A control method of a cooling system, a cooling system, a device, and a storage medium

Abstract

The application provides a cooling system control method, a cooling system, a device and a storage medium, and particularly relates to the technical field of data center cooling systems, and is used for improving the operation efficiency of a data center cooling system. The cooling system control method comprises the following steps: obtaining real-time operation data corresponding to a target device in the cooling system; using a target cooling system model corresponding to the target device to determine a performance parameter of the target device in a current operation state according to the operation data corresponding to the target device; determining a target operation parameter corresponding to the target device according to the performance parameter; and controlling the operation of the target device according to the target operation parameter.

Description

Technical Field

[0001] This application relates to the field of data center cooling system technology, and in particular to a control method, cooling system, device and storage medium for a cooling system. Background Technology

[0002] A data center cooling system is a specialized system used to reduce the temperature of equipment inside a data center. It typically consists of a series of cooling devices and components, such as chillers, cooling towers, cooling water pumps, and chilled water pumps. The control parameters of this series of cooling devices are closely related to the operating efficiency of the data center cooling system.

[0003] In related technologies, a large amount of historical operating data of data center cooling systems is often collected, and the cooling system model is trained based on this historical data. However, newly built data center cooling systems have limited historical operating data, resulting in inaccurate output results from the cooling system model and affecting the operating efficiency of the data center cooling system.

[0004] Therefore, improving the operating efficiency of data center cooling systems is an urgent problem to be solved. Summary of the Invention

[0005] This application provides a control method, cooling system, device, and storage medium for a cooling system, which can improve the operating efficiency of a data center cooling system.

[0006] To achieve the above objectives, the embodiments of this application adopt the following technical solutions:

[0007] Firstly, a method for controlling a cooling system is provided, the method comprising:

[0008] Acquire real-time operating data of the target equipment in the cooling system;

[0009] Using the target cooling system model corresponding to the target equipment, determine the performance parameters of the target equipment under the current operating state based on the corresponding operating data of the target equipment;

[0010] Determine the target operating parameters for the target equipment based on the performance parameters;

[0011] Control the operation of the target equipment according to the target operating parameters;

[0012] The target cooling system model corresponding to the target device was trained in the following way:

[0013] Multiple source domain training samples and multiple target domain training samples are obtained for the target device. The source domain training samples are determined based on historical operating data of other cooling systems; the target domain training samples are determined based on historical operating data of the corresponding cooling system.

[0014] The initial cooling system model corresponding to the target device is trained based on multiple source domain training samples to obtain the trained cooling system model corresponding to the target device.

[0015] The trained cooling system model corresponding to the target device is adjusted based on training samples from multiple target domains to obtain the target cooling system model corresponding to the target device.

[0016] The technical solution provided in this application provides at least the following beneficial effects: This application trains the initial cooling system model corresponding to the target device using source domain training samples, and adjusts the trained cooling system model corresponding to the target device using target domain training samples. This reduces the need for and dependence on the target device's operating data, and increases the generalization ability of the target cooling system model. In other words, it makes the target cooling system model more closely match the operating conditions of the target device, improving the accuracy of the target cooling system model. Furthermore, the target operating parameters determined using the target cooling system model corresponding to the target device are the optimal operating parameters for the target device. Controlling the operation of the target device using these target operating parameters can keep the target device in an optimal operating state, thereby improving the operating efficiency of the data center cooling system.

[0017] In some embodiments, when the target device is a chiller module, the real-time operating data includes: chilled water outlet temperature and cooling water inlet temperature; the above-mentioned determination of the performance parameters of the target device in the current operating state based on the operating data of the target device using the target cooling system model corresponding to the target device includes: determining multiple reference cooling capacities within the cooling capacity range of each chiller unit in the chiller module; for each of the multiple reference cooling capacities, determining the cooling load rate corresponding to the reference cooling capacity, and inputting the chilled water outlet temperature setpoint, cooling water inlet temperature, and cooling load rate into the target cooling system model corresponding to the target device to obtain the energy efficiency ratio corresponding to the reference cooling capacity; for each of the multiple reference cooling capacities, obtaining the power consumption corresponding to the reference cooling capacity based on the reference cooling capacity and the energy efficiency ratio corresponding to the reference cooling capacity; the performance parameters of the target device in the current operating state include the power consumption corresponding to each of the multiple reference cooling capacities.

[0018] In some embodiments, determining the target operating parameters corresponding to the target device based on performance parameters includes: for each of the multiple device activation quantities, determining multiple total cooling capacities and a first total power consumption corresponding to the device activation quantity based on multiple reference cooling capacities, the power consumption corresponding to each reference cooling capacities, and the device activation quantity; determining a first target total power consumption corresponding to the total cooling capacities greater than the cooling load demand among all the total cooling capacities and first total power consumption corresponding to the multiple device activation quantities; and using the reference cooling capacities and device activation quantities corresponding to the minimum power consumption among the first target total power consumption as the target operating parameters.

[0019] In some embodiments, when the target device is a cooling tower module, the real-time operating data includes: outdoor ambient wet-bulb temperature, target cooling water approximation, and cooling water temperature difference; using the target cooling system model corresponding to the target device, the performance parameters of the target device in the current operating state are determined based on the operating data corresponding to the target device, including: obtaining the target cooling water flow rate and multiple reference cooling tower fan frequencies within the cooling tower fan frequency range; for each of the multiple reference cooling tower fan frequencies, the reference cooling tower fan frequency, target cooling water flow rate, outdoor ambient wet-bulb temperature, and cooling water temperature difference are input into the target cooling system model corresponding to the target device to obtain the cooling tower approximation corresponding to the reference cooling tower fan frequency; the performance parameters of the target device in the current operating state include the cooling tower approximation corresponding to each of the multiple reference cooling tower fan frequencies.

[0020] In some embodiments, determining the target operating parameters corresponding to the target device based on performance parameters includes: for each of the multiple device activation quantities, determining multiple cooling tower approximation degrees corresponding to the device activation quantity based on multiple reference cooling tower fan operating frequencies and device activation quantities; among all cooling tower approximation degrees corresponding to the multiple device activation quantities, determining the target cooling tower fan operating frequency corresponding to the cooling tower approximation degree that is less than a preset approximation degree; and taking the target cooling tower fan operating frequency and device activation quantity corresponding to the minimum second total power consumption among the target cooling tower fan operating frequencies as the target operating parameters.

[0021] In some embodiments, when the target device is a cooling water pump module, the real-time operating data includes the cooling water pump operating frequency and the number of valves opened by the cooling water pump; using the target cooling system model corresponding to the target device, the performance parameters of the target device in the current operating state are determined based on the operating data corresponding to the target device, including: obtaining the target flow rate of cooling water and multiple reference pump operating frequencies within the range of cooling water pump operating frequencies; for each reference cooling water pump operating frequency among the multiple reference cooling water pump operating frequencies, the reference cooling water pump operating frequency and the number of valves opened by the cooling water pump are input into the target cooling system model corresponding to the target device to obtain the head and flow rate of the cooling water pump corresponding to the reference cooling water pump operating frequency; the performance parameters of the target device in the current operating state include the head and flow rate of the cooling water pump corresponding to each reference cooling water pump operating frequency among the multiple reference cooling water pump operating frequencies.

[0022] In some embodiments, determining the target operating parameters corresponding to the target device based on performance parameters includes: for each of the multiple device activation quantities, determining multiple first total water flow rates and multiple third total power consumption rates corresponding to the device activation quantity based on multiple reference cooling water pump operating frequencies, the water flow rate of the cooling water pump corresponding to each reference cooling water pump operating frequency, and the device activation quantity; among all the first total water flow rates and third total power consumption rates corresponding to the multiple device activation quantities, determining the second target total power consumption rate corresponding to the cooling water pump operating frequency where the first total water flow rate is greater than the first preset water flow rate and the cooling water pump head is greater than the first preset head; and using the reference cooling water pump operating frequency and device activation quantity corresponding to the smallest third total power consumption rate among the second target total power consumption rates as the target operating parameters.

[0023] In some embodiments, when the target device is a chilled water pump module, the real-time operating data includes the chilled water pump operating frequency and the number of valves opened by the chilled water pump; using the target cooling system model corresponding to the target device, the performance parameters of the target device in the current operating state are determined based on the operating data corresponding to the target device, including: obtaining the target flow rate of chilled water and multiple reference pump operating frequencies within the range of chilled water pump operating frequencies; for each reference chilled water pump operating frequency among the multiple reference chilled water pump operating frequencies, the reference chilled water pump operating frequency and the number of valves opened by the chilled water pump are input into the target cooling system model corresponding to the target device to obtain the head and flow rate of the chilled water pump corresponding to the reference chilled water pump operating frequency; the performance parameters of the target device in the current operating state include the head and flow rate of the chilled water pump corresponding to each reference chilled water pump operating frequency among the multiple reference chilled water pump operating frequencies.

[0024] In some embodiments, determining the target operating parameters corresponding to the target device based on performance parameters includes: for each of the multiple device activation quantities, determining multiple second total water flow rates and fourth total power consumption corresponding to the device activation quantity based on multiple reference chilled water pump operating frequencies, the chilled water pump flow rate corresponding to each reference chilled water pump operating frequency, and the device activation quantity; among all the second total water flow rates and fourth total power consumption corresponding to the multiple device activation quantities, determining the third target total power consumption corresponding to the chilled water pump operating frequency where the second total water flow rate is greater than the second preset water flow rate and the chilled water pump head is greater than the second preset head; and using the reference chilled water pump operating frequency and device activation quantity corresponding to the minimum third target total power consumption as the target operating parameters.

[0025] Secondly, embodiments of this application provide a cooling system, including:

[0026] The acquisition module is used to acquire real-time operating data corresponding to the target equipment in the cooling system.

[0027] The processing module is used to determine the performance parameters of the target device in the current operating state by utilizing the target cooling system model corresponding to the target device and the corresponding operating data of the target device.

[0028] The processing module is also used to determine the target operating parameters corresponding to the target device based on the performance parameters;

[0029] The processing module is also used to control the operation of the target device based on the target operating parameters;

[0030] The target cooling system model corresponding to the target device was trained in the following way:

[0031] The acquisition module acquires multiple source domain training samples and multiple target domain training samples corresponding to the target device. The source domain training samples are determined based on historical operating data corresponding to other cooling systems; the target domain training samples are determined based on historical operating data corresponding to the cooling system.

[0032] The processing module trains the initial cooling system model corresponding to the target device based on multiple source domain training samples to obtain the trained cooling system model corresponding to the target device.

[0033] The processing module adjusts the trained cooling system model corresponding to the target device based on multiple target domain training samples to obtain the target cooling system model corresponding to the target device.

[0034] Thirdly, a communication device is provided, comprising: a processor and a memory for storing processor-executable instructions; wherein the processor is configured to execute a control method for a cooling system as described in the first aspect and any possible implementation thereof.

[0035] Fourthly, a computer-readable storage medium is provided, on which computer instructions are stored, which, when executed on a communication device, cause the communication device to perform a control method for a cooling system as described in the first aspect and any possible implementation thereof.

[0036] For a detailed description of the second to fourth aspects and their various implementations in this invention, please refer to the detailed description in the first aspect and its various implementations. The beneficial effects of the second to fourth aspects and their various implementations can be found in the analysis of the beneficial effects of the first aspect and its various implementations, and will not be repeated here. Attached Figure Description

[0037] The accompanying drawings are provided to further understand the technical solutions of the present invention and constitute a part of the specification. They are used together with the embodiments of this application to explain the technical solutions of the present invention and do not constitute a limitation on the technical solutions of the present invention.

[0038] Figure 1 This is a schematic diagram of a cooling system provided in an embodiment of this application;

[0039] Figure 2 A schematic flowchart illustrating a control method for a cooling system provided in an embodiment of this application;

[0040] Figure 3 A schematic diagram of a model training process provided in an embodiment of this application;

[0041] Figure 4 This is a schematic flowchart illustrating another cooling system control method according to an embodiment of this application;

[0042] Figure 5 This is a schematic diagram of another cooling system provided in an embodiment of this application. Detailed Implementation

[0043] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described below are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0044] To facilitate the description of the technical solutions of this application, the terms "first" and "second" may be used to distinguish technical features with the same or similar functions. The terms "first" and "second" do not limit the number or execution order, nor do they imply that they are necessarily different. In this application, the terms "exemplary" or "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design scheme described as "exemplary" or "for example" should not be construed as being more preferred or advantageous than other embodiments or design schemes. The use of "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner for ease of understanding.

[0045] The terms “comprising” and “having”, and any variations thereof, used in the description of this application are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the steps or units listed, but may optionally include other steps or units not listed, or may optionally include other steps or units inherent to such process, method, product, or apparatus.

[0046] It is understood that the term "embodiment" used throughout the specification means that a specific feature, structure, or characteristic related to an embodiment is included in at least one embodiment of this application. Therefore, various embodiments throughout the specification do not necessarily refer to the same embodiment. Furthermore, these specific features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. It is understood that in the various embodiments of this application, the sequence number of each process does not imply a sequential order of execution; the execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of this application.

[0047] It is understood that in this application, "when," "under the circumstances," "if," and "if" all refer to the corresponding processing that will be carried out under certain objective circumstances, and are not time-limited, nor do they require that there must be a judgment action when implemented, nor do they imply any other limitations.

[0048] To facilitate understanding, we will first provide a brief introduction and explanation of some terms or basic concepts of technology involved in the embodiments of this application.

[0049] (I) Transfer Learning

[0050] Transfer learning is a learning process that uses the similarity between data, tasks, or models to apply a model learned in an old domain (source domain) to a new domain (target domain).

[0051] Transfer learning, by introducing knowledge from other projects, can build effective control models even when data is scarce, reducing the need for and dependence on data. Optimized control based on transfer learning methods can ensure that the models of various equipment in the cooling system are more generalizable in new data centers, adapting to and handling the diverse needs and changes in new projects, and promoting the large-scale application of energy-saving optimization control methods.

[0052] (II) Source Domain Dataset and Target Domain Dataset

[0053] The source domain dataset refers to the dataset used when training the model, while the target domain dataset is the new dataset or new domain to which the model will be applied. The source domain dataset contains a large amount of training data and a pre-trained model, while the target domain dataset contains less data. In this application, the source domain dataset can be operational data other than that of the cooling system of the newly built data center, and the target domain dataset can be the operational data of the cooling system of the newly built data center.

[0054] The above is an introduction to some of the concepts involved in the embodiments of this application, which will not be repeated below.

[0055] As described in the background section, a data center cooling system is a specialized system used to reduce the temperature of equipment inside a data center. It typically consists of a series of cooling devices and components, such as chillers, cooling towers, cooling water pumps, and chilled water pumps. The control parameters of this series of cooling devices are closely related to the operating efficiency of the data center cooling system.

[0056] In related technologies, a large amount of historical operating data from data center cooling systems is often collected, and the cooling system model is trained based on this historical data. However, newly built data center cooling systems typically have limited historical operating data, often lacking sufficient data for model training. This results in inaccurate outputs from the cooling system model, impacting the operational efficiency of the data center cooling system. Therefore, improving the operational efficiency of data center cooling systems is an urgent problem to be solved.

[0057] In view of this, the embodiments of this application train the initial cooling system model corresponding to the target device using source domain training samples, and adjust the trained cooling system model corresponding to the target device using target domain training samples. This reduces the need for and dependence on the target device's operating data, and increases the generalization ability of the target cooling system model. In other words, it makes the target cooling system model corresponding to the target device more closely match the operating conditions of the target device, improving the accuracy of the target cooling system model. Furthermore, the target operating parameters determined using the target cooling system model corresponding to the target device are the optimal operating parameters for the target device. Controlling the operation of the target device with these target operating parameters can keep the target device in an optimal operating state, thereby improving the operating efficiency of the data center cooling system.

[0058] The embodiments of this application will now be described in detail with reference to the accompanying drawings.

[0059] The control method for the cooling system provided in this application embodiment can be applied to cooling systems. Figure 1 This is a schematic diagram of a cooling system provided in an embodiment of this application.

[0060] like Figure 1 As shown, the cooling system 1 includes: a chiller unit module 10, a cooling tower module 20, a cooling water pump module 30, and a chilled water pump module 40. Among them,

[0061] The chiller module 10, also known as a refrigeration unit, chiller, ice water unit, or cooling equipment, is used to generate low-temperature cooling water through refrigerant circulation. The chiller module 10 is connected to the cooling tower module 20 via piping.

[0062] Optionally, the chiller module 10 may include a refrigerant circulation loop consisting of a compressor, condenser, expansion valve, and evaporator. The compressor draws in low-temperature, low-pressure refrigerant gas and then compresses it to increase its temperature and pressure, transforming it into a high-temperature, high-pressure gas. During this process, the refrigerant gas is compressed and converted into a high-temperature, high-pressure gas, releasing a large amount of heat. After entering the condenser, the high-temperature, high-pressure refrigerant gas exchanges heat with the external environment, transferring heat to the environment and thus lowering its own temperature, becoming a high-pressure liquid. Before entering the evaporator through the expansion valve, the high-pressure liquid undergoes throttling, causing a significant drop in both pressure and temperature, becoming a low-temperature, low-pressure liquid. After entering the evaporator, the low-temperature, low-pressure refrigerant liquid absorbs heat from the object being cooled, thus increasing its own temperature and becoming a low-temperature, low-pressure gas. The refrigerant gas in the evaporator is then drawn back into the compressor, beginning a new cycle.

[0063] Cooling tower module 20 is used to cool the cooling water flowing out of chiller module 10, which has absorbed heat. The cooling tower lowers the temperature of the cooling water by transferring heat to the atmosphere through heat exchange between the cooling water and the air.

[0064] Optionally, the cooling tower module 20 includes a cooling water circulation loop consisting of a fan and a water collection tank. The output end of the cooling tower module 20 is connected to the input end of the condenser, and the input end of the cooling tower module 20 is connected to the output end of the condenser. The cooling water in the cooling water circulation loop and the refrigerant in the refrigerant circulation loop exchange heat at the condenser of the chiller module 10.

[0065] The fan, serving as the power component of the cooling tower module 20, blows air into the module to promote the evaporation and heat dissipation of the cooling water. The water collection tank is used to receive and store the cooled water after cooling.

[0066] The cooling water pump module 30 is used to transport cooling water from the cooling tower module 20 to the chiller module 10, and to transport the cooled water to the equipment or system that needs to be cooled. Optionally, the output end of the cooling water pump in the cooling water pump module 30 is connected to the evaporator input end of the chiller in the chiller module 10 to provide cooled water to the chiller, and the input end of the cooling water pump is connected to the output end of the equipment being cooled.

[0067] The chilled water pump module 40 is used to deliver low-temperature chilled water generated by the chiller unit to equipment or systems that require chilled water cooling. Optionally, the output end of the chilled water pump in the chilled water pump module 40 is connected to the input end of the cooling tower module 20, and the input end of the chilled water pump is connected to the output end of the condenser in the chiller unit.

[0068] It is understood that the number of devices in the chiller module 10, cooling tower module 20, cooling water pump module 30 and chilled water pump module 40 can be one or more. This application embodiment does not limit the number of devices in the chiller module 10, cooling tower module 20, cooling water pump module 30 and chilled water pump module 40 in the cooling system 1.

[0069] The following description, in conjunction with the accompanying drawings, describes a control method for a cooling system provided in an embodiment of this application.

[0070] Figure 2 This is a flowchart illustrating a control method for a cooling system provided in an embodiment of this application. This control method can be applied to cooling systems. Figure 2 As shown, the control method for the cooling system provided in this application includes the following steps S1-S4.

[0071] S1. Obtain real-time operating data corresponding to the target device in the cooling system.

[0072] The target equipment can be one or more of the following: chiller unit module, cooling tower, cooling water pump, and chilled water pump.

[0073] Optionally, when the target equipment is a chiller module, the real-time operating data may include: chilled water outlet temperature and cooling water inlet temperature; when the target equipment is a cooling tower module, the real-time operating data may include: outdoor ambient wet-bulb temperature, cooling water target proximity, and cooling water temperature difference; when the target equipment is a cooling water pump module, the real-time operating data may include the cooling water pump operating frequency and the number of valves opened by the cooling water pump; when the target equipment is a chilled water pump module, the real-time operating data may include the chilled water pump operating frequency and the number of valves opened by the chilled water pump.

[0074] In one possible implementation, the cooling system includes multiple temperature sensors and a data acquisition system connected to a programmable logic controller (PLC). The temperature sensors detect the chilled water outlet temperature, cooling water inlet temperature, and outdoor wet-bulb temperature, respectively. The data acquisition system detects the number of cooling water pumps and the operating frequency of the chilled water pumps. The PLC can receive temperature data from the multiple temperature sensors and the number of cooling water pumps and the operating frequency of the chilled water pumps from the data acquisition system.

[0075] S2. Using the target cooling system model corresponding to the target equipment, determine the performance parameters of the target equipment under the current operating state based on the operating data of the target equipment.

[0076] Optionally, when the target equipment is a chiller module, the performance parameters of the target equipment in the current operating state include the power consumption corresponding to each of the multiple reference cooling capacities; when the target equipment is a cooling tower module, the performance parameters of the target equipment in the current operating state include the cooling tower approximation corresponding to each of the multiple reference cooling tower fan frequencies; when the target equipment is a cooling water pump module, the performance parameters of the target equipment in the current operating state include the head and flow rate of the cooling water pump corresponding to each of the multiple reference cooling water pump operating frequencies; when the target equipment is a chilled water pump module, the performance parameters of the target equipment in the current operating state include the head and flow rate of the chilled water pump corresponding to each of the multiple reference chilled water pump operating frequencies.

[0077] S3. Determine the target operating parameters corresponding to the target equipment based on the performance parameters.

[0078] Optionally, when the target equipment is a chiller unit module, the target operating parameters may include the number of chillers turned on and the chiller's cooling capacity; when the target equipment is a cooling tower module, the target operating parameters may include the cooling tower fan operating frequency and the number of cooling towers turned on in the cooling tower module; when the target equipment is a cooling water pump module, the target operating parameters may include the number of cooling water pump valves opened and the cooling water pump operating frequency; when the target equipment is a chilled water pump module, the target operating parameters may include the number of chilled water pump valves opened and the chilled water pump operating frequency.

[0079] S4. Control the operation of the target equipment according to the target operating parameters.

[0080] As one possible implementation, the target cooling system model corresponding to the target device is obtained through the following steps S401-S403:

[0081] S401. Obtain multiple source domain training samples and multiple target domain training samples corresponding to the target device.

[0082] The source domain training samples are determined based on historical operating data of other cooling systems; the target domain training samples are determined based on historical operating data of the corresponding cooling system. Other cooling systems can be similar to, but different from, the cooling systems described in this embodiment.

[0083] As one possible approach, source domain training samples can be generated by acquiring historical operating data related to the cooling system from an internal data warehouse.

[0084] As another possible implementation, source domain training samples can be generated by generating source domain data through a data generator or simulator.

[0085] Optionally, the following data preprocessing steps are included before generating source domain training samples and target domain training samples:

[0086] Step 1: Use the interquartile range (ICM) to select and remove outliers from both the source and target domains. The ICM is a statistic describing the dispersion of data, primarily used to identify and handle outliers. The ICM can be expressed as the following formula:

[0087] UL = Q³ + 1.5 × IQR

[0088] LL = Q1 - 1.5 × IQR

[0089] IQR = Q3 - Q1

[0090] Wherein, UL is the upper limit of the normal value, LL is the lower limit of the normal value, IQR is the difference between the third quartile and the first quartile, Q1 is the first quartile, and Q3 is the third quartile.

[0091] Step 2: Use linear interpolation to fill in outliers and missing values ​​in the source and target domain data.

[0092] Step 3: Use a normalization tool to scale the source and target domain datasets to a specific range (e.g., [0,1]). Formula:

[0093]

[0094] Where, x i Let x be the i-th data point in dataset X. iN This is the standardized value of x_i. max Let X be the maximum value in dataset X. min It is the minimum value in dataset X.

[0095] Understandably, removing or correcting errors and outliers in the data using the interquartile range method can improve data accuracy. Using linear interpolation to fill in outliers and missing values ​​in both the source and target domains ensures data integrity. Scaling the source and target datasets to the same order of magnitude using standardization tools makes different features comparable, and scaling the data to a specific range can lead to faster model training convergence.

[0096] S402. Train the initial cooling system model corresponding to the target device based on multiple source domain training samples to obtain the trained cooling system model corresponding to the target device.

[0097] One possible implementation involves inputting source domain training samples into the initial cooling system model corresponding to the target device to obtain prediction results for the source domain training samples in the source domain sample set. Based on the prediction results and the actual results of the source domain training samples, the loss value of the source domain sample set is determined. The convergence of the initial cooling system model corresponding to the target device is then determined based on the loss value of the sample set. If not, the weight parameters in the initial cooling system model corresponding to the target device are updated based on the loss value of the source domain sample set, and the process of inputting source domain training samples into the initial cooling system model corresponding to the target device continues to obtain prediction results for the source domain training samples in the source domain sample set. If convergence is achieved, the current initial cooling system model corresponding to the target device is determined as the trained cooling system model.

[0098] S403. Adjust the trained cooling system model corresponding to the target device based on multiple target domain training samples to obtain the target cooling system model corresponding to the target device.

[0099] One possible implementation involves inputting the target domain training samples into the trained cooling system model to obtain the prediction results corresponding to the target domain training samples in the target domain sample set. Based on the prediction results and the actual results of the target domain training samples, the loss value of the target domain sample set is determined. The convergence of the initial cooling system model corresponding to the target device is then determined based on the loss value of the sample set. If not, the weight parameters in the initial cooling system model corresponding to the target device are updated based on the loss value of the target domain sample set, and the process of inputting the target domain training samples into the initial cooling system model corresponding to the target device continues to obtain the prediction results corresponding to the target domain training samples in the target domain sample set. If convergence is achieved, the current cooling system model is determined as the target cooling system model.

[0100] Figure 2 The illustrated embodiments offer at least the following beneficial effects: In this application, the initial cooling system model corresponding to the target device is trained using source domain training samples, and the trained cooling system model corresponding to the target device is adjusted using target domain training samples. This reduces the need for and dependence on the target device's operational data, increasing the generalization ability of the target cooling system model. In other words, the target cooling system model corresponding to the target device better reflects the target device's operating conditions, improving the model's accuracy. Furthermore, the target operating parameters determined using the target cooling system model corresponding to the target device are the optimal operating parameters for the target device. Controlling the target device's operation with these target operating parameters allows the target device to operate in its optimal state, thereby improving the operational efficiency of the data center cooling system.

[0101] Figure 3 This is a schematic diagram illustrating a model training process provided in an embodiment of this application. Figure 3 As shown, the initial cooling system model corresponding to the chiller unit is the initial chiller unit model; the initial cooling system model corresponding to the cooling tower is the initial cooling tower model; the cooling system model corresponding to the cooling water pump is the initial cooling water pump model; and the cooling system model corresponding to the chilled water pump is the initial chilled water pump model.

[0102] Data cleaning, data completion, and data standardization are performed on the source domain data and target domain data to obtain preprocessed source domain data and preprocessed target domain data.

[0103] The preprocessed source domain data is input into the initial chiller unit model, initial cooling tower model, initial cooling water pump model, and initial chilled water pump model. These models are then trained to obtain the trained chiller unit model, trained cooling tower model, trained cooling water pump model, and trained chilled water pump model, respectively. The preprocessed target domain data is then input into these trained models for adjustment, resulting in the target chiller unit model, target cooling tower model, target cooling water pump model, and target chilled water pump model, respectively.

[0104] In some embodiments, step S2 above can be implemented as the following steps S201-S203:

[0105] S201. Determine multiple reference cooling capacities within the cooling capacity range of each chiller unit in the chiller unit module.

[0106] A chiller module can include one or more chiller units. The cooling capacity range of a chiller unit depends on its brand and model, and can be determined by the product of its cooling power and energy efficiency ratio. Typically, the cooling capacity of a chiller unit ranges from 30kW to 70kW, where kW is the power unit (kilowatt).

[0107] S202. For each of the multiple reference cooling caps, determine the cooling load rate corresponding to the reference cooling cap, and input the chilled water outlet temperature setpoint, cooling water inlet temperature and cooling load rate into the target cooling system model corresponding to the target equipment to obtain the energy efficiency ratio corresponding to the reference cooling cap.

[0108] The chilled water outlet temperature setting is either set by the cooling system designer at the factory or can be set by the user.

[0109] Optionally, the cooling load rate corresponding to the reference cooling capacity can be determined by the ratio between the cooling power corresponding to the reference cooling capacity and the rated cooling capacity.

[0110] Optionally, the inlet temperature of the cooling water can be determined by the outdoor ambient wet-bulb temperature, the set value of the cooling tower proximity, and the set value of the cooling water temperature difference. Among them, the set values ​​of the cooling tower proximity and the set value of the cooling water temperature difference can be set by the cooling system designer at the factory or by the user.

[0111] S203. For each of the multiple reference cooling capacities, the power consumption corresponding to the reference cooling cap is obtained based on the reference cooling cap and the energy efficiency ratio corresponding to the reference cooling cap.

[0112] As one possible implementation, the power consumption corresponding to the reference cooling capacity can be determined by the ratio between the reference cooling capacity and the energy efficiency ratio corresponding to the reference cooling capacity.

[0113] Optionally, the performance parameters of the target device in its current operating state include the power consumption corresponding to each of the multiple reference cooling capacities.

[0114] As can be seen from the above embodiments, by inputting the chilled water outlet temperature setpoint, the cooling water inlet temperature, and the cooling load rate corresponding to each of the multiple reference cooling capacities into the target cooling system model corresponding to the chiller unit module, the energy efficiency ratio corresponding to each of the multiple reference cooling capacities can be obtained. Then, the power consumption corresponding to each reference cooling cap can be determined based on the energy efficiency ratio corresponding to each reference cooling cap.

[0115] In some embodiments, step S3 above can be implemented as the following steps S301-S303:

[0116] S301. For each device activation quantity among multiple device activation quantities, based on multiple reference cooling capacities, the power consumption corresponding to each reference cooling capacities, and the device activation quantity, determine multiple total cooling capacities and a first total power consumption corresponding to the device activation quantity.

[0117] Understandably, the number of devices activated should be less than or equal to the total number of chillers in the chiller module.

[0118] As one possible implementation, the total cooling capacity corresponding to the number of devices turned on can be determined by the product of the number of devices turned on and the reference cooling capacity.

[0119] For example, when the number of devices turned on is 3 and the reference cooling capacity is akw, the total cooling capacity corresponding to akw is 3akw. When the number of devices turned on is 4 and the reference cooling capacity is bkw, the total cooling capacity corresponding to bkw is 4bkw.

[0120] As one possible implementation, the first total power consumption is determined by the product of the number of devices turned on and the power consumption corresponding to the reference cooling capacity. That is, the first total power consumption corresponding to the reference cooling capacity = the number of devices turned on * the power consumption corresponding to the reference cooling capacity.

[0121] S302. Among all the total cooling capacity and the first total power consumption corresponding to the number of multiple devices turned on, determine the first target total power consumption corresponding to the total cooling capacity that is greater than the cooling load requirement.

[0122] It is understandable that when the total cooling capacity is greater than the cooling load demand, it means that the cooling demand of the chiller module can meet the user's cooling needs under the current operating conditions. Therefore, the first total power consumption is the total power consumption of the chiller module when the cooling demand of the chiller module can meet the user's cooling needs.

[0123] S303. The reference cooling capacity and the number of devices turned on are taken as the target operating parameters, with the minimum total power consumption in the first target total power consumption being the reference cooling capacity and the number of devices turned on.

[0124] As can be seen from the above embodiments, by determining the total cooling capacity and first total power consumption of each scheme under different chiller unit operation numbers and different reference cooling capacities, the first total power consumption corresponding to the scheme where the total cooling capacity meets the cooling load demand can be taken as the first target total power consumption. Then, the reference cooling capacity and number of equipment operation corresponding to the minimum total power consumption are selected from the first target total power consumption as target operating parameters. In this way, controlling the number of chiller unit modules and cooling capacity with target operating parameters can minimize the power consumption of the chiller unit modules.

[0125] In some embodiments, step S2 above can be implemented as the following steps S204-S205:

[0126] S204. Obtain the target flow rate of cooling water and multiple reference cooling tower fan frequencies within the frequency range of the cooling tower fan.

[0127] The target flow rate of cooling water can be determined based on the number of cooling units in operation, the cooling capacity of the cooling units, and the target temperature difference of the cooling water. The frequency range of cooling tower fans is generally between 30Hz and 60Hz, where Hz is the unit of frequency, Hertz.

[0128] S205. For each reference cooling tower fan frequency among multiple reference cooling tower fan frequencies, input the reference cooling tower fan frequency, target cooling water flow rate, outdoor ambient wet-bulb temperature, and cooling water temperature difference into the target cooling system model corresponding to the target equipment to obtain the cooling tower approximation degree corresponding to the reference cooling tower fan frequency.

[0129] Among them, the approximation degree of a cooling tower is an important parameter for evaluating the performance of a cooling tower. It reflects the ability of the cooling tower to cool water to near the wet-bulb temperature of the air. The smaller the approximation degree of the cooling tower, the better the cooling effect of the cooling tower.

[0130] For example, when the reference cooling tower fan frequency is cHz, the cHz, the target cooling water flow rate, the outdoor ambient wet-bulb temperature, and the cooling water temperature difference are input into the target cooling system model corresponding to the cooling tower module to obtain the cooling tower approximation degree corresponding to cHz.

[0131] Optionally, the performance parameters of the target device under the current operating state include the cooling tower approximation degree corresponding to each of the multiple reference cooling tower fan frequencies.

[0132] As can be seen from the above embodiments, by inputting the reference cooling tower fan frequency, target cooling water flow rate, outdoor ambient wet-bulb temperature, and cooling water temperature difference into the target cooling system corresponding to the cooling tower module, the cooling tower approximation degree corresponding to multiple reference cooling tower fan frequencies can be obtained. The cooling tower approximation degree corresponding to multiple reference cooling tower fan frequencies can reflect the different cooling effects of the cooling tower under different reference cooling tower fan frequencies.

[0133] In some embodiments, step S3 above can be implemented as the following steps S304-S305:

[0134] S304. For each of the multiple device activation counts, determine the approximation degree of multiple cooling towers corresponding to the device activation count based on the operating frequency of multiple reference cooling tower fans and the device activation count.

[0135] It should be noted that the approximation degree of the cooling towers varies depending on the number of cooling towers activated in the cooling tower module. The number of activated devices is less than or equal to the number of cooling towers in the cooling tower module.

[0136] As one possible implementation, the approximation degree of the cooling tower corresponding to the number of devices turned on can be determined by the approximation degree of the cooling tower, the number of cooling towers turned on, the water flow rate through one cooling tower, and the total water flow rate through all cooling towers.

[0137] For example, the approximation degree of the cooling tower corresponding to the number of devices turned on = ∑(cooling tower approximation degree * water flow rate of one cooling tower) / total water flow rate of the cooling tower.

[0138] S305. Among all the cooling tower approximations corresponding to the number of devices turned on, determine the target cooling tower fan operating frequency corresponding to the cooling tower approximation that is less than the preset approximation; take the target cooling tower fan operating frequency and the number of devices turned on as the target operating parameters, which are the target total power consumption corresponding to the target cooling tower fan operating frequency and the second total power consumption that is the smallest among the second total power consumption.

[0139] The preset approximation degree can be set by the designer at the factory or by the user.

[0140] Understandably, if the cooling tower proximity is less than the preset proximity, it indicates that the cooling tower's cooling effect meets the usage requirements. The target cooling tower fan operating frequency is determined for each of the multiple cooling tower proximity values ​​that meet the cooling requirements. Then, based on different target cooling tower fan operating frequencies and different numbers of cooling towers operating, the second total power consumption for each combination is determined. The target cooling tower fan operating frequency and number of cooling towers operating corresponding to the minimum second total power consumption among the multiple combinations are used as target operating parameters. By controlling the number of cooling towers operating and the cooling tower fan operating frequency in the cooling tower module using these target operating parameters, the power consumption of the cooling tower module can be minimized.

[0141] In some embodiments, step S2 above can be implemented as the following steps S206-S207:

[0142] S206. Obtain the target flow rate of cooling water and the operating frequencies of multiple reference water pumps within the operating frequency range of the cooling water pump.

[0143] The target flow rate of cooling water can be determined based on the number of cooling units in operation, the cooling capacity of the cooling units, and the target temperature difference of the cooling water. The operating frequency range of the cooling water pump is typically between 30Hz and 50Hz.

[0144] S207. For each reference cooling water pump operating frequency among multiple reference cooling water pump operating frequencies, input the reference cooling water pump operating frequency and the number of valves opened by the cooling water pump into the target cooling system model corresponding to the target equipment to obtain the head and water flow rate of the cooling water pump corresponding to the reference cooling water pump operating frequency.

[0145] The head of a cooling water pump refers to the height to which the pump can lift or deliver water. The head affects the performance of the pump and the overall efficiency of the cooling system.

[0146] Optionally, the number of cooling water pump valves opened can be the same as the number of cooling towers opened in the cooling tower module.

[0147] Optionally, the performance parameters of the target device under the current operating state include the head and flow rate of the cooling water pump corresponding to each of the multiple reference cooling water pump operating frequencies.

[0148] As can be seen from the above embodiments, by inputting the reference cooling water pump operating frequency and the number of valves opened by the cooling water pump into the target cooling system model corresponding to the cooling water pump, the head and water flow rate of the cooling water pump corresponding to multiple reference cooling water pump operating frequencies can be obtained. Different cooling water pump heads can reflect different pumping capacities of the cooling water pumps.

[0149] In some embodiments, step S3 above can be implemented as the following steps S306-S308:

[0150] S306. For each of the multiple device activation quantities, based on the multiple reference cooling water pump operating frequencies, the cooling water pump flow rate corresponding to each reference cooling water pump operating frequency, and the device activation quantity, determine the multiple first total water flow rate and multiple third total power consumption corresponding to the device activation quantity.

[0151] The number of devices turned on is less than or equal to the number of cooling water pumps in the cooling system.

[0152] As one possible implementation, the first total water flow can be determined by the product of the cooling water pump flow rate and the number of pumps in operation. The third total power consumption can be determined by the operating frequency of the cooling water pumps and the number of pumps in operation.

[0153] S307. Among all the first total water flow and third total power consumption corresponding to the number of multiple devices turned on, determine the second target total power consumption corresponding to the operating frequency of the cooling water pump where the first total water flow is greater than the first preset water flow and the head of the cooling water pump is greater than the first preset head.

[0154] The first preset water flow rate and the first preset head can be set by the designer at the factory or by the user.

[0155] It is understandable that if the total flow rate is greater than the first preset flow rate and the head of the cooling water pump is greater than the first preset head, it indicates that the cooling water flow rate provided by the cooling water pump module can meet the cooling water flow rate requirements of the cooling system. By determining the second target power consumption corresponding to the operating frequency of the cooling water pump in multiple schemes that meet the cooling water flow rate requirements of the cooling system, the total power consumption of each scheme in multiple schemes that meet the cooling water flow rate requirements of the cooling system can be obtained.

[0156] S308. The reference cooling water pump operating frequency and the number of devices turned on are taken as the target operating parameters, corresponding to the third total power consumption with the smallest power consumption among the second target total power consumption.

[0157] The pump operating frequency and the number of devices turned on are taken as the target operating parameters when the solution with the lowest power consumption is selected from the solutions that meet the cooling water flow requirements of the cooling system. By controlling the number of cooling water pumps turned on and the pump operating frequency of the cooling water pump module with the target operating parameters, the power consumption of the cooling water pump module can be minimized.

[0158] In some embodiments, step S2 above can be implemented as the following steps S208-S209:

[0159] S208. Obtain the target flow rate of chilled water and the operating frequencies of multiple reference pumps within the operating frequency range of the chilled water pump.

[0160] The target flow rate of chilled water can be determined based on the number of chiller units in operation, the cooling capacity of the chiller units, and the target temperature difference of the chilled water. The operating frequency range of the chilled water pump is typically between 25Hz and 50Hz.

[0161] S209. For each reference chilled water pump operating frequency among multiple reference chilled water pump operating frequencies, input the reference chilled water pump operating frequency and the number of valves opened by the chilled water pump into the target cooling system model corresponding to the target equipment to obtain the head and water flow rate of the chilled water pump corresponding to the reference chilled water pump operating frequency.

[0162] The head of a chilled water pump refers to the height to which the pump can lift or deliver water. The head affects the performance of the pump and the overall efficiency of the cooling system.

[0163] Optionally, the number of valves on the chilled water pump can be the same as the number of valves on the chiller unit module.

[0164] Optionally, the performance parameters of the target equipment under the current operating state include the head and flow rate of the chilled water pump corresponding to each of the multiple reference chilled water pump operating frequencies.

[0165] As can be seen from the above embodiments, by inputting the reference chilled water pump operating frequency and the number of valves opened by the chilled water pump into the target cooling system model corresponding to the chilled water pump, the head and water flow rate of the chilled water pump corresponding to multiple reference chilled water pump operating frequencies can be obtained. Different chilled water pump heads can reflect different pumping capacities of the chilled water pumps.

[0166] In some embodiments, step S3 above can be implemented as the following steps S309-S311:

[0167] S309. For each of the multiple device activation quantities, based on the multiple reference chilled water pump operating frequencies, the chilled water pump flow rate corresponding to each reference chilled water pump operating frequency, and the device activation quantity, determine the multiple second total water flow rates and the fourth total power consumption corresponding to the device activation quantity.

[0168] As one possible implementation, the second total water flow rate can be determined by the product of the chilled water pump flow rate and the number of pumps in operation. The fourth total power consumption can be determined by the operating frequency of the chilled water pumps and the number of pumps in operation.

[0169] S310. Among all the second total water flow and fourth total power consumption corresponding to the number of multiple devices turned on, determine the third target total power consumption corresponding to the operating frequency of the chilled water pump where the second total water flow is greater than the second preset water flow and the head of the chilled water pump is greater than the second preset head.

[0170] The second preset water flow rate and the second preset head can be set by the designer at the factory or by the user.

[0171] It is understandable that if the second total water flow is greater than the second preset water flow rate and the chilled water pump head is greater than the second preset head, it indicates that the chilled water flow rate provided by the chilled water pump module can meet the chilled water demand of the cooling system. By determining the third target total power consumption corresponding to the operating frequency of the chilled water pump in multiple schemes that meet the chilled water flow rate demand of the cooling system, the total power consumption of each scheme that meets the chilled water flow rate demand of the cooling system can be obtained.

[0172] S311. The reference chilled water pump operating frequency and the number of devices turned on, corresponding to the minimum total power consumption in the third target total power consumption, are taken as the target operating parameters.

[0173] The pump operating frequency and the number of devices turned on are taken as the target operating parameters when the scheme with the lowest power consumption is selected to meet the chilled water flow requirements of the cooling system. By controlling the number of chilled water pumps turned on and the pump operating frequency of the chilled water module with the target operating parameters, the power consumption of the chilled water pump module can be minimized.

[0174] Figure 4 This is a schematic flowchart illustrating another cooling system control method according to an embodiment of this application. Figure 4 As shown,

[0175] The obtained cooling water inlet temperature, chilled water outlet temperature setpoints, and multiple cooling load rates are input into the target chiller model to obtain the energy consumption ratio corresponding to the cooling load. Combining the energy consumption ratio corresponding to the cooling load with the chiller module optimization strategy, the optimal number of chillers to operate and the optimal cooling load are obtained. Specifically, the chiller optimization strategy involves calculating the power consumption of the chiller module under different chiller operating numbers and cooling loads, determining the optimal number of chillers to operate with the minimum power consumption, and determining the optimal cooling load with the minimum power consumption.

[0176] The optimal number of chiller units to be activated is used as the number of chilled water pump valves to be activated. The number of activated chilled water pump valves and the operating frequencies of multiple chilled water pumps are input into the target chilled water pump model to obtain the head and flow rate of the chilled water pumps corresponding to the operating frequencies. Combining the head and flow rate of the chilled water pumps corresponding to the operating frequencies with the chilled water pump module optimization strategy, the optimal number of activated chilled water pumps and the optimal operating frequency of the chilled water pumps are obtained. Specifically, the chilled water pump module optimization strategy involves calculating the power consumption of the chilled water pump module under different numbers of activated chilled water pumps and different operating frequencies. The optimal number of activated chilled water pumps and the optimal operating frequency of the chilled water pumps are determined based on the minimum power consumption.

[0177] The obtained cooling water temperature difference, target cooling water flow rate, target cooling water approximation, outdoor wet-bulb temperature, and multiple cooling tower fan operating frequencies are input into the target cooling tower model to obtain the cooling tower approximation corresponding to the cooling tower fan operating frequency. Combining the cooling tower approximation corresponding to the cooling tower fan operating frequency with the cooling tower module optimization strategy, the optimal number of cooling towers to start and the optimal operating frequency of the cooling tower fans are obtained. Specifically, the cooling tower module optimization strategy involves calculating the power consumption of the cooling tower module under different cooling tower start numbers and different cooling tower fan operating frequencies. The optimal number of cooling towers to start, where power consumption is minimized, and the optimal operating frequency of the cooling tower fans, where power consumption is minimized, are determined as the optimal number of cooling towers to start and the optimal operating frequency of the cooling tower fans, respectively.

[0178] The optimal number of cooling towers to be opened is used as the number of cooling water pump valves to be opened. The number of opening cooling water pump valves and the operating frequencies of multiple cooling water pumps are input into the target cooling water pump model to obtain the head and flow rate of the cooling water pumps corresponding to multiple operating frequencies. Combining the head and flow rate of the cooling water pumps corresponding to the operating frequencies with the cooling water pump module optimization strategy, the optimal number of cooling water pumps to be opened and the optimal operating frequency of the cooling water pumps are obtained. Specifically, the cooling water pump module optimization strategy involves calculating the power consumption of the cooling water pump module under different numbers of opening cooling water pumps and different operating frequencies, determining the optimal number of opening cooling water pumps and the optimal operating frequency of the cooling water pumps when the power consumption is minimized.

[0179] The output cooling system optimization strategy can include the optimal number of chiller units to be turned on, the optimal cooling load of the chiller units, the optimal number of cooling towers to be turned on, the operating frequency of the cooling tower fans, the optimal number of cooling water pumps to be turned on, the operating frequency of the cooling water pumps, the optimal number of chilled water pumps to be turned on, and the operating frequency of the chilled water pumps.

[0180] As can be seen, the above mainly describes the solutions provided by the embodiments of this application from a methodological perspective. To achieve the above functions, the embodiments of this application provide corresponding hardware structures and / or software modules for executing each function. Those skilled in the art should readily recognize that, in conjunction with the modules and algorithm steps of the various examples described in the embodiments disclosed herein, the embodiments of this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed in a hardware or computer software-driven hardware manner depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this invention.

[0181] This application embodiment can divide the controller into functional modules according to the above method example. For example, each function can be divided into a separate functional module, or two or more functions can be integrated into one processing module. The integrated module can be implemented in hardware or as a software functional module. Optionally, the module division in this application embodiment is illustrative and only represents one logical functional division; other division methods may be used in actual implementation.

[0182] When dividing each function into modules according to its corresponding function. Figure 5 This is a schematic diagram of another cooling system provided in an embodiment of this application. Figure 5 As shown, the cooling system 5 includes: an acquisition module 501 and a processing module 502. Wherein,

[0183] The acquisition module 501 is used to acquire real-time operating data corresponding to the target device in the cooling system;

[0184] Processing module 502 is used to determine the performance parameters of the target device in the current operating state by using the target cooling system model corresponding to the target device and the corresponding operating data of the target device;

[0185] The processing module 502 is also used to control the operation of the target device according to the target operating parameters;

[0186] The target cooling system model corresponding to the target device was trained in the following way:

[0187] The acquisition module 501 acquires multiple source domain training samples and multiple target domain training samples corresponding to the target device. The source domain training samples are determined based on historical operating data corresponding to other cooling systems; the target domain training samples are determined based on historical operating data corresponding to the cooling system.

[0188] The processing module 502 trains the initial cooling system model corresponding to the target device based on multiple source domain training samples to obtain the trained cooling system model corresponding to the target device.

[0189] The processing module 502 adjusts the trained cooling system model corresponding to the target device based on multiple target domain training samples to obtain the target cooling system model corresponding to the target device.

[0190] In some embodiments, the processing module 502 is further configured to determine a plurality of reference cooling capacities within the cooling capacity range of each chiller unit in the chiller unit module;

[0191] The processing module 502 is also used to determine the cooling load rate corresponding to each of the multiple reference cooling caps, and input the chilled water outlet temperature setpoint, cooling water inlet temperature and cooling load rate into the target cooling system model corresponding to the target equipment to obtain the energy efficiency ratio corresponding to the reference cooling capacity.

[0192] The processing module 502 is also used to obtain the power consumption corresponding to the reference cooling capacity for each of the multiple reference cooling caps based on the reference cooling capacity and the energy efficiency ratio corresponding to the reference cooling capacity.

[0193] The performance parameters of the target device under its current operating state include the power consumption corresponding to each of the multiple reference cooling caps.

[0194] In some embodiments, the processing module 502 is further configured to, for each of the multiple device activation quantities, determine multiple total cooling capacities and a first total power consumption corresponding to the device activation quantity based on multiple reference cooling capacities, the power consumption corresponding to each reference cooling capacities, and the device activation quantity;

[0195] The processing module 502 is also used to determine, from all the total cooling capacities corresponding to the number of multiple devices turned on and the first total power consumption, the first target total power consumption corresponding to the total cooling capacity that is greater than the cooling load demand.

[0196] The processing module 502 is also used to take the reference cooling capacity and the number of devices turned on corresponding to the minimum total power consumption in the first target total power consumption as the target operating parameters.

[0197] In some embodiments, the acquisition module 501 is used to acquire the target flow rate of cooling water and multiple reference cooling tower fan frequencies within the frequency range of the cooling tower fan;

[0198] The processing module 502 is also used to input the reference cooling tower fan frequency, the target flow rate of cooling water, the outdoor wet-bulb temperature, and the cooling water temperature difference into the target cooling system model corresponding to the target equipment for each of the multiple reference cooling tower fan frequencies, so as to obtain the cooling tower approximation degree corresponding to the reference cooling tower fan frequency.

[0199] The performance parameters of the target equipment under its current operating state include the cooling tower approximation degree corresponding to each of the multiple reference cooling tower fan frequencies.

[0200] In some embodiments, the processing module 502 is further configured to determine, for each of the multiple device activation quantities, the multiple cooling tower approximation degrees corresponding to the device activation quantity based on the multiple reference cooling tower fan operating frequencies and the device activation quantity;

[0201] The processing module 502 is also used to determine the target cooling tower fan operating frequency corresponding to the cooling tower approximation degree that is less than the preset approximation degree among all cooling tower approximation degrees corresponding to the number of multiple devices turned on; and to take the target cooling tower fan operating frequency and the number of devices turned on as the target operating parameters, which are the target total power consumption corresponding to the target cooling tower fan operating frequency and the second total power consumption corresponding to the minimum second total power consumption.

[0202] In some embodiments, the acquisition module 501 is further configured to acquire the target flow rate of cooling water and a plurality of reference pump operating frequencies within the operating frequency range of the cooling water pump;

[0203] The processing module 502 is also used to input the reference cooling water pump operating frequency and the number of valves opened by the cooling water pump into the target cooling system model corresponding to the target equipment for each reference cooling water pump operating frequency among multiple reference cooling water pump operating frequencies, so as to obtain the head and water flow rate of the cooling water pump corresponding to the reference cooling water pump operating frequency.

[0204] The performance parameters of the target equipment under the current operating state include the head and flow rate of the cooling water pump corresponding to each of the multiple reference cooling water pump operating frequencies.

[0205] In some embodiments, the processing module 502 is further configured to, for each of the multiple device activation quantities, determine multiple first total water flow and multiple third total power consumption corresponding to the device activation quantity based on multiple reference cooling water pump operating frequencies, the water flow rate of the cooling water pump corresponding to each reference cooling water pump operating frequency, and the device activation quantity.

[0206] The processing module 502 is also used to determine, among all the first total water flow and third total power consumption corresponding to the number of multiple devices turned on, the second target total power consumption corresponding to the operating frequency of the cooling water pump where the first total water flow is greater than the first preset water flow and the head of the cooling water pump is greater than the first preset head.

[0207] The processing module 502 is also used to take the reference cooling water pump operating frequency and the number of devices turned on as the target operating parameters, which correspond to the minimum third total power consumption in the second target total power consumption.

[0208] In some embodiments, the acquisition module 501 is further configured to acquire the target flow rate of chilled water and the operating frequencies of multiple reference water pumps within the range of chilled water pump operating frequencies;

[0209] The processing module 502 is also used to input the reference chilled water pump operating frequency and the number of valves opened of the chilled water pump into the target cooling system model corresponding to the target equipment for each reference chilled water pump operating frequency among multiple reference chilled water pump operating frequencies, so as to obtain the head and water flow rate of the chilled water pump corresponding to the reference chilled water pump operating frequency.

[0210] The performance parameters of the target equipment under the current operating state include the head and flow rate of the chilled water pump corresponding to each of the multiple reference chilled water pump operating frequencies.

[0211] In some embodiments, the processing module 502 is further configured to, for each of the multiple device activation quantities, determine multiple second total water flow rates and fourth total power consumption corresponding to the device activation quantity based on multiple reference chilled water pump operating frequencies, the chilled water pump water flow rate corresponding to each reference chilled water pump operating frequency, and the device activation quantity.

[0212] The processing module 502 is also used to determine, among all the second total water flow and fourth total power consumption corresponding to the number of multiple devices turned on, the third target total power consumption corresponding to the operating frequency of the chilled water pump where the second total water flow is greater than the second preset water flow and the head of the chilled water pump is greater than the second preset head.

[0213] The processing module 502 is also used to take the reference chilled water pump operating frequency and the number of devices turned on corresponding to the minimum total power consumption in the third target total power consumption as the target operating parameters.

[0214] This application also provides a computer-readable storage medium including computer-executable instructions that, when run on a computer, cause the computer to execute any of the cooling system control methods provided in the above embodiments.

[0215] This application also provides a computer program product containing computer execution instructions, which, when run on a computer, causes the computer to execute any of the cooling system control methods provided in the above embodiments.

[0216] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented using software programs, implementation can be, in whole or in part, in the form of a computer program product. This computer program product includes one or more computer-executable instructions. When these computer-executable instructions are loaded and executed on a computer, all or part of the flow or function according to the embodiments of this application is generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer-executable instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer-executable instructions can be transmitted from one website, computer, server, or data center to another via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium accessible to a computer or a data storage device containing one or more servers, data centers, etc., that can be integrated with the medium. The available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media (e.g., solid-state drives (SSDs)).

[0217] Although this application has been described herein in conjunction with various embodiments, those skilled in the art, by reviewing the accompanying drawings, disclosure, and appended claims, will understand and implement other variations of the disclosed embodiments in carrying out the claimed application. In the claims, the word "comprising" does not exclude other components or steps, and "a" or "an" does not exclude multiple instances. A single processor or other unit can implement several functions listed in the claims. While different dependent claims may recite certain measures, this does not mean that these measures cannot be combined to produce good results.

[0218] Although this application has been described in conjunction with specific features and embodiments, it is obvious that various modifications and combinations can be made thereto without departing from the spirit and scope of this application. Accordingly, this specification and drawings are merely illustrative descriptions of the application as defined by the appended claims, and are considered to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from the spirit and scope of this application. Thus, if such modifications and modifications of this application fall within the scope of the claims of this application and their equivalents, this application is also intended to include such modifications and modifications.

[0219] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A control method for a cooling system, characterized in that, The method includes: Obtain real-time operating data corresponding to the target device in the cooling system; Using the target cooling system model corresponding to the target device, the performance parameters of the target device in its current operating state are determined based on the corresponding operating data. The target device includes a chiller module; the real-time operating data of the chiller module includes the cooling water inlet temperature and the chilled water outlet temperature. Multiple reference cooling capacities are determined within the cooling capacity range of each chiller unit in the chiller module. For each of the multiple reference cooling capacities, the cooling load rate corresponding to the reference cooling capacity is determined, and the chilled water outlet temperature setpoint, the cooling water inlet temperature, and the cooling load rate are input to the target cooling system model corresponding to the target device to obtain the energy efficiency ratio corresponding to the reference cooling capacity. For each of the multiple reference cooling capacities, the power consumption corresponding to the reference cooling capacity is obtained based on the reference cooling capacity and the corresponding energy efficiency ratio. The performance parameters of the target device in its current operating state include the power consumption corresponding to each of the multiple reference cooling capacities. Determining the target operating parameters corresponding to the target equipment based on the performance parameters specifically includes: for each of the multiple equipment activation quantities in the chiller unit module, determining multiple total cooling capacities and a first total power consumption corresponding to the equipment activation quantity based on multiple reference cooling capacities, the power consumption corresponding to each reference cooling capacity, and the equipment activation quantity; determining a first target total power consumption corresponding to the total cooling capacities greater than the cooling load demand among all the total cooling capacities and first total power consumption corresponding to the multiple equipment activation quantities; and using the reference cooling capacities and equipment activation quantities corresponding to the minimum power consumption among the first target total power consumption as the target operating parameters. Control the operation of the target device according to the target operating parameters; The target cooling system model corresponding to the target device was trained in the following way: Multiple source domain training samples and multiple target domain training samples corresponding to the target device are obtained. The source domain training samples are determined based on historical operating data corresponding to other cooling systems. The target domain training samples are determined based on historical operating data corresponding to the cooling system. The initial cooling system model corresponding to the target device is trained based on multiple source domain training samples to obtain the trained cooling system model corresponding to the target device. The trained cooling system model corresponding to the target device is adjusted based on multiple training samples of the target domain to obtain the target cooling system model corresponding to the target device.

2. The method according to claim 1, characterized in that, When the target device is a cooling tower module, the real-time operating data includes: outdoor ambient wet-bulb temperature, cooling water target proximity, and cooling water temperature difference. The step of determining the performance parameters of the target device in its current operating state using the target cooling system model corresponding to the target device and based on the operating data of the target device includes: Obtain the target cooling water flow rate and multiple reference cooling tower fan frequencies within the cooling tower fan frequency range; For each of the multiple reference cooling tower fan frequencies, the reference cooling tower fan frequency, the target cooling water flow rate, the outdoor wet-bulb temperature, and the cooling water temperature difference are input into the target cooling system model corresponding to the target equipment to obtain the cooling tower approximation degree corresponding to the reference cooling tower fan frequency. The performance parameters of the target device under its current operating state include the cooling tower approximation degree corresponding to each of the plurality of reference cooling tower fan frequencies.

3. The method according to claim 2, characterized in that, Determining the target operating parameters corresponding to the target device based on the performance parameters includes: For each of the multiple device activation quantities, the multiple cooling tower approximation degrees corresponding to the device activation quantity are determined based on the multiple reference cooling tower fan frequencies and the device activation quantity. Among all the cooling tower proximity degrees corresponding to the number of devices turned on, the target cooling tower fan frequency corresponding to the cooling tower proximity degree that is less than the preset proximity degree is determined; the target cooling tower fan frequency corresponding to the minimum second total power consumption among the target cooling tower fan frequencies and the number of devices turned on are taken as the target operating parameters.

4. The method according to claim 1, characterized in that, When the target device is a cooling water pump module, the real-time operating data includes the operating frequency of the cooling water pump and the number of valves opened by the cooling water pump. The step of determining the performance parameters of the target device in its current operating state using the target cooling system model corresponding to the target device and based on the operating data of the target device includes: Obtain the target flow rate of cooling water and the operating frequencies of multiple reference water pumps within the operating frequency range of the cooling water pump; For each reference cooling water pump operating frequency among multiple reference cooling water pump operating frequencies, the reference cooling water pump operating frequency and the number of valves opened by the cooling water pump are input into the target cooling system model corresponding to the target equipment to obtain the head and water flow rate of the cooling water pump corresponding to the reference cooling water pump operating frequency. The performance parameters of the target device under its current operating state include the head and flow rate of the cooling water pump corresponding to each of the multiple reference cooling water pump operating frequencies.

5. The method according to claim 4, characterized in that, Determining the target operating parameters corresponding to the target device based on the performance parameters includes: For each of the multiple device activation quantities, based on the multiple reference cooling water pump operating frequencies, the water flow rate of the cooling water pump corresponding to each reference cooling water pump operating frequency, and the device activation quantity, multiple first total water flow rates and multiple third total power consumption are determined for the device activation quantity. Among all the first total water flow and third total power consumption corresponding to the number of devices turned on, determine the second target total power consumption corresponding to the operating frequency of the cooling water pump where the first total water flow is greater than the first preset water flow and the head of the cooling water pump is greater than the first preset head. The reference cooling water pump operating frequency and the number of devices turned on are taken as the target operating parameters, corresponding to the third total power consumption that is the smallest among the second target total power consumption.

6. The method according to claim 1, characterized in that, When the target device is a chilled water pump module, the real-time operating data includes the chilled water pump operating frequency and the number of valves opened by the chilled water pump; The step of determining the performance parameters of the target device in its current operating state using the target cooling system model corresponding to the target device and based on the operating data of the target device includes: Obtain the target flow rate of chilled water and the operating frequencies of multiple reference pumps within the operating frequency range of the chilled water pump; For each reference chilled water pump operating frequency among multiple reference chilled water pump operating frequencies, the reference chilled water pump operating frequency and the number of valves opened by the chilled water pump are input into the target cooling system model corresponding to the target equipment to obtain the head and water flow rate of the chilled water pump corresponding to the reference chilled water pump operating frequency; The performance parameters of the target device under its current operating state include the head and flow rate of the chilled water pump corresponding to each of the plurality of reference chilled water pump operating frequencies.

7. The method according to claim 6, characterized in that, Determining the target operating parameters corresponding to the target device based on the performance parameters includes: For each of the multiple device activation quantities, based on the multiple reference chilled water pump operating frequencies, the chilled water pump flow rate corresponding to each reference chilled water pump operating frequency, and the device activation quantity, determine the multiple second total water flow rates and the fourth total power consumption corresponding to the device activation quantity; Among all the second total water flow and fourth total power consumption corresponding to the number of devices turned on, the third target total power consumption is determined to be the operating frequency of the chilled water pump that has a second total water flow greater than the second preset water flow and a chilled water pump head greater than the second preset head. The reference chilled water pump operating frequency and the number of devices turned on are taken as the target operating parameters, corresponding to the minimum total power consumption in the third target.

8. A cooling system, characterized in that, include: The acquisition module is used to acquire real-time operating data corresponding to the target device in the cooling system; A processing module is configured to utilize a target cooling system model corresponding to the target device to determine the performance parameters of the target device under its current operating state based on the corresponding operating data. The target device includes a chiller module; the real-time operating data of the chiller module includes the cooling water inlet temperature and the chilled water outlet temperature. Multiple reference cooling capacities are determined within the cooling capacity range of each chiller unit in the chiller module. For each of the multiple reference cooling capacities, a cooling load rate corresponding to the reference cooling capacity is determined, and the chilled water outlet temperature setpoint, the cooling water inlet temperature, and the cooling load rate are input to the target cooling system model corresponding to the target device to obtain the energy efficiency ratio corresponding to the reference cooling capacity. For each of the multiple reference cooling capacities, the power consumption corresponding to the reference cooling capacity is obtained based on the reference cooling capacity and the corresponding energy efficiency ratio. The performance parameters of the target device under its current operating state include the power consumption corresponding to each of the multiple reference cooling capacities. The processing module is further configured to determine the target operating parameters corresponding to the target device based on the performance parameters, specifically including: for each device activation quantity among the multiple device activation quantities of the chiller unit module, based on multiple reference cooling capacities, the power consumption corresponding to each reference cooling capacities, and the device activation quantity, determining multiple total cooling capacities and a first total power consumption corresponding to the device activation quantity; among all the total cooling capacities and first total power consumption corresponding to the multiple device activation quantities, determining a first target total power consumption corresponding to the total cooling capacities greater than the cooling load demand; and using the reference cooling capacities and device activation quantities corresponding to the minimum power consumption among the first target total power consumption as the target operating parameters. The processing module is also used to control the operation of the target device according to the target operating parameters; The target cooling system model corresponding to the target device was trained in the following way: The acquisition module acquires multiple source domain training samples and multiple target domain training samples corresponding to the target device. The source domain training samples are determined based on historical operating data corresponding to other cooling systems. The target domain training samples are determined based on historical operating data corresponding to the cooling system. The processing module trains the initial cooling system model corresponding to the target device based on multiple source domain training samples to obtain the trained cooling system model corresponding to the target device. The processing module adjusts the trained cooling system model corresponding to the target device based on multiple training samples of the target domain to obtain the target cooling system model corresponding to the target device.

9. A communication device, characterized in that, It includes a memory and a processor; the memory and the processor are coupled, the memory is used to store a computer program, and the processor executes the computer program to implement the control method of the cooling system as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that, when executed on a communication device, cause the communication device to perform the control method of the cooling system as described in any one of claims 1 to 7.

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