Control method and system for indirect evaporative cooling system

Abstract

The present application provides a control method for an indirect evaporative cooling system, comprising: creating an indirect evaporative cooling system refrigeration model and an indirect evaporative cooling system operation power consumption model; obtaining training data; training the indirect evaporative cooling system refrigeration model and the indirect evaporative cooling system operation power consumption model based on the training data to obtain a determined indirect evaporative cooling system refrigeration model and the indirect evaporative cooling system operation power consumption model; optimizing the operation cost of the indirect evaporative cooling system based at least on the determined model to obtain output parameter data of the indirect evaporative cooling system refrigeration model when the operation cost is optimized, thereby controlling the operation of the indirect evaporative cooling system. The present application can ensure the optimal operation cost of the indirect evaporative cooling system in different climates and locations, and controls the operation of the indirect evaporative cooling system based on the indoor supply air temperature and the indoor return air temperature, thereby quickly and accurately ensuring that the cooling capacity meets the needs of the cooled units.

Description

Technical Field

[0001] The present application relates to an indirect evaporative cooling system, and more particularly to a control method and system for an indirect evaporative cooling system. Background Art

[0002] To meet the global trend of digitalization, data centers are developing at an unprecedented pace, and their energy consumption is becoming increasingly important. For example, research shows that data centers account for 1.5% of total electricity consumption, with air conditioning systems contributing nearly 40% of this energy consumption. This is a key factor affecting the power usage effectiveness (PUE) of data centers, making energy conservation and consumption reduction a key consideration in the initial planning stages of major data centers. Indirect evaporative cooling, which utilizes natural cooling to improve energy efficiency, has been demonstrated in numerous large data centers around the world due to its energy efficiency and high applicability. Summary of the Invention

[0003] The inventors have observed that the control logic of the current indirect evaporative cooling system / unit is relatively traditional. For example, different operating modes of the indirect evaporative cooling system / unit are activated based on the dry-bulb and wet-bulb temperature values ​​of the outdoor air. More specifically, when the outdoor dry-bulb temperature is lower than a first set value, the outdoor fan system is activated, and the air in the cooled unit (e.g., the machine room) is heat-exchanged with the outdoor air (outside the cooled unit) only through the air-to-air heat exchange core. When the outdoor dry-bulb temperature is higher than the first set value and the wet-bulb temperature is lower than a second set value, the water mist system is activated for spraying, that is, by spraying water or water mist onto the air-to-air heat exchange core, the outdoor air temperature is lowered before heat-exchanging with the air in the cooled unit. When the outdoor air wet-bulb temperature is higher than the second set value, that is, when the spray mode cannot meet the temperature requirements of the cooled unit, the direct expansion refrigeration system is activated to further lower the air temperature of the cooled unit.

[0004] Currently, most indirect evaporative cooling systems / units on the market use variable-frequency fans and compressors to achieve energy savings and cost reductions. However, the inventors have discovered that conventional control logic for indirect evaporative cooling systems / units, which activate different operating modes solely based on the fixed wet-bulb and dry-bulb temperatures of the outdoor air, suffers from the following drawbacks: Firstly, this logic fails to account for the diverse climate distribution in my country, as well as the varying electricity and water costs in different locations, and therefore cannot guarantee the lowest total energy consumption / cost for system operation; secondly, the heat transfer efficiency of the air-to-air heat exchanger in wet mode is dependent on outdoor air parameters. Activating the direct expansion cooling system based on a predetermined wet-bulb temperature results in a cooling capacity that fails to meet the cooling requirements of the cooled units as the outdoor temperature rises.

[0005] To address the aforementioned issues, the present application provides a control method and system for an indirect evaporative cooling system. Specifically, the present application obtains multiple sets of control parameter data for controlling the indirect evaporative cooling system that meet the requirements of the cooled unit. Based on these multiple sets of control parameter data, the operating cost of the indirect evaporative cooling system is optimized, so that the total operating cost of the indirect evaporative cooling system is optimal under a certain set of control parameter data. When optimizing the operating cost of the indirect evaporative cooling system, the present application considers the total cost of electricity and water used by the indirect evaporative cooling system in different climates and locations, thereby ensuring that the total operating cost of the indirect evaporative cooling system is always optimal (e.g., lowest) in different climates and locations. In different climates, such as different regions, the unit price of electricity and water may vary (even significantly), and the unit price of water may also vary (even significantly). Therefore, even with the same set of control parameter data, the operating cost of the indirect evaporative cooling system may vary (even significantly). For example, the electricity / energy saving mode may be suitable for areas with abundant water resources, while the water saving mode may be suitable for areas with limited water resources, depending on the total cost of electricity and water used in these regions. This application considers the total operating cost of the indirect evaporative cooling system under different climates and locations while considering whether the control parameter data meets the needs of the cooled unit.

[0006] Furthermore, the present application controls the operation of the indirect evaporative cooling system based at least on the indoor supply air temperature provided by the indirect evaporative cooling system to the cooled unit and the indoor return air temperature returned from the cooled unit to the indirect evaporative cooling system to quickly and accurately meet the needs of the cooled unit, such as the cooling capacity demand. The present application monitors the indoor supply air temperature output by the indirect evaporative cooling system to ensure that the output meets the temperature demand received by the cooled unit, and monitors the indoor return air temperature returned from the cooled unit to the indirect evaporative cooling system to determine whether the cooled unit actually meets the temperature demand. When both the indoor supply air temperature and the indoor return air temperature meet the demand, it indicates that the indirect evaporative cooling system is accurately providing the cooling capacity required by the cooled unit. Otherwise, the cooling capacity actually required by the cooled unit is not met. For example, if the indoor supply air temperature output by the indirect evaporative cooling system does not meet the temperature demand received by the cooled unit, it may indicate that the indirect evaporative cooling system is not operating properly, and therefore the operation of the indirect evaporative cooling system needs to be adjusted. When the indoor return air temperature from the cooled unit to the indirect evaporative cooling system does not meet the actual temperature requirement of the cooled unit, this may indicate an increase in the actual load of the cooled unit or a component failure in the cooled unit, necessitating the corresponding adjustment of the indirect evaporative cooling system operation. Based on the monitored indoor supply air temperature and indoor return air temperature, the present application can adjust the operation of the indirect evaporative cooling system accordingly to accurately meet the cooling capacity requirements of the cooled unit.

[0007] More specifically, according to a first aspect of the present application, a control method for an indirect evaporative cooling system is provided. The control method includes the following steps S1-S4. In step S1, a refrigeration model and an operating power consumption model of the indirect evaporative cooling system are created. The refrigeration model of the indirect evaporative cooling system includes input parameters and output parameters, and the operating power consumption model of the indirect evaporative cooling system includes input parameters and output parameters. The output parameters of the refrigeration model of the indirect evaporative cooling system include control parameters of the indirect evaporative cooling system. The output parameters of the operating power consumption model of the indirect evaporative cooling system include operating power consumption of various components of the indirect evaporative cooling system. In step S2, training data is acquired, the training data including data on input parameters and output parameters of the refrigeration model of the indirect evaporative cooling system and data on input parameters and output parameters of the operating power consumption model of the indirect evaporative cooling system. In step S3, the refrigeration model and the operating power consumption model of the indirect evaporative cooling system are trained based on the acquired training data to obtain a determined refrigeration model and a determined operating power consumption model of the indirect evaporative cooling system. In step S4, the operating cost of the indirect evaporative cooling system is optimized based at least on the determined indirect evaporative cooling system refrigeration model and the determined indirect evaporative cooling system operation power consumption model to obtain data on the output parameters of the indirect evaporative cooling system refrigeration model corresponding to the optimization of the operating cost of the indirect evaporative cooling system, thereby controlling the operation of the indirect evaporative cooling system.

[0008] According to the first aspect of the present application, the output parameters of the refrigeration model of the indirect evaporative cooling system are used as input parameters of the operation power consumption model of the indirect evaporative cooling system.

[0009] According to the first aspect of the present application, the input parameters of the refrigeration model of the indirect evaporative cooling system include indoor supply air temperature and indoor return air temperature.

[0010] According to the first aspect of the present application, in step S4, steps S4.1-S4.3 are executed to obtain data on output parameters of the indirect evaporative cooling system refrigeration model corresponding to the operation cost optimization of the indirect evaporative cooling system. In step S4.1, field data of input parameters of the indirect evaporative cooling system refrigeration model is obtained. In step S4.2, the obtained field data is input into the determined indirect evaporative cooling system refrigeration model to obtain a data set of output parameters of the indirect evaporative cooling system refrigeration model. In step S4.3, the obtained data set of output parameters of the indirect evaporative cooling system refrigeration model is input into the indirect evaporative cooling system operation cost optimization model to obtain data on output parameters of the indirect evaporative cooling system refrigeration model corresponding to the operation cost optimization of the indirect evaporative cooling system.

[0011] According to the first aspect of the present application, the indirect evaporative cooling system operating cost optimization model is configured to: obtain an operating power consumption set consisting of the operating power consumption of each part of the indirect evaporative cooling system based on the obtained data set of output parameters of the indirect evaporative cooling system refrigeration model and the indirect evaporative cooling system operating power consumption model; obtain an operating cost set of the indirect evaporative cooling system based on the obtained operating power consumption set; and optimize the obtained operating cost set to obtain data on the output parameters of the indirect evaporative cooling system refrigeration model corresponding to the operation cost optimization of the indirect evaporative cooling system.

[0012] According to the first aspect of the present application, the operating power consumption of each part of the indirect evaporative cooling system includes electricity consumption and water consumption, and the operating cost of the indirect evaporative cooling system includes the electricity cost generated by the electricity consumption and the water cost generated by the water consumption.

[0013] According to a first aspect of the present application, the indirect evaporative cooling system is configured to cool a cooled unit. The indirect evaporative cooling system includes a water mist system. The output parameter of the refrigeration model of the indirect evaporative cooling system includes the speed of the nozzle moving motor of the water mist system, and the nozzle moving motor is configured to control the movement of the nozzle of the water mist system. The control method further includes obtaining a motor speed limit model. The motor speed limit model is configured to obtain a speed limit of the nozzle moving motor after the outdoor air is sprayed by the water mist system based on the outdoor air dry-bulb temperature and the outdoor air dry air humidity content of the cooled unit. In step S4, the speed limit of the nozzle moving motor after the outdoor air is sprayed by the water mist system at the current outdoor air dry-bulb temperature and the current outdoor air dry air humidity content is obtained, and the speed of the nozzle moving motor when the outdoor air is sprayed by the water mist system is limited to within the corresponding speed limit of the nozzle moving motor, thereby optimizing the operating cost of the indirect evaporative cooling system.

[0014] According to the first aspect of the present application, the motor speed limit model is obtained by the following operations: creating and obtaining a temperature and humidity model between the air humidity content and the air temperature of the outdoor air after being sprayed by the water spray system, wherein the temperature and humidity model takes the current outdoor air dry-bulb temperature and the current outdoor air dry air humidity content as constants; creating and obtaining a temperature and humidity limit model between the air humidity limit and the air temperature limit when the outdoor air is sprayed by the water spray system when the air humidity is saturated; creating and obtaining a speed and humidity limit model between the speed limit of the nozzle moving motor and the air humidity limit when the air humidity is saturated; and combining the obtained temperature and humidity model, the temperature and humidity limit model and the speed and humidity limit model to obtain the motor speed limit model.

[0015] According to the first aspect of the present application, the indirect evaporative cooling system further includes a direct expansion refrigeration system and a fan system. The input parameters of the refrigeration model of the indirect evaporative cooling system include the target parameters of the cooled unit and the operating parameters of the indirect evaporative cooling system. The output parameters of the refrigeration model of the indirect evaporative cooling system include control parameters for operating the water mist system, the direct expansion refrigeration system, and the fan system. The output parameters of the indirect evaporative cooling system operation power consumption model include the electricity consumption and water consumption of the water mist system of the indirect evaporative cooling system, the electricity consumption of the direct expansion refrigeration system, and the electricity consumption of the fan system. The operating cost of the indirect evaporative cooling system includes the sum of the electricity cost and water cost of the water mist system of the indirect evaporative cooling system, the electricity cost of the direct expansion refrigeration system, and the electricity cost of the fan system.

[0016] According to a first aspect of the present application, the direct expansion refrigeration system includes a compressor, a condenser, an evaporator, and a flow control valve. The fan system includes an indoor fan and an outdoor fan. The target parameter of the cooled unit is the target temperature of the cooled unit. The operating parameters of the indirect evaporative cooling system include the outdoor air temperature, outdoor air humidity, indoor supply air temperature, indoor supply air humidity, indoor return air temperature, and indoor return air humidity of the cooled unit. The control parameters for operating the water mist system include the speed of the nozzle moving motor, the control parameters for operating the direct expansion refrigeration system include the speed of the compressor and the opening of the flow control valve, and the control parameters for operating the fan system include the fan speed of the outdoor fan. The power consumption of the water mist system includes the power consumption of the nozzle moving motor, the water consumption of the water mist system includes the water spraying volume of the nozzle, the power consumption of the direct expansion refrigeration system includes the power consumption of the compressor, and the power consumption of the fan system includes the power consumption of the indoor fan and the power consumption of the outdoor fan. The electricity cost of the water mist system includes the electricity cost generated by the power consumption of the nozzle moving motor, the water cost of the water mist system includes the water cost generated by the water spraying volume of the nozzle, the electricity cost of the direct expansion refrigeration system includes the electricity cost generated by the power consumption of the compressor, and the electricity cost of the fan system includes the electricity cost generated by the power consumption of the indoor fan and the electricity cost generated by the power consumption of the outdoor fan.

[0017] According to the first aspect of the present application, the refrigeration model of the indirect evaporative cooling system and the operation power consumption model of the indirect evaporative cooling system are both fully connected deep neural network models.

[0018] According to the first aspect of the present application, a back propagation algorithm and a gradient descent optimization algorithm are used to train the indirect evaporative cooling system refrigeration model and the indirect evaporative cooling system operation power consumption model.

[0019] According to a first aspect of the present application, the indirect evaporative cooling system operating cost optimization model is configured to use a genetic algorithm to optimize the operating cost of the indirect evaporative cooling system, wherein the fitness function in the genetic algorithm is obtained based on the determined indirect evaporative cooling system operating power consumption model.

[0020] According to a first aspect of the present application, the indirect evaporative cooling system is applied to a test bench, and data obtained from the test bench is used as training data to train the indirect evaporative cooling system refrigeration model and the indirect evaporative cooling system operation power consumption model, thereby obtaining a determined indirect evaporative cooling system refrigeration model and the determined indirect evaporative cooling system operation power consumption model. The indirect evaporative cooling system is applied to a cooling unit on site, and data obtained from the cooling unit on site is used as further training data to further train the determined indirect evaporative cooling system refrigeration model and the determined indirect evaporative cooling system operation power consumption model, thereby adjusting the determined indirect evaporative cooling system refrigeration model and the determined indirect evaporative cooling system operation power consumption model, thereby obtaining a final determined indirect evaporative cooling system refrigeration model and the indirect evaporative cooling system operation power consumption model.

[0021] According to a second aspect of the present application, a computing system is provided, comprising a processor configured to execute the aforementioned control method to obtain a determined refrigeration model of an indirect evaporative cooling system and a determined operation power consumption model of the indirect evaporative cooling system.

[0022] According to a third aspect of the present application, a control system is provided. The control system includes an indirect evaporative cooling system module and an indirect evaporative cooling system operating cost optimization module. The indirect evaporative cooling system operating cost optimization module is connected to the indirect evaporative cooling system module. The indirect evaporative cooling system operating cost optimization module is configured to execute the aforementioned control method based on the current operating condition data of the indirect evaporative cooling system and the indirect evaporative cooling system module to optimize the operating cost of the indirect evaporative cooling system, thereby obtaining control data of the indirect evaporative cooling system corresponding to the indirect evaporative cooling system operating cost optimization, thereby controlling the operation of the indirect evaporative cooling system. The indirect evaporative cooling system module includes the determined indirect evaporative cooling system refrigeration model and the determined indirect evaporative cooling system operating power consumption model obtained by the aforementioned control method. Alternatively, the indirect evaporative cooling system module and the indirect evaporative cooling system operating cost optimization module each include the determined indirect evaporative cooling system refrigeration model and the determined indirect evaporative cooling system operating power consumption model obtained by the aforementioned control method.

[0023] According to a third aspect of the present application, the obtained determined refrigeration model of the indirect evaporative cooling system and the determined operating power consumption model of the indirect evaporative cooling system are deployed into the indirect evaporative cooling system module by the aforementioned computing system. Alternatively, the obtained determined refrigeration model of the indirect evaporative cooling system and the determined operating power consumption model of the indirect evaporative cooling system are deployed into the indirect evaporative cooling system module and the indirect evaporative cooling system operating cost optimization module, respectively, by the aforementioned computing system.

[0024] According to the third aspect of the present application, the control system includes a processor, which is configured to execute the aforementioned control method to obtain a determined indirect evaporative cooling system refrigeration model and a determined indirect evaporative cooling system operation power consumption model, and deploy the obtained determined indirect evaporative cooling system refrigeration model and the determined indirect evaporative cooling system operation power consumption model into the indirect evaporative cooling system module, or respectively deploy them into the indirect evaporative cooling system module and the indirect evaporative cooling system operation cost optimization module.

[0025] According to a fourth aspect of the present application, the present application provides an indirect evaporative cooling system. The indirect evaporative cooling system includes a water mist spray system, a direct expansion refrigeration system, a fan system, a detection device and a controller. The detection device is connected to at least one of the water mist spray system, the direct expansion refrigeration system and the fan system to detect the operating condition data of the at least one. The controller is configured to control the operation of at least one of the water mist spray system, the direct expansion refrigeration system and the fan system based on a control signal received from the aforementioned control system. The control system is configured to optimize the operating cost of the indirect evaporative cooling system based on the operating condition data detected by the detection device, so as to obtain the control data of the indirect evaporative cooling system corresponding to the optimization of the operating cost of the indirect evaporative cooling system, thereby generating the control signal. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] The drawings are not drawn to scale. In the drawings, each identical or nearly identical component that is represented in different figures is represented by a like reference numeral. For clarity, not every component may be labeled in every figure. In the drawings:

[0027] Figure 1 shows a block diagram structure of an overall system for controlling an indirect evaporative cooling system according to the present application;

[0028] Figure 2 Shown Figure 1 The block diagram structure of an embodiment of the indirect evaporative cooling system and detection device shown;

[0029] Figure 3 Shown Figure 1 A general flow chart of an embodiment of a control method for an indirect evaporative cooling system is shown;

[0030] Figure 4 Shown Figure 3 The block diagram structure of an embodiment of the indirect evaporative cooling system refrigeration model and the indirect evaporative cooling system operation power consumption model created in step 304 is shown;

[0031] Figure 5 Shown Figure 3 A detailed flow chart of one embodiment of step 308 is shown;

[0032] Figure 6 Shown Figure 3 A detailed flow chart of one embodiment of step 310 is shown;

[0033] Figure 7 Shown Figure 6 A detailed flow chart of one embodiment of step 606 is shown;

[0034] Figure 8 A flow chart showing an embodiment of a method for obtaining a motor speed limit model;

[0035] Figure 9 shows the psychrometric diagram of air;

[0036] Figure 10 Shown Figure 1 The block diagram structure of the PID control of the controller shown; and

[0037] Figure 11 Shown Figure 1 The structural block diagram of the computing system and control system shown. DETAILED DESCRIPTION

[0038] Various embodiments of the present application will be described below with reference to the accompanying drawings which constitute a part of this specification. It should be understood that, where possible, the same or similar reference numerals used in this application refer to the same components.

[0039] Figure 1A block diagram of an overall system for controlling an indirect evaporative cooling system according to the present application is shown. The present application creates and determines a cooling model and an operating power consumption model for the indirect evaporative cooling system. Array control parameter data for the indirect evaporative cooling system 100 is obtained based on the determined cooling model and operating power consumption model and field data of the indirect evaporative cooling system 100. The operating cost of the indirect evaporative cooling system 100 is optimized based on the obtained array control parameter data to obtain control parameter data, such as a set of control parameter data, for the indirect evaporative cooling system 100 when the operating cost of the indirect evaporative cooling system 100 is optimized (e.g., the total operating cost is optimal / lowest), thereby controlling the operation of the indirect evaporative cooling system 100.

[0040] like Figure 1 As shown, the overall system for controlling the indirect evaporative cooling system includes an indirect evaporative cooling system 100, a computing system 110, a control system 111 and a detection device 112. The indirect evaporative cooling system 100 is configured to cool the cooled unit 106. In one embodiment, the cooled unit 106 is a data center, for example, a computer room of a data center. In other embodiments, the cooled unit 106 includes other suitable units that need to be cooled by the indirect evaporative cooling system. The indirect evaporative cooling system 100 includes a controller 101 and a unit assembly 102. The controller 101 is configured to control the operation of the unit assembly 102 to provide the required cooling capacity to the cooled unit 106. The unit assembly 102 includes a water spray system 103, a direct expansion refrigeration system 104 and a fan system 105. An embodiment of the specific structure of the unit assembly 102 is described in detail. Figure 2 .

[0041] Detection device 112 is connected to indirect evaporative cooling system 100 and is configured to detect and obtain operating parameters of indirect evaporative cooling system 100. Detection device 112 includes several sensors, such as a temperature sensor, a humidity sensor, a speed sensor, an opening sensor, a flow sensor, etc. In some embodiments, detection device 112 is disposed external to indirect evaporative cooling system 100. In some embodiments, detection device 112 is integral to indirect evaporative cooling system 100.

[0042] The computing system 110 is configured to create an indirect evaporative cooling system refrigeration model and an indirect evaporative cooling system operating power consumption model, acquire training data, and train the indirect evaporative cooling system refrigeration model and the indirect evaporative cooling system operating power consumption model based on the acquired training data to obtain a determined indirect evaporative cooling system refrigeration model and a determined indirect evaporative cooling system operating power consumption model. The computing system 110 is connected to a detection device 112 via a connection 115 and is configured to acquire operating condition parameter data from the detection device 112 via the connection 115 and use this data as training data. The computing system 110 is connected to a control system 111 via a connection 113 and is configured to transmit the obtained determined indirect evaporative cooling system refrigeration model and the determined indirect evaporative cooling system operating power consumption model as a module package to the control system 111 via the connection 113. In one embodiment, the present application applies the indirect evaporative cooling system 100 to a test bench, which includes a cooled unit 106. At the test bench, the indirect evaporative cooling system 100 operates to cool the cooled unit 106. This application obtains operating parameter data of the indirect evaporative cooling system 100 during operation as training data. Based on this acquired training data, the computing system 110 trains the indirect evaporative cooling system refrigeration model and the indirect evaporative cooling system operation power consumption model to obtain a specific indirect evaporative cooling system refrigeration model and a specific indirect evaporative cooling system operation power consumption model. In one embodiment, the computing system 110 is a computer. In other embodiments, the computing system 110 includes other suitable devices or equipment. In one embodiment, the overall system for controlling the indirect evaporative cooling system does not include the computing system 110. Instead, the control system 111 performs the operations of the computing system 110 in this embodiment.

[0043] Considering that the determined indirect evaporative cooling system refrigeration model and the determined indirect evaporative cooling system operating power consumption model obtained based on the test bench data may deviate when the indirect evaporative cooling system 100 is applied to the cooled unit 106 actually operated by the user, the present application further obtains operating parameter data of the indirect evaporative cooling system 100 when the indirect evaporative cooling system 100 is applied to the cooled unit 106 actually operated by the user to further train the model, thereby obtaining an indirect evaporative cooling system refrigeration model and an indirect evaporative cooling system operating power consumption model adapted to the cooled unit 106 in the field. Specifically, after obtaining the determined indirect evaporative cooling system refrigeration model and the determined indirect evaporative cooling system operating power consumption model from the test bench, the present application further applies the indirect evaporative cooling system 100 to the field site of the cooled unit 106 actually operated by the user, and also applies the detection device 112 to the field site. The detection device 112 obtains the operating parameter data of the indirect evaporative cooling system 100 during operation at the cooled unit 106 in the field site as further training data. The computing system 110 further trains the determined indirect evaporative cooling system refrigeration model and the determined indirect evaporative cooling system operation power consumption model obtained on the test bench based on the further training data, and adjusts (for example, fine-tunes) the determined indirect evaporative cooling system refrigeration model and the determined indirect evaporative cooling system operation power consumption model to obtain the final determined indirect evaporative cooling system refrigeration model and the determined indirect evaporative cooling system operation power consumption model.

[0044] The control system 111 is configured to optimize the operating cost of the indirect evaporative cooling system 100 based on at least a determined indirect evaporative cooling system refrigeration model and a determined indirect evaporative cooling system operating power consumption model, thereby obtaining control parameter data for the indirect evaporative cooling system 100 at the time when the operating cost of the indirect evaporative cooling system 100 is optimized, thereby controlling the operation of the indirect evaporative cooling system 100. The control system 111 is connected to the detection device 112 via a connection line 116 and is configured to obtain real-time operating parameter data from the detection device 112 via the connection line 116 and use this data as field data. The control system 111 is connected to the indirect evaporative cooling system 100 via a connection line 114 and is configured to transmit the obtained control parameter data for the indirect evaporative cooling system 100 as a control signal to the indirect evaporative cooling system 100, such as its controller 101, via the connection line 114, to control the operation of the indirect evaporative cooling system 100. In one embodiment, the control parameter data for the indirect evaporative cooling system 100 is output parameter data of the corresponding indirect evaporative cooling system refrigeration model. In other embodiments, the control parameter data for the indirect evaporative cooling system 100 includes other suitable parameter data. The operating cost of the indirect evaporative cooling system 100 includes electricity cost and water cost.

[0045] When optimizing the operating cost of the indirect evaporative cooling system 100, the present application considers the total cost of electricity and water used by the indirect evaporative cooling system 100 under different climates and locations, thereby ensuring that the total operating cost of the indirect evaporative cooling system 100 is optimal, that is, the total operating cost is minimized. At the same time, the present application controls the water spray system 103, the direct expansion cooling system 104, and the fan system 105 of the indirect evaporative cooling system 100 based at least on the indoor supply air temperature provided by the indirect evaporative cooling system 100 to the cooled unit 106 and the indoor return air temperature returned from the cooled unit 106 to the indirect evaporative cooling system 100 (see Figure 2 ) operation, so that the indirect evaporative cooling system 100 operates in the required mode, thereby accurately ensuring that the cooling capacity meets the needs of the cooled unit 106.

[0046] Figure 2 Shown Figure 1 A block diagram of an embodiment of an indirect evaporative cooling system 100 and a detection device 112 is shown.

[0047] like Figure 2 As shown, the unit assembly 102 of the indirect evaporative cooling system 100 includes a water spray system 103, a direct expansion refrigeration system 104, and a fan system 105. The fan system 105 includes an indoor fan 18 and an outdoor fan 19.

[0048] The water mist spray system 103 includes an air-to-air heat exchanger 1, a hose connector 2, a water mist spray assembly support plate 10, a nozzle movement motor 11, a chain 12, a nozzle 13, a solenoid valve 14, a pressure regulating valve 15, a water pipe 16, a water pipe interface 21, and a slide bar 22. One end of the water pipe 16 is connected to an external water supply system (soft water) via the water pipe interface 21, and the other end is connected to a spiral hose 17. The middle of the water pipe 16 is fixedly connected to the solenoid valve 14 and the pressure regulating valve 15. The spiral hose 17 supplies water to the nozzle 13 via the hose connector 2. The solenoid valve 14 is configured to control the start and stop of the water mist spray system 103, and the pressure regulating valve 15 is configured to maintain a constant operating pressure at the nozzle 13. The hose connector 2 is also connected to the slide bar 22. The other side of the slide bar 22 is connected to the chain 12, and the other side of the chain 12 is connected to the nozzle movement motor 11. The nozzle movement motor 11 is configured to control the movement of the nozzle 13. The nozzle moving motor 11 controls the operation of the hose connector 2 and the nozzle 13 by manipulating the slide bar 22 through the chain 12. On the one hand, water mist is sprayed onto the surface of the air-to-air heat exchanger 1, and the temperature of the outdoor air (secondary air) is reduced by evaporative cooling. The temperature of the primary air is reduced by heat exchange between the air-to-air heat exchanger 1 and the indoor return air (the air returning to the air-to-air heat exchanger 1 from the cooled unit, such as the machine room, i.e., the primary air). At the same time, the water film on the surface of the air-to-air heat exchanger 1 also further reduces the temperature of the primary air by evaporative cooling. On the other hand, by controlling the dwell time of the slide bar 22, the working time of the nozzle 13 can be controlled, i.e., the amount of water mist sprayed can be controlled. The nozzle moving motor 11, the solenoid valve 14, the pressure regulating valve 15, the water pipe 16 and other components are fixed on the water mist spray assembly support plate 10.

[0049] The direct expansion refrigeration system 104 (also known as the DX system) includes an evaporator 3, a gas-liquid separator 5, a compressor 6, an oil separator 7, a condenser 4, a liquid reservoir 8, a flow regulating valve 9, and a refrigerant pipeline 23. In one embodiment, the flow regulating valve 9 is an expansion valve / capillary tube. In other embodiments, the flow regulating valve 9 includes other suitable structures. The outlet of the evaporator 3 is connected to the gas-liquid separator 5, the compressor 6, the oil separator 7, the condenser 4, the liquid reservoir 8, and the flow regulating valve 9 in sequence through the refrigerant pipeline 23, and the outlet of the flow regulating valve 9 is connected to the inlet of the evaporator 3. In the direct expansion refrigeration system 104, the liquid refrigerant directly evaporates in the coil of the evaporator 3 to absorb the heat of the indoor return air (primary air) outside the coil, thereby reducing the temperature of the primary air and achieving refrigeration. After absorbing heat, the refrigerant becomes low-temperature, low-pressure refrigerant vapor. After adiabatically compressing in compressor 6, it becomes high-temperature, high-pressure superheated vapor. It then enters condenser 4 for constant-pressure cooling, releasing heat to the secondary air (outdoor air). The refrigerant is cooled to a subcooled liquid refrigerant, which is then adiabatically throttled by flow control valve 9 to become a low-pressure refrigerant. Finally, it flows into evaporator 3 to complete a refrigeration cycle. Gas-liquid separator 5 is configured to prevent liquid refrigerant leaving evaporator 3 from entering compressor 6, thereby avoiding liquid hammer. Oil separator 7 is configured to separate the lubricating oil mixed in the refrigerant vapor at the outlet of compressor 6 and return the lubricating oil to the compressor's oil sump through its own control device. Liquid accumulator 8 is configured to compensate for changes in the condenser liquid level caused by load changes and to separate the liquid and gaseous refrigerant at the outlet of condenser 4.

[0050] Figure 2 FIG. 1 shows the layout of the detection device 112 provided at the indirect evaporative cooling system 100. Figure 2 As shown, an outdoor air inlet temperature and humidity sensor A is set at the entrance of the outdoor air (secondary air) and before the low-pressure water mist spraying system 103, which is configured to detect the outdoor air temperature T A and outdoor air humidity RH A A temperature sensor B is provided between the air-to-air heat exchanger 1 and the inlet of the condenser 4, which is configured to detect the temperature of the outdoor air (secondary air) at the inlet of the condenser 4. A condensation temperature sensor C is provided between the outlet of the condenser 4 and the outdoor fan 19, which is configured to detect the temperature of the outdoor air (secondary air) at the outlet of the condenser 4. An indoor return air temperature and humidity sensor D is provided between the inlet of the indoor return air (primary air) and the air-to-air heat exchanger 1, which is configured to detect the indoor return air temperature T D and indoor return air humidity RH D A temperature sensor E is provided between the air-to-air heat exchanger 1 and the inlet of the evaporator 3, which is configured to detect the temperature of the indoor return air at the inlet of the evaporator 3. An indoor supply air temperature and humidity sensor F is provided on the rear side of the indoor fan 18, which is configured to detect the indoor supply air temperature T Fand indoor air humidity RH F . The parameter data detected by the above-mentioned sensors can be used to control the normal operation of the indirect evaporative cooling system 100, and can also be used for the model training operation of this application. This application is not limited to the above-mentioned sensors, but also includes other suitable sensors for controlling other suitable operations of the indirect evaporative cooling system 100. For example, this application also includes a compressor speed sensor, which is arranged close to the compressor 6 for detecting the speed of the compressor 6; includes a fan speed sensor, which is arranged close to the outdoor fan 19 for detecting the fan speed of the outdoor fan 19; includes a motor speed sensor, which is arranged close to the nozzle moving motor 11 for detecting the speed of the nozzle moving motor 11; includes an opening sensor, which is arranged close to the flow control valve 9 for detecting the opening of the flow control valve 9, etc. In other embodiments, this application detects the above-mentioned parameters by setting other suitable sensors.

[0051] although Figure 2 The components of the unit assembly 102 and the arrangement of the detection device 112 of the indirect evaporative cooling system 100 are shown, but the present application is not limited to these specific components of the unit assembly 102 and the specific arrangement of the detection device 112. In other embodiments, the unit assembly 102 of the indirect evaporative cooling system 100 includes other suitable components or structures. In other embodiments, the detection device 112 includes other suitable sensors or other arrangements.

[0052] Figure 3 Shown Figure 1 FIG. 1 is a general flow chart of an embodiment of a control method of the indirect evaporative cooling system 100 .

[0053] like Figure 3 As shown, the control method 300 of the indirect evaporative cooling system 100 starts at step 302 and then proceeds from step 302 to step 304 .

[0054] At step 304, an indirect evaporative cooling system refrigeration model and an indirect evaporative cooling system operating power consumption model are created. The indirect evaporative cooling system refrigeration model includes input parameters and output parameters, and the indirect evaporative cooling system operating power consumption model includes input parameters and output parameters. The output parameters of the indirect evaporative cooling system refrigeration model include control parameters of the indirect evaporative cooling system 100. The output parameters of the indirect evaporative cooling system operating power consumption model include the operating power consumption of various components of the indirect evaporative cooling system 100. Then, step 304 is followed by step 306. In one embodiment, the output parameters of the indirect evaporative cooling system refrigeration model are used as input parameters of the indirect evaporative cooling system operating power consumption model. The operating power consumption of various components of the indirect evaporative cooling system 100 includes electricity consumption and water consumption. The operating cost of the indirect evaporative cooling system 100 includes electricity costs resulting from electricity consumption and water costs resulting from water consumption.

[0055] At step 306 , training data is acquired, including input and output parameters of the indirect evaporative cooling system refrigeration model and input and output parameters of the indirect evaporative cooling system operation power consumption model. The process then proceeds from step 306 to step 308 .

[0056] At step 308 , the indirect evaporative cooling system refrigeration model and the indirect evaporative cooling system operation power consumption model are trained based on the acquired training data to obtain a determined indirect evaporative cooling system refrigeration model and a determined indirect evaporative cooling system operation power consumption model. The process then proceeds from step 308 to step 310 .

[0057] At step 310, the operating cost of the indirect evaporative cooling system 100 is optimized based on at least the determined indirect evaporative cooling system refrigeration model and the determined indirect evaporative cooling system operation power consumption model to obtain the output parameter data of the indirect evaporative cooling system refrigeration model corresponding to the optimization of the operating cost of the indirect evaporative cooling system 100 (i.e., the control parameter data of the indirect evaporative cooling system 100), thereby controlling the operation of the indirect evaporative cooling system 100. Then, the process proceeds from step 310 to step 312. For a detailed flow of an embodiment of step 310, see Figure 6 .

[0058] At step 312, it is determined whether the indirect evaporative cooling system 100 needs to be shut down. If shutdown is not required, the process proceeds from step 312 to step 310 to continue controlling the operation of the indirect evaporative cooling system 100. If shutdown is required, the process proceeds from step 312 to step 314 to terminate the execution of the control method 300 for the indirect evaporative cooling system 100.

[0059] Figure 4 Shown Figure 3The block diagram structure of an embodiment of the indirect evaporative cooling system refrigeration model F(x) and the indirect evaporative cooling system operation power consumption model G(y) created in step 304 is shown. As mentioned above, Figure 3 At step 304, a refrigeration model of the indirect evaporative cooling system and an operation power consumption model of the indirect evaporative cooling system are created.

[0060] like Figure 4 As shown, in one embodiment, the refrigeration model F(x) of the indirect evaporative cooling system selects the following input parameters:

[0061] x=(T S ,T A ,RH A ,T F ,RH F ,T D ,RH D ),

[0062] Among them, T S The required operating temperature of the cooled unit 106 (ie, the target parameter of the cooled unit 106), T A is the outdoor air temperature, RH A is the outdoor air humidity, T F Indoor air supply temperature, RH F is the indoor air humidity, T D is the indoor return air temperature, RH D Indoor return air humidity.

[0063] In addition, the refrigeration model F(x) of the indirect evaporative cooling system selects the following output parameters:

[0064] y=(N P ,n 冷凝 ,n 喷头 ,D PF ),

[0065] where N P is the speed of the compressor 6 of the direct expansion refrigeration system 104, n 冷凝 is the fan speed of the outdoor fan 19, n 喷头 is the speed of the nozzle moving motor 11, D PF The present application controls the operation of the indirect evaporative cooling system 100 so that the indoor air supply temperature T F and indoor return air temperature T D The demand is met, and the cooling capacity can be accurately ensured to meet the cooling capacity demand of the cooled unit 106.

[0066] In addition, the indirect evaporative cooling system operation power consumption model G(y) selects the output parameters of the indirect evaporative cooling system refrigeration model F(x) as its input parameters, that is, the input parameters of the indirect evaporative cooling system operation power consumption model G(y) are as follows:

[0067] y=(N P , n 冷凝 , n 喷头 , D PF ),

[0068] In addition, the indirect evaporative cooling system operation power consumption model G(y) selects the following output parameters:

[0069] z=(P 喷头电机 , P IT风机 , P EA风机 , P 压缩机 , W 喷头 ),

[0070] Among them, P 喷头电机 is the power consumption of the nozzle moving motor 11, P IT风机 is the power consumption of the indoor fan 18, P EA风机 is the power consumption of the outdoor fan 19, P 压缩机 is the power consumption of compressor 6, W 喷头 is the amount of water sprayed by the nozzle 13.

[0071] In one embodiment, the indirect evaporative cooling system cooling model F(x) and the indirect evaporative cooling system operating power consumption model G(y) are both fully connected deep neural network models. This application assumes that the deep neural network model has m neurons in the input layer, n neurons in the output layer, and k hidden layers. The number of neurons in each hidden layer can be determined by the linear function L = g(m).

[0072] Figure 5 Shown Figure 3 Detailed flowchart of an embodiment of step 308 (training model) shown in FIG. Figure 3 At step 308 , the indirect evaporative cooling system cooling model and the indirect evaporative cooling system operating power consumption model are trained based on the acquired training data to obtain a determined indirect evaporative cooling system cooling model and a determined indirect evaporative cooling system operating power consumption model. In one embodiment, the indirect evaporative cooling system cooling model F(x) and the indirect evaporative cooling system operating power consumption model G(y) are both fully connected deep neural network models. In other embodiments, the indirect evaporative cooling system cooling model F(x) and the indirect evaporative cooling system operating power consumption model G(y) include other suitable models.

[0073] like Figure 5 As shown by Figure 3Step 306 in Go to Figure 5 In step 502, at step 502, the set of initialization weight values ​​and bias values ​​of the deep neural network model is set. Then, the process proceeds from step 502 to step 504. In one embodiment, the set of initialization weight values ​​and bias values ​​φ of the deep neural network model F(x) is set. f And the set of initialized weights and bias values ​​φ of the deep neural network model G(y) g .

[0074] At step 504, the operating parameter data of the indirect evaporative cooling system 100 is obtained. Then, the process proceeds from step 504 to step 506. In one embodiment, the operating parameter data of the indirect evaporative cooling system 100 includes the input parameter data of the indirect evaporative cooling system refrigeration model F(x) (e.g., the required operating temperature T of the cooled unit 106). S , outdoor air temperature T A , outdoor air humidity RH A , Indoor air supply temperature T F , Indoor air humidity RH F , Indoor return air temperature T D , Indoor return air humidity RH D ) and output parameter data (e.g., the speed N of the compressor 6 of the direct expansion refrigeration system 104 P , the fan speed n of the outdoor fan 19 冷凝 , the speed n of the nozzle moving motor 11 喷头 , the opening degree D of the flow control valve 9 PF The operating parameter data of the indirect evaporative cooling system 100 also includes the input parameter data of the indirect evaporative cooling system operation power consumption model G(y) (for example, the speed N of the compressor 6 of the direct expansion refrigeration system 104). P , the fan speed n of the outdoor fan 19 冷凝 , the speed n of the nozzle moving motor 11 喷头 , the opening degree D of the flow control valve 9 PF ) and output parameter data (for example, the power consumption P of the nozzle moving motor 11 喷头电机 , the power consumption P of the indoor fan 18 IT风机 , the power consumption P of the outdoor fan 19 EA风机 , the power consumption P of compressor 6 压缩机 , the water spraying amount W of nozzle 13 喷头 ).

[0075] At step 506, the output of each hidden layer neuron node is calculated according to the activation function. Then, the process proceeds from step 506 to step 508. In one embodiment, the output of each hidden layer neuron node is calculated according to the activation function of the deep neural network model F(x), and the output of each hidden layer neuron node is calculated according to the activation function of the deep neural network model G(y).

[0076] At step 508, the output layer neuron outputs are calculated based on the activation function. Then, the process proceeds to step 510. In one embodiment, the outputs of the output layer neurons of the deep neural network model F(x) are calculated based on the activation function of the deep neural network model F(x), and the outputs of the output layer neurons of the deep neural network model G(y) are calculated based on the activation function of the deep neural network model G(y).

[0077] At step 510, the error Loss between the predicted output of the output layer and the actual output of the system is calculated. Then, the process proceeds to step 512 from step 510. In one embodiment, the error Loss between the predicted output (predicted value) of the output layer of the deep neural network model (indirect evaporative cooling system refrigeration model) F(x) and the actual output (actual value / true value) of the indirect evaporative cooling system 100 is calculated. f , and calculate the error Loss between the predicted output (predicted value) of the output layer of the deep neural network model (indirect evaporative cooling system operation power consumption model) G(y) and the actual output (actual value / true value) of the indirect evaporative cooling system 100 g In one embodiment, the present application quantifies the difference (loss) between the predicted value output by the deep neural network model and the actual value output by the indirect evaporative cooling system 100 using a loss function. By minimizing the difference (loss), the parameters of the deep neural network model can be optimized and the prediction performance of the deep neural network model can be improved. In one embodiment, the loss function Loss of the deep neural network model F(x), G(y) is f 、Loss g They are as follows:

[0078]

[0079] Where F′(x) is the predicted value output by the indirect evaporative cooling system cooling model F(x), f(x) is the actual value of the corresponding output of the indirect evaporative cooling system 100, and x represents an input parameter of the indirect evaporative cooling system cooling model F(x); G′(y) is the predicted value output by the indirect evaporative cooling system operating power consumption model G(y), g(y) is the actual value of the corresponding output of the indirect evaporative cooling system 100, and y is the output parameter of the indirect evaporative cooling system cooling model F(x), which is also an input parameter of the indirect evaporative cooling system operating power consumption model G(y). In other embodiments, the loss functions of the deep neural network models F(x) and G(y) include other suitable functions.

[0080] At step 512, the error Loss (Loss f , Loss g ) meets the required value? In one embodiment, the error Loss of the deep neural network model F(x) is determined. f Is it less than or equal to the error threshold ε? f , and judge the error Loss of the deep neural network model G(y) g Is it less than or equal to the error threshold ε? g If the error Loss meets the required value, it indicates that the error Loss has converged, and the weight values ​​and bias values ​​of the deep neural network model F(x) and G(y) are obtained, that is, the determined indirect evaporative cooling system refrigeration model F(x) and the indirect evaporative cooling system operation power consumption model G(y) are obtained, so step 512 is transferred to Figure 3 Step 310 in . If the error Loss does not meet the required value, it indicates that the error Loss has not converged, and step 512 is jumped to step 514 to perform the operation of updating the weights and biases of the deep neural network model. In one embodiment, the present application uses a backpropagation algorithm and a gradient descent optimization algorithm to update the weights and biases of the deep neural network model. For example, during the training process, the gradients of the weights and biases are associated by calculating the loss function, and then the gradient descent algorithm is used to update the weights and biases to minimize the loss function. An embodiment of the method for updating the weights and biases of the deep neural network model is detailed in the following steps 514-518. In other embodiments, the present application may use other suitable algorithms to update the weights and biases of the deep neural network model.

[0081] At step 514, the error of the hidden layer is calculated. Then, the process proceeds to step 516. In one embodiment, the error of the hidden layer of the deep neural network model F(x) is calculated, and the error of the hidden layer of the deep neural network model G(y) is calculated.

[0082] At step 516, the error gradient is calculated. Then, the process proceeds to step 518 from step 516. In one embodiment, the error gradient of the deep neural network model F(x) is calculated, and the error gradient of the deep neural network model G(y) is calculated.

[0083] At step 518, the weight value and the bias value are adjusted according to the learning function. Then, the process goes to step 506 from step 518. In one embodiment, the weight value and the bias value of the deep neural network model F(x) are adjusted according to the learning function, and the weight value and the bias value of the deep neural network model G(y) are adjusted according to the learning function. The present application uses the operating parameter data of the indirect evaporative cooling system 100 to perform supervised learning on the deep neural network model to obtain the loss function Loss f With Loss g The set of weights and bias values ​​of the deep neural network model F(x), G(y) when the minimum is reached φ f With φ g .

[0084] In one embodiment, the present application uses an optimizer, such as the Adam optimizer, as an adaptive learning rate optimization algorithm. The learning rate is adjusted according to the gradient information of the previous moment. When the gradient of the previous moment is small, the learning rate is increased, and when the gradient of the previous moment is large, the learning rate is decreased. In this way, the learning rate is dynamically adjusted to accelerate the training of the neural network model, so that the weights and biases of the deep neural network model can quickly reach the optimal value, so that the loss function Loss f With Loss g Fastest convergence. For example, the optimizer updates weights and biases according to the following formula:

[0085]

[0086] Among them, φ t is the set of weights and bias values ​​of the model before updating; φ t+1 is the set of weights and bias values ​​of the updated model; is the gradient of the model's weight and bias at position t; β1 and β2 are the attenuation coefficients of the two exponentially weighted averages, which are generally constants; m t-1 and v t-1 It is the moving average of the gradient before deviation correction, initialized to 0 matrix; m t and v t is the bias-corrected moving average of the gradient; η is the learning rate, and ε is set to 1e-8 to avoid division by zero in the above formula (2.1).

[0087] The set of weights and bias values ​​of the above deep neural network model F(x), G(y) is φ f With φg Applied in φ t , update the weight value and bias value of the next position according to the above formula (2.1).

[0088] And, the gradient momentum of the next position is calculated by updating the following formula (2.2), and the gradient variance of the next position is calculated by updating the following formula (2.3):

[0089]

[0090] Among them, m t is the current momentum, m t-1 is the momentum of the next position, where momentum represents the exponentially weighted moving average of the gradient; v t is the variance of the current position, v t-1 Is the variance of the next position, where the variance represents the exponentially weighted moving average of the squared gradient. Initialize m t Initialize v to be a zero matrix t is a zero matrix.

[0091] By looping through the above three formulas (2.1), (2.2), and (2.3), the weight value and bias value of the previous position are continuously used to calculate the weight value and bias value of the next position until Loss f With Loss g convergence.

[0092] This application uses a portion of the training data as a validation set. This application also periodically evaluates model performance on the validation set to detect overfitting or undertraining of the trained model. Model performance is measured using accuracy and loss metrics. If model performance is inaccurate or overfitting, the learning rate, batch size, and model complexity are adjusted based on the validation set performance to improve model performance.

[0093] Figure 6 Show Figure 3 The detailed flow chart of an embodiment of step 310 (obtaining control parameter data when optimizing system operation cost) is shown in FIG. Figure 3 At step 310, the operating cost of the indirect evaporative cooling system 100 is optimized based on at least the determined indirect evaporative cooling system refrigeration model and the determined indirect evaporative cooling system operation power consumption model to obtain output parameter data of the indirect evaporative cooling system refrigeration model corresponding to the operation cost optimization of the indirect evaporative cooling system 100 (i.e., control parameter data of the indirect evaporative cooling system 100), thereby controlling the operation of the indirect evaporative cooling system 100.

[0094] like Figure 6 As shown by Figure 3Step 308 goes to Figure 6 In step 602 , the process obtains field data of input parameters of the refrigeration model of the indirect evaporative cooling system.

[0095] At step 604 , the acquired field data is input into the determined indirect evaporative cooling system refrigeration model to obtain a data set of output parameters of the indirect evaporative cooling system refrigeration model.

[0096] At step 606, the obtained data set of the output parameters of the indirect evaporative cooling system refrigeration model is input into the indirect evaporative cooling system operation cost optimization model to obtain the data of the output parameters of the indirect evaporative cooling system refrigeration model corresponding to the operation cost optimization of the indirect evaporative cooling system 100, thereby controlling the operation of the indirect evaporative cooling system 100. Then, step 606 is transferred to Figure 3 Step 312. In one embodiment, the indirect evaporative cooling system operating cost optimization model is configured to: obtain an operating power consumption set consisting of operating power consumptions of various parts of the indirect evaporative cooling system 100 based on the obtained data set of output parameters of the indirect evaporative cooling system refrigeration model and the indirect evaporative cooling system operating power consumption model; obtain an operating cost set of the indirect evaporative cooling system 100 based on the obtained operating power consumption set; and optimize the obtained operating cost set to obtain data on the output parameters of the indirect evaporative cooling system refrigeration model corresponding to the indirect evaporative cooling system operating cost optimization of the indirect evaporative cooling system 100.

[0097] The obtained output parameter data of the indirect evaporative cooling system cooling model F(x), i.e., the control parameter data of the indirect evaporative cooling system 100, can be used by the controller 101 of the indirect evaporative cooling system 100 (e.g., via an actuator of the indirect evaporative cooling system 100) to control the operation of the indirect evaporative cooling system 100, thereby adjusting the temperature of the cooled unit 106 (e.g., a data center) to meet the demand. The output parameter data of the indirect evaporative cooling system cooling model F(x) includes multiple sets of values ​​that are equal to or close to the actual demand, i.e., output parameter data sets / control parameter data sets. If these values ​​(output parameter data sets / control parameter data sets) cause the temperature of the data center to meet the temperature demand (e.g., reach the temperature threshold or be within the temperature threshold range), they can all be considered as a solution. The solution set consisting of all solutions is:

[0098] Y={((N P , n 冷凝 , n 喷头 , D PF )) i}, i∈{1, 2, 3, 4, 5,...I}.

[0099] Each solution (ie, each set of output parameter data / control parameter data) corresponds to a total operating cost Cost of the indirect evaporative cooling system 100 总 , solving for the optimal cost, such as the lowest cost, for the total system operating cost set corresponding to the solution set. In one embodiment, the present application uses a genetic algorithm to globally search the above solution set (i.e., the output parameter dataset / control parameter dataset of the indirect evaporative cooling system cooling model F(x)) to quickly solve for the control parameter data of the indirect evaporative cooling system 100 that satisfies the cooling demand of the cooled unit 106 (e.g., a data center) and minimizes the total operating cost of the indirect evaporative cooling system 100.

[0100] In one embodiment, the total operating cost of the indirect evaporative cooling system 100 = electricity price * electricity consumption + water price * water consumption, calculated as follows:

[0101] Cost 总 =C E ×(P 喷头电机 +P IT风机 +P EA风机 +P 压缩机 )+C W ×W 喷头 ,

[0102] Among them, C E is the local electricity price, P 喷头电机 is the power consumption of the nozzle moving motor 11, P IT风机 is the power consumption of the indoor fan 18, P EA风机 is the power consumption of the outdoor fan 19, P 压缩机 is the power consumption of compressor 6, C W is the local tap water price, W 喷头 is the amount of water sprayed by the nozzle 13. According to this formula, the total operating cost of the indirect evaporative cooling system 100 per unit time can be calculated. This application takes the indirect evaporative cooling system operation power consumption model G(y) as the optimization target, and takes the operating condition parameters output by the indirect evaporative cooling system refrigeration model F(x) as the input parameters of the indirect evaporative cooling system operation power consumption model G(y). The power consumption of each part of the indirect evaporative cooling system 100 is obtained through the indirect evaporative cooling system operation power consumption model G(y), including the power consumption P of the nozzle moving motor 11. 喷头电机 , the power consumption P of the indoor fan 18 IT风机 , the power consumption P of the outdoor fan 19 EA风机 , the power consumption P of compressor 6 压缩机 , the water spraying amount W of nozzle 13 喷头The present application can calculate the total operating cost of the indirect evaporative cooling system 100 according to the above total operating cost formula and the obtained power consumption of each part of the indirect evaporative cooling system 100 .

[0103] Since the input parameters of the indirect evaporative cooling system operating power consumption model G(y) are multiple sets of control parameters that meet the refrigeration conditions, which set of control parameters makes the indirect evaporative cooling system 100 operate most energy-efficiently? It is necessary to calculate the system operating power consumption through the indirect evaporative cooling system operating power consumption model G(y) to obtain the system operating cost and ensure that the total system operating cost is the lowest. This application uses the indirect evaporative cooling system operating power consumption model G(y) as a black box optimization target and uses a genetic algorithm to find the control parameter data when the system operating cost is optimal (for example, the lowest). An embodiment of the genetic algorithm optimization calculation is Figure 7 Shown in detail.

[0104] Figure 7 Shown Figure 6 A detailed flow chart of an embodiment of step 606 is shown.

[0105] like Figure 7 As shown by Figure 6 Step 604 in Go to Figure 7 At step 702, each individual in the population is initialized, i.e., the control parameters of the indirect evaporative cooling system 100 (i.e., the input parameters of the indirect evaporative cooling system operation power consumption model G(y)), such as the speed N of the compressor 6 of the direct expansion refrigeration system 104. P , the fan speed n of the outdoor fan 19 冷凝 , the speed n of the nozzle moving motor 11 喷头 , the opening degree D of the flow control valve 9 PF Then, go from step 702 to step 704.

[0106] At step 704, the fitness value of each acquired individual (i.e., the control parameter of the indirect evaporative cooling system 100) is calculated based on a fitness function. In one embodiment, the fitness function in the genetic algorithm is acquired based on the determined power consumption model G(y) for the indirect evaporative cooling system. For example, the fitness function is the same as the determined power consumption model G(y) for the indirect evaporative cooling system. The process then proceeds from step 704 to step 706.

[0107] At step 706 , individuals with higher fitness values ​​are selected from the fitness values ​​of the individuals and “retained.” Then, the process proceeds from step 706 to step 708 .

[0108] At step 708 , crossover is performed on the retained individuals to generate new individuals. Then, the process proceeds from step 708 to step 710 .

[0109] At step 710 , random mutation is performed on the new individuals generated after the crossover. Then, the process proceeds from step 710 to step 712 .

[0110] At step 712, the individuals after random mutation form a new individual group. Then, the process proceeds from step 712 to step 714.

[0111] At step 714 , a new individual is obtained based on the new individual group. Then, the process proceeds from step 714 to step 716 .

[0112] At step 716 , a new operating cost (ie, system operating cost) of the indirect evaporative cooling system 100 is calculated based on the new individual data obtained at step 714 . Then, the process proceeds from step 716 to step 718 .

[0113] At step 718, it is determined whether the maximum number of iterations has been reached. If not, the process proceeds from step 718 to step 704. If the maximum number of iterations has been reached, the process proceeds from step 718 to step 720.

[0114] At step 720, the control parameter data after the system operation cost is optimized is obtained, for example, the control parameter data when the total operation cost of the indirect evaporative cooling system 100 is the lowest. Then, step 720 is transferred to Figure 3 In one embodiment, the operation of the indirect evaporative cooling system 100 is controlled based on the obtained control parameter data after the system operation cost is optimized, and after a period of time (for example, a predetermined period of time), the system switches to Figure 3 Step 312.

[0115] When obtaining control parameter data for the indirect evaporative cooling system 100 corresponding to optimizing its operating costs, it is necessary to consider the limits of the control parameters. Control parameter data exceeding these limits may allow the indirect evaporative cooling system 100 to operate normally, but may increase its operating costs. Therefore, limiting the control parameter data for the indirect evaporative cooling system 100 to within these limits can optimize its operating costs. For example, after outdoor air is sprayed by the water mist system 103 and its moisture content reaches saturation, the air temperature will no longer decrease. Because the nozzles 13 in the water mist system 103 are driven to reciprocate by the nozzle motor 11, the moisture content of the air depends on the reciprocating speed of the nozzles 13. Therefore, when the moisture content of the outdoor air reaches saturation after being sprayed by the water mist system 103, the nozzle motor 11 has a corresponding maximum speed limit, i.e., a speed limit. Operating the nozzle moving motor 11 at a speed exceeding the speed limit still results in saturated air moisture content and no further decrease in air temperature. Therefore, operating the nozzle moving motor 11 beyond the motor speed limit generates unnecessary power consumption, resulting in wasted operating costs for the indirect evaporative cooling system 100. In one embodiment, to reduce operating costs for the indirect evaporative cooling system 100, the present application obtains the speed limit for the nozzle moving motor 11 after the outdoor air is sprayed by the water mist system 103 at the current outdoor air dry-bulb temperature and the current outdoor air dry air humidity content, and limits the speed of the nozzle moving motor 11 when the outdoor air is sprayed by the water mist system 103 to within the corresponding speed limit for the nozzle moving motor 11, thereby optimizing the operating costs of the indirect evaporative cooling system 100. In one embodiment, the present application uses a motor speed limit model to obtain the speed limit for the nozzle moving motor 11 after the outdoor air is sprayed by the water mist system 103. The motor speed limit model is configured to obtain a speed limit for the nozzle moving motor 11 after the outdoor air is sprayed by the water mist spraying system 103 based on the outdoor air dry bulb temperature and the outdoor air dry air humidity content of the cooled unit 106 .

[0116] Figure 8 A flow chart showing an embodiment of a method for obtaining a motor speed limit model.

[0117] like Figure 8 As shown, the method 800 for obtaining the motor speed limit model starts at step 802 . Then, the method proceeds from step 802 to step 804 .

[0118] At step 804, a psychrometric model is created and obtained, comparing the humidity content of the outdoor air after being sprayed by the water mist system 103 and the air temperature. Then, the process proceeds from step 804 to step 806. The psychrometric model uses the current outdoor air dry-bulb temperature and the current outdoor air dry-bulb humidity content as constants. In one embodiment, the psychrometric model is as follows:

[0119] T=K×(RR 初始 )+T 初始 ,

[0120] Among them, T is the temperature of the outdoor air after being sprayed, T 初始 is the current outdoor air dry bulb temperature, K is the coefficient of variation, R is the humidity content of the outdoor air after being sprayed, R 初始 is the current outdoor air dry air humidity content. For example, the current outdoor air dry bulb temperature T 初始 and the current outdoor air dry air humidity R 初始 By sensor A (see Figure 2 ) detection. This application sets a detection device (not shown) to detect and obtain the temperature T of the outdoor air after being sprayed and the dry air humidity R of the outdoor air after being sprayed within a predetermined time period. Assuming that the current outdoor air dry bulb temperature T 初始 and the current outdoor air dry air humidity R 初始 The temperature T of the outdoor air after spraying and the humidity R of the dry air after spraying obtained in the predetermined time period are input into the above-mentioned temperature and humidity model, and combined with the current outdoor air dry bulb temperature T 初始 and the current outdoor air dry air humidity R 初始 , we can get the coefficient of variation K in the above temperature and humidity model, and thus get the current outdoor air dry-bulb temperature T 初始 and the current outdoor air dry air humidity R 初始 The determined temperature and humidity model.

[0121] At step 806, a temperature and humidity limit model is created and obtained between the air humidity limit and the air temperature limit when the outdoor air is saturated after being sprayed by the water mist system 103. Then, step 806 is transferred to step 808. Figure 9 ) It can be seen that when water is sprayed on the air, the outdoor air is in contact with the water for a long time. The temperature of the water and the saturated air layer on its surface is the wet bulb temperature of the moist air, that is, the lowest temperature under the same enthalpy value. To lower the air temperature by spraying, it is necessary to increase the humidity. When the air humidity is saturated, the air temperature drops to the lowest point. After the outdoor air is sprayed, the air moisture content is saturated and the air temperature no longer drops. According to the psychrometric diagram of air (see Figure 9) The boundary curve (saturation boundary line) data at the lower right edge can be used to train a temperature and humidity limit model. In one embodiment, the temperature and humidity limit model uses an N-order polynomial model as follows:

[0122]

[0123] Among them, T is the air temperature limit when the air humidity is saturated, R is the air moisture content limit when the air humidity is saturated, a i is the coefficient of variation of the polynomial model. From the psychrometric diagram of air (see Figure 9 ) and input these collected data into the above polynomial model to obtain a determined temperature and humidity limit model. Therefore, the current outdoor air dry-bulb temperature T can be obtained from the temperature and humidity model and the temperature and humidity limit model. 初始 and the current outdoor air dry air humidity R 初始 The maximum humidity and temperature of the outdoor air after being sprayed.

[0124] At step 808, a speed and humidity limit model is created and obtained between the speed limit of the nozzle moving motor 11 and the air humidity limit when the air is saturated. Then, the process proceeds from step 808 to step 810. Since the nozzle 13 in the water mist spraying system 103 is driven to reciprocate by the nozzle moving motor 11, the level of moisture in the air depends on the reciprocating speed of the nozzle 13. Therefore, after the outdoor air is sprayed by the water mist spraying system 103, when the air humidity is saturated, the nozzle moving motor 11 has a corresponding maximum speed limit, i.e., a speed limit. For example, based on the data provided by the component manufacturer on the speed limit of the nozzle moving motor 11 and the air humidity limit when the air is saturated, a speed and humidity limit model can be fitted, for example, using a polynomial.

[0125] At step 810, the obtained temperature and humidity model, temperature and humidity limit model, and speed and humidity limit model are combined to obtain a motor speed limit model. Then, step 810 is transferred to step 812 to end the execution of the method 800 for obtaining the motor speed limit model. In one embodiment, the combination of the temperature and humidity model, the temperature and humidity limit model, and the speed and humidity limit model is called the motor speed limit model. The motor speed limit model composed of the temperature and humidity model, the temperature and humidity limit model, and the speed and humidity limit model can obtain the current outdoor air dry-bulb temperature T 初始 and the current outdoor air dry air humidity R 初始 The speed limit of the nozzle moving motor 11 corresponds to the saturated humidity of the outdoor air after being sprayed.

[0126] Figure 9 The psychrometric diagram of air is shown in Figure 1. Figure 9As shown in the figure, the abscissa represents the air humidity content, and the ordinate represents the air temperature. Several parallel oblique lines inclined relative to the abscissa and ordinate represent isoenthalpy lines. The boundary curve (saturation boundary line) at the lower right edge of the psychrometric chart shows the relationship between the air humidity content (i.e., the air humidity limit) and the air temperature (i.e., the air temperature limit) when the air humidity is saturated (100%).

[0127] Figure 10 Shown Figure 1 The block diagram of the PID control of the controller 101 is shown. The control parameter data corresponding to the system operation cost optimization, such as the output parameter data of the indirect evaporative cooling system operation power consumption model G(y) (for example, the power consumption of each part of the system) is obtained so that the total system operation cost is minimized, and the input parameter y of the indirect evaporative cooling system operation power consumption model G(y) is obtained. P , n 冷凝 , n 喷头 , D PF )), input into the controller 101, and use the PID algorithm to quickly make the actuator of the indirect evaporative cooling system 100 run to the specified input parameter value.

[0128] like Figure 10 As shown, input parameter data / control parameter data y=((N P , n 冷凝 , n 喷头 , D PF )) is used as the input of the PID algorithm (i.e., the target data), and then is superimposed after passing through the proportional control, differential control, and integral control modules. The superimposed result is input to the actuator of the indirect evaporative cooling system 100 to enable the indirect evaporative cooling system 100 to operate. The measuring element, such as the detection device 112 (see Figure 1 ), detects and obtains operating parameter data of the indirect evaporative cooling system 100 and uses it as feedback data. This feedback data is superimposed on the input parameter data / control parameter data and then passes through the proportional control, differential control, and integral control modules. This process is repeated until the indirect evaporative cooling system 100 reaches the desired input parameter data / control parameter data (i.e., target data).

[0129] Figure 11 Shown Figure 1 The structural block diagram of the computing system 110 and the control system 111 is shown.

[0130] like Figure 11As shown, computing system 110 includes memory 1101, processor 1102, input interface 1103, output interface 1104 and bus 1105. Memory 1101, processor 1102, input interface 1103, output interface 1104 are connected to bus 1105. Processor 1102 can read out a program (or instruction) from memory 1101 and execute the program (or instruction) to perform processing on data. Processor 1102 can also write data or program (or instruction) into memory 1101. Memory 1101 can store programs (instructions) or data. By executing the instructions in memory 1101, processor 1102 can control memory 1101, input interface 1103 and output interface 1104.

[0131] Input interface 1103 is configured to receive training data for an indirect evaporative cooling system cooling model and an indirect evaporative cooling system operating power consumption model via connection line 115. Input interface 1103 is further configured to convert the received data into data recognizable by processor 1102 and output the data to processor 1102. Processor 1102 is configured to process the received data (e.g., train the model) to obtain a determined indirect evaporative cooling system cooling model and an indirect evaporative cooling system operating power consumption model, and generate a program for the determined indirect evaporative cooling system cooling model and the indirect evaporative cooling system operating power consumption model. Processor 1102 is configured to create and obtain an indirect evaporative cooling system operating cost optimization model and generate a program for the determined indirect evaporative cooling system operating cost optimization model. Processor 1102 is further configured to combine the programs for the determined indirect evaporative cooling system cooling model and the indirect evaporative cooling system operating power consumption model and the determined indirect evaporative cooling system operating cost optimization model into a module package. The output interface 1104 is configured to receive the module package from the processor 1102 and send the module package to the control system 111 through the connection line 113 .

[0132] The control system 111 is configured to receive a module package from the computing system 110 via a connection line 113 and store the module package in a memory 1111. The control system 111 includes a memory 1111, a processor 1112, an input interface 1113, an output interface 1114, and a bus 1115. The memory 1111, the processor 1112, the input interface 1113, and the output interface 1114 are connected to the bus 1115. The processor 1112 can read a program (or instruction) from the memory 1111 and execute the program (or instruction) to perform processing on data. The processor 1112 can also write data or a program (or instruction) into the memory 1111. The memory 1111 can store a program (instruction) or data. By executing the instructions in the memory 1111, the processor 1112 can control the memory 1111, the input interface 1113, and the output interface 1114. The memory 1111 includes a data pre-processing module 1118 , an indirect evaporative cooling system module 1117 , and an operating cost optimization module 1116 .

[0133] Input interface 1113 is configured to receive a module package from computing system 110 via connection line 113, convert the module package into a module package recognizable by memory 1111, store the program for the determined indirect evaporative cooling system refrigeration model and the indirect evaporative cooling system operation power consumption model in the module package into indirect evaporative cooling system module 1117 in memory 1111, and store the program for the determined indirect evaporative cooling system operation cost optimization model in the module package into operation cost optimization module 1116 in memory 1111. In one embodiment, the module package is copied from computing system 110 to memory 1111 of control system 111 via a USB interface.

[0134] The input interface 1113 is further configured to receive field data of the indirect evaporative cooling system 100 detected by the detection device 112 via the connection line 116, convert the data into data recognizable by the processor 1112, and output the data to the processor 1112. The processor 1112 is configured to call the data preprocessing module 1118 to read its data and / or program (or instructions), and execute the program (or instructions) to process (e.g., pre-process) the received field data of the indirect evaporative cooling system 100. The processor 1112 is further configured to call the indirect evaporative cooling system module 1117 to read its data and / or program (or instructions), and execute the program (or instructions) to process the preprocessed data generated by the data preprocessing module 1118 (e.g., run the indirect evaporative cooling system refrigeration model) to obtain an output parameter data set of the indirect evaporative cooling system refrigeration model. The processor 1112 is further configured to call the operating cost optimization module 1116 to read its data and / or program (or instructions), and execute the program (or instructions) to process the output parameter data set generated by the indirect evaporative cooling system module 1117 to generate output parameter data (i.e., control parameter data) corresponding to the indirect evaporative cooling system refrigeration model when optimizing the operating costs of the indirect evaporative cooling system 100. In one embodiment, when executed, the operating cost optimization module 1116 calls the program of the indirect evaporative cooling system module 1117 that determines the indirect evaporative cooling system operating power consumption model, and combines the output parameter data generated by the indirect evaporative cooling system module 1117 to generate the control parameter data for optimizing the operating costs of the indirect evaporative cooling system 100. In one embodiment, the processor 1112 obtains an operating power consumption set consisting of the operating power consumption of each part of the indirect evaporative cooling system 100 based on the output parameter data set generated by the indirect evaporative cooling system module 1117 and the indirect evaporative cooling system operating power consumption model, obtains an operating cost set of the indirect evaporative cooling system 100 based on the obtained operating power consumption set, and optimizes the obtained operating cost set to obtain data on the output parameters of the indirect evaporative cooling system refrigeration model corresponding to the operation cost optimization of the indirect evaporative cooling system 100.

[0135] In one embodiment, the program for the determined refrigeration model of the indirect evaporative cooling system is stored in the indirect evaporative cooling system module 1117 in the memory 1111, and the program for the determined operating power consumption model of the indirect evaporative cooling system and the program for the determined operating cost optimization model of the indirect evaporative cooling system are stored in the operating cost optimization module 1116 in the memory 1111. When executed, the operating cost optimization module 1116 calls the program for the determined operating power consumption model of the indirect evaporative cooling system and combines the output parameter data set generated by the indirect evaporative cooling system module 1117 to generate control parameter data for optimizing the operating cost of the indirect evaporative cooling system 100.

[0136] The output interface 1114 is configured to receive control parameter data for optimizing the operating cost of the indirect evaporative cooling system 100 from the processor 1112, convert the data into a control signal suitable for the indirect evaporative cooling system 100, and send the control signal to the indirect evaporative cooling system 100 (e.g., its controller 101) via the connection line 114 to control the operation of the indirect evaporative cooling system 100.

[0137] All features disclosed in this specification (including any accompanying claims, abstracts, and drawings) and / or all steps of any method or process disclosed herein may be combined in any suitable combination, except where at least some of such features and / or steps are mutually exclusive. This application is not limited to the specific order of steps described in the specification, but includes other suitable order of steps for implementing this application.

[0138] Although the present application has been described in conjunction with the examples of the embodiments outlined above, it is likely that various alternatives, modifications, variations, improvements and / or substantial equivalents, whether known or currently or soon foreseeable, will be apparent to those skilled in the art. In addition, the technical effects and / or technical problems described in this specification are exemplary and not restrictive; so the disclosures in this specification may be used to solve other technical problems and have other technical effects and / or may solve other technical problems. Therefore, the examples of the embodiments of the present application as stated above are intended to be illustrative and not restrictive. Various changes may be made without departing from the spirit or scope of the present application. Therefore, the present application is intended to include all known or earlier developed alternatives, modifications, variations, improvements and / or substantial equivalents.

Claims

1. A control method (300) for an indirect evaporative cooling system, the control method (300) comprising: S1: creating an indirect evaporative cooling system refrigeration model and an indirect evaporative cooling system operation power consumption model, wherein the indirect evaporative cooling system refrigeration model includes input parameters and output parameters, and the indirect evaporative cooling system operation power consumption model includes input parameters and output parameters, wherein the output parameters of the indirect evaporative cooling system refrigeration model include control parameters of the indirect evaporative cooling system (100), and the output parameters of the indirect evaporative cooling system operation power consumption model include operation power consumption of various parts of the indirect evaporative cooling system (100), and the output parameters of the indirect evaporative cooling system refrigeration model are used as input parameters of the indirect evaporative cooling system operation power consumption model; S2: Acquire training data, wherein the training data includes data on input parameters and output parameters of the refrigeration model of the indirect evaporative cooling system and data on input parameters and output parameters of the power consumption model of the indirect evaporative cooling system; S3: training the indirect evaporative cooling system refrigeration model and the indirect evaporative cooling system operation power consumption model based on the acquired training data to obtain a determined indirect evaporative cooling system refrigeration model and a determined indirect evaporative cooling system operation power consumption model; and S4: Optimizing the operating cost of the indirect evaporative cooling system (100) based at least on the determined indirect evaporative cooling system refrigeration model and the determined indirect evaporative cooling system operation power consumption model, so as to obtain data of output parameters of the indirect evaporative cooling system refrigeration model corresponding to the optimization of the operating cost of the indirect evaporative cooling system (100), thereby controlling the operation of the indirect evaporative cooling system (100).

2. The control method (300) for an indirect evaporative cooling system according to claim 1, wherein: The input parameters of the refrigeration model of the indirect evaporative cooling system include indoor supply air temperature and indoor return air temperature.

3. The control method (300) for an indirect evaporative cooling system according to claim 1, wherein: In step S4, the following operations are performed to obtain data of output parameters of the refrigeration model of the indirect evaporative cooling system corresponding to the operation cost optimization of the indirect evaporative cooling system (100): S4.1: Obtaining field data of input parameters of a refrigeration model of the indirect evaporative cooling system; S4.2: Inputting the acquired field data into the determined indirect evaporative cooling system refrigeration model to obtain a data set of output parameters of the indirect evaporative cooling system refrigeration model; S4.3: Inputting the obtained data set of the output parameters of the refrigeration model of the indirect evaporative cooling system into the indirect evaporative cooling system operation cost optimization model to obtain data of the output parameters of the refrigeration model of the indirect evaporative cooling system corresponding to the operation cost optimization of the indirect evaporative cooling system (100).

4. The control method (300) for an indirect evaporative cooling system according to claim 3, wherein: The indirect evaporative cooling system operating cost optimization model is configured as follows: Based on the obtained data set of output parameters of the indirect evaporative cooling system refrigeration model and the indirect evaporative cooling system operation power consumption model, correspondingly obtaining an operation power consumption set composed of operation power consumption of various parts of the indirect evaporative cooling system (100); Obtaining an operating cost set of the indirect evaporative cooling system (100) based on the obtained operating power consumption set; as well as The acquired operating cost set is optimized to obtain data of output parameters of the indirect evaporative cooling system refrigeration model corresponding to the operation cost optimization of the indirect evaporative cooling system (100).

5. The control method (300) for an indirect evaporative cooling system according to claim 1, wherein: The operating power consumption of each part of the indirect evaporative cooling system (100) includes electricity consumption and water consumption, and the operating cost of the indirect evaporative cooling system (100) includes electricity cost generated by the electricity consumption and water cost generated by the water consumption.

6. The control method (300) for an indirect evaporative cooling system according to claim 1, wherein: The indirect evaporative cooling system (100) is configured to cool a cooled unit (106), the indirect evaporative cooling system (100) includes a water spray system (103), the output parameter of the refrigeration model of the indirect evaporative cooling system includes the rotation speed of the nozzle moving motor (11) of the water spray system (103), and the nozzle moving motor (11) is configured to control the movement of the nozzle (13) of the water spray system (103); The control method (300) further includes: obtaining a motor speed limit model, wherein the motor speed limit model is configured to obtain a speed limit of the nozzle moving motor (11) after the outdoor air is sprayed by the water mist spraying system (103) based on the outdoor air dry bulb temperature and the outdoor air dry air humidity content of the cooled unit (106); In the step S4, the speed limit of the nozzle moving motor (11) after the outdoor air at the current outdoor air dry-bulb temperature and the current outdoor air dry air humidity is sprayed by the water spray system (103) is obtained, and the speed of the nozzle moving motor (11) when the outdoor air is sprayed by the water spray system (103) is limited to within the corresponding speed limit of the nozzle moving motor (11), so as to optimize the operating cost of the indirect evaporative cooling system (100).

7. The control method (300) for an indirect evaporative cooling system according to claim 6, wherein: The motor speed limit model is obtained by performing the following operations: Creating and obtaining a psychrometric model between the air humidity content and the air temperature of the outdoor air after being sprayed by the water mist spraying system (103), wherein the psychrometric model uses the current outdoor air dry-bulb temperature and the current outdoor air dry-bulb humidity content as constants; Creating and obtaining a temperature and humidity limit model between an air moisture content limit and an air temperature limit when the outdoor air is sprayed by the water mist spraying system (103) and the air humidity is saturated; Creating and obtaining a speed and humidity limit model between the speed limit of the nozzle moving motor (11) and the air humidity limit when the air humidity is saturated; and The acquired temperature and humidity model, the temperature and humidity limit model, and the speed and humidity limit model are combined to obtain the motor speed limit model.

8. The control method (300) for an indirect evaporative cooling system according to claim 6, wherein: The indirect evaporative cooling system (100) further includes a direct expansion refrigeration system (104) and a fan system (105); The input parameters of the indirect evaporative cooling system refrigeration model include target parameters of the cooled unit (106) and operating parameters of the indirect evaporative cooling system (100), and the output parameters of the indirect evaporative cooling system refrigeration model include control parameters for operating the water spray system (103), the direct expansion refrigeration system (104), and the fan system (105); Output parameters of the indirect evaporative cooling system operation power consumption model include the power consumption and water consumption of the water spray system (103) of the indirect evaporative cooling system (100), the power consumption of the direct expansion refrigeration system (104), and the power consumption of the fan system (105); and The operating cost of the indirect evaporative cooling system (100) includes the sum of the electricity cost and water cost of the water spray system (103) of the indirect evaporative cooling system (100), the electricity cost of the direct expansion refrigeration system (104), and the electricity cost of the fan system (105).

9. The control method (300) for an indirect evaporative cooling system according to claim 8, wherein: The direct expansion refrigeration system (104) includes a compressor (6), a condenser (4), an evaporator (3) and a flow regulating valve (9), and the fan system (105) includes an indoor fan (18) and an outdoor fan (19), wherein: The target parameter of the cooled unit (106) is the target temperature of the cooled unit (106), and the operating parameters of the indirect evaporative cooling system (100) include the outdoor air temperature, outdoor air humidity, indoor supply air temperature, indoor supply air humidity, indoor return air temperature and indoor return air humidity of the cooled unit (106); The control parameters for operating the water mist spray system (103) include the rotation speed of the nozzle moving motor (11), the control parameters for operating the direct expansion refrigeration system (104) include the rotation speed of the compressor (6) and the opening of the flow regulating valve (9), and the control parameters for operating the fan system (105) include the fan speed of the outdoor fan (19); The power consumption of the water mist spraying system (103) includes the power consumption of the nozzle moving motor (11), the water consumption of the water mist spraying system (103) includes the water spraying amount of the nozzle (13), the power consumption of the direct expansion refrigeration system (104) includes the power consumption of the compressor (6), and the power consumption of the fan system (105) includes the power consumption of the indoor fan (18) and the power consumption of the outdoor fan (19); The electricity cost of the water mist system (103) includes the electricity cost generated by the power consumption of the nozzle moving motor (11), the water cost of the water mist system (103) includes the water cost generated by the water spraying amount of the nozzle (13), the electricity cost of the direct expansion refrigeration system (104) includes the electricity cost generated by the power consumption of the compressor (6), and the electricity cost of the fan system (105) includes the electricity cost generated by the power consumption of the indoor fan (18) and the electricity cost generated by the power consumption of the outdoor fan (19).

10. The control method (300) for an indirect evaporative cooling system according to claim 1, wherein: The indirect evaporative cooling system refrigeration model and the indirect evaporative cooling system operation power consumption model are both fully connected deep neural network models.

11. The control method (300) for an indirect evaporative cooling system according to claim 1, wherein a back propagation algorithm and a gradient descent optimization algorithm are used to train the indirect evaporative cooling system refrigeration model and the indirect evaporative cooling system operation power consumption model.

12. The control method (300) for an indirect evaporative cooling system according to claim 3, wherein: The indirect evaporative cooling system operation cost optimization model is configured to optimize the operation cost of the indirect evaporative cooling system (100) using a genetic algorithm, wherein a fitness function in the genetic algorithm is obtained based on the determined indirect evaporative cooling system operation power consumption model.

13. The control method (300) for an indirect evaporative cooling system according to claim 1, wherein: Applying the indirect evaporative cooling system (100) to a test bench, and using data obtained from the test bench as the training data to train the indirect evaporative cooling system refrigeration model and the indirect evaporative cooling system operation power consumption model, so as to obtain a determined indirect evaporative cooling system refrigeration model and the determined indirect evaporative cooling system operation power consumption model; and The indirect evaporative cooling system (100) is applied to a site of a cooled unit (106), and data obtained from the site of the cooled unit (106) is used as further training data to further train the determined indirect evaporative cooling system refrigeration model and the determined indirect evaporative cooling system operation power consumption model, so as to adjust the determined indirect evaporative cooling system refrigeration model and the determined indirect evaporative cooling system operation power consumption model, thereby obtaining a final determined indirect evaporative cooling system refrigeration model and indirect evaporative cooling system operation power consumption model.

14. A computing system (110), comprising: A processor (1102), the processor (1102) being configured to execute the control method (300) according to any one of claims 1 to 13 to obtain a determined refrigeration model of the indirect evaporative cooling system and a determined operation power consumption model of the indirect evaporative cooling system.

15. A control system (111), the control system (111) comprising: an indirect evaporative cooling system module (1117); and An indirect evaporative cooling system operating cost optimization module (1116), the indirect evaporative cooling system operating cost optimization module (1116) is connected to the indirect evaporative cooling system module (1117), and the indirect evaporative cooling system operating cost optimization module (1116) is configured to perform the control method (300) described in any one of claims 1 to 13 based on the current operating condition data of the indirect evaporative cooling system (100) and the indirect evaporative cooling system module (1117) to optimize the operating cost of the indirect evaporative cooling system (100), so as to obtain the control data of the indirect evaporative cooling system (100) corresponding to the operation cost optimization of the indirect evaporative cooling system (100), thereby controlling the operation of the indirect evaporative cooling system (100), wherein the indirect evaporative cooling system module (1117) comprises a determined indirect evaporative cooling system refrigeration model and a determined indirect evaporative cooling system operation power consumption model obtained by the control method (300) according to any one of claims 1 to 13; or The indirect evaporative cooling system module (1117) and the indirect evaporative cooling system operation cost optimization module (1116) respectively include a determined indirect evaporative cooling system refrigeration model and a determined indirect evaporative cooling system operation power consumption model obtained by the control method (300) according to any one of claims 1 to 13.

16. The control system (111) according to claim 15, wherein Deploying the obtained determined indirect evaporative cooling system refrigeration model and the determined indirect evaporative cooling system operation power consumption model into the indirect evaporative cooling system module (1117) through the computing system (110) according to claim 14; or The obtained determined indirect evaporative cooling system refrigeration model and the determined indirect evaporative cooling system operation power consumption model are respectively deployed to the indirect evaporative cooling system module (1117) and the indirect evaporative cooling system operation cost optimization module (1116) through the computing system (110) described in claim 14.

17. The control system (111) according to claim 15, wherein: The control system (111) includes a processor (1112), and the processor (1112) is configured to execute the control method (300) described in any one of claims 1 to 13 to obtain a determined indirect evaporative cooling system refrigeration model and a determined indirect evaporative cooling system operation power consumption model, and deploy the obtained determined indirect evaporative cooling system refrigeration model and the determined indirect evaporative cooling system operation power consumption model into the indirect evaporative cooling system module (1117), or respectively deploy them into the indirect evaporative cooling system module (1117) and the indirect evaporative cooling system operation cost optimization module (1116).

18. An indirect evaporative cooling system (100), comprising: Water mist system (103); Direct Expansion Refrigeration System (104); Fan system (105); a detection device (112), the detection device (112) being connected to at least one of the water spray system (103), the direct expansion refrigeration system (104), and the fan system (105) to detect operating condition data of the at least one; A controller (101), the controller (101) being configured to control the operation of at least one of the water mist system (103), the direct expansion refrigeration system (104), and the fan system (105) based on a control signal received from a control system (111) according to any one of claims 15 to 17, The control system (111) is configured to optimize the operating cost of the indirect evaporative cooling system (100) based on the operating condition data detected by the detection device (112), so as to obtain control data of the indirect evaporative cooling system (100) corresponding to the optimization of the operating cost of the indirect evaporative cooling system (100), thereby generating the control signal.

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