Control method and system of indirect evaporative cooling system

Abstract

The invention provides a control method for an indirect evaporative cooling system. The control method comprises the steps that an indirect evaporative cooling system refrigeration model and an indirect evaporative cooling system operation power consumption model are established; obtaining training data; training an indirect evaporative cooling system refrigeration model and an indirect evaporative cooling system operation power consumption model based on the training data to obtain the determined indirect evaporative cooling system refrigeration model and indirect evaporative cooling system operation power consumption model; and optimizing the operation cost of the indirect evaporative cooling system at least based on the determined model to obtain data of output parameters of the refrigeration model of the indirect evaporative cooling system when the operation cost is optimized so as to control the operation of the indirect evaporative cooling system. According to the method, the optimal operation cost of the indirect evaporative cooling system under different climates and different places can be guaranteed, operation of the indirect evaporative cooling system is controlled based on the indoor air supply temperature and the indoor air return temperature, and therefore it is rapidly and accurately guaranteed that the refrigerating capacity meets the requirement of the cooled unit.

Description

Technical Field

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

[0002] In order to comply with the global trend of digitalization, data centers are developing at an unprecedented speed, and their energy consumption is becoming more and more serious. For example, according to research, the total power consumption of data centers has accounted for 1.5% of the total electricity consumption of the whole society, and the air conditioning system accounts for nearly 40% of the energy consumption of data centers, which is a key factor affecting the power usage efficiency (PUE) value of data centers. Energy saving and consumption reduction have become one of the key considerations for major data centers in the early planning stage. The indirect evaporative cooling solution that uses natural cold sources to improve the energy efficiency ratio has been demonstrated and applied in many large data centers at home and abroad due to its energy saving and high applicability. Summary of the invention

[0003] The inventors have observed that the current indirect evaporative cooling system / unit has a relatively traditional control logic, for example, different operating modes of the indirect evaporative cooling system / unit are turned on according to the dry-bulb temperature value of the outdoor air. More specifically, when the outdoor dry-bulb temperature is lower than the first set value, the outdoor fan system is turned on, and the air in the cooled unit (for example, 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 temperature dry-bulb temperature is higher than the first set value and the wet-bulb temperature is lower than the second set value, the water mist system is turned on for spraying, that is, by spraying water or water mist to the air-to-air heat exchange core, the outdoor air temperature is lowered before heat exchange with the air in the cooled unit. When the outdoor air wet-bulb temperature is greater than the second set value, that is, when the spray mode cannot meet the temperature requirement of the cooled unit, the direct expansion refrigeration system is turned on to further reduce the air temperature of the cooled unit.

[0004] At present, most of the indirect evaporative cooling systems / units on the market have adopted variable frequency fans, variable frequency compressors, etc. to achieve energy saving and consumption reduction. However, the inventors found that the traditional control logic of the indirect evaporative cooling system / unit only starts different operation modes based on the fixed values ​​of the outdoor air dry-bulb temperature, which has the following disadvantages: on the one hand, my country's climate distribution is diverse, and the climate diversity is not taken into account, and the different electricity and water costs in different locations are not taken into account, which cannot guarantee the lowest total energy consumption / cost of the system operation; on the other hand, the heat exchange efficiency of the air-to-air heat exchanger in the wet mode is related to the outdoor air parameters. Starting the direct expansion refrigeration system according to the predetermined wet-bulb temperature will result in the cooling capacity not being able to meet the requirements of the cooled unit as the outdoor temperature increases.

[0005] In order to solve the above problems, 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, and optimizes the operating cost of the indirect evaporative cooling system based on the multiple sets of control parameter data, so that the total operating cost of the indirect evaporative cooling system is optimal under a certain 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 costs of the indirect evaporative cooling system in different climates and different locations, so as to ensure that the total operating cost of the indirect evaporative cooling system in different climates and different locations is always optimal (for example, the lowest). In different climates, such as different regions, the climate is different, and the unit price of electricity may be different (even very different), and the unit price of water may also be different (even very different), so even the same set of control parameter data will cause the operating cost of the indirect evaporative cooling system to be different (even very different). For example, the power saving / energy saving mode may be suitable for areas with abundant water resources, and the water saving mode may be suitable for areas with tight water resources, which specifically depends on the total cost generated by the electricity cost and water cost in these areas. This application considers the total operating cost of the indirect evaporative cooling system in different climates and locations while considering the control parameter data to meet the needs of the cooled unit.

[0006] Furthermore, the present application controls the operation of the indirect evaporative cooling system to quickly and accurately meet the needs of the cooled unit, such as the cooling capacity demand, 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. The present application uses the indoor supply air temperature output by the indirect evaporative cooling system as a monitoring target so that the output meets the temperature demand received by the cooled unit, and uses the indoor return air temperature returned from the cooled unit to the indirect evaporative cooling system as a monitoring target to determine whether the cooled unit actually meets the temperature demand. When it is detected that both the indoor supply air temperature and the indoor return air temperature meet the demand, it indicates that the indirect evaporative cooling system accurately provides the cooling capacity required by the cooled unit, otherwise the cooling capacity actually required by the cooled unit does not meet the demand. For example, when 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 operation of the indirect evaporative cooling system is inappropriate, 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, it may indicate that the actual load of the cooled unit has increased or a component of the cooled unit has failed, etc., and therefore the operation of the indirect evaporative cooling system needs to be adjusted accordingly. Based on the monitored indoor supply air temperature and indoor return air temperature, the present application can correspondingly adjust the operation of the indirect evaporative cooling system so that it accurately meets the cooling capacity requirement of the cooled unit.

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

[0008] According to a first aspect of the present application, 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 a first aspect of the present application, 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 the step S4, steps S4.1-S4.3 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. In step S4.1, field data of input parameters of the refrigeration model of the indirect evaporative cooling system are obtained. In step S4.2, the obtained field data is input into the determined refrigeration model of the indirect evaporative cooling system to obtain a data set of output parameters of the refrigeration model of the indirect evaporative cooling system. In step S4.3, the obtained data set of output parameters of the refrigeration model of the indirect evaporative cooling system is input into the operation cost optimization model of the indirect evaporative cooling system 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.

[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 operating power consumption of various parts of the indirect evaporative cooling system based on the acquired 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 acquired operating power consumption set; and optimize the acquired 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 a 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 the 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 spray system. The output parameter of the refrigeration model of the indirect evaporative cooling system includes the rotation speed of the nozzle moving motor of the water spray system, and the nozzle moving motor is configured to control the movement of the nozzle of the water spray system. The control method also includes obtaining a motor speed limit model. The motor speed limit model is configured to obtain the speed limit of the nozzle moving motor after the outdoor air is sprayed by the water spray system based on the outdoor air dry bulb temperature and the outdoor air dry air humidity content of the cooled unit. In the step S4, the speed limit of the nozzle moving motor after the outdoor air is sprayed by the water spray system under 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 spray system is limited to within the corresponding speed limit of the nozzle moving motor, so as to optimize the operation 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 saturated with air humidity after being sprayed by the water spray system; 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 also 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 the control parameters that operate the water spray 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 power consumption and water consumption of the water spray system of the indirect evaporative cooling system, the power consumption of the direct expansion refrigeration system and the power consumption of the fan system. The operating cost of the indirect evaporative cooling system includes the sum of the power cost and water cost of the water spray system of the indirect evaporative cooling system, the power cost of the direct expansion refrigeration system and the power cost of the fan system.

[0016] According to the first aspect of the present application, the direct expansion refrigeration system includes a compressor, a condenser, an evaporator and a flow regulating 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 spray 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 regulating valve, and the control parameters for operating the fan system include the fan speed of the outdoor fan. The power consumption of the water spray system includes the power consumption of the nozzle moving motor, the water consumption of the water spray system includes the water spraying amount 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 a 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 a first aspect of the present application, a back propagation algorithm and a gradient descent optimization algorithm are used to train the refrigeration model of the indirect evaporative cooling system and the operating power consumption model of the indirect evaporative cooling system.

[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 a 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 the data obtained from the test bench is used 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. The indirect evaporative cooling system is applied to a site of a cooled unit, and the data obtained from the site of the cooled unit 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, so as to obtain 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, the present application provides a computing system, wherein the computing system comprises a processor, wherein the processor is 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, the present application provides a control system. 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 perform 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, so as to obtain the control data of the indirect evaporative cooling system corresponding to the operating cost optimization of the indirect evaporative cooling system, 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 operation power consumption model obtained by the aforementioned control method. Alternatively, the indirect evaporative cooling system module and the indirect evaporative cooling system operation cost optimization module respectively include the determined indirect evaporative cooling system refrigeration model and the determined indirect evaporative cooling system operation 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 operation power consumption model of the indirect evaporative cooling system are deployed to the indirect evaporative cooling system module through the aforementioned computing system. Alternatively, the obtained determined refrigeration model of the indirect evaporative cooling system and the determined operation power consumption model of the indirect evaporative cooling system are deployed to the indirect evaporative cooling system module and the indirect evaporative cooling system operation cost optimization module respectively through 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 the fourth aspect of the present application, the present application provides an indirect evaporative cooling system. The indirect evaporative cooling system includes a water spray mist 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 spray mist 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 spray mist system, the direct expansion refrigeration system and the fan system based on the 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 represented in different figures is represented by the same reference numeral. For purposes of clarity, not every component may be labeled in every drawing. In the drawings:

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

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

[0029] Figure 3 Shows Figure 1 The overall flow chart of one embodiment of a control method for an indirect evaporative cooling system is shown;

[0030] Figure 4 Shows 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 Shows Figure 3 A detailed flowchart of an embodiment of step 308 is shown;

[0032] Figure 6 Shows Figure 3 A detailed flowchart of an embodiment of step 310 is shown;

[0033] Figure 7 Shows Figure 6 A detailed flowchart of an embodiment of step 606 is shown;

[0034] Figure 8 A flowchart of an embodiment of a method for obtaining a motor speed limit model is shown;

[0035] Fig. 9 The psychrometric diagram of air is shown;

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

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

[0038] Various specific 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 1The block diagram structure of the overall system for controlling the indirect evaporative cooling system according to the present application is shown. The present application creates and determines the refrigeration model of the indirect evaporative cooling system and the operating power consumption model of the indirect evaporative cooling system, obtains the array control parameter data of the indirect evaporative cooling system 100 based on the determined refrigeration model of the indirect evaporative cooling system and the operating power consumption model of the indirect evaporative cooling system and the field data of the indirect evaporative cooling system 100, optimizes the operating cost of the indirect evaporative cooling system 100 based on the obtained array control parameter data, so as to obtain the control parameter data of the indirect evaporative cooling system 100 when the operating cost of the indirect evaporative cooling system 100 is optimized (for example, the total operating cost is optimal / lowest), such as a set of control parameter data, so as to control 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 a 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] The detection device 112 is connected to the indirect evaporative cooling system 100 and is configured to detect and obtain the operating parameters of the indirect evaporative cooling system 100. The 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, the detection device 112 is disposed outside the indirect evaporative cooling system 100. In some embodiments, the detection device 112 is a part of the 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 operation power consumption model, obtain training data, and train the indirect evaporative cooling system refrigeration model and the indirect evaporative cooling system operation power consumption model based on the obtained training data to obtain a determined indirect evaporative cooling system refrigeration model and a determined indirect evaporative cooling system operation power consumption model. The computing system 110 is connected to the detection device 112 via a connection line 115, and is configured to obtain operating condition parameter data from the detection device 112 via the connection line 115, and use these data as training data. The computing system 110 is connected to the control system 111 via a connection line 113, and is configured to send the obtained determined indirect evaporative cooling system refrigeration model and the determined indirect evaporative cooling system operation power consumption model as a module package to the control system 111 via the connection line 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. The present application obtains the operating parameter data of the indirect evaporative cooling system 100 during operation as training data. The computing system 110 trains 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. 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, but the control system 111 performs the operation 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 operation power consumption model obtained according to the data of the test bench may have deviations when the indirect evaporative cooling system 100 is applied to the cooled unit 106 actually operated by the user, the present application further obtains the 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 to obtain the indirect evaporative cooling system refrigeration model and the indirect evaporative cooling system operation power consumption model adapted to the cooled unit 106 on site. Specifically, after obtaining the determined indirect evaporative cooling system refrigeration model and the determined indirect evaporative cooling system operation power consumption model from the test bench, the present application further applies the indirect evaporative cooling system 100 to the site of the cooled unit 106 actually operated by the user, and also applies the detection device 112 to the site. The operating parameter data of the indirect evaporative cooling system 100 when it is in operation is obtained at the site of the cooled unit 106 by the detection device 112 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 (e.g., 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 the determined refrigeration model of the indirect evaporative cooling system and the determined operating power consumption model of the indirect evaporative cooling system, so as to obtain the control parameter data of the indirect evaporative cooling system 100 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 the connection line 116, and is configured to obtain the real-time operating parameter data from the detection device 112 via the connection line 116, and use these data as field data. The control system 111 is connected to the indirect evaporative cooling system 100 via the connection line 114, and is configured to send the obtained control parameter data of 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 of the indirect evaporative cooling system 100 is the data of the output parameters of the corresponding indirect evaporative cooling system refrigeration model. In other embodiments, the control parameter data of 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 in 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 the lowest. At the same time, the present application controls the water spray system 103, the direct expansion refrigeration 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 Shows 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 mist 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 spray mist system 103 includes an air-to-air heat exchanger 1, a hose connector 2, a water spray mist assembly support plate 10, a nozzle moving 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) through 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 in sequence. The spiral hose 17 supplies water to the nozzle 13 through the hose connector 2. The solenoid valve 14 is configured to control the start and stop of the water spray mist system 103, and the pressure regulating valve 15 is configured to maintain a constant working pressure of 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 moving motor 11. The nozzle moving 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 operating 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 to reduce the temperature of the outdoor air (secondary air) through 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 through evaporative cooling. On the other hand, by controlling the residence 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 support plate 10 of the water mist spray assembly.

[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 and low-pressure refrigerant vapor, which becomes high-temperature and high-pressure superheated vapor after adiabatic compression by compressor 6, and then enters condenser 4 for constant pressure cooling, releasing heat to secondary air (outdoor air). The refrigerant is cooled into supercooled liquid refrigerant, and then adiabatically throttled by flow control valve 9 to become low-pressure refrigerant, and finally 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 to avoid 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 oil sump of the compressor through its own control device. Liquid storage tank 8 is configured to compensate for the change in condenser liquid level caused by load change, and separate the liquid and gaseous refrigerants at the outlet of condenser 4.

[0050] Figure 2 FIG. 1 shows the layout of the detection device 112 disposed 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 spray 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, and 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, and 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, and 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, and 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 at the rear side of the indoor fan 18, and is configured to detect the indoor supply air temperature T Fand indoor air humidity RH F . The parameter data detected by the above 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 the present application. The present application is not limited to the above sensors, but also includes other suitable sensors for controlling other suitable operations of the indirect evaporative cooling system 100. For example, the present 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, the present application detects the above parameters by setting other suitable sensors.

[0051] although Figure 2 The various components of the unit assembly 102 of the indirect evaporative cooling system 100 and the arrangement of the detection device 112 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 and other arrangements.

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

[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 operation power consumption model are created. 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. 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 operation power consumption model include the operation power consumption of each part of the indirect evaporative cooling system 100. Then, step 304 is transferred to 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 operation power consumption model. The operation power consumption of each part of the indirect evaporative cooling system 100 includes electricity consumption and water consumption, and the operation cost of the indirect evaporative cooling system 100 includes electricity cost generated by electricity consumption and water cost generated by water consumption.

[0055] At step 306 , training data is obtained, the training data including data of input parameters and output parameters of the refrigeration model of the indirect evaporative cooling system and data of input parameters and output parameters of the power consumption model of the indirect evaporative cooling system. Then, the process 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. Then, the process proceeds to step 310 from step 308.

[0057] At step 310, the operation 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, so as to obtain the 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., the control parameter data of the indirect evaporative cooling system 100), so as to control the operation of the indirect evaporative cooling system 100. Then, the process proceeds from step 310 to step 312. The detailed process of one embodiment of step 310 is shown in Figure 6 .

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

[0059] Figure 4 Shows 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. 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 is the required operating temperature of the cooled unit 106 (i.e., 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 is the opening degree of the flow control valve 9. 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 water spraying amount of the nozzle 13.

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

[0072] Figure 5 Shows Figure 3 A detailed flowchart of an embodiment of step 308 (training model) is shown in FIG. Figure 3 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. In one embodiment, the indirect evaporative cooling system refrigeration model F(x) and the indirect evaporative cooling system operation power consumption model G(y) are both fully connected deep neural network models. In other embodiments, the indirect evaporative cooling system refrigeration model F(x) and the indirect evaporative cooling system operation 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, a 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 ​​of the deep neural network model F(x) is set respectively. f And the set of initialization weights and bias values ​​of the deep neural network model G(y) φ g .

[0074] At step 504, the operating parameter data of the indirect evaporative cooling system 100 is obtained. Then, the process proceeds to step 506 from step 504. In one embodiment, the operating parameter data of the indirect evaporative cooling system 100 includes input parameter data of the refrigeration model F(x) of the indirect evaporative cooling system (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 to step 508 from step 506. 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 output is calculated according to the activation function. Then, the process proceeds to step 510 from step 508. In one embodiment, the output of the output layer neuron is calculated according to the activation function of the deep neural network model F(x), and the output of the output layer neuron is calculated according to 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] Among them, F'(x) is the predicted value output by the refrigeration model F(x) of the indirect evaporative cooling system, f(x) is the real value of the actual output of the indirect evaporative cooling system 100, and x represents the input parameter of the refrigeration model F(x) of the indirect evaporative cooling system; G'(y) is the predicted value output by the operation power consumption model G(y) of the indirect evaporative cooling system, g(y) is the real value of the actual output of the indirect evaporative cooling system 100, and y is the output parameter of the refrigeration model F(x) of the indirect evaporative cooling system, and is also the input parameter of the operation power consumption model G(y) of the indirect evaporative cooling system. 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 converges, and the weight values ​​and deviation values ​​of the deep neural network models 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 transferred 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 back propagation 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 described in detail 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 from step 514 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 from step 516 to step 518. 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 an 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 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 a zero matrix t is a zero matrix.

[0091] The above three formulas (2.1), (2.2), and (2.3) are repeated repeatedly, and 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 part of the training data as a validation set. This application also periodically evaluates the model performance on the validation set to detect whether the trained model is overfitting or undertrained. The detection accuracy and loss indicators are used to measure the model performance. If the model performance is not accurate enough or overfitting, the learning rate, batch size, and model complexity are adjusted according to the performance of the validation set to improve the model performance.

[0093] Figure 6 Show Figure 3 The detailed flowchart 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 operating 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 on-site data of the input parameters of the refrigeration model of the indirect evaporative cooling system are obtained. Then, the process proceeds from step 602 to step 604.

[0095] At step 604 , the acquired field data is input into the determined refrigeration model of the indirect evaporative cooling system to obtain a data set of output parameters of the refrigeration model of the indirect evaporative cooling system. Then, the process proceeds from step 604 to step 606 .

[0096] At step 606, the obtained data set of the output parameters of the refrigeration model of the indirect evaporative cooling system is input into the indirect evaporative cooling system operation cost optimization model to obtain the 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, 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 as follows: based on the acquired data set of the output parameters of the indirect evaporative cooling system refrigeration model and the indirect evaporative cooling system operating power consumption model, correspondingly acquire an operating power consumption set consisting of operating power consumptions of various parts of the indirect evaporative cooling system 100; based on the acquired operating power consumption set, acquire an operating cost set of the indirect evaporative cooling system 100; and optimize the acquired operating cost set to obtain 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.

[0097] The obtained output parameter data of the refrigeration model F(x) of the indirect evaporative cooling system, 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., through the 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., data center) to meet the demand. The output parameter data of the refrigeration model F(x) of the indirect evaporative cooling system 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) make the temperature of the data center meet the temperature demand (e.g., reach the temperature threshold, or be within the temperature threshold range), they can all be regarded as a solution, and the solution set composed 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 , solve 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 data set / control parameter data set of the refrigeration model F(x) of the indirect evaporative cooling system) to quickly solve the control parameter data of the indirect evaporative cooling system 100 when the refrigeration demand of the cooled unit 106 (e.g., a data center) is met and the total operating cost of the indirect evaporative cooling system 100 is the lowest.

[0100] In one embodiment, the total operating cost of the indirect evaporative cooling system 100 = electricity price * electricity quantity + water price * water quantity, and the calculation formula is 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. In this application, the indirect evaporative cooling system operation power consumption model G(y) is used as the optimization target, and the operating condition parameters output by the indirect evaporative cooling system refrigeration model F(x) are used 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 operation power consumption model G(y) are multiple groups of control parameters that meet the refrigeration conditions, which group of control parameters makes the operation of the indirect evaporative cooling system 100 most energy-efficient? It is necessary to calculate the system operation power consumption through the indirect evaporative cooling system operation power consumption model G(y) to obtain the system operation cost and ensure that the total system operation cost is the lowest. This application uses the indirect evaporative cooling system operation 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 operation cost is optimal (e.g., lowest). An embodiment of the genetic algorithm optimization calculation is Figure 7 Shown in detail.

[0104] Figure 7 Shows Figure 6 A detailed flowchart 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 individual (i.e., the control parameter of the indirect evaporative cooling system 100) is calculated according to the fitness function. In one embodiment, the fitness function in the genetic algorithm is obtained based on the determined indirect evaporative cooling system operation power consumption model G(y). For example, the fitness function is the same as the determined indirect evaporative cooling system operation power consumption model G(y). Then, the process proceeds to step 706 from step 704.

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

[0108] At step 708, the retained individuals are cross-pollinated in pairs 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 the maximum number of iterations has not been reached, 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 optimization, and after a period of time (for example, a predetermined period of time), the system switches to Figure 3 Step 312.

[0115] When obtaining the control parameter data of the indirect evaporative cooling system 100 corresponding to the optimization of the operating cost of the indirect evaporative cooling system 100, the limit of the control parameter needs to be considered. The control parameter data exceeding the limit of the control parameter can make the indirect evaporative cooling system 100 operate normally, but may increase the operating cost of the indirect evaporative cooling system 100. Therefore, limiting the control parameter data of the indirect evaporative cooling system 100 within its limit can optimize the operating cost of the indirect evaporative cooling system 100. For example, after the outdoor air is sprayed by the water spray mist system 103, the air temperature will no longer drop after the moisture content of the air is saturated. Since the nozzle 13 in the water spray mist system 103 is driven to reciprocate by the nozzle moving motor 11, the moisture content in the air depends on the reciprocating speed of the nozzle 13. Therefore, after the outdoor air is sprayed by the water spray mist system 103, when the moisture content of the air is saturated, the nozzle moving motor 11 has a corresponding maximum speed limit, that is, a speed limit. The nozzle moving motor 11 still makes the air saturated in moisture content and the air temperature no longer drops when it runs beyond the speed limit. Therefore, the operation of the nozzle moving motor 11 beyond the motor speed limit will generate unnecessary power consumption, resulting in a waste of operating costs of the indirect evaporative cooling system 100. In one embodiment, in order to save the operating cost of the indirect evaporative cooling system 100, the present application obtains the speed limit of the nozzle moving motor 11 after the outdoor air is sprayed by the water spray system 103 under 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 spray system 103 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. In one embodiment, the present application obtains the speed limit of the nozzle moving motor 11 after the outdoor air is sprayed by the water spray system 103 through a motor speed limit model. 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 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 of an embodiment of a method for obtaining a motor speed limit model is shown.

[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 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 103 is created and obtained. Then, step 804 is transferred to step 806. The temperature and humidity model uses the current outdoor air dry bulb temperature and the current outdoor air dry air humidity content as constants. In one embodiment, the 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 103 is as follows:

[0119] T=K×(RR 初始 )+T 初始 ,

[0120] Where 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 dry air humidity after the outdoor air is 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. The present 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 between the air humidity limit and the air temperature limit when the outdoor air is saturated with air humidity after being sprayed by the water spray system 103 is created and obtained. Then, the process proceeds from step 806 to step 808. Fig. 9 ) It can be seen that when water is used to spray air, the outdoor air is in contact with 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 humid 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 enthalpy-humidity diagram of air (see Fig. 9) The boundary curve (saturation boundary line) data at the lower right edge can be used to train the temperature and humidity limit model. In one embodiment, the temperature and humidity limit model uses an N-order polynomial model as follows:

[0122]

[0123] Where T is the air temperature limit when the air humidity is saturated, R is the air humidity limit when the air humidity is saturated, and a i is the coefficient of variation of the polynomial model. From the psychrometric diagram of air (see Fig. 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 between the speed limit of the nozzle moving motor 11 and the air humidity limit when the air humidity is saturated is created and obtained. Then, step 808 is transferred to step 810. Since the nozzle 13 in the water spray mist system 103 is driven by the nozzle moving motor 11 to reciprocate, 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 spray mist system 103, when the humidity content of the air is saturated, the nozzle moving motor 11 has a corresponding maximum speed limit, that is, the speed limit. For example, according to the data of the speed limit of the nozzle moving motor 11 and the air humidity limit when the air humidity is saturated provided by the component manufacturer, 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 humidity limit model are combined to obtain the 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 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 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 content of the outdoor air after being sprayed.

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

[0127] Fig.10 Shows Figure 1 The block diagram structure of the PID control of the controller 101 shown in FIG. 1 is a block diagram of the PID control of the controller 101. 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 input parameter y of the indirect evaporative cooling system operation power consumption model G(y) corresponding to the system total operation cost is minimized. P , n 冷凝 , n 喷头 , D PF )), input into the controller 101, and use the PID algorithm to quickly operate the actuator of the indirect evaporative cooling system 100 to the specified input parameter value.

[0128] like Fig.10 As shown, input parameter data / control parameter data y=((N P , n 冷凝 , n 喷头 , D PF )) as the input of the PID algorithm (i.e., the target data), and then superimposed after the proportional control, differential control and integral control modules, and 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 ), detect and obtain the operating parameter data of the indirect evaporative cooling system 100, and use it as feedback data. The feedback data is superimposed with the input parameter data / control parameter data and then passes through the proportional adjustment, differential adjustment and integral adjustment modules, and the above process is repeated until the indirect evaporative cooling system 100 reaches the required input parameter data / control parameter data (i.e., target data).

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

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

[0131] The input interface 1103 is configured to receive training data for the indirect evaporative cooling system refrigeration model and the indirect evaporative cooling system operation power consumption model through the connection line 115. The input interface 1103 is also configured to convert the received data into data recognizable by the processor 1102 and output the data to the processor 1102. The processor 1102 is configured to process the received data (e.g., training model) to obtain the determined indirect evaporative cooling system refrigeration model and the indirect evaporative cooling system operation power consumption model, and generate a program of the determined indirect evaporative cooling system refrigeration model and the indirect evaporative cooling system operation power consumption model. The processor 1102 is configured to create and obtain an indirect evaporative cooling system operation cost optimization model, and generate a program of the determined indirect evaporative cooling system operation cost optimization model. The processor 1102 is also configured to combine the programs of the determined indirect evaporative cooling system refrigeration model and the indirect evaporative cooling system operation power consumption model and the determined indirect evaporative cooling system operation 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 through 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 operation cost optimization module 1116 .

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

[0134] The input interface 1113 is also configured to receive the field data of the indirect evaporative cooling system 100 detected by the detection device 112 through 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 out its data and / or program (or instruction), and execute the program (or instruction) to process (e.g., preprocess) the received field data of the indirect evaporative cooling system 100. The processor 1112 is also configured to call the indirect evaporative cooling system module 1117 to read out its data and / or program (or instruction), and execute the program (or instruction) to process the preprocessed data generated by the data preprocessing module 1118 (e.g., run the indirect evaporative cooling system refrigeration model) to obtain the output parameter data set of the indirect evaporative cooling system refrigeration model. The processor 1112 is also configured to call the operation cost optimization module 1116 to read out its data and / or program (or instruction), and execute the program (or instruction) to process the output parameter data set generated by the indirect evaporative cooling system module 1117 to generate the output parameter data (i.e., control parameter data) of the indirect evaporative cooling system refrigeration model corresponding to the operation cost optimization of the indirect evaporative cooling system 100. In one embodiment, the operation cost optimization module 1116 calls the program of the determined indirect evaporative cooling system operation power consumption model in the indirect evaporative cooling system module 1117 when executing, and generates the control parameter data of the operation cost optimization of the indirect evaporative cooling system 100 in combination with the output parameter data set generated by the indirect evaporative cooling system module 1117. 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 the output parameter data of the indirect evaporative cooling system refrigeration model corresponding to the operating cost optimization of the indirect evaporative cooling system 100.

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

[0136] The output interface 1114 is configured to receive control parameter data when 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 attached claims, abstracts and drawings) and / or all steps of any method or process disclosed in this specification can be combined in any suitable combination, except for at least some mutually exclusive combinations of such features and / or steps. The application is not limited to the specific order of steps described in the specification, but includes other suitable order of steps for implementing the application.

[0138] Although the present application has been described in conjunction with the examples of the embodiments summarized above, various alternatives, modifications, changes, improvements and / or substantially equivalent solutions, whether known or currently or foreseeable in the near future, may be apparent to those skilled in the art. In addition, the technical effects and / or technical problems described in this specification are exemplary rather than restrictive; so the disclosure 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 rather than 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, changes, improvements and / or substantially equivalent solutions.

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 comprises input parameters and output parameters, and the indirect evaporative cooling system operation power consumption model comprises input parameters and output parameters, wherein the output parameters of the indirect evaporative cooling system refrigeration model comprise control parameters of the indirect evaporative cooling system (100), and the output parameters of the indirect evaporative cooling system operation power consumption model comprise operation power consumption of various parts of the indirect evaporative cooling system (100); S2: Acquire training data, wherein the training data includes data of input parameters and output parameters of the refrigeration model of the indirect evaporative cooling system and data of 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) at least based on the determined refrigeration model of the indirect evaporative cooling system and the determined operating power consumption model of the indirect evaporative cooling system, so as to obtain data of output parameters of the refrigeration model of the indirect evaporative cooling system 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 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.

3. 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.

4. 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 refrigeration model of the indirect evaporative cooling system to obtain a data set of output parameters of the refrigeration model of the indirect evaporative cooling system; S4.3: Input 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 the 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).

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

6. 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.

7. 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) comprises a water spray system (103), the output parameter of the refrigeration model of the indirect evaporative cooling system comprises the rotation speed of a nozzle moving motor (11) of the water spray system (103), and the nozzle moving motor (11) is configured to control the movement of a nozzle (13) of the water spray system (103); The control method (300) further comprises: 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 spray 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 is sprayed by the water spray system (103) at the current outdoor air dry-bulb temperature and the current outdoor air dry air humidity 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 within the corresponding speed limit of the nozzle moving motor (11) to optimize the operating cost of the indirect evaporative cooling system (100).

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

9. The control method (300) for an indirect evaporative cooling system according to claim 7, wherein: The indirect evaporative cooling system (100) further comprises a direct expansion refrigeration system (104) and a fan system (105); The input parameters of the refrigeration model of the indirect evaporative cooling system include target parameters of the cooled unit (106) and operating parameters of the indirect evaporative cooling system (100), and the output parameters of the refrigeration model of the indirect evaporative cooling system include control parameters for operating the water spray system (103), the direct expansion refrigeration system (104) and the fan system (105); The 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).

10. The control method (300) for an indirect evaporative cooling system according to claim 9, wherein: The direct expansion refrigeration system (104) comprises a compressor (6), a condenser (4), an evaporator (3) and a flow control valve (9); the fan system (105) comprises 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 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 spray mist system (103) includes the power consumption of the nozzle moving motor (11), the water consumption of the water spray mist 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 spray mist system (103) includes the electricity cost generated by the power consumption of the nozzle moving motor (11), the water cost of the water spray mist system (103) includes the water cost generated by the water spray volume 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).

11. The control method (300) for an indirect evaporative cooling system according to claim 1, wherein: 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.

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

13. The control method (300) for an indirect evaporative cooling system according to claim 4, wherein: The indirect evaporative cooling system operation cost optimization model is configured to use a genetic algorithm to optimize the operation cost of the indirect evaporative cooling system (100), wherein a fitness function in the genetic algorithm is obtained based on the determined indirect evaporative cooling system operation power consumption model.

14. 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 cooling unit (106) on site, and data obtained from the cooling unit (106) 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, 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 the final determined indirect evaporative cooling system refrigeration model and the indirect evaporative cooling system operation power consumption model.

15. A computing system (110), the computing system (110) comprising: A processor (1102), wherein the processor (1102) is configured to execute the control method (300) described in any one of claims 1-14 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.

16. A control system (111), the control system (111) comprising: Indirect evaporative cooling system module (1117); as well as An indirect evaporative cooling system operating cost optimization module (1116), the indirect evaporative cooling system operating cost optimization module (1116) being connected to the indirect evaporative cooling system module (1117), the indirect evaporative cooling system operating cost optimization module (1116) being configured to perform the control method (300) described in any one of claims 1 to 14 based on 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 control data of the indirect evaporative cooling system (100) corresponding to the operating 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 14; or The indirect evaporative cooling system module (1117) and the indirect evaporative cooling system operating cost optimization module (1116) respectively include a determined indirect evaporative cooling system refrigeration model and a determined indirect evaporative cooling system operating power consumption model obtained by the control method (300) described in any one of claims 1 to 14.

17. The control system (111) according to claim 16, 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) of claim 15; 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 15.

18. The control system (111) according to claim 16, 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 14 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 to the indirect evaporative cooling system module (1117), or deploy them respectively to the indirect evaporative cooling system module (1117) and the indirect evaporative cooling system operation cost optimization module (1116).

19. An indirect evaporative cooling system (100), the indirect evaporative cooling system (100) comprising: Water spray 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 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 spray 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 16 to 18, 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 the 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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