Air conditioning system and energy efficiency ratio determining method thereof

Abstract

The invention discloses an air conditioning system and an energy efficiency ratio determination method thereof, the air conditioning system comprises a water chilling unit and a controller, and the controller is configured to obtain the first condensation temperature of a condenser in the nth detection period through a first temperature sensor and obtain the first evaporation temperature of an evaporator in the nth detection period through a second temperature sensor; according to the operation parameters of the water chilling unit, the second condensation temperature of the condenser in the nth detection period and the second evaporation temperature of the evaporator in the nth detection period are determined; determining a first energy efficiency ratio of the nth detection period according to the first condensation temperature and the first evaporation temperature; determining a second energy efficiency ratio of the nth detection period according to the second condensation temperature and the second evaporation temperature; and determining an ideal energy efficiency ratio of the nth detection period according to the first energy efficiency ratio of the nth detection period and the second energy efficiency ratio of the nth detection period.

Description

Air conditioning system and method for determining energy efficiency ratio thereof

[0001] This application claims priority to Chinese patent application No. 202210822346.4 filed on July 13, 2022, and No. 202210867618.2 filed on July 21, 2022, the entire contents of which are incorporated by reference into this application. Technical Field

[0002] The present application relates to the technical field of air conditioning, and in particular to an air conditioning system and a method for determining an energy efficiency ratio thereof. Background Art

[0003] Central air-conditioning systems are widely used in public buildings. Chillers are one of the main energy-consuming equipment in central air-conditioning systems. Their operating performance is affected by many factors, among which the cooling load has a greater impact on the energy efficiency of the chiller. Reasonable operation strategies for regulating the chiller according to the cooling load can improve the operating performance of the chiller.

[0004] Summary of the Invention

[0005] In a first aspect, an air conditioning system is provided, comprising a chiller, a refrigerant circulation loop, a first temperature sensor, a second temperature sensor, and a controller. The refrigerant circulation loop is configured to circulate refrigerant through a loop consisting of a compressor, a condenser, and an evaporator. The first temperature sensor is configured to detect the condensing temperature of the condenser. The second temperature sensor is configured to detect the evaporating temperature of the evaporator. The controller is configured to: obtain a first condensing temperature of the nth detection cycle via the first temperature sensor, and obtain a first evaporating temperature of the nth detection cycle via the second temperature sensor, where n is an integer greater than 1; obtain operating parameters of the chiller, and determine a second condensing temperature of the condenser and a second evaporating temperature of the evaporator in the nth detection cycle based on the operating parameters; determine a first energy efficiency ratio of the nth detection cycle based on the first condensing temperature and the first evaporating temperature; determine a second energy efficiency ratio of the nth detection cycle based on the second condensing temperature and the second evaporating temperature; and determine an ideal energy efficiency ratio of the nth detection cycle based on the first energy efficiency ratio and the second energy efficiency ratio of the nth detection cycle.

[0006] In a second aspect, a method for determining an energy efficiency ratio of an air-conditioning system is provided, the air-conditioning system including a chiller, the method being applied to the chiller. The method comprises: obtaining a first condensing temperature of a condenser in an nth detection cycle using a first temperature sensor, and obtaining a first evaporating temperature of an evaporator in an nth detection cycle using a second temperature sensor, where n is an integer greater than 1; obtaining operating parameters of the chiller, and determining a second condensing temperature of the condenser in the nth detection cycle and a second evaporating temperature of the evaporator in the nth detection cycle based on the operating parameters; determining a first energy efficiency ratio in the nth detection cycle based on the first condensing temperature and the first evaporating temperature; determining a second energy efficiency ratio in the nth detection cycle based on the second condensing temperature and the second evaporating temperature; and determining an ideal energy efficiency ratio in the nth detection cycle based on the first energy efficiency ratio in the nth detection cycle and the second energy efficiency ratio in the nth detection cycle.

[0007] In a third aspect, an air conditioning system is provided, comprising: an evaporator, a condenser, and a controller. The controller is configured to: obtain an evaporation temperature, a condensation temperature, a first energy efficiency ratio, and a load rate of the air conditioning system based on operating data of the evaporator and the condenser within a preset time period; obtain a second energy efficiency ratio of the air conditioning system and a first corresponding relationship between the second energy efficiency ratio and the load rate of the air conditioning system based on the evaporation temperature, the condensation temperature, the first energy efficiency ratio, and the load rate; obtain a second corresponding relationship between the second energy efficiency ratio and the load rate of the air conditioning system using a back-propagation neural network model; and obtain a third corresponding relationship between a target energy efficiency ratio of the air conditioning system and the load rate of the air conditioning system using the first and second corresponding relationships based on a mean value clustering algorithm.

[0008] In a fourth aspect, a control method for an air-conditioning system is provided, comprising: obtaining the evaporation temperature, condensation temperature, first energy efficiency ratio and load rate of the air-conditioning system based on the operating data of the evaporator and the condenser within a preset time period; obtaining the second energy efficiency ratio of the air-conditioning system, and a first correspondence between the second energy efficiency ratio and the load rate of the air-conditioning system based on the evaporation temperature, the condensation temperature, the first energy efficiency ratio and the load rate; obtaining the second correspondence between the second energy efficiency ratio and the load rate of the air-conditioning system by using a back-propagation neural network model; and obtaining the third correspondence between the target energy efficiency ratio of the air-conditioning system and the load rate of the air-conditioning system by using the first correspondence and the second correspondence based on a mean clustering algorithm. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] FIG1 is a schematic structural diagram of a chiller provided in some embodiments of the present application;

[0010] FIG2 is a schematic structural diagram of another chiller provided in some embodiments of the present application;

[0011] FIG3 is a hardware configuration block diagram of a chiller provided in some embodiments of the present application;

[0012] FIG4 is a schematic diagram of interaction between a controller of a chiller and a terminal device provided by some embodiments of the present application;

[0013] FIG5 is a schematic diagram of a management interface of a chiller provided in some embodiments of the present application;

[0014] FIG6 is a flow chart of a method for determining an energy efficiency ratio of an air-conditioning system provided in some embodiments of the present application;

[0015] FIG7 is a flow chart of another method for determining the energy efficiency ratio of an air-conditioning system provided in some embodiments of the present application;

[0016] FIG8 is a flow chart of another method for determining the energy efficiency ratio of an air-conditioning system provided in some embodiments of the present application;

[0017] FIG9 is a flow chart of another method for determining the energy efficiency ratio of an air-conditioning system provided in some embodiments of the present application;

[0018] FIG10 is a flow chart of another method for determining the energy efficiency ratio of an air-conditioning system provided in some embodiments of the present application;

[0019] FIG11 is a flow chart of a method for controlling an air-conditioning system according to some embodiments of the present application;

[0020] FIG12 is a flow chart of another method for controlling an air-conditioning system according to some embodiments of the present application;

[0021] FIG13 is a flow chart of another method for controlling an air-conditioning system according to some embodiments of the present application;

[0022] FIG14 is a flow chart of another method for controlling an air-conditioning system according to some embodiments of the present application;

[0023] FIG15 is a flow chart of another method for controlling an air-conditioning system according to some embodiments of the present application;

[0024] FIG16 is a flow chart of another method for controlling an air-conditioning system according to some embodiments of the present application;

[0025] FIG17 is a flow chart of another method for controlling an air-conditioning system according to some embodiments of the present application;

[0026] FIG18 is a flow chart of another method for controlling an air-conditioning system according to some embodiments of the present application;

[0027] FIG19 is a neural network structure diagram of a chiller performance prediction model provided by some embodiments of the present application;

[0028] FIG20 is a schematic diagram of the hardware structure of a controller provided in some embodiments of the present application. DETAILED DESCRIPTION

[0029] The following will be combined with the accompanying drawings to clearly and completely describe some embodiments of the present disclosure. Obviously, the embodiments described are only some embodiments of the present disclosure, rather than all embodiments. Based on the embodiments provided by the present disclosure, all other embodiments obtained by ordinary technicians in this field are within the scope of protection of the present disclosure.

[0030] Unless the context requires otherwise, throughout the specification and claims, the term "comprise" and its other forms, such as the third person singular form "comprises" and the present participle form "comprising", are to be interpreted as open and inclusive, that is, "including, but not limited to". In the description of the specification, the terms "one embodiment", "some embodiments", "exemplary embodiments", "example", "specific example" or "some examples" are intended to indicate that the particular features, structures, materials or characteristics associated with the embodiment or example are included in at least one embodiment or example of the present disclosure. The schematic representation of the above terms does not necessarily refer to the same embodiment or example. In addition, the particular features, structures, materials or characteristics may be included in any one or more embodiments or examples in any appropriate manner.

[0031] The use of "adapted to" or "configured to" herein is intended to be open and inclusive language that does not exclude devices adapted or configured to perform additional tasks or steps.

[0032] Additionally, the use of “based on” is meant to be open and inclusive, as a process, step, calculation, or other action “based on” one or more stated conditions or values ​​may, in practice, be based on additional conditions or values ​​beyond those stated.

[0033] Adjusting the chiller's operating strategy based on the cooling load can improve the chiller's operating performance. Therefore, the higher the accuracy of the cooling load determination, the greater the benefit to the chiller's operating performance. Currently, the cooling load is determined based on the chiller's energy efficiency ratio. In related technologies, the chiller's energy efficiency ratio is determined solely based on the condensing temperature of the condenser and the evaporating temperature of the evaporator in the chiller detected by the temperature sensor. However, during the operation of the chiller, the temperature sensor is easily affected by various measurement noises, abnormal values, or system errors, resulting in the detected condensing temperature of the condenser and the evaporating temperature of the evaporator being inaccurate. This, in turn, results in the cooling load determined based on the inaccurate condensing temperature of the condenser and the evaporating temperature of the evaporator being inaccurate, making it impossible to reasonably adjust the chiller's operating strategy, affecting the chiller's operating performance.

[0034] Based on this, some embodiments of the present application provide a method for determining the energy efficiency ratio of an air conditioning system, the air conditioning system including a chiller. On the one hand, a temperature sensor is used to obtain a detected evaporation temperature value of an evaporator and a detected condensation temperature value of a condenser, and then a detected value of the energy efficiency ratio is determined based on the detected evaporation temperature value and the detected condensation temperature value. On the other hand, a calculated evaporation temperature value of the evaporator and a calculated condensation temperature value of the condenser are determined based on the operating parameters of the chiller, and then a calculated value of the energy efficiency ratio is determined based on the calculated evaporation temperature value and the calculated condensation temperature value. Then, based on the detected and calculated values ​​of the energy efficiency ratio, different energy efficiency ratios are selected as ideal energy efficiency ratios in different situations. This method can minimize the impact of temperature sensor anomalies on the determination of the ideal energy efficiency ratio, thereby improving the rationality and accuracy of the determination of the ideal energy efficiency ratio. Furthermore, by determining the cooling load of the chiller based on the highly accurate ideal energy efficiency ratio, a highly accurate cooling load can be obtained, thereby improving the accuracy of the cooling load determination of the chiller.

[0035] FIG1 is a schematic diagram of the structure of a chiller provided in accordance with an exemplary embodiment of the present application. It should be noted that the chillers involved in some embodiments of the present application may include different types of chillers, such as air-cooled chillers and water-cooled chillers. Depending on the type of compressor, they can be further divided into screw chillers, vortex chillers, centrifugal chillers, etc. For ease of description, different types of chillers are illustrated using the schematic diagram of the structure of the chiller shown in FIG1 as an example.

[0036] As shown in Figure 1, the chiller 1 includes a compressor 10, a condenser 11, a throttling device 12, an evaporator 13 and a controller 14 (see Figure 3). The compressor 10, the condenser 11, the throttling device 12 and the evaporator 13 are connected in sequence to form a refrigerant circulation loop. It should be noted that in some embodiments of the present application, the sequential connection only describes the sequential relationship between the various components, and other components may also be included between the various components. For example, as shown in Figure 2, a stop valve 15 and the like may be provided on the pipeline between the compressor 10 and the condenser 11.

[0037] During cooling, compressor 10 compresses low-temperature, low-pressure refrigerant gas into high-temperature, high-pressure refrigerant gas and discharges it to condenser 11. In condenser 11, the high-temperature, high-pressure refrigerant gas exchanges heat with the outdoor airflow, releasing heat. This heat is carried by the airflow to the outdoor ambient air, causing the refrigerant to undergo a phase change and condense into a liquid or gas-liquid two-phase refrigerant. The refrigerant flows out of condenser 11 and enters throttling element 12, where it is cooled and reduced in pressure to become low-temperature, low-pressure refrigerant. The low-temperature, low-pressure refrigerant enters evaporator 13, where it absorbs heat from the chilled water within evaporator 13, lowering the temperature of the chilled water there and achieving a cooling effect. The refrigerant then undergoes a phase change and evaporates into low-temperature, low-pressure refrigerant gas, which then flows back into compressor 10, achieving refrigerant recycling. In this embodiment, evaporator 13 is also connected to the user side. After the temperature of the chilled water within evaporator 13 is lowered, it enters the user side, where the chilled water in evaporator 13 can be replenished.

[0038] In some embodiments, the chilled water in the chiller 1 enters the evaporator 13, absorbs the cold energy of the evaporated refrigerant, lowers its temperature and becomes cold water, enters the water distributor, and then enters the surface cooler or cooling coil, exchanges heat with the treated air, and then returns to the chiller 1 for circulation and reprocessing.

[0039] In some embodiments, the chiller 1 also includes cooling water (also known as coolant). During the operation of the chiller 1, the various components generate a large amount of heat. If this heat is not removed promptly, the high-temperature components may be damaged due to excessive temperatures. By utilizing heat conduction, as the cooling water flows through the high-temperature components, heat is transferred from the high-temperature components into the cooling water, causing the water temperature to rise. The continuous flow of cooling water can then continuously remove the heat, thereby cooling the high-temperature components.

[0040] In some embodiments, the heat exchange process of the chiller 1 includes four steps: ① heat exchange between chilled water and the air in the cooling area; ② heat exchange between chilled water and the refrigerant in the evaporator; ③ heat exchange between cooling water and the refrigerant in the condenser; and ④ heat exchange between cooling water and the air in the cooling tower.

[0041] In some embodiments, compressor 10 may be a screw compressor.

[0042] In some embodiments, multiple compressors 10 may be provided, and the multiple compressors are connected in parallel.

[0043] In some embodiments, the evaporator 13 may be a flooded evaporator or a falling film evaporator.

[0044] In some embodiments, the throttling element 12 may be an electronic expansion valve, a capillary tube, or the like.

[0045] In some embodiments, the controller 14 is a device that generates an operation control signal based on an instruction opcode and a timing signal, thereby instructing the multi-split air conditioning system to execute the control instruction. For example, the controller can be a central processing unit (CPU), a general-purpose processor, a network processor (NP), a digital signal processor (DSP), a microcontroller unit (MCU), or any combination thereof.

[0046] In addition, the controller 14 may be configured to control the operation of various components inside the chiller 1 so that the various components of the chiller 1 operate to achieve various predetermined functions of the chiller 1 .

[0047] In some embodiments, during the operation of the chiller 1 , the controller 14 may obtain the electric power and cooling load rate of the chiller 1 .

[0048] In some embodiments, the controller 14 may calculate the cooling load rate of the chiller according to the real-time load value of the chiller 1 and the rated load value of the chiller 1. That is, the ratio of the real-time load value to the rated load value is used as the cooling load rate.

[0049] FIG3 shows a hardware configuration block diagram of a chiller provided by the present application according to an exemplary embodiment, as shown in FIG3 :

[0050] In some embodiments, the chiller 1 may include a first temperature sensor 21 . The first temperature sensor 21 may be disposed around the condenser 11 and configured to detect a condensing temperature of the condenser 11 .

[0051] In some embodiments, the chiller 1 may include a second temperature sensor 22 . The second temperature sensor 22 may be disposed around the evaporator 13 and configured to detect an evaporation temperature of the evaporator 13 .

[0052] In some embodiments, the chiller 1 may include a third temperature sensor 23 . The third temperature sensor 23 may be disposed on a chilled water supply pipeline of the chiller 1 and configured to detect a supply temperature value of the chilled water.

[0053] In some embodiments, the chiller 1 may include a fourth temperature sensor 24 . The fourth temperature sensor 24 may be disposed on a chilled water return pipe of the chiller 1 and configured to detect a return temperature value of the chilled water.

[0054] In some embodiments, the controller 14 may determine the supply and return temperature difference of the chilled water based on the supply temperature of the chilled water detected by the third temperature sensor 23 and the return temperature of the chilled water detected by the fourth temperature sensor 24 .

[0055] In some embodiments, the chiller 1 may include a fifth temperature sensor 25 . The fifth temperature sensor 25 may be disposed on a cooling water supply pipeline of the chiller 1 and configured to detect a supply temperature value of the cooling water.

[0056] In some embodiments, the chiller 1 may include a sixth temperature sensor 26 . The sixth temperature sensor 26 may be disposed on a cooling water return pipe of the chiller 1 and configured to detect a return temperature value of the cooling water.

[0057] In some embodiments, the controller 14 may determine the supply and return temperature difference of the cooling water based on the supply temperature of the cooling water detected by the fifth temperature sensor 25 and the return temperature of the cooling water detected by the sixth temperature sensor 26 .

[0058] In some embodiments, in order to improve the accuracy of determining the energy efficiency ratio, the supply and return water temperature difference of the chilled water and the supply and return water temperature difference of the cooling water can be obtained when the chiller 1 is fully loaded.

[0059] In some embodiments, the chiller 1 may include a seventh temperature sensor 27 . The seventh temperature sensor 27 may be disposed at a water outlet of the chiller 1 and configured to detect the outlet water temperature of the chiller 1 .

[0060] In some embodiments, the controller 14 may determine the heat exchange temperature difference of the condenser 11 based on the condensing temperature of the condenser 11 detected by the first temperature sensor 21 and the outlet water temperature detected by the seventh temperature sensor 27. The heat exchange temperature difference of the condenser 11 is used to represent the heat exchange condition of the condenser 11.

[0061] In some embodiments, the chiller 1 may include an eighth temperature sensor 27, which may be disposed on the evaporator 13 and configured to detect the ambient temperature of the environment in which the evaporator 13 is located. The environment in which the evaporator 13 is located may be air or other fluids.

[0062] In some embodiments, the controller 14 may determine the heat exchange temperature difference of the evaporator 13 based on the evaporation temperature of the evaporator 13 detected by the second temperature sensor 22 and the ambient temperature detected by the eighth temperature sensor 28. The heat exchange temperature difference of the evaporator 13 is used to represent the heat exchange condition of the evaporator 13.

[0063] In some embodiments, the chiller 1 may include a communicator 29 configured to establish a communication connection with other network entities, such as a communication connection with a terminal device.

[0064] In some embodiments, the chiller 1 may include a memory 30 configured to store software programs and data. The controller 14 executes the software programs or data stored in the memory 30 to perform various functions of the chiller 1 and process data.

[0065] FIG4 is a schematic diagram showing an interaction between a controller of a chiller and a terminal device provided in some embodiments of the present application. As shown in FIG4 , a terminal device 300 can establish a communication connection with the controller 14. Exemplarily, any known network communication protocol can be used to establish a communication connection. It should be noted that the terminal device 300 shown in FIG4 is only an example of a terminal device. The terminal device 300 in the present application can be a remote control, a mobile phone, a tablet computer, a personal computer (PC), a personal digital assistant (PDA), etc., and the present application does not impose any special restrictions on the specific form of the terminal device.

[0066] For example, taking the terminal device 300 as a mobile phone, the user can download the chiller management APP on the mobile phone, and the chiller management APP can be used to manage the chiller. The user can select the online device chiller 1 and select the control function that needs to be performed on the chiller 1 in the management options of the chiller 1. For example, as shown in Figure 5, a schematic diagram of the management interface of a chiller provided by the present application according to an exemplary embodiment, the management options of the chiller 1 displayed on the chiller management APP may include control functions such as power on, power off, and temperature adjustment. If it is detected that the user clicks on the power on option for the chiller 1 in the chiller management APP, the mobile phone can send a power on instruction to the chiller 1. In response to the power on instruction, the controller controls the various components in the chiller to start working.

[0067] As shown in FIG6 , some embodiments of the present application provide a method for determining the energy efficiency ratio of an air-conditioning system. The method can be applied to the controller 14 of the chiller 1 described above. The method includes the following steps:

[0068] S101 . Obtain a first condensing temperature of a condenser in an nth detection cycle through a first temperature sensor, and obtain a first evaporating temperature of an evaporator in an nth detection cycle through a second temperature sensor.

[0069] In some embodiments, during operation of the chiller, after the controller receives a control instruction from a user for determining a cooling load, the controller, in response to the control instruction for determining the cooling load, obtains a first condensing temperature of the condenser in the nth detection cycle via a first temperature sensor and obtains a first evaporating temperature of the evaporator in the nth detection cycle via a second temperature sensor. Here, n is a positive integer, for example, n is 20. A detection cycle can be pre-set when the chiller leaves the factory or can be set by the chiller's administrator via a terminal device. For example, a detection cycle is 5 seconds, and this is not limited to this.

[0070] The nth detection cycle can be understood as the current detection cycle, and the n-1 detection cycles before the nth detection cycle can be understood as the historical detection cycles before the current detection cycle.

[0071] In some embodiments, the detection period may be a moment, that is, obtaining the first condensing temperature of the condenser at the current moment and the first evaporating temperature of the evaporator at the current moment.

[0072] It is easy to understand that the first condensing temperature and the first evaporating temperature are both detected by corresponding temperature sensors. The first condensing temperature can be understood as the detection value of the condensing temperature of the condenser, and the first evaporating temperature can be understood as the detection value of the evaporating temperature of the evaporator.

[0073] In some embodiments, during the operation of the chiller, the controller can periodically obtain the first condensing temperature of the condenser in the nth detection cycle through the first temperature sensor and the first evaporating temperature of the evaporator in the nth detection cycle through the second temperature sensor, so as to timely determine the ideal energy efficiency ratio of the chiller, and then timely determine the cooling load of the chiller, and then timely adjust the operation strategy of the chiller according to the cooling load of the chiller, which helps to improve the operating performance of the chiller.

[0074] For example, the first condensing temperature of the condenser in the nth detection cycle can be recorded as T c1 , the first evaporation temperature of the evaporator in the nth detection cycle is recorded as T e1 .

[0075] S102: Acquire operating parameters of the chiller, and determine a second condensing temperature of the condenser in the nth detection cycle and a second evaporating temperature of the evaporator in the nth detection cycle based on the operating parameters.

[0076] It should be understood that the operating parameters of a chiller are the parameters generated by its components during operation. Taking one test cycle as a unit, these parameters include the chilled water supply and return temperature difference, the cooling water supply and return temperature difference, the evaporator heat exchange temperature difference, the condenser heat exchange temperature difference, the chilled water supply temperature, the cooling water return temperature, the chiller's cooling load rate, and the chiller's electrical power.

[0077] It is worth noting that in order to improve the accuracy of the subsequent determination of the ideal energy efficiency ratio, the operating parameters of the chiller are all taken from the values ​​when the chiller is at full load.

[0078] In some embodiments, as shown in FIG. 7 , determining the second condensing temperature of the condenser in the nth detection cycle and the second evaporating temperature of the evaporator in the nth detection cycle based on the operating parameters may include:

[0079] S1021. Determine a second condensing temperature of the condenser in the nth detection cycle based on the supply and return water temperature difference of the cooling water in the nth detection cycle, the heat exchange temperature difference of the condenser in the nth detection cycle, and the cooling load rate of the chiller in the nth detection cycle.

[0080] For example, the relationship between the supply and return water temperature difference of the cooling water in the nth detection cycle, the heat exchange temperature difference of the condenser in the nth detection cycle, the cooling load rate of the chiller in the nth detection cycle, and the second condensing temperature of the condenser in the nth detection cycle can be expressed as the following formula (1):

[0081] Among them, T c2 is the second condensing temperature of the condenser in the nth detection cycle, PLR is the cooling load rate of the chiller in the nth detection cycle, T c-rtn is the return water temperature of the cooling water in the nth detection cycle, T c-exc is the heat exchange temperature difference of the condenser in the nth detection cycle.

[0082] S1022. Determine the second evaporation temperature of the evaporator in the nth detection cycle based on the supply and return water temperature difference of the chilled water in the nth detection cycle, the heat exchange temperature difference of the evaporator in the nth detection cycle, the supply water temperature of the chilled water in the nth detection cycle, and the cooling load rate of the chiller in the nth detection cycle.

[0083] For example, the relationship between the supply and return water temperature difference of the chilled water in the nth detection cycle, the heat exchange temperature difference of the evaporator in the nth detection cycle, the supply water temperature of the chilled water in the nth detection cycle, the cooling load rate of the chiller in the nth detection cycle, and the second evaporation temperature of the evaporator in the nth detection cycle can be expressed as the following formula (2):

[0084] Among them, T e2 is the second evaporation temperature of the evaporator in the nth detection cycle, T e-sup is the supply temperature of chilled water in the nth detection cycle, T e-em The supply and return temperature difference of chilled water in the nth detection cycle, T c-exc is the heat exchange temperature difference of the evaporator in the nth detection cycle.

[0085] It is easy to understand that the second condensing temperature can be understood as a calculated value of the condensing temperature of the condenser, and the second evaporating temperature can be understood as a calculated value of the evaporating temperature of the evaporator.

[0086] S103 : Determine a first energy efficiency ratio of the nth detection cycle according to the first condensing temperature and the first evaporating temperature.

[0087] In some embodiments, the first energy efficiency ratio may be expressed as a COP (Coefficient of Performance), which refers to the cooling capacity obtained per unit power consumption. After obtaining a first condensing temperature of the condenser in the nth detection cycle and a first evaporating temperature of the evaporator in the nth detection cycle, the first energy efficiency ratio for the nth detection cycle may be determined based on the first condensing temperature of the condenser in the nth detection cycle and the first evaporating temperature of the evaporator in the nth detection cycle.

[0088] For example, the relationship between the first condensing temperature, the first evaporating temperature and the first energy efficiency ratio may be expressed as follows:

[0089] Wherein, K1 is the first energy efficiency ratio of the nth detection cycle.

[0090] S104: Determine a second energy efficiency ratio of the nth detection cycle according to the second condensation temperature and the second evaporation difficulty.

[0091] In some embodiments, after obtaining the second condensing temperature of the condenser in the nth detection cycle through the above step S1021 and obtaining the second evaporating temperature of the evaporator in the nth detection cycle through the above step S1022, the second energy efficiency ratio of the nth detection cycle can be determined based on the second condensing temperature of the condenser in the nth detection cycle and the second evaporating temperature of the evaporator in the nth detection cycle.

[0092] For example, the relationship between the second condensing temperature, the second evaporating temperature and the second energy efficiency ratio may be expressed as follows:

[0093] Wherein, K2 is the second energy efficiency ratio of the nth detection cycle.

[0094] It should be noted that some embodiments of the present application do not limit the order of executing step S103 and step S104. For example, step S103 may be executed first, then step S104; or step S104 may be executed first, then step S103; or step S103 and step S104 may be executed simultaneously.

[0095] S105 : Determine an ideal energy efficiency ratio of the nth detection cycle according to the first energy efficiency ratio of the nth detection cycle and the second energy efficiency ratio of the nth detection cycle.

[0096] It is easy to understand that the first energy efficiency ratio is obtained based on the first condensing temperature and the first evaporating temperature, which are detected by the temperature sensor. The first energy efficiency ratio can be understood as the detected value of the energy efficiency ratio. The second energy efficiency ratio is obtained based on the second condensing temperature and the second evaporating temperature, which are calculated based on the operating parameters of the chiller. The second energy efficiency ratio can be understood as the calculated value of the energy efficiency ratio.

[0097] Furthermore, in different situations, the detected value of the energy efficiency ratio or the calculated value of the energy efficiency ratio can be selected as the ideal energy efficiency ratio. Compared with the related art in which only the detected value of the energy efficiency ratio is used as the ideal energy efficiency ratio, the influence of the abnormality of the temperature sensor on the determination of the ideal energy efficiency ratio can be avoided, thereby improving the rationality and accuracy of the determination of the ideal energy efficiency ratio.

[0098] Compared to the related art method of determining the ideal energy efficiency ratio based solely on the detection values ​​of the condensing temperature of the condenser and the evaporating temperature of the evaporator detected by the temperature sensor, some embodiments of the present application provide a method for determining the energy efficiency ratio of an air-conditioning system. By comparing the detected value of the energy efficiency ratio with the calculated value of the energy efficiency ratio, the ideal energy efficiency ratio of the nth detection cycle is determined. This can reduce the impact of the temperature sensor error on the accuracy of the ideal energy efficiency ratio, improve the rationality of the determination of the ideal energy efficiency ratio, and improve the accuracy of the determination of the ideal energy efficiency ratio. Furthermore, by determining the cooling load of the chiller using the highly accurate ideal energy efficiency ratio, a highly accurate cooling load can be obtained, thereby improving the accuracy of the determination of the cooling load of the chiller.

[0099] In some embodiments, as shown in FIG8 , the implementation of step S105 may include the following steps:

[0100] S201 : Determine an ideal energy efficiency ratio of an nth detection cycle according to a first energy efficiency ratio of an nth detection cycle, a second energy efficiency ratio of an nth detection cycle, a first value range, and a second value range.

[0101] The first value range is determined based on an average value and a standard deviation of the first energy efficiency ratio of n detection cycles, and the second value range is determined based on an average value and a standard deviation of the second energy efficiency ratio of n detection cycles.

[0102] The method for determining the first energy efficiency ratio of each detection cycle in the n detection cycles can refer to the description of the above step S103, and the method for determining the second energy efficiency ratio of each detection cycle in the n detection cycles can refer to the description of the above step S104, which will not be repeated here.

[0103] For example, the average value of the first energy efficiency ratio of n detection cycles can be expressed as the following formula (5):

[0104] in, is the average value of the first energy efficiency ratio of n detection cycles, is the first energy efficiency ratio of the i-th detection cycle in n detection cycles.

[0105] For example, the standard deviation of the first energy efficiency ratio of n detection cycles can be expressed as the following formula (6):

[0106] Wherein, σ1 is the standard deviation of the first energy efficiency ratio of n detection cycles.

[0107] For example, the average value of the second energy efficiency ratio of n detection cycles can be expressed as the following formula (7):

[0108] in, is the average value of the second energy efficiency ratio of n detection cycles, is the second energy efficiency ratio of the i-th detection cycle in n detection cycles.

[0109] For example, the standard deviation of the second energy efficiency ratio of n detection cycles can be expressed as the following formula (8):

[0110] Wherein, σ2 is the standard deviation of the second energy efficiency ratio of n detection cycles.

[0111] In some embodiments, the first value range and the second value range may be pre-set by the manager of the chiller according to the operating conditions of the chiller and stored in the memory.

[0112] In some embodiments, the first value range and the second value range can also be calculated in real time by the controller according to preset rules and operating parameters of the chiller. For example, the first value range can be The second value range can be For another example, the first value range can also be The second value range can be Some embodiments of the present application do not limit the setting range of the first value range and the second value range.

[0113] In some embodiments, as shown in FIG9 , the implementation of step S201 may include the following steps:

[0114] S2011. When the first energy efficiency ratio of the nth detection cycle is not within the first value range, and the second energy efficiency ratio of the nth detection cycle is not within the second value range, the average value of the first energy efficiency ratio of the nth detection cycle and the second energy efficiency ratio of the nth detection cycle is used as the ideal energy efficiency ratio of the nth detection cycle.

[0115] It can be understood that when the first energy efficiency ratio of the nth detection cycle is not within the first value range, and the second energy efficiency ratio of the nth detection cycle is not within the second value range, it means that neither the first energy efficiency ratio nor the second energy efficiency ratio can effectively reflect the ideal energy efficiency ratio of the nth detection cycle. Therefore, the average value of the first energy efficiency ratio of the nth detection cycle and the second energy efficiency ratio of the nth detection cycle is taken as the ideal energy efficiency ratio of the nth detection cycle. Compared with the energy efficiency ratio determined by the evaporation temperature and condensation temperature detected by the sensor in the related art, the rationality of determining the ideal energy efficiency ratio and the accuracy of determining the ideal energy efficiency ratio are improved.

[0116] S2012: When the first energy efficiency ratio of the nth detection cycle is within the first value range and the second energy efficiency ratio of the nth detection cycle is not within the second value range, use the first energy efficiency ratio of the nth detection cycle as the ideal energy efficiency ratio of the nth detection cycle.

[0117] It can be understood that when the first energy efficiency ratio of the nth detection cycle is within the first value range and the second energy efficiency ratio of the nth detection cycle is not within the second value range, it means that the error of the temperature sensor is small, and the first condensing temperature of the condenser and the first evaporating temperature of the evaporator detected by the corresponding temperature sensor are relatively accurate, that is, the calculation result of the first energy efficiency ratio is better than the calculation result of the second energy efficiency ratio. The first energy efficiency ratio can better reflect the ideal energy efficiency ratio of the nth detection cycle than the second energy efficiency ratio, so the first energy efficiency ratio of the nth detection cycle is used as the ideal energy efficiency ratio of the nth detection cycle.

[0118] S2013: When the second energy efficiency ratio of the nth detection cycle is within the second value range, use the second energy efficiency ratio of the nth detection cycle as the ideal energy efficiency ratio of the nth detection cycle.

[0119] From the above description, it can be seen that the second energy efficiency ratio is calculated based on the operating parameters of the chiller. When the second energy efficiency ratio is detected to be within the second value range, it means that the second energy efficiency ratio can better reflect the ideal energy efficiency ratio of the nth detection cycle. Therefore, the second energy efficiency ratio of the nth detection cycle is used as the ideal energy efficiency ratio of the nth detection cycle.

[0120] For example, the ideal energy efficiency ratio of the nth detection cycle in different situations can be expressed as follows:

[0121] Among them, K F is the ideal energy efficiency ratio of the nth detection cycle.

[0122] For example, the following Table 1 is a K1, K2, σ1, σ2, and K F Partial value.

[0123] Table 1

[0124] The above embodiment focuses on how the ideal energy efficiency ratio of the nth detection cycle is determined. In some embodiments, the method for determining the energy efficiency ratio of an air-conditioning system provided in some embodiments of the present application also involves the use of the ideal energy efficiency ratio. As shown in Figure 10, after step S105, the method further includes the following steps:

[0125] S301. Determine the cooling load of the chiller in the nth detection cycle according to the electric power of the chiller in the nth detection cycle, the cooling load rate of the chiller in the nth detection cycle, and the ideal energy efficiency ratio in the nth detection cycle.

[0126] For example, the relationship among the electric power of the chiller in the nth detection cycle, the cooling load rate of the chiller in the nth detection cycle, the ideal energy efficiency ratio in the nth detection cycle, and the cooling load of the chiller in the nth detection cycle can be expressed as the following formula (10): Q i =(a*PLR 2 +b*PLR+c)*K F *P W Formula (10)

[0127] Among them, Q i is the cooling load of the chiller in the nth detection cycle, PLR is the cooling load rate of the chiller in the nth detection cycle, P W is the electric power of the chiller in the nth detection cycle, and a, b and c are all constants.

[0128] Among them, a, b and c are obtained based on the data training of the chiller. The data training is to collect the operating data of the chiller for a period of time, with PLR as the horizontal axis and the energy efficiency ratio (COP) of the chiller as the horizontal axis. F The ratio of is used as the vertical axis, and the data are fitted with a curve to obtain a, b and c.

[0129] Among them, COP and K F The relationship between can be expressed as the following formula (11):

[0130] The embodiment shown in FIG9 at least brings the following beneficial effects: Compared with the related art in which the ideal energy efficiency ratio of the chiller is obtained based on the condensing temperature and evaporating temperature detected by the temperature sensor, some embodiments of the present application provide a method for determining the energy efficiency ratio of an air-conditioning system, which not only determines the first energy efficiency ratio of the chiller based on the first condensing temperature and the first evaporating temperature detected by the temperature sensor, but also determines the second condensing temperature and the second evaporating temperature based on the operating parameters of the chiller, and then determines the second energy efficiency ratio of the chiller. Different energy efficiency ratios are selected as the ideal energy efficiency ratio of the chiller under different circumstances, reducing the impact of the temperature sensor error on the accuracy of the determination of the ideal energy efficiency ratio, and improving the rationality and accuracy of the determination of the ideal energy efficiency ratio. The cooling load of the chiller is then determined based on the highly accurate ideal energy efficiency ratio, thereby improving the accuracy of the determination of the cooling load of the chiller.

[0131] Furthermore, some embodiments of the present application provide a method for determining the energy efficiency ratio of an air-conditioning system. When determining the cooling load of a chiller based on an ideal energy efficiency ratio, the electrical power of the chiller is introduced as a basis for determining the cooling load of the chiller. It is understandable that, relative to the electrical power of the chiller, the temperature detected by the temperature sensor has a certain degree of delay and error and is also susceptible to various noises, while the detection of the electrical power of the chiller is more stable. Therefore, using the electrical power of the chiller as one of the bases for calculating the cooling load of the chiller improves the accuracy and stability of the chiller's cooling load determination.

[0132] Furthermore, the chiller manager can adjust the chiller's operating strategy based on the cooling load with higher accuracy. The operating strategy adjustment is more accurate, which helps to improve the operating performance of the chiller.

[0133] Based on the above air conditioning system, as shown in FIG11 , some embodiments of the present application provide a method for controlling an air conditioning system. The method may include the following steps:

[0134] S111 : The air-conditioning system obtains an evaporation temperature, a condensation temperature, a first energy efficiency ratio, and a load rate of the air-conditioning system according to operation data of the evaporator and the condenser within a preset time period.

[0135] In some embodiments, the operating data includes the evaporator's chilled water supply temperature, chilled water return temperature, chilled water flow rate and evaporation temperature, the condenser's cooling water supply temperature, cooling water return temperature, cooling water flow rate and condensation temperature, the air-conditioning system's cooling machine load rate, cooling machine power, etc.

[0136] In some embodiments, the preset time period may be six months, one year, etc., wherein the operating data corresponding to each time point within the preset time period may constitute a data group, and the time interval for obtaining the operating data may be 5 minutes, 10 minutes, etc. For example, a data group may be obtained every 5 minutes, and a data group may include the chilled water supply temperature, chilled water return temperature, chilled water flow rate, and evaporation temperature of the evaporator, the cooling water supply temperature, cooling water return temperature, cooling water flow rate, and condensation temperature of the condenser, and the chiller load rate and chiller power of the air-conditioning system at that time point.

[0137] S112: The air-conditioning system obtains a second energy efficiency ratio and a first corresponding relationship between the second energy efficiency ratio and a load rate of the air-conditioning system through the evaporation temperature, the condensation temperature, and the first energy efficiency ratio.

[0138] In some embodiments, the second energy efficiency ratio is DCOP (Dynamic coefficient of performance), which also represents the internal energy efficiency of the air-conditioning system. It mainly reflects the deviation between COP and ICOP (Ideal coefficient of performance). The DCOP can be used to determine the reason for the low operating efficiency of the air-conditioning system.

[0139] In some embodiments, the first correspondence may be a DCOP performance model of the air conditioning system established between the DCOP of the air conditioning system and the PLR ​​(Part Load Ratio) of the air conditioning system. Because the first correspondence is obtained or established based on actual operating data, the corresponding DCOP performance model can reflect the actual energy efficiency of the air conditioning system.

[0140] S113. The air-conditioning system uses a back-propagation neural network model to obtain a second corresponding relationship between the second energy efficiency ratio and the load rate of the air-conditioning system.

[0141] In some embodiments, the second correspondence may be a DCOP performance prediction model for the air conditioning system established by comparing the DCOP of the air conditioning system with the PLR ​​of the air conditioning system. Since the second correspondence is established through learning and prediction using a backpropagation neural network, the DCOP performance prediction model represented by the second correspondence can predict the energy efficiency of the air conditioning system.

[0142] In some embodiments, the back propagation neural network model is a BP (Back Propagation) neural network structure. The BP neural network is a multi-layer feedforward neural network trained according to the error back propagation algorithm.

[0143] In some embodiments, the refrigeration load rate of the air-conditioning system and the temperature difference between the evaporation temperature and the condensation temperature of the air-conditioning system in the operating data can be input into the back propagation neural network model to obtain a second energy efficiency ratio and a second corresponding relationship between the second energy efficiency ratio and the load rate of the air-conditioning system.

[0144] For example, a three-layer BP neural network structure is established. The input neurons of the neural network are: chiller load rate, the difference between condensing temperature and evaporating temperature; the number of network nodes in the hidden layer is seven; and the output neuron is DCOP. The BP neural network model uses the trainlm (Levenberg-Marquarelt) learning algorithm, the tansig function (bipolar sigmoid function) is selected as the transfer function of the hidden layer, and the Pureline function (linear function) is selected as the output layer. The learning rate of the BP neural network model can be set to 0.05, the maximum number of learning steps can be set to 1000, and the training target error can be set to 0.003. This allows the above-mentioned DCOP prediction model to be obtained through data training.

[0145] S114 : The air-conditioning system obtains a third corresponding relationship between the target energy efficiency ratio of the air-conditioning system and the load rate of the air-conditioning system by using the first corresponding relationship and the second corresponding relationship based on the mean clustering algorithm.

[0146] In some embodiments, the third correspondence represents a target performance model of the air-conditioning system that can achieve a target energy efficiency ratio.

[0147] In some embodiments, the mean clustering algorithm is a K-means clustering algorithm (K-means). The K-means algorithm is a classic distance-based clustering algorithm that uses distance as a similarity evaluation metric, i.e., it is believed that the closer the distance between two objects, the greater their similarity.

[0148] In some embodiments, based on the first correspondence, a third correspondence is established, which can be used to predict the performance of the air-conditioning system's operating status at the current time based on a DCOP performance model established using actual operating data of the air-conditioning system, and the performance can be expressed by a target energy efficiency ratio; based on the second correspondence, a third correspondence is established, which can be used to predict the performance of the air-conditioning system's operating status at the current time based on a DCOP performance prediction model of the air-conditioning system established based on a BP neural network, and the performance can be expressed by a target energy efficiency ratio.

[0149] Based on the operating data of the evaporator and condenser within a preset time period, a first energy efficiency ratio and a second energy efficiency ratio of the air-conditioning system are obtained, and then a first correspondence between the second energy efficiency ratio and the load rate of the air-conditioning system is established. A back-propagation neural network model is then used to predict a second correspondence between the second energy efficiency ratio and the load rate of the air-conditioning system. Based on a mean clustering algorithm, it is determined whether a third correspondence is established based on the first correspondence or the second correspondence. The control method of the present application can obtain multiple correspondences between the energy efficiency ratio of the air-conditioning system and the load rate of the air-conditioning system through different methods, and select a more appropriate correspondence from them to determine a target energy efficiency ratio. A more accurate cooling capacity can also be determined based on the target energy efficiency ratio, thereby reducing the operating energy consumption of the air-conditioning system. Furthermore, this can improve the prediction accuracy of the energy efficiency ratio of the air-conditioning system, solving the problem of low energy efficiency ratio prediction accuracy of the air-conditioning system performance model in related technologies. It also provides a prerequisite for energy consumption simulation, energy efficiency monitoring, and the formulation of reasonable energy-saving control strategies for air-conditioning systems.

[0150] In some embodiments, the first performance ratio, evaporation temperature, and cooling temperature of the air conditioning system can be further obtained by obtaining the direct load of the chiller and the cooling capacity of the chiller. Therefore, as shown in FIG12 , the above step S111 can include the following steps:

[0151] S211. Obtain the direct load of the chiller according to the specific heat capacity of water and the chilled water flow rate, chilled water return temperature, and chilled water supply temperature in the target data group in the operating data.

[0152] In some embodiments, the direct load of the chiller can be obtained using formula (12): Q d =c·m e ·(T e-rtn -T e-sup ) Formula (12)

[0153] Where: Q d represents the direct load of the chiller; c represents the specific heat capacity of water; m e represents the mass flow rate of chilled water; T e-rtn Indicates the chilled water return temperature; T e-supIndicates the chilled water supply temperature.

[0154] S212. Obtain the cooling capacity of the chiller according to the specific heat capacity of water and the cooling water flow rate, cooling water return temperature, and cooling water supply temperature in the target data group in the operating data.

[0155] In some embodiments, the cooling capacity of the refrigerator can be obtained using formula (13): Q c =c·m c ·(T c-sup -T c-rtn ) Formula (13)

[0156] Where: Q c Indicates the cooling capacity of the chiller; m c Indicates cooling water flow; T c-sup Indicates the cooling water supply temperature; T c-rtn Indicates the cooling water return temperature.

[0157] In some embodiments, considering the error in time measurement, the load imbalance rate can be obtained based on the chiller power, chiller direct load, and chiller cooling capacity; when the load imbalance rate is greater than or equal to a preset load imbalance rate, the data group corresponding to the load imbalance rate in the operating data is deleted to reduce the error in data collection. For example, the load imbalance rate can be calculated using formula (14):

[0158] Where: B a Indicates the load imbalance rate; P w Indicates the cooling power.

[0159] Exemplarily, the preset load imbalance rate may be 15%, that is, when the load imbalance rate calculated based on certain data in a data group is greater than or equal to 15%, all data in the data group corresponding to the load imbalance rate is removed from the operating data.

[0160] In the above embodiment, the data group corresponding to the abnormal load imbalance rate in the operating data is eliminated, thereby reducing the interference of the abnormal data group on the establishment of the air-conditioning system performance model, making the data group in the operating data more accurate, and laying the foundation for subsequently improving the accuracy of the air-conditioning system model.

[0161] In some embodiments, as shown in FIG13 , eliminating the data group corresponding to the abnormal data value of the first energy efficiency ratio may include the following steps:

[0162] S311 . Obtain a first energy efficiency ratio of the air-conditioning system according to the direct load of the cooling machine and the cooling machine power in the target data group in the operating data.

[0163] In some embodiments, the cooling capacity of the refrigerator can be obtained using formula (15):

[0164] Where: COP represents the first energy efficiency ratio.

[0165] S312: When the first energy efficiency ratio is outside a preset range, delete the data in the target data group from the operating data.

[0166] For example, the preset interval can be (μ1-2σ1, μ1+2σ1), where μ1 is the mean COP value for the month and σ1 is the standard deviation of the COP for the month. That is, when a COP value falls outside the interval (μ1-2σ1, μ1+2σ1), it is considered an outlier, and all data in the target data set corresponding to that COP value are removed from the operating data.

[0167] In the above embodiment, if the first energy efficiency ratio is outside the preset range, the data group corresponding to the first energy efficiency ratio in the operating data is deleted. In this embodiment, the data group corresponding to the abnormal first energy efficiency ratio in the operating data is deleted, thereby reducing the interference of the abnormal data group on the establishment of the air conditioning system performance model, making the data in the operating data more accurate, and laying the foundation for subsequently improving the accuracy of the air conditioning system model.

[0168] In some embodiments, as shown in FIG14 , obtaining the evaporation temperature of the air conditioning system may include the following steps:

[0169] S411. Obtain the evaporator logarithmic mean temperature difference corresponding to the target data group based on the temperature difference between the chilled water supply temperature and the evaporator evaporation temperature in the target data group, and the temperature difference between the chilled water return temperature and the evaporator evaporation temperature in the target data group.

[0170] In some embodiments, the logarithmic mean temperature difference of the evaporator can be obtained using formula (16) and formula (17): Δt ch,e =Δt e2 -Δt e1 Formula (17)

[0171] Where: Δt e2 , Δt e1 Respectively represent the temperature difference between the chilled water supply temperature and the evaporation temperature of the evaporator, and the chilled water return temperature and the evaporation temperature of the evaporator; Δt m,e Indicates the logarithmic mean temperature difference of the evaporator; Δt ch,e Indicates the temperature difference between the inlet and outlet of the evaporator.

[0172] S412. Obtain the evaporator heat transfer coefficient corresponding to the target data group according to the direct load of the chiller and the logarithmic mean temperature difference of the evaporator.

[0173] In some embodiments, the evaporator heat transfer coefficient can be obtained using formula (18):

[0174] Where: K e F e represents the evaporator heat transfer coefficient.

[0175] S413 , fitting the evaporator target heat transfer coefficient according to the evaporator heat transfer coefficients corresponding to different data groups.

[0176] In some embodiments, the target heat transfer coefficient of the evaporator can be obtained by regression fitting using formula (19):

[0177] Where: a1, b1, c1, d1, e1, f1 represent the parameters of the fitted evaporator model.

[0178] In some embodiments, the target heat transfer coefficient of the evaporator is obtained by regression fitting, and the evaporation temperature of the air-conditioning system can be quantitatively characterized in a curve, making the evaporation temperature of the air-conditioning system more comprehensive and accurate.

[0179] S414. Obtain the evaporation temperature of the air conditioning system according to the density of water, the specific heat capacity of water, the target heat transfer coefficient of the evaporator, the direct load of the chiller, the chilled water flow rate, and the chilled water supply temperature.

[0180] In some embodiments, the evaporation temperature of the air conditioning system can be obtained using formula (20):

[0181] Where: ρ represents the density of water; T e Indicates the evaporation temperature.

[0182] In the above embodiment, fitting the target evaporator heat transfer coefficient based on the evaporator heat transfer coefficients corresponding to different data groups in the operating data can help establish a heat transfer model of the evaporator, thereby making the obtained evaporation temperature data of the air-conditioning system more comprehensive and accurate.

[0183] In some embodiments, as shown in FIG15 , obtaining the condensing temperature of the air conditioning system may include the following steps:

[0184] S511. Obtain the condenser logarithmic mean temperature difference corresponding to the target data group based on the temperature difference between the cooling water supply temperature and the condenser condensing temperature in the target data group, and the temperature difference between the cooling water return temperature and the condenser condensing temperature in the target data group.

[0185] In some embodiments, the condenser logarithmic mean temperature difference can be obtained using formulas (21) and (22): Δt ch,c =Δt c2 -Δtc1 Formula (22)

[0186] Where: Δt c2 Indicates the temperature difference between the cooling water outlet temperature and the condenser condensation temperature; Δt c1 Indicates the temperature difference between the cooling water return temperature and the condenser condensation temperature; Δt m,c Indicates the logarithmic mean temperature difference of the condenser; Δt ch,c Indicates the temperature difference between the inlet and outlet of the condenser.

[0187] S512. Obtain the condenser heat transfer coefficient corresponding to the target data group according to the direct load of the chiller and the logarithmic mean temperature difference of the condenser.

[0188] In some embodiments, the condenser heat transfer coefficient can be obtained using formula (23):

[0189] Where: K c F c represents the condenser heat transfer coefficient.

[0190] S513. According to the condenser heat transfer coefficients corresponding to different data groups, a target heat transfer coefficient of the condenser is obtained by fitting.

[0191] In some embodiments, the target heat transfer coefficient of the condenser can be obtained by fitting using formula (24):

[0192] Where: a2, b2, c2, d2, e2, f2 represent the parameters of the fitted condenser model.

[0193] In some embodiments, the target heat transfer coefficient of the condenser is obtained by regression fitting, and the condensing temperature of the air-conditioning system can be quantitatively characterized in a curve, making the condensing temperature of the air-conditioning system more comprehensive and accurate.

[0194] S514. Obtain the condensing temperature of the air conditioning system according to the density of water, the specific heat capacity of water, the target heat transfer coefficient of the condenser, the direct load of the chiller, the cooling water flow rate, and the cooling water return temperature.

[0195] In some embodiments, the condensing temperature of the air conditioning system can be obtained using formula (25):

[0196] Where: ρ represents the density of water; T c Indicates the condensing temperature.

[0197] In the above embodiment, the target heat transfer coefficient of the condenser is obtained by fitting according to the condenser heat transfer coefficients corresponding to different data groups in the operating data; this can help establish a heat transfer model of the condenser, thereby making the obtained condensing temperature data of the air-conditioning system more comprehensive and accurate.

[0198] In some embodiments, the first correspondence between the second energy efficiency ratio and the load rate of the air conditioning system can be a first correspondence of the air conditioning system represented by a curve. Therefore, as shown in FIG16 , the above step S112 may include the following steps:

[0199] S611 , using the second energy efficiency ratio as the horizontal coordinate and the load rate corresponding to the second energy efficiency ratio as the vertical coordinate to generate a scatter plot consisting of a plurality of data points.

[0200] In some embodiments, the ICOP value of the air conditioning system can be calculated using formula (26), and the DCOP value can be calculated using formula (27):

[0201] Where: ICOP represents the ideal energy efficiency ratio; DCOP represents the second energy efficiency ratio.

[0202] In some embodiments, the DCOP value can be used as the horizontal axis and the cooling machine load rate PLR ​​corresponding to the DCOP value can be used as the vertical axis to generate a scatter plot consisting of several data points. The scatter plot can intuitively represent the relationship between the DCOP value of the air-conditioning system and its corresponding load rate PLR.

[0203] S612: Fit the data points on the scatter plot to obtain a first corresponding relationship represented by a curve.

[0204] In some embodiments, the first corresponding relationship represented by the curve can be obtained by establishing a DCOP performance model of the air conditioning system by data regression fitting. For example, the DCOP performance model of the air conditioning system can be obtained by regression fitting using formula (28) to identify and obtain the model coefficients: DCOP = A·PLR 2 +B·PLR+C Formula (28)

[0205] Where: PLR is the cooling machine load rate; A, B, and C are the parameters of the fitted air-conditioning system model.

[0206] In the above embodiment, the DCOP performance model curve of the air-conditioning system obtained based on a large amount of actual operating data is one of the bases for optimizing the operation of the air-conditioning system. The DCOP performance model of the air-conditioning system obtained by data regression fitting makes the construction of the air-conditioning system performance model more complete, so that it can be selectively applied to air-conditioning systems in different actual environments, thereby improving the prediction accuracy of the energy efficiency ratio of the air-conditioning system.

[0207] In some embodiments, the third correspondence of the air-conditioning system can be obtained by selectively using the first correspondence or the second correspondence based on the data similarity between the newly added current data group and the target data group in the operating data. Therefore, as shown in FIG17 , the above step S114 may include the following steps:

[0208] S711 . Based on the refrigeration machine load rate and the temperature difference between the evaporation temperature and the condensation temperature of the air-conditioning system, obtain data similarity between the current data group and the target data group in the operating data.

[0209] The current data group includes the operating data acquired after a preset time period, which can also be expressed as the operating data at the current time.

[0210] In some embodiments of the present application, the target data group can represent any data group in the running data. In some embodiments, the input data can be clustered using the K-means clustering analysis method, and the number of cluster centers is calculated by the elbow method to find the optimal number of cluster centers k in a preset interval, and the cluster centers p1, p2, ..., p k For example, the K-means clustering analysis method is used to cluster the load rate of the chiller, the temperature difference between the evaporation temperature and the condensation temperature. The number of cluster centers is found in the interval [2,10] by the elbow method, and the cluster centers p1, p2, ..., p k Then, use formula (29) to calculate the Euclidean distance d1, d2, ..., d from the cooling machine load rate, the temperature difference between the evaporation temperature and the condensation temperature to the cluster center. k :

[0211] Where: x j is the n-dimensional input parameter, p i is the cluster center, d i For input parameters to p i Euclidean distance to cluster centers.

[0212] In some embodiments, the data similarity may be data proximity, and the data similarity between the current data group and the target data group in the running data may be calculated using formula (30):

[0213] Where: I is the data similarity between the current data group and the target data group in the running data.

[0214] S712: When the data similarity is less than or equal to the preset similarity, a third corresponding relationship is established based on the first corresponding relationship; when the data similarity is greater than the preset similarity, a third corresponding relationship is established based on the second corresponding relationship.

[0215] In some embodiments, establishing the third correspondence can also represent the target performance model of the air conditioning system. The target performance model can be obtained using formula (31), thereby obtaining the target energy efficiency ratio from the target performance model. COP = ICOP·DCOP Formula (31)

[0216] The COP in formula (31) can represent the target energy efficiency ratio.

[0217] In some embodiments, the current data group may include the cooling machine operating load rate, the air conditioning system condensing temperature, and the air conditioning system evaporating temperature in the operating data obtained after a preset time. The data of the target data group in the operating data are the input neurons in the BP neural network: the cooling machine operating load rate, the difference between the condensing temperature and the evaporating temperature. The similarity between the current input data and the input neurons in the BP neural network can be calculated using formula (19), with I' (preset similarity) as the boundary. When the similarity is higher than I', it means that the current data group is close to the input neurons in the BP neural network, so the DCOP performance prediction model of the air conditioning system established by the BP neural network is selected to establish the target performance model of the air conditioning system; when the similarity is less than or equal to I', it means that the prediction accuracy of the DCOP performance model of the air conditioning system is higher, so the DCOP performance model of the air conditioning system is selected to establish the target performance model of the air conditioning system. In the above embodiment, the data similarity between the first data group and the current data group is obtained through the mean clustering algorithm, and the third correspondence is established based on the first correspondence or the second correspondence according to the size of the similarity, so that the establishment of the air-conditioning system performance model is based on the actual situation of the current data, which improves the prediction accuracy of the air-conditioning system model and can also obtain a more accurate target energy efficiency ratio and cooling capacity, thereby achieving the efficient, safe and energy-saving operation goals of the air-conditioning system.

[0218] The following describes the control methods in some embodiments of the present application using a centrifugal chiller with a rated cooling capacity of 440 kW as an example. The compressor is a centrifugal compressor, and both the evaporator and condenser are shell-and-tube heat exchangers, with water flowing inside the tubes and refrigerant R134a flowing outside. An electronic expansion valve is used. As shown in FIG18 , the steps for establishing a chiller performance model are as follows:

[0219] S811. Sensor data collection: A large amount of actual chiller operating data is collected through various sensors to obtain operating data of ten characteristics in a certain year: chilled water supply temperature, chilled water return temperature, chiller cooling load rate, chiller power, chilled water flow rate, cooling water supply temperature, cooling water return temperature, cooling water flow rate, evaporator evaporation temperature, and condenser condensation temperature.

[0220] S812. Data screening: All data from consecutive months are grouped as a set of running data for data screening.

[0221] 1) Calculation of load imbalance rate: First calculate the direct load of the chiller using formula (12), then calculate the cooling capacity of the chiller using formula (13), and then calculate the load imbalance rate using formula (14).

[0222] 2) Calculate the COP of the chiller: Use formula (12) to calculate the direct load of the chiller and then use formula (15) to calculate the COP of the chiller.

[0223] 3) Data elimination.

[0224] The rules for determining abnormal imbalance rate are as follows:

[0225] When the unbalance rate exceeds 15%, it is an abnormal value. That is, when the load unbalance rate calculated at a certain time point is greater than or equal to 15%, the data group at the time point corresponding to the load unbalance rate is removed from the operating data.

[0226] The rules for identifying COP outliers are as follows:

[0227] COP values ​​outside the interval (μ1-2σ1, μ1+2σ1) are considered outliers and are removed, where μ1 is the mean COP for the month and σ1 is the standard deviation of the COP for the month. Specifically, if the COP value calculated at a given point in time falls outside the interval (μ1-2σ1, μ1+2σ1), it is considered an outlier and the data set corresponding to that COP value at that point in time is removed from the operating data.

[0228] S813. Calculate the evaporation temperature and the condensation temperature using the data screening value.

[0229] 1) Calculate the evaporation temperature: Calculate the temperature difference between the chilled water supply temperature and the evaporator evaporation temperature, and the temperature difference between the chilled water return temperature and the evaporator evaporation temperature. Calculate the logarithmic mean temperature difference of the evaporator and the temperature difference between the inlet and outlet of the evaporator using formulas (15) and (17), respectively. Then use formula (18) to calculate the evaporator heat transfer coefficient K. e F e The evaporator heat transfer coefficient is regressed and fitted using formula (19) to quantitatively characterize the evaporator heat transfer coefficient K. e F e , the evaporation temperature is calculated using formula (20). The calculated regression fitting coefficients are shown in Table 2 below.

[0230] Table 2 Calculation results of fitting coefficients of evaporator heat transfer model

[0231] 2) Calculate the condensing temperature: Calculate the temperature difference between the cooling water return temperature and the condenser condensing temperature, and the cooling water outlet temperature and the condenser condensing temperature. Calculate the condenser logarithmic mean temperature difference and the condenser inlet and outlet temperature difference using formulas (21) and (22) respectively. Then calculate the condenser heat transfer coefficient K using formula (23). c F c , using formula (24) to regress and fit the condenser heat transfer coefficient, quantitatively characterize the condenser heat transfer coefficient K c F c , the condensation temperature is calculated using formula (25). The calculated regression fitting coefficients are shown in Table 3 below.

[0232] Table 3 Calculation results of fitting coefficients of condenser heat transfer model

[0233] S814. Establish the DCOP performance model of the chiller.

[0234] The chiller's evaporation temperature and condensation temperature are calculated using formulas (20) and (25). Formula (26) is then used to calculate the chiller's ideal COP, defined as the ICOP value. Formula (27) is used to calculate the DCOP value, which is the degree to which the chiller's actual COP approaches the ideal COP value. This value is called the chiller's coefficient of performance. Finally, the load rate PLR ​​is used as the horizontal axis, and the chiller's coefficient of performance DCOP is used as the vertical axis. The corresponding data points are listed in the coordinate system to form a dense scatter plot. Formula (28) is then used to perform regression fitting and identify the model coefficients, resulting in the chiller's DCOP performance model. The calculated regression fitting coefficients and corresponding model evaluation indicators are shown in Table 4 below.

[0235] Table 4 DCOP performance model fitting coefficient calculation results

[0236] The newly added chiller operation data was input into the trained DCOP performance model for calculation, and the model error evaluation indicators were obtained as shown in Table 5 below.

[0237] Table 5 New data error evaluation indicators for DCOP performance model

[0238] For input data that has not been trained with the model, the prediction results of the DCOP performance model are still relatively close to the measured results, and the DCOP performance model has good generalization ability.

[0239] S815. Establish a DCOP performance prediction model for the chiller through a BP neural network model.

[0240] Establish a BP neural network structure with three layers, as shown in Figure 19. The input neurons of the neural network are: the cooling machine load rate PLR, the difference between the condensing temperature and the evaporating temperature Tc -T e The network's hidden layer has seven nodes, and the output neuron is DCOP. The BP neural network model uses the trainlm learning algorithm, with the tansig function selected as the hidden layer transfer function and the Pureline function selected as the output layer. The BP neural network model's learning rate is set to 0.05, the maximum number of learning steps is set to 1000, and the training target error is set to 0.003. A DCOP prediction model for refrigeration units is obtained through data training.

[0241] The training data error evaluation indicators between the DCOP prediction results and the measured DCOP results are shown in Table 6. New chiller operating data was input into the trained DCOP prediction model for calculation, resulting in model error evaluation indicators, as shown in Table 7. This shows that the DCOP performance prediction model established using a neural network has high accuracy. However, when the new input data deviates from the model's training data, the error decreases, indicating that the model's generalization ability is poor.

[0242] Table 6 DCOP prediction model training data error evaluation index

[0243] Table 7. New data error evaluation indicators for DCOP prediction model

[0244] S816. Based on the K-means clustering algorithm, the DCOP performance model and the COP performance prediction model of the chiller are integrated to establish the chiller model.

[0245] 1) Clustering calculation to obtain cluster centers: K-means clustering analysis method is used to cluster the input data of DCOP prediction model. The cluster center number elbow method is used to find the optimal cluster center number k in the interval [2,10] to obtain cluster centers p1, p2, ..., p k In this embodiment, the number of cluster centers k is calculated by the elbow method and is set to 5. The Euclidean distances d1, d2, ..., d from the input parameters to the cluster centers are calculated by formula (18). k The coordinates of cluster centers p1, p2, p3, p4, p5, p6, and p7 are shown in Table 8.

[0246] Table 8 Cluster center calculation results

[0247] 2) Use the newly added data to calculate the cluster center proximity I.

[0248] 3) Using I' (preset similarity) as the boundary, when the proximity is higher than I', it indicates that the current data set is close to the input neurons in the BP neural network, so the air conditioning system DCOP performance prediction model established using the BP neural network is selected; when the proximity is less than or equal to I', it indicates that the air conditioning system DCOP performance model established using parameter identification has higher prediction accuracy, so the air conditioning system DCOP performance model established using parameter identification is selected. In this embodiment, I' is set to 0.8. New data error evaluation indicators are shown in Table 9.

[0249] Table 9 New data error evaluation indexes for chiller model

[0250] 4) Construct a refrigeration performance model.

[0251] The energy efficiency ratio (COP) of the chiller is calculated using formula (20). The newly added data error evaluation indicators are shown in Table 10. It can be seen that the prediction accuracy of the chiller model finally established is high, and the model's generalization and adaptability are also good.

[0252] Table 10 Evaluation index of COP prediction error of chiller model

[0253] Combined with the above examples, it can be seen that in the control method provided by some embodiments of the present application, the evaporation temperature and condensation temperature of the chiller can be quantitatively characterized in the form of curves by statistically analyzing the actual energy efficiency ratio of the chiller under different weather conditions and different cooling load rates, the ideal energy efficiency ratio under various conditions, the target heat transfer coefficient of the evaporator, and the target heat transfer coefficient of the condenser. Based on the physical framework of the chiller, the actual performance model of the chiller is established using actual data, and then the performance prediction model of the chiller is established through the BP neural network. Finally, the K-means clustering method is used to combine the actual performance model of the chiller with the high-precision prediction interval of the performance prediction model to obtain the target performance model of the chiller. The target performance model obtained by this solution is completely based on actual data, so it can well reflect the actual chiller performance under different load rates, greatly improve the prediction accuracy of the chiller model, and solve the problems that traditional chiller performance curves cannot be applied to actual projects, the existing chiller performance models have low prediction accuracy, and poor adaptability and versatility. Furthermore, the target energy efficiency ratio can be obtained according to the target performance model, so that a more reasonable and accurate cooling capacity can be determined according to the target energy efficiency ratio, which can effectively reduce the energy consumption of the chiller operation.

[0254] In some embodiments of the present application, the controller may be divided into functional modules according to the above method examples. For example, each functional module may be divided according to each function, or two or more functions may be integrated into one processing module. The above integrated modules may be implemented in the form of hardware or in the form of software functional modules. In some embodiments, the division of modules in some embodiments of the present application is schematic and is only a logical function division. In actual implementation, there may be other division methods.

[0255] Some embodiments of the present application also provide a hardware structure diagram of an air conditioner, as shown in Figure 20. The air conditioner 3000 includes a processor 3001 and, in some embodiments, a memory 3002 and a communication interface 3003 connected to the processor 3001. The processor 3001, the memory 3002, and the communication interface 3003 are connected via a bus 3004.

[0256] The processor 3001 may be a central processing unit (CPU), a general-purpose processor, a network processor (NP), a digital signal processor (DSP), a microprocessor, a microcontroller, a programmable logic device (PLD), or any combination thereof.

[0257] The memory 3002 may be a read-only memory (ROM) or other type of static storage device that can store static information and instructions, a random access memory (RAM) or other type of dynamic storage device that can store information and instructions, or an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM), or other optical disk storage.

[0258] The communication interface 3003 may be configured to communicate with other devices or a communication network.

[0259] Bus 3004 may be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. Bus 3004 may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, FIG20 shows only one thick line, but this does not mean that there is only one bus or only one type of bus.

[0260] The one or more memories 3002 are configured to store computer program code, which includes computer instructions. When the one or more processors 3002 execute the computer instructions, the controller performs the air conditioning system control method and the chiller ideal energy efficiency ratio determination method provided in the above possible implementation. The specific implementation can be found in the previous text and will not be further described here.

[0261] Those skilled in the art will understand that the disclosure scope of this application is not limited to the above specific embodiments, and certain elements of the embodiments may be modified and replaced without departing from the spirit of this application. The scope of this application is limited by the appended claims.

Claims

1. An air conditioning system comprising: Chillers; The refrigerant circulation loop circulates the refrigerant through the loop consisting of the compressor, condenser and evaporator; a first temperature sensor configured to detect a condensing temperature of the condenser; a second temperature sensor configured to detect an evaporation temperature of the evaporator; as well as The controller is configured as: Obtaining a first condensing temperature of an n-th detection period by a first temperature sensor, and obtaining a first evaporating temperature of an n-th detection period by a second temperature sensor, where n is an integer greater than 1; Obtaining operating parameters of the chiller, and determining a second condensing temperature of the condenser in an nth detection cycle and a second evaporating temperature of the evaporator in an nth detection cycle based on the operating parameters; determining a first energy efficiency ratio of the nth detection cycle according to the first condensing temperature and the first evaporating temperature; determining a second energy efficiency ratio of the nth detection cycle according to the second condensing temperature and the second evaporating temperature; An ideal energy efficiency ratio of the nth detection cycle is determined according to the first energy efficiency ratio of the nth detection cycle and the second energy efficiency ratio of the nth detection cycle.

2. The air conditioning system according to claim 1, wherein the controller is further configured to: Determining an ideal energy efficiency ratio of the nth period according to the first energy efficiency ratio of the nth detection period, the second energy efficiency ratio of the nth detection period, the first value range, and the second value range; in, The first value range is determined based on an average value and a standard deviation of the first energy efficiency ratio of n detection cycles, and the second value range is determined based on an average value and a standard deviation of the second energy efficiency ratio of n detection cycles.

3. The air conditioning system according to claim 2, wherein the controller is further configured to: When the first energy efficiency ratio of the nth detection cycle is not within the first value range, and the second energy efficiency ratio of the nth detection cycle is not within the second value range, taking the average of the first energy efficiency ratio of the nth detection cycle and the second energy efficiency ratio of the nth detection cycle as the ideal energy efficiency ratio of the nth detection cycle; or, When the first energy efficiency ratio of the nth detection cycle is within the first value range, and the second energy efficiency ratio of the nth detection cycle is not within the second value range, taking the first energy efficiency ratio of the nth cycle as the ideal energy efficiency ratio of the nth cycle; or, When the second energy efficiency ratio of the nth detection cycle is within the second value range, the second energy efficiency ratio of the nth detection cycle is used as the ideal energy efficiency ratio of the nth cycle.

4. The air conditioning system according to any one of claims 1 to 3, wherein the chiller comprises: Chilled water and cooling water; The operating parameters of the chiller include: the supply and return water temperature difference of the chilled water in each detection cycle, the supply and return water temperature difference of the cooling water in each detection cycle, the heat exchange temperature difference of the evaporator in each detection cycle, the heat exchange temperature difference of the condenser in each detection cycle, the supply water temperature of the chilled water in each detection cycle, the return water temperature of the cooling water in each detection cycle, and the cooling load rate of the chiller in each detection cycle; The controller is further configured to: determining a second condensing temperature of the condenser in the nth detection cycle based on the supply and return water temperature difference of the cooling water in the nth detection cycle, the heat exchange temperature difference of the condenser in the nth detection cycle, the return water temperature of the cooling water in the nth detection cycle, and the cooling load rate of the chiller in the nth detection cycle; as well as The second evaporation temperature of the evaporator in the nth detection cycle is determined based on the supply and return water temperature difference of the chilled water in the nth detection cycle, the heat exchange temperature difference of the evaporator in the nth detection cycle, the supply water temperature of the chilled water in the nth detection cycle, and the cooling load rate of the chiller in the nth detection cycle.

5. The air conditioning system according to claim 4, wherein the operating parameters of the chiller further include: The electric power of the chiller in each detection cycle; The controller is further configured to: After determining the ideal energy efficiency ratio of the nth detection cycle, the ideal energy efficiency ratio of the nth detection cycle is calculated based on the electric power of the chiller in the nth detection cycle, the cooling load rate of the chiller in the nth detection cycle, and the ideal energy efficiency ratio of the nth detection cycle. The efficiency ratio is used to determine the cooling load of the chiller in the nth detection cycle.

6. An air conditioning system comprising: evaporator; condenser; as well as A controller is electrically connected to the evaporator and the condenser, and is configured to: Obtaining the evaporation temperature, condensation temperature, first energy efficiency ratio, and load rate of the air-conditioning system based on the operating data of the evaporator and the condenser within a preset time period; Obtaining a second energy efficiency ratio of the air-conditioning system and a first corresponding relationship between the second energy efficiency ratio and the load rate of the air-conditioning system according to the evaporating temperature, the condensing temperature, the first energy efficiency ratio, and the load rate; Using a back-propagation neural network model, obtaining a second corresponding relationship between the second energy efficiency ratio and the load rate of the air-conditioning system; as well as The first corresponding relationship and the second corresponding relationship are used based on a mean value clustering algorithm to obtain a third corresponding relationship between the target energy efficiency ratio of the air-conditioning system and the load rate of the air-conditioning system.

7. The air conditioning system according to claim 6, wherein the operating data of the evaporator and the condenser within the preset time period comprises: The chilled water supply temperature, chilled water return temperature, chilled water flow rate and evaporation temperature of the evaporator, the cooling water supply temperature, cooling water return temperature, cooling water flow rate and condensation temperature of the condenser, and the refrigeration load rate and refrigeration power of the air-conditioning system; The operation data corresponding to each time point within the preset time period constitute a data group.

8. The air conditioning system according to claim 7, wherein the controller is further configured to: Obtaining a chiller direct load based on the specific heat capacity of water and the chilled water flow rate, the chilled water return temperature, and the chilled water supply temperature in the target data group in the operating data; Obtaining the cooling capacity of the chiller according to the specific heat capacity of water and the cooling water flow rate, the cooling water return temperature, and the cooling water supply temperature in the target data group in the operating data; Obtaining the evaporator logarithmic mean temperature difference corresponding to the target data group based on the temperature difference between the chilled water supply temperature and the evaporation temperature of the evaporator in the target data group, and the temperature difference between the chilled water return temperature and the evaporation temperature of the evaporator in the target data group; Obtaining the evaporator heat transfer coefficient corresponding to the target data group according to the direct load of the chiller and the logarithmic mean temperature difference of the evaporator; According to the evaporator heat transfer coefficients corresponding to different data groups, the target heat transfer coefficient of the evaporator is obtained by fitting; and The evaporation temperature of the air conditioning system is obtained according to the density of water, the specific heat capacity of water, the target heat transfer coefficient of the evaporator, the direct load of the chiller, the chilled water flow rate and the chilled water supply temperature.

9. The air conditioning system according to claim 7, wherein the controller is further configured to: Obtaining a chiller direct load based on the specific heat capacity of water and the chilled water flow rate, the chilled water return temperature, and the chilled water supply temperature in the target data group in the operating data; Obtaining the cooling capacity of the chiller according to the specific heat capacity of water and the cooling water flow rate, the cooling water return temperature, and the cooling water supply temperature in the target data group in the operating data; Obtaining the condenser logarithmic mean temperature difference corresponding to the target data group based on the temperature difference between the cooling water supply temperature and the condenser condensing temperature in the target data group, and the temperature difference between the cooling water return temperature and the condenser condensing temperature in the target data group; Obtaining a condenser heat transfer coefficient corresponding to the target data group according to the direct load of the chiller and the logarithmic mean temperature difference of the condenser; According to the condenser heat transfer coefficients corresponding to different data groups, the target heat transfer coefficient of the condenser is obtained by fitting; as well as The condensing temperature of the air-conditioning system is obtained according to the density of water, the specific heat capacity of water, the target heat transfer coefficient of the condenser, the direct load of the chiller, the cooling water flow rate and the cooling water return temperature.

10. The air conditioning system according to claim 8 or 9, wherein the controller is further configured to: According to the direct load of the refrigeration machine and the cooling capacity of the refrigeration machine, and the refrigeration Machine power, obtain load imbalance rate; and When the load imbalance rate is greater than or equal to a preset imbalance rate, the data in the target data group is deleted from the operating data.

11. The air conditioning system according to claim 8 or 9, wherein the controller is further configured to: Obtaining a first energy efficiency ratio of the air-conditioning system according to the direct load of the refrigeration machine and the refrigeration machine power in the target data group in the operating data; and When the first energy efficiency ratio is outside a preset range, the data in the target data group is deleted from the operating data.

12. The air conditioning system according to claim 6 or 7, wherein the controller is further configured to: Obtaining a second energy efficiency ratio of the air-conditioning system according to the evaporation temperature, the condensation temperature, the first energy efficiency ratio, and the load rate; The second energy efficiency ratio is used as the horizontal axis, and the load rate corresponding to the second efficiency ratio is used as the vertical axis to generate a scatter plot consisting of several data points; wherein, Each data point corresponds to a second energy efficiency ratio and a load factor; and The data points on the scatter plot are fitted to obtain the first corresponding relationship represented by a curve.

13. The air conditioning system according to claim 6 or 7, wherein the controller is further configured to: The refrigeration machine load rate in the operating data and the temperature difference between the evaporating temperature and the condensing temperature of the air-conditioning system are input into the back propagation neural network model to obtain a second energy efficiency ratio and a second corresponding relationship between the second energy efficiency ratio and the load rate of the air-conditioning system.

14. The air conditioning system according to claim 6 or 7, wherein the controller is further configured to: Based on the refrigeration machine load rate and the temperature difference between the evaporation temperature and the condensation temperature of the air-conditioning system, obtaining data similarity between a current data group and a target data group in the operating data; the current data group includes the operating data obtained after the preset time period; When the data similarity is less than or equal to a preset similarity, establishing a third corresponding relationship based on the first corresponding relationship; as well as In a case where the data similarity is greater than a preset similarity, a third corresponding relationship is established according to the second corresponding relationship.

15. A method for determining the energy efficiency ratio of an air conditioning system, the method being applied to a chiller in the air conditioning system, the method comprising: Obtaining a first condensing temperature of the condenser in an nth detection cycle by a first temperature sensor, and obtaining a first evaporating temperature of the evaporator in an nth detection cycle by a second temperature sensor, where n is an integer greater than 1; Obtaining operating parameters of the chiller, and determining a second condensing temperature of the condenser in an nth detection cycle and a second evaporating temperature of the evaporator in an nth detection cycle based on the operating parameters; determining a first energy efficiency ratio of the nth detection cycle according to the first condensing temperature and the first evaporating temperature; determining a second energy efficiency ratio of the nth detection cycle according to the second condensing temperature and the second evaporating temperature; An ideal energy efficiency ratio of the nth detection cycle is determined according to the first energy efficiency ratio of the nth detection cycle and the second energy efficiency ratio of the nth detection cycle.

16. A method for controlling an air conditioning system, comprising: Obtaining the evaporation temperature, condensation temperature, first energy efficiency ratio, and load rate of the air conditioning system based on the operating data of the evaporator and condenser within a preset time period; Obtaining a second energy efficiency ratio of the air-conditioning system and a first corresponding relationship between the second energy efficiency ratio and the load rate of the air-conditioning system according to the evaporating temperature, the condensing temperature, the first energy efficiency ratio, and the load rate; Using a back-propagation neural network model, obtaining a second corresponding relationship between the second energy efficiency ratio and the load rate of the air-conditioning system; as well as The first corresponding relationship and the second corresponding relationship are used based on a mean value clustering algorithm to obtain a third corresponding relationship between the target energy efficiency ratio of the air-conditioning system and the load rate of the air-conditioning system.

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