Air conditioner

Abstract

The invention discloses an air conditioner which comprises a controller, and the controller is configured to obtain a set temperature and determine a set temperature difference according to an indoor environment temperature and the set temperature; when the air conditioner starts refrigeration operation for the first time, a preset neural network algorithm or a preset fitting algorithm is adopted to determine an estimated target frequency and an estimated stable frequency; or when the air conditioner starts non-first refrigeration operation, the estimated target frequency and the estimated stable frequency are obtained; in the estimated frequency control stage, the air conditioner is controlled to execute the refrigeration operation; and when the set temperature difference does not exceed the preset temperature difference threshold value, the compressor is controlled to be reduced from the estimated target frequency to the estimated stable frequency according to the first frequency reduction rate, and after the compressor is controlled to operate for the first preset time according to the estimated stable frequency, the air conditioner is controlled to execute the fuzzy frequency control stage of the refrigeration operation, so that the user experience is improved, and the user experience is improved. And meanwhile, the energy efficiency and the energy-saving effect are also improved, so that efficient utilization of energy is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of air conditioners, and in particular to an air conditioner. Background Art

[0002] The temperature control of air conditioners usually adopts fuzzy algorithm control. When using fuzzy algorithm control, the set temperature difference (that is, the difference between the indoor ambient temperature and the set temperature) and the rate of change of the temperature difference are monitored in real time. Then, the preset fuzzy control table is queried based on this information to dynamically adjust the target operating frequency of the air conditioner to quickly adjust the indoor ambient temperature to close to the set temperature.

[0003] However, when the actual load of the room is relatively small, in the initial stage of air conditioner operation, the system often calculates an overly high target operating frequency, causing the compressor to operate at a high frequency. In fact, the energy efficiency ratio of the compressor operation will be significantly lower than that of medium and low frequencies. This not only reduces energy efficiency, but may also cause large and long-lasting overshoots in indoor ambient temperature, causing users to feel uncomfortable, thereby reducing the user experience and causing unnecessary energy consumption. Summary of the Invention

[0004] The present invention aims to solve at least one of the technical problems in the prior art. To this end, the object of the present invention is to provide an air conditioner.

[0005] The present invention provides an air conditioner, comprising: A refrigerant circulation loop, wherein the refrigerant undergoes a refrigeration cycle in a loop consisting of a compressor, a condenser, a throttling assembly, and an evaporator, wherein one of the condenser and the evaporator is an outdoor heat exchanger and the other is an indoor heat exchanger; Indoor ambient temperature sensor, used to detect indoor ambient temperature; Outdoor ambient temperature sensor, used to detect outdoor ambient temperature; A controller configured to: Obtaining a set temperature, and determining a set temperature difference according to the indoor ambient temperature and the set temperature; When the air conditioner starts the first cooling operation, a preset neural network algorithm or a preset fitting algorithm is used to determine the estimated target frequency and the estimated stable frequency; or, when the air conditioner starts the non-first cooling operation, the estimated target frequency and the estimated stable frequency are obtained by the controller based on the operating data of the fuzzy frequency control stage in the previous cooling operation of the air conditioner, the operating data including: the time when the compressor reaches the temperature for the first time, the stable operating frequency of the compressor after reaching the temperature, the first cooling rate, the energy saving coefficient, and the frequency revision coefficient calibrated based on the fan speed; each cooling operation of the air conditioner includes an estimated frequency control stage and a fuzzy frequency control stage; Controlling the air conditioner to perform the estimated frequency control phase of this cooling operation; The estimated frequency control stage includes: controlling the compressor to operate according to the estimated target frequency; When the set temperature difference does not exceed a preset temperature difference threshold, controlling the compressor to reduce the frequency from the estimated target frequency to the estimated stable frequency at a first frequency reduction rate, and controlling the compressor to operate at the estimated stable frequency for a first preset time, and then controlling the air conditioner to perform the fuzzy frequency control stage of the current cooling operation; The fuzzy frequency control stage includes: controlling the compressor to operate according to the estimated stable frequency.

[0006] In addition, the air conditioner according to the embodiment of the present invention may also have the following additional technical features: Furthermore, when using a preset neural network algorithm to determine the estimated target frequency and the estimated stable frequency, the controller is configured to: determine a first temperature difference between the outdoor ambient temperature and the set temperature; input the first temperature difference, the set temperature difference and the outdoor ambient temperature into a preset neural network model, and output an initial operating frequency after calculation through the preset neural network algorithm; based on the initial operating frequency, control the air conditioner to perform fuzzy frequency self-learning to obtain the estimated target frequency and the estimated stable frequency.

[0007] The above technical solution has the following advantages or beneficial effects: when the air conditioner is in cooling operation, it can more quickly and accurately meet the user's expected cooling needs and maintain stable and efficient operation.

[0008] Furthermore, when a preset fitting algorithm is used to determine the estimated target frequency and the estimated stable frequency, the controller is configured to: obtain a preset initial maximum operating frequency; determine a first temperature difference between the outdoor ambient temperature and the set temperature; determine a preset target frequency coefficient based on the set temperature difference, the outdoor ambient temperature and the first temperature difference; determine an iterative correction factor based on the energy-saving coefficient; obtain the initial operating frequency based on the preset target frequency coefficient, the iterative correction factor, the preset initial maximum operating frequency, the frequency correction coefficient based on fan speed calibration, the outdoor ambient temperature, the set temperature difference and the first temperature difference; based on the initial operating frequency, control the air conditioner to perform fuzzy frequency self-learning to obtain the estimated target frequency and the estimated stable frequency.

[0009] The above technical solution has the following advantages or beneficial effects: ensuring that the air conditioner can quickly and accurately achieve the cooling effect expected by the user when cooling is started, and maintain an efficient and energy-saving operating state.

[0010] Furthermore, when the air conditioner is controlled to perform fuzzy frequency self-learning based on the initial operating frequency to obtain the estimated target frequency and the estimated stable frequency, the controller is configured to: control the compressor to operate according to the initial operating frequency, and obtain multiple historical operating data obtained by the air conditioner performing multiple fuzzy frequency self-learning, wherein the historical operating data include: the first historical temperature-reaching time, the second historical temperature-reaching time, the historical stable operating frequency of the compressor after reaching temperature, and the frequency integral of the compressor from startup to the second historical temperature-reaching time of the compressor; averaging the multiple historical stable operating frequencies of the compressor after reaching temperature to obtain the estimated stable frequency; obtaining multiple corresponding target operating frequencies based on the multiple historical operating data, and averaging the multiple target operating frequencies to obtain the estimated target frequency.

[0011] The above technical solution has the following advantages or beneficial effects: it can ensure the accuracy of the estimated stable frequency and the estimated target frequency, so that the air conditioner can be adjusted according to the actual operating environment, achieving the purpose of efficient cooling and energy saving.

[0012] Further, when multiple corresponding target operating frequencies are obtained based on multiple historical operating data, the controller is configured to: determine the first cooling rate based on the set temperature difference and the first historical temperature-reaching time; determine the energy-saving coefficient based on the first cooling rate, wherein there is a pre-calibrated correspondence between the first cooling rate and the energy-saving coefficient; obtain multiple corresponding target operating frequencies based on multiple frequency integrals and their corresponding multiple energy-saving coefficients and multiple second historical temperature-reaching times.

[0013] The above technical solution has the following advantages or beneficial effects: it can ensure the accuracy of the estimated target frequency, enable the air conditioner to be adjusted according to the actual operating environment, and achieve the goals of efficient cooling and energy saving.

[0014] Furthermore, when obtaining the estimated target frequency, the controller is configured to: obtain the estimated target frequency of the air conditioner in the fuzzy frequency control stage in the last cooling operation; and obtain the estimated target frequency of the air conditioner when performing this cooling operation based on the product of the estimated target frequency of the fuzzy frequency control stage in the last cooling operation of the air conditioner, the energy-saving coefficient and the frequency revision coefficient calibrated based on the fan speed.

[0015] The above technical solution has the following advantages or beneficial effects: it takes into account the previous operating data of the air conditioner and at the same time incorporates the effects of energy efficiency optimization and fan speed adjustment, thereby achieving efficient cooling control.

[0016] Furthermore, when obtaining the estimated stable frequency, the controller is configured to: obtain the stable operating frequency of the compressor after reaching temperature in multiple fuzzy frequency control stages of the air conditioner before performing this cooling operation; and average the multiple stable operating frequencies of the compressor after reaching temperature to obtain the estimated stable frequency.

[0017] The above technical solution has the following advantages or beneficial effects: it can improve the accuracy of estimating the stable frequency, and help the air conditioner to maintain stable operation of the compressor while achieving efficient cooling.

[0018] Furthermore, when obtaining the estimated stable frequency, the controller is configured to: obtain the stable operating frequency of the compressor after reaching temperature in multiple fuzzy frequency control stages of the air conditioner before performing this cooling operation; and calculate the weighted average of the multiple stable operating frequencies of the compressor after reaching temperature to obtain the estimated stable frequency.

[0019] The above technical solution has the following advantages or beneficial effects: it can improve the accuracy of estimating the stable frequency, and help the air conditioner to maintain stable operation of the compressor while achieving efficient cooling.

[0020] Furthermore, when controlling the air conditioner to perform the estimated frequency control stage of this cooling operation, the controller is also configured to: obtain the second cooling rate of the compressor in the estimated frequency control stage; when the second cooling rate does not exceed the preset cooling rate threshold, correct the estimated target frequency according to the estimated target frequency, the energy-saving coefficient and the frequency correction coefficient calibrated based on the fan speed.

[0021] The above technical solution has the following advantages or beneficial effects: ensuring that the air conditioner can quickly respond to load changes, speed up the cooling speed, and make the indoor ambient temperature reach the set temperature as soon as possible without affecting the comfort.

[0022] Furthermore, when obtaining the second cooling rate of the compressor in the estimated frequency control stage, the controller is configured to: obtain the initial indoor ambient temperature and the operating time of the compressor; and determine the second cooling rate based on the initial indoor ambient temperature, the operating time of the compressor and the indoor ambient temperature.

[0023] The above technical solution has the following advantages or beneficial effects: the second cooling rate can be calculated in real time based on the real-time detected indoor ambient temperature, and the estimated target frequency can be corrected in real time according to the second cooling rate, thereby improving the accuracy of the estimated target frequency.

[0024] According to an embodiment of the present invention, when the air conditioner starts its first cooling operation, it will calculate the estimated target frequency and the estimated stable frequency based on whether the preset neural network model is supported, and then select a preset neural network algorithm or a preset fitting algorithm based on the judgment result to flexibly calculate the estimated target frequency and the estimated stable frequency. When the air conditioner starts a non-first cooling operation, the estimated target frequency and the estimated stable frequency are calculated based on the operating data of the air conditioner in the fuzzy frequency control stage of the previous cooling operation, and then the compressor is controlled to operate according to the estimated target frequency to control the air conditioner to execute the estimated frequency control stage of the current cooling operation, which can avoid large and long-lasting overshoots of the indoor ambient temperature. At the same time, the set temperature difference is compared with the preset temperature difference threshold. When the set temperature difference does not exceed the preset temperature difference threshold, the compressor is controlled to reduce the frequency from the estimated target frequency to the estimated stable frequency according to the first frequency reduction rate. This can gradually reduce the indoor ambient temperature and ensure the stability and comfort of the indoor ambient temperature. After that, the compressor is controlled to operate at the estimated stable frequency for the first preset time, and the air conditioner is controlled to switch to the fuzzy frequency control stage, that is, the compressor is controlled to operate at the estimated stable frequency, ensuring that the user can enjoy a comfortable cooling experience, improving the user experience, and also improving energy efficiency and energy saving effects, thereby achieving efficient use of energy.

[0025] Additional aspects and advantages of the present invention will be set forth in part in the description which follows and, in part, will be obvious from the description which follows, or may be learned by practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the following description of the embodiments with reference to the accompanying drawings, in which: Figure 1 is a structural diagram of an air conditioner according to an embodiment of the present invention; Figure 2 is a structural schematic diagram of a controller according to an embodiment of the present application; Figure 3 is a structural schematic diagram of an air conditioner according to another embodiment of the present application; Figure 4 is a schematic diagram of a preset neural network model according to an embodiment of the present application; Figure 5 is a schematic diagram of a predicted frequency control stage and a fuzzy frequency control stage according to an embodiment of the present application; Figure 6 is a flow chart of a control method of an air conditioner according to an embodiment of the present application; Figure 7 is a flow chart of obtaining a predicted target frequency according to an embodiment of the present application; Figure 8 is a flow chart of obtaining a predicted stable frequency according to an embodiment of the present application; Figure 9 is a flow chart of obtaining a predicted stable frequency according to another embodiment of the present application; Figure 10 is a flow chart of correcting a predicted target frequency according to an embodiment of the present application. DETAILED DESCRIPTION

[0027] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0028] In the description of the present application, it should be understood that the terms “center”, “upper”, “lower”, “front”, “back”, “left”, “right”, “vertical”, “horizontal”, “top”, “bottom”, “inner”, “outer” and the like indicate the orientation or positional relationship shown in the drawings based on the orientation or positional relationship shown in the drawings, and are only for the purpose of facilitating the description of the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as a limitation on the present application.

[0029] The terms “first”, “second” are only for the purpose of description, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the technical features indicated. Therefore, the features defined with “first”, “second” can explicitly or implicitly include one or more of the features. In the description of the present application, unless otherwise specified, the meaning of “multiple” is two or more.

[0030] In the description of the present invention, it should be noted that, unless otherwise expressly specified or limited, the terms "mounted," "connected," and "connected" should be understood in a broad sense. For example, they may refer to fixed, detachable, or integral connections; mechanical or electrical connections; direct or indirect connections through an intermediate medium; and internal communication between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on the specific circumstances.

[0031] The embodiment of the present invention provides an air conditioner 10, referring to Figure 1 The air conditioner 10 includes a refrigeration system for exchanging heat with indoor air to meet cooling or heating needs.

[0032] The refrigeration system includes a compressor, a condenser, an electronic expansion valve, and an evaporator. In the present invention, the air conditioner 10 performs a refrigeration cycle of the air conditioner 10 by using the compressor, the condenser, the electronic expansion valve, and the evaporator. The refrigeration cycle includes a series of processes involving compression, condensation, expansion, and evaporation, and supplies refrigerant to the conditioned and heat-exchanged air.

[0033] The compressor compresses high-temperature, high-pressure refrigerant gas and discharges the compressed gas. The discharged refrigerant gas flows into the condenser. The condenser condenses the compressed refrigerant into a liquid phase, releasing heat into the surrounding environment through the condensation process.

[0034] The electronic expansion valve expands the high-temperature, high-pressure liquid refrigerant condensed in the condenser into a low-pressure liquid refrigerant. The evaporator evaporates the refrigerant expanded in the electronic expansion valve and returns the low-temperature, low-pressure refrigerant gas to the compressor.

[0035] The evaporator can achieve a cooling effect by utilizing the latent heat of evaporation of the refrigerant to exchange heat with the material to be cooled. In the entire cycle, the air conditioner 10 can adjust the temperature of the indoor space.

[0036] The outdoor unit 2 of the air conditioner 10 refers to a portion of a refrigeration cycle including a compressor and an outdoor heat exchanger, the indoor unit 1 of the air conditioner 10 includes an indoor heat exchanger, and an electronic expansion valve may be provided in the indoor unit 1 or the outdoor unit 2 .

[0037] The indoor heat exchanger and the outdoor heat exchanger function as a condenser or an evaporator. When the indoor heat exchanger functions as a condenser, the air conditioner 10 functions as a heater in a heating mode, and when the indoor heat exchanger functions as an evaporator, the air conditioner 10 functions as a cooler in a cooling mode.

[0038] The air conditioner 10 of the present invention includes an indoor unit 1 and an outdoor unit 2. The indoor unit 1 and the outdoor unit 2 can be configured as an integrated unit or a split unit. The indoor unit 1 can be configured as a wall-mounted unit, a ceiling unit, a duct unit, etc., and the indoor unit 1 is installed at the top or ceiling of the indoor room.

[0039] Reference Figure 1 Taking an indoor hanging machine as an example, the indoor hanging machine is usually installed at a location such as an indoor wall. For another example, an indoor cabinet machine (not shown in the figure) is also a form of the indoor machine 1 .

[0040] Taking a split unit as an example, the air conditioner 10 includes an indoor unit 1 and an outdoor unit 2, wherein the outdoor unit 2 is usually set outdoors for heat exchange with the indoor environment.

[0041] Furthermore, as shown in the figure, the air conditioner 10 includes a controller 71 for controlling the operation of various components within the air conditioner 10, thereby enabling the various components of the air conditioner 10 to operate and realize various predetermined functions of the air conditioner 10. Furthermore, the air conditioner 10 is also provided with a control device 200. For example, the control device 200 is specifically configured as a remote control that is capable of communicating with the controller 71 using, for example, infrared or other communication methods. The remote control is used by the user to control the air conditioner 10 in various ways, thereby enabling interaction between the user and the air conditioner 10.

[0042] The indoor unit 1 of the air conditioner 10 in the embodiment of the present invention is arranged at the top or upper part of the room. Generally speaking, the installation height of the indoor unit 1 is higher than the user activity area. The indoor unit 1 includes a return air inlet and an air outlet connected to the room. The indoor air passes through the indoor unit 1 in the return air inlet and flows back to the room through the air outlet.

[0043] The refrigerant circulation circuit of the present invention circulates refrigerant through a loop consisting of a compressor, condenser, electronic expansion valve, and evaporator. One of the condenser and evaporator functions as an outdoor heat exchanger, while the other functions as an indoor heat exchanger. The indoor heat exchanger exchanges heat with the air in indoor unit 1, while the outdoor unit 2 heat exchanger exchanges heat with the air in outdoor unit 2, thereby achieving the cooling or heating requirements of air conditioner 10.

[0044] The indoor unit 1 also includes an indoor fan, which is arranged near the return air port or the air outlet of the indoor heat exchanger and is used to deliver the heat-exchanged air into the room. The indoor fan includes multiple gears for changing the outlet air flow speed of the outlet.

[0045] An air guide plate is provided at the position of the air outlet. The air guide plate adjusts the outflow direction of the air flowing through the air outlet by changing the relative rotation angle between the air guide plate and the air outlet, thereby affecting the indoor air temperature stratification.

[0046] In the illustrated embodiment of the present invention, the air conditioner 10 further includes a controller 71. Controller 71 is a device that generates an operation control signal based on an instruction opcode and a timing signal, thereby instructing the air conditioner 10 to execute the control instruction. For example, in response to a power-on or power-off instruction received from a user, controller 71 may execute an operation associated with the object selected by the power-on or power-off instruction.

[0047] The embodiment of the present invention also provides a hardware structure diagram of a controller 71, as shown in FIG. Figure 2 As shown, the controller 71 includes a processor 83 and, optionally, a memory 82 and a communication interface 84 connected to the processor 83. The processor 83, the memory 82 and the communication interface 84 are connected via a bus 81.

[0048] The processor 83 may be a central processing unit (CPU), a general-purpose processor (GP), a network processor (NP), a digital signal processor (DSP), a microprocessor (MCU), a microcontroller (MCU), a programmable logic device (PLD), or any combination thereof. The processor 83 may also be any other device having processing functionality, such as a circuit, a device, or a software module. The processor 83 may also include multiple CPUs, and the processor 83 may be a single-core (single CPU) processor 83 or a multi-core (multi CPU) processor 83. The processor 83 herein may refer to one or more devices, circuits, or processing cores for processing data (e.g., computer program instructions).

[0049] The memory 82 may be a read-only memory 82 (ROM) or other type of static storage device that can store static information and instructions, a random access memory 82 (RAM) or other type of dynamic storage device that can store information and instructions, or an electrically erasable programmable read-only memory 82 (EEPROM), a compact disc read-only memory (CD ROM) or other optical disc storage, an optical disc storage (including a compact disc, laser disc, optical disc, digital versatile disc, Blu-ray disc, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and can be accessed by a computer. The embodiment of the present invention does not impose any restrictions on this. The memory 82 may exist independently or be integrated with the processor 83. The memory 82 may contain computer program code. The processor 83 is used to execute the computer program code stored in the memory 82, thereby implementing the air conditioner control method provided in the embodiment of the present invention.

[0050] The communication interface 84 can be used to communicate with other devices or communication networks (such as Ethernet, radio access network (RAN), wireless local area network (WLAN), etc.). The communication interface 84 can be a module, a circuit, a transceiver or any device that can achieve communication.

[0051] The bus 81 may be a peripheral component interconnect (PCI) bus 81 or an extended industry standard architecture (EISA) bus 81. The bus 81 may be divided into an address bus 81, a data bus 81, a control bus 81, etc. For ease of representation, Figure 2 Only one thick line is used in the figure, but it does not mean that there is only one bus 81 or one type of bus 81.

[0052] Reference below Figure 3-Figure 10 An air conditioner according to an embodiment of the present invention is described.

[0053] Figure 3 FIG. 1 is a schematic diagram of the structure of an air conditioner according to an embodiment of the present invention. Figure 3As shown, an air conditioner 10 includes: a refrigerant circulation loop 11, an indoor ambient temperature sensor 12, an outdoor ambient temperature sensor 13 and a controller 71.

[0054] Among them, the refrigerant circulation loop 11 allows the refrigerant to undergo a refrigeration cycle in the loop composed of a compressor, condenser, throttling component, and evaporator. One of the condenser and the evaporator is an outdoor heat exchanger, and the other is an indoor heat exchanger; the indoor ambient temperature sensor 12 is used to detect the indoor ambient temperature; the outdoor ambient temperature sensor 13 is used to detect the outdoor ambient temperature.

[0055] The controller 71 is configured to: obtain the set temperature, determine the set temperature difference according to the indoor ambient temperature and the set temperature; when the air conditioner starts the first cooling operation, use the preset neural network algorithm or the preset fitting algorithm to determine the estimated target frequency and the estimated stable frequency; or, when the air conditioner 10 starts the non-first cooling operation, obtain the estimated target frequency and the estimated stable frequency, the estimated target frequency and the estimated stable frequency are calculated by the controller 71 based on the operating data of the fuzzy frequency control stage of the air conditioner 10 in the last cooling operation, and the operating data include: the time when the compressor reaches the temperature for the first time, the stable operating frequency after the compressor reaches the temperature, the first cooling rate, the energy saving coefficient and A frequency revision coefficient based on fan speed calibration; each cooling operation of the air conditioner 10 includes an estimated frequency control stage and a fuzzy frequency control stage; the air conditioner 10 is controlled to execute the estimated frequency control stage of this cooling operation; the estimated frequency control stage includes: controlling the compressor to operate at an estimated target frequency; when the set temperature difference does not exceed the preset temperature difference threshold, the compressor is controlled to reduce from the estimated target frequency to the estimated stable frequency at a first frequency reduction rate, and after the compressor is controlled to operate at the estimated stable frequency for a first preset time, the air conditioner 10 is controlled to execute the fuzzy frequency control stage of this cooling operation; the fuzzy frequency control stage includes: controlling the compressor to operate at the estimated stable frequency.

[0056] Among them, the throttling element includes, for example, an expansion valve, a capillary tube, or a throttle valve; the estimated target frequency refers to the frequency value at which the air conditioner 10 needs to run in order to reach the set temperature, and the estimated stable frequency is the frequency at which the air conditioner 10 needs to maintain operation after the indoor ambient temperature reaches the set temperature and remains stable.

[0057] For example, the set temperature difference is recorded as E, the indoor ambient temperature is recorded as Tin, and the set temperature is recorded as Ts; the time when the compressor reaches the temperature for the first time is recorded as tw1, the stable operating frequency of the compressor after reaching the temperature is recorded as Fw, the first cooling rate is recorded as ε1, the energy saving coefficient is recorded as σ (σ>0), and the frequency revision coefficient based on the fan speed calibration is recorded as λ; the preset temperature difference threshold is recorded as E1; and the first preset time is recorded as t1.

[0058] In an embodiment, a set temperature Ts set by a user and an indoor environment temperature Tin are acquired, and a set temperature difference E is determined according to a difference between the indoor environment temperature Tin and the set temperature Ts, that is, E = Tin-Ts.

[0059] When the air conditioner 10 starts a first refrigeration operation, it is first determined whether the controller 71 supports using a preset neural network model to calculate the estimated target frequency and the estimated stable frequency, if yes, the preset neural network algorithm is used to calculate the estimated target frequency and the estimated stable frequency, if no, the preset fitting algorithm is used to calculate the estimated target frequency and the estimated stable frequency, so that the estimated target frequency and the estimated stable frequency can be flexibly calculated.

[0060] When the air conditioner 10 starts a non-first refrigeration operation, based on the operation data of the fuzzy frequency control phase of the air conditioner 10 in the last refrigeration operation, that is, the time tw1 when the compressor reaches the temperature for the first time during the last refrigeration operation, the stable operation frequency Fw after the compressor reaches the temperature, the first temperature drop rate ε1, the energy saving coefficient σ, and the frequency revision coefficient λ based on the fan speed calibration, the estimated target frequency and the estimated stable frequency for the current refrigeration operation are calculated.

[0061] Then, the controller 71 controls the compressor to operate at the estimated target frequency, and compares the set temperature difference E with a preset temperature difference threshold E1, when the set temperature difference E does not exceed the preset temperature difference threshold E1, that is, E E1, the compressor is controlled to decrease from the estimated target frequency to the estimated stable frequency at a first frequency reduction rate, for example, when the estimated target frequency for the current refrigeration operation is 50Hz and the estimated stable frequency is 14Hz, the compressor is controlled to decrease from 50Hz to 14Hz at a first frequency reduction rate of 2Hz / s, so that the indoor environment temperature can be gradually reduced, ensuring the stability and comfort of the indoor environment temperature.

[0062] Then the compressor is controlled to operate at the estimated stable frequency of 14Hz for a first preset time t1, for example, after operating for 160s, the air conditioner 10 is controlled to execute the fuzzy frequency control phase of the current refrigeration operation, ensuring that the user can enjoy a comfortable refrigeration experience, while improving energy efficiency and energy saving effect, thereby realizing efficient use of energy and user experience.

[0063] Among them, the first preset time is, for example, the time of 2 or more calculation periods of the fuzzy frequency control phase, for example, one calculation period is 40s, and the first preset time is 4 calculation periods, so the first preset time t1 is 160s.

[0064] In one embodiment of the present invention, when a preset neural network algorithm is used to determine the estimated target frequency and the estimated stable frequency, the controller 71 is configured to: determine a first temperature difference between the outdoor ambient temperature and the set temperature; input the first temperature difference, the set temperature difference and the outdoor ambient temperature into a preset neural network model, and output the initial operating frequency after calculation through the preset neural network algorithm; based on the initial operating frequency, control the air conditioner 10 to perform fuzzy frequency self-learning to obtain the estimated target frequency and the estimated stable frequency.

[0065] For example, the outdoor ambient temperature is recorded as Tout and the first temperature difference is recorded as Tout_Ts.

[0066] In the embodiment, the outdoor ambient temperature Tout is detected by the outdoor ambient temperature sensor 13 , and the difference between the outdoor ambient temperature Tout and the set temperature Ts is used as the first temperature difference Tout_Ts, that is, Tout_Ts=Tout−Ts.

[0067] like Figure 4 As shown, when the controller 71 is able to use the preset neural network model to calculate the estimated target frequency and the estimated stable frequency, the first temperature difference Tout-Ts, the set temperature difference E and the outdoor ambient temperature Tout are used as input parameters of the preset neural network model. The preset neural network algorithm inside the preset neural network model can calculate the initial operating frequency and record it as F0(n).

[0068] Afterwards, the controller 71 will control the air conditioner 10 to perform fuzzy frequency self-learning based on the initial operating frequency. During the fuzzy frequency self-learning process, the air conditioner 10 will gradually adjust and optimize the initial operating frequency based on the actual operating results and feedback, and then determine the estimated target frequency and the estimated stable frequency, so that the air conditioner 10 can more quickly and accurately meet the user's expected cooling needs during cooling operation, and maintain stable and efficient operation.

[0069] Due to the differences between laboratory conditions and the actual user's home environment, such as room structure, thermal insulation performance, human activities and other factors, after the preset neural network model is set in the air conditioner 10, further training and adjustment are required according to the actual operating status to ensure that the initial operating frequency can accurately match the specific environment and needs of the user's home, thereby optimizing the operating efficiency of the air conditioner 10 and achieving more comfortable and energy-saving indoor environment temperature control.

[0070] In one embodiment of the present invention, when a preset fitting algorithm is used to determine the estimated target frequency and the estimated stable frequency, the controller 71 is configured to: obtain a preset initial maximum operating frequency; determine a first temperature difference between the outdoor ambient temperature and the set temperature; determine a preset target frequency coefficient based on the set temperature difference, the outdoor ambient temperature and the first temperature difference; determine an iterative correction factor based on the energy-saving coefficient; obtain the initial operating frequency based on the preset target frequency coefficient, the iterative correction factor, the preset initial maximum operating frequency, the frequency correction coefficient based on the fan speed calibration, the outdoor ambient temperature, the set temperature difference and the first temperature difference; based on the initial operating frequency, control the air conditioner to perform fuzzy frequency self-learning to obtain the estimated target frequency and the estimated stable frequency.

[0071] In an embodiment, when the controller 71 is unable to use the preset neural network model to calculate the estimated target frequency and the estimated stable frequency, it will first obtain the preset initial maximum operating frequency, for example, recorded as Fmax, and detect the outdoor ambient temperature Tout through the outdoor ambient temperature sensor 13, and use the difference between the outdoor ambient temperature Tout and the set temperature Ts as the first temperature difference Tout_Ts, that is, Tout_Ts=Tout-Ts.

[0072] Then, a preset target frequency coefficient is calculated according to the set temperature difference E, the outdoor ambient temperature Tout, and the first temperature difference Tout_Ts. For example, the preset target frequency coefficient is recorded as ξ(E, Tout, Tout_Ts).

[0073] An iterative correction factor is determined based on the energy-saving coefficient σ, so as to facilitate fine-tuning of the estimated target frequency in subsequent calculations to achieve better energy efficiency performance. For example, the iterative correction factor is denoted as β(n).

[0074] For example, in the process of calculating the iterative correction factor β(n), multiple energy-saving coefficients σ are obtained, and the multiple energy-saving coefficients σ are respectively recorded as σ(0), σ(1), σ(2), ..., σ(n), where n is a natural number greater than or equal to 1. Therefore, the product of the multiple energy-saving coefficients σ is used as the iterative correction factor β(n), that is, β(n)=σ(0)×σ(1)×σ(2)× ×σ(n), where σ(0)=1.0.

[0075] After obtaining the preset target frequency coefficient ξ(E, Tout, Tout_Ts) and the iterative correction factor β(n), the initial operating frequency is obtained by combining the preset initial maximum operating frequency Fmax, the frequency correction coefficient λ based on the fan speed calibration, the outdoor ambient temperature Tout, the set temperature difference E and the first temperature difference Tout_Ts. Subsequently, the controller 71 will use the initial operating frequency to start the air conditioner 10 and perform a fuzzy frequency self-learning process. During the fuzzy frequency self-learning process, the air conditioner 10 will also further adjust and optimize the initial operating frequency based on the actual operating conditions and feedback, and finally determine the estimated target frequency and the estimated stable frequency. Ensure that the air conditioner 10 can quickly and accurately achieve the user's expected cooling effect when the cooling is started, and maintain an efficient and energy-saving operating state.

[0076] In addition, in a specific embodiment, when obtaining a preset fitting algorithm, the advantages of laboratory testing and predicted neural network models will be combined, aiming to derive a preset fitting algorithm for the empty initial operating frequency applicable to a wider range of working conditions through limited laboratory test working condition data.

[0077] First, a preset neural network algorithm is trained on limited test conditions in the laboratory. This not only allows direct prediction of the initial operating frequency under these test conditions, but also allows for estimation of untested conditions, significantly increasing the amount of data available for analysis. Next, based on the actual parameters obtained from these laboratory tests and the parameters predicted by the preset neural network algorithm, a fitting method is used to construct a functional relationship between the estimated initial frequency and the set temperature difference E, the outer ring temperature Tout, and the outdoor and indoor temperature difference Tout_Ts. This yields a preset fitting algorithm. In this preset fitting algorithm, for example, the initial operating frequency is denoted as F0(n). The initial operating frequency F0(n) is obtained by multiplying the product of the initial estimated frequency coefficient ξ(E, Tout, Tout_Ts), the frequency correction coefficient λ based on fan speed calibration, and the iterative correction factor β(n), and then multiplying it by the preset initial maximum operating frequency Fmax. The initial operating frequency F0(n) is limited to between the preset minimum operating frequency Fmin and the preset maximum operating frequency Fmax. The preset minimum operating frequency Fmin can be directly obtained. That is: F0(n)= ξ(E,Tout,Tout_Ts) ×λ×β(n)×Fmax, Fmin≤F0(n)≤Fmax(1) The initial estimated frequency coefficient ξ(E, Tout, Tout_Ts) is a linear function of the set temperature difference E, the outdoor ambient temperature Tout, and the first temperature difference Tout_Ts, and is determined by coefficients k1, k2, k3, and k4, where k1, k2, k3, and k4 are known quantities. That is: ξ(E,Tout,Tout_Ts)=(k1×E(n)+k2×Tout(n)+k3× Tout_Ts(n)+k4)(2) The iterative correction factor β(n) is used to gradually adjust the initial estimated frequency F0(n) in actual applications to make it closer to the actual situation. Finally, by substituting the specific set temperature difference E, the outdoor ambient temperature Tout, and the first temperature difference Tout_Ts into (1) and (2), the initial operating frequency F0(n) can be calculated.

[0078] In one embodiment of the present invention, when the air conditioner is controlled to perform fuzzy frequency self-learning based on the initial operating frequency to obtain an estimated target frequency and an estimated stable frequency, the controller 71 is configured to: control the compressor to operate according to the initial operating frequency, and obtain multiple historical operating data obtained by the air conditioner 10 performing multiple fuzzy frequency self-learning, wherein the historical operating data include: the first historical temperature-reaching time, the second historical temperature-reaching time, the historical stable operating frequency after the compressor reaches temperature, and the frequency integral of the compressor from startup to the second historical temperature-reaching time of the compressor; averaging the historical stable operating frequencies of multiple compressors after reaching temperature to obtain the estimated stable frequency; obtaining multiple corresponding target operating frequencies based on multiple historical operating data, and averaging the multiple target operating frequencies to obtain the estimated target frequency.

[0079] Specifically, when the air conditioner 10 performs fuzzy frequency self-learning, it controls the compressor to operate according to the initial operating frequency F0(n), and in this process, obtains multiple historical operating data obtained by the air conditioner 10 performing multiple fuzzy frequency self-learning, wherein each time the fuzzy frequency self-learning is performed, the initial operating frequency F0(n) remains unchanged. For example, in the specific fuzzy frequency self-learning process, the obtained initial operating frequency is recorded as F0(x,y,z)(1).

[0080] For example, the temperature range determined by the obtained set temperature difference E, the outdoor ambient temperature Tout, and the first temperature difference Tout_Ts is recorded as the target temperature range. When the target temperature range is cooled for the first time, the initial operating frequency of the target temperature is obtained as F0(x,y,z)(1). The controller 71 controls the compressor to operate according to the initial operating frequency F0(x,y,z)(1). Then, the first historical time to reach the temperature is recorded as tw1(x,y,z)(1), the second historical time to reach the temperature is recorded as tw2(x,y,z)(1), the historical stable operating frequency after the compressor reaches the temperature is recorded as Fw_1(x,y,z)(1), and the frequency integral of the compressor from startup to the second historical time to reach the temperature is recorded as ∫[0, tw2(x,y,z)(1)]F(t)dt.

[0081] When the target temperature range is cooled for the second time, the compressor is also controlled to operate according to the initial operating frequency F0(x,y,z)(1). The time of the first historical temperature reaching is recorded as tw1(x,y,z)(2), the time of the second historical temperature reaching is recorded as tw2(x,y,z)(2), the historical stable operating frequency of the compressor after reaching the temperature is recorded as Fw_1(x,y,z)(2), and the frequency integral of the compressor from startup to the time of the second historical temperature reaching is recorded as ∫[0, tw2(x,y,z)(2)]F(t)dt.

[0082] By analogy, historical operation data of s times of fuzzy frequency self-learning can be obtained, where s is a natural number greater than or equal to 1.

[0083] When the target temperature range is cooled for the sth time, the compressor is controlled to operate according to the initial operating frequency F0(x,y,z)(1). Then, the first historical temperature-reaching time is recorded as tw1(x,y,z)(s), the second historical temperature-reaching time is recorded as tw2(x,y,z)(s), the historical stable operating frequency of the compressor after reaching the temperature is recorded as Fw_1(x,y,z)(s), and the frequency integral of the compressor from startup to the second historical temperature-reaching time is recorded as ∫[0, tw2(x,y,z)(s)]F(t)dt.

[0084] Thus, s historical operating data are obtained, and the historical stable operating frequencies of the s compressors after reaching temperature are averaged to obtain the estimated stable frequency. The estimated stable frequency is recorded as Fw_yc(x,y,z)(2), that is, Fw_yc(x,y,z)(2)=[(Fw_1(x,y,z)(1)+ Fw_1(x,y,z)(2) +…+ Fw_1(x,y,z)(s)] / s.

[0085] At the same time, s corresponding target operating frequencies are obtained based on the s historical operating data. For example, the s target operating frequencies are respectively recorded as F0_1(x,y,z)(1), F0_1(x,y,z)(2), ..., F0_1(x,y,z)(s), and the average of the s target operating frequencies is obtained to obtain the estimated target frequency. The estimated target frequency is recorded as F0(x,y,z)(2), that is, F0(x,y,z)(2)=[(F0_1(x,y,z)(1)+F0_1(x,y,z)(2)+…+ F0_1(x,y,z)(s)] / s.

[0086] This ensures the accuracy of the estimated stable frequency Fw_yc(x,y,z)(2) and the estimated target frequency F0(x,y,z)(2), allowing the air conditioner 10 to be adjusted according to the actual operating environment, achieving the goals of efficient cooling and energy saving.

[0087] In one embodiment of the present invention, when multiple corresponding target operating frequencies are obtained based on multiple historical operating data, the controller 71 is configured to: determine a first cooling rate based on a set temperature difference and the time for the first historical temperature to be reached; determine an energy-saving coefficient based on the first cooling rate, wherein there is a pre-calibrated correspondence between the first cooling rate and the energy-saving coefficient; obtain multiple corresponding target operating frequencies based on multiple frequency integrals and their corresponding multiple energy-saving coefficients and multiple second historical times for reaching the temperature.

[0088] In the embodiment, during the fuzzy frequency self-learning process, when the target temperature range is cooled for the first time, the first cooling rate ε1(1) is obtained according to the ratio of the set temperature difference E and the first historical temperature-reaching time tw1(x,y,z)(1), that is, ε1(1)=E / tw1(x,y,z)(1), and then Table 1 is queried to determine the energy-saving coefficient σ(x,y,z)(1) based on the first cooling rate ε1(1). Then, according to the obtained frequency integral ∫[0, tw2(x,y,z)(1)]F(t)dt, the energy-saving coefficient σ(x,y,z)(1) and the second historical temperature-reaching time tw2(x,y,z)(1), the target operating frequency F0_1(x,y,z)(1) is calculated, that is, F0_1(x,y,z)(1)=∫[0, tw2(x,y,z)(1)]F(t)dt×σ(x,y,z)(1) / tw2(x,y,z)(1).

[0089] Similarly, when the target temperature range is cooled for the second time, the first cooling rate ε1(2) will be obtained according to the ratio of the set temperature difference E and the first historical temperature reaching time tw1(x,y,z)(2), that is, ε1(2)=E / tw1(x,y,z)(2). Then, by querying Table 1, the energy saving coefficient σ(x,y,z)(2) can be determined based on the first cooling rate ε1(2). Then, according to the obtained frequency integral ∫[0, tw2(x,y,z)(2)]F(t)dt, the energy saving coefficient σ(x,y,z)(2) and the second historical temperature reaching time tw2(x,y,z)(2), the target operating frequency F0_1(x,y,z)(2) is calculated, that is, F0_1(x,y,z)(2)=∫[0, tw2(x,y,z)(2)]F(t)dt×σ(x,y,z)(2) / tw2(x,y,z)(2).

[0090] Similarly, when the target temperature range is cooled for the sth time, the first cooling rate ε1(s) will be obtained according to the ratio of the set temperature difference E and the first historical temperature-reaching time tw1(x,y,z)(s), that is, ε1(s)=E / tw1(x,y,z)(s). Then, by querying Table 1, the energy-saving coefficient σ(x,y,z)(s) can be determined based on the first cooling rate ε1(s). Then, according to the obtained frequency integral ∫[0, tw2(x,y,z)(s)]F(t)dt, the energy-saving coefficient σ(x,y,z)(s) and the second historical temperature-reaching time tw2(x,y,z)(s), the target operating frequency F0_1(x,y,z)(s) is calculated, that is, F0_1(x,y,z)(s)=∫[0, tw2(x,y,z)(s)]F(t)dt×σ(x,y,z)(s) / tw2(x,y,z)(s).

[0091] In a specific embodiment, a relationship between the frequency integral, the energy-saving coefficient, the second historical temperature-reaching time, and the target operating frequency can be obtained by deduction.

[0092] Specifically, when the air conditioner 10 reaches the set temperature, the room load ƒ(Tin, Rh, Tout) is equal to the output capacity Q of the air conditioner 10. 空调 , that is, Q 空调 = ƒ(Tin,Rh,Tout) = ƒ(Ts,Rhs,Tout) (Equation 1). Here, Ts is the set temperature, Rhs is the set relative humidity, Tin is the indoor ambient temperature, and Rh is the real-time relative humidity. When the indoor ambient temperature reaches the set temperature (Ts ≈ Tin), the set relative humidity Rhs is approximately equal to the relative humidity Rh, and RHs ≈ Rh.

[0093] The total load integral J required for the air conditioner 10 to reach the set temperature Ts within the time tw 房间 =∫[0,tw2]Q(t)dt, controls the compressor to run at a fixed frequency F0 for the estimated load J of tw2 time 空调 =Q(F0)×tw2. When the two are equal, that is, ∫[0,tw2]Q(t)dt=Q(F0)×tw2 (Formula 2), where Q(t) is the load of the air conditioner 10; Q(F0) is the load required for the compressor to run at a fixed frequency of F0 for tw2 time; tw is the total time required for the air conditioner 10 to start running and reach the set temperature; tw2 is the time from the air conditioner 10 starting running to the second time reaching the set temperature in the fuzzy frequency control stage, where tw tw2.

[0094] Assuming that air conditioner 10 has the same output capacity and energy efficiency ratio per unit frequency, the load Q(t) of air conditioner 10 can be considered a function of frequency F(t). Equation 2 can be equivalent to the frequency integral: ∫[0,tw²]F(t)dt×σ = F0(x,y,z)(n)×tw² (Equation 3). Here, ∫[0,tw]F(t)dt is the frequency integral of historical operating data during the fuzzy frequency control phase, which can be calculated using a proprietary AI (artificial intelligence) chip or a flash memory chip with equivalent computing power. F0(x,y,z)(n) is the estimated target frequency.

[0095] It's worth noting that during the fuzzy frequency control phase, the time it takes for the indoor ambient temperature to reach the setpoint for the first time is recorded as the first temperature-reaching time. Due to temperature overshoot, the indoor ambient temperature may fall below the setpoint, then rise again, reaching the setpoint for the second time. This is recorded as the second temperature-reaching time. If the indoor ambient temperature takes a long time to reach the setpoint, there's no overshoot, and the first temperature-reaching time is approximately equal to the second temperature-reaching time.

[0096] σ is the energy saving coefficient, dimensionless quantity, σ>0. First, run at the estimated target frequency F0(x,y,z)(n), and after reaching the set temperature Ts, switch to the fuzzy frequency control stage to continue fine temperature control. The initial frequency of the fuzzy frequency control stage after switching is the frequency recorded in the history to achieve stable operation at the set temperature. Figure 5 As shown in the figure, when σ=1, the energy saving generated is the energy saving generated by the transition from the fuzzy frequency control stage to the estimated frequency control stage. If σ<1, the lower the calculated estimated target frequency F0(x,y,z)(n), the energy saving generated also includes a further reduction in the estimated target frequency F0(x,y,z)(n), and the operating energy efficiency ratio is further improved. Therefore, the energy saving will be more obvious, but the first cooling rate ε1 will be reduced to a certain extent, and the user comfort will also be reduced to a certain extent.

[0097] Table 1 is a table showing the correspondence between the pre-calibrated first cooling rate and the energy-saving coefficient.

[0098]

[0099] Table 1 In one embodiment of the present invention, when obtaining the estimated target frequency, the controller 71 is configured to: obtain the estimated target frequency of the air conditioner 10 in the fuzzy frequency control stage of the last cooling operation; and obtain the estimated target frequency of the air conditioner 10 when performing this cooling operation based on the product of the estimated target frequency of the fuzzy frequency control stage of the air conditioner 10 in the last cooling operation, the energy-saving coefficient and the frequency revision coefficient calibrated based on the fan speed.

[0100] For example, the estimated target frequency of the fuzzy frequency control stage of the air conditioner 10 in the last cooling operation is recorded as F0(x,y,z)(n-1), and the estimated target frequency of the air conditioner 10 when performing the current cooling operation is recorded as F0(x,y,z)(n). Then, according to the energy-saving coefficient σ and the frequency correction coefficient λ based on the fan speed calibration, the estimated target frequency F0(x,y,z)(n-1) of the fuzzy frequency control stage of the air conditioner 10 in the last cooling operation, the energy-saving coefficient σ and the frequency correction coefficient based on the fan speed calibration are calculated. The product of λ and σ can be used to obtain the estimated target frequency F0(x,y,z)(n) of the air conditioner 10 during the current cooling operation, i.e., F0(x,y,z)(n)=F0(x,y,z)(n-1)×σ×λ. This process ensures that when the air conditioner 10 performs the estimated target frequency F0(x,y,z)(n) during the current cooling operation, the previous operating data of the air conditioner 10 is taken into account while also incorporating the effects of energy efficiency optimization and fan speed adjustment, thereby achieving efficient cooling control and an optimal balance between cooling efficiency and energy consumption. Here, n is a natural number greater than 1.

[0101] In one embodiment of the present invention, when obtaining the estimated stable frequency, the controller 71 is configured to: obtain the stable operating frequency of the compressor of the air conditioner 10 after reaching the temperature in multiple fuzzy frequency control stages before performing the current cooling operation; and calculate the average of the stable operating frequencies of the multiple compressors after reaching the temperature to obtain the estimated stable frequency.

[0102] In an embodiment, the controller 71 will collect the stable operating frequencies of the compressors after reaching temperature, which are obtained in multiple fuzzy frequency control stages before the air conditioner 10 performs the current cooling operation, wherein reaching temperature means reaching the set temperature, and then obtaining the frequency required for the compressor to operate stably under different working conditions and conditions. The controller 71 will calculate the stable operating frequencies of these compressors after reaching temperature, that is, obtain the average value of the stable operating frequencies of these compressors after reaching temperature, so as to estimate the stable operating frequency that the compressor may reach when performing the current cooling operation, that is, the estimated stable frequency. This can improve the accuracy of the estimated stable frequency, which helps the air conditioner 10 maintain stable operation of the compressor while achieving efficient cooling.

[0103] For example, before executing this cooling operation, the controller 71 obtains the stable operating frequencies of the three compressors after reaching temperature in the fuzzy frequency control stage, and records them as Fw(1), Fw(2) and Fw(3) respectively, and records the estimated stable frequency as Fw, that is, Fw=[Fw(1)+Fw(2)+Fw(3)] / 3.

[0104] In one embodiment of the present application, when the estimated stable frequency is obtained, the controller 71 is configured to: obtain the stable operating frequencies of the compressor after the compressor reaches a temperature in a plurality of fuzzy frequency control stages before the air conditioner performs the current refrigeration operation; and obtain the estimated stable frequency by taking a weighted average of the stable operating frequencies of the compressor after the compressor reaches a temperature.

[0105] In an embodiment, the controller 71 collects the stable operating frequencies of the compressor after the compressor reaches a temperature in a plurality of fuzzy frequency control stages before the air conditioner 10 performs the current refrigeration operation, and then obtains the frequency required for the compressor to stably operate under different conditions and conditions. The controller 71 calculates the stable operating frequencies of the compressor after the compressor reaches a temperature, that is, obtains the weighted average of the stable operating frequencies of the compressor after the compressor reaches a temperature by using the weighted moving average method, so as to estimate the stable operating frequency that the compressor can reach when performing the current refrigeration operation, that is, the estimated stable frequency. This can improve the accuracy of the estimated stable frequency and help the air conditioner 10 to achieve efficient refrigeration while maintaining stable operation of the compressor.

[0106] For example, before performing the current refrigeration operation, the controller 71 obtains three stable operating frequencies of the compressor after the compressor reaches a temperature in the fuzzy frequency control stage, and records them as Fw(1), Fw(2) and Fw(3), respectively. The weighted coefficients corresponding to the stable operating frequencies of the compressor after the compressor reaches a temperature Fw(1), Fw(2) and Fw(3) are denoted as η1, η2 and η3, respectively, where η1<η2<η3. The estimated stable frequency is denoted as Fw, that is, Fw=[η1×Fw(1)+η2×Fw(2)+η3×Fw(3)] / (η1+η2+η3).

[0107] In one embodiment of the present application, when the air conditioner 10 performs the estimated frequency control stage of the current refrigeration operation, the controller 71 is further configured to: obtain a second temperature drop rate of the compressor in the estimated frequency control stage; and correct the estimated target frequency according to the estimated target frequency, the energy saving coefficient and the frequency revision coefficient based on the fan speed calibration when the second temperature drop rate does not exceed a preset temperature drop rate threshold.

[0108] In an embodiment, in the estimated frequency control stage, the controller 71 gradually approaches an accurate estimated target frequency by iteration to ensure that the operating efficiency of the compressor matches the refrigeration demand and realizes stable operation. However, considering that the air conditioner 10 may experience load mutation in actual operation, for example, the user opens the window or the number of people in the room increases, which may cause the actual temperature drop rate to be slower than the expected temperature drop rate, so that the indoor environment temperature cannot reach the set temperature for a long time.

[0109] To cope with this situation, a second cooling rate is calculated based on the initial indoor ambient temperature, the compressor operating time, and the indoor ambient temperature, so as to correct the estimated target frequency based on the comparison result between the second cooling rate and the preset cooling rate threshold.

[0110] For example, the initial indoor ambient temperature is recorded as Tin0, the operating time of the compressor is recorded as t, the second cooling rate is recorded as ε2, and the estimated target frequency is recorded as F0(x, y, z)(n).

[0111] like Figure 6 As shown, if the calculated second cooling rate ε2 is obviously lower and does not exceed the preset cooling rate threshold, wherein the preset cooling rate threshold is, for example, 0.062°C / min, that is, when ε2≤0.062°C / min, the estimated target frequency needs to be immediately increased, specifically according to the estimated target frequency F0(x,y,z)(n), the energy-saving coefficient σ and the frequency correction coefficient λ based on the fan speed calibration, the estimated target frequency F0(x,y,z)(n) is corrected, that is, according to the ratio of the energy-saving coefficient σ of the estimated target frequency F0(x,y,z)(n) and the frequency correction coefficient λ based on the fan speed calibration, that is, according to the ratio of F0(x,y,z)(n) / σ / λ The target frequency F0(x,y,z)(n) is corrected, so that the estimated target frequency F0(x,y,z)(n) can be increased, and when the wind speed is set to low wind or medium wind, the estimated target frequency F0(x,y,z)(n) will be restored to the value corresponding to the high wind mode. At this time, the estimated target frequency F0(x,y,z)(n) will be corrected according to the product of the energy-saving coefficient σ of the estimated target frequency F0(x,y,z)(n) and the frequency revision coefficient λ based on the fan speed calibration to ensure that the air conditioner 10 can respond quickly to load changes, speed up the cooling speed, and make the indoor ambient temperature Tin reach the set temperature Ts as soon as possible without affecting the comfort.

[0112] In one embodiment of the present invention, when obtaining the second cooling rate of the compressor in the estimated frequency control stage, the controller 71 is configured to: obtain the initial indoor ambient temperature and the operating time of the compressor; and determine the second cooling rate based on the initial indoor ambient temperature, the operating time of the compressor and the indoor ambient temperature.

[0113] In an embodiment, after the compressor has been running for a period of time, the indoor ambient temperature Tin is detected in real time. According to the ratio of the difference between the initial indoor ambient temperature Tin0 and the indoor ambient temperature Tin to the compressor running time t2, the second cooling rate ε2 can be calculated, that is, ε2 = (Tin0-Tin) / t2. In this way, the second cooling rate ε2 can be calculated in real time based on the real-time detected indoor ambient temperature Tin, and then the estimated target frequency F0(x,y,z)(n) is corrected in real time according to the second cooling rate ε2, thereby improving the accuracy of the estimated target frequency F0(x,y,z)(n).

[0114] In summary, for example, in Example 1, the preset initial maximum operating frequency Fmax = 85 Hz, the preset initial minimum operating frequency Fmin = 10 Hz, the set temperature Ts = 26°C, s = 2 times, the preset temperature difference threshold E1 = 1.0°C, the calculation period of the fuzzy frequency control stage is 40 seconds, and the frequency correction coefficient λ based on the fan speed calibration is λ = 1.00 at high wind speed, λ = 1.02 at medium wind speed, and λ = 1.05 at low wind speed. Among them, the estimated target frequency F0(x,y,z)(n) is between the preset maximum operating frequency Fmax and the preset minimum operating frequency Fmin, that is, Fmin ≤ F0(x,y,z)(n) ≤ Fmax.

[0115] When the controller 71 supports the use of a preset neural network model for calculation, when the air conditioner 10 is turned on for cooling operation, the set temperature Ts is 26°C, the wind speed is high, the indoor ambient temperature Tin is detected to be 31°C, and the outdoor ambient temperature Tout is detected to be 34°C. The set temperature difference E is calculated to be 31-26=5°C, and the first temperature difference Tout_Ts is calculated to be 34-26=8°C. The three parameters of the outdoor ambient temperature Tout, the set temperature difference E, and the first temperature difference Tout_Ts are input into the preset neural network model. The initial operating frequency output by the preset neural network algorithm is recorded as F0(34,5,8)(1), for example, and F0(34,5,8)(1)=81Hz.

[0116] When the air conditioner 10 starts cooling operation for the first time, the air conditioner 10 is controlled to perform the first fuzzy frequency self-learning, and the compressor is controlled to operate according to the initial operating frequency F0(34,5,8)(1)=81Hz, and the room temperature is controlled according to the fuzzy frequency. When the compressor is started for 25 minutes, the indoor ambient temperature Tin reaches the set temperature Ts for the first time, that is, Tin=Ts=26℃, and the time for the compressor to reach the temperature for the first time is tw1(34,5,8)(1)=25min; the compressor continues to run, and the indoor ambient temperature Tin reaches the set temperature Ts for the second time, that is, when Tin=Ts=26℃, the time for the compressor to reach the temperature for the second time is tw2(34,5,8)(1)=30min; the stable frequency of the compressor after reaching the temperature is Fw_1(34,5,8)(1)=15Hz, and the time for the compressor to reach the temperature from the start is calculated. The frequency integral from the start (i.e., time 0) to 30 minutes is ∫[0,30]F(t)dt=1800Hz·min. At the same time, the first cooling rate ε1(1) is calculated based on the first historical temperature-reaching time tw1(34,5,8)(1)=25min and the set temperature difference E=5℃, i.e., ε1(1)=5 / 25=0.2℃ / min. According to Table 1, the energy-saving coefficient σ(34,5,8)(1)=0.8, and the target operating frequency F0_1(34,5,8)(1)=1800 / 30×0.8=48Hz is calculated.

[0117] Similarly, the fuzzy frequency self-learning is performed for the second time, that is, in the fuzzy frequency self-learning stage, when the second cooling operation is executed, the compressor is also controlled to operate according to the initial operating frequency F0(34,5,8)(1)=81Hz, and the room temperature is controlled according to the fuzzy frequency. When the compressor is started for 25 minutes, the indoor ambient temperature Tin reaches the set temperature Ts for the first time, that is, Tin=Ts=26℃, and the time for the compressor to reach the temperature for the first time is tw1(34,5,8)(2)=20min; the compressor continues to run, and the indoor ambient temperature Tin reaches the set temperature Ts for the second time, that is, when Tin=Ts=26℃, the time for the second time to reach the temperature is tw2(34,5,8)(2)=25min; the stable frequency of the compressor after reaching the temperature is Fw_1(34,5,8)(2)=13Hz, and the compressor is calculated. The frequency integral ∫[0,25]F(t)dt=1600Hz·min from startup (i.e., time 0) to 25 minutes is calculated based on the first historical temperature-reaching time tw1(34,5,8)(2)=20min and the set temperature difference E=5℃. The first cooling rate ε1(2) is calculated as ε1(2)=5 / 20=0.25℃ / min. According to Table 1, the energy-saving coefficient σ(34,5,8)(2)=0.8, and the target operating frequency F0_1(34,5,8)(2)=1600 / 25×0.8≈51Hz is obtained.

[0118] During the third cooling operation, it will enter the estimated frequency control stage.

[0119] In the estimated frequency control stage, the estimated target frequency for this cooling operation will be calculated based on the historical operating data of the fuzzy frequency control stage. Specifically, the average of the target operating frequencies obtained during the first two fuzzy frequency self-learnings will be calculated and used as the estimated target frequency F0(34,5,8)(2), that is, F0(34,5,8)(2)=(F0_1(34,5,8)(1)+F0_1(34,5,8)(2)) / 2≈50Hz. At this time, the compressor is controlled to operate at the estimated target frequency of 50Hz. The estimated stable frequency will be determined based on the historical stable operating frequencies of the compressor after reaching temperature obtained from the first two fuzzy frequency self-learnings, that is, Fw_yc(34,5,8)(2)=(Fw_1(34,5,8)(1)+Fw_1(34,5,8)(2)) / 2=14Hz.

[0120] Air conditioner 10 is then controlled to execute the estimated frequency control phase of this cooling operation, i.e., the compressor is controlled to operate at a constant 50 Hz. Simultaneously, the set temperature difference E is compared with a preset temperature difference threshold E1. When E ≤ 1.0°C, the compressor is controlled to reduce its frequency from 50 Hz to 14 Hz at a first frequency reduction rate of 2 Hz / s. After the compressor is controlled to operate at 14 Hz for a first preset time t1, i.e., after operating at 14 Hz for four fuzzy frequency calculation cycles (4 × 40 seconds = 160 seconds), air conditioner 10 is controlled to execute the fuzzy frequency control phase of this cooling operation, i.e., operate at the estimated stable frequency of 14 Hz.

[0121] When the air conditioner 10 starts cooling operation again, the estimated target frequency and estimated stable frequency of the cooling operation will be calculated based on the estimated target frequency and estimated stable frequency of the last cooling operation. For example, when the compressor is started and runs for 35 minutes, the indoor ambient temperature Tin reaches the set temperature Ts for the first time, that is, Tin=Ts=26℃, and the time to reach the temperature for the first time is 35 minutes. The actual stable operating frequency of the compressor after reaching the temperature is 12Hz. The first cooling rate ε1(3) is calculated based on the set temperature difference E and the time to reach the temperature for the first time, that is, ε1(3)=5 / 35≈0.14℃ / min. According to Table 1, the energy saving coefficient σ(5,3)(2)=0.87, based on the estimated target frequency of the last cooling operation of 50Hz and the energy saving coefficient σ(5,3)(2) and the fan speed. The frequency correction coefficient λ of the speed calibration (i.e., λ=1.00 at high wind speed) is used to determine the estimated target frequency F0(34,5,8)(3) for this cooling operation, i.e., F0(34,5,8)(3)=50×0.87×1≈44Hz; and the estimated stable frequency Fw_yc(34,5,8)(3) for this cooling operation is determined based on the average of the estimated stable frequency 14Hz obtained in the previous cooling operation and the stable operating frequency 12Hz after the compressor reaches temperature in this cooling operation, i.e., Fw_yc(34,5,8)(3)=(14+12) / 2=13Hz. The compressor is then controlled to operate at 44Hz.

[0122] During a new cooling operation, the compressor will be controlled to operate at the estimated target frequency of 44Hz for this cooling operation and remain unchanged. At the same time, the set temperature difference E will be compared with the preset temperature difference threshold E1. When E≤1.0℃, the compressor will be controlled to reduce the frequency from 44Hz to 13Hz at a first frequency reduction rate of 2Hz / s. After the compressor is controlled to operate at 13Hz for the first preset time t1, that is, after the compressor is controlled to operate at 13Hz for 4 fuzzy frequency calculation cycles (4×40s=160s), the air conditioner 10 will be controlled to execute the fuzzy frequency control stage of this cooling operation, that is, to operate at the estimated stable frequency of 13Hz.

[0123] After the compressor starts and runs for 51 minutes, the indoor ambient temperature Tin reaches the set temperature Ts for the first time, that is, Tin=Ts=26℃. The time it takes to reach the temperature for the first time is 51 minutes. The actual stable operating frequency of the compressor after reaching the temperature is 13Hz. The first cooling rate ε1(4) is calculated based on the set temperature difference E and the time it takes to reach the temperature for the first time, that is, ε1(4)=5 / 51≈0.10℃ / min. According to Table 1, the energy saving coefficient σ(34,5,8)(4)=1.00, and the estimated target frequency F0(34,5,8)(3) is calculated, that is, F0(34,5,8)(3)=44×1.00×1≈44Hz; the estimated temperature frequency Fw_yc(34,5,8)(3)=12Hz. The compressor is then controlled to run at 44Hz.

[0124] After n iterations of operation, air conditioner 10 estimates the target frequency F0(34,5,8)(n) = 46 Hz, and the calculated estimated stable frequency Fw_yc(34,5,8)(n) = 13 Hz. When air conditioner 10 starts cooling operation, the compressor is controlled to operate at the estimated target frequency of 46 Hz and remains constant. After the compressor has been running for 25 minutes, the second cooling rate ε2 is detected in real time. For example, if the initial indoor ambient temperature Tin0 = 31°C and the indoor ambient temperature Tin = 29.5°C, then ε2 = (Tin0 - Tin) / t = (31 - 29.5) / 25 ≈ 0.06°C / min. The estimated target frequency F0(34,5,8)(n) is significantly low, so it needs to be adjusted upward. Specifically, the estimated target frequency is corrected based on F0(x,y)(n) / σ / λ, resulting in an estimated target frequency of 46 × 1.15 × 1 ≈ 53 Hz.

[0125] At the same time, the set temperature difference E is compared with the preset temperature difference threshold E1. When E≤E1=1.0℃, the compressor is controlled to reduce the frequency from 53Hz to 13Hz at the first frequency reduction rate of 2Hz / s and maintain the first operation time t1, that is, after 4 fuzzy frequency calculation cycles 4×40s=160s, it switches to the fuzzy frequency control stage, that is, the compressor is controlled to operate at 13Hz. For example, when the compressor runs for 52 minutes after startup, the indoor ambient temperature Tin reaches the set temperature Ts for the first time, that is, Tin=Ts=26℃, and the time it takes to reach the temperature for the first time is 52 minutes. The actual stable operating frequency of the compressor after reaching the temperature is 13Hz. The first cooling rate ε1(n) is calculated based on the set temperature difference E and the time it takes to reach the temperature for the first time, that is, ε1(n)=5 / 52≈0.097℃ / min. According to Table 1, the energy saving coefficient σ(34,5,8)(n)=1.00. Based on the estimated target frequency of 50Hz and the energy saving coefficient σ(34,5 ,8)(n) and the frequency correction factor λ based on fan speed calibration (i.e., λ = 1.00 at high wind speed) are used to determine the estimated target frequency F0(34,5,8)(n) for this cooling operation. That is, F0(34,5,8)(n) = 53×1×1 ≈ 53 Hz. Furthermore, the estimated stable frequency Fw_yc(34,5,8)(n) for this cooling operation is determined based on the average of the estimated stable frequencies obtained from previous cooling operations and the stable operating frequency of 13 Hz after the compressor reaches temperature in this cooling operation. That is, Fw_yc(34,5,8)(n) ≈ 13 Hz. The compressor then operates at 53 Hz.

[0126] At the same time, in the process of continuously calculating the estimated target frequency in a loop, the parameters in the preset neural network model will be continuously updated, and then the initial operating frequency will be continuously updated, thereby improving the accuracy of the estimated target frequency calculation.

[0127] Example 2: The preset initial maximum operating frequency Fmax = 85 Hz, the preset initial minimum operating frequency Fmin = 10 Hz, the set temperature Ts = 26°C, s = 2 times, the compressor operating time t2 = 25 minutes, the preset temperature difference threshold E1 = 0.5°C, the calculation period of the fuzzy frequency control stage is 40 seconds, and the frequency correction coefficient λ based on fan speed calibration is λ = 1.00 at high wind speed, λ = 1.02 at medium wind speed, and λ = 1.05 at low wind speed. k1 = 0.086, k2 = 0.039, k3 = 0.058, and k4 = -0.838. The estimated target frequency F0(x,y,z)(n) is between the preset maximum operating frequency Fmax and the preset minimum operating frequency Fmin, that is, Fmin ≤ F0(x,y,z)(n) ≤ Fmax.

[0128] When the controller 71 does not support the use of the preset neural network model for calculation, when the air conditioner 10 starts cooling operation, the set temperature Ts is 25°C, the wind speed is high, the detected indoor ambient temperature Tin = 28°C, and the outdoor ambient temperature Tout = 30°C. The set temperature difference E = Tin-Ts = 28-25 = 3°C, the first temperature difference Tout_Ts = Tout-Ts = 30-25 = 5°C are calculated, and the parameters are substituted into (1) and (2). The initial operating frequency can be obtained by the preset fitting algorithm and recorded as F0(30,3,5)(1), that is, F0(30,3,5)(1) = (0.086×3+0.039×30+0.058×5-0.838)×1.00×85 = 75Hz.

[0129] When the air conditioner 10 starts cooling operation for the first time, the air conditioner 10 is controlled to perform the first fuzzy frequency self-learning, and the compressor is controlled to operate according to the initial operating frequency F0(30,3,5)(1)=75Hz, and the room temperature is controlled according to the fuzzy frequency. When the compressor is started for 25 minutes, the indoor ambient temperature Tin reaches the set temperature Ts for the first time, that is, Tin=Ts=25℃, and the time for the compressor to reach the temperature for the first time is tw1(30,3,5)(1)=25min; the compressor continues to run, and the indoor ambient temperature Tin reaches the set temperature Ts for the second time, that is, when Tin=Ts=25℃, the time for the compressor to reach the temperature for the second time is tw2(30,3,5)(1)=30min; the stable frequency of the compressor after reaching the temperature is Fw_1(30,3,5)(1)=15Hz, and the time for the compressor to reach the temperature from the start is calculated. The frequency integral from time 0 to 30 minutes is ∫[0,30]F(t)dt=1800Hz·min. At the same time, the first cooling rate ε1(1) is calculated based on the first historical temperature-reaching time tw1(30,3,5)(1)=25min and the set temperature difference E=3℃, that is, ε1(1)=3 / 25=0.12℃ / min. According to Table 1, the energy-saving coefficient σ(30,3,5)(1)=0.95, and the target operating frequency F0_1(30,3,5)(1)=1800 / 30×0.95≈57Hz is calculated.

[0130] Similarly, the fuzzy frequency self-learning is performed for the second time, that is, in the fuzzy frequency self-learning stage, when the second cooling operation is executed, the compressor is also controlled to operate according to the initial operating frequency F0(30,3,5)(1)=75Hz, and the room temperature is controlled according to the fuzzy frequency. When the compressor is started for 20 minutes, the indoor ambient temperature Tin reaches the set temperature Ts for the first time, that is, Tin=Ts=25℃, and the time for the compressor to reach the temperature for the first time is tw1(30,3,5)(2)=20min; the compressor continues to run, and the indoor ambient temperature Tin reaches the set temperature Ts for the second time, that is, Tin=Ts=25℃, and the time for the second time to reach the temperature is tw2(30,3,5)(2)=25min; the stable frequency of the compressor after reaching the temperature is Fw_1(30,3,5)(2)=13Hz, and the time for the compressor to reach the temperature from the start is calculated. The frequency integral from time 0 to 25 minutes is ∫[0,25]F(t)dt=1600Hz·min. According to the first historical temperature-reaching time tw1(30,3,5)(2)=20min and the set temperature difference E=5℃, the first cooling rate ε1(2) is calculated, that is, ε1(2)=3 / 20=0.15℃ / min. According to Table 1, the energy saving coefficient σ(30,3,5)(2)=0.87, and the target operating frequency F0_1(30,3,5)(2)=1600 / 25×0.87≈56Hz is calculated.

[0131] During the third cooling operation, it will enter the estimated frequency control stage.

[0132] In the estimated frequency control stage, the estimated target frequency for this cooling operation will be calculated based on the historical operating data of the fuzzy frequency control stage. Specifically, the average of the target operating frequencies obtained during the first two fuzzy frequency self-learnings will be calculated and used as the estimated target frequency F0(30,3,5)(2), that is, F0(30,3,5)(2)=(F0_1(30,3,5)(1)+F0_1(30,3,5)(2)) / 2≈57Hz. At this time, the compressor is controlled to operate at 57Hz. The estimated stable frequency will be determined based on the historical stable operating frequencies of the compressor after reaching temperature obtained from the first two fuzzy frequency self-learnings, that is, Fw_yc(30,3,5)(2)=(Fw_1(30,3,5)(1)+Fw_1(30,3,5)(2)) / 2=14Hz.

[0133] Afterwards, the air conditioner 10 is controlled to execute the estimated frequency control phase of the current refrigeration operation, i.e. the compressor is controlled to run at 57 Hz and remain unchanged. At the same time, the set temperature difference E is compared with the preset temperature difference threshold E1, when E≤0.5℃, the compressor is controlled to decrease from 57 Hz to 14 Hz at a first frequency reduction rate of 2 Hz / s, and after the compressor is controlled to run at 14 Hz for a first preset time t1, i.e. the compressor is controlled to run at 14 Hz for 4 fuzzy frequency calculation periods (4x40s=160s), the air conditioner 10 is controlled to execute the fuzzy frequency control phase of the current refrigeration operation, i.e. run at the estimated stable frequency of 14 Hz.

[0134] When the air conditioner 10 is started to execute the refrigeration operation again, the estimated target frequency and the estimated stable frequency of the current refrigeration operation are calculated according to the estimated target frequency and the estimated stable frequency of the last refrigeration operation. For example, when the compressor is started to run for 35 min, at this time, the indoor environment temperature Tin reaches the set temperature Ts for the first time, i.e. Tin=Ts=25℃, the time of reaching the temperature for the first time is 35 min, and the stable running frequency of the compressor after reaching the temperature is actually obtained as 12 Hz. The first temperature reduction rate ε1(3) is calculated according to the set temperature difference E and the time of reaching the temperature for the first time, i.e. ε1(3)=3 / 35≈0.086℃ / min, the table 1 is inquired, the energy saving coefficient σ(30,3,5)(2)=1, the estimated target frequency F0(30,3,5)(3) of the current refrigeration operation is determined according to the estimated target frequency 57 Hz of the last refrigeration operation, the energy saving coefficient σ(30,3,5)(2)=1 and the frequency revision coefficient λ based on the fan speed calibration (i.e. λ=1.00 at high wind), i.e. F0(30,3,5)(3)=57x1x1≈57Hz, and the estimated stable frequency Fw_yc(30,3,5)(3) of the current refrigeration operation is determined according to the average of the estimated stable frequency 14 Hz obtained in the last refrigeration operation and the stable running frequency 12 Hz of the compressor after reaching the temperature of the current refrigeration operation, i.e. Fw_yc(30,3,5)(3)=(14+12) / 2=13Hz. Afterwards, the compressor is controlled to run at 57 Hz.

[0135] After n iterations of operation, the estimated target frequency F0(30,3,5)(n) of air conditioner 10 is 55 Hz, and the calculated estimated target frequency Fw_yc(30,3,5)(n) is 11 Hz. When air conditioner 10 starts a new cooling operation, the user sets a mid-stroke setting. The estimated target frequency is corrected based on the frequency correction factor λ=1.02, which is based on the fan speed calibration at the time of the mid-stroke. The corrected estimated target frequency is 56 Hz, and the compressor is controlled to operate at 56 Hz and remains constant. When the compressor runs for 25 minutes, the real-time detected indoor ambient temperature Tin = 26.5°C, and the initial indoor ambient temperature Tin0 = 28°C, then the second cooling rate ε2 = (28-26.5) / 25≈0.06°C / min. At this time, the second cooling rate ε2 is lower than the preset cooling rate threshold (for example, 0.062°C / min), that is, ε2<0.062°C / min. At this time, the estimated target frequency F0(30,3,5)(n) needs to be corrected. The estimated target frequency F0(30,3,5)(n) will be increased according to the energy-saving coefficient σ and the frequency correction coefficient λ based on the fan speed calibration. The increased estimated target frequency F0(30,3,5)(n)≈64Hz.

[0136] The set temperature difference E is compared with the preset temperature difference threshold E1. When E≤0.5℃, the compressor is controlled to reduce the frequency from 64Hz to 11Hz at the first frequency reduction rate of 2Hz / s, and maintain the first operating time t1, that is, after 4 fuzzy frequency calculation cycles 4×40s=160s, it switches to the fuzzy frequency control stage, that is, controlling the compressor to operate at 11Hz. After the compressor starts and runs for 30 minutes, the indoor ambient temperature Tin reaches the set temperature Ts for the first time, that is, Tin = Ts = 25°C. The time it takes to reach this temperature is 30 minutes. The actual stable operating frequency of the compressor after reaching this temperature is 13 Hz. Based on the set temperature difference E and the time it takes to reach this temperature, the first cooling rate ε1(n) is calculated, that is, ε1(n) = 3 / 30 ≈ 0.1°C / min. Looking up Table 1, the energy-saving coefficient σ(30,3,5)(n) = 1.00, and the estimated target frequency F0(30,3,5)(n) ≈ 63 Hz is calculated. The estimated stable frequency Fw_yc(30,3,5)(n)) is then calculated to be ≈ 13 Hz. The compressor is then controlled to operate at 63 Hz.

[0137] At the same time, in the process of continuously calculating the estimated target frequency in a loop, the preset fitting algorithm will be continuously updated, and then the initial operating frequency will be continuously updated, thereby improving the accuracy of the estimated target frequency calculation.

[0138] According to the air conditioner 10 of the embodiment of the present invention, when the air conditioner 10 starts the first cooling operation, the estimated target frequency and the estimated stable frequency will be calculated based on whether the controller 71 supports the use of a preset neural network model, and then the preset neural network algorithm or the preset fitting algorithm will be selected according to the judgment result to flexibly calculate the estimated target frequency and the estimated stable frequency. When the air conditioner 10 starts a non-first cooling operation, the estimated target frequency and the estimated stable frequency are calculated based on the operating data of the fuzzy frequency control stage of the air conditioner 10 in the previous cooling operation. Then, the controller 71 controls the compressor to operate according to the estimated target frequency to control the air conditioner 10 to execute the estimated frequency control stage of this cooling operation, which can avoid large overshoot of the indoor ambient temperature and long duration. At the same time, the set temperature difference is compared with the preset temperature difference threshold. When the set temperature difference does not exceed the preset temperature difference threshold, the controller 71 controls the compressor to reduce the frequency from the estimated target frequency to the estimated stable frequency according to the first frequency reduction rate, so as to gradually reduce the indoor ambient temperature and ensure the stability and comfort of the indoor ambient temperature. After that, the compressor is controlled to operate at the estimated stable frequency for the first preset time, and the air conditioner 10 is controlled to switch to the fuzzy frequency control stage, that is, the compressor is controlled to operate at the estimated stable frequency, ensuring that the user can enjoy a comfortable cooling experience, improving the user's usage experience, and also improving energy efficiency and energy saving effects, thereby achieving efficient use of energy.

[0139] Reference below Figure 6 A method for controlling an air conditioner according to an embodiment of the present invention is described.

[0140] like Figure 6 As shown, the air conditioner control method according to the embodiment of the present invention at least includes steps S1 to S4.

[0141] Step S1, obtaining a set temperature, and determining a set temperature difference according to the indoor ambient temperature and the set temperature.

[0142] Step S2: When the air conditioner starts the first cooling operation, the preset neural network algorithm or the preset fitting algorithm is used to determine the estimated target frequency and the estimated stable frequency; or, when the air conditioner starts the non-first cooling operation, the estimated target frequency and the estimated stable frequency are obtained. The estimated target frequency and the estimated stable frequency are calculated by the controller based on the operating data of the fuzzy frequency control stage of the air conditioner in the last cooling operation. The operating data include: the time when the compressor reaches the temperature for the first time, the stable operating frequency of the compressor after reaching the temperature, the first cooling rate, the energy-saving coefficient and the frequency revision coefficient based on the fan speed calibration; each cooling operation of the air conditioner includes an estimated frequency control stage and a fuzzy frequency control stage.

[0143] Step S3 , controlling the air conditioner to execute the estimated frequency control phase of the current cooling operation. The estimated frequency control phase includes: controlling the compressor to operate according to the estimated target frequency.

[0144] Step S4, when the set temperature difference does not exceed the preset temperature difference threshold, the compressor is controlled to reduce the frequency from the estimated target frequency to the estimated stable frequency according to the first frequency reduction rate, and the compressor is controlled to operate at the estimated stable frequency for the first preset time, and then the air conditioner is controlled to execute the fuzzy frequency control stage of this cooling operation. The fuzzy frequency control stage includes: controlling the compressor to operate at the estimated stable frequency.

[0145] In one embodiment of the present invention, when a preset neural network algorithm is used to determine the estimated target frequency and the estimated stable frequency, it includes: determining a first temperature difference between the outdoor ambient temperature and the set temperature; inputting the first temperature difference, the set temperature difference and the outdoor ambient temperature into a preset neural network model, and outputting an initial operating frequency after calculation through the preset neural network algorithm; based on the initial operating frequency, controlling the air conditioner to perform fuzzy frequency self-learning to obtain the estimated target frequency and the estimated stable frequency.

[0146] In one embodiment of the present invention, when a preset fitting algorithm is used to determine the estimated target frequency and the estimated stable frequency, it includes: obtaining a preset initial maximum operating frequency; determining a first temperature difference between the outdoor ambient temperature and the set temperature; determining a preset target frequency coefficient based on the set temperature difference, the outdoor ambient temperature and the first temperature difference; determining an iterative correction factor based on the energy-saving coefficient; obtaining the initial operating frequency based on the preset target frequency coefficient, the iterative correction factor, the preset initial maximum operating frequency, a frequency revision coefficient based on fan speed calibration, the outdoor ambient temperature, the set temperature difference and the first temperature difference; based on the initial operating frequency, controlling the air conditioner to perform fuzzy frequency self-learning to obtain the estimated target frequency and the estimated stable frequency.

[0147] In one embodiment of the present invention, when controlling the air conditioner to perform fuzzy frequency self-learning based on the initial operating frequency to obtain an estimated target frequency and an estimated stable frequency, it includes: controlling the compressor to operate according to the initial operating frequency, and obtaining multiple historical operating data obtained by the air conditioner performing multiple fuzzy frequency self-learning, wherein the historical operating data include: the first historical time of reaching temperature, the second historical time of reaching temperature, the historical stable operating frequency after the compressor reaches temperature, and the frequency integral of the compressor from startup to the second historical time of the compressor reaching temperature; averaging the historical stable operating frequencies of multiple compressors after reaching temperature to obtain the estimated stable frequency; obtaining multiple corresponding target operating frequencies based on multiple historical operating data, and averaging the multiple target operating frequencies to obtain the estimated target frequency.

[0148] In one embodiment of the present invention, when obtaining multiple corresponding target operating frequencies based on multiple historical operating data, it includes: determining a first cooling rate based on a set temperature difference and the time for the first historical temperature reaching temperature; determining an energy-saving coefficient based on the first cooling rate, wherein there is a pre-calibrated correspondence between the first cooling rate and the energy-saving coefficient; obtaining multiple corresponding target operating frequencies based on multiple frequency integrals and their corresponding multiple energy-saving coefficients and multiple second historical temperature reaching times.

[0149] In one embodiment of the present invention, Figure 7 As shown, when obtaining the estimated target frequency, it includes: obtaining the estimated target frequency of the air conditioner in the fuzzy frequency control stage in the last cooling operation; according to the product of the estimated target frequency of the air conditioner in the fuzzy frequency control stage in the last cooling operation, the energy-saving coefficient and the frequency revision coefficient calibrated based on the fan speed, the estimated target frequency of the air conditioner when performing this cooling operation is obtained.

[0150] In one embodiment of the present invention, Figure 8 As shown, when obtaining the estimated stable frequency, it includes: obtaining the stable operating frequency of the compressor after reaching the temperature in multiple fuzzy frequency control stages of the air conditioner before performing the current cooling operation; averaging the stable operating frequencies of multiple compressors after reaching the temperature to obtain the estimated stable frequency.

[0151] In one embodiment of the present invention, Figure 9 As shown, when obtaining the estimated stable frequency, it includes: obtaining the stable operating frequency of the compressor after reaching the temperature in multiple fuzzy frequency control stages of the air conditioner before performing the current cooling operation; and calculating the weighted average of the stable operating frequencies of the multiple compressors after reaching the temperature to obtain the estimated stable frequency.

[0152] In one embodiment of the present invention, Figure 10 As shown, when controlling the air conditioner to perform the estimated frequency control stage of this cooling operation, it includes: obtaining the second cooling rate of the compressor in the estimated frequency control stage; when the second cooling rate does not exceed the preset cooling rate threshold, the estimated target frequency is corrected according to the estimated target frequency, the energy-saving coefficient and the frequency correction coefficient calibrated based on the fan speed.

[0153] In one embodiment of the present invention, Figure 10 As shown, when obtaining the second cooling rate of the compressor in the estimated frequency control stage, it includes: obtaining the initial indoor ambient temperature and the operating time of the compressor; determining the second cooling rate according to the initial indoor ambient temperature, the operating time of the compressor and the indoor ambient temperature.

[0154] According to the control method for an air conditioner according to an embodiment of the present invention, when the air conditioner starts its first cooling operation, the estimated target frequency and the estimated stable frequency are calculated based on whether a preset neural network model is supported. Then, based on the judgment result, a preset neural network algorithm or a preset fitting algorithm is selected to flexibly calculate the estimated target frequency and the estimated stable frequency. When the air conditioner starts a non-initial cooling operation, the estimated target frequency and the estimated stable frequency are calculated based on the operating data of the air conditioner during the fuzzy frequency control phase of the previous cooling operation. The compressor is then controlled to operate at the estimated target frequency to control the air conditioner to execute the estimated frequency control phase of the current cooling operation, thereby avoiding large and prolonged overshoots of the indoor ambient temperature. At the same time, the set temperature difference is compared with the preset temperature difference threshold. When the set temperature difference does not exceed the preset temperature difference threshold, the compressor is controlled to reduce the frequency from the estimated target frequency to the estimated stable frequency according to the first frequency reduction rate. This can gradually reduce the indoor ambient temperature and ensure the stability and comfort of the indoor ambient temperature. After that, the compressor is controlled to operate at the estimated stable frequency for the first preset time, and the air conditioner is controlled to switch to the fuzzy frequency control stage, that is, the compressor is controlled to operate at the estimated stable frequency, ensuring that the user can enjoy a comfortable cooling experience, improving the user experience, and also improving energy efficiency and energy saving effects, thereby achieving efficient use of energy.

[0155] Throughout this specification, reference to terms such as "one embodiment," "some embodiments," "illustrative embodiments," "example," "specific example," or "some examples" means that a specific feature, structure, material, or characteristic described in conjunction with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example.

[0156] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to the embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the claims and their equivalents.

Claims

1. An air conditioner, characterized in that: include: A refrigerant circulation loop, wherein the refrigerant undergoes a refrigeration cycle in a loop consisting of a compressor, a condenser, a throttling element, and an evaporator, wherein one of the condenser and the evaporator is an outdoor heat exchanger and the other is an indoor heat exchanger; Indoor ambient temperature sensor, used to detect indoor ambient temperature; Outdoor ambient temperature sensor, used to detect outdoor ambient temperature; A controller configured to: Obtaining a set temperature, and determining a set temperature difference according to the indoor ambient temperature and the set temperature; When the air conditioner starts the first cooling operation, a preset neural network algorithm or a preset fitting algorithm is used to determine the estimated target frequency and the estimated stable frequency; or, when the air conditioner starts the non-first cooling operation, the estimated target frequency and the estimated stable frequency are obtained by the controller based on the operating data of the fuzzy frequency control stage in the previous cooling operation of the air conditioner, the operating data including: the time when the compressor reaches the temperature for the first time, the stable operating frequency of the compressor after reaching the temperature, the first cooling rate, the energy saving coefficient, and the frequency revision coefficient calibrated based on the fan speed; each cooling operation of the air conditioner includes an estimated frequency control stage and a fuzzy frequency control stage; Controlling the air conditioner to perform the estimated frequency control stage of this cooling operation; The estimated frequency control stage includes: controlling the compressor to operate according to the estimated target frequency; When the set temperature difference does not exceed a preset temperature difference threshold, controlling the compressor to reduce the frequency from the estimated target frequency to the estimated stable frequency at a first frequency reduction rate, and controlling the compressor to operate at the estimated stable frequency for a first preset time, and then controlling the air conditioner to perform the fuzzy frequency control stage of the current cooling operation; The fuzzy frequency control stage includes: controlling the compressor to operate according to the estimated stable frequency.

2. The air conditioner according to claim 1, characterized in that When the preset neural network algorithm is used to determine the estimated target frequency and the estimated stable frequency, the controller is configured to: determining a first temperature difference between the outdoor ambient temperature and the set temperature; Inputting the first temperature difference, the set temperature difference, and the outdoor ambient temperature into a preset neural network model, and outputting an initial operating frequency after calculation by the preset neural network algorithm; Based on the initial operating frequency, the air conditioner is controlled to perform fuzzy frequency self-learning to obtain the estimated target frequency and the estimated stable frequency.

3. The air conditioner according to claim 1, characterized in that When the preset fitting algorithm is used to determine the estimated target frequency and the estimated stable frequency, the controller is configured to: Get the preset initial maximum operating frequency; determining a first temperature difference between the outdoor ambient temperature and the set temperature; determining a preset target frequency coefficient according to the set temperature difference, the outdoor ambient temperature, and the first temperature difference; determining an iterative correction factor according to the energy saving coefficient; Obtaining an initial operating frequency according to the preset target frequency coefficient, the iterative correction factor, the preset initial maximum operating frequency, the frequency revision coefficient based on fan speed calibration, the outdoor ambient temperature, the set temperature difference, and the first temperature difference; Based on the initial operating frequency, the air conditioner is controlled to perform fuzzy frequency self-learning to obtain the estimated target frequency and the estimated stable frequency.

4. The air conditioner according to claim 2 or 3, characterized in that: When controlling the air conditioner to perform fuzzy frequency self-learning based on the initial operating frequency to obtain the estimated target frequency and the estimated stable frequency, the controller is configured to: Controlling the compressor to operate according to the initial operating frequency, and acquiring a plurality of historical operating data obtained by performing multiple fuzzy frequency self-learning on the air conditioner, wherein the historical operating data includes: a first historical time when the temperature is reached, a second historical time when the temperature is reached, a historical stable operating frequency of the compressor after reaching the temperature, and a frequency integral of the compressor from startup to the second historical time when the compressor reaches the temperature; averaging the historical stable operating frequencies of a plurality of the compressors after reaching temperature to obtain the estimated stable frequency; A plurality of corresponding target operating frequencies are obtained according to the plurality of historical operating data, and the estimated target frequency is obtained by averaging the plurality of target operating frequencies.

5. The air conditioner according to claim 4, characterized in that When a plurality of corresponding target operating frequencies are obtained according to the plurality of historical operating data, the controller is configured to: Determining the first cooling rate according to the set temperature difference and the first historical temperature reaching time; determining the energy-saving coefficient according to the first cooling rate, wherein a pre-calibrated correspondence relationship exists between the first cooling rate and the energy-saving coefficient; A plurality of corresponding target operating frequencies are obtained according to the plurality of frequency integrals and the plurality of energy-saving coefficients corresponding thereto and the plurality of second historical temperature-reaching times.

6. The air conditioner according to claim 1, characterized in that When acquiring the estimated target frequency, the controller is configured to: Obtaining an estimated target frequency of the air conditioner during the fuzzy frequency control phase in the last cooling operation; The estimated target frequency of the air conditioner during the current cooling operation is obtained based on the product of the estimated target frequency of the fuzzy frequency control stage in the previous cooling operation of the air conditioner, the energy-saving coefficient and the frequency revision coefficient calibrated based on the fan speed.

7. The air conditioner according to claim 1, wherein: When obtaining the estimated stable frequency, the controller is configured to: Obtaining a stable operating frequency of the compressor after reaching temperature during a plurality of fuzzy frequency control stages before the air conditioner performs the current cooling operation; The estimated stable frequency is obtained by averaging the stable operating frequencies of the plurality of compressors after they reach temperature.

8. The air conditioner according to claim 1, wherein: When obtaining the estimated stable frequency, the controller is configured to: Obtaining a stable operating frequency of the compressor after reaching temperature during a plurality of fuzzy frequency control stages before the air conditioner performs the current cooling operation; The estimated stable frequency is obtained by calculating a weighted average of the stable operating frequencies of the plurality of compressors after they reach temperature.

9. The air conditioner according to claim 1, wherein: When controlling the air conditioner to perform the estimated frequency control stage of the current cooling operation, the controller is further configured to: Obtaining a second cooling rate of the compressor in the estimated frequency control stage; When the second cooling rate does not exceed a preset cooling rate threshold, the estimated target frequency is corrected according to the estimated target frequency, the energy-saving coefficient, and the frequency revision coefficient calibrated based on the fan speed.

10. The air conditioner according to claim 9, characterized in that When obtaining the second cooling rate of the compressor in the estimated frequency control stage, the controller is configured to: Obtaining an initial indoor ambient temperature and the operating time of the compressor; A second cooling rate is determined according to the initial indoor ambient temperature, the operating time of the compressor, and the indoor ambient temperature.

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