A defrosting control method and apparatus

Abstract

This invention provides a defrosting control method and apparatus, an air conditioner, and a non-transitory computer-readable medium. In the method provided by this invention, operating parameters of the air conditioner and / or outdoor environmental parameters are first collected cyclically at a preset period. Then, the corresponding frost change parameters of the air conditioner are calculated. Next, the frost change parameters are used to assess the degree of influence of the frost layer on the air conditioner's operating capacity. Finally, the defrosting operation of the air conditioner is controlled based on the degree of influence parameters. Based on the defrosting control method proposed in this invention, the real-time frost rate can be calculated according to the actual operating process, and the degree of influence of the real-time frost layer on the system capacity can be calculated according to a formula to determine whether defrosting should be performed. Through the solution of this invention, the air conditioner can defrost at the optimal time, improving the heating comfort of the air conditioner.

Description

Technical Field

[0001] This invention relates to the field of intelligent control, and more specifically to a defrosting control method and apparatus, an air conditioner, and a non-transitory computer-readable medium. Background Technology

[0002] When outdoor temperatures are low, frost will form on the condenser, affecting the performance of the air conditioner, thus often requiring defrosting control. Currently, the main methods for determining when to initiate defrosting in household air conditioners, besides the gradually abandoned timed defrosting method, are based on the external pipe temperature. While these methods are simple and reliable, they are prone to misjudgments such as "not defrosting when there is frost" or "defrosting when there is no frost." With the development of neural network technology, methods using neural network algorithms to predict frost layers are no longer uncommon.

[0003] Patent CN1111156657A proposes a frost thickness identification method based on a probabilistic neural network. It uses parameters such as outdoor ambient temperature, relative humidity, heat exchanger external pipe temperature, heating capacity, power, and suction pressure as inputs to the neural network, and the frost thickness status as the model's output. The air conditioner calculates the frost thickness in real time during operation and determines whether defrosting should be initiated based on the obtained frost thickness status. Patent CN109882994A proposes a method to determine whether frost has formed and its thickness based on the difference and variation pattern between the outdoor heat exchanger evaporation temperature and the defrosting temperature. Patent CN113865009A proposes a frost thickness determination method that uses outdoor ambient temperature, humidity, outdoor fan speed, and external pipe temperature as neural network inputs, and ultimately outputs the amount of frost.

[0004] All three methods can determine the thickness or amount of frost, and the neural networks used in some of these methods have strong adaptability and accuracy. However, in actual air conditioning operation, the same amount of frost can have different effects on the system under different operating conditions. Therefore, it is not possible to simply determine whether defrosting should be performed based on the amount or thickness of frost. The actual effect of the frost amount obtained by the above models is limited.

[0005] The information disclosed in the background section above is only used to further understand the background of the present invention, and therefore may include information known to those skilled in the art that does not constitute prior art. Summary of the Invention

[0006] This invention provides a defrosting control method and apparatus that enables air conditioners to defrost at the optimal time, thereby improving the comfort of air conditioning heating.

[0007] According to a first aspect of the present invention, a defrosting control method is provided, comprising:

[0008] The operating parameters of the air conditioner and / or outdoor environmental parameters are collected cyclically at a preset period.

[0009] Calculate the corresponding frost change parameters of the air conditioner based on the operating parameters of the air conditioner and / or the outdoor environmental parameters;

[0010] The aforementioned frost change parameters are used to evaluate the degree of impact of frost layer on the air conditioner's operating capacity.

[0011] The defrosting operation of the air conditioner is controlled based on the aforementioned influence parameter.

[0012] Optionally, calculating the corresponding frost change parameters of the air conditioner based on the operating parameters of the air conditioner and / or the outdoor environmental parameters includes:

[0013] The environmental parameters and / or operating parameters are input as input data into the neural network model;

[0014] The neural network model is used to calculate the frost change parameters corresponding to the air conditioner based on the input data; the frost change parameters include the frost rate.

[0015] Optionally, the step of using the neural network model to calculate the frost change parameters corresponding to the air conditioner based on the input data includes:

[0016] The neural network model is used to calculate the frosting speed of the air conditioner at the current moment based on the input data;

[0017] Read the accumulated frost amount of the air conditioner in the previous moment at the current moment, and calculate the new frost amount of the air conditioner based on the frost rate corresponding to the current moment and the frost amount in the previous moment.

[0018] Optionally, the parameters used to assess the impact of frost layer on the air conditioner's operating capacity using the frost change parameters include:

[0019] The rate of change of the impact of the frost rate on the equipment's operating capacity is calculated based on the frost change parameters using the frost rate-water vapor partial pressure difference correction model.

[0020] The parameter of the degree of influence of frost layer on the air conditioner's operating capacity is calculated by accumulating the rate of change of the degree of influence of the equipment's operating capacity.

[0021] Optionally, the frost rate-water vapor partial pressure difference correction model is as follows:

[0022]

[0023] f represents the rate of change of the impact on equipment operating capacity; ΔP represents the water vapor partial pressure difference; α and β are correction coefficients; m represents the frosting rate; Vpa V pb There are two preset thresholds.

[0024] Optionally, controlling the defrosting operation of the air conditioner based on the influence parameter includes:

[0025] The influence level parameter is compared with a preset threshold. If the influence level parameter is greater than the preset threshold, the air conditioner is controlled to defrost.

[0026] Optionally, before inputting the environmental parameters and / or operating parameters as input data into the neural network model, the method further includes:

[0027] Collect frost data of the corresponding equipment type of the air conditioner under different environments, and calculate the real-time frost speed under different environmental conditions and / or different operating conditions. Use the different environmental conditions and / or different operating conditions and the corresponding real-time frost speed as the basic data for training the neural network.

[0028] Constructing a neural network;

[0029] The neural network is trained using the aforementioned basic data to obtain a neural network model capable of calculating the frosting speed and amount based on environmental parameters and / or operating parameters.

[0030] Optionally, the step of cyclically collecting the air conditioner's operating parameters and / or outdoor environmental parameters at a preset cycle includes:

[0031] The outdoor heat exchanger temperature sensor and the outer ring temperature sensor are used to cyclically collect the outdoor pipe temperature and the outdoor ambient temperature at preset cycles; and / or,

[0032] Outdoor ambient humidity is collected cyclically by a humidity sensor at a preset cycle; and / or,

[0033] The speed of the outdoor fan of the air conditioner is read cyclically at a preset period.

[0034] According to a second aspect of the present invention, a defrosting control device is provided, comprising one or more processors and a non-transitory computer-readable storage medium storing program instructions, wherein when the one or more processors execute the program instructions, the one or more processors are configured to implement the method described in any one of the first aspects.

[0035] According to a third aspect of the invention, a non-transitory computer-readable storage medium is provided, having stored thereon program instructions that, when executed by one or more processors, enable the one or more processors to implement the method according to any one of the first aspects.

[0036] According to a fourth aspect of the invention, an air conditioner is provided that employs the method described in any of the first aspects, or includes the defrosting control device of the second aspect, or the non-transitory computer-readable storage medium of the third aspect.

[0037] This invention provides a defrosting control method and device. By calculating the frost formation rate and frost amount and sequentially judging the impact of the frost layer on the system's capacity, the defrosting timing can be accurately determined. Equipment using this defrosting method can fully utilize the advantages of both defrosting methods, improving operational comfort while ensuring defrosting effectiveness. It enables the equipment to defrost at the optimal time, enhancing the user experience. Attached Figure Description

[0038] To more clearly illustrate the technical solution of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0039] Figure 1 A schematic flowchart of a defrosting control method according to an embodiment of the present invention is shown;

[0040] Figure 2 A schematic diagram of the neural network structure according to an embodiment of the present invention is shown;

[0041] Figure 3 A schematic diagram of the neural network model training process according to an embodiment of the present invention is shown;

[0042] Figure 4 A control system logic diagram according to an embodiment of the present invention is shown;

[0043] Figure 5 A schematic diagram of a defrosting control system according to an embodiment of the present invention is shown. Detailed Implementation

[0044] This invention relates to a series of applications, including application number 202111082305.8, the entire contents of which are referenced and incorporated herein. As used herein, the terms “first,” “second,” etc., can be used to describe elements in exemplary embodiments of the invention. These terms are used only to distinguish one element from another, and the inherent features or order of the corresponding elements are not limited by the term. Unless otherwise defined, all terms used herein (including technical or scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. Terms defined in common dictionaries are interpreted as having the same meaning as in the context of the relevant technical field, and not as having an ideal or overly formal meaning, unless explicitly defined as having such a meaning in this invention.

[0045] Those skilled in the art will understand that the apparatus and methods of the present invention described herein and illustrated in the accompanying drawings are non-limiting exemplary embodiments, and the scope of the invention is defined only by the claims. Features illustrated or described in conjunction with an exemplary embodiment may be combined with features of other embodiments. Such modifications and variations are included within the scope of the invention.

[0046] In the following description, exemplary embodiments of the present invention will be described in detail with reference to the accompanying drawings. In the drawings, detailed descriptions of known functions or configurations are omitted to avoid unnecessarily obscuring the key technical aspects of the invention. Furthermore, throughout the description, the same reference numerals always refer to the same circuits, modules, or units, and for the sake of brevity, repeated descriptions of the same circuits, modules, or units are omitted.

[0047] Furthermore, it should be understood that one or more of the following methods or aspects can be performed by at least one control system, control unit, or controller. The terms "control unit," "controller," "control module," or "main control module" can refer to a hardware device including a memory and a processor, and the term "air conditioning" can refer to a device similar to a refrigeration unit. The memory or computer-readable storage medium is configured to store program instructions, and the processor is specifically configured to execute the program instructions to perform one or more processes, which will be further described below. Moreover, it should be understood that, as those skilled in the art will recognize, the following methods can be performed by including a processor in conjunction with one or more other components.

[0048] Figure 1 This is a schematic flowchart of a defrosting control method according to an embodiment of the present invention. See also... Figure 1 As is known, the defrosting control method provided in this embodiment of the invention may include at least the following steps:

[0049] Step S101: Collect the operating parameters of the air conditioner and / or outdoor environmental parameters in a preset cycle.

[0050] The preset cycle in this embodiment can be 1 second, 2 seconds or other interval cycles. The outdoor environmental parameters are the environmental parameters such as temperature and humidity of the outdoor environment in the space where the air conditioner is located, while the operating parameters refer to the state parameters of the air conditioner during operation, such as the relevant state parameters of each component and device during operation.

[0051] Step S102: Calculate the corresponding frosting variation parameters of the air conditioner based on the air conditioner's operating parameters and / or outdoor environmental parameters. After obtaining the corresponding environmental parameters and / or operating parameters of the air conditioner, the corresponding frosting variation parameters can be calculated based on these parameters. Optionally, the air conditioner's frosting variation parameters may include parameters such as frosting rate and frosting amount.

[0052] Step S103: Evaluate the degree of influence of frost layer on the air conditioner's operating capacity using frost change parameters. In this embodiment, the degree of influence of frost layer on the air conditioner's operating capacity can be obtained through learning a neural network model.

[0053] Step S104: Control the defrosting operation of the air conditioner based on the degree of influence parameter. After evaluating and obtaining the degree of influence parameter of the frost layer on the operating capacity of the air conditioner, the defrosting operation of the air conditioner can be controlled using this degree of influence parameter.

[0054] This invention provides a defrosting control method. In this method, the operating parameters of the air conditioner and / or outdoor environmental parameters are first collected cyclically at a preset period. Then, the corresponding frost change parameters of the air conditioner are calculated. Next, the frost change parameters are used to assess the degree of influence of the frost layer on the air conditioner's operating capacity. Finally, the defrosting operation of the air conditioner is controlled based on the degree of influence parameters. Based on the defrosting control method proposed in this invention, the thickness of the frost layer can be accurately calculated, and its influence on the system capacity can be corrected to determine the degree of influence. Defrosting is then determined based on this degree of influence, thereby significantly improving the accuracy of defrosting timing and ultimately improving the comfort of the air conditioner's heating operation.

[0055] The defrosting control methods mentioned in the above embodiments will be described in detail below.

[0056] First, as described in step S101, the operating parameters of the air conditioner and / or the environmental parameters of the deployed environment are collected.

[0057] The environmental parameters in this embodiment may include outdoor ambient temperature and outdoor ambient humidity, and the operating parameters may include outdoor pipe temperature and outdoor fan speed. Optionally, step S101, when acquiring the parameters, may include: cyclically collecting the corresponding outdoor pipe temperature and outdoor ambient temperature of the air conditioner through the outdoor heat exchanger temperature sensor and the outer ring temperature sensor at a preset cycle; and / or, cyclically collecting the outdoor ambient humidity through the humidity sensor at a preset cycle; and / or, cyclically reading the outdoor fan speed of the air conditioner at a preset cycle. That is, the outdoor temperature, outdoor humidity, outdoor fan speed, and outdoor pipe temperature of the air conditioner are cyclically acquired within the preset cycle as the environmental and operating parameters of the air conditioner.

[0058] After collecting various parameters of the air conditioner, step S102 is executed to calculate the frost change parameters of the air conditioner using the parameters.

[0059] In this embodiment, when calculating the frost change parameters, environmental parameters and / or operating parameters are first input into the neural network model as input data; then, the neural network model is used to calculate the frost change parameters corresponding to the air conditioner based on the input data.

[0060] Based on the basic data obtained in step S201, the neural network can be trained to obtain a neural network model that has been trained to a convergent state. The neural network model can then be used to calculate the corresponding frost variation parameters of the air conditioner based on the air conditioner's operating parameters and / or outdoor environmental parameters. That is, step S102 may further include A1 to A2.

[0061] A1, using the neural network model to calculate the frosting speed of the air conditioner at the current moment based on the input data;

[0062] A2, read the accumulated frost amount of the air conditioner in the previous moment at the current moment, and calculate the new frost amount of the air conditioner based on the frost rate corresponding to the current moment and the frost amount in the previous moment. Specifically, the frost amount in the previous moment + the frost rate in the previous moment × time (default can be 1 second) = the new frost amount.

[0063] In other words, the calculated frost change parameters can include two types of parameters: one is to use a neural network model to calculate the frost rate of the air conditioner at the current moment based on the input data; the other is to read the accumulated frost amount of the air conditioner at the current moment and calculate the new frost amount of the air conditioner based on the frost rate at the current moment and the frost amount at the previous moment.

[0064] After collecting various parameters of the air conditioner, the initial frost amount is set to 0. During the first calculation, the frost rate and frost amount (i.e., frost thickness) are directly calculated using a neural network model based on the aforementioned parameters. Subsequent calculations of the frost amount are based on the frost amount from the previous moment and the frost rate calculated using the current parameters. Since the defrost count and frost amount may not be zero when the air conditioner is turned off, but the existing frost layer will gradually disappear naturally after the air conditioner is left idle, to avoid system calculation errors, the defrost count and frost amount are reset to zero when the system is started (usually simultaneously with the air conditioner's startup).

[0065] In other words, such as Figure 2 As shown, the neural network model takes existing frost thickness, outdoor temperature, outdoor humidity, external fan assembly temperature, and external pipe temperature as inputs, and outputs new frost amount and frost rate. This embodiment utilizes a neural network model to learn the frost rate, effectively improving the intelligence of equipment operation. By learning historical patterns, it accurately determines the real-time status of the equipment during operation, and then provides quantitative parameters for control reference, thereby guiding subsequent equipment operation control.

[0066] The aforementioned neural network model is pre-trained and combined with... Figure 3 It can be seen that the training process of a neural network model can be as follows:

[0067] S301, collect frost data of the corresponding equipment type of air conditioner under different environments, and calculate the real-time frost speed under different environmental conditions and / or different operating conditions, and use the different environmental conditions and / or different operating conditions and the corresponding real-time frost speed as the basic data for training the neural network.

[0068] Optionally, a large number of experiments can be conducted in the laboratory to measure the frosting conditions of the corresponding model under different outdoor fan speeds, outdoor temperatures and humidity levels, and external pipe temperatures. Then, based on the obtained frosting data, the real-time frosting rate under different outdoor fan speeds, outdoor temperatures and humidity levels, external pipe temperatures, and frost thicknesses can be calculated, and this data can be used as the basic data for network training.

[0069] S302, Construct a neural network.

[0070] Due to the limited computing power of the air conditioner's mainboard, a three- to five-layer BP (BackPropagation) neural network was used. The input layer had five neurons, corresponding to five inputs (existing frost thickness, outdoor temperature, outdoor humidity, outdoor fan speed, and outdoor pipe temperature). The intermediate layers could have 5-10 neurons, and the output layer had one neuron, corresponding to the defrosting speed. The sum of the model's output and the existing frost thickness represented the new frost amount. Positive and negative sigmoid functions were chosen as the activation functions for the intermediate layers, while a non-negative sigmoid function was chosen for the output layer. The neural network was then trained using the data obtained from the above experiments and embedded into the air conditioner's mainboard.

[0071] S303 uses basic data to train a neural network to obtain a neural network model that can calculate the frosting rate and frosting amount based on environmental parameters and / or operating parameters.

[0072] Generally speaking, when an air conditioner is running continuously, it will make real-time or periodic judgments on the defrosting status. Therefore, during the continuous operation of the air conditioner, the neural network model can calculate the frosting speed and obtain the new frosting amount based on the real-time collected input parameters, and then execute step S103 to evaluate the degree of influence on the air conditioner's operating capacity based on the new frosting amount and frosting speed.

[0073] Optionally, when evaluating the degree of influence on the air conditioner's operating capacity, the frost rate-water vapor partial pressure difference correction model can be used to calculate the rate of change of the degree of influence on the equipment's operating capacity based on the frost change parameters; then, the degree of influence of the frost layer on the air conditioner's operating capacity can be calculated cumulatively based on the rate of change of the degree of influence on the equipment's operating capacity.

[0074] The correction model for frosting rate-water vapor partial pressure difference is as follows:

[0075]

[0076] f represents the rate of change of the impact on equipment operating capacity; ΔP represents the water vapor partial pressure difference; α and β are correction coefficients; m represents the frosting rate; V pa V pb There are two preset thresholds, where V pa The value range of V is 10 to 15. pb The value range is 20 to 40. The above two parameters can be set according to different models, but this embodiment does not limit them.

[0077] The frosting rate-water vapor partial pressure difference (f-ΔP) correction model is proposed to determine the optimal frosting amount for defrosting. It is based on the differences in frost structure and system variation characteristics under different operating conditions. The influence of the frost layer on system capacity is defined as F, which more intuitively reflects the relationship between the frost layer and the system capacity decay rate. f represents the rate of change of F. After obtaining f, F can be accumulated. For example, if the network runs once per second during calculation, the new F is calculated as: F from the previous moment + f * 1. After this correction, even under different operating conditions, the same F value indicates that the frost layer has the same influence on the system capacity, i.e., the capacity decay rate is consistent, thus facilitating subsequent defrosting decisions.

[0078] Finally, step S104 is executed to control the defrosting operation of the air conditioner based on the influence level parameter.

[0079] In an optional embodiment of the present invention, the influence level parameter is compared with a preset threshold. If the influence level parameter is greater than the preset threshold, the air conditioner is controlled to perform defrosting. The preset threshold can be set according to different needs, and this embodiment of the present invention does not limit its setting.

[0080] In summary, such as Figure 4 As shown, after the air conditioner is turned on, the frost level S is first reset to zero. Then, the outdoor ambient temperature and humidity, external pipe temperature, and external fan speed are acquired through relevant sensors. These, along with the frost level being zero, are input into the neural network for calculation to obtain the frost rate. A new frost level S is then calculated sequentially for the next cycle to calculate the new frost rate. The obtained frost rate is used to calculate the rate of change f of the impact on system capacity using a correction formula. This is accumulated to obtain the degree of impact on system capacity F, which is compared with a preset value F0 (F0 ranges from 900 to 1100). If F is greater than F0, it indicates that the frost layer has a significant impact on system capacity and defrosting is required. Otherwise, the air conditioner operates normally without defrosting. The data is updated and calculated in real time, and F continuously accumulates. After defrosting is complete, both the frost level S and F are reset to zero, and the next cycle begins.

[0081] This invention provides a defrosting control method that calculates the frost formation rate and frost amount and sequentially determines the impact of the frost layer on the system's capacity, thereby accurately determining the defrosting timing. Air conditioners using this defrosting method can fully utilize the advantages of both defrosting methods, improving the comfort of air conditioner operation while ensuring defrosting effectiveness.

[0082] The air conditioner of this embodiment of the invention may be equipped with Figure 5 The control system shown is as follows: Figure 2As shown, this control system mainly involves the control of components such as the outdoor unit mainboard, temperature sensors, humidity sensors, indoor and outdoor fans, compressor, four-way valve, and electronic expansion valve. The temperature sensors include an outdoor heat exchanger temperature sensor and an outer loop temperature sensor, used to acquire the outdoor pipe temperature and outdoor ambient temperature, respectively; the humidity sensor is used to acquire the outdoor ambient humidity. The outdoor unit mainboard mainly includes a data acquisition module, a frost amount calculation module, a module for calculating the impact on system capacity, a defrost judgment module, and a defrost control module. Among them:

[0083] (1) The data acquisition module is used to obtain the temperature of the outer pipe and the ambient temperature from the temperature sensing bulb, the outdoor ambient humidity from the humidity sensor, and the speed of the outdoor fan. Then the data is transmitted to the frost amount calculation module for frost amount calculation.

[0084] (2) The frost amount calculation module is mainly composed of a neural network. It calculates the current frost rate based on the collected real-time outdoor temperature, outdoor relative humidity, condenser external pipe temperature, and external fan speed and the existing frost amount, and calculates the new frost amount based on the frost rate.

[0085] (3) The system capability impact calculation module calculates the impact of the frost layer on the system capability based on the obtained frost rate.

[0086] (4) The defrosting judgment module determines whether defrosting is needed based on the calculated impact on the system and relevant logic.

[0087] (5) The defrosting control module distributes the corresponding defrosting commands to each actuator based on the output of the defrosting judgment module.

[0088] The compressor control module, four-way valve control module, electronic expansion module, and indoor and outdoor fan control module perform the relevant operations of non-reversing thin defrosting or conventional reversing defrosting according to the received defrosting command.

[0089] According to one or more embodiments of the present invention, the present invention also provides a non-transitory computer-readable storage medium having program instructions stored thereon. When the program instructions are executed by one or more processors, the one or more processors are used to implement the methods or processes shown in the various embodiments of the present invention above. According to one embodiment of the present invention, the air conditioner rinsing control method of the present invention is stored as a program in a readable storage medium. In addition to storing the program that implements each function in a computer, it can also be stored in a recording medium such as a USB flash drive, a portable hard drive, an optical disc, or a hard disk.

[0090] According to one or more embodiments of the present invention, the present invention also provides a defrosting control device, which includes one or more processors and a non-transitory computer-readable storage medium storing program instructions. When the one or more processors execute the program instructions, the one or more processors are used to implement the methods or processes shown in the various embodiments of the present invention above.

[0091] According to one or more embodiments of the present invention, the present invention also includes an air conditioner that employs the method described above, or includes the defrosting control device of the present invention, or has the non-transitory computer-readable storage medium described above.

[0092] According to one or more embodiments of the present invention, the defrosting control method of the present invention can implement the processing of the control method described above using encoded instructions (e.g., computer and / or machine-readable instructions) stored on a non-transitory computer and / or machine-readable medium (e.g., hard disk drive, flash memory, read-only memory, optical disk, digital multifunction disk, cache, random access memory, and / or any other storage device or storage disk), storing information for any time period (e.g., extended time periods, permanent, transient instances, temporary caches, and / or information caches) in the non-transitory computer and / or machine-readable medium. As used herein, the term "non-transitory computer-readable medium" is explicitly defined to include any type of computer-readable storage device and / or storage disk, excluding propagated signals and transmission media.

[0093] According to one or more embodiments of the present invention, the main control system or control module of the air conditioner may include one or more processors and may also internally include a non-transitory computer-readable medium. Specifically, in the defrosting control device of the present invention (main control system or control module), a microcontroller (MCU) may be included, which is arranged in the air conditioner for defrosting control of various operations of the air conditioner and implementation of various functions. The processor of the air conditioner with rinsing control function may be, such as, but not limited to, one or more single-core or multi-core processors. The processor(s) may include any combination of general-purpose processors and special-purpose processors (e.g., graphics processors, application processors, etc.). The processor may be coupled thereto and / or may include memory / storage device, and may be configured to execute instructions stored in memory / storage device to implement various applications and / or operating systems running on the controller in the present invention.

[0094] This invention provides a defrosting control method and apparatus, an air conditioner, and a non-transitory computer-readable medium. In the method provided by this invention, operating parameters of the air conditioner and / or outdoor environmental parameters are first collected cyclically at a preset period. Then, the corresponding frost change parameters of the air conditioner are calculated. Next, the frost change parameters are used to assess the degree of influence of the frost layer on the air conditioner's operating capacity. Finally, the defrosting operation of the air conditioner is controlled based on the degree of influence parameters. Based on the defrosting control method proposed in this invention, the real-time frost rate can be calculated according to the actual operating process, and the degree of influence of the real-time frost layer on the system capacity can be calculated according to a formula to determine whether defrosting should be performed. In this way, the air conditioner can defrost at the optimal time, improving the heating comfort of the air conditioner.

[0095] The accompanying drawings and detailed description of the invention, cited above as examples, serve to explain the invention but do not limit its meaning or scope as described in the claims. Therefore, those skilled in the art can readily make modifications from the above description. Furthermore, those skilled in the art can remove some of the components described herein without degrading performance, or add other components to improve performance. Additionally, those skilled in the art can change the order of steps in the method described herein depending on the process or equipment environment. Therefore, the scope of the invention should not be determined by the embodiments described above, but rather by the claims and their equivalents.

[0096] Although the invention has been described in conjunction with embodiments now considered to be achievable, it should be understood that the invention is not limited to the disclosed embodiments, but rather is intended to cover various modifications and equivalent configurations included within the spirit and scope of the appended claims.

Claims

1. A defrosting control method, characterized in that, include: The operating parameters of the air conditioner and / or outdoor environmental parameters are collected cyclically at a preset period. Calculate the corresponding frost change parameters of the air conditioner based on the operating parameters of the air conditioner and / or the outdoor environmental parameters; The aforementioned frost change parameters are used to evaluate the degree of impact of frost layer on the air conditioner's operating capacity. The defrosting operation of the air conditioner is controlled based on the aforementioned influence parameters; in: The frosting variation parameters include the frosting speed; The parameters used to assess the impact of frost layer on the air conditioner's operating capacity using the frost change parameters include: The rate of change of the impact of the frost rate on the equipment's operating capacity is calculated based on the frost change parameters using the frost rate-water vapor partial pressure difference correction model. The parameter of the degree of influence of frost layer on the air conditioner's operating capacity is calculated by accumulating the rate of change of the degree of influence of the equipment's operating capacity.

2. The method according to claim 1, characterized in that, The calculation of the corresponding frost change parameters of the air conditioner based on the operating parameters of the air conditioner and / or the outdoor environmental parameters includes: The environmental parameters and / or operating parameters are input as input data into the neural network model; The neural network model is used to calculate the frost change parameters corresponding to the air conditioner based on the input data.

3. The method according to claim 2, characterized in that, The step of using the neural network model to calculate the frost change parameters corresponding to the air conditioner based on the input data includes: The neural network model is used to calculate the frosting speed of the air conditioner at the current moment based on the input data; Read the accumulated frost amount of the air conditioner in the previous moment at the current moment, and calculate the new frost amount of the air conditioner based on the frost rate corresponding to the current moment and the frost amount in the previous moment.

4. The method according to claim 3, characterized in that, The frost rate-water vapor partial pressure difference correction model is as follows: in, f Indicates the rate of change in the degree of impact on equipment operating capacity, △ P This represents the difference in partial pressure of water vapor; α、β For correction factor, m Indicates the speed of frosting; V pa , V pb There are two preset thresholds.

5. The method according to claim 1, characterized in that, The method of controlling the defrosting operation of the air conditioner based on the influence parameter includes: The influence level parameter is compared with a preset threshold. If the influence level parameter is greater than the preset threshold, the air conditioner is controlled to defrost.

6. The method according to any one of claims 2-4, characterized in that, Before inputting the environmental parameters and / or operating parameters as input data into the neural network model, the method further includes: Collect frost data of the corresponding equipment type of the air conditioner under different environments, and calculate the real-time frost speed under different environmental conditions and / or different operating conditions. Use the different environmental conditions and / or different operating conditions and the corresponding real-time frost speed as the basic data for training the neural network. Constructing a neural network; The neural network is trained using the aforementioned basic data to obtain a neural network model capable of calculating the frosting speed and amount based on environmental parameters and / or operating parameters.

7. The method according to any one of claims 1-5, characterized in that, The step of cyclically collecting the air conditioner's operating parameters and / or outdoor environmental parameters at a preset cycle includes: The outdoor heat exchanger temperature sensor and the outer ring temperature sensor are used to cyclically collect the outdoor pipe temperature and the outdoor ambient temperature at preset cycles; and / or, Outdoor ambient humidity is collected cyclically by a humidity sensor at a preset cycle; and / or, The speed of the outdoor fan of the air conditioner is read cyclically at a preset period.

8. A defrosting control device comprising one or more processors and a non-transitory computer-readable storage medium storing program instructions, wherein when the one or more processors execute the program instructions, the one or more processors are configured to implement the method according to any one of claims 1-7.

9. A non-transitory computer-readable storage medium having stored program instructions thereon, which, when executed by one or more processors, are configured to implement the method according to any one of claims 1-7.

10. An air conditioner employing the method of any one of claims 1-7, or comprising the apparatus of claim 8, or having a non-transitory computer-readable storage medium as described in claim 9.

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