Defrosting control method and device, air conditioner and computer readable storage medium

Abstract

The embodiment of the invention discloses a defrosting control method and device, an air conditioner and a computer readable storage medium, and relates to the technical field of air conditioners. The defrosting control method comprises the steps that the current of a draught fan is obtained; acquiring a plurality of current characteristics of the current; acquiring environment temperature and environment humidity; determining a dynamic current threshold according to the environment temperature and the environment humidity; based on the multiple current characteristics, the environment temperature, the environment humidity and the dynamic current threshold value, the frosting probability is determined; and when the frosting probability is larger than a frosting probability threshold value, defrosting control is carried out. Therefore, according to the scheme, whether frosting occurs or not can be accurately judged, and accurate defrosting control is achieved.

Description

TECHNICAL FIELD

[0001] Embodiments of the present application relate to the technical field of air conditioners, and specifically relate to a defrosting control method and device, an air conditioner, and a computer readable storage medium. BACKGROUND

[0002] Air conditioner frosting can cause heat exchange efficiency to decrease, heating capacity to weaken, and can also cause equipment load to increase, components to wear out, and even faults. Traditional defrosting methods (such as timed defrosting and temperature-time method) have problems of high false triggering rate and large energy consumption.

[0003] Related technologies determine whether frosting occurs by changes in the amplitude of a fan current, which cannot cope with different environmental conditions, and the amplitude of the fan current is easily disturbed by voltage fluctuations and mechanical wear, so the determination result is not accurate enough, which leads to inaccurate defrosting control and reduces energy efficiency. SUMMARY

[0004] Embodiments of the present application provide a defrosting control method and device, an air conditioner, and a computer readable storage medium, which can accurately determine whether frosting occurs to achieve accurate defrosting control.

[0005] In a first aspect, embodiments of the present application provide a defrosting control method, comprising:

[0006] Obtaining a current of a fan;

[0007] Obtaining a plurality of current characteristics of the current;

[0008] Obtaining an ambient temperature and an ambient humidity;

[0009] Determining a dynamic current threshold value according to the ambient temperature and the ambient humidity;

[0010] Determining a frosting probability based on the plurality of current characteristics, the ambient temperature, the ambient humidity, and the dynamic current threshold value;

[0011] Defrosting when the frosting probability is greater than a frosting probability threshold value.

[0012] In one of the embodiments, the determining of the dynamic current threshold value according to the ambient temperature and the ambient humidity comprises:

[0013] Obtaining a fan rated current under a standard working condition;

[0014] Obtaining a standard temperature and a standard humidity;

[0015] Determining a temperature difference value between the ambient temperature and the standard temperature;

[0016] Determining a humidity difference value between the ambient humidity and the standard humidity;

[0017] adjusting the fan rated current based on the temperature difference and the humidity difference, to obtain the dynamic current threshold.

[0018] In one of the embodiments, the multiple current characteristics include: fluctuation frequency and current harmonic distortion rate; and the defrosting includes:

[0019] when the fluctuation frequency is greater than a fluctuation frequency setting value and the current harmonic distortion rate is less than a current harmonic distortion rate setting value, performing short-time defrosting;

[0020] when the fluctuation frequency is not greater than the fluctuation frequency setting value or the current harmonic distortion rate is not less than the current harmonic distortion rate setting value, performing standard defrosting.

[0021] In one of the embodiments, the multiple current characteristics include current effective value;

[0022] The determining of the frosting probability based on the multiple current characteristics, the environment temperature, the environment humidity and the dynamic current threshold includes:

[0023] when the current effective value is greater than the dynamic current threshold, determining the frosting probability based on the multiple current characteristics, the environment temperature and the environment humidity.

[0024] In one of the embodiments, the multiple current characteristics include current effective value;

[0025] The determining of the frosting probability based on the multiple current characteristics, the environment temperature, the environment humidity and the dynamic current threshold includes:

[0026] inputting the multiple current characteristics, the environment temperature and the environment humidity into a random forest frosting diagnosis model, to obtain binary classification weights respectively output by multiple decision trees of the random forest frosting diagnosis model;

[0027] determining a sum of the binary classification weights respectively output by the multiple decision trees as the frosting probability;

[0028] wherein, the multiple current characteristics, the environment temperature and the environment humidity are respectively one-to-one corresponding to the decision trees;

[0029] the decision tree corresponding to the current effective value outputs the binary classification weights based on the current effective value and the dynamic current threshold.

[0030] In one of the embodiments, the inputting of the multiple current characteristics, the environment temperature and the environment humidity into a random forest frosting diagnosis model, to obtain binary classification weights respectively output by multiple decision trees of the random forest frosting diagnosis model includes:

[0031] inputting the plurality of current features, the ambient temperature and the ambient humidity into a plurality of decision trees of the random forest frosting diagnosis model respectively;

[0032] determining a decision value corresponding to each of the plurality of current features, the ambient temperature and the ambient humidity respectively; wherein the decision value corresponding to the current effective value is the dynamic current threshold value;

[0033] obtaining the binary classification weight output by the plurality of decision trees according to a size relationship between input data and the decision value corresponding to the input data respectively.

[0034] In one of the embodiments, the plurality of current features further include fluctuation frequency and current harmonic distortion rate.

[0035] The obtaining the binary classification weight output by the plurality of decision trees according to a size relationship between input data and the decision value corresponding to the input data respectively includes:

[0036] when the current effective value is not greater than the dynamic current threshold value, obtaining a target weight output by the decision tree; the target weight is 0;

[0037] when the current effective value is greater than the dynamic current threshold value, obtaining a first weight output by the decision tree;

[0038] when the fluctuation frequency is greater than a fluctuation frequency setting value, obtaining the target weight output by the decision tree;

[0039] when the fluctuation frequency is not greater than the fluctuation frequency setting value, obtaining a second weight output by the decision tree;

[0040] when the ambient temperature is within a set temperature range, obtaining the target weight output by the decision tree;

[0041] when the ambient temperature is not within the set temperature range, obtaining a third weight output by the decision tree;

[0042] when the current harmonic distortion rate is less than a current harmonic distortion rate setting value, obtaining the target weight output by the decision tree;

[0043] when the current harmonic distortion rate is not less than the current harmonic distortion rate setting value, obtaining a fourth weight output by the decision tree;

[0044] when the ambient humidity is less than a humidity setting value, obtaining the target weight output by the decision tree;

[0045] when the ambient humidity is not less than the humidity setting value, obtaining a fifth weight output by the decision tree.

[0046] Secondly, embodiments of this application provide a defrosting control device, comprising:

[0047] The current acquisition module is used to acquire the current of the fan.

[0048] A feature acquisition module is used to acquire various current features of the current.

[0049] Temperature and humidity acquisition module, used to acquire ambient temperature and humidity;

[0050] A threshold determination module is used to determine a dynamic current threshold based on the ambient temperature and the ambient humidity;

[0051] The probability determination module is used to determine the frosting probability based on the multiple current characteristics, the ambient temperature, the ambient humidity, and the dynamic current threshold.

[0052] The defrosting control module is used to perform defrosting when the frosting probability is greater than the frosting probability threshold.

[0053] Thirdly, embodiments of this application also provide an air conditioner, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, it implements the steps in the defrosting control method described above.

[0054] Fourthly, embodiments of this application also provide a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps in the defrosting control method described above.

[0055] The embodiments of this application have the following beneficial effects:

[0056] The dynamic current threshold is determined based on ambient temperature and humidity, thus it can handle different environmental conditions. The frosting probability is determined based on multiple current characteristics, ambient temperature, ambient humidity and dynamic current threshold, which solves the problem of inaccurate judgment results caused by relying on a single current amplitude. Therefore, the frosting probability can be accurately predicted, and thus accurate defrosting control can be achieved based on the frosting probability. Attached Figure Description

[0057] To more clearly illustrate the technical solutions in this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the 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.

[0058] Figure 1is a step schematic diagram of a defrosting control method provided by an embodiment of the present application;

[0059] Figure 2 is a structural schematic diagram of a defrosting control device provided by an embodiment of the present application;

[0060] Figure 3 is a structural schematic diagram of an air conditioner provided by an embodiment of the present application. DETAILED DESCRIPTION

[0061] The technical solutions in the present application will be described in detail below with reference to the drawings in the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by a person skilled in the art without creative work fall within the protection scope of the present application.

[0062] In one embodiment, as shown in Figure 1 a defrosting control method is provided, although a logical sequence is shown in the step schematic diagram, in some cases, the steps shown or described can be performed in an order different from that shown in the drawing. The following will be described in detail respectively. It should be noted that the description order of the following embodiments is not limited as the priority order of the embodiments.

[0063] According to the defrosting control method shown in Figure 1 , the method at least includes steps S110 to S160, which will be described in detail as follows:

[0064] In step S110, the current of the fan is acquired.

[0065] The fan is used to drive air flow to realize heat exchange or air circulation. When the air conditioner is frosting, the air flow resistance will increase, which increases the load of the fan motor and the current. Therefore, the current of the fan can be collected by the current transformer, so as to subsequently determine whether frosting based on the current of the fan.

[0066] In step S120, multiple current characteristics of the current are acquired.

[0067] The sampling frequency of the analog-to-digital converter (ADC) can be controlled by the microcontroller; after the current of the fan is collected by the current transformer, the current is signal-conditioned, the ADC periodically samples the conditioned current signal according to the sampling frequency, and obtains a digital signal, so as to subsequently determine multiple current characteristics based on the digital signal.

[0068] Optionally, the sampling frequency of the ADC can be 1 kHz, so as to capture subtle changes (such as amplitude increase, harmonic component increase) of the current waveform, avoid feature loss due to insufficient sampling points, and ensure calculation accuracy while reducing calculation pressure.

[0069] The plurality of current characteristics of the current can include, but are not limited to, any one or more of the following: root mean square (RMS) of the current, fluctuation frequency of the current, and total harmonic distortion (THD) of the current.

[0070] The root mean square of the current refers to an equivalent current value of alternating current that produces the same thermal effect as direct current in the same time, and can reflect the average work capacity of the alternating current. In one embodiment, a microcontroller unit (MCU) in the current can be used to calculate the current data sequence sampled, and the root mean square of the current can be calculated according to a physical conversion formula of a plurality of instantaneous current values and the root mean square. In another embodiment, the root mean square of the current can be directly calculated in real time based on a root mean square conversion chip.

[0071] The fluctuation frequency of the current refers to the number of times of completing periodic changes per unit time, and can reflect the speed of alternating changes of the magnitude and direction of the current over time. The fluctuation frequency can be determined by the proportion of energy of the current in a specific frequency band; if the frequency band of the current is mainly concentrated in the specific frequency band (the proportion of energy of the current in the specific frequency band is high), the fluctuation frequency is low; if the frequency band of the current is mainly concentrated outside the specific frequency band (the proportion of energy of the current in the specific frequency band is low), the fluctuation frequency is high; wherein the specific frequency band can be 0.1-10 Hz. In one embodiment, the current signal can be converted into a frequency domain signal, and the fluctuation frequency of the current can be determined by the frequency band in which the frequency domain signal is located.

[0072] The total harmonic distortion of the current is used to characterize the nonlinearity of the load. In one embodiment, the current signal can be converted into a frequency domain signal, the amplitudes of each harmonic and the amplitude of the fundamental wave are obtained, and the ratio of the root mean square of the amplitudes of each harmonic to the amplitude of the fundamental wave is determined as the total harmonic distortion of the current. The total harmonic distortion of the current of 3-5 harmonics can be calculated.

[0073] In step S130, the ambient temperature and the ambient humidity are obtained.

[0074] The ambient temperature collected by the temperature sensor can be obtained. The ambient humidity collected by the humidity sensor can be obtained.

[0075] In step S140, the dynamic current threshold is determined according to the ambient temperature and the ambient humidity.

[0076] A standard temperature and a standard humidity can be acquired. The standard temperature and the standard humidity can be preset temperatures and humidities. A difference between the ambient temperature and the standard temperature is determined as a temperature difference; a difference between the ambient humidity and the standard humidity is determined as a humidity difference.

[0077] A fan rated current under a standard working condition (standard temperature and standard humidity) is acquired, and the fan rated current is adjusted based on the temperature difference and the humidity difference to obtain a dynamic current threshold. The greater the absolute value of the temperature difference, the greater the current difference between the dynamic current threshold and the fan rated current; the greater the absolute value of the humidity difference, the greater the current difference between the dynamic current threshold and the fan rated current.

[0078] The dynamic current threshold is a critical value for triggering defrosting; the determined dynamic current threshold is a current threshold that meets the current ambient temperature and ambient humidity, so that different environmental conditions can be coped with. Under the current ambient temperature and ambient humidity, the problem of high false positive rate under low temperature and high humidity is solved based on the dynamic current threshold, and whether frosting occurs or not can be accurately determined.

[0079] Optionally, the dynamic current threshold can be adjusted in real time every minute.

[0080] In step S150, a frosting probability is determined based on the plurality of current characteristics, the ambient temperature, the ambient humidity, and the dynamic current threshold.

[0081] The plurality of current characteristics, the ambient temperature, and the ambient humidity have different performances when frosting and not frosting. Therefore, the frosting probability can be comprehensively determined based on the plurality of current characteristics, the ambient temperature, and the ambient humidity.

[0082] The determination result corresponding to the current effective value can be determined based on the current effective value and the dynamic current threshold.

[0083] In step S160, defrosting control is performed when the frosting probability is greater than a frosting probability threshold.

[0084] A preset frosting probability threshold is acquired. The frosting probability threshold can be determined according to big data, experiments, or a neural network model. When the frosting probability is not greater than the frosting probability threshold, it is indicated that frosting does not occur, so that defrosting can not be performed at present. When the frosting probability is greater than the frosting probability threshold, it is indicated that frosting occurs, so that defrosting can be controlled to be performed to restore the heating efficiency of the air conditioner and protect the equipment safety of the air conditioner.

[0085] The technical scheme of the embodiment of the application is adopted, the dynamic current threshold is determined according to the environmental temperature and the environmental humidity, and therefore different environmental conditions can be coped with based on the dynamic current threshold; the frosting probability is determined based on the multiple current characteristics, the environmental temperature, the environmental humidity and the dynamic current threshold, the problem that the frosting judgment result is inaccurate due to the single current amplitude is solved, so that the frosting probability can be accurately predicted, and then accurate defrosting control can be realized according to the frosting probability.

[0086] On the basis of the above technical scheme, as an embodiment, the dynamic current threshold is determined according to the environmental temperature and the environmental humidity, and can include: obtaining a fan rated current under a standard condition; obtaining a standard temperature and a standard humidity; determining a temperature difference value of the environmental temperature and the standard temperature; determining a humidity difference value of the environmental humidity and the standard humidity; adjusting the fan rated current based on the temperature difference value and the humidity difference value to obtain the dynamic current threshold.

[0087] The fan rated current under the standard condition is a theoretical current value of the fan when the fan normally operates under the standard temperature and the standard humidity. The standard temperature and the standard humidity can be preset temperatures and humidities.

[0088] The difference value of the environmental temperature and the standard temperature is determined as the temperature difference value, and the difference value of the environmental humidity and the standard humidity is determined as the humidity difference value.

[0089] In one of the embodiments, the dynamic current threshold can be determined by the following formula:

[0090] I' = I * (1 + k1 * ΔT + k2 * ΔRH + k3 * a);

[0091] wherein, I' is the dynamic current threshold; I is the fan rated current under the standard condition; k1 is a temperature compensation coefficient, which can represent the sensitivity of the current to the motor temperature change; ΔT is the temperature difference value; k2 is a humidity compensation coefficient, which can represent the sensitivity of the current to the air humidity change; ΔRH is the humidity difference value; k3 is an aging compensation coefficient, which represents the sensitivity of the current to the long-term performance decay of the motor; a is an aging factor, which can be determined through a long-term aging experiment of the motor. The k1, k2 and k3 can be determined in advance according to the experiment.

[0092] The dynamic current threshold is determined based on the aging factor and the aging compensation coefficient, and the motor efficiency decay caused by the long-term operation is considered, so that the dynamic current threshold obtained is more accurate.

[0093] In another embodiment, the dynamic current threshold can be determined by the following formula: I' = I * (1 + k1 * ΔT + k2 * ΔRH); wherein the meanings of the characters can be referred to the foregoing.

[0094] By adopting the technical solutions in the embodiments of the present application, the determined dynamic current threshold is a current threshold meeting the current ambient temperature and ambient humidity, and the current ambient temperature and ambient humidity are used to determine whether frosting occurs based on the dynamic current threshold, so that the accuracy of the determination can be improved, and whether defrosting is performed can be accurately controlled.

[0095] Based on the above technical solutions, as an embodiment, the defrosting can have two modes of short-time defrosting and standard defrosting. The defrosting duration of the standard defrosting is longer than that of the short-time defrosting. Optionally, the defrosting duration of the short-time defrosting can be set to 2 min to 3 min at most; and the defrosting duration of the standard defrosting can be set to 3 min to 15 min in general. In the early stage of frosting, the frosting duration is short, and the frost layer is thin, so the short-time defrosting can be performed; in the non-early stage of frosting, the frosting duration is long, and the frost layer is thick, so the standard defrosting can be performed.

[0096] When the frosting probability is greater than the frosting probability threshold, defrosting needs to be performed, and at this time, whether it is the early stage of frosting can be further determined by the fluctuation frequency and the current harmonic distortion rate, so as to determine whether the short-time defrosting or the standard defrosting is performed. When the fluctuation frequency is greater than the fluctuation frequency setting value, and the current harmonic distortion rate is less than the current harmonic distortion rate setting value, it is determined that the current is the early stage of frosting, so only the short-time defrosting needs to be performed. When the fluctuation frequency is not greater than the fluctuation frequency setting value, or the current harmonic distortion rate is not less than the current harmonic distortion rate setting value, it is determined that the current is not the early stage of frosting, so the standard defrosting can be performed.

[0097] In the early stage of frosting, the frost layer is just formed on the surface of the evaporator, and is not uniform, which can cause mechanical vibration and thus high-frequency fluctuation of the current. Therefore, whether the current is the early stage of frosting can be determined according to whether the fluctuation frequency of the current is greater than the fluctuation frequency setting value. The fluctuation frequency setting value can be determined according to experiments or big data. Optionally, the fluctuation frequency setting value can be 3 Hz to 12 Hz according to experiments; and optionally, the fluctuation frequency setting value can be 7 Hz.

[0098] At the initial stage of frosting, the initial formation of frost layer only slightly reduces the heat exchange efficiency of the evaporator, and the fan can still maintain the stable evaporation pressure by slightly adjusting the rotating speed, etc. At this time, the current waveform is close to a sine wave, and the amplitude of harmonic components (such as 3rd and 5th harmonics) is low. At the late stage of frosting, after the frost layer completely covers the evaporator, the heat exchange efficiency is significantly reduced, and the evaporation temperature continues to decrease. The fan needs to output a larger torque to maintain the pressure, resulting in a current waveform with a truncated top or distortion, and the amplitude of low-frequency harmonics (such as 3rd and 5th harmonics) significantly increases. Therefore, whether it is currently at the initial stage of frosting can be determined according to whether the current harmonic distortion rate is less than the current harmonic distortion rate set value. The current harmonic distortion rate set value can be determined according to experiments or big data. Alternatively, the value range of the current harmonic distortion rate set value can be 3% to 8%; alternatively, the current harmonic distortion rate set value can be 5%.

[0099] By adopting the technical solutions of the embodiments of the present application, when defrosting is needed, it can be more accurately determined whether it is at the initial stage of defrosting, so as to determine whether to perform short-time defrosting or standard defrosting, which can reduce the invalid defrosting energy consumption caused by too long defrosting time, avoid the chain failure caused by the failure of defrosting due to too short defrosting time, thereby reducing energy consumption cost, reducing failure downtime, and prolonging the service life of the air conditioner.

[0100] On the basis of the above technical solutions, as an embodiment, based on the plurality of current characteristics, the environment temperature, the environment humidity, and the dynamic current threshold, the frosting probability can be determined, which can include: when the current effective value is greater than the dynamic current threshold, the frosting probability is determined based on the plurality of current characteristics, the environment temperature, and the environment humidity.

[0101] On the one hand, the influence of frosting on the current effective value is more obvious, and when the current effective value is not greater than the dynamic current threshold, the probability of frosting is low. Therefore, whether the frosting probability needs to be accurately determined based on the plurality of current characteristics, the environment temperature, and the environment humidity can be determined based on whether the current effective value is greater than the dynamic current threshold.

[0102] When the current effective value is not greater than the dynamic current threshold, the frosting probability does not need to be determined based on the plurality of current characteristics, the environment temperature, and the environment humidity; when the current effective value is greater than the dynamic current threshold, the frosting probability is determined based on the plurality of current characteristics, the environment temperature, and the environment humidity.

[0103] On the other hand, as described later, the binary classification weight corresponding to the non-0 general current effective value is high (usually 0.30-0.45), so when the current effective value is not greater than the dynamic current threshold, the output binary classification weight is 0, and even if the binary classification weights corresponding to other current features, ambient temperature and ambient humidity are all non-0, the sum is at most 0.55-0.70, and the frosting probability threshold is usually greater than 0.70 (usually 0.80), so when the current effective value is not greater than the dynamic current threshold, the determined frosting probability is also less than the frosting probability threshold, and it is determined that no frosting has occurred. Therefore, when the current effective value is not greater than the dynamic current threshold, the frosting probability can be directly determined without being based on multiple current features, ambient temperature and ambient humidity.

[0104] The technical solution of the embodiment of the present application can first determine whether the current effective value is greater than the dynamic current threshold, thereby determining whether it is necessary to determine the frosting probability based on multiple current features, ambient temperature and ambient humidity, thereby reducing the number of determinations and saving computing resources.

[0105] On the basis of the above technical solution, as an embodiment, determining the frosting probability based on multiple current features, ambient temperature, ambient humidity and dynamic current threshold can include: inputting the multiple current features, ambient temperature and ambient humidity into a random forest frosting diagnosis model to obtain the binary classification weights output by each of the multiple decision trees of the random forest frosting diagnosis model; and determining the sum of the binary classification weights output by each of the multiple decision trees as the frosting probability; wherein the multiple current features, ambient temperature and ambient humidity are one-to-one corresponding to the decision trees; and the decision tree corresponding to the current effective value outputs the binary classification weight based on the current effective value and the dynamic current threshold.

[0106] The random forest frosting diagnosis model can be used to determine the frosting probability, and the random forest frosting diagnosis model is constructed based on the random forest diagnosis model. The random forest frosting diagnosis model is a machine learning model based on ensemble learning, which constructs multiple decision trees, and integrates the diagnosis results of the multiple decision trees to realize accurate diagnosis and prediction of the frosting probability.

[0107] The multiple current features, ambient temperature and ambient humidity are one-to-one corresponding to the decision trees. After inputting the multiple current features, ambient temperature and ambient humidity into the random forest frosting diagnosis model, the random forest frosting diagnosis model will input the inputted multiple current features, ambient temperature and ambient humidity into the corresponding decision trees respectively, and the decision trees will output binary classification weights according to the input data. The binary classification weight corresponding to each decision tree includes weights of 0 and non-0 weights; wherein the non-0 weights corresponding to different decision trees can be the same or different.

[0108] In one embodiment, the random forest frosting diagnosis model can be pre-trained in a supervised manner, so that each decision tree learns how to predict accurate binary classification weights according to the input data. Specifically, each decision tree can learn the decision value corresponding to the input data, and learn the non-zero weight in the binary classification weight corresponding to the input data. In other words, the decision value corresponding to the input data, and the non-zero weight in the binary classification weight corresponding to the input data, are the optimal parameters obtained through supervised training, so the accuracy of the binary classification weight output by the decision tree is also high.

[0109] In another embodiment, the decision value and non-zero weight corresponding to each decision tree in the random forest frosting diagnosis model can be pre-determined and assigned to the random forest frosting diagnosis model. The decision value and non-zero weight can be determined through experiments, big data or other machine learning models.

[0110] The decision value corresponding to the current effective value is a dynamic current threshold, so the decision tree corresponding to the current effective value can output a binary classification weight according to the current effective value and the dynamic current threshold. Each of the other decision trees can output a corresponding binary classification weight according to the input data and the decision value corresponding to the input data.

[0111] The binary classification weights output by each of the plurality of decision trees are added together, and the sum is determined as the frosting probability. For example, the binary classification weight corresponding to the current effective value is 0.4, the binary classification weight corresponding to the fluctuation frequency is 0, the binary classification weight corresponding to the environmental temperature is 0.2, the binary classification weight corresponding to the current harmonic distortion rate is 0, and the binary classification weight corresponding to the environmental humidity is 0.1, so the frosting probability can be determined as 0.4+0+0.2+0+0.1=0.7. If the frosting probability threshold is 0.80, the frosting probability is less than the frosting probability threshold, and it is determined that no frosting occurs, and defrosting is not required.

[0112] By using the technical solution of the embodiments of the present application, the plurality of decision trees of the random forest frosting diagnosis model can reduce the interference of single data noise on the diagnosis result, improve the accuracy of the determined frosting probability, and based on the random forest frosting diagnosis model, a plurality of output data can be quickly predicted, which is efficient and can meet the real-time monitoring requirements.

[0113] On the basis of the above technical solutions, as an embodiment, the multiple current characteristics, the ambient temperature and the ambient humidity are input into the random forest frosting diagnosis model to obtain the binary classification weights output by each of the multiple decision trees of the random forest frosting diagnosis model, which can include: inputting the multiple current characteristics, the ambient temperature and the ambient humidity into the multiple decision trees of the random forest frosting diagnosis model respectively; determining the decision values corresponding to the multiple current characteristics, the ambient temperature and the ambient humidity respectively; wherein the decision value corresponding to the current effective value is a dynamic current threshold; and obtaining the binary classification weights output by the multiple decision trees according to the size relationship between the input data and the decision values corresponding to the input data.

[0114] The decision value corresponding to the current effective value can be a dynamic current threshold; the decision value corresponding to the fluctuation frequency can be a fluctuation frequency setting value; the decision value corresponding to the ambient temperature can be a set temperature range; the decision value corresponding to the current harmonic distortion rate can be a current harmonic distortion rate setting value; and the decision value corresponding to the ambient humidity can be a humidity setting value. The fluctuation frequency setting value, the set temperature range, the current harmonic distortion rate setting value and the humidity setting value can be obtained through supervised training, or can be determined in advance through experiments, big data or other machine learning models.

[0115] In an embodiment, it is determined through experiments that the fluctuation frequency setting value is usually in the range of 3 Hz to 12 Hz, the current harmonic distortion rate setting value is usually in the range of 3% to 8%, and the humidity setting value is usually in the range of 60% to 80%.

[0116] The multiple current characteristics, the ambient temperature and the ambient humidity have different performances when frosting and non-frosting. Therefore, the frosting probability can be comprehensively judged based on the multiple current characteristics, the ambient temperature and the ambient humidity. The current effective value can represent the fan load change, the fluctuation frequency can reflect the mechanical vibration in the early stage of frosting, the judgment based on the ambient temperature can correct the temperature influence, the current harmonic distortion rate is nonlinearly related to frosting, and the judgment based on the ambient humidity can correct the humidity influence. The multiple decision trees can output binary classification weights according to the size relationship between the input data and the decision values corresponding to the input data.

[0117] When the current effective value is greater than the dynamic current threshold, the corresponding decision tree outputs a first weight; when the fluctuation frequency is not greater than the fluctuation frequency setting value, the corresponding decision tree outputs a second weight; when the ambient temperature is not in the set temperature range, the corresponding decision tree outputs a third weight; when the current harmonic distortion rate is not less than the current harmonic distortion rate setting value, the corresponding decision tree outputs a fourth weight; and when the ambient humidity is not less than the humidity setting value, the corresponding decision tree outputs a fifth weight. The first weight, the second weight, the third weight, the fourth weight and the fifth weight are all non-zero weights.

[0118] The technical scheme of the embodiment of the application is based on multiple current characteristics, environmental temperature and environmental humidity to predict the frosting probability, considers the change caused by frosting from multiple dimensions, solves the problem that the result is inaccurate due to the frosting probability prediction according to a single current amplitude, so that the frosting probability can be accurately predicted, and then accurate defrosting control can be realized according to the frosting probability.

[0119] On the basis of the above technical scheme, as an embodiment, the output binary classification weight can comprise: obtaining a target weight output by the decision tree when the current effective value is not greater than the dynamic current threshold; the target weight being 0; obtaining a first weight output by the decision tree when the current effective value is greater than the dynamic current threshold; obtaining the target weight output by the decision tree when the fluctuation frequency is greater than the fluctuation frequency setting value; obtaining a second weight output by the decision tree when the fluctuation frequency is not greater than the fluctuation frequency setting value; obtaining the target weight output by the decision tree when the environmental temperature is within the set temperature range; obtaining a third weight output by the decision tree when the environmental temperature is not within the set temperature range; obtaining the target weight output by the decision tree when the current harmonic distortion rate is less than the current harmonic distortion rate setting value; obtaining a fourth weight output by the decision tree when the current harmonic distortion rate is not less than the current harmonic distortion rate setting value; obtaining the target weight output by the decision tree when the environmental humidity is less than the humidity setting value; and obtaining a fifth weight output by the decision tree when the environmental humidity is not less than the humidity setting value.

[0120] When the current effective value is greater than the dynamic current threshold, the corresponding decision tree outputs the first weight, and otherwise the decision tree outputs the target weight; when the fluctuation frequency is not greater than the fluctuation frequency setting value, the corresponding decision tree outputs the second weight, and otherwise the decision tree outputs the target weight; when the environmental temperature is not within the set temperature range, the corresponding decision tree outputs the third weight, and otherwise the decision tree outputs the target weight; when the current harmonic distortion rate is not less than the current harmonic distortion rate setting value, the corresponding decision tree outputs the fourth weight, and otherwise the decision tree outputs the target weight; and when the environmental humidity is not less than the humidity setting value, the corresponding decision tree outputs the fifth weight, and otherwise the decision tree outputs the target weight. The target weight is 0, and the first weight, the second weight, the third weight, the fourth weight and the fifth weight are all non-0 weights, and the first weight, the second weight, the third weight, the fourth weight and the fifth weight can be the same or different.

[0121] In one embodiment, it is found through multiple experiments that the first weight generally ranges from 0.30 to 0.45, the second weight generally ranges from 0.20 to 0.35, the third weight generally ranges from 0.15 to 0.25, the fourth weight generally ranges from 0.10 to 0.20, and the fifth weight generally ranges from 0.05 to 0.15. The settings of the first weight, the second weight, the third weight, the fourth weight, and the fifth weight also take into account the influence degree of the frosting on different characteristics (the effective value of the current, the fluctuation frequency, and the current harmonic distortion rate) and the influence degree of different characteristics (the ambient temperature and the ambient humidity) on the frosting, and the greater the influence degree, the higher the corresponding weight. For example, the frosting has a greater influence on the effective value of the current, and the frosting has a smaller influence on the current harmonic distortion rate, so the first weight is generally greater than the fourth weight; the influence of the ambient humidity on the frosting is lower than the influence of the ambient temperature on the frosting, so the fifth weight is generally less than the third weight.

[0122] In one of the embodiments, the plurality of decision trees can make decisions and output binary classification weights according to the corresponding relationship shown in Table 1.

[0123] Table 1

[0124]

[0125] By using the technical solutions of the embodiments of the present application, different weights are set according to the influence degree of the frosting on different characteristics and the influence degree of different characteristics on the frosting, so as to improve the accuracy of the predicted frosting probability.

[0126] In one embodiment, the defrosting control method can be applied to an air conditioner, wherein the air conditioner can include but is not limited to one or more of an air source heat pump, an air conditioner, and a multi-split air conditioner.

[0127] In another embodiment, the defrosting control method can be applied to other electronic devices, which can communicate with the air conditioner based on a low-power wide-area wireless communication technology (Long Range Radio, LoRa), so as to obtain current data from the air conditioner, and obtain the ambient temperature and the ambient humidity from the air conditioner or other sensors, and then determine the frosting probability according to the current data, the ambient temperature, and the ambient humidity, and determine whether to perform defrosting, perform short-time defrosting, or perform standard defrosting, and issue a defrosting control command to the air conditioner to control the air conditioner to perform defrosting when defrosting is needed.

[0128] As an embodiment, the frosting probability can also be determined by combining the pressure difference between the two sides of the evaporator and infrared thermal imaging, and then whether to defrost is controlled. The pressure difference between the two sides of the evaporator can reflect the air flow condition, and frosting will cause blockage between the fins, so the pressure difference between the two sides of the evaporator increases. Infrared thermal imaging can detect the temperature distribution of the evaporator surface, and frosting will affect heat conduction, causing uneven temperature distribution or local temperature drop. By combining the pressure difference between the two sides of the evaporator and infrared thermal imaging to determine the frosting probability, false judgments based on a single parameter can be avoided.

[0129] Alternatively, a machine learning model (such as a random forest diagnosis model) can be used to fuse these features to predict the frosting probability, and then determine whether frosting occurs and whether defrosting is needed, according to the rate of change of the pressure difference between the two sides of the evaporator, the temperature mean, standard deviation and temperature gradient of the infrared image, and the ambient temperature and humidity.

[0130] As an embodiment, the frosting probability can also be determined by the fan noise spectrum, and then whether to defrost is controlled. Frosting will affect air flow, and then change the operating state of the fan. The fan needs more power to maintain air volume under frosting conditions, or the airflow is uneven due to the presence of frost, so different noises will be produced. Noise spectrum analysis can decompose the noise signal into different frequency components. Under normal operation, the noise spectrum of the fan may have a specific pattern, and frosting may cause changes in certain frequency components, such as an increase in high-frequency noise or the appearance of a new frequency peak. Therefore, whether frosting occurs can be determined according to the fan noise spectrum.

[0131] To better implement the defrosting control method of the present application, the present application also provides a defrosting control device based on the above defrosting control method. The meanings of the terms are the same as in the above defrosting control method, and the specific implementation details can be referred to the description in the method embodiment.

[0132] Please refer to Figure 2 , Figure 2 is a structural schematic diagram of the defrosting control device provided by the embodiment of the present application, wherein the defrosting control device comprises:

[0133] The current acquisition module 201 is configured to acquire the current of the fan.

[0134] The feature acquisition module 202 is configured to acquire a plurality of current features of the current.

[0135] The temperature and humidity acquisition module 203 is configured to acquire the ambient temperature and the ambient humidity.

[0136] The threshold determination module 204 is configured to determine a dynamic current threshold according to the ambient temperature and the ambient humidity.

[0137] The probability determination module 205 is configured to determine a frosting probability based on the multiple current characteristics, the ambient temperature, the ambient humidity, and the dynamic current threshold value.

[0138] The defrosting control module 206 is configured to perform defrosting when the frosting probability is greater than a frosting probability threshold value.

[0139] In one embodiment, the threshold value determination module 204 is specifically configured to:

[0140] Obtain a fan rated current under a standard working condition;

[0141] Obtain a standard temperature and a standard humidity;

[0142] Determine a temperature difference between the ambient temperature and the standard temperature;

[0143] Determine a humidity difference between the ambient humidity and the standard humidity;

[0144] Adjust the fan rated current based on the temperature difference and the humidity difference to obtain the dynamic current threshold value.

[0145] In one embodiment, the multiple current characteristics include a fluctuation frequency and a current harmonic distortion rate, and the defrosting control module 206 is specifically configured to:

[0146] Perform short-time defrosting when the fluctuation frequency is greater than a fluctuation frequency setting value and the current harmonic distortion rate is less than a current harmonic distortion rate setting value;

[0147] Perform standard defrosting when the fluctuation frequency is not greater than the fluctuation frequency setting value or the current harmonic distortion rate is not less than the current harmonic distortion rate setting value.

[0148] In one embodiment, the multiple current characteristics include a current effective value, and the probability determination module 205 is specifically configured to:

[0149] Determine the frosting probability based on the multiple current characteristics, the ambient temperature, and the ambient humidity when the current effective value is greater than the dynamic current threshold value.

[0150] In one embodiment, the multiple current characteristics include a current effective value, and the probability determination module 205 is specifically configured to:

[0151] Input the multiple current characteristics, the ambient temperature, and the ambient humidity into a random forest frosting diagnosis model to obtain a binary classification weight output by each of multiple decision trees of the random forest frosting diagnosis model;

[0152] Determine a sum of the binary classification weights output by each of the multiple decision trees as the frosting probability.

[0153] wherein the plurality of current features, the ambient temperature and the ambient humidity each correspond to one of the decision trees;

[0154] The decision tree corresponding to the current effective value outputs the binary classification weight based on the current effective value and the dynamic current threshold.

[0155] In one of the embodiments, the inputting the plurality of current features, the ambient temperature and the ambient humidity into the random forest frosting diagnosis model to obtain the binary classification weight output by each of the plurality of decision trees of the random forest frosting diagnosis model comprises:

[0156] inputting the plurality of current features, the ambient temperature and the ambient humidity into the plurality of decision trees of the random forest frosting diagnosis model respectively;

[0157] determining the decision value corresponding to each of the plurality of current features, the ambient temperature and the ambient humidity; wherein the decision value corresponding to the current effective value is the dynamic current threshold;

[0158] obtaining the binary classification weight output by each of the plurality of decision trees according to the size relationship between the input data and the decision value corresponding to the input data.

[0159] In one of the embodiments, the plurality of current features further comprises fluctuation frequency and current harmonic distortion rate;

[0160] The obtaining the binary classification weight output by each of the plurality of decision trees according to the size relationship between the input data and the decision value corresponding to the input data comprises:

[0161] when the current effective value is not greater than the dynamic current threshold, obtaining the target weight output by the decision tree; the target weight is 0;

[0162] when the current effective value is greater than the dynamic current threshold, obtaining the first weight output by the decision tree;

[0163] when the fluctuation frequency is greater than the fluctuation frequency setting value, obtaining the target weight output by the decision tree;

[0164] when the fluctuation frequency is not greater than the fluctuation frequency setting value, obtaining the second weight output by the decision tree;

[0165] when the ambient temperature is within the set temperature range, obtaining the target weight output by the decision tree;

[0166] when the ambient temperature is not within the set temperature range, obtaining the third weight output by the decision tree;

[0167] When the current harmonic distortion rate is less than the current harmonic distortion rate set value, the target weight output by the decision tree is obtained;

[0168] When the current harmonic distortion rate is not less than the current harmonic distortion rate set value, the fourth weight output by the decision tree is obtained;

[0169] When the ambient humidity is less than the set humidity value, the target weight output by the decision tree is obtained;

[0170] When the ambient humidity is not less than the set humidity value, the fifth weight output by the decision tree is obtained.

[0171] The technical solution of this application embodiment is adopted in which the dynamic current threshold is determined based on the ambient temperature and ambient humidity. Therefore, the dynamic current threshold can cope with different environmental conditions. The frosting probability is determined based on multiple current characteristics, ambient temperature, ambient humidity and dynamic current threshold, which solves the problem that the judgment result is inaccurate when judging frosting based on a single current amplitude. Thus, the frosting probability can be accurately predicted, and accurate defrosting control can be achieved based on the frosting probability.

[0172] Specific limitations regarding the defrosting control device can be found in the limitations of the defrosting control method described above, and will not be repeated here. Each module in the aforementioned defrosting control device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in the computer device in hardware form, or stored in the memory of the computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0173] In addition, this application also provides an air conditioner, such as Figure 3 As shown, it illustrates the structural diagram of the air conditioner involved in this application, specifically:

[0174] The air conditioner may include components such as a processor 301 with one or more processing cores and a memory 302 with one or more computer-readable storage media. Those skilled in the art will understand that... Figure 3 The air conditioner structure shown does not constitute a limitation on the air conditioner and may include more or fewer components than shown, or combine certain components, or have different component arrangements. Wherein:

[0175] The processor 301 is the control center of the air conditioner, and connects all parts of the air conditioner through various interfaces and lines, and performs various functions and processes data of the air conditioner by running or executing software programs and / or modules stored in the memory 302 and calling data stored in the memory 302, thereby monitoring the air conditioner as a whole. Optionally, the processor 301 can include one or more processing cores; preferably, the processor 301 can integrate an application processor and a modem processor, wherein the application processor mainly processes operating systems, user interfaces and application programs, etc., and the modem processor mainly processes wireless communication. It can be understood that the above-mentioned modem processor can also not be integrated into the processor 301.

[0176] The memory 302 can be used to store software programs and modules, and the processor 301 executes various functions and processes data by running the software programs and modules stored in the memory 302. The memory 302 can mainly include a program storage area and a data storage area, wherein the program storage area can store operating systems, application programs required by at least one function, etc.; and the data storage area can store data created according to the use of the air conditioner, etc. In addition, the memory 302 can include a high-speed random access memory, and can also include a non-volatile memory, such as at least one magnetic disk storage device, a flash memory device, or other volatile solid-state memory device. Accordingly, the memory 302 can also include a memory controller to provide access for the processor 301 to the memory 302.

[0177] In one embodiment, the air conditioner further includes a power supply 303 for supplying power to various components, and preferably, the power supply 303 can be logically connected to the processor 301 through a power management system, so as to realize functions such as management of charging, discharging and power consumption management through the power management system. The power supply 303 can also include one or more than one direct current or alternating current power supply, a recharging system, a power supply device debugging circuit, a power supply converter or inverter, a power supply state indicator, etc.

[0178] In one embodiment, the air conditioner can further include an input unit 304, which can be used to receive input digital or character information, and generate keyboard, mouse, joystick, optical or trackball signal inputs related to user settings and function controls.

[0179] Although not shown, the air conditioner can also include a display unit, etc., which will not be described here. Specifically, in the present embodiment, the processor 301 in the air conditioner will load executable files corresponding to the processes of one or more than one application program into the memory 302 according to the following instructions, and run the application programs stored in the memory 302 by the processor 301, thereby realizing the steps in any defrosting control method provided by the present application.

[0180] Those skilled in the art can understand that, Figure 3 The structure shown in the figure is only a block diagram of part of the structure related to the scheme of the present application, and does not constitute a limitation on the air conditioner to which the scheme of the present application is applied. A specific air conditioner can include more or fewer components than those shown in the figure, or combine certain components, or have a different component arrangement.

[0181] In one embodiment, an air conditioner is provided, including a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the method described in any embodiment of the present application.

[0182] In one embodiment, a computer readable storage medium is provided, storing a computer program, and the computer program is executed by a processor to implement the method described in any embodiment of the present application.

[0183] In some embodiments, a computer program product is also provided, including a computer program or instructions, and the computer program or instructions are executed by a processor to implement the method described in any embodiment of the present application.

[0184] The specific implementation of each of the above operations can be referred to the previous embodiments, which will not be repeated here.

[0185] Those skilled in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructions, or by relevant hardware controlled by the instructions, which can be stored in a computer readable storage medium and loaded and executed by a processor.

[0186] To this end, the present application provides a computer readable storage medium, storing a computer program, which can be loaded by a processor to execute the steps in any defrosting control method provided by the present application.

[0187] The specific implementation of each of the above operations can be referred to the previous embodiments, which will not be repeated here.

[0188] The computer readable storage medium can include a read only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0189] Since the instructions stored in the computer readable storage medium can execute the steps in any defrosting control method provided by the present application, the beneficial effects that can be achieved by any defrosting control method provided by the present application can be achieved, which will be described in detail in the previous embodiments, and will not be repeated here.

[0190] Finally, it is to be understood that the phraseology or terminology such as "first" and "second" etc. used herein is merely intended to differentiate one entity or operation from another without necessarily requiring or implying any actual such relationship or order between such entities or operations. Moreover, the terms "comprises", "comprising", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can also include other elements not expressly listed or inherent to such process, method, article, or apparatus. An element proceeded by "comprises... a" does not, without more constraints, exclude the existence of additional identical elements in the process, method, article, or apparatus that comprises the element.

[0191] The above provides a defrosting control method, device, air conditioner and computer readable storage medium. The principles and implementation manners of the present application are described by using specific examples. The above example is only used to help understand the method and core idea of the present application. For those skilled in the art, the specific implementation manners and application ranges can be changed according to the idea of the present application. In conclusion, the content of the specification should not be understood as a limitation of the present application.

Claims

1. A defrosting control method characterized by, The method comprises: obtaining a current of a fan; obtaining a plurality of current characteristics of the current; obtaining an ambient temperature and an ambient humidity; determining a dynamic current threshold according to the ambient temperature and the ambient humidity; determining a frosting probability based on the plurality of current characteristics, the ambient temperature, the ambient humidity and the dynamic current threshold; carrying out defrosting when the frosting probability is greater than a frosting probability threshold.

2. The method of claim 1, wherein, The determining of the dynamic current threshold according to the ambient temperature and the ambient humidity comprises: obtaining a fan rated current under a standard working condition; obtaining a standard temperature and a standard humidity; determining a temperature difference between the ambient temperature and the standard temperature; determining a humidity difference between the ambient humidity and the standard humidity; adjusting the fan rated current based on the temperature difference and the humidity difference to obtain the dynamic current threshold.

3. The method of claim 1, wherein, The plurality of current characteristics comprises a fluctuation frequency and a current harmonic distortion rate; the carrying out of defrosting comprises: carrying out short-time defrosting when the fluctuation frequency is greater than a fluctuation frequency setting value and the current harmonic distortion rate is less than a current harmonic distortion rate setting value; carrying out standard defrosting when the fluctuation frequency is not greater than the fluctuation frequency setting value or the current harmonic distortion rate is not less than the current harmonic distortion rate setting value.

4. The method of claim 1, wherein, The plurality of current characteristics comprises a current effective value; The determining of the frosting probability based on the plurality of current characteristics, the ambient temperature, the ambient humidity and the dynamic current threshold comprises: determining the frosting probability based on the plurality of current characteristics, the ambient temperature and the ambient humidity when the current effective value is greater than the dynamic current threshold.

5. The method of claim 1, wherein, The plurality of current characteristics comprises a current effective value; The determining of the frosting probability based on the plurality of current characteristics, the ambient temperature, the ambient humidity and the dynamic current threshold comprises: inputting the plurality of current characteristics, the ambient temperature and the ambient humidity into a random forest frosting diagnosis model to obtain a binary classification weight output by each of a plurality of decision trees of the random forest frosting diagnosis model; determining a sum of the binary classification weight output by each of the plurality of decision trees as the frosting probability; wherein the plurality of current characteristics, the ambient temperature and the ambient humidity are respectively one-to-one corresponding to the decision trees; a decision tree corresponding to the current effective value outputs the binary classification weight based on the current effective value and the dynamic current threshold.

6. The method of claim 5, wherein, The inputting of the plurality of current characteristics, the ambient temperature and the ambient humidity into the random forest frosting diagnosis model to obtain the binary classification weight output by each of the plurality of decision trees of the random forest frosting diagnosis model comprises: inputting the plurality of current characteristics, the ambient temperature and the ambient humidity into the plurality of decision trees of the random forest frosting diagnosis model respectively; determining a decision value corresponding to each of the plurality of current characteristics, the ambient temperature and the ambient humidity; wherein the decision value corresponding to the current effective value is the dynamic current threshold; obtaining the binary classification weight output by each of the plurality of decision trees according to a size relationship between input data and the decision value corresponding to the input data.

7. The method of claim 6, wherein, The plurality of current characteristics further include: a fluctuation frequency and a current harmonic distortion rate; The obtaining of the plurality of decision trees respectively according to the size relationship between the input data and the decision value corresponding to the input data outputs the binary classification weight, including: When the current effective value is not greater than the dynamic current threshold, obtaining the target weight output by the decision tree; the target weight is 0; When the current effective value is greater than the dynamic current threshold, obtaining the first weight output by the decision tree; When the fluctuation frequency is greater than the fluctuation frequency setting value, obtaining the target weight output by the decision tree; When the fluctuation frequency is not greater than the fluctuation frequency setting value, obtaining the second weight output by the decision tree; When the ambient temperature is within the set temperature range, obtaining the target weight output by the decision tree; When the ambient temperature is not within the set temperature range, obtaining the third weight output by the decision tree; When the current harmonic distortion rate is less than the current harmonic distortion rate setting value, obtaining the target weight output by the decision tree; When the current harmonic distortion rate is not less than the current harmonic distortion rate setting value, obtaining the fourth weight output by the decision tree; When the ambient humidity is less than the humidity setting value, obtaining the target weight output by the decision tree; When the ambient humidity is not less than the humidity setting value, obtaining the fifth weight output by the decision tree.

8. A defrosting control device characterized by comprising: Comprising: A current acquisition module for acquiring the current of the fan; A feature acquisition module for acquiring a plurality of current characteristics of the current; A temperature and humidity acquisition module for acquiring the ambient temperature and the ambient humidity; A threshold determination module for determining a dynamic current threshold according to the ambient temperature and the ambient humidity; A probability determination module for determining a frosting probability based on the plurality of current characteristics, the ambient temperature, the ambient humidity, and the dynamic current threshold; A defrosting control module for defrosting when the frosting probability is greater than a frosting probability threshold.

9. An air conditioner characterized by comprising: The computer readable storage medium stores a computer program, and the computer program is executed by the processor to realize the steps in the defrosting control method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer readable storage medium stores a computer program, and the computer program is executed by the processor to realize the steps in the defrosting control method according to any one of claims 1 to 7.

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