Refrigerator defrosting control method and device, refrigerator and storage medium

Abstract

The invention relates to the technical field of intelligent equipment, and discloses a refrigerator defrosting control method and device, a refrigerator and a storage medium. The method comprises the steps that under the condition that the current accumulative operation time of a compressor reaches first set time, the current internet state of the refrigerator is obtained; under the condition that the current internet state is an offline state, the corresponding first peak shifting defrosting time is predicted according to a defrosting time sequence calibration model, and the defrosting time sequence calibration model is generated after big data analysis is conducted according to historical trough defrosting time data; and at the first peak shifting defrosting time, refrigerator defrosting operation is conducted. Thus, under the condition that no network exists, the refrigerator can still conduct defrosting operation in the electricity trough time period, reasonable allocation of resources is achieved, and resource waste is reduced.

Description

Technical Field

[0001] The present application relates to the field of smart device technology, for example, to a method, device, refrigerator, and storage medium for refrigerator defrost control. Background Art

[0002] Energy conservation and environmental protection have always been a major concern in the home appliance industry. Currently, society is promoting energy-saving strategies that prioritize electricity consumption during peak and off-peak hours and allocate resources rationally. This shifting of electricity consumption can effectively alleviate pressure on resource supply and reduce resource waste. A refrigerator is a refrigeration device that maintains a constant low temperature and is a consumer product designed to keep food or other items cold. It contains a compressor, an ice maker, a cabinet or box for ice formation, and a storage box with a refrigeration unit.

[0003] After a period of use, refrigerators tend to develop frost in the cabinet, requiring timely defrosting. However, the defrost heater has a relatively high power rating. Furthermore, the compressor continues to operate at high frequency for an extended period of time to maintain the refrigerator's temperature after defrosting. As a 24 / 7 standby appliance, refrigerators perform the energy-intensive defrosting process during off-peak hours, effectively shifting electricity usage and reducing electricity bills in areas with off-peak electricity pricing.

[0004] Currently, the heating wire can be turned on and off by timing or by judging the time based on whether the Internet is online. However, once the refrigerator loses power, the time needs to be reset, or, when the Internet is offline, the corresponding staggered defrost control cannot be performed.

[0005] It should be noted that the information disclosed in the above background technology section is only used to enhance the understanding of the background of this application, and therefore may include information that does not constitute prior art known to ordinary technicians in this field. Summary of the Invention

[0006] In order to provide a basic understanding of some aspects of the disclosed embodiments, a brief summary is given below. The summary is not an extensive review, nor is it intended to identify key / critical elements or delineate the scope of protection of these embodiments, but rather serves as a prelude to the detailed description that follows.

[0007] The embodiments of the present disclosure provide a method, apparatus, device, and storage medium for refrigerator defrost control, so as to solve the technical problem that the application scenarios of refrigerator off-peak defrost control need to be expanded.

[0008] In some embodiments, the method comprises:

[0009] When the current accumulated running time of the compressor reaches a first set time, obtaining the current Internet status of the refrigerator;

[0010] When the current Internet status is offline, the corresponding first off-peak defrost time is predicted based on the defrost time series calibration model, wherein the defrost time series calibration model is generated after big data analysis based on historical trough defrost time data;

[0011] During the first off-peak defrost time, the refrigerator is defrosted.

[0012] In some embodiments, generating a defrost time series calibration model after performing big data analysis based on historical trough defrost time data includes:

[0013] According to the historical trough defrost time data, an autocorrelation time series is constructed, and the autocorrelation coefficient and regression coefficient of the time series are obtained;

[0014] Determine the time series forecasting model that matches the autocorrelation coefficient and regression coefficient;

[0015] Conduct learning and model training, determine the corresponding model order and parameters when the variance corresponding to the time series prediction model is minimized, and establish the corresponding prediction equation.

[0016] In some embodiments, determining the corresponding model order and parameters includes:

[0017] When the time series prediction model is an autoregressive AR model, learning and model training are performed to determine each autoregressive parameter corresponding to the minimum variance;

[0018] When the time series prediction model is a moving average MA model, learning and model training are performed to determine each moving average corresponding to the minimum variance;

[0019] When the time series prediction model is an autoregressive moving average ARMA model, learning and model training are performed to determine each autoregressive parameter and each moving average corresponding to the minimum variance.

[0020] In some embodiments, further comprising:

[0021] When the current accumulated running time of the compressor reaches a second set time, obtaining the current defrost times of the refrigerator, wherein the second set time is less than the first set time;

[0022] When the current defrost times is zero, the refrigerator is defrosted and the current defrost times is updated to 1.

[0023] In some embodiments, further comprising:

[0024] When the current Internet status is online, specify the off-peak time on the Internet to perform defrost operation on the refrigerator.

[0025] In some embodiments, before performing the refrigerator defrosting operation, the method further includes:

[0026] Clear the current accumulated running time to zero.

[0027] In some embodiments, the apparatus comprises:

[0028] a status determination module configured to obtain a current Internet status of the refrigerator when the current accumulated running time of the compressor reaches a first set time;

[0029] The time prediction module is configured to, when the current Internet status is offline, predict a corresponding first off-peak defrost time based on a defrost time series calibration model, wherein the defrost time series calibration model is generated after big data analysis based on historical trough defrost time data;

[0030] The first defrost module is configured to perform refrigerator defrosting operation during a first off-peak defrosting time.

[0031] In some embodiments, the device for refrigerator defrost control includes a processor and a memory storing program instructions, and the processor is configured to execute the above-mentioned refrigerator defrost control method when executing the program instructions.

[0032] In some embodiments, the refrigerator includes a device body; the above-mentioned device for refrigerator defrost control is installed on the device body.

[0033] In some embodiments, the storage medium stores program instructions, and when the program instructions are run, the above-mentioned method for refrigerator defrost control is executed.

[0034] The method, device, and refrigerator for refrigerator defrost control provided by the embodiments of the present disclosure can achieve the following technical effects:

[0035] By conducting big data analysis and model training on historical valley defrost time data, a corresponding defrost time series calibration model can be obtained. Therefore, after the current cumulative running time of the refrigerator compressor reaches the first set time, if the refrigerator is offline, the first off-peak defrost time in the next electricity consumption valley time period corresponding to the current time can be predicted according to the defrost time series calibration model, and the refrigerator can be defrosted at the first off-peak defrost time. In this way, even in the absence of a network, the refrigerator can also perform defrost operation in the electricity consumption valley time period, ensuring that off-peak defrost can be achieved even when offline, realizing reasonable allocation of resources and reducing resource waste.

[0036] The above general description and the following description are exemplary and explanatory only and are not intended to limit the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] One or more embodiments are exemplarily described by corresponding drawings. These exemplary descriptions and drawings do not limit the embodiments. Elements with the same reference numerals in the drawings are shown as similar elements. The drawings do not constitute a scale limitation. In addition,

[0038] Figure 1 This is a flow chart of a refrigerator defrosting control method provided by an embodiment of the present disclosure;

[0039] Figure 2 This is a schematic diagram of a process for generating a defrost time series calibration model provided by an embodiment of the present disclosure;

[0040] Figure 3 This is a flow chart of a refrigerator defrosting control method provided by an embodiment of the present disclosure;

[0041] Figure 4 This is a structural diagram of a defrost control device for a refrigerator provided by an embodiment of the present disclosure;

[0042] Figure 5 This is a structural diagram of a defrost control device for a refrigerator provided by an embodiment of the present disclosure;

[0043] Figure 6 This is a structural diagram of a defrost control device for a refrigerator provided by an embodiment of the present disclosure;

[0044] Figure 7 It is a schematic diagram of a refrigerator provided by an embodiment of the present disclosure. DETAILED DESCRIPTION

[0045] In order to be able to understand the features and technical content of the embodiments of the present disclosure in more detail, the implementation of the embodiments of the present disclosure is described in detail below in conjunction with the accompanying drawings. The accompanying drawings are for reference only and are not used to limit the embodiments of the present disclosure. In the following technical description, for the sake of convenience of explanation, a full understanding of the disclosed embodiments is provided through multiple details. However, one or more embodiments can still be implemented without these details. In other cases, to simplify the drawings, well-known structures and devices can be simplified for display.

[0046] In the description and claims of the embodiments of the present disclosure, as well as in the accompanying drawings, the terms "first," "second," and the like are used to distinguish similar items and are not necessarily used to describe a particular order or precedence. It should be understood that the terms used in this manner are interchangeable where appropriate to describe the embodiments of the present disclosure herein. In addition, the terms "including," "having," and any variations thereof are intended to cover non-exclusive inclusions.

[0047] Unless otherwise stated, the term "plurality" means two or more.

[0048] In the embodiment of the present disclosure, the character " / " indicates that the preceding and following objects are in an "or" relationship. For example, A / B means: A or B.

[0049] The term "and / or" describes an association between objects, indicating that three relationships can exist. For example, A and / or B means: A or B, or A and B.

[0050] In the disclosed embodiment, after the refrigerator compressor has cumulatively run for a period of time, if the defrost operation is performed during the off-peak period of electricity consumption, the time can be recorded. In this way, big data analysis and model training can be performed based on the recorded time, i.e., the historical off-peak defrost time data, to obtain a corresponding defrost time series calibration model. Therefore, when the refrigerator is offline, the defrost time series calibration model can be used to predict the defrost time within the next off-peak period of electricity consumption, i.e., the first off-peak defrost time. Then, the defrost operation is performed during the first off-peak defrost time. It can be seen that even in the absence of a network, the refrigerator can also perform defrost operation within the off-peak period of electricity consumption, ensuring that off-peak defrost can be achieved even when offline, realizing reasonable allocation of resources, reducing resource waste, and making the refrigerator more economical and environmentally friendly during use.

[0051] The figure is a flow chart of a refrigerator defrosting control method provided by an embodiment of the present disclosure.

[0052] based on Figure 1 , the refrigerator defrost control process includes:

[0053] Step 101: When the current accumulated running time of the compressor reaches a first set time, the current Internet status of the refrigerator is obtained.

[0054] After the refrigerator compressor has been working for a period of time, the refrigerator may need to perform defrosting operation. Therefore, the first set time can be determined according to the refrigerator performance, operating season, region, etc., so that the refrigerator defrost control can be performed when the current cumulative operating time of the compressor reaches the first set time.

[0055] In the disclosed embodiment, to achieve greater energy conservation, environmental protection, and economical use of the refrigerator, the refrigerator can be defrosted during periods of low electricity usage. Therefore, it is necessary to determine the specific time point for the refrigerator to defrost. When the refrigerator is online, the internet-specified off-peak time can be used as the specific time point for the refrigerator to defrost. When the refrigerator is offline, it is necessary to predict the defrost time point during the next off-peak period that matches the current time through big data analysis, learning, and model training. Therefore, when the current cumulative operating time of the compressor reaches the first set time, the current internet status of the refrigerator must be obtained.

[0056] Of course, in some embodiments, when the refrigerator is initially used and has not undergone a defrost operation, the defrost operation can be performed immediately as long as the current accumulated running time of the refrigerator compressor reaches the second set time. After the refrigerator has already undergone a defrost operation, the current accumulated running time must reach the first set time before the defrost operation can be performed at a specific defrost time, wherein the first set time is greater than the second set time, and the specific defrost time can be the off-peak time specified by the Internet or the predicted defrost time.

[0057] That is, when the current cumulative running time of the compressor reaches the second set time, the current defrost times of the refrigerator are obtained, where the second set time is less than the first set time; when the current defrost times is zero, the refrigerator defrost operation is performed, and the current defrost times are updated to 1.

[0058] Step 102: When the current Internet status is offline, predict the corresponding first off-peak defrost time according to the defrost time series calibration model, wherein the defrost time series calibration model is generated after big data analysis based on historical trough defrost time data.

[0059] If the refrigerator's current internet connection is offline, big data analysis and model training are needed to predict the defrost time point during the next electricity consumption off-peak period, i.e., the first off-peak defrost time. Therefore, in some embodiments, a defrost time series calibration model can be generated by performing big data analysis based on historical off-peak defrost time data.

[0060] Regardless of whether the refrigerator is online or offline, once the defrost operation is performed at a certain time during the electricity consumption valley period, the time can be recorded and become the historical valley defrost time data, and can become the sample time for big data analysis and model training. Therefore, in some embodiments, generating a defrost time series calibration model includes: constructing an autocorrelated time series based on the historical valley defrost time data, and obtaining the autocorrelation coefficient and regression coefficient of the time series; determining a time series prediction model that matches the autocorrelation coefficient and the regression coefficient; performing learning and model training, and determining the corresponding model order and parameters when the variance corresponding to the time series prediction model is minimized, and establishing a corresponding prediction equation.

[0061] For example: Based on the historical trough defrost time data, a time series is formed, and each sequence value Y of the time series t 、Y t-1 ,……,Y t-k , the simple correlation between them is called autocorrelation. The degree of autocorrelation is given by the autocorrelation coefficient ρ k A measure that indicates the degree of correlation between observations in a time series that are k periods apart.

[0062]

[0063] Where n is the sample size, k is the lag period, Representative sample size mean

[0064] The regression coefficient φ kk =Corr(y t ,y t-k |y t-1 ,…,y t-k+1 ), k = 0, 1, 2, ...

[0065] The seasonal autoregressive integrated moving average exogenous model SARIMAX is a powerful time series analysis and prediction model. In the embodiment of the present disclosure, historical trough defrost time data can be used as sample data, and corresponding learning and model training can be performed based on the sample data and the SARIMAX model to obtain a corresponding defrost time series calibration model.

[0066] Among them, each part of the SARIMAX model can be used as an independent model, or different models can be combined and used. For example, it can include independent models such as the autoregressive AR model and the moving average MA model, and it can also include the autoregressive moving average ARMA model that combines the AR model and the MA model.

[0067] In this way, the autocorrelation coefficient ρ is determined k and the regression coefficient φ kkAfter that, the time series forecasting model that matches the autocorrelation coefficient and regression coefficient can be determined.

[0068] Autocovariance difference function γ k =(1+θ1 2 +…+θ q 2 )σ 2 ,k=0

[0069] (-θ k +θ1θ k+1 +…+θ q θ q-k )σ 2 , 1≤k≤q

[0070] 0, k>q

[0071] Among them, θ1, θ2,..., θ q is the moving average; σ 2 is the variance.

[0072] After taking the historical trough defrost time data as sample data, if the autocovariance function γ of the corresponding sample data is k After truncating at step q, the autocorrelated time series Y can be determined t , is the MA(q) sequence; if the regression coefficient φ of the sample data kk If we truncate at step p, we can determine Y t is an AR(p) sequence; if γ k 、φ kk There is no truncation, but only a negative exponential decay. At this time, we can preliminarily determine Y t is an ARMA series. Therefore, by performing corresponding learning and training based on different models or model combinations within the SARIMAX model, a time series prediction model that matches the autocorrelation coefficient and regression coefficient can be determined. The MA(q) series corresponds to the MA model, the AR(p) series corresponds to the AR model, and the ARMA series corresponds to the ARMA model.

[0073] Different SARIMAX models correspond to different parameters. When the time series prediction model is an autoregressive AR model, the corresponding parameters can be autoregressive parameters When the time series prediction model is a moving average MA model, the corresponding parameters can be the moving averages θ1, θ2, ..., θ q , and when the time series prediction model is the autoregressive moving average ARMA model, the corresponding parameters can be the autoregressive parameters and moving averages θ1, θ2, ..., θ q .

[0074] In this way, according to the historical trough defrost time data, there is a corresponding time series prediction model, and the variance σ corresponding to the time series prediction model is 2 When the time series prediction model is the minimum, learning and model training are performed to determine each autoregressive parameter and the corresponding model order corresponding to the minimum variance; or, each moving average and the corresponding model order corresponding to the minimum variance are determined; or, each autoregressive parameter, each moving average, and the model order corresponding to the minimum variance are determined. Determining the corresponding model order and parameters includes: when the time series prediction model is an autoregressive AR model, learning and model training are performed to determine each autoregressive parameter corresponding to the minimum variance; when the time series prediction model is a moving average MA model, learning and model training are performed to determine each moving average corresponding to the minimum variance; when the time series prediction model is an autoregressive moving average ARMA model, learning and model training are performed to determine each autoregressive parameter and each moving average corresponding to the minimum variance.

[0075] Therefore, the corresponding prediction equations can be established as follows:

[0076] Under the AR model,

[0077] Under the MA model, Y t =ε t -θ1ε t-1 -θ2ε t-2 -……-θ q ε t-q .

[0078] Under the ARM model, it can be:

[0079]

[0080] p is the order of the model, q is the order of the model, ε t For error.

[0081] Therefore, the defrost time in the next trough period can be predicted according to the prediction equation, that is, the corresponding first off-peak defrost time can be predicted.

[0082] Of course, in the embodiment of the present disclosure, it is also possible to not determine the time series prediction model, and directly select a model in the SARIMAX model, and perform learning and model training based on the historical trough defrost time data to obtain the corresponding defrost time series calibration model. For example, directly based on the AR model, learning and model training are performed to obtain the corresponding defrost time prediction equation, or directly based on the ARMA model, learning and model training are performed to obtain the corresponding defrost time prediction equation, etc. Alternatively, it is not limited to based on the SARIMAX model, and based on the historical trough defrost time data, learning and model training are performed to obtain the corresponding defrost time series calibration model. It is also possible to perform learning and model training based on other time prediction models and the historical trough defrost time data to obtain the corresponding defrost time series calibration model, and the specific examples are not listed one by one.

[0083] Step 103: During the first off-peak defrost time, the refrigerator performs defrost operation.

[0084] In this way, as long as the first off-peak defrosting time comes, the refrigerator will immediately start

[0085] It can be seen that in the embodiment of the present disclosure, big data analysis and model training are performed on the historical valley defrost time data to obtain a corresponding defrost time series calibration model. Therefore, after the current cumulative operating time of the refrigerator compressor reaches the first set time, if the refrigerator is offline, the defrost time within the next valley time, that is, the first off-peak defrost time, can be predicted according to the defrost time series calibration model, and the refrigerator defrost operation can be performed during the first off-peak defrost time. In this way, even in the absence of a network, the refrigerator can also perform defrost operation within the electricity valley time period, ensuring that off-peak defrost can be achieved even when offline, realizing reasonable allocation of resources and reducing resource waste.

[0086] Of course, in some embodiments, when the current Internet status is online, the refrigerator defrost operation is performed during the off-peak power time specified on the Internet.

[0087] Regardless of whether the refrigerator is offline or online, the current accumulated running time must be reset to zero before the refrigerator defrosts. This ensures the continuity of the refrigerator defrost control.

[0088] In some embodiments, regardless of whether the refrigerator is offline or online, after defrosting, the first off-peak defrost time or the internet-specified off-peak time can be recorded as historical off-peak defrost time data. Therefore, when the sample data is updated, corresponding learning and training can be performed based on the SARIMAX model, resulting in a corresponding defrost time series calibration model. This allows the defrost time series calibration model to be updated in a timely manner, further ensuring the accuracy of the predicted off-peak defrost time.

[0089] The operation flow is summarized into a specific embodiment below to illustrate the defrosting control process for a refrigerator provided by the embodiment of the present invention.

[0090] In this embodiment, corresponding learning and training are performed based on the recorded historical trough defrost time data and the SARIMAX model to obtain a corresponding defrost time series calibration model.

[0091] Figure 2 This is a flow chart of a process for generating a defrost time series calibration model provided by an embodiment of the present disclosure. Figure 2 As shown in Figure 2, the process of generating a defrost time series calibration model includes:

[0092] Step 201: The refrigerator constructs an autocorrelated time series based on historical trough defrost time data.

[0093] The autocorrelated time series can be Y t 、Y t-1 ,……,Y t-k .

[0094] Step 202: Determine whether the time series is stationary. If so, proceed to step 204; otherwise, proceed to step 203.

[0095] Step 203: Perform differential processing based on the data in the time series, obtain the processed time series, and return to step 202.

[0096] The relevant use for sequence stationary judgment and the corresponding difference processing process can be applied here, so I will not go into details.

[0097] Step 204: Calculate the autocorrelation coefficient ρ corresponding to the time series k and the regression coefficient φ kk .

[0098] Step 205: According to the autocorrelation coefficient ρ k and the regression coefficient φ kk , the refrigerator determines the prediction sequence corresponding to the time series, and determines the time series prediction model corresponding to the measured sequence.

[0099] The prediction sequence may include: MA sequence, AR sequence, ARMA sequence, and thus, the corresponding time series prediction model may be MA model, AR model, ARMA model.

[0100] Of course, in some embodiments, step 205 may not be performed, and a model in the SARIMAX model may be directly selected as the time series prediction model.

[0101] Step 206: The refrigerator estimates the order and parameters of the time series prediction model and obtains the corresponding variance.

[0102] Step 207: Determine whether the variance is the minimum variance. If so, proceed to step 208; otherwise, return to step 206.

[0103] Steps 206 and 207 are a process of repeated learning and training, that is, learning and training are performed based on historical trough defrost time data.

[0104] Step 208: The refrigerator generates a corresponding prediction equation according to the order and parameters corresponding to the minimum variance.

[0105] Among them, under the AR model,

[0106] Under the MA model, Y t =ε t -θ1ε t-1 -θ2ε t-2 -……-θ q ε t-q .

[0107] Under the ARM model, it can be:

[0108]

[0109] p is the order of the model, q is the order of the model, ε t For error.

[0110] In this way, after big data analysis based on historical valley defrost time data, a defrost time series calibration model can be generated. This model can then be used to predict the defrost time during the next valley period corresponding to the current time when the refrigerator is offline, and then perform the corresponding defrost operation.

[0111] Figure 3 FIG. 1 is a flow chart of a refrigerator defrosting control method provided by an embodiment of the present disclosure. Figure 3 As shown in the figure, the refrigerator defrost control process includes:

[0112] Step 301: The refrigerator obtains the current accumulated running time of the compressor.

[0113] Step 302: Determine whether the current defrost count is zero. If so, proceed to step 303; otherwise, proceed to step 305.

[0114] Step 303: Determine whether the current accumulated running time reaches the second set time. If so, execute step 304; otherwise, return to step 303.

[0115] Step 304: the refrigerator resets the current accumulated running time to zero, performs defrosting operation, and updates the current defrosting times to 1.

[0116] Step 305: Determine whether the current accumulated running time reaches the first set time. If so, execute step 306; otherwise, return to step 305.

[0117] The first set time is greater than the second set time.

[0118] Step 306: The refrigerator obtains the current Internet status.

[0119] Step 307: Determine whether the current Internet status is online. If so, execute step 308; otherwise, execute step 310.

[0120] Step 308: The off-peak time is specified on the Internet, the refrigerator resets the current accumulated running time to zero, and performs defrosting operation.

[0121] Step 309: After determining that the defrost operation is completed, the refrigerator determines the off-peak time specified by the Internet as the historical off-peak defrost time data, which becomes the sample data for model training.

[0122] Step 310: The refrigerator predicts a corresponding first off-peak defrost time according to the defrost time series calibration model.

[0123] The first off-peak defrost time is a time within the next electricity consumption valley time period that matches the current time.

[0124] Step 311: During the first off-peak defrosting time, the refrigerator resets the current accumulated running time to zero and performs the defrosting operation.

[0125] Step 312: After determining that the defrost operation is completed, the refrigerator determines the first off-peak defrost time as the historical valley defrost time data, which becomes sample data for model training.

[0126] As can be seen, in this embodiment, after the current cumulative operating time of the refrigerator compressor reaches the first set time, if the refrigerator is offline, the defrost time series calibration model can be used to predict the corresponding first off-peak defrost time. The refrigerator then defrosts during the first off-peak defrost time. This allows the refrigerator to defrost during off-peak periods even without a network connection, ensuring off-peak defrosting even when offline, achieving rational resource allocation, and reducing resource waste. Furthermore, the first off-peak defrost time or the internet-specified off-peak time can be recorded as historical off-peak defrost time data. This allows the defrost time series calibration model to be updated promptly based on updated sample data, further ensuring the accuracy of the predicted off-peak defrost time.

[0127] According to the above process for refrigerator defrost control, a device for refrigerator defrost control can be constructed.

[0128] Figure 4 Schematic diagram of a defrost control device for a refrigerator provided by an embodiment of the present disclosure. Figure 4 As shown, the defrost control device 400 for a refrigerator includes: a state determination module 410 , a time prediction module 420 and a first defrost module 430 .

[0129] The status determination module 410 is configured to obtain the current Internet status of the refrigerator when the current accumulated running time of the compressor reaches a first set time.

[0130] The time prediction module 420 is configured to predict the corresponding first off-peak defrost time based on the defrost time series calibration model when the current Internet status is offline, wherein the defrost time series calibration model is generated after big data analysis based on historical trough defrost time data.

[0131] The first defrost module 430 is configured to perform refrigerator defrosting operation during a first off-peak defrosting time.

[0132] In some embodiments, further comprising:

[0133] The module is configured to construct an autocorrelation time series based on historical trough defrost time data, and obtain the autocorrelation coefficient and regression coefficient of the time series.

[0134] The learning and training module is configured to determine a time series prediction model that matches the autocorrelation coefficient and the regression coefficient, and perform learning and model training. When the variance corresponding to the time series prediction model is minimized, the corresponding model order and parameters are determined, and the corresponding prediction equation is established.

[0135] In some embodiments, the learning and training module is specifically configured to perform learning and model training when the time series prediction model is an autoregressive AR model, and determine each autoregressive parameter corresponding to the minimum variance; when the time series prediction model is a moving average MA model, perform learning and model training, and determine each moving average corresponding to the minimum variance; when the time series prediction model is an autoregressive moving average ARMA model, perform learning and model training, and determine each autoregressive parameter and each moving average corresponding to the minimum variance.

[0136] In some embodiments, further comprising:

[0137] The second defrost module is configured to obtain the current defrost times of the refrigerator when the current accumulated running time of the compressor reaches the second set time, wherein the second set time is less than the first set time; when the current defrost times is zero, perform defrost operation on the refrigerator and update the current defrost times to 1.

[0138] In some embodiments, further comprising:

[0139] The third defrost module is configured to perform a defrost operation on the refrigerator during off-peak hours specified by the Internet when the current Internet status is online.

[0140] In some embodiments, the first defrost module 430 , the second defrost module, or the third defrost module is further configured to reset the current accumulated running time to zero before performing the defrosting operation of the refrigerator.

[0141] The following describes an example of a refrigerator defrost control process of the device for refrigerator defrost control provided by an embodiment of the present invention.

[0142] In this embodiment, the first set time is greater than the second set time. Figure 5 Schematic diagram of a defrost control device for a refrigerator provided by an embodiment of the present disclosure. Figure 5 As shown, the defrost control device 400 for a refrigerator includes: a state determination module 410, a time prediction module 420, a first defrost module 430, a structure acquisition module 440, a learning and training module 450, a second defrost module 460, and a third defrost module 470.

[0143] The module 440 constructs an autocorrelation time series based on the historical trough defrost time data, and calculates the autocorrelation coefficient ρ corresponding to the time series when the time series is in a stationary state. k and the regression coefficient φ kk. When the time series is in an unsteady state, the data in the time series is differentially processed until a time series in a steady state is obtained, and the corresponding autocorrelation coefficient ρ is obtained. k and the regression coefficient φ kk ..

[0144] The learning training module 450 is based on the autocorrelation coefficient ρ k and the regression coefficient φ kk , determine the prediction sequence corresponding to the time series, and determine the time series prediction model corresponding to the measured sequence, then perform learning and model training, determine the order and parameters corresponding to the minimum variance, and generate the corresponding prediction equation.

[0145] In this way, when the current accumulated running time of the compressor reaches the second set time and the current number of defrosts is zero, the second defrost module 460 can reset the current accumulated running time of the refrigerator, perform the defrost operation of the refrigerator, and update the current number of defrosts to 1. When the current accumulated running time of the compressor reaches the first set time and the current number of defrosts is not zero, the state determination module 410 obtains the current Internet state. If the current Internet state is online, at the Internet-specified off-peak time, the third defrost module 470 can reset the current accumulated running time and perform the defrost operation of the refrigerator. After determining that the defrost operation is completed, the Internet-specified off-peak time is determined as the historical off-peak defrost time data, which becomes the sample data for model training.

[0146] Of course, when the current Internet status is offline, the time prediction module 420 can obtain the prediction equation in the learning and training module 450 to predict the defrost time in the next electricity consumption valley time period that matches the current time, that is, the first off-peak defrost time. Therefore, at the first off-peak defrost time, the first defrost module 430 will clear the current accumulated running time and perform the refrigerator defrost operation. After determining that the defrost operation is completed, the first off-peak defrost time can be determined as the historical valley defrost time data, which becomes the sample data for model training.

[0147] As can be seen, in this embodiment, the refrigerator defrost control device can analyze the corresponding SARIMAX model based on big data, and perform corresponding learning and training based on recorded historical valley defrost time data to obtain a corresponding defrost time series calibration model. Thus, after the current cumulative operating time of the refrigerator compressor reaches the first set time, if the refrigerator is offline, the refrigerator defrost control device can predict the corresponding first off-peak defrost time based on the defrost time series calibration model and perform refrigerator defrost operation at the first off-peak defrost time. Thus, even without a network connection, the refrigerator can still perform defrost operation during valley power usage periods, ensuring off-peak defrost even when offline, achieving rational resource allocation and reducing resource waste.

[0148] Combine Figure 6 The embodiment of the present disclosure provides a device 600 for controlling defrosting of a refrigerator, comprising:

[0149] Processor 1000 and memory 1001 may also include a communication interface 1002 and a bus 1003. Processor 1000, communication interface 1002, and memory 1001 may communicate with each other via bus 1003. Communication interface 1002 may be used for information transmission. Processor 1000 may invoke logic instructions in memory 1001 to execute the method for refrigerator defrost control described in the above embodiment.

[0150] In addition, the logic instructions in the memory 1001 can be implemented in the form of software functional units and can be stored in a computer-readable storage medium when sold or used as an independent product.

[0151] Memory 1001, as a computer-readable storage medium, can be used to store software programs and computer-executable programs, such as program instructions / modules corresponding to the methods in the embodiments of the present disclosure. Processor 1000 executes the program instructions / modules stored in memory 1001 to perform functional applications and data processing, thereby implementing the method for refrigerator defrost control in the above-mentioned method embodiment.

[0152] The memory 1001 may include a program storage area and a data storage area. The program storage area may store an operating system and at least one application required for a function; the data storage area may store data generated based on the use of the terminal device. Furthermore, the memory 1001 may include high-speed random access memory and non-volatile memory.

[0153] An embodiment of the present disclosure provides a defrost control device for a refrigerator, comprising: a processor and a memory storing program instructions, wherein the processor is configured to execute a defrost control method for a refrigerator when executing the program instructions.

[0154] Combine Figure 7 , an embodiment of the present disclosure provides a refrigerator, comprising: a device body; and the above-mentioned defrost control device 400 (600) for a refrigerator. The defrost control device 400 (600) for a refrigerator is installed on the device body. The installation relationship described here is not limited to placement inside the product, but also includes installation connections with other components of the product, including but not limited to physical connections, electrical connections or signal transmission connections, etc. It can be understood by those skilled in the art that the defrost control device 400 (600) for a refrigerator can be adapted to a feasible device body, thereby realizing other feasible embodiments.

[0155] An embodiment of the present disclosure provides a storage medium storing program instructions, which, when executed, execute the above-mentioned method for refrigerator defrost control.

[0156] An embodiment of the present disclosure provides a computer program product, which includes a computer program stored on a storage medium. The computer program includes program instructions. When the program instructions are executed by a computer, the computer executes the above-mentioned refrigerator defrost control method.

[0157] The aforementioned storage medium may be a transient computer-readable storage medium or a non-transitory computer-readable storage medium.

[0158] The technical solution of the embodiments of the present disclosure may be embodied in the form of a software product, which is stored in a storage medium and includes one or more instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in the embodiments of the present disclosure. The aforementioned storage medium may be a non-transitory storage medium, including: a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and other media that can store program code, or a transient storage medium.

[0159] The above description and accompanying drawings sufficiently illustrate the embodiments of the present disclosure to enable those skilled in the art to practice them. Other embodiments may include structural, logical, electrical, process, and other changes. The embodiments represent only possible variations. Unless expressly required, individual components and functions are optional, and the order of operations may vary. Portions and features of some embodiments may be included in or substituted for portions and features of other embodiments. The scope of the embodiments of the present disclosure includes the entire scope of the claims and all available equivalents thereof. When used in this application, although the terms "first," "second," etc. may be used in this application to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, a first element can be called a second element, and similarly, a second element can be called a first element, without changing the meaning of the description, as long as all occurrences of "first element" are consistently renamed and all occurrences of "second element" are consistently renamed. The first element and the second element are both elements, but they may not be the same element. Furthermore, the terms used in this application are only used to describe the embodiments and are not intended to limit the claims. As used in the description of the embodiments and claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include the plural forms as well. Similarly, the term "and / or" as used in this application refers to any and all possible combinations of one or more of the associated listings. In addition, when used in this application, the term "comprise" and its variations "comprises" and / or comprising refer to the presence of stated features, wholes, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or groups of these. Without further limitation, an element defined by the phrase "comprising a..." does not exclude the presence of other identical elements in the process, method or apparatus comprising the element. In this article, each embodiment may focus on the differences from other embodiments, and the same and similar parts between the embodiments can be referred to each other. For the methods, products, etc. disclosed in the embodiments, if they correspond to the method part disclosed in the embodiments, then the relevant parts can be referred to the description of the method part.

[0160] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software may depend on the specific application and design constraints of the technical solution. The technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the embodiments of the present disclosure. The technicians will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0161] In the embodiments disclosed herein, the disclosed methods and products (including but not limited to devices, equipment, etc.) can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units can be merely a logical functional division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between each other shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, and can be electrical, mechanical or other forms. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the units may be selected to implement this embodiment according to actual needs. In addition, the functional units in the embodiments of the present disclosure may be integrated into a processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0162] The flowcharts and block diagrams in the accompanying drawings show the possible implementation architectures, functions and operations of the systems, methods and computer program products according to the embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, program segment or part of the code, and the module, program segment or part of the code contains one or more executable instructions for implementing the specified logical functions. In some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, or they can sometimes be executed in the opposite order, which can depend on the functions involved. In the descriptions corresponding to the flowcharts and block diagrams in the accompanying drawings, the operations or steps corresponding to different boxes can also occur in an order different from that disclosed in the description, and sometimes there is no specific order between different operations or steps. For example, two consecutive operations or steps can actually be executed substantially in parallel, or they can sometimes be executed in the opposite order, which can depend on the functions involved. Each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented by a dedicated hardware-based system that performs the specified function or action, or may be implemented by a combination of dedicated hardware and computer instructions.

Claims

1. A method for controlling defrosting of a refrigerator, characterized in that: include: When the current accumulated running time of the compressor reaches a first set time, obtaining the current Internet status of the refrigerator; When the current Internet status is offline, the corresponding first off-peak defrost time is predicted based on the defrost time series calibration model, wherein the defrost time series calibration model is generated after big data analysis based on historical trough defrost time data; During the first off-peak defrost time, the refrigerator is defrosted.

2. The method according to claim 1, characterized in that The method of generating a defrost time series calibration model after performing big data analysis based on historical trough defrost time data includes: According to the historical trough defrost time data, an autocorrelation time series is constructed, and the autocorrelation coefficient and regression coefficient of the time series are obtained; Determine the time series forecasting model that matches the autocorrelation coefficient and regression coefficient; Conduct learning and model training, determine the corresponding model order and parameters when the variance corresponding to the time series prediction model is minimized, and establish the corresponding prediction equation.

3. The method according to claim 2, characterized in that Determining the corresponding model order and parameters includes: When the time series prediction model is an autoregressive AR model, learning and model training are performed to determine each autoregressive parameter corresponding to the minimum variance; When the time series prediction model is a moving average MA model, learning and model training are performed to determine each moving average corresponding to the minimum variance; When the time series prediction model is an autoregressive moving average ARMA model, learning and model training are performed to determine each autoregressive parameter and each moving average corresponding to the minimum variance.

4. The method according to claim 1, wherein Also includes: When the current accumulated running time of the compressor reaches a second set time, obtaining the current defrost times of the refrigerator, wherein the second set time is less than the first set time; When the current defrost times is zero, the refrigerator is defrosted and the current defrost times is updated to 1.

5. The method according to claim 1, wherein Also includes: When the current Internet status is online, specify the off-peak time on the Internet to perform defrost operation on the refrigerator.

6. The method according to claim 1, wherein: Before the refrigerator defrost operation is performed, the method further includes: Clear the current accumulated running time to zero.

7. A device for controlling defrosting of a refrigerator, characterized in that: include: a status determination module configured to obtain a current Internet status of the refrigerator when the current accumulated running time of the compressor reaches a first set time; The time prediction module is configured to, when the current Internet status is offline, predict a corresponding first off-peak defrost time based on a defrost time series calibration model, wherein the defrost time series calibration model is generated after big data analysis based on historical trough defrost time data; The first defrost module is configured to perform refrigerator defrosting operation during a first off-peak defrosting time.

8. A device for controlling defrosting of a refrigerator, comprising a processor and a memory storing program instructions, characterized in that: The processor is configured to perform the method for refrigerator defrost control according to any one of claims 1 to 6 when executing the program instructions.

9. A refrigerator, characterized in that: include: Equipment body; The device for refrigerator defrost control as claimed in claim 7 or 8 is installed on the device body.

10. A storage medium storing program instructions, characterized in that: When the program instructions are executed, the method for controlling refrigerator defrost as described in any one of claims 1 to 6 is executed.

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