A refrigerator defrosting control method, system, device and apparatus

Abstract

The application is suitable for the technical field of refrigerators, and provides a refrigerator defrosting control method, system, device and apparatus, comprising: constructing a linear regression model based on historical refrigerator external environment temperature and historical door opening frequency data, obtaining temperature threshold data and frequency threshold data; obtaining a refrigerator early warning signal by real-time monitoring of temperature change trend and frequency change trend of the refrigerator in a unit time area; calculating a frost formation speed in the refrigerator based on the refrigerator early warning signal; dynamically adjusting defrosting parameters of the refrigerator through a dynamic adjustment algorithm; starting a defrosting program of the refrigerator based on the defrosting parameters, recording running data of the defrosting program, and optimizing the dynamic adjustment algorithm based on the running data to generate a defrosting control strategy, thereby avoiding unnecessary energy consumption and refrigerant waste, helping to reduce energy consumption and emissions of the refrigerator, and thus improving user experience and satisfaction and reducing user operation burden and maintenance cost.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of refrigerators, and particularly relates to a refrigerator defrosting control method, system, device and apparatus. BACKGROUND

[0002] After the refrigerator evaporator is used for a period of time, frost will gradually form on the surface of the fins. As the thickness of the frost layer increases, the heat transfer rate of the evaporator will decrease significantly, thereby affecting the refrigeration efficiency of the refrigerator. In order to maintain the refrigeration performance of the refrigerator, the frost layer must be removed regularly.

[0003] In the prior art, the most commonly used defrosting method for a refrigerator is to periodically melt the frost on the evaporator by using electric heating. The defrosting control parameters usually include cumulative power-on time, cumulative compressor running time, and detection of the temperature in the cabinet, etc. When the cumulative power-on time or the compressor running time reaches a preset value, the defrosting heater is started to defrost. Although this defrosting control method is simple and easy to implement, it has obvious defects. Specifically, the above defrosting control method fails to comprehensively reflect the actual degree of frosting of the refrigerator evaporator, and also fails to consider the influence of changes in the use environment on the frosting speed. For example, the number of times the refrigerator door is opened will directly affect the amount of humid hot air entering the refrigerator, and thus affect the frosting speed of the evaporator. If the user opens the refrigerator door less frequently, the amount of humid hot air entering the refrigerator will decrease, and the frosting will naturally decrease. In this case, if the defrosting program is still forcibly started according to the fixed cumulative time, not only will the overall energy consumption of the refrigerator increase, but the service life of the evaporator may also be damaged due to excessive defrosting. In addition, the defrosting strategy of the traditional refrigerator is mostly triggered based on fixed time intervals or preset conditions, and lacks real-time response capability to changes in the environment and the actual running state of the refrigerator. This not only may lead to unnecessary increase in energy consumption, but also may affect the stability of the internal temperature of the refrigerator due to untimely defrosting, thereby negatively affecting the preservation effect of food.

[0004] In view of the above, it is necessary to develop a more intelligent and efficient refrigerator defrosting control method to overcome the drawbacks of the prior art. SUMMARY

[0005] The embodiments of the present application provide a refrigerator defrosting control method, system, device and apparatus, which can solve one of the above-mentioned problems in the prior art.

[0006] In a first aspect, the embodiments of the present application provide a refrigerator defrosting control method, comprising:

[0007] Based on historical refrigerator external environment temperature and historical door opening frequency data, a linear regression model is constructed to obtain temperature threshold data and frequency threshold data of the refrigerator in a unit time area at a preset time;

[0008] Based on the temperature threshold data and the frequency threshold data, a refrigerator early warning signal is obtained by monitoring the temperature change trend and the frequency change trend of the refrigerator in a unit time area in real time;

[0009] Based on the refrigerator early warning signal, a frost formation speed in the refrigerator is calculated;

[0010] If the frost formation speed is greater than a preset threshold speed, a defrosting parameter of the refrigerator is dynamically adjusted through a dynamic adjustment algorithm;

[0011] Based on the defrosting parameter, a defrosting program of the refrigerator is started, running data of the defrosting program is recorded, and the dynamic adjustment algorithm is optimized based on the running data to generate a defrosting control strategy.

[0012] Further, based on historical refrigerator external environment temperature and historical door opening frequency data, a linear regression model is constructed to obtain temperature threshold data and frequency threshold data of the refrigerator in a unit time area at a preset time, including:

[0013] Based on historical refrigerator external environment temperature and historical door opening frequency data, a temperature data set and a door opening frequency data set are obtained;

[0014] Based on the temperature data set and the door opening frequency data set, the degree of correlation between the environment temperature and the door opening frequency is determined;

[0015] Based on the degree of correlation, a linear regression model is fitted and generated with the environment temperature as the independent variable and the door opening frequency as the dependent variable;

[0016] Based on the temperature data set, an environment temperature prediction model is constructed to predict the environment temperature of the refrigerator in each unit time area at a preset time through the environment temperature prediction model, generate an environment temperature prediction data set, and the environment temperature in the unit time area is the temperature threshold data in the corresponding unit time area;

[0017] Based on the linear regression model and the environment temperature prediction data set, the door opening frequency of the refrigerator in each unit time area at a preset time is predicted to generate a door opening frequency prediction data set at a preset time, and the door opening frequency in the unit time area is the frequency threshold data in the corresponding unit time area.

[0018] Further, based on historical refrigerator external environment temperature and historical door opening frequency data, a temperature data set and a door opening frequency data set are obtained, including:

[0019] The external environment temperature of the refrigerator is obtained through an environment temperature sensor, and a temperature time series data set is generated;

[0020] Obtain the occurrence time of the refrigerator door opening event from the door magnetic switch record, and generate a door opening event time series data set;

[0021] Based on the same unit time granularity, the preset time is divided into a plurality of same unit time regions;

[0022] Based on the unit time region, the temperature time series data set and the door opening event time series data set are time-aligned to generate a temperature data set and a door opening frequency data set.

[0023] Further, based on the temperature threshold data and the frequency threshold data, the refrigerator early warning signal is obtained by monitoring the temperature change trend and the frequency change trend of the refrigerator in the unit time region in real time, including:

[0024] Real-time acquisition of refrigerator external environment temperature and door opening frequency data, linear regression fitting of refrigerator external environment temperature and door opening frequency data in the unit time region is carried out respectively by using the least square method, and the temperature slope value of the temperature change trend and the frequency slope value of the frequency change trend in the unit time region are obtained;

[0025] Compare the temperature slope value with the temperature threshold data under the corresponding unit time region to obtain temperature abnormal data points;

[0026] Compare the frequency slope value with the frequency threshold data under the corresponding unit time region to obtain frequency abnormal data points;

[0027] An ARIMA time series prediction model is used to obtain the refrigerator early warning signal within the preset time based on the temperature abnormal data points and the frequency abnormal data points.

[0028] Further, the calculation of the frost formation speed in the refrigerator includes:

[0029] Based on the law of conservation of energy, a differential equation of refrigerator cooling capacity change and frost amount is established, and the frost formation speed is obtained by solving the differential equation;

[0030] The differential equation of the refrigerator cooling capacity change and the frost amount is:

[0031]

[0032] Wherein, represents the refrigeration capacity of the refrigerator, represents the cooling capacity lost due to frost of the refrigerator, and Q_heat_loss represents the cooling capacity lost due to heat transfer.

[0033] Further, the calculation formula of the is specifically:

[0034]

[0035] wherein, represents the frost amount, h_frost represents the latent heat of unit mass frost, represents the frost speed of the refrigerator;

[0036] the The calculation formula is specifically:

[0037]

[0038] wherein, U represents the heat transfer coefficient of the refrigerator, A represents the area of heat exchange between the inside of the refrigerator and the external environment, T_in represents the internal temperature of the refrigerator, T_out represents the external environment temperature of the refrigerator, represents the frequency threshold data, and k represents the influence coefficient of the refrigerator opening event.

[0039] Further, if the frost speed is greater than the preset threshold speed, the defrosting parameters of the refrigerator are dynamically adjusted through a dynamic adjustment algorithm, including:

[0040] calculating the deviation value between the frost speed and the preset threshold speed;

[0041] based on the deviation value, a gradient descent method is used to obtain a new defrosting parameter value;

[0042] based on the new defrosting parameter value and the old defrosting parameter, a weighted average method is used to update the defrosting parameters of the refrigerator to the first defrosting parameters.

[0043] Further, based on the operation data, the dynamic adjustment algorithm is optimized to generate a defrosting control strategy, including

[0044] obtaining the external environment temperature of the refrigerator and the opening frequency data during the defrosting operation, and using the Pearson correlation coefficient to calculate the influence weight of the external environment temperature of the refrigerator and the opening frequency data on the frost speed, to obtain a weight distribution result;

[0045] According to the weight distribution result, the external environment temperature of the refrigerator or the opening frequency data is taken as the independent variable, and the frost speed is taken as the dependent variable, a linear regression equation is fitted, and a frost speed prediction model is generated;

[0046] The frost speed prediction model is applied to the dynamic adjustment algorithm to generate a defrosting control strategy, and the defrosting control strategy is to adjust the compressor power and fan speed of the refrigerator refrigeration system.

[0047] Further, the frost speed prediction model is applied to the dynamic adjustment algorithm to generate a defrosting control strategy, including:

[0048] obtain a predicted frosting speed of the refrigerator based on the frosting speed prediction model;

[0049] input the collected running data in the defrosting operation process of the refrigerator into a preset decision tree model, and output a second defrosting parameter through the decision tree model;

[0050] based on the second defrosting parameter, dynamically adjust the compressor power and fan speed of the refrigerator.

[0051] Further, the generating a defrosting control strategy further comprises:

[0052] preprocess the collected running data to obtain a time series data set, and based on the time series data set, construct a multi-dimensional feature vector;

[0053] use a time series analysis algorithm to model and analyze the multi-dimensional feature vector to obtain a defrosting duration;

[0054] calculate the correlation coefficients of each dimension feature in the multi-dimensional feature vector and the defrosting effect to obtain key factors highly related to the defrosting effect;

[0055] based on the key factors, cluster different defrosting effects in the historical data through a clustering algorithm to obtain defrosting strategy parameters, the defrosting strategy parameters being parameter thresholds when a defrosting program is started;

[0056] update the defrosting duration and the defrosting strategy parameters to the defrosting program.

[0057] In a second aspect, the embodiments of the present application provide a refrigerator defrosting control system, comprising:

[0058] a first processing module configured to construct a linear regression model based on historical refrigerator external environment temperature and historical door opening frequency data, and obtain temperature threshold data and frequency threshold data of the refrigerator in a unit time region at a preset time;

[0059] a second processing module configured to obtain a refrigerator warning signal by monitoring the temperature change trend and frequency change trend of the refrigerator in the unit time region based on the temperature threshold data and the frequency threshold data;

[0060] a third processing module configured to calculate a frosting speed in the refrigerator based on the refrigerator warning signal;

[0061] a fourth processing module configured to dynamically adjust the defrosting parameters of the refrigerator through a dynamic adjustment algorithm if the frosting speed is greater than a preset threshold speed;

[0062] The fifth processing module is configured to start a defrosting program of the refrigerator based on the defrosting parameter, record running data of the defrosting program, and optimize the dynamic adjustment algorithm based on the running data to generate a defrosting control strategy.

[0063] In a third aspect, the embodiments of the present application provide a computer device, which comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the refrigerator defrosting control method when executing the computer program.

[0064] In a fourth aspect, the embodiments of the present application provide a computer readable storage medium, which comprises a computer program stored in the computer readable storage medium, and the computer program is executable on a processor to implement the refrigerator defrosting control method.

[0065] Compared with the prior art, the embodiments of the present application have the following beneficial effects:

[0066] The refrigerator defrosting control method provided by the present application can predict and set the temperature threshold data and the frequency threshold data of the refrigerator in a unit time region at a preset time by constructing a linear regression model and using historical refrigerator external environment temperature and historical door opening frequency data, which helps to identify external environmental factors that may cause frost acceleration in advance, thereby improving the predictability and accuracy of defrosting. Based on the set temperature threshold data and frequency threshold data, the temperature change trend and the frequency change trend of the refrigerator in a unit time region are monitored in real time, and once an anomaly is found, a refrigerator warning signal is immediately generated. This real-time monitoring mechanism helps to discover and handle potential frost problems in a timely manner, avoiding the impact of thick frost layer on the performance of the refrigerator. After receiving the warning signal, the frost speed in the refrigerator is calculated, and the defrosting parameters of the refrigerator are dynamically adjusted according to the size of the frost speed, so as to ensure the refrigeration efficiency of the refrigerator during the defrosting process and prolong the service life of the refrigerator. After starting the defrosting program, the dynamic adjustment algorithm is continuously optimized based on the running data, and this continuous optimization mechanism can continuously improve the accuracy and efficiency of defrosting control, forming a more scientific and reasonable defrosting control strategy. Through accurate control of the defrosting process, unnecessary energy consumption and refrigerant waste are avoided, which helps to reduce the energy consumption and emissions of the refrigerator, thereby improving the user experience and satisfaction and reducing the operation burden and maintenance cost of the user. BRIEF DESCRIPTION OF DRAWINGS

[0067] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiments or the prior art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0068] Figure 1 is a flowchart of a refrigerator defrosting control method according to an embodiment of the present application;

[0069] Figure 2 is a structural diagram of a refrigerator defrosting control system according to an embodiment of the present application;

[0070] Figure 3 is a structural diagram of a computer device according to an embodiment of the present application. DETAILED DESCRIPTION

[0071] In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular sequences of steps, techniques, etc., in order to provide a thorough understanding of the embodiments of the present application. However, it will be apparent to those skilled in the art that the present application can be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known methods, devices, circuits, and

[0072] It is to be understood that the terminology "includes", "has", "holds", "contains" and / or "comprising", when used in this specification and in the following claims, indicates the presence of the described features, integers, steps, operations, elements, and / or components, but does not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0073] It is also to be understood that the terminology "and / or" when used in this specification and in the following claims, refers to at least one of the items, or any combination of one or more of the items, and includes any possible combination of the items.

[0074] As used in this specification and in the claims, the terms "if" and "when" can be interpreted to mean "upon" or "in response to determining," or "in response to detecting," depending on the context. Similarly, the phrase "if it is determined" or "if [a described condition or event] is detected" can be interpreted to mean "upon determining" or "in response to determining," or "upon detecting [the described condition or event]" or "in response to detecting [the described condition or event]," depending on the context.

[0075] In addition, in the description of the specification and the appended claims, the terms "first", "second", "third", etc. are used only to distinguish descriptions, and cannot be understood as indicating or implying relative importance.

[0076] Reference to“one embodiment” or“an embodiment” or“some embodiments” or“one implementation” or“some implementations” means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment. The appearances of the phrase“in one embodiment” or“in some embodiments” or“in other embodiments” or“in other implementations” in various places in the specification are not necessarily all referring to the same embodiment, nor are they necessarily referring to some, but not all, embodiments. The terms“including,”“comprising,”“having,” and variations thereof are meant to encompass the item listed thereafter, but do not exclude additional, unrecited items. The terms“a” or“an,” or the terms“one or more,” or the term“at least one,” or the term“one or more of’ when used in this context are understood to encompass one or more than one instance. Only claims may use the terms“consisting of” or“consisting essentially of.” Thus, use of the term“comprising” or“having” or“including” or variations thereof is not meant to be limiting.

[0077] Referring to Figure 1 The application is a refrigerator defrosting control method, comprising the following steps:

[0078] S100, based on historical refrigerator external environment temperature and historical door opening frequency data, a linear regression model is constructed to obtain temperature threshold data and frequency threshold data of the refrigerator in a unit time region at a preset time;

[0079] In this application, by constructing a linear regression model, using historical refrigerator external environment temperature and historical door opening frequency data, the temperature threshold data and frequency threshold data of the refrigerator in a unit time region at a preset time can be predicted and set, which helps to identify the external environmental factors that may cause frost acceleration in advance, thereby improving the predictability and accuracy of defrosting.

[0080] In some embodiments, the above step S100 comprises:

[0081] Based on the historical refrigerator external environment temperature and the historical door opening frequency data, a temperature data set and a door opening frequency data set are obtained;

[0082] Based on the temperature data set and the door opening frequency data set, the correlation between the environmental temperature and the door opening frequency is determined;

[0083] Based on the correlation, a linear regression model is fitted and generated with the environmental temperature as the independent variable and the door opening frequency as the dependent variable;

[0084] Based on the temperature data set, an environmental temperature prediction model is constructed, and the environmental temperature of the refrigerator in each unit time region at a preset time is predicted by the environmental temperature prediction model to generate an environmental temperature prediction data set, and the environmental temperature in the unit time region is the temperature threshold data in the corresponding unit time region;

[0085] Based on the linear regression model and the ambient temperature prediction dataset, the door opening frequency of the refrigerator in each unit time region within a preset time is predicted, and a door opening frequency prediction dataset within a preset time is generated, wherein the door opening frequency in each unit time region is a frequency threshold value data corresponding to the unit time region.

[0086] In this embodiment, the Pandas library is used to load the temperature dataset and the door opening frequency dataset, ensuring that the temperature dataset and the door opening frequency dataset are aligned on the timestamp, i.e., each ambient temperature data point and each door opening frequency data point are one-to-one corresponding on the timestamp. In addition, the data needs to be cleaned to remove missing values or outliers, ensuring the accuracy and integrity of the data. Specifically, the correlation coefficient between the ambient temperature and the door opening frequency is calculated according to the corr() function in the Pandas library, and the correlation coefficient r is obtained. It can be understood that the value range of the correlation coefficient r is between -1 and 1, wherein 1 represents complete positive correlation, -1 represents complete negative correlation, and 0 represents no correlation. If the calculated correlation coefficient r is greater than a set threshold t, it is judged that there is a strong correlation between the ambient temperature and the door opening frequency. In a preferred embodiment, the value of t is 0.7.

[0087] In this embodiment, if there is a strong correlation between the ambient temperature and the door opening frequency, the ambient temperature is taken as the independent variable (X) and the door opening frequency is taken as the dependent variable (y), and a linear regression model is fitted. By training the linear regression model, the regression coefficient (slope) and the intercept are obtained.

[0088] In this embodiment, the temperature dataset is cleaned to remove missing values, outliers or duplicate data, ensuring the accuracy and consistency of the data. Then, according to the characteristics of the temperature data and the prediction requirements, useful features are extracted, for example, the date such as season, month, week, etc. can be extracted; the time interval, which is equivalent to the preset time, the unit time granularity such as hour, minute, etc. is taken as the time feature, and the time series analysis technology (such as moving average, exponential smoothing, etc.) is used to identify the long-term trend in the ambient temperature data. Through seasonal analysis method, the seasonal change in ambient temperature data is analyzed to understand the influence of different seasons on ambient temperature, and through periodic analysis, the periodic pattern in the ambient temperature data is identified, such as daily diurnal variation, weekly periodic fluctuation, etc. Based on the above analysis results, a suitable machine learning model such as LSTM model is selected, and the above time features are input into the machine learning model for training to generate an ambient temperature prediction model. Based on the ambient temperature prediction model, according to the prediction requirements, the corresponding features such as time interval and unit time granularity are input. In a feasible embodiment, the preset time is 24 hours and the unit time granularity is 1 hour, which is input into the ambient temperature prediction model. Finally, the ambient temperature in each unit time region within the next 24 hours is output by the ambient temperature prediction model, and the ambient temperature prediction dataset is generated.

[0089] In this embodiment, the environmental temperature prediction model can analyze the current environment corresponding to the input preset time to obtain the environmental temperature in the preset time on the corresponding date, so that the finally generated predicted environmental temperature conforms to seasonal changes and periodic changes. It can be understood that, according to different dates, time intervals and unit time granularity, the data in the generated environmental temperature prediction data set is different, and the corresponding door opening frequency prediction data set also corresponds to different data.

[0090] In this embodiment, the linear regression model and the environmental temperature prediction data set are used to obtain the door opening frequency prediction data set corresponding to the environmental temperature prediction data set. It can be understood that, in the case of time alignment, each unit time region corresponds to a temperature threshold data and a frequency threshold data, which correspond to the predicted environmental temperature in the environmental temperature prediction data set and the predicted door opening frequency in the door opening frequency prediction data set. Taking the environmental temperature prediction data set and the door opening frequency prediction result data set generated by taking the above preset time as 24 hours and the unit time granularity as 1 hour as an example, the environmental temperature prediction data set includes the predicted environmental temperature of each hour in the next 24 hours, and the 24 predicted environmental temperatures are respectively stored in the corresponding unit time region. Similarly, the door opening frequency prediction data set includes the predicted door opening frequency of each hour in the next 24 hours, and the 24 predicted door opening frequencies correspond to the 24 predicted environmental temperatures one by one and are stored in the corresponding unit time region. Specifically, if the above preset time 24 hours is a future day, the 24 unit time regions can be divided into 00:00-01:00, 01:00-02:00, ……, 23:00-24:00 in turn, each unit time region includes the temperature threshold data generated by the above environmental temperature prediction model, and then the frequency threshold data corresponding to each unit time region is generated according to the linear regression model and the temperature threshold data.

[0091] In some embodiments, the temperature data set and the door opening frequency data set are obtained based on historical refrigerator external environment temperature and historical door opening frequency data, including:

[0092] The refrigerator external environment temperature is obtained by the environmental temperature sensor, and a temperature time sequence data set is generated;

[0093] The occurrence time of the refrigerator door opening event is obtained from the door magnetic switch record, and a door opening event time sequence data set is generated;

[0094] The preset time is divided into a plurality of same unit time regions based on the same unit time granularity;

[0095] Based on the unit time region, the temperature time series data set and the door opening event time series data set are time-aligned to generate a temperature data set and a door opening frequency data set.

[0096] In this embodiment, for the unit time region in the temperature data set and the door opening frequency data set, the same unit time granularity is required to be based on for division, so that the finally generated temperature data set and door opening frequency data set can be aligned in time. Similarly, the preset time in the temperature data set and the door opening frequency data set also needs to be in the same time region. Specifically, if the data of the temperature data set in an embodiment is the external environment temperature of the refrigerator in a certain month, in the case of a unit time granularity of 1 hour, the temperature time series data set can be divided in time sequence. For the door opening frequency data set, the occurrence time of the refrigerator door opening event and the corresponding occurrence frequency in the same month are obtained, and they are divided in time sequence. The temperature time series data set and the door opening event time series data set belonging to the same unit time region are matched.

[0097] In this embodiment, when obtaining the historical external environment temperature of the refrigerator and the historical door opening frequency data, the historical external environment temperature of the refrigerator and the historical door opening frequency data of the corresponding time point can be analyzed and divided to generate the temperature data set and the door opening frequency data set according to the time date to be predicted, thereby simplifying the data processing process and improving the accuracy of data prediction. For example, in an embodiment, the time to be predicted is February 18, and the historical external environment temperature of the refrigerator and the historical door opening frequency data in February can be selected for data analysis and division. In order to ensure the accuracy of the data, the historical data in February in the previous at least 5 years can be obtained for analysis.

[0098] S200, based on the temperature threshold data and the frequency threshold data, by real-time monitoring the temperature change trend and the frequency change trend of the refrigerator in the unit time region, obtaining a refrigerator early warning signal;

[0099] In this application, based on the set temperature threshold data and frequency threshold data, the temperature change trend and the frequency change trend of the refrigerator in the unit time region are monitored in real time. Once an anomaly is found, a refrigerator early warning signal is immediately generated. This real-time monitoring mechanism helps to discover and handle potential frost problems in time and avoid excessive frost affecting the performance of the refrigerator.

[0100] In some embodiments, the above step S200 includes:

[0101] The external environment temperature of the refrigerator and the door opening frequency data are obtained in real time. The least square method is used to linearly regress and fit the external environment temperature of the refrigerator and the door opening frequency data in the unit time region, to obtain the temperature slope value of the temperature change trend and the frequency slope value of the frequency change trend in the unit time region.

[0102] The temperature slope value is compared with the temperature threshold value data in the corresponding unit time region to obtain temperature abnormal data points;

[0103] The frequency slope value is compared with the frequency threshold value data in the corresponding unit time region to obtain frequency abnormal data points;

[0104] An ARIMA time series prediction model is adopted to obtain a refrigerator early warning signal within a preset time based on the temperature abnormal data points and the frequency abnormal data points.

[0105] In this embodiment, the collected refrigerator external environment temperature and door opening frequency data are cleaned to remove noise and outliers, and then the refrigerator external environment temperature and door opening frequency data are sorted in time sequence to ensure the continuity of the data. According to the time of the obtained refrigerator external environment temperature and door opening frequency data, the corresponding unit time region is selected from the environment temperature prediction data set and the door opening frequency prediction data set, and the temperature threshold value data and the frequency threshold value data corresponding to the current time of the refrigerator external environment temperature and door opening frequency data are determined.

[0106] In this embodiment, the least square method is adopted to perform linear regression fitting on the refrigerator external environment temperature and door opening frequency data in the unit time region respectively, to obtain a linear equation of temperature change with time and a linear equation of door opening frequency change with time. Based on the above two linear equations, the temperature slope value of the temperature change trend and the frequency slope value of the frequency change trend in the unit time region can be calculated.

[0107] In this embodiment, the temperature slope value is compared with the corresponding temperature threshold value data. If it exceeds the temperature threshold value data, it is marked as a temperature abnormal data point. Similarly, the frequency slope value is compared with the corresponding frequency threshold value data. If it exceeds the frequency threshold value data, it is marked as a frequency abnormal data point.

[0108] For the ARIMA time series prediction model, historical temperature abnormal data points and frequency abnormal data points are used as training data to train the ARIMA time series prediction model. Based on the trained ARIMA time series prediction model, the temperature abnormal signal and the frequency abnormal signal within a preset time are predicted. If the prediction result shows that there is a temperature abnormality or a frequency abnormality within a future preset time, such as 24 hours, 48 hours, etc., a refrigerator early warning signal is generated.

[0109] S300, based on the refrigerator early warning signal, calculate the frost formation speed in the refrigerator;

[0110] In this embodiment, when a refrigerator warning signal is generated, it indicates that there is a temperature abnormality or a frequency abnormality in the refrigerator. The temperature abnormality or the frequency abnormality will affect the degree of frost in the refrigerator to a certain extent. Therefore, when generating a refrigerator warning signal, it is necessary to calculate the current frost speed in the refrigerator and dynamically adjust the defrost operation of the refrigerator based on this to ensure the refrigeration efficiency of the refrigerator during the defrost process and extend the service life of the refrigerator.

[0111] In some embodiments, calculating the frost formation rate in the refrigerator includes:

[0112] Based on the law of conservation of energy, a differential equation is established to determine the change in refrigerator cooling capacity and the amount of frost. By solving the differential equation, the frost rate is obtained.

[0113] The differential equation of the refrigerator cooling capacity change and frost amount is:

[0114]

[0115] in, Indicates the refrigerator's cooling capacity. It represents the cooling capacity lost due to frost in the refrigerator, and Q_heat_loss represents the cooling capacity lost due to heat transfer.

[0116] In this embodiment, the differential equation model can be used to accurately predict the frosting speed of the refrigerator under different operating conditions, thereby continuously monitoring the frosting state inside the refrigerator and promptly detecting and handling potential frosting problems.

[0117] In some embodiments, the The calculation formula is as follows:

[0118]

[0119] in, represents the amount of frost, h_frost represents the potential heat of frost per unit mass, Indicates the frost speed of the refrigerator;

[0120] described The calculation formula is as follows:

[0121]

[0122] Among them, U represents the heat transfer coefficient of the refrigerator, A represents the area of ​​heat exchange between the inside of the refrigerator and the external environment, T_in represents the internal temperature of the refrigerator, and T_out represents the external environment temperature of the refrigerator. represents the frequency threshold data, and k represents the influence coefficient of the refrigerator door opening event.

[0123] It can be understood that the calculation formula of Q_heat_loss and Q_frost is substituted into the above differential equation of refrigerator capacity change and frost amount, and the following calculation formula is obtained:

[0124]

[0125] Then, the frost speed is solved by the above differential equation, and the following calculation formula is obtained:

[0126]

[0127] It can be understood that through the above formula, the frost speed of the refrigerator can be obtained, where F represents the frequency threshold data, and further, it represents the frequency threshold data of the unit time region corresponding to the current time point, which is used to measure the influence of the door opening frequency on the frost speed, k is an influence coefficient, which represents the proportional relationship between the heat exchange amount caused by each door opening and the door opening frequency. The coefficient depends on multiple factors such as the size of the refrigerator door, the insulation performance, and the temperature difference between the inside and outside of the refrigerator, and it is usually fitted through experimental data. For example, the temperature change and heat exchange amount inside and outside the refrigerator are measured under different conditions, and then the value of k is estimated. Specifically, refrigerator doors with different sizes and insulation performances are selected for experiments, the internal temperature of the refrigerator is adjusted and controlled, and temperature sensors are equipped to accurately measure the temperature difference between the inside and outside of the refrigerator. The experimental time, environmental temperature and other external conditions are determined to ensure the repeatability of the experiment. Different door opening frequencies are set, such as opening the door once every minute, opening the door once every five minutes, etc. After each door opening, the temperature change inside the refrigerator is recorded, and a calorimeter or other equipment is used to measure the heat exchange amount caused by the door opening. According to the experimental data, the heat exchange amount caused by each door opening is calculated, and the door opening frequency and the corresponding heat exchange amount are statistically analyzed to observe their trend and relationship. Regression analysis methods such as linear regression, polynomial regression, etc. are used to fit the mathematical relationship between the door opening frequency as the independent variable and the heat exchange amount as the dependent variable. The influence coefficient k is extracted from the fitting result, which represents the proportional relationship between the heat exchange amount caused by each door opening and the door opening frequency.

[0128] S400, if the frost speed is greater than a preset threshold speed, dynamically adjusting the defrosting parameters of the refrigerator through a dynamic adjustment algorithm;

[0129] In some embodiments, the above step S400 includes:

[0130] calculating the deviation value between the frost speed and the preset threshold speed;

[0131] based on the deviation value, using gradient descent method to obtain a new defrosting parameter value;

[0132] Based on the new defrost parameter value and the old defrost parameter, the defrost parameter of the refrigerator is updated to the first defrost parameter by adopting a weighted average method.

[0133] In this embodiment, an objective function is defined to measure the deviation between the current frosting speed and a preset threshold speed. The objective function is usually a loss function, such as mean square error (MSE) or absolute error (MAE). The preset threshold speed is statistically derived from a large amount of experimental data. At this threshold speed, the temperature and humidity environment inside the refrigerator are maintained. Specifically, the objective function is:

[0134]

[0135] in, Indicates the current defrost parameters Frosting speed, Indicates the preset threshold speed, It represents the loss value, that is, the deviation value between the frosting speed and the preset threshold speed.

[0136] Based on the above loss values, calculate the objective function with respect to the defrost parameters The gradient is a vector that points to the direction in which the function value grows fastest. Specifically, in the gradient descent method, the parameters are updated in the opposite direction of the gradient to minimize the objective function. Specifically, by Taking the derivative we get:

[0137]

[0138] in, It represents the derivative of the frost formation rate with respect to the defrost parameter.

[0139] Based on the above defrost parameters The gradient derivative function generates the defrost parameters, specifically, ,in, is the learning rate, which determines the step size of parameter updates; These are the old defrost parameters before the update; is the new defrost parameter generated.

[0140] In this embodiment, in order to improve the stability of the dynamic adjustment of the defrost parameters, the update process between the old defrost parameters and the new defrost parameters is smoothed by a weighted average method, specifically: ,in, Is the weighted average coefficient, which determines the weight of the new parameter value and the old parameter value in the update process. Specifically, if you want to respond to the new parameter value faster, you can increase If you want to keep the current parameter value relatively stable, you can reduce The value of is a first defrosting parameter of the refrigerator after being updated, the first defrosting parameter being used to adjust a defrosting strategy of the refrigeration system to be closer to the preset threshold speed.

[0141] S500, based on the defrosting parameter, starting a defrosting program of the refrigerator, recording running data of the defrosting program, and optimizing the dynamic adjustment algorithm based on the running data to generate a defrosting control strategy.

[0142] In this embodiment, based on the first defrosting parameter generated after being updated, the defrosting program of the refrigerator is started, and the defrosting control strategy is generated. Specifically, the defrosting control strategy is to adjust the compressor power and fan speed of the refrigeration system of the refrigerator, so that the frosting speed of the refrigerator meets the preset threshold speed. In some embodiments, the defrosting control strategy also includes the determination of the defrosting duration. During the starting process of the defrosting program of the refrigerator, the dynamic adjustment algorithm also needs to be adjusted according to the corresponding running data, so that the defrosting control strategy can be dynamically adjusted according to the corresponding environmental parameters during the actual running process of the defrosting program, to ensure the balance between the refrigeration effect and the defrosting speed. In addition, the continuous optimization mechanism of the dynamic adjustment algorithm can continuously optimize the precision and efficiency of the defrosting control, and form a more scientific and reasonable defrosting control strategy. The running data includes the internal temperature of the refrigerator, the internal humidity of the refrigerator, the external environmental temperature of the refrigerator, the door opening frequency data and the frosting speed.

[0143] In some embodiments, the optimization of the dynamic adjustment algorithm based on the running data to generate the defrosting control strategy includes

[0144] The external environmental temperature of the refrigerator and the door opening frequency data during the defrosting operation process are obtained, the Pearson correlation coefficient is used to calculate the influence weight of the external environmental temperature of the refrigerator and the door opening frequency data on the frosting speed, and the weight distribution result is obtained;

[0145] According to the weight distribution result, the external environmental temperature of the refrigerator or the door opening frequency data is taken as the independent variable, and the frosting speed is taken as the dependent variable, a linear regression equation is fitted, and a frosting speed prediction model is generated;

[0146] The frosting speed prediction model is applied to the dynamic adjustment algorithm to generate a defrosting control strategy, and the defrosting control strategy is to adjust the compressor power and fan speed of the refrigeration system of the refrigerator.

[0147] In the embodiment, the Pearson correlation coefficient is used to calculate the correlation between the external environment temperature of the refrigerator and the opening frequency data and the frosting speed, and a correlation coefficient matrix is obtained. According to the correlation coefficient matrix, the influence weight of the external environment temperature and the opening frequency on the frosting speed is determined, and a weight distribution table is generated. Specifically, when the correlation coefficient is any value between 0 and -1, it indicates that the corresponding external environment temperature of the refrigerator or the opening frequency data and the frosting speed do not have correlation, so in the weight distribution table, it is assigned a value of 0, and the correlation coefficient greater than 0 is taken as the weight value, and the weight distribution table is perfected.

[0148] In the embodiment, if the weight of the external environment temperature in the weight distribution table is higher than that of the opening frequency, the external environment temperature is taken as the independent variable, and the frosting speed is taken as the dependent variable, and a linear regression equation is fitted to generate a frosting speed prediction model. If the weight of the opening frequency in the weight distribution table is higher than that of the external environment temperature, the opening frequency data is taken as the independent variable, and the frosting speed is taken as the dependent variable, and a linear regression equation is fitted to generate a frosting speed prediction model. If the weights of the opening frequency and the external environment temperature in the weight distribution table are equivalent, the opening frequency data and the external environment temperature are taken as the independent variables, and the frosting speed is taken as the dependent variable, and a multiple linear regression model is fitted to generate a frosting speed prediction model.

[0149] In some embodiments, the application of the frosting speed prediction model to the dynamic adjustment algorithm generates a defrosting control strategy, including:

[0150] Based on the frosting speed prediction model, the predicted frosting speed of the refrigerator is obtained;

[0151] The running data collected in real time during the defrosting operation of the refrigerator is input into a preset decision tree model, and a second defrosting parameter is output by the decision tree model;

[0152] Based on the second defrosting parameter, the compressor power and the fan speed of the refrigerator are dynamically adjusted.

[0153] In the embodiment, the running data includes the internal temperature of the refrigerator, the internal humidity of the refrigerator, the external environment temperature of the refrigerator, the opening frequency data and the frosting speed. The frosting speed is obtained by the frosting speed prediction model, the second defrosting parameter is output by the decision tree model, and the defrosting control strategy is further regulated.

[0154] For the decision tree model, the features of the decision tree model include the internal temperature of the refrigerator, the internal humidity of the refrigerator, the external environment temperature of the refrigerator, the door opening frequency data and the frosting speed, each feature corresponds to a node in the decision tree, according to the different feature values, the data is divided into different sub-nodes, when the data traverses to the leaf node of the decision tree, it means that it has been finely divided according to a series of feature values. At the leaf node, the model will calculate the optimal defrosting parameters according to the defrosting operation records under similar conditions in the historical data, the above defrosting parameters include the compressor power and fan speed of the refrigerator, which are derived from the operation with the best defrosting effect in the historical data, and then when the real-time collected running data in the defrosting operation process of the refrigerator is input into the decision tree model, the second defrosting parameters conforming to the current frosting condition can be obtained.

[0155] In some embodiments, the generating the defrosting control strategy further includes:

[0156] The collected running data is preprocessed to obtain a time series data set, and a multi-dimensional feature vector is constructed based on the time series data set;

[0157] A time series analysis algorithm is used to model and analyze the multi-dimensional feature vector to obtain a defrosting duration;

[0158] The correlation coefficients of each dimension feature in the multi-dimensional feature vector and the defrosting effect are calculated to obtain key factors highly related to the defrosting effect;

[0159] Based on the key factors, different defrosting effects in the historical data are clustered by a clustering algorithm to obtain defrosting strategy parameters, the defrosting strategy parameters being parameter threshold values when the defrosting program is started;

[0160] The defrosting duration and the defrosting strategy parameters are updated to the defrosting program.

[0161] In this embodiment, the collected time series data of the internal temperature of the refrigerator, the internal humidity of the refrigerator, the external environment temperature of the refrigerator, the door opening frequency data and the frosting speed are preprocessed, outliers are removed, time alignment and data normalization are performed, and a standardized time series data set is obtained. Based on the standardized time series data set, time statistical features such as mean and variance, and frequency domain features such as Fourier transform coefficients are extracted, and a multi-dimensional feature vector of the defrosting process is constructed. A time series analysis algorithm such as an ARIMA model is used to model and analyze the multi-dimensional feature vector, to depict the temperature and humidity change law of the defrosting process and the dynamic characteristics of the device frosting speed, and to predict the optimal defrosting time under the current environmental conditions using the ARIMA model. At the same time, the correlation coefficients of each dimension of the multi-dimensional feature vector and the defrosting effect are calculated to obtain key factors highly related to the defrosting effect. Through the K-Means clustering algorithm, different defrosting effects in the historical data are clustered according to the key factors, and the defrosting strategy parameters corresponding to the optimal defrosting mode are mined, such as defrosting start temperature and humidity threshold. The defrosting strategy parameters and the predicted optimal defrosting time are updated to the configuration file of the defrosting program, and then the refrigerator monitors the environmental parameters in real time according to the updated defrosting strategy parameters and the predicted optimal defrosting time, starts the defrosting program when the defrosting strategy parameters are met, and controls the duration of the defrosting according to the predicted optimal defrosting time. At the same time, real-time collection of defrosting effect data and return to the historical data set form a strategy optimization closed loop, continuously improving the system defrosting performance.

[0162] In this application, by precisely controlling the defrosting process, unnecessary energy consumption and refrigerant waste are avoided, which helps to reduce the energy consumption and emissions of the refrigerator, thereby improving the user's experience and satisfaction, and reducing the user's operation burden and maintenance cost.

[0163] Referring to Figure 2 The refrigerator defrosting control system provided by the application comprises:

[0164] The first processing module 201 is configured to construct a linear regression model based on historical refrigerator external environment temperature and historical door opening frequency data, and obtain temperature threshold data and frequency threshold data of the refrigerator in a unit time region at a preset time.

[0165] The second processing module 202 is configured to obtain a refrigerator early warning signal by monitoring the temperature change trend and the frequency change trend of the refrigerator in the unit time region in real time based on the temperature threshold data and the frequency threshold data.

[0166] The third processing module 203 is configured to calculate the frosting speed in the refrigerator based on the refrigerator early warning signal.

[0167] The fourth processing module 204 is configured to dynamically adjust the defrosting parameters of the refrigerator by a dynamic adjustment algorithm if the frost formation speed is greater than a preset threshold speed.

[0168] The fifth processing module 205 is configured to start a defrosting program of the refrigerator based on the defrosting parameters, record running data of the defrosting program, and optimize the dynamic adjustment algorithm based on the running data to generate a defrosting control strategy.

[0169] It can be understood that the contents in the refrigerator defrosting control method embodiment shown in Figure 1 are all applicable to the refrigerator defrosting control system embodiment, the refrigerator defrosting control system embodiment specifically implements the functions same as the refrigerator defrosting control method embodiment shown in Figure 1 , and achieves the beneficial effects same as the refrigerator defrosting control method embodiment shown in Figure 1 .

[0170] It should be noted that the information interaction and execution process between the above systems, since based on the same concept as the method embodiments, the specific functions and the technical effects brought by them can be referred to the method embodiments part, and will not be repeated here.

[0171] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above functional units and modules is exemplified, and in actual application, the above functions can be completed by different functional units and modules according to needs, that is, the internal structure of the system is divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated in one processing unit, or each unit can exist physically, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or software. In addition, the specific name of each functional unit and module is only for easy distinction, and does not limit the protection scope of the present application. The specific working process of the units and modules in the system can refer to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0172] Please refer to Figure 3 , the present embodiment further provides a computer device 3, comprising a memory 302, a processor 301 and a computer program 303 stored in the memory 302, when the computer program 303 is executed on the processor 301, the refrigerator defrosting control method of any one of the above methods is realized.

[0173] The computer device 3 can be a desktop computer, a notebook computer, a palm computer, a cloud server, and the like. The computer device 3 can include, but is not limited to, a processor 301 and a memory 302. Those skilled in the art can understand that Figure 3 The computer device 3 is only an example and does not limit the computer device 3, which can include more or fewer components than shown, or combine some components, or include different components, such as an input / output device, a network access device, and the like.

[0174] The processor 301 can be a central processing unit (CPU), and can also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic components, discrete hardware components, and the like. The general-purpose processor can be a microprocessor or can also be any conventional processor.

[0175] The memory 302 can be an internal storage unit of the computer device 3 in some embodiments, such as a hard disk or a memory of the computer device 3. The memory 302 can also be an external storage device of the computer device 3 in other embodiments, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, and the like. Further, the memory 302 can include both the internal storage unit and the external storage device of the computer device 3. The memory 302 is used to store an operating system, an application program, a boot loader, data, and other programs, such as program codes of the computer program, and the like. The memory 302 can also be used to temporarily store data that has been output or will be output.

[0176] The embodiment of the present application also provides a computer readable storage medium, which stores a computer program. When the computer program is run by a processor, the refrigerator defrosting control method is realized.

[0177] In this embodiment, the integrated unit, if implemented in the form of a software functional unit and sold or used as an independent product, can be stored in a computer readable storage medium. Based on such understanding, all or part of the processes in the above-mentioned embodiment methods can be instructed by a computer program to relevant hardware, and the computer program can be stored in a computer readable storage medium. When the computer program is executed by a processor, the steps of the above-mentioned various method embodiments can be implemented. The computer program includes computer program code, which can be in the form of source code, object code, executable files or some intermediate forms. The computer readable medium at least includes any entity or device capable of carrying the computer program code to the photographing device / computer equipment, recording medium, computer memory, read-only memory (ROM), random access memory (RAM) and software distribution medium. For example, U disk, mobile hard disk, magnetic disk or optical disk, etc.

[0178] The above-mentioned embodiments are only used to illustrate the technical solutions of the present application, rather than limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacements for some technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should be included in the protection scope of the present application.

Claims

1. A refrigerator defrosting control method, characterized in that: include: Based on the historical refrigerator external environment temperature and historical door opening frequency data, a linear regression model is constructed to obtain the temperature threshold data and frequency threshold data of the refrigerator in a unit time area under the preset time; Based on the temperature threshold data and the frequency threshold data, a refrigerator warning signal is obtained by real-time monitoring of the temperature change trend and the frequency change trend of the refrigerator within a unit time area; Calculating a frost formation rate in the refrigerator based on the refrigerator warning signal; If the frosting speed is greater than a preset threshold speed, dynamically adjusting the defrosting parameters of the refrigerator through a dynamic adjustment algorithm; Based on the defrost parameters, a defrost program of the refrigerator is started, operation data of the defrost program is recorded, and based on the operation data, the dynamic adjustment algorithm is optimized to generate a defrost control strategy.

2. The method according to claim 1, wherein The method constructs a linear regression model based on historical refrigerator external environment temperature and historical door opening frequency data to obtain temperature threshold data and frequency threshold data of the refrigerator within a unit time area under a preset time, including: Based on the historical refrigerator external environment temperature and historical door opening frequency data, a temperature dataset and a door opening frequency dataset are obtained; Determining the degree of correlation between ambient temperature and door opening frequency based on the temperature data set and the door opening frequency data set; Based on the correlation, a linear regression model is generated by fitting with the ambient temperature as the independent variable and the door opening frequency as the dependent variable; Based on the temperature data set, an ambient temperature prediction model is constructed, and the ambient temperature of the refrigerator in each unit time zone within a preset time period is predicted by the ambient temperature prediction model to generate an ambient temperature prediction data set, where the ambient temperature in the unit time zone is the temperature threshold data in the corresponding unit time zone; Based on the linear regression model and the ambient temperature prediction data set, the door opening frequency of the refrigerator in each unit time zone within the preset time is predicted, and a door opening frequency prediction data set within the preset time is generated, and the door opening frequency in the unit time zone is the frequency threshold data in the corresponding unit time zone.

3. The method according to claim 2, wherein The method of obtaining a temperature dataset and a door opening frequency dataset based on historical refrigerator external environment temperature and historical door opening frequency data includes: The ambient temperature outside the refrigerator is obtained through the ambient temperature sensor, and a temperature time series dataset is generated; Obtain the occurrence time of the refrigerator door opening event from the door magnetic switch record and generate a door opening event time series dataset; Based on the same unit time granularity, the preset time is divided into multiple identical unit time areas; Based on the unit time region, the temperature time series dataset and the door opening event time series dataset are time-aligned to generate a temperature dataset and a door opening frequency dataset.

4. The method according to claim 2, wherein The obtaining of a refrigerator warning signal by real-time monitoring of the temperature change trend and the frequency change trend of the refrigerator within a unit time area based on the temperature threshold data and the frequency threshold data includes: The refrigerator's external ambient temperature and door opening frequency data are acquired in real time. The least squares method is used to perform linear regression fitting on the refrigerator's external ambient temperature and door opening frequency data within a unit time region to obtain the temperature slope value of the temperature change trend and the frequency slope value of the frequency change trend within a unit time region. Comparing the temperature slope value with the temperature threshold data under the corresponding unit time area to obtain temperature anomaly data points; Comparing the frequency slope value with the frequency threshold data under the corresponding unit time area to obtain a frequency abnormality data point; An ARIMA time series prediction model is used to obtain a refrigerator warning signal within a preset time based on the temperature anomaly data points and the frequency anomaly data points.

5. The method according to claim 1, wherein The calculating of the frost formation speed in the refrigerator comprises: Based on the law of conservation of energy, a differential equation is established to determine the change in refrigerator cooling capacity and the amount of frost. By solving the differential equation, the frost rate is obtained. The differential equation of the refrigerator cooling capacity change and frost amount is: ； in, Indicates the refrigerator's cooling capacity. It represents the cooling capacity lost due to frost in the refrigerator, and Q_heat_loss represents the cooling capacity lost due to heat transfer.

6. The method according to claim 5, wherein described The calculation formula is as follows: ； in, represents the amount of frost, h_frost represents the potential heat of frost per unit mass, Indicates the frost speed of the refrigerator; described The calculation formula is as follows: ； Among them, U represents the heat transfer coefficient of the refrigerator, A represents the area of ​​heat exchange between the inside of the refrigerator and the external environment, T_in represents the internal temperature of the refrigerator, and T_out represents the external environment temperature of the refrigerator. represents the frequency threshold data, and k represents the influence coefficient of the refrigerator door opening event.

7. The method according to claim 1, wherein If the frosting speed is greater than a preset threshold speed, dynamically adjusting the defrosting parameters of the refrigerator through a dynamic adjustment algorithm includes: Calculating a deviation value between the frosting speed and the preset threshold speed; Based on the deviation value, a new defrost parameter value is obtained using a gradient descent method; Based on the new defrost parameter value and the old defrost parameter, the defrost parameter of the refrigerator is updated to the first defrost parameter by adopting a weighted average method.

8. The method according to claim 1, wherein Based on the operating data, the dynamic adjustment algorithm is optimized to generate a defrost control strategy, including Obtain the refrigerator's external ambient temperature and door opening frequency data during the defrost operation, and use the Pearson correlation coefficient to calculate the weight of the refrigerator's external ambient temperature and door opening frequency data on the frosting speed to obtain the weight distribution result; According to the weight distribution result, the external ambient temperature of the refrigerator or the door opening frequency data is used as an independent variable, and the frosting speed is used as a dependent variable, and a linear regression equation is fitted to generate a frosting speed prediction model; The frosting speed prediction model is applied to a dynamic adjustment algorithm to generate a defrost control strategy, wherein the defrost control strategy is to adjust the compressor power and fan speed of the refrigerator refrigeration system.

9. The method according to claim 8, wherein The frosting speed prediction model is applied to a dynamic adjustment algorithm to generate a defrost control strategy, including: Obtaining a predicted frosting speed of the refrigerator based on the frosting speed prediction model; Inputting real-time operating data collected during the defrosting operation of the refrigerator into a preset decision tree model, and outputting a second defrosting parameter through the decision tree model; Based on the second defrost parameter, the compressor power and fan speed of the refrigerator are dynamically adjusted.

10. The method according to claim 9, wherein The generating of the defrost control strategy further includes: Preprocessing the collected operating data to obtain a time series data set, and constructing a multi-dimensional feature vector based on the time series data set; Using a time series analysis algorithm, modeling and analyzing the multi-dimensional feature vector to obtain the defrost duration; Calculate the correlation coefficient between each dimension feature in the multi-dimensional feature vector and the defrosting effect, and obtain the key factors that are highly correlated with the defrosting effect; Based on the key factors, different defrosting effects in the historical data are clustered by a clustering algorithm to obtain defrosting strategy parameters, where the defrosting strategy parameters are thresholds of various parameters when the defrosting program is started; The defrost duration and the defrost strategy parameters are updated into the defrost program.

11. A refrigerator defrosting control system, characterized in that: include: The first processing module is used to build a linear regression model based on the historical refrigerator external environment temperature and historical door opening frequency data to obtain the temperature threshold data and frequency threshold data of the refrigerator in a unit time area under a preset time; A second processing module is configured to obtain a refrigerator warning signal by monitoring the temperature change trend and frequency change trend of the refrigerator in a unit time area in real time based on the temperature threshold data and the frequency threshold data; A third processing module is configured to calculate a frost formation rate in the refrigerator based on the refrigerator warning signal; A fourth processing module is configured to dynamically adjust the defrosting parameters of the refrigerator through a dynamic adjustment algorithm if the frosting speed is greater than a preset threshold speed; The fifth processing module is used to start the defrost program of the refrigerator based on the defrost parameters, record the operating data of the defrost program, and optimize the dynamic adjustment algorithm based on the operating data to generate a defrost control strategy.

12. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the method according to any one of claims 1 to 10 is implemented.

13. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 10 is implemented.

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