Temperature control method and device for freezing chamber, electronic equipment and medium

Abstract

The embodiment of the invention provides a temperature control method and device for a freezing chamber, electronic equipment and a medium. The method can comprise the steps that predicted defrosting time for executing a defrosting program next time is obtained on the basis of counted first historical defrosting data, the first historical defrosting data are historical data of executing the defrosting program in a freezing chamber by refrigeration equipment, a preset cooling value is determined according to a defrosting temperature rising value in the first historical defrosting data, and the preset cooling value is used for executing the defrosting program next time according to the preset cooling value. And the preset cooling value and the predicted defrosting time are sent to the refrigeration equipment, so that the refrigeration equipment reduces the temperature of a freezing chamber in the refrigeration equipment by the preset cooling value before the predicted defrosting time.

Description

Technical Field

[0001] This specification relates to the field of refrigeration technology, and particularly to a temperature control method, device, electronic device, and medium for a freezer compartment. Background Art

[0002] Currently, moisture in refrigeration equipment condenses into a frost layer on the surface of the evaporator. The frost layer hinders the heat exchange of the evaporator, thereby affecting the refrigeration effect. To ensure good refrigeration capacity, the refrigeration equipment will execute a defrosting program.

[0003] However, when the refrigeration equipment executes the defrosting program, the temperature in the freezer compartment of the refrigeration equipment will rise. For the food stored in the freezer compartment, the temperature rise will accelerate the spoilage and nutrient loss of the food, affecting the freshness preservation effect and edible taste of the food. Summary of the Invention

[0004] To overcome the problems existing in the related art, this specification provides a temperature control method, device, electronic device, and medium for a freezer compartment.

[0005] According to the first aspect of any embodiment of this specification, a temperature control method for a freezer compartment is provided, which is applied to a server. The method includes:

[0006] Based on the statistically obtained first historical defrosting data, obtain the predicted defrosting time for the next execution of the defrosting program. The first historical defrosting data is the historical data of the refrigeration equipment when the defrosting program is executed in the freezer compartment;

[0007] Determine a preset temperature reduction value according to the defrosting temperature rise value in the first historical defrosting data;

[0008] Send the preset temperature reduction value and the predicted defrosting time to the refrigeration equipment, so that the refrigeration equipment reduces the temperature of the freezer compartment in the refrigeration equipment by the preset temperature reduction value before the predicted defrosting time.

[0009] According to the second aspect of any embodiment of this specification, a temperature control method for a freezer compartment is provided, which is applied to a refrigeration equipment. The method includes:

[0010] Receive the predicted defrosting time and the preset temperature reduction value sent by the server. The predicted defrosting time is obtained by the server based on the statistically obtained first historical defrosting data of the refrigeration equipment when the defrosting program is executed in the freezer compartment, and the preset temperature reduction value is determined according to the defrosting temperature rise value in the first historical defrosting data;

[0011] Before the predicted defrosting time, reduce the temperature of the freezer compartment in the refrigeration equipment by the preset temperature reduction value.

[0012] According to a third aspect of any embodiment of this specification, a temperature control device for a freezer is provided, which is applied to a server. The device includes:

[0013] A predicted time acquisition module, configured to acquire a predicted defrost time for the next execution of the defrost program based on the statistically recorded first historical defrost data, where the first historical defrost data is the historical data of the refrigeration device executing the defrost program in the freezer;

[0014] A temperature reduction value determination module, configured to determine a preset temperature reduction value according to the defrost temperature increase value in the first historical defrost data;

[0015] A data sending module, configured to send the preset temperature reduction value and the predicted defrost time to the refrigeration device, so that the refrigeration device reduces the temperature of the freezer in the refrigeration device by the preset temperature reduction value before the predicted defrost time.

[0016] According to a fourth aspect of any embodiment of this specification, a temperature control device for a freezer is provided, which is applied to a refrigeration device. The device includes:

[0017] A data receiving module, configured to receive the predicted defrost time and the preset temperature reduction value sent by the server, where the predicted defrost time is obtained by the server based on the statistically recorded first historical defrost data of the refrigeration device executing the defrost program in the freezer, and the preset temperature reduction value is determined according to the defrost temperature increase value in the first historical defrost data;

[0018] A freezer temperature reduction module, configured to reduce the temperature of the freezer in the refrigeration device by the preset temperature reduction value before the predicted defrost time.

[0019] According to a fifth aspect of any embodiment of this specification, an electronic device is provided, including:

[0020] A processor;

[0021] A memory for storing instructions executable by the processor;

[0022] Wherein, the processor realizes the method described in any embodiment of this specification by running the executable instructions.

[0023] According to a sixth aspect of any embodiment of this specification, a computer-readable storage medium is provided, on which computer instructions are stored, and when the instructions are executed by a processor, the steps of the method described in any embodiment of this specification above are realized.

[0024] The technical solutions provided by the embodiments of this specification may include the following beneficial effects:

[0025] According to the above embodiments, based on the first historical defrosting data of the refrigeration device performing the defrosting program in the freezer, the predicted defrosting time for the next execution of the defrosting program is obtained. According to the defrosting temperature rise value in the first historical defrosting data, a preset temperature drop value is determined, and the preset temperature drop value and the predicted defrosting time are sent to the refrigeration device, so that the refrigeration device can reduce the temperature of the freezer in the refrigeration device by the preset temperature drop value before the predicted defrosting time. Since the freezer can be pre-cooled before the predicted defrosting time for the next execution of the defrosting program, the temperature rise amplitude caused by the execution of the defrosting program can be reduced, and the temperature fluctuation of the freezer can be controlled within a smaller temperature range, thereby ensuring the edible taste and nutritional retention of the stored food materials and extending the shelf life of the food materials.

[0026] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit this specification. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] The accompanying drawings herein are incorporated into the specification and constitute a part of this specification, showing embodiments consistent with the present application, and are used together with the specification to explain the principles of the present application.

[0028] Figure 1 is a flowchart of a method for controlling the temperature of a freezer shown according to an exemplary embodiment of this specification;

[0029] Figure 2 is a flowchart of another method for controlling the temperature of a freezer shown according to an exemplary embodiment of this specification;

[0030] Figure 3 is a flowchart of a method for obtaining a predicted defrosting time shown according to an exemplary embodiment of this specification;

[0031] Figure 4 is an interaction diagram of a method for controlling temperature shown according to an exemplary embodiment of this specification;

[0032] Figure 5 is a schematic structural diagram of an electronic device shown according to an exemplary embodiment of this specification;

[0033] Figure 6 is a block diagram of a device for controlling the temperature of a freezer shown according to an exemplary embodiment of this specification;

[0034] Figure 7 is a block diagram of another device for controlling the temperature of a freezer shown according to an exemplary embodiment of this specification. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0035] Exemplary embodiments will be described in detail herein, and examples thereof are shown in the accompanying drawings. When the following description refers to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this specification. On the contrary, they are merely examples of devices and methods consistent with some aspects of this specification as detailed in the appended claims.

[0036] The terms used in this specification are for the purpose of describing particular embodiments only and are not intended to limit this specification. The singular forms "a", "the", and "said" used in this specification and the appended claims are also intended to include the plural forms unless the context clearly dictates otherwise. It should also be understood that the term "and / or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.

[0037] It should be understood that although the terms first, second, third, etc. may be used in this specification to describe various information, such information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of this specification, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the word "if" as used herein may be interpreted as "when" or "while" or "in response to determining".

[0038] Currently, when the refrigeration equipment defrosts, it will cause the temperature in the freezer of the refrigeration equipment to rise. The rising temperature in the freezer will accelerate the deterioration of food ingredients and the loss of nutrients, affect the fresh-keeping effect and edible taste of food ingredients, and shorten the shelf life of stored food ingredients.

[0039] To solve the above problems, an embodiment of this specification proposes a temperature control method for a freezer. To further illustrate this specification, the following embodiments are provided:

[0040] Please refer to Figure 1 , Figure 1 is a flowchart of a temperature control method for a freezer shown in an exemplary embodiment according to this specification. This temperature control method can be applied to a server. A server is a computer device that manages resources and provides data processing services for refrigeration equipment, such as a cloud server, etc. A refrigeration equipment is an electronic device with a refrigeration function, such as a refrigerator, a freezer, etc., and this refrigeration equipment has a freezer.

[0041] This temperature control method may include the following steps:

[0042] Step 102: Based on the statistically analyzed first historical defrost data, obtain the predicted defrost time for the next execution of the defrost program. The first historical defrost data is the historical data of the refrigeration device executing the defrost program in the freezer compartment.

[0043] In this step, the server can receive the first historical defrost data reported by the refrigeration device and perform statistics on the first historical defrost data. For example, statistically analyze the first historical defrost data of refrigeration devices that have been in an operating state for the most recent T days, or statistically analyze the first historical defrost data of the most recent T executions of the defrost program by refrigeration devices that have been in an operating state. T is a positive integer greater than 1.

[0044] Among them, there is an association relationship between the refrigeration device and the server. The association method can be that the refrigeration device has been bound to platforms such as an APP or a public account. The refrigeration device can report the historical defrost data of the executed defrost program to the server through the binding relationship with platforms such as an APP or a public account.

[0045] The first historical defrost data is the historical data of the refrigeration device executing the defrost program in the freezer compartment, including: the unique identifier of the refrigeration device, the start time of the defrost program, the end time of the defrost program, the execution duration of the defrost program, and the defrost temperature rise value when the defrost program is executed, etc.

[0046] Among them, the unique identifier of the refrigeration device, which is used to identify the model of the refrigeration device, can be a Media Access Control IDentifier (MACID). The defrost program is a program for defrosting the freezer compartment in the refrigeration device. The defrost temperature rise value is the highest temperature value that the freezer compartment rises each time the refrigeration device executes the defrost program.

[0047] The server can analyze information such as the execution time, execution interval, and execution times of the defrost program based on the statistically analyzed first historical defrost data, predict the predicted defrost time for the next execution of the defrost program, and obtain the predicted defrost time for the next execution of the defrost program.

[0048] The method for obtaining the predicted defrost time, for example, can be to predict the defrost time for the next execution of the defrost program according to the change rule of the start time of the defrost program to obtain the predicted defrost time; it can also be to calculate the interval time between the start time and the end time of the defrost program, predict the interval time for the next execution of the defrost program according to the change rule of the interval time, and obtain the predicted defrost time based on the predicted interval data.

[0049] The server can also obtain the area where the refrigeration equipment is located according to the IP address of the refrigeration equipment. Statistically analyze the historical defrost data of a batch of refrigeration equipment that is in the same area as the refrigeration equipment and has the same model as the batch historical defrost data. Based on the first historical defrost data, referring to the variation law of the batch historical defrost data, predict the predicted defrost time for the next execution of the defrost program. Among them, the same area can be the same city, province, etc. as the refrigeration equipment.

[0050] Further, the server can preprocess the first historical defrost data before obtaining the predicted defrost time for the next execution of the defrost program based on the statistically analyzed first historical defrost data. Among them, the preprocessing is used to clean up invalid historical defrost data.

[0051] Invalid historical defrost data includes, but is not limited to: outliers, missing values, and duplicate values. Among them, outliers are data that exceed the preset normal range, missing values are data that are not reported successfully due to reasons such as network environment, and duplicate values are historical defrost data reported repeatedly by the same refrigeration equipment.

[0052] As described above, by preprocessing the historical defrost data and cleaning up invalid historical defrost data, the quality and accuracy of the first historical defrost data can be ensured. Based on the accurate first historical defrost data, predict the predicted defrost time for the next execution of the defrost program, thereby making the predicted defrost time more accurate.

[0053] Step 104: Determine a preset temperature drop value according to the defrost temperature rise value in the first historical defrost data.

[0054] In this step, the server can select the highest defrost temperature rise value in the first historical defrost data as the preset temperature drop value according to the defrost temperature rise value in the first historical defrost data statistically analyzed in step 102.

[0055] The server can also calculate the sum value and quantity of the defrost temperature rise values in the first historical defrost data, calculate the average temperature rise value, and use the average temperature rise value as the preset temperature drop value. Calculate the average temperature rise value according to the following formula (1):

[0056]

[0057] Among them, y represents the average temperature rise value, n represents the quantity of the defrost temperature rise values in the first historical defrost data, and x represents the sum value of n defrost temperature rise values.

[0058] For example, the defrost temperature rise values for five executions of the defrost program in the first historical defrost data are: 3, 5, 7, 9, and 11. The quantity of the defrost temperature rise values in the first historical defrost data is 5, and the sum value of the defrost temperature rise values is 35, then the average temperature rise value is 7. The server can use 7 as the preset temperature drop value.

[0059] As described above, by calculating the average temperature rise value based on the sum and quantity of the temperature rise values during defrosting in the freezer when the defrosting program is executed, and using the average temperature rise value as the preset temperature drop value, the refrigeration device reduces the temperature of the freezer in the intelligent device by the average temperature rise value before the predicted defrosting time. The temperature of the freezer reduced in advance is closer to the actual situation and more accurate, thereby further reducing the temperature fluctuation of the freezer.

[0060] The server can also calculate the batch average temperature rise value of a batch of refrigeration devices based on the sum value and quantity of the temperature rise values in the batch historical defrosting data, and adjust the preset temperature drop value with reference to the batch average temperature rise value, so as to obtain a more accurate temperature drop amplitude by combining the actual defrosting conditions of refrigeration devices of the same model in the same area.

[0061] Step 106: Send the preset temperature drop value and the predicted defrosting time to the refrigeration device, so that the refrigeration device reduces the temperature of the freezer in the refrigeration device by the preset temperature drop value before the predicted defrosting time.

[0062] In this step, the server can send the preset temperature drop value and the predicted defrosting time to the refrigeration device. Before the predicted defrosting time arrives, the refrigeration device reduces the temperature of the freezer in the refrigeration device by the preset temperature drop value. Pre-cooling before the predicted defrosting time can eliminate the need to wait for the temperature drop operation first after the refrigeration device meets the defrosting conditions. When the predicted defrosting time arrives, the temperature of the freezer in the refrigeration device has been pre-cooled, and the defrosting program can be executed immediately.

[0063] The server can also determine the cooling duration based on the preset temperature drop value and the cooling rate of the freezer temperature. The cooling duration is the time required for the temperature of the freezer in the refrigeration device to be reduced by the preset temperature drop value. Exemplarily, if the preset temperature drop value is 7 and the cooling rate of the freezer temperature is 10 minutes per degree drop, the cooling duration is the product of the preset temperature drop value and the cooling rate, that is, 7 * 10 = 70 minutes.

[0064] The server can also send the cooling duration to the refrigeration device, so that the refrigeration device cools the freezer within the cooling duration before the predicted defrosting time arrives, which can ensure that the temperature of the freezer has been reduced by the preset temperature drop value before the predicted defrosting time, and more accurately guarantee the freshness of the food in the freezer.

[0065] It can be understood that when the error between the predicted defrost time and the preset temperature reduction value and the actual execution of the defrost program is small, the server can regularly obtain the predicted defrost time and the preset temperature reduction value, and send the predicted defrost time and the preset temperature reduction value within a certain period to the refrigeration device. The refrigeration device cools down in advance according to the predicted defrost time and the preset temperature reduction value sent by the server within a certain period. The server does not need to recalculate the predicted data for the next execution of the defrost program every time the defrost program is executed, thereby reducing the resource consumption of the server.

[0066] The temperature control method for the freezer compartment in this embodiment obtains the predicted defrost time for the next execution of the defrost program by using the first historical defrost data of the refrigeration device performing the defrost program in the freezer compartment based on statistics. According to the defrost temperature increase value in the first historical defrost data, the preset temperature reduction value is determined, and the preset temperature reduction value and the predicted defrost time are sent to the refrigeration device, so that the refrigeration device can reduce the temperature of the freezer compartment in the refrigeration device by the preset temperature reduction value before the predicted defrost time. Before the predicted defrost time of the next execution of the defrost program, the freezer compartment can be cooled in advance, ensuring that the temperature of each compartment in the refrigeration device does not rise due to the operation of the evaporator and the heating wire during defrost. Since the preset temperature reduction value is determined according to the defrost temperature increase value in the historical defrost data, the temperature increase amplitude caused by the execution of the defrost program can be accurately reduced, and the temperature fluctuation of the freezer compartment can be controlled within a smaller temperature range, thereby ensuring the edible taste and nutritional retention of the stored food materials and extending the shelf life of the food materials.

[0067] Please refer to Figure 2 , Figure 2 is a flowchart of another temperature control method for a freezer compartment shown in this specification according to an exemplary embodiment. This temperature control method can be applied to a refrigeration device, and this temperature control method can include the following steps:

[0068] Step 202: Receive the predicted defrost time and the preset temperature reduction value sent by the server. The predicted defrost time is obtained by the server based on the first historical defrost data of the refrigeration device performing the defrost program in the freezer compartment, and the preset temperature reduction value is determined according to the defrost temperature increase value in the first historical defrost data.

[0069] In this step, the refrigeration device can report the historical defrost data of the executed defrost program to the server. The server can statistically analyze the historical defrost data reported by the refrigeration device, analyze the execution law of the defrost program based on the statistically obtained first historical defrost data, and obtain the predicted defrost time for the next execution of the defrost program.

[0070] The server can also statistically analyze the batch historical defrosting data of the batch refrigeration equipment, and based on the first historical defrosting data, refer to the variation law of the batch historical defrosting data to predict the predicted defrosting time for the next execution of the defrosting program.

[0071] The server can determine a preset temperature reduction value according to the average value, maximum value, etc. of the defrosting temperature rise values in the first historical defrosting data, and refer to the defrosting temperature rise values in the batch historical defrosting data. The refrigeration equipment can receive the predicted defrosting time and the preset temperature reduction value sent by the server.

[0072] The server can also determine the temperature reduction duration according to the preset temperature reduction value and the temperature reduction speed of the freezer temperature. The refrigeration equipment can also receive the temperature reduction duration sent by the server.

[0073] Step 204: Before the predicted defrosting time, reduce the temperature of the freezer in the refrigeration equipment by the preset temperature reduction value.

[0074] In this step, the refrigeration equipment can cool down the freezer before the predicted defrosting time, and reduce the temperature of the freezer in the refrigeration equipment by the preset temperature reduction value.

[0075] If the refrigeration equipment also receives the temperature reduction duration sent by the server, it can cool down the freezer within the temperature reduction duration before the predicted defrosting time, and reduce the temperature of the freezer in the refrigeration equipment by the preset temperature reduction value.

[0076] In the temperature control method of the freezer in this embodiment, by receiving the preset temperature reduction value sent by the server and the predicted defrosting time obtained based on the historical defrosting data of the intelligent equipment executing the defrosting program, before the predicted defrosting time, the temperature of the freezer in the intelligent equipment is reduced by the preset temperature reduction value. Before the predicted defrosting time of the next execution of the defrosting program, the freezer can be cooled down in advance, ensuring that the temperature of each compartment in the refrigeration equipment will not rise due to the operation of the evaporator and the heating wire during defrosting. Since the preset temperature reduction value is determined according to the defrosting temperature rise value in the historical defrosting data, the temperature rise amplitude caused by executing the defrosting program can be accurately reduced, and the temperature fluctuation of the freezer can be controlled within a smaller temperature range, thereby ensuring the edible taste and nutritional retention of the stored food ingredients and extending the shelf life of the food ingredients.

[0077] In the foregoing embodiment, the temperature control process before executing the defrosting program is introduced. In the following embodiment, a more detailed description will be given on how to obtain the predicted defrosting time, and it can be applied to any of the above embodiments.

[0078] Please refer to Figure 3 , Figure 3FIG. 0 is a flowchart of a method for obtaining predicted defrost time according to an exemplary embodiment of this specification. The method for obtaining predicted defrost time may include the following steps:

[0079] Step 302: Based on the first historical defrost data, train the defrost prediction model to obtain the trained defrost prediction model.

[0080] In this step, based on the first historical defrost data, the server may use the start times of several executions of the defrost program in the first historical defrost data as input parameters, and the actual interval data between the last execution of the defrost program and the adjacent next execution of the defrost program as output parameters to train the defrost prediction model to obtain the trained defrost prediction model.

[0081] Among them, the defrost prediction model is used to predict the interval period between the last execution of the defrost program and the next execution of the defrost program, and may be an Autoregressive Integrated Moving Average Model (ARIMA).

[0082] Further, the server may predict the first interval period between the previous execution of the defrost program and the adjacent previous execution of the defrost program according to the first historical defrost data. Use the start times of several executions of the defrost program before the previous execution of the defrost program in the first historical defrost data as input parameters to obtain the first interval period output by the defrost prediction model.

[0083] The server may predict the second interval period between the previous execution of the defrost program and the adjacent previous execution of the defrost program corresponding to a batch of refrigeration devices according to the statistically collected batch historical defrost data. Respectively use the start times of several executions of the defrost program before the previous execution of the defrost program in the batch historical defrost data as input parameters to obtain the second interval period corresponding to the batch of refrigeration devices output by the defrost prediction model.

[0084] The server may calculate the actual interval period between the previous execution of the defrost program and the adjacent previous execution of the defrost program, calculate the first error between the actual interval period and the first interval period, and further calculate the second error between the actual interval period and the second interval period.

[0085] The server may compare the first error with the second error. In the case where the first error is less than or equal to the second error, use the first historical defrost data to train the defrost prediction model. In the case where the first error is greater than the second error, use the batch historical defrost data to train the defrost prediction model.

[0086] As described above, by obtaining the first error between the actual interval period of the last defrost program execution and the first interval period corresponding to the first historical defrost data, and the second error between the actual interval period and the second interval period corresponding to the batch historical defrost data, when the first error is less than or equal to the second error, the first historical defrost data is used to train the defrost prediction model; when the first error is greater than the second error, the batch historical defrost data is used to train the defrost prediction model. Between the refrigeration equipment and the historical defrost data of the same model of refrigeration equipment in the same city, the historical defrost data corresponding to the prediction result with a smaller error is selected to train the defrost prediction model, so that the prediction result output by the defrost prediction is closer to the actual interval period, making the predicted interval period more accurate.

[0087] Step 304: Input the second historical defrost data into the defrost prediction model, and obtain the predicted interval period output by the defrost prediction model.

[0088] In this step, the server can input the second historical defrost data into the defrost prediction model to obtain the predicted interval period between the next execution of the defrost program and the previous adjacent execution of the defrost program output by the defrost prediction model. Among them, the second historical defrost data is the historical defrost data within a preset time selected from the first historical defrost data. The preset time can preferably be a certain recent time period, such as the most recent seven days, the most recent thirty days, etc.

[0089] Step 306: Add the predicted interval period to the previous execution time of the defrost program as the predicted defrost time.

[0090] In this step, the server can add the previous execution time of the defrost program to the predicted interval period obtained in step 304 as the predicted defrost time for the next execution of the defrost program.

[0091] As described above, by training the defrost prediction model based on the first historical defrost data, a trained defrost prediction model is obtained. Input the second historical defrost data into the defrost prediction model, obtain the predicted interval period output by the defrost prediction model, add the predicted interval period to the previous execution time of the defrost program as the predicted defrost time, and obtain an intelligent predicted interval period according to the historical defrost data and the defrost prediction model, thereby improving the accuracy of predicting the defrost time.

[0092] Furthermore, the server can continuously monitor the actual operation status of the refrigeration equipment and update the first historical defrost data. The rolling window method can be used to select the latest historical defrost data, that is, according to the time sequence of the data acquisition in the updated first historical defrost data, the latest first historical defrost data with a preset time length is selected as the third historical defrost data to generate a new rolling window.

[0093] The defrost prediction model is trained using the third historical defrost data in the new rolling window. Based on the third historical defrost data, the predicted interval period output by the defrost prediction model is corrected, and model parameters such as the autoregressive term order, differencing order, and moving average term order in the defrost prediction model are adjusted to update the defrost prediction model. As time goes by, the rolling window will continuously increase, and the defrost prediction model will also be continuously updated.

[0094] As described above, by updating the first historical defrost data, the latest first historical defrost data of a preset time length is selected according to the data acquisition time sequence in the updated first historical defrost data, and the third historical defrost data. Based on the third historical defrost data, the defrost prediction model is updated to adapt to new defrost data, seasonal changes, environmental changes, etc.

[0095] Furthermore, the server can compare the actual interval period of a batch of refrigeration devices in the same area and of the same model as the refrigeration device with the predicted interval period corresponding to the batch of refrigeration devices to determine the accuracy rate of the predicted interval period.

[0096] The server can perform error analysis on abnormal refrigeration devices with an accuracy rate lower than the preset threshold and obtain the device operation data of the abnormal refrigeration devices and normal refrigeration devices with an accuracy rate higher than the preset threshold. Among them, the device operation data is the operation data recorded during the operation of the refrigeration device, such as: the number of door openings and closings, the duration of door openings and closings, power consumption, the health status of components, etc.

[0097] The server can determine the device operation data associated with the predicted interval period corresponding to the abnormal refrigeration device and analyze the differences between the device operation data of the abnormal refrigeration device and the normal refrigeration device. If the differences between the device operation data of the abnormal refrigeration device and the normal refrigeration device are relatively large in terms of hardware data such as power consumption and the health status of components, the server can remind the user to check whether there are hardware failure problems with the abnormal refrigeration device.

[0098] If the differences between the device operation data of the abnormal refrigeration device and the normal refrigeration device are relatively large in terms of user usage habit data such as the number of door openings and closings and the duration of door openings and closings, the server can provide a personalized defrost prediction model for the abnormal refrigeration device based on the user usage habit data in the device operation data on the basis of the defrost prediction model.

[0099] Add the feature data corresponding to the user usage habits data to the personalized defrost prediction model. For example, since the user stores more food in the refrigeration device, the door opening and closing time of the refrigeration device is longer. The server can add the door opening and closing time to the input parameters of the personalized defrost prediction model, adjust the model parameters such as the autoregressive term order, differencing order, and moving average term order in the personalized defrost prediction model, and train the personalized defrost prediction model.

[0100] As described above, by determining the accuracy rate of the predicted interval corresponding to the batch of refrigeration devices according to the actual interval of the batch of refrigeration devices in the same area and of the same model as the refrigeration device, determining the device operation data associated with the predicted interval corresponding to the abnormal refrigeration device whose accuracy rate of the predicted interval is lower than the preset threshold, and adjusting the defrost prediction model based on the device operation data, it is possible to analyze the influencing factors of the refrigeration devices with low accuracy rate according to the device operation data and adjust the defrost prediction model, so that the prediction result of the defrost prediction model is closer to the actual situation and has a higher accuracy rate.

[0101] To further illustrate the temperature control process between the server and the refrigeration device, please refer to Figure 4 , Figure 4 which shows an interaction diagram of a temperature control method. The temperature control method may include the following steps:

[0102] Step 402: The refrigeration device reports the first historical defrost data.

[0103] In this step, the refrigeration device may report the first historical defrost data of the refrigeration device executing the defrost program to the server.

[0104] Step 404: The server preprocesses the first historical defrost data.

[0105] In this step, the server may preprocess the first historical defrost data and clean the outliers, missing values, duplicate values, etc. in the first historical defrost data.

[0106] Step 406: The server trains the defrost prediction model based on the first historical defrost data and the batch of historical defrost data.

[0107] In this step, the server selects the historical defrost data corresponding to the prediction result with a smaller error from the actual interval based on the first historical defrost data and the batch of historical defrost data, and trains the defrost prediction model.

[0108] Step 408: The server inputs the recent second historical defrost data into the defrost prediction model and obtains the predicted interval output by the defrost prediction model.

[0109] In this step, the server inputs the second historical defrost data into the defrost prediction model trained in step 406, and obtains the predicted interval period output by the defrost prediction model.

[0110] Step 410: The server adds the predicted interval period to the previous execution time of the defrost program as the predicted defrost time.

[0111] In this step, the server can add the predicted interval period obtained in step 408 to the previous execution time of the defrost program as the predicted defrost time.

[0112] Step 412: The server calculates the average temperature rise value and uses it as the preset temperature drop value.

[0113] In this step, the server can calculate the average temperature rise value based on the sum value and quantity of the defrost temperature rise values in the first historical defrost data, and use the average temperature rise value as the preset temperature drop value.

[0114] Step 414: The server calculates the temperature drop duration.

[0115] In this step, the server can calculate the temperature drop duration required for the freezer to reduce the preset temperature drop value according to the preset temperature drop value determined in step 412 and the temperature drop rate of the freezer temperature.

[0116] Step 416: The server sends the preset temperature drop value, the predicted defrost time, and the temperature drop duration.

[0117] In this step, the server can send the preset temperature drop value, the predicted defrost time, and the temperature drop duration to the refrigeration equipment.

[0118] Step 418: The refrigeration equipment reduces the temperature of the freezer by the preset temperature drop value within the temperature drop duration before the predicted defrost time.

[0119] In this step, the refrigeration equipment can reduce the temperature of the freezer of the refrigeration equipment by the preset temperature drop value within the temperature drop duration before the predicted defrost time.

[0120] Step 420: The server updates the defrost prediction model based on the third historical defrost data.

[0121] In this step, the server can update the first historical defrost data, select the latest first historical defrost data of a preset time length as the third historical defrost data. Based on the third historical defrost data, adjust the model parameters of the defrost prediction model to update the defrost prediction model.

[0122] Step 422: The server determines the equipment operation data associated with the predicted interval period corresponding to the abnormal refrigeration equipment, and adjusts the defrost prediction model.

[0123] In this step, the server can perform error analysis on abnormal refrigeration devices with an accuracy rate lower than a preset threshold, determine the device operation data associated with the predicted interval period corresponding to the abnormal refrigeration device, adjust the defrost prediction model, and provide a personalized defrost prediction model.

[0124] Figure 5 FIG. is a schematic structural diagram of an electronic device shown according to an exemplary embodiment of the present specification. The electronic device can be, for example, a mobile phone, a computer, a digital broadcast terminal, a messaging device, a game console, a tablet device, a personal digital assistant, a server, a smart home appliance, etc. The electronic device can be, for example, a mobile phone, a computer, a digital broadcast terminal, a messaging device, a game console, a tablet device, a personal digital assistant, a server, a smart home appliance, etc. Refer to Figure 5 , at the hardware level, the electronic device includes a processor 502, an internal bus 504, a network interface 506, a memory 508, and a non-volatile memory 510. Of course, it may also include other hardware required for other services. The processor 502 reads the corresponding computer program from the non-volatile memory 510 into the memory 508 and then runs it, forming a temperature control device for the freezer compartment at the logical level. Of course, in addition to the software implementation method, the present application does not exclude other implementation methods, such as logic devices or a combination of software and hardware. That is to say, the execution subject of the following processing flow is not limited to each logic unit, and can also be hardware or a logic device.

[0125] Figure 6 FIG. is a block diagram of a temperature control device for a freezer compartment shown according to an exemplary embodiment of the present specification. Refer to Figure 6 , the device may include: a predicted time acquisition module 602, a temperature reduction value determination module 604, and a data sending module 606, where:

[0126] The predicted time acquisition module 602 is configured to obtain a predicted defrost time for the next execution of the defrost program based on the statistically collected first historical defrost data, where the first historical defrost data is the historical data of the refrigeration device executing the defrost program in the freezer compartment.

[0127] The temperature reduction value determination module 604 is configured to determine a preset temperature reduction value according to the defrost temperature increase value in the first historical defrost data.

[0128] The data sending module 606 is configured to send the preset temperature reduction value and the predicted defrost time to the refrigeration device, so that the refrigeration device reduces the temperature of the freezer compartment in the refrigeration device by the preset temperature reduction value before the predicted defrost time.

[0129] In one example, before the prediction time acquisition module 602 obtains the predicted defrosting time for the next execution of the defrosting program based on the statistically-based first historical defrosting data, it is further configured to:

[0130] Preprocess the first historical defrosting data, where the preprocessing is used to clean invalid historical defrosting data.

[0131] In one example, when the prediction time acquisition module 602 is configured to obtain the predicted defrosting time for the next execution of the defrosting program based on the statistically-based first historical defrosting data, it includes: training a defrosting prediction model based on the first historical defrosting data to obtain the trained defrosting prediction model; inputting second historical defrosting data into the defrosting prediction model to obtain a predicted interval period output by the defrosting prediction model; and adding the predicted interval period to the last execution time of the defrosting program as the predicted defrosting time.

[0132] In one example, the prediction time acquisition module 602 is further configured to: update the first historical defrosting data; select the latest first historical defrosting data of a preset time length as the third historical defrosting data according to the time sequence of the data acquisition time in the updated first historical defrosting data; and update the defrosting prediction model based on the third historical defrosting data.

[0133] In one example, when the prediction time acquisition module 602 is configured to train a defrosting prediction model based on the first historical defrosting data to obtain the trained defrosting prediction model, it includes: predicting a first interval period for the last execution of the defrosting program according to the first historical defrosting data; predicting a second interval period for the last execution of the defrosting program according to the statistically-based batch historical defrosting data, where the batch historical defrosting data is the historical defrosting data of a batch of refrigeration devices in the same region as the refrigeration device and of the same model; obtaining a first error between the actual interval period for the last execution of the defrosting program and the first interval period, and a second error between the actual interval period and the second interval period; training the defrosting prediction model with the first historical defrosting data when the first error is less than or equal to the second error; and training the defrosting prediction model with the batch historical defrosting data when the first error is greater than the second error.

[0134] In one example, the prediction time acquisition module 602 is further configured to: determine the accuracy rate of the predicted interval period corresponding to the batch of refrigeration devices according to the actual interval period of the batch of refrigeration devices that are in the same area as the refrigeration device and have the same model; determine the device operation data associated with the predicted interval period corresponding to the abnormal refrigeration device, where the abnormal refrigeration device is a refrigeration device whose accuracy rate of the predicted interval period is lower than a preset threshold; and adjust the defrost prediction model based on the device operation data.

[0135] In one example, when the temperature reduction value determination module 604 is configured to determine a preset temperature reduction value according to the defrost temperature increase value in the first historical defrost data, it includes: calculating an average temperature increase value according to the sum and quantity of the defrost temperature increase values, and using the average temperature increase value as the preset temperature reduction value.

[0136] Figure 7 It is a block diagram of another temperature control device for a freezer shown in this specification according to an exemplary embodiment. Refer to Figure 7 , the device may include: a data reception module 702 and a freezer temperature reduction module 704, where:

[0137] The data reception module 702 is configured to receive the predicted defrost time and the preset temperature reduction value sent by the server, where the predicted defrost time is obtained by the server based on the first historical defrost data of the refrigeration device performing the defrost procedure in the freezer, and the preset temperature reduction value is determined according to the defrost temperature increase value in the first historical defrost data.

[0138] The freezer temperature reduction module 704 is configured to reduce the temperature of the freezer in the refrigeration device by the preset temperature reduction value before the predicted defrost time.

[0139] For the implementation processes of the functions and roles of each unit in the above device, please refer to the implementation processes of the corresponding steps in the above method for details, and will not be elaborated here.

[0140] For the device embodiment, since it basically corresponds to the method embodiment, the relevant parts can refer to the partial description of the method embodiment. The device embodiments described above are only illustrative. The modules described as separate components may or may not be physically separated. The components shown as modules may or may not be physical modules, that is, they may be located in one place or distributed to multiple network modules. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution in this specification. Those of ordinary skill in the art can understand and implement it without creative efforts.

[0141] In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions, such as a memory including instructions, is also provided. The above instructions can be executed by a processor of a temperature control device of a freezer to implement the method described in any of the above embodiments.

[0142] Among them, the non-transitory computer-readable storage medium may be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, etc., and the present application does not limit this.

[0143] In an exemplary embodiment, a computer program product including a computer program / instructions is also provided. The above computer program / instructions can be executed by a processor of a data synchronization device to implement the method described in any of the above embodiments.

[0144] The above describes specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be executed in a different order than in the embodiments and still achieve the desired result. Additionally, the processes depicted in the drawings do not necessarily require the specific order or sequential order shown to achieve the desired result. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0145] Those skilled in the art will readily conceive of other embodiments of this specification after considering the specification and practicing the invention herein. This specification is intended to cover any variations, uses, or adaptations of this specification, which follow the general principles of this specification and include common general knowledge or conventional technical means in the technical field not claimed in this application. The specification and embodiments are only regarded as exemplary, and the true scope and spirit of this specification are pointed out by the following claims.

[0146] It should be understood that this specification is not limited to the exact structures described above and shown in the drawings, and various modifications and changes can be made without departing from its scope. The scope of this specification is only limited by the appended claims.

[0147] The above are only the preferred embodiments of this specification and are not intended to limit this specification. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of this specification shall be included within the scope of protection of this specification.

Claims

1. A temperature control method for a freezer, characterized in that, Applied to the server side, the method includes: Based on the statistically first historical defrost data, obtaining the predicted defrost time for the next execution of the defrost program, where the first historical defrost data is the historical data of the refrigeration device executing the defrost program in the freezer; Determining a preset temperature decrease value according to the defrost temperature increase value in the first historical defrost data; Sending the preset temperature decrease value and the predicted defrost time to the refrigeration device, so that the refrigeration device reduces the temperature of the freezer in the refrigeration device by the preset temperature decrease value before the predicted defrost time.

2. The method according to claim 1, wherein Before obtaining the predicted defrost time for the next execution of the defrost program based on the statistically first historical defrost data, the method further includes: Preprocessing the first historical defrost data, where the preprocessing is used to clean invalid historical defrost data.

3. The method according to claim 1, wherein The obtaining the predicted defrost time for the next execution of the defrost program based on the statistically first historical defrost data includes: Training a defrost prediction model based on the first historical defrost data to obtain the trained defrost prediction model; Inputting second historical defrost data into the defrost prediction model and obtaining the predicted interval period output by the defrost prediction model; Adding the predicted interval period to the previous execution time of the defrost program as the predicted defrost time.

4. The method according to claim 3, wherein The method further includes: Updating the first historical defrost data; Selecting the latest first historical defrost data of a preset time length as the third historical defrost data according to the data acquisition time sequence in the updated first historical defrost data; Updating the defrost prediction model based on the third historical defrost data.

5. The method according to claim 3, characterized in that, The training the defrost prediction model based on the first historical defrost data to obtain the trained defrost prediction model includes: Predicting the first interval period of the previous execution of the defrost program according to the first historical defrost data; Predicting the second interval period of the previous execution of the defrost program according to the statistically batch historical defrost data, where the batch historical defrost data is the historical defrost data of a batch of refrigeration devices in the same area and of the same model as the refrigeration device; Obtaining the first error between the actual interval period of the previous execution of the defrost program and the first interval period, and the second error between the actual interval period and the second interval period; In the case where the first error is less than or equal to the second error, training the defrost prediction model using the first historical defrost data; In the case where the first error is greater than the second error, training the defrost prediction model using the batch historical defrost data.

6. The method according to claim 3, characterized in that, The method further includes: Determining the accuracy rate of the predicted interval period corresponding to the batch of refrigeration devices according to the actual interval period of the batch of refrigeration devices in the same area and of the same model as the refrigeration device; Determining the device operation data associated with the predicted interval period corresponding to the abnormal refrigeration device, where the abnormal refrigeration device is a refrigeration device with an accuracy rate of the predicted interval period lower than a preset threshold; Adjusting the defrost prediction model based on the device operation data.

7. The method according to claim 1, wherein Determining a preset temperature reduction value according to the defrost temperature rise value in the first historical defrost data includes: Calculating an average temperature rise value based on the sum and quantity of the defrost temperature rise values, and using the average temperature rise value as the preset temperature reduction value.

8. A temperature control method for a freezer, characterized in that, When applied to a refrigeration device, the method includes: Receiving a predicted defrost time and a preset temperature reduction value sent by a server, where the predicted defrost time is obtained by the server based on the first historical defrost data of the refrigeration device performing a defrost program in the freezer, and the preset temperature reduction value is determined according to the defrost temperature rise value in the first historical defrost data; Before the predicted defrost time, reducing the temperature of the freezer in the refrigeration device by the preset temperature reduction value.

9. A temperature control device for a freezer compartment, characterized in that When applied to a server, the device includes: A predicted time acquisition module, configured to obtain a predicted defrost time for the next execution of the defrost program based on the first historical defrost data, where the first historical defrost data is the historical data of the refrigeration device performing the defrost program in the freezer; A temperature reduction value determination module, configured to determine a preset temperature reduction value according to the defrost temperature rise value in the first historical defrost data; A data sending module, configured to send the preset temperature reduction value and the predicted defrost time to the refrigeration device, so that the refrigeration device reduces the temperature of the freezer in the refrigeration device by the preset temperature reduction value before the predicted defrost time.

10. A temperature control device for a freezer compartment, characterized in that, When applied to a refrigeration device, the device includes: A data receiving module, configured to receive a predicted defrost time and a preset temperature reduction value sent by a server, where the predicted defrost time is obtained by the server based on the first historical defrost data of the refrigeration device performing a defrost program in the freezer, and the preset temperature reduction value is determined according to the defrost temperature rise value in the first historical defrost data; A freezer temperature reduction module, configured to reduce the temperature of the freezer in the refrigeration device by the preset temperature reduction value before the predicted defrost time.

11. An electronic device, characterized in that, Including: A processor; A memory for storing instructions executable by the processor; Wherein, the processor implements the method according to any one of claims 1-8 by running the executable instructions.

12. A computer-readable storage medium having computer instructions stored thereon, characterized in that, When the instructions are executed by the processor, the steps of the method according to any one of claims 1-8 are implemented.

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