Purification method and device of food material storage equipment and electronic equipment

Abstract

The invention discloses a purification method and device of food material storage equipment and electronic equipment. The method comprises the following steps: acquiring a historical use time sequence of target food material storage equipment in a first preset time period; determining a predicted use time sequence of the target food material storage equipment in a next time period corresponding to a first preset time period based on the historical use time sequence; according to the predicted use time in the predicted use time sequence, the purification time of the target food material storage equipment is determined; and the target food material storage equipment is purified within the purification time. By adopting the method, the user can be effectively prevented from being influenced by peculiar smell and bacteria when using the target food material storage equipment, and the use experience of the user is improved.

Description

Technical Field

[0001] This application relates to the field of computer technology, and more specifically, to a purification method, apparatus, and electronic device for food storage equipment. Background Technology

[0002] Currently, with the development of technology, various devices are becoming increasingly intelligent. For example, some food storage devices equipped with purification functions can automatically purify the food storage equipment (e.g., disinfection, sterilization, deodorization, etc.). However, in related technologies, the purification of food storage devices is usually performed on a timed basis, such as once every certain period of time, but this purification method usually does not achieve good purification results. Summary of the Invention

[0003] In view of the above problems, this application proposes a purification method, device and electronic device for food storage equipment, which can obtain the predicted usage time of the food storage equipment, purify the food storage equipment at the purification time corresponding to the predicted usage time, and the purification time is located before the predicted usage time corresponding to the purification time, so that the purification of the food storage equipment is completed before the user uses the food storage equipment, thereby improving the user's experience of using the food storage equipment.

[0004] In a first aspect, embodiments of this application provide a purification method for a food storage device, the method comprising: acquiring a historical usage time sequence of a target food storage device within a first preset time period; determining a predicted usage time sequence of the target food storage device within a next time period corresponding to the first preset time period based on the historical usage time sequence; determining a purification time for the target food storage device according to the predicted usage time in the predicted usage time sequence; and purifying the target food storage device during the purification time.

[0005] Secondly, embodiments of this application provide a purification device for a food storage device. The device includes: a time acquisition module for acquiring a historical usage time sequence of a target food storage device within a first preset time period; a time prediction module for determining a predicted usage time sequence of the target food storage device within the next time period corresponding to the first preset time period based on the historical usage time sequence; a purification time determination module for determining a purification time for the target food storage device based on the predicted usage time in the predicted usage time sequence; and a purification module for purifying the target food storage device during the purification time.

[0006] Thirdly, embodiments of this application provide an electronic device including one or more processors; a memory; and one or more application programs, wherein the one or more application programs are stored in the memory and configured to be executed by the one or more processors, and the one or more application programs are configured to perform the methods described above.

[0007] Fourthly, embodiments of this application provide a computer-readable storage medium storing program code, which can be invoked by a processor to execute the above-described method.

[0008] The purification method, apparatus, and electronic device for food storage equipment provided in this application obtain the historical usage time sequence of the target food storage equipment within a first preset time period; based on the historical usage time sequence, determine the predicted usage time sequence of the target food storage equipment in the next time period corresponding to the first preset time period; thereby realizing the analysis of the user's historical usage habits for the target food storage equipment to determine the user's predicted usage time in the next time period, and then determining the purification time for the target food storage equipment based on the predicted usage time sequence; purifying the target food storage equipment during the purification time, so that the food storage equipment has been purified when the user uses the target food storage equipment according to their usage habits, avoiding the situation of strong odor when the user uses the food storage equipment, thereby improving the user's experience of using the food storage equipment. Attached Figure Description

[0009] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0010] Figure 1 A schematic flowchart of a purification method for a food storage device according to an embodiment of this application is shown;

[0011] Figure 2 It shows Figure 1 A flowchart illustrating step S130;

[0012] Figure 3 It shows Figure 1 Another flowchart of step S130;

[0013] Figure 4 This application shows another schematic flowchart of a purification method for a food storage device provided in one embodiment of the present application;

[0014] Figure 5 This paper illustrates another schematic flowchart of a purification method for a food storage device provided in an embodiment of this application.

[0015] Figure 6 This paper illustrates another schematic flowchart of a purification method for a food storage device provided in an embodiment of this application;

[0016] Figure 7 This invention illustrates a module block diagram of a purification device for a food storage apparatus according to an embodiment of the present application.

[0017] Figure 8 A block diagram of an electronic device for performing a purification method of a food storage device according to an embodiment of this application is shown;

[0018] Figure 9 An embodiment of this application shows a storage unit for storing or carrying program code that implements a purification method for a food storage device according to an embodiment of this application. Detailed Implementation

[0019] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings.

[0020] Please see Figure 1 , Figure 1 This is a schematic flowchart of a purification method for a food storage device provided in one embodiment of this application. The method can be applied to electronic devices, which may be servers, terminal devices, or target food storage devices.

[0021] In some embodiments, the server can be a standalone physical server, a server cluster or distributed system consisting of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDN), and big data and artificial intelligence platforms.

[0022] Terminal devices can be smartphones, tablets, laptops, desktop computers, smart voice interaction devices, smart home appliances, in-vehicle terminals, etc., but are not limited to these.

[0023] Food storage equipment can include ambient temperature food storage equipment and low temperature food storage equipment. Ambient temperature food storage equipment is used to store food at room temperature. The food stored is usually food that can be preserved at room temperature for a long time. Ambient temperature food storage equipment includes: grain storage equipment, edible oil storage equipment, etc., such as rice storage containers or flour storage containers with built-in processors and communication chips. Low temperature food storage equipment is used to store food at temperatures below room temperature. The food stored is usually food that is easily perishable at room temperature. Low temperature food storage equipment includes: refrigerators, freezers, etc., without specific limitations here.

[0024] The purification method for the target food storage device may specifically include the following steps S110 to S140.

[0025] Step S110: Obtain the historical usage time series of the target food storage equipment within the first preset time period.

[0026] Among them, the target food storage equipment refers to the food storage equipment that needs to be purified. The first preset time period can be one week, one month, three months or half a year, etc., which can be set according to actual needs.

[0027] The historical usage time series within the first preset time period refers to the usage records of the target food storage equipment over a specific period of time (such as the past week, month, three months, or half a year). This typically includes the opening and closing times of the target food storage equipment, as well as other relevant usage data. This data is usually arranged chronologically to form a time series.

[0028] It is worth mentioning that the above-mentioned "opening" refers to opening the door of the food storage device, and correspondingly, "closing" refers to closing the door of the food storage device. Considering that users usually store food in the food storage device for a short period of time, the above-mentioned historical usage time series may include historical opening time series or historical closing time series.

[0029] In this embodiment, the aforementioned historical usage time series includes a historical start time series. The historical start time series includes the start time of the target food storage device.

[0030] There are several ways to obtain historical usage time series data.

[0031] In one possible implementation, the historical usage time series of the target food storage device within a first preset time period can be read from the log file of the target food storage device.

[0032] In another possible implementation, if the target food storage device is associated with a cloud server and the target food storage device can upload usage data to the cloud server in real time, then the historical usage time series of the target food storage device within a first preset time period can be obtained from the database associated with the cloud server.

[0033] Step S120: Based on the historical usage time series, determine the predicted usage time series of the target food storage device in the next time period corresponding to the first preset time period.

[0034] The duration of the next time period corresponding to the first preset time period can be one day, two days, or one week, etc.

[0035] In one possible implementation, step S120 may be: using a time prediction model to predict the predicted usage time series of the target food storage device in the next time period corresponding to the first preset time period based on the historical usage time series.

[0036] The aforementioned time prediction model can be an LSTM (Long Short-Term Memory) model, an ARIMA (Autoregressive Integrated Moving Average) model, or any other model that can predict the usage time series of the target food storage device within the next time period corresponding to the first preset time period based on historical usage time series.

[0037] In another possible implementation, periodic statistical analysis can be performed on the historical usage time series to obtain periodic statistical results, such as obtaining historical usage statistics for different time periods within a day; and the predicted usage time for the next time period can be determined based on the periodic statistical results.

[0038] For example, historical usage time series can be aggregated and statistically analyzed according to the 24 hours of a day to obtain the number of times used in different time periods within a day. Based on the number of times used in different time periods within a day, the probability of different time periods in the next time period (the next day) can be predicted. Target time periods with a probability greater than a preset probability threshold can be selected, and the earliest time in each selected target time period can be determined as the predicted usage time in the next time period.

[0039] Step S130: Determine the purification time for the target food storage equipment based on the predicted usage time in the predicted usage time series.

[0040] The predicted usage time series can include one or more predicted usage times.

[0041] To ensure that the purification of the target food storage equipment is completed before the predicted usage time and that the equipment is in optimal condition during use, a preliminary preset duration can be determined based on experience or the characteristics of the target food storage equipment, such as twenty minutes, half an hour, forty minutes, or one hour.

[0042] Step S130 above may be: determining the time point that is a first preset time before the predicted usage time as the purification time for the target food storage device.

[0043] Step S130 above can also be to determine the purification time of the target food storage device by selecting at least one target predicted usage time from the predicted usage time in the predicted usage time series and determining the time point located before the target predicted usage time as the purification time of the target food storage device.

[0044] One method for selecting at least one target predicted usage time from the predicted usage times in the predicted usage time series is to select the predicted usage time within a specified time period as the target predicted usage time based on the user's usage habits. For example, a user may frequently use the refrigerator in the morning, noon, and evening, so the predicted usage time within these time periods can be selected as the target predicted usage time.

[0045] Alternatively, the earliest predicted usage time can be selected from the predicted usage times in the predicted usage time series as the target predicted usage time. Then, a predicted usage time that is after the earliest predicted usage time and whose time interval with the earliest predicted usage time reaches a preset time interval can be selected as the target predicted usage time. This target predicted usage time can be used as the new earliest predicted time to obtain at least one target predicted usage time.

[0046] Step S140: Purify the target food storage device during the purification time.

[0047] Purifying the target food storage equipment includes one or more of the following: sterilization, disinfection, and deodorization.

[0048] It is worth mentioning that the target food storage device can be equipped with purification equipment or components (such as activated carbon filters for deodorization and removal of harmful gases, ultraviolet lamps for killing bacteria and viruses, etc.). The target food storage device can control the purification equipment or components to start working during the purification time to purify the target food storage device.

[0049] By employing the purification method for the aforementioned food storage equipment, firstly, the historical usage time series of the target food storage equipment within a first preset time period is obtained. Based on this historical usage time series, a predicted usage time series for the target food storage equipment in the next time period corresponding to the first preset time period is determined. This allows for the analysis of the user's historical usage habits regarding the target food storage equipment to predict the user's predicted usage time in the next time period. Subsequently, based on the predicted usage time in the predicted usage time series, the purification time for the target food storage equipment is determined. The target food storage equipment is then purified during this purification time, ensuring that the equipment is purified by the time the user uses it according to their habits. This avoids strong odors when the user uses the equipment, thereby improving the user experience. Furthermore, by predicting the predicted usage time series for the next time period corresponding to the first preset time period based on the historical usage time series of the target food storage equipment within the first preset time period, dynamic adjustments can be made according to the user's real-time needs when using the target food storage equipment to meet their personalized requirements.

[0050] Considering that users may frequently use the target food storage device within a short period of time during a fixed time of day, while the purification time for the target food storage device may be relatively long, in order to improve the purification effect of the target food storage device and avoid the problem of the target food storage device becoming unusable due to improper purification time scheduling, in one possible implementation method, please refer to... Figure 2 The above step S130 includes:

[0051] Step S131: Determine the earliest usage time within the specified time range in the predicted usage time series as the first reference time.

[0052] The specified time range can be one or more. If there are multiple ranges, the earliest time within each specified time range of the predicted usage time will be determined as the first reference time. That is, there can be multiple first reference times.

[0053] For example, the target food storage device is a household refrigerator. Users typically use the refrigerator during the morning, noon, and evening. Specifically, the refrigerator is frequently used during three time periods: 7-9 AM, 11-1 PM, and 5-7 PM. If the predicted usage times in the predicted usage time series include 8:30 AM, 11:10 AM, 12:30 PM, 5:10 PM, and 6:40 PM, then the earliest time within each of these three time periods (7-9 AM, 11-1 PM, and 5-7 PM) can be determined as the first reference time. That is, 8:30 AM, 11:10 AM, and 5:10 PM are each determined as the first reference time.

[0054] Step S132: Determine the time point that is a first preset time before the first reference time as the purification time for the target food storage device.

[0055] The first preset duration can be determined based on the purification time required by the target food storage device. For example, if the purification time required by the target food storage device is a specified duration, the first preset duration can be set to be greater than the specified duration to ensure that the target food storage device has completed purification before the predicted usage time.

[0056] By using the steps S131-S132 above, the earliest usage time within the specified time range of the predicted usage time in the predicted usage time series is determined as the first reference time. Purification is performed before the first reference time, which ensures that the refrigerator has completed the purification process before the user uses it, thereby providing the user with a clean and fresh usage environment.

[0057] Please see Figure 3 Considering that users may use the refrigerator multiple times within a specified time period, in order to ensure a better user experience each time, in one possible implementation of this application, step S130 further includes:

[0058] Step S133: Determine the purification frequency within the specified time range based on the predicted usage time within the specified time range.

[0059] In one possible implementation, if there are multiple predicted usage times within a specified time range, such as at least two, the purification frequency can be determined based on the number of predicted usage times within the specified time range, the maximum duration between predicted usage times within the specified time range, and a first preset correspondence. The first preset correspondence table stores multiple purification frequencies, as well as the range of predicted usage times and the maximum duration range corresponding to each purification frequency.

[0060] For example, the first preset correspondence table is as follows:

[0061] Purification frequency The predicted range of usage time Maximum duration range 1 1-2 <1.5 hours 2 3-5 >1.5 hours

[0062] Based on the first preset correspondence mentioned above, if the number of predicted usage times within a specified time range is 3, and the maximum duration between predicted usage times within a specified time range is greater than 1.5 hours, the purification frequency can be determined to be 2 times according to the first preset correspondence mentioned above.

[0063] Step S134: If the purification frequency is greater than 1, determine the second reference time based on the predicted usage time within the specified time range and the purification frequency.

[0064] In one possible implementation, the predicted usage time within a specified time range that has a duration greater than a second preset duration between the predicted usage time and the first predicted time within the specified time range can be determined as the second reference time.

[0065] The second preset duration can be 1 hour, 40 minutes, etc., which can be set according to actual needs.

[0066] In another possible approach, the second reference time can be determined as the predicted usage time with the longest duration between the predicted usage time within the specified time range and the first predicted time within the specified time range.

[0067] Step S135: Determine the time point that is a first preset time before the second reference time as the purification time for the target food storage device.

[0068] By employing steps S133-S135 above, and determining the purification frequency based on predicted usage time within a specified time range, and setting a second reference time accordingly, purification operations can be rationally scheduled within the specified time period of frequent user use. This ensures that users experience a cleaner and fresher environment each time they open the target food storage device, thereby improving user satisfaction.

[0069] Furthermore, by analyzing the predicted usage time and maximum duration within a specified time range, not only the frequency of use is considered, but also the interval between uses. For example, if a user opens the refrigerator multiple times in a short period of time, the frequency of purification can be increased; if the user does not open the target food storage device for a long time, the frequency of purification will be reduced, thereby ensuring that the device can still maintain a good purification state during periods of high user frequency.

[0070] In some scenarios, the target food storage device may also have different purification intensities. In one possible implementation, the method further includes: determining the purification intensity of the target food storage device within the specified time range based on the number of predicted usage times within the specified time range.

[0071] In one possible implementation, if there are multiple predicted usage times within a specified time range, such as at least two, the purification intensity can be determined based on the number of predicted usage times within the specified time range, the maximum duration between predicted usage times within the specified time range, and a second preset correspondence. The second preset correspondence table stores multiple purification intensities, as well as the range of the number of predicted usage times and the maximum duration range corresponding to each purification intensity.

[0072] For example, the second preset correspondence table is as follows:

[0073] Purification intensity The predicted range of usage time Maximum duration range Low purification intensity 1-2 <1.5 hours High purification intensity 3-5 >1.5 hours

[0074] Based on the above second preset correspondence, if the number of predicted usage times within a specified time range is 3, and the maximum duration between predicted usage times within a specified time range is greater than 1.5 hours, the purification intensity can be determined to be high purification intensity based on the above second preset correspondence.

[0075] Among them, different purification intensities correspond to one or more differences in the time consumed, the power of the purification equipment or components, the purification range, etc.

[0076] For example, a low-intensity purification process can take several minutes, and its purification scope includes air circulation and partial area disinfection; a high-intensity purification process can take half an hour, and its purification scope includes air circulation and full-area disinfection.

[0077] Step S140 above can also be: purifying the target food storage device according to the purification intensity corresponding to the purification time during the purification time, wherein the purification intensity corresponding to the purification time is the purification intensity of the target food storage device within a specified time range of the predicted usage time corresponding to the purification time.

[0078] By adopting the above method, high-intensity purification can be used during periods of high user usage, while low-intensity purification can be used during periods of low usage. This ensures purification effectiveness while avoiding unnecessary energy waste, thus achieving the goal of optimizing energy efficiency.

[0079] Please see Figure 4 In one possible implementation, before performing step S140, the method further includes:

[0080] Step S150: Obtain the food information stored in the target food storage device.

[0081] The aforementioned ingredient information may include one or more of the following: ingredient name, quantity, purchase date, and expected consumption date.

[0082] In one possible implementation, the method for obtaining the food information stored in the target food storage device can be to receive the food information stored in the target food storage device input by the user.

[0083] Users can input information such as the name of the food, the purchase date, and the expected consumption date through the interactive interface of the target food storage device or the interactive interface of the terminal device associated with the target food storage device.

[0084] In another possible implementation, step S150 above may also involve acquiring an image captured by a camera in the target food storage device and identifying the image to obtain information about the food stored in the food storage device.

[0085] Specifically, when performing image recognition, computer vision techniques (such as convolutional neural networks (CNNs) can be used to identify food ingredients in the image and output the recognition results. The recognition results can include information such as the name and quantity of the food ingredients.

[0086] Step S160: Determine the purification intensity of the target food storage device based on the food information stored in the target food storage device.

[0087] Different purification intensities may imply different purification methods, durations, and resource consumption. The purification intensity can be determined by comprehensively considering factors such as the type, quantity, and freshness of the ingredients.

[0088] Specifically, the purification intensity can be determined based on the characteristics of different ingredients, such as their perishability or distinctive odors. A mapping table or rule base can be established to associate different ingredients with corresponding purification intensity levels. For example, meat and seafood, as well as foods with pungent odors (such as durian and jackfruit), require higher purification intensity, while dried goods require lower intensity. The purification intensity can also be determined based on the quantity of ingredients; for instance, a larger quantity may require more intensive purification to prevent cross-contamination and bacterial growth. Furthermore, the freshness of the ingredients can be estimated based on their purchase date and expected consumption date, and the purification intensity can be adjusted accordingly.

[0089] The purification intensity can also be determined by combining information on multiple ingredients (such as at least two of the ingredients’ types, quantities, and dates). This involves establishing a correspondence between multiple purification intensities and ingredient information, and determining the purification intensity based on the ingredient information and the correspondence.

[0090] Step S140 above can also be: purifying the target food storage device according to the purification intensity corresponding to the purification time during the purification time.

[0091] Wherein, the purification intensity corresponding to the purification time is the purification intensity of the target food storage device within the specified time range of the predicted usage time corresponding to the purification time.

[0092] By employing the above methods to determine the purification intensity based on ingredient information, the safety and quality of food storage are improved, providing users with a better experience. Furthermore, it can extend the shelf life of ingredients and reduce food waste.

[0093] In one possible implementation, step S120 includes: using a time prediction model to predict the predicted usage time series of the target food storage device in the next time period corresponding to the first preset time period based on the historical usage time series.

[0094] Please see Figure 5 In this implementation, the time prediction model is trained in the following manner:

[0095] Step S210: Obtain training samples, which include the actual usage time series of the next time period corresponding to the sample usage time series of the food storage device.

[0096] In one possible implementation, the training samples can be obtained by acquiring log data of the food storage device. The log data typically includes the historical usage times of the food storage device. The usage time series of a first preset time period in the log information is used as the sample usage time series, and the usage time series of the next time period adjacent to the first preset time period is used as the actual usage time series.

[0097] In another possible implementation, historical usage data uploaded by food storage devices or users through a client can also be received, and the usage time series of the first preset time period can be used as the sample usage time series, and the usage time series of the next time period adjacent to the first preset time period can be used as the actual usage time series.

[0098] It is worth mentioning that the usage data of the aforementioned food storage equipment can be collected through sensors installed on the food storage equipment.

[0099] Step S220: Use the feature extraction network in the time prediction model to extract features from the time series of the sample to obtain the feature representation of the time series of the sample.

[0100] Feature extraction networks refer to neural networks used to encode features from training samples. Examples include convolutional neural networks, pooling neural networks, and Transformer networks.

[0101] In one possible implementation of this application, when linearly encoding a sample using a time series, the sample using a time series can be treated as a whole for feature extraction, or the sample using a time series can be divided into time series signals corresponding to multiple time windows, and feature extraction can be performed on the time series signal corresponding to each time window. The durations of the multiple time windows can be the same, and can also be the same as the duration of the next time period.

[0102] Step S230: Use the classification prediction network in the time prediction model to perform classification prediction based on the feature representation of the sample using the time series, and obtain the sample prediction using the time series.

[0103] The aforementioned classification network can be a regressor, such as a linear regressor, ridge regressor, decision tree regressor, support vector machine regressor, or neural network regressor (e.g., long short-term memory network), as long as it can predict the sample usage time series in the next time period based on the characteristics of the sample usage time series.

[0104] Step S240: Based on the predicted time series and the actual time series of the samples, determine the model loss of the time prediction model.

[0105] Specifically, the model loss can be obtained by using a loss function based on the predicted and actual usage time series of samples for the next time period. The loss function can be a mean squared error (MSE) loss function or a mean absolute error (MAE) loss function commonly used in regression tasks. These loss functions measure the error between the predicted and actual usage time series. By minimizing the loss function, the model can learn more accurate predictions.

[0106] Step S250: Update the model parameters of the time prediction model based on the model loss.

[0107] After obtaining the model loss, the model parameters can be adjusted based on the model loss to minimize it. The training termination condition can be that the number of iterations of the object detection model reaches a preset number, or the model loss is less than a preset loss threshold.

[0108] By employing steps S210-S250 above and utilizing training samples, a time prediction model is trained. This model can analyze the usage habits of different users' food storage devices, thereby generating personalized predicted usage time series for each user. Subsequently, purification times are determined based on the predicted usage time series. For example, if it is predicted that users will not use the device for a certain period, energy consumption can be temporarily reduced to save energy; if it is predicted that users will frequently use the device, purification can be carried out in advance to improve the user experience.

[0109] Please see Figure 6 As shown, this application provides a purification method for a food storage device. Taking a refrigerator as an example, the purification method is executed by a server associated with the target refrigerator, and includes the following steps:

[0110] Step 1: Remote Data Analysis. When connected to the internet, the refrigerator collects data on the time each time it is opened or closed by the user, and reports this data to the server via a wireless network. The server receives and stores this data, and the cloud server uses the received data to obtain a historical usage time series within a first preset time period.

[0111] Step 2: Personalized Cleaning Recommendations. The server uses a time prediction model to predict the user's usage time series in the next time period (the next day) based on historical usage time series. For example, it predicts that the user will use the refrigerator at 7:00 AM, 11:00 AM, 6:00 PM, and 8:00 PM.

[0112] Step 3: Dynamically determine the purification time. Perform steps S131-S135 as described above to dynamically adjust the purification time and intensity based on the user's real-time needs. For example, if the user opens the refrigerator multiple times in a short period, the purification frequency and intensity will increase; if the user does not open the refrigerator for a long time, the purification frequency and intensity will decrease.

[0113] It's worth mentioning that the aforementioned time prediction model uses machine learning algorithms to continuously learn and optimize user habits, making the cleanup operation more aligned with user needs and improving user experience. For example, if a user's habits change during a certain period, the model can predict the usage time series for the next period based on the historical usage time series during that period. This allows for adjustments to the timing and intensity of cleanup based on the predicted usage time series to adapt to the user's new habits. These are the specific steps of this embodiment. In this way, we can achieve personalized analysis and recommendations based on each user's specific situation, improving the user experience while also saving energy.

[0114] It should be noted that, after determining the purification time and intensity, the server can send the purification time and intensity to the refrigerator, so that the refrigerator, upon receiving the sent purification time and intensity, will purify the target food storage device according to the purification intensity at the specified purification time. Alternatively, the server can obtain a predicted usage time series using a time prediction model and then send the predicted usage time series to the refrigerator, so that the refrigerator, upon receiving the predicted usage time series, dynamically determines the purification time and intensity and performs purification according to the purification intensity at the specified purification time.

[0115] By adopting the above steps, the beneficial effects of this embodiment are as follows: 1. Personalized Analysis: This technical solution, through remote data analysis, can obtain the refrigerator door opening and closing times of each user at different times. This allows for personalized analysis and recommendations of the user's predicted usage time sequence in the next time period, rather than analyzing based on the user's overall usage data, thereby improving the accuracy and relevance of the analysis. 2. Pre-cleaning: This solution can perform purification in advance of the user's predicted usage time, rather than after the user uses the refrigerator. This avoids the user being affected by odors and bacteria while using the refrigerator, improving the user experience. 3. Dynamic Adjustment: This technical solution can dynamically adjust based on the user's real-time needs (i.e., based on the user's historical usage time sequence within a first preset time period before the current moment), rather than performing fixed purification. This better meets the user's personalized needs and improves the user's quality of life. Based on the above, it can be seen that this technical solution, compared with related technologies, pays more attention to personalization, intelligence, and convenience, and can better meet user needs and improve the user experience.

[0116] Please see Figure 7 , Figure 7 A block diagram of a purification device 300 for a food storage device according to an embodiment of this application is shown. The following will focus on... Figure 7The process shown is described in detail. The purification device 200 of the food storage equipment is applied to the above-mentioned electronic equipment. The purification device 200 of the food storage equipment includes: a time acquisition module 310, a time prediction module 320, a purification time determination module 330, and a purification module 340.

[0117] The time acquisition module 310 is used to acquire the historical usage time sequence of the target food storage device within a first preset time period; the time prediction module 320 is used to determine the predicted usage time sequence of the target food storage device within the next time period corresponding to the first preset time period based on the historical usage time sequence; the purification time determination module 330 is used to determine the purification time for the target food storage device according to the predicted usage time in the predicted usage time sequence; and the purification module 340 is used to purify the target food storage device at the purification time.

[0118] In one possible implementation, the time prediction module 320 is further configured to use a time prediction model to predict the predicted usage time series of the target food storage device in the next time period corresponding to the first preset time period based on the historical usage time series.

[0119] In one possible implementation, the purification device 300 of the food storage equipment further includes a sample acquisition module, a feature extraction module, a classification prediction module, a loss determination module, and a parameter adjustment module. The sample acquisition module is used to acquire training samples, which include the actual usage time series within the next time period corresponding to the sample usage time series of the food storage equipment. The feature extraction module is used to extract features from the sample usage time series using the feature extraction network in the time prediction model to obtain a feature representation of the sample usage time series. The classification prediction module is used to perform classification prediction based on the feature representation of the sample usage time series using the classification prediction network in the time prediction model to obtain a predicted usage time series. The loss determination module is used to determine the model loss of the time prediction model based on the predicted usage time series and the actual usage time series. The parameter adjustment module is used to update the model parameters of the time prediction model based on the model loss.

[0120] In one possible implementation, the purification time determination module 330 includes a first reference time determination submodule and a first purification time determination submodule. The first reference time determination submodule is used to determine the earliest usage time within a specified time range in the predicted usage time sequence as the first reference time; the first purification time determination submodule is used to determine the time point before the first reference time as the purification time for the target food storage device.

[0121] In one possible implementation, the purification time determination module 330 includes a purification frequency determination submodule, a second reference time determination module, and a second purification time determination submodule. The purification frequency determination submodule is used to determine the purification frequency within the specified time range based on the predicted usage time within the specified time range. The second reference time determination submodule is used to determine a second reference time based on the predicted usage time within the specified time range and the purification frequency when the purification frequency is greater than 1. The second purification time determination submodule is used to determine a time point that is a first preset duration before the second reference time as the purification time for the target food storage device.

[0122] In one possible implementation, the purification device 300 of the food storage device further includes: a purification intensity determination module, configured to determine the purification intensity of the target food storage device within the specified time range based on the number of predicted usage times within the specified time range; the purification module is further configured to purify the target food storage device at the purification intensity corresponding to the purification time during the purification time, wherein the purification intensity corresponding to the purification time is the purification intensity of the target food storage device within the specified time range of the predicted usage time corresponding to the purification time.

[0123] In one possible implementation, the purification device 300 of the food storage device further includes: a food acquisition module and a purification intensity determination module. The food acquisition module is used to acquire information about the food stored in the target food storage device; the purification intensity determination module is used to determine the purification intensity of the target food storage device based on the information about the food stored in the target food storage device; the purification module is also used to purify the target food storage device according to the purification intensity during the purification time.

[0124] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the above-described device and module can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0125] In the several embodiments provided in this application, the coupling between modules can be electrical, mechanical, or other forms of coupling.

[0126] Furthermore, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module. The integrated modules described above can be implemented in hardware or as software functional modules.

[0127] Please see Figure 8 , Figure 8A schematic diagram of the structure of an electronic device provided in an embodiment of this application is shown. The electronic device 400 may include the following components: a memory 410, one or more processors 420, and one or more application programs, wherein the one or more application programs are stored in the memory 410 and are used to cause the electronic device 400 to execute a method for generating a food purchase list applied to the electronic device when invoked by one or more processors 420.

[0128] The processor 420 may include one or more processing cores. The processor 420 connects to various parts within the electronic device 400 using various interfaces and lines, and performs various functions and processes data by running or executing instructions, programs, code sets, or instruction sets stored in memory, and by calling data stored in memory. Optionally, the processor may be implemented using at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor may integrate one or a combination of several of the following: Central Processing Unit (CPU), Graphics Processing Unit (GPU), and modem. It is understood that the aforementioned modem may also not be integrated into the processor 420, but may be implemented separately through a communication chip.

[0129] The memory 410 may include random access memory (RAM) or read-only memory (ROM). The memory 410 may be used to store instructions, programs, code, code sets, or instruction sets. The memory 410 may include a program storage area and a data storage area. The program storage area may store instructions for implementing an operating system, instructions for implementing at least one function, instructions for implementing the various method embodiments described above, etc. The data storage area may store data created during the use of the electronic device.

[0130] Please see Figure 9 This diagram illustrates a structural block diagram of a computer-readable storage medium provided in an embodiment of this application. The computer-readable medium 500 stores program code that can be called by a processor to execute the methods described in the above method embodiments.

[0131] The computer-readable storage medium 500 may be an electronic memory such as flash memory, EEPROM (Electrically Erasable Programmable Read-Only Memory), EPROM, hard disk, or ROM. Optionally, the computer-readable storage medium 500 includes a non-volatile computer-readable storage medium. The computer-readable storage medium 500 has storage space for program code 510 that performs any of the method steps described above. This program code can be read from or written to one or more computer program products. The program code 510 may, for example, be compressed in a suitable form.

[0132] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A purification method for a food storage device, characterized in that, The method includes: Obtain the historical usage time series of the target food storage equipment within the first preset time period; Based on the historical usage time series, the predicted usage time series of the target food storage device in the next time period corresponding to the first preset time period is determined; The purification time for the target food storage equipment is determined based on the predicted usage time in the predicted usage time series. The target food storage device is purified during the purification period.

2. The method according to claim 1, characterized in that, The step of determining the predicted usage time series of the target food storage device within the next time period corresponding to the first preset time period based on the historical usage time series includes: Using a time prediction model, the predicted usage time series of the target food storage equipment is predicted within the next time period corresponding to the first preset time period, based on the historical usage time series.

3. The method according to claim 2, characterized in that, The time prediction model was trained in the following way: Acquire training samples, which include the actual usage time series of the next time period corresponding to the sample usage time series of the food storage device; The feature extraction network in the time prediction model is used to extract features from the time series of the sample to obtain the feature representation of the time series of the sample. The classification prediction network in the time prediction model is used to perform classification prediction based on the feature representation of the sample using the time series, thereby obtaining the sample prediction using the time series. Based on the predicted time series and the actual time series of the samples, the model loss of the time prediction model is determined. The model parameters of the time prediction model are updated based on the model loss.

4. The method according to claim 1, characterized in that, The step of determining the purification time for the target food storage equipment based on the predicted usage time in the predicted usage time series includes: The earliest usage time within a specified time range in the predicted usage time series is determined as the first reference time; The time point that is a first preset duration before the first reference time is determined as the purification time for the target food storage device.

5. The method according to claim 4, characterized in that, The step of determining the purification time for the target food storage equipment based on the predicted usage time in the predicted usage time series further includes: The purification frequency within the specified time range is determined based on the predicted usage time within the specified time range; If the purification frequency is greater than 1, a second reference time is determined based on the predicted usage time within the specified time range and the purification frequency; The time point that is a first preset duration before the second reference time is determined as the purification time for the target food storage device.

6. The method according to claim 4, characterized in that, The method further includes: The purification intensity of the target food storage equipment within the specified time range is determined based on the number of predicted usage times within the specified time range. The purification of the target food storage device during the purification time includes: The target food storage device is purified according to the purification intensity corresponding to the purification time, wherein the purification intensity corresponding to the purification time is the purification intensity of the target food storage device within a specified time range of the predicted usage time corresponding to the purification time.

7. The method according to any one of claims 1-6, characterized in that, Before purifying the target food storage device during the purification time, the method further includes: Obtain the food information stored in the target food storage device; The purification intensity of the target food storage device is determined based on the food information stored in the target food storage device. The purification of the target food storage device during the purification time includes: The target food storage device is purified according to the purification intensity during the purification time.

8. A purification device for a food storage facility, characterized in that, The device includes: The time acquisition module is used to acquire the historical usage time series of the target food storage equipment within a first preset time period; The time prediction module is used to determine the predicted usage time series of the target food storage device in the next time period corresponding to the first preset time period based on the historical usage time series; The purification time determination module is used to determine the purification time for the target food storage equipment based on the predicted usage time in the predicted usage time series. A purification module is used to purify the target food storage device during the purification time.

9. An electronic device, characterized in that, include: processor; A memory storing computer-readable instructions that, when executed by the processor, implement the method as described in any one of claims 1-8.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-readable instructions that, when executed by a processor, implement the method as described in any one of claims 1-8.

11. A computer program product comprising computer-readable instructions, characterized in that, When executed by a processor, the computer-readable instructions implement the steps of the method according to any one of claims 1-8.

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