Health management method and system for intelligent refrigerator

Abstract

The invention relates to the technical field of data processing, and provides a health management method and system for an intelligent refrigerator. The method comprises the steps that firstly, a recipe plan of a user is obtained and analyzed, food materials needed by dishes in the recipe are obtained, the target maturity of the food materials is determined, secondly, the maturity of the food materials in the refrigerator is monitored, the maturity when the eating time is up is predicted, whether the maturity meets the target maturity or not is judged, and if yes, the food materials are obtained. Whether to adjust the preservation strategy of the food materials or not is determined based on the judgment result. The food materials are preserved through the method, so that the health of a user can be met, and the personalized requirements of the user for the taste and flavor of dishes can be met.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and particularly to a health management method and system for an intelligent refrigerator. Background Art

[0002] With the progress of technology, refrigerators are becoming more and more intelligent. Currently, intelligent refrigerators can preserve food ingredients in different zones. For the food ingredients placed in different zones, the intelligent refrigerator can adopt different preservation strategies based on the type of food ingredients to ensure the safety and health of the food ingredients.

[0003] However, the existing preservation solutions of intelligent refrigerators do not consider that different dishes have different requirements for the maturity of food ingredients. Different maturities of food ingredients not only affect user health but also affect the taste, flavor, etc. of the dishes. For some dishes, even if they contain the same kind of food ingredient, different requirements for the maturity of the same food ingredient will be needed when making different dishes. The traditional single preservation strategy that does not consider the processing of different dishes with food ingredients cannot meet the personalized needs of users.

[0004] Therefore, how to preserve food ingredients to meet both user health and user personalized needs is an urgent problem to be solved in this field. Summary of the Invention

[0005] The purpose of the present invention is to provide a health management method, system, medium and electronic device for an intelligent refrigerator, so as to at least partially solve the above problems.

[0006] According to one aspect of the present disclosure, a health management method for an intelligent refrigerator is proposed, including: obtaining a recipe plan of a user, the recipe plan including recipe dishes and their eating times; analyzing the recipe plan to obtain the first food ingredients required for the first dish in the recipe and the target maturity of the first food ingredients processed into the first dish; monitoring the maturity of the first food ingredients in the refrigerator at the current moment; based on the maturity at the current moment, determining whether the maturity of the first food ingredients can meet the target maturity at the first time from the current moment to the eating time. If so, continue to preserve the first food ingredients according to the current preservation strategy. If not, adjust the preservation strategy of the first food ingredients.

[0007] Optionally, analyzing the recipe plan to obtain the first food ingredients required for the first dish in the recipe and the target maturity of the first food ingredients corresponding to the first dish includes obtaining the target maturity of the first food ingredients through statistical analysis of the dishes and their evaluation big data, wherein the dishes and their evaluation big data are obtained by crawling Internet data.

[0008] Optionally, big data of dishes and their evaluations are obtained by crawling Internet data, including dividing dishes into a first type of dishes and a second type of dishes. For the first type of dishes, a first amount of Internet data is randomly crawled, and for the second type of dishes, a second amount of Internet data is crawled within a recent predetermined time range, wherein the first type of dishes are classic dishes, the second type of dishes are new dishes, and the first amount is less than the second amount.

[0009] Optionally, adjusting the preservation strategy of the first ingredient includes: if the maturity of the first ingredient is lower than the target maturity after a first period of time, adopting a first strategy to process the first ingredient; if the maturity of the first ingredient is higher than the target maturity after the first period of time, adopting a second strategy to process the first ingredient, wherein the first strategy is to ripen the first ingredient, and the second strategy is to delay the maturation of the first ingredient.

[0010] Optionally, a first strategy is adopted to process the first ingredient, including dividing the first ingredient into a first type first ingredient and a second type first ingredient based on the difficulty of ripening, wherein the first type first ingredient is easier to ripen than the second type first ingredient, and for the first type first ingredient, the first strategy is initiated after a second time has passed from the current moment, and for the second type first ingredient, the first strategy is initiated after a third time has passed from the current moment, wherein the second time is greater than the third time, and both the second time and the third time are less than the first time.

[0011] According to one aspect of the present disclosure, a health management system for a smart refrigerator is proposed, including: a first acquisition module, used to obtain a user's recipe plan, the recipe plan including recipe dishes and their consumption time; a first analysis module, used to analyze the recipe plan, obtain a first ingredient required for a first dish in the recipe, and a target maturity of the first ingredient processed into the first dish; a monitoring module, used to monitor the maturity of the first ingredient in the refrigerator at the current moment; an adjustment module, used to judge whether the maturity of the first ingredient from the current moment to the first time of consumption can meet the target maturity based on the maturity at the current moment, and if so, continue to preserve the first ingredient according to the current preservation strategy, and if not, adjust the preservation strategy of the first ingredient.

[0012] Optionally, the first analysis module is further used to obtain the target maturity of the first ingredient by performing statistical analysis on big data of dishes and their evaluations, wherein the big data of dishes and their evaluations are obtained by crawling Internet data.

[0013] Optionally, the first analysis module is also used to classify dishes into first type dishes and second type dishes. For the first type dishes, a first amount of Internet data is randomly crawled; for the second type dishes, a second amount of Internet data within a recent predetermined time range is crawled, wherein the first type dishes are classic dishes, the second type dishes are new dishes, and the first quantity is less than the second quantity.

[0014] The present disclosure also provides a computer-readable storage medium storing a computer program, including steps in the method of any of the above embodiments when the computer program is run by a processor.

[0015] An embodiment of the present application also provides an electronic device, which includes a memory and a processor. A computer program is stored in the memory, and the processor executes the steps in the method of any of the above embodiments by calling the computer program stored in the memory.

[0016] The present invention relates to a method for health management of an intelligent refrigerator. The method includes, first, obtaining and analyzing a user's recipe plan to obtain the ingredients required for the dishes in the recipe and determining the target maturity of the ingredients. Second, monitoring the maturity of the ingredients in the refrigerator, predicting the maturity at the time of consumption, and determining whether the maturity at the time of consumption meets the target maturity. Based on the determination result, decide whether to adjust the fresh-keeping strategy of the ingredients. By this method, fresh-keeping the ingredients can not only meet the user's health but also meet the user's personalized needs for the taste and flavor of the dishes. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a schematic diagram of a method for health management of an intelligent refrigerator provided by an embodiment of the present application.

[0018] Figure 2 It is a schematic diagram of a health management system of an intelligent refrigerator provided by an embodiment of the present application.

[0019] Figure 3 It is a schematic diagram of the structure of an electronic device provided by an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0020] The following further describes the specific embodiments of the present invention with reference to the drawings. It should be noted here that the description of these embodiments is for helping to understand the present invention, but does not limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0021] It should be noted that in the present application, "first", "second" and various numerical numbers are used for distinction for the convenience of description, and do not limit the scope of the embodiments of the present application. For example, to distinguish different classification results, etc., rather than for describing a specific order or sequence. It should be understood that the objects described in this way can be interchanged under appropriate circumstances so as to be able to describe the solutions other than the embodiments of the present application.

[0022] Specifically, Figure 1 shows a specific implementation flowchart of a method for health management of an intelligent refrigerator provided by an embodiment of the present application. Please refer toFigure 1 For the health management method of the intelligent refrigerator provided by this application, the specific steps are as follows: Step S1, obtain the user's recipe plan, where the recipe plan includes the recipe dishes and their eating times.

[0023] In this embodiment, the intelligent refrigerator communicates with a user terminal (such as a smart phone, a tablet computer, or a smart home terminal, etc.) to receive the recipe plan uploaded by the user. The recipe plan not only includes the names of the dishes to be made, but also the eating times corresponding to each dish. For example, the user sets the recipe plan for this week on the mobile APP. The dinner on Wednesday is scrambled eggs with tomatoes, and the lunch on Saturday is fruit salad. The intelligent refrigerator obtains the above recipe plan information through data interaction with the APP, and structurally stores the dishes and their corresponding eating times to prepare for subsequent data analysis. It can be understood that for an intelligent refrigerator that supports human-computer interaction, the user can directly input the recipe plan through the human-computer interaction interface.

[0024] Step S2, analyze the recipe plan to obtain the first ingredients required for the first dish in the recipe, and the target maturity of the first ingredients processed into the first dish.

[0025] Analyze the recipe plan through the processor built in the intelligent refrigerator to extract the required ingredients and their corresponding target maturities. Taking "scrambled eggs with tomatoes" as an example, the required ingredients are tomatoes and eggs. The target maturity of tomatoes is 85% (with a good taste and a moderate sour and sweet taste). For the ingredient eggs, since as long as the eggs are not spoiled, they will not affect the taste and flavor of the dish too much. Therefore, in order to improve the system efficiency and reduce energy consumption, the maturity of eggs is no longer analyzed and monitored. It can be understood that the first dish in this embodiment is such that, among the ingredients it contains, the maturity of at least one ingredient will affect the taste and flavor of the dish. Usually, the maturity of fruit ingredients is likely to affect the taste and flavor, such as tomatoes, apples, pears, avocados, peaches, mangoes, pitayas, grapes, bananas, etc.

[0026] In some embodiments, the target maturity of food ingredients is obtained through big data statistical analysis on the Internet. Specifically, a preset crawler program automatically obtains data related to dishes and their evaluations from the Internet, and uses statistical analysis methods to process the crawled data, extract evaluations related to the taste, nutrition, and maturity of the dishes, and then determine the target maturity of the relevant food ingredients. For example, for the classic dish of scrambled eggs with tomatoes, the system of the smart refrigerator randomly crawls relevant data from Internet platforms such as food forums and recipe websites, including user evaluations of this dish, ingredient selection suggestions, etc. Exemplarily, by analyzing big data, it is found that most users think that scrambled eggs with tomatoes made with tomatoes with a maturity of 70%-90% have a better taste. Therefore, the target maturity of tomatoes is set to 85%. It can be understood that the target maturity can also be a range value, such as 80%-90%.

[0027] In some embodiments, the target maturity can be determined by importing existing statistical data on the Internet, such as the survey results on the maturity of ingredients involved in dishes published by survey institutions or food platforms. For example, based on the public data, for the target dish, the maturity of the ingredient with a relatively high proportion of user votes or positive reviews is selected as the target maturity of the ingredient, which can save the process of crawling and analyzing data.

[0028] Step S3, monitor the maturity of the first ingredient in the refrigerator at the current moment.

[0029] The smart refrigerator is internally equipped with maturity monitoring devices, such as near-infrared spectroscopy sensors, image recognition sensors, gas sensors, etc., which can monitor the maturity of the first ingredient at the current moment in real time. Taking tomatoes as an example, the near-infrared spectroscopy sensor emits near-infrared light to penetrate the tomato skin and detect the internal chemical composition and structural changes, so as to judge the maturity. The image recognition sensor captures the external image of the tomato through a camera and uses deep learning algorithms to analyze its color, shape, texture and other features. The gas sensor mainly monitors the gas composition inside the refrigerator, such as the ethylene concentration. Ethylene is a plant hormone that can promote fruit ripening, and its concentration change is related to the maturity of tomatoes. By monitoring the ethylene concentration, the ripening situation of tomatoes can be further assisted in judgment. The data obtained by these sensors will be transmitted to the core control system of the smart refrigerator, and after algorithm processing and analysis, the current maturity of the tomatoes is finally obtained. For specific algorithms, for example, the maturity can be judged according to the deep learning models corresponding to different features taken by the sensors, and the maturity obtained based on different features is fused to obtain the final maturity. The deep learning models can adopt existing models, which will not be elaborated in this embodiment.

[0030] Step S4: Based on the maturity at the current moment, determine whether the maturity of the first ingredient can meet the target maturity at the first time from the current moment to the consumption time. If so, continue to preserve the first ingredient according to the current preservation strategy. If not, adjust the preservation strategy of the first ingredient.

[0031] In this embodiment, a maturity prediction model is used to determine whether the maturity of the first ingredient can meet the target maturity after the first time. The maturity prediction model can adopt a known time series prediction model, such as a long short-term memory network (LSTM). This model can handle and learn the long-term dependencies in time series data and is suitable for predicting the change of ingredient maturity over time. Specifically, the input layer of the model includes at least the current maturity of the input ingredient, the current time, environmental parameters (such as temperature, humidity, etc.) and other features that affect the ingredient maturity. The hidden layer adopts multiple LSTM layers, and each layer contains a certain number of neurons, for example, 3 layers, which are used to capture and learn the complex patterns and rules of the change of ingredient maturity. The output layer outputs the maturity value of the ingredient at the predicted time point, that is, the maturity of the ingredient predicted by the model. The loss function uses the mean squared error (MSE) as the loss function to measure the difference between the model prediction value and the actual maturity value, so as to adjust the model parameters during the training process to make the prediction value closer to the true value. The training process of the model can adopt the known LSTM training process. The data collection is specifically to collect the maturity data of the ingredient under different times and different environmental conditions, including the current maturity, the current time, environmental parameters (temperature, humidity, etc.) and the change of maturity after a period of time, as the training data set, or generate the training data set by generating the data set. Other steps of model training include data preprocessing, data set division, model initialization, forward propagation, loss calculation, backpropagation and optimization, iterative training, model evaluation, model adjustment and optimization, etc. These specific processes are not elaborated in this embodiment.

[0032] Specifically, the smart refrigerator obtains the current tomato ripeness data (e.g., 8:00 am on Monday), the first time period (the duration from 8:00 am on Monday to 6:00 pm on Wednesday), and information such as the temperature and humidity inside the refrigerator, and inputs them into the ripeness prediction model. After model operation, it is predicted that at 6:00 pm on Wednesday, the ripeness of the tomatoes is expected to reach 60%. Based on the previous analysis, the target ripeness of the tomatoes when making scrambled eggs with tomatoes is 85%. Therefore, the smart refrigerator determines that after the first time period, the ripeness of the tomatoes does not meet the target ripeness. Specifically, after the first time period, the ripeness of the tomatoes is expected to be lower than the target ripeness. The smart refrigerator adjusts the preservation strategy for the tomatoes according to the ripeness prediction result. According to the situation of this embodiment, the smart refrigerator ripens the tomatoes so that the tomatoes can meet the target ripeness when making scrambled eggs with tomatoes at 6:00 pm on Wednesday. It can be understood that the smart refrigerator in this embodiment has multiple compartments, and each compartment can independently control the preservation strategy. The specific compartment setting and compartment control can be based on existing technologies, and this embodiment does not make any restrictions. For the specific setting of the ripening process, the ripening device inside the smart refrigerator can release an appropriate amount of ethylene gas into the compartment where the tomatoes are stored, and at the same time, appropriately adjust the temperature and humidity of the tomato compartment. The adjustment values of the ethylene gas concentration, temperature, and humidity can be predicted by known machine learning algorithms, or a corresponding relationship table of ethylene gas concentration, temperature, humidity, and time parameters and ripeness established based on historical data can be used to select an appropriate parameter combination, which will not be elaborated in this embodiment.

[0033] In one embodiment, if after model operation, it is predicted that at 6:00 pm on Wednesday, the ripeness of the tomatoes is expected to reach 100%. Based on the previous analysis, the target ripeness of the tomatoes when making scrambled eggs with tomatoes is 85%. Therefore, the smart refrigerator determines that after the first time period, the ripeness of the tomatoes does not meet the target ripeness. Specifically, after the first time period, the ripeness of the tomatoes is expected to be higher than the target ripeness. The smart refrigerator adjusts the preservation strategy for the tomatoes according to the ripeness prediction result. According to the situation of this embodiment, the smart refrigerator improves the preservation level of the tomatoes so that the tomatoes can meet the target ripeness when making scrambled eggs with tomatoes at 6:00 pm on Wednesday. The temperature and humidity of the tomato compartment can be appropriately adjusted, for example, reducing the temperature and humidity of the compartment storing the tomatoes and / or by using a gas absorption or gas emission device installed inside the refrigerator to absorb or discharge the ethylene gas released by the tomatoes or other fruits, reducing the ethylene concentration in the tomato compartment, and ultimately delaying the ripening speed of the tomatoes. The specific adjustment values can be predicted by machine learning algorithms or selected by looking up the comparison relationship table as described above, which will not be elaborated in this embodiment.

[0034] In one embodiment, if, after model operation, it is predicted that the ripeness of tomatoes can meet the target ripeness at 18:00 on Wednesday evening, the current freshness preservation strategy is adopted and the freshness preservation strategy is not adjusted any further. It can be understood that in the above embodiments, as long as the actual ripeness at the first time is within the range, it is considered to meet the target ripeness. It can be understood that due to various restrictions, such as the ingredients being too raw when put into the refrigerator, or the ingredients being difficult to ripen, and the eating time arriving soon after being put into the refrigerator, etc., despite strategy adjustment, it is still possible that the ingredients have not reached the target ripeness when the eating time arrives.

[0035] In one embodiment, big data on dishes and their evaluations is obtained by crawling Internet data. The dishes are divided into first-type dishes and second-type dishes. For the first-type dishes, a first quantity of Internet data is randomly crawled. For the second-type dishes, a second quantity of Internet data within a recent reservation time range is crawled. The first-type dishes are classic dishes, and the second-type dishes are new dishes. Specifically, crawling Internet data and analyzing the crawled data both consume a certain amount of computing power and network resources. To reduce unnecessary resource waste and improve efficiency, in this embodiment, the dishes are classified. For classic dishes, users' tastes are relatively fixed, and a first quantity of data can be randomly crawled for analysis. The setting of the first quantity can be relatively less than the second quantity because users' tastes for classic dishes are relatively consistent. For example, for the dish of scrambled eggs with tomatoes, which belongs to the first type of classic dishes, the smart refrigerator randomly crawls 500 pieces of Internet data for analysis. For new dishes such as light meals, generally, light meals include vegetables, fruits, grains and tubers, animal foods, nuts, dairy products, etc. For the vegetable and fruit ingredients among them, different users may have different requirements for their maturity. At the same time, light meals are new dishes that have become popular in recent years and belong to the second type of dishes. Therefore, users' tastes may change to some extent. To meet users' current taste needs, exemplarily, 1000 pieces of Internet data in the past six months can be crawled for statistical analysis. The specific statistical analysis process can be to select, from the crawled data, keywords related to the maturity of the dishes in users' evaluations of the dishes, such as keywords like "raw", "cooked", "crispy", "soft", "hard", "sour", "sweet", "tender", "old", etc., as well as whether it is a positive or negative review. For example, the evaluations of the scrambled eggs with tomatoes dish crawled from Dianping (a food APP): "The tomatoes are sour and very appetizing", "The tomatoes are too mushy and look unappetizing", "The eggs are fragrant, the tomatoes are of moderate hardness, recommended", "It has all the colors, smells, and tastes, very perfect", etc. Through the analysis of a large number of similar evaluations, the preferences of different users for the maturity of tomatoes in scrambled eggs with tomatoes can be statistically analyzed. In some embodiments, for subsequent comparison, the preferences can be normalized and quantified. For example, being partial to raw (corresponding to evaluations of tomatoes such as sour and hard, etc.) corresponds to a tomato maturity of "less than 60%", being partial to cooked (corresponding to evaluations of tomatoes such as mushy and shapeless, etc.) corresponds to a tomato maturity of "greater than 90%", and being just right (corresponding to evaluations of tomatoes such as neither soft nor hard, having all the colors, smells, and tastes, neither sour nor sweet, of moderate hardness, etc.) corresponds to a tomato maturity of "60% - 90%". For example, after statistical analysis, the proportion of those who like it to be just right is relatively large among all evaluations, and accordingly, the target maturity is set to "60% - 90%". It can be understood that the above is only an example, and the target maturity can be determined according to the actual situation, specific dishes, and specific statistical analysis results.

[0036] In some embodiments, the first food ingredients are divided into first - type first food ingredients and second - type first food ingredients based on the ripening difficulty, where the first - type first food ingredients are easier to ripen than the second - type first food ingredients. For the first - type first food ingredients, the first strategy is started after a second time from the current moment, and for the second - type first food ingredients, the first strategy is started after a third time from the current moment, where the second time is greater than the third time, and both the second time and the third time are less than the first time. Specifically, when ripening food ingredients, considering that different food ingredients have different ripening difficulties, the time to start ripening is dynamically set to further enhance the accuracy and robustness of regulation. Exemplarily, relatively easy - to - ripen food ingredients include bananas, tomatoes, peaches, pears, etc., while relatively difficult - to - ripen food ingredients include apples, mangoes, kiwis, grapes, etc. For relatively easy - to - ripen food ingredients, such as tomatoes (exemplarily, the corresponding dish is scrambled eggs with tomatoes), the ripening strategy can be started relatively closer to the eating time in combination with the eating time to avoid over - ripening due to ripening at the eating time. For relatively difficult - to - ripen food ingredients, such as apples (exemplarily, the corresponding dish is a light meal), the ripening strategy can be started earlier to make it reach the target maturity or be as close as possible to the target maturity at the eating time.

[0037] A health management method for an intelligent refrigerator corresponding to the above - mentioned embodiments Figure 2 The block diagram of a health management system of an intelligent refrigerator provided by an embodiment of the present application is shown. For the sake of convenience of description, only the parts related to the embodiments of the present application are shown.

[0038] See Figure 2 As shown, a health management system 200 of an intelligent refrigerator provided by an embodiment of the present application includes: a first acquisition module, configured to acquire the recipe plan of the user, where the recipe plan includes recipe dishes and their eating times; a first analysis module, configured to analyze the recipe plan to obtain the first food ingredients required for the first dish in the recipe and the target maturity of the first food ingredients corresponding to the processed first dish; a monitoring module, configured to monitor the maturity of the first food ingredients in the refrigerator at the current moment; an adjustment module, configured to judge, based on the maturity at the current moment, whether the maturity of the first food ingredients can meet the target maturity at the first time from the current moment to the eating time. If so, continue to preserve the first food ingredients according to the current preservation strategy. If not, adjust the preservation strategy of the first food ingredients.

[0039] In some embodiments, the system 200 further includes a first analysis module, which is further configured to obtain the target maturity of the first food ingredients through statistical analysis of dish and its evaluation big data, where the dish and its evaluation big data are obtained by crawling Internet data.

[0040] In some embodiments, the system 200 further includes a first analysis module, which is further configured to classify dishes into first-type dishes and second-type dishes. For the first-type dishes, a first quantity of Internet data is randomly crawled, and for the second-type dishes, a second quantity of Internet data within a recent reservation time range is crawled. The first-type dishes are classic dishes, the second-type dishes are new dishes, and the first quantity is less than the second quantity.

[0041] For the specific implementation of each of the above operations, reference may be made to the foregoing embodiments, which will not be elaborated herein.

[0042] Correspondingly, an embodiment of the present application further provides an electronic device, which may be a mobile terminal or a server. As Figure 3 shown, Figure 3 FIG. is a schematic structural diagram of the electronic device provided by the embodiment of the present application. For the sake of convenience of description, only the parts related to the embodiment of the present application are shown.

[0043] The electronic device 300 includes a processor 301 having one or more processing cores, a memory 302 having one or more computer-readable storage media, and a computer program stored on the memory 302 and executable on the processor. Among them, the processor 301 is electrically connected to the memory 302. Those skilled in the art can understand that the structure of the electronic device shown in the figure does not constitute a limitation on the electronic device, and may include more or fewer components than shown, or combine certain components, or arrange different components.

[0044] The processor 301 is the control center of the electronic device 300, connects various parts of the entire electronic device 300 through various interfaces and lines, and executes various functions of the electronic device 300 and processes data by running or loading software programs (computer programs) and / or units stored in the memory 302, and calling data stored in the memory 302, thereby monitoring the electronic device 300 as a whole.

[0045] In the embodiment of the present application, the processor 301 in the electronic device 300 will load the instructions corresponding to the processes of one or more application programs into the memory 302 according to the following steps, and the processor 301 will run the application programs stored in the memory 302 to implement various functions: obtaining a recipe plan of a user, the recipe plan including recipe dishes and their eating times; analyzing the recipe plan to obtain the first ingredients required for the first dish in the recipe and the target maturity of the first ingredients corresponding to the first dish after processing; monitoring the maturity of the first ingredients in the refrigerator at the current moment; based on the maturity at the current moment, judging whether the maturity of the first ingredients can meet the target maturity during the first time from the current moment to the eating time. If so, continue to preserve the first ingredients according to the current preservation strategy. If not, adjust the preservation strategy of the first ingredients.

[0046] For the specific implementation of each of the above operations, reference may be made to the foregoing embodiments, which will not be elaborated herein.

[0047] Optionally, as Figure 3 shown, the electronic device 300 further includes: a health management unit 303, a communication unit 304, an input unit 305, and a power supply 306. Among them, the processor 301 is electrically connected to the health management unit 303, the communication unit 304, the input unit 305, and the power supply 306 respectively. Those skilled in the art can understand that Figure 3 the structure of the electronic device shown in

[0048] does not constitute a limitation on the electronic device, and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0049] The health management unit 303 can be used for the health management of the smart refrigerator.

[0050] The communication unit 304 can be used to communicate with other devices.

[0051] The input unit 305 can be used to receive input digital, character information, or user characteristic information (such as fingerprints, irises, facial information, etc.), and generate keyboard, mouse, joystick, optical, or trackball signal inputs related to user settings and function controls.

[0052] In the above embodiments, the descriptions of the various embodiments have their own emphases. For the parts not detailed in a certain embodiment, reference may be made to the relevant descriptions of other embodiments.

[0053] Those of ordinary skill in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructions, or by controlling relevant hardware through instructions. The instructions can be stored in a computer-readable storage medium and loaded and executed by a processor.

[0054] Therefore, an embodiment of the present application provides a computer-readable storage medium, in which multiple computer programs are stored, and the computer programs can be loaded by a processor to execute the steps of a method for health management of a smart refrigerator provided by an embodiment of the present application.

[0055] For the specific implementation of each of the above operations, reference may be made to the previous embodiments, which will not be elaborated herein.

[0056] Among them, the computer-readable storage medium may include: read-only memory (ROM, Read Only Memory), random access memory (RAM, Random Access Memory), magnetic disk or optical disk, etc.

[0057] Since the computer program stored in this storage medium can execute the steps in any of the health management methods of the intelligent refrigerator provided in the embodiments of the present application, the beneficial effects of any of the health management methods of the intelligent refrigerator provided in the embodiments of the present application can be achieved. For details, refer to the previous embodiments, which will not be elaborated herein.

[0058] This application is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to the embodiments of the present application. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate for implementing in the process Figure 1 one process or multiple processes and / or blocks Figure 1 a device for the functions specified in one block or multiple blocks.

[0059] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured product including an instruction device, and the instruction device implements in the process Figure 1 one process or multiple processes and / or blocks Figure 1 a device for the functions specified in one block or multiple blocks.

[0060] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process. Thus, the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 a device for the functions specified in one block or multiple blocks.

[0061] The embodiments of the present invention have been described in detail above in conjunction with the accompanying drawings, but the present invention is not limited to the described embodiments. For those skilled in the art, without departing from the principles and spirit of the present invention, various changes, modifications, substitutions, and variations made to these embodiments still fall within the protection scope of the present invention.

Claims

1. A health management method for a smart refrigerator, characterized in that: include: Obtain the user's recipe plan, which includes recipes, dishes and their consumption times; Analyze the recipe plan to obtain a first ingredient required for a first dish in the recipe and a target maturity of the first ingredient processed into the first dish; monitor the maturity of the first ingredient in the refrigerator at the current moment; Based on the maturity at the current moment, determine whether the maturity of the first ingredient can meet the target maturity when the first time from the current moment to the consumption time is judged. If so, continue to preserve the first ingredient according to the current preservation strategy; if not, adjust the preservation strategy of the first ingredient.

2. The health management method of a smart refrigerator according to claim 1, characterized in that: The analyzing the recipe plan to obtain the first ingredient required for the first dish in the recipe and the target maturity of the first ingredient processed into the first dish includes obtaining the target maturity of the first ingredient by performing statistical analysis on big data of dishes and their evaluations, wherein the big data of dishes and their evaluations are obtained by crawling Internet data.

3. The health management method of a smart refrigerator according to claim 2, characterized in that: The dishes and their evaluation big data are obtained by crawling Internet data, Including, the dishes are divided into a first type of dishes and a second type of dishes, for the first type of dishes, a first amount of Internet data is randomly crawled, and for the second type of dishes, a second amount of Internet data within a recent predetermined time range is crawled, wherein the first type of dishes are classic dishes, the second type of dishes are new dishes, and the first quantity is less than the second quantity.

4. The health management method of a smart refrigerator according to claim 1, characterized in that: The adjustment of the preservation strategy for the first food ingredient includes: if the maturity of the first food ingredient is lower than the target maturity after a first period of time, adopting a first strategy to process the first food ingredient; if the maturity of the first food ingredient is higher than the target maturity after the first period of time, adopting a second strategy to process the first food ingredient, wherein the first strategy is to ripen the first food ingredient, and the second strategy is to delay the maturation of the first food ingredient.

5. The health management method of a smart refrigerator according to claim 4, characterized in that: The adopting the first strategy to process the first ingredient includes: dividing the first ingredient into a first type of first ingredient and a second type of first ingredient based on the difficulty of ripening, wherein the first type of first ingredient is easier to ripen than the second type of first ingredient; for the first type of first ingredient, starting the first strategy after a second time has passed from the current moment, and for the second type of first ingredient, starting the first strategy after a third time has passed from the current moment, wherein the second time is greater than the third time, and both the second time and the third time are less than the first time.

6. A health management system for a smart refrigerator, characterized in that: include: The first acquisition module is used to acquire the user's recipe plan, which includes recipe dishes and their consumption time; A first analysis module is used to analyze the recipe plan to obtain a first ingredient required for a first dish in the recipe and a target maturity of the first ingredient to be processed into the first dish; A monitoring module, used to monitor the maturity of the first food in the refrigerator at the current moment; The adjustment module is used to judge whether the maturity of the first ingredient can meet the target maturity from the current moment to the first time of the consumption time based on the maturity at the current moment; if so, continue to preserve the first ingredient according to the current preservation strategy; if not, adjust the preservation strategy of the first ingredient.

7. The health management system of a smart refrigerator according to claim 6, characterized in that: The first analysis module is also used to obtain the target maturity of the first ingredient by performing statistical analysis on the big data of dishes and their evaluations, wherein the big data of dishes and their evaluations are obtained by crawling Internet data.

8. The health management system of a smart refrigerator according to claim 7, characterized in that: The first analysis module is also used to classify the dishes into first type dishes and second type dishes. For the first type dishes, a first amount of Internet data is randomly crawled, and for the second type dishes, a second amount of Internet data within a recent predetermined time range is crawled, wherein the first type dishes are classic dishes, and the second type dishes are new dishes, and the first quantity is less than the second quantity.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 5 is performed.

10. An electronic device, characterized in that: It comprises a memory storing executable program code and a processor coupled to the memory; wherein the processor calls the executable program code stored in the memory to execute the method according to any one of claims 1-5.

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