Sugar-free cooking optimization method and system based on group nutritional data analysis

Abstract

The present application relates to the field of rice cooker cooking control and discloses a desugaring cooking optimization method and system based on group nutritional data analysis. The method comprises: obtaining user group information input by a user, and obtaining health index curve information of the user within a preset time period; determining the rice water control program, the draining control program, and the heating control program of the draining type rice cooker based on the user group information through a cooking control model; determining the rice water control optimization program, the draining control optimization program, and the heating control optimization program based on the user group information, the health index curve information, the rice water control program, the draining control program, and the heating control program through a desugaring cooking optimization model, and completing rice cooking through the rice water control optimization program, the draining control optimization program, and the heating control optimization program. The draining type rice cooker of the present application can meet the cooking needs of different groups of people and improve the user experience of the draining type rice cooker.

Description

Technical Field

[0001] The present application relates to the technical field of electric rice cooker cooking control, and more specifically, to a desugaring cooking optimization method and system based on group nutritional data analysis. Background Art

[0002] A drain-type rice cooker is an intelligent cooking device that uses the physical drainage principle to reduce the sugar content of rice. Its core technology leverages the water-soluble nature of the starch and sugars released from rice grains during the boiling process. At a specific stage in the cooking process, a built-in porous drainage structure or water pump circulation system drains the sugar-laden rice soup, thereby reducing the digestible sugar content of the rice and achieving a sugar-free effect. Compared to traditional rice cookers, drain-type rice cookers meet consumers' demand for a low-sugar diet to a certain extent and are particularly suitable for those who prioritize healthy eating.

[0003] Existing drain-type rice cookers have significant shortcomings in precisely adjusting the degree of sugar removal for different populations. Different populations (such as diabetics, obese people, pregnant women, and the elderly) have vastly different nutritional needs for rice. For example, diabetics need to strictly control the glycemic index of rice, obese people are more concerned with the precise ratio of calories and carbohydrates, and pregnant women need to maintain a balanced diet while controlling sugar intake. However, existing devices use fixed settings for key parameters such as draining timing, draining volume, rice-to-water ratio, and cooking temperature, lacking dynamic adjustment mechanisms tailored to the specific needs of different populations. Therefore, optimizing the cooking process of drain-type rice cookers is crucial to improving the user experience for different populations. Summary of the Invention

[0004] The purpose of this application is to provide a desugaring cooking optimization method and system based on group nutritional data analysis, which solves the technical problem that the drainage type electric rice cooker is less effective in meeting the cooking needs of different groups of people, and achieves the technical effect that the drainage type electric rice cooker meets the cooking needs of different groups of people.

[0005] An embodiment of the present application provides a desugaring cooking optimization method based on group nutritional data analysis, the method comprising: obtaining user group information input by a user, and obtaining health index curve information of the user within a preset time period; wherein the user group information includes disease-sensitive group information, weight management group information, special physiological stage group information and exercise function group information, and the health index curve information includes body fat rate curve information and blood sugar curve information; through a cooking control model, according to the user group information, determining the rice water control program, draining control program and heating control program of the draining type rice cooker; through a desugaring cooking optimization model, according to the user group information, health index curve information, rice water control program, draining control program and heating control program, determining the rice water control optimization program, draining control optimization program and heating control optimization program, and completing rice cooking through the rice water control optimization program, draining control optimization program and heating control optimization program; wherein the rice water control program and the rice water control optimization program are used to control the rice-water ratio before draining, the draining control program and the draining control optimization program are used to control the draining process, and the heating control program and the heating control optimization program are used to control the heating parameters before and after draining.

[0006] In a possible implementation, the method also includes: obtaining a health indicator collection time corresponding to the health indicator curve information, the health indicator collection time being the time span for collecting the health indicator curve information; when the user group information to which the user belongs is weight management group information, special physiological stage group information or exercise function group information, and when the health indicator collection time is less than a preset health indicator collection time, obtaining a taste score and a softness score fed back by the user; determining a rice water control first-level optimization program, a draining control first-level optimization program and a heating control first-level optimization program according to the taste score, the softness score, the rice water control program, the draining control program and the heating control program through a first-level optimization control model; when the health indicator collection time is greater than or equal to the preset health indicator collection time, determining a rice water control second-level optimization program, a draining control second-level optimization program and a heating control second-level optimization program according to the user group information, the health indicator curve information, the rice water control first-level optimization program, the draining control first-level optimization program and the heating control first-level optimization program through a desugaring cooking optimization model, and completing rice cooking through the rice water control second-level optimization program, the draining control second-level optimization program and the heating control second-level optimization program.

[0007] In another possible implementation, the rice water control second-level optimization program, the draining control second-level optimization program and the heating control second-level optimization program are determined through the sugar-removing cooking optimization model according to user group information, health index curve information, the rice water control first-level optimization program, the draining control first-level optimization program and the heating control first-level optimization program, including: obtaining the cooling rate of the heating control first-level optimization program within a preset temperature range after draining, and obtaining the enzymatic reaction heating time period of the heating control first-level optimization program before and after draining; wherein, the preset temperature range is 98°C to 85°C, the rice water temperature in the enzymatic reaction heating time period is 50 to 60°C, and different rice types correspond to enzymatic reaction heating time periods of different lengths; through the sugar-removing cooking optimization model, according to user group information, health index curve information, and the enzymatic reaction heating time period of the heating control first-level optimization program, the enzymatic reaction heating optimization time period in the heating control second-level optimization program is determined, the cooling rate adjustment amplitude of the cooling rate is limited to be less than the preset cooling rate adjustment amplitude, and the duration adjustment amplitude of other heating time periods of the heating control first-level optimization program is limited to be less than the preset duration adjustment amplitude.

[0008] In another possible implementation, the desugaring cooking optimization model is used to determine the rice water control secondary optimization program, the draining control secondary optimization program and the heating control secondary optimization program based on user group information, health index curve information, the rice water control first-level optimization program, the draining control first-level optimization program and the heating control first-level optimization program. It also includes: obtaining the user's physical information, the user's physical information includes age, gender and food softness preference information; through the constraint margin identification model, according to the user's health index curve information and user physical information, determine the preset cooling rate adjustment range and the preset time adjustment range.

[0009] In another possible implementation, the method also includes: obtaining the health indicator collection time corresponding to the health indicator curve information, the health indicator collection time being the time span for collecting the health indicator curve information; when the user group information to which the user belongs is disease-sensitive group information, and when the health indicator collection time is less than the preset health indicator collection time, obtaining the rice and water control basic optimization program, the draining control basic optimization program and the heating control basic optimization program corresponding to the disease-sensitive group information through the cooking control database, the rice and water control basic optimization program, the draining control basic optimization program and the heating control basic optimization program are used to meet the basic cooking needs of the disease-sensitive group corresponding to the disease-sensitive group information; when the health indicator collection time is greater than or equal to the preset health indicator collection time, determining the rice and water control secondary optimization program, the draining control secondary optimization program and the heating control secondary optimization program according to the disease-sensitive group information, the health indicator curve information, the rice and water control basic optimization program, the draining control basic optimization program and the heating control basic optimization program through the desugaring cooking optimization model, and completing the rice cooking through the rice and water control secondary optimization program, the draining control secondary optimization program and the heating control secondary optimization program.

[0010] In another possible implementation, the rice water control secondary optimization program, the drainage control secondary optimization program and the heating control secondary optimization program are determined according to the disease sensitive group information, the health index curve information, the rice water control basic optimization program, the drainage control basic optimization program and the heating control basic optimization program through the desugaring cooking optimization model, including: obtaining the associated parameters and non-associated parameters of the rice water control secondary optimization program, the drainage control secondary optimization program and the heating control secondary optimization program, and obtaining the adjustment weights corresponding to the associated parameters and the non-associated parameters respectively; wherein the associated parameters include the rice-water ratio, the enzymatic reaction heating optimization time period and the cooling rate, and the non-associated parameters include the control parameters other than the associated parameters in the rice water control secondary optimization program, the drainage control secondary optimization program and the heating control secondary optimization program; through the desugaring cooking optimization model, according to the disease sensitive group information, the health index curve information, the rice water control basic optimization program, the drainage control basic optimization program, the heating control basic optimization program, the associated parameters and the non-associated parameters respectively corresponding to the adjustment weights, determine the associated parameter adjustment values ​​corresponding to the associated parameters in the rice water control secondary optimization program, the drainage control secondary optimization program and the heating control secondary optimization program, and the non-associated parameter adjustment values ​​corresponding to the non-associated parameters.

[0011] In another possible implementation, the sugar-free cooking optimization model is used to determine the rice water control secondary optimization program, the draining control secondary optimization program and the heating control secondary optimization program according to the disease sensitive group information, the health index curve information, the rice water control basic optimization program, the draining control basic optimization program and the heating control basic optimization program. It also includes: obtaining the disease sensitivity level corresponding to the user's disease sensitive group information; wherein the disease sensitivity level includes the disease sensitivity levels corresponding to diabetes, cardiovascular disease and kidney disease respectively; according to the disease sensitivity level, determining the adjustment weights corresponding to the associated parameters and non-associated parameters respectively.

[0012] In another possible implementation, the method also includes: when the user group information to which the user belongs is weight management group information, special physiological stage group information or exercise function group information, determining the health indicator decline score according to the health indicator curve information through the health indicator evaluation model; when the health indicator decline score is greater than or equal to the preset health indicator decline score, and when the health indicator collection time is less than the preset health indicator collection time, obtaining the rice water control basic optimization program, drainage control basic optimization program and heating control basic optimization program corresponding to the preset health indicator decline score through the cooking control database; when the health indicator collection time is greater than or equal to the preset health indicator collection time, determining the rice water control secondary optimization program, drainage control secondary optimization program and heating control secondary optimization program according to the user group information, health indicator curve information, rice water control basic optimization program, drainage control basic optimization program and heating control basic optimization program through the desugar cooking optimization model, and completing rice cooking through the rice water control secondary optimization program, drainage control secondary optimization program and heating control secondary optimization program.

[0013] In another possible implementation, the rice and water control basic optimization program, the draining control basic optimization program and the heating control basic optimization program corresponding to the preset health index degradation score are obtained through the cooking control database, including: determining the health index degradation level corresponding to the health index degradation score; obtaining different rice and water control basic optimization programs, draining control basic optimization programs and heating control basic optimization programs corresponding to the health index degradation level through the cooking control database, and the parameter thresholds of different rice and water control basic optimization programs, draining control basic optimization programs and heating control basic optimization programs are different.

[0014] An embodiment of the present application also provides a sugar-free cooking optimization system based on group nutritional data analysis, comprising a unit for executing any of the methods described above.

[0015] Compared with the prior art, the embodiments of the present application have the following beneficial effects:

[0016] An embodiment of the present application provides a desugaring cooking optimization method based on group nutritional data analysis, the method comprising: obtaining user group information input by a user, and obtaining health index curve information of the user within a preset time period; wherein the user group information includes disease-sensitive group information, weight management group information, special physiological stage group information and exercise function group information, and the health index curve information includes body fat rate curve information and blood sugar curve information; through a cooking control model, according to the user group information, determining the rice water control program, draining control program and heating control program of the draining type rice cooker; through a desugaring cooking optimization model, according to the user group information, health index curve information, rice water control program, draining control program and heating control program, determining the rice water control optimization program, draining control optimization program and heating control optimization program, and completing rice cooking through the rice water control optimization program, draining control optimization program and heating control optimization program; wherein the rice water control program and the rice water control optimization program are used to control the rice-water ratio before draining, the draining control program and the draining control optimization program are used to control the draining process, and the heating control program and the heating control optimization program are used to control the heating parameters before and after draining. The method in the embodiment of the present application can provide highly personalized sugar-free cooking solutions for user groups with different health needs through integrated analysis of user group information and health index curve information, thereby improving the user's health experience of using sugar-free cooking. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the embodiments or descriptions of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0018] Figure 1 A schematic flow chart of the first sugar-free cooking optimization method based on population nutritional data analysis provided in an embodiment of the present application;

[0019] Figure 2 A schematic diagram of the workflow of the first sugar-free cooking optimization method based on population nutritional data analysis provided in an embodiment of the present application;

[0020] Figure 3 A schematic diagram of the workflow of the second sugar-free cooking optimization method based on population nutritional data analysis provided in an embodiment of the present application;

[0021] Figure 4 A schematic diagram of the workflow of the third sugar-free cooking optimization method based on population nutritional data analysis provided in an embodiment of the present application;

[0022] Figure 5 A schematic flow chart of a fourth sugar-free cooking optimization method based on population nutritional data analysis provided in an embodiment of the present application;

[0023] Figure 6 A schematic diagram of the logical structure of a sugar-free cooking optimization system based on group nutritional data analysis provided in an embodiment of the present application. DETAILED DESCRIPTION

[0024] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, integers, steps, operations, elements and / or components, but does not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or collections thereof.

[0025] It will also be understood that the term "and / or" used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0026] As used in this specification and the appended claims, the term "if" can be interpreted as "when" or "upon" or "in response to determining" or "in response to detecting," depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" can be interpreted as meaning "upon determination" or "in response to determining" or "upon detection of [described condition or event]" or "in response to detecting [described condition or event]," depending on the context.

[0027] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0028] References to "one embodiment" or "some embodiments" in this specification mean that a particular feature, structure, or characteristic described in conjunction with that embodiment is included in one or more embodiments of the present application. Thus, phrases such as "in one embodiment," "in some embodiments," "in other embodiments," and "in other embodiments" appearing in various places in this specification do not necessarily refer to the same embodiment, but rather mean "one or more but not all embodiments," unless otherwise specifically emphasized. The terms "including," "comprising," "having," and variations thereof all mean "including but not limited to," unless otherwise specifically emphasized.

[0029] Existing drain-type rice cookers have significant deficiencies in achieving fine-tuning of the degree of sugar removal for different groups of people.

[0030] Based on the above reasons, an embodiment of the present application provides a sugar-free cooking optimization method based on group nutritional data analysis, which includes: obtaining user group information to which the user belongs input by the user, and obtaining health index curve information of the user within a preset time period; wherein the user group information includes disease-sensitive group information, weight management group information, special physiological stage group information and exercise function group information, and the health index curve information includes body fat rate curve information and blood sugar curve information; through a cooking control model, according to the user group information, the rice water control program, the draining control program and the heating control program of the draining type rice cooker are determined; through a sugar-free cooking optimization model, according to the user group information, the health index curve information, the rice water control program, the draining control program and the heating control program, the rice water control optimization program, the draining control optimization program and the heating control optimization program are determined, and rice cooking is completed through the rice water control optimization program, the draining control optimization program and the heating control optimization program; wherein the rice water control program and the rice water control optimization program are used to control the rice-water ratio before draining, the draining control program and the draining control optimization program are used to control the draining process, and the heating control program and the heating control optimization program are used to control the heating parameters before and after draining. The method in the embodiment of the present application can provide highly personalized sugar-free cooking solutions for user groups with different health needs through integrated analysis of user group information and health index curve information, thereby improving the user's health experience of using sugar-free cooking.

[0031] In some scenarios, a desugaring cooking optimization method based on group nutritional data analysis in an embodiment of the present application can be applied to the control of a drain-type rice cooker, which can improve the desugaring cooking effect of the drain-type rice cooker and improve the user's health experience.

[0032] The following is a detailed description of a sugar-free cooking optimization method based on group nutritional data analysis provided in an embodiment of the present application with reference to specific examples.

[0033] Figure 1 This is a flow chart of the first sugar-free cooking optimization method based on group nutritional data analysis provided in the embodiment of the present application, as shown in FIG. Figure 1 As shown, the method for optimizing the desugaring cooking method based on population nutritional data analysis includes S110 to S120, and S110 to S120 are described in detail below.

[0034] S110: Obtain user group information input by the user, and obtain health indicator curve information of the user within a preset time period. The user group information includes disease-sensitive group information, weight management group information, special physiological stage group information, and exercise function group information. The health indicator curve information includes body fat percentage curve information and blood sugar curve information.

[0035] Figure 2This is a schematic diagram of the workflow of the first sugar-free cooking optimization method based on group nutritional data analysis provided in the embodiment of the present application, as shown in FIG. Figure 2 As shown, in this method, the user group information to which the user belongs, which is input by the user, can be first obtained. The user group information includes disease-sensitive group information, weight management group information, special physiological stage group information and exercise function group information. Then, the user's demand characteristics for sugar-free cooking can be determined through the disease-sensitive group information, weight management group information, special physiological stage group information and exercise function group information, so as to construct a sugar-free cooking strategy suitable for the user in a targeted manner.

[0036] For example, when implementing the sugar-free cooking optimization method based on group nutritional data analysis, the user group information to which the user belongs input by the user can be obtained through the user interaction interface. This information can reflect the user's specific nutritional needs and health goals.

[0037] In this method, the user's health index curve information within a preset time period can also be obtained. The health index curve information includes body fat rate curve information and blood sugar curve information, and the sugar-free cooking strategy can be controlled specifically according to the user's body fat rate and blood sugar status.

[0038] For example, the user's health indicator curve information within a preset time period can also be obtained through the user's input in the rice cooker control App. These curve information can be collected through connected health monitoring devices or manual input by the user, so as to dynamically track the user's physiological change trends.

[0039] For example, in the rice cooker application, users can select that they belong to the diabetes-sensitive group and upload their blood sugar monitoring data for the past week. The system will automatically parse this information to support the subsequent optimization process.

[0040] S120: Determine the rice water control program, draining control program, and heating control program of the draining type rice cooker based on the user group information using the cooking control model. Determine the rice water control optimization program, draining control optimization program, and heating control optimization program based on the desugaring cooking optimization model using the user group information, health index curve information, the rice water control program, draining control program, and heating control program. Cooking rice is completed using the rice water control optimization program, draining control optimization program, and heating control optimization program. The rice water control program and the rice water control optimization program are used to control the rice-to-water ratio before draining, the draining control program and the draining control optimization program are used to control the draining process, and the heating control program and the heating control optimization program are used to control heating parameters before and after draining.

[0041] like Figure 2As shown, after obtaining user group information, the cooking control model can be used to determine the initial control program for the draining rice cooker based on this user group information. This includes a rice-water control program, a draining control program, and a heating control program. The rice-water control program controls the rice-water ratio before draining, the draining control program controls specific parameters during the draining process, and the heating control program controls heating parameters such as temperature and time before and after draining. The cooking control model can analyze key characteristics of user group information and customize basic program settings for different groups.

[0042] For example, for weight management groups, a lower initial rice-to-water ratio can be set in this method to reduce starch content, while for sports function groups, the heating parameters can be adjusted to retain more energy substances.

[0043] Exemplarily, the user group information may be labeled user group information, or the user group information may be features corresponding to the user group information represented by a vector. The cooking control model and the desugaring cooking optimization model may process and learn the user group information as feature input.

[0044] In this method, if Figure 2 As shown, based on the determination of the initial control program, these programs can be further optimized through the desugaring cooking optimization model. Specifically, the desugaring cooking optimization model can be combined with user group information, health index curve information and the initial rice water control program, draining control program and heating control program to generate optimized rice water control optimization program, draining control optimization program and heating control optimization program.

[0045] For example, the optimization process can dynamically adjust parameters based on deep learning and nutritional data analysis to balance sugar removal and cooking quality. For example, if the health indicator curve information shows that the user's blood sugar curve fluctuates significantly, the optimization model can adjust the draining control optimization process to extend the draining time to more effectively remove dissolved sugars, while also using the heating control optimization process to reduce the high temperature section to prevent nutrient loss.

[0046] Illustratively, the electric rice cooker in the present method may be connected to an external water source to provide additional water to the draining process of the electric rice cooker to improve the draining effect.

[0047] Exemplarily, the cooking control model may be a deep learning model trained using sample user group information, labeled sample rice and water control programs, sample draining control programs, and sample heating control programs.

[0048] Exemplarily, the sugar-free cooking optimization model can be a deep learning model trained by sample user group information, sample health index curve information, sample rice and water control program, sample drainage control program, sample heating control program, labeled sample rice and water control optimization program, sample drainage control optimization program and sample heating control optimization program.

[0049] In this method, the rice cooking process can be completed subsequently through the rice-water control optimization program, the drainage control optimization program and the heating control optimization program. The optimization program can be automatically executed on the drainage type rice cooker to realize the whole process control from rice-water mixing to drainage and heating, and finally complete the rice cooking process.

[0050] The beneficial effect of the above implementation method is that, through the integrated analysis of user group information and health indicator curve information, it can provide highly personalized sugar-free cooking plans for user groups with different health needs, especially automatically strengthening sugar control for diabetic-sensitive groups, thereby improving the accuracy and safety of diet management.

[0051] The beneficial effect of the above implementation method is that, through the synergy of the cooking control model and the desugaring cooking optimization model, the parameters can be dynamically optimized based on real-time health data on the basis of the initial program, ensuring the maximization of the desugaring effect while taking into account the taste and nutritional retention of rice, and ultimately achieving a systematic improvement in nutritional indicators and cooking quality.

[0052] In some implementations, the above method further includes S210 to S230, and S210 to S230 are described in detail below.

[0053] S210 , obtaining a health indicator collection time duration corresponding to the health indicator curve information, where the health indicator collection time duration is a time span for collecting the health indicator curve information.

[0054] In this implementation method, when implementing the sugar-free cooking optimization method based on group nutritional data analysis, the health indicator collection time corresponding to the health indicator curve information can be obtained. The health indicator collection time represents the time span for collecting the health indicator curve information. The health indicator collection time represents the credibility of the health indicator curve information, and then it can be determined how to optimize the cooking process of the rice cooker based on the health indicator collection time.

[0055] For example, in the control of an electric rice cooker, the system can automatically calculate the monitoring period of the user's blood sugar curve data, such as the duration of the past week or month, which helps to evaluate the integrity and reliability of health data.

[0056] S220: When the user group information to which the user belongs is weight management group information, special physiological stage group information, or exercise function group information, and when the health indicator collection time is less than the preset health indicator collection time, obtain the taste score and softness score fed back by the user. A first-level optimization control model is used to determine a first-level optimization program for rice water control, a first-level optimization program for draining control, and a first-level optimization program for heating control based on the taste score, the softness score, the rice water control program, the draining control program, and the heating control program.

[0057] Figure 3 This is a schematic diagram of the workflow of the second sugar-free cooking optimization method based on group nutritional data analysis provided in the embodiment of the present application, as shown in FIG. Figure 3 As shown, in this implementation, when the user group information to which the user belongs is weight management group information, special physiological stage group information or exercise function group information, and the health indicator collection time is less than the preset health indicator collection time, it means that the data amount of the health indicator curve information is insufficient. At this time, the taste score and softness score feedback from the user can be obtained, so as to first optimize the taste score and softness score for the user group that has no disease control needs, and optimize the cooking process of the rice cooker in advance.

[0058] For example, the user can input these ratings through the touch interface of the rice cooker or the mobile application. For example, after the rice is cooked, the user can rate the chewiness and softness of the rice.

[0059] After obtaining the taste score and softness score from user feedback, the first-level optimization control model can be used to determine the rice water control first-level optimization program, the draining control first-level optimization program and the heating control first-level optimization program based on the taste score, the softness score, the rice water control program, the draining control program and the heating control program. The rice water control first-level optimization program, the draining control first-level optimization program and the heating control first-level optimization program are the cooking control programs after the first-level optimization of the rice cooking process, which are used for primary optimization of taste and softness.

[0060] For example, the first-level optimization control model can analyze when the taste score is low and adjust the first-level optimization program of rice-water control to increase the rice-water ratio before draining, thereby improving the texture of the rice while maintaining the consistency of the heating parameters.

[0061] Exemplarily, the first-level optimization control model can be a deep learning model trained based on the sample taste score, sample softness score, sample rice water control program, sample drainage control program, sample heating control program, labeled sample rice water control first-level optimization program, sample drainage control first-level optimization program and sample heating control first-level optimization program.

[0062] S230. When the health indicator collection time is greater than or equal to the preset health indicator collection time, the desugaring cooking optimization model is used to determine the rice water control secondary optimization program, the draining control secondary optimization program, and the heating control secondary optimization program according to the user group information, the health indicator curve information, the rice water control primary optimization program, the draining control primary optimization program, and the heating control primary optimization program, and the rice cooking is completed through the rice water control secondary optimization program, the draining control secondary optimization program, and the heating control secondary optimization program.

[0063] like Figure 3 As shown, when the health index collection time is greater than or equal to the preset health index collection time, it means that the data amount of the health index curve information is sufficient. At this time, the desugaring cooking optimization model can be used to determine the rice water control second-level optimization program, the draining control second-level optimization program and the heating control second-level optimization program according to the user group information, the health index curve information, and the rice water control first-level optimization program, the draining control first-level optimization program and the heating control first-level optimization program. The rice water control second-level optimization program, the draining control second-level optimization program and the heating control second-level optimization program are the desugaring cooking methods targetedly optimized according to the user's health index curve information.

[0064] When cooking in a rice cooker, if the user belongs to the weight management group and has a long history of blood sugar monitoring, the sugar-free cooking optimization model can further optimize the draining control secondary optimization program based on the primary optimization program, extend the draining time to reduce sugar content, and adjust the heating control secondary optimization program to ensure nutrient retention.

[0065] After obtaining the rice-water control secondary optimization program, the draining control secondary optimization program and the heating control secondary optimization program, the rice cooking can be completed through the rice-water control secondary optimization program, the draining control secondary optimization program and the heating control secondary optimization program, realizing the automatic execution of the entire process.

[0066] The beneficial effect of the above implementation method is that by dynamically judging the health indicator collection time and user group type, it can flexibly adapt to scenarios with different data integrity. When health data is insufficient, it will give priority to introducing user sensory feedback for preliminary optimization to ensure that the taste and softness of the rice meet immediate needs, thereby improving user experience and cooking satisfaction.

[0067] The beneficial effect of the above-mentioned implementation method is that when there is sufficient health data, in-depth optimization is carried out based on the first-level optimization results to achieve fine-tuning of the desugaring effect and nutritional indicators, thereby taking into account the systematic improvement of long-term health goals and cooking quality.

[0068] In some implementations, in the above-mentioned S230, the desugaring cooking optimization model is used to determine the rice water control secondary optimization program, the draining control secondary optimization program and the heating control secondary optimization program according to user group information, health index curve information, the rice water control primary optimization program, the draining control primary optimization program and the heating control primary optimization program, including S231 to S232. S231 to S232 are described in detail below.

[0069] S231. Obtain the cooling rate of the first-level heating control optimization program within a preset temperature range after draining, and obtain the enzymatic reaction heating time periods of the first-level heating control optimization program before and after draining. The preset temperature range is 98° C. to 85° C., the rice water temperature during the enzymatic reaction heating time period is 50° C. to 60° C., and different rice varieties correspond to different enzymatic reaction heating time periods.

[0070] In this implementation, when executing the second-level optimization program determination process, the cooling rate of the first-level optimization program for heating control within the preset temperature range after draining can be obtained. The preset temperature range can be understood as the temperature of rice water dropping from near boiling point to a temperature range suitable for safe operation. The preset temperature range can be 98°C to 85°C.

[0071] At the same time, the enzymatic reaction heating time period set in the first-level optimization program of heating control before and after draining can be obtained. The rice water temperature during the enzymatic reaction heating time period is maintained within a specific range to promote starch conversion. Different types of rice require different optimal enzymatic reaction durations. The rice water temperature during the enzymatic reaction heating time period is 50 to 60°C.

[0072] For example, by analyzing the parameters recorded by the first-level optimization program, the cooling rate data in the temperature range of 98°C to 85°C can be extracted, and the heating period in which the rice water temperature is maintained in the range of 50°C to 60°C for activating amylase can be identified.

[0073] S232. Through the desugaring cooking optimization model, according to the user group information, the health index curve information, and the enzymatic reaction heating time period of the heating control first-level optimization program, the enzymatic reaction heating optimization time period in the heating control second-level optimization program is determined, the cooling rate adjustment range of the cooling rate is limited to be less than the preset cooling rate adjustment range, and the duration adjustment range of other heating time periods of the heating control first-level optimization program is limited to be less than the preset duration adjustment range.

[0074] After obtaining the above parameters through S231, the enzymatic reaction heating time period can be finely adjusted through the desugaring cooking optimization model combined with user group information and health index curve information to determine the enzymatic reaction heating optimization time period in the heating control secondary optimization program. During the adjustment process, the cooling rate adjustment range of the cooling rate is limited to be smaller than the preset cooling rate adjustment range, and the duration adjustment range of other heating time periods of the heating control primary optimization program is limited to be smaller than the preset duration adjustment range, so that the adjustment range of the cooling rate can be limited to a smaller range during the adjustment process to avoid sudden temperature changes affecting the starch conversion efficiency.

[0075] At the same time, the duration of other heating time periods in the non-enzymatic reaction stage in the first-level optimization program of heating control can be limited to be significantly modified to ensure the stability of the main framework of the cooking process, so as to retain the effect of optimizing taste and softness in the first-level optimization program of heating control.

[0076] For example, when the user belongs to a diabetes-sensitive group and the blood sugar curve shows significant fluctuations after a meal, the sugar-free cooking optimization model can moderately extend the enzymatic reaction heating optimization time period to allow the starch to be more fully converted into resistant starch, while strictly controlling the cooling rate adjustment range in the range of 98°C to 85°C. For example, it only allows the cooling rate to be fine-tuned by about 0.5°C per minute, and keeps the duration of the boiling stage basically unchanged, allowing only a fine-tuning of no more than 2% of the duration, thereby retaining the effect of optimizing taste and softness in the first-level optimization program of heating control.

[0077] Exemplarily, the desugaring cooking optimization model can be a deep learning model trained based on sample user group information, sample health index curve information, the enzymatic reaction heating time period of the sample heating control first-level optimization program, and the enzymatic reaction heating optimization time period in the marked sample heating control second-level optimization program.

[0078] The beneficial effect of the above implementation method is that, through the targeted optimization of the heating time period of the enzymatic reaction and the intelligent constraint of the adjustment range of key parameters, the desugaring effect for specific groups can be maximized without destroying the stability of the cooking process and retaining the effect of optimizing the taste and softness in the first-level optimization program of heating control. For example, the resistant starch production rate can be enhanced for the metabolic disease group while retaining the palatability of rice.

[0079] The beneficial effect of the above implementation method is that the hierarchical adjustment strategy takes into account both parameter optimization space and execution reliability. For example, the limited adjustment of the cooling rate avoids the undercooked phenomenon caused by thermal shock, and the duration protection mechanism of other heating periods ensures the consistency and repeatability of the cooking process.

[0080] In some implementations, in the above-mentioned S230, the desugaring cooking optimization model is used to determine the rice water control secondary optimization program, the draining control secondary optimization program and the heating control secondary optimization program according to user group information, health index curve information, the rice water control primary optimization program, the draining control primary optimization program and the heating control primary optimization program, and also includes S233 to S234. S233 to S234 are described in detail below.

[0081] S233. Obtain the user's physical condition information, which includes age, gender, and food softness preference information.

[0082] In this implementation method, user physical information describing the user's physiological characteristics can be obtained. The user physical information may include key data such as age, gender, and food softness preference. The optimization constraint margin in the secondary optimization of the sugar-free cooking optimization model can then be determined based on the user physical information.

[0083] For example, users can input their age group, gender, and preferred rice hardness level (e.g., slightly hard or soft) through a mobile terminal application. This information can reflect an individual's specific needs for food texture.

[0084] S234. Determine the preset cooling rate adjustment range and the preset duration adjustment range based on the user's health index curve information and the user's physical fitness information through the constraint margin identification model.

[0085] After obtaining the user's physical fitness information, the constraint margin identification model can be used to analyze the correlation between the user's health index curve information and the user's physical fitness information, and dynamically determine the allowable parameter adjustment amplitude threshold to improve the cooking control effect of the sugar-free cooking optimization model within the individual's special requirements for food texture.

[0086] For example, the constraint margin identification model can evaluate the sensitivity of user physiological characteristics to changes in cooking parameters and establish a safety margin for subsequent optimization processes.

[0087] For example, when the user's health indicator curve information shows that the blood sugar control ability is weak and the user's physical condition information is that of an elderly person, the model can automatically set stricter cooling rate adjustment range limits to avoid sudden temperature changes affecting the starch conversion effect. At the same time, based on the general preference of the elderly for softness, the restrictions on the adjustment range of the enzymatic reaction heating time period are appropriately relaxed to retain more optimization space for improving taste. This constraint identification method can take into account individual preferences while ensuring health effects.

[0088] The beneficial effect of the above implementation method is that, by integrating the cross-analysis of user physical information and health indicators, it can accurately identify the parameter tolerance boundaries of different groups, provide customized constraint margin configuration for groups with weaker digestive ability, and achieve a refined balance between health goals and taste preferences.

[0089] The beneficial effect of the above implementation method is that the dynamic identification mechanism of the constraint margin enhances the adaptability of the system, automatically relaxes the softness optimization restrictions for elderly users and tightens the temperature adjustment range for users with abnormal metabolism, so that the optimization program meets both nutritional needs and ergonomic characteristics.

[0090] The beneficial effect of the above implementation method is that the introduction of user food preference information prevents the optimization from deviating excessively from subjective expectations, retains adjustment space for the enzymatic reaction of users who prefer a hard taste, and improves the acceptance and practicality of the optimization plan.

[0091] In some implementations, the above method further includes S310 to S330, and S310 to S330 are described in detail below.

[0092] S310: Obtain the health indicator collection time corresponding to the health indicator curve information. The health indicator collection time is the time span for collecting the health indicator curve information.

[0093] In this implementation, when implementing the sugar-free cooking optimization method based on group nutritional data analysis, the health indicator collection time corresponding to the health indicator curve information can also be obtained. The health indicator collection time represents the time span for collecting the health indicator curve information.

[0094] For example, in the rice cooker application, the system can automatically calculate the monitoring period of the user's blood glucose curve data, such as the cumulative duration of the past few weeks or months, which helps to evaluate the completeness and representativeness of the health indicator curve information.

[0095] S320. When the user group information to which the user belongs is disease-sensitive group information, and when the health indicator collection time is less than the preset health indicator collection time, the basic rice and water control optimization program, the basic drainage control optimization program and the basic heating control optimization program corresponding to the disease-sensitive group information are obtained through the cooking control database. The basic rice and water control optimization program, the basic drainage control optimization program and the basic heating control optimization program are used to meet the basic cooking needs of the disease-sensitive group corresponding to the disease-sensitive group information.

[0096] Figure 4 The schematic diagram of the workflow of the third sugar-free cooking optimization method based on group nutritional data analysis provided in the embodiment of the present application is as follows: Figure 4As shown, when the user group information to which the user belongs is disease-sensitive group information, and the health indicator collection time is less than the preset health indicator collection time, it means that the integrity and representativeness of the health indicator curve information are insufficient. At this time, the basic optimization program for rice and water control, the basic optimization program for draining control, and the basic optimization program for heating control corresponding to the disease-sensitive group information can be obtained through the cooking control database. The basic optimization program is designed to meet the basic cooking needs of the disease-sensitive group, ensure the minimum desugaring effect and nutrient retention, and then select the corresponding control program for the disease-sensitive group information to complete the cooking method that can meet the basic disease control needs corresponding to the disease-sensitive group information.

[0097] For example, if the user belongs to a diabetes-sensitive group but the health data only covers short-term monitoring, the system can extract the preset rice-water control basic optimization program from the database, set a lower rice-water ratio to reduce the starch content, while the draining control basic optimization program controls the standard draining time, and the heating control basic optimization program maintains basic heating parameters, thereby generating safe rice that conforms to the common characteristics of the group.

[0098] S330. When the health indicator collection time is greater than or equal to the preset health indicator collection time, the desugaring cooking optimization model is used to determine the rice water control secondary optimization program, the draining control secondary optimization program, and the heating control secondary optimization program according to the disease sensitive group information, the health indicator curve information, the rice water control basic optimization program, the draining control basic optimization program, and the heating control basic optimization program, and the rice cooking is completed through the rice water control secondary optimization program, the draining control secondary optimization program, and the heating control secondary optimization program.

[0099] When the health indicator collection time is greater than or equal to the preset health indicator collection time, it means that the integrity and representativeness of the health indicator curve information are sufficient. At this time, the desugaring cooking optimization model can be used to determine the rice water control secondary optimization program, the draining control secondary optimization program and the heating control secondary optimization program according to the disease sensitive group information, the health indicator curve information, and the rice water control basic optimization program, the draining control basic optimization program and the heating control basic optimization program. Then, the cooking process can be further optimized and controlled according to the rice water control secondary optimization program, the draining control secondary optimization program and the heating control secondary optimization program.

[0100] For example, if the user has a long history of blood sugar monitoring, the health index curve information can be analyzed through the sugar-free cooking optimization model, and the draining control secondary optimization program can be further adjusted based on the basic optimization program to extend the draining time, optimize the ratio setting of the rice water control secondary optimization program, and fine-tune the temperature parameters of the heating control secondary optimization program to ensure the maximum desugaring effect.

[0101] The beneficial effect of the above implementation method is that by dynamically judging the collection time of health indicators and the type of user group, the basic optimization program in the database can be applied first when data is insufficient, the basic health needs of disease-sensitive groups can be met, and a stable low-sugar rice solution can be provided for diabetic users to avoid nutritional risks caused by missing data.

[0102] The beneficial effect of the above implementation method is that when there is sufficient health data, in-depth optimization is carried out based on the basic program to achieve personalized desugaring adjustment, and a secondary optimization program is customized based on the long-term blood sugar curve to improve the accuracy and adaptability of the cooking effect.

[0103] In some implementations, in the above-mentioned S330, through the desugaring cooking optimization model, according to the disease sensitive group information, health index curve information, the rice water control basic optimization program, the drainage control basic optimization program and the heating control basic optimization program, the rice water control secondary optimization program, the drainage control secondary optimization program and the heating control secondary optimization program are determined, including S331 to S332. S331 to S332 are described in detail below.

[0104] S331. Obtain associated parameters and non-associated parameters for the rice-water control secondary optimization program, the drainage control secondary optimization program, and the heating control secondary optimization program, and obtain adjustment weights corresponding to the associated parameters and non-associated parameters, respectively. The associated parameters include the rice-water ratio, the enzymatic reaction heating optimization time period, and the cooling rate; the non-associated parameters include control parameters other than the associated parameters in the rice-water control secondary optimization program, the drainage control secondary optimization program, and the heating control secondary optimization program.

[0105] In this implementation, when executing the secondary optimization program parameter determination process, the parameter association relationships existing in the rice and water control secondary optimization program, the drainage control secondary optimization program and the heating control secondary optimization program can be identified, and the control programs can be optimized in a targeted manner based on the parameter association relationships.

[0106] In this implementation, the associated parameter set and the non-associated parameter set can be specifically obtained. The associated parameters include key variables such as the rice-to-water ratio, the enzymatic reaction heating optimization time period and the cooling rate that directly affect the core indicators of desugaring, while the non-associated parameters include other auxiliary control parameters in the program.

[0107] In this implementation, the adjustment weights corresponding to the above two types of parameters can also be obtained simultaneously. These adjustment weights reflect the difference in contribution of different parameters to the final desugaring effect.

[0108] For example, two types of parameters, rice-to-water ratio (associated parameter) and holding time after cooking (non-associated parameter), can be automatically identified and assigned different optimization weights based on historical data.

[0109] S332. Through the desugaring cooking optimization model, according to the disease sensitive group information, health index curve information, rice water control basic optimization program, drainage control basic optimization program, heating control basic optimization program, and the adjustment weights corresponding to the associated parameters and non-associated parameters, determine the associated parameter adjustment values ​​corresponding to the associated parameters in the rice water control secondary optimization program, the drainage control secondary optimization program, and the heating control secondary optimization program, and the non-associated parameter adjustment values ​​corresponding to the non-associated parameters.

[0110] After obtaining the parameter classification and the weight information corresponding to the parameter classification, the disease sensitive group information, health indicator curve information and basic optimization program configuration can be comprehensively analyzed through the sugar-free cooking optimization model. The sugar-free cooking optimization model can combine the adjustment weights corresponding to the associated parameters and non-associated parameters to determine the rice water control secondary optimization program and the drainage control secondary optimization program respectively, and determine the associated parameter adjustment values ​​corresponding to the associated parameters and the non-associated parameter adjustment values ​​corresponding to the non-associated parameters in the heating control secondary optimization program.

[0111] For example, when the user's blood sugar curve information shows that the blood sugar fluctuates significantly after a meal, a higher weight can be assigned to the enzymatic reaction heating optimization time period in the associated parameters, thereby generating a parameter adjustment value to extend this period to enhance the starch conversion effect; while for non-associated parameters such as the insulation temperature after draining, only slight adjustments are made based on lower weights.

[0112] The beneficial effect of the above implementation method is that through parameter correlation analysis and differentiated weight allocation, it is possible to focus on key variables for precise regulation, enhance the adjustment depth of the rice-water ratio and enzymatic reaction parameters for diabetic-sensitive groups, and avoid invalid disturbances of non-critical parameters while ensuring core health indicators.

[0113] The beneficial effect of the above implementation method is that the application of dynamic weights realizes the optimal allocation of resources, gives higher adjustment priority to parameters that significantly affect the desugaring effect, concentrates the optimization resources on the most critical control dimensions, and improves the efficiency of the method.

[0114] The beneficial effect of the above implementation method is that the coordinated adjustment of associated parameters and non-associated parameters ensures the overall cooking effect, maintains the stability of rice texture while optimizing sugar control indicators, and achieves a systematic balance between health attributes and eating quality.

[0115] In some implementations, in the above-mentioned S330, the desugaring cooking optimization model is used to determine the rice water control secondary optimization program, the draining control secondary optimization program and the heating control secondary optimization program according to the disease sensitive group information, the health index curve information, the rice water control basic optimization program, the draining control basic optimization program and the heating control basic optimization program, and also includes S333 to S334. S333 to S334 are described in detail below.

[0116] S333: Obtain the disease sensitivity level corresponding to the user's disease sensitive group information, wherein the disease sensitivity level includes the disease sensitivity levels corresponding to diabetes, cardiovascular disease, and kidney disease, respectively.

[0117] In this implementation, when implementing the parameter configuration of the secondary optimization program, the disease sensitivity level corresponding to the user's disease sensitive group information can be obtained, and classification and grading can be performed based on the user's medical records or health declarations.

[0118] For example, the health information import module can be used to input the user's sensitivity levels for different health conditions such as diabetes, cardiovascular disease, and kidney disease (such as the three-level classification of diabetes: high risk, medium risk, and low risk). Kidney disease can be included in the high-level disease sensitivity level. These graded data can reflect the user's sensitivity to specific nutrients and cooking processes.

[0119] S334. Determine adjustment weights corresponding to the associated parameters and non-associated parameters respectively according to the disease sensitivity level. Different disease sensitivity levels correspond to different adjustment weights corresponding to the associated parameters and non-associated parameters respectively.

[0120] In this implementation, after the disease sensitivity level is determined, the optimization weight configuration strategy can be dynamically set according to the nutritional intervention characteristics of different disease types.

[0121] For example, when the user's diabetes sensitivity level is high, a higher adjustment weight can be assigned to the rice-to-water ratio parameter in the associated parameters (such as setting it to a weight coefficient of 0.8), while the adjustment weights of non-associated parameters (such as the insulation time parameter) maintain the basic weight (such as a weight coefficient of 0.2).

[0122] For another example, when the user belongs to the medium level of the kidney disease sensitive group, the enzymatic reaction time parameter can be assigned a medium weight (such as 0.6), and the adjustment range weight of the cooling rate parameter can be limited (such as 0.4) to avoid the enzymatic reaction time affecting the user's kidney disease indicators. In addition, the adjustment weights of the associated parameters and non-associated parameters corresponding to different disease sensitivity levels of the kidney disease sensitive group are different, so that the optimal desugaring cooking parameters can be determined according to different disease sensitivity levels.

[0123] The beneficial effect of the above implementation method is that through the mapping relationship between disease sensitivity levels and nutritional factors, parameter adjustment strategies and health risk levels can be accurately matched. For example, the optimization weights of sugar control parameters can be automatically strengthened for users at risk of high blood sugar, thereby achieving precise health risk prevention and control; for example, the intensity of parameter adjustments related to protein dissolution can be limited for people who are sensitive to kidney disease, thereby maintaining nutritional balance while ensuring special health goals.

[0124] The beneficial effect of the above implementation method is that the dynamic hierarchical weight configuration optimizes the allocation of system resources, avoids the decline in cooking quality caused by over-optimization of low health risk parameters, and improves the overall optimization efficiency and economy.

[0125] Figure 5 A schematic diagram of a fourth method for optimizing desugaring cooking based on group nutritional data analysis provided in an embodiment of the present application is shown in FIG. Figure 5 As shown, the above method further includes S410 to S430, and S410 to S430 are described in detail below.

[0126] S410: When the user group information to which the user belongs is weight management group information, special physiological stage group information, or exercise function group information, a health index decline score is determined according to the health index curve information through a health index evaluation model.

[0127] When implementing cooking optimization for specific user groups, when the user belongs to the weight management group information, special physiological stage group information or sports function group information, the health indicator curve information can be dynamically analyzed through the health indicator evaluation model to calculate the health indicator decline score that reflects the change in health trends. The health indicator decline score can comprehensively evaluate the change amplitude and duration of key indicators such as the blood sugar curve and the body fat rate curve.

[0128] For example, when the user selects the "weight management group", the recent upward slope of the blood sugar curve and the fluctuation characteristics of the body fat percentage curve can be analyzed to generate a quantitative health risk score value.

[0129] S420. When the health index decrease score is greater than or equal to the preset health index decrease score, and when the health index collection time is less than the preset health index collection time, the rice and water control basic optimization program, the draining control basic optimization program, and the heating control basic optimization program corresponding to the preset health index decrease score are obtained through the cooking control database.

[0130] In this implementation, if Figure 5As shown, when the health index decline score is greater than or equal to the preset threshold and the health index collection time is short, it means that the data integrity of the health index decline score is insufficient. At this time, in order to ensure the best health protection effect for the user, the basic optimization program solutions preset for the preset health index decline score can be obtained through the cooking control database. These basic optimization programs include the rice and water control basic optimization program, the draining control basic optimization program and the heating control basic optimization program, which can provide a cooking solution with a higher health level that meets the health protection needs.

[0131] For example, when the rice cooker detects that the user's blood sugar deterioration score exceeds the standard and the data collection period is less than one month, the system automatically retrieves the low glycemic index plan from the database. The low glycemic index plan can reduce the higher draining frequency and specific rice-water ratio that can reduce the efficiency of carbohydrate dissolution.

[0132] S430. When the health indicator collection time is greater than or equal to the preset health indicator collection time, the desugaring cooking optimization model is used to determine the rice water control secondary optimization program, the draining control secondary optimization program, and the heating control secondary optimization program according to the user group information, the health indicator curve information, the rice water control basic optimization program, the draining control basic optimization program, and the heating control basic optimization program, and the rice cooking is completed through the rice water control secondary optimization program, the draining control secondary optimization program, and the heating control secondary optimization program.

[0133] like Figure 5 As shown, when the health indicator collection time reaches a sufficient period, it means that the data integrity of the health indicator collection is high, and in-depth adjustments can be made based on the aforementioned basic optimization program. During the adjustment, the desugaring cooking optimization model can be used to integrate user group characteristics, long-term health indicator data and basic program configuration to generate a more refined secondary optimization program and complete the cooking execution.

[0134] For example, when the system determines that the user has a full quarter of health monitoring data, it can make further adjustments based on the basic low-glycemic plan: extend the enzymatic reaction stage by 15% to increase the production of resistant starch, and optimize the temperature drop curve during the drainage stage, ultimately achieving a finished rice product that has both health protection and a balanced taste.

[0135] The beneficial effect of the above implementation method is that through the dual criteria of health trend scoring mechanism and collection duration, it can intelligently trigger different levels of protection strategies, quickly launch safe cooking plans in short-term data scenarios, and effectively block the risk path of continuous deterioration of health indicators.

[0136] The beneficial effect of the above implementation method is that long-term data-driven deep optimization achieves a dynamic balance between health goals and cooking quality. The enzymatic reaction parameters can be fine-tuned according to the characteristics of the quarterly blood sugar curve, maintaining the palatability of rice while ensuring health benefits.

[0137] In some implementations, in the above-mentioned S420, the rice and water control basic optimization program, the draining control basic optimization program and the heating control basic optimization program corresponding to the preset health indicator reduction score are obtained through the cooking control database, including S421 to S422. S421 to S422 are described in detail below.

[0138] S421. Determine the health indicator decline level corresponding to the health indicator decline score.

[0139] In this implementation, when executing the basic optimization program matching process, the corresponding health indicator decline level can be determined based on the health indicator decline score. The health indicator decline level division can reflect the severity gradient of the health risk.

[0140] For example, in the rice cooker application, the health decline level can be divided into three levels (such as mild, moderate, and severe) through scoring intervals, and each level corresponds to different nutritional intervention intensity requirements.

[0141] S422. Obtain different rice and water control basic optimization programs, draining control basic optimization programs, and heating control basic optimization programs corresponding to the health index degradation levels through the cooking control database, and the parameter thresholds of different rice and water control basic optimization programs, draining control basic optimization programs, and heating control basic optimization programs are different.

[0142] After determining the level of decline in health indicators, the basic optimization program combination exclusive to that level can be obtained through the cooking control database. The program settings corresponding to different levels have differentiated parameter threshold characteristics. For mild decline levels, the system can obtain a basic program that limits the rice-to-water ratio to a regular floating range. The program sets a regular draining time threshold and a standard heating rate; when a moderate decline level is identified, a program with a higher rice-to-water ratio limit value can be obtained, and the extended draining control threshold and medium-intensity heating adjustment parameters are triggered at the same time; for severe decline levels, the database can provide the highest level rice-to-water ratio control threshold, matching the maximum allowable draining time parameters and enhanced heating correction scheme; this gradient program configuration achieves precise correspondence between health risks and intervention intensity.

[0143] The beneficial effect of the above implementation method is that, through the precise graded response to the health decline level, it can achieve intelligent matching of risk level and intervention intensity, and then automatically adapt the moderate intensity program parameter threshold for users with moderate health risks, thereby ensuring the health protection effect and avoiding excessive intervention leading to a decline in cooking quality.

[0144] The beneficial effect of the above implementation method is that the gradient parameter threshold design enhances the system adaptability, thereby providing differentiated rice-water ratio control range and drainage time configuration for different risk levels, so that the basic optimization program has both clear safety boundaries and reasonable operating space.

[0145] An embodiment of the present application also provides a sugar-free cooking optimization system based on group nutritional data analysis, comprising a unit for executing any of the methods described above.

[0146] Figure 6 A schematic diagram of the logical structure of a sugar-free cooking optimization system based on group nutritional data analysis provided in an embodiment of the present application is shown in FIG. Figure 6 As shown, the system 1 of this embodiment includes a processing unit 11, a storage unit 12, and a transceiver unit 13. The processing unit 11 is used to process data, the storage unit 12 is used to store data, and the transceiver unit 13 is used to send and receive data. The processing unit 11, the storage unit 12, and the transceiver unit 13 cooperate with each other to implement the above method. The beneficial effects of the embodiment of the present application have been described in the above method and will not be repeated here.

[0147] It should be noted that the information interaction, execution process, etc. between the above-mentioned devices / units are based on the same concept as the method embodiment of this application. Their specific functions and technical effects can be found in the method embodiment section and will not be repeated here.

[0148] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0149] If the integrated unit is implemented as a software functional unit and sold or used as a standalone product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application can implement all or part of the process steps in the above-mentioned method embodiments by using a computer program to instruct the relevant hardware. The computer program can be stored in a computer-readable storage medium. When executed by a processor, the computer program can implement the steps of each of the above-mentioned method embodiments. The computer program includes computer program code, which can be in source code form, object code form, executable file, or some intermediate form. The computer-readable medium can include at least: any entity or device capable of carrying computer program code to a camera / terminal device, recording medium, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signals, telecommunication signals, and software distribution media. Examples include USB flash drives, removable hard drives, magnetic disks, or optical disks. In some jurisdictions, based on legislation and patent practice, computer-readable media cannot be electric carrier signals or telecommunication signals.

[0150] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant description of other embodiments.

[0151] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0152] In the embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the modules or units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0153] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0154] The above-described embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present application, and should all be included in the scope of protection of the present application.

Claims

1. A desugaring cooking optimization method based on group nutritional data analysis, characterized in that: The method comprises: Obtaining user group information input by the user, and obtaining the user's health indicator curve information within a preset time period; wherein the user group information includes disease-sensitive group information, weight management group information, special physiological stage group information, and exercise function group information, and the health indicator curve information includes body fat percentage curve information and blood sugar curve information; Through the cooking control model, according to the user group information, the rice water control program, the draining control program and the heating control program of the draining type rice cooker are determined; through the desugaring cooking optimization model, according to the user group information, the health index curve information, the rice water control program, the draining control program and the heating control program, the rice water control optimization program, the draining control optimization program and the heating control optimization program are determined, and the rice cooking is completed through the rice water control optimization program, the draining control optimization program and the heating control optimization program; among them, the rice water control program and the rice water control optimization program are used to control the rice-water ratio before draining, the draining control program and the draining control optimization program are used to control the draining process, and the heating control program and the heating control optimization program are used to control the heating parameters before and after draining.

2. The method according to claim 1, wherein The method further comprises: Obtain the health indicator collection time corresponding to the health indicator curve information, where the health indicator collection time is the time span for collecting the health indicator curve information; When the user group information to which the user belongs is weight management group information, special physiological stage group information, or exercise function group information, and when the health indicator collection time is less than the preset health indicator collection time, obtaining the taste score and the softness score fed back by the user; determining the rice water control first-level optimization program, the draining control first-level optimization program, and the heating control first-level optimization program according to the taste score, the softness score, the rice water control program, the draining control program, and the heating control program through the first-level optimization control model; When the health indicator collection time is greater than or equal to the preset health indicator collection time, the desugaring cooking optimization model is used to determine the rice water control second-level optimization program, the draining control second-level optimization program and the heating control second-level optimization program according to the user group information, the health indicator curve information, the rice water control first-level optimization program, the draining control first-level optimization program and the heating control first-level optimization program, and the rice cooking is completed through the rice water control second-level optimization program, the draining control second-level optimization program and the heating control second-level optimization program.

3. The method according to claim 2, wherein Through the sugar-free cooking optimization model, according to user group information, health index curve information, rice and water control first-level optimization program, draining control first-level optimization program and heating control first-level optimization program, the rice and water control second-level optimization program, the draining control second-level optimization program and the heating control second-level optimization program are determined, including: Obtaining a cooling rate within a preset temperature range after draining in the first-level optimization program for heating control, and obtaining an enzymatic reaction heating time period before and after draining in the first-level optimization program for heating control; wherein the preset temperature range is 98° C. to 85° C., the rice water temperature during the enzymatic reaction heating time period is 50° C. to 60° C., and different rice varieties correspond to different enzymatic reaction heating time periods; Through the desugaring cooking optimization model, based on user group information, health index curve information, and the enzymatic reaction heating time period of the first-level heating control optimization program, the enzymatic reaction heating optimization time period in the second-level heating control optimization program is determined, the cooling rate adjustment range of the cooling rate is limited to be less than the preset cooling rate adjustment range, and the duration adjustment range of other heating time periods of the first-level heating control optimization program is limited to be less than the preset duration adjustment range.

4. The method according to claim 3, wherein Through the sugar-free cooking optimization model, according to the user group information, health index curve information, the rice and water control first-level optimization program, the draining control first-level optimization program and the heating control first-level optimization program, the rice and water control second-level optimization program, the draining control second-level optimization program and the heating control second-level optimization program are determined, and further comprising: Obtain the user's physical information, including age, gender, and food softness preference; Through the constraint margin identification model, the preset cooling rate adjustment range and the preset duration adjustment range are determined according to the user's health index curve information and user physical information.

5. The method according to claim 4, wherein The method further comprises: Obtain the health indicator collection time corresponding to the health indicator curve information, where the health indicator collection time is the time span for collecting the health indicator curve information; When the user group information to which the user belongs is disease-sensitive group information, and when the health indicator collection time is less than the preset health indicator collection time, the rice and water control basic optimization program, the draining control basic optimization program, and the heating control basic optimization program corresponding to the disease-sensitive group information are obtained through the cooking control database, and the rice and water control basic optimization program, the draining control basic optimization program, and the heating control basic optimization program are used to meet the basic cooking needs of the disease-sensitive group corresponding to the disease-sensitive group information; When the health indicator collection time is greater than or equal to the preset health indicator collection time, the desugaring cooking optimization model is used to determine the rice water control secondary optimization program, the draining control secondary optimization program and the heating control secondary optimization program according to the disease sensitive group information, the health indicator curve information, the rice water control basic optimization program, the draining control basic optimization program and the heating control basic optimization program, and the rice cooking is completed through the rice water control secondary optimization program, the draining control secondary optimization program and the heating control secondary optimization program.

6. The method according to claim 5, wherein Through the desugaring cooking optimization model, according to the disease sensitive group information, health index curve information, rice water control basic optimization program, drainage control basic optimization program and heating control basic optimization program, the rice water control secondary optimization program, drainage control secondary optimization program and heating control secondary optimization program are determined, including: Obtain associated parameters and non-associated parameters of the rice-water control secondary optimization program, the drainage control secondary optimization program, and the heating control secondary optimization program, and obtain adjustment weights corresponding to the associated parameters and non-associated parameters, respectively; wherein the associated parameters include the rice-water ratio, the enzymatic reaction heating optimization time period, and the cooling rate, and the non-associated parameters include control parameters other than the associated parameters in the rice-water control secondary optimization program, the drainage control secondary optimization program, and the heating control secondary optimization program; Through the desugaring cooking optimization model, according to the disease sensitive group information, health index curve information, rice water control basic optimization program, drainage control basic optimization program, heating control basic optimization program, associated parameters and non-associated parameters respectively corresponding to the adjustment weights, determine the associated parameter adjustment values ​​corresponding to the associated parameters in the rice water control secondary optimization program, drainage control secondary optimization program, heating control secondary optimization program, and non-associated parameter adjustment values ​​corresponding to the non-associated parameters.

7. The method according to claim 6, wherein Through the desugaring cooking optimization model, according to the disease sensitive group information, health index curve information, rice water control basic optimization program, drainage control basic optimization program and heating control basic optimization program, the rice water control secondary optimization program, drainage control secondary optimization program and heating control secondary optimization program are determined, and also include: Obtain the disease sensitivity level corresponding to the user's disease sensitive group information; wherein the disease sensitivity level includes the disease sensitivity levels corresponding to diabetes, cardiovascular disease, and kidney disease respectively; According to the disease sensitivity level, the adjustment weights corresponding to the associated parameters and non-associated parameters are determined.

8. The method according to claim 7, wherein The method further comprises: When the user group information to which the user belongs is weight management group information, special physiological stage group information, or exercise function group information, the health index decline score is determined according to the health index curve information through the health index evaluation model; When the health index decrease score is greater than or equal to the preset health index decrease score, and when the health index collection time is less than the preset health index collection time, the rice and water control basic optimization program, the draining control basic optimization program, and the heating control basic optimization program corresponding to the preset health index decrease score are obtained through the cooking control database; When the health indicator collection time is greater than or equal to the preset health indicator collection time, the desugaring cooking optimization model is used to determine the rice water control secondary optimization program, the draining control secondary optimization program and the heating control secondary optimization program according to the user group information, health indicator curve information, the rice water control basic optimization program, the draining control basic optimization program and the heating control basic optimization program, and the rice cooking is completed through the rice water control secondary optimization program, the draining control secondary optimization program and the heating control secondary optimization program.

9. The method according to claim 8, wherein Obtaining the rice and water control basic optimization program, the draining control basic optimization program, and the heating control basic optimization program corresponding to the preset health indicator reduction score through the cooking control database, including: Determine the health indicator decline level corresponding to the health indicator decline score; Different rice and water control basic optimization programs, draining control basic optimization programs and heating control basic optimization programs corresponding to the health index degradation level are obtained through the cooking control database. The parameter thresholds of different rice and water control basic optimization programs, draining control basic optimization programs and heating control basic optimization programs are different.

10. A sugar-free cooking optimization system based on group nutritional data analysis, characterized in that: Comprising means for performing the method according to any one of claims 1 to 9.

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