Nutritionally intelligent management method and intelligent management system

Abstract

The application discloses a kind of nutritional intelligent management method and intelligent management system, method includes: through data acquisition terminal, the daily dietary intake data of user is sent to analysis platform end;Analysis platform end received daily dietary intake data of user is stored but is not handled;In preset period, analysis platform end obtains the health-related index data of user fluctuation from index terminal, and judges whether the key health index data in fluctuation health-related index data meets set standard;If key health index data does not meet set standard, analysis platform end is stored but is not handled daily dietary intake data of user is handled into analysis data;Analysis platform end does not meet set standard key health index data and analysis data are handled into input data, input first analysis model, output daily recommended recipe.The technical scheme of the present application aims to guide different users to carry out reasonable dietary management.

Description

Technical Field

[0001] This invention relates to the field of intelligent management system technology, specifically to a nutritional intelligent management method and intelligent management system. Background Technology

[0002] Nutrition is a vital material basis for human life, growth, development, health, and genetic inheritance. Proper nutrition is a prerequisite for ensuring the body's material needs, physiological activities, health, immunity, and optimal reproductive health. Nutrition is crucial to health, improved quality of life, and socio-economic development. The four cornerstones of health are: a balanced diet, adequate exercise, quitting smoking and limiting alcohol consumption, and mental well-being. Therefore, providing adequate nutrition is highly beneficial to public health.

[0003] Diet is the primary source of nutrition. The foods we consume are diverse, with varying nutrient contents. However, public awareness of nutrition and health, as well as nutritional education, are far from widespread. Consequently, dietary design and catering management often fail to meet the body's nutritional needs, resulting in the following situation:

[0004] First, the food supply side lacks awareness of proper nutrition and food safety, and cannot meet personalized nutritional needs;

[0005] Second, diners have low nutritional and health literacy and cannot choose nutritious and healthy meals according to their individual needs.

[0006] Third, modern nutritional science knowledge and health strategies have not been well integrated into the food supply chain.

[0007] Fourth, the extended food chain and the diversified and complex catering supply network make it difficult to carry out real-time hygiene supervision, etc.

[0008] Nutritional management is complex across different population groups, primarily due to differences in age, developmental stage, occupation, and disease factors. Currently, the most effective method for determining nutritional needs is to consult a doctor or nutritionist, in addition to referring to nutritional guidelines tailored to each group. However, this consultation approach is not always convenient.

[0009] Modern information technology is a dynamic technology that uses the combination of computer technology and telecommunications technology based on microelectronics to acquire, process, store, transmit, and use information such as sound, images, text, numbers, and various sensor signals.

[0010] The core of modern information technology is informatics. Modern information technology includes ERP, GPS, RFID, etc., which can be understood and learned through ERP knowledge and applications, GPS knowledge and applications, and EDI knowledge and applications. Modern information technology is a very broad group of technologies, including microelectronics, optoelectronics, communication technology, network technology, sensing technology, control technology, display technology, etc.

[0011] Therefore, it is necessary to propose a method for intelligent nutrition management using modern information technology to guide different users in making reasonable dietary management. Summary of the Invention

[0012] The main objective of this invention is to provide a method for intelligent nutrition management, which aims to guide different users in making reasonable dietary management.

[0013] To achieve the above objectives, this invention proposes a nutritional intelligent management method, applied to an intelligent management system. The intelligent management system includes an analysis platform and an indicator terminal and a data acquisition terminal, respectively connected to the analysis platform via signals. The analysis platform is equipped with a first analysis model. The nutritional intelligent management method includes the following steps:

[0014] The user's daily dietary data is sent to the analysis platform via the data acquisition terminal.

[0015] Within a preset period, the analysis platform will store the daily dietary data received from users but will not process it.

[0016] During the preset period, the analysis platform obtains fluctuating health-related indicator data of users from the indicator terminal, and determines whether the key health indicator data in the fluctuating health-related indicator data meets the set standards.

[0017] If the key health indicator data does not meet the set standards, the analysis platform will process the user's daily dietary data that has been stored but not yet processed into analysis data.

[0018] The analysis platform processes the key health indicator data that does not meet the set standards and the analysis data into input data, and inputs them into the first analysis model.

[0019] The first analysis model outputs daily recommended recipes, which are then sent to the data acquisition terminal.

[0020] Preferably, the analysis platform is further equipped with a second analysis model; the intelligent nutrition management method further includes:

[0021] After the daily recommended recipes are generated, the user's daily dietary data is sent to the analysis platform via the data acquisition terminal.

[0022] The analysis platform stores the user's daily dietary data after the daily recommended recipes are generated, and processes the user's daily dietary data into analysis data.

[0023] The analysis platform inputs the analysis data from the two periods before and after the daily recommended recipe generation into the second analysis model;

[0024] The second analysis model compares the ratio of the evaluation values ​​of the two sets of analysis data to see if they reach a preset value.

[0025] If the preset value is not reached, the analysis platform sends an alarm notification to the data acquisition terminal.

[0026] Preferably, after the step of comparing the ratio of the evaluation values ​​of the two analysis data points in the second analysis model to see if it reaches a preset value, the method further includes:

[0027] If the preset value is reached, the analysis platform will input the analysis data generated after the daily recommended recipes are generated into the first analysis model;

[0028] After the daily recommended recipes are generated, the analysis platform obtains the user's fluctuating health-related indicator data from the indicator terminal and determines whether the key health indicator data in the fluctuating health-related indicator data meets the set standards.

[0029] After the daily recommended recipes are generated, if the key health indicator data does not meet the set standards, the analysis platform will process the user's daily dietary data that has been stored but not yet processed into analysis data.

[0030] The analysis platform processes the key health indicator data that do not meet the set standards after the daily recommended recipes are generated, along with the analysis data, into input data, and inputs it into the first analysis model.

[0031] The first analysis model outputs an updated daily recommended recipe and sends the updated daily recommended recipe to the data acquisition terminal.

[0032] Preferably, the step of sending the user's daily dietary data to the analysis platform via the data acquisition terminal includes:

[0033] The data acquisition terminal provides a list of optional meals, wherein the list of optional meals records the recipe information for each meal, including the meal name, food name, the amount of food corresponding to each food name, and the cooking method of the food; and after the daily recommended recipe is generated, the list of optional meals becomes the daily recommended recipe.

[0034] Obtain the meals selected by the user from the list of available meals, and use all the meals selected by the user on that day as the daily meal data;

[0035] The data acquisition terminal sends the acquired daily dietary data to the analysis platform.

[0036] Preferably, the data acquisition terminal is equipped with an RFID reader; the step of sending the user's daily dietary data to the analysis platform via the data acquisition terminal includes:

[0037] Each of the available meals for the day will be assigned an RFID tag;

[0038] Record dietary information for each meal in RFID tags;

[0039] Before the meal, the RFID tag corresponding to the user's selected meal is read through the data acquisition terminal;

[0040] The data acquisition terminal sends the RFID tags it reads to the analysis platform.

[0041] Preferably, the analysis platform processes the user's daily dietary data into analytical data in the following manner:

[0042] Obtain a nutrition database, which stores nutritional data corresponding to food names, including the types and proportions of nutrients contained in the food name;

[0043] The analysis platform processes the user's daily dietary data into a data unit by querying the nutrition database.

[0044] The analysis platform processes consecutive data units into analytical data.

[0045] Preferably, the intelligent nutrition management method further includes:

[0046] Based on the fluctuating health-related indicator data, the time period corresponding to the analysis data is divided into several nutrient intake time periods, and the intake standard value for the type of nutrient to be ingested in each nutrient intake time period is set separately.

[0047] The second analysis model's step of comparing the ratio of the evaluation values ​​of the two analysis data sets before and after the analysis to see if it reaches a preset value includes:

[0048] The second analysis model queries the intake standard value for each type of nutrient in each data unit of each analysis data and calculates the evaluation value for each data unit.

[0049] The evaluation value of the analytical data is determined based on the evaluation values ​​of all data units in each analytical data set;

[0050] The evaluation values ​​corresponding to the two sets of analysis data are compared to determine whether the ratio of the evaluation values ​​of the two sets of analysis data reaches the preset value.

[0051] Preferably, the step of the analysis platform obtaining user fluctuation health-related indicator data from the indicator terminal includes:

[0052] The analysis platform queries the user's daily health-related indicator data from the indicator terminal;

[0053] Determine whether the user's daily health-related indicator data contains any indicators updated that day;

[0054] The health-related indicator data updated on the same day will be used as the health-related indicator data.

[0055] For health-related indicators that have not been updated on the current day, the latest historical health-related indicator data will be used as the health-related indicator data.

[0056] The step of dividing a preset period into several nutritional intake time periods based on the fluctuating health-related indicator data includes:

[0057] Calculate the fluctuation range of key health indicator data in health-related indicator data;

[0058] Based on the fluctuation range of key health indicator data, the preset period is divided into several nutritional intake time periods.

[0059] Preferably:

[0060] The analysis platform processes the user's daily dietary data into a data unit by querying the nutrition database, specifically as follows:

[0061] A i =(a i1 ,a i2 ,…a in );

[0062] A i This represents the i-th data unit in the preset period; a ij This represents the intake of the j-th type of nutrient on the i-th day of a preset period; 1≤i≤m, 1≤j≤n; m represents the total number of days in the preset period; n represents the total number of nutrient types.

[0063] The analysis platform processes consecutive data units into analytical data, specifically as follows:

[0064]

[0065] Where A represents the data to be analyzed;

[0066] For each nutritional intake period within the preset timeframe, specific intake standards are set for the types of nutrients that need to be consumed, as follows:

[0067] B i =(b i1 ,b i2 ,…b in );

[0068] B i b represents the set of intake criteria for the i-th data unit within a preset period; ij This represents the standard intake value of the j-th nutrient type consumed by the user on day i within a preset period;

[0069] The second analysis model calculates the evaluation value for each data unit, specifically:

[0070]

[0071] The specific steps for determining the evaluation value of the analytical data based on the evaluation values ​​of all data units in each analytical data set are as follows:

[0072]

[0073] The evaluation values ​​corresponding to the two sets of analysis data are compared to determine whether the ratio of the evaluation values ​​between the two sets of analysis data reaches the preset value. Specifically:

[0074] Obtain the ratio of the evaluation value of the previous analysis data to the evaluation value of the next analysis data. When the ratio reaches a preset value, it is determined that the ratio of the evaluation values ​​of the two analyses has reached the preset value.

[0075] Furthermore, to achieve the above objectives, the present invention also proposes an intelligent management system that applies the intelligent nutrition management method described in any of the above claims. The intelligent management system includes an analysis platform terminal and an indicator terminal and a data acquisition terminal that are respectively connected to the analysis platform terminal via signals. The analysis platform terminal is equipped with a first analysis model.

[0076] In the technical solution of this invention, the user's daily dietary data is sent to the analysis platform via a data acquisition terminal. The analysis platform stores the daily dietary data but does not process it. Simultaneously, the analysis platform continuously acquires fluctuating health-related indicator data of the user through an indicator terminal. When key health indicator data within the fluctuating health-related indicator data meets a set standard, the analysis platform processes the stored but unprocessed daily dietary data into analytical data. The analysis platform then processes the key health indicator data that does not meet the set standard and the analytical data into input data, which is input into the first analysis model. The first analysis model outputs a daily recommended diet plan to the user based on their dietary habits and key indicators. The daily recommended diet plan output by the first analysis model is sent to the data acquisition terminal, allowing the user to select meals based on the daily recommended diet plan, thereby achieving intelligent nutrition management based on the user's dietary habits and key indicator fluctuations. Attached Figure Description

[0077] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the structures shown in these drawings without creative effort.

[0078] Figure 1 This is a flowchart of an embodiment of the intelligent nutrition management method of the present invention. Detailed Implementation

[0079] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0080] It should be noted that all directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of the present invention are only used to explain the relative positional relationship and movement of each component in a certain specific posture (as shown in the figure). If the specific posture changes, the directional indication will also change accordingly.

[0081] Furthermore, in this invention, descriptions involving "first," "second," etc., are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0082] In this invention, unless otherwise explicitly specified and limited, the terms "connection," "fixed," etc., should be interpreted broadly. For example, "fixed" can mean a fixed connection, a detachable connection, or an integral part; it can mean a mechanical connection or an electrical connection; it can mean a direct connection or an indirect connection through an intermediate medium; it can mean the internal communication of two components or the interaction between two components, unless otherwise explicitly limited. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0083] Furthermore, the technical solutions of the various embodiments of the present invention can be combined with each other, but only if they are feasible for those skilled in the art. If the combination of technical solutions is contradictory or cannot be implemented, it should be considered that such combination of technical solutions does not exist and is not within the scope of protection claimed by the present invention.

[0084] Please see Figure 1 In a first embodiment of the present invention, the intelligent nutrition management method is applied to an intelligent management system, which includes an analysis platform and an indicator terminal and a data acquisition terminal respectively connected to the analysis platform via signals; the analysis platform is equipped with a first analysis model; the intelligent nutrition management method includes the following steps:

[0085] Step S10: Send the user's daily dietary data to the analysis platform via the data acquisition terminal;

[0086] Step S20: Within a preset period, the analysis platform will store the daily dietary data received from users but will not process it.

[0087] Step S30: During the preset period, the analysis platform obtains fluctuating health-related indicator data of the user from the indicator terminal, and determines whether the key health indicator data in the fluctuating health-related indicator data meets the set standards.

[0088] Step S40: If the key health indicator data does not meet the set standards, the analysis platform will process the user's daily dietary data that has been stored but not yet processed into analysis data.

[0089] Step S50: The analysis platform processes the key health indicator data that does not meet the set standards and the analysis data into input data and inputs them into the first analysis model.

[0090] Step S60: Output daily recommended recipes through the first analysis model and send the daily recommended recipes to the data acquisition terminal.

[0091] In the technical solution of this invention, the user's daily dietary data is sent to the analysis platform via a data acquisition terminal. The analysis platform stores the daily dietary data but does not process it. Simultaneously, the analysis platform continuously acquires fluctuating health-related indicator data of the user through an indicator terminal. When key health indicator data within the fluctuating health-related indicator data meets a set standard, the analysis platform processes the stored but unprocessed daily dietary data into analytical data. The analysis platform then processes the key health indicator data that does not meet the set standard and the analytical data into input data, which is input into the first analysis model. The first analysis model outputs a daily recommended diet plan to the user based on their dietary habits and key indicators. The daily recommended diet plan output by the first analysis model is sent to the data acquisition terminal, allowing the user to select meals based on the daily recommended diet plan, thereby achieving intelligent nutrition management based on the user's dietary habits and key indicator fluctuations.

[0092] Sending users' daily dietary data to the analysis platform via the data acquisition terminal helps the first analysis model analyze users' dietary preferences and patterns. The fluctuations in key health indicator data help the first analysis model adjust daily recommended recipes for different groups of people, thereby guiding different users to conduct reasonable dietary management.

[0093] The aforementioned indicator terminals can be connected to the analysis platform as needed. For example, the indicator terminals can be smart wearable devices used to detect users' physiological indicators (such as sports watches, or wearable devices with blood pressure and blood sugar testing functions), or they can be hospital terminals. Furthermore, the indicator terminals connected to the analysis platform can include both smart wearable devices and hospital terminals.

[0094] Health-related indicator data obtained from indicator terminals are generally fluctuating and do not remain constant. When an indicator terminal has the function of acquiring multiple types of data, the analysis platform does not focus on all the data, but only on the data that can be improved through diet, or the data that can be used to measure dietary health.

[0095] Meanwhile, the data acquisition terminal can also set dietary restrictions for users, or intelligently analyze the user's dietary restrictions by analyzing health-related indicator data and the user's daily dietary data obtained from the platform.

[0096] The data acquisition terminal can be a smart device, such as a mobile phone, a smart wearable device, or other smart handheld device.

[0097] In this invention, health-related indicator data includes at least one of the following: weight, body fat percentage, blood pressure, blood glucose, blood lipids, uric acid, and waist circumference, or other indicators used to measure health status. The health-related indicator data can be determined according to user settings or default settings, and the data type of the health-related indicator data obtained by the analysis platform conforms to the set type.

[0098] Key health indicators refer to data that are currently significantly abnormal for the user. Examples include blood pressure, blood sugar, blood lipids, and uric acid. The determination of key health indicators can be done as follows:

[0099] The analysis platform continuously acquires various health-related indicator data from each connected indicator terminal and determines whether each health-related indicator data is normal. It also identifies continuously abnormal health-related indicator data that can be controlled through diet from the fluctuations of various health-related indicator data as key health indicator data.

[0100] If the user does not have any obviously abnormal data, key health indicators can be set as the data that the user is concerned about, such as weight or body fat percentage.

[0101] The preset period refers to a time period set by the user or the system, and the length of the preset period is no less than one week. Setting a preset time helps determine the length of dietary data obtained by the analysis platform, thereby enabling the analysis platform to obtain the user's dietary preferences and patterns over a continuous period of time, while minimizing the error of occasional events, which helps the primary analysis model make more reasonable dietary recommendations.

[0102] Within a preset period, the analysis platform stores the daily dietary data received from users but does not process it. The platform then retrieves fluctuating health-related indicator data from the indicator terminal and determines whether key health indicators within this fluctuating data meet set standards. Meeting these standards means not reaching a set rate of change or a set warning value. If key health indicator data does not meet the standards, it is considered to be in a special abnormal situation. Dietary recommendations should be provided to the user before the preset period ends to proactively address this abnormality. For example, excessive blood sugar fluctuations or blood sugar levels significantly exceeding warning values ​​may indicate a special abnormal situation.

[0103] If the analysis platform determines that the user's key health indicator data meets the set standards within a preset period, then the analysis platform will process the fluctuations of the key health indicator data and the analysis data during the preset period into input data and input it into the first analysis model only when the preset period ends.

[0104] Including key health indicators that do not meet set standards as part of the input data helps to provide scientific guidance on nutritional intake for different population groups. For example, people with high cholesterol should consume less total fat than people with normal cholesterol levels, thus requiring them to include lower-fat meals in their daily recommended diet.

[0105] Based on the first embodiment of the present invention, in the second embodiment of the present invention, the analysis platform terminal is further provided with a second analysis model; the intelligent nutrition management method further includes:

[0106] Step S70: After the daily recommended recipe is generated, the user's daily dietary data is sent to the analysis platform via the data acquisition terminal.

[0107] Step S80: The analysis platform stores the user's daily dietary data after the daily recommended recipe is generated, and processes the user's daily dietary data into analysis data.

[0108] Step S90: The analysis platform inputs the analysis data from the two periods before and after the daily recommended recipe generation into the second analysis model.

[0109] Step S100: The second analysis model compares the ratio of the evaluation values ​​of the two sets of analysis data before and after to see if it reaches a preset value;

[0110] In step S110, if the preset value is not reached, the analysis platform sends an alarm notification to the data acquisition terminal.

[0111] In this embodiment, the length of the recipe recommendation period can be equal to the length of the period of the user's daily dietary data corresponding to the first analysis of data.

[0112] The second analytical model is used to analyze whether users have corrected their original nutritional deficiencies by following the daily recommended meal plans after they are generated. In other words, the role of the second analytical model is to verify whether users have actually used the daily recommended meal plans.

[0113] Specifically, after the daily recommended recipes are generated, when the analysis platform detects that the time has reached the end of the recommended recipe period, the analysis platform processes the user's daily dietary data into analytical data, and compares the ratio of the evaluation values ​​of the two analytical data before and after the second model to see if it reaches the preset value.

[0114] In step S110, the evaluation value is used to assess the degree to which the user's daily dietary data deviates from a healthy diet; the ratio of the two evaluation values ​​before and after the generation of the daily recommended diet is used to determine the extent to which the user adopts the daily recommended diet. When the ratio of the evaluation values ​​does not reach a set value, it indicates that the user's usage rate of the daily recommended diet is very low, and the daily recommended diet does not achieve the effect of correcting the diet. Therefore, the analysis platform sends an alarm to the data acquisition terminal to remind the user of the health risks associated with not following the daily recommended diet. Furthermore, if the preset value is not reached, the analysis platform can also send an alarm notification to the associated terminal of the data acquisition terminal. The associated terminal can be a hospital terminal or a relative's terminal.

[0115] Based on the second embodiment of the present invention, in the third embodiment of the present invention, after step S100, the method further includes:

[0116] Step S120: If the preset value is reached, the analysis platform will input the analysis data generated after the daily recommended recipes are generated into the first analysis model.

[0117] Step S130: After the daily recommended recipe is generated, the analysis platform obtains the user's fluctuating health-related indicator data from the indicator terminal, and determines whether the key health indicator data in the fluctuating health-related indicator data meets the set standards.

[0118] Step S140: After the daily recommended diet is generated, if the key health indicator data does not meet the set standards, the analysis platform will process the user's daily dietary data that has been stored but not processed into analysis data.

[0119] Step S150: The analysis platform processes the key health indicator data that do not meet the set standards after the daily recommended recipes are generated and the analysis data into input data, and inputs it into the first analysis model.

[0120] Step S160: Output an updated daily recommended recipe through the first analysis model and send the updated daily recommended recipe to the data acquisition terminal.

[0121] In step S120, if the ratio of the evaluation values ​​of the two sets of analysis data compared by the second analysis model reaches a preset value, it indicates that the user has adopted the daily recommended diet to a high degree. For such users, the daily dietary data and key health indicator data fluctuation records can be continuously provided in each round, and the above data can be input into the first analysis model to continuously derive the daily recommended diet for different stages.

[0122] The daily recommended menu has a recommended time period. This recommended time period cannot exceed a preset period because the daily recommended menu in this invention is formulated based on the fluctuations in the user's health-related indicator data over a period of time. Since the user's health-related indicator data will vary every so often due to medication or dietary adjustments, different daily recommended menus are formulated based on the fluctuations over each period. After the fluctuations in dietary data and health-related indicator data are obtained again in the next time period, the daily recommended menu is updated, allowing for dynamic adjustments and more scientific dietary management.

[0123] Based on the second to third embodiments of the present invention, in the fourth embodiment of the present invention, step S10 includes:

[0124] Step S11: The data acquisition terminal provides a list of optional meals, wherein the list of optional meals records the recipe information consumed each day. The recipe information includes the name of the meal, the name of the food, the amount of food corresponding to each food name, the cooking method of the food, and the number of meals (breakfast, lunch, and dinner). After the daily recommended recipe is generated, the list of optional meals is the daily recommended recipe. The name of the meal is the name of the dish, such as braised pork with potatoes. The name of the food is the various foods included in a dish. For example, braised pork with potatoes includes potatoes and pork.

[0125] Step S12: Obtain the meals selected by the user from the list of available meals, and use all the meals selected by the user on that day as the daily meal data;

[0126] Step S13: The data acquisition terminal sends the acquired daily dietary data to the analysis platform.

[0127] The fourth and fifth embodiments are used to provide different technical solutions for recording users' daily dietary data.

[0128] In the fourth embodiment, if a user is using the intelligent management system for the first time, the system can acquire the user's self-inputted health-related indicator data through a data acquisition terminal, thereby providing a targeted daily dietary list. The dietary list is then displayed on the data acquisition terminal.

[0129] Users can select meal names from the available meal list to quickly enter daily meal data.

[0130] Of course, users may also choose other meals from outside the available meal list. In this case, the name of the meal chosen by the user can be entered into the data acquisition terminal to form the user's daily dietary data. The data can be entered by filling in the input interface of the data acquisition terminal or by taking a picture and recognizing it through the camera of the data acquisition terminal.

[0131] After users have used the intelligent management system for a period of time, if a daily recommended menu is generated, the list of available meals will be the recommended menu for that day.

[0132] Based on the second to third embodiments of the present invention, in the fifth embodiment of the present invention, the data acquisition terminal is equipped with an RFID reader; step S10 includes:

[0133] Step S14: Assign an RFID tag to each of the optional meals for the day;

[0134] Step S15: Record the dietary information of each meal in the RFID tag;

[0135] Step S16: Before the meal, the RFID tag corresponding to the meal selected by the user is read through the data acquisition terminal;

[0136] In step S17, the data acquisition terminal sends the RFID tag it reads to the analysis platform.

[0137] The fifth embodiment and the fourth embodiment can be used in combination, or only one of them can be used. The application scenario of the fifth embodiment is suitable for dining in the lobby, such as a canteen or restaurant. In the canteen or restaurant, the same type of food is marked with RFID tags. By bringing the receipt acquisition terminal close to the RFID tag, the food selected by the user can be quickly obtained, thereby realizing the rapid entry of the user's daily dietary data.

[0138] Based on the fourth or fifth embodiment of the present invention, in the sixth embodiment of the present invention, the analysis platform processes the user's daily dietary data into analytical data in the following manner:

[0139] Step S41: Obtain a nutrition database, wherein the nutrition database stores nutritional data corresponding to food names, wherein the nutritional data includes the type and proportion of nutrients contained in the food name;

[0140] Step S42: The analysis platform processes the user's daily dietary data into a data unit by querying the nutrition database.

[0141] Step S43: The analysis platform processes the continuous data units into analysis data.

[0142] In this embodiment, the daily dietary data is processed into a data unit in order to standardize the measurement criteria of the daily dietary data.

[0143] For example, a user's daily diet might include: breakfast consisting of 1 egg and 200ml of milk; lunch consisting of 200g of rice, 200g of green vegetables, and 1 serving of stir-fried potato slices with meat; and dinner consisting of 200g of rice, 200g of loofah, and 1 serving of stir-fried carrots with meat.

[0144] The process of converting the day's dietary data into data units includes the following steps:

[0145] First, by consulting a nutrition database, the total intake of each type of nutrient for the day is calculated from the dietary data. For example: energy (C kJ); protein (D g); total fat (E g); cholesterol (E mg); total carbohydrates (F g); salt (G mg).

[0146] Secondly, obtain the set sorting of various nutrient types, and arrange the total intake of each nutrient type for the day according to the set sorting to form a row matrix. When the total intake of a certain nutrient type for the day is 0, fill in 0 in the corresponding position in the row matrix.

[0147] Finally, the row matrices obtained from each data unit are arranged vertically according to the date order, and the row matrices are merged to obtain an analysis data matrix composed of consecutive data units.

[0148] Based on the first to sixth embodiments of the present invention, in the seventh embodiment of the present invention, the intelligent nutrition management method further includes:

[0149] Step S170: Based on the fluctuating health-related indicator data, divide the time period corresponding to the analysis data into several nutrient intake time periods, and set intake standard values ​​for the types of nutrients that need to be ingested in each nutrient intake time period.

[0150] Step S100 includes:

[0151] Step S101: The second analysis model queries the intake standard value for each type of nutrient in each data unit of each analysis data and calculates the evaluation value for each data unit.

[0152] Step S102: Determine the evaluation value of the analysis data based on the evaluation values ​​of all data units in each analysis data.

[0153] Step S103: Compare the evaluation values ​​corresponding to the two sets of analysis data to determine whether the ratio of the evaluation values ​​of the two sets of analysis data reaches the preset value.

[0154] In step S170, the reason for dividing the time period corresponding to the analysis data into several nutrient intake time periods based on the fluctuating health-related indicator data is that the recommended intake of corresponding nutrient types varies during the fluctuation of health-related indicator data. For example, if blood lipids are normal for a period of time during the analysis data period, the daily total fat intake can be based on the normal standard intake. If blood lipids rise above the normal range for a period of time, the daily total fat intake needs to be reduced. Therefore, based on the fluctuating health-related indicator data during the analysis period, the time period corresponding to the analysis data can be divided into several nutrient intake time periods, and intake standard values ​​can be set for the types of nutrients that need to be consumed in each nutrient intake time period. This helps to obtain the daily dietary assessment value by measuring the deviation between the actual intake of each nutrient type and the intake standard value; and by measuring the daily dietary assessment value of the time period corresponding to the analysis data, the overall dietary assessment value of the time period corresponding to the analysis data can be measured.

[0155] Based on the first to seventh embodiments of the present invention, in the eighth embodiment of the present invention, the step of the analysis platform obtaining user fluctuation health-related indicator data from the indicator terminal includes:

[0156] Step S31: The analysis platform queries the user's daily health-related indicator data from the indicator terminal;

[0157] Step S32: Determine whether there are any updated indicator items in the user's daily health-related indicator data;

[0158] Step S33: Use the updated health-related indicator data for the day as the health-related indicator data;

[0159] Step S34: For indicators that have not been updated on the current day, the latest historical health-related indicator data will be used as the health-related indicator data. For example, in the preset period from March 1, 2022 to March 15, 2022, if updated blood lipid data is found on March 7, 2022 and March 12, 2022, the previous blood lipid data will be used from March 1, 2022 to March 6, 2022; the blood lipid data from March 7, 2022 to March 11, 2022 will be used from March 7, 2022; and the blood lipid data from March 12, 2022 to March 15, 2022 will be used from March 12, 2022.

[0160] Step S35, the step of dividing the preset period into several nutritional intake time periods based on the fluctuating health-related indicator data, includes:

[0161] Step S36: Calculate the fluctuation range of key health indicator data in the health-related indicator data;

[0162] Step S37: Based on the fluctuation range of key health indicator data, the preset period is divided into several nutritional intake time periods.

[0163] Specifically, the health-related indicator data for each test fluctuates. The fluctuation range is preset to measure whether the fluctuation is significant. If the fluctuation is significant, there is no need to divide the nutritional intake into different time periods. If the fluctuation is slight, there is no need to divide the nutritional intake into different time periods.

[0164] Based on the eighth embodiment of the present invention, in the ninth embodiment of the present invention:

[0165] The analysis platform processes the user's daily dietary data into a data unit by querying the nutrition database, specifically as follows:

[0166] A i =(a i1 ,a i2 ,…a in );

[0167] A i This represents the i-th data unit in the preset period; a ij This represents the intake of the j-th type of nutrient on the i-th day of a preset period; 1≤i≤m, 1≤j≤n; m represents the total number of days in the preset period; n represents the total number of nutrient types.

[0168] The analysis platform processes consecutive data units into analytical data, specifically as follows:

[0169]

[0170] Where A represents the data to be analyzed;

[0171] For each nutritional intake period within the preset timeframe, specific intake standards are set for the types of nutrients that need to be consumed, as follows:

[0172] B i =(b i1 ,b i2 ,…b in );

[0173] B i b represents the set of intake criteria for the i-th data unit within a preset period; ij This represents the standard intake value of the j-th nutrient type consumed by a user on day i within a preset period; 1≤i≤m, 1≤j≤n; m represents the total number of days in the preset period; n represents the total number of nutrient types.

[0174] The second analysis model calculates the evaluation value for each data unit, specifically:

[0175]

[0176] The specific steps for determining the evaluation value of the analytical data based on the evaluation values ​​of all data units in each analytical data set are as follows:

[0177]

[0178] The evaluation values ​​corresponding to the two sets of analysis data are compared to determine whether the ratio of the evaluation values ​​between the two sets of analysis data reaches the preset value. Specifically:

[0179] Obtain the ratio of the evaluation value of the previous analysis data to the evaluation value of the next analysis data. When the ratio reaches a preset value, it is determined that the ratio of the evaluation values ​​of the two analyses has reached the preset value.

[0180] Specifically, the evaluation value of each data analysis indicates the degree of deviation between actual nutrient intake and standard nutrient intake;

[0181] In the process of comparing the evaluation values ​​of the two analysis data, if the previous evaluation value is large (large deviation) and the subsequent evaluation value is small (small deviation), then the ratio of the two is large. If the ratio of the two can reach the preset value (indicating that the diet has been corrected in the subsequent analysis), then no warning is needed.

[0182] Reaching the preset value indicates that the first recommended recipe is effective. Then, recommended recipes need to be generated continuously based on the fluctuations in subsequent dietary conditions and health-related indicators.

[0183] Conversely, if both the previous and subsequent assessment values ​​are large (large deviation), the ratio will be small, and the ratio will fail to reach the preset value (indicating that the dietary correction was not achieved in the subsequent assessment), thus requiring an early warning.

[0184] Furthermore, to achieve the above objectives, the present invention also proposes an intelligent management system that applies the nutritional intelligent management method described in any of the above claims. The intelligent management system includes an analysis platform terminal and an indicator terminal and a data acquisition terminal that are respectively connected to the analysis platform terminal via signals; the analysis platform terminal is equipped with a first analysis model.

[0185] As a further extension of the present invention, the following solutions may also be included:

[0186] ① The user's daily diet plan (i.e., desired dietary plan), basic health status, and body assessment data (height, weight, etc.) are sent to the first analysis platform (which is a platform for assessing and analyzing the user's physical condition). Based on the user's health status and body assessment data, the first analysis platform recommends the user's dietary energy reference intake, etc.

[0187] ② Based on the types and quantities of food included in the user's desired dietary plan, calculate the energy and nutrient intake provided by the plan, and send this information to the second analysis platform. The second analysis platform compares this nutrient intake with the recommended nutrient intake for the user's physical condition, calculates the energy-to-nutrient supply ratio, and also calculates indicators such as the energy source ratio, protein source ratio, and dietary regimen to analyze whether it meets the preset ratios.

[0188] ③ If the preset ratio is met, the final recommended diet is generated and delivered to the user for daily meal preparation and distribution.

[0189] If the preset ratio is not met, the quantity and proportion of food in the user's desired diet will be adjusted according to the characteristics of the data presentation. Then, the calculation procedure in item ② above on the second analysis platform will be entered. The diet will be adjusted until it meets the user's daily dietary nutrient reference intake standard. This diet is the daily personalized recommended diet provided to the user.

[0190] Therefore, based on the above extensions, this intelligent management system can provide users with:

[0191] The calculation includes various models such as individual recommended dietary design, family recommended dietary design, organism canteen recommended dietary design, and recommended menu design for orderers.

[0192] Among them, the expected diet refers to the meals that users order and expect. It often focuses on personal preferences such as taste, without making reasonable judgments on nutrition or providing quantitative meal plans.

[0193] The first analysis platform uses user health status and body evaluation data as the basic basis for recommending a reasonable diet to users, that is, how much energy and nutrients should be provided to users appropriately.

[0194] The second analysis platform performs quantitative meal planning calculations based on the desired dietary recipes. By adjusting the types and quantities of food in the desired dietary recipes and appropriately adjusting the types and quantities of food according to the individual's health condition, it generates designed food. It calculates and judges whether the energy and nutrients provided by the designed recipes meet the preset values ​​for reasonable nutrition for the individual, and finally generates a daily personalized recommended diet.

[0195] Recommended recipes are entered into the user's device, allowing users to view and use them at any time.

[0196] Expected meal recipes: These are the meal recipes ordered by users.

[0197] Meal design recipes: These are preliminary designed recipes generated during the software analysis process.

[0198] Recommended dietary recipes: These refer to recipes designed specifically for the user, scientifically formulated to meet sustainable and balanced nutritional needs. This incorporates information technology and nutritional science calculations to create personalized health recipes. Therefore, these three concepts in this invention have a specific order and varying degrees of rationality.

[0199] The above are merely preferred embodiments of the present invention and do not limit the patent scope of the present invention. Any equivalent structural transformations made under the concept of the present invention using the description and drawings of the present invention, or direct / indirect applications in other related technical fields, are included within the patent protection scope of the present invention.

Claims

1. A method for intelligent nutrition management, characterized in that, The method is applied to an intelligent management system, which includes an analysis platform and an indicator terminal and a data acquisition terminal respectively connected to the analysis platform via signals. The analysis platform is equipped with a first analysis model and a second analysis model. The intelligent nutrition management method includes the following steps: The data acquisition terminal sends the user's daily dietary intake data to the analysis platform. Within a preset period, the analysis platform will store the daily dietary intake data received from users but will not process it. During the preset period, the analysis platform obtains fluctuating health-related indicator data of users from the indicator terminal, and determines whether the key health indicator data in the fluctuating health-related indicator data meets the set standards. If the key health indicator data does not meet the set standards, the analysis platform will process the user's daily dietary intake data that has been stored but not yet processed into analysis data. The analysis platform processes the key health indicator data that does not meet the set standards and the analysis data into input data, and inputs them into the first analysis model. The first analysis model outputs daily recommended recipes, which are then sent to the data acquisition terminal. The intelligent nutrition management method further includes: whether the ratio of the evaluation values ​​of the two sets of analysis data before and after the second analysis model reaches a preset value; the step of whether the ratio of the evaluation values ​​of the two sets of analysis data before and after the second analysis model reaches a preset value includes: the second analysis model queries the intake standard value for each type of nutrition in each data unit of each analysis data, and calculates the evaluation value of each data unit; and determines the evaluation value of the analysis data based on the evaluation values ​​of all data units in each analysis data. The analysis platform processes the user's daily dietary data into analytical data in the following manner: It acquires a nutrition database, which stores nutritional data corresponding to food names, including the type and proportion of nutrients contained in the food name; the analysis platform queries the nutrition database to process the user's daily dietary data into a data unit; and the analysis platform processes consecutive data units into analytical data. The analysis platform processes the user's daily dietary data into a data unit by querying the nutrition database, specifically as follows: ; A i This represents the i-th data unit in the preset period; a ij This represents the intake of the j-th type of nutrient on the i-th day of a preset period; 1≤i≤m, 1≤j≤n; m represents the total number of days in the preset period; n represents the total number of nutrient types. The analysis platform processes consecutive data units into analytical data, specifically as follows: ； Where A represents the data to be analyzed; For each nutritional intake period within the preset timeframe, specific intake standards are set for the types of nutrients that need to be consumed, as follows: ； B i b represents the set of intake criteria for the i-th data unit within a preset period; ij This represents the standard intake value of the j-th nutrient type consumed by the user on day i within a preset period; The second analysis model calculates the evaluation value for each data unit, specifically: ； The specific steps for determining the evaluation value of the analytical data based on the evaluation values ​​of all data units in each analytical data set are as follows: ； The evaluation values ​​corresponding to the two sets of analysis data are compared to determine whether the ratio of the evaluation values ​​between the two sets of analysis data reaches the preset value. Specifically: Obtain the ratio of the evaluation value of the previous analysis data to the evaluation value of the next analysis data. When the ratio reaches a preset value, it is determined that the ratio of the evaluation values ​​of the two analyses has reached the preset value.

2. The intelligent nutrition management method according to claim 1, characterized in that, The intelligent nutrition management method also includes: After the daily recommended recipe is generated, the user's daily dietary intake data is sent to the analysis platform via the data acquisition terminal. The analysis platform stores the user's daily dietary data after the daily recommended recipes are generated, and processes the user's daily dietary data into analysis data. The analysis platform inputs the analysis data from the two periods before and after the daily recommended recipe generation into the second analysis model; If the preset value is not reached, the analysis platform sends an alarm notification to the data acquisition terminal.

3. The intelligent nutrition management method according to claim 2, characterized in that, After the step of comparing the ratio of the evaluation values ​​of the two analysis data to see if they reach a preset value, the second analysis model further includes: If the preset value is reached, the analysis platform will input the analysis data generated after the daily recommended recipes are generated into the first analysis model; After the daily recommended recipes are generated, the analysis platform obtains the user's fluctuating health-related indicator data from the indicator terminal and determines whether the key health indicator data in the fluctuating health-related indicator data meets the set standards. After the daily recommended recipes are generated, if the key health indicator data does not meet the set standards, the analysis platform will process the user's daily dietary data that has been stored but not yet processed into analysis data. The analysis platform processes the key health indicator data that do not meet the set standards after the daily recommended recipes are generated, along with the analysis data, into input data, and inputs it into the first analysis model. The first analysis model outputs an updated daily recommended recipe and sends the updated daily recommended recipe to the data acquisition terminal.

4. The intelligent nutrition management method according to claim 2, characterized in that, The step of sending the user's daily dietary data to the analysis platform via the data acquisition terminal includes: The data acquisition terminal provides a list of optional meals, wherein the list records the dietary information of each meal, including the meal name, food name, the amount of food corresponding to each food name, and the cooking method of the food; and after the daily recommended recipe is generated, the list of optional meals becomes the daily recommended recipe. Obtain the meals selected by the user from the list of available meals, and use all the meals selected by the user on that day as the daily meal data; The data acquisition terminal sends the acquired daily dietary data to the analysis platform.

5. The intelligent nutrition management method according to claim 2, characterized in that, The data acquisition terminal is equipped with an RFID reader; the step of sending the user's daily dietary data to the analysis platform via the data acquisition terminal includes: Each of the available meals for the day will be assigned an RFID tag; Record daily dietary intake information in RFID tags; Before the meal, the RFID tag corresponding to the user's selected meal is read through the data acquisition terminal; The data acquisition terminal sends the RFID tags it reads to the analysis platform.

6. The intelligent nutrition management method according to claim 1, characterized in that, The intelligent nutrition management method also includes: Based on the fluctuating health-related indicator data, the time period corresponding to the analysis data is divided into several nutrient intake time periods, and intake standard values ​​are set for the types of nutrients that need to be ingested in each nutrient intake time period.

7. The intelligent nutrition management method according to claim 6, characterized in that, The steps of the analysis platform obtaining user fluctuation health-related indicator data from the indicator terminal include: The analysis platform queries the user's daily health-related indicator data from the indicator terminal; Determine whether the user's daily health-related indicator data contains any indicators updated that day; Use the daily updated indicator data as health-related indicator data; For indicators that have not been updated on the current day, the latest historical indicator data will be used as the health-related indicator data; The step of dividing a preset period into several nutritional intake time periods based on the fluctuating health-related indicator data includes: Calculate the fluctuation range of key health indicator data in health-related indicator data; Based on the fluctuation range of key health indicator data, the preset period is divided into several nutritional intake time periods.

8. An intelligent management system, characterized in that, The intelligent management system, using the nutritional intelligent management method as described in any one of claims 1 to 7, includes an analysis platform terminal and an indicator terminal and a data acquisition terminal respectively connected to the analysis platform terminal via signals; the analysis platform terminal is equipped with a first analysis model and a second analysis model.

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