CN115985470A - Intelligent nutrition management method and intelligent management system - Google Patents
Intelligent nutrition management method and intelligent management system Download PDFInfo
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Abstract
The invention discloses a nutrition intelligent management method and an intelligent management system, wherein the method comprises the following steps: sending daily meal intake data of a user to an analysis platform end through a data acquisition terminal; the analysis platform end stores the received daily meal intake data of the user but does not process the data; in a preset period, the analysis platform end acquires the fluctuating health related index data of the user from the index terminal and judges whether the key health index data in the fluctuating health related index data meet a set standard or not; if the key health index data do not meet the set standard, the analysis platform end processes the stored daily meal intake data of the user which is not processed into analysis data; and the analysis platform end processes the key health index data and the analysis data which do not accord with the set standard into input data, inputs the input data into the first analysis model and outputs the daily recommended recipes. The technical scheme provided by the invention aims to guide different users to carry out reasonable diet management.
Description
Technical Field
The invention relates to the technical field of intelligent management systems, in particular to a nutrition intelligent management method and an intelligent management system.
Background
Food nutrition is an important material basis for human life maintenance, growth and development, body health and genetic inheritance. Reasonable nutrition is a prerequisite for guaranteeing the material needs of organisms, physiological activities, body health, immunity, and good birth and good care. The method has the advantages of healthy nutrition matters, improved quality and economic and social development, wherein the four healthy base stones are respectively as follows: reasonable diet, proper exercise, smoking cessation, drinking restriction and psychological balance. Therefore, the provision of nutrition is very beneficial to the health of the population.
Diet is a major source of nutrition. The foods ingested by the diet are various, the nutrient content of each food is different, the nutrition health scientific literacy and nutrition health education of people are far from popularizing, the diet design and the catering management are difficult to meet the nutritional requirements of human bodies, and the following current situations exist:
firstly, the catering supply side lacks reasonable nutrition and food safety awareness and cannot meet individual nutritional requirements;
secondly, the diners have lower nutritional and healthy literacy and can not select nutritional and healthy meals according to individual demands;
thirdly, modern nutrition science knowledge and health strategies are not well integrated into the links of catering supply chains;
fourth, the extension of food chains and the diversification and complexity of catering supply networks make it difficult to carry out real-time health supervision, etc.
Nutritional management is complex among different populations, differing primarily in age, developmental stage, occupation, and disease factors. In the prior art, the most effective method for determining nutritional needs is to consult a doctor or dietician, and furthermore, to refer to a nutritional guideline set for each group. However, this way of consulting is not convenient.
Modern Information Technology (Information Technology) is an active Technology for acquiring, processing, storing, propagating and using Information of acoustic, image, textual, digital and various sensor signals by means of a combination of micro-electronics-based computer Technology and telecommunication Technology.
At the heart of modern information technology is informatics. Modern information technologies include ERP, GPS, RFID, etc., and can be learned and learned from ERP knowledge and applications, GPS knowledge and applications, EDI knowledge and applications. Modern information technology is a very broad group of technologies, including microelectronics, optoelectronics, communications, networking, sensing, control, display, and so on.
Therefore, there is a need to provide a method for implementing intelligent nutrition management by using modern information technology to guide different users to perform reasonable meal management.
Disclosure of Invention
The invention mainly aims to provide a nutrition intelligent management method, aiming at guiding different users to carry out reasonable diet management.
In order to achieve the purpose, the intelligent nutrition management method provided by the invention is applied to an intelligent management system, wherein the intelligent management system comprises an analysis platform end, and an index terminal and a data acquisition terminal which are respectively in signal connection with the analysis platform end; a first analysis model is arranged at the end of the analysis platform; the intelligent nutrition management method comprises the following steps:
sending daily meal data of a user to the analysis platform end through the data acquisition terminal;
in a preset period, the analysis platform end stores the received daily meal data of the user but does not process the received daily meal data;
in the preset period, the analysis platform end acquires the fluctuating health related index data of the user from the index terminal and judges whether the key health index data in the fluctuating health related index data meet the set standard or not;
if the key health index data do not meet the set standard, the analysis platform end processes the stored daily diet data of the user which is not processed into analysis data;
the analysis platform end processes the key health index data which do not meet the set standard and the analysis data into input data, and inputs the input data into the first analysis model;
and outputting the daily recommended recipes through the first analysis model, and sending the daily recommended recipes to a data acquisition terminal.
Preferably, the analysis platform end is further provided with a second analysis model; the intelligent nutrition management method further comprises the following steps:
after the daily recommended recipes are generated, the daily dietary data of the user are sent to the analysis platform end through the data acquisition terminal;
the analysis platform end stores the daily diet data of the user after the daily recommended recipe is generated, and processes the daily diet data of the user after the daily recommended recipe is generated into analysis data;
the analysis platform end inputs the analysis data twice before and after the generation of the daily recommended recipes into the second analysis model;
comparing whether the ratio of the evaluation values of the analysis data of the first time and the second time reaches a preset value or not by the second analysis model;
and if the preset value is not reached, the analysis platform end sends an alarm prompt to the data acquisition terminal.
Preferably, after the step of comparing whether the ratio of the evaluation values of the two previous and subsequent analysis data of the second analysis model reaches the preset value, the method further includes:
if the preset value is reached, the analysis platform end inputs the analysis data generated by the daily recommended recipes into the first analysis model;
after the daily recommended recipes are generated, the analysis platform end obtains the fluctuating health related index data of the user from the index terminal and judges whether the key health index data in the fluctuating health related index data meet the set standard or not;
after the daily recommended recipes are generated, if the key health index data do not meet the set standard, the analysis platform end processes the stored daily dietary data of the user without processing into analysis data;
the analysis platform end processes key health index data which do not accord with set standards and are generated by daily recommended recipes and the analysis data into input data, and inputs the input data into the first analysis model;
and outputting the updated daily recommended recipes through the first analysis model, and sending the updated daily recommended recipes to a data acquisition terminal.
Preferably, the step of sending the daily meal data of the user to the analysis platform end through the data acquisition terminal includes:
providing a selection diet list at the data acquisition terminal, wherein the selection diet list records the recipe information of each diet, and the recipe information comprises diet names, food amount corresponding to each food name and food cooking modes; after the daily recommended recipes are generated, the optional diet list is the daily recommended recipes;
obtaining the meals selected by the user from the meal list for selection, and taking all the meals selected by the user on the current day as meal data on the current day;
and the data acquisition terminal sends the acquired daily meal data to the analysis platform terminal.
Preferably, the data acquisition terminal is provided with an RFID reader; the step of sending the daily meal data of the user to the analysis platform end through the data acquisition terminal comprises the following steps:
assigning an RFID tag to each meal of the current day selectable meal;
recording meal information of each meal in the RFID tag;
reading an RFID tag corresponding to a meal selected by a user through a data acquisition terminal before the meal;
and the data acquisition terminal sends the read RFID tag to the analysis platform end.
Preferably, the analysis platform end processes the daily meal data of the user into analysis data, and the analysis is carried out by referring to the following modes:
acquiring a nutrition database, wherein nutrition data corresponding to food names are stored in the nutrition database, and the nutrition data comprise nutrition types and nutrition proportions contained in the food names;
the analysis platform end processes the meal data of each day of the user into a data unit by inquiring the nutrition database;
and the analysis platform end processes each continuous data unit into analysis data.
Preferably, the intelligent nutrition management method further comprises:
dividing a time period corresponding to the analysis data into a plurality of nutrition intake time periods according to the fluctuating health-related index data, and respectively setting an intake standard value for the nutrition types required to be taken in each nutrition intake time period;
the step of comparing whether the ratio of the evaluation values of the two analysis data before and after the comparison of the second analysis model reaches a preset value or not comprises the following steps:
the second analysis model queries an intake standard value of each nutrition type in each data unit in each analysis data respectively, and calculates to obtain an evaluation value of each data unit;
determining an evaluation value of each analysis data according to the evaluation values of all data units in the analysis data;
and comparing the evaluation values corresponding to the analysis data of the previous time and the analysis data of the next time respectively to determine whether the ratio of the evaluation values of the analysis data of the previous time and the analysis data of the next time reaches a preset value.
Preferably, the step of acquiring, by the analysis platform side, the fluctuating health-related index data of the user from the index terminal includes:
the analysis platform end inquires health related index data of the user every day from the index terminal;
judging whether the current day updating index items exist in the health related index data of the user every day;
updating the health related index data on the same day as the health related index data;
adopting historical latest health related index data as health related index data for the health related index items which are not updated on the current day;
the step of dividing a preset period into a number of nutrient intake time periods according to the fluctuating health-related index data comprises:
calculating the fluctuation range of key health index data in the health related index data;
and dividing the preset period into a plurality of nutrition intake time periods according to the fluctuation range of the key health index data.
Preferably:
the analysis platform end processes the meal data of each day of the user into a data unit by inquiring the nutrition database, and the method specifically comprises the following steps:
A i ＝(a i1 ,a i2 ,…a in )；
A i indicating the ith data unit in a preset period; a is ij Representing the intake of the jth nutrition type on the ith day of the user in a preset period; i is more than or equal to 1 and less than or equal to m, and j is more than or equal to 1 and less than or equal to n; m represents the total days corresponding to the preset period; n represents the total number of nutritional types;
the analysis platform end processes each continuous data unit into analysis data, and specifically comprises the following steps:
wherein A is analysis data;
respectively setting an intake standard value for the type of the nutrition required to be taken in each nutrition intake time period in the preset time period, specifically:
B i ＝(b i1 ,b i2 ,…b in )；
B i a set of intake criteria representing the ith data unit in a preset period; b ij The intake standard value of the jth nutrition type which is taken by the user on the ith day in a preset period;
the second analysis model calculates an evaluation value of each data unit, specifically:
the determining the evaluation value of the analysis data according to the evaluation values of all the data units in each analysis data specifically includes:
comparing the evaluation values corresponding to the analysis data of the previous time and the next time respectively to determine whether the ratio of the evaluation values of the analysis data of the previous time and the next time reaches a preset value, specifically:
and obtaining the ratio of the evaluation value of the previous analysis data to the evaluation value of the next analysis data, and when the ratio reaches a preset value, determining that the ratio of the evaluation values of the previous analysis data and the next analysis data reaches the preset value.
In addition, in order to achieve the above object, the present invention further provides an intelligent management system, which applies the nutrition intelligent management method according to any one of the above mentioned items, wherein the intelligent management system comprises an analysis platform end, an index terminal and a data acquisition terminal, wherein the index terminal and the data acquisition terminal are respectively connected with the analysis platform end through signals; and a first analysis model is arranged at the end of the analysis platform.
According to the technical scheme, daily dietary data of a user are sent to an analysis platform end through a data acquisition terminal, and the analysis platform end stores the daily dietary data but does not process the daily dietary data; meanwhile, the analysis platform end continuously acquires the fluctuating health related index data of the user through the index terminal, and when the key health related index data in the fluctuating health related index data meet the set standard, the analysis platform end processes the stored and unprocessed daily diet data of the user into analysis data; processing key health index data which do not meet set standards and the analysis data into input data by an analysis platform end, and inputting the input data into the first analysis model; the first analysis model is used for outputting daily recommended recipes to the user according to the dietary habits and key indexes of the user. The daily recommended recipes output by the first analysis model are sent to the data acquisition terminal, so that the user can conveniently select meals according to the daily recommended recipes, and intelligent nutrition management is achieved according to the meal habits and key index fluctuations of the user.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the structures shown in the drawings without creative efforts.
Fig. 1 is a flowchart of an embodiment of the intelligent nutrition management method of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that all directional indicators (such as up, down, left, right, front, back ...) in the embodiments of the present invention are only used to explain the relative positional relationship between the components, the motion situation, etc. in a specific posture (as shown in the attached drawings), and if the specific posture is changed, the directional indicator is changed accordingly.
In addition, the descriptions related to "first", "second", etc. in the present invention are only for descriptive purposes and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the feature. In the description of the present invention, "a plurality" means at least two, e.g., two, three, etc., unless specifically limited otherwise.
In the present invention, unless otherwise expressly stated or limited, the terms "connected," "secured," and the like are to be construed broadly, and for example, "secured" may be a fixed connection, a removable connection, or an integral part; can be mechanically or electrically connected; they may be directly connected or indirectly connected through intervening media, or they may be connected internally or in any other suitable relationship, unless expressly stated otherwise. The specific meanings of the above terms in the present invention can be understood according to specific situations by those of ordinary skill in the art.
In addition, the technical solutions in the embodiments of the present invention may be combined with each other, but it must be based on the realization of the technical solutions by those skilled in the art, and when the technical solutions are contradictory to each other or cannot be realized, such a combination of the technical solutions should not be considered to exist, and is not within the protection scope of the present invention.
Referring to fig. 1, in a first embodiment of the present invention, the nutrition intelligent management method is applied to an intelligent management system, where the intelligent management system includes an analysis platform terminal, and an index terminal and a data acquisition terminal respectively connected to the analysis platform terminal through signals; a first analysis model is arranged at the end of the analysis platform; the intelligent nutrition management method comprises the following steps:
step S10, sending daily meal data of a user to the analysis platform end through the data acquisition terminal;
step S20, in a preset period, the analysis platform end stores the received daily meal data of the user but does not process the meal data;
step S30, in the preset period, the analysis platform end acquires the fluctuating health related index data of the user from the index terminal and judges whether the key health index data in the fluctuating health related index data meets the set standard;
step S40, if the key health index data do not meet the set standard, the analysis platform end processes the stored daily diet data of the unprocessed user into analysis data;
step S50, the analysis platform end processes the key health index data which do not accord with the set standard and the analysis data into input data, and inputs the input data into the first analysis model;
and S60, outputting a daily recommended recipe through the first analysis model, and sending the daily recommended recipe to a data acquisition terminal.
According to the technical scheme, daily dietary data of a user are sent to an analysis platform end through a data acquisition terminal, and the analysis platform end stores the daily dietary data but does not process the daily dietary data; meanwhile, the analysis platform end continuously acquires the fluctuating health related index data of the user through the index terminal, and when the key health related index data in the fluctuating health related index data meet the set standard, the analysis platform end processes the stored daily diet data of the user which is not processed into analysis data; processing key health index data which do not meet set standards and the analysis data into input data by an analysis platform end, and inputting the input data into the first analysis model; the first analysis model is used for outputting daily recommended recipes to the user according to the dietary habits and key indexes of the user. The daily recommended recipes output by the first analysis model are sent to the data acquisition terminal, so that the user can conveniently select meals according to the daily recommended recipes, and intelligent nutrition management is achieved according to the meal habits and key index fluctuations of the user.
The daily diet data of the user are sent to the analysis platform end through the data acquisition terminal, the first analysis model is facilitated to analyze the diet preference and the rule of the user, the fluctuation condition of key health index data is facilitated to adjust the daily recommended recipes according to the health conditions of different crowds, and therefore the different users are guided to carry out reasonable diet management.
The index terminal can be connected to the analysis platform end according to needs, for example, the index terminal can be an intelligent wearable device (for example, a sports watch or a wearable device with a blood pressure and blood sugar testing function) for detecting physiological indexes of a user, and can also be a hospital terminal. Meanwhile, the index terminal connected with the analysis platform end through signals can also comprise intelligent wearable equipment and a hospital terminal.
The health-related index data acquired from the index terminal is generally in a fluctuating state and does not remain unchanged. When the index terminal has a function of acquiring various data, the analysis platform end does not pay attention to all data, but only to data that can be improved by meals, or data that can be used to measure the health of meals.
Meanwhile, the data acquisition terminal can also set diet taboos of the user, or intelligently analyze the diet taboos of the user through the health related index data acquired by the analysis platform terminal and the daily diet data of the user.
The data acquisition terminal can be a smart device, such as a mobile phone, a smart wearable device or other smart handheld devices.
In the present invention, the health-related index data includes: at least one of body weight, body fat rate, blood pressure, blood sugar, blood fat, uric acid, and waist circumference, and other indicators for measuring health condition. The health related index data can be determined according to user setting or default setting, and the type of the health related index data acquired by the analysis platform end accords with a set type.
The key health indicator data refers to data that is currently significantly abnormal for the user. Such as blood pressure, blood sugar, blood lipids, uric acid. Wherein, the determination of the key health index data can be performed by referring to the following modes:
the analysis platform end continuously acquires each health related index data from each connected index terminal, judges whether each health related index data is normal or not, and acquires health related index data which is continuously abnormal and can be controlled through meals from the fluctuation process of each health related index data as key health index data.
If the user does not have significantly abnormal data, the key health indicator data may be set as data of interest to the user, such as weight or body fat rate.
The preset period refers to a period of time set by a user or a system, and the length of the preset period is not less than one week. The preset time is beneficial to determining the length of the meal data acquired by the analysis platform end, so that the meal preference and the rule of the user in a period of continuous time can be acquired at the analysis platform end, the error of the accidental event is ignored as much as possible, and the first analysis model is beneficial to making more reasonable meal recommendation.
And in a preset period, the analysis platform end stores the received daily meal data of the user without processing, acquires the fluctuating health related index data of the user from the index terminal and judges whether the key health index data in the fluctuating health related index data meet a set standard. Wherein, the meeting of the setting standard means that the setting change rate is not reached or the setting warning value is not reached. When the key health index data do not meet the set standard, the key health index data are considered to be in the special abnormal condition, and the diet recommendation needs to be given to the user in advance before the end point of the preset period is reached, so that the special abnormal condition can be improved in advance. For example, when the blood sugar of the user fluctuates too much or the blood sugar significantly exceeds the alarm value, the user may be in a special abnormal condition.
If the analysis platform end considers that the key health index data of the user meet the set standard in the preset period, the analysis platform end can process the fluctuation of the key health index data in the preset period and the analysis data into input data and input the input data into the first analysis model when the end point of the preset period is reached.
The key health index data which do not meet the set standard are also used as a part of input data, and scientific guidance on the nutrient intake of different crowds is facilitated through the key health index data. For example, the total intake of fat in hyperlipidemic people is lower than in normal people, and thus a diet with a relatively lower amount of fat in the recommended daily diet is required.
Based on the first embodiment of the present invention, in the second embodiment of the present invention, the analysis platform end is further provided with a second analysis model; the intelligent nutrition management method further comprises the following steps:
step S70, after the daily recommended recipes are generated, daily meal data of the user are sent to the analysis platform end through the data acquisition terminal;
step S80, storing the daily diet data of the user after the daily recommended recipes are generated by the analysis platform end, and processing the daily diet data of the user after the daily recommended recipes are generated into analysis data;
step S90, the analysis platform end inputs the analysis data twice before and after the generation of the daily recommended recipes into the second analysis model;
s100, comparing whether the ratio of evaluation values of the analysis data of the previous time and the next time reaches a preset value or not by a second analysis model;
and step S110, if the preset value is not reached, the analysis platform end sends an alarm prompt to the data acquisition terminal.
In this embodiment, the length of the recipe recommendation period may be equal to the length of the period of the user's daily meal data corresponding to the first analysis data.
And the second analysis model is used for analyzing whether the original nutrition intake defect is corrected by the daily recommended recipe after the daily recommended recipe is generated by the user. I.e. the second analysis model is used to verify whether the user has used the daily recommended recipes.
Specifically, after the recommended recipes are generated every day, when the analysis platform end detects that the time reaches the end of the recipe recommendation period, the analysis platform end processes the daily diet data of the user into analysis data, and whether the ratio of the evaluation values of the two analysis data before and after comparison through the second model reaches a preset value or not is judged.
In step S110, the evaluation value is used to evaluate the degree of deviation of daily meal data of the user from a healthy meal; the ratio of the two evaluation values before and after the generation of the daily recommended recipe is used for judging the degree of adopting the daily recommended recipe by the user. When the ratio of the evaluated values does not reach a set value, the utilization rate of the user to the daily recommended recipes is low, and the effect of correcting the diet cannot be achieved by the daily recommended recipes. Therefore, the analysis platform end sends an alarm to the data acquisition terminal to prompt the user that health risks exist when the recipe diet is not recommended every day. Further, if the preset value is not reached, the analysis platform end can also send an alarm prompt to a related terminal of the data acquisition terminal. The associated terminal may be a hospital terminal or a relative terminal.
Based on the second embodiment of the present invention, in a third embodiment of the present invention, after the step S100, the method further includes:
step S120, if a preset value is reached, the analysis platform end inputs the analysis data generated by the daily recommended recipes into the first analysis model;
step S130, after the daily recommended recipes are generated, the analysis platform end obtains the fluctuating health related index data of the user from the index terminal and judges whether the key health index data in the fluctuating health related index data meet the set standard;
step S140, after the daily recommended recipes are generated, if the key health index data do not meet the set standard, the analysis platform end processes the stored daily meal data of the unprocessed user into analysis data;
step S150, the analysis platform end processes the key health index data which do not accord with the set standard and the analysis data after the daily recommended recipe is generated into input data, and inputs the input data into the first analysis model;
and step S160, outputting the updated daily recommended recipes through the first analysis model, and sending the updated daily recommended recipes to the data acquisition terminal.
In step S120, if the ratio of the evaluation values of the analysis data obtained before and after the comparison by the second analysis model reaches a preset value, which indicates that the degree of the user using the daily recommended recipes is high, the daily dietary data and the fluctuation record of the key health index data can be continuously provided for each round of the user, and the data is input into the first analysis model to continuously obtain the daily recommended recipes at different stages.
The daily recommended recipe has a recipe recommendation period. The recommended diet time period does not exceed the preset time period at the longest, the reason is that the recommended diet every day in the invention is formulated according to the fluctuation of health related index data of the user for a period of time, and the health related index data of the user are different due to the conditioning of medicines or meals at intervals. According to the fluctuation of each period of time, different daily recommended recipes are formulated, and after the fluctuation of the meal data and the health related index data is obtained again in the next period of time, the daily recommended recipes are updated, so that the recommended recipes are dynamically adjusted, and more scientific meal management and control can be realized.
Based on the second to third embodiments of the present invention, in a fourth embodiment of the present invention, the step S10 includes:
step S11, providing a selection diet list at the data acquisition terminal, wherein the selection diet list records diet information ingested every day, and the diet information comprises diet names, food amounts corresponding to the food names, food cooking modes and food meals (morning, noon and evening); after the daily recommended recipes are generated, selecting a diet list as the daily recommended recipes; meal names, i.e., dish names, such as potato meat; the food name is one dish and includes various kinds of foods, for example, the food of the potato-roasted meat includes potato, pork, and the like.
Step S12, obtaining the meals selected by the user from the meal list for selection, and taking all the meals selected by the user on the current day as meal data on the current day;
and S13, the data acquisition terminal sends the acquired daily meal data to the analysis platform terminal.
The fourth embodiment and the fifth embodiment are used for providing different technical solutions for recording daily meal data of a user.
In a fourth embodiment, if the user uses the intelligent management system for the first time, the data acquisition terminal can acquire the health related index data input by the user, so as to provide the selected meal list on a targeted basis every day. And displays the meal list on the data acquisition terminal.
The user may select a meal name from the list of alternative meals to enable rapid entry of daily meal data.
Certainly, the user may also select other meals from the meal list to be selected, and at this time, the meal name selected by the user is entered into the data acquisition terminal, so that the daily meal data of the user can be formed. The recording mode can be as follows: and filling in through an input interface of the data acquisition terminal, or photographing and identifying through a camera of the data acquisition terminal.
And after the user uses the intelligent management system for a period of time, if the daily recommended recipes are generated, selecting the diet list as the daily recommended recipes.
Based on the second to third embodiments of the present invention, in a fifth embodiment of the present invention, the data acquisition terminal is provided with an RFID reader; the step S10 includes:
step S14, each meal of the optional meals on the current day is allocated with an RFID label;
step S15, recording the meal information of each meal in the RFID tag;
step S16, reading an RFID label corresponding to the meal selected by the user through a data acquisition terminal before the meal;
and S17, the data acquisition terminal sends the read RFID tag to the analysis platform end.
The fifth embodiment and the fourth embodiment may be used in combination, or only one of them may be used. The application scenario of the fifth embodiment is suitable for a meal of a lobby, such as a dining hall, or a restaurant. The same meal is marked in the dining room or the restaurant through the RFID tag, and the meal selected by the user can be quickly acquired by the receipt acquisition terminal close to the RFID tag, so that the daily meal data of the user can be quickly input.
In a sixth embodiment of the present invention, based on the fourth or fifth embodiment of the present invention, the analysis platform end processes the daily meal data of the user into analysis data, and refers to the following manner:
step S41, acquiring a nutrition database, wherein the nutrition database stores nutrition data corresponding to food names, and the nutrition data comprises nutrition types and nutrition proportions contained in the food names;
step S42, the analysis platform end processes the diet data of each day of the user into a data unit by inquiring a nutrition database;
and S43, processing each continuous data unit into analysis data by the analysis platform end.
In the present embodiment, the processing of meal data for each day into one data unit is to unify the metrics of meal data for each day.
For example, a meal ingested by a user on a certain day includes: the breakfast comprises 1 egg and 200ml milk, the Chinese meal comprises 200g of rice, 200g of green vegetables and 1 part of fried meat of potato slices, and the supper comprises 200g of rice, 200g of towel gourd and 1 part of fried meat of carrot.
Processing the meal data for the day into data units comprises the steps of:
first, by querying the nutrition database, the daily meal data is converted to total intake for each nutrition type on the day, for example: energy C kilojoule; protein D g; total fat E g; cholesterol E mg; the total amount of carbohydrates is F grams; salt G mg.
Secondly, acquiring set ordering of various nutrition types, and arranging the total daily intake of each nutrition type converted according to the set ordering to form a row matrix, wherein when the daily total intake of a certain nutrition type is 0, 0 is filled in the corresponding position in the row matrix;
and finally, according to the date sequence, arranging the row matrixes obtained by each data unit longitudinally, and carrying out row matrix combination treatment to obtain an analysis data matrix consisting of continuous data units.
In a seventh embodiment of the present invention based on the first to sixth embodiments, the intelligent nutrition management method further includes:
step S170, dividing the time period corresponding to the analysis data into a plurality of nutrition intake time periods according to the fluctuating health-related index data, and respectively setting an intake standard value for the nutrition type required to be taken in each nutrition intake time period;
the step S100 includes:
s101, respectively inquiring an intake standard value of each nutrition type in each data unit in each analysis data by the second analysis model, and calculating to obtain an evaluation value of each data unit;
step S102, determining the evaluation value of the analysis data according to the evaluation values of all data units in each analysis data;
step S103, comparing the evaluation values respectively corresponding to the analysis data of the previous and the next times to determine whether the ratio of the evaluation values of the analysis data of the previous and the next times reaches a preset value.
In step S170, the reason why the period corresponding to the analysis data is divided into a plurality of nutrition intake time periods according to the fluctuating health-related index data is that the recommended intake amounts of the corresponding nutrition types are different in the fluctuation process of the health-related index data. For example, when the blood lipid is normal for a certain period of time in the period corresponding to the analysis data, the intake of the total fat amount per day can be referred to the normal standard intake. When the blood lipid rises to exceed the normal interval in the middle of the period, the intake of the total fat amount per day needs to be reduced. Therefore, according to the fluctuating health-related index data in the analysis period, the period corresponding to the analysis data may be divided into a plurality of nutrient intake periods, and intake standard values are set for the types of nutrients to be taken in each of the nutrient intake periods, respectively. Thereby contributing to obtaining an estimated value of the diet of the day by the deviation between the actual intake amount and the intake standard value for each nutrition type per day; by analyzing the evaluation value of the daily meal of the period corresponding to the data, the overall evaluation value of the meal of the period corresponding to the data can be measured.
Based on the first to seventh embodiments of the present invention, in an eighth embodiment of the present invention, the step of the analysis platform side obtaining the health related index data of the user fluctuation from the index terminal includes:
step S31, the analysis platform end inquires health related index data of the user every day from the index terminal;
step S32, judging whether the current day updating index item exists in the health related index data of each day of the user;
step S33, updating the health related index data on the current day as the health related index data;
step S34, adopting the historical latest health related index data as the health related index data of the index items which are not updated on the current day; for example, in a period from 3/1/2022 to 3/15/2022, if the blood lipid data updated on the current day is found in 3/7/2022 and 3/12/2022, respectively, the previous blood lipid data is adopted from 3/1/2022 to 3/6/2022; and the blood lipid data of 3-7 months in 2022 to 11 months in 2022 are adopted; the blood lipid data of 3, 12 and 2022 years from 3, 12 and 2022, 15 and 3, 12 and 2022 years are adopted.
Step S35, the step of dividing a preset period into a plurality of nutrient intake time periods according to the fluctuating health-related index data comprises:
step S36, calculating the fluctuation range of key health index data in the health related index data;
and step S37, dividing a preset period into a plurality of nutrition intake time periods according to the fluctuation range of the key health index data.
Specifically, the health-related index data detected each time fluctuate, and whether the fluctuation presents a remarkable fluctuation condition is measured by presetting a fluctuation amplitude, so that different nutrition intake time periods do not need to be divided when the remarkable fluctuation condition is achieved, and different nutrition intake time periods do not need to be divided when the fluctuation condition is slight.
Based on the eighth embodiment of the present invention, in the ninth embodiment of the present invention:
the analysis platform end processes the meal data of each day of the user into a data unit by inquiring the nutrition database, and the method specifically comprises the following steps:
A i ＝(a i1 ,a i2 ,…a in )；
A i indicating the ith data unit in a preset period; a is ij Representing the intake of the jth nutrition type on the ith day of the user in a preset period; i is more than or equal to 1 and less than or equal tom,1 is more than or equal to j and less than or equal to n; m represents the total days corresponding to the preset period; n represents the total number of nutritional types;
the analysis platform end processes each continuous data unit into analysis data, and specifically comprises the following steps:
wherein A is analysis data;
respectively setting an intake standard value for the type of the nutrition required to be taken in each nutrition intake time period in the preset time period, specifically:
B i ＝(b i1 ,b i2 ,…b in )；
B i a set of intake criteria representing the ith data unit in a preset period; b ij The intake standard value of the jth nutrition type which is taken by the user on the ith day in a preset period; i is more than or equal to 1 and less than or equal to m, and j is more than or equal to 1 and less than or equal to n; m represents the total days corresponding to the preset period; n represents the total number of nutritional types;
the second analysis model calculates an evaluation value of each data unit, specifically:
the determining the evaluation value of the analysis data according to the evaluation values of all the data units in each analysis data specifically includes:
comparing the evaluation values corresponding to the analysis data of the previous time and the next time respectively to determine whether the ratio of the evaluation values of the analysis data of the previous time and the next time reaches a preset value, specifically:
and obtaining the ratio of the evaluation value of the previous analysis data to the evaluation value of the next analysis data, and when the ratio reaches a preset value, determining that the ratio of the evaluation values of the previous analysis data and the next analysis data reaches the preset value.
Specifically, the evaluation value of each analysis data indicates the magnitude of the deviation of an actual intake nutrition from the standard intake nutrition;
in the comparison process of the evaluation values of the analysis data of the previous time and the analysis data of the next time, if the previous evaluation value is large (deviation degree is large) and the next evaluation value is small (deviation degree is small), the ratio of the two is large, and the ratio of the two can reach a preset value (indicating that the meal is corrected in the next time), so that early warning is not needed.
If the preset value is reached, the first recommended recipe is effective, and the recommended recipe is output urgently according to the following meal condition and the fluctuation of the health related index data.
On the contrary, if the previous evaluation value is large (deviation degree is large) and the next evaluation value is large (deviation degree is large), the ratio is small, and the ratio of the two values fails to reach the preset value (indicating that no correction of diet is obtained at the next time), so that early warning is needed.
In addition, in order to achieve the above object, the present invention further provides an intelligent management system, which applies any one of the above nutrition intelligent management methods, wherein the intelligent management system comprises an analysis platform end, and an index terminal and a data acquisition terminal which are respectively connected with the analysis platform end through signals; and a first analysis model is arranged at the end of the analysis platform.
As a further development of the present invention, the following scheme may be further included:
(1) and sending the daily recipe (namely the expected diet recipe) declared by the user, the basic health condition and the body evaluation data (height, weight and the like) of the user to a first analysis platform at the analysis platform end (the first analysis platform belongs to a judgment and analysis platform of the body condition of the user). The first analysis platform recommends the dietary energy reference intake of the user and the like according to the health condition and physical evaluation data of the user.
(2) And calculating the energy and nutrient intake quantity provided by the recipe according to the food types and the quantities of the foods contained in the expected diet recipe declared by the user, and transmitting the energy and nutrient intake quantity provided by the recipe to a second analysis platform at the end of the analysis platform. And comparing the nutrient intake with the recommended nutrient intake adapted to the physical conditions of the user by the second analysis platform, calculating an energy and nutrient supply ratio, and meanwhile, calculating indexes such as an energy source ratio, a protein source ratio, a diet system and the like, and analyzing whether the indexes meet the preset ratio or not.
(3) If the ratio accords with the preset ratio, forming a final recommended meal, and conveying the final recommended meal to the user side for the user to prepare a meal for catering daily;
and if the food quantity and the proportion of the food in the expected diet recipe of the user are not in accordance with the preset ratio, adjusting the quantity and the proportion of the food in the expected diet recipe of the user according to the characteristics presented by the data, and entering the calculation program in the item (2) of the second analysis platform, wherein the recipe is adjusted until the reference intake standard of the nutrients of the daily diet of the user can be met, and the recipe is the daily personalized recommended recipe provided for the user.
Thus, according to the above extension, the intelligent management system can provide:
calculating in various modes such as individual recommended diet design, family recommended diet design, body canteen recommended diet design, and ordering person recommended diet design.
Wherein: the expected diet recipe refers to a diet which is ordered and expected by a user, personal preferences such as taste are often emphasized, reasonable nutrition judgment is not carried out, and quantitative catering is not carried out;
a first analysis platform: the health condition and body evaluation data of the user are basic judgments for recommending reasonable meals for the user, namely, the user needs to be provided with proper energy and nutrients;
a second analysis platform: and carrying out quantitative catering calculation according to the expected diet recipe, forming a designed food by adjusting the set number of the food types in the expected diet recipe and properly adjusting the food types and the diet number according to the health condition, calculating and judging whether the energy and the nutrients provided by the designed recipe reach the preset value of the individual reasonable nutrition, and finally forming a daily personalized recommended recipe.
And (4) inputting the recommended recipes into a user side, and checking and using the recipes at any time by the user.
Desired dietary recipes: is a dietary recipe for ordering dishes by a user.
Diet design recipe: is a preliminary design recipe generated during the software analysis.
Recommending a diet recipe: the recipe is a scientific recipe which is designed aiming at the user in a personalized way and can meet the sustainable and reasonable nutritional requirement. An information means and a personalized health recipe of nutrition scientific calculation are introduced. Therefore, the three concepts in the invention have different sequences and rationalization degrees.
The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications and equivalents of the present invention, which are made by the contents of the present specification and the accompanying drawings, or directly/indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (10)
1. An intelligent nutrition management method is characterized by being applied to an intelligent management system, wherein the intelligent management system comprises an analysis platform end, and an index terminal and a data acquisition terminal which are respectively in signal connection with the analysis platform end; a first analysis model is arranged at the end of the analysis platform; the intelligent nutrition management method comprises the following steps:
sending daily meal intake data of the user to the analysis platform end through the data acquisition terminal;
in a preset period, the analysis platform end stores the received daily meal intake data of the user but does not process the data;
in the preset period, the analysis platform end acquires the fluctuating health related index data of the user from the index terminal and judges whether the key health index data in the fluctuating health related index data meet the set standard or not;
if the key health index data do not meet the set standard, the analysis platform end processes the stored daily meal intake data of the user without processing into analysis data;
the analysis platform end processes the key health index data which do not meet the set standard and the analysis data into input data, and inputs the input data into the first analysis model;
and outputting a daily recommended recipe through the first analysis model, and sending the daily recommended recipe to a data acquisition terminal.
2. The intelligent nutrition management method according to claim 1, wherein the analysis platform end is further provided with a second analysis model; the intelligent nutrition management method further comprises the following steps:
after the daily recommended recipes are generated, the daily meal intake data of the user are sent to the analysis platform end through the data acquisition terminal;
the analysis platform end stores the daily meal data of the user after the daily recommended recipe is generated, and processes the daily meal data of the user after the daily recommended recipe is generated into analysis data;
the analysis platform end inputs the analysis data twice before and after the generation of the daily recommended recipes into the second analysis model;
comparing whether the ratio of the evaluation values of the analysis data of the first time and the evaluation value of the evaluation data of the second time reaches a preset value or not by the second analysis model;
and if the preset value is not reached, the analysis platform end sends an alarm prompt to the data acquisition terminal.
3. The intelligent nutrition management method according to claim 2, wherein after the step of comparing whether the ratio of the evaluation values of the two analysis data before and after the second analysis model reaches the preset value, the method further comprises:
if the preset value is reached, the analysis platform end inputs the analysis data generated by the daily recommended recipes into the first analysis model;
after the daily recommended recipes are generated, the analysis platform end acquires the fluctuating health related index data of the user from the index terminal and judges whether the key health index data in the fluctuating health related index data meet the set standard or not;
after the daily recommended recipes are generated, if the key health index data do not meet the set standard, the analysis platform end processes the stored daily dietary data of the user without processing into analysis data;
the analysis platform end processes key health index data which do not accord with set standards and are generated by the daily recommended recipes and the analysis data into input data, and inputs the input data into the first analysis model;
and outputting the updated daily recommended recipes through the first analysis model, and sending the updated daily recommended recipes to a data acquisition terminal.
4. The intelligent management method for nutrition according to claim 2, wherein the step of sending the daily meal data of the user to the analysis platform end through the data acquisition terminal comprises:
providing an optional meal list at the data acquisition terminal, wherein meal information of each meal is recorded in the optional meal list, and the meal information comprises meal names, food amount corresponding to each food name and food cooking modes; after the daily recommended recipes are generated, the optional diet list is the daily recommended recipes;
obtaining the meals selected by the user from the meal list for selection, and taking all the meals selected by the user on the current day as meal data on the current day;
and the data acquisition terminal sends the acquired daily meal data to the analysis platform terminal.
5. The intelligent nutrition management method according to claim 2, wherein the data acquisition terminal is provided with an RFID reader; the step of sending the daily meal data of the user to the analysis platform end through the data acquisition terminal comprises the following steps:
assigning an RFID tag to each meal of the current day selectable meal;
recording meal intake information for each day in the RFID tag;
reading an RFID tag corresponding to a meal selected by a user through a data acquisition terminal before the meal;
and the data acquisition terminal sends the read RFID tag to the analysis platform end.
6. A nutrition intelligent management method according to claim 4 or 5, wherein the analysis platform end processes daily meal data of a user into analysis data by referring to the following way:
acquiring a nutrition database, wherein nutrition data corresponding to food names are stored in the nutrition database, and the nutrition data comprise nutrition types and nutrition proportions contained in the food names;
the analysis platform end processes the meal data of each day of the user into a data unit by inquiring the nutrition database;
and the analysis platform end processes each continuous data unit into analysis data.
7. The intelligent nutrition management method according to claim 6, further comprising:
dividing the time period corresponding to the analysis data into a plurality of nutrition intake time periods according to the fluctuating health related index data, and respectively setting an intake standard value for the nutrition type required to be taken in each nutrition intake time period;
the step of comparing whether the ratio of the evaluation values of the two analysis data before and after the comparison of the second analysis model reaches a preset value or not comprises the following steps:
the second analysis model queries an intake standard value of each nutrition type in each data unit in each analysis data respectively, and calculates to obtain an evaluation value of each data unit;
determining an evaluation value of each analysis data according to the evaluation values of all data units in the analysis data;
and comparing the evaluation values corresponding to the analysis data of the previous time and the analysis data of the next time respectively to determine whether the ratio of the evaluation values of the analysis data of the previous time and the analysis data of the next time reaches a preset value.
8. The intelligent nutrition management method according to claim 7, wherein the step of obtaining the fluctuating health related index data of the user from the index terminal by the analysis platform terminal comprises:
the analysis platform end inquires health related index data of the user every day from the index terminal;
judging whether the current day updating index items exist in the health related index data of the user every day;
taking the current day updating index data as health related index data;
adopting the latest historical index data as the health related index data for the index items which are not updated in the current day;
the step of dividing a preset period into a number of nutrient intake time periods according to the fluctuating health-related index data comprises:
calculating the fluctuation range of key health index data in the health related index data;
and dividing the preset period into a plurality of nutrition intake time periods according to the fluctuation range of the key health index data.
9. The intelligent management method of nutrition according to claim 7, wherein:
the analysis platform end processes the meal data of each day of the user into a data unit by inquiring the nutrition database, and the method specifically comprises the following steps:
A i ＝(a i1 ,a i2 ,…a in )；
A i indicating the ith data unit in a preset period; a is ij Representing the intake of the jth nutrition type on the ith day of the user in a preset period; i is more than or equal to 1 and less than or equal to m, and j is more than or equal to 1 and less than or equal to n; m represents the total days corresponding to the preset period; n represents the total number of nutritional types;
the analysis platform end processes each continuous data unit into analysis data, and specifically comprises the following steps:
wherein A is analysis data;
respectively setting an intake standard value for the type of the nutrition required to be taken in each nutrition intake time period in the preset time period, specifically:
B i ＝(b i1 ,b i2 ,…b in )；
B i a set of intake criteria representing the ith data unit in a preset period; b ij The intake standard value of the jth nutrition type which is taken by the user on the ith day in the preset period is represented;
the second analysis model calculates an evaluation value of each data unit, specifically:
the determining the evaluation value of the analysis data according to the evaluation values of all the data units in each analysis data specifically includes:
comparing the evaluation values corresponding to the analysis data of the previous time and the next time respectively to determine whether the ratio of the evaluation values of the analysis data of the previous time and the next time reaches a preset value, specifically:
and obtaining the ratio of the evaluation value of the previous analysis data to the evaluation value of the next analysis data, and when the ratio reaches a preset value, determining that the ratio of the evaluation values of the previous analysis data and the next analysis data reaches the preset value.
10. An intelligent management system, which is characterized in that the intelligent nutrition management method according to any one of claims 1 to 9 is applied, and the intelligent management system comprises an analysis platform end, and an index terminal and a data acquisition terminal which are respectively connected with the analysis platform end through signals; and a first analysis model is arranged at the end of the analysis platform.
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| CN116884573A (en) * | 2023-09-08 | 2023-10-13 | 北京逯博士行为医学科技研究院有限公司 | Dietary nutrition configuration method based on optimal carbon technology |
| CN118051825A (en) * | 2024-01-10 | 2024-05-17 | 常州久沃网络科技有限公司 | Supervision data processing management method based on big data analysis |
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| CN116884573A (en) * | 2023-09-08 | 2023-10-13 | 北京逯博士行为医学科技研究院有限公司 | Dietary nutrition configuration method based on optimal carbon technology |
| CN116884573B (en) * | 2023-09-08 | 2023-12-01 | 北京逯博士行为医学科技研究院有限公司 | Dietary nutrition configuration method based on optimal carbon technology |
| CN118051825A (en) * | 2024-01-10 | 2024-05-17 | 常州久沃网络科技有限公司 | Supervision data processing management method based on big data analysis |
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