Postpartum diet management system and method

Abstract

The invention relates to the technical field of puerpera diet management, in particular to a postpartum diet management system and method. The server comprises a data acquisition module, a dimension extraction module, an evaluation module, a diet customization module, a baby data acquisition module and a diet optimization module; wherein the infant data acquisition module is used for monitoring physical state data of an infant born by a puerpera in real time at the first time after the puerpera is postpartum; the judgment module is used for determining the to-be-supplemented nutrient elements missed by the infant and the corresponding missing content according to the body state data corresponding to the infant in each time period; and the diet optimization module is used for optimizing the basic diet scheme according to the basic diet scheme corresponding to each time period of the lying-in woman and the to-be-supplemented nutrient elements and the deficiency content required by the baby born by the lying-in woman in each time period to form a diet optimization scheme.

Description

Technical Field

[0001] The present invention relates to the technical field of maternal diet management, and particularly relates to a postpartum diet management system and method. Background Art

[0002] Existing postpartum diet management systems mainly focus on the monitoring and assessment of the physical condition of parturients. Usually, personalized diet plans are formulated by collecting the basic physiological data of parturients (such as weight, blood pressure, blood glucose level, etc.).

[0003] However, these systems have the following several significant limitations:

[0004] Most existing systems only focus on the physical recovery of parturients and ignore the health needs of infants. This one-way attention mode cannot comprehensively consider the synergy effect of maternal and infant health, and may lead to an incomplete or unreasonable diet plan.

[0005] Breast milk is the main source of early nutrition for infants, and its composition is directly affected by the mother's diet. However, most existing systems do not establish a direct connection between the maternal diet and the composition of breast milk, making it difficult to ensure that breast milk can meet all the nutritional elements required for infant growth. The data of parturients and infants are often managed separately, lacking an integrated platform to comprehensively analyze the relationship between the two. This information island phenomenon limits the comprehensive understanding of the overall health of mothers and infants and affects the effectiveness of health management.

[0006] Based on this, there is an urgent need for a postpartum diet management system and method that can solve the information island problem between the data corresponding to parturients and infants in the existing technology, resulting in an unreasonable diet plan for parturients, so as to ensure that the formulated diet plan for parturients can help parturients recover while also meeting the needs of infants. Summary of the Invention

[0007] One of the purposes of the present invention is to provide a postpartum diet management system and method that can solve the information island problem between the data corresponding to parturients and infants in the existing technology, resulting in an unreasonable diet plan for parturients, so as to ensure that the formulated diet plan for parturients can help parturients recover while also meeting the needs of infants.

[0008] To achieve the above purpose, a postpartum diet management system is provided, including a server;

[0009] The server includes:

[0010] A data collection module for real-time collection of the basic postpartum information corresponding to the parturient at the first time after the parturient gives birth;

[0011] A dimension extraction module, configured to extract and determine a set of physical state assessment dimensions corresponding to a parturient based on the collected basic postnatal information and a preset dimension extraction strategy;

[0012] An evaluation module, configured to evaluate the physical state of a parturient according to the extracted and determined set of physical state assessment dimensions, and form corresponding evaluation data;

[0013] A diet customization module, configured to formulate a basic diet plan corresponding to each time period for the parturient according to the formed evaluation data;

[0014] An infant data acquisition module, configured to monitor in real time the physical state data of the infant born to the parturient at the first moment after childbirth;

[0015] A judgment module, configured to determine the missing nutrient elements to be supplemented and the corresponding missing contents for the infant according to the physical state data corresponding to the infant in each time period;

[0016] A diet optimization module, configured to optimize the basic diet plan according to the basic diet plan corresponding to each time period for the parturient, and the nutrient elements to be supplemented and the missing contents required by the infant born to the parturient in each time period, so as to form a diet optimization plan.

[0017] The technical principle and effect of this solution: In this solution, first, starting from the first moment after the parturient gives birth, the physical state data of the parturient and the infant are collected in real time. These data include but are not limited to weight, blood pressure, blood glucose level, milk secretion volume, etc. (for the parturient), and the growth and development indicators of the infant (such as weight, body length, head circumference, etc.).

[0018] Then, based on a preset dimension extraction strategy, the collected data is processed. This module uses a standardization formula to convert the original data into a unified scale and calculates the similarity between each dimension, so as to identify the set of dimensions most relevant to the parturient's physical recovery (i.e., the set of physical state assessment dimensions). This step ensures that the subsequent analysis is based on the most representative data.

[0019] After that, according to the extracted set of physical state assessment dimensions, considering the parturient's health condition comprehensively, detailed evaluation data is formed. These data not only reflect the current physical state but also predict the future recovery trend, providing a basis for a personalized diet plan. According to the evaluation data, a basic diet plan for the parturient in each time period is formulated. This plan aims to meet the parturient's own nutritional needs and support her physical recovery and breastfeeding.

[0020] Synchronously monitor the physical condition data of the baby, especially paying attention to the baby's growth and development and any potential signs of nutritional deficiency. This part of the data is crucial for adjusting the diet plan of the parturient. Analyze the baby's physical condition data to determine the missing nutritional elements to be supplemented and their specific deficiency amounts. For example, if it is detected that the baby has insufficient vitamin D or iron, it will be clearly recorded.

[0021] Combine the basic diet plan of the parturient with the nutritional elements to be supplemented and the deficiency amounts required by the baby to optimize the original diet plan. The optimized plan not only takes into account the parturient's own recovery needs but also specifically adds nutritional components that can promote the healthy growth of the baby to ensure that the needs of both are fully met.

[0022] In this plan, by associating the parturient's diet with the nutritional elements required by the baby, the system can customize a personalized diet plan for each parturient. This customization not only helps the parturient's own recovery but also directly improves the quality of breast milk, thereby better meeting the growth needs of the baby.

[0023] Precisely identify the specific nutritional elements missing in the baby and targetedly adjust the parturient's diet structure. For example, if the baby lacks a certain vitamin or mineral, the gap can be filled by increasing foods rich in that nutrient to ensure that the baby obtains sufficient nutritional support.

[0024] Early identify possible nutritional deficiency problems in the baby and take timely measures to correct them. This can effectively prevent developmental delays or other health problems caused by malnutrition and improve the overall health level of the baby. That is, it can solve the information island problem between the data corresponding to the parturient and the baby in the existing technology, resulting in an unreasonable diet plan for the parturient, so as to ensure that the formulated diet plan for the parturient can help the parturient recover while also meeting the needs of the baby.

[0025] Furthermore, the preset feature extraction strategy is as follows:

[0026] According to the collected basic postpartum information, determine each dimension corresponding to the basic postpartum information and the index values corresponding to each dimension; the collected basic postpartum information includes the current moment's basic postpartum information, the previous moment's, and the information corresponding to the moment before the previous moment.

[0027] According to each dimension corresponding to the basic postpartum information at the current moment, the previous moment, and the moment before the previous moment, and the index values corresponding to each dimension, convert the corresponding dimensions and the index values corresponding to the dimensions into a first matrix:

[0028] The first matrix is:

[0029]

[0030] Wherein, x t,n is the index value corresponding to the nth dimension at time t;

[0031] Based on the converted first matrix, according to a preset dimension standardization formula, standardize the data of each dimension in the first matrix, and update the first matrix after standardization to form a second matrix;

[0032] The preset dimension standardization formula is:

[0033]

[0034] Wherein, x t,i is the index value of the ith dimension at time t, x k,i is the index value of the ith dimension at the kth time point, and m is the total number of time points;

[0035] The formed second matrix is:

[0036]

[0037] Based on the formed second matrix, according to a preset dimension similarity calculation formula, calculate the dimension similarity between each dimension;

[0038] The preset dimension similarity calculation formula is:

[0039]

[0040] Wherein, R ij (t s , t l ) is the dimension similarity between the ith dimension and the jth dimension between time points t s and t l , is the standardized value corresponding to the ith dimension at the mth time series position at time point t s ; t s and t l respectively represent two different time points, which can be any two of t, t - 1 or t - 2;

[0041] Based on the calculated dimension similarity between each dimension, according to a preset first similarity threshold, cluster each dimension to form several first dimension sets;

[0042] Dynamically adjust the preset second similarity threshold according to the total number of dimensions in each first dimension set and the number of different dimensions, form the second similarity threshold corresponding to each dimension set, and based on the second similarity threshold corresponding to each dimension set, perform another clustering on each dimension set to form multiple sub-dimension sets;

[0043] In all sub-dimension sets, randomly select a dimension as the physical state evaluation dimension corresponding to the parturient, and form a corresponding physical state evaluation dimension set.

[0044] Beneficial effects: In this solution, the basic postpartum information at the current moment, the previous moment, and the moment before the previous moment is collected to ensure the time continuity and integrity of the data. This multi-time-point data collection can capture the changing trend of the parturient's physical state and provide more abundant information support. Calculate the similarity between each dimension based on the preset dimension similarity calculation formula. This step reveals the correlation between different dimensions, helps to identify the changing trend of each dimension, and thus provides a basis for further analysis. Dynamically adjust the second similarity threshold according to the total number of dimensions in each first dimension set to adapt to the density difference within different clusters. There may be significant differences in the total number and distribution of dimensions in each first dimension set formed by the initial clustering. By dynamically adjusting the second similarity threshold, the system can be optimized according to the actual data density within each cluster to ensure that each sub-dimension set has a high degree of internal consistency. A static similarity threshold may not be applicable to all clusters, especially in the case of uneven data distribution. The dynamic adjustment mechanism enables the system to flexibly respond to different data characteristics and improves the accuracy and reliability of the clustering results.

[0045] Furthermore, the diet optimization module includes:

[0046] A prediction module, which is used to output the milk nutrient elements and the corresponding element contents corresponding to the parturient in each time period based on the preset milk nutrient element prediction model according to the basic diet plan corresponding to the parturient in each time period;

[0047] A processing module, which is used to sequentially determine whether the milk nutrient elements at each moment contain the nutrient elements to be supplemented according to the milk nutrient elements and element contents corresponding to the parturient in each time period, the nutrient elements to be supplemented and the missing contents required by the baby in the corresponding time periods;

[0048] If not, it is determined that the current basic diet plan at this moment is unreasonable, and based on the nutrient elements to be supplemented, the corresponding foods are determined and added to the basic diet plan to form the diet optimization plan at the corresponding moment;

[0049] If so, calculate the content difference between the element content corresponding to the nutrient to be supplemented in the breast milk nutrients and the missing content corresponding to the nutrient to be supplemented;

[0050] Based on the calculated content difference and the preset diet adjustment strategy, adjust the basic diet plan at the corresponding moment to form an optimized diet plan at the corresponding moment.

[0051] Beneficial effects: By outputting the breast milk nutrients and their contents corresponding to the parturient in each time period through the prediction module and comparing them with the nutrients to be supplemented required by the infant, the system can accurately identify which nutrients need to be supplemented additionally. This personalized customization not only meets the specific needs of the infant but also ensures the quality of breast milk, providing the best nutritional guarantee for the infant. It not only focuses on the recovery and health of the parturient herself but also particularly considers the growth needs of the infant. By optimizing the diet structure of the parturient, the quality of breast milk is indirectly improved, providing better nutritional guarantee for the infant, and achieving a win-win situation for the health of both the mother and the infant.

[0052] Furthermore, the preset diet adjustment strategy is as follows:

[0053] Based on the calculated content difference, if the content difference is less than the first preset threshold, retrieve the food list corresponding to the nutrient to be supplemented corresponding to the content difference, and determine the content value of each food in the food list containing the nutrient to be supplemented;

[0054] Based on the absorption degree of the parturient for each food at each moment and the absorption degree of the infant for the nutrient to be supplemented at each moment, determine the attenuation coefficient for each food, and based on the attenuation coefficient, update the content value of each food in the food list containing the nutrient to be supplemented, and select the food with the largest updated content value and add it to the basic diet plan;

[0055] If the content difference is greater than or equal to the first preset threshold, retrieve the food list corresponding to the nutrient to be supplemented corresponding to the content difference, and based on the content value of each food in the food list containing the nutrient to be supplemented, select the food with the largest content value and add it to the basic diet plan;

[0056] After completing the addition of foods corresponding to all nutrients to be supplemented, form an optimized diet plan.

[0057] Beneficial effects: In this solution, based on the calculated smaller content difference, the system will retrieve the food arrangement list corresponding to the nutrient to be supplemented and determine the specific content of this nutrient in each food. Considering the absorption differences of food by parturients and infants at different time periods, the system will determine the attenuation coefficient corresponding to each food. These coefficients reflect the actual effective content of the nutrients in the food after processes such as digestion and metabolism. Update the nutrient content values of each food in the food arrangement list according to the attenuation coefficient, and then select the food with the largest updated content value and add it to the basic diet plan. Considering the absorption differences of various foods by parturients and infants at different time periods, the actual effective nutrient components are reflected by determining the attenuation coefficients corresponding to each food. This enables the finally selected food to more precisely meet the needs of the infant. The absorption efficiency of the same food may vary significantly among different individuals. By introducing the attenuation coefficient, the system can be adjusted according to the actual situation of specific individuals, avoiding one-size-fits-all recommendations, and thus providing more personalized nutritional support. The updated content value reflects the true and effective content after processes such as digestion and metabolism, avoiding the problem of excessive intake that may be caused by simply relying on the original content. This is particularly important for some nutrients with potential risks (such as vitamin A, iron, etc.), and can effectively prevent side effects caused by excessive supplementation.

[0058] When the content difference is large, the system directly selects the food containing the highest nutrient to be supplemented from the food arrangement list and adds it to the basic diet plan to quickly make up for the large nutritional gap.

[0059] The present invention also provides a postpartum diet management method using the above-mentioned postpartum diet management system. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 It is a logic block diagram of the postpartum diet management system in Embodiment 1 of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0061] The following is a further detailed description through specific embodiments:

[0062] Embodiment 1

[0063] A postpartum diet management system is basically as Figure 1 shown, including a server;

[0064] The server includes:

[0065] A data collection module for collecting in real time the basic postpartum information corresponding to the parturient at the first time after the parturient gives birth;

[0066] A dimension extraction module, configured to extract and determine a set of physical state evaluation dimensions corresponding to a parturient based on the collected basic postpartum information and a preset dimension extraction strategy;

[0067] The preset feature extraction strategy is as follows:

[0068] Based on the collected basic postpartum information, determine each dimension corresponding to the basic postpartum information and the index value corresponding to each dimension; the collected basic postpartum information includes the current moment's basic postpartum information, the previous moment's, and the information corresponding to the moment before the previous moment;

[0069] Based on each dimension corresponding to the basic postpartum information at the current moment, the previous moment, and the moment before the previous moment, and the index value corresponding to each dimension, convert the corresponding dimensions and the index values corresponding to the dimensions into a first matrix:

[0070] The first matrix is:

[0071]

[0072] In the formula, x t,n is the index value corresponding to the nth dimension at time t;

[0073] Based on the converted first matrix, and based on a preset dimension standardization formula, standardize the data of each dimension in the first matrix, and update the first matrix after standardization to form a second matrix;

[0074] The preset dimension standardization formula is:

[0075]

[0076] In the formula, x t,i is the index value of the ith dimension at time t, x k,i is the index value of the ith dimension at the kth time point, and m is the total number of time points;

[0077] The formed second matrix is:

[0078]

[0079] Based on the formed second matrix, and based on a preset dimension similarity calculation formula, calculate the dimension similarity between each dimension;

[0080] The preset dimension similarity calculation formula is:

[0081]

[0082] In the formula, R ij (ts , t l ) is the time point t s and t l is the dimensional similarity between the i-th dimension and the j-th dimension between t is at the time point t s at the m-th time series position, the standardized value corresponding to the i-th dimension; t s and t l respectively represent two different time points, which can be any two of t, t - 1 or t - 2; in this embodiment, the dimensional similarity calculation formula further includes a weighting coefficient A, and the weighting coefficient is related to R ij (t s , t l )'s product is the final dimensional similarity;

[0083] Among them,

[0084] In the formula, ∝ is the weight value, w(t s , m, i) is the missing sensitivity corresponding to the i-th dimension at the m-th time series position at the time point t s , b(t l , m, j) is the standard content value corresponding to the i-th dimension at the m-th time series position at the time point t l ; by setting the weighting coefficient, the finally obtained dimensional similarity is more reliable. Multiple important factors are comprehensively considered, including time sensitivity, missing sensitivity, and standard content value, etc. This makes the final evaluation result more comprehensive and reliable. It can effectively reduce these potential biases and provide a more accurate similarity evaluation. Adjust the weights of the data at each time point according to specific application requirements, so as to more accurately capture the change trend.

[0085] According to the calculated dimensional similarity between each dimension, based on a preset first similarity threshold, cluster each dimension to form several first dimension sets;

[0086] According to the total number of dimensions in each first dimension set and the number of different dimensions, dynamically adjust the preset second similarity threshold to form the second similarity threshold corresponding to each dimension set, and based on the second similarity threshold corresponding to each dimension set, perform another clustering on each dimension set to form multiple sub-dimension sets;

[0087] In all sub-dimension sets, randomly select one dimension as the physical state evaluation dimension corresponding to the parturient, and form a corresponding physical state evaluation dimension set.

[0088] An evaluation module, configured to evaluate the physical condition corresponding to the parturient according to the extracted and determined set of physical condition evaluation dimensions, and form corresponding evaluation data;

[0089] A diet customization module, configured to formulate a basic diet plan corresponding to the parturient in each time period according to the formed evaluation data;

[0090] An infant data acquisition module, configured to monitor in real time the physical condition data of the infant born to the parturient immediately after childbirth; monitor the physical condition data of the infant in real time, especially the growth and development indicators. For example, record data such as the weight, length, and head circumference of the infant, and pay attention to the growth rate and nutritional status of the infant.

[0091] A judgment module, configured to determine the missing nutritional elements to be supplemented and the corresponding missing contents for the infant according to the physical condition data corresponding to the infant in each time period;

[0092] A diet optimization module, configured to optimize the basic diet plan according to the basic diet plan corresponding to the parturient in each time period, and the nutritional elements to be supplemented and the missing contents required by the infant born to the parturient in each time period, and form a diet optimization plan.

[0093] The diet optimization module includes:

[0094] A prediction module, configured to output the milk nutritional elements and the corresponding element contents corresponding to the parturient in each time period according to the basic diet plan corresponding to the parturient in each time period, based on a preset milk nutritional element prediction model; in this embodiment, the milk nutritional element prediction model is constructed using the existing BP neural network technology.

[0095] A processing module, configured to sequentially determine whether the milk nutritional elements contain the nutritional elements to be supplemented at each moment according to the milk nutritional elements and the element contents corresponding to the parturient in each time period, and the nutritional elements to be supplemented and the missing contents required by the infant in the corresponding each time period;

[0096] If not, it is determined that the current basic diet plan at this moment is unreasonable, and based on the nutritional elements to be supplemented, the corresponding foods are determined and added to the basic diet plan to form a diet optimization plan at the corresponding moment;

[0097] If so, according to the element content corresponding to the nutritional element to be supplemented in the milk nutritional elements, and the missing content corresponding to the nutritional element to be supplemented, calculate the content difference between the element content corresponding to the nutritional element to be supplemented and the missing content;

[0098] According to the calculated content difference, based on a preset diet adjustment strategy, adjust the basic diet plan at the corresponding moment to form a diet optimization plan at the corresponding moment.

[0099] This embodiment also discloses a postpartum diet management method, which uses the above-mentioned postpartum diet management system.

[0100] The above are only embodiments of the present invention. Specific structures and common knowledge such as characteristics well-known in the art are not described in detail here. Those of ordinary skill in the art know all the common general technical knowledge in the technical field to which the invention belongs before the filing date or the priority date, can know all the prior arts in this field, and have the ability to apply the conventional experimental means before this date. Those of ordinary skill in the art can, under the inspiration given in this application and in combination with their own abilities, improve and implement this solution. Some typical well-known structures or well-known methods should not become an obstacle for those of ordinary skill in the art to implement this application. It should be noted that for those skilled in the art, without departing from the structure of the present invention, several modifications and improvements can be made, which should also be regarded as the protection scope of the present invention, and these will not affect the implementation effect of the present invention and the practicality of the patent. The protection scope required by this application should be based on the content of its claims, and the specific implementation manners and the like recorded in the specification can be used to interpret the content of the claims.

Claims

1. A postpartum diet management system, characterized in that: Including the server side; The server includes: The data collection module is used to collect the basic information of the parturient immediately after giving birth. The dimension extraction module is used to extract and determine the physical condition assessment dimension set corresponding to the parturient according to the collected basic postpartum information and based on the preset dimension extraction strategy; An evaluation module is used to evaluate the physical condition of the parturient according to the extracted and determined physical condition evaluation dimension set, and form corresponding evaluation data; The diet customization module is used to formulate a basic diet plan for the mother in different time periods based on the formed evaluation data; The baby data acquisition module is used to monitor the physical status data of the baby born by the mother in real time immediately after giving birth; A judgment module is used to determine the nutritional elements to be supplemented and the corresponding missing content of the infant according to the physical condition data of the infant in each time period; The diet optimization module is used to optimize the basic diet plan according to the basic diet plan corresponding to the mother in each time period, as well as the nutrients to be supplemented and the missing content required by the baby born by the mother in each time period, to form a diet optimization plan.

2. A postpartum diet management system according to claim 1, characterized in that: The preset feature extraction strategy is: According to the collected basic postpartum information, determine each dimension corresponding to the basic postpartum information and the index value corresponding to each dimension; the collected basic postpartum information includes the basic postpartum information at the current moment, the basic postpartum information at the previous moment, and the basic postpartum information corresponding to the previous moment; According to the dimensions corresponding to the basic postpartum information corresponding to the current moment, the previous moment and the previous moment and the index values ​​corresponding to the dimensions, the corresponding dimensions and the index values ​​corresponding to the dimensions are converted into the first matrix: The first matrix is: In the formula, x t,n is the index value corresponding to the nth dimension at time t; According to the converted first matrix, based on a preset dimension normalization formula, the data of each dimension in the first matrix is ​​normalized, and the first matrix is ​​updated after normalization to form a second matrix; The preset dimension normalization formula is: In the formula, x t,i is the index value of the i-th dimension at time t, x k,i is the index value of the i-th dimension at the k-th time point, and m is the total number of time points; The second matrix formed is: According to the formed second matrix, based on a preset dimension similarity calculation formula, the dimension similarity between the dimensions is calculated; The preset dimensional similarity calculation formula is: In the formula, R ij (t s ,t l ) is the time point t s and t l The dimensional similarity between the i-th dimension and the j-th dimension, At time point t s The standardized value corresponding to the i-th dimension at the m-th time series position; t s and t l Respectively represent two different time points, which are any two time points among t, t-1 or t-2; According to the calculated dimensional similarities between the dimensions, based on a preset first similarity threshold, clustering the dimensions to form a plurality of first dimension sets; According to the total number of dimensions in each first dimension set and the number of different dimensions, the preset second similarity threshold is dynamically adjusted to form the second similarity threshold corresponding to each dimension set, and based on the second similarity threshold corresponding to each dimension set, each dimension set is clustered again to form multiple sub-dimension sets; In all sub-dimension sets, one dimension is randomly selected as the physical condition assessment dimension corresponding to the parturient, and a corresponding physical condition assessment dimension set is formed.

3. A postpartum diet management system according to claim 2, characterized in that: The diet optimization module includes: The prediction module is used to output the corresponding milk nutrient elements and the corresponding element contents of the mother in each time period according to the basic diet plan corresponding to the mother in each time period and based on the preset milk nutrient element prediction model; A processing module is used to determine whether the nutrient elements in the breast milk at each time point contain the nutrient elements to be supplemented according to the nutrient elements and element contents of the breast milk corresponding to each time period, the nutrient elements to be supplemented required by the infant in each corresponding time period, and the missing contents; If not, it is determined that the current basic diet plan at that moment is unreasonable, and based on the nutritional elements to be supplemented, the corresponding food is determined and added to the basic diet plan to form a diet optimization plan at the corresponding moment; If yes, then according to the element content corresponding to the nutritional element to be supplemented in the milk nutritional elements and the missing content corresponding to the nutritional element to be supplemented, calculate the content difference between the element content corresponding to the nutritional element to be supplemented and the missing content; According to the calculated content difference and based on the preset diet adjustment strategy, the basic diet plan at the corresponding time is adjusted to form an optimized diet plan at the corresponding time.

4. A postpartum diet management system according to claim 3, characterized in that: The preset dietary adjustment strategy is: According to the calculated content difference, if the content difference is less than the first preset threshold, based on the nutrient element to be supplemented corresponding to the content difference, the food arrangement table corresponding to the nutrient element to be supplemented is retrieved, and the content value of the nutrient element to be supplemented contained in each food in the food arrangement table is determined; According to the absorption degree of each food corresponding to the mother at each moment, and the absorption degree of the nutritional element to be supplemented by the baby at each moment, the attenuation coefficient corresponding to each food is determined, and based on the attenuation coefficient, the content value of the nutritional element to be supplemented contained in each food in the food arrangement table is updated, and the food with the largest updated content value is selected and added to the basic diet plan; If the content difference is greater than or equal to the first preset threshold, based on the nutrient element to be supplemented corresponding to the content difference, the food arrangement table corresponding to the nutrient element to be supplemented is retrieved, and according to the content value of the nutrient element to be supplemented contained in each food in the food arrangement table, the food with the largest content value is selected and added to the basic diet plan; After completing the addition of foods corresponding to all the nutrients to be supplemented, a dietary optimization plan is formed.

5. A postpartum diet management method, characterized in that: A postpartum diet management system using any one of claims 1 to 4.

Images