Nutrition monitoring system based on data processing and use method thereof

Abstract

The invention relates to the technical field of nutrition monitoring, in particular to a nutrition monitoring system based on data processing and a using method of the nutrition monitoring system. The analysis module is used for determining key monitoring parameters according to user body nutrition data feature types; the monitoring module is used for determining a monitoring method according to the change trend of a plurality of key monitoring parameters within a first preset time length; the early warning module is used for determining whether to send out early warning or not according to whether the user behavior is qualified or not under the corresponding monitoring method; the adjusting module is used for determining whether to adjust the nutrition monitoring process parameters or not according to the trend tendency of the key monitoring parameters of the user in a second preset duration under the corresponding monitoring method and the environmental influence degree under the condition that the early warning module does not give out the early warning; the accuracy of the user nutrition monitoring process is improved by improving the accuracy of user body nutrition data analysis.

Description

Technical Field

[0001] The present invention relates to the technical field of nutrition monitoring, and in particular to a nutrition monitoring system based on data processing and a method of using the system. Background Art

[0002] In today's society, people are paying more and more attention to health, and balanced nutrition has become one of the key factors in maintaining good health. However, traditional nutrition monitoring methods have many limitations and cannot meet people's needs for accurate and personalized nutrition monitoring. On the one hand, people's understanding of their own nutritional status is often vague. Usually, people can only judge their nutritional status through some subjective feelings or simple physical indicators (such as weight, blood pressure, etc.), but these indicators cannot fully reflect the body's nutritional needs. On the other hand, the existing nutrition monitoring system has deficiencies in data processing and analysis. Some systems can only provide simple nutritional statistics and lack in-depth analysis of key monitoring parameters. In addition, most existing systems use a single monitoring method and cannot be flexibly adjusted according to the changing trends of key monitoring parameters.

[0003] Chinese patent application publication number: CN109872799A discloses a dining nutrition monitoring system and a nutrition monitoring mobile meal delivery vehicle, the system includes a weighing and selling terminal, a cloud nutrition monitoring server and a nutrition and dining information query terminal, the weighing and selling terminal is connected to the cloud nutrition monitoring server in communication, and the nutrition and dining information query terminal is connected to the cloud nutrition monitoring server in communication; the weighing and selling terminal serves as a dish weighing and selling terminal, collects meal sales information for diners, and provides the meal sales information to the cloud nutrition monitoring server; the cloud nutrition monitoring server generates meal nutrition information for diners based on the meal sales information for diners provided by the weighing and selling terminal; the user queries the meal nutrition information generated by the cloud nutrition monitoring server through the nutrition and dining information query terminal. The dining nutrition monitoring system is installed on the nutrition monitoring mobile meal delivery vehicle. This invention is of great significance for improving users' eating habits and promoting users' dietary health.

[0004] It can be seen that the existing technology has the problem of inaccurate analysis of the user's body nutrition data, which leads to the inability to select an accurate monitoring method and causes low accuracy in the nutrition monitoring process. Summary of the invention

[0005] To this end, the present invention provides a nutrition monitoring system based on data processing and a method of using the same, so as to overcome the problem in the prior art that the analysis of the user's body nutrition data is not accurate enough, resulting in the inability to select an accurate monitoring method and causing low accuracy in the nutrition monitoring process.

[0006] To achieve the above object, the present invention provides a nutrition monitoring system based on data processing, comprising: A collection and storage module, comprising a dietary data collection unit for collecting dietary data, a body nutrition data collection unit for collecting body nutrition data of a user, and a data storage unit for storing the dietary data and the body nutrition data; An analysis module connected to the acquisition and storage module, for determining key monitoring parameters according to a characteristic type of the user's body nutrition data, wherein the characteristic type of the user's body nutrition data is determined according to whether there are abnormal values ​​in the user's body nutrition data; A monitoring module, which is connected to the analysis module and is used to determine a monitoring method according to the change trend of a number of key monitoring parameters within a first preset time period, wherein the monitoring method includes a multi-factor monitoring method, a dietary monitoring method, and a sampling monitoring method; An early warning module, which is connected to the monitoring module and is used to determine whether to issue an early warning according to whether the user behavior is qualified under the corresponding monitoring method; An adjustment module is connected to the early warning module and is used to determine whether to adjust the nutrition monitoring process parameters according to the trend of change of the user's key monitoring parameters within the second preset time period under the corresponding monitoring method and the degree of environmental impact when the early warning module does not issue an early warning. The nutrition monitoring process parameters include a first preset time period and a health parameter range.

[0007] Furthermore, the data analysis module determines the key monitoring parameters according to the characteristic type of the user's body nutrition data, including: The data analysis module determines that the key monitoring parameter is a high-risk key monitoring parameter according to the normal characteristic type of the user's body nutrition data; The data analysis module determines that the key monitoring parameter is an abnormal key monitoring parameter according to the abnormal characteristic type of the user's physical nutrition data.

[0008] Furthermore, the data analysis module determines the characteristic type of the user's body nutrition data according to whether there are abnormal values ​​in the user's body nutrition data, including: The data analysis module determines that the characteristic type of the user's body nutrition data is abnormal according to the presence of abnormal values ​​in the user's body nutrition data; The data analysis module determines that the characteristic type of the user's body nutrition data is normal based on the absence of abnormal values ​​in the user's body nutrition data.

[0009] Furthermore, the nutrition monitoring module determines the monitoring method according to the change trend of several key monitoring parameters within the first preset time period, including: The nutrition monitoring module determines to monitor the user using a multi-factor monitoring method according to the change trend of the key monitoring parameters being far from the healthy parameter range and having fluctuations; The nutrition monitoring module determines to monitor the user using a diet monitoring method according to the change trend of the key monitoring parameters being far away from the healthy parameter range and without fluctuation; The nutrition monitoring module determines to monitor the user by a sampling monitoring method according to the changing trend of the key monitoring parameters being close to the health parameter range; The changing trend of the key monitoring parameter is that the minimum value of the absolute value of the difference between the key monitoring parameter after the change and the maximum value or minimum value of the health parameter range becomes larger as it moves away from the health parameter range, and the changing trend of the key monitoring parameter is that the minimum value of the absolute value of the difference between the key monitoring parameter after the change and the maximum value or minimum value of the health parameter range becomes smaller as it approaches the health parameter range.

[0010] Furthermore, the health parameter range is the average value of the parameter of several healthy users of the same age, and judging whether there is fluctuation in the key monitoring parameter includes that the standard deviation of the change value of the key monitoring parameter within the first preset time period is greater than the preset standard deviation.

[0011] Furthermore, the early warning module determines whether to issue an early warning according to whether the user behavior is qualified under the corresponding monitoring method, including: The early warning module issues an early warning based on the determination that the user behavior is unqualified under the corresponding monitoring method.

[0012] Further, the adjustment module determines whether to adjust the nutrition monitoring process parameters including: The adjustment module determines to adjust the nutrition monitoring process parameters according to the negative trend of the key monitoring parameters of the user within the second preset time period under the corresponding monitoring method and the low degree of environmental impact, under the condition that the early warning module does not issue an early warning.

[0013] Further, judging that the trend of change of the key monitoring parameter of the user is negative includes that the absolute value of the difference between the final value of the key monitoring parameter within the second preset time period and the average value of the health parameter range is greater than the preset difference.

[0014] Furthermore, the adjustment amount of the first preset time period is positively correlated with the absolute value of the difference between the final value of the key monitoring parameter within the second preset time period and the average value of the health parameter range.

[0015] On the other hand, the present invention also provides a method for using a nutrition monitoring system based on data processing, comprising: Acquire the user's dietary data and the user's physical nutritional data, and store the dietary data and the user's physical nutritional data in a database; Determine the key monitoring parameters of the user based on the characteristic type of the user's body nutrition data, wherein the characteristic type of the user's body nutrition data is determined according to whether there is an abnormal value in the user's body nutrition data; Determining a monitoring method based on the changing trends of a number of key monitoring parameters within a first preset time period, wherein the monitoring method includes a multi-factor monitoring method, a dietary monitoring method, and a sampling monitoring method; Determine whether to issue an early warning based on whether the user's behavior is qualified under the corresponding monitoring method; Under the condition that the early warning module does not issue an early warning, it is determined whether to adjust the nutrition monitoring process parameters based on the trend of change of the user's key monitoring parameters within the second preset time period under the corresponding monitoring method and the degree of environmental impact. The nutrition monitoring process parameters include the first preset time period and the health parameter range.

[0016] Compared with the prior art, the beneficial effect of the present invention is that the present invention can accurately judge whether there are abnormal values ​​in the data by analyzing the user's body nutrition data in detail, so as to determine the characteristic type of the user's body nutrition data, which enables the monitoring system to pay attention to users with different health conditions in a targeted manner, and provide accurate direction for subsequent monitoring and intervention. For users with normal body nutrition data, determining high-risk key monitoring parameters can warn potential health risks in advance. For example, when the user's serum total protein content is close to the edge of the normal range, although it is still within the normal range, the system has identified it as a high-risk key monitoring parameter, and pays close attention to its change trend, so as to take preventive measures before the problem occurs. For users with abnormal body nutrition data, determining abnormal key monitoring parameters can clarify the specific problem, and provide a basis for formulating personalized treatment and nutrition intervention programs. For example, if the user's blood sugar value exceeds the normal range, the system determines it as an abnormal key monitoring parameter, and then can take corresponding diet adjustments or exercise suggestions for high blood sugar or low blood sugar. The above method improves the accuracy of the analysis of the user's body nutrition data and then selects appropriate monitoring parameters to improve the accuracy of the nutrition monitoring process.

[0017] Furthermore, the present invention adopts different monitoring methods according to different changing trends of key monitoring parameters, and can provide users with more accurate and personalized nutrition monitoring services. For example, when the key monitoring parameters are far away from the healthy parameter range and there are fluctuations, a multi-factor monitoring method is adopted to comprehensively consider the user's exercise data and diet data, so as to more comprehensively understand the factors affecting the user's health, thereby formulating more targeted intervention measures. For the case where the key monitoring parameters are far away from the healthy parameter range and there are no fluctuations, a diet monitoring method is adopted to focus on the user's diet data, which can more specifically adjust the user's eating habits and improve the nutritional status. When the key monitoring parameters are close to the healthy parameter range, a sampling monitoring method is adopted to monitor the user's diet data at a sampling interval (such as 6 hours), which can not only reduce the monitoring cost and user burden while ensuring the monitoring effect, but also promptly discover possible problems to ensure that the user's nutritional status continues to improve. The above method improves the accuracy of the analysis of the user's physical nutrition data and then selects appropriate monitoring parameters to improve the accuracy of the nutrition monitoring process.

[0018] Furthermore, in the case where no warning is issued, the adjustment module of the present invention determines whether to adjust the nutrition monitoring process parameters according to the trend of change of the key monitoring parameters and the degree of environmental impact, thereby realizing dynamic tracking and adjustment of the user's health status. When the trend of change of the key monitoring parameters is negative and the degree of environmental impact is low, it indicates that the user's health status may be developing in an unfavorable direction. Even if the warning is not triggered, the system can make adjustments in time, thereby improving the sensitivity and foresight of monitoring. The trend of change of the key monitoring parameters is judged by the second preset time length, thereby providing a time window for evaluating the long-term health trend of the user. By comparing the absolute value of the difference between the final value of the key monitoring parameter during this period and the average value of the health parameter range and the preset difference, it is possible to accurately judge whether the user's health status is improving or deteriorating. Considering the impact of environmental factors such as temperature and humidity on the user's health, the user's health status can be more comprehensively evaluated to avoid misjudgment caused by environmental changes. The setting of the first adjustment coefficient and the second adjustment coefficient makes the adjustment of the first preset time length and the health parameter range more flexible and accurate. The above method improves the accuracy of the analysis of the user's physical nutrition data and then selects appropriate monitoring parameters to improve the accuracy of the nutrition monitoring process. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 A structural diagram of a nutrition monitoring system based on data processing according to an embodiment of the present invention; Figure 2 A schematic diagram of the structure of a storage module in a nutrition monitoring system based on data processing according to an embodiment of the present invention; Figure 3It is a work flow chart of the adjustment module in the nutrition monitoring system based on data processing according to an embodiment of the present invention; Figure 4 The present invention is a flowchart of a method for using a nutrition monitoring system based on data processing according to an embodiment of the present invention. DETAILED DESCRIPTION

[0020] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0021] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the protection scope of the present invention.

[0022] It should be noted that, in the description of the present invention, terms such as "up", "down", "left", "right", "inside" and "outside" indicating directions or positional relationships are based on the directions or positional relationships shown in the drawings. This is merely for the convenience of description and does not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it cannot be understood as a limitation on the present invention.

[0023] In addition, it should be noted that in the description of the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0024] See also Figure 1 - Figure 3 As shown, Figure 1 A structural diagram of a nutrition monitoring system based on data processing according to an embodiment of the present invention; Figure 2 A schematic diagram of the structure of a storage module in a nutrition monitoring system based on data processing according to an embodiment of the present invention; Figure 3 The present invention is a flowchart of the adjustment module in the nutrition monitoring system based on data processing according to an embodiment of the present invention.

[0025] The nutrition monitoring system based on data processing according to the embodiment of the present invention includes: A collection and storage module, comprising a dietary data collection unit for collecting dietary data, a body nutrition data collection unit for collecting body nutrition data of a user, and a data storage unit for storing the dietary data and the body nutrition data; An analysis module connected to the acquisition and storage module, for determining key monitoring parameters according to a characteristic type of the user's body nutrition data, wherein the characteristic type of the user's body nutrition data is determined according to whether there are abnormal values ​​in the user's body nutrition data; A monitoring module, which is connected to the analysis module and is used to determine a monitoring method according to the change trend of a number of key monitoring parameters within a first preset time period, wherein the monitoring method includes a multi-factor monitoring method, a dietary monitoring method, and a sampling monitoring method; An early warning module, which is connected to the monitoring module and is used to determine whether to issue an early warning according to whether the user behavior is qualified under the corresponding monitoring method; An adjustment module is connected to the early warning module and is used to determine whether to adjust the nutrition monitoring process parameters according to the trend of change of the user's key monitoring parameters within the second preset time period under the corresponding monitoring method and the degree of environmental impact when the early warning module does not issue an early warning. The nutrition monitoring process parameters include a first preset time period and a health parameter range.

[0026] The dietary data in the embodiment of the present invention includes but is not limited to "food types, food weight and dietary time", and the user's body nutritional data includes but is not limited to "protein content, vitamin content and fat content".

[0027] Specifically, the data analysis module determines the key monitoring parameters according to the user's body nutrition data feature type under the condition of determining the key monitoring parameters; If the characteristic type of the user's body nutrition data is normal, the data analysis module determines that the key monitoring parameter is a high-risk key monitoring parameter; If the characteristic type of the user's physical nutrition data is abnormal, the data analysis module determines that the key monitoring parameter is an abnormal key monitoring parameter.

[0028] The high-risk key monitoring parameter described in the embodiment of the present invention is a monitoring parameter whose minimum value of the absolute difference between the maximum value or the minimum value of the health parameter range is less than one tenth of the minimum value of the health parameter range. For example, the health parameter range of total serum protein is 60g / L-80g / L. If the user's total serum protein content is 62g / L, the total serum protein content is a high-risk key monitoring parameter. The abnormal key monitoring parameter is a monitoring parameter that is not within the health parameter range, but the above values ​​are not limited to this. Those skilled in the art can also adjust the values ​​according to actual needs.

[0029] Specifically, the data analysis module determines the characteristic type of the user's body nutrition data according to whether there is an abnormal value in the user's body nutrition data under the condition of determining the characteristic type of the user's body nutrition data; If there are abnormal values ​​in the user's body nutrition data, the data analysis module determines that the characteristic type of the user's body nutrition data is abnormal; If there is no abnormal value in the user's body nutrition data, the data analysis module determines that the characteristic type of the user's body nutrition data is normal.

[0030] The abnormal value in the embodiment of the present invention is a body nutrition data value of the user's body nutrition data that is not within the healthy parameter range.

[0031] The present invention can accurately judge whether there are abnormal values ​​in the data by analyzing the user's body nutrition data in detail, so as to determine the characteristic type of the user's body nutrition data, which enables the monitoring system to pay attention to users with different health conditions in a targeted manner, and provide accurate directions for subsequent monitoring and intervention. For users with normal body nutrition data, high-risk key monitoring parameters are determined, and potential health risks can be warned in advance. For example, when the user's serum total protein content is close to the edge of the normal range, although it is still within the normal range, the system has identified it as a high-risk key monitoring parameter, and pays close attention to its change trend, so as to take preventive measures before the problem occurs. For users with abnormal body nutrition data, abnormal key monitoring parameters are determined, and specific problems can be clearly identified, providing a basis for formulating personalized treatment and nutrition intervention programs. For example, if the user's blood sugar value exceeds the normal range, the system determines it as an abnormal key monitoring parameter, and then can take corresponding diet adjustments or exercise suggestions for the situation of high blood sugar or low blood sugar, and the accuracy of the analysis of the user's body nutrition data is improved by the above method, and then the appropriate monitoring parameters are selected to improve the accuracy of the nutrition monitoring process.

[0032] Specifically, the nutrition monitoring module determines the monitoring method according to the change trend of several key monitoring parameters within the first preset time period under the condition of determining the monitoring method; If the change trend of the key monitoring parameter is far away from the healthy parameter range and there is fluctuation, the nutrition monitoring module determines to monitor the user using a multi-factor monitoring method; If the change trend of the key monitoring parameter is far away from the healthy parameter range and there is no fluctuation, the nutrition monitoring module determines to monitor the user by the diet monitoring method; If the changing trend of the key monitoring parameter is close to the healthy parameter range, the nutrition monitoring module determines to monitor the user by a sampling monitoring method.

[0033] In an embodiment of the present invention, the first preset time period can be set to 7 days, and the change trend of the key monitoring parameter is away from the health parameter range, including the minimum value of the absolute value of the difference between the changed key monitoring parameter and the maximum value or minimum value of the health parameter range becomes larger, and the change trend of the key monitoring parameter is close to the health parameter range, including the minimum value of the absolute value of the difference between the changed key monitoring parameter and the maximum value or minimum value of the health parameter range becomes smaller. For example, if the health parameter range of fat content is 15% to 20%, and the fat content of the user changes from 12% to 10% within the first preset time period, then the change trend of fat content is away from the health parameter range, and the health parameter range is determined by the minimum and maximum values ​​of this parameter of several healthy users of the same age. It is judged whether there is fluctuation in the key monitoring parameter, including that the standard deviation of the change value of the key monitoring parameter within the first preset time period is greater than the preset standard deviation, and the value of the preset standard deviation is the average value of the standard deviation of the change value of the key monitoring parameter within several sections of the first preset time period, but the above value is not limited to this, and technicians in this field can also adjust the value according to actual needs.

[0034] In the embodiment of the present invention, monitoring users by the multi-factor monitoring method includes monitoring the user's exercise data and diet data, monitoring users by the diet monitoring method includes monitoring the user's diet data, and monitoring users by the sampling monitoring method includes monitoring the user's diet data at sampling intervals, and the sampling interval can be set to 6 hours.

[0035] The present invention adopts different monitoring methods according to different changing trends of key monitoring parameters, and can provide users with more accurate and personalized nutrition monitoring services. For example, when the key monitoring parameters are far away from the healthy parameter range and there are fluctuations, a multi-factor monitoring method is adopted to comprehensively consider the user's exercise data and diet data, so as to more comprehensively understand the factors affecting the user's health, thereby formulating more targeted intervention measures. For the situation where the key monitoring parameters are far away from the healthy parameter range and there are no fluctuations, a diet monitoring method is adopted to focus on the user's diet data, which can more specifically adjust the user's eating habits and improve the nutritional status. When the key monitoring parameters are close to the healthy parameter range, a sampling monitoring method is adopted to monitor the user's diet data at a sampling interval (such as 6 hours), which can not only reduce the monitoring cost and user burden while ensuring the monitoring effect, but also promptly discover possible problems to ensure that the user's nutritional status continues to improve. The above method improves the accuracy of the analysis of the user's physical nutrition data and then selects appropriate monitoring parameters to improve the accuracy of the nutrition monitoring process.

[0036] Specifically, the early warning module determines whether to issue an early warning, including determining whether to issue an early warning according to whether the user behavior is qualified under the corresponding monitoring method; If the user behavior is unqualified under the corresponding monitoring method, the warning module determines to issue a warning; If the user behavior is qualified under the corresponding monitoring method, the warning module determines not to issue a warning.

[0037] The method for judging whether the user's behavior is qualified under the multi-factor monitoring method in the embodiment of the present invention includes whether the user's exercise data and diet data are regular. If they are regular, the user's behavior is qualified. The judgment of whether the exercise data and diet data are regular can be based on the variance of the exercise data and the variance of the diet data within 5 days to judge whether the exercise data is regular and whether the diet data is regular. The exercise data can be selected as calories, and the diet data can be selected as the key monitoring parameter content contained in the food, but the above values ​​are not limited to this, and technicians in this field can also adjust the values ​​according to actual needs.

[0038] The determination of whether the user's behavior is qualified under the dietary monitoring method and the determination of whether the user's behavior is qualified under the sampling monitoring method in the embodiment of the present invention includes whether the user increases the weight of food containing key monitoring parameters. If increased, it is determined to be qualified.

[0039] Specifically, the adjustment module determines whether to adjust the nutrition monitoring process parameters under the condition that the early warning module does not issue an early warning, according to the trend of change of the key monitoring parameters of the user within the second preset time period under the corresponding monitoring method and the degree of environmental impact; If the trend of the key monitoring parameters of the user within the second preset time period under the corresponding monitoring method under the condition that the early warning module does not issue an early warning is negative and the degree of environmental impact is low, the adjustment module determines that the nutrition monitoring process parameters need to be adjusted; If the trend of the user's key monitoring parameters within the second preset time period under the corresponding monitoring method under the condition that the early warning module does not issue an early warning is negative and the environmental impact level is high, or the trend of the user's key monitoring parameters is positive, the adjustment module determines that there is no need to adjust the nutrition monitoring process parameters.

[0040] In an embodiment of the present invention, the second preset time period can be set to 30 days, and the judgment that the trend of the user's key monitoring parameters is negative includes that the absolute value of the difference between the final value of the key monitoring parameter within the second preset time period and the average value of the health parameter range is greater than the preset difference, and the preset difference is the absolute value of the difference between the initial value of the key monitoring parameter within the second preset time period and the average value of the health parameter range, but the above value is not limited to this, and technical personnel in this field can also adjust the value according to actual needs.

[0041] The determination of the degree of environmental impact in the embodiment of the present invention includes determining that the degree of environmental impact is low based on that the environmental parameters are within a preset environmental parameter range, and the environmental impact parameters include but are not limited to "temperature and humidity", and the minimum value of the preset environmental parameter range is seven-tenths of the average value of the environmental parameters of the user's living environment within one year, and the maximum value of the preset environmental parameter range is thirteen-tenths of the average value of the environmental parameters of the user's living environment within one year, but the above values ​​are not limited to this, and technical personnel in this field may also adjust the values ​​according to actual needs.

[0042] Specifically, under the condition that the adjustment module determines that the nutrition monitoring process parameters need to be adjusted, the adjustment module determines to adjust the first preset time length with a first adjustment coefficient, and adjusts the minimum value of the health parameter range with the first adjustment coefficient and adjusts the maximum value of the health parameter range with the second adjustment coefficient.

[0043] The adjustment of the health parameter range by the second adjustment coefficient in the embodiment of the present invention includes setting the value range of the first adjustment coefficient to 1.05-1.21, the value of the first adjustment coefficient is preferably 1.16, the value range of the second adjustment coefficient is set to 0.82-0.97, the value of the second adjustment coefficient is preferably 0.89, and the adjustment amount of the first preset time length is positively correlated with the absolute value of the difference between the final value of the key monitoring parameter within the second preset time length and the average value of the health parameter range, but the above values ​​are not limited to this, and technical personnel in this field can also adjust the value according to actual needs.

[0044] In the case where no warning is issued, the adjustment module of the present invention determines whether to adjust the nutrition monitoring process parameters according to the trend of change of the key monitoring parameters and the degree of environmental impact, thereby realizing dynamic tracking and adjustment of the user's health status. When the trend of change of the key monitoring parameters is negative and the degree of environmental impact is low, it indicates that the user's health status may be developing in an unfavorable direction. Even if the warning is not triggered, the system can make adjustments in time, thereby improving the sensitivity and foresight of monitoring. The trend of change of the key monitoring parameters is judged by the second preset time length, thereby providing a time window for evaluating the long-term health trend of the user. By comparing the absolute value of the difference between the final value of the key monitoring parameters during this period and the average value of the health parameter range and the preset difference, it is possible to accurately judge whether the user's health status is improving or deteriorating. Considering the impact of environmental factors such as temperature and humidity on the user's health, the user's health status can be more comprehensively evaluated to avoid misjudgment caused by environmental changes. The setting of the first adjustment coefficient and the second adjustment coefficient makes the adjustment of the first preset time length and the health parameter range more flexible and accurate. The above method improves the accuracy of the analysis of the user's body nutrition data and then selects appropriate monitoring parameters to improve the accuracy of the nutrition monitoring process.

[0045] In one embodiment of the present invention, it can be applied to a smart lunch box, which has multiple functional modules, including various built-in sensors, a data processing unit, a storage unit, a display and prompt module, and a communication module.

[0046] The dietary data collection unit collects dietary data by installing a high-precision weight sensor and an image recognition camera inside the lunch box. The weight sensor can accurately measure the weight of the food put into the lunch box each time, while the image recognition camera can identify the type of food, such as vegetables, meat, grains, fruits, etc., to obtain detailed dietary data.

[0047] The body nutrition data collection unit connects and communicates with wearable devices (such as smart bracelets, smart body fat scales, etc.). These wearable devices can monitor the user's physical indicators in real time, such as weight, body fat percentage, blood sugar, blood pressure and other body nutrition-related data, and transmit these data to the data processing unit of the smart lunch box.

[0048] Data storage unit: The smart lunch box has a built-in large-capacity storage chip for storing the collected dietary data and body nutrition data, which is convenient for subsequent analysis and processing as well as data retrieval and viewing.

[0049] Display and prompt module: The lunch box is equipped with an LCD screen to show the user the current nutritional intake, monitoring results and related prompt information. At the same time, it is also equipped with a sound prompt device and a vibration motor to issue early warning reminders.

[0050] Communication module: It has Bluetooth and Wi-Fi functions. On the one hand, it can connect to the user's mobile phone and other smart terminals, allowing users to view more detailed data reports, set personalized parameters, etc. On the other hand, it can also interact with the cloud server to facilitate data backup and update system functions.

[0051] Every day, the user puts the prepared meals into the smart lunch box. The weight sensor inside the lunch box will immediately measure the weight of the food and identify the type of food through the image recognition camera. For example, it can identify that 100 grams of oatmeal, 50 grams of blueberries and 1 boiled egg are put into the breakfast. These dietary data will be recorded in real time and stored in the data storage unit of the lunch box. At the same time, the smart bracelet connected to the lunch box monitors the user's heart rate, sleep status and other data, and the smart body fat scale measures the user's current weight, body fat percentage and other body nutrition data, which are also transmitted to the smart lunch box for storage.

[0052] The data processing unit of the lunch box (corresponding to the analysis module function) will analyze the stored body nutrition data to determine whether there are abnormal values. For example, if it is found that the user's body fat rate is higher than a certain percentage of the average body fat rate of healthy users of the same age group for a week (such as more than 10%), it is determined that there are abnormal values ​​in the body nutrition data. At this time, the characteristic type of the user's body nutrition data is abnormal, and then the key monitoring parameters are determined to be abnormal key monitoring parameters, such as focusing on monitoring blood sugar, blood pressure, and blood lipids related to body fat; if all body nutrition data are within the normal fluctuation range of healthy users of the same age group and there are no abnormal values, the characteristic type of the body nutrition data is determined to be normal, and the key monitoring parameters are determined to be high-risk key monitoring parameters, focusing on whether the intake of vitamins, minerals, etc. meets the standards.

[0053] The smart lunch box will use every three days as the first preset time (this time can be adjusted appropriately by the user on the mobile APP according to their own situation) to analyze the changing trends of key monitoring parameters during this period. Assuming that the user's blood sugar index (one of the key monitoring parameters) continues to rise during these three days and gradually moves away from the healthy parameter range (set according to the average blood sugar value and reasonable fluctuation range of a large number of healthy users of the same age group), and the standard deviation of the blood sugar value change is greater than the preset standard deviation (indicating fluctuations), then the lunch box will determine to use a multi-factor monitoring method to monitor the user, which means that it is necessary not only to pay attention to dietary intake, but also to comprehensively consider the impact of other life factors such as exercise and sleep on blood sugar.

[0054] If it is found that the user's dietary fiber intake continues to be lower than the healthy parameter range within the first preset time period and the value is relatively stable without fluctuations, the smart lunch box will decide to monitor the user using the diet monitoring method, focusing on analyzing the dietary fiber content in each meal, and reminding the user to increase the intake of foods rich in dietary fiber.

[0055] If the user's dietary fiber intake is monitored to gradually approach the healthy parameter range within the first preset time period and is close to the average intake level of healthy users of the same age group, the smart lunch box will adopt a sampling monitoring method, occasionally conduct spot checks on this indicator, and appropriately relax the monitoring frequency.

[0056] Taking the dietary monitoring method as an example, if it is determined that this method is used and it is found that the user has consumed very little vegetables for many consecutive days, far below the reasonable range to meet the body's nutritional needs, and does not meet the requirements of a healthy diet, the user's behavior is judged to be unqualified. The early warning module of the smart lunch box will pop up a prompt message through the display screen (such as "You have not consumed enough vegetables recently, please increase your vegetable intake"), and the sound prompt device will issue a reminder sound, and the vibration motor will also vibrate slightly to warn the user.

[0057] Assuming that the early warning module does not issue an early warning, taking every two weeks as the second preset time, it is found that the trend of the user's key monitoring parameters (such as calcium intake) is negative, that is, the absolute value of the difference between the final value of calcium intake and the average value of the health parameter range (the average calcium intake of healthy users of the same age group) in these two weeks is greater than the preset difference (for example, the absolute value of the difference is greater than 50 mg), and after analysis, it is judged that the degree of environmental impact is low (for example, the user's recent living environment, exercise volume and other external factors are relatively stable, and there are no special circumstances that affect calcium absorption). At this time, the adjustment module will adjust the parameters of the nutrition monitoring process. Specifically, due to the large gap between calcium intake and the health parameter range, the first preset time will be appropriately increased, such as from every three days to every five days to conduct a comprehensive analysis of the trend of changes in key monitoring parameters, so as to track the user's nutritional status more carefully; at the same time, the lower limit of calcium intake in the health parameter range will be appropriately narrowed, and the relatively loose health standards will be tightened a little, so as to prompt subsequent monitoring to find problems more accurately and guide users to improve their nutritional intake.

[0058] See also Figure 4 As shown, Figure 4 The present invention is a flowchart of a method for using a nutrition monitoring system based on data processing according to an embodiment of the present invention.

[0059] Specifically, a method for using a nutrition monitoring system based on data processing includes: Step S1, obtaining the user's dietary data and the user's body nutritional data, and storing the dietary data and the user's body nutritional data in a database; Step S2, determining the key monitoring parameters of the user based on the characteristic type of the user's body nutrition data, wherein the characteristic type of the user's body nutrition data is determined according to whether there are abnormal values ​​in the user's body nutrition data; Step S3, determining a monitoring method based on the change trends of a number of key monitoring parameters within a first preset time period, wherein the monitoring method includes a multi-factor monitoring method, a dietary monitoring method, and a sampling monitoring method; Step S4, determining whether to issue an early warning based on whether the user behavior is qualified under the corresponding monitoring method; Step S5, under the condition that the early warning module does not issue an early warning, determine whether to adjust the nutrition monitoring process parameters based on the trend of change of the user's key monitoring parameters within the second preset time period under the corresponding monitoring method and the degree of environmental impact. The nutrition monitoring process parameters include the first preset time period and the health parameter range.

[0060] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

[0061] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A nutrition monitoring system based on data processing, characterized in that: include: A collection and storage module, comprising a dietary data collection unit for collecting dietary data, a body nutrition data collection unit for collecting body nutrition data of a user, and a data storage unit for storing the dietary data and the body nutrition data; An analysis module connected to the acquisition and storage module, for determining key monitoring parameters according to a characteristic type of the user's body nutrition data, wherein the characteristic type of the user's body nutrition data is determined according to whether there are abnormal values ​​in the user's body nutrition data; A monitoring module, which is connected to the analysis module and is used to determine a monitoring method according to the change trend of a number of key monitoring parameters within a first preset time period, wherein the monitoring method includes a multi-factor monitoring method, a dietary monitoring method, and a sampling monitoring method; An early warning module, which is connected to the monitoring module and is used to determine whether to issue an early warning according to whether the user behavior is qualified under the corresponding monitoring method; An adjustment module is connected to the early warning module and is used to determine whether to adjust the nutrition monitoring process parameters according to the trend of change of the user's key monitoring parameters within the second preset time period under the corresponding monitoring method, and the degree of environmental impact, under the condition that the early warning module does not issue an early warning. The nutrition monitoring process parameters include a first preset time period and a health parameter range.

2. The nutrition monitoring system based on data processing according to claim 1, characterized in that: The data analysis module determines the key monitoring parameters according to the characteristic type of the user's body nutrition data, including: The data analysis module determines that the key monitoring parameter is a high-risk key monitoring parameter according to the normal characteristic type of the user's body nutrition data; The data analysis module determines that the key monitoring parameter is an abnormal key monitoring parameter according to the abnormal characteristic type of the user's physical nutrition data.

3. The nutrition monitoring system based on data processing according to claim 2, characterized in that: The data analysis module determines the characteristic type of the user's body nutrition data according to whether there is an abnormal value in the user's body nutrition data, including: The data analysis module determines that the characteristic type of the user's body nutrition data is abnormal according to the presence of abnormal values ​​in the user's body nutrition data; The data analysis module determines that the characteristic type of the user's body nutrition data is normal based on the absence of abnormal values ​​in the user's body nutrition data.

4. The nutrition monitoring system based on data processing according to claim 3, characterized in that: The nutrition monitoring module determines the monitoring method according to the change trend of several key monitoring parameters within the first preset time period, including: The nutrition monitoring module determines to monitor the user using a multi-factor monitoring method according to the change trend of the key monitoring parameters being far from the healthy parameter range and having fluctuations; The nutrition monitoring module determines to monitor the user using a diet monitoring method according to the change trend of the key monitoring parameters being far away from the healthy parameter range and without fluctuation; The nutrition monitoring module determines to monitor the user by a sampling monitoring method according to the changing trend of the key monitoring parameters being close to the health parameter range; The changing trend of the key monitoring parameter is that the minimum value of the absolute value of the difference between the key monitoring parameter after the change and the maximum value or minimum value of the health parameter range becomes larger as it moves away from the health parameter range, and the changing trend of the key monitoring parameter is that the minimum value of the absolute value of the difference between the key monitoring parameter after the change and the maximum value or minimum value of the health parameter range becomes smaller as it approaches the health parameter range.

5. The nutrition monitoring system based on data processing according to claim 4, characterized in that: The health parameter range is the average value of the parameter of several healthy users of the same age. Judging whether there is fluctuation in the key monitoring parameter includes that the standard deviation of the change value of the key monitoring parameter within the first preset time period is greater than the preset standard deviation.

6. The nutrition monitoring system based on data processing according to claim 5, characterized in that: The early warning module determines whether to issue an early warning according to whether the user behavior is qualified under the corresponding monitoring method, including: The early warning module issues an early warning based on the determination that the user behavior is unqualified under the corresponding monitoring method.

7. The nutrition monitoring system based on data processing according to claim 6, characterized in that: The adjustment module determines whether to adjust the nutrition monitoring process parameters, including: The adjustment module determines to adjust the nutrition monitoring process parameters according to the negative trend of the key monitoring parameters of the user within the second preset time period under the corresponding monitoring method and the low degree of environmental impact, under the condition that the early warning module does not issue an early warning.

8. The nutrition monitoring system based on data processing according to claim 7, characterized in that: Determining that the trend of change of the key monitoring parameter of the user is negative includes that the absolute value of the difference between the final value of the key monitoring parameter within the second preset time period and the average value of the health parameter range is greater than the preset difference.

9. The nutrition monitoring system based on data processing according to claim 8, characterized in that: The adjustment amount of the first preset time period is positively correlated with the absolute value of the difference between the final value of the key monitoring parameter within the second preset time period and the average value of the health parameter range.

10. A method for using the nutrition monitoring system based on data processing according to any one of claims 1 to 9, characterized in that: include: Acquire the user's dietary data and the user's physical nutritional data, and store the dietary data and the user's physical nutritional data in a database; Determine the key monitoring parameters of the user based on the characteristic type of the user's body nutrition data, wherein the characteristic type of the user's body nutrition data is determined according to whether there is an abnormal value in the user's body nutrition data; Determining a monitoring method based on the changing trends of a number of key monitoring parameters within a first preset time period, wherein the monitoring method includes a multi-factor monitoring method, a dietary monitoring method, and a sampling monitoring method; Determine whether to issue an early warning based on whether the user's behavior is qualified under the corresponding monitoring method; Under the condition that the early warning module does not issue an early warning, it is determined whether to adjust the nutrition monitoring process parameters based on the trend of change of the user's key monitoring parameters within the second preset time period under the corresponding monitoring method and the degree of environmental impact. The nutrition monitoring process parameters include the first preset time period and the health parameter range.

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