A data processing based nutrition monitoring system and method of use thereof

Abstract

The present application relates to the technical field of nutrition monitoring, and particularly relates to a nutrition monitoring system based on data processing and a use method thereof, which comprises an acquisition and storage module; an analysis module for determining key monitoring parameters according to user body nutrition data characteristic types; a monitoring module for determining a monitoring method according to the change trend of a plurality of key monitoring parameters within a first preset time length; a warning module for determining whether to issue a warning according to whether the user behavior is qualified under the corresponding monitoring method; and an adjustment module for determining whether to adjust the nutrition monitoring process parameters according to the change trend of the key monitoring parameters of the user within a second preset time length under the corresponding monitoring method and the environmental influence degree under the condition that the warning module does not issue a warning; the present application improves the accuracy of user body nutrition data analysis and further improves the accuracy of the user nutrition monitoring process.

Description

Technical Field

[0001] This invention relates to the field of nutrition monitoring technology, and in particular to a nutrition monitoring system based on data processing and its usage method. Background Technology

[0002] In today's society, people are paying increasing attention to health, and balanced nutrition has become one of the key factors in maintaining good health. However, traditional nutritional monitoring methods have many limitations and cannot meet people's needs for accurate and personalized nutritional 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, existing nutritional monitoring systems are insufficient in data processing and analysis. Some systems can only provide simple nutritional composition statistics and lack in-depth analysis of key monitoring parameters. Moreover, most existing systems adopt a single monitoring method and cannot be flexibly adjusted according to the changing trends of key monitoring parameters.

[0003] Chinese Patent Application Publication No. CN109872799A discloses a meal nutrition monitoring system and a mobile food delivery vehicle for nutrition monitoring. The system includes a weighing and selling terminal, a cloud-based nutrition monitoring server, and a nutrition and meal information query terminal. The weighing and selling terminal and the nutrition and meal information query terminal are both communicatively connected. The weighing and selling terminal, acting as a food weighing and selling terminal, collects meal information for diners and provides this information to the cloud-based nutrition monitoring server. The cloud-based nutrition monitoring server generates meal nutrition information for diners based on the meal information provided by the weighing and selling terminal. Users can query the meal nutrition information generated by the cloud-based nutrition monitoring server through the nutrition and meal information query terminal. The meal nutrition monitoring system is installed on the mobile food delivery vehicle. This invention is of great significance for improving users' eating habits and promoting their dietary health.

[0004] This shows that existing technologies have the problem of insufficient accuracy in analyzing users' body nutrition data, which leads to the inability to select accurate monitoring methods and results in low accuracy in the nutrition monitoring process. Summary of the Invention

[0005] To address this issue, the present invention provides a nutrition monitoring system based on data processing and its usage method, in order to overcome the problem in the prior art where insufficient precision in analyzing the user's body nutrition data leads to the inability to select an accurate monitoring method, resulting in low accuracy in the nutrition monitoring process.

[0006] To achieve the above objectives, the present invention provides a nutrition monitoring system based on data processing, comprising: The data acquisition and storage module includes a diet data acquisition unit for acquiring diet data, a body nutrition data acquisition unit for acquiring user body nutrition data, and a data storage unit for storing the diet data and the body nutrition data. An analysis module, which is connected to the acquisition and storage module, is used to determine key monitoring parameters based on the characteristic type of the user's body nutrition data. The characteristic type of the user's body nutrition data is determined based on whether there are outliers in the user's body nutrition data. A monitoring module, which is connected to the analysis module, is used to determine the monitoring method based on the changing trends of several key monitoring parameters within a first preset time period. 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, is used to determine whether to issue an early warning based on whether the user's behavior is qualified under the corresponding monitoring method. An adjustment module, connected to the early warning module, is used to determine whether to adjust the nutritional monitoring process parameters based on the trend of key monitoring parameters of the user and the degree of environmental influence within a second preset time period under the corresponding monitoring method, provided that the early warning module does not issue an early warning. The nutritional monitoring process parameters include a first preset time period and a range of health parameters.

[0007] Furthermore, the data analysis module determines key monitoring parameters based on the user's nutritional data characteristics, including: The data analysis module determines key monitoring parameters as high-risk key monitoring parameters based on the user's normal body nutrition data characteristics. The data analysis module identifies key monitoring parameters as abnormal key monitoring parameters based on the abnormal characteristics of the user's body nutrition data.

[0008] Furthermore, the data analysis module determines the user's nutritional data feature type based on whether there are outliers in the user's nutritional data, including: The data analysis module determines that the user's body nutrition data feature type is abnormal based on the presence of outliers in the user's body nutrition data. The data analysis module determines that the user's nutritional data feature type is normal based on the absence of outliers in the user's nutritional data.

[0009] Furthermore, the nutrition monitoring module determines the monitoring method based on the changing trends of several key monitoring parameters within a first preset time period, including: The nutrition monitoring module determines that the user is monitored using a multi-factor monitoring method based on the changing trends of key monitoring parameters, which are far from the range of healthy parameters and fluctuate. The nutrition monitoring module determines that the user is monitored using a diet monitoring method based on the fact that the changing trend of key monitoring parameters is far from the range of healthy parameters and there is no fluctuation. The nutrition monitoring module determines the user to be monitored using a sampling monitoring method based on the changing trend of key monitoring parameters, which are close to the range of healthy parameters. The changing trend of the key monitoring parameters is as follows: when the key monitoring parameters move away from the health parameter range, the minimum absolute value of the difference between the maximum or minimum value of the changed key monitoring parameters and the health parameter range increases; when the key monitoring parameters move closer to the health parameter range, the minimum absolute value of the difference between the maximum or minimum value of the changed key monitoring parameters and the health parameter range decreases.

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

[0011] Furthermore, the early warning module determines whether to issue an early warning based on whether the user's behavior is acceptable under the corresponding monitoring method, including: The early warning module issues an early warning based on the user's non-compliance under the corresponding monitoring method.

[0012] Furthermore, the adjustment module determines whether to adjust the parameters of the nutrition monitoring process, including: The adjustment module determines to adjust the nutritional monitoring process parameters based on the fact that the trend of the user's key monitoring parameters within the second preset time period under the corresponding monitoring method is negative and the degree of environmental impact is low, provided that the early warning module does not issue an early warning.

[0013] Furthermore, determining that the trend of a user's key monitoring parameters is negative includes the absolute value of the difference between the final value of the key monitoring parameters and the average value of the health parameter range within a second preset time period being greater than a preset difference.

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

[0015] On the other hand, the present invention also provides a method of using a nutrition monitoring system based on data processing, comprising: Acquire the user's diet data and the user's body nutrition data, and store the diet data and the user's body nutrition data in the database; The key monitoring parameters of a user are determined based on the feature type of the user's body nutrition data, and the feature type of the user's body nutrition data is determined based on whether there are outliers in the user's body nutrition data. The monitoring method is determined based on the changing trends of several key monitoring parameters within a first preset time period. The monitoring method includes a multi-factor monitoring method, a dietary monitoring method, and a sampling monitoring method. Whether to issue an alert depends on whether the user's behavior is acceptable under the corresponding monitoring methods. If the warning module does not issue a warning, the decision on whether to adjust the nutrition monitoring process parameters is based on the trend of the user's key monitoring parameters and the degree of environmental impact within a second preset time period under the corresponding monitoring method. The nutrition monitoring process parameters include the first preset time period and the range of health parameters.

[0016] Compared with existing technologies, the beneficial effects of this invention are that, through detailed analysis of user's nutritional data, it can accurately determine whether there are outliers, thereby identifying the characteristic type of the user's nutritional data. This allows the monitoring system to specifically target users with different health conditions, providing accurate direction for subsequent monitoring and intervention. For users with normal nutritional data, identifying high-risk key monitoring parameters can provide early warning of potential health risks. For example, when a user's serum total protein level is close to the edge of the normal range, although it is currently still within the normal range, the system has already identified it as a high-risk key monitoring parameter, closely monitoring its changing trend so that preventive measures can be taken before problems occur. For users with abnormal nutritional data, identifying abnormal key monitoring parameters can clarify the specific problem, providing a basis for developing personalized treatment and nutritional intervention plans. For example, if a user's blood glucose level exceeds the normal range, the system identifies it as an abnormal key monitoring parameter, and can then take corresponding dietary adjustments or exercise recommendations for hyperglycemia or hypoglycemia. Through the above methods, the accuracy of the analysis of the user's nutritional data is improved, thereby selecting appropriate monitoring parameters to improve the accuracy of the nutritional monitoring process.

[0017] Furthermore, this invention employs different monitoring methods based on the varying trends of key monitoring parameters, enabling more accurate and personalized nutritional monitoring services for users. For example, when key monitoring parameters are far from the healthy parameter range and fluctuate, a multi-factor monitoring method is used, comprehensively considering the user's exercise and dietary data. This allows for a more comprehensive understanding of the factors affecting the user's health, thereby enabling the development of more targeted intervention measures. When key monitoring parameters are far from the healthy parameter range and do not fluctuate, a dietary monitoring method is used to focus on the user's dietary data, allowing for more targeted adjustments to the user's eating habits and improvement of nutritional status. When key monitoring parameters are close to the healthy parameter range, a sampling monitoring method is used, monitoring the user's dietary data at sampling intervals (e.g., 6 hours). This reduces monitoring costs and user burden while ensuring monitoring effectiveness, and also allows for timely detection of potential problems, ensuring a continuous improvement in the user's nutritional status. These methods improve the accuracy of analyzing the user's body nutrition data, thereby selecting appropriate monitoring parameters to enhance the accuracy of the nutritional monitoring process.

[0018] Furthermore, the adjustment module of this invention determines whether to adjust the nutritional monitoring process parameters based on the trend of key monitoring parameters and the degree of environmental influence when no warning is issued. This enables dynamic tracking and adjustment of the user's health status. When the trend of key monitoring parameters is negative and the degree of environmental influence is low, it indicates that the user's health status may be developing in an unfavorable direction. Even without triggering a warning, the system can make timely adjustments, improving the sensitivity and foresight of monitoring. Using a second preset duration to determine the trend of key monitoring parameters provides a time window for assessing the user's long-term health trend. By comparing the absolute value of the difference between the final value of the key monitoring parameters and the average value of the health parameter range during this period with the preset difference, it is possible to accurately determine 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, it is possible to more comprehensively assess the user's health status and avoid misjudgments caused by environmental changes. The setting of the first adjustment coefficient and the second adjustment coefficient makes the adjustment of the first preset duration and the health parameter range more flexible and accurate. Through the above methods, the accuracy of the analysis of the user's body nutrition data is improved, thereby selecting appropriate monitoring parameters to improve the accuracy of the nutritional monitoring process. Attached Figure Description

[0019] Figure 1 This is a schematic diagram of the structure of the nutrition monitoring system based on data processing according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the structure of the data storage module in the nutrition monitoring system based on data processing according to an embodiment of the present invention; Figure 3This is a flowchart illustrating the workflow of the adjustment module in the data processing-based nutrition monitoring system according to an embodiment of the present invention. Figure 4 This is a flowchart illustrating the usage method of the nutrition monitoring system based on data processing, as described in an embodiment of the present invention. Detailed Implementation

[0020] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0021] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0022] It should be noted that in the description of this invention, the terms "upper", "lower", "left", "right", "inner", "outer", etc., which indicate directions or positional relationships, are based on the directions or positional relationships shown in the accompanying drawings. This is only for the convenience of description and is not intended to indicate or imply that the device or element must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation of this invention.

[0023] Furthermore, it should be noted that, in the description of this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0024] Please see Figure 1 - Figure 3 As shown, Figure 1 This is a schematic diagram of the structure of the nutrition monitoring system based on data processing according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the structure of the data storage module in the nutrition monitoring system based on data processing according to an embodiment of the present invention; Figure 3 This is a flowchart illustrating the workflow of the adjustment module in the nutrition monitoring system based on data processing, as described in an embodiment of the present invention.

[0025] The present invention provides a data processing-based nutrition monitoring system, comprising: The data acquisition and storage module includes a diet data acquisition unit for acquiring diet data, a body nutrition data acquisition unit for acquiring user body nutrition data, and a data storage unit for storing the diet data and the body nutrition data. An analysis module, which is connected to the acquisition and storage module, is used to determine key monitoring parameters based on the characteristic type of the user's body nutrition data. The characteristic type of the user's body nutrition data is determined based on whether there are outliers in the user's body nutrition data. A monitoring module, which is connected to the analysis module, is used to determine the monitoring method based on the changing trends of several key monitoring parameters within a first preset time period. 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, is used to determine whether to issue an early warning based on whether the user's behavior is qualified under the corresponding monitoring method. An adjustment module, connected to the early warning module, is used to determine whether to adjust the nutritional monitoring process parameters based on the trend of key monitoring parameters of the user and the degree of environmental influence within a second preset time period under the corresponding monitoring method, provided that the early warning module does not issue an early warning. The nutritional monitoring process parameters include a first preset time period and a range of health parameters.

[0026] The dietary data mentioned in this embodiment of the invention includes, but is not limited to, "food type, food weight and eating time", and the user's body nutrition 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 based on the characteristics of the user's body nutrition data, after determining the key monitoring parameters. If the user's nutritional data characteristics are normal, the data analysis module determines that the key monitoring parameter is a high-risk key monitoring parameter. If the user's nutritional data characteristics are abnormal, the data analysis module determines the key monitoring parameters as abnormal key monitoring parameters.

[0028] In this embodiment of the invention, the high-risk key monitoring parameter is defined as the minimum value of the absolute difference between the value of the monitoring parameter and the maximum or minimum value of the health parameter range, which is less than one-tenth of the minimum value of the health parameter range. For example, the health parameter range for serum total protein is 60g / L-80g / L. If a user's serum total protein content is 62g / L, then the serum total 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. However, the above values ​​are not limited to these, and those skilled in the art can adjust the values ​​according to actual needs.

[0029] Specifically, the data analysis module determines the user's body nutrition data feature type based on whether there are outliers in the user's body nutrition data, after determining the user's body nutrition data feature type. If there are outliers in the user's nutritional data, the data analysis module determines that the user's nutritional data feature type is abnormal; If there are no abnormal values ​​in the user's nutritional data, the data analysis module determines that the user's nutritional data characteristic type is normal.

[0030] In this embodiment of the invention, the abnormal value is the user's body nutrition data value that is not within the range of healthy parameters.

[0031] This invention, through detailed analysis of user nutritional data, can accurately determine the presence of outliers and thus identify the characteristics of the user's nutritional data. This allows the monitoring system to target users with different health conditions, providing accurate direction for subsequent monitoring and intervention. For users with normal nutritional data, identifying high-risk key monitoring parameters can provide early warning of potential health risks. For example, when a user's serum total protein level is close to the edge of the normal range, although it is currently still within the normal range, the system has already identified it as a high-risk key monitoring parameter, closely monitoring its changing trend so that preventive measures can be taken before problems occur. For users with abnormal nutritional data, identifying abnormal key monitoring parameters can clarify the specific problem, providing a basis for developing personalized treatment and nutritional intervention plans. For example, if a user's blood glucose level exceeds the normal range, the system identifies it as an abnormal key monitoring parameter, and can then provide corresponding dietary adjustments or exercise recommendations for hyperglycemia or hypoglycemia. These methods improve the accuracy of user nutritional data analysis and enable the selection of appropriate monitoring parameters to enhance the accuracy of the nutritional monitoring process.

[0032] Specifically, the nutrition monitoring module determines the monitoring method based on the changing trends of several key monitoring parameters within a first preset time period, provided that the monitoring method is determined. If the trend of the key monitoring parameters is far from the range of healthy parameters and fluctuates, the nutrition monitoring module determines to monitor the user using a multi-factor monitoring method. If the trend of the key monitoring parameters is far from the range of healthy parameters and there is no fluctuation, the nutrition monitoring module determines to monitor the user using a diet monitoring method. If the trend of the key monitoring parameters is close to the range of healthy parameters, the nutrition monitoring module determines to monitor the user using a sampling monitoring method.

[0033] In this embodiment of the invention, the first preset duration can be set to 7 days. The trend of change of the key monitoring parameter is that it moves away from the healthy parameter range, including the minimum absolute value of the difference between the changed key monitoring parameter and the maximum or minimum value of the healthy parameter range increases. The trend of change of the key monitoring parameter is that it moves closer to the healthy parameter range, including the minimum absolute value of the difference between the changed key monitoring parameter and the maximum or minimum value of the healthy parameter range decreases. For example, if the healthy parameter range for body fat content is 15% to 20%, and the user's body fat content changes from 12% to 10% within the first preset duration, then the trend of change of body fat content is that it moves away from the healthy parameter range. The healthy parameter range is determined by the minimum and maximum values ​​of this parameter for several healthy users of the same age. Determining whether there is fluctuation in the key monitoring parameter includes the standard deviation of the change value of the key monitoring parameter within the first preset duration being greater than the preset standard deviation. The preset standard deviation is the average of the standard deviations of the change values ​​of the key monitoring parameter within several segments of the first preset duration. However, the above value is not limited to this, and those skilled in the art can adjust the value according to actual needs.

[0034] In this embodiment of the invention, the multi-factor monitoring method for monitoring users includes monitoring users' exercise data and dietary data. The dietary monitoring method for monitoring users includes monitoring users' dietary data. The sampling monitoring method for monitoring users includes monitoring users' dietary data at sampling intervals, where the sampling interval can be set to 6 hours.

[0035] This invention employs different monitoring methods based on the varying trends of key monitoring parameters, enabling more accurate and personalized nutritional monitoring services for users. For example, when key monitoring parameters are far from the healthy parameter range and fluctuate, a multi-factor monitoring method is used, comprehensively considering the user's exercise and dietary data. This allows for a more comprehensive understanding of the factors affecting the user's health, thereby enabling the development of more targeted intervention measures. When key monitoring parameters are far from the healthy parameter range and do not fluctuate, a dietary monitoring method is used to focus on the user's dietary data, allowing for more targeted adjustments to the user's eating habits and improvement of nutritional status. When key monitoring parameters are close to the healthy parameter range, a sampling monitoring method is used, monitoring the user's dietary data at sampling intervals (e.g., 6 hours). This method reduces monitoring costs and user burden while ensuring monitoring effectiveness, and can promptly identify potential problems, ensuring the user's nutritional status continues to improve. Through these methods, the accuracy of analyzing the user's body nutrition data is improved, allowing for the selection of appropriate monitoring parameters to enhance the accuracy of the nutritional monitoring process.

[0036] Specifically, the warning module determines whether to issue a warning based on whether the user's behavior is acceptable under the corresponding monitoring method. If the user's behavior is deemed unqualified under the corresponding monitoring method, the early warning module determines to issue an early warning. If the user's behavior is deemed acceptable under the corresponding monitoring method, the early warning module will determine not to issue an early warning.

[0037] In this embodiment of the invention, determining whether a user's behavior is acceptable under the multi-factor monitoring method includes whether the user's exercise data and dietary data are regular. If they are regular, the user's behavior is acceptable. The regularity of exercise data and dietary data can be determined based on the variance of exercise data and the variance of dietary data over 5 days. The exercise data can be selected as calories, and the dietary data can be selected as the content of key monitoring parameters contained in food. However, the above values ​​are not limited to these, and those skilled in the art can adjust the values ​​according to actual needs.

[0038] In this embodiment of the invention, determining whether a user's behavior is acceptable under the dietary monitoring method and the sampling monitoring method includes whether the user increases the weight of food containing key monitoring parameters. If the user increases the weight, the behavior is deemed acceptable.

[0039] Specifically, the adjustment module determines whether to adjust the nutritional monitoring process parameters based on the trend of key monitoring parameters of the user and the degree of environmental impact within a second preset time period under the corresponding monitoring method, provided that the early warning module does not issue an early warning. If, under the corresponding monitoring method where the early warning module does not issue an early warning, the trend of the user's key monitoring parameters is negative and the degree of environmental impact is low within the second preset time period, the adjustment module determines that the nutritional monitoring process parameters need to be adjusted. If, under the corresponding monitoring method where the early warning module does not issue an early warning, the trend of the user's key monitoring parameters is negative and the degree of environmental impact is high within the second preset time period, or if the trend of the user's key monitoring parameters is positive, the adjustment module determines that no adjustment is needed to the nutritional monitoring process parameters.

[0040] In this embodiment of the invention, the second preset duration can be set to 30 days. The judgment of the trend of the user's key monitoring parameters is negative, which means that the absolute value of the difference between the final value of the key monitoring parameters and the average value of the health parameter range within the second preset duration is greater than a preset difference. The preset difference is the absolute value of the difference between the initial value of the key monitoring parameters and the average value of the health parameter range within the second preset duration. However, the above value is not limited to this, and those skilled in the art can also adjust the value according to actual needs.

[0041] In this embodiment of the invention, determining the degree of environmental impact includes judging the degree of environmental impact as low based on environmental parameters being within a preset environmental parameter range. The environmental impact parameters include, but are not limited to, "temperature and humidity". The minimum value of the preset environmental parameter range is seven-tenths of the average environmental parameters of the user's living environment over one year, and the maximum value of the preset environmental parameter range is thirteen-tenths of the average environmental parameters of the user's living environment over one year. However, the above values ​​are not limited to these values, and those skilled in the art can adjust the values ​​according to actual needs.

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

[0043] In this embodiment of the invention, adjusting the health parameter range with a second adjustment coefficient includes setting the value range of the first adjustment coefficient to 1.05-1.21, preferably 1.16, and setting the value range of the second adjustment coefficient to 0.82-0.97, preferably 0.89. The adjustment amount of the first preset duration is positively correlated with the absolute value of the difference between the final value of the key monitoring parameter and the average value of the health parameter range within the second preset duration. However, the above values ​​are not limited to these, and those skilled in the art can adjust the values ​​according to actual needs.

[0044] In the absence of an early warning, the adjustment module of this invention determines whether to adjust the nutritional monitoring parameters based on the trend of key monitoring parameters and the degree of environmental influence. This enables dynamic tracking and adjustment of the user's health status. When the trend of key monitoring parameters is negative and the degree of environmental influence is low, it indicates that the user's health status may be deteriorating. Even without triggering an early warning, the system can make timely adjustments, improving the sensitivity and foresight of monitoring. Using a second preset duration to determine the trend of key monitoring parameters provides a time window for assessing the user's long-term health trend. By comparing the absolute value of the difference between the final value of the key monitoring parameter and the average value of the health parameter range during this period with the preset difference, it is possible to accurately determine whether the user's health status is improving or deteriorating. Considering the impact of environmental factors such as temperature and humidity on user health, it is possible to more comprehensively assess the user's health status and avoid misjudgments caused by environmental changes. The setting of the first and second adjustment coefficients makes the adjustment of the first preset duration and the health parameter range more flexible and accurate. Through the above methods, the accuracy of the analysis of the user's body nutrition data is improved, thereby selecting appropriate monitoring parameters to improve the accuracy of the nutritional monitoring process.

[0045] In one embodiment of the present invention, it can be applied to a smart lunchbox. The smart lunchbox 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 acquisition unit collects dietary data by installing a high-precision weight sensor and an image recognition camera inside the lunchbox. The weight sensor accurately measures the weight of the food placed in the lunchbox each time, while the image recognition camera can identify the type of food, such as distinguishing between different categories like vegetables, meat, grains, or fruits, thereby obtaining 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 body 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 smart lunchbox's data processing unit.

[0048] Data storage unit: The smart lunchbox has a built-in large-capacity storage chip to store the collected dietary and nutritional data, facilitating subsequent analysis, processing, and data retrieval.

[0049] Display and prompt module: The lunchbox is equipped with an LCD screen to show users current nutritional intake, monitoring results, and relevant prompts. It also features an audio prompt and a vibration motor to issue warnings and reminders.

[0050] Communication module: Equipped with Bluetooth and Wi-Fi capabilities, it can connect to users' mobile phones and other smart terminals to facilitate viewing more detailed data reports and setting personalized parameters; on the other hand, it can also interact with cloud servers to facilitate data backup and system function updates.

[0051] Users place their prepared meals into the smart lunchbox daily. An internal weight sensor immediately measures the food's weight, and an image recognition camera identifies the food types. For example, if a breakfast meal includes 100 grams of oatmeal, 50 grams of blueberries, and a boiled egg, this dietary data is recorded in real-time and stored in the lunchbox's data storage unit. Simultaneously, a smart bracelet connected to the lunchbox monitors the user's heart rate and sleep patterns, while a smart body fat scale measures the user's current weight, body fat percentage, and other nutritional data. This data is also transmitted to the smart lunchbox for storage.

[0052] The lunchbox's data processing unit (corresponding to the analysis module function) analyzes the stored body nutrition data to determine if any outliers exist. For example, if a user's body fat percentage is found to be higher than the average body fat percentage of healthy users of the same age group by a certain percentage (e.g., exceeding 10%) for a consecutive week, the body nutrition data is determined to have an outlier. In this case, the user's body nutrition data characteristic type is abnormal, and key monitoring parameters are identified as abnormal key monitoring parameters, such as 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 outliers, the body nutrition data characteristic type is determined to be normal, and key monitoring parameters are identified as high-risk key monitoring parameters, focusing on whether the intake of vitamins, minerals, etc., is up to standard.

[0053] The smart lunchbox will use a three-day preset time period (this period can be adjusted by the user on the mobile app according to their own situation) to analyze the changing trends of key monitoring parameters during this period. If the user's blood glucose level (one of the key monitoring parameters) shows a continuous rise and gradually deviates from the healthy parameter range (set based on the average blood glucose level and reasonable fluctuation range of a large number of healthy users of the same age) over these three days, and the standard deviation of the blood glucose value change is greater than the preset standard deviation (indicating fluctuation), then the lunchbox will determine to use a multi-factor monitoring method to monitor the user. This means that it will not only focus on dietary intake but also comprehensively consider the impact of other lifestyle factors such as exercise and sleep on blood glucose.

[0054] If the smart lunchbox detects that the user's dietary fiber intake is consistently below the healthy parameter range and the value is relatively stable without fluctuation within the first preset time period, it will determine to monitor the user using dietary monitoring methods, 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 smart lunchbox detects that the user's dietary fiber intake gradually approaches 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, the smart lunchbox will use a sampling monitoring method to occasionally check this indicator and appropriately relax the monitoring frequency.

[0056] Taking dietary monitoring as an example, if it is determined that after using this method, it is found that the user's vegetable intake is extremely low for several consecutive days, far below the reasonable range to meet the body's nutritional needs, and does not meet the requirements of a healthy diet, then the user's behavior is deemed unqualified. The smart lunchbox's warning module will then display a prompt message on the screen (such as "Your vegetable intake has been insufficient recently, please increase your vegetable intake"), and at the same time, the sound prompt device will emit a reminder sound, and the vibration motor will also vibrate slightly to issue a warning to the user.

[0057] Assuming no warning is issued by the early warning module, and using a second preset timeframe of two weeks, if the trend of a user's key monitoring parameter (such as calcium intake) is found to be negative—meaning the absolute value of the difference between the final calcium intake and the average value of the healthy parameter range (average calcium intake of healthy users in the same age group) within these two weeks is greater than a preset difference (e.g., the absolute value of the difference is greater than 50 mg)—and analysis determines that the degree of environmental influence is low (e.g., the user's recent living environment, exercise level, and other external factors are relatively stable, and there are no special circumstances affecting calcium absorption), then the adjustment module will adjust the parameters of the nutrition monitoring process. Specifically, because the calcium intake deviates significantly from the healthy parameter range, the first preset timeframe will be appropriately increased, for example, from every three days to every five days, to conduct a comprehensive trend analysis of the key monitoring parameter changes, in order to track the user's nutritional status more meticulously. At the same time, the lower limit of calcium intake within the healthy parameter range will be appropriately narrowed, tightening the previously relatively lenient health standard, prompting subsequent monitoring to more accurately identify problems and guide users to improve their nutritional intake.

[0058] Please see Figure 4 As shown, Figure 4 This is a flowchart illustrating the usage method of the nutrition monitoring system based on data processing, as described in an embodiment of the present invention.

[0059] Specifically, a method of using a data processing-based nutrition monitoring system includes: Step S1: Obtain the user's diet data and the user's body nutrition data, and store the diet data and the user's body nutrition data in the database; Step S2: Determine the user's key monitoring parameters based on the user's body nutrition data feature type, wherein the user's body nutrition data feature type is determined based on whether there are outliers in the user's body nutrition data. Step S3: Determine the monitoring method based on the changing trends of several key monitoring parameters within a first preset time period. The monitoring method includes a multi-factor monitoring method, a dietary monitoring method, and a sampling monitoring method. Step S4: Determine whether to issue an alert based on whether the user's behavior is acceptable under the corresponding monitoring method; Step S5: If the warning module does not issue a warning, determine whether to adjust the nutrition monitoring process parameters based on the trend of the user's key monitoring parameters and the degree of environmental influence within the second preset time period under the corresponding monitoring method. The nutrition monitoring process parameters include the first preset time period and the range of health parameters.

[0060] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles 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 all fall within the scope of protection of the present invention.

[0061] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A nutrition monitoring system based on data processing, characterized in that, include: The data acquisition and storage module includes a diet data acquisition unit for acquiring diet data, a body nutrition data acquisition unit for acquiring user body nutrition data, and a data storage unit for storing the diet data and the body nutrition data. An analysis module, which is connected to the acquisition and storage module, is used to determine key monitoring parameters based on the characteristic type of the user's body nutrition data. The characteristic type of the user's body nutrition data is determined based on whether there are outliers in the user's body nutrition data. A monitoring module, which is connected to the analysis module, is used to determine the monitoring method based on the changing trends of several key monitoring parameters within a first preset time period. The monitoring module determines to monitor the user using a multi-factor monitoring method based on the fact that the changing trends of the key monitoring parameters are far from the range of healthy parameters and fluctuate. Based on the fact that the changing trends of key monitoring parameters are far from the range of healthy parameters and there is no fluctuation, it is determined that users will be monitored using dietary monitoring methods. Based on the changing trend of key monitoring parameters, users are identified as being monitored using a sampling monitoring method when the parameters are approaching the range of healthy parameters. The changing trend of the key monitoring parameters is when the absolute value of the difference between the changed key monitoring parameters and the maximum or minimum value of the range of healthy parameters increases, while the changing trend of the key monitoring parameters is when the absolute value of the difference between the changed key monitoring parameters and the maximum or minimum value of the range of healthy parameters decreases. An early warning module, which is connected to the monitoring module, is used to determine whether to issue an early warning based on whether the user's behavior is qualified under the corresponding monitoring method. An adjustment module, which is connected to the early warning module, is used to adjust the nutritional monitoring process parameters based on the negative trend of the user's key monitoring parameters and the low degree of environmental impact within a second preset time period under the corresponding monitoring method, provided that the early warning module does not issue an early warning. The nutritional monitoring process parameters include a first preset time period and a range of health parameters. Among them, judging the trend of the user's key monitoring parameters as negative includes the absolute value of the difference between the final value of the key monitoring parameters and the average value of the health parameter range within the second preset time period being greater than a preset difference.

2. The nutrition monitoring system based on data processing according to claim 1, characterized in that, The analysis module determines key monitoring parameters based on the characteristics of the user's body nutrition data, including: The analysis module determines key monitoring parameters as high-risk key monitoring parameters based on the user's normal body nutrition data characteristics. The analysis module identifies key monitoring parameters as abnormal key monitoring parameters based on the abnormal characteristics of the user's body nutrition data.

3. The nutrition monitoring system based on data processing according to claim 2, characterized in that, The analysis module determines the user's nutritional data feature types based on whether there are outliers in the user's nutritional data, including: The analysis module determines that the user's body nutrition data feature type is abnormal based on the presence of outliers in the user's body nutrition data. The analysis module determines that the user's nutritional data feature type is normal based on the absence of outliers in the user's nutritional data.

4. The nutrition monitoring system based on data processing according to claim 3, characterized in that, The range of health parameters is the average value of parameters of several healthy users of the same age. The determination of whether there is fluctuation in key monitoring parameters includes whether the standard deviation of the change value of key monitoring parameters within a first preset time period is greater than the preset standard deviation.

5. The nutrition monitoring system based on data processing according to claim 4, characterized in that, The early warning module determines whether to issue an early warning based on whether the user's behavior is acceptable under the corresponding monitoring method, including: The early warning module issues an early warning based on the user's non-compliance under the corresponding monitoring method.

6. The nutrition monitoring system based on data processing according to claim 1, characterized in that, The adjustment amount of the first preset duration is positively correlated with the absolute value of the difference between the final value of the key monitoring parameter and the average value of the health parameter range within the second preset duration.

7. A method of using the data processing-based nutrition monitoring system according to any one of claims 1-6, characterized in that, include: Acquire the user's diet data and the user's body nutrition data, and store the diet data and the user's body nutrition data in the database; The key monitoring parameters of a user are determined based on the feature type of the user's body nutrition data, and the feature type of the user's body nutrition data is determined based on whether there are outliers in the user's body nutrition data. The monitoring method is determined based on the changing trends of several key monitoring parameters within a first preset time period. The monitoring method includes a multi-factor monitoring method, a dietary monitoring method, and a sampling monitoring method. Whether to issue an alert depends on whether the user's behavior is acceptable under the corresponding monitoring methods. If the warning module does not issue a warning, the decision on whether to adjust the nutrition monitoring process parameters is based on the trend of the user's key monitoring parameters and the degree of environmental impact within a second preset time period under the corresponding monitoring method. The nutrition monitoring process parameters include the first preset time period and the range of health parameters.

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