Big data-based pregnant and lying-in woman health dynamic monitoring system and method

Abstract

The invention provides a pregnant and lying-in woman health dynamic monitoring system and method based on big data, and relates to the technical field of data processing, and the method comprises the steps: inputting the physiological data of a pregnant and lying-in woman, the doctor's advice information and the current pregnancy period into a health planning model, and obtaining standard energy consumption and standard nutrition parameters; obtaining current-day exercise data and current-day diet data of the pregnant and lying-in woman; on the basis of the current-day exercise data, the physiological data and the current pregnancy period, the current-day energy consumption of the pregnant and lying-in woman is determined; inputting the daily diet data and the standard nutrition types into a nutrition analysis model to obtain daily intake parameters corresponding to each standard nutrition type; comparing the energy consumption of the day with standard energy consumption to obtain an exercise monitoring result of the pregnant and lying-in woman; and comparing the daily intake parameter corresponding to each standard nutrition type with the standard intake parameter corresponding to each standard nutrition type to obtain a nutrition monitoring result of the pregnant and lying-in woman. The accuracy of the health assessment result of the pregnant and lying-in woman can be improved.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a dynamic monitoring system and method for maternal health based on big data. Background Technology

[0002] Nutritional intake and physical activity during pregnancy and postpartum are core factors affecting the short-term and long-term health of mothers and infants. Currently, nutritional and exercise guidance for pregnant women suffers from serious deficiencies in standardization and static approaches. Dietary records, exercise data, and hospital clinical test results and medical advice are stored in different systems, creating data silos. Nutritionists or doctors cannot monitor the health status of pregnant women in real time, nor can they know the mother's current health condition, leading to often inaccurate health assessments. Summary of the Invention

[0003] In view of the shortcomings of the prior art, the purpose of this invention is to provide a method for dynamic monitoring of maternal health based on big data, which can solve the technical problem that the health assessment results of pregnant women in the prior art are not reasonable enough.

[0004] A first aspect of this invention proposes a method for dynamic monitoring of maternal health based on big data, comprising:

[0005] The acquired physiological data, medical advice information, and current pregnancy status of pregnant women are input into a pre-built health planning model to obtain the standard energy consumption and standard nutritional parameters output by the health planning model; wherein, the standard nutritional parameters include standard nutritional types and standard intake parameters corresponding to each standard nutritional type.

[0006] Acquire the pregnant woman's daily exercise data and daily diet data; wherein, the daily exercise data includes at least the daily exercise duration and daily exercise energy consumption; the daily diet data includes at least the names of the foods consumed that day and the weight of each food item.

[0007] Based on the daily exercise data, the physiological data, and the current gestational age, the pregnant woman's daily energy consumption is determined.

[0008] The daily dietary data and the standard nutrient type are input into a pre-built nutrient analysis model to obtain the daily intake parameters corresponding to each standard nutrient type output by the nutrient analysis model.

[0009] The daily energy consumption is compared with the standard energy consumption to obtain the exercise monitoring results of the pregnant woman.

[0010] The daily intake parameters for each standard nutrient type are compared with the standard intake parameters for each standard nutrient type to obtain the nutritional monitoring results of the pregnant and postpartum women.

[0011] Optionally, before inputting the acquired physiological data, medical advice information, and current pregnancy stage of the pregnant woman into a pre-constructed health planning model to obtain the standard energy consumption and standard nutritional parameters output by the health planning model, the method further includes:

[0012] Obtain medical record images;

[0013] The medical record image is subjected to text recognition to obtain the text information of the medical record;

[0014] Semantic analysis is performed on the textual information of the medical records to determine the exercise recommendation information and nutrition recommendation information contained in the textual information of the medical records; wherein, the exercise recommendation information includes recommended exercise type and recommended exercise duration, and the nutrition recommendation information includes recommended nutrition type and recommended intake parameters corresponding to each recommended nutrition type;

[0015] The exercise recommendations and nutrition recommendations are identified as medical orders.

[0016] Optionally, determining the pregnant woman's daily energy expenditure based on the daily exercise data, the physiological data, and the current gestational age specifically includes:

[0017] The age, weight, and height of the pregnant woman are determined from the physiological data.

[0018] Based on the age, weight, and height, the basal energy expenditure of the pregnant woman is determined;

[0019] Determine the energy consumption parameters corresponding to the current pregnancy stage;

[0020] Based on the basic energy consumption, the duration of exercise on the day, the energy consumption of exercise on the day, and the energy consumption parameters, the daily energy consumption of the pregnant woman is determined.

[0021] Optionally, determining the pregnant woman's basal energy expenditure based on her age, weight, and height specifically includes:

[0022] Obtain the age parameter corresponding to the age, the weight parameter corresponding to the weight, the height parameter corresponding to the height, and the basic parameters;

[0023] The product of the age and the age parameter is determined as the first data;

[0024] The product of the weight and the weight parameter is determined as the second data;

[0025] The product of the height and the height parameter is determined as the third data;

[0026] The sum of the first data, the second data, and the basic parameters is determined as the fourth data;

[0027] The difference between the fourth data and the third data is determined as the basal energy consumption of the pregnant woman.

[0028] Optionally, determining the pregnant woman's daily energy consumption based on the baseline energy consumption, the daily exercise duration, the daily exercise energy consumption, and the energy consumption parameters specifically includes:

[0029] The basic daily consumption time is determined based on the total daily duration and the daily exercise duration.

[0030] The basic energy consumption for the day is determined based on the basic consumption duration and the basic energy consumption for that day.

[0031] The daily basal energy consumption, the daily exercise energy consumption, and the sum of the energy consumption parameters are determined as the daily energy consumption of the pregnant woman.

[0032] Optionally, comparing the daily energy consumption with the standard energy consumption to obtain the exercise monitoring results of the pregnant woman specifically includes:

[0033] Determine the absolute value of the difference between the daily energy consumption and the standard energy consumption;

[0034] If the absolute value is less than or equal to the preset energy consumption threshold, then the exercise test result of the pregnant woman is determined to be normal energy consumption.

[0035] If the absolute value is greater than the preset energy consumption threshold and the current energy consumption is greater than the standard energy consumption, then the exercise test result of the pregnant woman is determined to be excessive energy consumption.

[0036] If the absolute value is greater than the preset energy consumption threshold and the current energy consumption is less than the standard energy consumption, then the exercise detection result of the pregnant woman is determined to be insufficient energy consumption.

[0037] Optionally, comparing the daily intake parameters corresponding to each standard nutrient type with the standard intake parameters corresponding to each standard nutrient type to obtain the nutritional monitoring results of the pregnant and postpartum women specifically includes:

[0038] The daily intake parameters corresponding to each standard nutrient type are compared with the standard intake parameters corresponding to each standard nutrient type to obtain a set of high-intake nutrient types and a set of low-intake nutrient types. Among them, the daily intake parameters corresponding to the high-intake nutrient types in the set of high-intake nutrient types are all higher than the standard intake parameters corresponding to the high-intake nutrient types, and the daily intake parameters corresponding to the low-intake nutrient types in the set of low-intake nutrient types are all lower than the standard intake parameters corresponding to the low-intake nutrient types.

[0039] The types of nutrients with low intake included in the set of low-intake nutrient types are identified as the nutritional monitoring results of the pregnant and postpartum women.

[0040] Optionally, after obtaining the exercise monitoring results of the pregnant woman, the method further includes:

[0041] If the exercise monitoring results indicate that the pregnant woman's energy consumption is too low, then the absolute value will be determined as the energy consumption difference.

[0042] Obtain at least one type of exercise that matches the current pregnancy stage;

[0043] Based on the energy consumption difference and the physiological data, determine the exercise replenishment duration corresponding to the exercise type;

[0044] The exercise type and the corresponding exercise supplementation duration are determined as the exercise compensation scheme, and the exercise compensation scheme is output.

[0045] A second aspect of this invention provides a dynamic monitoring system for maternal health based on big data, comprising: a processor and a memory;

[0046] The memory stores programs or instructions that can run on the processor, which, when executed by the processor, implement the steps of the big data-based dynamic monitoring method for maternal health as described in the first aspect.

[0047] A third aspect of the present invention provides a readable storage medium on which a program or instructions are stored, which, when executed by a processor, implement the steps of the big data-based dynamic monitoring method for maternal health as described in the first aspect.

[0048] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following:

[0049] In this embodiment of the invention, the health planning model integrates physiological data, medical advice, and pregnancy stage to generate personalized standard energy consumption and nutritional parameters. The nutritional analysis model, through intelligent matching of daily dietary data with standard nutritional types, accurately quantifies actual intake, avoiding subjective biases from manual estimation. Simultaneously, the system dynamically corrects energy consumption calculations through collaborative analysis of daily exercise and physiological data, ensuring that exercise monitoring results accurately reflect the individual's actual metabolic state. Ultimately, by comparing energy consumption and nutritional intake, health risks (such as excessive / insufficient exercise, nutritional imbalance) can be comprehensively identified, significantly improving the accuracy and clinical guidance value of health assessment results, and providing pregnant and postpartum women with safer and more scientific health management solutions. Attached Figure Description

[0050] The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Throughout the drawings, the same reference numerals denote the same parts. Obviously, the drawings described below are merely some embodiments of the present invention, and those skilled in the art can obtain other drawings based on these drawings without any creative effort.

[0051] Figure 1 This is a flowchart illustrating a method for dynamic monitoring of maternal health based on big data, provided in an embodiment of the present invention.

[0052] Figure 2 This is a schematic diagram of the structure of a dynamic monitoring system for maternal health based on big data, provided in an embodiment of the present invention. Detailed Implementation

[0053] To enable those skilled in the art to better understand the technical solutions in the embodiments of the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. It should be understood that these descriptions are merely exemplary and are not intended to limit the scope of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0054] The following description, in conjunction with the accompanying drawings, details the method for dynamic monitoring of maternal health based on big data provided by the present invention through specific embodiments and application scenarios.

[0055] Reference manual attached Figure 1 The diagram illustrates a flowchart of a method for dynamic monitoring of maternal health based on big data, provided by an embodiment of the present invention.

[0056] This invention provides a method for dynamic monitoring of maternal health based on big data, which may include the following steps:

[0057] S101: Input the acquired physiological data, medical advice information, and current pregnancy status of the pregnant woman into the pre-built health planning model to obtain the standard energy consumption and standard nutritional parameters output by the health planning model.

[0058] In this embodiment, the standard nutritional parameters include standard nutrient types and corresponding standard intake parameters for each standard nutrient type. Pregnancy can be divided into early pregnancy, mid-pregnancy, and late pregnancy. For different stages of pregnancy, the types of nutrients, nutrient intake, types of exercise, and duration of exercise required by pregnant women are all different.

[0059] As an optional implementation, the following steps may also be performed before S101:

[0060] Obtain medical record images;

[0061] The medical record image is subjected to text recognition to obtain the text information of the medical record;

[0062] Semantic analysis is performed on the textual information of the medical records to determine the exercise recommendation information and nutrition recommendation information contained in the textual information of the medical records; wherein, the exercise recommendation information includes recommended exercise type and recommended exercise duration, and the nutrition recommendation information includes recommended nutrition type and recommended intake parameters corresponding to each recommended nutrition type;

[0063] The exercise recommendations and nutrition recommendations are identified as medical orders.

[0064] This implementation method involves first acquiring medical record images, converting them into textual information through text recognition, and then performing semantic analysis to accurately extract exercise and nutrition recommendations, including recommended exercise types, durations, and recommended nutrient types and intake parameters. This information is then designated as medical orders. This approach avoids information omissions or errors that may occur with manual entry of medical orders, ensuring the completeness and accuracy of the information. It provides a reliable basis for subsequent dynamic monitoring of maternal health based on medical orders, thereby improving the overall effectiveness of health assessment and management.

[0065] S102: Obtain the pregnant woman's daily exercise data and daily diet data.

[0066] In this embodiment of the application, the daily exercise data includes at least the daily exercise duration and the daily exercise energy consumption; the daily diet data includes at least the daily food names and the weight of each daily food name.

[0067] In this embodiment of the application, wearable devices such as sports watches can be used to detect the daily exercise data of pregnant women, and the daily exercise data of pregnant women collected by wearable devices can be obtained.

[0068] In this embodiment, the daily diet data can be manually entered by the pregnant woman or by capturing food images through an image acquisition device, thereby enabling the input of the daily diet data.

[0069] Specifically, the method of acquiring food images through image acquisition devices to input daily dietary data can be as follows:

[0070] Food images are captured using image acquisition equipment;

[0071] Feature recognition is performed on the food image to determine the name of the food and the name of the tableware contained in the food image for the day;

[0072] Input the names of the food and tableware for the day into a pre-trained food weight analysis model to obtain the food weight corresponding to the food name for the day.

[0073] S103: Based on the daily exercise data, the physiological data, and the current gestational age, determine the pregnant woman's daily energy consumption.

[0074] As an optional implementation, S103 may determine the pregnant woman's daily energy consumption based on the daily exercise data, the physiological data, and the current gestational age in the following ways:

[0075] The age, weight, and height of the pregnant woman are determined from the physiological data.

[0076] Based on the age, weight, and height, the basal energy expenditure of the pregnant woman is determined;

[0077] Determine the energy consumption parameters corresponding to the current pregnancy stage;

[0078] Based on the basic energy consumption, the duration of exercise on the day, the energy consumption of exercise on the day, and the energy consumption parameters, the daily energy consumption of the pregnant woman is determined.

[0079] This implementation method calculates basal energy expenditure by accurately extracting age, weight, and height information from physiological data. This data is then combined with energy expenditure parameters corresponding to the current stage of pregnancy, as well as actual exercise duration and energy expenditure data for the day, to comprehensively determine the pregnant woman's daily energy expenditure. This method fully considers individual physiological differences and the special needs of pregnancy, avoiding the one-sidedness of estimations based on a single factor. It makes the calculated energy expenditure more closely reflect the actual metabolic state of pregnant women, providing accurate data support for subsequent exercise monitoring and health assessment, and effectively improving the scientific rigor and effectiveness of health management.

[0080] As an optional implementation, determining the pregnant woman's basal energy expenditure based on her age, weight, and height may include:

[0081] Obtain the age parameter corresponding to the age, the weight parameter corresponding to the weight, the height parameter corresponding to the height, and the basic parameters;

[0082] The product of the age and the age parameter is determined as the first data;

[0083] The product of the weight and the weight parameter is determined as the second data;

[0084] The product of the height and the height parameter is determined as the third data;

[0085] The sum of the first data, the second data, and the basic parameters is determined as the fourth data;

[0086] The difference between the fourth data and the third data is determined as the basal energy consumption of the pregnant woman.

[0087] This implementation method involves multiplying age, weight, and height by corresponding parameters to obtain relevant data, and then using addition and subtraction operations to determine basal energy expenditure. This calculation method comprehensively considers the individual differences in age, weight, and height among pregnant and postpartum women, and compared to estimations based on single factors or experience, it more accurately reflects the physiological characteristics of each pregnant or postpartum woman. The basal energy expenditure data obtained using this method provides a reliable basis for accurately calculating daily energy expenditure, helps to more scientifically assess the energy metabolism of pregnant and postpartum women, and thus develops health management plans that better meet their physical needs, improving the accuracy of health monitoring.

[0088] In this embodiment of the application, the formula for calculating the basal energy consumption of pregnant and postpartum women is as follows:

[0089] ;

[0090] Wherein, BMR represents the pregnant woman's basal energy expenditure per day, with a baseline parameter of 655.1; g represents her weight in kilograms, with a weight parameter of 9.563; h represents her height in centimeters, with a height parameter of 1.85; and a represents her age, with an age parameter of 4.676. That is, 9.563g represents the first data point, 1.85h represents the second data point, 4.676a represents the third data point, and 655.1 + 9.563g + 1.85h represents the fourth data point.

[0091] As an optional implementation, the method for determining the pregnant woman's daily energy consumption based on the baseline energy consumption, the daily exercise duration, the daily exercise energy consumption, and the energy consumption parameters may include:

[0092] The basic daily consumption time is determined based on the total daily duration and the daily exercise duration.

[0093] The basic energy consumption for the day is determined based on the basic consumption duration and the basic energy consumption for that day.

[0094] The daily basal energy consumption, the daily exercise energy consumption, and the sum of the energy consumption parameters are determined as the daily energy consumption of the pregnant woman.

[0095] This implementation method calculates the basal metabolic rate (BMR) by combining the total daily activity time with the total exercise time, thus determining the daily BMR. This BMR is then summed with the daily exercise energy expenditure and other energy expenditure parameters to obtain the total daily energy expenditure. This calculation method comprehensively considers both the pregnant and postpartum woman's BMR in a non-exercise state and the energy expenditure from exercise, while also incorporating energy expenditure parameters specific to pregnancy, making the calculation results more closely reflect the actual metabolic situation of pregnant and postpartum women. Compared to simple estimation, it accurately reflects the daily energy expenditure, providing reliable data for health assessment, helping to develop more scientific and reasonable health management strategies, and improving the level of health management for pregnant and postpartum women.

[0096] In this embodiment of the application, the formula for calculating the daily energy consumption of pregnant and postpartum women is as follows;

[0097] ;

[0098] Wherein, K represents the daily energy consumption of the pregnant woman, the total duration of the day is 24, i.e. 24 hours, t represents the daily exercise duration in hours, 24-t represents the daily basal energy consumption duration, (24-t)BMR represents the daily basal energy consumption; N represents the daily exercise energy consumption, and m represents the energy consumption parameter.

[0099] S104: Input the daily diet data and the standard nutrition type into the pre-built nutrition analysis model to obtain the daily intake parameters corresponding to each standard nutrition type output by the nutrition analysis model.

[0100] S105: Compare the daily energy consumption with the standard energy consumption to obtain the exercise monitoring results of the pregnant woman.

[0101] As an optional implementation, S105 may compare the daily energy consumption with the standard energy consumption to obtain the exercise monitoring results of the pregnant woman, including:

[0102] Determine the absolute value of the difference between the daily energy consumption and the standard energy consumption;

[0103] If the absolute value is less than or equal to the preset energy consumption threshold, then the exercise test result of the pregnant woman is determined to be normal energy consumption.

[0104] If the absolute value is greater than the preset energy consumption threshold and the current energy consumption is greater than the standard energy consumption, then the exercise test result of the pregnant woman is determined to be excessive energy consumption.

[0105] If the absolute value is greater than the preset energy consumption threshold and the current energy consumption is less than the standard energy consumption, then the exercise detection result of the pregnant woman is determined to be insufficient energy consumption.

[0106] This implementation method calculates the absolute value of the difference between daily energy consumption and standard energy consumption, and compares it with a preset energy consumption threshold to accurately classify energy consumption into three states: normal, excessive, and insufficient. This quantitative judgment method avoids subjective and vague assessments, clearly identifying whether the pregnant or postpartum woman's exercise energy consumption meets health needs. Based on the clear results, exercise plans can be adjusted in a timely and targeted manner; for example, the intensity of exercise can be appropriately reduced when energy consumption is excessive, and the amount of exercise can be increased when energy consumption is insufficient. This effectively ensures that the pregnant or postpartum woman's exercise is within a reasonable range, improves the accuracy and practicality of health monitoring, and contributes to scientific health management.

[0107] As an optional implementation, the following steps may also be performed after S105:

[0108] If the exercise monitoring results indicate that the pregnant woman's energy consumption is too low, then the absolute value will be determined as the energy consumption difference.

[0109] Obtain at least one type of exercise that matches the current pregnancy stage;

[0110] Based on the energy consumption difference and the physiological data, determine the exercise replenishment duration corresponding to the exercise type;

[0111] The exercise type and the corresponding exercise supplementation duration are determined as the exercise compensation scheme, and the exercise compensation scheme is output.

[0112] This implementation method, when detecting insufficient energy expenditure in pregnant or postpartum women, precisely quantifies the energy deficit using the absolute value of the difference. It then selects suitable exercise types based on the current stage of pregnancy and calculates personalized exercise supplementation time based on physiological data, generating a targeted exercise compensation plan. This dynamic adjustment mechanism avoids a "one-size-fits-all" approach to exercise recommendations. It effectively compensates for insufficient energy expenditure while fully considering the pregnant or postpartum woman's physical condition and needs during pregnancy, ensuring the safety and effectiveness of exercise intervention. This helps pregnant and postpartum women scientifically increase their exercise volume and improve their overall health management.

[0113] S106: Compare the daily intake parameters corresponding to each standard nutrient type with the standard intake parameters corresponding to each standard nutrient type to obtain the nutritional monitoring results of the pregnant and postpartum women.

[0114] As an optional implementation, S106 comparing the daily intake parameter corresponding to each standard nutrient type with the standard intake parameter corresponding to each standard nutrient type to obtain the nutritional monitoring results of the pregnant woman may include:

[0115] The daily intake parameters corresponding to each standard nutrient type are compared with the standard intake parameters corresponding to each standard nutrient type to obtain a set of high-intake nutrient types and a set of low-intake nutrient types. Among them, the daily intake parameters corresponding to the high-intake nutrient types in the set of high-intake nutrient types are all higher than the standard intake parameters corresponding to the high-intake nutrient types, and the daily intake parameters corresponding to the low-intake nutrient types in the set of low-intake nutrient types are all lower than the standard intake parameters corresponding to the low-intake nutrient types.

[0116] The types of nutrients with low intake included in the set of low-intake nutrient types are identified as the nutritional monitoring results of the pregnant and postpartum women.

[0117] This implementation method, by precisely comparing daily intake parameters with standard intake parameters, clearly identifies sets of nutrients that are over- or under-ingested, enabling rapid identification of deviations in nutrient intake. In particular, identifying under-ingested nutrients as the focus of nutritional monitoring results directly addresses potential nutritional deficiencies in pregnant and postpartum women, providing a clear direction for subsequent targeted nutritional supplementation. This comparison method is simple and effective, avoiding complex analysis, and quickly identifies key nutritional deficiencies, facilitating timely adjustments to dietary plans, ensuring pregnant and postpartum women receive sufficient and balanced nutrition, and improving the efficiency and practicality of nutritional monitoring.

[0118] As an optional implementation, after S106, the following steps may also be performed:

[0119] Get a set of foods that match your current stage of pregnancy; the set includes a variety of recommended foods that match your current stage of pregnancy.

[0120] Identify multiple target foods from a food set that contain the highest amounts of the low-intake nutrient types.

[0121] The difference in intake is determined by comparing the daily intake parameter corresponding to the type of nutrient intake that is reduced with the standard intake parameter corresponding to that type of nutrient intake.

[0122] The appropriate amount of each target food should be determined based on this difference in intake.

[0123] The food compensation scheme is determined by identifying each target food and the quantity of each target food that should be consumed, and then the food compensation scheme is output.

[0124] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following:

[0125] In this embodiment of the invention, by comparing energy consumption and nutrient intake, health risks (such as excessive / insufficient exercise, nutritional imbalance) can be comprehensively identified, significantly improving the accuracy of health assessment results and clinical guidance value, and providing pregnant and postpartum women with safer and more scientific health management solutions.

[0126] Reference manual attached Figure 2 The diagram shows a structural schematic of a dynamic monitoring system for maternal health based on big data, provided by an embodiment of the present invention.

[0127] This invention provides a dynamic monitoring system 20 for maternal health based on big data, comprising: a processor 201 and a memory 202;

[0128] The memory 202 stores programs or instructions that can run on the processor 201. When the program or instructions are executed by the processor 201, they implement the steps of the above-mentioned big data-based dynamic monitoring method for maternal health and achieve the same technical effect. To avoid repetition, the present invention will not elaborate further.

[0129] It should be understood that the processor 201 in this embodiment of the invention may be a central processing unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor.

[0130] It should also be understood that the memory 202 in the embodiments of the present invention can be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of random access memory are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM), and direct memory bus RAM (DR RAM).

[0131] The above embodiments can be implemented, in whole or in part, by software, hardware (such as circuits), firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of the present invention are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0132] It should be understood that, in various embodiments of the present invention, the order of the above-mentioned process numbers does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0133] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0134] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the devices, apparatuses, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0135] In the several embodiments provided by this invention, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0136] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0137] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0138] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0139] This invention provides a readable storage medium comprising: storing a program or instructions on the readable storage medium, wherein when the program or instructions are executed by a processor, the program or instructions implement the steps of the above-described method for dynamic monitoring of maternal health based on big data, and can achieve the same technical effect. To avoid repetition, this invention will not elaborate further.

[0140] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the embodiments of the present invention, and are not intended to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the protection scope of the present invention.

Claims

1. A method for dynamic monitoring of maternal health based on big data, characterized in that, The system includes: The acquired physiological data, medical advice information, and current pregnancy status of pregnant women are input into a pre-built health planning model to obtain the standard energy consumption and standard nutritional parameters output by the health planning model; wherein, the standard nutritional parameters include standard nutritional types and standard intake parameters corresponding to each standard nutritional type. Acquire the pregnant woman's daily exercise data and daily diet data; wherein, the daily exercise data includes at least the daily exercise duration and daily exercise energy consumption; the daily diet data includes at least the names of the foods consumed that day and the weight of each food item. Based on the daily exercise data, the physiological data, and the current gestational age, the pregnant woman's daily energy consumption is determined. The daily dietary data and the standard nutrient type are input into a pre-built nutrient analysis model to obtain the daily intake parameters corresponding to each standard nutrient type output by the nutrient analysis model. The daily energy consumption is compared with the standard energy consumption to obtain the exercise monitoring results of the pregnant woman. The daily intake parameters for each standard nutrient type are compared with the standard intake parameters for each standard nutrient type to obtain the nutritional monitoring results of the pregnant and postpartum women.

2. The method for dynamic monitoring of maternal health based on big data according to claim 1, characterized in that, Before inputting the acquired physiological data, medical advice information, and current pregnancy stage of the pregnant woman into a pre-constructed health planning model to obtain the standard energy consumption and standard nutritional parameters output by the health planning model, the method further includes: Obtain medical record images; The medical record image is subjected to text recognition to obtain the text information of the medical record; Semantic analysis is performed on the textual information of the medical records to determine the exercise recommendation information and nutrition recommendation information contained in the textual information of the medical records; wherein, the exercise recommendation information includes recommended exercise type and recommended exercise duration, and the nutrition recommendation information includes recommended nutrition type and recommended intake parameters corresponding to each recommended nutrition type; The exercise recommendations and nutrition recommendations are identified as medical orders.

3. The method for dynamic monitoring of maternal health based on big data according to claim 1, characterized in that, The determination of the pregnant woman's daily energy expenditure based on the daily exercise data, the physiological data, and the current gestational age specifically includes: The age, weight, and height of the pregnant woman are determined from the physiological data. Based on the age, weight, and height, the basal energy expenditure of the pregnant woman is determined; Determine the energy consumption parameters corresponding to the current pregnancy stage; Based on the basic energy consumption, the duration of exercise on the day, the energy consumption of exercise on the day, and the energy consumption parameters, the daily energy consumption of the pregnant woman is determined.

4. The method for dynamic monitoring of maternal health based on big data according to claim 3, characterized in that, The determination of the pregnant woman's basal energy expenditure based on her age, weight, and height specifically includes: Obtain the age parameter corresponding to the age, the weight parameter corresponding to the weight, the height parameter corresponding to the height, and the basic parameters; The product of the age and the age parameter is determined as the first data; The product of the weight and the weight parameter is determined as the second data; The product of the height and the height parameter is determined as the third data; The sum of the first data, the second data, and the basic parameters is determined as the fourth data; The difference between the fourth data and the third data is determined as the basal energy consumption of the pregnant woman.

5. The method for dynamic monitoring of maternal health based on big data according to claim 4, characterized in that, The determination of the pregnant woman's daily energy consumption based on the baseline energy consumption, the daily exercise duration, the daily exercise energy consumption, and the energy consumption parameters specifically includes: The basic daily consumption time is determined based on the total daily duration and the daily exercise duration. The basic energy consumption for the day is determined based on the basic consumption duration and the basic energy consumption for that day. The daily basal energy consumption, the daily exercise energy consumption, and the sum of the energy consumption parameters are determined as the daily energy consumption of the pregnant woman.

6. The method for dynamic monitoring of maternal health based on big data according to claim 1, characterized in that, The step of comparing the daily energy consumption with the standard energy consumption to obtain the exercise monitoring results of the pregnant woman specifically includes: Determine the absolute value of the difference between the daily energy consumption and the standard energy consumption; If the absolute value is less than or equal to the preset energy consumption threshold, then the exercise test result of the pregnant woman is determined to be normal energy consumption. If the absolute value is greater than the preset energy consumption threshold and the current energy consumption is greater than the standard energy consumption, then the exercise test result of the pregnant woman is determined to be excessive energy consumption. If the absolute value is greater than the preset energy consumption threshold and the current energy consumption is less than the standard energy consumption, then the exercise detection result of the pregnant woman is determined to be insufficient energy consumption.

7. The method for dynamic monitoring of maternal health based on big data according to claim 6, characterized in that, The comparison of the daily intake parameters corresponding to each standard nutrient type with the standard intake parameters corresponding to each standard nutrient type to obtain the nutritional monitoring results of the pregnant and postpartum women specifically includes: The daily intake parameters corresponding to each standard nutrient type are compared with the standard intake parameters corresponding to each standard nutrient type to obtain a set of high-intake nutrient types and a set of low-intake nutrient types. Among them, the daily intake parameters corresponding to the high-intake nutrient types in the set of high-intake nutrient types are all higher than the standard intake parameters corresponding to the high-intake nutrient types, and the daily intake parameters corresponding to the low-intake nutrient types in the set of low-intake nutrient types are all lower than the standard intake parameters corresponding to the low-intake nutrient types. The types of nutrients with low intake included in the set of low-intake nutrient types are identified as the nutritional monitoring results of the pregnant and postpartum women.

8. The method for dynamic monitoring of maternal health based on big data according to claim 7, characterized in that, After obtaining the exercise monitoring results of the pregnant woman, the method further includes: If the exercise monitoring results indicate that the pregnant woman's energy consumption is too low, then the absolute value will be determined as the energy consumption difference. Obtain at least one type of exercise that matches the current pregnancy stage; Based on the energy consumption difference and the physiological data, determine the exercise replenishment duration corresponding to the exercise type; The exercise type and the corresponding exercise supplementation duration are determined as the exercise compensation scheme, and the exercise compensation scheme is output.

9. A dynamic monitoring system for maternal health based on big data, characterized in that, include: Processor and memory; The memory stores programs or instructions that can run on the processor, and when the programs or instructions are executed by the processor, they implement the steps of the big data-based dynamic monitoring method for maternal health as described in any one of claims 1 to 8.

10. A readable storage medium, characterized in that, The readable storage medium stores a program or instructions, which, when executed by a processor, implement the steps of the big data-based dynamic monitoring method for maternal health as described in any one of claims 1 to 8.

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