Comprehensive nutrition monitoring system based on intelligent data calculation

Abstract

The invention relates to the technical field of nutrition monitoring, in particular to a comprehensive nutrition monitoring system based on intelligent data computation.The system comprises a data acquisition port, a data checking port and a nutrition abnormity tracing port, and the data acquisition port is used for monitoring physiological indexes, behaviors and disease conditions of a patient in real time through multiple devices; the physiological indexes comprise age, height, weight, body fat, muscle conditions, energy intake, protein and body functions; the data checking port is used for taking three days as a monitoring period, constructing a comparison group for the physiological indexes of the patient in the same time period of three continuous monitoring periods, calculating a data abnormal value and judging whether to enter a nutrition abnormality analysis process or not; and the nutrition abnormity tracing port is used for positioning the time period of nutrition abnormity and searching the main cause of nutrition abnormity.

Description

Technical Field

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

[0002] In the current field of health management and nutritional testing, traditional technologies mainly rely on periodic physical examinations or single tests to obtain patients' physiological indicators. These methods have certain limitations. Most traditional solutions only focus on a single or a few physiological indicators, fail to achieve comprehensive analysis of multi-dimensional data, and are prone to missing hidden nutritional abnormalities. Relying on single or periodic examinations, it is impossible to capture the continuous changes in patients' physiological indicators, resulting in difficulty in timely detection of abnormal conditions. Traditional methods lack continuous comparison of data from different time periods and often rely on manual experience and judgment, resulting in low data processing efficiency and strong subjectivity. When detecting nutritional abnormalities, current technologies find it difficult to effectively correlate patients' daily behaviors with changes in physiological indicators and cannot fundamentally reveal the causes of abnormalities. Summary of the Invention

[0003] In response to the shortcomings of the existing technology, the present invention proposes a comprehensive nutrition monitoring system based on intelligent data calculation. The present invention uses a variety of sensor devices through a data acquisition port to monitor multiple physiological indicators and behavioral data of patients in real time, ensuring that the data source is comprehensive and real-time; a comparison group is constructed using data from the same time period of three consecutive monitoring cycles, and abnormal values ​​of physiological indicators are calculated by setting weight factors to achieve quantitative judgment of abnormal states and improve detection accuracy; through the nutrition abnormality tracing port, the system can not only locate the specific time period when the abnormality occurs, but also use the matching calculation of behavioral data and abnormal indicators to find out the main causes of nutritional abnormalities, providing a basis for precise intervention; the real-time monitoring and intelligent traceability capabilities of the solution enable nutritional abnormalities to be identified at an early stage, facilitating rapid response and intervention by medical personnel, thereby improving the health management effect of patients.

[0004] To achieve the above object, the technical solution of the present invention is as follows:

[0005] A comprehensive nutrition monitoring system based on intelligent data calculation, the system includes a data acquisition port, a data verification port and a nutrition abnormality tracing port. The data acquisition port is used to monitor patients' physiological indicators, patient behavior and patient disease conditions in real time through multiple devices. The physiological indicators include age, height, weight, body fat, muscle condition, energy intake, protein, and body function; the data verification port is used to use three days as a monitoring cycle, construct a comparison group of the patient's physiological indicators in the same time period of three consecutive monitoring cycles, calculate data abnormalities and determine whether to enter the nutrition abnormality analysis process; the nutrition abnormality tracing port is used to locate the time period of nutrition abnormality and find the main cause of nutrition abnormality.

[0006] A further improvement of the present invention is that the data verification port includes a data extraction module, a data verification module and a threshold judgment module; it is used to extract the patient's physiological indicators in the same time period of three consecutive monitoring cycles; the data verification module is used to calculate the abnormal value of the physiological indicator data; the threshold judgment module is used to judge the size relationship between the abnormal value of the physiological indicator data and the abnormal value threshold, and when the data abnormal value is greater than the abnormal value threshold, the nutritional abnormality analysis process is entered.

[0007] A further improvement of the present invention is that the nutritional abnormality tracing port includes a nutritional abnormality time positioning module and a nutritional abnormality behavior search module. The nutritional abnormality time positioning module is used to run the nutritional abnormality time positioning strategy, calculate the comprehensive confirmation values ​​of different time periods and arrange them in descending order, and take the nutritional abnormality time source of the time period with the largest comprehensive confirmation value; the nutritional abnormality behavior search module is used to run the abnormal behavior search strategy, calculate the matching values ​​of the patient's different behaviors and the patient's abnormal physiological indicators, and take the patient behavior with the largest matching value as the main cause of the nutritional abnormality.

[0008] A further improvement of the present invention is that the data verification module calculates abnormal values ​​of physiological indicator data, and the specific formula for calculating abnormal values ​​of physiological indicator data is:

[0009]

[0010] in, is the value of the i-th physiological indicator of the patient in a certain period of time during this monitoring cycle, is the value of the i-th physiological indicator of the patient in the same time period of the previous monitoring cycle, is the value of the i-th physiological indicator in the same time period of the patient's first two monitoring cycles, α1 and α2 are weight factors respectively, α1+α2=1, and the value of i is 1-n.

[0011] A further improvement of the present invention is that the nutritional abnormality time positioning module runs a nutritional abnormality time positioning strategy; the nutritional abnormality time positioning strategy includes the following specific steps:

[0012] S11, extracting various physiological indicators of the patient at different time periods during this monitoring cycle;

[0013] S12. Calculate the comprehensive confirmation value of the patient at different time periods during this monitoring cycle. The specific formula for calculating the comprehensive confirmation value is:

[0014]

[0015] Among them, P k is the comprehensive confirmation value of the patient's monitoring period k, is the value of the i-th physiological indicator of the patient in the k-th time period of this monitoring cycle, is the value of the i-th physiological indicator of the patient in the k-1 time period of this monitoring cycle, β i is the weight factor of the i-th physiological index value;

[0016] S13. Arrange the comprehensive confirmation values ​​of the patient in different time periods of this monitoring cycle in descending order, and take the nutritional abnormality time source of the time period with the largest comprehensive confirmation value.

[0017] A further improvement of the present invention is that the nutritional abnormal behavior search module runs an abnormal behavior search strategy, and the abnormal behavior search strategy includes the following specific steps:

[0018] S21, extract the patient's monitoring period behavior data set A and the patient's monitoring period abnormal physiological index set B, and obtain the associated physiological index set A of the single behavior element in the patient's monitoring period behavior data set A m , where A m Represents the set of physiological indicators associated with the mth behavior element of the patient's behavior data set A in this monitoring period;

[0019] S22. Calculate matching values ​​between the patient's different behaviors and the patient's abnormal physiological indicators. The specific formula for calculating the matching value is:

[0020]

[0021] Among them, N(A m ∩B) represents the set A m The number of elements that intersect with set B, N(A m ∪B) represents the set A m The number of elements in the union with set B, Q m Indicates the matching degree of the behavioral data of the mth patient in this monitoring period;

[0022] S23. Arrange the matching values ​​of all behavioral data elements in the patient's behavioral data set A during this monitoring period in descending order, and take the behavioral data element with the largest matching value as the main cause of nutritional abnormalities.

[0023] The technical effects of the present invention are as follows:

[0024] The present invention utilizes a variety of sensor devices through the data acquisition port to monitor multiple physiological indicators and behavioral data of patients in real time, ensuring that the data source is comprehensive and real-time; uses data from the same time period of three consecutive monitoring cycles to construct a comparison group, and calculates abnormal values ​​of physiological indicators by setting weight factors to achieve quantitative judgment of abnormal conditions and improve detection accuracy; through the nutritional abnormality tracing port, the system can not only locate the specific time period when the abnormality occurs, but also use the matching calculation of behavioral data and abnormal indicators to find out the main cause of nutritional abnormalities, providing a basis for precise intervention; the real-time monitoring and intelligent traceability capabilities of the solution enable nutritional abnormalities to be identified at an early stage, facilitating medical personnel to respond and intervene quickly, thereby improving the patient's health management effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Other features, objects and advantages of the present invention will become more apparent upon reading the detailed description of non-limiting embodiments made with reference to the following drawings:

[0026] Figure 1 This is a structural diagram of a comprehensive nutrition monitoring system based on intelligent data calculation according to the present invention. DETAILED DESCRIPTION

[0027] Example 1

[0028] A comprehensive nutrition monitoring system based on intelligent data calculation includes a data acquisition port, a data verification port and a nutrition abnormality tracing port. The data acquisition port is used to monitor the patient's physiological indicators, patient behavior and patient disease conditions in real time through multiple devices. The physiological indicators include age, height, weight, body fat, muscle condition, energy intake, protein, and body function; the data verification port is used to use three days as a monitoring cycle, construct a comparison group of the patient's physiological indicators in the same time period of three consecutive monitoring cycles, calculate data abnormalities and determine whether to enter the nutrition abnormality analysis process; the nutrition abnormality tracing port is used to locate the time period of nutrition abnormality and find the main cause of nutrition abnormality.

[0029] In this embodiment, the data verification port includes a data extraction module, a data verification module and a threshold judgment module; the data extraction module is used to extract the patient's physiological indicators in the same time period of three consecutive monitoring cycles; the data verification module is used to calculate the abnormal value of the physiological indicator data; the threshold judgment module is used to judge the size relationship between the abnormal value of the physiological indicator data and the abnormal value threshold, and enter the nutritional abnormality analysis process when the data abnormal value is greater than the abnormal value threshold.

[0030] In this embodiment, the nutritional abnormality tracing port includes a nutritional abnormality time positioning module and a nutritional abnormality behavior search module. The nutritional abnormality time positioning module is used to run the nutritional abnormality time positioning strategy, calculate the comprehensive confirmation values ​​of different time periods and arrange them in descending order, and take the nutritional abnormality time source of the time period with the largest comprehensive confirmation value; the nutritional abnormality behavior search module is used to run the abnormal behavior search strategy, calculate the matching values ​​of the patient's different behaviors and the patient's abnormal physiological indicators, and take the patient behavior with the largest matching value as the main cause of the nutritional abnormality.

[0031] In this embodiment, the data verification module calculates abnormal values ​​of physiological indicator data. The specific formula for calculating abnormal values ​​of physiological indicator data is:

[0032]

[0033] in, is the value of the i-th physiological indicator of the patient in a certain period of time during this monitoring cycle, is the value of the i-th physiological indicator of the patient in the same time period of the previous monitoring cycle, is the value of the i-th physiological indicator in the same time period of the patient's first two monitoring cycles, α1 and α2 are weight factors respectively, α1+α2=1, and the value of i is 1-n.

[0034] In this embodiment, the nutritional abnormality time location module executes a nutritional abnormality time location strategy; the nutritional abnormality time location strategy includes the following specific steps:

[0035] S11, extracting various physiological indicators of the patient at different time periods during this monitoring cycle;

[0036] S12. Calculate the comprehensive confirmation value of the patient at different time periods during this monitoring cycle. The specific formula for calculating the comprehensive confirmation value is:

[0037]

[0038] Among them, P k is the comprehensive confirmation value of the patient's monitoring period k, is the value of the i-th physiological indicator of the patient in the k-th time period of this monitoring cycle, is the value of the i-th physiological indicator of the patient in the k-1 time period of this monitoring cycle, β i is the weight factor of the i-th physiological index value;

[0039] S13. Arrange the comprehensive confirmation values ​​of the patient in different time periods of this monitoring cycle in descending order, and take the nutritional abnormality time source of the time period with the largest comprehensive confirmation value.

[0040] In this embodiment, the nutritional abnormal behavior search module runs an abnormal behavior search strategy, and the abnormal behavior search strategy includes the following specific steps:

[0041] S21, extract the patient's monitoring period behavior data set A and the patient's monitoring period abnormal physiological index set B, and obtain the associated physiological index set A of the single behavior element in the patient's monitoring period behavior data set A m , where A m Represents the set of physiological indicators associated with the mth behavior element of the patient's behavior data set A in this monitoring period;

[0042] S22. Calculate matching values ​​between the patient's different behaviors and the patient's abnormal physiological indicators. The specific formula for calculating the matching value is:

[0043]

[0044] Among them, N(A m ∩B) represents the set A m The number of elements that intersect with set B, N(A m ∪B) represents the set A m The number of elements in the union with set B, Q m Indicates the matching degree of the behavioral data of the mth patient in this monitoring period;

[0045] S23. Arrange the matching values ​​of all behavioral data elements in the patient's behavioral data set A during this monitoring period in descending order, and take the behavioral data element with the largest matching value as the main cause of nutritional abnormalities.

[0046] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean 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 application.

[0047] It should be understood that determining B based on A does not mean determining B based solely on A. B can also be determined based on A and / or other information.

[0048] The above embodiments can be implemented in whole or in part through software, hardware, firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. A computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions according to the embodiments of the present invention are generated in whole or in part. 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 a wired network or / and a wireless network. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains a collection of one or more available media. The available media can be magnetic media (e.g., floppy disks, hard disks, tapes), optical media (e.g., DVDs), or semiconductor media. The semiconductor media can be a solid-state drive.

[0049] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in the present invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0050] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0051] In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only one type. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0052] The units described as separate components may or may not be physically separate, and 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 these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0053] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0054] Throughout this specification, references to terms such as "one embodiment," "example," or "specific example" indicate that the specific features, structures, materials, or characteristics described in conjunction with that embodiment or example are included in at least one embodiment or example of the present invention. In this specification, schematic representations of these terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0055] The preferred embodiments of the present invention disclosed above are intended only to help illustrate the present invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the present invention to specific embodiments. Obviously, many modifications and variations are possible based on the contents of this specification. These embodiments are selected and described in detail in this specification to better explain the principles and practical applications of the present invention, thereby enabling those skilled in the art to better understand and utilize the present invention. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A comprehensive nutrition monitoring system based on intelligent data calculation, characterized in that: The system includes a data acquisition port, a data verification port, and a nutritional abnormality tracing port. The data acquisition port is used to monitor the patient's physiological indicators, patient behavior, and patient disease status in real time through multiple devices. The physiological indicators include age, height, weight, body fat, muscle condition, energy intake, protein, and body function. The data verification port is used to construct a comparison group based on the patient's physiological indicators in the same time period of three consecutive monitoring cycles, using three days as a monitoring cycle, calculate data abnormalities, and determine whether to enter the nutritional abnormality analysis process. The nutritional abnormality tracing port is used to locate the time period of nutritional abnormality and find the main cause of the nutritional abnormality.

2. A comprehensive nutrition monitoring system based on intelligent data calculation according to claim 1, characterized in that: The data verification port includes a data extraction module, a data verification module and a threshold judgment module; the data extraction module is used to extract the patient's physiological indicators in the same time period of three consecutive monitoring cycles; the data verification module is used to calculate the abnormal values ​​of the physiological indicator data; the threshold judgment module is used to judge the size relationship between the abnormal value of the physiological indicator data and the abnormal value threshold, and enter the nutritional abnormality analysis process when the data abnormal value is greater than the abnormal value threshold.

3. A comprehensive nutrition monitoring system based on intelligent data calculation according to claim 2, characterized in that: The nutritional abnormality tracing port includes a nutritional abnormality time positioning module and a nutritional abnormality behavior search module. The nutritional abnormality time positioning module is used to run the nutritional abnormality time positioning strategy, calculate the comprehensive confirmation values ​​of different time periods and arrange them in descending order, and take the nutritional abnormality time source of the time period with the largest comprehensive confirmation value; the nutritional abnormality behavior search module is used to run the abnormal behavior search strategy, calculate the matching values ​​of the patient's different behaviors and the patient's abnormal physiological indicators, and take the patient behavior with the largest matching value as the main cause of the nutritional abnormality.

4. The comprehensive nutrition monitoring system based on intelligent data calculation according to claim 3, characterized in that: The data checking module calculates abnormal values ​​of physiological indicator data. The specific formula for calculating abnormal values ​​of physiological indicator data is: in, is the value of the i-th physiological indicator of the patient in a certain period of time during this monitoring cycle, is the value of the i-th physiological indicator of the patient in the same time period of the previous monitoring cycle, is the value of the i-th physiological indicator in the same time period of the patient's first two monitoring cycles, α1 and α2 are weight factors respectively, α1+α2=1, and the value of i is 1-n.

5. The comprehensive nutrition monitoring system based on intelligent data calculation according to claim 4, characterized in that: The nutritional abnormality time positioning module executes the nutritional abnormality time positioning strategy; the nutritional abnormality time positioning strategy includes the following specific steps: S11, extracting various physiological indicators of the patient at different time periods during this monitoring cycle; S12. Calculate the comprehensive confirmation value of the patient at different time periods during this monitoring cycle. The specific formula for calculating the comprehensive confirmation value is: Among them, P k is the comprehensive confirmation value of the patient's monitoring period k, is the value of the i-th physiological indicator of the patient in the k-th time period of this monitoring cycle, is the value of the i-th physiological indicator of the patient in the k-1 time period of this monitoring cycle, β i is the weight factor of the i-th physiological index value; S13. Arrange the comprehensive confirmation values ​​of the patient in different time periods of this monitoring cycle in descending order, and take the nutritional abnormality time source of the time period with the largest comprehensive confirmation value.

6. The comprehensive nutrition monitoring system based on intelligent data calculation according to claim 5, characterized in that: The abnormal nutritional behavior search module runs an abnormal behavior search strategy, which includes the following specific steps: S21, extract the patient's monitoring period behavior data set A and the patient's monitoring period abnormal physiological index set B, and obtain the associated physiological index set A of the single behavior element in the patient's monitoring period behavior data set A m , where A m Represents the set of physiological indicators associated with the mth behavior element of the patient's behavior data set A in this monitoring period; S22. Calculate matching values ​​between the patient's different behaviors and the patient's abnormal physiological indicators. The specific formula for calculating the matching value is: Among them, N(A m ∩B) represents the set A m The number of elements that intersect with set B, N(A m ∪B) represents the set A m The number of elements in the union with set B, Q m Indicates the matching degree of the behavioral data of the mth patient in this monitoring period; S23. Arrange the matching values ​​of all behavioral data elements in the patient's behavioral data set A during this monitoring period in descending order, and take the behavioral data element with the largest matching value as the main cause of nutritional abnormalities.

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