Physical and psychological health degree judgment system based on sleep long historical time sequence data

Abstract

The invention discloses a physical and psychological health degree judgment system based on sleep long historical time sequence data, which relates to the technical field of physical and psychological health analysis and comprises a sleep data processing subsystem, a health data judgment subsystem and a data judgment feedback subsystem. The sleep data processing subsystem is used for collecting related data in the aspect of personal sleep, storing and classifying each item of data by taking a person as a unit, establishing a plurality of corresponding data sets and arranging the data sets of the same category according to a time sequence; and the health data judgment subsystem is used for processing the data set collected in the sleep data processing subsystem and utilizing data mining and statistical analysis technologies. According to the system, the user perception indexes, the lifestyle, the physiological indexes and the past health conditions of the user are combined, the current health information of the user is obtained, suggestion information is given according to the health conditions, the accuracy and convenience of human body health state judgment are improved, the physical and psychological health state is conditioned, and the physical and psychological health state of the user is effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of physical and mental health analysis, and in particular to a physical and mental health level judgment system based on long-term sleep history time series data. Background Art

[0002] With the general improvement of people's quality of life, the increase of life pressure, and the increasingly serious trend of population aging, the general public is increasingly concerned about their physical and mental health. The changes in the living structure of modern society have led to threats to people's physical and mental health. The number of people suffering from sudden diseases and mental illnesses has shown an increasing trend. People have also paid more attention to their physical and mental health. People are studying how to grasp their current health status in a timely manner, and adjust according to the current health status to achieve reasonable management and improvement of physical and mental health.

[0003] At present, people's physical and mental health is obviously not enough. Their physical health can only be obtained through physical examinations in large hospitals or physical examination centers. This requires regular examinations at specific institutions. It is impossible to understand the physical and mental health status in time, nor can it carry out targeted conditioning and management of the physical and mental health status. Moreover, with the increase of life pressure, people have relatively fewer ways to understand mental health issues. Long-term accumulation of emotions is not conducive to maintaining physical and mental health. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention provides a system for determining the physical and mental health level based on long-term historical time series data of sleep, which solves the problems raised in the above-mentioned background technology.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: a physical and mental health judgment system based on long-term sleep history time series data, including a sleep data processing subsystem, a health data judgment subsystem, and a data judgment feedback subsystem;

[0006] The sleep data processing subsystem is used to collect personal sleep-related data, classify the data by individual, establish multiple corresponding data sets, and arrange the data sets of the same category in chronological order;

[0007] The health data judgment subsystem processes the health-related data based on the data set collected in the sleep data processing subsystem and uses data mining and statistical analysis techniques to provide health status assessment, disease prediction, and health management suggestions;

[0008] Among them, the health data judgment subsystem consists of a standard model library, a data indicator comparison module, a trend prediction module, and a result analysis module;

[0009] The standard model library is used to store and manage the database of health standards, which contains health-related standards, including weight-to-height ratio, sleep duration, eating habits, daily routine duration, daily exercise duration, calorie consumption, fat content, muscle content, BMI index, and heart rate range; the database also includes disease diagnosis names, disease diagnosis feature data, drug function categories, and laboratory test indicators, which are used to manage ICD codes, SNOMED, ​​LONIC, and pathological diagnosis name sets. The standard model library is equipped with information coverage units and information entry units for updating and expanding the database;

[0010] The data index comparison module compares the collected personal sleep data with the parameter index range in the database one by one in fixed time intervals, and arranges the comparison results according to the collection time sequence. The index categories involved in the comparison module are divided into physiological indicators and functional indicators;

[0011] in,

[0012] Physiological indicators include but are not limited to heart rate, body temperature, blood pressure, and blood oxygen concentration;

[0013] Functional indicators include, but are not limited to, cardiopulmonary function and emotional state;

[0014] The trend prediction module evaluates the difference based on the comparison results of the collected sleep data and the indicators in the database. The difference data needs to be comprehensively judged based on the individual's other real-time data before health analysis to maintain the accuracy of the evaluation, and an extended prediction is made based on the evaluation results. The individual health data is predicted in stages with fixed time intervals as the basic data for result analysis;

[0015] The result analysis module is used to analyze the current results and trends of health data, judge the degree of user's physical and mental health based on the data trends, and give the analysis results;

[0016] like:

[0017] If the user's personal data is close to the median value of the health index, it is judged as "healthy state";

[0018] If the user's personal data is close to the MAX value or MIN value of the health index, it is judged as "sub-health status"

[0019] If one or more items of the user's personal data exceed or fall below the range of health indicators, the user is judged as "unhealthy", and the user's comparison results and trend data are compared with the disease diagnosis data in the database, and the single item or multiple items with the smallest difference are selected, and other data indicators except disease characteristic data are compared at the same time;

[0020] The data judgment and feedback subsystem provides users with real-time feedback and suggestions based on the judgment results of their sleep data;

[0021] If the user is in a "healthy state" or "sub-healthy state", optimization will be performed based on the user's living habits and sleeping habits;

[0022] If the user is in an "unhealthy state", suggestions for adjustments will be given to the user's living habits, sleeping habits and emotional state.

[0023] Optionally, the sleep data processing subsystem consists of a data collection module, a data storage module, a data processing module, a data management module, and a data visualization module.

[0024] Optionally, the data collection module acts on a data source to obtain sleep data, and the data source is divided into personal data and third-party data;

[0025] in,

[0026] Personal data includes portable smart sports bracelets, mobile health record apps, and smart home sleep devices;

[0027] Third-party data includes medical institution visits, physical examination reports, genetic disease records of immediate family members, and medical test reports.

[0028] Optionally, the data storage module is provided with a data storage device inside, which is used to store the collected data in a storage medium, and uses a cluster-based data storage method. The data storage module also includes a data access unit for accessing and extracting sleep data.

[0029] Optionally, the data processing module is used to decompose the sleep data, establish a data model based on the sleep data analysis structure, and predict the floating trend of the sleep data; this includes cleaning, converting, analyzing and mining the collected sleep data. Data cleaning is used to remove noise and inconsistency in the data to ensure data quality; data conversion is used to convert the data into a unified format; data analysis and mining are used for in-depth analysis of the data.

[0030] Optionally, the data management module ensures the quality and value of the sleep data during the storage period, which includes data organization, protection, processing, integration, distribution and archiving operations.

[0031] Optionally, the data visualization module performs statistics and modeling on the sleep data and provides feedback in the form of videos, texts, and charts, and uses one or more of Excel, SPSS, and Python as sleep data analysis tools.

[0032] The present invention provides a system for judging the physical and mental health based on long-term sleep history time series data, which has the following beneficial effects:

[0033] The physical and mental health judgment system based on long-term sleep time series data combines user perception indicators, lifestyle, physiological indicators and the user's past health status to obtain the user's current health information and provide advice information based on the health status, thereby improving the accuracy and convenience of judging the human health status, regulating the physical and mental health status, and effectively improving the user's physical and mental health status;

[0034] Moreover, in actual application, the system can effectively optimize or supplement and improve the audience's sub-health status. It can not only help the audience effectively prevent physical and mental disorders, achieve partial self-help intervention for physical and mental health, and provide professional physical and mental health intervention guidance, but also enable full-time physical and mental health practitioners to quickly establish a relationship of mutual trust with the audience in a better way, and establish a more harmonious and friendly communication method and bridge to achieve the goal of quickly resolving the audience's problems. DETAILED DESCRIPTION

[0035] The technical solutions in the embodiments of the present invention will be described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0036] In the description of the present invention, unless otherwise specified, "plurality" means two or more than two; the terms "upper", "lower", "left", "right", "inner", "outer", "front end", "rear end", "head", "tail" and the like indicate directions or positional relationships, which are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the devices or elements referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as limiting the present invention. In addition, the terms "first", "second", "third" and the like are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0037] In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "connected" and "connection" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0038] A physical and mental health judgment system based on long-term sleep history time series data, including a sleep data processing subsystem, a health data judgment subsystem, and a data judgment feedback subsystem;

[0039] The sleep data processing subsystem is used to collect personal sleep-related data, classify the data by individual, establish multiple corresponding data sets, and arrange the data sets of the same category in chronological order;

[0040] The sleep data processing subsystem consists of a data collection module, a data storage module, a data processing module, a data management module, and a data visualization module;

[0041] The data collection module acts on the data source to obtain sleep data. The data source is divided into personal data and third-party data. Personal data uses portable smart sports bracelets and smart home sleep devices. Third-party data includes medical records, physical examination reports, genetic disease records of direct relatives, and medical test reports.

[0042] A data storage module, which has a data storage device inside, for storing the collected data in a storage medium, and uses a cluster-based method to store data. The data storage module also includes a data access unit for accessing and extracting sleep data;

[0043] The data processing module is used to decompose sleep data, establish a data model based on the sleep data analysis structure, and predict the floating trend of sleep data; it includes cleaning, conversion, analysis and mining of the collected sleep data. Data cleaning is used to remove noise and inconsistency in the data to ensure data quality; data conversion is used to convert data into a unified format; data analysis and mining are used for in-depth analysis of the data;

[0044] Data management module, which ensures the quality and value of sleep data during its preservation period, including data organization, protection, processing, integration, distribution and archiving operations;

[0045] The data visualization module collects statistics and models sleep data and provides feedback in the form of text and charts. Excel is used as a sleep data analysis tool.

[0046] The health data judgment subsystem processes the health-related data based on the data set collected in the sleep data processing subsystem and uses data mining and statistical analysis techniques to provide health status assessment, disease prediction, and health management suggestions;

[0047] Among them, the health data judgment subsystem consists of a standard model library, a data indicator comparison module, a trend prediction module, and a result analysis module;

[0048] The standard model library is used to store and manage the database of health standards, which contains health-related standards, including weight-to-height ratio, sleep duration, eating habits, daily routine duration, daily exercise duration, calorie consumption, fat content, muscle content, BMI index, and heart rate range; the database also includes disease diagnosis names, disease diagnosis feature data, drug function categories, and laboratory test indicators, which are used to manage ICD codes, SNOMED, ​​LONIC, and pathological diagnosis name sets. The standard model library is equipped with information coverage units and information entry units for updating and expanding the database;

[0049] The data index comparison module compares the collected personal sleep data with the parameter index range in the database one by one in fixed time intervals, and arranges the comparison results according to the collection time sequence. The index categories involved in the comparison module are divided into physiological indicators and functional indicators;

[0050] in,

[0051] Physiological indicators include heart rate, body temperature, blood pressure, blood oxygen concentration, etc.

[0052] Functional indicators include cardiopulmonary function, emotional state and other indicators;

[0053] The trend prediction module evaluates the difference based on the comparison results of the collected sleep data and the indicators in the database. The difference data needs to be comprehensively judged based on the individual's other real-time data before health analysis to maintain the accuracy of the evaluation, and an extended prediction is made based on the evaluation results. The individual health data is predicted in stages with fixed time intervals as the basic data for result analysis;

[0054] The result analysis module is used to analyze the current results and trends of health data, judge the degree of user's physical and mental health based on the data trends, and give the analysis results;

[0055] If the user's personal data is close to the median value of the health indicators, it will be judged as "healthy state" and optimized according to the user's living habits and sleeping habits.

[0056] If multiple items in the user's personal data exceed the range of health indicators, the user will be judged as "unhealthy", and the user's comparison results and trend data will be compared with the disease diagnosis data in the database. The single item or multiple items with the smallest difference will be selected, and other data indicators except disease characteristic data will be compared to give adjustment suggestions for the user's living habits, sleeping habits and emotional state.

[0057] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A system for judging physical and mental health based on long-term sleep history time series data, characterized in that: Including sleep data processing subsystem, health data judgment subsystem, data judgment feedback subsystem; The sleep data processing subsystem is used to collect personal sleep-related data, classify the data by individual, establish multiple corresponding data sets, and arrange the data sets of the same category in chronological order; The health data judgment subsystem processes the health-related data based on the data set collected in the sleep data processing subsystem and uses data mining and statistical analysis techniques to provide health status assessment, disease prediction, and health management suggestions; Among them, the health data judgment subsystem consists of a standard model library, a data indicator comparison module, a trend prediction module, and a result analysis module; The standard model library is used to store and manage the database of health standards, which contains health-related standards, including weight-to-height ratio, sleep duration, eating habits, daily routine duration, daily exercise duration, calorie consumption, fat content, muscle content, BMI index, and heart rate range; the database also includes disease diagnosis names, disease diagnosis feature data, drug function categories, and laboratory test indicators, which are used to manage ICD codes, SNOMED, ​​LONIC, and pathological diagnosis name sets. The standard model library is equipped with information coverage units and information entry units for updating and expanding the database; The data index comparison module compares the collected personal sleep data with the parameter index range in the database one by one in fixed time intervals, and arranges the comparison results according to the collection time sequence. The index categories involved in the comparison module are divided into physiological indicators and functional indicators; in, Physiological indicators include but are not limited to heart rate, body temperature, blood pressure, and blood oxygen concentration; Functional indicators include, but are not limited to, cardiopulmonary function and emotional state; The trend prediction module evaluates the difference based on the comparison results of the collected sleep data and the indicators in the database. The difference data needs to be comprehensively judged based on the individual's other real-time data before health analysis to maintain the accuracy of the evaluation, and an extended prediction is made based on the evaluation results. The individual health data is predicted in stages with fixed time intervals as the basic data for result analysis; The result analysis module is used to analyze the current results and trends of health data, judge the degree of user's physical and mental health based on the data trends, and give the analysis results; like: If the user's personal data is close to the median value of the health index, it is judged as "healthy state"; If the user's personal data is close to the MAX value or MIN value of the health index, it is judged as "sub-health status" If one or more items of the user's personal data exceed or fall below the range of health indicators, the user is judged as "unhealthy", and the user's comparison results and trend data are compared with the disease diagnosis data in the database, and the single item or multiple items with the smallest difference are selected, and other data indicators except disease characteristic data are compared at the same time; The data judgment and feedback subsystem provides users with real-time feedback and suggestions based on the judgment results of their sleep data; If the user is in a "healthy state" or "sub-healthy state", optimization will be performed based on the user's living habits and sleeping habits; If the user is in an "unhealthy state", adjustment suggestions will be given to the user's living habits, sleeping habits and emotional state.

2. A system for judging physical and mental health based on long-term sleep time series data according to claim 1, characterized in that: The sleep data processing subsystem is composed of a data collection module, a data storage module, a data processing module, a data management module, and a data visualization module.

3. A system for judging physical and mental health based on long-term sleep time series data according to claim 2, characterized in that: The data collection module acts on the data source to obtain sleep data, and the data source is divided into personal data and third-party data; in, Personal data includes portable smart sports bracelets, mobile health record apps, and smart home sleep devices; Third-party data includes medical institution visits, physical examination reports, genetic disease records of immediate family members, and medical test reports.

4. A system for judging physical and mental health based on long-term sleep time series data according to claim 2, characterized in that: The data storage module has a data storage device inside, which is used to store the collected data in a storage medium, and uses a cluster-based method to store data. The data storage module also includes a data access unit, which is used to access and extract sleep data.

5. A system for judging physical and mental health based on long-term sleep time series data according to claim 2, characterized in that: The data processing module is used to decompose the sleep data, establish a data model according to the sleep data analysis structure, and predict the floating trend of the sleep data; This includes cleaning, converting, analyzing and mining the collected sleep data. Data cleaning is used to remove noise and inconsistencies in the data to ensure data quality; Data transformation is used to convert data into a unified format; data analysis and mining are used to conduct in-depth analysis of data.

6. A system for judging physical and mental health based on long-term sleep time series data according to claim 2, characterized in that: The data management module ensures the quality and value of sleep data during the storage period, including data organization, protection, processing, integration, distribution and archiving operations.

7. A system for judging physical and mental health based on long-term sleep history time series data according to claim 2, characterized in that: The data visualization module performs statistics and modeling on the sleep data and provides feedback in the form of videos, texts, and charts. The sleep data analysis tool uses one or more of Excel, SPSS, and Python.