Health data tracking method and system

Abstract

The invention discloses a health data tracking method and system, and the method comprises the following steps: a data collection step, a data transmission step, a data analysis step, a data feedback step, and a data storage step, in the data collection step, the data collection range of a wearable device and a mobile terminal device is further expanded, and in the data analysis step, the data collection range of the wearable device and the mobile terminal device is further expanded. A deep learning algorithm is used for carrying out deeper trend analysis and prediction on long-term health data of a user, in the data feedback step, besides a conventional feedback mode, an interactive feedback function and personalized health challenge task recommendation are added, in the data storage step, a more advanced data encryption and backup technology is adopted, and the health challenge task recommendation efficiency is improved. A conventional encryption mode is adopted; according to the system, comprehensive and accurate data acquisition and integration are realized through multiple devices, accurate analysis and prediction are realized by using an innovative formula and deep learning, personalized feedback and intervention are provided, and a valuable innovative solution is provided for the fields of health management and medical treatment.

Description

Technical Field

[0001] The present invention relates to the technical field of health data tracking, and more specifically, to a health data tracking method and system. Background Art

[0002] In today's society, people are paying more and more attention to their health. With the accelerated pace of life, increased work pressure and the influence of environmental factors, various health problems have gradually become prominent. The incidence of chronic diseases, such as cardiovascular disease and diabetes, is rising. These diseases not only bring physical pain to patients, but also bring heavy burdens to families and society. Therefore, effective health management has become crucial. Through the continuous tracking and analysis of personal health data, potential health risks can be discovered in time, and corresponding prevention and intervention measures can be taken to improve people's health level and quality of life.

[0003] The limitations of traditional health data tracking are as follows:

[0004] Incomplete data collection: Traditional health data collection methods mainly rely on hospital physical examinations and patient self-reporting. Hospital physical examinations are usually only conducted periodically and cannot reflect changes in a person's health status in real time. Self-reporting is often subjective and inaccurate. For example, patients may miss some important symptoms or misdescribe their health status. In addition, traditional methods lack effective means of collecting some key daily behavior data, psychological state data, and environmental data, and cannot fully understand the various factors that affect health.

[0005] Lack of data integration and analysis: Even if some health data is collected, these data are often scattered in different medical institutions, health management platforms or personal records, lacking effective integration. This makes it difficult for doctors and health management professionals to fully understand the health status of patients, and it is impossible to conduct systematic data analysis and trend prediction. Moreover, traditional analysis methods are mainly based on simple statistics and empirical judgments, lack the application of big data and artificial intelligence technology, and it is difficult to dig out the potential connections and patterns hidden in the data.

[0006] Feedback and intervention are not timely: Under the traditional health management model, patients can usually only get advice and guidance from doctors after a physical examination or when obvious symptoms appear. This lagging feedback mechanism cannot meet people's needs for real-time health management. For example, for some patients with chronic diseases who need to adjust their lifestyle or treatment plans in a timely manner, the lack of timely feedback and intervention may lead to worsening of their condition. In addition, the traditional health management method lacks personalized services and cannot provide accurate advice and solutions based on an individual's unique health status and needs.

[0007] Outdated technical means: Traditional health data tracking devices and systems have single functions and limited accuracy. For example, some common home health monitoring devices can only measure basic physiological indicators, such as blood pressure, heart rate, etc., and the accuracy and stability of the measurement results need to be improved. At the same time, these devices lack interconnection and interoperability, making it impossible to share and coordinate data management. In terms of data transmission and storage, there are also security and reliability issues, which can easily lead to the leakage or loss of personal health data.

[0008] Technological development provides opportunities for innovative health data tracking:

[0009] The rapid development of sensor technology, mobile Internet technology, artificial intelligence technology and big data technology has provided new possibilities for solving the problems of traditional health data tracking.

[0010] Advances in sensor technology: New sensors can achieve high-precision measurements of more physiological indicators, such as blood oxygen saturation, bioelectrical impedance, etc. At the same time, the miniaturization and intelligence of sensors allow them to be integrated into wearable devices to achieve real-time and continuous collection of personal health data. For example, smart bracelets and smart watches can not only measure heart rate and exercise steps, but also monitor sleep quality and environmental factors through built-in sensors.

[0011] Popularization of mobile Internet technology: The widespread use of mobile terminal devices provides a convenient channel for the collection and transmission of health data. Through devices such as smartphones and tablets, users can record their diet, exercise, psychological state and other data anytime and anywhere, and upload these data to cloud servers in real time. At the same time, mobile Internet technology also provides a platform for communication and interaction between users and healthcare institutions, realizing remote medical care and health consultation services.

[0012] Application of artificial intelligence and big data technologies: Artificial intelligence algorithms can quickly analyze and process large amounts of health data and discover potential patterns and trends in the data. Through machine learning and deep learning technologies, personalized health models can be established to accurately assess and predict the health status of users. Big data technology can integrate health data from different sources, enable data sharing and collaborative analysis, and provide a more comprehensive perspective for medical research and health management.

[0013] Introduction of blockchain technology: The distributed ledger and encryption verification mechanism of blockchain technology can ensure the security and non-tamperability of health data, solving the potential safety hazards in traditional data storage and transmission. At the same time, blockchain technology also provides new solutions for the sharing and mutual trust of medical data, promoting the informatization and coordinated development of the medical industry.

[0014] To sum up, the existing health data tracking methods can no longer meet people's needs for comprehensive, accurate and real-time health management. There is an urgent need for an innovative health data tracking system and method, combined with advanced technical means, to achieve comprehensive collection, efficient integration, in-depth analysis and timely feedback of personal health data, and provide strong support for people's health management. Summary of the invention

[0015] Technical issues to be solved

[0016] The object of the present invention is to provide a health data tracking method comprising the following steps:

[0017] Data collection steps:

[0018] Wearable devices are used to collect the user's physiological data in real time, including but not limited to heart rate (represented by P), blood pressure (represented by Q), blood oxygen saturation (represented by R), body temperature (represented by S), etc. The heart rate collection frequency is 1 to 10 times per minute, the blood pressure collection interval is 1 to 3 times per hour, the blood oxygen saturation detection frequency is 1 to 5 times per minute, and the body temperature measurement frequency is 1 to 2 times per hour.

[0019] With the help of mobile terminal devices, users' daily behavior data is collected, such as exercise steps (represented by T), exercise trajectory, dietary information, etc. The exercise steps are counted in real time, and the exercise trajectory is obtained through the positioning function with a positioning accuracy of 5 to 10 meters. Dietary information is recorded manually by the user or collected through image recognition technology.

[0020] Collect the user's psychological state data, such as the emotional state (represented by M) obtained through questionnaires or sensor detection, including indicators such as pleasure and anxiety.

[0021] Collect user's environmental exposure data, including but not limited to air quality index (expressed by E), ultraviolet intensity (expressed by F), etc.

[0022] Introducing the Health Basic Index (HBI) calculation formula: HBI = a 1 ×P_factor+a 2 ×Q_factor+a 3 ×R_factor+a 4 ×S_factor+a 5 ×T_factor+a 6 ×Behavior_factor+a 7 ×M_factor+a 8 ×Environmental_factor+a 9 ×U_factor+a 10 ×N_factor+a11 ×E_factor+a 12 ×F_factor(where a 1 to a 12 is a weight coefficient determined based on medical research and a large amount of experimental data, and a 1 +a 2 +a 3 +a 4 +a 5 +a 6 +a 7 +a 8 +a 9 +a 10 +a 11 +a 12 =1; P_factor is a health assessment factor related to heart rate, which is set according to the normal range and fluctuation of heart rate; Q_factor is a health assessment factor related to blood pressure, which is determined according to the normal range and stability of blood pressure; R_factor is a factor related to blood oxygen saturation, which is obtained by combining normal blood oxygen values ​​and change patterns; S_factor is a factor related to body temperature, which is calculated according to the normal body temperature range and change characteristics; T_factor is a factor related to exercise steps, which is obtained by comparing with the daily recommended exercise volume; Behavior_factor is a comprehensive assessment factor for daily behavior, which covers a comprehensive assessment of factors such as the rationality of exercise trajectory and balanced diet; M_factor is a psychological state assessment factor, which is comprehensively assessed based on indicators such as the positivity and stability of emotional state; Environmental_factor is a comprehensive environmental assessment factor, which includes the comfort of living environment, etc.; U_factor is a sleep quality assessment factor, including sleep duration, deep sleep ratio, etc.; N_factor is a social interaction assessment factor, which covers social frequency, social relationship quality, etc.; E_factor is an air quality-related influencing factor; F_factor is an ultraviolet-related influencing factor).

[0023] Introduce the calculation formula of health dynamic change index (HDC): HDC=√((ΔP 2 +ΔQ 2 +ΔR 2 +ΔS 2 +ΔT 2 +ΔM 2 +ΔE 2 +ΔF 2 +ΔU 2 +ΔN 2 ) / 10)(wherein ΔP, ΔQ, ΔR, ΔS, ΔT, ΔM, ΔE, ΔF, ΔU, and ΔN respectively represent the changes of the corresponding data within a certain period of time). This index is used to measure the short-term dynamic changes of the user's health status.

[0024] Data transfer steps:

[0025] Wearable devices and mobile terminal devices transmit the collected data to the data processing center in real time through Bluetooth, Wi-Fi or mobile network. Encryption technology is used to ensure data security during transmission. The encryption algorithm adopts the Advanced Encryption Standard with a key length of 128 to 256 bits. At the same time, the data is compressed with a compression ratio of 2:1 to 5:1.

[0026] Data analysis steps:

[0027] After receiving the data, the data processing center uses big data analysis technology and artificial intelligence algorithms to analyze and process the data. Based on the comparison of historical data and real-time data, it determines whether the user's health status is normal. For example, when the heart rate exceeds the normal range (60 times / minute to 100 times / minute) or the blood pressure is higher than the normal level for many consecutive times (systolic blood pressure ≥140mmHg and / or diastolic blood pressure ≥90mmHg), the system automatically issues an early warning prompt. At the same time, based on the basic health index calculation formula, the user's HBI value is calculated in real time, and its changing trend and influencing factors are deeply analyzed. Combined with the health dynamic change index, the short-term fluctuations in the user's health status are evaluated.

[0028] Data feedback steps:

[0029] Feedback the results of the analysis to users and relevant healthcare institutions. Users can view their health data and analysis reports through mobile terminal applications to understand their health status and improvement directions. Healthcare institutions can obtain data based on user authorization and provide users with telemedicine services and health consultation suggestions. The feedback includes detailed health data interpretation, HBI value meaning, HDC value and its meaning, and personalized health improvement plans.

[0030] Data storage steps:

[0031] The collected raw data, analysis and processing results, and user personal information are stored in the database of the cloud server. The database adopts a distributed storage architecture to ensure the security and reliability of the data. The data storage period is not less than 5 years, and the data is classified and managed to facilitate subsequent query and analysis.

[0032] Furthermore, in the data collection step, the data collection scope of wearable devices and mobile terminal devices is further expanded. Wearable devices can collect users' bioelectrical impedance data (represented by G) to evaluate information such as body composition and water content. Mobile terminal devices can collect users' work stress data (represented by H), which is obtained through user self-assessment or work-related application data integration. These data are incorporated into the calculation factors of the basic health index and the dynamic health change index to further improve the evaluation system. At this time, the calculation formula of HBI is updated to: HBI = a 1 ×P_factor+a 2 ×Q_factor+a 3 ×R_factor+a 4 ×S_factor+a 5 ×T_factor+a 6 ×Behavior_factor+a 7 ×M_factor+a 8 ×Environmental_factor+a 9 ×U_factor+a 10 ×N_factor+a 11 ×E_factor+a 12 ×F_factor+a 13 ×G_factor+a 14 ×H_factor(where a 13 and a 14 The calculation formula of HDC is correspondingly expanded to: HDC=√((ΔP 2 +ΔQ 2 +ΔR 2 +ΔS 2 +ΔT 2 +ΔM 2 +ΔE 2 +ΔF 2 +ΔU 2 +ΔN 2 +ΔG 2 +ΔH 2 ) / 12).

[0033] In the data analysis step, deep learning algorithms are used to conduct more in-depth trend analysis and prediction of users' long-term health data. By learning from users' historical data, combined with the changing trend of HBI and the short-term fluctuation of HDC, as well as the dynamic changes of various factors, it predicts possible health problems in the future and gives targeted preventive measures in advance.

[0034] In the data feedback step, in addition to conventional feedback methods, interactive feedback functions and personalized health challenge task recommendations are added. According to the user's HBI value, HDC value and health status, customized health advice and challenge tasks are provided to users. After the user completes the task, he or she can receive corresponding rewards or incentives to encourage the user to actively improve his or her health status. At the same time, users are supported to share their health data in an anonymous manner on the health community platform to communicate and compare with other users, but the privacy security of user data and clear authorization must be ensured.

[0035] In the data storage step, more advanced data encryption and backup technologies are used. In addition to conventional encryption methods, blockchain technology is introduced to perform distributed storage and encryption verification of key data to ensure that the data cannot be tampered with and is secure. At the same time, data is backed up regularly, and the backup data is encrypted and stored off-site, with a backup frequency of at least once a week. The calculation process of HBI and HDC and related intermediate data are recorded in detail and stored separately for subsequent data analysis and model optimization.

[0036] Furthermore, a health data tracking system includes:

[0037] Data acquisition module:

[0038] Contains data acquisition units for wearable devices and mobile terminal devices. Wearable devices are equipped with high-precision sensors, such as heart rate sensors, blood pressure sensors, blood oxygen sensors, body temperature sensors, etc., for collecting user physiological data. Mobile terminal devices are installed with special applications for collecting users' daily behavior data, sleep quality data, environmental data, psychological state data, social activity data, bioelectrical impedance data, and work pressure data. At the same time, this module has the function of calculating relevant evaluation factors in real time according to the calculation formula of the basic health index and the calculation formula of the dynamic health change index, providing an accurate data basis for the calculation of HBI and HDC.

[0039] Data transmission module:

[0040] Responsible for transmitting the data collected by the data acquisition module to the data processing center safely and efficiently. Supports multiple communication methods and has data encryption, compression and breakpoint resume functions. During the transmission process, key data related to HBI and HDC are specially marked and transmitted first to ensure the timeliness and accuracy of the data.

[0041] Data Analysis Module:

[0042] Deployed in the data processing center, it uses big data analysis technology and artificial intelligence algorithms to process the received data. It includes data cleaning, mining, establishing user health models, and real-time calculation and analysis of users' basic health index and health dynamic change index according to HBI and HDC calculation formulas. Through in-depth analysis of user health data, it evaluates user health status and predicts potential risks.

[0043] Data feedback module:

[0044] Feedback the processing results of the data analysis module to users and relevant healthcare institutions. Provide multiple feedback methods, such as mobile terminal applications, emails, text messages, etc. The feedback content revolves around HBI and HDC, and explains in detail their calculation methods, the meaning of the results, and personalized health recommendations and improvement measures based on HBI and HDC. At the same time, it supports the implementation of interactive feedback functions and the recommendation of personalized health challenge tasks.

[0045] Data storage module:

[0046] It is used to store the collected raw data, analysis and processing results, and user personal information. It adopts a distributed storage architecture with high reliability and scalability. It specially stores and manages the relevant data of HBI and HDC, and establishes indexes and data warehouses to facilitate rapid retrieval and in-depth analysis. It uses blockchain technology to encrypt, store and verify key data.

[0047] User management module:

[0048] Responsible for user registration, login, personal information management, data sharing permission settings, etc. Users can manage their own health data, set the data sharing scope and privacy protection level, and ensure the security and privacy of user data.

[0049] System Management Module:

[0050] Manage and maintain the entire health data tracking system. This includes system configuration, monitoring, upgrades, and troubleshooting. Ensure the stable operation of the system and the security and reliability of data. Also, be responsible for regularly updating and optimizing the computational models and related algorithms of HBI and HDC to adapt to the ever-changing medical research results and user needs.

[0051] Security Management Module:

[0052] Multiple security measures are adopted to ensure the security of user data. Including data encryption, access control, identity authentication and other functions. The user's sensitive information is strictly encrypted and stored, and only authorized users and healthcare institutions can access the relevant data. At the same time, strict permission management is implemented for access to the system to prevent unauthorized access and data leakage. Blockchain technology is used to strengthen data security and traceability management.

[0053] Interface management module:

[0054] Provide interfaces with external devices and systems to facilitate data interaction and integration with other health management devices, medical information systems, etc. Support multiple data formats and communication protocols to ensure system compatibility and scalability. For example, it can seamlessly connect with smart medical devices, health management platforms, etc. to achieve data sharing and collaborative management.

[0055] Statistical analysis module:

[0056] Statistical analysis of users' health data, HBI, and HDC is performed to provide data support for system optimization and improvement. Various statistical reports and analysis charts can be generated to help users and healthcare institutions better understand users' health status and trends. At the same time, through the analysis of a large amount of user data, a reference basis is provided for medical research and health management. Supports the generation of personalized health recommendations and plans based on HBI, HDC, and various factor data.

[0057] Furthermore, the wearable device in the data acquisition module has an adaptive adjustment function, which can automatically adjust the sensor acquisition parameters according to the user's activity status, sleep status and environment to improve the accuracy and reliability of the data. For example, the sampling frequency of the heart rate sensor is automatically increased in the state of exercise, the collection method of sleep quality data is optimized in the sleep state, and the detection parameters of the environmental sensor are automatically adjusted in different environments. At the same time, according to the changes in the user's bioimpedance, the acquisition frequency and accuracy of the bioimpedance data are dynamically adjusted.

[0058] The breakpoint-resume function of the data transmission module automatically saves the transmitted data when the network is interrupted, and continues to transmit the remaining data after the network is restored to ensure data integrity. At the same time, the transmission speed and mode are automatically adjusted according to the network status to adapt to different network environments. For HBI and HDC key data, multiple verification and encryption transmission mechanisms are used to ensure data accuracy and security.

[0059] The artificial intelligence algorithm of the data analysis module can continuously learn and update, and optimize the analysis model and prediction algorithm according to the accumulation of user health data and the progress of medical research. Focus on the optimization of HBI and HDC, adjust the weight coefficient and calculation method to make it more accurately reflect the health status of users. Combined with deep learning technology, it can mine potential associations and patterns in user data to provide a more accurate basis for personalized health recommendations.

[0060] The data feedback module can be personalized according to the user's needs and preferences, providing a variety of feedback content and display methods. For example, customized health reports and suggestions are provided based on the user's health goals and focus. It supports the smooth operation of interactive feedback functions and provides users with a good interactive experience. Based on the results of HBI and HDC, personalized health challenge tasks and improvement measures are recommended to users.

[0061] The data storage module uses distributed storage and redundant backup technology to ensure data security and recoverability. Encrypted storage and backup management of HBI and HDC related data are performed to prevent data loss and damage. Blockchain technology is used to ensure data immutability and traceability, providing a higher level of protection for data security storage.

[0062] The user management module provides multiple authentication methods, such as password, fingerprint recognition, facial recognition, etc., to ensure the security of user accounts. Users can flexibly set data sharing permissions and clarify the scope and method of sharing HBI and HDC related data. Users are supported to independently manage and edit their own health data to more accurately reflect the user's actual health status.

[0063] The system management module has remote monitoring and management functions. Administrators can monitor the system's operating status in real time and promptly discover and resolve system failures. At the same time, based on the data feedback from the statistical analysis module, the system's performance and functions are continuously optimized. Security audits and vulnerability repairs are performed regularly to ensure the security and stability of the system.

[0064] The security management module conducts security audits and risk assessments regularly to promptly discover and fix security vulnerabilities. Strict logging of user data access and use is performed to ensure data traceability. Blockchain technology’s encryption verification and distributed storage features are used to strengthen data security management and anti-tampering capabilities.

[0065] The interface management module supports integration with third-party applications and platforms, and enables data sharing and interaction through open APIs and other means. At the same time, it ensures the security and stability of the interface to prevent data leakage and system attacks. It supports seamless connection with a variety of health management devices and medical information systems, expanding the application scope and functions of the system.

[0066] The statistical analysis module can generate personalized health recommendations and improvement plans based on the user's health data and HBI and HDC. By analyzing the data of the user group, health trends and potential problems can be discovered, providing a reference for health management and medical research. At the same time, it supports the visualization of data, which makes it easier for users and healthcare institutions to understand their health status intuitively. Using big data analysis technology and machine learning algorithms, it can explore the potential value and rules in the data, providing strong support for the optimization and improvement of the system.

[0067] Beneficial Effects

[0068] Compared with the prior art, the advantages of the present invention are:

[0069] 1. Through wearable and mobile terminal devices, multi-dimensional collection of physiological, daily behavior, psychological, environmental and extended data is achieved, covering physiological indicators such as heart rate and blood pressure, behavioral data such as exercise and diet, emotional state and environmental factors such as air quality. Sensor technology ensures high-precision real-time collection, such as high-frequency heart rate and other data monitoring, which provides the possibility for early detection of health problems. At the same time, multi-source data is integrated to facilitate correlation analysis and in-depth understanding of health influencing factors and changing trends.

[0070] 2. The HBI calculation formula integrates multiple key data factors and weight coefficients to accurately quantify the basic health status. HDC monitors short-term dynamic changes in health in real time. The deep learning algorithm uses long-term data to predict future health problems and give preventive measures in advance, realizing the transition from passive management to active preventive intervention.

[0071] 3. Provide customized health advice based on data analysis, such as exclusive diet, exercise and psychological adjustment programs. Interactive feedback challenge tasks and reward mechanisms can increase user participation and promote the formation of good living habits. Feedback results to healthcare institutions in a timely manner, realize remote medical support, and provide professional advice quickly.

[0072] 4. Use advanced encryption standards to ensure transmission security, and blockchain technology to achieve distributed storage and encryption verification to prevent data tampering and leakage. Regular backup and off-site storage ensure recoverability. Users can set data permissions and privacy protection levels independently, and strict identity verification ensures account security.

[0073] In summary, this patent excels in data collection, analysis, feedback and security management, providing innovative solutions for health management and medical fields. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] Figure 1 is a schematic block diagram of the health data tracking method of the present invention;

[0075] Figure 2It is a schematic block diagram of the health data tracking system of the present invention. DETAILED DESCRIPTION

[0076] Example: See Figure 1-2 , a health data tracking method comprises the following steps:

[0077] Data collection steps:

[0078] Wearable devices are used to collect the user's physiological data in real time, including but not limited to heart rate (represented by P), blood pressure (represented by Q), blood oxygen saturation (represented by R), body temperature (represented by S), etc. The heart rate collection frequency is 1 to 10 times per minute, the blood pressure collection interval is 1 to 3 times per hour, the blood oxygen saturation detection frequency is 1 to 5 times per minute, and the body temperature measurement frequency is 1 to 2 times per hour.

[0079] With the help of mobile terminal devices, users' daily behavior data is collected, such as exercise steps (represented by T), exercise trajectory, dietary information, etc. The exercise steps are counted in real time, and the exercise trajectory is obtained through the positioning function with a positioning accuracy of 5 to 10 meters. Dietary information is recorded manually by the user or collected through image recognition technology.

[0080] Collect the user's psychological state data, such as the emotional state (represented by M) obtained through questionnaires or sensor detection, including indicators such as pleasure and anxiety.

[0081] Collect user's environmental exposure data, including but not limited to air quality index (expressed by E), ultraviolet intensity (expressed by F), etc.

[0082] Introducing the Health Basic Index (HBI) calculation formula: HBI = a 1 ×P_factor+a 2 ×Q_factor+a 3 ×R_factor+a 4 ×S_factor+a 5 ×T_factor+a 6 ×Behavior_factor+a 7 ×M_factor+a 8 ×Environmental_factor+a 9 ×U_factor+a 10 ×N_factor+a 11 ×E_factor+a 12 ×F_factor(where a 1 to a 12 is a weight coefficient determined based on medical research and a large amount of experimental data, and a 1 +a2 +a 3 +a 4 +a 5 +a 6 +a 7 +a 8 +a 9 +a 10 +a 11 +a 12 =1; P_factor is a health assessment factor related to heart rate, which is set according to the normal range and fluctuation of heart rate; Q_factor is a health assessment factor related to blood pressure, which is determined according to the normal range and stability of blood pressure; R_factor is a factor related to blood oxygen saturation, which is obtained by combining normal blood oxygen values ​​and change patterns; S_factor is a factor related to body temperature, which is calculated according to the normal body temperature range and change characteristics; T_factor is a factor related to exercise steps, which is obtained by comparing with the daily recommended exercise volume; Behavior_factor is a comprehensive assessment factor for daily behavior, which covers a comprehensive assessment of factors such as the rationality of exercise trajectory and balanced diet; M_factor is a psychological state assessment factor, which is comprehensively assessed based on indicators such as the positivity and stability of emotional state; Environmental_factor is a comprehensive environmental assessment factor, which includes the comfort of living environment, etc.; U_factor is a sleep quality assessment factor, including sleep duration, deep sleep ratio, etc.; N_factor is a social interaction assessment factor, which covers social frequency, social relationship quality, etc.; E_factor is an air quality-related influencing factor; F_factor is an ultraviolet-related influencing factor).

[0083] Introduce the calculation formula of health dynamic change index (HDC): HDC=√((ΔP 2 +ΔQ 2 +ΔR 2 +ΔS 2 +ΔT 2 +ΔM 2 +ΔE 2 +ΔF 2 +ΔU 2 +ΔN 2 ) / 10)(wherein ΔP, ΔQ, ΔR, ΔS, ΔT, ΔM, ΔE, ΔF, ΔU, and ΔN respectively represent the changes of the corresponding data within a certain period of time). This index is used to measure the short-term dynamic changes of the user's health status.

[0084] Data transfer steps:

[0085] Wearable devices and mobile terminal devices transmit the collected data to the data processing center in real time through Bluetooth, Wi-Fi or mobile network. Encryption technology is used to ensure data security during transmission. The encryption algorithm adopts the Advanced Encryption Standard with a key length of 128 to 256 bits. At the same time, the data is compressed with a compression ratio of 2:1 to 5:1.

[0086] Data analysis steps:

[0087] After receiving the data, the data processing center uses big data analysis technology and artificial intelligence algorithms to analyze and process the data. Based on the comparison of historical data and real-time data, it determines whether the user's health status is normal. For example, when the heart rate exceeds the normal range (60 times / minute to 100 times / minute) or the blood pressure is higher than the normal level for many consecutive times (systolic blood pressure ≥140mmHg and / or diastolic blood pressure ≥90mmHg), the system automatically issues an early warning prompt. At the same time, based on the basic health index calculation formula, the user's HBI value is calculated in real time, and its changing trend and influencing factors are deeply analyzed. Combined with the health dynamic change index, the short-term fluctuations in the user's health status are evaluated.

[0088] Data feedback steps:

[0089] Feedback the results of the analysis to users and relevant healthcare institutions. Users can view their health data and analysis reports through mobile terminal applications to understand their health status and improvement directions. Healthcare institutions can obtain data based on user authorization and provide users with telemedicine services and health consultation suggestions. The feedback includes detailed health data interpretation, HBI value meaning, HDC value and its meaning, and personalized health improvement plans.

[0090] Data storage steps:

[0091] The collected raw data, analysis and processing results, and user personal information are stored in the database of the cloud server. The database adopts a distributed storage architecture to ensure the security and reliability of the data. The data storage period is not less than 5 years, and the data is classified and managed to facilitate subsequent query and analysis.

[0092] Furthermore, in the data collection step, the data collection scope of wearable devices and mobile terminal devices is further expanded. Wearable devices can collect users' bioelectrical impedance data (represented by G) to evaluate information such as body composition and water content. Mobile terminal devices can collect users' work stress data (represented by H), which is obtained through user self-assessment or work-related application data integration. These data are incorporated into the calculation factors of the basic health index and the dynamic health change index to further improve the evaluation system. At this time, the calculation formula of HBI is updated to: HBI = a 1×P_factor+a 2 ×Q_factor+a 3 ×R_factor+a 4 ×S_factor+a 5 ×T_factor+a 6 ×Behavior_factor+a 7 ×M_factor+a 8 ×Environmental_factor+a 9 ×U_factor+a 10 ×N_factor+a 11 ×E_factor+a 12 ×F_factor+a 13 ×G_factor+a 14 ×H_factor(where a 13 and a 14 The calculation formula of HDC is correspondingly expanded to: HDC=√((ΔP 2 +ΔQ 2 +ΔR 2 +ΔS 2 +ΔT 2 +ΔM 2 +ΔE 2 +ΔF 2 +ΔU 2 +ΔN 2 +ΔG 2 +ΔH 2 ) / 12).

[0093] In the data analysis step, deep learning algorithms are used to conduct more in-depth trend analysis and prediction of users' long-term health data. By learning from users' historical data, combined with the changing trend of HBI and the short-term fluctuation of HDC, as well as the dynamic changes of various factors, it predicts possible health problems in the future and gives targeted preventive measures in advance.

[0094] In the data feedback step, in addition to conventional feedback methods, interactive feedback functions and personalized health challenge task recommendations are added. According to the user's HBI value, HDC value and health status, customized health advice and challenge tasks are provided to users. After the user completes the task, he or she can receive corresponding rewards or incentives to encourage the user to actively improve his or her health status. At the same time, users are supported to share their health data in an anonymous manner on the health community platform to communicate and compare with other users, but the privacy security of user data and clear authorization must be ensured.

[0095] In the data storage step, more advanced data encryption and backup technologies are used. In addition to conventional encryption methods, blockchain technology is introduced to perform distributed storage and encryption verification of key data to ensure that the data cannot be tampered with and is secure. At the same time, data is backed up regularly, and the backup data is encrypted and stored off-site, with a backup frequency of at least once a week. The calculation process of HBI and HDC and related intermediate data are recorded in detail and stored separately for subsequent data analysis and model optimization.

[0096] Furthermore, a health data tracking system includes:

[0097] Data acquisition module:

[0098] Contains data acquisition units for wearable devices and mobile terminal devices. Wearable devices are equipped with high-precision sensors, such as heart rate sensors, blood pressure sensors, blood oxygen sensors, body temperature sensors, etc., for collecting user physiological data. Mobile terminal devices are installed with special applications for collecting users' daily behavior data, sleep quality data, environmental data, psychological state data, social activity data, bioelectrical impedance data, and work pressure data. At the same time, this module has the function of calculating relevant evaluation factors in real time according to the calculation formula of the basic health index and the calculation formula of the dynamic health change index, providing an accurate data basis for the calculation of HBI and HDC.

[0099] Data transmission module:

[0100] Responsible for transmitting the data collected by the data acquisition module to the data processing center safely and efficiently. Supports multiple communication methods and has data encryption, compression and breakpoint resume functions. During the transmission process, key data related to HBI and HDC are specially marked and transmitted first to ensure the timeliness and accuracy of the data.

[0101] Data Analysis Module:

[0102] Deployed in the data processing center, it uses big data analysis technology and artificial intelligence algorithms to process the received data. It includes data cleaning, mining, establishing user health models, and real-time calculation and analysis of users' basic health index and health dynamic change index according to HBI and HDC calculation formulas. Through in-depth analysis of user health data, it evaluates user health status and predicts potential risks.

[0103] Data feedback module:

[0104] Feedback the processing results of the data analysis module to users and relevant healthcare institutions. Provide multiple feedback methods, such as mobile terminal applications, emails, text messages, etc. The feedback content revolves around HBI and HDC, and explains in detail their calculation methods, the meaning of the results, and personalized health recommendations and improvement measures based on HBI and HDC. At the same time, it supports the implementation of interactive feedback functions and the recommendation of personalized health challenge tasks.

[0105] Data storage module:

[0106] It is used to store the collected raw data, analysis and processing results, and user personal information. It adopts a distributed storage architecture with high reliability and scalability. It specially stores and manages the relevant data of HBI and HDC, and establishes indexes and data warehouses to facilitate rapid retrieval and in-depth analysis. It uses blockchain technology to encrypt, store and verify key data.

[0107] User management module:

[0108] Responsible for user registration, login, personal information management, data sharing permission settings, etc. Users can manage their own health data, set the data sharing scope and privacy protection level, and ensure the security and privacy of user data.

[0109] System Management Module:

[0110] Manage and maintain the entire health data tracking system. This includes system configuration, monitoring, upgrades, and troubleshooting. Ensure the stable operation of the system and the security and reliability of data. Also, be responsible for regularly updating and optimizing the computational models and related algorithms of HBI and HDC to adapt to the ever-changing medical research results and user needs.

[0111] Security Management Module:

[0112] Multiple security measures are adopted to ensure the security of user data. Including data encryption, access control, identity authentication and other functions. The user's sensitive information is strictly encrypted and stored, and only authorized users and healthcare institutions can access the relevant data. At the same time, strict permission management is implemented for access to the system to prevent unauthorized access and data leakage. Blockchain technology is used to strengthen data security and traceability management.

[0113] Interface management module:

[0114] Provide interfaces with external devices and systems to facilitate data interaction and integration with other health management devices, medical information systems, etc. Support multiple data formats and communication protocols to ensure system compatibility and scalability. For example, it can seamlessly connect with smart medical devices, health management platforms, etc. to achieve data sharing and collaborative management.

[0115] Statistical analysis module:

[0116] Statistical analysis of users' health data, HBI, and HDC is performed to provide data support for system optimization and improvement. Various statistical reports and analysis charts can be generated to help users and healthcare institutions better understand users' health status and trends. At the same time, through the analysis of a large amount of user data, a reference basis is provided for medical research and health management. Supports the generation of personalized health recommendations and plans based on HBI, HDC, and various factor data.

[0117] Furthermore, the wearable device in the data acquisition module has an adaptive adjustment function, which can automatically adjust the sensor acquisition parameters according to the user's activity status, sleep status and environment to improve the accuracy and reliability of the data. For example, the sampling frequency of the heart rate sensor is automatically increased in the state of exercise, the collection method of sleep quality data is optimized in the sleep state, and the detection parameters of the environmental sensor are automatically adjusted in different environments. At the same time, according to the changes in the user's bioimpedance, the acquisition frequency and accuracy of the bioimpedance data are dynamically adjusted.

[0118] The breakpoint-resume function of the data transmission module automatically saves the transmitted data when the network is interrupted, and continues to transmit the remaining data after the network is restored to ensure data integrity. At the same time, the transmission speed and mode are automatically adjusted according to the network status to adapt to different network environments. For HBI and HDC key data, multiple verification and encryption transmission mechanisms are used to ensure data accuracy and security.

[0119] The artificial intelligence algorithm of the data analysis module can continuously learn and update, and optimize the analysis model and prediction algorithm according to the accumulation of user health data and the progress of medical research. Focus on the optimization of HBI and HDC, adjust the weight coefficient and calculation method to make it more accurately reflect the health status of users. Combined with deep learning technology, it can mine potential associations and patterns in user data to provide a more accurate basis for personalized health recommendations.

[0120] The data feedback module can be personalized according to the user's needs and preferences, providing a variety of feedback content and display methods. For example, customized health reports and suggestions are provided based on the user's health goals and focus. It supports the smooth operation of interactive feedback functions and provides users with a good interactive experience. Based on the results of HBI and HDC, personalized health challenge tasks and improvement measures are recommended to users.

[0121] The data storage module uses distributed storage and redundant backup technology to ensure data security and recoverability. Encrypted storage and backup management of HBI and HDC related data are performed to prevent data loss and damage. Blockchain technology is used to ensure data immutability and traceability, providing a higher level of protection for data security storage.

[0122] The user management module provides multiple authentication methods, such as password, fingerprint recognition, facial recognition, etc., to ensure the security of user accounts. Users can flexibly set data sharing permissions and clarify the scope and method of sharing HBI and HDC related data. Users are supported to independently manage and edit their own health data to more accurately reflect the user's actual health status.

[0123] The system management module has remote monitoring and management functions. Administrators can monitor the system's operating status in real time and promptly discover and resolve system failures. At the same time, based on the data feedback from the statistical analysis module, the system's performance and functions are continuously optimized. Security audits and vulnerability repairs are performed regularly to ensure the security and stability of the system.

[0124] The security management module conducts security audits and risk assessments regularly to promptly discover and fix security vulnerabilities. Strict logging of user data access and use is performed to ensure data traceability. Blockchain technology’s encryption verification and distributed storage features are used to strengthen data security management and anti-tampering capabilities.

[0125] The interface management module supports integration with third-party applications and platforms, and enables data sharing and interaction through open APIs and other means. At the same time, it ensures the security and stability of the interface to prevent data leakage and system attacks. It supports seamless connection with a variety of health management devices and medical information systems, expanding the application scope and functions of the system.

[0126] The statistical analysis module can generate personalized health recommendations and improvement plans based on the user's health data and HBI and HDC. By analyzing the data of the user group, health trends and potential problems can be discovered, providing a reference for health management and medical research. At the same time, it supports the visualization of data, which makes it easier for users and healthcare institutions to understand their health status intuitively. Using big data analysis technology and machine learning algorithms, it can explore the potential value and rules in the data, providing strong support for the optimization and improvement of the system.

[0127] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments. The above embodiments and descriptions are only preferred examples of the present invention and are not intended to limit the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A health data tracking method, characterized in that: The following steps are involved: S1. Data collection: Wearable devices are used to collect the user's physiological data in real time, including heart rate, blood pressure, blood oxygen saturation, and body temperature. The heart rate is collected 1 to 10 times per minute, the blood pressure is collected 1 to 3 times per hour, the blood oxygen saturation is collected 1 to 5 times per minute, and the body temperature is measured 1 to 2 times per hour. Collect users' daily behavior data through mobile terminal devices, including exercise steps, exercise trajectory, and dietary information. Exercise steps are counted in real time. The exercise trajectory is obtained through the positioning function with a positioning accuracy of 5 to 10 meters. Dietary information is recorded manually by users or collected through image recognition technology; Collect users’ environmental exposure data, including air quality index and UV intensity; Introduce the calculation formula of Health Basic Index HBI: HBI=a1×P_factor+a2×Q_factor+a3×R_factor+a4×S_factor+a5×T_factor+a6×Behavior_factor+a7×M_factor+a8×Environmental_factor+a9×U_factor+a 10 ×N_factor+a 11 ×E_factor+a 12 ×F_factor, where a1 to a 12 is the weight coefficient, and a1+a2+a3...+a 12 =1; P_factor is a health assessment factor related to heart rate, Q_factor is a health assessment factor related to blood pressure, R_factor is a factor related to blood oxygen saturation, S_factor is a factor related to body temperature, T_factor is a factor related to exercise steps, Behavior_factor is a comprehensive assessment factor for daily behavior, M_factor is a psychological state assessment factor, Environmental_factor is a comprehensive environmental assessment factor, U_factor is a sleep quality assessment factor, including sleep duration and deep sleep ratio; N_factor is a social interaction assessment factor, E_factor is an air quality-related influencing factor; F_factor is an ultraviolet-related influencing factor; Introduce the calculation formula of health dynamic change index HDC: HDC=√((ΔP2+ΔQ2+ΔR 2 +ΔS2+ΔT2+ΔM 2 +ΔE2+ΔF2+ΔU2+ΔN 2 ) / 10), where ΔP, ΔQ, ΔR, ΔS, ΔT, ΔM, ΔE, ΔF, ΔU, and ΔN respectively represent the changes of the corresponding data within a certain time period. This index is used to measure the short-term dynamic changes of the user's health status. S2. Data transmission: Wearable devices and mobile terminal devices transmit the collected data to the data processing center in real time via Bluetooth, Wi-Fi or mobile network. Encryption technology is used to ensure data security during transmission. The encryption algorithm adopts the Advanced Encryption Standard with a key length of 128 to 256 bits. At the same time, the data is compressed with a compression ratio of 2:1 to 5:

1. S3. Data analysis: After receiving the data, the data processing center uses big data analysis technology and artificial intelligence algorithms to analyze and process the data. Based on the comparison of historical data and real-time data, it determines whether the user's health status is normal. When the heart rate exceeds the range of 60 beats / minute to 100 beats / minute or the blood pressure is systolic ≥140mmHg or diastolic ≥90mmHg for more than three consecutive times, the system automatically issues an early warning. At the same time, based on the health basic index calculation formula, the user's HBI value is calculated in real time, and its change trend and influencing factors are deeply analyzed. Combined with the health dynamic change index, the short-term fluctuation of the user's health status is evaluated; S4. Data feedback: The results of the analysis and processing will be fed back to users and relevant healthcare institutions. Users can view their health data and analysis reports through mobile terminal applications to understand their own health status and improvement directions. Healthcare institutions can obtain data based on user authorization and provide users with telemedicine services and health consultation suggestions. The feedback content includes detailed health data interpretation, the meaning of HBI values, HDC values ​​and their meanings, and health improvement plans. S5. Data storage: The collected raw data, analysis and processing results, user personal information and other data are stored in the database of the cloud server. The database adopts a distributed storage architecture to ensure the security and reliability of the data. The data storage period is not less than 5 years, and the data is classified and managed to facilitate subsequent query and analysis.

2. The health data tracking method according to claim 1, characterized in that: In step S1, the data collection scope of wearable devices and mobile terminal devices is expanded. Wearable devices collect the user's bioelectrical impedance data G for evaluating body composition and water content information. Mobile terminal devices can collect the user's work pressure data H, which is obtained through user self-assessment or work-related application data integration. These data are included in the calculation factors of the basic health index and the dynamic health change index. At this time, the calculation formula of HBI is updated as follows: HBI = a1×P_factor+a2×Q_factor+a3×R_factor+a4×S_factor+a5×T_factor+a6×Behavior_factor+a7×M_factor+a8×Environmental_factor+a9×U_factor+a 10 ×N_factor+a 11 ×E_factor+a 12 ×F_factor+a 13 ×G_factor+a 14 ×H_factor, where a 13 and a 14 is the corresponding weight coefficient, and the calculation formula of HDC is correspondingly expanded to: HDC=√((ΔP2+ΔQ2+ΔR 2 +ΔS2+ΔT2+ΔM 2 +ΔE2+ΔF2+ΔU2+ΔN 2 +ΔG2+ΔH 2 ) / 12); In step S2, deep learning algorithms are used to conduct more in-depth trend analysis and prediction of users' long-term health data. By learning from users' historical data, combined with the changing trend of HBI and the short-term fluctuation of HDC, as well as the dynamic changes of various factors, possible health problems in the future are predicted, and targeted preventive measures are given in advance. In step S4, through interactive feedback functions and personalized health challenge task recommendations, users are provided with customized health advice and challenge tasks based on their HBI values, HDC values, and health conditions. After completing the tasks, users can receive corresponding rewards or incentives. Users are supported to share their health data in an anonymous manner on the health community platform to communicate and compare with other users, but the privacy of user data and clear authorization must be ensured; In step S5, data encryption and backup technology are used, and blockchain technology is introduced to perform distributed storage and encryption verification of key data to ensure that the data cannot be tampered with and is secure. Data is backed up and the backup data is encrypted and stored off-site. The backup frequency is at least once a week, and the calculation process of HBI and HDC and related intermediate data are recorded in detail and stored separately.

3. A system for implementing the health data tracking method according to claims 1-2, characterized in that: include: Data collection module: It includes data collection units for wearable devices and mobile terminal devices. Wearable devices are equipped with heart rate sensors, blood pressure sensors, blood oxygen sensors, and body temperature sensors. Mobile terminal devices are installed with applications for collecting users' daily behavior data, sleep quality data, environmental data, psychological state data, social activity data, bioelectrical impedance data, and work pressure data. This module has the function of calculating relevant evaluation factors in real time according to the basic health index calculation formula and the dynamic health change index calculation formula; Data transmission module: transmits the data collected by the data acquisition module to the data processing center, with data encryption, compression and breakpoint resume functions. During the transmission process, key data related to HBI and HDC are specially marked and transmitted with priority; Data analysis module: deployed in the data processing center, using big data analysis technology and artificial intelligence algorithms to process the received data, including data cleaning, mining, establishing user health models, and real-time calculation and analysis of the user's basic health index and health dynamic change index according to the HBI and HDC calculation formulas; Data feedback module: Feedback the processing results of the data analysis module to users and relevant healthcare institutions. The feedback content revolves around HBI and HDC, supporting the implementation of interactive feedback functions and the recommendation of personalized health challenge tasks; Data storage module: used to store collected raw data, analysis and processing results, and user personal information data. It adopts a distributed storage architecture with high reliability and scalability. It specifically stores and manages HBI and HDC related data, and establishes indexes and data warehouses. User management module: used for user registration, login, personal information management, and data sharing permission setting functions, and setting the data sharing scope and privacy protection level; System management module: manages and maintains the entire health data tracking system, including system configuration, monitoring, upgrades and troubleshooting functions; Security management module: It has data encryption, access control, and identity authentication functions. It strictly encrypts and stores users' sensitive information. Only authorized users and healthcare institutions can access relevant data. Blockchain technology is used to strengthen data security and traceability management. Interface management module: provides interfaces with external devices and systems; Statistical analysis module: performs statistical analysis on the user's health data and HBI, HDC, provides data support for system optimization and improvement, generates statistical reports and analysis charts, and supports the generation of health recommendations and plans based on HBI, HDC and various factor data.

4. A health data tracking system according to claim 3, characterized in that: The wearable device in the data acquisition module has an adaptive adjustment function, which can automatically adjust the sensor acquisition parameters according to the user's activity status, sleep status and environment, automatically increase the sampling frequency of the heart rate sensor in the exercise state, optimize the collection method of sleep quality data in the sleep state, and automatically adjust the detection parameters of the environmental sensor in different environments. According to the changes in the user's bioimpedance, the collection frequency and accuracy of the bioimpedance data are dynamically adjusted.

5. A health data tracking system according to claim 3, characterized in that: The breakpoint-resume function of the data transmission module automatically saves the transmitted data when the network is interrupted, and continues to transmit the remaining data after the network is restored. It automatically adjusts the transmission speed and mode according to the network conditions to adapt to different network environments.

6. A health data tracking system according to claim 3, characterized in that: The data storage module adopts distributed storage and redundant backup technology to encrypt and store and backup the relevant data of HBI and HDC, and uses blockchain technology to ensure the data is tamper-proof and traceable.

7. A health data tracking system according to claim 3, characterized in that: The user management module provides password, fingerprint, and facial recognition authentication methods. Users can flexibly set data sharing permissions and support users to independently manage and edit their own health data; The system management module has remote monitoring and management functions. Administrators can monitor the system's operating status in real time and discover and solve system failures in a timely manner. The interface management module supports integration with third-party applications and platforms, and enables data sharing and interaction through open APIs; The statistical analysis module can generate personalized health recommendations and improvement plans based on the user's health data, HBI, and HDC, and supports visual display of data.

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