Intelligent house health data synchronous analysis and management method

Abstract

The invention discloses an intelligent house health data synchronous analysis and management method, which relates to the technical field of health data synchronous analysis, and comprises the following steps: collecting health data of a user in real time through a sensor and health monitoring equipment in an intelligent home system; according to the invention, adaptive data synchronization is realized through an intelligent routing algorithm, the synchronization mode is dynamically adjusted according to the network condition, and timely and accurate transmission of health data is ensured. Multi-dimensional health data analysis is combined with historical health information, personalized health early warning and management suggestions are provided, and the health management accuracy is improved. The system flexibly adjusts the data acquisition frequency according to the health change of the user, optimizes the uploading strategy, timely feeds back and adjusts the health management scheme according to the feedback of the user, ensures the personalized service, and improves the accuracy and reliability of health management.

Description

Technical Field

[0001] The present invention relates to the technical field of health data synchronous analysis, and in particular to a method for managing health data synchronous analysis in a smart house. Background Art

[0002] Smart home health data synchronization analysis and management involves the collaborative work of smart home systems and health monitoring devices to collect and synchronize family members' health data, such as body temperature, heart rate, blood pressure, and sleep quality, in real time. Data analytics technology is then used to process, store, and analyze these data, providing residents with comprehensive health assessments and personalized recommendations. The system automatically monitors and records data, syncing it to the user's smart device via a cloud platform or local device. This helps users understand their health status in real time, identify health risks promptly, and optimize their lifestyles or adjust their living environment to improve their quality of life and overall well-being.

[0003] Existing technologies have the following shortcomings: In existing smart home health data synchronization, analysis, and management technologies, data synchronization delays can lead to ineffective health monitoring, potentially leading to serious consequences. Smart home systems rely on real-time communication between sensors and devices to obtain health data. Network issues or device failures can cause data transmission delays or even loss. For high-risk users, such as the elderly or those with chronic diseases, if critical health data is not synchronized and analyzed in a timely manner, opportunities for disease warning or intervention may be missed. Continuous synchronization delays can lead to inaccurate health assessments or missed early symptoms, hindering timely treatment and even endangering lives. Therefore, ensuring the real-time and reliable synchronization of data is crucial.

[0004] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not form the prior art that is already known to a person of ordinary skill in the art. Summary of the Invention

[0005] The purpose of the present invention is to provide a method for synchronizing, analyzing, and managing health data in smart homes. This method uses an intelligent routing algorithm to achieve adaptive data synchronization, dynamically adjusting the synchronization mode based on network conditions to ensure timely and accurate transmission of health data. Multi-dimensional health data analysis, combined with historical health information, provides personalized health warnings and management recommendations, improving the accuracy of health management. The system flexibly adjusts data collection frequency based on changes in user health, optimizes upload strategies, provides timely feedback, and adjusts health management plans based on user feedback, ensuring personalized services and improving the accuracy and reliability of health management, thereby addressing the problems in the aforementioned background technology.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a method for synchronous analysis and management of health data of a smart house, comprising the following steps:

[0007] Collect users’ health data in real time through sensors and health monitoring devices in smart home systems;

[0008] Pre-process the collected health data to ensure the accuracy and reliability of the data;

[0009] By establishing an efficient data transmission channel, we ensure real-time synchronization of health data between devices and the cloud platform. We also use intelligent network optimization algorithms to automatically adjust transmission strategies to ensure stability and low latency during data transmission.

[0010] Conduct multi-dimensional analysis of synchronized health data, combining the user's historical health status, personal characteristics and living habits to generate a health trend analysis report;

[0011] Based on the data analysis results, abnormal health warnings are generated in real time and pushed to users or health managers via mobile terminals to ensure timely intervention measures;

[0012] Based on early warning information and user feedback, adjust health data collection strategies, optimize health management plans, and ensure that user health problems are resolved in a timely manner.

[0013] Preferably, the health data collection step includes using multiple sensors to work together to ensure multi-point monitoring of the same health indicator in different environments, and jointly collecting environmental and human health data through temperature and humidity sensors and body temperature sensors to perform data fusion, thereby improving the accuracy and comprehensiveness of health data.

[0014] Preferably, the data preprocessing step further includes filling in missing values in the health data by using interpolation, nearest neighbor method, and time series-based regression model to ensure data integrity, thereby avoiding analysis errors caused by missing data and ensuring the accuracy of subsequent data analysis.

[0015] Preferably, the data synchronization step further includes:

[0016] Utilize intelligent routing algorithms to automatically detect network conditions and adaptively adjust synchronization modes based on network bandwidth and latency;

[0017] Use encryption algorithms during data transmission to ensure the privacy and security of health data and prevent data leakage during synchronization;

[0018] In the event of device failure or network interruption, health data is saved through a cache mechanism and automatically uploaded after the network is restored or the device is repaired to ensure data integrity;

[0019] Through load balancing technology, synchronization tasks are distributed to different devices or servers to ensure stable operation under high load conditions.

[0020] Preferably, the data analysis step further includes performing time series forecasting analysis on the health data, and based on the time series model, making long-term trend forecasts on the user's health status to timely identify potential health risks. The specific steps are as follows:

[0021] Standardize user health data to eliminate dimensional differences between data;

[0022] Use time series prediction algorithms to build a time series model for health data and predict the changing trend of user health data in the future;

[0023] Based on the prediction results, it is determined whether there are major changes in the user's health status and health warning information is generated.

[0024] Preferably, the health change prediction formula is used in the data analysis step, and the calculation expression is as follows:

[0025] First, the health data is weighted and the weight coefficient is set;

[0026] The weighted average method is used to calculate the user's comprehensive health index. The calculation expression is as follows:

[0027]

[0028] Where H t is the comprehensive health index, which represents the overall health status of the user at time t, w i is the weight coefficient of the i-th health indicator, X i (t) is the value of the i-th health indicator at time t, and n is the total number of health indicators;

[0029] Regression analysis method is used to predict future health status, and the influence of different health indicators on overall health status is adjusted by weighting coefficients. The calculation expression is as follows:

[0030]

[0031] Where H t+1 is the comprehensive health index at the future time t+1, α and β are regression model coefficients, and α is used to represent the comprehensive health index H t The degree of impact on future health status, β is used to represent various health indicators X i (t) the degree of influence on future health status, ∈ is the error term;

[0032] The health status is evaluated based on the prediction results. If the comprehensive health index is lower than a certain threshold, a health risk warning is automatically generated.

[0033] Preferably, the abnormal warning step further includes:

[0034] Based on the outlier detection algorithm of health data, the user's real-time health data is monitored in real time;

[0035] When a health data exceeds a preset threshold, the system will issue an alarm and push it to the user and their health manager through multiple channels to ensure timely response;

[0036] It can automatically adjust the warning threshold according to the user's historical health records and current health status to achieve personalized health warning management;

[0037] The generation of health warning adopts health prediction based on multivariate analysis. The prediction steps are as follows:

[0038] First, define the contribution of each health indicator to health risk and calculate the risk factor of each indicator based on historical data;

[0039] Each health indicator is normalized to unify its dimension, and the risk score of each health indicator is calculated. The calculation expression is as follows:

[0040]

[0041] R i (t) is the standardized score of the i-th health indicator at time t, μ i is the mean value of the i-th health indicator, σ i is the standard deviation;

[0042] The total risk index is calculated by weighted aggregation, and the calculation expression is as follows:

[0043]

[0044] Where c i is the risk coefficient of the i-th health indicator, R total (t) is the comprehensive health risk index, which represents the user's overall health risk level at time t;

[0045] If R total (t) is greater than the set risk threshold, a health risk warning will be generated and notified to the user and his / her health manager.

[0046] Preferably, the data feedback and adjustment step further includes:

[0047] Automatically generate personalized health management reports based on health data analysis and early warning information, and recommend measures for users to improve their health status;

[0048] Based on the user's living habits and health status, an intelligent recommendation algorithm is used to provide tailored lifestyle adjustment suggestions.

[0049] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0050] The present invention uses an adaptive data synchronization solution based on an intelligent routing algorithm to dynamically adjust the data upload mode according to the real-time network status, ensuring that health data can be transmitted in a timely and accurate manner under different network environments. Regardless of whether the network bandwidth is good or there are problems such as delays and packet loss, the system can intelligently switch the synchronization mode and reduce the risk of synchronization delays or data loss through adaptive synchronization mechanisms, load balancing, caching, and intelligent retries. This efficient data synchronization mechanism ensures that the smart home health system can monitor the user's health status in real time, upload key health data in a timely manner, and avoid health monitoring failures due to synchronization problems, thereby improving the reliability and security of the entire health management system. In particular, in the health monitoring of high-risk users (such as the elderly and patients with chronic diseases), it can provide accurate health data support and reduce blind spots in health monitoring.

[0051] Based on multi-dimensional health data analysis, the present invention can comprehensively assess the user's health status and improve the accuracy and personalization level of health management through personalized health warnings and management suggestions. By comprehensively analyzing multiple health indicators such as body temperature, heart rate, blood pressure, and combining the user's historical health data and living habits, the system can more accurately judge the user's health trends, thereby discovering potential health risks in advance. For example, the system can make trend predictions based on real-time heart rate changes and historical data, issue abnormal warnings in a timely manner, and guide users to take preventive measures. This multi-dimensional health data analysis can not only help users understand their health status more clearly, but also provide tailored health suggestions based on the different needs and characteristics of individuals, help users improve their living habits, optimize health management effects, and thus enhance users' health awareness and self-management capabilities.

[0052] The present invention can meet individualized health management needs to the greatest extent possible while maintaining efficient system operation by flexibly adjusting the frequency of data collection and uploading according to changes in the user's health status. When the user's health status changes, the system can automatically increase the collection frequency or optimize the data upload strategy to ensure that any health changes are captured in a timely manner and provide rapid feedback. For example, when the user's health status is abnormal (such as a fast heart rate or large fluctuations in body temperature), the system will automatically increase the collection frequency and prioritize uploading key data for timely analysis and intervention. In addition, users are allowed to provide feedback on health management plans, and health recommendations are adjusted based on user feedback. This flexible data collection and feedback mechanism enables the system to dynamically optimize health management plans based on the user's specific needs and health status, provide more accurate and personalized services, and ensure that health management is more in line with the user's actual needs. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction to the drawings required for use in the embodiments will be given below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0054] Figure 1 This is a flow chart of the method for synchronous analysis and management of smart house health data of the present invention. DETAILED DESCRIPTION

[0055] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these example embodiments are provided so that the description of this disclosure will be thorough and complete and will fully convey the concepts of the example embodiments to those skilled in the art.

[0056] The present invention provides Figure 1 The smart house health data synchronization analysis and management method shown includes the following steps:

[0057] Through sensors and health monitoring devices in the smart home system, real-time collection of user health data, including but not limited to body temperature, heart rate, blood pressure, sleep quality, etc.

[0058] The steps of collecting health data include using multiple sensors to work together to ensure multi-point monitoring of the same health indicator in different environments, collecting environmental and human health data through temperature and humidity sensors and body temperature sensors, and performing data fusion to improve the accuracy and comprehensiveness of health data.

[0059] Preprocess the collected health data, including denoising, data calibration, and outlier detection, to ensure data accuracy and reliability;

[0060] The data preprocessing step further includes filling in missing values in health data by using interpolation, nearest neighbor method, and time series-based regression models to ensure data integrity, thereby avoiding analytical errors caused by missing data and ensuring the accuracy of subsequent data analysis.

[0061] By establishing an efficient data transmission channel, we ensure real-time synchronization of health data between devices and the cloud platform. We also use intelligent network optimization algorithms to automatically adjust transmission strategies to ensure stability and low latency during data transmission.

[0062] The data synchronization step further includes:

[0063] Utilize intelligent routing algorithms to automatically detect network conditions and adaptively adjust synchronization modes based on network bandwidth and latency;

[0064] Use encryption algorithms during data transmission to ensure the privacy and security of health data and prevent data leakage during synchronization;

[0065] In the event of device failure or network interruption, health data is saved through a cache mechanism and automatically uploaded after the network is restored or the device is repaired to ensure data integrity;

[0066] Through load balancing technology, synchronization tasks are distributed to different devices or servers to ensure stable operation under high load conditions.

[0067] Conduct multi-dimensional analysis of synchronized health data, combining the user's historical health status, personal characteristics and living habits to generate a health trend analysis report;

[0068] The data analysis step further includes time series forecasting analysis of health data. Based on time series models (such as ARIMA and LSTM), long-term trend forecasts of user health status are made to promptly identify potential health risks. The specific steps are as follows:

[0069] Standardize user health data to eliminate dimensional differences between data;

[0070] Use time series prediction algorithms to build a time series model for health data and predict the changing trend of user health data in the future;

[0071] Based on the prediction results, it is determined whether there are major changes in the user's health status and health warning information is generated.

[0072] The health change prediction formula is used in the data analysis step, and the calculation expression is as follows:

[0073] First, health data (such as heart rate, body temperature, blood pressure, etc.) are weighted and weight coefficients are set (for example, the weight of body temperature is 0.3, the weight of heart rate is 0.5, and the weight of blood pressure is 0.2);

[0074] The weighted average method is used to calculate the user's comprehensive health index. The calculation expression is as follows:

[0075]

[0076] Where H t is the comprehensive health index, which represents the overall health status of the user at time t, w i is the weight coefficient of the i-th health indicator, X i (t) is the value of the i-th health indicator at time t, and n is the total number of health indicators;

[0077] Regression analysis method is used to predict future health status, and the influence of different health indicators on overall health status is adjusted by weighting coefficients. The calculation expression is as follows:

[0078]

[0079] Where H t+1 It is the comprehensive health index at the future time t+1, which indicates the expected health status of the user in the future. α and β are regression model coefficients. α is used to represent the comprehensive health index H t The degree of impact on future health status, β is used to represent various health indicators X i (t) the degree of influence on future health status, ∈ is the error term;

[0080] The health status is evaluated based on the prediction results. If the comprehensive health index is lower than a certain threshold, a health risk warning is automatically generated.

[0081] Based on the data analysis results, abnormal health warnings are generated in real time and pushed to users or health managers via mobile terminals to ensure timely intervention measures;

[0082] The abnormal warning step further includes:

[0083] Based on outlier detection algorithms for health data (such as the statistical Z-score method or machine learning-based anomaly detection model), real-time monitoring of users' real-time health data;

[0084] When a health data exceeds a preset threshold, the system will issue an alarm and push it to the user and their health manager through multiple channels (such as SMS, APP notification, etc.) to ensure timely response;

[0085] It can automatically adjust the warning threshold according to the user's historical health records and current health status to achieve personalized health warning management.

[0086] The generation of health warning adopts health prediction based on multivariate analysis. The prediction steps are as follows:

[0087] First, define the contribution of each health indicator (such as body temperature, heart rate, blood pressure, etc.) to health risk, and calculate the risk factor of each indicator based on historical data;

[0088] Each health indicator is normalized to unify its dimension, and the risk score of each health indicator is calculated. The calculation expression is as follows:

[0089]

[0090] R i(t) is the standardized score of the i-th health indicator at time t, μ i is the mean value of the i-th health indicator, σ i is the standard deviation;

[0091] The total risk index is calculated by weighted aggregation, and the calculation expression is as follows:

[0092]

[0093] Where c i is the risk coefficient of the i-th health indicator, R total (t) is the comprehensive health risk index, which represents the user's overall health risk level at time t;

[0094] If R total (t) is greater than the set risk threshold, a health risk warning will be generated and notified to the user and his / her health manager.

[0095] Adjust health data collection strategies and optimize health management plans based on early warning information and user feedback to ensure that user health issues are resolved promptly;

[0096] The data feedback and adjustment steps further include:

[0097] Automatically generate personalized health management reports based on health data analysis and early warning information, and recommend measures for users to improve their health, such as adjusting their daily routine, diet, and exercise;

[0098] Based on the user's living habits and health status, an intelligent recommendation algorithm is used to provide tailored lifestyle adjustment suggestions;

[0099] Users adjust their lifestyles based on the recommendations in the report and provide feedback on the adjustment results. Through continuous data monitoring, the adjustment effects will be evaluated and the health management plan will be further optimized.

[0100] Implementation 1: In modern smart home systems, the timeliness and accuracy of health data synchronization are crucial, especially for monitoring the health of the elderly and those with chronic diseases. Delays or data loss can lead to serious health risks. Therefore, this implementation proposes an adaptive data synchronization solution based on an intelligent routing algorithm to ensure timely and accurate synchronization of health data in various network environments.

[0101] The key to this implementation is to design an intelligent routing algorithm that can dynamically adjust the data synchronization method according to the real-time status of the network. After the user's health data collection system (such as a smart bracelet, smart mattress, thermometer, etc.) collects data, this data needs to be transmitted to the cloud platform through the network for storage and analysis. Due to the uncertainty of the network environment, factors such as network bandwidth, latency, and packet loss rate may affect the upload speed of data, thereby causing synchronization delays or losses. Therefore, it is first necessary to ensure reliable data transmission by monitoring the device and network status in real time.

[0102] The system will monitor the network status in real time in the background, including parameters such as bandwidth, latency, and packet loss rate. When the network condition is normal, the system automatically selects the real-time synchronization mode and uploads the data to the cloud platform immediately to ensure the timeliness and accuracy of the data. If the network bandwidth is insufficient or there is a delay, the system will switch to the delayed synchronization mode. The data will first be stored in the local cache and automatically uploaded after the network is restored. For extreme situations such as network failures, the system will adopt multiple backup mechanisms to improve the stability of data transmission through multi-channel transmission (such as Wi-Fi and 4G networks working at the same time), and combine encryption technology to ensure the security and integrity of the data transmission process. The system will perform synchronization tests regularly. If a problem is found in a data channel, it will automatically switch to the backup channel to avoid data loss due to the failure of a single transmission method.

[0103] The system was designed using intelligent load balancing technology. When multiple devices are synchronizing data simultaneously, the system rationally schedules data upload tasks based on network bandwidth allocation, avoiding network congestion and synchronization delays, ensuring smooth and timely data transmission from each device to the cloud platform. This intelligent load balancing mechanism effectively prevents delayed processing or inability to upload health data from certain devices in high-concurrency scenarios, thereby improving data synchronization efficiency across the entire system.

[0104] In addition, an intelligent retry mechanism has been incorporated into the system. When the system detects an anomaly during data transmission, such as a network outage or device failure, the data is not lost directly. Instead, the data to be transmitted is stored in a local cache and automatically uploaded after the network is restored or the device is repaired. During the data upload process, an adaptive algorithm is used to select the appropriate upload time, ensuring maximum data transmission speed when the network is in good condition, thereby avoiding the loss or delay of healthy data due to network problems.

[0105] This adaptive data synchronization solution based on intelligent routing algorithms not only ensures the real-time and stability of data synchronization, but also effectively avoids data synchronization delays in extreme network environments through adaptive adjustment, load balancing, and intelligent retry mechanisms, ensuring the accuracy and timeliness of user health data, thereby providing users with timely health warnings and intervention recommendations.

[0106] Implementation Method 2: This implementation method proposes an intelligent health early warning system based on multi-dimensional health data analysis. It aims to provide users with personalized, accurate health management recommendations through real-time monitoring and in-depth analysis of multiple health indicators. Traditional smart home health monitoring systems often focus on a single health metric, such as body temperature or heart rate, and are unable to effectively integrate multiple data points to comprehensively assess a user's health status. This implementation method, by combining multi-dimensional health data and utilizing advanced analytical methods and models, enables dynamic prediction of health conditions and personalized interventions.

[0107] First, this implementation uses multiple sensors to collect real-time health data from users, including but not limited to body temperature, heart rate, blood pressure, and sleep quality. This health data isn't collected solely on a single device, but rather through the collaborative work of multiple sensors, such as smart bracelets, mattress sensors, and blood pressure monitors, to collect multi-dimensional health data and ensure comprehensive and accurate monitoring.

[0108] After data collection, the system preprocesses the health data, including data denoising and outlier processing, to ensure the accuracy of the input data. The system then uses a comprehensive assessment model (such as weighted averaging or machine learning algorithms) to weight each health indicator, assigning different weights based on its importance to generate a comprehensive health index. This comprehensive health index can intuitively reflect the user's overall health status and provide a basis for subsequent health warnings.

[0109] During the data analysis process, this implementation introduces a time series prediction model (such as ARIMA, LSTM, etc.) to predict the trend of health data. By modeling historical health data, the system can predict the user's health status within a certain period of time in the future, thereby achieving early warning of health problems. For example, the system will predict the user's possible heart problems in the next few hours based on the trend of changes in the user's heart rate, or determine whether there is a risk of sleep disorders based on the trend of changes in sleep quality. These predictions can not only help users become aware of potential health problems earlier, but also provide a scientific basis for health managers to guide them to take timely and effective intervention measures.

[0110] When the system's analysis results indicate that a user's health data deviates from the normal range, the system will issue a warning through an intelligent early warning mechanism. For example, when a user's heart rate exceeds the safe range, the system will notify the user via a mobile app, text message, or voice prompt, reminding them to take immediate action, such as rest or seeking medical attention. In addition, the system can generate personalized health management recommendations based on the user's health data, such as dietary adjustments, optimized work and rest schedules, and exercise recommendations, to help users improve their health.

[0111] This multi-dimensional health data analysis and personalized health warning solution can achieve a comprehensive assessment of the user's health status, and provide users with refined health management services through personalized warnings and intervention recommendations. Especially in the health monitoring of high-risk groups such as the elderly and patients with chronic diseases, it can greatly improve the effectiveness of health management.

[0112] Implementation Method 3: To address the mismatch between user health data collection and upload frequencies, this implementation method designs a dynamic adjustment scheme for data return and intelligent feedback mechanisms. This scheme dynamically adjusts data collection and return strategies based on the user's health status, enabling personalized and precise management of health data collection and analysis, improving health management efficiency.

[0113] First, the system automatically assesses the frequency and accuracy of data collection based on the user's health status and real-time data monitoring. Under normal circumstances, for users with relatively stable health conditions, the system will reduce the collection frequency to conserve resources and extend device battery life. For example, the system can adjust the sensor collection frequency based on fluctuations in the user's body temperature, heart rate, and other data. When health conditions are relatively stable, the collection frequency may be reduced to once every 30 minutes or once an hour. However, if the user's health status changes, such as an abnormal heart rate or fluctuating body temperature, the system will automatically increase the collection frequency to promptly capture changes in health data and analyze them.

[0114] Secondly, the system adjusts the frequency and method of data uploads based on network conditions and data transmission requirements. Under normal network conditions, health data is uploaded to the cloud platform in real time for analysis and storage. When network conditions are unstable, the system will delay uploads or cache the data locally, resuming uploads when the network is restored. Furthermore, the system intelligently determines the urgency of the uploaded data. If a user's health condition presents a potential risk, the system prioritizes uploading this important data, ensuring the timely delivery of critical health information.

[0115] The system also features an intelligent feedback mechanism, allowing users to adjust health recommendations based on their needs and health status. For example, if a user feels uncomfortable with a particular health recommendation or if it's ineffective, they can provide feedback in the system, which will automatically adjust their health management plan based on the feedback. For example, a user might report that their sleep quality is "unsatisfactory," and the system will adjust sleep environment optimization recommendations based on this feedback, such as adjusting bedroom temperature or improving mattress quality. The effectiveness of these adjustments will be evaluated based on subsequent changes in health data.

[0116] This dynamic adjustment scheme of data transmission and intelligent feedback mechanism can not only flexibly adjust the collection frequency and upload strategy according to the user's health status, but also provide personalized health management solutions through continuous data monitoring and feedback adjustment, thereby ensuring that the user's health receives comprehensive and timely attention and management.

[0117] The present invention uses an adaptive data synchronization solution based on an intelligent routing algorithm to dynamically adjust the data upload mode according to the real-time network status, ensuring that health data can be transmitted in a timely and accurate manner under different network environments. Regardless of whether the network bandwidth is good or there are problems such as delays and packet loss, the system can intelligently switch the synchronization mode and reduce the risk of synchronization delays or data loss through adaptive synchronization mechanisms, load balancing, caching, and intelligent retries. This efficient data synchronization mechanism ensures that the smart home health system can monitor the user's health status in real time, upload key health data in a timely manner, and avoid health monitoring failures due to synchronization problems, thereby improving the reliability and security of the entire health management system. In particular, in the health monitoring of high-risk users (such as the elderly and patients with chronic diseases), it can provide accurate health data support and reduce blind spots in health monitoring.

[0118] Based on multi-dimensional health data analysis, the present invention can comprehensively assess the user's health status and improve the accuracy and personalization level of health management through personalized health warnings and management suggestions. By comprehensively analyzing multiple health indicators such as body temperature, heart rate, blood pressure, and combining the user's historical health data and living habits, the system can more accurately judge the user's health trends, thereby discovering potential health risks in advance. For example, the system can make trend predictions based on real-time heart rate changes and historical data, issue abnormal warnings in a timely manner, and guide users to take preventive measures. This multi-dimensional health data analysis can not only help users understand their health status more clearly, but also provide tailored health suggestions based on the different needs and characteristics of individuals, help users improve their living habits, optimize health management effects, and thus enhance users' health awareness and self-management capabilities.

[0119] The present invention can meet individualized health management needs to the greatest extent possible while maintaining efficient system operation by flexibly adjusting the frequency of data collection and uploading according to changes in the user's health status. When the user's health status changes, the system can automatically increase the collection frequency or optimize the data upload strategy to ensure that any health changes are captured in a timely manner and provide rapid feedback. For example, when the user's health status is abnormal (such as a fast heart rate or large fluctuations in body temperature), the system will automatically increase the collection frequency and prioritize uploading key data for timely analysis and intervention. In addition, users are allowed to provide feedback on health management plans, and health recommendations are adjusted based on user feedback. This flexible data collection and feedback mechanism enables the system to dynamically optimize health management plans based on the user's specific needs and health status, provide more accurate and personalized services, and ensure that health management is more in line with the user's actual needs.

[0120] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formulas are set by technicians in this field according to actual conditions.

[0121] The above description is merely illustrative of certain exemplary embodiments of the present invention. It goes without saying that those skilled in the art will be able to modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and description are illustrative in nature and should not be construed as limiting the scope of protection of the claims.

[0122] It should be noted that, in this document, if there are relational terms such as first and second, etc., they are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "comprises", "comprising" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device that includes a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprising a ..." does not exclude the presence of other identical elements in the process, method, article or device that includes the element.

[0123] 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.

[0124] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0125] 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.

[0126] 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.

[0127] In addition, each functional unit in each embodiment of the present application 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.

[0128] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

[0129] The above description is merely illustrative of certain exemplary embodiments of the present invention. It goes without saying that those skilled in the art will be able to modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and description are illustrative in nature and should not be construed as limiting the scope of protection of the claims.

Claims

1. A method for synchronous analysis and management of health data of a smart house, characterized in that: The following steps are involved: Collect users’ health data in real time through sensors and health monitoring devices in smart home systems; Pre-process the collected health data to ensure the accuracy and reliability of the data; By establishing an efficient data transmission channel, we ensure real-time synchronization of health data between devices and the cloud platform. We also use intelligent network optimization algorithms to automatically adjust transmission strategies to ensure stability and low latency during data transmission. Conduct multi-dimensional analysis of synchronized health data, combining the user's historical health status, personal characteristics and living habits to generate a health trend analysis report; Based on the data analysis results, abnormal health warnings are generated in real time and pushed to users or health managers via mobile terminals to ensure timely intervention measures; Based on early warning information and user feedback, adjust health data collection strategies, optimize health management plans, and ensure that user health problems are resolved in a timely manner.

2. The smart house health data synchronization analysis and management method according to claim 1, characterized in that: The steps of collecting health data include using multiple sensors to work together to ensure multi-point monitoring of the same health indicator in different environments, collecting environmental and human health data through temperature and humidity sensors and body temperature sensors, and performing data fusion to improve the accuracy and comprehensiveness of health data.

3. The smart house health data synchronization analysis and management method according to claim 1, characterized in that: The data preprocessing step further includes filling in missing values in health data by using interpolation, nearest neighbor method, and time series-based regression models to ensure data integrity, thereby avoiding analytical errors caused by missing data and ensuring the accuracy of subsequent data analysis.

4. The smart house health data synchronization analysis and management method according to claim 1, characterized in that: The data synchronization step further includes: Utilize intelligent routing algorithms to automatically detect network conditions and adaptively adjust synchronization modes based on network bandwidth and latency; Use encryption algorithms during data transmission to ensure the privacy and security of health data and prevent data leakage during synchronization; In the event of device failure or network interruption, health data is saved through a cache mechanism and automatically uploaded after the network is restored or the device is repaired to ensure data integrity; Through load balancing technology, synchronization tasks are distributed to different devices or servers to ensure stable operation under high load conditions.

5. The smart house health data synchronization analysis and management method according to claim 1, characterized in that: The data analysis step further includes time series forecasting analysis of health data. Based on the time series model, the long-term trend of the user's health status is predicted to identify potential health risks in a timely manner. The specific steps are as follows: Standardize user health data to eliminate dimensional differences between data; Use time series prediction algorithms to build a time series model for health data and predict the changing trend of user health data in the future; Based on the prediction results, it is determined whether there are major changes in the user's health status and health warning information is generated.

6. The smart house health data synchronization analysis and management method according to claim 1, characterized in that: The health change prediction formula is used in the data analysis step, and the calculation expression is as follows: First, the health data is weighted and the weight coefficient is set; The weighted average method is used to calculate the user's comprehensive health index. The calculation expression is as follows: Where H t is the comprehensive health index, which represents the overall health status of the user at time t, w i is the weight coefficient of the i-th health indicator, X i (t) is the value of the i-th health indicator at time t, and n is the total number of health indicators; Regression analysis method is used to predict future health status, and the influence of different health indicators on overall health status is adjusted by weighting coefficients. The calculation expression is as follows: Where H t+1 is the comprehensive health index at the future time t+1, α and β are regression model coefficients, and α is used to represent the comprehensive health index H t The degree of impact on future health status, β is used to represent various health indicators X i (t) the degree of influence on future health status, ∈ is the error term; The health status is evaluated based on the prediction results. If the comprehensive health index is lower than a certain threshold, a health risk warning is automatically generated.

7. The method for synchronous analysis and management of smart house health data according to claim 1, characterized in that: The abnormal warning step further includes: Based on the outlier detection algorithm of health data, the user's real-time health data is monitored in real time; When a health data exceeds a preset threshold, the system will issue an alarm and push it to the user and their health manager through multiple channels to ensure timely response; It can automatically adjust the warning threshold according to the user's historical health records and current health status to achieve personalized health warning management; The generation of health warning adopts health prediction based on multivariate analysis. The prediction steps are as follows: First, define the contribution of each health indicator to health risk and calculate the risk factor of each indicator based on historical data; Each health indicator is normalized to unify its dimension, and the risk score of each health indicator is calculated. The calculation expression is as follows: R i (t) is the standardized score of the i-th health indicator at time t, μ i is the mean value of the i-th health indicator, σ i is the standard deviation; The total risk index is calculated by weighted aggregation, and the calculation expression is as follows: Where c i is the risk coefficient of the i-th health indicator, R total (t) is the comprehensive health risk index, which represents the user's overall health risk level at time t; If R total (t) is greater than the set risk threshold, a health risk warning will be generated and notified to the user and his / her health manager.

8. The smart house health data synchronization analysis and management method according to claim 1, characterized in that: The data feedback and adjustment steps further include: Automatically generate personalized health management reports based on health data analysis and early warning information, and recommend measures for users to improve their health status; Based on the user's living habits and health status, an intelligent recommendation algorithm is used to provide tailored lifestyle adjustment suggestions.

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