Chinese and Western combined full life cycle life management system
By collecting and analyzing user regional data and basic data, a physical scoring model is established, which solves the problem of low data accuracy in life cycle management and achieves accurate quantification of physical status and improved practicality of feedback.
Patent Information
- Application Number
- CN202510781315.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-06-12
- Publication Date
- 2025-09-16
AI Technical Summary
The existing full life cycle management system lacks comprehensive health monitoring and feedback, resulting in low data accuracy and poor reliability, and increased labor and time costs.
By combining Chinese and Western methods, we collect user regional data, basic data, and temperature and humidity fluctuation data, perform data preprocessing and analysis, establish a body scoring model, generate body scoring reference values, and improve the accuracy and adaptability of the scoring model through iterative optimization modules.
It achieves accurate quantification of the user's physical condition, improves the accuracy of the scoring model and the practicality of feedback, and enhances the adaptability and practicality of the system.
Smart Images
Figure CN120656722A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of full life cycle life management, and more specifically, to a full life cycle life management system that combines Chinese and Western methods. Background Art
[0002] The integration of Chinese and Western medicine is to combine the knowledge and methods of traditional Chinese medicine with the knowledge and methods of Western medicine. On the basis of improving clinical efficacy, the mechanism is clarified to obtain new medical knowledge. With the rapid development of our country in recent years and the overall progress of medical level, people pay more attention to their own health than ever before. As a medical method that has continued for thousands of years in our country, Chinese medicine can effectively enhance the body's resistance and improve physical condition with its unique diagnostic methods. Assisted by the precise diagnosis and treatment methods of modern medicine, the rational allocation of functional nutrition and modern intelligent monitoring equipment, the goal of life management throughout the life cycle is achieved, providing a new path for people's health and longevity.
[0003] However, in the existing full life cycle life management process, people often only focus on one-way health monitoring and lack comprehensive reference and corresponding feedback, which leads to people's lack of sufficiently accurate and comprehensive understanding of their own physical condition. In addition, traditional full life cycle life management mostly requires manual one-way detection and treatment feedback of the user's physical condition, which not only reduces the data reliability and availability of full life cycle life management, but also increases the labor and time costs of medical personnel. Therefore, how to effectively quantify people's physical condition and improve the accuracy of data reference, the reliability of feedback and the adaptability of data has become a major problem that the current full life cycle life management industry needs to solve.
[0004] In view of this, the present invention proposes a Chinese-Western integrated full life cycle management system to solve the above problems. Summary of the Invention
[0005] In order to overcome the above-mentioned defects of the prior art and achieve the above-mentioned objectives, the present invention provides the following technical solutions, including: User login module, used for users to log in to the management system and read user regional data sets, which include regional environmental data and regional disease data; User basic data collection module, used to collect user basic data sets, including user genetic data, user medical history data, user traditional Chinese medicine constitution data and user baseline physiological data; User region data collection module, used to collect temperature and humidity fluctuation data, and calculate it in combination with the user region data set to obtain environmental characteristic data; Furthermore, the calculation method based on the user region dataset includes: A1 calculates environmental characteristic data based on the temperature and humidity fluctuation data combined with the regional environmental data in the user's regional dataset. The specific calculation formula is: ; Get environmental characteristic data, where For the Regional environmental data, For regional disease data, is the temperature and humidity fluctuation data, is the environmental weight factor, is the climate sensitivity coefficient; A2, based on a preset time unit, continuously updates the regional disease data in the user regional data set to obtain second regional disease data. The specific calculation formula for continuous updating is: ; Get the disease data of the second region, where is the time attenuation coefficient; The user data preprocessing module is used to preprocess the user basic data set and calculate it in combination with the environmental feature data to obtain composite feature data; Furthermore, the method of preprocessing the user basic data set includes: The user gene data in the user basic data set is Z-score normalized to obtain the second user gene data. The user TCM constitution data is vectorized by one-hot encoding to obtain the second user TCM constitution data. The second user basic data set is obtained. The specific calculation formula for Z-score normalization is: ; Obtain the second user's gene data, wherein, For user genetic data, is the mean, is the standard deviation; The calculation method for combining the environmental characteristic data is to obtain composite characteristic data by fusing the second user's genetic data, the second user's TCM constitution data, the user's baseline physiological data and the environmental characteristic data; The body score reference value generating module is used to analyze the second user basic data set and the environmental feature data to obtain a body score reference value; Furthermore, the physical score reference value generation module also includes a historical data retrieval module, a manual input module, a model support module and a score transmission module, wherein: The historical data retrieval module is used to retrieve the historical second user basic data set and historical environmental feature data stored in the database; The manual input module is used to support manual input of standard physical score reference values and score deviation thresholds; The model support module is used to support the system in establishing the required calculation model; The score transmission module is used to support the system in transmitting the body score reference value; Furthermore, the specific steps of analyzing the second user basic data set and the environmental characteristic data are as follows: Step 1: Based on the historical data retrieval module, a set of historical second user basic data sets and historical environmental feature data stored in the database are retrieved, and corresponding grouping and labeling are performed from the earliest to the latest timestamps. The labeling results are Q1, Q2, Q3, ..., Qn, and the labeling results are used as the sample set; Step 2: Based on the model support module, the sample set is divided into an 80% training set and a 20% validation set to establish an initial scoring model; Step 3: Substitute into the calculation formula: ; Get the initial body score reference value, where: For the Second user genetic data at time The value of For the Second user's TCM constitution data in time Rating, For the Baseline physiological data of users at time The historical average For the past Environmental characteristic data at a time point, is the intercept term, 、 、 and is the weight factor and satisfies , is the error term; Step 4: Based on the initial body score reference value obtained in step 3, calculate the standard body score reference value of the manual input module to obtain the score deviation value. When the score deviation value is less than or equal to the score deviation threshold, continue to output the initial body score reference value to step 5. When the score deviation value is greater than the score deviation threshold, modify the weight factor. 、 、 and , and substitute the correction result into step 3 to continue training the initial scoring model. The specific calculation formula of the scoring deviation value is: ; Get the score deviation value, where It is the reference value of standard body score; Step 5: Based on the user baseline physiological data in step 3, use exponential moving average to obtain the second user baseline physiological data, and input the second user baseline physiological data into the initial scoring model in step 3. The specific calculation formula of the exponential moving average is: ; Obtain the second user's baseline physiological data, wherein, is the smoothing coefficient; Step 6: Repeat steps 3 to 5 until the preset number of iterations is reached to obtain a scoring model; Step 7: Input the second user basic data set and environmental feature data into the scoring model, output a physical score reference value, and transmit the physical score reference value to the physical score decision module based on the scoring transmission module; Furthermore, the weight factor is modified 、 、 and The specific methods include: By substituting into the calculation formula: ; The corrected result is obtained, where Contains 1, 2, 3 and 4, For the Original input data, is the covariance, is the variance, and the original input data includes the second user's genetic data, the second user's TCM constitution data, the user's baseline physiological data, and environmental characteristic data; The iterative optimization module is used to input new sample data and adjust the weight factor Perform optimization and obtain adjustment results; Furthermore, the weight factor Optimization methods include: Weight factor The specific calculation formula for optimization is: ; Get the adjustment results, is the historical data retention coefficient, Update the value for the parameter; The body score decision module is used to compare the body score reference value and obtain a body score feedback report; Furthermore, the method of comparing the body score reference value includes: When the physical score reference value is less than or equal to the physical score standard deviation, a primary prevention report is generated; when the physical score reference value is greater than the physical score standard deviation but less than or equal to twice the physical score standard deviation, a secondary intervention report is generated; when the physical score reference value is greater than twice the physical score standard deviation, a tertiary intervention report is generated; The primary prevention report includes an explanation of the user's current good physical score and provides lifestyle recommendations based on the user's regional data set and user basic data set; The secondary intervention report includes an explanation of the user's current poor physical score, reminding the user to combine targeted nutrition plans, medical measures and intelligent monitoring of physical condition to reverse the user's sub-health condition; The third-level intervention report includes an explanation of the user's current extremely poor physical score, requiring the user to combine a functional nutrition enhancement program, in-depth medical intervention, comprehensive Chinese medicine methods, and intelligent monitoring in-depth intervention to reverse the user's existing disease state; Package the primary prevention report, secondary intervention report and tertiary intervention report to obtain the physical score feedback report; The data output module is used to process and output the physical score feedback report; Furthermore, the method of processing and outputting the physical score feedback report includes: Identify the content of the physical score feedback report. When the physical score feedback report is a secondary intervention report or a tertiary intervention report, push the secondary intervention report or the tertiary intervention report to the user receiving end and the doctor receiving end in real time. When the physical score feedback report is a primary prevention report, push it to the user receiving end and the doctor receiving end according to the preset time unit. Furthermore, S1: a user logs in to the management system and reads a user regional data set, which includes regional environmental data and regional disease data; S2: Collect user basic data sets, which include user genetic data, user medical history data, user traditional Chinese medicine constitution data, and user baseline physiological data; S3: Collect temperature and humidity fluctuation data and calculate it based on the user's regional data set to obtain environmental characteristic data; S4: Preprocess the user basic data set and calculate it in combination with the environmental feature data to obtain composite feature data; S5: Analyze the second user basic data set and environmental characteristic data to obtain a physical score reference value; S6: Input new sample data and adjust the weight factor Perform optimization and obtain adjustment results; S7: Compare the body score reference value to obtain a body score feedback report; S8: Process and output the physical score feedback report.
[0006] The technical effects and advantages of the present invention's integrated Chinese and Western full life cycle management system are as follows: The present invention logs in to the management system through the user and reads the user's regional data set, which includes regional environmental data and regional disease data; collects the user's basic data set, which includes user gene data, user medical history data, user traditional Chinese medicine constitution data and user baseline physiological data; collects temperature and humidity fluctuation data, and calculates it in combination with the user's regional data set to obtain environmental characteristic data; pre-processes the user's basic data set, and calculates it in combination with the environmental characteristic data to obtain composite characteristic data; analyzes the second user basic data set and the environmental characteristic data to obtain a physical score reference value; inputs new sample data, and adjusts the weight factor. Optimize and obtain adjustment results; compare the body score reference value to obtain a body score feedback report; process the body score feedback report and output it, so that the user's physical condition can be effectively quantified. In addition, through the established scoring model, combined with further re-examination of the scoring model, the accuracy of the body score reference value can be greatly enhanced, and at the same time, the adaptability of the scoring model in the face of multiple data sources and dynamic data fluctuations can be effectively improved, so that the system can further improve the accuracy of the scoring, and through the new samples input by the iterative optimization module, while ensuring that the system has the ability to continuously update and optimize, the accuracy of the scoring is further improved. By relying on the body score decision module, the data can be converted into a practical and effective feedback report for the user, greatly improving the practicality of the system. Overall, the present invention has the significant advantages of high scoring accuracy, strong feedback practicality and good system adaptability. BRIEF DESCRIPTION OF THE DRAWINGS
[0007] Figure 1 This is a schematic diagram of a Chinese-Western integrated full life cycle life management system of the present invention; Figure 2 This is a schematic diagram of a full life cycle life management method combining Chinese and Western methods according to the present invention. DETAILED DESCRIPTION
[0008] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.
[0009] The terms used in the embodiments of the present invention are for the purpose of describing specific embodiments only and are not intended to limit the present invention. The singular forms "a," "an," "the," and "the" used in the embodiments of the present invention and the appended claims are also intended to include plural forms, and unless the context clearly indicates otherwise, "a plurality" generally includes at least two.
[0010] As used herein, the words "if" and "if" may be interpreted as "at the time of" or "when" or "in response to determining" or "in response to detecting," depending on the context. Similarly, the phrases "if it is determined" or "if (stated condition or event) is detected" may be interpreted as "when it is determined" or "in response to the determination" or "when detecting (stated condition or event)" or "in response to detecting (stated condition or event)," depending on the context.
[0011] In addition, the step sequence in the following method embodiments is only an example and not a strict limitation.
[0012] In practice, the server-side device deployed by the Power Internet of Things anomaly detection system may consist of one or more devices. The aforementioned Power Internet of Things anomaly detection system can be implemented as a service instance, a virtual machine, or a hardware device. For example, the Power Internet of Things anomaly detection system can be implemented as a service instance deployed on one or more devices in a cloud node. Simply put, the Power Internet of Things anomaly detection system can be understood as software deployed on a cloud node, providing the Power Internet of Things anomaly detection system to each user end. Alternatively, the Power Internet of Things anomaly detection system can be implemented as a virtual machine deployed on one or more devices in a cloud node. This virtual machine contains application software for managing each user end. Alternatively, the Power Internet of Things anomaly detection system can be implemented as a server-side device composed of numerous hardware devices of the same or different types, with one or more hardware devices configured to provide the Power Internet of Things anomaly detection system to each user end.
[0013] In terms of implementation, the power Internet of Things anomaly detection system and the user end are mutually compatible. Specifically, if the power Internet of Things anomaly detection system is an application installed on a cloud service platform, the user end is the client that establishes a communication connection with the application. Alternatively, if the power Internet of Things anomaly detection system is implemented as a website, the user end is implemented as a webpage. Alternatively, if the power Internet of Things anomaly detection system is implemented as a cloud service platform, the user end is implemented as a mini-program within an instant messaging application.
[0014] like Figure 1 , which is a system architecture diagram of a power Internet of Things anomaly detection system provided by one embodiment of the present invention.
[0015] The power Internet of Things anomaly detection system of the present invention can be set in a cloud server. In terms of implementation, it can be used as one or more service devices, or it can be installed as an application on the cloud (such as a mobile service operator's server, server cluster, etc.), or it can be developed as a website. According to the functions implemented, the power Internet of Things anomaly detection system can include a user login module, a user basic data acquisition module, a user regional data acquisition module, a user data preprocessing module, a physical score reference value generation module, an iterative optimization module, a physical score decision module, and a data output module. The module of the present invention can also be referred to as a unit, which refers to a series of computer program segments that can be executed by an electronic device processor and can complete fixed functions, which are stored in the memory of the electronic device.
[0016] In the embodiment of the present invention, in the power Internet of Things anomaly detection system, each of the above modules can be implemented independently and called with other modules. The call here can be understood as a module that can connect to multiple modules of another type and provide corresponding services to the multiple modules it is connected to. For example, the sharing evaluation module can call the same information acquisition module to obtain the information collected by the information acquisition module. Based on the above characteristics, in the power Internet of Things anomaly detection system provided by the embodiment of the present invention, the scope of application of the power Internet of Things anomaly detection system architecture can be adjusted by adding modules and directly calling them without modifying the program code, thereby realizing cluster-type horizontal expansion, so as to achieve the purpose of quickly and flexibly expanding the power Internet of Things anomaly detection system. In actual applications, the above modules can be set in the same device or different devices, or they can be set in virtual devices, such as service instances in cloud servers.
[0017] Example 1 See also Figure 1 As shown, the embodiment of the present invention is a Chinese-Western integrated full life cycle management system, the system includes: The user login module is used for users to log in to the management system and read the user's regional data set, which includes regional environmental data and regional disease data; It should be explained that the real-time air quality release platform collects the comprehensive air quality values, water hardness values, and ultraviolet intensity values of the designated user's area to obtain regional environmental data; the public health database collects the regional disease weights of the designated user's area to obtain regional disease data; The user basic data collection module is used to collect user basic data sets, which include user gene data, user medical history data, user traditional Chinese medicine constitution data and user baseline physiological data; It should be explained that by providing saliva or blood samples, the longevity-related gene sites of the designated user are collected to obtain the user's genetic data; by connecting with the electronic health record, the structured diagnostic records of the designated user are collected to obtain the user's medical history data; by using the four diagnostic instruments, the user's physical category is collected to obtain the user's traditional Chinese medicine physical data; by using flow cytometry, the telomere length of the designated user's lymphocytes is collected and measured to obtain the user's baseline physiological data; The user region data collection module is used to collect temperature and humidity fluctuation data and calculate it in combination with the user region data set to obtain environmental characteristic data; It should be explained that the temperature and humidity fluctuation values of the area where the specified user is located are collected through the meteorological API to obtain the temperature and humidity fluctuation data; Further, the calculation method based on the user region dataset includes: A1 calculates environmental characteristic data based on the temperature and humidity fluctuation data combined with the regional environmental data in the user's regional dataset. The specific calculation formula is: ; Get environmental characteristic data, where For the Regional environmental data, For regional disease data, is the temperature and humidity fluctuation data, is the environmental weight factor, is the climate sensitivity coefficient; A2, based on a preset time unit, continuously updates the regional disease data in the user regional data set to obtain second regional disease data. The specific calculation formula for continuous updating is: ; Get the disease data of the second region, where is the time attenuation coefficient; The user data preprocessing module is used to preprocess the user basic data set and calculate it in combination with the environmental feature data to obtain composite feature data; Furthermore, the method of preprocessing the user basic data set includes: The user gene data in the user basic data set is Z-score normalized to obtain the second user gene data. The user TCM constitution data is vectorized by one-hot encoding to obtain the second user TCM constitution data. The second user basic data set is obtained. The specific calculation formula for Z-score normalization is: ; Obtain the second user's gene data, wherein, For user genetic data, is the mean, is the standard deviation; The calculation method for combining the environmental characteristic data is to obtain composite characteristic data by fusing the second user's genetic data, the second user's TCM constitution data, the user's baseline physiological data and the environmental characteristic data; The body score reference value generating module is used to analyze the second user basic data set and the environmental feature data to obtain a body score reference value; Furthermore, the physical score reference value generation module also includes a historical data retrieval module, a manual input module, a model support module and a score transmission module, wherein: The historical data retrieval module is used to retrieve the historical second user basic data set and historical environmental feature data stored in the database; The manual input module is used to support manual input of standard physical score reference values and score deviation thresholds; The model support module is used to support the system in establishing the required calculation model; The score transmission module is used to support the system in transmitting the body score reference value; Furthermore, the specific steps of analyzing the second user basic data set and the environmental characteristic data are as follows: Step 1: Based on the historical data retrieval module, a set of historical second user basic data sets and historical environmental feature data stored in the database are retrieved, and corresponding grouping and labeling are performed from the earliest to the latest timestamps. The labeling results are Q1, Q2, Q3, ..., Qn, and the labeling results are used as the sample set; Step 2: Based on the model support module, the sample set is divided into an 80% training set and a 20% validation set to establish an initial scoring model; Step 3: Substitute into the calculation formula: ; Get the initial body score reference value, where: For the Second user genetic data at time The value of For the Second user's TCM constitution data in time Rating, For the Baseline physiological data of users at time The historical average For the past Environmental characteristic data at a time point, is the intercept term, 、 、 and is the weight factor and satisfies , is the error term; Step 4: Based on the initial body score reference value obtained in step 3, calculate the standard body score reference value of the manual input module to obtain the score deviation value. When the score deviation value is less than or equal to the score deviation threshold, continue to output the initial body score reference value to step 5. When the score deviation value is greater than the score deviation threshold, modify the weight factor. 、 、 and , and substitute the correction result into step 3 to continue training the initial scoring model. The specific calculation formula of the scoring deviation value is: ; Get the score deviation value, where It is the reference value of standard body score; Step 5: Based on the user baseline physiological data in step 3, use exponential moving average to obtain the second user baseline physiological data, and input the second user baseline physiological data into the initial scoring model in step 3. The specific calculation formula of the exponential moving average is: ; Obtain the second user's baseline physiological data, wherein, is the smoothing coefficient; Step 6: Repeat steps 3 to 5 until the preset number of iterations is reached to obtain a scoring model; Step 7: Input the second user basic data set and environmental feature data into the scoring model, output a physical score reference value, and transmit the physical score reference value to the physical score decision module based on the scoring transmission module; Furthermore, the weight factor is modified 、 、 and The specific methods include: By substituting into the calculation formula: ; The corrected result is obtained, where Contains 1, 2, 3 and 4, For the Original input data, is the covariance, is the variance, and the original input data includes the second user's genetic data, the second user's TCM constitution data, the user's baseline physiological data, and environmental characteristic data; The iterative optimization module is used to input new sample data and adjust the weight factor Perform optimization and obtain adjustment results; Furthermore, the weight factor Optimization methods include: Weight factor The specific calculation formula for optimization is: ; Get the adjustment results, is the historical data retention coefficient, Update the value for the parameter; It should be explained that the parameter update values are obtained based on training of new sample data; The body score decision module is used to compare the body score reference value and obtain a body score feedback report; Furthermore, the method of comparing the body score reference value includes: When the physical score reference value is less than or equal to the physical score standard deviation, a primary prevention report is generated; when the physical score reference value is greater than the physical score standard deviation but less than or equal to twice the physical score standard deviation, a secondary intervention report is generated; when the physical score reference value is greater than twice the physical score standard deviation, a tertiary intervention report is generated; The primary prevention report includes an explanation of the user's current good physical score and provides lifestyle recommendations based on the user's regional data set and user basic data set; The secondary intervention report includes an explanation of the user's current poor physical score, reminding the user to combine targeted nutrition plans, medical measures and intelligent monitoring of physical condition to reverse the user's sub-health condition; The third-level intervention report includes an explanation of the user's current extremely poor physical score, requiring the user to combine a functional nutrition enhancement program, in-depth medical intervention, comprehensive Chinese medicine methods, and intelligent monitoring in-depth intervention to reverse the user's existing disease state; Package the primary prevention report, secondary intervention report and tertiary intervention report to obtain the physical score feedback report; The data output module is used to process and output the physical score feedback report; Furthermore, the method of processing and outputting the physical score feedback report includes: Identify the content of the physical score feedback report. When the physical score feedback report is a secondary intervention report or a tertiary intervention report, push the secondary intervention report or the tertiary intervention report to the user receiving end and the doctor receiving end in real time. When the physical score feedback report is a primary prevention report, push it to the user receiving end and the doctor receiving end according to the preset time unit. This embodiment has the beneficial effects of logging into the management system through the user and reading the user's regional data set, which includes regional environmental data and regional disease data; collecting the user's basic data set, which includes user gene data, user medical history data, user TCM constitution data and user baseline physiological data; collecting temperature and humidity fluctuation data, and combining it with the user's regional data set to calculate and obtain environmental characteristic data; preprocessing the user's basic data set, and combining it with the environmental characteristic data to calculate and obtain composite characteristic data; analyzing the second user's basic data set and the environmental characteristic data to obtain a reference value for the body score; inputting new sample data, and adjusting the weight factor. Optimize and obtain adjustment results; compare the body score reference value to obtain a body score feedback report; process the body score feedback report and output it, so that the user's physical condition can be effectively quantified. In addition, through the established scoring model, combined with further re-examination of the scoring model, the accuracy of the body score reference value can be greatly enhanced, and at the same time, the adaptability of the scoring model in the face of multiple data sources and dynamic data fluctuations can be effectively improved, so that the system can further improve the accuracy of the scoring, and through the new samples input by the iterative optimization module, while ensuring that the system has the ability to continuously update and optimize, the accuracy of the scoring is further improved. By relying on the body score decision module, the data can be converted into a practical and effective feedback report for the user, greatly improving the practicality of the system. Overall, the present invention has the significant advantages of high scoring accuracy, strong feedback practicality and good system adaptability.
[0018] Example 2 See also Figure 2 As shown, for parts not described in detail in this embodiment, please refer to the description of Example 1. A method for life management combining Chinese and Western medicine is provided, the method comprising: S1: a user logs in to the management system and reads a user regional data set, the user regional data set including regional environmental data and regional disease data; S2: Collect user basic data sets, which include user genetic data, user medical history data, user traditional Chinese medicine constitution data, and user baseline physiological data; S3: Collect temperature and humidity fluctuation data and calculate it based on the user's regional data set to obtain environmental characteristic data; S4: Preprocess the user basic data set and calculate it in combination with the environmental feature data to obtain composite feature data; S5: Analyze the second user basic data set and environmental characteristic data to obtain a physical score reference value; S6: Input new sample data and adjust the weight factor Perform optimization and obtain adjustment results; S7: Compare the body score reference value to obtain a body score feedback report; S8: Process and output the physical score feedback report.
[0019] Example 3 It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.
[0020] Therefore, the embodiments should be considered in all respects as illustrative and non-restrictive, and the scope of the invention is defined by the appended claims rather than the foregoing description, and all changes that come within the meaning and range of equivalents of the claims are intended to be embraced therein. Any reference to a figure in a claim should not be construed as limiting the claim to which it relates.
[0021] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence (AI) refers to the theories, methods, techniques, and application systems that use digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to achieve optimal results.
[0022] Furthermore, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices recited in a system claim may also be implemented by a single unit or device through software or hardware. Terms such as "first" and "second" are used to indicate names and do not imply any particular order.
[0023] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not limiting. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention.
Claims
1. A life cycle management system combining Chinese and Western methods, characterized by: The system includes: a user basic data collection module, a user regional data collection module, a user data pre-processing module, a body score reference value generation module, a body score decision module, and a data output module, wherein: The user basic data collection module is used to collect user basic data sets, which include user gene data, user medical history data, user traditional Chinese medicine constitution data and user baseline physiological data; The user region data collection module is used to collect temperature and humidity fluctuation data and calculate it in combination with the user region data set to obtain environmental characteristic data; The user data preprocessing module is used to preprocess the user basic data set and calculate it in combination with the environmental feature data to obtain composite feature data; The body score reference value generating module is used to analyze the second user basic data set and the environmental feature data to obtain a body score reference value; The body score decision module is used to compare the body score reference value and obtain a body score feedback report; The data output module is used to process and output the physical score feedback report.
2. The Chinese-Western integrated full life cycle management system according to claim 1 is characterized in that: It also includes a user login module and an iterative optimization module, among which: The user login module is used for users to log in to the management system and read the user's regional data set, which includes regional environmental data and regional disease data; The iterative optimization module is used to input new sample data and adjust the weight factor Perform optimization and obtain adjustment results.
3. The Chinese-Western integrated full life cycle management system according to claim 1 is characterized in that: Methods for performing calculations based on user region datasets include: A1 calculates environmental characteristic data based on the temperature and humidity fluctuation data combined with the regional environmental data in the user's regional dataset. The specific calculation formula is: ； Get environmental characteristic data, where For the Regional environmental data, For regional disease data, is the temperature and humidity fluctuation data, is the environmental weight factor, is the climate sensitivity coefficient; A2, based on a preset time unit, continuously updates the regional disease data in the user regional data set to obtain second regional disease data. The specific calculation formula for continuous updating is: ； Get the disease data of the second region, where is the time decay coefficient.
4. The Chinese-Western integrated full life cycle management system according to claim 1 is characterized in that: Methods for preprocessing user basic data sets include: The user gene data in the user basic data set is Z-score normalized to obtain the second user gene data. The user TCM constitution data is vectorized by one-hot encoding to obtain the second user TCM constitution data. The second user basic data set is obtained. The specific calculation formula for Z-score normalization is: ； Obtain the second user's gene data, wherein, For user genetic data, is the mean, is the standard deviation; The method of combining the environmental characteristic data for calculation is to obtain composite characteristic data by fusing the second user's gene data, the second user's TCM constitution data, the user's baseline physiological data and the environmental characteristic data.
5. The Chinese-Western integrated full life cycle management system according to claim 1 is characterized in that: The physical score reference value generation module also includes a historical data retrieval module, a manual input module, a model support module and a score transmission module, wherein: The historical data retrieval module is used to retrieve the historical second user basic data set and historical environmental feature data stored in the database; The manual input module is used to support manual input of standard physical score reference values and score deviation thresholds; The model support module is used to support the system in establishing the required calculation model; The score transmission module is used to support the system in transmitting the body score reference value.
6. The Chinese-Western integrated full life cycle management system according to claim 5 is characterized in that: The specific steps for analyzing the second user basic data set and environmental characteristic data are as follows: Step 1: Based on the historical data retrieval module, a set of historical second user basic data sets and historical environmental feature data stored in the database are retrieved, and corresponding grouping and labeling are performed from the earliest to the latest timestamps. The labeling results are Q1, Q2, Q3, ..., Qn, and the labeling results are used as the sample set; Step 2: Based on the model support module, the sample set is divided into an 80% training set and a 20% validation set to establish an initial scoring model; Step 3: Substitute into the calculation formula: ； Get the initial body score reference value, where: For the Second user genetic data at time The value of For the Second user's TCM constitution data in time Rating, For the Baseline physiological data of users at time The historical average For the past Environmental characteristic data at a time point, is the intercept term, 、 、 and is the weight factor and satisfies , is the error term; Step 4: Based on the initial body score reference value obtained in step 3, calculate the standard body score reference value of the manual input module to obtain the score deviation value. When the score deviation value is less than or equal to the score deviation threshold, continue to output the initial body score reference value to step 5. When the score deviation value is greater than the score deviation threshold, modify the weight factor. 、 、 and , and substitute the correction result into step 3 to continue training the initial scoring model. The specific calculation formula of the scoring deviation value is: ； Get the score deviation value, where It is the reference value of standard body score; Step 5: Based on the user baseline physiological data in step 3, use exponential moving average to obtain the second user baseline physiological data, and input the second user baseline physiological data into the initial scoring model in step 3. The specific calculation formula of the exponential moving average is: ; Obtain the second user's baseline physiological data, wherein, is the smoothing coefficient; Step 6: Repeat steps 3 to 5 until the preset number of iterations is reached to obtain a scoring model; Step 7: Input the second user basic data set and environmental feature data into the scoring model, output a body score reference value, and transmit the body score reference value to the body score decision module based on the scoring transmission module.
7. The Chinese-Western integrated full life cycle management system according to claim 6 is characterized in that: Modified weight factor 、 、 and The specific methods include: By substituting into the calculation formula: ； The corrected result is obtained, where Contains 1, 2, 3 and 4, For the Original input data, is the covariance, The original input data includes the second user's gene data, the second user's TCM constitution data, the user's baseline physiological data and environmental characteristic data.
8. The Chinese-Western integrated full life cycle management system according to claim 7 is characterized in that: Weight factor Optimization methods include: Weight factor The specific calculation formula for optimization is: ； Get the adjustment results, is the historical data retention coefficient, Update the value for the parameter.
9. The Chinese-Western integrated full life cycle management system according to claim 1 is characterized in that: Methods for comparing body score reference values include: When the physical score reference value is less than or equal to the physical score standard deviation, a primary prevention report is generated; when the physical score reference value is greater than the physical score standard deviation but less than or equal to twice the physical score standard deviation, a secondary intervention report is generated; when the physical score reference value is greater than twice the physical score standard deviation, a tertiary intervention report is generated; The primary prevention report includes an explanation of the user's current good physical score and provides lifestyle recommendations based on the user's regional data set and user basic data set; The secondary intervention report includes an explanation of the user's current poor physical score, reminding the user to combine targeted nutrition plans, medical measures and intelligent monitoring of physical condition to reverse the user's sub-health condition; The third-level intervention report includes an explanation of the user's current extremely poor physical score, requiring the user to combine a functional nutrition enhancement program, in-depth medical intervention, comprehensive Chinese medicine methods, and intelligent monitoring in-depth intervention to reverse the user's existing disease state; The primary prevention report, secondary intervention report and tertiary intervention report are packaged to obtain a physical score feedback report.
10. A Chinese-Western integrated full life cycle life management method, implemented by a Chinese-Western integrated full life cycle life management system according to any one of claims 1 to 9, characterized in that: The following steps are included: S1: The user logs in to the management system and reads the user's regional data set, which includes regional environmental data and regional disease data; S2: Collect user basic data sets, which include user genetic data, user medical history data, user traditional Chinese medicine constitution data, and user baseline physiological data; S3: Collect temperature and humidity fluctuation data and calculate it based on the user's regional data set to obtain environmental characteristic data; S4: Preprocess the user basic data set and calculate it in combination with the environmental feature data to obtain composite feature data; S5: Analyze the second user basic data set and environmental characteristic data to obtain a physical score reference value; S6: Input new sample data and adjust the weight factor Perform optimization and obtain adjustment results; S7: Compare the body score reference value to obtain a body score feedback report; S8: Process and output the physical score feedback report.