Disease risk assessment method and system based on patient whole life cycle health data

Abstract

This invention discloses a method and system for disease risk assessment based on a patient's full life-cycle health data, relating to the field of health management. It includes: a data acquisition module for collecting and storing user disease-related physical state parameters; and a visualization module for receiving the stored physical state parameters in real time, obtaining the user's disease risk coefficient based on these parameters, and creating and updating a trend chart representing the user's health status in real time. This invention integrates user's full life-cycle health data to achieve dynamic assessment and early warning of disease risk, conducts risk analysis based on the real-time updated health trend chart, and performs risk prediction by combining historical early warning information, effectively improving the timeliness and accuracy of the assessment.

Description

Technical Field

[0001] This invention relates to the field of health management technology, specifically to a method and system for disease risk assessment based on patients' health data throughout their entire life cycle. Background Technology

[0002] Chronic disease management involves continuously monitoring patients' health data throughout their entire lifespan, combined with interventions such as medication, diet, and exercise, to dynamically assess disease progression and the risk of complications.

[0003] The invention patent application with application number 202410896835.3 discloses a big data analysis and monitoring system for eye health throughout the entire life cycle. This application aims to solve the problem that "at present, the development of eye health throughout the entire life cycle is affected by the uneven distribution of medical levels in different regions, the lack of medical testing equipment and the uneven quality of medical technology. The early detection and diagnosis rates of many fundus diseases in primary hospitals are low, causing many preventable eye diseases to miss the best time for diagnosis and treatment, resulting in serious damage to the visual quality of patients. Furthermore, due to the uneven medical levels, patients have difficulty understanding their own health in a timely manner after treatment, resulting in poor treatment effects and affecting the treatment and recovery of patients' eye diseases."

[0004] However, for chronic disease management, the main purpose of management is to suppress the risk and probability of disease. However, current technology focuses on the detection accuracy of chronic disease-related physical state parameters, making it difficult to intelligently and quickly assess the real-time risk of the disease. To address this, a disease risk assessment system based on patients' health data throughout their entire life cycle was proposed. Summary of the Invention

[0005] In view of the above-mentioned shortcomings of the existing technology, the present invention provides a disease risk assessment method and system based on the patient's full life cycle health data, which can effectively solve the problems of the existing technology.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions; This invention discloses a disease risk assessment system based on patients' health data throughout their entire life cycle, comprising: The system comprises four modules: a data acquisition module, a data collection module, and a visualization module. The data acquisition module collects and stores user disease-related physical status parameters. The visualization module receives these parameters in real-time, obtains the user's disease risk coefficient based on these parameters, and creates and updates a trend chart representing the user's health status. The analysis module monitors the real-time trend chart in the visualization module and performs a user disease risk analysis each time the trend chart is updated. The early warning module receives the user's disease risk analysis results from the analysis module and triggers early warnings based on these results. The recording and prediction module records historical early warning trigger information from the early warning module and predicts whether the user has abnormal disease risk based on this historical information.

[0007] Furthermore, the user's disease-related physical status collected by the acquisition module all originates from a portable physical status parameter acquisition device adapted to the user's disease. When the acquisition module stores the user's disease-related physical status parameters, it marks the acquisition time for each physical status parameter and distinguishes them based on each acquisition operation, so as to store them separately in the acquisition module. The portable body status parameter acquisition device is manually operated by the user to complete the acquisition of body status parameters. The body status parameters acquired in real time by each portable body status parameter acquisition device are fed back to the system's acquisition module in real time via wireless network or Bluetooth network. The acquisition module is equipped with a control unit at its lower level, which is used to control the acquisition module to run continuously based on a preset operating cycle.

[0008] Furthermore, the operating cycle of the control unit provided to the acquisition module conforms to: A basic operating cycle is set, and the control unit controls the acquisition module to run continuously based on the basic operating cycle. After the basic operating cycle is applied a second time and the second basic operating cycle ends, the operating cycle provided by the control unit to the acquisition module is adjusted according to the user's disease risk coefficient. The user's disease risk coefficient during the first basic operating cycle is less than or equal to the user's disease risk coefficient during the second basic operating cycle. ; In the formula: This refers to the operational cycle after adjustment; Based on the basic operating cycle; The user's disease risk coefficient output for the first and second basic operating cycles; This indicates taking the minimum value within the parentheses; For correction functions; The disease risk coefficient of users in the first basic operating cycle is greater than that of users in the second basic operating cycle. ; In the formula: This indicates taking the maximum value within the parentheses.

[0009] Furthermore, the control unit outputs the operating cycle in real time based on formulas (1) and (2) for continuous operation of the acquisition module: In the third and subsequent output cycles, the control unit replaces the user's disease risk coefficient based on the performance of the most recent two cycles. Replace with the latest completed cycle. This ensures that the output of the next cycle is completed before the end of each cycle of the acquisition module, so that the acquisition module can use it for subsequent operation. The correction function The values ​​follow the rules in formula (1). When it is zero for the first time, =a, in formula (1) When it is zero for the second consecutive time, =2a, and so on; Where 'a' is a minimum value preset by the system user, initially set to 0.01.

[0010] Furthermore, the formula for calculating the user's disease risk coefficient is as follows: ; In the formula: The set of parameters used in the risk accumulation calculation; Let i be the value of the i-th parameter; This is the standard value of the i-th parameter; The set of parameters participating in the collaborative buffer computation; For the j-th parameter value; The j-th parameter is the standard value; The set of parameters used in the overall deviation calculation; For the j-th parameter value; This is the standard value of the s-th parameter; in, , , The three are subordinate: , , This represents the total number of parameter types in the set.

[0011] Furthermore, after the visualization module obtains the user's disease risk coefficient, the trend chart representing the user's health status is a line chart, and the line chart synchronously updates the user's disease risk coefficient represented in the trend chart based on the newly received body status parameters from the visualization module. The trend chart representing the user's health status is marked with a safe range after it is first created; In the trend chart, the safe area is a rectangle, with its left boundary coinciding with the vertical axis of the trend chart and its lower boundary coinciding with the horizontal axis of the trend chart.

[0012] Furthermore, the user disease risk analysis logic in the analysis module is as follows: Read the three latest user disease risk coefficients shown in the trend chart, denoted as R1, R2, and R3. R1 is the earliest user disease risk coefficient compared to R3, and R3 is the latest user disease risk coefficient. ; In the formula: This represents the vertical distance between the corresponding point of R1 in the trend chart and the upper boundary of the safe zone. Similarly; If the above formula is true, it means that the user's disease risk is abnormal; otherwise, it means that the user's disease risk is not abnormal. When the user's disease risk is abnormal, the warning module is triggered. The early warning module is integrated with an audio module and a communication unit. The audio module stores preset early warning audio, and the communication unit stores preset monitoring user contact information. During the operation of the early warning module, the audio module synchronously emits early warning audio, and the communication unit synchronously sends preset early warning information to the monitored target based on the preset monitoring user contact information.

[0013] Furthermore, the historical early warning triggering information recorded in the recording and prediction module refers to the user's disease-related physical state parameters within a specified time threshold before the early warning is triggered; When the recording and prediction module predicts whether a user has an abnormal disease risk, it follows the following: After the acquisition module acquires new user disease-related physical status parameters, it identifies the highest similarity between the newly acquired user disease-related physical status parameters and the historical warning trigger information in each group in the recording and prediction module. When the highest similarity identified three times in a row shows a linear increasing trend, and the three highest similarities all point to the same historical warning trigger information, the recording and prediction module predicts that the user has an abnormal disease risk.

[0014] Furthermore, the acquisition module is connected to a control unit via a wireless network, the acquisition module is connected to a visualization module and an analysis module via a wireless network, the analysis module is connected to an early warning module via a wireless network, the early warning module is connected to a recording and prediction module via a wireless network, and the recording and prediction module is connected to the visualization module via a wireless network.

[0015] On the other hand, disease risk assessment methods based on patients' health data throughout their entire life cycle include: Collect and store user disease-related physical status parameters; based on the real-time updates of these parameters, synchronously analyze user disease risk coefficients; create and update a trend chart representing user health status based on the analysis results; extract user disease risk coefficients from the trend chart and analyze whether the user's disease risk is abnormal; trigger preset warning logic when the analysis result indicates abnormal user disease risk; record relevant information about historical warning triggers and predict in real time whether the user has abnormal disease risk based on this information.

[0016] Compared with the known prior art, the technical solution provided by this invention has the following beneficial effects: This invention provides a disease risk assessment method and system based on a patient's full life-cycle health data. During execution, this method and system integrate the user's full life-cycle health data to achieve dynamic assessment and early warning of disease risks. Risk analysis is conducted based on real-time updated health trend charts, and risk prediction is performed by combining historical early warning information, effectively improving the timeliness and accuracy of the assessment. Simultaneously, the operating cycle of the data collection equipment can be dynamically adjusted according to the risk coefficient, ensuring both the continuity and flexibility of data collection and optimizing the collection frequency when risks change. Furthermore, by marking safe intervals on trend charts and combining multi-parameter analysis to determine risk anomalies, early warnings are made more accurate. Information can also be simultaneously sent to caregivers, providing comprehensive protection for the user's health and effectively assisting in the early detection and intervention of diseases. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0018] Figure 1 This is a schematic diagram of the structure of a disease risk assessment system based on the patient's health data throughout their entire life cycle; Figure 2 This is a flowchart illustrating a disease risk assessment method based on a patient's health data throughout their entire life cycle. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0020] The present invention will be further described below with reference to embodiments.

[0021] Example 1: This embodiment presents a disease risk assessment system based on the patient's full life-cycle health data, such as... Figure 1 As shown, it includes: The data acquisition module is used to collect and store the user's disease-related physical status parameters. The user's disease-related physical status collected by the acquisition module all come from portable physical status parameter acquisition devices adapted to the user's disease. When the acquisition module stores the user's disease-related physical status parameters, it marks the acquisition time for each physical status parameter and distinguishes them based on each acquisition operation so that they can be stored separately in the acquisition module. It should be noted that the criteria for determining "fit" in "portable physical condition parameter acquisition device adapted to the user's disease" should be based on the type of core monitoring parameter of the user's disease, and the measurement accuracy and measurement range of the portable device should match the clinical needs of the core monitoring parameter. The portable body status parameter acquisition device is manually operated by the user to complete the acquisition of body status parameters. The body status parameters collected in real time by each portable body status parameter acquisition device are fed back to the system's acquisition module in real time via wireless network or Bluetooth network. The acquisition module is equipped with a control unit, which is used to control the acquisition module to run continuously based on a preset operating cycle. The operating cycle provided by the control unit to the acquisition module follows the following rules: A basic operating cycle is set, and the control unit controls the acquisition module to run continuously based on the basic operating cycle. After the basic operating cycle is applied a second time and the second basic operating cycle ends, the operating cycle provided by the control unit to the acquisition module is adjusted according to the user's disease risk coefficient. The user's disease risk coefficient during the first basic operating cycle is less than or equal to the user's disease risk coefficient during the second basic operating cycle. ; The above formula aims to dynamically adjust the operating cycle of the data collection module when the user's disease risk coefficient does not decrease. By introducing a base operating cycle, the minimum value of the risk coefficient between two consecutive cycles, and a correction function, the adjusted cycle can adapt to the trend of risk changes and more accurately match the user's health monitoring needs. In the formula: This refers to the operational cycle after adjustment; Based on the basic operating cycle; The user's disease risk coefficient output for the first and second basic operating cycles; This indicates taking the minimum value within the parentheses; For correction functions; The disease risk coefficient of users in the first basic operating cycle is greater than that of users in the second basic operating cycle. ; In the formula: This indicates taking the maximum value within the parentheses; The above formula is designed for situations where the user's disease risk coefficient decreases. By introducing the maximum value of the risk coefficient between two consecutive periods, the basic operating cycle is adjusted to adapt to the monitoring needs when the risk decreases, and to avoid excessive data collection that would waste resources. The control unit outputs the operating cycle in real time based on formulas (1) and (2) for continuous operation of the acquisition module: In the third and subsequent output cycles, the control unit replaces the user's disease risk coefficient based on the performance of the most recent two cycles. Replace with the latest completed cycle. This ensures that the output of the next cycle is completed before the end of each cycle of the acquisition module, so that the acquisition module can use it for subsequent operation. Correction function The values ​​follow the rules in formula (1). When it is zero for the first time, =a, in formula (1) When it is zero for the second consecutive time, =2a, and so on; Where 'a' is a minimum value preset by the system user, initially set to 0.01; The visualization module is used to receive the body status parameters stored in the acquisition module in real time, obtain the user's disease risk coefficient based on the body status parameters, and create and update a trend chart representing the user's health status in real time. The formula for calculating the user's disease risk coefficient is: ; In the formula: The set of parameters used in the risk accumulation calculation; Let i be the value of the i-th parameter; This is the standard value of the i-th parameter; The set of parameters participating in the collaborative buffer computation; For the j-th parameter value; The j-th parameter is the standard value; The set of parameters used in the overall deviation calculation; For the j-th parameter value; This is the standard value of the s-th parameter; in, , , The three are subordinate: , , This represents the total number of parameter types in the set; It should be noted that the specific criteria for dividing the parameter set involved in risk accumulation calculation, the parameter set involved in collaborative buffer calculation, and the parameter set involved in overall deviation calculation should refer to the correlation strength between each physiological parameter and the target disease in medical literature, the physiological interaction mechanism between parameters, and the independent and collaborative contributions of parameter abnormalities to disease risk in clinical statistics. The above formula constructs a risk coefficient by integrating three types of parameters (risk accumulation parameter, synergistic buffer parameter, and overall deviation parameter), comprehensively considering the contribution of different physiological parameters to disease risk. Among them, the risk accumulation parameter reflects the cumulative effect of individual parameter deviation from the standard, the synergistic buffer parameter reflects the mutual influence between parameters, and the overall deviation parameter assesses the degree of deviation of the overall health status. In terms of dimensions, the ratio of each parameter value to the corresponding standard value is dimensionless, and the calculation result of the three types of parameter sets is also dimensionless, making the final risk coefficient a unified indicator that can be compared horizontally, which meets the quantitative needs of risk assessment. After the visualization module obtains the user's disease risk coefficient, it creates a trend chart representing the user's health status as a line chart. The line chart is updated synchronously with the user's disease risk coefficient based on the newly received body status parameters from the visualization module. After the trend chart representing a user's health status is created for the first time, a safe range is simultaneously marked on the trend chart. In the trend chart, the safe area is a rectangle, with its left boundary coinciding with the vertical axis of the trend chart and its lower boundary coinciding with the horizontal axis of the trend chart. The analysis module is used to monitor the trend charts that are updated in real time in the visualization module, and to perform a user disease risk analysis every time the trend chart is updated. The user disease risk analysis logic in the analysis module is as follows: Read the three latest user disease risk coefficients shown in the trend chart, denoted as R1, R2, and R3. R1 is the earliest user disease risk coefficient compared to R3, and R3 is the latest user disease risk coefficient. ; In the formula: This represents the vertical distance between the corresponding point of R1 in the trend chart and the upper boundary of the safe zone. Similarly; If the above formula is true, it means that the user's disease risk is abnormal; otherwise, it means that the user's disease risk is not abnormal. When the user's disease risk is abnormal, the warning module is triggered. It should be noted that the method for determining the upper boundary of the safe area in the trend chart needs to be based on the clinical normal reference range of the target disease, combined with the individual baseline characteristics of the user such as age, gender, and medical history, and made individual adjustments. The distribution range of health parameters of similar patients in the clinical database can be introduced as an initial reference, and dynamically calibrated according to the user's long-term monitoring data. The early warning module is used to receive the user's disease risk analysis results from the analysis module and trigger early warnings based on the user's disease analysis results; The early warning module is integrated with an audio module and a communication unit. The audio module stores preset early warning audio, and the communication unit stores preset monitoring user contact information. During the operation of the early warning module, the audio module synchronously emits early warning audio, and the communication unit synchronously sends preset early warning information to the monitored target based on the preset monitoring user contact information. In addition, when the communication unit of the early warning module sends early warning information to the preset monitoring users, if the first transmission fails, a retry mechanism needs to be initiated. The number of retryes shall not exceed the preset number, and the interval between each retry shall be the preset duration. When there are multiple preset monitoring users, the information shall be sent in sequence according to the preset priority order. The priority can be set based on factors such as the kinship between the guardian and the user and the geographical distance. The recording and prediction module is used to record relevant information about historical warnings triggered by the early warning module, and to predict whether the user has abnormal disease risk based on the relevant information about historical warnings triggered. The historical warning trigger information recorded in the recording and prediction module refers to the user's disease-related physical status parameters within a specified time threshold before the warning is triggered. When predicting whether a user has an abnormal disease risk in the recording and prediction module, the following applies: After the acquisition module acquires new user disease-related physical status parameters, it identifies the highest similarity between the newly acquired user disease-related physical status parameters and the historical warning trigger information in each group in the recording and prediction module. When the highest similarity identified three times in a row shows a linear increasing trend and all three highest similarities point to the same historical warning trigger information, the recording and prediction module predicts that the user has an abnormal disease risk. The acquisition module is connected to the control unit via a wireless network. The acquisition module is connected to the visualization module and the analysis module via a wireless network. The analysis module is connected to the early warning module via a wireless network. The early warning module is connected to the recording and prediction module via a wireless network. The recording and prediction module is connected to the visualization module via a wireless network.

[0022] In this embodiment, the acquisition module collects and stores user disease-related physical status parameters. The control unit synchronously controls the acquisition module to run continuously based on a preset operating cycle. The visualization module runs in real time to receive the physical status parameters stored in the acquisition module, obtains the user's disease risk coefficient based on the physical status parameters, and creates and updates a trend chart representing the user's health status in real time. The analysis module monitors the real-time updated trend chart in the visualization module, performs a user disease risk analysis once each time the trend chart is updated, and receives the user disease risk analysis results from the analysis module through the early warning module. The early warning module triggers an early warning based on the user's disease analysis results. Finally, the recording and prediction module records the relevant information of the early warning module's historical early warning triggers and predicts whether the user has abnormal disease risk based on the relevant information of the historical early warning triggers.

[0023] In the above embodiments, the system can collect users' health-related data in real time, generate and update health trend charts, continuously analyze risks, and issue sound warnings and notify guardians when risks are abnormal. It also records historical data before warnings and predicts potential risks by comparing the similarity between new data and historical data, helping to detect health problems in a timely manner, prevent them in advance, and improve the timeliness and effectiveness of health management throughout the entire life cycle.

[0024] Example 2: At the implementation level, based on Example 1, this example refers to... Figure 2 The disease risk assessment system based on the patient's full life-cycle health data in Example 1 will be further described in detail below: Disease risk assessment methods based on patients' health data throughout their entire life cycle include: Collect and store user disease-related physical status parameters. Based on the real-time collection and updating of user disease-related physical status parameters, the user's disease risk coefficient is analyzed synchronously, and a trend chart representing the user's health status is created and updated in real time based on the analysis results of the user's disease risk coefficient. Obtain the user's disease risk coefficient from the trend chart representing the user's health status, and analyze whether the user's disease risk is abnormal based on the obtained user disease risk coefficient; When the analysis results indicate an abnormal risk of disease for the user, a pre-set warning logic is triggered; Record relevant information about users' historical alerts, and predict in real time whether users have abnormal disease risks based on this information.

[0025] In summary, the methods and systems described in the above embodiments integrate users' health data throughout their entire lifecycle to achieve dynamic assessment and early warning of disease risks. Risk analysis is conducted based on real-time updated health trend charts, and risk prediction is performed by combining historical early warning information. This effectively improves the timeliness and accuracy of the assessment. Furthermore, the operating cycle of the data collection equipment can be dynamically adjusted according to the risk coefficient, ensuring both the continuity and flexibility of data collection and optimizing the collection frequency when risks change. In addition, by marking safe intervals on trend charts and combining multi-parameter analysis to determine risk anomalies, early warnings are made more accurate. Information can also be sent synchronously to guardians, providing comprehensive protection for users' health and effectively assisting in the early detection and intervention of diseases.

[0026] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions will not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A disease risk assessment system based on patients' full life-cycle health data, characterized in that, include: The data acquisition module is used to collect and store the user's disease-related physical status parameters. The visualization module is used to receive the body status parameters stored in the acquisition module in real time, obtain the user's disease risk coefficient based on the body status parameters, and create and update a trend chart representing the user's health status in real time. The analysis module is used to monitor the trend charts that are updated in real time in the visualization module, and to perform a user disease risk analysis every time the trend chart is updated. The early warning module is used to receive the user's disease risk analysis results from the analysis module and trigger early warnings based on the user's disease analysis results; The recording and prediction module is used to record relevant information about historical warnings triggered by the early warning module, and to predict whether a user has abnormal disease risk based on the relevant information about historical warnings triggered.

2. The disease risk assessment system based on patient's full life-cycle health data according to claim 1, characterized in that, The user's disease-related physical status collected by the acquisition module all come from portable physical status parameter acquisition devices adapted to the user's disease. When the acquisition module stores the user's disease-related physical status parameters, it marks the acquisition time for each physical status parameter and distinguishes them based on each acquisition operation so that they can be stored separately in the acquisition module. The portable body status parameter acquisition device is manually operated by the user to complete the acquisition of body status parameters. The body status parameters acquired in real time by each portable body status parameter acquisition device are fed back to the system's acquisition module in real time via wireless network or Bluetooth network. The acquisition module is equipped with a control unit at its lower level, which is used to control the acquisition module to run continuously based on a preset operating cycle.

3. The disease risk assessment system based on patient's full life-cycle health data according to claim 1, characterized in that, The operating cycle provided by the control unit to the acquisition module follows the following: A basic operating cycle is set, and the control unit controls the acquisition module to run continuously based on the basic operating cycle. After the basic operating cycle is applied a second time and the second basic operating cycle ends, the operating cycle provided by the control unit to the acquisition module is adjusted according to the user's disease risk coefficient. The user's disease risk coefficient during the first basic operating cycle is less than or equal to the user's disease risk coefficient during the second basic operating cycle. ; In the formula: This refers to the operational cycle after adjustment; Based on the basic operating cycle; The user's disease risk coefficient output for the first and second basic operating cycles; This indicates taking the minimum value within the parentheses; For correction functions; The disease risk coefficient of users in the first basic operating cycle is greater than that of users in the second basic operating cycle. ; In the formula: This indicates taking the maximum value within the parentheses.

4. The disease risk assessment system based on patient's full life-cycle health data according to claim 3, characterized in that, The control unit outputs the operating cycle in real time based on formulas (1) and (2) for continuous operation of the acquisition module: In the third and subsequent output cycles, the control unit replaces the user's disease risk coefficient based on the performance of the most recent two cycles. Replace with the latest completed cycle. This ensures that the output of the next cycle is completed before the end of each cycle of the acquisition module, so that the acquisition module can use it for subsequent operation. The correction function The values ​​follow the rules in formula (1). When it is zero for the first time, =a, in formula (1) When it is zero for the second consecutive time, =2a, and so on; Where 'a' is a minimum value preset by the system user, initially set to 0.

01.

5. The disease risk assessment system based on patient's full life-cycle health data according to claim 1, characterized in that, The formula for calculating the user's disease risk coefficient is as follows: ； In the formula: The set of parameters used in the risk accumulation calculation; Let i be the value of the i-th parameter; This is the standard value of the i-th parameter; The set of parameters participating in the collaborative buffer computation; For the j-th parameter value; The j-th parameter is the standard value; The set of parameters used in the overall deviation calculation; For the j-th parameter value; This is the standard value of the s-th parameter; in, , , The three are subordinate: , , This represents the total number of parameter types in the set.

6. The disease risk assessment system based on patient's full life-cycle health data according to claim 1, characterized in that, After the visualization module obtains the user's disease risk coefficient, it creates a trend chart representing the user's health status as a line chart. The line chart updates the user's disease risk coefficient in the trend chart synchronously based on the newly received body status parameters from the visualization module. The trend chart representing the user's health status is marked with a safe range after it is first created; In the trend chart, the safe area is a rectangle, with its left boundary coinciding with the vertical axis of the trend chart and its lower boundary coinciding with the horizontal axis of the trend chart.

7. The disease risk assessment system based on patient's full life-cycle health data according to claim 1, characterized in that, The user disease risk analysis logic in the analysis module is as follows: Read the three latest user disease risk coefficients shown in the trend chart, denoted as R1, R2, and R3. R1 is the earliest user disease risk coefficient compared to R3, and R3 is the latest user disease risk coefficient. ； In the formula: This represents the vertical distance between the corresponding point of R1 in the trend chart and the upper boundary of the safe zone. Similarly; If the above formula is true, it means that the user's disease risk is abnormal; otherwise, it means that the user's disease risk is not abnormal. When the user's disease risk is abnormal, the warning module is triggered. The early warning module is integrated with an audio module and a communication unit. The audio module stores preset early warning audio, and the communication unit stores preset monitoring user contact information. During the operation of the early warning module, the audio module synchronously emits early warning audio, and the communication unit synchronously sends preset early warning information to the monitored target based on the preset monitoring user contact information.

8. The disease risk assessment system based on patient's full life-cycle health data according to claim 1, characterized in that, The historical warning trigger information recorded in the recording and prediction module refers to the user's disease-related physical status parameters within a specified time threshold before the warning is triggered. When the recording and prediction module predicts whether a user has an abnormal disease risk, it follows the following: After the acquisition module acquires new user disease-related physical status parameters, it identifies the highest similarity between the newly acquired user disease-related physical status parameters and the historical warning trigger information in each group in the recording and prediction module. When the highest similarity identified three times in a row shows a linear increasing trend, and the three highest similarities all point to the same historical warning trigger information, the recording and prediction module predicts that the user has an abnormal disease risk.

9. The disease risk assessment system based on patient's full life-cycle health data according to claim 1, characterized in that, The acquisition module is connected to a control unit via a wireless network. The acquisition module is also connected to a visualization module and an analysis module via a wireless network. The analysis module is connected to an early warning module via a wireless network. The early warning module is connected to a recording and prediction module via a wireless network. The recording and prediction module is connected to the visualization module via a wireless network.

10. A method for disease risk assessment based on patient's full life-cycle health data, wherein the method is an implementation method of the disease risk assessment system based on patient's full life-cycle health data as described in any one of claims 1-9, characterized in that, include: Collect and store user disease-related physical status parameters. Based on the real-time collection and updating of user disease-related physical status parameters, the user's disease risk coefficient is analyzed synchronously, and a trend chart representing the user's health status is created and updated in real time based on the analysis results of the user's disease risk coefficient. Obtain the user's disease risk coefficient from the trend chart representing the user's health status, and analyze whether the user's disease risk is abnormal based on the obtained user disease risk coefficient; When the analysis results indicate an abnormal risk of disease for the user, a pre-set warning logic is triggered; Record relevant information about users' historical alerts, and predict in real time whether users have abnormal disease risks based on this information.

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