A method, system, product and medium for managing data rights suitable for the elderly

Abstract

A method, system, product and medium for managing data permissions for the elderly relate to the field of electronic digital data processing. The method comprises: collecting operation trajectory data of a target user during elderly-friendly interaction with a terminal device. Extracting features according to the operation timing, constructing a micro-motion feature vector, associating the change data of the vectors at adjacent moments, forming an operation chain sequence according to the timing, segmenting the sequence according to preset intervals, calculating the discreteness of the micro-motion features, and establishing an operation stability curve containing position offset, pressure fluctuation amplitude, and response interval. Within two preset time windows, the deviation of the curve from the user's historical baseline value is calculated respectively. When the deviation between the two windows exceeds the corresponding threshold, the user's permission to access the elderly-friendly data is adjusted. Implementing this method can improve the accuracy of the adjustment of the elderly's permission to use the terminal device while ensuring the normal use needs of the elderly for the terminal device.

Description

Technical Field

[0001] The present application relates to the field of electronic digital data processing, and in particular to an aging-friendly data rights management method, system, product and medium. Background Art

[0002] In the intelligent management of elderly care institutions, the permissions that seniors have for daily self-service operations through smart terminals need to be properly managed. These permissions mainly include activity reservations, meal selections, personal information viewing, and service bill browsing.

[0003] In related technologies, fixed system operation permissions can be assigned to seniors. For example, seniors can use smart terminals to view personal information, participate in entertainment activities, transfer money, make event reservations, and choose meals. This allows seniors to complete some basic operations independently, improving service efficiency and enhancing their self-care ability.

[0004] However, this permission management method cannot respond to changes in the cognitive status of the elderly in a timely manner. For example, when an elderly person has cognitive impairment, maintaining full operating permissions may lead to erroneous operations, while excessively restricting permissions will affect their normal usage needs, making it difficult to achieve accurate adjustment of permissions. Summary of the Invention

[0005] The present application provides an aging-friendly data permission management method and device, which is used to improve the accuracy of permission adjustment for the elderly to use terminal devices while ensuring the normal use needs of the elderly for the terminal devices.

[0006] In the first aspect, the present application provides a method for aging-friendly data authority management, which is applied to the aging-friendly data authority management system, the method comprising: collecting operation trajectory data of the target user when performing aging-friendly data interaction on the terminal device, the operation trajectory data including contact position, contact pressure, sliding speed and operation timing; extracting features of the operation trajectory data according to the operation timing, constructing a micro-motion feature vector based on the contact position coordinates, contact pressure value and sliding speed value obtained at each sampling moment, performing correlation analysis on the micro-motion feature vectors at adjacent sampling moments, and combining the variation data obtained by the correlation analysis in time sequence to form an operation chain sequence; and performing correlation analysis on the operation chain sequence according to a preset time interval. The system divides the sequence into segments, calculates the discreteness of the micro-motion features in each segment, and establishes an operation stability curve based on the discreteness. The operation stability curve includes the offset degree of the finger contact position, the fluctuation amplitude of the pressure change, and the time interval of the operation response; calculates the deviation of the operation stability curve from the user's historical baseline value within a preset first time window and a preset second time window respectively; when the deviation within the preset first time window exceeds a first preset threshold and the deviation within the preset second time window exceeds a second preset threshold, adjusts the target user's aging-friendly data access rights. The aging-friendly data access rights include the target user's data operation rights, function usage rights, and interactive verification rights for the terminal device.

[0007] In the above embodiment, the target user's operation trajectory data is collected and micro-movement feature vectors are constructed. After forming an operation chain sequence, an operation stability curve is established. The deviation from the user's historical baseline value is calculated within two time windows. When the deviation exceeds the threshold, the data access rights are adjusted in a timely manner. Based on real-time operation behavior analysis, the permission level is dynamically adjusted to match the permission management with the elderly person's current cognitive and operational capabilities, avoiding the risk of incorrect operation in cognitive impairment while ensuring normal use.

[0008] In combination with some embodiments of the first aspect, in some embodiments, the step of performing feature extraction on the operation trajectory data according to the operation sequence, and constructing a micro-motion feature vector based on the contact position coordinates, contact pressure value and sliding speed value obtained at each sampling moment, specifically includes: obtaining the positional relationship between the contact position coordinates and the current interface function key, and determining the touch tolerance range around the contact position coordinates based on the positional relationship; calculating the degree of offset of the contact position coordinates within the touch tolerance range; constructing a pressure attenuation curve based on the contact pressure value, and calculating the touch force stability based on the slope of the pressure attenuation curve; calculating the speed fluctuation characteristics of the sliding operation based on the sliding speed value; and combining the offset degree, touch force stability and speed fluctuation characteristics to form a micro-motion feature vector.

[0009] In the above example, the touch tolerance range is determined by the relationship between the touch point position and the function key position. The touch force stability is calculated by combining the slope of the pressure decay curve. The fluctuation characteristics are analyzed by the sliding velocity value. These characteristics are combined to form a micro-motion feature vector. This complete touch operation feature extraction system has been established, enabling refined quantitative analysis of elderly people's operating behavior and providing accurate feature basis for subsequent permission adjustments.

[0010] In combination with some embodiments of the first aspect, in some embodiments, the operation chain sequence is segmented according to a preset time interval, and the step of calculating the discreteness of the micro-motion features in each sequence specifically includes: dividing the operation chain sequence into multiple timing feature segments according to the preset time interval; obtaining the time interval and touch duration between adjacent touch operations, and when the time interval is less than a preset third threshold, determining it as a repeated touch operation; obtaining the offset distance between the target position and the actual response position of the repeated touch operation, and when the offset distance is greater than a preset fourth threshold, performing position mapping correction to obtain a corrected touch operation; obtaining the target interface switching sequence corresponding to the repeated touch operation, and merging the repeated touch operations that trigger the same interface switch into one valid operation; calculating the discreteness of the micro-motion features of the timing feature segment based on the ratio of the number of operations of the corrected touch operation to the number of original touch operations and the ratio of the number of valid operations to the number of original touch operations.

[0011] In the above example, the operation chain sequence is segmented into time-series feature segments, repeated touch operations are identified and position mapping is corrected, repeated operations that trigger the same interface transition are merged as valid operations, and the discreteness of micro-movement features is calculated. This constructs a time-series analysis framework for operation behavior, eliminates data interference caused by repeated operations and accidental touches by elderly people, improves the accuracy of feature extraction, and makes the judgment basis for permission adjustment more reliable.

[0012] In combination with some embodiments of the first aspect, in some embodiments, when the deviation within a preset first time window exceeds a first preset threshold and the deviation within a preset second time window exceeds a second preset threshold, the aging-friendly data access rights of the target user are adjusted, and the aging-friendly data access rights include the target user's data operation rights, function use rights and interactive verification rights to the terminal device, specifically including: characteristic encoding of the degree of deviation of the finger contact position in the operation stability curve, the fluctuation amplitude of the pressure change and the time interval of the operation response, to obtain the operation feature code at the current moment, calculating the similarity between the operation feature code and the historical feature code of the target user, marking the abnormal operation type of the target user when the similarity is lower than the similarity threshold, and adjusting the data operation permission to the first preset level; obtaining the time of the operation response At a time point in the interval that is greater than a preset interval threshold, the micro-motion feature before the time point is recorded as the starting action, and the micro-motion feature after the time point is recorded as the ending action. The operation hesitation state is determined based on the feature difference between the starting action and the ending action, and the target user's function usage authority is restricted to the second preset level according to the operation hesitation state; the pressure control feature value is calculated based on the fluctuation amplitude of the pressure change in the operation stability curve, and the operation subject feature of the target user is judged according to the pressure control feature value. When the matching degree between the operation subject feature and the preset feature template is lower than the matching threshold, the interactive verification authority requirement is increased to the third preset level; when the similarity of the operation feature code continuously exceeds the similarity threshold and the fluctuation range of the pressure control feature value is within the preset interval, the data operation authority, function usage authority and interactive verification authority are restored to the original level.

[0013] In the above embodiment, the operation stability curve is feature-encoded to generate an operation signature code. Abnormal operation types are labeled based on signature code similarity. The difference in the characteristics of the starting and ending actions is used to determine the operation hesitation state. The pressure control characteristic value is combined to determine the characteristics of the operator. A multi-dimensional permission adjustment mechanism has been established, enabling hierarchical management and control of data operation permissions, function access permissions, and interactive verification permissions, ensuring security while maintaining flexibility in permission adjustment.

[0014] In combination with some embodiments of the first aspect, in some embodiments, after the step of adjusting the target user's aging-friendly data access rights when the deviation within a preset first time window exceeds a first preset threshold and the deviation within a preset second time window exceeds a second preset threshold, the method also includes: collecting posture sensor data of the terminal device, the posture sensor data including acceleration data, angular velocity data and spatial orientation data; aligning the posture sensor data with the operation chain sequence in time sequence, extracting the device shaking frequency, tilt angle and spatial displacement; analyzing the correlation between the device shaking frequency and the offset degree of the finger contact position in the operation stability curve, and judging whether the terminal device is in a stable holding state; when it is detected that the terminal device is in an unstable holding state, marking the current operation as a suspicious operation; counting the number of suspicious operations within a preset time period, and when the number of suspicious operations exceeds the preset number threshold, triggering the identity re-authentication process and freezing the target user's sensitive data access rights.

[0015] In the above embodiment, the terminal device's posture sensor data is collected and aligned with the operation chain sequence, the correlation between the device's shaking frequency and the degree of contact position deviation is analyzed, and the number of suspicious operations is counted to trigger identity re-authentication. Incorporating the device's holding state into the basis for permission management judgment, an operation behavior assessment system based on multi-source data fusion is established, improving the accuracy of abnormal operation identification and the security of permission management.

[0016] In combination with some embodiments of the first aspect, in some embodiments, the step of analyzing the correlation between the device shaking frequency and the degree of deviation of the finger contact position in the operation stability curve to determine whether the terminal device is in a stable holding state specifically includes: obtaining the correspondence between the device shaking frequency and the degree of deviation of the finger contact position, and identifying the target user's holding habit type based on the correspondence, the holding habit type including one-handed holding, two-handed holding and supported holding; dividing the corresponding operation area range and the maximum fault tolerance time of a single operation according to the holding habit type; when it is detected that the finger contact position exceeds the operation area range corresponding to the current holding habit type, it is determined to be a deviation operation; counting the number of deviation operations in unit time, and when the number of deviation operations exceeds the preset number threshold, migrating the high-frequency use function to the optimal operation area corresponding to the holding habit type.

[0017] In the above embodiment, the device's grip type is identified based on the frequency of device shaking and the degree of contact point deviation. The corresponding operation area range and error tolerance time are then divided. The number of deviation operations is counted, and frequently used functions are migrated. This establishes an adaptive operation area division mechanism that adapts to different gripping habits, optimizes the interface layout, reduces the risk of misoperation caused by unstable grip, and enhances the personalized adaptation of the operating experience.

[0018] In combination with some embodiments of the first aspect, in some embodiments, after the step of adjusting the target user's aging-friendly data access rights when the deviation within a preset first time window exceeds a first preset threshold and the deviation within a preset second time window exceeds a second preset threshold, the method also includes: obtaining the target user's heart rate data and blood pressure data to generate a physiological characteristic data set of the target user; when the heart rate data in the physiological characteristic data set exceeds a preset heart rate range or the blood pressure data exceeds a preset blood pressure range, obtaining the acceleration data and angular velocity data of the terminal device; judging whether the terminal device is in a stable state based on the acceleration data and angular velocity data; when the terminal device is in a stable state and the physiological characteristic data set continues to exceed the preset range for a preset duration, sending alarm data containing the physiological characteristic data set and location information to the preset communication terminal, and adjusting the deviation threshold of the target user's access operation to emergency contact information and medical information to a preset minimum value.

[0019] In the above embodiment, the target user's heart rate and blood pressure data are collected to generate a physiological characteristic dataset. Combined with the terminal device's stability assessment, an alert is sent when physiological indicators are abnormal and persistently exceed a preset range. This also reduces the deviation threshold for emergency information access operations. By linking physiological health status with the rights management mechanism, an emergency response mechanism for emergencies is established. This ensures the rapid accessibility of critical information while safeguarding the lives of the elderly, achieving a user-friendly approach to rights management.

[0020] In a second aspect, an embodiment of the present application provides an aging-friendly data rights management system, which includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to enable the aging-friendly data rights management system to execute the method described in the first aspect and any possible implementation method of the first aspect.

[0021] In a third aspect, an embodiment of the present application provides a computer program product comprising instructions. When the above-mentioned computer program product runs on an aging-friendly data rights management system, the above-mentioned aging-friendly data rights management system executes the method described in the first aspect and any possible implementation method of the first aspect.

[0022] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium comprising instructions. When the instructions are executed on an aging-friendly data rights management system, the aging-friendly data rights management system executes the method described in the first aspect and any possible implementation of the first aspect.

[0023] It is understandable that the aging-friendly data rights management system provided in the second aspect, the computer program product provided in the third aspect, and the computer storage medium provided in the fourth aspect are all used to execute the methods provided in the embodiments of this application. Therefore, the beneficial effects that can be achieved can be referenced to the beneficial effects of the corresponding methods and will not be repeated here.

[0024] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages:

[0025] 1. This application collects the target user's operation trajectory data and constructs micro-movement feature vectors to form an operation chain sequence, then establishes an operation stability curve. The deviation from the user's historical baseline value is calculated within two time windows. When the deviation exceeds the threshold, the data access permission is adjusted in a timely manner. Based on real-time operation behavior analysis, the permission level is dynamically adjusted to match the permission management with the current cognitive and operational capabilities of the elderly. This not only avoids the risk of incorrect operation in the state of cognitive impairment, but also ensures the use needs under normal conditions.

[0026] 2. This application was approved to determine the touch tolerance range by obtaining the positional relationship between the contact point position and the function button, calculate the touch force stability by combining the slope of the pressure decay curve, analyze the fluctuation characteristics by the sliding speed value, and combine these characteristics to form a micro-motion feature vector. A complete touch operation feature extraction system was established, which achieved a refined quantitative analysis of the operation behavior of the elderly and provided an accurate feature basis for subsequent permission adjustments.

[0027] 3. This application divides the operation chain sequence into time-series feature segments, identifies repeated touch operations, performs position mapping correction, merges repeated operations that trigger the same interface switch as valid operations, and calculates the discreteness of micro-movement features. This builds a time-series analysis framework for operation behavior, eliminates data interference caused by repeated operations and accidental touches by elderly people, improves the accuracy of feature extraction, and makes the judgment basis for permission adjustment more reliable. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 This is a flow chart of the method for managing data rights suitable for the elderly in an embodiment of the present application;

[0029] Figure 2 This is another flowchart of the method for managing data rights suitable for the elderly in an embodiment of the present application;

[0030] Figure 3 This is another flowchart of the method for managing data rights suitable for the elderly in an embodiment of the present application;

[0031] Figure 4 This is a schematic diagram of the physical device structure of the aging-friendly data rights management system in an embodiment of the present application. DETAILED DESCRIPTION

[0032] The terms used in the following examples of the present application are only for the purpose of describing specific embodiments and are not intended to limit the present application. As used in the specification of the present application, the singular expressions "a", "an", "above", "the", and "this" are intended to include plural expressions as well, unless the context clearly indicates otherwise. It should also be understood that the term "and / or" used in the present application refers to any or all possible combinations of one or more of the listed items.

[0033] In the following, the terms "first" and "second" are used for descriptive purposes only and should not be understood to imply or suggest relative importance or implicitly indicate the number of the technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the embodiments of this application, unless otherwise specified, "plurality" means two or more.

[0034] For ease of understanding, the application scenarios of the embodiments of the present application are introduced below.

[0035] In modern elderly care facilities, smart terminals are widely used in the daily lives of seniors. Seniors can use these terminals to perform self-service tasks, such as making medical appointments, selecting meals, registering for activities, and paying bills. However, the cognitive abilities and operating habits of seniors vary significantly and can change over time. For example, some may experience temporary cognitive impairments, leading to repeated or incorrect operations; others may experience unstable touchscreen operations due to hand tremors. These situations can lead to incorrect data access and function usage, posing security risks. In practical applications, elderly care facilities require a technical solution that can dynamically adjust data access permissions based on the individual's actual operating status, ensuring security while not impacting normal needs.

[0036] Currently, nursing homes typically use a fixed level of authority management. For example, the elderly are divided into three levels of authority: A / B / C. Level A allows all operations, including transfers, payments, and information modifications; Level B only allows viewing information and low-risk operations; and Level C requires the assistance of family members or caregivers to operate. This static authority management method has obvious flaws: first, once the authority level is set, it is rarely adjusted and cannot respond to short-term fluctuations in the elderly's condition in a timely manner; second, all functions within the same authority level are either fully open or fully restricted, lacking refined management; third, individual differences in operating habits are not taken into account, and some elderly people may be overly restricted while others are underprotected. For example, an elderly person who is usually proficient in operating may experience a brief operational abnormality due to physical discomfort, but the system still allows them to perform high-risk operations.

[0037] By adopting this application solution, the system can monitor the operating characteristics of elderly people in real time and dynamically adjust permissions. For example, for an elderly user, the system collects data such as the location, pressure, and speed of their touch operations to build a personalized operating characteristic model. If the system detects frequent deviations in the user's touch point position or abnormal fluctuations in pressure, it determines that the user is currently in an unstable operating state. At this time, it automatically reduces their access rights to sensitive data, such as temporarily limiting transfer amounts or requiring re-authentication. Furthermore, the system uses the device's posture sensor data to determine whether the operating anomaly is due to unstable device grip. If abnormalities are detected in the user's physiological indicators (such as heart rate and blood pressure), access restrictions on emergency contact information and medical information are automatically lifted, and an alert is sent to relevant personnel. When the user's status returns to normal and the operating characteristics become stable again, the system restores the original permission settings accordingly. This dynamic permission management mechanism based on real-time operating characteristics ensures data security while meeting the special needs of emergency situations.

[0038] For ease of understanding, the following describes the process of the method provided by this implementation in combination with the above scenario. Figure 1 , which is a flow chart of the method for managing aging-friendly data rights in an embodiment of the present application.

[0039] S101 : Collecting operation trajectory data of a target user when performing aging-friendly data interaction on a terminal device, where the operation trajectory data includes contact position, contact pressure, sliding speed, and operation timing.

[0040] Among them, the target users refer to elderly users who perform aging-friendly data interaction operations on terminal devices; terminal devices refer to smart devices with touch functions, such as smart phones, tablets, etc.; operation trajectory data refers to the operation trace data left by users when performing operations on the touch screen; the contact position is used to indicate the coordinate position of the finger when it touches the screen; the contact pressure refers to the pressure when the finger presses the screen; the sliding speed indicates the movement rate of the finger when sliding on the screen; the operation timing is used to indicate the time sequence of each operation action.

[0041] This step is performed when elderly users use terminal devices to interact with aging-friendly data. Specifically, the system uses the touch sensor of the terminal device to collect the user's operation trajectory data in real time, including the position coordinates of the finger when it contacts the screen, the pressing force, the sliding speed, and other information, and records the time series of these operation data. This data can reflect the user's operating habits and status characteristics, providing basic data support for subsequent analysis. During the collection process, the system will preprocess the data to filter out noise data to ensure data accuracy and availability.

[0042] In some embodiments, operation trajectory data can be collected in a variety of ways: optionally, the finger contact position can be collected through the capacitive sensor of the touch screen, the pressure can be calculated based on the change in the contact capacitance value, the sliding speed can be calculated based on the position and time of adjacent sampling points, and the timestamps of each sampling point can be recorded to form the operation sequence; optionally, the contact pressure distribution can be directly collected through the pressure sensor array, the contact position trajectory can be extracted through image processing methods, the sliding speed can be calculated in combination with the inertial sensor data, and a unified time base can be established to record the operation sequence. It is understandable that other sensor combinations or data collection methods can also be used to obtain operation trajectory data, which are not limited here.

[0043] S102. Extract features from the operation trajectory data according to the operation sequence, construct a micro-motion feature vector based on the contact position coordinates, contact pressure value, and sliding speed value obtained at each sampling moment, perform correlation analysis on the micro-motion feature vectors at adjacent sampling moments, and combine the change data obtained from the correlation analysis in time sequence to form an operation chain sequence.

[0044] Among them, feature extraction refers to extracting representative feature information from the original operation trajectory data; sampling moment refers to the discrete time point in the data acquisition process; micro-motion feature vector is used to represent the operation feature combination of a single sampling moment; association analysis refers to the comparative analysis of feature vectors at adjacent moments; change data refers to the change information between adjacent feature vectors; and the operation chain sequence is used to represent the complete operation process characteristics.

[0045] This step is performed after the operation trajectory data collection is complete. Specifically, the system first processes the collected trajectory data in chronological order, extracting characteristic parameters such as contact position coordinates, pressure value, and velocity value at each sampling moment. These parameters are combined to form a vector representing the microscopic operation characteristics at that moment. It then performs correlation analysis on the characteristic vectors at adjacent moments and calculates the changes in each characteristic parameter. These changes reflect the dynamic characteristics of the operation process. Finally, these change data are combined in chronological order to construct a complete operation chain sequence, which is used to characterize the characteristic changes of the entire operation process.

[0046] In some embodiments, feature extraction and sequence construction can be achieved in a variety of ways: optionally, the original data is segmented using a sliding window method, statistical features (such as mean, variance, etc.) are extracted within each window, the difference values ​​of the features of adjacent windows are calculated, and finally the difference values ​​are serialized to form an operation chain; optionally, a deep learning model is used to learn and extract feature representations directly from the original data, and a temporal association between features is established through a recurrent neural network, and a feature sequence representing the complete operation process is output. It is understandable that other feature engineering methods or machine learning algorithms can also be used to achieve the extraction of operation features and sequence construction, which are not limited here.

[0047] S103. Segment the operation chain sequence according to preset time intervals, calculate the discreteness of the micro-motion features in each segment, and establish an operation stability curve based on the discreteness. The operation stability curve includes the degree of deviation of the finger contact position, the fluctuation amplitude of the pressure change, and the time interval of the operation response.

[0048] Among them, the preset time interval refers to the fixed time length pre-set by the system for dividing the operation chain sequence; discreteness refers to the discrete degree of the micro-motion feature distribution, which is used to measure the stability of the operation; the operation stability curve refers to the timing characteristic curve reflecting the stability of the user operation; the offset degree refers to the deviation distance of the contact position relative to the target position; the fluctuation amplitude is used to indicate the severity of the pressure change; the time interval of the operation response represents the time difference between consecutive operations.

[0049] This step is performed after the construction of the operation chain sequence is completed. Specifically, the system first divides the operation chain sequence into multiple time segments according to a preset time interval (such as 5 seconds), and each segment contains all the micro-motion features within this period of time. For each time segment, the degree of discreteness of the micro-motion features is calculated, including calculating statistical indicators such as the standard deviation of the contact position, the fluctuation range of the pressure value, and the coefficient of variation of the operation time interval. These discreteness indicators are then connected in chronological order to form a characteristic curve that reflects the change of operation stability over time. The curve contains three dimensions: the changing trend of the contact position offset, the stability of the pressure control, and the continuity of the operation rhythm.

[0050] In some embodiments, discreteness calculation and stability curve construction can be achieved in a variety of ways: optionally, first calculate the mean and standard deviation of the Euclidean distance between the contact position and the target position in each time segment, perform wavelet transform on the pressure value sequence to extract the fluctuation characteristics, calculate the variance of the time intervals of adjacent operations, and finally normalize these three groups of indicators to synthesize the stability curve; optionally, use a clustering algorithm to perform cluster analysis on the micro-motion characteristics of each time segment, calculate the ratio of the intra-class distance to the inter-class distance as the discreteness index, and extract the time series change characteristics of the cluster center at the same time, and combine the statistical characteristics of the operation response time to construct a multidimensional stability curve. It is understandable that other statistical analysis or machine learning methods can also be used to achieve quantitative evaluation of operational stability and curve construction, which is not limited here.

[0051] S104: Calculate the deviation between the operation stability curve and the user's historical benchmark value in the preset first time window and the preset second time window respectively.

[0052] Among them, the preset first time window represents the time range for short-term stability evaluation; the preset second time window refers to the time range for long-term stability evaluation; the historical benchmark value represents the statistical reference value of the user's past operation characteristics; the deviation is used to indicate the degree of difference between the current operation characteristics and the historical benchmark.

[0053] This step is performed after the operational stability curve is constructed. Specifically, the system simultaneously maintains two time windows of different scales: a short-term window might cover the last 10 minutes, and a long-term window might cover the last two hours. Within these two time windows, the difference between the operational stability curve and the user's historical baseline value is calculated. The calculation considers multiple characteristic dimensions of the curve, including its overall trend, fluctuation frequency, and peak distribution, and a weighted combination is used to generate a comprehensive deviation index. This dual-time window design can simultaneously capture both short-term fluctuations and long-term trends in user operational status.

[0054] In some embodiments, the calculation of the deviation can be achieved in a variety of ways: optionally, first extract the time domain features of the operational stability curve, calculate the mean, standard deviation, kurtosis and other statistics, then compare it with the historical benchmark value to obtain the normalized difference value, and finally obtain the comprehensive deviation through weighted summation; optionally, use the dynamic time warping algorithm to calculate the time series similarity between the current operational stability curve and the historical benchmark curve, combine the frequency domain features and shape features of the curve, and obtain the final deviation score through multi-level feature fusion. It is understandable that other time series data analysis methods or pattern recognition algorithms can also be used to implement the calculation of the deviation of operational features, which is not limited here.

[0055] S105. When the deviation within the preset first time window exceeds a first preset threshold and the deviation within the preset second time window exceeds a second preset threshold, adjust the target user's aging-friendly data access rights, which include the target user's data operation rights, function use rights, and interactive verification rights for the terminal device.

[0056] Among them, the first preset threshold represents the warning value of short-term operational stability deviation; the second preset threshold refers to the warning value of long-term operational stability deviation; data operation authority is used to indicate the scope of user access and modification of system data; function use authority indicates the scope of system functions that users can use; interactive verification authority refers to the required level of user identity authentication by the system; aging-friendly data access rights represent a hierarchical authority system customized for the characteristics of elderly users.

[0057] This step is performed when an abnormality in the stability of user operations is detected. Specifically, the system will monitor the operation deviation in short-term and long-term time windows in real time. When the short-term deviation exceeds the first preset threshold (such as 1.5 times the historical benchmark) and the long-term deviation exceeds the second preset threshold (such as 1.3 times the historical benchmark) at the same time, it means that the user's operating status may have changed significantly, and the system will start the permission adjustment mechanism. Permission adjustment involves three levels: reducing the operating permissions of sensitive data to prevent misoperation, limiting the scope of use of specific functions to reduce risks, and increasing identity authentication requirements to strengthen security protection. This multi-dimensional permission adjustment can maintain the availability of the system while ensuring security.

[0058] In some embodiments, permission adjustment can be achieved in a variety of ways: optionally, first analyze the specific characteristics of the operation deviation (such as position offset, pressure instability, etc.), select the corresponding permission adjustment strategy according to the different types of anomalies, then gradually reduce the sensitivity of the data operation permission, and adjust the restricted range of function use at the same time, and finally set the corresponding identity authentication requirements according to the degree of anomaly; optionally, use a machine learning model to evaluate the user's operating status in real time, establish a mapping relationship between operation anomalies and permission levels, dynamically calculate the adjustment range of various permissions, and make adaptive adjustments based on user feedback. It is understandable that other intelligent decision-making algorithms or permission management mechanisms can also be used to achieve dynamic adjustment of aging-friendly data access rights, which is not limited here.

[0059] The following is a more detailed description of the process of the method provided by this implementation. Figure 2 , which is another flow chart of the aging-friendly data authority management method in an embodiment of the present application.

[0060] S201 : Collecting operation trajectory data of a target user when performing aging-friendly data interaction on a terminal device, where the operation trajectory data includes contact position, contact pressure, sliding speed, and operation timing.

[0061] The target users are elderly operators who use terminal devices. Terminal devices refer to devices with touch functions such as smartphones and tablets. The operation trajectory data includes the following elements: contact position refers to the coordinate point where the finger touches the screen, contact pressure refers to the force value of the finger pressing the screen, sliding speed refers to the rate at which the finger moves on the screen, and operation timing refers to the time sequence of each operation action.

[0062] The terminal device collects user operation data in real time via the capacitive sensor array on the touch screen. Touch point location is collected at a 60Hz frequency, recording the X and Y coordinates of the touch point. Touch pressure is sampled at 200Hz by a pressure sensor, acquiring pressure values ​​in the range of 0-1023. Sliding velocity is calculated based on the position and time differences between two adjacent sampling points to generate a velocity vector. For operation timing, the timestamp of each touch event is recorded. This data is preprocessed by the device driver to remove outliers and noise, and then saved as a standard data stream. During the acquisition process, multi-touch data is recorded separately to form independent operation traces.

[0063] S202: Obtain the positional relationship between the touch point coordinates and the current interface function keys, and determine a touch tolerance range around the touch point coordinates based on the positional relationship.

[0064] The touch point position coordinates represent the two-dimensional coordinate values ​​when the finger touches the screen. The interface function buttons represent clickable interactive elements on the screen. The position relationship refers to the spatial relationship between the touch point and the center point of the button. The touch tolerance range represents the maximum range within which the touch point is allowed to deviate from the center of the button.

[0065] The terminal device first obtains the location information of all function keys on the current interface, including the center coordinates and boundary range of each key. For each detected touch point, the Euclidean distance between it and the nearest function key is calculated. Based on the size and spacing of the keys, a basic error tolerance radius is set, typically 1 / 3 of the key width. For keys at the edge, the error tolerance range is appropriately reduced in the direction perpendicular to the edge to avoid accidentally touching adjacent keys. At the same time, based on the frequency of key usage, the error tolerance range is expanded for frequently used keys to improve operational accuracy.

[0066] S203: Calculate the degree of deviation of the touch point position coordinates within the touch error tolerance range.

[0067] The offset degree indicates the deviation between the actual position of the touch point and the center of the key, and is calculated by calculating the distance between the two. The larger the value, the lower the operation accuracy.

[0068] The calculation process first obtains the real-time coordinates (x, y) of the touch point and the center coordinates (x0, y0) of the corresponding button, and calculates the Euclidean distance d = √((x-x0)² + (y-y0)²). This distance is then divided by the radius R of the touch tolerance range to obtain a standardized offset ratio p = d / R. When p < 1, the touch point is within the tolerance range; when p ≥ 1, the touch point is outside the tolerance range. The angle θ = arctan((y-y0) / (x-x0)) of the touch point relative to the center of the button is also calculated to analyze the directional characteristics of the offset. For continuous touch operations, the mean and standard deviation of the offset degree are calculated throughout the entire process to evaluate the stability of the operation.

[0069] S204: Construct a pressure decay curve based on the contact pressure value, and calculate the touch force stability according to the slope of the pressure decay curve.

[0070] The contact pressure value indicates the force applied when a finger presses the screen. The pressure decay curve refers to the function curve of the contact pressure changing over time. The slope indicates how quickly the pressure changes. Touch force stability refers to the user's ability to maintain stable pressure during the touch process.

[0071] The pressure decay curve construction process first samples the original pressure value sequence in time series with a sampling interval of 5ms. For each touch event, the pressure value P(t) is recorded throughout the entire process from the start of contact to leaving the screen. The pressure value sequence is fitted with an exponential function using the least squares method to obtain a decay curve of the form P(t)=P0e^(-λt), where P0 is the initial pressure value and λ is the decay coefficient. At each time point t, the instantaneous slope of the curve k(t)=-λP0e^(-λt) is calculated. The touch force stability S is obtained by calculating the root mean square value of the slope: S=sqrt(∑k(t)² / n), where n is the number of sampling points. A smaller S value indicates more stable pressure control, and vice versa, a larger pressure fluctuation.

[0072] S205 : Calculate the speed fluctuation characteristics of the sliding operation according to the sliding speed value.

[0073] The sliding speed value represents the instantaneous rate at which the finger moves on the screen, and the speed fluctuation characteristic refers to the characteristic indicator of speed change during the sliding process.

[0074] To calculate the velocity fluctuation characteristics, first segment the sliding trajectory into fixed time windows (e.g., 100ms). For each segment, calculate the velocity vector v(t) = (dx / dt, dy / dt), where dx and dy are the position differences between adjacent sampling points, and dt is the sampling time interval. Calculate the velocity magnitude sequence |v(t)| = sqrt((dx / dt)² + (dy / dt)²). Extract the following features: velocity mean μv = ∑|v(t)| / n, velocity standard deviation σv = sqrt(∑(|v(t)| - μv)² / n), and acceleration mean μa = ∑(|v(t+dt)| - |v(t)|) / dt / n. Combine these features to form the velocity fluctuation feature vector [μv, σv, μa].

[0075] S206: Combining the offset degree, touch force stability, and speed fluctuation characteristics to form a micro-motion feature vector.

[0076] The micro-motion feature vector represents a multidimensional numerical vector that describes the characteristics of a single operation action, and contains feature quantities in three dimensions: position, pressure, and speed.

[0077] The feature vector construction process standardizes the various features calculated above. The degree of deviation is normalized using the z-score: z = (x-μ) / σ, where x is the original deviation value, μ and σ are the mean and standard deviation of the historical data, respectively. Touch force stability and speed fluctuation characteristics are also standardized. The standardized feature value range is unified to the interval [-1, 1]. The final micro-motion feature vector is in the form of: [degree of deviation, touch force stability, speed mean, speed standard deviation, acceleration mean]. Each component corresponds to a specific operation feature, and the vector as a whole reflects the characteristic distribution of the micro-motion.

[0078] S207 , performing correlation analysis on the micro-motion feature vectors at adjacent sampling moments, and combining the variation data obtained from the correlation analysis in time sequence to form an operation chain sequence.

[0079] Adjacent sampling moments represent two consecutive sampling time points, association analysis refers to the comparative analysis of the feature vectors of consecutive time points, variation data represents the change value of each component of the feature vector, and operation chain sequence refers to a sequence composed of multiple variation data in chronological order.

[0080] The correlation analysis process calculates the eigenvectors V(t) and V(t+1) for each pair of adjacent time points t and t+1. First, the difference ΔV = V(t+1)-V(t) between the components of the eigenvectors is calculated to obtain a difference vector reflecting changes in position, pressure, and velocity. The Euclidean distance d = ||V(t+1)-V(t)|| of the eigenvectors is then calculated to reflect the overall degree of change. Simultaneously, the angle θ = arccos((V(t)·V(t+1)) / (||V(t)||·||V(t+1)||)) between the eigenvectors is calculated to reflect the direction of change. The difference vector ΔV, distance d, and angle θ are combined to form the variation data [ΔV, d, θ]. All variation data are connected in chronological order to form an operation chain sequence that describes the characteristic changes of the entire operation process.

[0081] S208: Divide the operation chain sequence into multiple time series feature segments according to preset time intervals.

[0082] The preset time interval represents a fixed time length for dividing the operation chain sequence, and the time series feature segment refers to the sub-segment of the operation chain sequence obtained by dividing the time interval.

[0083] The segmentation process first determines a preset time interval T (usually set to 1 - 3 seconds). Starting from the starting time t0 of the operation chain sequence, the sequence is divided into consecutive segments of length T, [t0, t0 + T), [t0 + T, t0 + 2T),... The change amount data within each time segment is sorted out, retaining the start and end timestamps of the segment, the number of change amount data included, and the specific values of each change amount data. If the length of the last segment is less than T, it is taken as a separate feature segment. This segmentation method based on a fixed time interval facilitates subsequent comparative analysis of operation characteristics in different time periods.

[0084] S209. Obtain the time interval and touch duration between adjacent touch operations. When the time interval is less than a preset third threshold, it is determined as a repeated touch operation.

[0085] The time interval refers to the difference between the start times of two adjacent touch operations, the touch duration refers to the duration from the start to the end of a single touch operation, and a repeated touch operation refers to similar touch operations repeatedly performed by the user in a short period of time.

[0086] The specific execution process is to traverse all touch operation events, record the start time ts and end time te of each operation. Calculate the time interval Δt = ts(i + 1) - ts(i) between adjacent operations, where i represents the operation number. At the same time, calculate the duration d = te(i) - ts(i) of each operation. Compare the calculated time interval Δt with a preset third threshold T3 (for example, set to 500 ms). When Δt < T3, it indicates that the two operations occur consecutively in a short period of time, and such operations are marked as repeated touch operations. For the marked repeated touch operations, record information such as their occurrence time, duration, and operation position for subsequent analysis of operation characteristics.

[0087] S210. Obtain the offset distance between the target position and the actual response position of the repeated touch operation. When the offset distance is greater than a preset fourth threshold, perform position mapping correction to obtain a corrected touch operation.

[0088] The target position represents the center position of the interface element that the user intends to touch, the actual response position represents the coordinate position where the touch event actually occurs, the offset distance refers to the spatial distance between the two positions, and position mapping correction refers to mapping the deviated touch position to the nearest valid target position.

[0089] The execution process of position mapping correction first obtains the actual touch point coordinates (x, y) of repeated touch operations and the center coordinate set {(xi, yi)} of all interactive elements on the current interface. Calculate the distance di = √((x-xi)² + (y-yi)²) from the touch point to each interactive element. Find the interactive element with the smallest distance as the target position (xt, yt). Calculate the offset distance d = √((x-xt)² + (y-yt)²). Compare the offset distance d with the preset fourth threshold T4 (such as set to 5% of the screen width). When d>T4, perform position mapping correction: correct the response position of the touch event from (x, y) to the target position (xt, yt), and update the relevant touch parameters, including the touch point position, pressure value and timestamp.

[0090] S211: Acquire a target interface switching sequence corresponding to repeated touch operations, and merge repeated touch operations that trigger the same interface switching into one valid operation.

[0091] The target interface switching sequence represents the sequence of interface state changes caused by touch operations, and the effective operation refers to the operation behavior that produces actual effects in the interface interaction.

[0092] The process of performing interface switching analysis on repeated touch operations is: record the interface state identifier (such as interface ID or path) before and after each touch operation. Construct an interface switching graph, where nodes represent interface states and edges represent state transitions caused by touch operations. For continuous touch operations with a time interval less than a preset third threshold, extract the corresponding interface switching sequence. When multiple repeated touch operations result in the same interface switching sequence (such as A→B→A→B), merge these operations into one valid operation, and retain the earliest operation as the representative. Record the number of operations before the merge and the number of valid operations after the merge.

[0093] S212 , calculating the micro-motion feature dispersion of the time series feature segment according to the ratio of the number of corrected touch operations to the number of original touch operations and the ratio of the number of valid operations to the number of original touch operations.

[0094] Corrected touch operation refers to the operation after position mapping correction, valid operation refers to the operation retained after interface switching analysis, and micro-motion feature dispersion refers to the degree of dispersion of operation feature distribution.

[0095] The dispersion calculation process uses two ratio metrics: the correction ratio Rc = Nc / N and the effective ratio Re = Ne / N, where Nc is the number of correction operations, Ne is the number of effective operations, and N is the number of original operations. The micro-motion feature dispersion D of the time series feature segment is calculated as (1-Rc)*w1+(1-Re)*w2, where w1 and w2 are weight coefficients such that w1+w2=1. The weights are set based on the impact of the correction and effective operations on operational stability, typically w1=0.4 and w2=0.6. The dispersion D ranges from [0 to 1]. Larger values ​​indicate more unstable operations, requiring more correction and merging.

[0096] S213 . Establish an operation stability curve according to the discreteness. The operation stability curve includes the degree of deviation of the finger contact position, the fluctuation amplitude of the pressure change, and the time interval of the operation response.

[0097] Discreteness is a numerical indicator that quantifies the degree of dispersion of micro-motion characteristics, with a value range of [0, 1]. The operation stability curve is a three-dimensional time series that describes the changing trend of user operation characteristics over time. The offset degree uses the physical distance to represent the deviation between the touch point and the target. The fluctuation amplitude reflects the range of pressure value. The time interval measures the rhythm characteristics of the operation.

[0098] The operational stability curve was constructed using a sliding window method, with a window size of 1 minute and a sliding step of 10 seconds. For each window interval, three-dimensional feature values ​​were extracted: the touch point position offset was calculated by averaging the Euclidean distance between the actual and target positions of all touch operations within the window, expressed in pixels; the pressure fluctuation was calculated by calculating the range (maximum minus minimum) of the pressure value sequence within the window, expressed in units of the device's original pressure values; and the time interval was calculated by averaging the time differences between adjacent operations within the window, expressed in milliseconds. For each feature dimension, the raw data was normalized: P'(t) = (P(t) - Pmin) / (Pmax - Pmin), where Pmin and Pmax are the minimum and maximum values ​​of the historical data, respectively. F'(t) and T'(t) were normalized using the same method. The resulting operational stability curve was a standardized three-dimensional time series: [P'(t), F'(t), T'(t)]. To ensure smoothness, the raw series was smoothed using an exponential moving average method with a smoothing coefficient of α = 0.3. Each dimension of the curve contains independent stability information: P'(t) reflects spatial accuracy, F'(t) reflects force control ability, and T'(t) reflects operational consistency.

[0099] S214 : Calculate the deviation between the operation stability curve and the user's historical benchmark value in the preset first time window and the preset second time window respectively.

[0100] The preset time window defines the time range for calculating the deviation. The first time window (short-term) is usually set to 10 minutes, and the second time window (long-term) is set to 2 hours. The historical benchmark value is a standard characteristic curve obtained based on the user's operation data statistics over the past 7 days. The deviation quantifies the degree of difference between the current operation characteristics and the historical benchmark.

[0101] Deviation calculation uses a hierarchical comparison method. First, the root mean square error (RMSEP) of each dimension is calculated within the short-term window W1: the position deviation root mean square error (RMSEP) = sqrt(∑(P'(t) - Pb'(t))² / n), where P'(t) is the current curve value, Pb'(t) is the baseline curve value, and n is the number of sampling points. Similarly, the pressure deviation (RMSEF) and time deviation (RMSET) are calculated. The weighted combination of the three-dimensional root mean square errors yields the composite deviation D1 = wpRMSEP + wfRMSEF + wt*RMSET. The weight coefficients are determined using principal component analysis: wp = 0.4, wf = 0.35, and wt = 0.25. The same method is used to calculate D2 for the long-term window W2. This dual-time-scale analysis method captures both short-term fluctuations and long-term trends in operational characteristics. The deviation calculation results are used for subsequent anomaly assessment and authority adjustments.

[0102] S215. Feature encode the degree of deviation of the finger contact position, the fluctuation amplitude of the pressure change, and the time interval of the operation response in the operation stability curve to obtain the operation feature code at the current moment, calculate the similarity between the operation feature code and the historical feature code of the target user, mark the abnormal operation type of the target user when the similarity is lower than the similarity threshold, and adjust the data operation permission to the first preset level.

[0103] Feature coding is the process of converting continuous operation features into discrete digital codes; the operation feature code uses a 15-bit binary number to represent the current operation feature; the historical feature code records the standard feature pattern during normal user operation; similarity is used to quantitatively evaluate the degree of feature matching; abnormal operation types include position offset abnormalities, pressure control abnormalities, and rhythm imbalance abnormalities; the first preset level is the most stringent authority control level.

[0104] The feature encoding adopts a hierarchical quantization strategy. The position offset encoding EPA allocates 5 bits: the normalized offset value is divided into 5 levels at an interval of 0.2, and each level is represented by a different 5-bit binary code. For example,

[00000] represents the minimum offset, and

[11111] represents the maximum offset. The pressure fluctuation encoding EPB allocates 5 bits: the normalized pressure fluctuation value is also divided into 5 levels and encoded. The time interval encoding EPC allocates 5 bits: the normalized time interval value is encoded into a 5-bit binary number. The three parts of the encoding are concatenated to obtain a 15-bit feature code [EPA|EPB|EPC]. The feature code comparison uses the weighted Hamming distance: HD = ∑wi * |bit_i - bit_i'|, where bit_i and bit_i' are the i-th bit values of the current feature code and the historical feature code respectively, and wi is the weight of this bit. The similarity S = 1 - HD / 15 is calculated. A similarity threshold ST = 0.7 is set. When S < ST, the abnormal type is determined by analyzing the differences in each part of the encoding. Once an abnormality is detected, the data operation permission is immediately reduced to the first preset level: the data modification permission is disabled, the authentication frequency is increased, and the execution of sensitive operations is restricted. The system continuously monitors the change in similarity. When the similarity continuously exceeds the threshold for a set time (such as 30 minutes), the permission level is gradually restored.

[0105] S216. Obtain the time points in the time interval of the operation response that are greater than the preset interval threshold. Denote the micro-action features before the time point as the starting action, and the micro-action features after the time point as the ending action. Determine the operation hesitation state based on the feature differences between the starting action and the ending action, and restrict the function usage permission of the target user to the second preset level according to the operation hesitation state.

[0106] The preset interval threshold represents the time standard for judging operation interruption, generally set to 3 times the normal operation interval; the micro-action features include the feature vectors of parameters such as the contact position, pressure, and speed; the starting action refers to the last operation feature before the interruption; the ending action refers to the first operation feature after the interruption; the operation hesitation state represents the state of hesitation or uncertainty that the user shows during the operation process; the second preset level refers to the medium permission level between the normal permission and the strictest restriction.

[0107] The determination process of the operation hesitation state first analyzes the time series of operation responses. Set a preset interval threshold T_threshold = 3 * T_normal, where T_normal is the average time interval of the user's historical operations. Scan the time series and mark all time points {ti} greater than T_threshold. For each time point ti, extract the micro-action feature vectors before and after it: V_start = [P_s, F_s, T_s] and V_end = [P_e, F_e, T_e]. Calculate the feature difference vector ΔV = V_end - V_start to obtain the position difference ΔP, pressure difference ΔF, and time difference ΔT. Set the difference thresholds [θp, θf, θt]. When |ΔP| > θp or |ΔF| > θf or |ΔT| > θt, it is determined as the operation hesitation state. Once the hesitation state is detected, immediately reduce the function usage permission to the second preset level: disable critical function operations, increase operation confirmation steps, and extend the operation response time. The system records the duration and occurrence frequency of the hesitation state for subsequent permission restoration judgment.

[0108] S217. Calculate the pressure control eigenvalue based on the fluctuation amplitude of the pressure change in the operation stability curve, and judge the operation subject characteristics of the target user according to the pressure control eigenvalue. When the matching degree of the operation subject characteristics and the preset characteristic template is lower than the matching threshold, increase the interactive verification permission requirement to the third preset level.

[0109] The pressure control eigenvalue is a quantitative index characterizing the user's pressure control ability; the operation subject characteristics refer to the unique operation habit characteristics of the user; the preset characteristic template stores the standard operation characteristics when the user passes the verification; the matching threshold defines the lowest requirement for feature matching; the third preset level is the most stringent identity verification level.

[0110] The pressure control feature extraction adopts a multi-level analysis method. First, perform frequency-domain analysis on the pressure fluctuation curve F(t), and obtain the spectral features [f1, f2,..., fn] through fast Fourier transform. Calculate the energy distribution E(f) and frequency center fc of the main frequency components. Extract the time-domain features including: average pressure value μF, standard deviation σF, kurtosis KF, and skewness SF. Combine these features to form the pressure control feature vector PCV = [E(f), fc, μF, σF, KF, SF]. Calculate the Euclidean distance d = ||PCV - TPL|| between the current PCV and the preset characteristic template TPL. The feature matching degree M = exp(-d² / σ²), where σ is the normalization parameter. When M < M_threshold (the matching threshold, usually set to 0.8), increase the interactive verification permission to the third preset level: require biometric authentication, shorten the session validity period, and increase the verification frequency.

[0111] S218. When the similarity of the operation feature code continuously exceeds the similarity threshold and the fluctuation range of the pressure control feature value is within the preset interval, the data operation permission, function use permission and interaction verification permission are restored to the original level. The aging-friendly data access permission includes the target user's data operation permission, function use permission and interaction verification permission for the terminal device.

[0112] Continuous exceeding means that the conditions are continuously met over a period of time; the preset interval defines the pressure fluctuation range for normal operation; the original level refers to the user's initial permission setting; aging-friendly data access rights are a permission management system that includes multiple dimensions.

[0113] The permission recovery process uses a multi-condition joint judgment mechanism. First, the similarity S(t) of the operation signature code is monitored, requiring that within the time window W (usually set to 30 minutes), min(S(t))>ST, where ST is the similarity threshold. Simultaneously, the fluctuation range of the pressure control characteristic value PCV(t) is monitored, requiring max(|PCV(t) - μPCV|)<2σPCV, where μPCV and σPCV are the mean and standard deviation of the historical data, respectively. When both conditions are met simultaneously and the duration exceeds the preset observation period (e.g., 15 minutes), permission settings are gradually restored: first, data viewing permissions are restored, and after observing for 5 minutes without anomalies, data modification permissions are restored; then, basic function usage permissions are restored, and after observing for 10 minutes without anomalies, all function permissions are restored; finally, the authentication frequency is reduced to restore to the standard interactive verification requirements. The entire recovery process adopts a progressive strategy to ensure operational security.

[0114] The following is a more detailed description of the process of the method provided by this implementation. Figure 3 , which is another flow chart of the aging-friendly data authority management method in an embodiment of the present application.

[0115] The following describes the aging-friendly data rights management system in the embodiment of the present invention from the perspective of hardware processing. Figure 3 , which is a schematic diagram of the physical device structure of the aging-friendly data rights management system in an embodiment of the present application.

[0116] It should be noted that Figure 3 The structure of the aging-friendly data rights management system shown is only an example and should not limit the functions and scope of use of the embodiments of the present invention.

[0117] S301 : Collecting attitude sensor data of a terminal device, where the attitude sensor data includes acceleration data, angular velocity data, and spatial orientation data.

[0118] Attitude sensor data refers to the raw data collected by the terminal device's built-in motion sensor. Acceleration data represents the device's acceleration changes in three directions, measured in m / s². Angular velocity data represents the device's rotational speed around three axes, measured in rad / s. Spatial orientation data represents the device's orientation angle relative to the Earth's coordinate system, including pitch, roll, and heading angles.

[0119] Attitude sensor data is collected through the device's IMU (inertial measurement unit). Acceleration data is collected using a three-axis accelerometer with a sampling frequency of 100Hz and a measurement range of ±16g. Each sampling point contains three components: [ax, ay, az]. Angular velocity data is collected using a three-axis gyroscope with a sampling frequency of 200Hz and a measurement range of ±2000° / s. Angular velocity values ​​along the three axes: [wx, wy, wz]. Spatial orientation data is obtained using a magnetometer combined with an accelerometer at a sampling frequency of 50Hz, recording the three Euler angles: [pitch, roll, yaw]. All sensor data is Kalman filtered to remove noise and undergo temperature compensation calibration. During data collection, the timestamp of each sampling point is recorded to facilitate subsequent alignment with operational data. The system establishes a sensor data cache queue to update and save the last 30 seconds of data in real time.

[0120] S302: Align the attitude sensor data with the operation chain sequence in time sequence to extract the device shaking frequency, tilt angle, and spatial displacement.

[0121] Timing alignment refers to unifying data with different sampling frequencies to the same time base; the device shake frequency represents the periodic characteristics of the device's vibration; the tilt angle refers to the angle of the device relative to the horizontal plane; and the spatial displacement represents the distance the device moves in three-dimensional space.

[0122] The timing alignment process first resamples all sensor data to a uniform 100Hz sampling rate. Time windows (1-second width and 0.1-second step size) are established, and feature extraction is performed on the data within each window. The device shake frequency is determined by performing a fast Fourier transform on the acceleration data, extracting the top three frequency components with the highest energy [f1, f2, f3]. The tilt angle θ is calculated from the acceleration components: θ = arccos(az / sqrt(ax²+ay²+az²)). The spatial displacement is obtained by double integration of the acceleration data, using a zero-velocity update algorithm to eliminate integral drift. The extracted features are aligned with the operation chain sequence by timestamp to form a feature-operation correspondence table. A sliding average of the features is calculated to smooth instantaneous fluctuations.

[0123] S303: Obtain a correspondence between the device shaking frequency and the degree of deviation of the finger contact position, and identify the target user's holding habit type based on the correspondence, where the holding habit type includes one-handed holding, two-handed holding, and supported holding.

[0124] The correspondence refers to the correlation between the device's physical state and operational characteristics; the gripping habit type represents the different ways users hold the device; one-handed grip means holding and operating the device with one hand; two-handed grip means operating the device with both hands; and supported grip means operating the device while supporting it on an object such as a table.

[0125] Grip habit recognition uses a feature correlation analysis method. First, the correlation coefficient matrix R between the device shake frequency feature vector F = [f1, f2, f3] and the contact position offset vector P = [dx, dy] is calculated. Singular value decomposition is performed on R to extract the main feature patterns. The feature patterns are matched with pre-calibrated grip type templates: one-hand grip is characterized by high-frequency shake (>2Hz) with offset concentrated on one side; two-hand grip is characterized by low-frequency shake (<1Hz) with evenly distributed offset; supported grip is characterized by extremely low-frequency shake (<0.5Hz) with a small offset range. The maximum likelihood estimation method is used to select the most matching grip type. The system records the recognition results and their confidence levels for subsequent interactive adaptation. When the confidence level is lower than the threshold, the current recognition result is maintained unchanged to avoid interaction instability caused by frequent switching.

[0126] S304: Divide the corresponding operation area range and the maximum error tolerance time of a single operation according to the holding habit type.

[0127] The operation area refers to the area on the screen that is easy to operate based on the holding method; the maximum tolerance time indicates the longest response delay allowed for a single operation; a single operation refers to the complete interaction process from touching the screen to leaving the screen.

[0128] The operational area is divided using a grid analysis method. For one-handed grip, the screen is divided into a 6×8 grid, each 120×120 pixels. The thumb's reachable area is determined by analyzing the natural range of motion of the gripping hand: a sector-shaped area is drawn with the grip point as the center and the maximum thumb reach as the radius R (typically 70% of the screen width). Within this area, the grids are categorized into three levels based on the characteristics of thumb joint motion: optimal operation area (within 0.3R from the grip point, with a tolerance of 2000ms), moderate operation area (within 0.3R-0.6R, with a tolerance of 1500ms), and marginal operation area (within 0.6R-1.0R, with a tolerance of 1000ms). For two-handed grip, a 3×8 grid on each side of the screen serves as the primary operation area, and a 4×8 grid in the middle serves as the collaborative area. The tolerance is uniformly set to 1800ms. For supported grip, the entire screen is divided into a uniform 8×8 grid, and the error tolerance time for all areas is set to 2500ms.

[0129] S305: When it is detected that the finger contact position exceeds the operation area corresponding to the current holding habit type, it is determined to be a deviation operation.

[0130] Deviation operation refers to the interactive behavior in which the finger contact falls outside the expected operation area; detection refers to the process of real-time monitoring and judgment of the contact position.

[0131] Deviation detection uses a real-time boundary determination algorithm. The system maintains a data structure for the operational area boundaries corresponding to the current grip type: One-handed grip uses a polar coordinate system (r, θ) to describe the boundaries, where r is the maximum reach of the thumb and θ is the angular range of the gripping palm. Two-handed grip uses a rectangular boundary set {[x1, y1, x2, y2]} to describe the main operational areas on both sides and the central collaboration area. Supported grip uses an availability marker matrix of the full-screen grid. When a touch event is detected, the touch point coordinates (x, y) are obtained and converted to the corresponding coordinate system for boundary checking. For one-handed grip, the distance d and angle α between the touch point and the grip point are calculated. A deviation is determined when d > r or α exceeds the preset angle range. For two-handed grip, the touch point is checked to see if it falls within any rectangular boundary. For supported grip, the availability marker of the grid where the touch point is located is checked. The judgment result records the touch point coordinates, timestamp, and deviation type (distance deviation / angle deviation / area deviation).

[0132] S306. Count the number of deviation operations in a unit time. When the number of deviation operations exceeds a preset threshold, migrate the frequently used functions to the optimal operation area corresponding to the holding habit type.

[0133] Unit time refers to the length of the time window for statistical deviation operations; the number of deviation operations refers to the number of deviations that occur within the time window; the preset number threshold defines the number of deviations that trigger function migration; high-frequency use functions refer to interface elements that users frequently access; the optimal operation area refers to the screen area that is easiest to operate under a specific holding method.

[0134] The function migration process adopts an adaptive layout adjustment strategy. The system uses a sliding time window (width of 10 minutes) to count deviation operations. For each function button, the number of deviations count and the total number of visits total in the window are recorded, and the deviation rate ratio = count / total is calculated. When the count exceeds the preset threshold N (such as 10 times) and the ratio exceeds 0.3, the function migration is triggered. During migration, the optimal operation area for the current holding type is first identified: one-handed holding is a fan-shaped area within 0.3R from the holding point, two-handed holding is the center part of the main operation area on both sides, and supported holding is the central area of ​​the screen. Find available space in the optimal area and use the quadratic adaptation priority algorithm to assign a new position to the function to be migrated. After migration, the position of the surrounding function buttons is fine-tuned to maintain the overall balance of the interface layout. The system records the migration history and establishes a correspondence between the function position and the holding method for subsequent layout optimization.

[0135] S307: When it is detected that the terminal device is in an unstable holding state, mark the current operation as a suspicious operation.

[0136] An unstable holding state refers to a state in which the device posture changes drastically or deviates from the normal holding range; a suspicious operation refers to an interactive behavior performed in an unstable state that does not conform to the normal operating mode; a terminal device refers to a mobile device such as a smartphone or tablet used by the user.

[0137] Unstable grip detection is based on real-time sensor data analysis. The system acquires triaxial acceleration [ax, ay, az] and angular velocity [wx, wy, wz] data from the IMU, with a sampling frequency of 200Hz. The system calculates the combined acceleration a = sqrt(ax² + ay² + az²) and the combined angular velocity w = sqrt(wx² + wy² + wz²). Two thresholds are set: an acceleration threshold Ta = 1.5g (g is the acceleration due to gravity) and an angular velocity threshold Tw = 50° / s. When a > Ta or w > Tw for more than 100ms, a posture anomaly flag is triggered. Simultaneously, the device tilt angle θ = arccos(az / a) is calculated. When θ exceeds the normal grip angle range (-30° to 60°), the angle anomaly flag is triggered. Combining these two anomaly flags, if either flag is triggered, an unstable grip is detected. After detecting an unstable state, the current timestamp and abnormality type (abnormal posture / abnormal angle) are recorded, and all operations within 500ms before and after the time point are marked as suspicious operations.

[0138] S308: Count the number of suspicious operations within a preset time period. When the number of suspicious operations exceeds a preset threshold, trigger the identity re-authentication process and freeze the target user's access rights to sensitive data.

[0139] The preset time period refers to the time range for counting suspicious operations; the preset number threshold represents the number of suspicious operations that triggers security measures; the identity re-authentication process refers to the security procedure for verifying user identity; sensitive data refers to important information that requires special protection.

[0140] Suspicious operation statistics adopt the sliding time window method. Set the preset time period W to 5 minutes, and update the statistical results every 10 seconds. Count the number of suspicious operations and their type distribution within the window W. Set the preset number threshold N=5 times. When count>N, start the hierarchical response mechanism: first trigger the primary verification, and require the user to enter the unlock password; if the primary verification fails or count>2N, start the identity re-authentication process: the biometric authentication interface (fingerprint or facial recognition) pops up, and the access rights to sensitive data are temporarily frozen. The scope of sensitive data includes: personal information, payment vouchers, private communication records, etc. During the freezing period, these data only support viewing basic information, and modification, deletion, forwarding and other operations are prohibited. After the re-authentication is successful, the freezing status will be gradually lifted, and the viewing permission will be restored first. After observing for 5 minutes without abnormalities, the full access right will be restored.

[0141] S309: Acquire the heart rate data and blood pressure data of the target user to generate a physiological characteristic data set of the target user.

[0142] Heart rate data refers to changes in the user's heart rate; blood pressure data represents the measured values ​​of systolic and diastolic blood pressure; and the physiological characteristic dataset is a multidimensional data set that describes the user's physical condition.

[0143] Physiological characteristic data is collected using built-in or external biosensors in the device. Heart rate data is collected through a photoplethysmography (PPG) sensor with a sampling frequency of 100Hz, recording the heart rate waveform for 60 seconds. The raw PPG signal is bandpass filtered (0.5-4Hz) to remove baseline drift and high-frequency noise, and the heart rate value HR and heart rate variability index HRV are extracted using a peak detection algorithm. Blood pressure data is collected using an electronic sphygmomanometer, and systolic pressure SP and diastolic pressure DP are recorded for each measurement. For three consecutive blood pressure values, the mean and standard deviation are calculated. The heart rate and blood pressure data are aligned by timestamp to construct a physiological feature vector: [HR, HRV, SP, DP]. The feature vector is normalized to generate a standardized physiological characteristic data set. The system maintains a sliding window of data for the last 24 hours to monitor the changing trends of physiological indicators.

[0144] S310. When the heart rate data in the physiological characteristic data set exceeds a preset heart rate range or the blood pressure data exceeds a preset blood pressure range, the acceleration data and angular velocity data of the terminal device are acquired.

[0145] The preset heart rate range refers to the standard range of normal human heart rate, which is 50-100 beats / minute for adults at rest. The preset blood pressure range refers to the normal value range of blood pressure, which is 90-140 mmHg systolic pressure and 60-90 mmHg diastolic pressure for adults. Acceleration data represents the quantitative value of the device's acceleration change in three-dimensional space. Angular velocity data represents the change in the device's rotational speed around the three spatial axes.

[0146] The physiological trait monitoring system performs real-time data acquisition and analysis. Heart rate data is collected via a PPG sensor with a sampling frequency set to 100Hz. The raw signal undergoes a 50Hz low-pass filter to remove power frequency interference and a 0.5Hz high-pass filter to eliminate baseline drift. Heart rate (HR) is calculated once per second, along with the average (HRavg) and standard deviation (HRstd) for the last 60 seconds. An abnormal heart rate is flagged when |HR - HRavg| > θHR (θHR set to 15 beats / minute). Blood pressure data is collected every 60 seconds, recording systolic pressure (SP) and diastolic pressure (DP). The average (SPavg) and standard deviation (DPstd) of the last five measurements are calculated. Abnormal blood pressure is flagged when |SP - SPavg| > θSP (θSP set to 20 mmHg) or |DP - DPavg| > θDP (θDP set to 15 mmHg). Upon detecting abnormal physiological indicators, IMU data collection was immediately initiated: the accelerometer sampling frequency was 200 Hz, with a range of ±16 g; the angular velocity sensor sampling frequency was 200 Hz, with a range of ±2000° / s. The IMU data was preprocessed using a Kalman filter to remove high-frequency noise, retaining the most recent 10 seconds of processed data for subsequent analysis.

[0147] S311. Determine whether the terminal device is in a stable state based on the acceleration data and the angular velocity data.

[0148] The stable state refers to the state in which the device remains stationary or moves only slightly; the judgment process refers to the calculation method that determines the device's motion state by analyzing sensor data.

[0149] Device stability is determined using a multi-feature fusion analysis method. First, the composite three-axis acceleration value a = sqrt(ax² + ay² + az²) and the composite three-axis angular velocity value w = sqrt(wx² + wy² + wz²) are calculated. Statistical features are calculated using a 1-second time window (200 sampling points): the acceleration mean μa and standard deviation σa, and the angular velocity mean μw and standard deviation σw. Stability criteria are set: the difference in acceleration from gravity, |μa-g|, < 0.1g and σa < 0.1g; the absolute value of angular velocity, |μw|, < 5° / s and σw < 2° / s. Continuous judgment is performed using a 60-point sliding window. When the judgment criteria are met for 10 consecutive windows, the device is considered stable. A state machine is used to manage stability judgment results, with a 100ms state transition delay to prevent state jitter caused by transient disturbances. The start time, duration, and statistical features of the stable state are recorded for subsequent abnormal state analysis.

[0150] S312. When the terminal device is in a stable state and the physiological characteristic data set continues to exceed the preset range for a preset duration, alarm data containing the physiological characteristic data set and location information is sent to the preset communication terminal, and the deviation threshold of the target user's access operation to emergency contact information and medical information is adjusted to a preset minimum value.

[0151] The preset duration refers to the shortest observation time that the abnormal state needs to last; the preset communication terminal refers to the device terminal that receives the alarm information; the alarm data refers to the data packet containing detailed information about the abnormal situation; the deviation threshold refers to the maximum deviation range allowed for the operating behavior; the preset minimum value refers to the lowest deviation setting supported by the system.

[0152] The alarm system employs a three-level response mechanism. The first level is abnormal status confirmation: when physiological characteristic data continuously exceeds the preset range and the device remains stable, a duration timer (preset to 180 seconds) is initiated. The second level is alarm data generation: a structured data packet is constructed, containing the abnormality type (heart rate / blood pressure), physiological data set (current value, historical statistics, and changing trends), device status (battery level, signal strength), and location information (GPS coordinates, positioning accuracy). The third level is emergency response: alarm data is sent to the preset communication terminal via a preset communication channel (4G network / Wi-Fi). Simultaneously, the system configuration is adjusted to lower the deviation threshold for access to emergency contacts and medical information from the default value of 1.0 to a preset minimum of 0.2. Physiological indicators are continuously monitored. Once all indicators return to normal for 30 minutes, the system automatically restores the original permission settings. The entire incident process, including the abnormality trigger time, alarm transmission status, response measures executed, and recovery process data, is recorded and archived in an event report.

[0153] like Figure 4As shown, the aging-friendly data rights management system includes a central processing unit (CPU) 401, which can perform various appropriate actions and processes based on programs stored in a read-only memory (ROM) 402 or programs loaded from a storage unit 408 into a random access memory (RAM) 403, such as executing the methods described in the above embodiments. RAM 403 also stores various programs and data required for system operation. CPU 401, ROM 402, and RAM 403 are interconnected via a bus 404. An input / output (I / O) interface 405 is also connected to bus 404.

[0154] The following components are connected to the I / O interface 405: an input section 406 including an audio input device, push button switches, and the like; an output section 407 including a liquid crystal display (LCD), an audio output device, indicator lights, and the like; a storage section 408 including a hard disk and the like; and a communication section 409 including a network interface card such as a LAN (Local Area Network) card or a modem. The communication section 409 performs communication processing via a network such as the Internet. A drive 410 is also connected to the I / O interface 405 as needed. Removable media 411, such as a magnetic disk, an optical disk, a magneto-optical disk, or a semiconductor memory, is installed in the drive 310 as needed, so that computer programs read from the removable media can be installed in the storage section 308 as needed.

[0155] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program including a computer program for executing the methods illustrated in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via the communication section 309 and / or installed from removable media 311. When executed by the central processing unit (CPU) 301, the computer program performs the various functions defined in the present invention.

[0156] It should be noted that specific examples of computer-readable storage media may include, but are not limited to, an electrical connection having one or more conductors, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, portable compact disc read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0157] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present invention. Each box in the flowchart or block diagram can represent a module, program segment, or part of the code, and the above-mentioned module, program segment, or part of the code contains one or more executable instructions for implementing the specified logical functions. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings.

[0158] Specifically, the aging-friendly data rights management system of this embodiment includes a processor and a memory. The memory stores a computer program. When the computer program is executed by the processor, the aging-friendly data rights management method provided by the above embodiment is implemented.

[0159] As another aspect, the present invention further provides a computer-readable storage medium, which may be included in the aging-friendly data rights management system described in the above embodiments, or may exist independently without being incorporated into the aging-friendly data rights management system. The storage medium carries one or more computer programs, which, when executed by a processor of the aging-friendly data rights management system, enable the aging-friendly data rights management system to implement the aging-friendly data rights management method provided in the above embodiments.

[0160] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

[0161] As used in the above embodiments, the term “when” may be interpreted to mean “if” or “after” or “in response to determining that” or “in response to detecting that”, depending on the context. Similarly, the phrases “upon determining that” or “if (stated condition or event) is detected” may be interpreted to mean “if determining that” or “in response to determining that” or “upon detecting (stated condition or event)” or “in response to detecting (stated condition or event)”, depending on the context.

[0162] Those skilled in the art will appreciate that all or part of the process steps in the above-described method embodiments can be implemented by a computer program instructing the relevant hardware. The program can be stored in a computer-readable storage medium, and when executed, the program can include the process steps in the above-described method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM or random access memory (RAM), magnetic disks, or optical disks.

Claims

1. A data rights management method suitable for the elderly, characterized by: Applied to an aging-friendly data rights management system, the method includes: Collecting operation trajectory data of the target user when interacting with aging-friendly data on the terminal device, wherein the operation trajectory data includes contact position, contact pressure, sliding speed and operation timing; Extracting features from the operation trajectory data according to the operation time sequence, constructing a micro-motion feature vector based on the contact position coordinates, contact pressure value, and sliding speed value obtained at each sampling moment, performing correlation analysis on the micro-motion feature vectors at adjacent sampling moments, and combining the variation data obtained from the correlation analysis in time sequence to form an operation chain sequence; The operation chain sequence is segmented according to preset time intervals, the discreteness of the micro-motion characteristics in each segment is calculated, and an operation stability curve is established based on the discreteness, wherein the operation stability curve includes the degree of deviation of the finger contact position, the fluctuation amplitude of the pressure change, and the time interval of the operation response; Calculating the deviation between the operation stability curve and the user's historical benchmark value within a preset first time window and a preset second time window respectively; When the deviation within the preset first time window exceeds a first preset threshold and the deviation within the preset second time window exceeds a second preset threshold, the aging-friendly data access rights of the target user are adjusted, and the aging-friendly data access rights include the target user's data operation rights, function usage rights and interactive verification rights for the terminal device.

2. The method according to claim 1, characterized in that The step of extracting features from the operation trajectory data according to the operation sequence and constructing a micro-motion feature vector based on the contact position coordinates, contact pressure value, and sliding speed value obtained at each sampling moment specifically includes: Obtaining a positional relationship between the touch point coordinates and a current interface function key, and determining a touch tolerance range around the touch point coordinates based on the positional relationship; Calculating the degree of deviation of the touch point position coordinates within the touch fault tolerance range; constructing a pressure decay curve based on the contact point pressure value, and calculating the touch force stability according to the slope of the pressure decay curve; calculating a speed fluctuation characteristic of the sliding operation according to the sliding speed value; The offset degree, the touch force stability, and the speed fluctuation feature are combined to form the micro-motion feature vector.

3. The method according to claim 1, characterized in that The step of segmenting the operation chain sequence according to preset time intervals and calculating the discreteness of the micro-motion features in each segment of the sequence specifically includes: Dividing the operation chain sequence into multiple time sequence feature segments according to preset time intervals; Acquire the time interval and touch duration between adjacent touch operations, and determine that the touch operation is repeated when the time interval is less than a preset third threshold; Acquiring an offset distance between a target position of the repeated touch operation and an actual response position, and performing position mapping correction when the offset distance is greater than a preset fourth threshold to obtain a corrected touch operation; Obtaining a target interface switching sequence corresponding to the repeated touch operations, and merging repeated touch operations that trigger the same interface switching into one valid operation; The micro-motion feature dispersion of the time sequence feature segment is calculated according to the ratio of the number of correction touch operations to the number of original touch operations and the ratio of the number of valid operations to the number of original touch operations.

4. The method according to claim 1, wherein When the deviation within the preset first time window exceeds a first preset threshold and the deviation within the preset second time window exceeds a second preset threshold, adjusting the aging-friendly data access rights of the target user, wherein the aging-friendly data access rights include the target user's data operation rights, function use rights, and interactive verification rights for the terminal device, specifically includes: Performing feature encoding on the deviation degree of the finger contact point position, the fluctuation amplitude of the pressure change, and the time interval of the operation response in the operation stability curve to obtain an operation feature code at the current moment, calculating a similarity between the operation feature code and the historical feature code of the target user, marking the target user's operation type as abnormal when the similarity is lower than a similarity threshold, and adjusting the data operation permission to a first preset level; Obtaining a time point greater than a preset interval threshold in the time interval of the operation response, recording the micro-movement feature before the time point as a starting action, and recording the micro-movement feature after the time point as a terminating action, determining an operation hesitation state based on a feature difference between the starting action and the terminating action, and limiting the function usage permission of the target user to a second preset level based on the operation hesitation state; calculating a pressure control characteristic value based on the fluctuation amplitude of the pressure change in the operation stability curve, determining an operation subject feature of the target user based on the pressure control characteristic value, and raising the interactive verification authority requirement to a third preset level when a matching degree between the operation subject feature and a preset feature template is lower than a matching threshold; When the similarity of the operation feature code continuously exceeds the similarity threshold and the fluctuation range of the pressure control feature value is within a preset interval, the data operation permission, the function use permission and the interactive verification permission are restored to their original levels.

5. The method according to claim 1, wherein After the step of adjusting the target user's access rights to the age-friendly data when the deviation within the preset first time window exceeds a first preset threshold and the deviation within the preset second time window exceeds a second preset threshold, the method further includes: Collecting attitude sensor data of the terminal device, wherein the attitude sensor data includes acceleration data, angular velocity data, and spatial orientation data; Aligning the attitude sensor data with the operation chain sequence in time sequence to extract the device shaking frequency, tilt angle, and spatial displacement; Analyzing the correlation between the shaking frequency of the device and the degree of deviation of the finger contact position in the operation stability curve to determine whether the terminal device is in a stable holding state; When it is detected that the terminal device is in an unstable holding state, marking the current operation as a suspicious operation; Count the number of suspicious operations within a preset time period. When the number of suspicious operations exceeds a preset threshold, trigger the identity re-authentication process and freeze the target user's access rights to sensitive data.

6. The method according to claim 5, characterized in that The step of analyzing the correlation between the device shaking frequency and the degree of deviation of the finger contact position in the operation stability curve to determine whether the terminal device is in a stable holding state specifically includes: Obtaining a correspondence between the device shaking frequency and the degree of deviation of the finger contact position, and identifying a target user's gripping habit type based on the correspondence, where the gripping habit type includes one-hand gripping, two-hand gripping, and supported gripping; Divide the corresponding operation area range and the maximum error tolerance time of a single operation according to the holding habit type; When it is detected that the finger contact position exceeds the operation area corresponding to the current holding habit type, it is determined to be a deviation operation; The number of deviation operations in a unit time is counted, and when the number of deviation operations exceeds a preset threshold, the frequently used function is migrated to the optimal operation area corresponding to the holding habit type.

7. The method according to claim 1, characterized in that After the step of adjusting the target user's access rights to the age-friendly data when the deviation within the preset first time window exceeds a first preset threshold and the deviation within the preset second time window exceeds a second preset threshold, the method further includes: Acquire the heart rate data and blood pressure data of the target user to generate a physiological characteristic data set of the target user; When the heart rate data in the physiological characteristic data set exceeds a preset heart rate range or the blood pressure data exceeds a preset blood pressure range, acquiring acceleration data and angular velocity data of the terminal device; Determining whether the terminal device is in a stable state according to the acceleration data and the angular velocity data; When the terminal device is in a stable state and the physiological characteristic data set continues to exceed the preset range for a preset duration, alarm data containing the physiological characteristic data set and location information is sent to the preset communication terminal, and the deviation threshold of the target user's access operation to emergency contact information and medical information is adjusted to a preset minimum value.

8. An aging-friendly data rights management system, characterized in that: The aging-friendly data rights management system includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to enable the aging-friendly data rights management system to execute the method described in any one of claims 1-7.

9. A computer-readable storage medium comprising instructions, characterized in that: When the instruction is executed on the aging-friendly data rights management system, the aging-friendly data rights management system is enabled to execute the method according to any one of claims 1 to 7.

10. A computer program product, characterized in that When the computer program product is run on the aging-friendly data rights management system, the aging-friendly data rights management system is enabled to execute the method according to any one of claims 1 to 7.

Images