Ageing-suitable data authority management method and system, product and medium

Abstract

The invention discloses an aging-suitable data authority management method and system, a product and a medium, and relates to the field of electrical digital data processing, and the method comprises the steps: collecting operation track data of a target user during aging-suitable interaction of terminal equipment; the method comprises the following steps: extracting features according to an operation time sequence, constructing micro-action feature vectors, associating variable quantity data of adjacent moment vectors, forming an operation chain sequence according to the time sequence, segmenting the sequence according to a preset interval, calculating micro-action feature dispersion, and establishing an operation stability curve containing position offset, pressure fluctuation amplitude and response interval. In the two preset time windows, the deviation degrees of the curve and the historical reference value of the user are calculated respectively; and when the deviation degree of the two windows exceeds a corresponding threshold value, adjusting the aging-suitable data access permission of the user. By implementing the method, the accuracy of adjusting the authority of the old to use the terminal equipment can be improved under the condition of ensuring the normal use requirement of the old on the terminal equipment.

Description

Technical Field

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

[0002] In the intelligent management of elderly care institutions, the permissions for the elderly to perform daily self-service operations through intelligent terminals need to be reasonably managed. These operation permissions mainly include activity reservation, meal selection, personal information viewing, and service bill browsing, etc.

[0003] In the related art, fixed system operation permissions can be assigned to the elderly. For example, the elderly can view personal information, carry out entertainment activities, transfer money, make activity reservations, select meals, etc. through intelligent terminals, enabling the elderly to independently complete some basic operations, improving service efficiency, and enhancing the self-care ability of the elderly.

[0004] However, this permission management method cannot respond in a timely manner to changes in the cognitive state of the elderly. For example, when the elderly have cognitive impairments, maintaining all operation permissions may lead to incorrect operations, while overly restricting permissions will affect their normal usage requirements, making it difficult to achieve precise adjustment of permissions. Summary of the Invention

[0005] This application provides an aging-friendly data permission management method and device, which are used to improve the accuracy of adjusting the permissions for the elderly to use terminal devices while ensuring the normal usage requirements of the elderly for terminal devices.

[0006] In a first aspect, the present application provides an aging-friendly data permission management method, which is applied to an aging-friendly data permission management system. The method includes: collecting operation trajectory data of a target user during aging-friendly data interaction on a terminal device, where 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 timing, constructing a micro-action feature vector based on the contact position coordinates, contact pressure values, and sliding speed values at each sampling moment obtained, performing correlation analysis on the micro-action feature vectors at adjacent sampling moments, and combining the variation data obtained from the correlation analysis in sequence to form an operation chain sequence; segmenting the operation chain sequence at a preset time interval, calculating the dispersion of the micro-action features in each segment sequence, and establishing an operation stability curve based on the dispersion. The operation stability curve includes the deviation degree of the finger contact position, the fluctuation range of pressure change, and the time interval of operation response; calculating the deviation degree between the operation stability curve and the user's historical reference value within a preset first time window and a preset second time window respectively; when the deviation degree within the preset first time window exceeds a first preset threshold and the deviation degree within the preset second time window exceeds a second preset threshold, adjusting the aging-friendly data access permission of the target user, where the aging-friendly data access permission includes the data operation permission, function usage permission, and interaction verification permission of the target user to the terminal device.

[0007] In the above embodiment, the operation trajectory data of the target user is collected and a micro-action feature vector is constructed. After forming the operation chain sequence, an operation stability curve is established. The deviation degree from the user's historical reference value is calculated within two time windows, and when the deviation degree exceeds the threshold, the data access permission is adjusted in a timely manner. The permission level is dynamically adjusted based on real-time operation behavior analysis, making the permission management match the current cognitive and operation capabilities of the elderly, avoiding the risk of incorrect operations in the state of cognitive impairment, and ensuring the usage requirements in the normal state.

[0008] Combined with some embodiments of the first aspect, in some embodiments, the step of extracting features from the operation trajectory data according to the operation timing and constructing a micro-action feature vector based on the contact position coordinates, contact pressure values, and sliding speed values at each sampling moment obtained specifically includes: obtaining the position relationship between the contact position coordinates and the position of the function buttons on the current interface, determining the touch tolerance range around the contact position coordinates according to the position relationship; calculating the deviation degree of the contact position coordinates within the touch tolerance range; constructing a pressure decay curve based on the contact pressure value, and calculating the touch force application stability according to the slope of the pressure decay curve; calculating the speed fluctuation feature of the sliding operation according to the sliding speed value; and combining the deviation degree, touch force application stability, and speed fluctuation feature to form a micro-action feature vector.

[0009] In the above embodiments, the positional relationship between the contact point position and the function button is obtained to determine the touch tolerance range, the touch force application stability is calculated in combination with the slope of the pressure decay curve, the fluctuation characteristics are analyzed through the sliding speed value, and these characteristics are combined to form a micro-motion feature vector. A complete touch operation feature extraction system is established, realizing the refined quantitative analysis of the operation behavior of the elderly and providing an accurate feature basis for subsequent permission adjustment.

[0010] In combination with some embodiments of the first aspect, in some embodiments, the step of segmenting the operation chain sequence at a preset time interval and calculating the dispersion of the micro-motion features in each segment sequence specifically includes: segmenting the operation chain sequence into multiple time-series feature segments at a preset time interval; obtaining the time interval and touch duration between adjacent touch operations, and determining them as repeated touch operations when the time interval is less than a preset third threshold; obtaining the offset distance between the target position and the actual response position of the repeated touch operation, and performing position mapping correction when the offset distance is greater than a preset fourth threshold to obtain the 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 switching into one effective operation; calculating the dispersion of the micro-motion features 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 effective operations to the number of original touch operations.

[0011] In the above embodiments, the operation chain sequence is segmented into time-series feature segments, the repeated touch operations are identified and position mapping correction is performed, the repeated operations that trigger the same interface switching are merged into effective operations, and the dispersion of the micro-motion features is calculated. A time-series analysis framework for operation behavior is constructed, eliminating the data interference caused by the repeated operations and accidental touches of the elderly, improving the accuracy of feature extraction, and making the judgment basis for permission adjustment more reliable.

[0012] In some embodiments in combination with some embodiments of the first aspect, 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 age-friendly data access permissions of the target user are adjusted. The age-friendly data access permissions include the steps of the target user's data operation permissions, function usage permissions, and interaction verification permissions on the terminal device, and specifically include: performing feature encoding on the offset degree of the finger contact position, the fluctuation range 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, 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 points in the time interval of the operation response that are greater than the preset interval threshold, recording the micro-action features before the time point as the starting action, recording the micro-action features after the time point as the ending action, determining the operation hesitation state based on the feature differences between the starting action and the ending action, and restricting the function usage permission of the target user to the second preset level according to the operation hesitation state; calculating the pressure control feature value based on the fluctuation range of the pressure change in the operation stability curve, judging the operation subject feature of the target user according to the pressure control feature value, and increasing the interaction verification permission requirement to the third preset level when the matching degree between the operation subject feature and the preset feature template is lower than the 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 the preset interval, restoring the data operation permission, function usage permission, and interaction verification permission to the original level.

[0013] In the above embodiments, feature encoding is performed on the operation stability curve to obtain the operation feature code, the abnormal operation type is marked based on the feature code similarity, the operation hesitation state is determined according to the feature differences between the starting action and the ending action, and the operation subject feature is judged in combination with the pressure control feature value. A multi-dimensional permission adjustment mechanism is established, realizing hierarchical control of data operation permissions, function usage permissions, and interaction verification permissions, and maintaining the flexibility of permission adjustment while ensuring security.

[0014] In some embodiments in combination with some embodiments of the first aspect, after the step of adjusting the access permission of the age-friendly data of the target user when the deviation degree within the preset first time window exceeds the first preset threshold and the deviation degree within the preset second time window exceeds the second preset threshold, the method further includes: collecting attitude sensor data of the terminal device, where the attitude sensor data includes acceleration data, angular velocity data, and spatial orientation data; performing time series alignment on the attitude sensor data and the operation chain sequence, and extracting the device shaking frequency, tilt angle, and spatial displacement; analyzing the correlation between the device shaking frequency and the deviation degree 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; counting the number of suspicious operations within a preset time period, and when the number of suspicious operations exceeds the preset number threshold, triggering an identity re-verification process and freezing the access permission of the sensitive data of the target user.

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

[0016] In some embodiments in combination with some embodiments of the first aspect, the step of analyzing the correlation between the device shaking frequency and the deviation degree 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 corresponding relationship between the device shaking frequency and the deviation degree of the finger contact position, and identifying the holding habit type of the target user based on the corresponding relationship, where the holding habit type includes single-handed holding, two-handed holding, and supported holding; dividing the corresponding operation area range and the maximum fault tolerance time for 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, determining it as a deviation operation; counting the number of deviation operations within a unit time, and when the number of deviation operations exceeds the preset number threshold, migrating the frequently used functions to the best operation area corresponding to the holding habit type.

[0017] In the above embodiments, the holding habit type is identified based on the device shaking frequency and the deviation degree of the contact position, the corresponding operation area range and fault tolerance time are divided, the number of deviation operations is counted, and the frequently used functions are migrated. An adaptive operation area division mechanism suitable for different holding habits is constructed, optimizing the operation interface layout, reducing the risk of misoperation caused by unstable holding, and improving the personalized adaptation degree of the operation experience.

[0018] In some embodiments in combination with some embodiments of the first aspect, after the step of adjusting the access permission of the age-friendly data of the target user when the deviation within the preset first time window exceeds the first preset threshold and the deviation within the preset second time window exceeds the second preset threshold, the method further includes: obtaining the heart rate data and blood pressure data of the target user, and generating a physiological characteristic data set of the target user; when the heart rate data in the physiological characteristic data set exceeds the preset heart rate range or the blood pressure data exceeds the 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 according to the acceleration data and angular velocity data; when the terminal device is in a stable state and the physiological characteristic data set continuously exceeds the preset range for a preset duration, sending warning data including the physiological characteristic data set and location information to a preset communication terminal, and adjusting the deviation threshold of the access operation of the target user to the emergency contact information and medical information to a preset minimum value.

[0019] In the above embodiments, the heart rate data and blood pressure data of the target user are obtained to generate a physiological characteristic data set, and in combination with the judgment of the stable state of the terminal device, warning data is sent when the physiological index is abnormal and continuously exceeds the preset range, and at the same time, the deviation threshold of the access operation of the emergency information is reduced. The physiological health status is associated with the permission management mechanism, an emergency response mechanism for emergencies is established, while ensuring the safety of the elderly, the rapid accessibility of key information is ensured, and the humanized adjustment of permission management is realized.

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

[0021] In a third aspect, an embodiment of the present application provides a computer program product containing instructions, when the computer program product runs on the age-friendly data permission management system, making the age-friendly data permission management system execute the method described in the first aspect and any possible implementation manner in the first aspect.

[0022] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, including instructions, when the instructions run on the age-friendly data permission management system, making the age-friendly data permission management system execute the method described in the first aspect and any possible implementation manner in the first aspect.

[0023] Understandably, the aging-friendly data permission management system provided in the second aspect above, 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 method provided in the embodiments of the present application. Therefore, the beneficial effects they can achieve can refer to the beneficial effects in the corresponding method, which will not be elaborated here.

[0024] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. In the present application, by collecting the operation trajectory data of the target user, constructing a micro-action feature vector, forming an operation chain sequence, establishing an operation stability curve, and calculating the deviation from the user's historical benchmark value within two time windows, the data access permission is adjusted in a timely manner when the deviation exceeds the threshold. Based on real-time operation behavior analysis, the permission level is dynamically adjusted to match the current cognitive and operation capabilities of the elderly, avoiding the risk of incorrect operations in the state of cognitive impairment and ensuring the usage requirements in the normal state.

[0025] 2. In the present application, by obtaining the positional relationship between the contact position and the function button to determine the touch tolerance range, calculating the touch force application stability in combination with the slope of the pressure decay curve, and analyzing the fluctuation characteristics through the sliding speed value, these characteristics are combined to form a micro-action feature vector. A complete touch operation feature extraction system is established, realizing refined quantitative analysis of the operation behavior of the elderly and providing an accurate feature basis for subsequent permission adjustment.

[0026] 3. In the present application, by splitting the operation chain sequence into time-series feature segments, identifying repeated touch operations and performing position mapping correction, merging repeated operations that trigger the same interface switch into valid operations, and calculating the micro-action feature dispersion. A time-series analysis framework for operation behavior is constructed, eliminating data interference caused by the repeated operations and accidental touches of the elderly, improving the accuracy of feature extraction, and making the judgment basis for permission adjustment more reliable. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 is a flowchart of an aging-friendly data permission management method in an embodiment of the present application; Figure 2 is another flowchart of an aging-friendly data permission management method in an embodiment of the present application; Figure 3 is another flowchart of an aging-friendly data permission management method in an embodiment of the present application; Figure 4 is a schematic structural diagram of an entity device of an aging-friendly data permission management system in an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0028] The terms used in the following embodiments of this application are only for the purpose of describing specific embodiments and are not intended to limit this application. As used in the specification of this application, the singular forms "a", "an", "the above", "the", and "this" are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term "and / or" used in this application refers to any or all possible combinations including one or more of the listed items.

[0029] Hereinafter, the terms "first" and "second" are only used for descriptive purposes and should not be construed as implying or suggesting relative importance or implicitly indicating the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of such features. In the description of the embodiments of this application, unless otherwise stated, the meaning of "a plurality" is two or more.

[0030] For ease of understanding, the application scenarios of the embodiments of this application are introduced below.

[0031] In modern elderly care institutions, intelligent terminal devices have been widely used in the daily life services of the elderly. The elderly can perform self-service operations such as medical appointment booking, meal selection, activity registration, and bill payment through intelligent terminals. However, there are significant differences in the cognitive abilities and operation habits of the elderly, and these may change dynamically over time. For example, some elderly people may experience short-term cognitive impairments, resulting in repeated operations or incorrect operations; some elderly people may have unstable touch operations due to hand tremors. These situations may all cause incorrect data access and function usage, posing safety hazards. In practical applications, elderly care institutions need a technical solution that can dynamically adjust data access permissions according to the actual operation status of the elderly, ensuring both usage safety and not affecting normal needs.

[0032] Currently, elderly care institutions usually adopt a management method of fixed permission grading. For example, the elderly are divided into three permission levels: A / B / C. Level A can perform all operations, including transfer payments, information modification, etc.; Level B is only allowed to view information and perform low-risk operations; Level C requires assistance from family members or caregivers to operate. This static permission management method has obvious defects: First, once the permission level is set, it is rarely adjusted and cannot respond in a timely manner to short-term fluctuations in the elderly's status. Second, all functions within the same permission level are either all open or all restricted, lacking refined management. Third, it does not take into account individual differences in operation habits, and may overly restrict some elderly people while providing insufficient protection for others. For example, an elderly person who is usually proficient in operations may have short-term operation abnormalities due to physical discomfort, but the system still allows them to perform high-risk operations.

[0033] After adopting the solution of this application, the system can monitor the operation characteristics of the elderly in real time and adjust the dynamic permissions accordingly. Taking an elderly user as an example, the system constructs a personalized operation characteristic model by collecting data such as the position, pressure, and speed of their touch operations. When it detects that the position of the touch point of the user appears to deviate frequently and the pressing force fluctuates abnormally, the system will determine that the user is currently in an unstable operation state. At this time, the access permission to sensitive data will be automatically reduced, such as temporarily restricting the transfer amount and requiring re-authentication. At the same time, the system will also judge whether the abnormal operation is caused by the unstable holding of the device based on the data of the device's attitude sensor. If it is found that the user's physiological indicators (such as heart rate and blood pressure) also appear abnormal, the access restrictions on the emergency contact information and medical information will be automatically lifted, and an alarm will be sent to the relevant personnel. When the user's state returns to normal and the operation characteristics become stable again, the system will restore the original permission settings accordingly. This dynamic permission management mechanism based on real-time operation characteristics not only ensures data security but also meets the special needs in emergency situations.

[0034] For the sake of easy understanding, the method provided in this embodiment will be described in terms of its process in combination with the above scenario. Please refer to Figure 1 , which is a schematic flowchart of a process of the data permission management method for the elderly-friendly in the embodiment of this application.

[0035] S101. Collect the operation trajectory data of the target user during the elderly-friendly data interaction on the terminal device. The operation trajectory data includes the contact point position, contact point pressure, sliding speed, and operation time sequence.

[0036] Among them, the target user refers to an elderly user who performs elderly-friendly data interaction operations on the terminal device; the terminal device refers to an intelligent device with a touch function, such as a smart phone, a tablet computer, etc.; the operation trajectory data refers to the operation trace data left by the user when operating on the touch screen; the contact point position is used to represent the coordinate position when the finger touches the screen; the contact point pressure refers to the magnitude of the pressure when the finger presses the screen; the sliding speed represents the moving rate when the finger slides on the screen; the operation time sequence is used to represent the time sequence in which each operation action occurs.

[0037] This step is executed when the elderly user uses the terminal device to perform elderly-friendly data interaction operations. Specifically, the system collects the operation trajectory data of the user in real time through the touch sensor of the terminal device, including information such as the position coordinates, pressing force, and sliding speed when the finger touches the screen, and records the time series in which these operation data occur. These data can reflect the user's operation habits and state 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 the accuracy and availability of the data.

[0038] In some embodiments, the acquisition of operation trajectory data can be achieved in various ways: Optionally, the position of the finger contact point is acquired through the capacitive sensor of the touch screen, the pressure magnitude is calculated according to the change of the contact capacitance value, the sliding speed is calculated based on the positions and times of adjacent sampling points, and the time stamps of each sampling point are recorded simultaneously to form the operation time sequence; Optionally, the contact pressure distribution is directly acquired through the pressure sensor array, the position trajectory of the contact point is extracted by means of image processing methods, the sliding speed is calculated in combination with the inertial sensor data, and a unified time reference is established to record the operation time sequence. It can be understood that other sensor combinations or data acquisition methods can also be adopted to obtain the operation trajectory data, which is not limited herein.

[0039] S102. Extract features from the operation trajectory data according to the operation time sequence, construct a micro-operation feature vector based on the contact position coordinates, contact pressure value, and sliding speed value at each sampling moment obtained, perform correlation analysis on the micro-operation feature vectors at adjacent sampling moments, and combine the change amount data obtained from the correlation analysis in time sequence to form an operation chain sequence.

[0040] Among them, feature extraction means extracting representative feature information from the original operation trajectory data; the sampling moment refers to the discrete time points in the data acquisition process; the micro-operation feature vector is used to represent the combination of operation features at a single sampling moment; correlation analysis means performing a comparative analysis on the feature vectors at adjacent moments; the change amount data refers to the change information between adjacent feature vectors; and the operation chain sequence is used to represent the characteristics of the complete operation process.

[0041] This step is executed after the acquisition of the operation trajectory data is completed. Specifically, the system first processes the acquired trajectory data in chronological order, extracts feature parameters such as the contact position coordinates, pressure value, and speed value at each sampling moment, and combines these parameters to form a vector representing the microscopic operation characteristics at that moment. Then, correlation analysis is performed on the feature vectors at adjacent moments to calculate the change amounts of each feature parameter, and these change amounts reflect the dynamic characteristics in the operation process. Finally, these change amount data are combined in time sequence to construct a complete operation chain sequence, which is used to represent the characteristic changes of the entire operation process.

[0042] In some embodiments, feature extraction and sequence construction can be achieved in various ways: Optionally, the sliding window method is used to segment the original data, statistical features (such as mean, variance, etc.) are extracted within each window, the difference values of the features in adjacent windows are calculated, and finally the difference values are serialized to form an operation chain; Optionally, a deep learning model is used to directly learn and extract feature representations from the original data, the temporal correlation between features is established through a recurrent neural network, and a feature sequence representing the complete operation process is output. It can be understood that other feature engineering methods or machine learning algorithms can also be adopted to achieve the extraction of operation features and sequence construction, which is not limited herein.

[0043] S103. Segment the operation chain sequence at a preset time interval, calculate the dispersion of the micro-action features in each segment sequence, and establish an operation stability curve based on the dispersion. The operation stability curve includes the deviation degree of the finger contact position, the fluctuation amplitude of the pressure change, and the time interval of the operation response.

[0044] Among them, the preset time interval represents the fixed time length preset by the system for segmenting the operation chain sequence; the dispersion refers to the degree of dispersion of the distribution of micro-action features and is used to measure the stability of the operation; the operation stability curve represents a time-series feature curve reflecting the stability of the user's operation; the deviation degree refers to the deviation distance of the contact position relative to the target position; the fluctuation amplitude is used to represent the severity of the pressure change; the time interval of the operation response represents the time difference between consecutive operations.

[0045] This step is executed after the operation chain sequence is constructed. 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-action features within this period of time. For each time segment, calculate the degree of dispersion of the micro-action features therein, 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. Then connect these dispersion indicators in chronological order to form a feature curve reflecting the change of operation stability over time. This curve includes three dimensions: the change trend of the contact position deviation, the stability degree of pressure control, and the coherence of the operation rhythm.

[0046] In some embodiments, the calculation of the dispersion and the construction of the stability curve can be achieved in various ways: Optionally, first calculate the mean Euclidean distance and standard deviation between the contact position in each time segment and the target position, perform wavelet transform on the pressure value sequence to extract the fluctuation features, calculate the variance of the time interval between adjacent operations, and finally normalize these three groups of indicators and synthesize the stability curve; Optionally, use a clustering algorithm to perform clustering analysis on the micro-action features of each time segment, calculate the ratio of the within-class distance to the between-class distance as the dispersion index, and at the same time extract the time-series change features of the clustering center, and construct a multi-dimensional stability curve in combination with the statistical features of the operation response time. It can be understood that other statistical analysis or machine learning methods can also be used to achieve the quantitative evaluation of operation stability and the construction of the curve, which is not limited here.

[0047] S104. Calculate the deviation degrees of the operation stability curve from the user's historical reference values within a preset first time window and a preset second time window respectively.

[0048] Among them, the preset first time window represents the time range for short-term stability assessment; the preset second time window refers to the time range for long-term stability assessment; the historical baseline value represents the statistical reference value of the user's past operation characteristics; the deviation degree is used to represent the difference degree between the current operation characteristics and the historical baseline.

[0049] This step is executed after the construction of the operation stability curve is completed. Specifically, the system will maintain two different-scale time windows simultaneously. For example, the short-term window may be the last 10 minutes, and the long-term window may be the last 2 hours. Within these two time windows, the differences between the operation stability curve and the user's historical baseline value are calculated respectively. During the calculation process, multiple characteristic dimensions of the curve are considered, including the overall trend of the curve, the fluctuation frequency, the peak distribution, etc., and a comprehensive deviation index is obtained through weighted combination. This design of dual time windows can capture both the short-term fluctuations and the long-term change trends of the user's operation state.

[0050] In some embodiments, the calculation of the deviation degree can be achieved in multiple ways: Optionally, first extract the time-domain characteristics of the operation stability curve, calculate statistics such as the mean, standard deviation, and kurtosis, then compare with the historical baseline value to obtain the normalized difference value, and finally obtain the comprehensive deviation degree through weighted summation; Optionally, use the dynamic time warping algorithm to calculate the temporal similarity between the current operation stability curve and the historical baseline curve, combine the frequency-domain characteristics and shape characteristics of the curve, and obtain the final deviation score through multi-level feature fusion. It can be understood that other time series data analysis methods or pattern recognition algorithms can also be used to implement the calculation of the deviation degree of the operation characteristics, which is not limited here.

[0051] S105. When the deviation degree within the preset first time window exceeds the first preset threshold and the deviation degree within the preset second time window exceeds the second preset threshold, adjust the access permission of the target user's age-friendly data. The access permission of the age-friendly data includes the data operation permission, function usage permission, and interaction verification permission of the target user to the terminal device.

[0052] Among them, the first preset threshold represents the warning value of the short-term operation stability deviation; the second preset threshold refers to the warning value of the long-term operation stability deviation; the data operation permission is used to represent the access and modification range of the user to the system data; the function usage permission represents the range of system functions that the user can use; the interaction verification permission refers to the level of requirement for the system to authenticate the user; the access permission of the age-friendly data represents the hierarchical permission system customized for the characteristics of elderly users.

[0053] This step is executed when an abnormality in the user operation stability is detected. Specifically, the system monitors the operation deviation degrees within short-term and long-term time windows in real time. When the short-term deviation degree exceeds the first preset threshold (such as set to 1.5 times the historical benchmark) and the long-term deviation degree simultaneously exceeds the second preset threshold (such as set to 1.3 times the historical benchmark), it indicates that the user's operation state may have changed significantly, and the system will activate the permission adjustment mechanism. The permission adjustment involves three levels: reducing the operation permissions for sensitive data to prevent misoperations, restricting the usage scope of specific functions to reduce risks, and increasing the authentication requirements to enhance security protection. Such multi-dimensional permission adjustment can maintain the availability of the system while ensuring security.

[0054] In some embodiments, the permission adjustment can be achieved in various 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 different types of abnormalities, then gradually reduce the sensitivity of the data operation permissions, and at the same time adjust the restricted scope of function usage. Finally, set the corresponding authentication requirements according to the degree of abnormality; Optionally, use a machine learning model to evaluate the user's operation state in real time, establish a mapping relationship between operation abnormalities and permission levels, dynamically calculate the adjustment amplitudes of various permissions, and perform adaptive adjustment according to the user's feedback. It can be understood that other intelligent decision algorithms or permission management mechanisms can also be used to achieve the dynamic adjustment of the age-friendly data access permissions, which is not limited here.

[0055] The following provides a further and more specific process description of the method provided in this embodiment. Please refer to Figure 2 , which is another process schematic diagram of the age-friendly data permission management method in the embodiments of the present application.

[0056] S201. Collect the operation trajectory data of the target user during the age-friendly data interaction on the terminal device. The operation trajectory data includes the contact position, contact pressure, sliding speed, and operation timing sequence.

[0057] The target user refers to an elderly operator using the terminal device. The terminal device refers to a device with a touch function such as a smartphone or a tablet computer. The operation trajectory data includes the following elements: The contact position refers to the coordinate point where the finger touches the screen, the contact pressure refers to the force value when the finger presses the screen, the sliding speed refers to the rate at which the finger moves on the screen, and the operation timing sequence refers to the time sequence of each operation action.

[0058] The terminal device collects real-time user operation data through the capacitive sensor array on the touch screen. For the contact position, the acquisition frequency is 60 Hz, and the X and Y coordinate values of the contact are recorded; for the contact pressure, it is sampled at a frequency of 200 Hz through a pressure sensor to obtain the pressure value within the range of 0 - 1023; for the sliding speed, the velocity vector is calculated based on the position difference and time difference between two adjacent sampling points; for the operation timing, the timestamp of each touch event is recorded. These data are preprocessed through the device driver, and after removing outliers and noise, they are saved as a data stream in a standard format. During the acquisition process, the data of multi-touch are recorded separately to form independent operation trajectories.

[0059] S202. Obtain the positional relationship between the contact position coordinates and the position of the function buttons on the current interface, and determine the touch tolerance range around the contact position coordinates according to the positional relationship.

[0060] The contact position coordinates represent the two-dimensional coordinate values when the finger touches the screen, the interface function buttons represent the clickable interactive elements on the screen, the positional relationship refers to the spatial relationship between the contact and the center point of the button, and the touch tolerance range represents the maximum range allowed for the contact to deviate from the button center.

[0061] The terminal device first obtains the position information of all function buttons on the current interface, including the center coordinates and boundary ranges of each button. For each detected contact, calculate the Euclidean distance between it and the nearest function button. According to the size and spacing of the buttons, set the basic tolerance radius, usually 1 / 3 of the button width. For the buttons at the edge positions, appropriately reduce the tolerance range in the direction perpendicular to the edge to avoid accidental touch of adjacent buttons. At the same time, combine the usage frequency of the buttons, and expand the tolerance range for the frequently used buttons to improve the operation accuracy.

[0062] S203. Calculate the deviation degree of the contact position coordinates within the touch tolerance range.

[0063] The deviation degree represents the deviation amount between the actual position of the contact and the button center, and is obtained by calculating the distance between the two. The larger this value is, the lower the operation accuracy.

[0064] In the calculation process, first obtain the real-time coordinates (x, y) of the contact and the center coordinates (x0, y0) of the corresponding button, and calculate the Euclidean distance d = √((x - x0)²+(y - y0)²). Then divide this distance value by the radius R of the touch tolerance range to obtain the normalized deviation ratio p = d / R. When p < 1, it means the contact is within the tolerance range, and when p ≥ 1, it means the contact is outside the tolerance range. At the same time, calculate the angle θ = arctan((y - y0) / (x - x0)) of the contact relative to the button center, which is used to analyze the direction characteristics of the deviation. For continuous touch operations, calculate the mean and standard deviation of the deviation degree during the whole process to evaluate the stability of the operation.

[0065] S204. Construct a pressure decay curve based on the contact pressure value, and calculate the touch force application stability according to the slope of the pressure decay curve.

[0066] The contact pressure value represents the force value when the finger presses the screen. The pressure decay curve refers to the function curve of the contact pressure changing with time. The slope represents the degree of pressure change. The touch force application stability refers to the ability of the user to maintain a stable pressure during the touch process.

[0067] The construction process of the pressure decay curve first performs time series sampling on the original pressure value sequence, with a sampling interval of 5 ms. For each touch event, record the pressure value P(t) throughout the process from contact to leaving the screen. Use the least squares method to perform exponential function fitting on the pressure value sequence to obtain a decay curve in the form of P(t)=P0e^(-λt), where P0 is the initial pressure value and λ is the decay coefficient. Calculate the instantaneous slope k(t)=-λP0e^(-λt) of the curve at each time point t. The touch force application 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. The smaller the S value, the more stable the pressure control; conversely, it indicates larger pressure fluctuations.

[0068] S205. Calculate the speed fluctuation characteristics of the sliding operation according to the sliding speed value.

[0069] The sliding speed value represents the instantaneous speed at which the finger moves on the screen. The speed fluctuation characteristics refer to the characteristic indicators of speed change during the sliding process.

[0070] To calculate the speed fluctuation characteristics, first segment the sliding trajectory according to a fixed time window (such as 100 ms). For each segment of the trajectory, 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 sequence of speed magnitudes |v(t)| = sqrt((dx / dt)²+(dy / dt)²). Extract the following characteristic quantities: the average speed μv = ∑|v(t)| / n, the standard deviation of speed σv = sqrt(∑(|v(t)| - μv)² / n), and the average acceleration μa = ∑(|v(t + dt)| - |v(t)|) / dt / n. Combine these characteristic quantities to form the speed fluctuation characteristic vector [μv, σv, μa].

[0071] S206. Combine the degree of deviation, touch force application stability, and speed fluctuation characteristics to form a micro - motion characteristic vector.

[0072] The micro - motion characteristic vector represents a multi - dimensional numerical vector describing the characteristics of a single operation action, including characteristic quantities in three dimensions: position, pressure, and speed.

[0073] In the process of constructing the feature vector, the various features calculated above are standardized. The degree of deviation is standardized by z-score: z = (x - μ) / σ, where x is the original deviation value, and μ and σ are the mean and standard deviation of historical data respectively. The characteristics of touch force stability and speed fluctuation are also standardized. The range of the standardized feature values is unified to the interval [-1, 1]. The final form of the micro-motion feature vector is: [degree of deviation, touch force stability, mean speed, standard deviation of speed, mean acceleration]. Each component corresponds to a specific operation feature, and the vector as a whole reflects the feature distribution of the micro-motion.

[0074] S207. Perform correlation analysis on the micro-motion feature vectors at adjacent sampling times, and combine the change amount data obtained from the correlation analysis in time sequence to form an operation chain sequence.

[0075] Adjacent sampling times refer to two consecutive sampling time points. Correlation analysis refers to the comparative analysis of the feature vectors at consecutive time points. The change amount data represents the change values of the components of the feature vector. The operation chain sequence refers to a sequence composed of multiple change amount data in time order.

[0076] In the correlation analysis process, the feature vectors V(t) and V(t + 1) at each pair of adjacent times t and t + 1 are calculated. First, calculate the difference ΔV = V(t + 1) - V(t) of each component of the feature vector to obtain a difference vector reflecting the changes in position, pressure, and speed. Then calculate the Euclidean distance d = ||V(t + 1) - V(t)|| of the feature vector, which reflects the overall degree of change. At the same time, calculate the included angle θ = arccos((V(t) · V(t + 1)) / (||V(t)|| · ||V(t + 1)||)) of the feature vector, which reflects the direction of change. Combine the difference vector ΔV, the distance d, and the angle θ to form the change amount data [ΔV, d, θ]. Connect all the change amount data in time order to form an operation chain sequence describing the feature changes of the entire operation process.

[0077] S208. Cut the operation chain sequence into multiple time-sequence feature segments according to a preset time interval.

[0078] The preset time interval represents the fixed time length for dividing the operation chain sequence. The time-sequence feature segment refers to a sub-segment of the operation chain sequence obtained by dividing according to the time interval.

[0079] 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, and the start and end timestamps of the segment, the number of change amount data included, and the specific values of each change amount data are retained. 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.

[0080] 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.

[0081] 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.

[0082] 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 continuously 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 location for subsequent operation feature analysis.

[0083] 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.

[0084] 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.

[0085] The execution process of position mapping correction first obtains the actual contact coordinates (x, y) of repeated touch operations and the set of center coordinates of all interactive elements on the current interface {(xi, yi)}. Calculate the distance di from the contact point to each interactive element: di = √((x - xi)² + (y - yi)²). 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 a 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 relevant touch parameters, including the contact position, pressure value, and timestamp.

[0086] S211. Obtain the target interface switching sequence corresponding to the repeated touch operation, and merge the repeated touch operations that trigger the same interface switching into one effective operation.

[0087] The target interface switching sequence represents the sequence of interface state changes caused by touch operations, and an effective operation refers to an operation behavior that produces an actual effect in interface interaction.

[0088] The process of performing interface switching analysis on repeated touch operations is as follows: Record the interface state identifiers (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 consecutive touch operations with a time interval less than a preset third threshold, extract their corresponding interface switching sequences. When multiple repeated touch operations result in the same interface switching sequence (such as A → B → A → B), merge these operations into one effective operation, and retain the operation with the earliest time as the representative. Record the number of operations before merging and the number of effective operations after merging.

[0089] S212. Calculate the micro-action feature dispersion of the timing 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 effective operations to the number of original touch operations.

[0090] The corrected touch operation represents the operation after position mapping correction, the effective operation represents the operation retained after interface switching analysis, and the micro-action feature dispersion represents the dispersion degree of the operation feature distribution.

[0091] 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 timing feature segment is calculated as D = (1 - Rc)*w1 + (1 - Re)*w2, where w1 and w2 are weight coefficients and satisfy w1 + w2 = 1. The weights are set according to the influence degrees of the correction operations and the effective operations on the operation stability. Usually, w1 = 0.4 and w2 = 0.6. The value range of the dispersion D is [0, 1]. The larger the value, the more unstable the operation is, and more correction and merging processes are required.

[0092] S213. Establish an operation stability curve based on the dispersion. This operation stability curve includes the deviation degree of the finger contact position, the fluctuation amplitude of the pressure change, and the time interval of the operation response.

[0093] The dispersion is a numerical index quantifying the dispersion degree of the micro-motion features, and its value range is [0, 1]; the operation stability curve is a three-dimensional time series describing the change trend of the user operation features over time; the deviation degree is represented by the physical distance indicating the deviation between the contact point and the target; the fluctuation amplitude reflects the change range of the pressure value; the time interval measures the rhythm feature of the operation.

[0094] The operation stability curve is constructed using the sliding window method. The window size is set to 1 minute, and the sliding step is 10 seconds. For each window interval, three-dimensional feature values are extracted: the contact position deviation value is obtained by calculating the average Euclidean distance between the actual positions and the target position of all touch operations within the window, with the unit of pixel; the pressure fluctuation value is obtained by calculating the range (the maximum value minus the minimum value) of the pressure value sequence within the window, using the original pressure value unit of the device; the time interval value is obtained by calculating the average time difference between adjacent operations within the window, with the unit of millisecond. For each feature dimension, the original data is normalized: P'(t) = (P(t) - Pmin) / (Pmax - Pmin), where Pmin and Pmax are the minimum and maximum values of the historical data respectively, and F'(t) and T'(t) are normalized using the same method. Finally, the operation stability curve in the form of a standardized three-dimensional time series [P'(t), F'(t), T'(t)] is obtained. To ensure the smoothness of the curve, the exponential moving average method is used to smooth the original sequence, and the smoothing coefficient α = 0.3. Each dimension of the curve contains independent stability information: P'(t) reflects the spatial accuracy, F'(t) reflects the force control ability, and T'(t) reflects the operation coherence.

[0095] S214. Calculate the deviation degrees of the operation stability curve from the user's historical reference values within the preset first time window and the preset second time window respectively.

[0096] 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 the standard feature curve statistically obtained based on the user's operation data in the past 7 days. The deviation quantifies the degree of difference between the current operation feature and the historical benchmark.

[0097] The deviation calculation adopts a hierarchical comparison method. First, calculate the root mean square error of each dimension within the short-term window W1: the root mean square error of position deviation RMSEP = sqrt(∑(P'(t) - Pb'(t))² / n), where P'(t) is the current curve value, Pb'(t) is the benchmark curve value, and n is the number of sampling points. Similarly, calculate the pressure deviation RMSEF and the time deviation RMSET. The root mean square errors of the three dimensions are weighted and combined to obtain the comprehensive deviation D1 = wpRMSEP + wfRMSEF + wt * RMSET, and the weight coefficients are determined by the principal component analysis method, wp = 0.4, wf = 0.35, wt = 0.25. The same method is used to calculate D2 for the long-term window W2. This dual-time-scale analysis method can capture both the short-term fluctuations and long-term trend changes of the operation features. The calculation results of the deviation are used for subsequent anomaly judgment and permission adjustment.

[0098] S215. Feature code the deviation degree 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. When the similarity is lower than the similarity threshold, mark the abnormal operation type of the target user and adjust the data operation permission to the first preset level.

[0099] 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 the user's normal operation. The similarity is used to quantitatively evaluate the feature matching degree. The abnormal operation types include position deviation anomaly, pressure control anomaly, and rhythm imbalance anomaly. The first preset level is the strictest permission control level.

[0100] 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 intervals 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 type of anomaly is determined by analyzing the differences in each part of the encoding. Once an anomaly 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.

[0101] 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.

[0102] The preset interval threshold represents the time standard for judging operation interruption, and is generally set to 3 times the normal operation interval; the micro-action features include the feature vectors of parameters such as 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 most restrictive level.

[0103] 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 lower 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 recovery judgment.

[0104] 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 between the operation subject characteristics and the preset feature template is lower than the matching threshold, raise the interactive verification permission requirement to the third preset level.

[0105] The pressure control eigenvalue is a quantitative index characterizing the user's pressure control ability; the operation subject characteristics refer to the user's unique operation habit characteristics; the preset feature template stores the standard operation characteristics when the user passes the verification; the matching threshold defines the minimum requirement for feature matching; the third preset level is the most stringent identity verification level.

[0106] 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 characteristics [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 characteristics 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 feature 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), raise the interactive verification permission to the third preset level: require biometric authentication, shorten the session validity period, and increase the verification frequency.

[0107] 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, restore the data operation permission, function usage permission, and interaction verification permission to the original level. The age-friendly data access permission includes the data operation permission, function usage permission, and interaction verification permission of the target user for the terminal device.

[0108] Continuously exceeding means continuously meeting the conditions within 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 settings; the age-friendly data access permission is a permission management system that includes multiple dimensions.

[0109] The permission restoration process adopts a multi-condition joint judgment mechanism. First, monitor the similarity S(t) of the operation feature code, and it is required to satisfy min(S(t)) > ST within the time window W (usually set to 30 minutes), where ST is the similarity threshold. At the same time, monitor the fluctuation range of the pressure control feature value PCV(t), and it is required that max(|PCV(t) - μPCV|) < 2σPCV, where μPCV and σPCV are the mean and standard deviation of the historical data respectively. When these two conditions are simultaneously satisfied and the duration exceeds the preset observation period (such as 15 minutes), gradually restore the permission settings: first, restore the data viewing permission, and restore the data modification permission after observing for 5 minutes without abnormalities; then restore the basic function usage permission, and restore all function permissions after observing for 10 minutes without abnormalities; finally, reduce the authentication frequency to the standard interaction verification requirements. The entire restoration process adopts a progressive strategy to ensure operation safety.

[0110] Next, a more specific process description of the method provided in this embodiment will be given. Please refer to Figure 3 , which is another process schematic diagram of the age-friendly data permission management method in the embodiment of the present application.

[0111] Next, the age-friendly data permission management system in the embodiment of the present invention application will be described from the perspective of hardware processing. Please refer to Figure 3 , which is a schematic structural diagram of an entity device of the age-friendly data permission management system in the embodiment of the present application.

[0112] It should be noted that Figure 3 The structure of the age-friendly data permission management system shown is only an example and should not bring any limitations to the functions and usage scope of the embodiments of the present invention.

[0113] S301. Collect the attitude sensor data of the terminal device. The attitude sensor data includes acceleration data, angular velocity data, and spatial orientation data.

[0114] Attitude sensor data refers to the raw data collected by the motion sensor built into the terminal device; acceleration data represents the acceleration change of the device in three directions, in units of m / s²; angular velocity data represents the rotation speed of the device around three axes, in units of rad / s; spatial orientation data represents the orientation angle of the device relative to the earth's coordinate system, including pitch angle, roll angle and heading angle.

[0115] The attitude sensor data is collected through the IMU (inertial measurement unit) of the device. The acceleration data is collected using a three-axis acceleration sensor, with a sampling frequency set to 100Hz, a measurement range of ±16g, and each sampling point contains three components [ax, ay, az]. The angular velocity data is collected using a three-axis gyroscope, with a sampling frequency of 200Hz, a measurement range of ±2000° / s, and records the angular velocity values of the three axes [wx, wy, wz]. The spatial orientation data is obtained by combining a magnetometer with an accelerometer, with a sampling frequency of 50Hz, and records the three Euler angles [pitch, roll, yaw]. All sensor data is Kalman filtered to remove noise and temperature compensation calibration is performed. The timestamp of each sampling point is recorded during the data collection process to facilitate subsequent alignment with the operation data. The system establishes a sensor data cache queue to update and save the data of the last 30 seconds in real time.

[0116] S302: align the gesture sensor data with the operation chain sequence in time sequence, and extract the device shaking frequency, tilt angle, and spatial displacement.

[0117] Timing alignment refers to unifying data with different sampling frequencies to the same time base; the device shaking frequency represents the periodic characteristics of the device 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.

[0118] The timing alignment process first resamples all sensor data to a uniform sampling rate of 100Hz. A time window (width of 1 second, step size of 0.1 second) is established, and features are extracted from the data in each window: the device shaking frequency is obtained by fast Fourier transforming the acceleration data, and the first three frequency components with the largest energy [f1, f2, f3] are extracted; the tilt angle θ is calculated by the acceleration component: θ=arccos(az / sqrt(ax²+ay²+az²)); the spatial displacement is obtained by double integration of the acceleration data, and the zero-speed update algorithm is used to eliminate integral drift. The extracted features are aligned with the operation chain sequence by timestamp to form a feature-operation correspondence table. The sliding average of the features is calculated to smooth instantaneous fluctuations.

[0119] S303. Obtain the correspondence between the device shaking frequency and the offset degree of the finger contact position, and identify the holding habit type of the target user based on the correspondence. The holding habit type includes single-handed holding, two-handed holding, and supported holding.

[0120] The correspondence refers to the correlation between the physical state of the device and the operation characteristics; the holding habit type represents different holding methods of the user using the device; single-handed holding means holding and operating with one hand; two-handed holding means operating with the cooperation of both hands; supported holding means supporting the device on an object such as a desktop for operation.

[0121] The holding habit recognition adopts the feature correlation analysis method. First, calculate the cross-correlation coefficient matrix R of the device shaking frequency feature vector F = [f1, f2, f3] and the contact position offset vector P = [dx, dy]. Perform singular value decomposition on R to extract the main feature patterns. Match the feature patterns with the pre-calibrated holding type templates: the single-handed holding feature is high-frequency shaking (>2 Hz) and the offset is concentrated on one side; the two-handed holding feature is low-frequency shaking (<1 Hz) and the offset is evenly distributed; the supported holding feature is extremely low-frequency shaking (<0.5 Hz) and the offset range is small. Use the maximum likelihood estimation method to select the most matching holding type. The system records the recognition result and its confidence level for subsequent interaction adaptation. When the confidence level is lower than the threshold, keep the current recognition result unchanged to avoid interaction instability caused by frequent switching.

[0122] S304. Divide the corresponding operation area range and the maximum fault tolerance time for a single operation according to the holding habit type.

[0123] The operation area range refers to the area range on the screen that is easy to operate based on the holding method; the maximum fault tolerance time represents 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.

[0124] The operation area is divided by grid analysis. For the one-handed grip type, the screen is divided into 6×8 grids, each with a size of 120×120 pixels. The thumb reachable area is determined by analyzing the natural range of motion of the holding palm: a fan-shaped area is drawn with the grip point as the center and the maximum extension length of the thumb as the radius R (usually 70% of the screen width). In this area, the grids are marked into three levels according to the characteristics of thumb joint activity: the best operation area (within 0.3R of the grip point, with a tolerance time of 2000ms), the moderate operation area (within 0.3R-0.6R, with a tolerance time of 1500ms), and the reluctant operation area (within 0.6R-1.0R, with a tolerance time of 1000ms). For the two-handed grip type, 3×8 grids are divided on both sides of the screen as the main operation area, and the middle 4×8 grids are used as the collaboration area. The tolerance time is uniformly set to 1800ms. For supported holding, the entire screen is divided into a uniform 8×8 grid, and the error tolerance time of all areas is set to 2500ms.

[0125] 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.

[0126] Deviation operation refers to the interactive behavior in which the finger touch point falls outside the expected operation area; detection refers to the process of real-time monitoring and determining the touch point position.

[0127] Deviation operation detection uses a real-time boundary determination algorithm. The system maintains the boundary data structure of the operation area corresponding to the current holding type: one-handed holding uses the polar coordinate system (r, θ) to describe the boundary, r is the maximum reachable distance of the thumb, and θ is the range of the activity angle of the holding palm; two-handed holding uses the rectangular boundary set {[x1, y1, x2, y2]} to describe the main operation area on both sides and the middle collaboration area; supported holding uses the availability mark 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 holding, the distance d and angle α between the touch point and the holding point are calculated. When d>r or α exceeds the preset angle range, it is determined to be a deviation. For two-handed holding, check whether the touch point falls within any rectangular boundary. For supported holding, check the availability mark of the grid where the touch point is located. The judgment result records the touch point coordinates, timestamp, and deviation type (distance deviation / angle deviation / area deviation).

[0128] S306. Count the number of deviation operations within a unit time, and when the number of deviation operations exceeds a preset threshold, migrate the high-frequency use function to the optimal operation area corresponding to the holding habit type.

[0129] Unit time refers to the length of the time window for counting deviation operations; the number of deviation operations represents the number of deviations that occur within the time window; the preset number threshold defines the deviation number standard for triggering function migration; frequently used functions refer to the interface elements that users often access; the optimal operation area refers to the screen area that is most easily operable under a specific holding method.

[0130] 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, record its deviation count count and total access times total within the window, and calculate the deviation rate ratio = count / total. When count exceeds the preset threshold N (such as 10 times) and ratio exceeds 0.3, trigger function migration. When migrating, first identify the optimal operation area for the current holding type: the fan-shaped area within 0.3R from the holding point for single-handed holding, the central part of the two main operation areas for two-handed holding, and the central area of the screen for supported holding. Search for available space within the optimal area, and use the quadratic fit first algorithm to allocate a new position for the function to be migrated. After migration, fine-tune the positions of the surrounding function buttons to maintain the overall balance of the interface layout. The system records the migration history and establishes the correspondence between function positions and holding methods for subsequent layout optimization.

[0131] S307. When it is detected that the terminal device is in an unstable holding state, mark the current operation as a suspicious operation.

[0132] An unstable holding state refers to a state where the device's posture changes violently or deviates from the normal holding range; a suspicious operation refers to an interaction behavior that does not conform to the normal operation mode under an unstable state; a terminal device refers to a mobile device such as a smartphone or tablet used by the user.

[0133] The detection of the unstable holding state is based on real-time sensor data analysis. The system obtains the three-axis acceleration [ax, ay, az] and angular velocity [wx, wy, wz] data from the IMU, and sets the sampling frequency to 200Hz. Calculate the combined acceleration a = sqrt(ax² + ay² + az²) and the combined angular velocity w = sqrt(wx² + wy² + wz²). Set two determination thresholds: the acceleration threshold Ta = 1.5g (g is the acceleration due to gravity), and the angular velocity threshold Tw = 50° / s. When a > Ta or w > Tw lasts for more than 100ms, trigger the posture anomaly mark. At the same time, calculate the device tilt angle θ = arccos(az / a), and when θ exceeds the normal holding angle range [-30°, 60°], trigger the angle anomaly mark. Combining the two anomaly marks, if any one of the marks is triggered, it is determined as an unstable holding state. After detecting the unstable state, record the current timestamp and the anomaly type (posture anomaly / angle anomaly), and mark all operations within 500ms before and after this time point as suspicious operations.

[0134] 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.

[0135] The preset time period refers to the time range for counting suspicious operations; the preset number threshold represents the number of suspicious operations that trigger security measures; the identity re-authentication process refers to the security procedure for verifying the user's identity; sensitive data refers to important information that requires special protection.

[0136] 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 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 is gradually lifted, and the viewing permission is restored first. After observing for 5 minutes without abnormalities, the full access right is restored.

[0137] S309: Acquire the heart rate data and blood pressure data of the target user, and generate a physiological characteristic data set of the target user.

[0138] Heart rate data refers to changes in the user's heart rate; blood pressure data refers to the measured values of systolic and diastolic blood pressure; and the physiological characteristic data set is a multidimensional data set that describes the user's physical state.

[0139] Physiological characteristic data collection uses built-in or external biosensors of 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 original 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 through the 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.

[0140] S310. When the heart rate data in the physiological characteristic dataset exceeds the preset heart rate range or the blood pressure data exceeds the preset blood pressure range, obtain the acceleration data and angular velocity data of the terminal device.

[0141] The preset heart rate range refers to the standard interval of the normal human heart rate. For adults at rest, it is 50 - 100 beats per minute; the preset blood pressure range refers to the normal value range of blood pressure. For adults, the systolic blood pressure is 90 - 140 mmHg, and the diastolic blood pressure is 60 - 90 mmHg; the acceleration data represents the quantization value of the acceleration change of the device in three-dimensional space; the angular velocity data represents the change value of the rotation speed of the device around three spatial axes.

[0142] The physiological characteristic monitoring system performs real-time data collection and analysis. The heart rate data is collected by a PPG sensor, and the sampling frequency is set to 100 Hz. The original signal is filtered by a 50 Hz low-pass filter to remove power frequency interference, and then filtered by a 0.5 Hz high-pass filter to eliminate baseline drift. The heart rate value HR is calculated once per second, and at the same time, the average value HRavg and standard deviation HRstd of the last 60 seconds are calculated. When |HR - HRavg| > θHR (θHR is set to 15 beats per minute), it is marked as abnormal heart rate. The blood pressure data is collected once every 60 seconds, recording the systolic blood pressure SP and diastolic blood pressure DP, and calculating the average values SPavg, DPavg and standard deviations SPstd, DPstd of the last 5 measurements. When |SP - SPavg| > θSP (θSP is set to 20 mmHg) or |DP - DPavg| > θDP (θDP is set to 15 mmHg), it is marked as abnormal blood pressure. After detecting abnormal physiological indicators, immediately start IMU data collection: the acceleration sensor sampling frequency is 200 Hz, and the range is ±16 g; the angular velocity sensor sampling frequency is 200 Hz, and the range is ±2000° / s. The IMU data is preprocessed using Kalman filtering to filter out high-frequency noise, and the processed data of the last 10 seconds is retained for subsequent analysis.

[0143] S311. Determine whether the terminal device is in a stable state according to the acceleration data and angular velocity data.

[0144] The stable state refers to the state where the device remains stationary or has only slight movement; the judgment process refers to the calculation method of determining the motion state of the device by analyzing the sensor data.

[0145] The device stability judgment adopts a multi - feature fusion analysis method. First, calculate the combined value of three - axis acceleration \(a = \sqrt{a_x^2 + a_y^2 + a_z^2}\) and the combined value of three - axis angular velocity \(w=\sqrt{w_x^2 + w_y^2 + w_z^2}\). Use a 1 - second time window (200 sampling points) to calculate statistical features: the mean value of acceleration \(\mu_a\) and the standard deviation \(\sigma_a\), the mean value of angular velocity \(\mu_w\) and the standard deviation \(\sigma_w\). Set the stable - state judgment conditions: the difference between the acceleration and the gravitational acceleration \(|\mu_a - g|\lt0.1g\) and \(\sigma_a\lt0.1g\); the absolute value of the angular velocity \(|\mu_w|\lt5° / s\) and \(\sigma_w\lt2° / s\). Use a 60 - point sliding window for continuous judgment. When 10 consecutive windows meet the judgment conditions, confirm that the device is in a stable state. Use a state machine to manage the stability judgment results, set a 100 - ms state - switching delay to avoid state jitter caused by instantaneous disturbances. Record the start time, duration, and statistical features of the stable state for subsequent abnormal - state analysis.

[0146] S312. When the terminal device is in a stable state and the physiological - characteristic data set continuously exceeds the preset range for the preset duration, send warning data including the physiological - characteristic data set and location information to the preset communication terminal, and adjust the deviation - threshold value of the target user's access operations to emergency - contact information and medical information to the preset minimum value.

[0147] 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 warning information; the warning data refers to the data packet containing the detailed information of the abnormal situation; the deviation - threshold value refers to the maximum deviation range allowed for the operation behavior; the preset minimum value refers to the lowest deviation - setting supported by the system.

[0148] The warning system adopts a three - level response mechanism. The first level is the abnormal - state confirmation: when the physiological - characteristic data continuously exceeds the preset range and the device remains in a stable state, start a duration timer (preset to 180 seconds). The second level is the warning - data generation: construct a structured data packet containing the abnormal type (heart rate / blood pressure), physiological - data set (current value, historical statistics, change trend), device state (battery level, signal strength), and location information (GPS coordinates, positioning accuracy). The third level is the emergency response: send the warning data to the preset communication terminal through the preset communication channel (4G network / WIFI); at the same time, adjust the system configuration, and reduce the deviation - threshold value of the access operations to emergency - contact information and medical information from the default value of 1.0 to the preset minimum value of 0.2. Continuously monitor the physiological indicators. When all indicators return to normal and remain so for 30 minutes, the system automatically restores the original permission settings. Record the entire event process, including the abnormal - trigger time, warning - sending status, implementation of response measures, and recovery - process data, and generate an event report for archiving.

[0149] Such as Figure 4As shown, the age-friendly data permission management system includes a Central Processing Unit (CPU) 401, which can perform various appropriate actions and processes according to the program stored in the Read-Only Memory (ROM) 402 or the program loaded from the storage section 408 into the Random Access Memory (RAM) 403, such as executing the method described in the above embodiments. In the RAM 403, various programs and data required for system operation are also stored. The CPU 401, ROM 402, and RAM 403 are connected to each other via a bus 404. An Input / Output (I / O) interface 405 is also connected to the bus 404.

[0150] The following components are connected to the I / O interface 405: an input section 406 including an audio input device, a button switch, etc.; an output section 407 including a Liquid Crystal Display (LCD), an audio output device, an indicator light, etc.; a storage section 408 including a hard disk, etc.; and a communication section 409 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. 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. A removable medium 411, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 310 as needed so that a computer program read from it can be installed into the storage section 308 as needed.

[0151] Specifically, according to an embodiment of the present invention, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present invention includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program contains a computer program for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network via the communication section 309, and / or installed from the removable medium 311. When the computer program is executed by the Central Processing Unit (CPU) 301, various functions defined in the present invention are executed.

[0152] It should be noted that specific examples of computer-readable storage media may include, but are not limited to: electrical connections with one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fibers, portable compact disc read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the above. In the present invention, a computer-readable storage medium can be any tangible medium that contains or stores a program, and this program can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0153] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of the present invention. Among them, each block in the flowchart or block diagram may represent a module, a program segment, or a part of code, and the above-mentioned module, program segment, or part of code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order from that marked in the accompanying drawings.

[0154] Specifically, the age-friendly data permission management system of this embodiment includes a processor and a memory. A computer program is stored on the memory. When the computer program is executed by the processor, it implements the age-friendly data permission management method provided in the above embodiment.

[0155] On the other hand, the present invention also provides a computer-readable storage medium. This storage medium can be included in the age-friendly data permission management system described in the above embodiment; or it can exist separately and not be assembled into the age-friendly data permission management system. The above storage medium carries one or more computer programs. When the above one or more computer programs are executed by a processor of the age-friendly data permission management system, the age-friendly data permission management system is enabled to implement the age-friendly data permission management method provided in the above embodiment.

[0156] As mentioned 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 foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and 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 various embodiments of the present application.

[0157] As used in the foregoing embodiments, depending on the context, the term "when" may be construed to mean "if" or "after" or "in response to determining" or "in response to detecting". Similarly, depending on the context, the phrase "upon determining" or "if (the stated condition or event) is detected" may be construed to mean "if determined" or "in response to determining" or "when (the stated condition or event) is detected" or "in response to detecting (the stated condition or event)".

[0158] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the foregoing embodiments can be implemented. The processes can be completed by relevant hardware instructed by a computer program, and the program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the foregoing method embodiments. The foregoing storage media include: various media such as ROM or random access memory RAM, magnetic disks, or optical discs that can store program codes.

Claims

1. An aging-friendly data permission management method, characterized in that, Applied to an aging-friendly data permission management system, the method includes: Collecting operation trajectory data of a target user during aging-friendly data interaction on a terminal device, where the operation trajectory data includes contact position, contact pressure, sliding speed, and operation timing; Performing feature extraction on the operation trajectory data according to the operation timing, constructing a micro-action feature vector based on the contact position coordinates, contact pressure values, and sliding speed values at each sampling moment obtained, performing correlation analysis on the micro-action feature vectors at adjacent sampling moments, and combining the change amount data obtained from the correlation analysis in sequence to form an operation chain sequence; Segmenting the operation chain sequence at a preset time interval, calculating the discreteness of the micro-action features in each segment sequence, and establishing an operation stability curve based on the discreteness. The operation stability curve includes the deviation degree of the finger contact position, the fluctuation amplitude of the pressure change, and the time interval of the operation response; Calculating the deviation degrees of the operation stability curve from the user's historical reference values within a preset first time window and a preset second time window respectively; When the deviation degree within the preset first time window exceeds a first preset threshold and the deviation degree within the preset second time window exceeds a second preset threshold, adjusting the aging-friendly data access permissions of the target user. The aging-friendly data access permissions include the target user's data operation permissions, function usage permissions, and interaction verification permissions for the terminal device.

2. The method according to claim 1, wherein The step of performing feature extraction on the operation trajectory data according to the operation timing and constructing a micro-action feature vector based on the contact position coordinates, contact pressure values, and sliding speed values at each sampling moment obtained specifically includes: Obtaining the positional relationship between the contact position coordinates and the position of the function keys on the current interface, and determining the touch tolerance range around the contact position coordinates according to the positional relationship; Calculating the deviation degree of the contact position coordinates within the touch tolerance range; Constructing a pressure decay curve based on the contact pressure value, and calculating the touch force application stability according to the slope of the pressure decay curve; Calculating the speed fluctuation feature of the sliding operation according to the sliding speed value; Combining the deviation degree, the touch force application stability, and the speed fluctuation feature to form the micro-action feature vector.

3. The method according to claim 1, wherein The step of segmenting the operation chain sequence at a preset time interval and calculating the discreteness of the micro-action features in each segment sequence specifically includes: Segmenting the operation chain sequence into multiple time-series feature segments at a preset time interval; Obtaining 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; Obtaining 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, performing position mapping correction to obtain a corrected touch operation; Obtaining the target interface switching sequence corresponding to the repeated touch operation, and combining the repeated touch operations that trigger the same interface switching into one effective operation; Calculate the micro - motion feature dispersion of the timing feature segment according to the ratio of the number of operations for correcting the 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.

4. The method according to claim 1, wherein When the deviation within the preset first time window exceeds the first preset threshold and the deviation within the preset second time window exceeds the second preset threshold, adjust the age - friendly data access rights of the target user. The steps for the age - friendly data access rights, which include the data operation rights, function usage rights, and interaction verification rights of the target user for the terminal device, specifically include: Perform feature encoding on the offset degree 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. When the similarity is lower than the similarity threshold, mark the abnormal operation type of the target user and adjust the data operation rights to the first preset level. Obtain the time points in the time interval of the operation response that are greater than the preset interval threshold. Denote the micro - motion features before the time point as the starting action and the micro - motion 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 limit the function usage rights of the target user to the second preset level according to the operation hesitation state. Calculate the pressure control feature value based on the fluctuation amplitude of the pressure change in the operation stability curve. Judge the operation subject feature of the target user 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, increase the requirement for the interaction verification rights 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, restore the data operation rights, function usage rights, and interaction verification rights to the original levels.

5. The method according to claim 1, wherein After the step of adjusting the age - friendly data access rights of the target user when the deviation within the preset first time window exceeds the first preset threshold and the deviation within the preset second time window exceeds the second preset threshold, the method further includes: Collect the attitude sensor data of the terminal device, where the attitude sensor data includes acceleration data, angular velocity data, and spatial orientation data. Align the attitude sensor data and the operation chain sequence in time series, and extract the device shaking frequency, tilt angle, and spatial displacement. Analyze the correlation between the device shaking frequency and the offset degree 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, mark 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 the preset number threshold, trigger the identity re - authentication process and freeze the access rights to the sensitive data of the target user.

6. The method according to claim 5, wherein The step of analyzing the correlation between the shaking frequency of the device and the deviation degree of the finger contact position in the operation stability curve to determine whether the terminal device is in a stable holding state specifically includes: Obtain the corresponding relationship between the shaking frequency of the device and the deviation degree of the finger contact position, and identify the holding habit type of the target user based on the corresponding relationship. The holding habit type includes single-handed holding, two-handed holding, and supported holding; Divide the corresponding operation area range and the maximum error tolerance time for 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 as an off-operation; Count the number of off-operations within a unit time. When the number of off-operations exceeds a preset number threshold, migrate the frequently used functions to the optimal operation area corresponding to the holding habit type.

7. The method according to claim 1, wherein After the step of adjusting the access permission for the target user's age-friendly data when the deviation degree within the preset first time window exceeds the first preset threshold and the deviation degree within the preset second time window exceeds the second preset threshold, the method further includes: Obtain the heart rate data and blood pressure data of the target user, and generate a physiological characteristic data set of the target user; When the heart rate data in the physiological characteristic data set exceeds the preset heart rate range or the blood pressure data exceeds the preset blood pressure range, obtain the acceleration data and angular velocity data of the terminal device; Judge 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 continuously exceeds the preset range for a preset continuous duration, send warning data including the physiological characteristic data set and location information to a preset communication terminal, and adjust the deviation degree threshold for the target user's access operations to emergency contact information and medical information to a preset minimum value.

8. An aging-friendly data permission management system, characterized in that, The age-friendly data permission 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 age-friendly data permission management system to execute the method according to any one of claims 1-7.

9. A computer-readable storage medium, comprising instructions, characterized in that, When the instruction runs on the age-friendly data permission management system, it enables the age-friendly data permission management system to execute the method according to any one of claims 1-7.

10. A computer program product, characterized in that, When the computer program product runs on the age-friendly data permission management system, it enables the age-friendly data permission management system to execute the method according to any one of claims 1-7.

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