Physiological sign data monitoring method and system based on brain health

Abstract

The invention relates to the technical field of data processing, and provides a physiological sign data monitoring method and system based on brain health. Obtaining dietary data and physiological data of the user based on a preset composite sensor architecture; extracting a first feature representing a physiological short-term fluctuation rule and a second feature representing a physiological long-term fluctuation rule from the physiological data, and determining an influence factor of diet on the physiological state of the user in combination with diet data; obtaining current dietary information and target physiological data, and determining dietary adjustment information in combination with the influence factors; and in the process of applying the dietary adjustment information, monitoring the physiological data through the composite sensor architecture until the target physiological data is reached. By constructing a composite sensor architecture, short-term and long-term physiological features are extracted from data, full-chain health management from data acquisition and analysis to personalized intervention is realized, and the accuracy, personalization and effectiveness of user physiological sign monitoring are remarkably improved.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a method and system for monitoring physiological sign data based on brain health. Background Art

[0002] With improved living standards and a growing understanding of health, people are using their brains more intensively, and their interest in brain health, healthy diet, and physical well-being is growing. Methods based on monitoring physiological signs of brain health can provide personalized nutritional recommendations tailored to the individual's physiological state. For example, tailored diet plans can be developed for patients with brain diseases or those who use their brains more intensively.

[0003] In practice, different people have different ways of absorbing, utilizing, and metabolizing nutrients. This requires that physiological sign monitoring methods account for individual differences in their practical application. Furthermore, these methods require long-term, continuous monitoring data. Consequently, current physiological sign data monitoring methods face challenges with both low monitoring efficiency and a low degree of personalization. Summary of the Invention

[0004] The present application provides a method and system for monitoring physiological sign data based on brain health, which can at least to some extent solve the problems of low monitoring efficiency and personalization faced by physiological sign data monitoring methods.

[0005] Other features and advantages of the present application will become apparent from the following detailed description, or may be learned in part by practice of the present application.

[0006] According to one aspect of the present application, a method for monitoring physiological sign data based on brain health is provided, including: obtaining a user's dietary data and physiological data based on a preset composite sensor architecture; extracting a first feature representing a short-term physiological fluctuation pattern and a second feature representing a long-term physiological fluctuation pattern based on the physiological data; determining, based on the first feature and the second feature, in combination with the dietary data, an influence factor of the diet corresponding to the dietary data on the user's physiological state; obtaining current dietary information and target physiological data, and determining dietary adjustment information in combination with the influencing factor; and in the process of applying the dietary adjustment information, monitoring the physiological data through the composite sensor architecture until the target physiological data is reached.

[0007] In the present application, based on the aforementioned scheme, the preset composite sensor architecture, before obtaining the user's dietary data and physiological data, also includes: constructing a plasma metasurface design, constructing a gold nanocolumn array on a flexible polydimethylsiloxane substrate, and attaching aptamers to the surface for detecting the concentration of antioxidant molecules in sweat; constructing a capillary force-driven microfluidic network, combined with an anti-evaporation coating composed of fluorinated polymers, to detect the molecular concentration of antioxidant molecules in tissue fluid; based on a video monitoring network, collecting the user's dietary data; and constructing a composite sensor architecture based on the plasma metasurface design, the capillary force-driven microfluidic network and the video monitoring network.

[0008] In the present application, based on the aforementioned scheme, the first feature characterizing the short-term physiological fluctuation law and the second feature characterizing the long-term physiological fluctuation law are extracted from the physiological data, including: based on a preset window length, the physiological data is windowed to generate a physiological data sequence; high-frequency components are extracted from the physiological data sequence, and the information intensity corresponding to the physiological data sequence is determined based on the high-frequency components as the first feature characterizing the short-term physiological fluctuation law; trend items and fluctuation items are extracted from the physiological data sequence as the second feature characterizing the long-term physiological fluctuation law.

[0009] In the present application, based on the aforementioned scheme, the first feature and the second feature are combined with the dietary data to determine the influence factor of the diet corresponding to the dietary data on the physiological state of the user, including: performing timestamp matching on the first feature, the second feature and the dietary data to determine the data feature group; analyzing the data feature group through multiple linear regression to determine the influence factor of the diet corresponding to the dietary data on the physiological state of the user.

[0010] In the present application, based on the aforementioned scheme, the current dietary information and target physiological data are obtained, combined with the influencing factors, to determine the dietary adjustment information, including: obtaining the user's current dietary information and target physiological data; based on the current dietary information, the target physiological data and the influencing factors, performing linear programming to determine the recipe elements and eating methods as the dietary adjustment information.

[0011] In the present application, based on the aforementioned scheme, in the process of applying the dietary adjustment information, the physiological data is monitored through the composite sensor architecture until the target physiological data is reached, including: sending the dietary adjustment information to the user terminal to remind the user to adjust the diet; monitoring the physiological data through the composite sensor architecture to capture the nonlinear absorption, metabolism and clearance process of antioxidants in the body, and dynamically associating the metabolic kinetics of dietary components with the physiological antioxidant response according to the monitoring situation to update the dietary adjustment information in real time until the target physiological data is reached.

[0012] In the present application, based on the aforementioned scheme, after obtaining the user's dietary data and physiological data, it also includes: generating statistical graphs of the dietary data and the physiological data; transmitting the dietary data, the physiological data and the statistical graphs to the control terminal, and displaying them in the interface of the control terminal.

[0013] According to one aspect of the present application, a system for monitoring physiological sign data of brain health is provided, comprising:

[0014] an acquisition unit, configured to acquire dietary data and physiological data of a user based on a preset composite sensor architecture;

[0015] a feature unit for extracting, based on the physiological data, a first feature representing a short-term physiological fluctuation pattern and a second feature representing a long-term physiological fluctuation pattern;

[0016] an associating unit, configured to determine, based on the first feature and the second feature and in combination with the dietary data, an influence factor of the diet corresponding to the dietary data on the physiological state of the user;

[0017] A diet unit is used to obtain current diet information and target physiological data, and determine diet adjustment information in combination with the influencing factors;

[0018] A monitoring unit is configured to monitor the physiological data through the composite sensor architecture during the application of the dietary adjustment information until target physiological data is reached.

[0019] In the present application, based on the aforementioned scheme, the preset composite sensor architecture, before obtaining the user's dietary data and physiological data, also includes: constructing a plasma metasurface design, constructing a gold nanocolumn array on a flexible polydimethylsiloxane substrate, and attaching aptamers to the surface for detecting the concentration of antioxidant molecules in sweat; constructing a capillary force-driven microfluidic network, combined with an anti-evaporation coating composed of fluorinated polymers, to detect the molecular concentration of antioxidant molecules in tissue fluid; based on a video monitoring network, collecting the user's dietary data; and constructing a composite sensor architecture based on the plasma metasurface design, the capillary force-driven microfluidic network and the video monitoring network.

[0020] In the present application, based on the aforementioned scheme, the first feature characterizing the short-term physiological fluctuation law and the second feature characterizing the long-term physiological fluctuation law are extracted from the physiological data, including: based on a preset window length, the physiological data is windowed to generate a physiological data sequence; high-frequency components are extracted from the physiological data sequence, and the information intensity corresponding to the physiological data sequence is determined based on the high-frequency components as the first feature characterizing the short-term physiological fluctuation law; trend items and fluctuation items are extracted from the physiological data sequence as the second feature characterizing the long-term physiological fluctuation law.

[0021] In the present application, based on the aforementioned scheme, the first feature and the second feature are combined with the dietary data to determine the influence factor of the diet corresponding to the dietary data on the physiological state of the user, including: performing timestamp matching on the first feature, the second feature and the dietary data to determine the data feature group; analyzing the data feature group through multiple linear regression to determine the influence factor of the diet corresponding to the dietary data on the physiological state of the user.

[0022] In the present application, based on the aforementioned scheme, the current dietary information and target physiological data are obtained, combined with the influencing factors, to determine the dietary adjustment information, including: obtaining the user's current dietary information and target physiological data; based on the current dietary information, the target physiological data and the influencing factors, performing linear programming to determine the recipe elements and eating methods as the dietary adjustment information.

[0023] In the present application, based on the aforementioned scheme, in the process of applying the dietary adjustment information, the physiological data is monitored through the composite sensor architecture until the target physiological data is reached, including: sending the dietary adjustment information to the user terminal to remind the user to adjust the diet; monitoring the physiological data through the composite sensor architecture to capture the nonlinear absorption, metabolism and clearance process of antioxidants in the body, and dynamically associating the metabolic kinetics of dietary components with the physiological antioxidant response according to the monitoring situation to update the dietary adjustment information in real time until the target physiological data is reached.

[0024] In the present application, based on the aforementioned scheme, after obtaining the user's dietary data and physiological data, it also includes: generating statistical graphs of the dietary data and the physiological data; transmitting the dietary data, the physiological data and the statistical graphs to the control terminal, and displaying them in the interface of the control terminal.

[0025] According to one aspect of the present application, a computer-readable medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the method for monitoring physiological sign data based on brain health as described in the above embodiments is implemented.

[0026] According to one aspect of the present application, an electronic device is provided, comprising: one or more processors; a storage device for storing one or more programs, which, when executed by the one or more processors, enables the one or more processors to implement the method for monitoring physiological sign data based on brain health as described in the above embodiments.

[0027] According to one aspect of the present application, a computer program product or computer program is provided, comprising computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the method for monitoring brain health-based physiological sign data provided in the various optional implementations described above.

[0028] In the technical solution of the present application, by constructing a composite sensor architecture, short-term and long-term physiological characteristics are extracted from physiological data, and combined with dietary data, the influencing factors of diet on physiological state are scientifically determined; the metabolic kinetics of dietary components are dynamically correlated with the physiological antioxidant response, and the nonlinear absorption, metabolism and clearance process of antioxidants in the body is captured. Based on these influencing factors and target physiological indicators, personalized dietary adjustment plans are intelligently generated; at the same time, physiological data are continuously monitored, and dietary recommendations are dynamically adjusted until the user's brain health goals are achieved, and key information and adjustment effects are intuitively presented to the user through visualization, realizing the full-chain management of brain health from data collection and analysis to personalized intervention, and significantly improving the accuracy, personalization and effectiveness of user physiological sign monitoring based on brain health.

[0029] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] The accompanying drawings are incorporated into and constitute a part of the specification, illustrate embodiments consistent with the present application, and together with the specification, are used to explain the principles of the present application. Obviously, the drawings described below are only some embodiments of the present application, and those skilled in the art can derive other drawings based on these drawings without inventive effort.

[0031] Figure 1 The flowchart of a method for monitoring physiological sign data of brain health in one embodiment of the present application is schematically shown.

[0032] Figure 2 The flowchart of extracting features from physiological data in one embodiment of the present application is schematically shown.

[0033] Figure 3 The following schematically shows a schematic diagram of a physiological sign data monitoring system based on brain health in one embodiment of the present application.

[0034] Figure 4 A schematic diagram of the structure of a computer system suitable for implementing an electronic device according to an embodiment of the present application is shown. DETAILED DESCRIPTION

[0035] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this application will be thorough and complete and will fully convey the concepts of the example embodiments to those skilled in the art.

[0036] In addition, described feature, structure or characteristic can be combined in one or more embodiments in any suitable manner.In the following description, many specific details are provided so as to provide a full understanding of the embodiments of the present application. However, it will be appreciated by those skilled in the art that the technical scheme of the present application can be put into practice without one or more of the specific details, or other methods, components, devices, steps etc. can be adopted. In other cases, known methods, devices, implementations or operations are not shown or described in detail to avoid blurring the various aspects of the application.

[0037] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically separate entities. That is, these functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0038] The flowcharts shown in the accompanying drawings are for illustrative purposes only and do not necessarily include all contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may be decomposed, while others may be combined or partially combined. Therefore, the actual execution order may vary depending on the actual situation.

[0039] The implementation details of the technical solution of this application are described in detail below:

[0040] Figure 1 FIG2 shows a flow chart of a method for monitoring brain health based on physiological sign data according to an embodiment of the present application. Figure 1 As shown, the method for monitoring physiological sign data based on brain health includes at least steps S110 to S150, which are described in detail as follows:

[0041] In step S110 , the user's dietary data and physiological data are acquired based on a preset composite sensor architecture.

[0042] In this embodiment, based on a pre-set composite sensor architecture, the user's dietary data and physiological data are accurately captured and processed in real time. The dietary data includes information such as food intake, nutritional composition, and intake time monitored by various sensors, while the physiological data includes physiological indicators such as heart rate, blood pressure, and body temperature.

[0043] This dietary and physiological data is then cleaned, formatted, and statistically analyzed to generate intuitive statistical graphs. This data and its corresponding statistical graphs are then securely and reliably transmitted to the control terminal via an efficient data transmission protocol. Ultimately, the control terminal presents this information, enabling users to clearly understand their dietary intake and physiological status, enabling them to make more scientific and rational health decisions.

[0044] In one embodiment of the present application, before obtaining the user's dietary data and physiological data based on the preset composite sensor architecture, the following steps are further included:

[0045] A plasmonic metasurface design was constructed, with a gold nanopillar array constructed on a flexible polydimethylsiloxane substrate and aptamers attached to the surface for detecting the concentration of antioxidant molecules in sweat;

[0046] Constructing a capillary force-driven microfluidic network combined with an anti-evaporation coating composed of a fluorinated polymer to detect the molecular concentration of antioxidant molecules in tissue fluid;

[0047] Collect users' dietary data based on the video surveillance network;

[0048] A composite sensor architecture is constructed based on the ion metasurface design, the capillary force-driven microfluidic network, and the video monitoring network.

[0049] In one embodiment of this application, a plasmonic metasurface design is constructed on the sensor surface, and a gold nanopillar array is constructed on a flexible polydimethylsiloxane substrate. The gold nanopillar array can have a diameter of 50 nm and a spacing of 200 nm. Aptamers are attached to the plasmonic metasurface and used to detect the concentration of antioxidant molecules such as vitamin C or glutathione in sweat.

[0050] At the same time, a capillary force-driven microfluidic network is constructed, using a capillary force-driven microfluidic network, for example, with a channel width of 100 μm and a depth of 50 μm, to achieve continuous collection and detection of sweat or tissue fluid. At the same time, an anti-evaporation coating composed of fluorinated polymers is combined to maintain fluid stability to detect the molecular concentration of antioxidant molecules in tissue fluid.

[0051] It should be noted that antioxidants in the human body help protect the brain from damage by free radicals. They can effectively neutralize free radicals and reduce damage to brain cells. Long-term lack of antioxidants may lead to the accumulation of free radicals in the brain, increasing the risk of neurodegenerative diseases (such as Alzheimer's disease). At the same time, people with higher levels of antioxidants in their blood perform better in tests of memory, logical reasoning, and visual attention. This means that antioxidants play an important role in preventing brain aging and preventing progressive cognitive impairment. Therefore, improving the body's antioxidant levels through a reasonable diet, moderate exercise, good living habits, etc., not only helps to improve the state of the brain, but also can prevent the occurrence of brain diseases to a certain extent.

[0052] Optionally, in this embodiment, the physiological data further includes real-time antioxidant indicators, oxidative stress markers, metabolic rate, heart rate variability, etc., and the physiological data are sampled in time series.

[0053] In addition, this embodiment also includes a video surveillance network for collecting user dietary data through video surveillance. For example, a camera can capture images of each meal of the user each day, identify the images, and detect the type and amount of food consumed, such as the content of trace elements such as polyphenols, vitamin C, and carotenoids, as well as the timestamp and intake amount. This data is then used as dietary data.

[0054] Specifically, in the process of detecting the trace element content in food in this embodiment, the food type and food volume can be identified through video images, and the corresponding trace element content ratio can be found and determined based on the food type. Then, the corresponding content of each trace element in the food is determined based on the trace element content ratio and the food volume as dietary data.

[0055] In addition to the above-mentioned sensing methods, in order to obtain more comprehensive physiological data, the composite sensor architecture in this embodiment also includes a heart rate monitor, a blood pressure monitor, a thermometer, and a brain image acquisition device, etc.

[0056] Optionally, in this embodiment, the physiological data also includes brain images, which are used to determine the user's brain state in a timely manner.

[0057] The above process, through the construction of a composite sensor architecture with a temporally and spatially distributed structure composed of a plasma metasurface design, a capillary-force-driven microfluidic network, and a video surveillance network, enables comprehensive, real-time acquisition of a user's dietary and physiological data. This highly integrated sensor architecture not only improves data accuracy and reliability but also greatly simplifies the data acquisition process, making it easier for users to monitor their physiological signs. Simultaneously, the metabolic kinetics of dietary components are dynamically correlated with the physiological antioxidant response, capturing the nonlinear absorption, metabolism, and clearance of antioxidants in the body, providing a comprehensive data foundation for this example.

[0058] In one embodiment of the present application, after obtaining the user's dietary data and physiological data, the method further includes:

[0059] generating statistical graphs of the dietary data and the physiological data;

[0060] The dietary data, the physiological data and the statistical graph are transmitted to a control terminal and displayed on an interface of the control terminal.

[0061] In this embodiment, after obtaining the user's dietary and physiological data, the collected dietary and physiological data are processed and analyzed. This step includes data cleaning, formatting, and necessary statistical calculations to ensure data accuracy and consistency. Subsequently, corresponding statistical graphics, such as bar charts, line charts, and scatter plots, are generated based on this processed data to intuitively display the changing trends, distribution, or relationships between dietary intake and physiological indicators.

[0062] The generated dietary data, physiological data, and statistical graphs are then packaged into an appropriate data format and transmitted to the control terminal via a network communication protocol. Upon receiving the transmitted data, the control terminal parses the data packets, extracts the dietary data, physiological data, and statistical graphs, and presents them in a user-friendly format, allowing users to easily understand their dietary intake and physiological status.

[0063] This embodiment generates statistical graphs of dietary and physiological data and transmits them to a control terminal for display. This not only improves the readability and comprehensibility of the data, but also allows users to more easily understand their health status and dietary effects. Furthermore, the remote data transmission function facilitates remote monitoring and guidance of users by medical professionals.

[0064] In step S120, based on the physiological data, a first feature representing a short-term physiological fluctuation pattern and a second feature representing a long-term physiological fluctuation pattern are extracted.

[0065] In this embodiment, physiological data acquired using a composite sensor architecture is deeply analyzed to extract two key features: primary features, which characterize short-term fluctuations in physiological parameters, such as diurnal variations in heart rate and blood pressure; and secondary features, which reveal long-term trends in physiological status, such as monthly or annual changes in weight and body fat percentage. These extracted features accurately capture and quantify a user's physiological status, providing solid data support for subsequent health monitoring, risk assessment, and personalized recommendations.

[0066] like Figure 2 As shown, in one embodiment of the present application, based on the physiological data, a first feature representing a short-term physiological fluctuation pattern and a second feature representing a long-term physiological fluctuation pattern are extracted therefrom, including:

[0067] S210, performing window processing on the physiological data based on a preset time window length to generate a physiological data sequence;

[0068] S220, extracting a high-frequency component from the physiological data sequence, and determining, based on the high-frequency component, information intensity corresponding to the physiological data sequence as the first feature characterizing a short-term physiological fluctuation pattern;

[0069] S230 , extracting a trend term and a fluctuation term from the physiological data sequence as a second feature representing a long-term physiological fluctuation law.

[0070] In this embodiment, short-term fluctuations reflect the response of the physiological system to immediate changes in the internal and external environment. The composite sensor architecture can continuously monitor skin conductivity and sweat metabolites to capture transient physiological responses during exercise or stress.

[0071] First, a fixed time window length is determined. The time window length can be selected based on the type of physiological data, the sampling frequency of the physiological data, or the short-term fluctuation period expected to be analyzed. For example, if heart rate data is being analyzed, the time window length can be set to 1 minute or 5 minutes to capture heart rate changes over a short period of time. Based on the preset time window length, the physiological data is windowed, that is, the physiological data in each time window is sampled based on the time window length to generate a physiological data sequence. Through windowing, a continuous data stream can be converted into a discrete, easy-to-process data sequence, which facilitates subsequent feature extraction and analysis.

[0072] Afterwards, frequency domain analysis techniques are applied to the physiological data sequence to extract the high-frequency components. High-frequency components represent short-term fluctuations or rapid changes in physiological data. The high-frequency components W(s,τ) extracted from the physiological data sequence are:

[0073]

[0074] Where s represents the scale parameter of the high-frequency component, τ represents the time shift parameter, x(t) represents the physiological data sequence, and t represents the timestamp. represents the wavelet function, ∞ is positive infinity, -∞ is positive infinity, It is calculated of calculus.

[0075] Afterwards, the information intensity E of the physiological data in each window is calculated based on the extracted high-frequency components. k for:

[0076]

[0077] Among them, i and j represent the data time series in the physiological data sequence, s i and s j They represent the scale parameters of the high-frequency components corresponding to the i and j time series respectively, N represents the data volume of the physiological data sequence, and In is the logarithmic function with the natural constant e as the base.

[0078] The above process, by extracting the information intensity corresponding to the physiological data sequence as the first feature, can capture the acute antioxidant response triggered by dietary intake, such as the surge in real-time antioxidant indicators within 2-4 hours after vitamin C intake.

[0079] In one embodiment of the present application, long-term fluctuations are reflected in the periodic rhythms and trend changes of the physiological system, and a time series decomposition model can be used to separate information such as trend terms, period terms, and residual terms.

[0080] Specifically, the trend item represents the overall change trend of the physiological data, and the trend item can be extracted from the physiological data using a moving average method or linear regression.

[0081] The fluctuation term reflects random fluctuations or cyclical changes in physiological data outside of the trend. Subtracting the trend term from the physiological data yields the fluctuation parameter, which is then subjected to sign extraction to obtain the fluctuation term. For example, in seasonal rhythms, systolic blood pressure is on average 5-10 mmHg higher in winter than in summer, which is related to temperature fluctuations and vasoconstriction. Extracting the trend term and fluctuation term as secondary features helps reveal long-term fluctuation patterns in physiological data, providing more comprehensive physiological data support for the subsequent construction of the joint optimization function.

[0082] This process, through windowing physiological data and extracting high-frequency components, trends, and fluctuations, accurately captures both short-term and long-term physiological fluctuation patterns. This provides crucial data support for subsequent dietary impact analysis, making the results more accurate and reliable.

[0083] In step S130 , based on the first feature and the second feature and in combination with the dietary data, an influence factor of the diet corresponding to the dietary data on the physiological state of the user is determined.

[0084] In this embodiment, the analysis results of the first and second features are integrated with simultaneously collected dietary data. Through data correlation analysis, the specific factors affecting the user's physiological state are calculated and determined. This process not only considers the immediate correlation between dietary components and short-term physiological fluctuations, but also deeply explores the potential impact of long-term eating habits on physiological trends. This provides users with scientific dietary adjustment recommendations aimed at optimizing their physiological health and preventing potential health risks.

[0085] In one embodiment of the present application, determining, based on the first feature and the second feature and in combination with the dietary data, an impact factor of the diet corresponding to the dietary data on the physiological state of the user includes:

[0086] performing timestamp matching on the first feature, the second feature, and the dietary data to determine a data feature group;

[0087] The data feature group is analyzed by multiple linear regression to determine the influence factor of the diet corresponding to the dietary data on the physiological state of the user.

[0088] The extracted first and second features, along with dietary data, are integrated to form the basis for analysis. By precisely aligning the first and second features with the dietary data using timestamps, physiological characteristics and dietary data can be standardized to eliminate time differences.

[0089] Since the generation times corresponding to the first feature, the second feature and the dietary data are different, in an embodiment of the present application, timestamp matching is performed on the first feature, the second feature and the dietary data to determine a data feature group, and the features and data corresponding to when the generation time difference is less than the set threshold are combined into a data feature group for performing multivariate linear regression analysis in the same group.

[0090] Furthermore, traditional timestamp matching uses fixed time window alignment (such as ±5 minutes), which cannot handle the differences caused by asynchronous device acquisition delay and individual metabolic differences. Based on this, the metabolic difference factor C is first determined based on the user's current body temperature. meta for:

[0091]

[0092] Wherein, k represents the metabolic sensitivity coefficient, which is 0.5 / ℃ by default; T user Indicates the user's real-time body temperature. For example, when the real-time body temperature is 37 degrees, the metabolic difference factor C meta 0.5; real-time body temperature every ± 1 ℃ leads to metabolic difference factor C meta Change 0.23; e is a natural constant.

[0093] Afterwards, the dynamic calibration function W(t i ,t j )for:

[0094]

[0095] Among them, exp is the exponential function with the natural constant e as the base; t i Indicates the timestamp corresponding to the dietary data; t j represents the acquisition timestamp of physiological data, i.e., the timestamp corresponding to the first and second features; σ represents the time tolerance parameter, such as 15 minutes; Δ baseThis represents the baseline latency obtained by training based on the user's personal historical data. Specifically, the baseline latency is determined by analyzing the user's historical dietary data, historical physiological data, and device delay information. An estimation model is trained using this historical data to determine the time difference between the user's eating and the onset of physiological changes. This time difference serves as the baseline latency for the user.

[0096] A dynamic calibration function is used to calculate the correlation value between two sets of data. When the correlation value is less than or equal to a set threshold, the two sets of data belong to the same data feature group. This process achieves metabolic adaptive spatiotemporal modeling through the dynamic calibration function, avoiding the uncertainty introduced by the timing of asynchronous multi-source data. It breaks through the mechanical matching of the original fixed time window, is compatible with sensor errors and individual differences, and significantly improves the subsequent model's ability to analyze the "diet-physiology" causal relationship, providing a reliable temporal correlation foundation for personalized nutrition recommendations.

[0097] After obtaining the data feature set, we perform short-term and long-term multiple linear regression analysis on the data feature set to determine the correlation between the dietary data and the short-term and long-term physiological characteristics. Based on the results of the correlation analysis, we determine the impact factor of the diet on the user's physiological state. This impact factor is a quantitative indicator in the form of a matrix that represents the degree and duration of the impact of the diet on the physiological state.

[0098] This process, by matching data feature groups with timestamps and using multiple linear regression analysis, can accurately determine the factors that influence diet on a user's physiological state. This influencing factor analysis technique helps reveal the complex relationship between diet and physiological state, providing a scientific basis for dietary adjustments.

[0099] In step S140 , current dietary information and target physiological data are acquired, and dietary adjustment information is determined in combination with the influencing factors.

[0100] After obtaining the user's current dietary information and target physiological data, the system uses previously identified influencing factors as key parameters to comprehensively assess the specific impact of the current diet on the user's physiological state and intelligently determine targeted dietary adjustments based on this information. This process aims to generate personalized dietary recommendations for users to help them achieve or approach their ideal physiological state, thereby effectively improving their health and quality of life.

[0101] In one embodiment of the present application, obtaining current dietary information and target physiological data, combining the influencing factors, and determining dietary adjustment information includes:

[0102] Obtain the user's current dietary information and target physiological data;

[0103] Based on the current dietary information, the target physiological data and the influencing factors, linear programming is performed to determine recipe elements and consumption methods as the dietary adjustment information.

[0104] In one embodiment of the present application, a user's current dietary information and target physiological data are obtained, where target physiological indicators may include information such as blood sugar and body fat percentage. Dietary adjustment information is intelligently generated based on the user's current dietary information, target physiological data, target physiological indicators, and influencing factors. Dietary adjustment information may include recommended food types, quantities, cooking methods, and consumption times, aiming to help users optimize their diet and achieve their target physiological state.

[0105] Specifically, in the process of generating dietary adjustment information, linear programming is performed based on the current dietary information, target physiological data and the influencing factors. In this process, the user's target physiological indicators are first converted into dynamic nutritional needs, and the energy and nutritional constraints that change over time are determined in combination with the user's current physiological data, such as real-time body temperature, exercise volume and other personalized parameters. For example, blood sugar regulation needs will be quantified as an upper limit for carbohydrate intake, and this upper limit is dynamically adjusted with the metabolic rate, rather than a fixed value, so as to enhance brain health by monitoring blood sugar to avoid chronic diseases such as diabetes. In this way, a nutritional demand model that fits the individual's metabolic rhythm is constructed. Based on the nutritional demand model, a multi-objective function is established that includes physiological goals for brain health (such as sleep time, memory), user preferences, nutritional balance and influencing factors. Solve the multi-objective function, and according to the solution of the multi-objective function, combine the user's current dietary information to determine the recipe elements, and simultaneously plan the eating methods such as eating time and order as dietary adjustment information.

[0106] This process, based on target physiological indicators and influencing factors, uses linear programming to determine recipe elements and consumption methods, thereby generating personalized dietary adjustment information. This dietary adjustment information not only meets the user's brain health needs, but also improves the user's diet quality and brain health.

[0107] In step S150 , during the process of applying the dietary adjustment information, the physiological data is monitored by the composite sensor architecture until the target physiological data is reached.

[0108] After applying dietary adjustment information, the user's physiological data is continuously monitored in real time or periodically through a composite sensor architecture. Utilizing high-precision sensing technology and data analysis, it accurately tracks changing trends in physiological status and dynamically evaluates them against target physiological data. This process not only ensures the effective implementation of dietary adjustment plans but also automatically adjusts recommendations when necessary until the user's physiological data reaches or approaches the preset health goals, providing users with comprehensive, personalized health management services.

[0109] In one embodiment of the present application, in the process of applying the dietary adjustment information, monitoring the physiological data through the composite sensor architecture until the target physiological data is reached includes:

[0110] Sending the dietary adjustment information to the user terminal to remind the user to adjust the diet;

[0111] The physiological data is monitored by the composite sensor architecture to capture the nonlinear absorption, metabolism and clearance process of antioxidants in the body;

[0112] The metabolic kinetics of the dietary components are dynamically associated with the physiological antioxidant response according to the monitoring situation, so as to update the dietary adjustment information in real time until the target physiological data is achieved.

[0113] In this embodiment, the dietary adjustment information is sent to the user terminal to remind the user to adjust the diet, and in the process of applying the dietary adjustment information, the user's physiological data is continuously monitored through the composite sensor architecture. In this process, the composite sensor architecture can keenly capture the complex dynamic process of antioxidants in the body, including its nonlinear absorption, metabolism, and final clearance stages. Based on the data obtained from the above monitoring and the influencing factors calculated above, the metabolic kinetics of dietary components and the physiological antioxidant response are dynamically correlated and analyzed to better fit the user's current physiological state. This process will continue until the user's physiological data reaches the preset health target range, thereby achieving personalized nutritional management and optimization.

[0114] Optionally, when the physiological data approaches or reaches the target value, the system will give the user timely feedback to encourage him or her to continue to adhere to the current diet plan; if the physiological data deviates from the target value, the system will automatically adjust the diet adjustment information to provide the user with more personalized diet recommendations.

[0115] In applying dietary adjustment information, this embodiment uses a composite sensor architecture to monitor physiological data in real time and updates the dietary adjustment information based on the monitoring results until the target physiological data is achieved. This real-time monitoring and adjustment mechanism ensures the effectiveness and flexibility of the dietary adjustment plan, allowing users to achieve their health goals more quickly.

[0116] In the technical solution of the present application, a user's dietary data and physiological data are acquired based on a preset composite sensor architecture. Based on the physiological data, a first feature representing the short-term physiological fluctuation pattern and a second feature representing the long-term physiological fluctuation pattern are extracted. Based on the first and second features, combined with the dietary data, the influencing factor of the diet corresponding to the dietary data on the user's physiological state is determined. Current dietary information and target physiological data are acquired, and dietary adjustment information is determined based on the influencing factors. During the application of the dietary adjustment information, the physiological data is monitored through the composite sensor architecture until the target physiological data is achieved. This solution constructs a composite sensor architecture to extract short-term and long-term physiological characteristics from the data, and scientifically determines the influencing factors of the diet on the physiological state based on the dietary data. Based on these influencing factors and the target physiological indicators, a personalized dietary adjustment plan is intelligently generated. Simultaneously, physiological data is continuously monitored, dietary recommendations are dynamically adjusted until health goals are achieved, and key information and adjustment effects are intuitively presented to the user through visualization. This achieves a full chain of health management from data collection and analysis to personalized intervention, significantly improving the accuracy, personalization, and effectiveness of user physiological sign monitoring.

[0117] The following describes an embodiment of the device of the present application, which can be used to execute the method for monitoring physiological signs data based on brain health in the above-mentioned embodiment of the present application. It is understandable that the device can be a computer program (including program code) running on a computer device, for example, the device is an application software; the device can be used to execute the corresponding steps in the method provided in the embodiment of the present application. For details not disclosed in the embodiment of the device of the present application, please refer to the embodiment of the method for monitoring physiological signs data based on brain health in the above-mentioned embodiment of the present application.

[0118] Figure 3 A block diagram of a system for monitoring physiological sign data of brain health according to an embodiment of the present application is shown.

[0119] Reference Figure 3 As shown, a system for monitoring physiological sign data of brain health according to one embodiment of the present application includes:

[0120] An acquisition unit 310 is configured to acquire dietary data and physiological data of a user based on a preset composite sensor architecture;

[0121] A feature unit 320 is configured to extract, based on the physiological data, a first feature representing a short-term physiological fluctuation pattern and a second feature representing a long-term physiological fluctuation pattern;

[0122] An associating unit 330 is configured to determine, based on the first feature and the second feature and in combination with the dietary data, an influence factor of the diet corresponding to the dietary data on the physiological state of the user;

[0123] The dietary unit 340 is used to obtain current dietary information and target physiological data, and determine dietary adjustment information in combination with the influencing factors;

[0124] The monitoring unit 350 is configured to monitor the physiological data through the composite sensor architecture during the application of the dietary adjustment information until target physiological data is reached.

[0125] In the present application, based on the aforementioned scheme, the preset composite sensor architecture, before obtaining the user's dietary data and physiological data, also includes: constructing a plasma metasurface design, constructing a gold nanocolumn array on a flexible polydimethylsiloxane substrate, and attaching aptamers to the surface for detecting the concentration of antioxidant molecules in sweat; constructing a capillary force-driven microfluidic network, combined with an anti-evaporation coating composed of fluorinated polymers, to detect the molecular concentration of antioxidant molecules in tissue fluid; based on a video monitoring network, collecting the user's dietary data; and constructing a composite sensor architecture based on the plasma metasurface design, the capillary force-driven microfluidic network and the video monitoring network.

[0126] In the present application, based on the aforementioned scheme, the first feature characterizing the short-term physiological fluctuation law and the second feature characterizing the long-term physiological fluctuation law are extracted from the physiological data, including: based on a preset window length, the physiological data is windowed to generate a physiological data sequence; high-frequency components are extracted from the physiological data sequence, and the information intensity corresponding to the physiological data sequence is determined based on the high-frequency components as the first feature characterizing the short-term physiological fluctuation law; trend items and fluctuation items are extracted from the physiological data sequence as the second feature characterizing the long-term physiological fluctuation law.

[0127] In the present application, based on the aforementioned scheme, the first feature and the second feature are combined with the dietary data to determine the influence factor of the diet corresponding to the dietary data on the physiological state of the user, including: performing timestamp matching on the first feature, the second feature and the dietary data to determine the data feature group; analyzing the data feature group through multiple linear regression to determine the influence factor of the diet corresponding to the dietary data on the physiological state of the user.

[0128] In the present application, based on the aforementioned scheme, the current dietary information and target physiological data are obtained, combined with the influencing factors, to determine the dietary adjustment information, including: obtaining the user's current dietary information and target physiological data; based on the current dietary information, the target physiological data and the influencing factors, performing linear programming to determine the recipe elements and eating methods as the dietary adjustment information.

[0129] In the present application, based on the aforementioned scheme, in the process of applying the dietary adjustment information, the physiological data is monitored through the composite sensor architecture until the target physiological data is reached, including: sending the dietary adjustment information to the user terminal to remind the user to adjust the diet; monitoring the physiological data through the composite sensor architecture to capture the nonlinear absorption, metabolism and clearance process of antioxidants in the body, and dynamically associating the metabolic kinetics of dietary components with the physiological antioxidant response according to the monitoring situation to update the dietary adjustment information in real time until the target physiological data is reached.

[0130] In the present application, based on the aforementioned scheme, after obtaining the user's dietary data and physiological data, it also includes: generating statistical graphs of the dietary data and the physiological data; transmitting the dietary data, the physiological data and the statistical graphs to the control terminal, and displaying them in the interface of the control terminal.

[0131] In the technical solution of the present application, a user's dietary data and physiological data are acquired based on a preset composite sensor architecture. Based on the physiological data, a first feature representing the short-term physiological fluctuation pattern and a second feature representing the long-term physiological fluctuation pattern are extracted. Based on the first and second features, combined with the dietary data, the influencing factor of the diet corresponding to the dietary data on the user's physiological state is determined. Current dietary information and target physiological data are acquired, and dietary adjustment information is determined based on the influencing factors. During the application of the dietary adjustment information, the physiological data is monitored through the composite sensor architecture until the target physiological data is achieved. This solution constructs a composite sensor architecture to extract short-term and long-term physiological characteristics from the data, and scientifically determines the influencing factors of the diet on the physiological state based on the dietary data. Based on these influencing factors and the target physiological indicators, a personalized dietary adjustment plan is intelligently generated. Simultaneously, physiological data is continuously monitored, dietary recommendations are dynamically adjusted until health goals are achieved, and key information and adjustment effects are intuitively presented to the user through visualization. This achieves a full chain of health management from data collection and analysis to personalized intervention, significantly improving the accuracy, personalization, and effectiveness of user physiological sign monitoring.

[0132] Figure 4 A schematic diagram of the structure of a computer system suitable for implementing an electronic device according to an embodiment of the present application is shown.

[0133] It should be noted that the computer system of the electronic device in this embodiment is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0134] In this embodiment, the computer system includes a central processing unit 401, which can perform various appropriate actions and processes based on programs stored in a read-only memory 402 or programs loaded from a storage unit 408 into a random access memory 403, such as executing the method for monitoring physiological signs of brain health described in the above embodiment. The random access memory 403 also stores various programs and data required for system operation. The central processing unit 401, the read-only memory 402, and the random access memory 403 are interconnected via a bus 404. An input / output interface 405 is also connected to the bus 404.

[0135] The following components are connected to the input / output interface 405: an input section 406 including a keyboard, a mouse, and the like; an output section 407 including devices such as a cathode ray tube (CRT), a liquid crystal display (LCD), and a speaker; a storage section 408 including a hard disk and the like; and a communication section 409 including a network interface card such as a LAN (Local Area Network) card or a modem. The communication section 409 performs communication processing via a network such as the Internet. A drive 410 is also connected to the input / output interface 405 as needed. Removable media 411, such as a magnetic disk, an optical disk, a magneto-optical disk, or a semiconductor memory, is installed in the drive 410 as needed, so that computer programs read therefrom can be installed into the storage section 408 as needed.

[0136] In particular, according to an embodiment of the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present application includes a computer program product that includes a computer program carried on a computer-readable medium, the computer program including a computer program for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via the communication section 409 and / or installed from a removable medium 411. When the computer program is executed by the central processing unit 401, the various functions defined in the system of the present application are performed.

[0137] It should be noted that the computer-readable medium shown in the embodiments of the present application can be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media can include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device. In the present application, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, which carries a computer-readable computer program. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. A computer program embodied on a computer-readable medium may be transmitted using any suitable medium, including but not limited to wireless, wired, or any suitable combination thereof.

[0138] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present application. Among them, each box in the flowchart or block diagram can represent a module, program segment, or part of the code, and the above-mentioned module, program segment, or part of the code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0139] The units involved in the embodiments described in this application may be implemented by software or hardware, and the units described may also be set in a processor. In some cases, the names of these units do not constitute limitations on the units themselves.

[0140] According to one aspect of the present application, a computer program product or computer program is provided, the computer program product or computer program including computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the methods provided in the various optional implementations described above.

[0141] As another aspect, the present application also provides a computer-readable medium, which may be included in the electronic device described in the above embodiments, or may exist independently without being incorporated into the electronic device. The computer-readable medium carries one or more programs, and when the one or more programs are executed by the electronic device, the electronic device implements the method for monitoring physiological sign data based on brain health described in the above embodiments.

[0142] It should be noted that, although several modules or units of the device for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiment of the application, the features and functions of two or more modules or units described above can be concretized in one module or unit. On the contrary, the features and functions of one module or unit described above can be further divided into multiple modules or units to be concretized.

[0143] Through the description of the above embodiments, it is easy for those skilled in the art to understand that the example embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solution according to the embodiments of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which can be a personal computer, a server, a touch terminal, or a network device, etc.) to execute the method according to the embodiments of the present application.

[0144] Those skilled in the art will readily conceive of other embodiments of the present application after considering the specification and practicing the embodiments disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present application that follow the general principles of this application and include common knowledge or customary techniques in the art that are not disclosed herein.

[0145] It should be understood that the present application is not limited to the exact structures described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present application is limited only by the appended claims.

Claims

1. A method for monitoring physiological sign data based on brain health, characterized in that: include: Based on a preset composite sensor architecture, obtaining dietary data and physiological data of the user, wherein the physiological data includes brain images; Extracting, based on the physiological data, a first feature representing a short-term physiological fluctuation pattern and a second feature representing a long-term physiological fluctuation pattern; determining, based on the first feature and the second feature and in combination with the dietary data, an influence factor of the diet corresponding to the dietary data on the physiological state of the user; Obtaining current dietary information and target physiological data, and determining dietary adjustment information in combination with the influencing factors; During the application of the dietary adjustment information, the physiological data is monitored by the composite sensor architecture until target physiological data is reached.

2. The method for monitoring physiological sign data based on brain health according to claim 1, characterized in that: Before obtaining the user's dietary data and physiological data based on the preset composite sensor architecture, the method further includes: A plasmonic metasurface design was constructed, with a gold nanopillar array constructed on a flexible polydimethylsiloxane substrate and aptamers attached to the surface for detecting the concentration of antioxidant molecules in sweat; Constructing a capillary force-driven microfluidic network combined with an anti-evaporation coating composed of a fluorinated polymer to detect the molecular concentration of antioxidant molecules in tissue fluid; Collect users' dietary data based on the video surveillance network; The composite sensor architecture is constructed based on the ion metasurface design, the capillary force driven microfluidic network and the video monitoring network.

3. The method for monitoring physiological sign data based on brain health according to claim 1, characterized in that: The extracting, based on the physiological data, a first feature representing a short-term physiological fluctuation pattern and a second feature representing a long-term physiological fluctuation pattern, includes: Based on a preset window length, performing window processing on the physiological data to generate a physiological data sequence; Extracting a high-frequency component from the physiological data sequence, and determining, based on the high-frequency component, information intensity corresponding to the physiological data sequence as the first feature characterizing a short-term physiological fluctuation pattern; A trend term and a fluctuation term are extracted from the physiological data sequence as a second feature representing a long-term physiological fluctuation law.

4. The method for monitoring physiological sign data based on brain health according to claim 1, characterized in that: The determining, based on the first feature and the second feature and in combination with the dietary data, an influence factor of the diet corresponding to the dietary data on the physiological state of the user includes: performing timestamp matching on the first feature, the second feature, and the dietary data to determine a data feature group; The data feature group is analyzed by multiple linear regression to determine the influence factor of the diet corresponding to the dietary data on the physiological state of the user.

5. The method for monitoring physiological sign data based on brain health according to claim 1, characterized in that: The obtaining of current dietary information and target physiological data, and combining the influencing factors to determine dietary adjustment information includes: Obtain the user's current dietary information and target physiological data; Based on the current dietary information, the target physiological data and the influencing factors, linear programming is performed to determine recipe elements and consumption methods as the dietary adjustment information.

6. The method for monitoring physiological sign data based on brain health according to claim 5, characterized in that: In the process of applying the dietary adjustment information, monitoring the physiological data by using the composite sensor architecture until the target physiological data is reached includes: Sending the dietary adjustment information to the user terminal to remind the user to adjust the diet; The physiological data is monitored by the composite sensor architecture to capture the nonlinear absorption, metabolism and clearance process of antioxidants in the body; The metabolic kinetics of the dietary components are dynamically associated with the physiological antioxidant response according to the monitoring situation, so as to update the dietary adjustment information in real time until the target physiological data is achieved.

7. The method for monitoring physiological sign data based on brain health according to any one of claims 1 to 6, characterized in that: After obtaining the user's dietary data and physiological data, the method further includes: generating statistical graphs of the dietary data and the physiological data; The dietary data, the physiological data and the statistical graph are transmitted to a control terminal and displayed on an interface of the control terminal.

8. A physiological sign data monitoring system based on brain health, characterized in that: include: an acquisition unit, configured to acquire dietary data and physiological data of a user based on a preset composite sensor architecture, wherein the physiological data includes a brain image; a feature unit for extracting, based on the physiological data, a first feature representing a short-term physiological fluctuation pattern and a second feature representing a long-term physiological fluctuation pattern; an associating unit, configured to determine, based on the first feature and the second feature and in combination with the dietary data, an influence factor of the diet corresponding to the dietary data on the physiological state of the user; A diet unit is used to obtain current diet information and target physiological data, and determine diet adjustment information in combination with the influencing factors; A monitoring unit is configured to monitor the physiological data through the composite sensor architecture during the application of the dietary adjustment information until target physiological data is reached.

9. The system for monitoring physiological signs of brain health according to claim 8, characterized in that: The acquisition unit is further configured to: A plasmonic metasurface design was constructed, with a gold nanopillar array constructed on a flexible polydimethylsiloxane substrate and aptamers attached to the surface for detecting the concentration of antioxidant molecules in sweat; Constructing a capillary force-driven microfluidic network combined with an anti-evaporation coating composed of a fluorinated polymer to detect the molecular concentration of antioxidant molecules in tissue fluid; Collect users' dietary data based on the video surveillance network; A composite sensor architecture is constructed based on the ion metasurface design, the capillary force-driven microfluidic network, and the video monitoring network.

10. The physiological sign data monitoring system based on brain health according to claim 8, characterized in that: The extracting, based on the physiological data, a first feature representing a short-term physiological fluctuation pattern and a second feature representing a long-term physiological fluctuation pattern, includes: Based on a preset window length, performing window processing on the physiological data to generate a physiological data sequence; Extracting a high-frequency component from the physiological data sequence, and determining, based on the high-frequency component, information intensity corresponding to the physiological data sequence as the first feature characterizing a short-term physiological fluctuation pattern; A trend term and a fluctuation term are extracted from the physiological data sequence as a second feature representing a long-term physiological fluctuation law.

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