A cardiovascular health customized service pushing system and method

Abstract

The application provides a cardiovascular health customized service pushing system and method, the method comprises the following steps: acquiring a correlation data set, performing cardiovascular health risk assessment for a user, and outputting the current risk level of the user; extracting the current behavior mode of the user and the scene state thereof, judging whether the user has environmental stress factors or behavior-induced factors through correlation analysis of the behavior mode and the scene state; matching the cardiovascular health intervention strategy corresponding to the user according to the risk level and the risk factor type corresponding to the user, and generating customized cardiovascular health service pushing information; acquiring the high-frequency active time period of the user according to the historical behavior information of the user, and determining the adaptive pushing time of the cardiovascular health service pushing information; and pushing the cardiovascular health intervention strategy to the user based on the adaptive time pushing and the preset channel. The application predicts the risk of the user through a dynamic baseline and generates corresponding customized services, thereby improving the individualization degree of cardiovascular health management services.

Description

Technical Field

[0001] This application relates to the field of data processing technology, specifically a cardiovascular health customized service delivery system and method. Background Technology

[0002] A significant problem in current personalized cardiovascular health services is the failure to adequately consider individual differences. Cardiovascular health is a complex and multidimensional field, with significant variations in each person's physiological condition, lifestyle, and environmental factors, all of which collectively influence the health of the cardiovascular system. However, existing personalized cardiovascular health services often adopt a "one-size-fits-all" approach, ignoring these individual differences. This standardized service model may be based on widely accepted health guidelines or research findings, but it fails to fully capture the unique physiological and psychological characteristics of each individual. Ignoring individual differences not only leads to a lack of personalization in customized health services but may also fail to effectively meet the actual needs of individuals. This can significantly reduce the effectiveness of the service and, in some cases, even pose unnecessary risks to patients. Therefore, how to develop health plans that conform to individual characteristics has become a key issue that needs to be addressed. Summary of the Invention

[0003] To overcome the aforementioned problems in the prior art, this application provides a cardiovascular health customized service push system and method, which adopts the following technical solution:

[0004] Firstly, this application provides a customized cardiovascular health service delivery system, including:

[0005] Based on timestamps, the system associates the user’s current external environment information, user behavior information, user physiological data and user basic profile to obtain an associated dataset.

[0006] Combining the user's basic profile and the user's physiological data in the associated dataset, a cardiovascular health risk assessment is performed based on the user's new baseline, and the user's current risk level is output.

[0007] Extract the user's current behavior pattern and the scene state, and obtain the user's corresponding risk factor type by analyzing the correlation between the behavior pattern and the scene state;

[0008] Based on the risk level and the type of risk factors corresponding to the user, a cardiovascular health intervention strategy is matched with the user, and customized cardiovascular health service push information is generated. Based on the user's historical behavior information, the user's high-frequency active time period is obtained to determine the appropriate push time for cardiovascular health service push information.

[0009] Based on the adapted push time and preset channels, the customized cardiovascular health service push information is pushed to the user.

[0010] Furthermore, the process of associating the user's current external environment information, user behavior information, user physiological data, and user basic profile based on timestamps to obtain an associated dataset includes:

[0011] Different data are initially sorted according to timestamps, and data within a preset time error range are divided into the same time dimension. The external environment information, user behavior information, and user physiological data are integrated within the same time dimension to generate a multi-dimensional feature record, while retaining the original timestamp records of different data as unique identifiers.

[0012] Furthermore, after acquiring the associated dataset, the process also includes updating the user's baseline data, namely: grouping the multi-day data within the same time period in the associated dataset according to time segments; calculating the mean, standard deviation, and distribution range of different physiological indicators in the corresponding scenarios to form an initial baseline; and using a sliding window and exponentially weighted moving average method to integrate the latest collected data into the initial baseline according to certain weights, thereby updating the initial baseline to obtain a new baseline.

[0013] Furthermore, the method of using a sliding window and exponentially weighted moving average to integrate the latest collected data into the initial baseline according to certain weights, and updating the initial baseline to obtain a new baseline, includes:

[0014] When new data is generated, the new data is added to the window, the oldest set of data is removed, and the length of the window remains unchanged. The new baseline is calculated using the exponentially weighted moving average method for the data in the window. The new data is given a higher weight α, and the historical weighted average is given a weight (1-α). Then the new baseline = α × new data + (1-α) × initial baseline.

[0015] Furthermore, based on the user's new baseline, a cardiovascular health risk assessment is performed, and the user's current risk level is output. This includes: screening real-time physiological data based on the user's new baseline to obtain abnormal data and key feature combinations; using the abnormal data and key feature combinations as input to a preset cardiovascular risk assessment model to obtain a risk score; and mapping the risk score to a preset level range to obtain the user's current risk level.

[0016] Furthermore, the step of extracting the user's current behavioral pattern and the current scene state, and obtaining the user's corresponding risk factor type through correlation analysis of the behavioral pattern and the scene state, includes: extracting behavioral data within a preset time period, identifying user behavioral patterns and abnormal behaviors through time-series analysis; determining the user's current scene state based on the location information, environmental parameters, and timestamps in the correlated dataset, wherein the scene state includes environmental features and time features; identifying abnormal matching results between the behavioral pattern and the current scene state based on the mapping relationship between the behavioral pattern and the scene state; and, based on the abnormal matching results and a preset cardiovascular risk factor database, determining whether the user has environmental stress factors or behavioral triggering factors, and outputting the corresponding risk factor type.

[0017] Furthermore, the step of matching cardiovascular health intervention strategies corresponding to the user based on the risk level and the type of risk factors corresponding to the user, and generating customized cardiovascular health service push information, includes: determining the level of intervention based on the risk level; determining the focus of intervention based on the type of risk factors corresponding to the user; and combining the matched intervention level and focus of intervention with the user's basic profile to generate customized cardiovascular health service push information for the user.

[0018] Secondly, this application also provides a cardiovascular health customized service delivery system, including:

[0019] The associated dataset acquisition module is used to associate data based on timestamps with the user's current external environment information, user behavior information, user physiological data and user basic profile to obtain associated datasets.

[0020] The risk prediction module is used to combine the user's basic profile and the user's physiological data in the associated dataset to conduct a cardiovascular health risk assessment based on the user's new baseline and output the user's current risk level.

[0021] The behavior and scenario correlation analysis module is used to extract the user's current behavior pattern and the scenario state. By analyzing the correlation between the behavior pattern and the scenario state, the risk factor type corresponding to the user is obtained.

[0022] The time-appropriate push module is used to match the cardiovascular health intervention strategy corresponding to the user based on the risk level and the type of risk factors corresponding to the user, and generate customized cardiovascular health service push information. It obtains the user's high-frequency active time period based on the user's historical behavior information to determine the appropriate push time for cardiovascular health service push information.

[0023] The customized service push module is used to push customized cardiovascular health service information to users based on the adapted push time and preset channels.

[0024] Thirdly, this application provides an electronic device, comprising:

[0025] One or more processors; a memory; and one or more computer programs, wherein the one or more computer programs are stored in the memory, and the one or more computer programs include instructions that, when executed by the device, cause the device to perform the method as described in the first aspect.

[0026] Fourthly, this application provides a computer-readable storage medium storing a computer program that, when run on a computer, causes the computer to perform the method described in the first aspect.

[0027] Fifthly, this application provides a computer program that, when executed by a computer, performs the method described in the first aspect.

[0028] In one possible design, the program in the fifth aspect can be stored wholly or partially on a storage medium packaged with the processor, or it can be stored wholly or partially on a memory not packaged with the processor.

[0029] This application has the following beneficial effects:

[0030] 1. Based on timestamps, the system correlates the user's current external environment information, user behavior information, user physiological data, and user basic profile to obtain a correlated dataset. Combining the user's basic profile and the user's physiological data in the correlated dataset, a cardiovascular health risk assessment is performed based on the user's new baseline, and the user's current risk level is output. By obtaining the user's current risk level, this application can determine the intervention level for the user during the process of providing customized cardiovascular health services, avoiding one-size-fits-all health advice and providing personalized strategies based on the user's risk level, thus preventing excessive intervention.

[0031] 2. Extract the user's current behavior pattern and the current scene state, and obtain the user's corresponding risk factor type by analyzing the correlation between the behavior pattern and the scene state; This application analyzes the correlation between the user's behavior pattern and the current scene state, which can comprehensively judge the interaction between behavior and environment and reduce misjudgment caused by a single data source.

[0032] 3. Based on the risk level and the user's corresponding risk factor type, a cardiovascular health intervention strategy is matched to the user, and customized cardiovascular health service push information is generated. The user's high-frequency active time periods are obtained based on historical behavioral information to determine the appropriate push time for the cardiovascular health service push information. Based on the appropriate push time and preset channels, the customized cardiovascular health service push information is pushed to the user. This application matches customized intervention plans for users based on risk level and specific risk factor type, providing users with precise intervention plans. When the user is at a high risk level, immediate intervention can be obtained; when the user is at a low risk level, health advice is generated for the user. Attached Figure Description

[0033] Figure 1 This is an exemplary system architecture diagram to which embodiments of this application can be applied;

[0034] Figure 2 This is a flowchart of the cardiovascular health customized service push method according to an embodiment of this application;

[0035] Figure 3 Update the baseline flowchart for the cardiovascular health customized service push method in this application embodiment;

[0036] Figure 4 This is a flowchart illustrating the risk factor type acquisition process for the cardiovascular health customized service delivery method according to an embodiment of this application.

[0037] Figure 5 This is a system flowchart of an embodiment of this application;

[0038] Figure 6 This is a schematic diagram of a computer device according to an embodiment of this application. Detailed Implementation

[0039] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein in the specification of the application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application; the terms "comprising" and "having," and any variations thereof, in the specification, claims, and foregoing drawings of this application, are intended to cover non-exclusive inclusion. The terms "first," "second," etc., in the specification, claims, or foregoing drawings of this application are used to distinguish different objects, not to describe a particular order.

[0040] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0041] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings.

[0042] like Figure 1 As shown, system architecture 100 may include terminal devices 101, 102, and 103, a network 104, and a server 105. Network 104 serves as the medium for providing communication links between terminal devices 101, 102, and 103 and server 105. Network 104 may include various connection types, such as wired or wireless communication links, or fiber optic cables, etc.

[0043] Users can use terminal devices 101, 102, and 103 to interact with server 105 via network 104 to receive or send messages, etc. Various communication client applications can be installed on terminal devices 101, 102, and 103, such as web browser applications, shopping applications, search applications, instant messaging tools, email clients, social media platform software, etc.

[0044] Terminal devices 101, 102, and 103 can be various electronic devices with displays and support web browsing, including but not limited to smartphones, tablets, e-book readers, MP3 players (Moving Picture Experts Group Audio Layer III), MP4 players (Moving Picture Experts Group Audio Layer IV), laptops, and desktop computers, etc.

[0045] Server 105 can be a server that provides various services, such as a backend server that supports the pages displayed on terminal devices 101, 102, and 103.

[0046] It should be noted that the cardiovascular health customized service push method provided in this application embodiment is generally executed by a server / terminal device, and correspondingly, the cardiovascular health customized service push system is generally set in the server / terminal device.

[0047] It should be understood that Figure 1The number of terminal devices, networks, and servers shown is merely illustrative. Depending on implementation needs, any number of terminal devices, networks, and servers can be included.

[0048] Continue to refer to Figure 2 The figure shows a flowchart of a method for pushing customized cardiovascular health services according to this application. The method includes the following steps:

[0049] Step S1: Obtain current external environmental information, user behavior information, user physiological data, and user basic profile. Based on timestamps, associate the external environmental information, user behavior information, user physiological data, and user basic profile to obtain an associated dataset. In this embodiment, the external environmental information includes temperature, humidity, air quality index, etc.; the user behavior information includes daily activity level, sleep duration, dietary records, device interaction time, and lifestyle habits, etc.; user behavior data can be obtained through wearable devices, mobile phone sensors, and interaction records with related applications. The user physiological data includes heart rate, blood pressure, blood oxygen, etc.; the user basic profile includes age, gender, past medical history, and medication usage, and can be obtained through the user's historical medical records.

[0050] In this embodiment of the application, the external environment information, user behavior information, user physiological data, and user basic profile are correlated based on timestamps to obtain a correlated dataset, including:

[0051] Different data are initially sorted according to timestamps, and data within a preset time error range are divided into the same time dimension. Within the same time dimension, the external environmental information, user behavior information, and user physiological data are integrated to generate a multi-dimensional feature record, while retaining the original timestamp records of different data as unique identifiers.

[0052] Assuming the user's current time is 09:20:38 on May 20, 2025, based on the time module, and the detected external environmental information is sunny, temperature 25℃, humidity 65%, and air quality index 42, under the same timestamp, the user is currently walking at 102 steps per minute using the smartwatch's activity tracking function, having exercised continuously for 15 minutes. Based on the user's wearable device, the user's resting heart rate is 92 beats / minute, blood pressure is 128 / 84 mmHg, blood oxygen saturation is 96%, and body temperature is 36.7℃. The user's basic profile information is male, 50 years old, height 175cm, weight 75kg, with a family history of hypertension, diagnosed with mild hypertension in 2019, taking amlodipine, non-smoker, occasional alcohol consumption, and usual exercise habits of brisk walking or jogging. The user's data is then correlated to obtain the following correlated dataset: [timestamp, room temperature 25℃, humidity 65%, AQI=42, sunny day, 15min walking, cadence 102 steps / min, resting heart rate 92, baseline 85, blood pressure 128 / 84, blood oxygen 96%, body temperature 36.7℃, male, 50 years old, history of hypertension, taking amlodipine]. This correlated dataset can be used in step S2 for user risk level assessment and risk factor acquisition, thereby determining whether to provide patients with customized cardiovascular services.

[0053] In this embodiment of the application, after obtaining the associated dataset, the method further includes updating the user's baseline data. The process of obtaining the user's baseline data is as follows:

[0054] Step 31: Group the multi-day data within the same time period in the associated dataset according to time segments.

[0055] Step 32: Calculate the mean, standard deviation and distribution range of different physiological indicators in the corresponding scenarios to form an initial baseline.

[0056] Step 33: By using a sliding window and exponentially weighted moving average method, the latest collected data is integrated into the initial baseline according to a certain weight, and the initial baseline is updated. This allows the user's baseline to be dynamically adapted to the user's physiological condition, seasonal changes, and lifestyle habits, ensuring the accuracy and personalization of risk assessment.

[0057] In this embodiment of the application, the newly collected data is integrated into the initial baseline according to a certain weight using a sliding window and exponentially weighted moving average method. Updating the initial baseline includes:

[0058] The window size is set to N, where N can represent the most recent ten minutes or the most recent 100 data points. When new data is generated, it is added to the window, and the oldest set of data is removed, keeping the window length N constant. A new baseline is calculated for the data within the window using an exponentially weighted moving average method. The new data is assigned a higher weight α, and the historical weighted average is assigned a weight of (1-α). The formula is: New Baseline = α × New Data + (1-α) × Initial Baseline. This application maintains sensitivity to recent data changes during curve smoothing. Through real-time dynamic adjustment of the baseline, this application can monitor the user's latest status.

[0059] Step S2: Combining the user's basic profile and the user's physiological data in the associated dataset, perform a cardiovascular health risk assessment based on the user's new baseline and output the user's current risk level.

[0060] In one possible implementation, the step of combining the user's basic profile and the user's physiological data in the associated dataset, and performing a cardiovascular health risk assessment based on the user's new baseline to output the user's current risk level includes:

[0061] Based on the user's new baseline, real-time physiological data is screened to identify abnormal data and key feature combinations. These abnormal data and key feature combinations are then used as input to a pre-defined cardiovascular risk assessment model to obtain a risk score. By mapping the risk score to a pre-defined level range, the user's current risk level is determined. For example, a risk level can be quantified using a score of 0-100, where 0-30 is low risk, 31-60 is medium risk, and above 61 is high risk. Through feature importance analysis, the contribution of different input features to the total risk score is obtained, and the contributions are ranked to identify the main risk factors.

[0062] For example, using a sliding window and exponentially weighted moving average method, the latest collected data is integrated into the initial baseline according to certain weights, and the initial baseline is updated. By comparing the deviation of the user's real-time data from the new baseline, the heart rate deviation is +8%, and the systolic blood pressure deviation is +3 mmHg, and the user's three most recent measurements are all higher than the baseline. The preset cardiovascular risk assessment model, combined with the prior weight of the user having hypertension, uses elevated heart rate and mild hypertension as the main feature inputs. Through the calculation of the preset cardiovascular risk assessment model, the risk score is 56 points, corresponding to a medium risk level. Through feature importance analysis, it is found that the current risk factors are mainly continuous elevated heart rate (60%), mild hypertension above baseline (35%), and early morning activity assessment (5%).

[0063] Step S3: Extract the user's current behavior pattern and the scene state. By analyzing the correlation between the behavior pattern and the scene state, obtain the user's corresponding risk factor type, which includes environmental stress factors or behavioral triggering factors.

[0064] In this embodiment, the step of extracting the user's current behavior pattern and the current scene state, and obtaining the user's corresponding risk factor type through correlation analysis of the behavior pattern and the scene state, is described in the following reference. Figure 4 ,include:

[0065] Step 41: Extract behavioral data within a preset time period and identify user behavior patterns and abnormal behaviors through time series analysis.

[0066] Step 42: Based on the location information, environmental parameters and timestamps in the associated dataset, determine the current scene state of the user, where the scene state includes environmental features and time features.

[0067] Step 43: Based on the mapping relationship between behavioral patterns and scene states, identify abnormal matching results between behavioral patterns and the current scene state. Based on the abnormal matching results and a preset cardiovascular risk factor database, determine whether the user has environmental stress factors or behavioral triggering factors, and output the corresponding risk factor type. Environmental stress factors include environments such as low oxygen and high temperature; behavioral triggering factors include behaviors such as prolonged sitting and strenuous exercise.

[0068] Step S4: Based on the risk level and the user's corresponding risk factor type, match the cardiovascular health intervention strategy corresponding to the user, and generate customized cardiovascular health service push information. Based on the user's historical behavior information, obtain the user's high-frequency active time period and determine the appropriate push time for the cardiovascular health service push information.

[0069] In this embodiment of the application, the step of matching a cardiovascular health intervention strategy corresponding to the user based on the risk level and the type of risk factors corresponding to the user, and generating customized cardiovascular health service push information, includes:

[0070] The level of intervention is determined based on the risk level. For low-risk levels, health advice is sent to users, while for medium- and high-risk levels, warning information is provided or information is pushed out immediately and action is taken.

[0071] Based on the factor type corresponding to the user, the intervention focus is determined. For environmental stress factors, the intervention focus can be on improving the environment; for behavioral triggering factors, the intervention focus can be on adjusting lifestyle.

[0072] By combining the matched intervention level and focus with the user's basic profile, personalized cardiovascular health service push information is generated, ensuring that the intervention recommendations are consistent with the user's actual situation.

[0073] In this embodiment of the application, the step of determining the appropriate push time for cardiovascular health service information based on the user's high-frequency active time period obtained from the user's historical behavior information includes:

[0074] User behavior information within a preset time period is acquired, and the user's activity frequency and interaction time with the device are calculated through time series statistics within each preset time period. Peak activity intervals for users are identified using preset activity thresholds, and a core time window is selected based on the proportion of these peak activity intervals occurring within a preset duration. Based on the timeliness requirements of cardiovascular health service push information, an appropriate push time is determined within the core time window to ensure users receive cardiovascular health service push information in a high-response state. This appropriate push time is associated with a push task list for pushing cardiovascular health service push information to users within the appropriate push time.

[0075] Step S5: Based on the adapted push time and preset channel, push the customized cardiovascular health service information to the user.

[0076] In this embodiment of the application, when the user is at a low to medium risk level, the appropriate push time is the user's high-frequency active period; when the user is at a high risk level, the appropriate push time is an immediate push, and intervention measures are taken.

[0077] Based on the user's basic profile information—male, 50 years old, 175cm tall, weighing 75kg, with a family history of hypertension, and waking up and moving around between 7:00 and 8:00 AM—and based on the user's interaction habits on the client app, the optimal push notification time was determined to be 7:30 AM, and channels frequently used by the user were selected for delivery. The customized cardiovascular health service push notification included the following message: "This morning, your blood pressure was 18 mmHg higher than usual, and your heart rate remained at 92 bpm, slightly higher than your dynamic baseline. We suggest you practice deep breathing 5 minutes before breakfast, drink some water, and avoid strenuous exercise. If your blood pressure continues to rise, please seek medical attention immediately."

[0078] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. This computer program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the methods described above. The aforementioned storage medium can be a non-volatile storage medium such as a magnetic disk, optical disk, or read-only memory (ROM), or random access memory (RAM).

[0079] It should be understood that although the steps in the flowcharts of the accompanying figures are shown sequentially as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the accompanying figures may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0080] Continue to refer to Figure 5 The cardiovascular health customized service push system described in this embodiment includes: a related dataset acquisition module 501, a risk prediction module 502, a behavior and scenario association analysis module 503, an adaptation time push module 504, and a customized service push module 505, wherein:

[0081] The associated dataset acquisition module 501 is used to associate user's current external environment information, user behavior information, user physiological data and user basic profile based on timestamps to obtain associated datasets.

[0082] The risk prediction module 502 is used to combine the user's basic profile and the user's physiological data in the associated dataset to perform a cardiovascular health risk assessment based on the user's new baseline and output the user's current risk level.

[0083] The behavior and scenario association analysis module 503 is used to extract the user's current behavior pattern and the scenario state. By analyzing the association between the behavior pattern and the scenario state, the risk factor type corresponding to the user is obtained.

[0084] The time-adaptive push module 504 is used to match the cardiovascular health intervention strategy corresponding to the user based on the risk level and the type of risk factors corresponding to the user, and generate customized cardiovascular health service push information. It obtains the user's high-frequency active time period based on the user's historical behavior information and determines the appropriate push time for the cardiovascular health service push information.

[0085] The customized service push module 505 is used to push the customized cardiovascular health service push information to the user based on the adapted push time and preset channel.

[0086] This application obtains a correlated dataset by linking the user's current external environment information, user behavior information, user physiological data, and user basic profile based on timestamps. Combining the user's basic profile and the user's physiological data in the correlated dataset, a cardiovascular health risk assessment is performed based on the user's new baseline, outputting the user's current risk level. The application extracts the user's current behavioral patterns and the current scenario state, and through correlation analysis of behavioral patterns and scenario states, determines whether the user has environmental stressors or behavioral triggers. Based on the risk level and the user's corresponding risk factor type, a cardiovascular health intervention strategy is matched to the user, and customized cardiovascular health service push information is generated. The application also determines the appropriate push time for cardiovascular health service information by obtaining the user's high-frequency active time periods based on historical behavioral information. Based on the appropriate push time and preset channels, the cardiovascular health intervention strategy is pushed to the user. This application improves the personalization of cardiovascular health management services by using a dynamic baseline to predict user risk and generate corresponding customized services.

[0087] To address the aforementioned technical problems, embodiments of this application also provide a computer device. Please refer to [link / reference needed]. Figure 6 , Figure 6 This is a basic structural block diagram of the computer device in this embodiment.

[0088] The computer device 6 includes a memory 6a, a processor 6b, and a network interface 6c that are interconnected via a system bus. It should be noted that only the computer device 6 with components 6a-6c is shown in the figure; however, it should be understood that it is not required to implement all the shown components, and more or fewer components can be implemented alternatively. Those skilled in the art will understand that the computer device described here is a device capable of automatically performing numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes, but is not limited to, microprocessors, application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), digital signal processors (DSPs), embedded devices, etc.

[0089] The computer device can be a desktop computer, laptop, handheld computer, or cloud server, etc. The computer device can interact with the user via a keyboard, mouse, remote control, touchpad, or voice control.

[0090] The memory 6a includes at least one type of readable storage medium, including flash memory, hard disk, multimedia card, card-type memory (e.g., SD or DX memory), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, disk, optical disk, etc. In some embodiments, the memory 6a may be an internal storage unit of the computer device 6, such as the hard disk or memory of the computer device 6. In other embodiments, the memory 6a may also be an external storage device of the computer device 6, such as a plug-in hard disk, smart media card (SMC), secure digital card (SD), flash card, etc., equipped on the computer device 6. Of course, the memory 6a may also include both the internal storage unit and its external storage device of the computer device 6. In this embodiment, the memory 6a is typically used to store the operating system and various application software installed on the computer device 6, such as the program code of a method for pushing customized cardiovascular health services. In addition, the memory 6a can also be used to temporarily store various types of data that have been output or will be output.

[0091] In some embodiments, the processor 6b may be a central processing unit (CPU), controller, microcontroller, microprocessor, or other data processing chip. The processor 6b is typically used to control the overall operation of the computer device 6. In this embodiment, the processor 6b is used to run program code stored in the memory 6a or process data, for example, to run the program code for the cardiovascular health customized service push method.

[0092] The network interface 6c may include a wireless network interface or a wired network interface, which is typically used to establish communication connections between the computer device 6 and other electronic devices.

[0093] This application also provides another embodiment, namely, a non-volatile computer-readable storage medium storing a program for a cardiovascular health customized service push method, wherein the cardiovascular health customized service push can be executed by at least one processor to cause the at least one processor to perform the steps of the cardiovascular health customized service push method as described above.

[0094] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0095] Obviously, the embodiments described above are only some embodiments of this application, not all embodiments. The accompanying drawings show preferred embodiments of this application, but do not limit the patent scope of this application. This application can be implemented in many different forms; rather, the purpose of providing these embodiments is to provide a more thorough and comprehensive understanding of the disclosure of this application. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing specific embodiments, or make equivalent substitutions for some of the technical features. Any equivalent structures made using the content of this application's specification and drawings, directly or indirectly applied to other related technical fields, are similarly within the scope of patent protection of this application.

Claims

1. A method for pushing customized cardiovascular health services, characterized in that, include: Based on timestamps, the system associates the user’s current external environment information, user behavior information, user physiological data and user basic profile to obtain an associated dataset. Combining the user's basic profile and the user's physiological data in the associated dataset, a cardiovascular health risk assessment is performed based on the user's new baseline, and the user's current risk level is output. Extract the user's current behavior pattern and the scene state, and obtain the user's corresponding risk factor type by analyzing the correlation between the behavior pattern and the scene state; Based on the risk level and the type of risk factors corresponding to the user, a cardiovascular health intervention strategy is matched with the user, and customized cardiovascular health service push information is generated. Based on the user's historical behavior information, the user's high-frequency active time period is obtained to determine the appropriate push time for cardiovascular health service push information. Based on the adapted push time and preset channels, the customized cardiovascular health service push information is pushed to the user.

2. The method for pushing customized cardiovascular health services according to claim 1, characterized in that, The process involves associating the user's current external environment information, user behavior information, user physiological data, and user basic profile based on timestamps to obtain an associated dataset, including: The different data are initially sorted according to timestamps, and the data within a preset time error range are divided into the same time dimension. The external environment information, user behavior information, and user physiological data are integrated within the same time dimension to generate a multi-dimensional feature record, while retaining the original timestamp records of different data as unique identifiers.

3. The method for pushing customized cardiovascular health services according to claim 1, characterized in that, After obtaining the associated dataset, the process also includes updating the user's baseline data, namely: Group the data from multiple days within the same time period in the associated dataset according to time segments; Calculate the mean, standard deviation, and distribution range of different physiological indicators in the corresponding scenarios to form an initial baseline; By using a sliding window and exponentially weighted moving average method, the latest collected data is integrated into the initial baseline according to certain weights, and the initial baseline is updated to obtain a new baseline.

4. The method for pushing customized cardiovascular health services according to claim 3, characterized in that, The method of using a sliding window and exponentially weighted moving average to integrate the latest collected data into the initial baseline according to certain weights, and updating the initial baseline to obtain a new baseline, includes: When new data is generated, add the new data to the window, remove the oldest set of data, and keep the window length unchanged. The new baseline is calculated using the exponentially weighted moving average method on the data within the window. The new data is assigned a higher weight α, and the historical weighted average is assigned a weight (1-α). Then the new baseline = α × new data + (1-α) × initial baseline.

5. The method for pushing customized cardiovascular health services according to claim 1, characterized in that, The process of combining the user's basic profile and the user's physiological data in the associated dataset to perform a cardiovascular health risk assessment based on the user's new baseline, and outputting the user's current risk level, includes: Based on the user's new baseline, real-time physiological data is screened to obtain abnormal data and key feature combinations; Abnormal data and key feature combinations are used as input to a preset cardiovascular risk assessment model to obtain a risk score. By mapping the risk score to a preset level range, the user's current risk level is obtained.

6. The method for pushing customized cardiovascular health services according to claim 1, characterized in that, The process involves extracting the user's current behavioral patterns and the current scenario state, and then analyzing the correlation between the behavioral patterns and the scenario state to obtain the user's corresponding risk factor types, including: Extract behavioral data within a preset time period and identify user behavior patterns and abnormal behaviors through time series analysis; Based on the location information, environmental parameters and timestamps in the associated dataset, the current scene state of the user is determined, where the scene state includes environmental features and time features. Based on the mapping relationship between behavioral patterns and scene states, abnormal matching results between behavioral patterns and current scene states are identified. Based on the abnormal matching results and a preset cardiovascular risk factor database, it is determined whether the user has environmental stress factors or behavioral triggering factors, and the corresponding risk factor type is output.

7. The method for pushing customized cardiovascular health services according to claim 1, characterized in that, The process involves matching cardiovascular health intervention strategies to users based on their risk levels and corresponding risk factor types, and generating customized cardiovascular health service push information, including: The level of intervention is determined based on the aforementioned risk level; Based on the factor types corresponding to the users, the focus of intervention is determined; By combining the matched intervention level and focus with the user's basic profile, personalized cardiovascular health service push information is generated for each user.

8. A cardiovascular health customized service push system, used to implement the cardiovascular health customized service push method of claims 1-7, characterized in that, include: The associated dataset acquisition module is used to associate data based on timestamps with the user's current external environment information, user behavior information, user physiological data and user basic profile to obtain associated datasets. The risk prediction module is used to combine the user's basic profile and the user's physiological data in the associated dataset to conduct a cardiovascular health risk assessment based on the user's new baseline and output the user's current risk level. The behavior and scenario correlation analysis module is used to extract the user's current behavior pattern and the scenario state. By analyzing the correlation between the behavior pattern and the scenario state, the risk factor type corresponding to the user is obtained. The time-appropriate push module is used to match the cardiovascular health intervention strategy corresponding to the user based on the risk level and the type of risk factors corresponding to the user, and generate customized cardiovascular health service push information. It obtains the user's high-frequency active time period based on the user's historical behavior information to determine the appropriate push time for cardiovascular health service push information. The customized service push module is used to push customized cardiovascular health service information to users based on the adapted push time and preset channels.

9. An electronic device, characterized in that, include: One or more processors; Memory, used to store one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the method as described in any one of claims 1-7.

10. A computer-readable storage medium storing computer instructions thereon, characterized in that, When executed by the processor, this instruction implements the steps of the method as described in any one of claims 1-7.

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