An image generation method and related apparatus

Abstract

The embodiment of the application provides a kind of portrait generation method and related equipment, belong to data processing technical field.The method includes: obtaining the member basic information of target member, obtains corresponding multi-source health data according to member basic information;Member portrait feature is generated based on multi-source health data;Weight calculation and aggregation are carried out to member portrait feature, and comprehensive evaluation index is obtained;According to comprehensive evaluation index, the member portrait label of target member is generated.The embodiment of the application can generate high objectivity high credibility dynamic member portrait, clearly member health and behavior state, reduce portrait deviation, be conducive to staff to carry out member management and service, provide reliable technical foundation for personalized health management, precision health intervention.

Description

Technical Field

[0001] This application relates to the field of data processing technology, and in particular to a portrait generation method and related equipment. Background Technology

[0002] In related technologies, with the popularization of health management platforms and wearable health devices, these platforms have gradually accumulated a large amount of data related to members' health status and health management behaviors. However, most existing member profiling technologies focus on basic attributes or single behavioral data, making it difficult to continuously reflect the true health status of members.

[0003] In summary, the technical problems existing in the relevant technologies need to be improved. Summary of the Invention

[0004] The main objective of this application is to propose a portrait generation method and related equipment, which aims to generate dynamic member portraits with high objectivity and credibility.

[0005] To achieve the above objectives, one aspect of this application proposes a portrait generation method, the method comprising: Obtain the target member's basic membership information, and obtain corresponding multi-source health data based on the basic membership information; Member profile features are generated based on the multi-source health data. These features include health status features, health participation features, health consumption features, health device usage compliance features, and platform activity features. The member profile features are weighted and aggregated to obtain a comprehensive evaluation index, which includes a health status index, a health participation index, and a health management compliance index. Based on the comprehensive evaluation indicators, member profile tags are generated for the target members. The member profile tags include health risk level tags, health participation activity tags, and health device usage compliance tags.

[0006] In some embodiments, the method further includes: Abnormal data in the multi-source health data are removed or corrected to obtain corrected multi-source health data. The missing data in the multi-source health data are filled in to obtain the completed multi-source health data. The multi-source health data is time-aligned to obtain aligned multi-source health data; The multi-source health data is normalized or interval mapped to obtain normalized multi-source health data.

[0007] In some embodiments, the multi-source health data includes behavioral characteristics, health indicators, health management participation levels, consumption behavior, and device usage. The step of generating member profile features based on the multi-source health data includes: Based on the behavioral characteristics and health indicators, corresponding user tag indicators are generated using preset classification rules; Based on the level of participation in health management, consumption behavior, and device usage, corresponding user segmentation dimension indicators are generated. Member profile features are extracted based on the user tag metrics and the user segmentation dimensions.

[0008] In some embodiments, the step of weighting and aggregating the member profile features to obtain a comprehensive evaluation index includes: According to the preset weight configuration rules, corresponding feature weights are assigned to the health status feature, the health participation feature, the health consumption feature, the health device usage compliance feature, and the platform activity feature; The member profile features are weighted and aggregated based on the aforementioned feature weights to generate a comprehensive evaluation index.

[0009] In some embodiments, the method further includes: Obtain historical data analysis results; The feature weights are adjusted based on the historical data analysis results to obtain the adjusted feature weights.

[0010] In some embodiments, the method further includes: Store the member profile tags in the member profile database; In response to the first instruction, the member profile tag is displayed on the user profile page of the target member.

[0011] To achieve the above objectives, another aspect of this application provides a portrait generation apparatus, the apparatus comprising: The data acquisition module is used to acquire the basic membership information of the target member and acquire corresponding multi-source health data based on the basic membership information. The feature generation module is used to generate member profile features based on the multi-source health data. The member profile features include health status features, health participation features, health consumption features, health device usage compliance features, and platform activity features. The weight calculation module is used to calculate and aggregate the weights of the member profile features to obtain a comprehensive evaluation index, which includes a health status index, a health participation index, and a health management compliance index. The profile generation module is used to generate member profile tags for the target member based on the comprehensive evaluation indicators. The member profile tags include health risk level tags, health participation activity tags, and health device usage compliance tags.

[0012] To achieve the above objectives, another aspect of this application provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the method described above.

[0013] To achieve the above objectives, another aspect of the embodiments of this application proposes a computer-readable storage medium storing a computer program that, when executed by a processor, implements the methods described above.

[0014] To achieve the above objectives, another aspect of the embodiments of this application proposes a computer program product, including a computer program that, when executed by a processor, implements the aforementioned method.

[0015] The embodiments of this application include at least the following beneficial effects: This application provides a portrait generation method, apparatus, electronic device, storage medium, and program product. This solution obtains the basic membership information of the target member and acquires corresponding multi-source health data based on the basic membership information; it generates member portrait features based on the multi-source health data, which can integrate complex multi-source health data into analyzable portrait features, thus improving the granularity and accuracy of the member portrait; it performs weight calculation and aggregation on the member portrait features to obtain comprehensive evaluation indicators, which is conducive to generating tags and facilitating management and display; based on the comprehensive evaluation indicators, it generates member portrait tags for the target member, which can generate dynamic member portraits with high objectivity and credibility, clarify the member's health and behavioral status, reduce portrait bias, and facilitate staff in managing and serving members, providing a reliable technical foundation for personalized health management and precise health intervention. Attached Figure Description

[0016] Figure 1 This is a flowchart of the portrait generation method provided in the embodiments of this application; Figure 2 yes Figure 1 The flowchart of step S102 in the document; Figure 3 yes Figure 1 The flowchart of step S103 in the process; Figure 4 This is a flowchart illustrating a specific implementation of the portrait generation method provided in this application when applied to a health management system. Figure 5 This is a schematic diagram of the image generation device provided in the embodiments of this application; Figure 6 This is a schematic diagram of the hardware structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit it. In the following description, when referring to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with those of this application; they are merely examples of apparatuses and methods consistent with some aspects of the embodiments of this application as detailed in the appended claims.

[0018] It is understood that the terms “first,” “second,” etc., used in this application may be used herein to describe various concepts, but unless otherwise stated, these concepts are not limited by these terms. These terms are only used to distinguish one concept from another. For example, without departing from the scope of the embodiments of this application, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the words “if,” “when,” or “in response to a determination” as used herein may be interpreted as “when…” or “when…” or “in response to a determination.”

[0019] As used in this application, the terms "at least one", "multiple", "each", "any", etc., "at least one" includes one, two or more, "multiple" includes two or more, "each" refers to each of the corresponding multiples, and "any" refers to any one of the multiples.

[0020] 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 belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0021] In related technologies, with the popularization of health management platforms and wearable health devices, these platforms have gradually accumulated a large amount of data related to members' health status and health management behaviors. However, most existing member profiling technologies focus on basic attributes or single behavioral data, making it difficult to continuously reflect the true health status of members.

[0022] In summary, the technical problems existing in the relevant technologies need to be improved.

[0023] In view of this, this application provides a profile generation method and related equipment. This method obtains the basic membership information of the target member and acquires corresponding multi-source health data based on this information. It generates member profile features based on the multi-source health data, which can integrate complex multi-source health data into analyzable profile features, improving the granularity and accuracy of the member profile. Weight calculation and aggregation of the member profile features yield comprehensive evaluation indicators, facilitating tag generation for easy management and display. Based on the comprehensive evaluation indicators, member profile tags for the target member are generated, resulting in a highly objective and reliable dynamic member profile that clearly defines the member's health and behavioral status, reduces profile bias, and benefits staff in member management and services. This provides a reliable technical foundation for personalized health management and precise health intervention. The profile generation method provided in this application relates to the field of data processing technology. The profile generation method provided in this application can be applied to a terminal, a server, or software running on a terminal or server. In some embodiments, the terminal may be a smartphone, tablet, laptop, desktop computer, smart speaker, smartwatch, or in-vehicle terminal, but is not limited thereto; the server may be configured as an independent physical server, or as a server cluster or distributed system composed of multiple physical servers, or as a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms. The server may also be a node server in a blockchain network; the software may be an application that implements the portrait generation method, but is not limited to the above forms.

[0024] This application can be used in a wide variety of general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics devices, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices. This application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform specific tasks or implement specific abstract data types. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0025] It should be noted that in all specific embodiments of this application, when processing data related to user identity or characteristics, such as user information, user behavior data, user historical data, and user location information, user permission or consent is obtained first. Furthermore, the collection, use, and processing of this data comply with relevant laws, regulations, and standards. In addition, when embodiments of this application require access to sensitive personal information of users, separate permission or consent from the user is obtained through pop-ups or redirection to confirmation pages. Only after obtaining the user's separate permission or consent is the necessary user-related data required for the proper functioning of these embodiments acquired.

[0026] Figure 1 This is an optional flowchart of the portrait generation method provided in the embodiments of this application. Figure 1 The method may include, but is not limited to, steps S101 to S106.

[0027] Step S101: Obtain the target member's basic information and obtain the corresponding multi-source health data based on the basic information.

[0028] In some embodiments, a target member is selected, and their basic membership information is obtained. Based on this information, corresponding multi-source health data is then retrieved. This multi-source health data includes behavioral characteristics, health indicators, level of participation in health management, consumption behavior, and device usage.

[0029] Optionally, abnormal data in the multi-source health data can be removed or corrected to obtain corrected multi-source health data; missing data in the multi-source health data can be filled in to obtain filled multi-source health data; time alignment can be performed on the multi-source health data to obtain aligned multi-source health data; and normalization or interval mapping can be performed on the multi-source health data to obtain normalized multi-source health data.

[0030] Understandably, supporting automatic periodic updates or manual real-time updates of target member profiles helps improve the credibility and authenticity of these profiles.

[0031] In this embodiment, the basic membership information of the target member is obtained, and corresponding multi-source health data is obtained based on the basic membership information. This facilitates the generation of multi-source member profiles and improves the accuracy, comprehensiveness and credibility of the profiles.

[0032] Step S102: Generate member profile features based on multi-source health data.

[0033] Specifically, member profile characteristics include health status characteristics, health participation characteristics, health consumption characteristics, health device usage compliance characteristics, and platform activity characteristics.

[0034] In some embodiments, corresponding user tag indicators are generated based on behavioral characteristics and health indicators through preset classification rules; corresponding user segmentation dimension indicators are generated based on the degree of participation in health management, consumption behavior and device usage; and member profile features are extracted based on user tag indicators and user segmentation dimensions.

[0035] Optionally, a pre-trained classification model can be used to generate member profile features based on multi-source health data.

[0036] In this embodiment, member profile features are generated based on multi-source health data, which can transform complex multi-source health data into analyzable profile features, thereby improving the granularity and accuracy of member profiles.

[0037] Step S103: Calculate and aggregate the weights of the member profile features to obtain a comprehensive evaluation index.

[0038] Specifically, the comprehensive evaluation indicators include the health status index, the health participation index, and the health management compliance index.

[0039] In some embodiments, according to preset weight configuration rules, corresponding feature weights are assigned to health status features, health participation features, health consumption features, health device usage compliance features, and platform activity features; based on the feature weights, the member profile features are weighted and aggregated to generate a comprehensive evaluation index.

[0040] Optionally, obtain historical data analysis results; adjust the feature weights based on the historical data analysis results to obtain the adjusted feature weights.

[0041] In other embodiments, the member profile features can be weighted based on expert experience or based on a trained machine learning model to obtain a comprehensive evaluation index.

[0042] In this embodiment, the member profile features are weighted and aggregated to obtain a comprehensive evaluation index. This can transform multi-source and multi-dimensional features into a comprehensive index, which is beneficial for generating tags and facilitating management and display.

[0043] Step S104: Generate member profile tags for the target members based on the comprehensive evaluation indicators.

[0044] Specifically, member profile tags include health risk level tags, health participation activity tags, and health device usage compliance tags.

[0045] In some embodiments, threshold rules are obtained, and the comprehensive evaluation indicators are used to generate health risk level labels, health participation activity labels, and health device use compliance labels through threshold rules.

[0046] It is understandable that the threshold rules include risk thresholds, activity thresholds, and compliance thresholds. Among them, risk thresholds include low-risk thresholds and medium-risk thresholds; activity thresholds include low-activity thresholds and medium-activity thresholds; and compliance thresholds include low-compliance thresholds and medium-compliance thresholds.

[0047] Optionally, the trained machine learning classification model generates member profile tags for the target members based on comprehensive evaluation indicators.

[0048] In some embodiments, member profile tags are stored in a member profile database.

[0049] Optionally, in response to the first instruction, a member profile tag is displayed on the target member's user profile page. The first instruction is triggered when the target member's user profile page is opened or refreshed, and is used to retrieve and display the target member's member profile tag on the user profile page from the member profile database.

[0050] Understandably, member profile generation can be performed periodically through tasks. If the tags do not exist in the database when the user profile page is opened, member profile tags can be generated in real time, thereby improving the accuracy and timeliness of member profiles.

[0051] In this embodiment, member profile tags for target members are generated based on comprehensive evaluation indicators. This helps to generate structured member profiles, clarify members' health and behavioral status, reduce profile bias, and facilitate member management and services for staff.

[0052] Steps S101 to S104, as illustrated in this embodiment, involve obtaining the target member's basic information and acquiring corresponding multi-source health data based on that information; generating member profile features based on the multi-source health data, which integrates complex multi-source health data into analyzable profile features, thus improving the granularity and accuracy of the member profile; calculating and aggregating the weights of the member profile features to obtain comprehensive evaluation indicators, which facilitates the generation of tags for easy management and display; and generating member profile tags for the target member based on the comprehensive evaluation indicators, which generates highly objective and reliable dynamic member profiles, clearly defining the member's health and behavioral status, reducing profile bias, and facilitating member management and services by staff, providing a reliable technical foundation for personalized health management and precise health intervention.

[0053] Please see Figure 2 In some embodiments, step S102 may include, but is not limited to, steps S201 to S203: Step S201: Generate corresponding user tag indicators based on behavioral characteristics and health indicators using preset classification rules.

[0054] In step S201 of some embodiments, members are divided into several tag categories according to preset classification rules, and corresponding user tag indicators are generated.

[0055] The tag categories include, but are not limited to, healthy and active, device-dependent, and low-engagement types. User tag metrics are intermediate processing results and serve as one of the input bases for extracting member profile features.

[0056] Step S202: Generate corresponding user segmentation dimension indicators based on the level of participation in health management, consumption behavior, and device usage.

[0057] In step S202 of some embodiments, members are divided into different levels based on their participation in health management, consumption behavior, and device usage, and corresponding user segmentation dimension indicators are generated.

[0058] Among them, the membership levels include high-activity, medium-activity, and low-activity levels. The user segmentation dimension is also an intermediate processing result, which, together with the user tag index, serves as a reference for subsequent member profile feature extraction.

[0059] Step S203: Extract member profile features based on user tag metrics and user segmentation dimensions.

[0060] In step S203 of some embodiments, user tag categories and hierarchical results are used as constraints, and multi-source health data are combined to generate member profile feature values ​​for each dimension according to feature extraction rules or through feature extraction models, thereby making the feature extraction process more targeted and discriminative.

[0061] Please see Figure 3 In some embodiments, step S103 may include, but is not limited to, steps S301 to S304: Step S301: According to the preset weight configuration rules, assign corresponding feature weights to health status features, health participation features, health consumption features, health device usage compliance features, and platform activity features.

[0062] In step S301 of some embodiments, corresponding weights are assigned to different types of member profile features according to preset weight configuration rules.

[0063] Understandably, feature weights can be further optimized, or evaluation metrics can be generated directly based on feature weights.

[0064] Step S302: Obtain historical data analysis results.

[0065] Optionally, the weights can be further optimized based on the results of historical data analysis.

[0066] Step S303: Adjust the feature weights based on the historical data analysis results to obtain the adjusted feature weights.

[0067] Optionally, the weight parameters can be dynamically adjusted based on historical data analysis results or model training results.

[0068] In step S303 of some embodiments, the feature weights are adjusted based on the results of historical data analysis to obtain the adjusted feature weights.

[0069] Step S304: Perform weighted aggregation processing on the member profile features based on feature weights to generate a comprehensive evaluation index.

[0070] In step S304 of some embodiments, various features are weighted and aggregated based on feature weights to generate at least one comprehensive evaluation index.

[0071] Figure 4 This is a flowchart illustrating a specific implementation of the portrait generation method provided in this application when applied to a health management system. Figure 4 The methods may include, but are not limited to, the following steps: Step 1: Obtain basic member information and multi-source health-related data.

[0072] In some embodiments, multi-source data corresponding to the target member is acquired. This multi-source data includes at least member basic attribute data, health service participation behavior data, health product consumption behavior data, health data collection data, health management platform behavior data, and health device usage data.

[0073] Specifically, member basic attribute data includes gender, age, and occupation; health service participation behavior data includes frequency and duration of attendance; health product consumption behavior data includes purchase price and frequency of health care products; health collection data includes physiological indicators such as blood pressure, blood lipids, heart rate, and bone density; health management platform behavior data includes frequency of member visits to the health management platform and duration of stay on key pages; and health device usage data includes physiological indicator data obtained from health devices, as well as the usage time and frequency of health devices.

[0074] Step 2: Clean and standardize the multi-source health-related data.

[0075] In some embodiments, the data obtained in step 1 is preprocessed. The preprocessing operations include removing or correcting abnormal data, completing missing data, aligning data at different time granularities, and normalizing or mapping data at different scales, thereby obtaining standardized data with a uniform format that can be used for feature extraction.

[0076] Step 3: Extract member profile features based on preset rules or models.

[0077] In some embodiments, before feature extraction, the system first performs user tag classification and user segmentation on the standardized data to guide the generation of subsequent feature indicators.

[0078] Optionally, regarding user tag classification, the system divides members into several tag categories (such as "healthy and active", "device dependent", "low-participation" etc.) according to preset classification rules based on behavioral characteristics and health indicators in standardized data, and generates corresponding user tag indicators. User tag indicators are intermediate processing results and serve as one of the input bases for member profile feature extraction.

[0079] Furthermore, in terms of user segmentation, the system divides members into different levels (such as high-activity, medium-activity, and low-activity) based on their participation in health management, consumption behavior, and device usage, and generates corresponding user segmentation dimension indicators. These user segmentation dimensions are also intermediate processing results and, together with user tag indicators, serve as a reference for subsequent member profile feature extraction.

[0080] Understandably, based on the aforementioned user tag indicators and user segmentation dimensions, the system further extracts member profile features. The specific process is as follows: using user tag categories and segmentation results as constraints, combined with the original standardized data, member profile feature values ​​for each dimension are generated according to feature extraction rules or through feature extraction models, thereby making the feature extraction process more targeted and discriminative.

[0081] In some embodiments, member profile features are generated based on standardized data according to preset feature extraction rules or through a feature extraction model. Specifically, multidimensional member profile features include health status features, health participation features, health consumption features, and health device usage adherence features. Health status features reflect the overall level and trend of members' health indicators; health participation features reflect the degree of members' active participation in health management activities; health consumption features reflect members' consumption behavior in health products; health device usage adherence features reflect members' continuous use of health devices; and platform activity features reflect members' activity level on the health management platform.

[0082] Among them, the health device usage compliance characteristic is used to characterize the member's continuous use and stability of health devices within a certain time period.

[0083] Specifically, the data sources and methods for extracting various member profile features are explained below: (1) Health Status Characteristics: Based on health data collection (physiological indicators such as blood pressure, blood lipids, heart rate, and bone density) and physiological indicator data from health equipment usage data, interval mapping rules are used for extraction. Each physiological indicator is classified and mapped according to a preset health reference interval (normal, borderline, abnormal) to generate a health status score and abnormal indicator marking. For example, if a member's blood pressure measurement values ​​are higher than 140 / 90 mmHg for three consecutive times, the corresponding blood pressure status characteristic is marked as high risk.

[0084] (2) Health participation characteristics: Based on health service participation behavior data (attendance frequency and duration), statistical aggregation rules are used to extract the data, count the number of attendees and the average duration of attendees within a specified time period, and generate a participation activity score by combining the stratified threshold. For example, if the frequency of attendees exceeds 8 times and the average duration exceeds 60 minutes in the past 30 days, they are classified as high participation level.

[0085] (3) Health consumption characteristics: Based on health product consumption behavior data (purchase price, purchase frequency), statistical aggregation and consumption stratification rules are used to extract consumption frequency indicators and consumption amount indicators.

[0086] (4) Health device usage compliance characteristics: Based on health device usage data (usage duration, usage frequency), time series analysis rules are used to extract data, and the percentage of days used and duration stability (coefficient of variation) are statistically analyzed to generate a compliance score. For example, if the percentage of days used in the past 30 days exceeds 80% and the daily duration coefficient of variation is less than 0.3, it is classified as a high compliance level.

[0087] (5) Platform activity characteristics: Based on the behavioral data of the health management platform (access frequency, page dwell time), the behavioral weight rules are used to extract the data and generate a comprehensive score of platform activity by combining the two dimensions.

[0088] Step 4: Calculate and aggregate the weights of member profile features.

[0089] In some embodiments, according to preset weighting rules, corresponding weights are assigned to different types of member profile features, and weighted aggregation processing is performed on various features to generate at least one comprehensive evaluation index. The comprehensive evaluation index includes at least a health status index, a health participation index, and a health management compliance index. Optionally, the weight parameters can be dynamically adjusted based on historical data analysis results or model training results.

[0090] Specifically, the weighting rules can be implemented using one or a combination of the following methods: Method 1: Experience-based weight allocation based on business needs. Professionals directly specify the weight coefficients of each feature according to business objectives. For example, in a health intervention scenario, the weight of the health status feature is set to 0.35, the weight of the health device usage compliance feature is set to 0.30, the weight of the health participation feature is set to 0.20, the weight of the health consumption feature is set to 0.10, and the weight of the platform activity feature is set to 0.05. Method 2: Based on the weight allocation of the correlation between features and comprehensive evaluation indicators, calculate the correlation coefficient between each feature and the target evaluation indicator (such as health risk level), and use the normalized correlation coefficient as the weight of the corresponding feature. Method 3: Based on the feature importance weight allocation of decision tree or random forest models, the importance score of each feature is output through model training and mapped to weight coefficients. The weight parameters are dynamically adjusted as the model training results are updated.

[0091] Step 5: Generate member profile tags and store them.

[0092] In some embodiments, based on the comprehensive evaluation indicators obtained in step 4, corresponding member profile tags are generated for members. The member profile tags include at least health risk level tags, health participation activity tags, and health device usage compliance tags. The member profile tags are stored in the member profile database for subsequent health management services.

[0093] Specifically, in the schematic structure of the member profile tag, the profile tag set of a single member consists of three types of tags. Taking member A as an example: the health risk level tag is "medium risk" (assigned based on the health status index), the health participation activity tag is "high participation" (assigned based on the health participation index), and the health device use compliance tag is "high compliance" (assigned based on the health management compliance index).

[0094] The three types of tags mentioned above together constitute a comprehensive profile of the member, which can be used to trigger corresponding health intervention strategies, such as sending health warning reminders to members with "medium risk and low participation" and providing advanced health management solutions to members with "high compliance and high participation". If further visualizations of member profile tags are needed, they can be presented in the form of illustrations in subsequent versions.

[0095] It should be noted that this application has at least the following beneficial effects: 1) This application improves the objectivity and credibility of member profile results by introducing health device usage data; 2) This application can characterize the continuity and compliance of members' health management behavior, avoiding the bias of profiling based solely on static attributes or single behaviors; 3) This application supports the unified fusion and processing of multi-source health data, and is suitable for long-term and dynamic health management scenarios; 4) This application provides a reliable technical foundation for personalized health management and precision health intervention.

[0096] Please see Figure 5 This application also provides an image generation apparatus that can implement the above-described method. The apparatus includes: The data acquisition module 501 is used to acquire the basic membership information of the target member and acquire corresponding multi-source health data based on the basic membership information. The feature generation module 502 is used to generate member profile features based on multi-source health data. The member profile features include health status features, health participation features, health consumption features, health device usage compliance features, and platform activity features. The weight calculation module 503 is used to calculate and aggregate the weights of member profile features to obtain comprehensive evaluation indicators, which include health status index, health participation index and health management compliance index. The profile generation module 504 is used to generate member profile tags for target members based on comprehensive evaluation indicators. Member profile tags include health risk level tags, health participation activity tags, and health device usage compliance tags.

[0097] It is understood that the content of the above method embodiments is applicable to the present device embodiments. The specific functions implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0098] This application also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the above-described method. This electronic device can be any smart terminal, including tablet computers, in-vehicle computers, etc.

[0099] It is understood that the content of the above method embodiments is applicable to this device embodiment. The specific functions implemented by this device embodiment are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0100] Please see Figure 6 , Figure 6 The hardware structure of an electronic device according to another embodiment is illustrated. The electronic device includes: The processor 601 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application. The memory 602 can be implemented as a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 602 can store the operating system and other applications. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 602 and is called and executed by the processor 601 using the methods described in the embodiments of this application. The input / output interface 603 is used to implement information input and output; The communication interface 604 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.). Bus 605 transmits information between various components of the device (e.g., processor 601, memory 602, input / output interface 603, and communication interface 604); The processor 601, memory 602, input / output interface 603, and communication interface 604 are connected to each other within the device via bus 605.

[0101] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method.

[0102] It is understood that the content of the above method embodiments is applicable to this storage medium embodiment. The specific functions implemented in this storage medium embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0103] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0104] It is understood that the content of the above method embodiments is applicable to the embodiments of this program product. The specific functions implemented by the embodiments of this program product are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0105] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0106] The portrait generation method, apparatus, electronic device, storage medium, and program product provided in this application obtain basic member information of the target member and acquire corresponding multi-source health data based on the basic member information; generate member portrait features based on the multi-source health data, which can integrate complex multi-source health data into analyzable portrait features, thus improving the granularity and accuracy of member portraits; perform weight calculation and aggregation on member portrait features to obtain comprehensive evaluation indicators, which is conducive to generating tags for easy management and display; generate member portrait tags for the target member based on the comprehensive evaluation indicators, which can generate dynamic member portraits with high objectivity and credibility, clarify the member's health and behavioral status, reduce portrait bias, facilitate staff in member management and services, and provide a reliable technical foundation for personalized health management and precise health intervention.

[0107] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

[0108] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of this application, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0109] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0110] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0111] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0112] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0113] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0114] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0115] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0116] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes multiple instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0117] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.

Claims

1. A method for generating an image, characterized in that, The method includes the following steps: Obtain the target member's basic membership information, and obtain corresponding multi-source health data based on the basic membership information; Member profile features are generated based on the multi-source health data. These features include health status features, health participation features, health consumption features, health device usage compliance features, and platform activity features. The member profile features are weighted and aggregated to obtain a comprehensive evaluation index, which includes a health status index, a health participation index, and a health management compliance index. Based on the comprehensive evaluation indicators, member profile tags are generated for the target members. The member profile tags include health risk level tags, health participation activity tags, and health device usage compliance tags.

2. The method according to claim 1, characterized in that, The method further includes: Abnormal data in the multi-source health data are removed or corrected to obtain corrected multi-source health data. The missing data in the multi-source health data are filled in to obtain the completed multi-source health data. The multi-source health data is time-aligned to obtain aligned multi-source health data; The multi-source health data is normalized or interval mapped to obtain normalized multi-source health data.

3. The method according to claim 1, characterized in that, The multi-source health data includes behavioral characteristics, health indicators, health management participation levels, consumption behavior, and device usage. The generation of member profile features based on the multi-source health data includes: Based on the behavioral characteristics and health indicators, corresponding user tag indicators are generated using preset classification rules; Based on the level of participation in health management, consumption behavior, and device usage, corresponding user segmentation dimension indicators are generated. Member profile features are extracted based on the user tag metrics and the user segmentation dimensions.

4. The method according to claim 1, characterized in that, The process of weighting and aggregating the features of the member profile to obtain a comprehensive evaluation index includes: According to the preset weight configuration rules, corresponding feature weights are assigned to the health status feature, the health participation feature, the health consumption feature, the health device usage compliance feature, and the platform activity feature; The member profile features are weighted and aggregated based on the aforementioned feature weights to generate a comprehensive evaluation index.

5. The method according to claim 4, characterized in that, The method further includes: Obtain historical data analysis results; The feature weights are adjusted based on the historical data analysis results to obtain the adjusted feature weights.

6. The method according to claim 1, characterized in that, The method further includes: Store the member profile tags in the member profile database; In response to the first instruction, the member profile tag is displayed on the user profile page of the target member.

7. A portrait generation device, characterized in that, The device includes: The data acquisition module is used to acquire the basic membership information of the target member and acquire corresponding multi-source health data based on the basic membership information. The feature generation module is used to generate member profile features based on the multi-source health data. The member profile features include health status features, health participation features, health consumption features, health device usage compliance features, and platform activity features. The weight calculation module is used to calculate and aggregate the weights of the member profile features to obtain a comprehensive evaluation index, which includes a health status index, a health participation index, and a health management compliance index. The profile generation module is used to generate member profile tags for the target member based on the comprehensive evaluation indicators. The member profile tags include health risk level tags, health participation activity tags, and health device usage compliance tags.

8. An electronic device, characterized in that, The electronic device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the method according to any one of claims 1 to 6.

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1 to 6.

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