Multi-scene intelligent pension health system based on AIOT technology
The multi-scenario smart elderly care and health system using AIoT technology enables unified management and data integration of multi-source smart health devices, generates high-quality data assets, solves the data collaboration problem in different scenarios, and provides efficient, accurate, and personalized elderly care service solutions.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- SHENZHEN AS TECH CO LTD
- Filing Date
- 2026-03-03
- Publication Date
- 2026-06-26
Smart Images
Figure CN122290935A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of Internet of Things (IoT) technology, and in particular to a multi-scenario smart elderly care and health system based on AIoT technology. Background Technology
[0002] With the increasing diversification and sophistication of elderly care service demands, AIoT technologies such as artificial intelligence, the Internet of Things, and big data are being gradually introduced into the elderly care service field. This provides a technological foundation for building a smart elderly care and health platform, aiming to improve the quality of life and safety of the elderly through intelligent means.
[0003] However, existing smart elderly care and health platforms are mostly developed for specific scenarios (such as home or institutional care) and generally adopt a decentralized architecture: elderly care application modules for different scenarios maintain and process application data collected by different smart health devices (such as health monitoring devices, safety monitoring devices, and life service devices). This makes it difficult to effectively integrate and coordinate application data between different modules, thus restricting the large-scale, personalized, and intelligent development of elderly care services. Summary of the Invention
[0004] The main purpose of this application is to provide a multi-scenario smart elderly care and health system based on AIoT technology, which aims to solve the technical problem that it is difficult to effectively integrate and coordinate the application data of various application modules in the smart elderly care and health platform.
[0005] To achieve the above objectives, this application proposes a multi-scenario smart elderly care and health system based on AIoT technology, the system comprising: an equipment management module, a data management module, and a data mining module; The device management module is used to collect monitoring data generated by different smart health devices and send the monitoring data to the data management module. The device management module maintains communication connections with each of the smart health devices. The data management module is used to perform data standardization processing on the monitoring data, obtain data assets, and store the data assets in the data asset library; The data mining module is used to obtain target data assets from the data asset library according to the application scenario requirements, and input the target data assets into the preset decision support model corresponding to the application scenario requirements to obtain scenario decision support information.
[0006] In one embodiment, the device management module is used to scan for several smart health devices in the current network; The device management module is used to determine the device type of each of the smart health devices and determine the corresponding communication protocol method according to the device type; The device management module is used to establish communication connections with each of the intelligent monitoring devices according to the respective communication protocols.
[0007] In one embodiment, the data management module is used to receive the monitoring data, and to perform data cleaning and data transformation on the monitoring data to obtain data assets; The data management module is used to determine the data category to which the data asset belongs, and to encrypt and store the data asset in the data asset library corresponding to the data category; The data management module is used to generate a data asset catalog corresponding to the data asset library based on the storage results. The data asset catalog includes the mapping relationship between different users and different data assets.
[0008] In one embodiment, the data mining module is further configured to determine target users and target tasks based on application scenario requirements, wherein the application scenario requirements are determined based on the system integration application; The data mining module is also used to obtain the target data assets corresponding to the target user from the data asset library according to the data asset catalog; The data mining module is further configured to determine a corresponding preset decision support model based on the target task, and input the target data asset into the preset decision support model to obtain scenario decision support information.
[0009] In one embodiment, the data mining module is further configured to initialize a decision support model; The data mining module is also used to obtain training data assets from the data asset library based on a preset time interval; The data mining module is also used to train the initialized decision support model according to the training data assets in accordance with the training task strategy, so as to obtain the preset decision support model corresponding to different tasks.
[0010] In one embodiment, the device management module is further configured to determine the user terminal associated with the target user; The data mining module is also used to determine whether the scenario decision support information meets the preset distribution conditions; The data mining module is further configured to send the scenario decision support information to the user terminal if the condition is met.
[0011] In one embodiment, the system further includes: an application support module; The device management module is also used to receive application access requests sent by user terminals and send the access requests to the application support module; The application support module is used to determine the target system integrated application based on the application access request when the application access request is received. The application support module is further configured to obtain scenario decision support information corresponding to the target system integration application from the data mining module, and send the scenario decision support information corresponding to the target system integration application to the user terminal.
[0012] In one embodiment, the application support module is further configured to query the permission configuration information of the target system integrated application, and perform permission verification on the application access request based on the permission configuration information; The application support module is also used to send the scenario decision support information corresponding to the target system integrated application to the user terminal when the verification is successful.
[0013] In one embodiment, the system further includes: an open ecosystem module, which maintains several standardized application programming interfaces; The ecosystem open module is used to receive access applications submitted by third-party applications based on the standardized application programming interface; The ecosystem open module is also used to review the access application and register the third-party application as a system integration application when the review is passed.
[0014] In one embodiment, the ecosystem open module is further configured to obtain the container image corresponding to the third-party application; The application support module is also used to obtain the container image when the third-party application is registered as a system integration application; The application support module is also used to extract the configuration file of the container image, parse the configuration file, and generate the permission configuration information of the system integrated application based on the parsing result.
[0015] This application proposes a multi-scenario smart elderly care and health system based on AIoT technology, including: an equipment management module, a data management module, and a data mining module; the equipment management module is used to collect monitoring data generated by different smart health devices and send the monitoring data to the data management module, and the equipment management module maintains communication connections with each smart health device; the data management module is used to perform data standardization processing on the monitoring data to obtain data assets and store the data assets in a data asset library; the data mining module is used to obtain target data assets from the data asset library according to the application scenario requirements, and input the target data assets into a preset decision support model corresponding to the application scenario requirements to obtain scenario decision support information.
[0016] Because this application's system uses a device management module to uniformly access and maintain the communication connections of various smart health devices, it achieves centralized management and data collection from multiple smart health devices, ensuring the continuity and stability of monitoring data acquisition. Simultaneously, the system uses a data management module to standardize the raw monitoring data, forming data assets with unified specifications. This effectively solves the problems of inconsistent data formats and varying quality from multiple sources, providing a high-quality data foundation. Furthermore, the data mining module can call upon data assets according to scenario requirements and generate accurate scenario-based decision support information through preset decision support models, thereby providing efficient, accurate, and personalized solutions for elderly care services in different scenarios. Attached Figure Description
[0017] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.
[0018] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0019] Figure 1 This is a functional block diagram of the first embodiment of the multi-scenario smart elderly care and health system based on AIoT technology in this application; Figure 2 This is a functional block diagram of the second embodiment of the multi-scenario smart elderly care and health system based on AIoT technology in this application; Figure 3 This is a functional block diagram of the third embodiment of the multi-scenario smart elderly care and health system based on AIoT technology in this application; Figure 4 This is a system architecture diagram of the multi-scenario smart elderly care and health system based on AIoT technology in this application.
[0020] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation
[0021] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.
[0022] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.
[0023] Currently, the elderly care service sector faces the following problems: Existing elderly care platform systems are mostly decentralized, lacking a unified digital infrastructure for resource integration and management. This results in poor data flow between different applications, hindering the formation of synergistic effects. Third-party elderly care applications for different scenarios are often developed by different vendors, employing inconsistent technical standards and interfaces. Modules are difficult to plug in and combine flexibly, severely limiting the system's scalability and adaptability. Furthermore, existing systems are mostly limited to their own service scope, lacking an open ecosystem, making it difficult to integrate external elderly care service resources and providing comprehensive and diversified services for the elderly.
[0024] To address the aforementioned shortcomings, this application proposes a multi-scenario smart elderly care and health system based on AIoT technology. This system includes an equipment management module, a data management module, and a data mining module, as well as an application support module and an ecosystem open module. The equipment management, data management, and data mining modules enable interconnection and interoperability among different smart health devices and intelligent data analysis. The application support and ecosystem open modules allow for the integration of third-party applications, thereby solving the problems of resource fragmentation, poor compatibility, insufficient data value mining, and weak ecosystem collaboration capabilities in existing elderly care systems. This provides efficient, precise, and personalized solutions for elderly care services in different scenarios.
[0025] Based on this, the embodiments of this application provide a multi-scenario smart elderly care and health system based on AIoT technology, see reference. Figure 1 , Figure 1 This is a functional block diagram of the first embodiment of the multi-scenario smart elderly care and health system based on AIoT technology in this application.
[0026] In this embodiment, the system includes: a device management module 10, a data management module 20, and a data mining module 30.
[0027] The device management module 10 is used to collect monitoring data generated by different smart health devices and send the monitoring data to the data management module 20; It should be noted that the device management module 10 can maintain communication connections with different smart health devices, which may include health monitoring devices (heart rate monitors, blood pressure monitors, blood glucose meters, etc.), safety monitoring devices (smart cameras, smoke detectors, infrared sensors, etc.), and life service devices (smart speakers, smart home appliance controllers, etc.).
[0028] It should be understood that the device management module 10 can have multiple built-in communication protocols (such as Wi-Fi, Bluetooth, NB-IoT, etc.) to establish and maintain communication connections with the aforementioned smart health devices of different brands and types.
[0029] Understandably, the device management module 10 can periodically send probe messages within the current network (local area network and / or low power wide area network) to passively listen for or actively query for possible smart health terminals. Then, it can determine the corresponding device type (e.g., Wi-Fi devices, BLE devices, and LoRa / NB-IoT devices) based on the information of the scanned smart health terminals. Furthermore, it can determine the corresponding communication protocol based on different device types, thereby establishing communication connections with each smart monitoring device based on different communication protocol methods.
[0030] For example, for Wi-Fi devices, HTTP(S) or MQTT can be used to complete TLS handshake and account token authentication; for BLE devices, an L2CAP channel can be established to negotiate the MTU and subscribe to feature value notifications; for LoRa / NB-IoT devices, Join requests can be sent according to regional frequency bands, and a reliable link can be created after obtaining DevAddr and session keys.
[0031] After establishing a communication connection with different smart health devices, the device management module 10 can collect monitoring data from different smart health devices in real time or periodically, and then transmit the monitoring data to the data management module 20.
[0032] The data management module 20 is used to perform data standardization processing on the monitoring data, obtain data assets, and store the data assets in the data asset library.
[0033] It should be understood that, since the monitoring data collected by different smart health devices are often multi-source and heterogeneous, the data management module 20 can first perform data standardization processing on these monitoring data, including data cleaning, data transformation and data integration. Data cleaning can remove abnormal and erroneous data from the monitoring data, data transformation can unify the data format and units of the monitoring data, and data integration can associate monitoring data from different sources.
[0034] After the above data standardization process, the monitoring data can be labeled with corresponding semantic tags to serve as data assets and stored in data asset repositories corresponding to different data categories. These data asset repositories can adopt a distributed storage architecture and can be physical databases based on different logical partitions, such as a database of basic information about the elderly, a database of health indicators, a database of device logs, and a database of behavioral activities.
[0035] In addition, data assets can be encrypted before or after being written to a database to ensure data security and privacy, in compliance with legal and regulatory requirements.
[0036] Furthermore, in order to achieve effective management of data assets, after storage is completed, a global data asset catalog can be created or updated based on all existing data assets in the data asset library. This data asset catalog can maintain the correspondence between different users and different data assets, where the user is the user to whom the monitoring data collected by different smart health devices belongs.
[0037] In its implementation, after receiving the monitoring data, the data management module 20 can perform data cleaning and data transformation to obtain data assets; then, it determines the data category to which the data assets belong and encrypts and stores the data assets in the data asset library corresponding to the data category; finally, it generates a data asset catalog corresponding to the data asset library based on the storage results. The data asset catalog includes the mapping relationship between different users and different data assets.
[0038] The data mining module 30 is used to obtain target data assets from the data asset library according to the application scenario requirements, and input the target data assets into the preset decision support model corresponding to the application scenario requirements to obtain scenario decision support information.
[0039] It should be noted that this application scenario requirement can be based on system integration applications, which can be application modules integrated into the application layer of the system for different scenarios. Specifically, this application scenario requirement could be users initiating data requests based on system integration applications in different scenarios; or it could be data requests triggered periodically by system integration applications based on their corresponding scenarios.
[0040] For example, the system integration application may include: a home-based elderly care application module and an institutional elderly care application module; then the application scenario requirement from the home-based elderly care application module can be: "needing fall risk warning service", and the application scenario requirement from the institutional elderly care application module can be: "needing a group health trend analysis report".
[0041] It should be understood that the application scenario requirements may also include target users and target tasks. The target users can be the users that are of interest in the application scenario requirements, and the target tasks are the specific problems that need to be solved.
[0042] Following the example above, for the fall risk warning service, the target user can be an elderly person living alone (A), and the target task can be to predict the fall risk in the next 24 hours; for the group health trend analysis, the target user can be all elderly people in the institution, and the target task can be to analyze the population whose blood pressure control is not up to standard this month and its patterns.
[0043] Next, the data mining module 30 can query the data asset library maintained in the data management module 20 and specifically obtain the data assets corresponding to the target user based on the data asset catalog of the data asset library, that is, obtain the target data assets.
[0044] Following the example above, to analyze the fall risk of Elder A, the mapping relationship of the data asset catalog can be used to extract only Elder A's own historical gait data, activity data, and smart sensor data in Elder A's home, without involving the data assets of other elderly people.
[0045] To conduct institutional group analysis, the mapping relationship of the data asset catalog can be used to extract blood pressure records, medication records, etc. of all elderly people with hypertension.
[0046] It should also be noted that the data mining module 30 can be pre-deployed with machine learning, deep learning and other algorithm models (pre-set decision support models), which can achieve different types of target tasks such as health risk prediction, behavioral habit analysis and abnormal situation prediction.
[0047] The steps of deploying preset decision support models corresponding to different tasks in the data mining module 30 may specifically include: initializing the decision support model; obtaining training data assets from the data asset library based on preset time intervals; and training the initialized decision support model according to the training data assets in accordance with the training task strategy to obtain preset decision support models corresponding to different tasks.
[0048] Initializing the decision support model can involve selecting or building a suitable machine learning algorithm framework (e.g., choosing an LSTM neural network for time-series behavioral analysis, or a random forest model for risk classification) and setting its initial parameters. This provides a "blank" or "basic" starting point for subsequent training, ensuring the model learns the specific knowledge of the elderly care field from scratch.
[0049] The preset time interval is used to specify the rhythm and frequency of model training, which can be "once a week", "once a month", or "after accumulating 1000 new data points". This ensures that the model can periodically learn the latest knowledge and patterns, adapting to possible changes in the health status and behavioral habits of older adults.
[0050] Training data assets can be historical data sets with "labels" or "known results" extracted from a data asset repository. For example, heart rate and activity data of all elderly people over the past three months, along with records of whether these elderly people actually experienced falls. This ensures that the source of training data is high-quality, cleaned, and standardized data assets, rather than raw, messy data.
[0051] Training strategies can include data partitioning strategies, training objectives, and optimization objectives. Data partitioning strategies involve dividing training data into a "training set" (for learning) and a "validation set" (for testing). Training objectives are the desired accuracy or loss rate that the model needs to achieve. Optimization objectives can be specific metrics that the model needs to optimize based on different tasks, such as maximizing "prediction accuracy" or minimizing "false positive rate."
[0052] During training, training data assets are input into the initial decision support model. The model continuously adjusts its internal parameters through algorithms, attempting to find the complex mapping relationship between inputs (such as activity data) and outputs (such as fall risk) from the data, thereby obtaining a preset decision support model suitable for different tasks.
[0053] After acquiring the target data asset, the data mining module 30 can input the target data asset into the preset decision support model corresponding to the target task, so that the model can output scenario decision support information with guiding significance.
[0054] It should be noted that the decision support information in this scenario can serve as a request response to the aforementioned application scenario requirements. This decision support information can include, for example, health logs, disease risks, abnormal behavior judgment results, and behavior analysis reports.
[0055] In the specific implementation, the data mining module 30 can determine the target user and target task according to the application scenario requirements, which are determined based on the system integration application; then, it can obtain the target data asset corresponding to the target user from the data asset library according to the data asset catalog; finally, it can determine the corresponding preset decision support model according to the target task, and input the target data asset into the preset decision support model to obtain scenario decision support information.
[0056] This system, through its device management module, uniformly accesses and maintains the communication connections of various smart health devices, achieving centralized management and data collection from multiple sources and ensuring the continuity and stability of monitoring data acquisition. Simultaneously, the system standardizes the raw monitoring data through its data management module, forming data assets with unified specifications. This effectively solves the problems of inconsistent data formats and varying quality from multiple sources, providing a high-quality data foundation. Furthermore, the data mining module can invoke data assets according to scenario requirements and generate accurate scenario-based decision support information through preset decision support models, thereby providing efficient, accurate, and personalized solutions for elderly care services in different scenarios.
[0057] Based on the first embodiment of this application, in the second embodiment of this application, the content that is the same as or similar to that in the first embodiment described above can be referred to the above description, and will not be repeated hereafter. Based on this, please refer to... Figure 2 , Figure 2 This is a functional block diagram of the first embodiment of the multi-scenario smart elderly care and health system based on AIoT technology in this application.
[0058] In this embodiment, to illustrate the interaction process between the system and the user, the system further includes an application support module 40.
[0059] The device management module 10 is also used to receive application access requests sent by user terminals and send the access requests to the application support module.
[0060] It should be noted that the user terminal can be a terminal device for different users, such as the mobile phone of the elderly or their family members, the work tablet of the caregiver, etc., or other electronic devices that can be connected to this multi-scenario smart elderly care and health system based on AIoT technology.
[0061] The user terminal can send an application access request to the system. This application access request can be a specific request as described in the aforementioned application scenario requirements. For example, a family member of an elderly person (A) wants to check the elderly person's health report for this week.
[0062] The application support module 40 is used to determine the target system integrated application based on the application access request when the application access request is received.
[0063] It should be understood that parsing the application access request can identify the system integration application most closely associated with it, and thus identify it as the target system integration application.
[0064] The application support module 40 is further configured to obtain scenario decision support information corresponding to the target system integration application from the data mining module 30, and send the scenario decision support information corresponding to the target system integration application to the user terminal.
[0065] It is understandable that after determining the target application, the application support module 40 can directly obtain the scenario decision support information corresponding to the target system integration application from the data mining module 30, which stores decision support information for different scenarios; or, it can trigger the data mining module 30 to perform the aforementioned operation of obtaining the target data asset from the data asset library according to the application scenario requirements, and inputting the target data asset into the preset decision support model corresponding to the application scenario requirements to obtain scenario decision support information.
[0066] Next, after obtaining the scenario decision support information, the application support module 40 can encapsulate the scenario decision support information and combine it with some interface data, and finally send it to the user terminal that initiated the request. This allows the holder of the user terminal to know the result of his request on the user terminal interface, realizing the system's proactive response to the user request.
[0067] In addition, to ensure the security of scenario decision support information, after obtaining the scenario decision support information, the application support module 40 can first query the permission configuration information of the target system integrated application, and verify the permission of the application access request according to the permission configuration information; then, when the verification is successful, the scenario decision support information corresponding to the target system integrated application is sent to the user terminal.
[0068] It should be noted that the permission configuration information can be determined when different system integration applications are integrated into the system. This includes the correspondence between different request roles and different data operations and data scopes. Request roles can include: the user, the user's family member, the caregiver, and the administrator; data operations can include: readable, writable, and manageable; data scopes can include: the user's own data, the data of each user in the care group, and all data of the application.
[0069] The application support module 40 can parse the application access request, extract the requesting user role (which can be identified based on the user's terminal login system ID) and the request content (the specific data requested), and then match the user identity and request content with the aforementioned permission configuration information to achieve permission verification.
[0070] If the verification passes, it is determined that the requesting user role has the right to access the requested specific data. At this time, the application support module 40 will send the scenario decision support information corresponding to the target system integrated application to the user terminal normally. Conversely, if the verification fails, it is determined that the requesting user role does not have the right to access the requested specific data. The application support module 40 will not send the requested decision information, but will return an error message of "insufficient permissions" or "access denied" to the user terminal.
[0071] The above access control ensures that users' sensitive health data (such as disease risk prediction) can only be accessed by authorized users, thereby preventing data leakage.
[0072] Furthermore, considering that different application scenarios may require the system to actively send information to the user terminal, the device management module 10 is also used to determine the user terminal associated with the target user.
[0073] It should be understood that the device management module 10 can also maintain information on all user terminals connected to the system and their association with users. When the data mining module 30 generates scenario decision support information, the device management module 10 can first determine the user terminals associated with the target user.
[0074] For example, if the target user is an elderly person who has fallen, the user terminal associated with the target user can be the elderly person's own wristband (used to issue sound and light reminders), a family member's mobile phone, or a computer in the community care center.
[0075] The data mining module 30 is also used to determine whether the scenario decision support information meets the preset distribution conditions.
[0076] It should be understood that the preset distribution conditions can be business rules pre-set according to different information types. For example, when the information type is an emergency alarm, the distribution time is immediate; when the information type is a risk reminder, the distribution time is daily; and when the information type is a periodic report, the distribution time is upon request (distributed only when an application access request is received).
[0077] Specifically, the data mining module can first determine the information type of the scenario decision support information pair, then query the business rules corresponding to the information type, and determine whether the scenario decision support information should be issued and when it should be issued based on the business rules.
[0078] The data mining module 30 is further configured to send the scenario decision support information to the user terminal if the scenario is true.
[0079] It should be noted that when the scenario decision support information meets the preset distribution conditions, the scenario decision information is directly sent to the user terminal associated with the target user based on the determined distribution time. The user terminal can then notify the user and its associated users through ringing, pop-up vibration, or other means, thereby realizing the system's proactive intervention and real-time warning to the user.
[0080] This embodiment of the system, through the collaboration of the device management module and the data mining module, enables the system to automatically judge and target early warning information. It can automatically and accurately send key scenario decision support information (such as fall alarms and health risk reminders) to associated user terminals (such as devices of family members and caregivers) according to preset rules (such as urgency level and time strategy), thereby ensuring timely intervention and closed-loop processing of high-risk events. Simultaneously, by introducing an application support module as a request scheduling and security intermediary, the system can respond to proactive access requests from user terminals, locate the target system integration application based on the request content, and securely return the corresponding scenario decision support information. This allows users to obtain personalized and intelligent analysis results on demand, achieving a two-way service capability combining proactive early warning and on-demand querying, improving the system's practicality and response efficiency in real-world elderly care scenarios.
[0081] Based on the first and second embodiments of this application, in the third embodiment of this application, the content that is the same as or similar to that in embodiments one and two above can be referred to the above description, and will not be repeated hereafter. Based on this, please refer to... Figure 3 , Figure 3 This is a functional block diagram of the first embodiment of the multi-scenario smart elderly care and health system based on AIoT technology in this application.
[0082] In this embodiment, to illustrate the interaction process between the system and the user, the system further includes an open ecosystem module 50, which maintains several standardized application programming interfaces.
[0083] The open ecosystem module 50 is used to receive access applications submitted by third-party applications based on the standardized application programming interface.
[0084] It should be noted that the open ecosystem module 50 provides a unified, predefined, standardized application programming interface (API). This standardized API specifies the development specifications and technical interfaces for third-party developers, i.e., how third-party applications can access this system (data format, communication protocol, authentication method, etc.), thereby enabling the expansion of the system's application modules.
[0085] It should be understood that third-party developers can submit access applications through the standardized API after developing their third-party applications in accordance with the above development specifications. These third-party applications can be from external elderly care service institutions, enterprises, or research units.
[0086] The ecosystem open module 50 is also used to review the access application and register the third-party application as a system integration application when the review is passed.
[0087] Understandably, the open ecosystem module 50 can conduct a formal review of access applications to determine whether the technical implementation of the third-party application complies with the system's standards and specifications, whether the interface calls are correct, and whether there are any obvious security risks.
[0088] Furthermore, upon successful review, the third-party application is registered in the application layer of this system as an integrated application.
[0089] Furthermore, to illustrate how third-party applications can be registered in this system, the ecosystem open module 50 is also used to obtain the container image corresponding to the third-party application.
[0090] The application support module 40 is also used to obtain the container image when the third-party application is registered as a system integration application.
[0091] The application support module 40 is further configured to extract the configuration file of the container image, parse the configuration file, and generate permission configuration information for the system integrated application based on the parsing result.
[0092] It's important to note that a container image can be a standardized, lightweight application package containing all the code, environment, and dependencies required for a third-party application to run. This container image can embed a configuration file that declaratively describes the basic information and resource requirements of the third-party application.
[0093] When the third-party application is registered as a system integration application, the application support module 40 can obtain the container image of the third-party application from the ecosystem open module 50, parse it, and automatically generate the permission configuration information of the third-party application based on the parsing result.
[0094] For example, the application support module 40 can read the required data fields and required operation fields in the configuration file, and then convert them into a set of executable permission policy rules. For example, application B can read the health data table, but cannot write to the health data table. Finally, the above permission policy is bound to preset role types (user, family member, caregiver, administrator) to obtain the permission configuration information of the third-party application.
[0095] Furthermore, this can be combined with Figure 4 The application scenarios of system integration in this application are illustrated by example. Figure 4 This is a system architecture diagram of the multi-scenario smart elderly care and health system based on AIoT technology in this application.
[0096] Depend on Figure 4 It can be seen that the above-mentioned equipment management module 10, data management module 20, data mining module 30, application support module 40 and ecosystem open module 50 in the system of this application can be regarded as the data base of the system; while the third-party applications connected to the system, i.e. system integration applications, can be maintained by the application layer of the system, which are represented in the figure as different application modules: home-based elderly care application module, community elderly care application module, institutional elderly care application module, high-end sanatorium application module and elderly care training room application module, etc.
[0097] The home-based elderly care application module can be further divided into: health monitoring sub-module, safety monitoring sub-module, smart home control sub-module, remote care sub-module, etc. Figure 4 (Not shown in the text, used only for the functional description of this module). The health monitoring submodule collects the elderly's health data through the AIoT Smart Connection Center, uploads it to the data management module 20, and after analysis by the data mining module 30, provides the elderly with health assessments and suggestions; the safety monitoring submodule monitors the safety status of the elderly's home environment in real time, and in case of fire, water leakage, etc., it promptly issues alarms and notifies family members and the community; the smart home control submodule enables remote control of smart home appliances, facilitating the elderly's daily life; the remote care submodule supports family members to make video calls with the elderly and check on the elderly's activities.
[0098] The community-based elderly care application module can be further divided into: community service appointment sub-module, activity organization sub-module, health lecture sub-module, volunteer management sub-module, etc. Figure 4 (Not shown in the text, used only for the functional description of this module). The Community Service Appointment submodule allows seniors to book home care, rehabilitation nursing, and other services provided by the community online; the Activity Organization submodule is responsible for the planning, organization, and registration management of various community elderly care activities; the Health Lecture submodule regularly holds health knowledge lectures to provide health guidance for seniors; and the Volunteer Management submodule recruits, trains, and dispatches community volunteers to provide volunteer services for seniors.
[0099] The institutional elderly care application module can be further divided into: a sub-module covering elderly information management, a nursing plan, a dietary management, and a medical connection, etc. Figure 4 (Not shown in the text, used only for the functional description of this module). The Elderly Information Management submodule stores the basic information, health status, and nursing records of the elderly; the Nursing Plan submodule develops personalized nursing plans based on the elderly's health status and arranges for nursing staff to implement them; the Dietary Management submodule develops reasonable dietary plans based on the elderly's dietary needs and health status, and manages the procurement of ingredients and the preparation of meals; the Medical Integration submodule enables information exchange with hospitals to provide convenient medical services for the elderly, such as appointment registration and remote diagnosis and treatment.
[0100] The high-end sanatorium application module can be further divided into: high-end health assessment sub-module, personalized rehabilitation sub-module, premium lifestyle service sub-module, health tracking sub-module, etc. Figure 4 (Not shown in the text, used only for the functional description of this module). The high-end health assessment submodule uses advanced testing equipment and assessment methods to provide comprehensive health assessments for the elderly; the personalized rehabilitation submodule develops personalized rehabilitation plans based on the elderly's health status and rehabilitation needs, and is guided by professional rehabilitation therapists; the premium lifestyle services submodule provides high-end accommodation, catering, entertainment and other services to meet the elderly's high-quality life needs; the health tracking submodule tracks changes in the elderly's health status in real time and adjusts rehabilitation plans and service content in a timely manner.
[0101] The elderly care training lab application module can be further divided into: simulation teaching sub-module, skills assessment sub-module, case library sub-module, and teacher management sub-module, etc. Figure 4(Not shown in the text, used only for subsequent module function descriptions). The simulation teaching submodule utilizes technologies such as virtual reality (VR) and augmented reality (AR) to simulate various elderly care scenarios and nursing operations, providing trainees with an immersive learning experience; the skills assessment submodule assesses and evaluates trainees' nursing skills and generates assessment reports; the case library submodule collects and organizes a large number of elderly care cases to provide reference for teaching and practice; the teacher management submodule manages training teachers, including teacher information, teaching plans, and teaching evaluations.
[0102] The different application modules maintained at the application layer of the aforementioned system are functionally independent, accessed through the open ecosystem module 50 and maintained by the application support module 40. This allows for the arbitrary plugging and unplugging and combination of application modules in different scenarios, greatly improving the system's scalability and adaptability, and helping to meet the diverse needs of different scenarios.
[0103] This embodiment introduces an open ecosystem module 50 to receive and review access applications from third-party applications using a standardized application programming interface (API), and registers them as system integration applications. This achieves an ecological expansion of the platform's service capabilities and solves the problems of slow functional iteration and difficulty in accessing external services under the traditional closed architecture. Furthermore, through collaboration with the application support module 40, the container image of a third-party application is automatically obtained and its configuration file is parsed during registration. Based on this, the permission configuration information of the application is automatically generated, realizing the automation and standardization of permission management during the integration of third-party applications. This not only significantly improves the access efficiency of system functional modules, but also ensures the security and controllability of the platform's external access services through permission control.
[0104] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the multi-scenario smart elderly care and health system based on AIoT technology in this application. Any simple modifications based on this technical concept are within the protection scope of this application.
[0105] It should be noted that all user-related data involved in this application (e.g., monitoring data generated by different smart health devices) was obtained with the user's permission or consent. In other words, when this application is used in specific products or technologies, user permission is required to acquire and process the relevant data, and the processing of the data must comply with the relevant laws, regulations, and regulatory standards of the relevant countries and regions. For example, before different smart health devices collect and generate monitoring data and send the data to the system of this application, a monitoring data acquisition prompt may be displayed on the user's terminal. Only after receiving confirmation from the user regarding the monitoring data acquisition prompt will the monitoring data be collected, generated, and sent to the system of this application.
[0106] In this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or system. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element.
[0107] The sequence numbers of the embodiments in this application are merely for description and do not represent the superiority or inferiority of the embodiments. 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 read-only memory / random access memory, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of this application.
[0108] The above are merely preferred embodiments of this application and do not limit the scope of this application. Any equivalent structural or procedural transformations made based on the description and drawings of this application, or direct or indirect applications in other related technical fields, are similarly included within the protection scope of this application.
Claims
1. A multi-scenario smart elderly care and health system based on AIoT technology, characterized in that, The system includes: an equipment management module, a data management module, and a data mining module; The device management module is used to collect monitoring data generated by different smart health devices and send the monitoring data to the data management module. The device management module maintains communication connections with each of the smart health devices. The data management module is used to perform data standardization processing on the monitoring data, obtain data assets, and store the data assets in the data asset library; The data mining module is used to obtain target data assets from the data asset library according to the application scenario requirements, and input the target data assets into the preset decision support model corresponding to the application scenario requirements to obtain scenario decision support information.
2. The system as described in claim 1, characterized in that, The device management module is used to scan several smart health devices in the current network; The device management module is used to determine the device type of each of the smart health devices and determine the corresponding communication protocol method according to the device type; The device management module is used to establish communication connections with each of the intelligent monitoring devices according to the respective communication protocols.
3. The system as described in claim 1, characterized in that, The data management module is used to receive the monitoring data, perform data cleaning and data transformation on the monitoring data, and obtain data assets. The data management module is used to determine the data category to which the data asset belongs, and to encrypt and store the data asset in the data asset library corresponding to the data category; The data management module is used to generate a data asset catalog corresponding to the data asset library based on the storage results. The data asset catalog includes the mapping relationship between different users and different data assets.
4. The system as described in claim 3, characterized in that, The data mining module is also used to determine target users and target tasks based on application scenario requirements, which are determined based on system integration applications. The data mining module is also used to obtain the target data assets corresponding to the target user from the data asset library according to the data asset catalog; The data mining module is further configured to determine a corresponding preset decision support model based on the target task, and input the target data asset into the preset decision support model to obtain scenario decision support information.
5. The system as described in claim 4, characterized in that, The data mining module is also used to initialize the decision support model; The data mining module is also used to obtain training data assets from the data asset library based on a preset time interval; The data mining module is also used to train the initialized decision support model according to the training data assets in accordance with the training task strategy, so as to obtain the preset decision support model corresponding to different tasks.
6. The system as described in claim 4, characterized in that, The device management module is also used to determine the user terminal associated with the target user; The data mining module is also used to determine whether the scenario decision support information meets the preset distribution conditions; The data mining module is further configured to send the scenario decision support information to the user terminal if the scenario is true.
7. The system as described in claim 4, characterized in that, The system also includes: an application support module; The device management module is also used to receive application access requests sent by user terminals and send the access requests to the application support module; The application support module is used to determine the target system integrated application based on the application access request when the application access request is received. The application support module is further configured to obtain scenario decision support information corresponding to the target system integration application from the data mining module, and send the scenario decision support information corresponding to the target system integration application to the user terminal.
8. The system as described in claim 7, characterized in that, The application support module is also used to query the permission configuration information of the application integrated into the target system, and to verify the permission of the application access request based on the permission configuration information. The application support module is also used to send the scenario decision support information corresponding to the target system integrated application to the user terminal when the verification is successful.
9. The system as described in claim 7, characterized in that, The system also includes: an open ecosystem module, which maintains several standardized application programming interfaces; The ecosystem open module is used to receive access applications submitted by third-party applications based on the standardized application programming interface; The ecosystem open module is also used to review the access application and register the third-party application as a system integration application when the review is passed.
10. The system as described in claim 9, characterized in that, The ecosystem open module is also used to obtain the container image corresponding to the third-party application; The application support module is also used to obtain the container image when the third-party application is registered as a system integration application; The application support module is also used to extract the configuration file of the container image, parse the configuration file, and generate the permission configuration information of the system integrated application based on the parsing result.