Intelligent health-care cloud platform and data interaction method thereof

Abstract

The invention belongs to the field of intelligent health care, and particularly relates to an intelligent health care cloud platform and a data interaction method thereof, and the intelligent health care cloud platform comprises an application layer, an interface layer and a base layer. The application layer is used for receiving the external instruction sent by the base layer, outputting the processed instruction as an output port of the intelligent health-care platform and providing service for a terminal user; the interface layer is used as a data interaction intermediate module to realize data uploading and distribution between the application layer and the base layer, and is also used for issuing an equipment instruction in the application layer to ensure accurate and efficient transmission of data between different protocols and systems; and the base layer is used for carrying out artificial intelligence operation and data analysis through the data obtained by docking the protocols of the interface layer, processing the analyzed data, and finally outputting and distributing the data to each device in the application layer through the interface layer. According to the invention, various devices and functions are integrated into the cloud platform in a mode of a new generation AI intelligent health-care large model formed by artificial intelligence and data analysis.

Description

Technical Field

[0001] The present invention belongs to the field of smart health care, and specifically relates to a smart health care cloud platform and a data interaction method thereof. Background Art

[0002] In recent years, with the acceleration of the aging process of the global population, the number of elderly people has continued to rise. The elderly population has the characteristics of a large base, a high proportion, rapid growth, and many empty nests. In addition to the basic living needs of food, clothing, housing and transportation, the elderly need companionship, and the demand for health care services has shown explosive growth. At the same time, people's attention to health and quality of life is also continuing to increase, which makes the traditional health care model increasingly difficult to meet the needs of modern society.

[0003] It is understood that the current home-based elderly care equipment is basically testing equipment and rehabilitation equipment. There are many varieties of these two types of equipment on the market, but they are basically in an independent state. Independence is conducive to the management of the equipment by family members and caregivers, but not conducive to the integration and analysis of data.

[0004] Traditional healthcare services rely heavily on manual information collection and management, which is inefficient and prone to errors. For example, nursing homes and community service staff often use paper documents or simple spreadsheets to record elderly health information. This not only consumes a lot of manpower and time, but also makes it difficult to ensure the accuracy and timeliness of the data. Errors or omissions in the data can lead to misjudgments of the elderly's health status, which in turn affects subsequent healthcare service decisions.

[0005] In terms of device management, the use of various smart devices, such as rehabilitation equipment and medical assistance devices, in the healthcare sector is increasing. However, these devices lack effective interoperability mechanisms. Data from disparate devices cannot be centrally integrated and analyzed, making it difficult for medical staff and healthcare providers to fully understand the health status and device usage of the elderly. For example, the heart rate and movement data recorded by smart bracelets cannot be correlated and analyzed with the usage data of rehabilitation equipment, making it difficult to extract more valuable health information.

[0006] Furthermore, when it comes to health early warning, traditional healthcare models often rely on regular manual checkups or voluntary reporting of health conditions by seniors, resulting in a delay in the detection of health issues. Many chronic diseases or health emergencies go undetected, delaying the optimal treatment opportunity. Furthermore, traditional health early warning methods lack personalization and are unable to accurately provide warnings tailored to each senior's specific health status and medical history.

[0007] Traditional healthcare models lack efficient data processing and analysis capabilities. Faced with massive amounts of elderly health data and healthcare service data, they are unable to conduct in-depth mining and analysis, making it difficult to extract valuable information to support healthcare service optimization and decision-making. For example, it is impossible to analyze the elderly's long-term health data to predict potential health risks and develop personalized healthcare plans in advance.

[0008] In summary, the existing healthcare model has many shortcomings in information management, device interconnection, health warnings, and data processing. Therefore, there is an urgent need for an innovative smart healthcare solution to achieve intelligent, efficient, and personalized healthcare services to meet the growing demand for healthcare. Summary of the Invention

[0009] The purpose of the present invention is to provide a smart health care cloud platform and its data interaction method. By leveraging the capabilities of AI big data and large models, it is aimed at the elderly, integrates data for analysis, and provides rich smart home care application scenarios to solve the elderly's daily health care services, health management, psychological comfort, safety protection and other needs. Through mobile terminals, wearable devices, service robots and other smart devices as carriers, it completes daily communication with the elderly and basic life services, becoming a software carrier that is a true life partner for the elderly.

[0010] The technical solution adopted by the present invention to achieve the above-mentioned purpose is: a smart health care cloud platform, including: an application layer, an interface layer and a basic layer;

[0011] The application layer is used to receive external instructions sent by the basic layer. It also serves as the output port of the smart health care platform, outputting processed instructions and providing services to end users.

[0012] The interface layer is used as an intermediate module for data interaction, enabling data upload and distribution between the application layer and the basic layer. It also issues instructions to devices in the application layer to ensure accurate and efficient data transmission between different protocols and systems.

[0013] The basic layer is used to perform artificial intelligence operations and data analysis on the data obtained through the various protocols of the interface layer. After processing the analyzed data, the final output is distributed to various devices in the application layer through the interface layer.

[0014] The application layer includes: client, smart device, third-party service platform and server;

[0015] The client is any one of a large screen, PC or mobile phone, used for display status display, device control, data analysis, and uploading to the public cloud;

[0016] The smart devices include rehabilitation equipment, medical assistance equipment, smart robots, and third-party medical and health management equipment; they are used to upload user usage data to the public cloud in preparation for health data analysis;

[0017] The third-party service platform includes call center services and community service systems, which are used to provide health consultation and emergency response services, and upload the corresponding service processes to the public cloud;

[0018] The server side is used to adopt a distributed server architecture to provide hardware and network environment for system operation.

[0019] The interface layer includes: the interface layer includes: a Json protocol parsing module, a byte stream protocol parsing module, an Http protocol, a WebSocket protocol and an MQTT protocol.

[0020] The client communicates with the public cloud via any one of the Http protocol, WebSocket protocol or MQTT protocol;

[0021] The smart device and the server both communicate with the public cloud via HTTP or MQTT protocol;

[0022] The third-party service platform communicates with the public cloud via HTTP protocol;

[0023] The data interaction between the application layer and the basic layer adopts the Json string format, and is connected through the Json protocol parsing module and the byte stream protocol parsing module to achieve data interaction.

[0024] The basic layer includes: a data storage module, a background management module, a data analysis module and an artificial intelligence calculation module;

[0025] The data analysis module is used to perform operation data analysis and health warning analysis on the smart device operation data and user health data obtained from the interface layer, compare the analysis results with the original user information stored in the background management module, and send the comparison results to the data storage module for data storage; if the compared data shows health problems, the health warning mechanism is triggered and an alarm information is sent to relevant personnel through the application layer;

[0026] The artificial intelligence operation module is used to perform image processing and audio and video processing on the images, audio and video data collected by the smart device, and send the processed images and audio to the data storage module for data storage;

[0027] The background management module is used to store original user information and wait for the data analysis module to call;

[0028] The data storage module is used to receive data processed by the data analysis module and the artificial intelligence operation module, and feed it back to various sub-modules of the application layer through the interface layer to achieve closed-loop management of data and provide decision-making basis and service support for the application layer.

[0029] The application layer provides services for the end users themselves, including: providing real-time monitoring of the status of nursing homes and community services, displaying them in the form of statistical graphics and detailed data lists; it also supports the access of third-party smart devices and service systems, provides an extensible interface, and reserves multiple access methods for subsequent access to more types of data.

[0030] A data interaction method for a smart healthcare cloud platform includes the following steps:

[0031] 1) The data analysis module obtains various data such as smart device operation data and user health data from the interface layer, and uses preset algorithms and models to analyze the data to analyze the operation time and failure frequency of smart devices;

[0032] 2) Simultaneously, health warning analysis is performed, and the analysis results are compared with the original user information stored in the background management module to identify anomalies in the data. If the compared data indicates health problems, the health warning mechanism is triggered and an alert is sent to relevant personnel through the application layer. At the same time, all analysis results are sent to the application layer.

[0033] 3) The artificial intelligence computing module receives the image, audio and video data collected by the intelligent device, and uses the image recognition algorithm and audio analysis algorithm to perform image processing and audio and video processing on the image, audio and video data;

[0034] 4) Sending the results obtained after image processing and audio and video processing to the data storage module for storage and then sending them to the application layer;

[0035] 5) The backend management module continuously stores original user information, including basic health information, past medical history, and service records, and provides corresponding data for timely access based on the needs of the data analysis module;

[0036] 6) The data storage module receives the data processed by the data analysis module and the artificial intelligence operation module, classifies, stores and manages the data, and feeds it back to the various sub-modules of the application layer through the interface layer to provide decision support and service basis for the application layer.

[0037] The step 1) is specifically as follows:

[0038] 1-1) Data collection and processing

[0039] Receive smart device operation data from the interface layer, and unify the data format into a system-recognizable format after parsing according to the transmission protocol; perform preliminary cleaning of the data to remove obviously erroneous or incomplete data records; and classify and store the data by device type and device number.

[0040] 1-2) Calculate the running time

[0041] For each device, extract the device start and stop timestamps. If the device is running continuously, record the timestamps at fixed intervals, and then calculate the duration of each device operation by using the timestamp difference.

[0042] Accumulate the device running time within a period of time to obtain the total running time of the device in that period;

[0043] 1-3) Calculate the failure frequency

[0044] Define fault indicators, including device error codes and abnormal status signals. When these fault indicators are detected, record the time and device number of the fault. Count the number of faults per device per unit time. Also, use moving average or exponential smoothing time series analysis methods to predict future fault frequencies.

[0045] 1-4) Physiological indicator data acquisition and preprocessing

[0046] The user's physiological indicator data is obtained from the interface layer. The data is also formatted and cleaned, and missing and outliers are processed. Missing values are supplemented using mean filling and linear interpolation. Outliers, such as data that exceeds the normal physiological range by several times, are corrected or excluded by comparing with historical data.

[0047] 1-5) All analysis results are sent to the data storage module for storage, and then sent to the application layer through the interface layer for status display, device control and data analysis display.

[0048] The step 2) is specifically as follows:

[0049] 2-1) Use the K-Means clustering algorithm to cluster the current analysis results with similar data in the original user information. Observe the position of the new data point in the cluster to determine whether it belongs to the normal cluster range. If a user's current value is in an isolated cluster in the cluster analysis and deviates significantly from the normal value cluster, the corresponding point is considered an outlier.

[0050] 2-2) If the matched data indicates health issues, a health warning mechanism is triggered. A mapping relationship between warning levels and corresponding treatment measures is established to determine the warning level. A mild warning can be pushed through the client to remind the user to take rest; a moderate warning can not only push a reminder but also notify community service personnel for follow-up; a severe warning will directly contact a medical institution and initiate emergency rescue procedures;

[0051] 2-3) Send alert information including health status details and recommended measures to users, medical staff or relevant managers through the application layer client and third-party service platform channels.

[0052] The step 6) is specifically as follows:

[0053] 6-1) The data storage module receives data requests from each sub-module of the application layer through the interface layer and sends them in a specific protocol format, including the requested data type, query conditions, and target application layer sub-module identification information;

[0054] 6-2) Parsing the received data request to extract key information including data type, query conditions, and target application layer submodule;

[0055] 6-3) Based on the parsed request information, query and filter the stored data; and feed the queried and filtered data back to the various sub-modules of the application layer through the interface layer; before feeding back, convert the data format according to the requirements of the sub-modules in the application layer and the protocol in the interface layer;

[0056] 6-4) When providing feedback, ensure the integrity and accuracy of the data and record relevant information for subsequent monitoring and analysis.

[0057] The present invention has the following beneficial effects and advantages:

[0058] 1. The data storage module in this invention categorizes, stores, and manages various types of data, applying multiple algorithms to create, update, and delete data indexes, ensuring efficient data storage and rapid querying. For example, when processing massive amounts of smart device operating data and user health data, it can quickly locate and extract required information, providing a solid data foundation for subsequent data analysis and decision-making. Furthermore, the algorithmic steps involved in data interaction and processing ensure data accuracy and consistency, avoiding the data errors and inconsistencies encountered in traditional models.

[0059] 2. The present invention uses the health warning analysis algorithm in the data analysis module, combined with the processing of image and audio and video data by the artificial intelligence operation module, to monitor the user's physiological indicators in real time and conduct personalized health risk assessments based on each user's original information and historical data. Once a health problem is discovered, the early warning mechanism can be quickly triggered and the warning level can be accurately determined. Compared with the traditional method of relying on regular manual inspections or active reporting by the elderly, the timeliness of discovering health problems is greatly improved, and precious treatment time is gained for users. Moreover, the personalized early warning method can provide more accurate and targeted health warnings based on the health status and medical history of different users.

[0060] 3. This invention enables interconnectivity among various smart devices. Smart devices upload data to the public cloud via specific protocols and then interact with other modules via the interface layer. This enables medical staff and health care providers to fully understand the health status and device usage of the elderly. They can correlate and analyze the heart rate and exercise data recorded by the smart bracelet with the usage data of rehabilitation equipment, thereby uncovering more valuable health information and providing a basis for developing more scientific rehabilitation plans and health management programs.

[0061] 4. The application layer of the present invention provides convenient services for end users. Through large screens, PCs, mobile phones and other clients, users can obtain real-time monitoring information on the status of nursing homes and community services, and perform equipment control and data analysis. The access of third-party service platforms enriches the service content, and the distributed architecture of the server side ensures the stable operation of the system. At the same time, the powerful data analysis and artificial intelligence computing capabilities of the basic layer provide decision support and service basis for the application layer, enabling health care services to meet user needs more accurately and efficiently, and improving the overall quality of health care services.

[0062] 5. This smart healthcare cloud platform boasts excellent scalability and compatibility. The application layer supports access to third-party smart devices and service systems, with multiple access methods reserved. Multiple protocol parsing modules and different protocol options at the interface layer enable the platform to adapt to the data interaction needs of diverse devices and systems. This facilitates the continuous introduction of new healthcare technologies and services, enabling it to continuously meet the evolving needs of the healthcare sector and drive innovation and progress within the smart healthcare industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 This is the interconnection framework diagram of the components of the smart healthcare platform;

[0064] Figure 2 A topological diagram of the projects included in the application layer;

[0065] Figure 3 It is the protocol framework contained in the interface layer;

[0066] Figure 4 A framework diagram of the projects included in the base layer;

[0067] Among them, 1 is the smart health care platform, 2 is the application layer, 3 is the interface layer, 4 is the basic layer, 5 is the client, 6 is the smart device, 7 is the third-party service platform, 8 is the server, 9 is the public cloud, 10 is the Json protocol parsing module, 11 is the byte stream protocol parsing module, 12 is the Http protocol, 13 is the WebSocket protocol, 14 is the MQTT protocol, 15 is the data storage module, 16 is the background management module, 17 is the data analysis module, and 18 is the artificial intelligence calculation module. DETAILED DESCRIPTION

[0068] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments.

[0069] The smart health care cloud platform 1 of the present invention is composed of an application layer 2, an interface layer 3 and a basic layer 4. Figure 1 shown.

[0070] Specifically, application layer 2, as the input port of the smart health care platform 1, is used to receive external instructions sent by the basic layer. At the same time, as the output port of the smart health care platform 1, it outputs processed instructions to provide services for the end users themselves, and provides real-time monitoring of the status of nursing homes and community services, including elderly health information, community service conditions, etc., displayed in the form of statistical graphics and detailed data lists; application layer 2 also supports the access of third-party smart devices and service systems, provides an extensible interface, and reserves multiple access methods for subsequent access to more types of data.

[0071] Among them, such as Figure 2 As shown, the application layer 2 includes a client 5, a smart device 6, a third-party service platform 7 and a server 8.

[0072] The client 5 uses a large screen, PC, mobile phone, etc. to display functional interfaces such as status display, device control, and data analysis, and uploads them to the public cloud 9;

[0073] Smart devices 6 include rehabilitation equipment, medical assistance equipment, smart robots, third-party medical and health management equipment, etc. Smart devices 6 upload user usage data to the public cloud 9 to prepare for health data analysis.

[0074] Third-party service platform 7, including call center services and community service systems. The call center service provides health consultation, emergency response and other services; the community service system provides access to data such as family nursing bed statistics, training and employment services, and uploads the corresponding service processes to the public cloud 9;

[0075] The server side 8 adopts a distributed server architecture, including a web application server, a file server, and a database server, providing the hardware and network environment for system operation. These three types of servers can run on the same server depending on the service scale.

[0076] Recombination Figure 2 As shown, the data obtained by the client 5, smart device 6, third-party service platform 7, and server 8 interact with the public cloud 9 through the interface layer 3. The interface layer 3 is the intermediate module for data exchange between the client 5, smart device 6, third-party service platform 7, and the health care cloud platform 1, enabling data upload and distribution between various systems, issuing instructions to smart devices, and other operations.

[0077] like Figure 3 As shown, the interface layer 3 is used as a data interaction intermediate module to realize data upload and distribution between the application layer 2 and the basic layer 4. It also issues operations on device instructions in the application layer 2 to ensure accurate and efficient data transmission between different protocols and systems;

[0078] The interface layer 3 includes: a Json protocol parsing module 10, a byte stream protocol parsing module 11, an Http protocol 12, a WebSocket protocol 13 and an MQTT protocol 14.

[0079] The client 5 can choose Http protocol 12, WebSocket protocol 13 or MQTT protocol 14 to communicate with the public cloud 9, the smart device 6 can choose HTTP protocol 12, MQTT protocol 14 to communicate with the public cloud 9, the third-party service platform 7 uses HTTP protocol 12 to communicate with the public cloud 9, and the server 8 can also choose HTTP protocol 12, MQTT protocol 14 to communicate with the public cloud 9. The data interaction method uses Json string format for Json protocol parsing module 10 and byte stream protocol parsing module 11.

[0080] The basic layer 4 is used to perform artificial intelligence calculations and data analysis on the data obtained through the various protocols of the interface layer 3. After processing the analyzed data, the final output is distributed to various devices in the application layer 2 through the interface layer 3.

[0081] Combine Figure 4 As shown, the basic layer 4 includes a data storage module 15, a background management module 16, a data analysis module 17, and an artificial intelligence computing module 18. The basic layer 4 provides data storage and computing capabilities for the smart healthcare cloud platform 1 and manages user and device information.

[0082] The data analysis module 17 is used to perform operation data analysis and health warning analysis on the smart device operation data and user health data obtained from the interface layer, compare the analysis results with the original user information stored in the background management module 16, and send the comparison results to the data storage module 15 for data storage. If the compared data indicates health problems, the health warning mechanism is triggered and an alarm information is sent to relevant personnel through the application layer;

[0083] The artificial intelligence operation module 18 is used to perform image processing and audio and video processing on the images, audio and video data collected by the smart device, and send the processed images and audio to the data storage module 15 for data storage;

[0084] The backend management module 16 is used to store the original user information and wait for the data analysis module 17 to call;

[0085] The data storage module 15 is used to receive data processed by the data analysis module 17 and the artificial intelligence operation module 18, and feed it back to various sub-modules of the application layer 2 through the interface layer 3, thereby realizing closed-loop management of data and providing decision-making basis and service support for the application layer 2.

[0086] For example Figure 4 As shown, in the data storage module of this smart health care cloud platform, the database and file management jointly build a comprehensive and efficient data storage system.

[0087] The database component includes relational, time-series, and triple databases, each of which performs its own function to meet the storage needs of different data. Relational databases, with their mature transaction processing capabilities and advantages in structured data management, are responsible for storing large amounts of structured health care data, such as basic user information, device configuration parameters, service records, etc. By defining clear table structures and relationships, data addition, deletion, modification, and query operations can be easily performed, providing a solid foundation for data analysis and the implementation of business logic. For example, when recording the health records of the elderly, various physiological indicators, medical records, etc. can be stored in a structured table format, making it easy for medical staff to query and analyze changes in the elderly's health status at any time.

[0088] Time-series databases are specifically optimized for storing data with time-series characteristics. In the healthcare sector, user physiological data continuously collected by smart devices, such as heart rate, blood pressure, blood sugar, and other indicators that change over time, as well as device operating status data, are all suitable for storage in time-series databases. They can efficiently handle the insertion, query, and aggregation operations of time-series data, quickly analyze data trends over time, and provide key support for health warnings and equipment maintenance. For example, by performing trend analysis on the heart rate data of elderly people over a period of time, abnormal heart rate fluctuations can be detected in a timely manner, providing early warning of potential health risks.

[0089] The triple database is primarily used to store data related to knowledge graphs. It clearly expresses the semantic relationships between various health care data in the form of "subject-verb-object" triples. Within this platform, it can be used to construct knowledge graphs for elderly health and device association graphs. For example, the triple database can describe the relationship between a particular elderly disease and its associated symptoms and treatments, as well as the associations between different smart devices in terms of functions and usage scenarios, providing rich semantic information support for intelligent decision-making and data analysis.

[0090] File management encompasses video, audio, and log management. Video and audio files are of significant value in healthcare services. For example, videos of elderly individuals' daily lives captured by smart cameras can be used to analyze their behavioral patterns and activities, identifying falls and unusual behavior. Voice communication records between medical staff and the elderly can serve as a crucial basis for evaluating health consultations and service quality. The file management system ensures the orderly storage, categorized management, and rapid retrieval of these video and audio files, ensuring timely access when needed.

[0091] Log management records key information during system operation, including user operation logs, device operation logs, and data processing logs. This log information is crucial for system troubleshooting, performance optimization, and data tracing. For example, when a system anomaly occurs, analyzing operation logs can quickly pinpoint the problem. Analyzing device operation logs can also help understand device usage and potential failure risks, enabling proactive maintenance.

[0092] Through the collaborative work of database and file management, the data storage module provides comprehensive and reliable data storage support for the smart health care cloud platform, ensuring that all types of health care data can be properly managed and efficiently utilized.

[0093] The working principle of the basic layer 4 is as follows: the basic layer 4 performs artificial intelligence calculations and data analysis on the data obtained through the various protocols of the interface layer 3. Artificial intelligence calculations include image processing and audio and video processing, and data analysis includes operation data analysis and health warning analysis. That is, the data obtained is compared with the original user information stored in the background management module 16, and the conclusion is drawn and then stored in the data storage module 15, and then fed back to the various sub-modules of the application layer 2 through the interface layer 3. If the compared data has health problems, a health warning is issued; the data analysis module 17 and the artificial intelligence calculation module 18 constitute the AI smart health care model; the data storage module 15 and the background management module 16 constitute the storage and management of the application layer 2 data.

[0094] Based on the smart health care cloud platform of the present invention, the present invention also provides a data interaction method of the smart health care cloud platform, comprising the following steps:

[0095] 1) The data analysis module 17 obtains various data such as the operation data of the smart device 6 and the user's health data from the interface layer 3, and uses the preset algorithms and models to analyze the operation data of the data to analyze the operation time and failure frequency of the smart device 6;

[0096] 1-1) Data collection and processing

[0097] Receive the operating data of the smart device 6 from the interface layer 3, and unify the data format into a system-recognizable format after parsing according to the transmission protocol; perform preliminary cleaning of the data to remove obviously erroneous or incomplete data records; and classify and store the data by device type and device number.

[0098] 1-2) Calculate the running time

[0099] For each device, extract the device start and stop timestamps. If the device is running continuously, record the timestamps at fixed intervals, and then calculate the duration of each device operation by using the timestamp difference.

[0100] Accumulate the device running time within a period of time to obtain the total running time of the device in that period;

[0101] 1-3) Calculate the failure frequency

[0102] Define fault indicators, including device error codes and abnormal status signals. When these fault indicators are detected, record the time and device number of the fault. Count the number of faults per device per unit time. Also, use moving average or exponential smoothing time series analysis methods to predict future fault frequencies.

[0103] 1-4) Physiological indicator data acquisition and preprocessing

[0104] The user's physiological indicator data is obtained from the interface layer. The data is also formatted and cleaned, and missing and outliers are processed. Missing values are supplemented using mean filling and linear interpolation. Outliers, such as data that exceeds the normal physiological range by several times, are corrected or excluded by comparing with historical data.

[0105] 1-5) All analysis results are sent to the data storage module 15 for storage, and then sent to the application layer 2 via the interface layer 3 for status display, device control, and data analysis display.

[0106] 2) Perform health warning analysis at the same time, compare the analysis results with the original user information stored in the background management module 16, and find out any anomalies in the data; if the compared data indicates health problems, trigger the health warning mechanism and send an alert message to relevant personnel through the application layer 2; and send all analysis results to the application layer 2 at the same time;

[0107] 2-1) Use the K-Means clustering algorithm to cluster the current analysis results with similar data in the original user information. Observe the position of the new data point in the cluster to determine whether it belongs to the normal cluster range. If a user's current value is in an isolated cluster in the cluster analysis and deviates significantly from the normal value cluster, the corresponding point is considered an outlier.

[0108] 2-2) If the matched data indicates health issues, a health warning mechanism is triggered. A mapping relationship between warning levels and corresponding treatment measures is established to determine the warning level. A mild warning can be pushed through the client to remind the user to take rest; a moderate warning can not only push a reminder but also notify community service personnel for follow-up; a severe warning will directly contact a medical institution and initiate emergency rescue procedures;

[0109] 2-3) Send alert information including health status details and recommended measures to users, medical staff or relevant management personnel through the client 5 of the application layer 2 and the third-party service platform 7.

[0110] 3) The artificial intelligence computing module 18 receives the image, audio and video data collected by the smart device 6, and uses the image recognition algorithm and the audio analysis algorithm to perform image processing and audio and video processing on the image, audio and video data;

[0111] 4) The results obtained after image processing and audio and video processing are sent to the data storage module 15 for storage and then sent to the application layer 2;

[0112] 5) The backend management module 16 continuously stores original user information, including basic health information, past medical history, and service records, and provides corresponding data for timely access according to the needs of the data analysis module 17;

[0113] 6) The data storage module 15 receives the data processed by the data analysis module 17 and the artificial intelligence operation module 18, classifies, stores and manages the data, and feeds back to the various sub-modules of the application layer 2 through the interface layer 3 to provide decision support and service basis for the application layer 2.

[0114] 6-1) The data storage module 15 receives data requests from the various submodules of the application layer 2 through the interface layer 3 and sends them in a specific protocol format, including the requested data type, query conditions, and target application layer submodule identification information;

[0115] 6-2) Parsing the received data request to extract key information including data type, query conditions, and target application layer submodule;

[0116] 6-3) Based on the parsed request information, query and filter the stored data; and feed the queried and filtered data back to the various submodules of Application Layer 2 through Interface Layer 3. Before feeding back, convert the data format according to the requirements of the submodules in Application Layer 2 and the protocols in Interface Layer 3;

[0117] 6-4) When providing feedback, ensure the integrity and accuracy of the data and record relevant information for subsequent monitoring and analysis.

[0118] This invention leverages next-generation large-scale model technology to improve the fluency and accuracy of communication between the smart health care cloud platform and the elderly. Through continuous deep learning through large-scale model self-training, the cloud platform's carrier products can be imbued with human-like emotions, enabling better communication with users and providing a positive communication experience for the elderly. Furthermore, it allows the elderly to control surrounding smart home devices with simple commands, providing greater convenience in entertainment, exercise, medical consultations, and home health care services, significantly increasing their user engagement and enthusiasm. Intelligent hardware devices can also enable remote communication, real-time monitoring, and sleep management between family members and the elderly. It also addresses the need for collecting and managing vital health data for elderly people at home. By connecting with a range of monitoring devices and health care equipment at home, it becomes a terminal entry point for the health care ecosystem, aggregated into the smart health care platform as a unified management platform, effectively connecting vital health data for elderly people at home and becoming a vital data resource for the elderly care industry. The smart health care cloud platform acts as the master data terminal, connecting and driving devices and integrating data, which is then aggregated into the smart health care platform. Combined with intelligent cloud AI capabilities, this data can be intelligently managed, recorded, organized, and analyzed around the clock.

[0119] Those skilled in the art will understand that the above description is only a preferred embodiment of the present invention, and the features described in the various embodiments and / or claims of the present disclosure may be combined or coupled in various ways, even if such a combination or coupling is not explicitly described in the present disclosure. It is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art may still modify the technical solutions described in the aforementioned embodiments or make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention shall be included in the scope of protection of the present invention.

[0120] Although preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they are aware of the basic inventive concepts. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention. Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, the present invention is intended to include such changes and modifications as fall within the scope of the claims and their equivalents.

Claims

1. A smart health care cloud platform, characterized in that: include: Application layer (2), interface layer (3) and basic layer (4); The application layer (2) is used to receive external instructions sent by the basic layer and at the same time, serves as the output port of the smart health care platform (1), outputs the processed instructions, and provides services for the end users themselves; The interface layer (3) is used as a data exchange intermediate module to realize data upload and distribution between the application layer (2) and the basic layer (4), and also issues operation instructions to the devices in the application layer (2) to ensure accurate and efficient data transmission between different protocols and systems; The basic layer (4) is used to perform artificial intelligence calculations and data analysis on the data obtained through the various protocols of the interface layer (3), and after processing the analyzed data, the final output is distributed to various devices in the application layer (2) through the interface layer (3).

2. The smart health care cloud platform according to claim 1, characterized in that: The application layer (2) includes: a client (5), a smart device (6), a third-party service platform (7) and a server (8); The client (5) is any one of a large screen, a PC or a mobile phone, used for displaying status, device control, data analysis, and uploading to a public cloud (9); The smart device (6) includes rehabilitation equipment, medical auxiliary equipment, smart robots, and third-party medical health management equipment; it is used to upload the user's usage data to the public cloud (9) to prepare for health data analysis; The third-party service platform (7) includes a call center service and a community service system, which is used to provide health consultation and emergency treatment services, and upload the corresponding service process to the public cloud (9); The server side (8) is used to adopt a distributed server architecture to provide hardware and network environment for system operation.

3. The smart health care cloud platform according to claim 1, characterized in that: The interface layer comprises: an interface layer (3), comprising: a Json protocol parsing module (10), a byte stream protocol parsing module (11), an Http protocol (12), a WebSocket protocol (13) and an MQTT protocol (14).

4. The smart health care cloud platform according to claim 2, characterized in that: The client (5) communicates with the public cloud (9) via any one of the Http protocol (12), the WebSocket protocol (13) or the MQTT protocol (14); The smart device (6) and the server (8) both communicate with the public cloud (9) via HTTP protocol (12) or MQTT protocol (14); The third-party service platform (7) communicates with the public cloud (9) via the HTTP protocol (12); The data interaction between the application layer (2) and the basic layer (4) adopts the Json character string format and is connected through the Json protocol parsing module (10) and the byte stream protocol parsing module (11) to achieve data interaction.

5. The smart health care cloud platform according to claim 1, characterized in that: The basic layer includes: a data storage module (15), a background management module (16), a data analysis module (17) and an artificial intelligence operation module (18); The data analysis module (17) is used to perform operation data analysis and health warning analysis on the smart device operation data and user health data obtained from the interface layer, and compare the analysis results with the original user information stored in the background management module (16), and send the comparison results to the data storage module (15) for data storage; if the compared data has health problems, the health warning mechanism is triggered, and an alarm message is sent to relevant personnel through the application layer; The artificial intelligence operation module (18) is used to perform image processing and audio and video processing on the image, audio and video data collected by the intelligent device, and send the processed image and audio to the data storage module (15) for data storage; The background management module (16) is used to store original user information and wait for the data analysis module (17) to call; The data storage module (15) is used to receive data processed by the data analysis module (17) and the artificial intelligence operation module (18), and feed back the data to various sub-modules of the application layer (2) through the interface layer (3), so as to realize closed-loop management of data and provide decision-making basis and service support for the application layer (2).

6. The smart health care cloud platform according to claim 1, characterized in that: The application layer (2) provides services for the end users themselves, including: providing real-time monitoring of the status of nursing homes and community services, and displaying them in the form of statistical graphics and detailed data lists; it also supports the access of third-party smart devices and service systems, provides an extensible interface, and reserves multiple access methods for subsequent access to more types of data.

7. The data interaction method of a smart health care cloud platform according to claim 1 is characterized in that: The following steps are involved: 1) The data analysis module (17) obtains various data such as operation data of the smart device (6) and user health data from the interface layer (3), and uses a preset algorithm and model to perform operation data analysis on the data, and analyzes the operation time and failure frequency of the smart device (6); 2) Perform health warning analysis at the same time, compare the analysis results with the original user information stored in the background management module (16), and find out the abnormal points in the data; if the compared data has health problems, trigger the health warning mechanism, and send an alarm message to relevant personnel through the application layer (2); at the same time, send all analysis results to the application layer (2); 3) The artificial intelligence operation module (18) receives the image, audio and video data collected by the intelligent device (6), and uses the image recognition algorithm and the audio analysis algorithm to perform image processing and audio and video processing on the image, audio and video data; 4) Sending the results obtained after the image processing and the audio and video processing to the data storage module (15) for storage and then sending to the application layer (2); 5) The backend management module (16) continuously stores the original user information, including basic health information, past medical history, and service records, and timely provides corresponding data for inquiries according to the needs of the data analysis module (17); 6) The data storage module (15) receives the data processed by the data analysis module (17) and the artificial intelligence operation module (18), classifies, stores and manages the data, and feeds back the data to the various sub-modules of the application layer (2) through the interface layer (3), thereby providing decision support and service basis for the application layer (2).

8. The data interaction method of a smart health care cloud platform according to claim 1 is characterized in that: The step 1) is specifically: 1-1) Data collection and processing The operation data of the intelligent device (6) is received from the interface layer (3), and the data format is unified into a system-recognizable format after being parsed according to the transmission protocol; the data is preliminarily cleaned to remove data records with obvious errors or incomplete data; and the data is classified and stored according to the device type and device number. 1-2) Calculate the running time For each device, extract the device start timestamp and stop timestamp; if the device runs continuously, record the timestamp once at a fixed time interval, and then calculate the duration of each device run by the timestamp difference; Accumulate the device running time within a period of time to obtain the total running time of the device in the period of time; 1-3) Calculate the failure frequency Define fault identifiers including equipment error codes and abnormal status signals. When these fault identifiers are detected, record the time when the fault occurred and the equipment number; and count the number of faults that occur per device per unit time; at the same time, use the moving average method or exponential smoothing method time series analysis method to predict the possible fault frequency in the future; 1-4) Acquisition and preprocessing of physiological index data The user's physiological index data is obtained from the interface layer, and the data format is also unified and cleaned, and missing values ​​and abnormal values ​​are processed; for missing values, mean filling and linear interpolation are used to supplement them; for abnormal values, such as data that exceeds the normal physiological range by several times, they are corrected or excluded by comparing with historical data; 1-5) All analysis results are sent to the data storage module (15) for storage, and then sent to the application layer (2) via the interface layer (3) for status display, device control and data analysis display.

9. The data interaction method of a smart health care cloud platform according to claim 1 is characterized in that: The step 2) is specifically: 2-1) Use the K-Means clustering analysis algorithm to cluster the current analysis results with similar data in the original user information; by observing the position of the new data point in the cluster, determine whether it belongs to the normal cluster range; if a user's current value is in an isolated cluster in the cluster analysis and deviates greatly from the normal value cluster, the corresponding point of the value is an outlier; 2-2) If the compared data has health problems, the health warning mechanism is triggered, and a mapping relationship between the warning level and the corresponding treatment measures is established to determine the warning level. A mild warning can be pushed through the client to remind the user to take a rest; a moderate warning can not only push reminders, but also notify community service personnel for follow-up; a severe warning directly contacts the medical institution and initiates the emergency rescue process; 2-3) Sending alert information including health status details and recommended measures to users, medical staff or relevant managers through the client (5) of the application layer (2) and the third-party service platform (7).

10. The data interaction method of the smart health care cloud platform according to claim 1 is characterized in that: The step 6) is specifically: 6-1) The data storage module (15) receives data requests sent by each submodule of the application layer (2) through the interface layer (3) and sends them in a specific protocol format, including the requested data type, query conditions, and target application layer submodule identification information; 6-2) Parse the received data request and extract key information including data type, query conditions and target application layer submodule; 6-3) According to the parsed request information, query and filter the stored data; and feed back the query and filtered data to each submodule of the application layer (2) through the interface layer (3); Before feedback, the data is formatted according to the requirements of the submodules in the application layer (2) and the protocol in the interface layer (3); 6-4) When providing feedback, ensure the integrity and accuracy of the data and record relevant information for subsequent monitoring and analysis.

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