Data service system and digital human portrait construction method
By configuring the digital patient model and data call interface, a unified digital person portrait data is formed, which solves the problems of large system overhead and poor scalability in the prior art, and realizes efficient data processing and task execution.
Patent Information
- Application Number
- CN202410097843.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-01-24
- Publication Date
- 2025-07-25
AI Technical Summary
In the prior art, in order to improve the quality of patient data, different patient portraits are constructed for different patients, resulting in large system overhead and poor dynamic scalability.
By configuring the digital patient model and data call interface, a unified digital person portrait data is formed, including indicator data, indicator data, indicator data, behavior data and sensor data, and in response to call requests of different digital therapy tasks, matching the corresponding data to trigger task execution.
It realizes that all patients share a unified digital patient model, reduces system overhead, improves dynamic scalability and system flexibility, and ensures data security and independent execution of tasks.
Smart Images
Figure CN120376012A_ABST
Abstract
Description
Technical Field
[0001] The present disclosure relates to the field of big data technology, and in particular, to a data service system and a method for constructing a digital human portrait. Background Art
[0002] Digital therapy refers to the use of digital technology and information and communication technology to improve and optimize the treatment and management process of healthcare. In the medical field, digital therapy can include the use of mobile applications, internet platforms, sensor devices, and other digital tools to monitor the health status of patients, provide personalized treatment plans, conduct remote diagnosis and treatment, and perform health management and prevention.
[0003] Digital therapy can help healthcare professionals better understand the condition and needs of patients, provide more accurate and personalized treatment plans, and improve the efficiency and quality of healthcare. At the same time, digital therapy can also make it more convenient for patients to access medical services, and improve the treatment compliance and self-management ability of patients.
[0004] However, in the process of implementing the present application, the inventors found that the implementation of digital therapy is based on patient data, and the quality of patient data greatly affects the accuracy of data therapy.
[0005] Therefore, in the prior art, in order to improve the quality of patient data, different patient portraits are directly constructed for different patients, which increases the system overhead and has poor dynamic scalability. Summary of the Invention
[0006] The present disclosure provides a data service system and a method for constructing a digital human portrait.
[0007] The technical solution provided by the present disclosure is as follows:
[0008] A data service system, which includes: a digital patient model and a data call interface. The digital patient model is used to form digital human portrait data of a patient, and the digital human portrait data includes: indication data, indication item data, index data, behavior data, and sensor data. The data call interface is used to respond to a call request for different digital therapy tasks, and specifically match at least one of the indication data, indication item data, index data, behavior data, and sensor data from the digital portrait data to trigger the execution of the digital therapy task, where:
[0009] The digital patient model at least includes: an indication data model, an indication item data model, an index data model, a behavior data model, and a sensing signal model constructed based on the patient;
[0010] Among them, the indication data model is used to encapsulate the indication data of the patient based on the patient ID, the indication item data model is used to encapsulate the indication item data of the patient based on the patient ID, the index data model is used to encapsulate the index data of the patient based on the patient ID, the behavior data model is used to encapsulate the behavior data of the patient based on the patient ID, and the sensing signal model is used to encapsulate the sensor data of the patient based on the patient ID.
[0011] Optionally, a namespace set is set on the data service system. The namespace set includes several namespaces. When responding to the call requests of different digital therapy tasks, the application table of each digital therapy task is obtained, and the application table of one digital therapy task is associated with a namespace to isolate different digital therapy tasks from each other.
[0012] Optionally, a Web application service directory is created on the data service system. A Web call service is deployed in the Web application service directory. When the data call interface responds to the call requests of different digital therapy tasks, a Web call service is started to specifically match at least one of the indication data, indication item data, index data, behavior data, and sensor data from the digital portrait data to trigger the execution of the digital therapy task.
[0013] Optionally, a remote call service root is configured on the data call interface to trigger the remote call service root to start a Web call service when responding to the call requests of different digital therapy tasks, so as to specifically match at least one of the indication data, indication item data, index data, behavior data, and sensor data from the digital portrait data to trigger the execution of the digital therapy task.
[0014] Optionally, when the indication data model, indication item data model, index data model, behavior data model, and sensing signal model encapsulate their corresponding data, the data is divided into several data records and encapsulated in a columnar manner.
[0015] Optionally, when the indication data model, indication item data model, index data model, behavior data model, and sensing signal model encapsulate their corresponding data, according to the type of the data, the data is mapped to the business domain or information domain or time domain, and encapsulated separately in the business domain or information domain or time domain.
[0016] Optionally, a data configuration engine library is provided on the data service system. The data configuration engine library includes indication data configuration items, indication item data configuration items, metric data configuration items, behavior data configuration items, and sensor data configuration items. Encodings for the corresponding data are defined in the indication data configuration items, indication item data configuration items, metric data configuration items, behavior data configuration items, and sensor data configuration items.
[0017] Optionally, a common data operation engine data packet is provided on the data service system. The common data operation engine data packet is used to uniformly convert call requests for different digital therapy tasks into interface definitions, so that the data call interface can match the interface definitions to respond to call requests for different digital therapy tasks.
[0018] Optionally, a data proxy layer is provided on the data service system. The data proxy layer is used to intercept call requests for different digital therapy tasks when routing at least one of the indication data, indication item data, metric data, behavior data, and sensor data to the digital patient model.
[0019] A method for constructing a digital human portrait, comprising:
[0020] Receiving indication data, indication item data, metric data, behavior data, and sensor data;
[0021] Performing the following steps to describe a digital patient model for the digital human portrait data of a patient, so as to, in response to call requests for different digital therapy tasks, specifically match at least one of the indication data, indication item data, metric data, behavior data, and sensor data from the digital portrait data to trigger the execution of the digital therapy task:
[0022] Encapsulating the indication data of the patient based on the patient ID to obtain an indication data model;
[0023] Encapsulating the indication item data of the patient based on the patient ID to obtain an indication item data model;
[0024] Encapsulating the metric data of the patient based on the patient ID to obtain a metric data model;
[0025] Encapsulating the behavior data of the patient based on the patient ID to obtain a behavior data model;
[0026] Encapsulating the sensor data of the patient based on the patient ID to obtain a sensing signal model.
[0027] In this application, by configuring a digital patient model and a data calling interface on a data service system, the digital patient model is used to form digital human portrait data of a patient. The digital human portrait data includes: indication data, indication item data, index data, behavior data, and sensor data. The data calling interface is used to respond to a calling request for different digital therapy tasks, and specifically match at least one of the indication data, indication item data, index data, behavior data, and sensor data from the digital portrait data to trigger the execution of the digital therapy task. Among them: The digital patient model at least includes: an indication data model, an indication item data model, an index data model, a behavior data model, and a sensing signal model constructed based on the patient; wherein, the indication data model is used to encapsulate the indication data of the patient based on the patient ID, the indication item data model is used to encapsulate the indication item data of the patient based on the patient ID, the index data model is used to encapsulate the index data of the patient based on the patient ID, the behavior data model is used to encapsulate the behavior data of the patient based on the patient ID, and the sensing signal model is used to encapsulate the sensor data of the patient based on the patient ID. Thus, all patients share a unified digital patient model. When forming personalized digital human portrait data, only the indication data, indication item data, index data, behavior data, and sensor data of different patients need to be uniformly input into the same indication data model, indication item data model, index data model, behavior data model, and sensing signal model for instantiation, and then the digital human portrait data corresponding to the patient can be obtained, thereby reducing the system overhead and increasing the dynamic scalability of the system.
[0028] It should be understood that the content described in the summary of the invention section is not intended to limit the key or important features of the embodiments of the present disclosure, nor to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. Brief Description of the Drawings
[0029] In combination with the accompanying drawings and referring to the following detailed description, the above and other features, advantages, and aspects of the embodiments of the present disclosure will become more obvious. The drawings are used to better understand the solution and do not constitute a limitation to the present disclosure. In the drawings, the same or similar reference numerals represent the same or similar elements, where:
[0030] Figure 1 It is a schematic diagram of a data service system according to an embodiment of the present application.
[0031] Figure 2 It is a schematic diagram of the data to be encapsulated by the index data model.
[0032] Figure 3 It is a schematic flowchart of a method for constructing a digital human portrait according to an embodiment of the present application.
[0033] Figure 4 It is a schematic structural diagram of the electronic device in this embodiment.
[0034] Figure 5 It is the hardware structure of the electronic device in this embodiment. Detailed implementation manners
[0035] To make the objectives, technical solutions and advantages of the embodiments of the present disclosure clearer, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present disclosure. Apparently, the described embodiments are some but not all of the embodiments of the present disclosure. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present disclosure without creative efforts shall fall within the scope of protection of the present disclosure.
[0036] In addition, the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in this article generally represents an "or" relationship between the associated objects before and after.
[0037] Figure 1 It is a schematic diagram of a data service system according to an embodiment of the present application. As Figure 1 shown, it includes: a digital patient model and a data call interface. The digital patient model is used to form digital human portrait data of a patient. The digital human portrait data includes: indication data, indication item data, index data, behavior data, and sensor data. The data call interface is used to respond to a call request for different digital therapy tasks, and specifically match at least one of the indication data, indication item data, index data, behavior data, and sensor data from the digital portrait data to trigger the execution of the digital therapy task.
[0038] The digital patient model at least includes: an indication data model, an indication item data model, an index data model, a behavior data model, and a sensing signal model constructed based on the patient.
[0039] The indication data model is used to encapsulate the indication data of the patient based on the patient ID. The indication item data model is used to encapsulate the indication item data of the patient based on the patient ID. The index data model is used to encapsulate the index data of the patient based on the patient ID. The behavior data model is used to encapsulate the behavior data of the patient based on the patient ID. The sensing signal model is used to encapsulate the sensor data of the patient based on the patient ID.
[0040] Optionally, methods such as integrating Internet of Things data, in-hospital examination data, and scale collection can be combined to obtain a person's physiological signs, behavioral data, and derived data generated therefrom. By mining the derived data, the physiological and case status of the patient can be identified, enabling medical professionals to better understand the patient's physiological state, pathological condition, and behavioral manifestations, providing stronger support for precision medicine and personalized treatment.
[0041] Further, the data is sorted and classified to obtain indication data, indication item data, index data, behavioral data, and sensor data.
[0042] Specifically, for example, indication data may include variables that measure a patient's specified physiological or pathological state, such as blood pressure; indication item data refers to the individual components that make up an indication, such as systolic blood pressure and diastolic blood pressure; index data refers to the quantified results obtained by calculating or combining indications or indication items, such as mean arterial pressure; behavioral data, for example, refers to a patient's physiological or behavioral manifestations under specific conditions, such as exercise; sensor data refers to the signals collected by sensors worn on the patient, which may include physiological parameters such as heart rate and body temperature, and is related to the previously mentioned indication data, indication item data, and index data, thus forming a comprehensive data structure.
[0043] Therefore, for the same patient, the indication data, indication item data, index data, behavioral data, and sensor data are used to describe the mapping of the patient to a digital patient model, ensuring that the digital patient model has a high degree of scientific basis and clinical applicability.
[0044] Optionally, a namespace set is provided on the data service system. The namespace set includes several namespaces. When responding to the call requests of different digital therapy tasks, the application table of each digital therapy task is obtained, and the application table of a digital therapy task is associated with a namespace to isolate different digital therapy tasks from each other.
[0045] To this end, by associating the application tables of different digital therapy tasks with different namespaces, mutual isolation between different digital therapy tasks can be achieved, enabling multiple parallel digital therapy tasks to run independently within their own namespaces without interfering with or affecting each other, thereby improving the stability and security of the system. Additionally, the namespace set can provide flexible management and configuration for different digital therapy tasks, including resource allocation, permission control, monitoring, etc., thus better meeting the requirements of different digital therapy tasks and enhancing the flexibility and customizability of the system. Moreover, through the setting of the namespace set, the system can be more easily extended and new digital therapy tasks can be added. Each new digital therapy task can be assigned to an independent namespace without the need for large-scale modification of the entire system, improving the scalability and maintainability of the system.
[0046] Optionally, for example, by creating multiple virtualized environments on the data service system, loading a digital patient model in each virtualized environment, instantiating a digital therapy task to obtain a digital therapy instance when executing the corresponding digital therapy task, and establishing an application table corresponding to the digital therapy instance, and storing the application table under a namespace to isolate different digital therapy tasks.
[0047] To this end, by creating multiple virtualized environments on the data service system and running a digital patient model in each environment, the isolation between different digital therapy tasks can be ensured, guaranteeing data security and the independence of task execution and avoiding interference between different tasks. Additionally, since each virtual environment can operate and be managed independently, when new digital therapy tasks need to be added, simply load a new digital patient model in the existing virtual environment or create a new virtual environment to handle the new tasks, enabling the system to flexibly respond to different scales of task requirements. Through virtualization technology, resource sharing and reuse can be achieved. For example, multiple virtual environments can share computing resources, improving resource utilization while ensuring the performance of each task during processing. In addition, the optimal configuration of resources can reduce energy consumption and operating costs. Since all virtual environments are managed in a data service system, administrators can centrally monitor, manage, and maintain each virtual environment. This greatly simplifies the management complexity of the system and improves management efficiency. By instantiating digital therapy tasks and storing application tables, the system provides great flexibility and can easily modify, update, or expand the application tables as needed to meet the ever-changing digital therapy requirements. The isolation of namespaces ensures the data security of different digital therapy tasks. Even if a task suffers a security threat or data leakage, it will not affect the data security of other tasks. Since each virtual environment is independent, the failure of one virtual environment will not affect other environments, improving the reliability of the entire system and ensuring the continuous execution of digital therapy tasks.
[0048] Optionally, the namespace can adopt a three - part naming, which includes environmental parameters and a digital therapy task number. For example, for test_1044_namespace, through this three - part naming, it can be clearly known that this namespace belongs to the test test environment and the digital therapy number is 1044. An application table is established for this digital therapy instance under the namespace to achieve data isolation for different environments and different instances.
[0049] Optionally, a Web application service directory is created on the data service system, and a Web call service is deployed in the Web application service directory. When the data call interface responds to call requests for different digital therapy tasks, one of the Web call services is started to specifically match at least one of indication data, indication item data, metric data, behavior data, and sensor data from the digital portrait data to trigger the execution of the digital therapy task.
[0050] Therefore, by combining digital therapy tasks with Web call services, the design principle of high cohesion and low coupling can be achieved. Thus, each digital therapy task has a clear function, and the relationship with other tasks is as simple as possible, improving the maintainability and scalability of the system. Moreover, by deploying Web call services in the Web application service directory, a flexible call method can be achieved, enabling different digital therapy tasks to call corresponding Web services through HTTP requests and also enabling interaction between tasks. Additionally, when the data call interface responds to call requests for different digital therapy tasks, a Web call service is started to match the required indication data, indication item data, metric data, behavior data, sensor data, etc. from the digital portrait data, realizing real - time data processing, thereby improving the system's response speed and meeting the requirements of real - time or near - real - time digital therapy. Furthermore, by processing in the Web call service, the security of data during transmission and processing can be ensured, and by adopting the Web application service directory and Web call services, digital therapy tasks can be conveniently monitored and managed. Finally, the Web application service directory and Web call services can be easily integrated with other systems and services. For example, by combining digital therapy tasks with the functions of other systems through methods such as APIs and SDKs, the automation of business processes can be achieved, and the deployment and expansion of Web services are also relatively convenient, and service instances can be dynamically increased or decreased according to requirements.
[0051] Optionally, a remote call service root is configured on the data call interface to trigger the remote call service root to start one of the Web call services to specifically match at least one of indication data, indication item data, metric data, behavior data, and sensor data from the digital portrait data when responding to call requests for different digital therapy tasks, thereby triggering the execution of the digital therapy tasks.
[0052] In this embodiment, since the remote call service root (or referred to as the remote service root) is equivalent to specifying a unified entry point for processing remote call requests from different digital therapy tasks, one of the Web call services is started to specifically match at least one of indication data, indication item data, metric data, behavior data, and sensor data from the digital portrait data, thereby triggering the execution of the digital therapy tasks.
[0053] Optionally, when encapsulating their corresponding data, the indication data model, indication item data model, metric data model, behavior data model, and sensing signal model divide the data into several data records and encapsulate them in a columnar manner.
[0054] Therefore, splitting the data into multiple records can better utilize the indexing and query functions of the database, improving data retrieval and processing speed. At the same time, columnar storage can reduce the complexity and storage space of data storage, improving storage efficiency. In addition, splitting the data into multiple records can facilitate the expansion and update of data, adding new data fields without affecting other data records. Moreover, splitting the data into multiple records and encapsulating them in a columnar manner can better ensure data consistency and integrity, avoiding data errors and losses caused by excessive data or complex structures. Finally, splitting the data into multiple records and encapsulating them in a columnar manner is conducive to statistical analysis and mining of data, extracting valuable information for convenient interaction with digital therapy tasks.
[0055] Optionally, when encapsulating their corresponding data, the indication data model, indication item data model, metric data model, behavior data model, and sensing signal model map the data to the business domain or information domain or time domain according to the data type and perform encapsulation separately in the business domain or information domain or time domain.
[0056] Exemplarily, as Figure 2 shown, for example, for the metric data model, the encapsulated data includes:
[0057] Config_id represents the index definition ID, source_type represents the index source ID, and its function is to identify the source of the index. Source_id represents the unique identifier of the source. Person_id represents the person to whom the index belongs. Code, name, data_type, unit, and value respectively represent the encoding, name, data set type, unit, value, etc. of the index data. Measurement_time, create_time, update_time, and expires_time respectively represent the measurement time of the index data, the generation time in the database, the last modification time, and the expiration time.
[0058] Map the above data to the business domain, information domain, or time domain, as Figure 2 shown, where BusinessDomain represents the business domain, Information Domain represents the information domain, and Time Domain is the time domain.
[0059] It should be noted that not all data can necessarily be mapped to the business domain, information domain, and time domain. It is determined according to the actual content of the data.
[0060] Therefore, by mapping the data to different domains according to the type, the data structure can be made clearer, which is convenient for developers and business personnel to understand the meaning and use of the data, and improves the readability and maintainability of the data. Moreover, classifying and encapsulating the data according to different domains can improve the efficiency of data processing and retrieval, reduce the complexity of data processing, and improve the performance and response speed of the system. Also, mapping the data to different domains according to the type can better ensure the consistency and integrity of the data, and avoid data errors and losses caused by excessive data or complex structures. Finally, mapping the data to different domains according to the type can facilitate the interaction with digital therapy tasks. In addition, mapping the data to different domains can support more complex query and statistical requirements, meet the diverse needs of the business for data, and improve the flexibility and adaptability of the system.
[0061] Optionally, a data configuration engine library is set on the data service system. The data configuration engine library includes indication data configuration items, indication item data configuration items, index data configuration items, behavior data configuration items, and sensor data configuration items. The indication data configuration items, indication item data configuration items, index data configuration items, behavior data configuration items, and sensor data configuration items define the encodings of the corresponding data.
[0062] The encoded representation of indication data represents the encoding of specific diseases, health indicators, physiological parameters, etc., and is used to identify and distinguish different types of indication data. The encoded representation of indication item data represents different indication items, such as the encoding of blood pressure, blood sugar, heart rate, etc., and is used to identify and distinguish different indication item data. The encoded representation of metric data represents the encoding of specific medical metrics, laboratory test results, biomarkers, etc., and is used to identify and distinguish different metric data. The encoded representation of behavior data represents the encoding of a patient's behavioral activities, lifestyle habits, exercise conditions, etc., and is used to identify and distinguish different behavior data. The encoded representation of sensor data represents the encoding of different types of sensor devices, sensor data acquisition channels, data formats, etc., and is used to identify and distinguish different sensor data.
[0063] Therefore, by defining encodings for various types of data, data standardization can be achieved, enabling the unified management and processing of data from different sources and in different formats, and improving the comparability and interoperability of data. Additionally, defining a unified data encoding can avoid confusion and errors caused by inconsistent data representations, ensuring the consistency and accuracy of data. Moreover, it facilitates the retrieval and processing of data, improving the speed and efficiency of data processing. Furthermore, defining data encodings enables the convenient expansion and update of data by adding new data types and encodings without affecting the processing of other data. Finally, through the data configuration engine library, various data configuration items can be centrally managed and maintained, simplifying the complexity of data management, improving management efficiency, and enabling the rapid acquisition and configuration of various types of data, supporting the rapid development and deployment of the system, shortening the project cycle, and improving development efficiency.
[0064] In addition, through the above-mentioned data configuration engine library, the separation of configuration and data is achieved, enabling the easy update of configuration information without interfering with the actual data, thereby reducing potential risks and maintenance difficulties. It enhances the readability and security of the data. Separating the configuration information makes the data structure clearer and easier to understand, and also helps to better control access rights to the configuration information, protecting the security of the data.
[0065] Optionally, a data operation engine common data packet is set on the data service system, and the data operation engine common data packet is used to uniformly convert the call requests of different digital therapy tasks into interface definitions, so that the data call interface matches the interface definition to respond to the call requests of different digital therapy tasks.
[0066] To this end, through the common data packet of the data operation engine, the call requests of different digital therapy tasks can be uniformly processed, achieving decoupling of system function modules, reducing system complexity, and improving system maintainability and scalability. By uniformly converting call requests into interface definitions, the matching of data call interfaces and interface definitions can be achieved to respond to call requests of different digital therapy tasks, improving system flexibility and adaptability. Through the common data packet of the data operation engine, the processing process of call requests for different digital therapy tasks can be simplified, development difficulty reduced, and development efficiency improved. At the same time, the call requests of different digital therapy tasks can be uniformly processed, improving data processing efficiency and response speed. Through the common data packet of the data operation engine, call requests of different digital therapy tasks can be supported, realizing the integration and interconnection of heterogeneous systems, and improving data utilization and value. Moreover, through the common data packet of the data operation engine, call requests of different digital therapy tasks can be uniformly managed and monitored, improving system manageability and maintainability.
[0067] Optionally, a data proxy layer is provided on the data service system, and the data proxy layer is used to intercept call requests of different digital therapy tasks when routing at least one of the indication data, indication item data, metric data, behavior data, and sensor data to the digital patient model.
[0068] To this end, the data proxy layer can intercept and filter data transmission requests, thereby improving data security and privacy protection, ensuring that only authorized data can be transmitted to the digital patient model, and effectively preventing unauthorized data access and leakage. In addition, the data proxy layer can implement routing and management of different types of data, and effectively distribute and schedule data transmission requests according to data types and the requirements of the target model, ensuring that data can be correctly transmitted to the corresponding digital patient model. Through the interception and scheduling of the data proxy layer, data transmission can be optimized, including operations such as data compression, encryption, and caching, thereby improving data transmission efficiency and performance. The data proxy layer can implement authorization control of data transmission requests, and only requests that meet specific conditions and permissions can pass through the proxy layer, effectively controlling data access and usage permissions. Through the data proxy layer, the system can be more easily extended and adjusted, adding new digital therapy tasks or adjusting data transmission strategies to adapt to changing business requirements.
[0069] Figure 3 Schematic diagram of the process of a digital human portrait construction method according to an embodiment of the present application. As Figure 3 shown, it includes:
[0070] Receiving indication data, indication item data, metric data, behavior data, and sensor data;
[0071] Perform the following steps to describe a digital patient model of the digital human portrait data of a patient, so as to match at least one of indication data, indication item data, index data, behavior data, and sensor data from the digital portrait data in response to a call request for different digital therapy tasks, and trigger the execution of the digital therapy tasks:
[0072] Package the indication data of the patient based on the patient ID to obtain an indication data model;
[0073] Package the indication item data of the patient based on the patient ID to obtain an indication item data model;
[0074] Package the index data of the patient based on the patient ID to obtain an index data model;
[0075] Package the behavior data of the patient based on the patient ID to obtain a behavior data model;
[0076] Package the sensor data of the patient based on the patient ID to obtain a sensing signal model.
[0077] Figure 4 This is a schematic structural diagram of the electronic device in this embodiment; the electronic device may include:
[0078] One or more processors 301;
[0079] A computer-readable medium 302, which can be configured to store one or more programs,
[0080] When the one or more programs are executed by the one or more processors, the one or more processors implement the method described in the above embodiment.
[0081] Figure 5 This is the hardware structure of the electronic device in this embodiment; as Figure 5 shown, the hardware structure of the electronic device may include: a processor 401, a communication interface 402, a computer-readable medium 403, and a communication bus 404;
[0082] Wherein, the processor 401, the communication interface 402, and the computer-readable medium 403 complete mutual communication through the communication bus 404;
[0083] Optionally, the communication interface 402 may be an interface of a communication module, such as an interface of a GSM module;
[0084] Wherein, the processor 401 may be specifically configured to execute the method of any of the above embodiments.
[0085] The processor 401 may be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. It can implement or execute the various methods, steps, and logic block diagrams disclosed in the embodiments of the present application. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.
[0086] The computer-readable medium 403 may be, but is not limited to, a Random Access Memory (RAM), a Read Only Memory (ROM), a Programmable Read-Only Memory (PROM), an Erasable Programmable Read-Only Memory (EPROM), an Electric Erasable Programmable Read-Only Memory (EEPROM), etc.
[0087] In particular, according to embodiments of the present disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product that includes a computer program carried on a computer-readable medium, the computer program including program code configured to execute the methods shown in the flowcharts. In such an embodiment, the computer program can be downloaded and installed from a network through a communication section, and / or installed from a removable medium. When the computer program is executed by a central processing unit (CPU), the above-described functions defined in the methods of the present application are performed. It should be noted that the computer-readable medium described in the present application can be a computer-readable signal medium or a computer-readable storage medium or any combination of the two. The computer-readable medium can, for example, but is not limited to, be an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples of the computer-readable storage medium can include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, a random access storage medium (RAM), a read-only storage medium (ROM), an erasable programmable read-only storage medium (EPROM or flash memory), an optical fiber, a portable compact disk read-only storage medium (CD-ROM), an optical storage medium, a magnetic storage medium, or any suitable combination of the above. In the present application, the computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In the present application, the computer-readable signal medium can include a data signal propagated in a baseband or as part of a carrier wave, in which the computer-readable program code is carried. Such a propagated data signal can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The computer-readable signal medium can also be any computer-readable medium other than the computer-readable storage medium, which can send, propagate, or transmit a program configured to be used by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted by any suitable medium, including but not limited to: wireless, wire, optical cable, RF, etc., or any suitable combination of the above.
[0088] Computer program code configured to perform the operations of the present application can be written in one or more programming languages or combinations thereof. The programming languages include object-oriented programming languages such as Java, Smalltalk, C++, and also include conventional procedural programming languages such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, executed as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer can be connected to the user's computer through any kind of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (e.g., through the Internet using an Internet service provider).
[0089] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagram can represent a module, a program segment, or a part of code that contains one or more executable instructions configured to implement a specified logical function. There are specific sequential relationships in the above specific embodiments, but these sequential relationships are only exemplary. In specific implementations, these steps may be fewer, more, or the execution order may be adjusted. That is, in some alternative implementations, the functions marked in the blocks may also occur in a different order than that marked in the accompanying drawings. For example, two consecutively represented blocks can actually be executed substantially in parallel, and they can sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and the combination of blocks in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system that performs the specified functions or operations, or can be implemented by a combination of dedicated hardware and computer instructions.
[0090] As another aspect, the present application also provides a computer-readable medium having a computer program stored thereon, which when executed by a processor implements the method described in the above embodiments.
[0091] As another aspect, the present application also provides a computer-readable medium, which can be included in the device described in the above embodiments; or it can exist separately and not be assembled into the device. The above computer-readable medium carries one or more programs, and when the one or more programs are executed by the device, the device is caused to implement the method described in the above embodiments.
[0092] In various embodiments of the present disclosure, the expressions "first", "second", "the first" or "the second" may modify various components regardless of order and / or importance, but these expressions do not limit the corresponding components. The above expressions are only configured for the purpose of distinguishing an element from other elements. For example, a first user device and a second user device represent different user devices, although both are user devices. For example, without departing from the scope of the present disclosure, the first element may be referred to as the second element, and similarly, the second element may be referred to as the first element.
[0093] When an element (e.g., a first element) is referred to as "(operatively or communicatively) coupled" or "(operatively or communicatively) coupled to" or "connected to" another element (e.g., a second element), it should be understood that the one element is directly connected to the other element or the one element is indirectly connected to the other element via yet another element (e.g., a third element). Conversely, it can be understood that when an element (e.g., a first element) is referred to as "directly connected" or "directly coupled" to another element (a second element), then no element (e.g., a third element) is inserted between the two.
[0094] The above description is only a preferred embodiment of the present application and an explanation of the applied technical principles. Those skilled in the art should understand that the scope of the invention involved in the present application is not limited to the technical solution formed by the specific combination of the above technical features, and should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above inventive concept. For example, the above features are mutually replaced with the (but not limited to) technical features having similar functions disclosed in the present application to form a technical solution.
Claims
1. A data service system, characterized in that, Including: A digital patient model and a data call interface. The digital patient model is used to form digital human portrait data of a patient. The digital human portrait data includes: indication data, indication item data, index data, behavior data, and sensor data. The data call interface is used to respond to call requests for different digital therapy tasks, and specifically match at least one of the indication data, indication item data, index data, behavior data, and sensor data from the digital portrait data to trigger the execution of the digital therapy task. Among them: The digital patient model at least includes: an indication data model, an indication item data model, an index data model, a behavior data model, and a sensing signal model constructed based on the patient; Among them, the indication data model is used to encapsulate the indication data of the patient based on the patient ID. The indication item data model is used to encapsulate the indication item data of the patient based on the patient ID. The index data model is used to encapsulate the index data of the patient based on the patient ID. The behavior data model is used to encapsulate the behavior data of the patient based on the patient ID. The sensing signal model is used to encapsulate the sensor data of the patient based on the patient ID.
2. The data service system according to claim 1, wherein A namespace set is set on the data service system. The namespace set includes several namespaces. When responding to call requests for different digital therapy tasks, obtain the application table of each digital therapy task, and associate the application table of a digital therapy task with a namespace to isolate different digital therapy tasks from each other.
3. The data service system according to claim 1, wherein A Web application service directory is created on the data service system. A Web call service is deployed in the Web application service directory. When the data call interface responds to call requests for different digital therapy tasks, start a Web call service to specifically match at least one of the indication data, indication item data, index data, behavior data, and sensor data from the digital portrait data to trigger the execution of the digital therapy task.
4. The data service system according to claim 3, wherein A remote call service root is configured on the data call interface to trigger the remote call service root to start a Web call service when responding to call requests for different digital therapy tasks, so as to specifically match at least one of the indication data, indication item data, index data, behavior data, and sensor data from the digital portrait data to trigger the execution of the digital therapy task.
5. The data service system according to claim 1, wherein When the indication data model, indication item data model, index data model, behavior data model, and sensing signal model encapsulate their corresponding data, the data is divided into several data records and encapsulated in a columnar manner.
6. The data service system according to claim 1, wherein When the indication data model, indication item data model, index data model, behavior data model, and sensing signal model encapsulate their corresponding data, according to the type of data, the data is mapped to the business domain or the information domain or the time domain, and encapsulated separately in the business domain or the information domain or the time domain.
7. The data service system according to claim 1, wherein A data configuration engine library is set up on the data service system. The data configuration engine library includes indication data configuration items, indication item data configuration items, metric data configuration items, behavior data configuration items, and sensor data configuration items. Encodings of corresponding data are defined in the indication data configuration items, indication item data configuration items, metric data configuration items, behavior data configuration items, and sensor data configuration items.
8. The data service system according to claim 1, wherein A data operation engine common data packet is set up on the data service system. The data operation engine common data packet is used to uniformly convert call requests for different digital therapy tasks into interface definitions, so that the data call interface matches the interface definition to respond to call requests for different digital therapy tasks.
9. The data service system according to claim 1, characterized in that A data proxy layer is set up on the data service system. The data proxy layer is used to intercept call requests for different digital therapy tasks when routing at least one of the indication data, indication item data, metric data, behavior data, and sensor data to the digital patient model.
10. A method for constructing a digital human portrait, characterized in that, Including: Receiving indication data, indication item data, metric data, behavior data, and sensor data; Performing the following steps to describe a digital patient model of the digital human portrait data of a patient, so as to specifically match at least one of the indication data, indication item data, metric data, behavior data, and sensor data from the digital portrait data in response to call requests for different digital therapy tasks to trigger the execution of the digital therapy tasks: Encapsulating the indication data of the patient based on the patient ID to obtain an indication data model; Encapsulating the indication item data of the patient based on the patient ID to obtain an indication item data model; Encapsulating the metric data of the patient based on the patient ID to obtain a metric data model; Encapsulating the behavior data of the patient based on the patient ID to obtain a behavior data model; Encapsulating the sensor data of the patient based on the patient ID to obtain a sensing signal model.