Nursing remote training platform integrating internet search and news aggregation

Abstract

The invention relates to the technical field of nursing training, in particular to a nursing remote training platform fusing internet search and news aggregation, the platform comprises a data fusion port, a knowledge matching port and a training recommendation port, the data fusion port is used for integrating multi-source dynamic data; the knowledge matching port is used for calculating the matching degree of the student ability and the knowledge system; and the disease tracing port is used for generating a personalized learning path.

Description

Technical Field

[0001] The present invention relates to the technical field of nursing training, and in particular to a nursing remote training platform integrating Internet search and news aggregation. Background Art

[0002] Current nursing distance training systems primarily rely on two types of technologies: static knowledge base systems and basic adaptive learning systems. These systems use simple rules to push learning content (e.g., by job level) based on pre-built course libraries (e.g., nursing practice videos and theoretical documents). Furthermore, they use collaborative filtering to recommend learning paths for similar learners based on test score analysis and weaknesses. However, due to the rapid evolution of nursing knowledge, the WHO updates over 15% of its medical guidelines annually, and traditional curriculum updates lag behind. Furthermore, with diverse practice scenarios, the skills required for primary care (community care) and intensive care (ICU) care differ significantly, and public health emergencies require the real-time integration of the latest protective measures. Relying solely on static data such as course completion and test scores ignores learners' active exploration and fails to identify implicit learning needs. Course update cycles are long, and access to real-time medical news on the internet is inadequate, leading to a disconnect between training content and clinical practice. Furthermore, a closed-loop feedback mechanism linking knowledge base, news, and search data is lacking. This makes the development of a nursing distance training platform that integrates internet search and news aggregation crucial. Summary of the Invention

[0003] To address the shortcomings of existing technologies, this paper proposes a remote nursing training platform that integrates internet search and news aggregation. This platform captures individual needs through internet search behavior analysis, leverages a news aggregation engine to connect to cutting-edge industry trends, and combines a multi-dimensional quantitative model to reconstruct the underlying logic of nursing training. Its core value lies in upgrading "static knowledge transfer" to a "dynamic capability evolution system," making it particularly suitable for medical talent training in the VUCA (Volatile, Uncertain, Complex, Ambiguous) era.

[0004] To achieve the above object, the technical solution of the present invention is as follows:

[0005] A nursing remote training platform that integrates Internet search and news aggregation includes a data fusion port, a knowledge matching port, and a training recommendation port. The data fusion port is used to integrate multi-source dynamic data; the knowledge matching port is used to calculate the matching degree between trainees' abilities and knowledge systems; and the disease tracing port is used to generate personalized learning paths.

[0006] A further improvement of the present invention is that the data fusion port includes a knowledge point acquisition module, a behavior log acquisition module, a news aggregation module and an Internet search module; the knowledge point acquisition module is used to extract nursing knowledge units from the course library; the behavior log acquisition module is used to record students' learning time, wrong question records, and search keywords; the news aggregation module is used to capture medical news in real time and label it; and the Internet search module is used to crawl the latest nursing cases from authoritative medical websites.

[0007] A further improvement of the present invention is that the knowledge matching port includes a data extraction module, a matching degree calculation module and a threshold judgment module. The data extraction module is used to obtain the student's current knowledge state S = {S1, ..., S i ,...S m}, i represents the student's knowledge status label, and the value of i is 1-m; the matching degree calculation module is used to calculate the matching degree between the student and the knowledge field; the threshold judgment module is used to mark it as a knowledge gap when the matching degree is less than the threshold, and generate a knowledge gap matrix.

[0008] A further improvement of the present invention is that the training recommendation port includes a behavior association module, a dynamic weight module and a comprehensive recommendation module. The behavior association module is used to calculate the compatibility between trainee behavior and knowledge gaps, and calculate behavior association scores in different fields; the dynamic weight module is used to calculate news aggregation weights; and the comprehensive recommendation module is used to output a learning plan based on the comprehensive recommendation value formula.

[0009] A further improvement of the present invention is that the matching degree calculation module calculates the matching degree between the student and the knowledge domain, and the matching degree calculation formula is:

[0010]

[0011] Among them, M j represents the matching degree between the student and the knowledge domain k, ω i represents the weight of knowledge point i, δ(S i ∈C k ) represents the indicator function. When the students master the knowledge domain S i And S i When it belongs to knowledge domain k, the value is 1, |C k | represents the total number of knowledge points in knowledge domain k.

[0012] A further improvement of the present invention is that the specific formula for calculating the behavior association score is:

[0013]

[0014]

[0015] Among them, Q b represents the compatibility between behavior b and knowledge gap G, |B b ∩G| represents the number of knowledge points not mastered when behavior b occurs, I(B b ∈H k ) represents the indicator function, which takes the value 1 if and only if behavior b has training value for domain k, R k represents the behavior relevance score of knowledge domain k.

[0016] A further improvement of the present invention is that the dynamic weight module calculates the news aggregation weight, and the calculation formula for calculating the news aggregation weight is:

[0017]

[0018] Among them, f(N n ,k) represents the frequency of news n and field k, recency(N n ) represents the number of days of news freshness, and λ represents the decay factor.

[0019] A further improvement of the present invention is that the comprehensive recommendation module outputs a learning plan according to a comprehensive recommendation value formula, and the calculation formula of the comprehensive recommendation value is:

[0020] F k =αM k +βR k +γT k ;

[0021] Among them, F k Indicates the comprehensive recommended value.

[0022] The technical effects of the present invention are as follows:

[0023] This invention captures individual needs through internet search behavior analysis, leverages a news aggregation engine to connect to cutting-edge industry trends, and combines multi-dimensional quantitative models to reconstruct the underlying logic of nursing training. Its core value lies in upgrading "static knowledge transfer" to a "dynamic capability evolution system," making it particularly suitable for medical talent training in the VUCA (Volatile, Uncertain, Complex, Ambiguous) era. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Other features, objects and advantages of the present invention will become more apparent upon reading the detailed description of non-limiting embodiments made with reference to the following drawings:

[0025] Figure 1 This is a structural diagram of the nursing remote training platform that integrates Internet search and news aggregation of the present invention. DETAILED DESCRIPTION

[0026] Example 1

[0027] A nursing remote training platform that integrates Internet search and news aggregation includes a data fusion port, a knowledge matching port, and a training recommendation port. The data fusion port is used to integrate multi-source dynamic data; the knowledge matching port is used to calculate the matching degree between trainees' abilities and knowledge systems; and the disease tracing port is used to generate personalized learning paths.

[0028] In this embodiment, the data fusion port includes a knowledge point acquisition module, a behavior log acquisition module, a news aggregation module and an Internet search module; the knowledge point acquisition module is used to extract nursing knowledge units from the course library; the behavior log acquisition module is used to record students' learning time, wrong question records, and search keywords; the news aggregation module is used to capture and label medical news in real time; and the Internet search module is used to crawl the latest nursing cases from authoritative medical websites.

[0029] Example 2

[0030] Different from the first embodiment, in this embodiment, the knowledge matching port includes a data extraction module, a matching degree calculation module and a threshold judgment module. The data extraction module is used to obtain the student's current knowledge state S = {S1, ..., S i ,...S m}, i represents the student's knowledge status label, and the value of i is 1-m; the matching degree calculation module is used to calculate the matching degree between the student and the knowledge field; the threshold judgment module is used to mark it as a knowledge gap when the matching degree is less than the threshold, and generate a knowledge gap matrix.

[0031] In this embodiment, the matching degree calculation module calculates the matching degree between the student and the knowledge domain. The matching degree calculation formula is:

[0032]

[0033] Among them, M j represents the matching degree between the student and the knowledge domain k, ω i represents the weight of knowledge point i, δ(S i ∈C k ) represents the indicator function. When the students master the knowledge domain S i And S i When it belongs to knowledge domain k, the value is 1, |C k | represents the total number of knowledge points in knowledge domain k.

[0034] Example 3

[0035] In this embodiment, the training recommendation port includes a behavior association module, a dynamic weight module and a comprehensive recommendation module. The behavior association module is used to calculate the compatibility between trainee behavior and knowledge gaps, and calculate behavior association scores in different fields; the dynamic weight module is used to calculate the news aggregation weight; the comprehensive recommendation module is used to output a learning plan based on the comprehensive recommendation value formula.

[0036] In this embodiment, the specific formula for calculating the behavior association score is:

[0037]

[0038] Among them, Q b represents the compatibility between behavior b and knowledge gap G, |B b ∩G| represents the number of knowledge points not mastered when behavior b occurs, I(B b ∈H k ) represents the indicator function, which takes the value 1 if and only if behavior b has training value for domain k, R k represents the behavior relevance score of knowledge domain k.

[0039] Example 4

[0040] Furthermore, in this embodiment, the dynamic weight module calculates the news aggregation weight, and the calculation formula for calculating the news aggregation weight is:

[0041]

[0042] Among them, f(N n ,k) represents the frequency of news n and field k, recency(N n ) represents the number of days of news freshness, and λ represents the decay factor.

[0043] In this embodiment, the comprehensive recommendation module outputs a learning plan according to a comprehensive recommendation value formula. The calculation formula of the comprehensive recommendation value is:

[0044] F k =αM k +βR k +γT k ;

[0045] Among them, F k Indicates the comprehensive recommended value.

[0046] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0047] It should be understood that determining B based on A does not mean determining B based solely on A. B can also be determined based on A and / or other information.

[0048] The above embodiments can be implemented in whole or in part through software, hardware, firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. A computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions according to the embodiments of the present invention are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via a wired network or / and a wireless network. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains a collection of one or more available media. The available media can be magnetic media (e.g., floppy disks, hard disks, tapes), optical media (e.g., DVDs), or semiconductor media. The semiconductor media can be a solid-state drive.

[0049] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in the present invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0050] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0051] In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only one type. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0052] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0053] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0054] Throughout this specification, references to terms such as "one embodiment," "example," or "specific example" indicate that the specific features, structures, materials, or characteristics described in conjunction with that embodiment or example are included in at least one embodiment or example of the present invention. In this specification, schematic representations of these terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0055] The preferred embodiments of the present invention disclosed above are intended only to help illustrate the present invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the present invention to specific embodiments. Obviously, many modifications and variations are possible based on the contents of this specification. These embodiments are selected and described in detail in this specification to better explain the principles and practical applications of the present invention, thereby enabling those skilled in the art to better understand and utilize the present invention. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A remote nursing training platform integrating Internet search and news aggregation, characterized by: The platform includes a data fusion port, a knowledge matching port and a training recommendation port. The data fusion port is used to integrate multi-source dynamic data; The knowledge matching port is used to calculate the matching degree between the student's ability and the knowledge system; the disease tracing port is used to generate a personalized learning path.

2. The nursing remote training platform integrating Internet search and news aggregation according to claim 1 is characterized in that: The data fusion port includes a knowledge point collection module, a behavior log collection module, a news aggregation module and an Internet search module; The knowledge point collection module is used to extract nursing knowledge units from the course library; the behavior log collection module is used to record students' learning time, wrong questions, and search keywords; The news aggregation module is used to capture and label medical news in real time; the Internet search module is used to crawl the latest nursing cases from authoritative medical websites.

3. The nursing remote training platform integrating Internet search and news aggregation according to claim 2 is characterized in that: The knowledge matching port includes a data extraction module, a matching degree calculation module and a threshold judgment module. The data extraction module is used to obtain the student's current knowledge state S = {S1, ..., S i ,...S m }, i represents the student's knowledge status label, and the value of i is 1-m; the matching degree calculation module is used to calculate the matching degree between the student and the knowledge field; the threshold judgment module is used to mark it as a knowledge gap when the matching degree is less than the threshold, and generate a knowledge gap matrix.

4. The nursing remote training platform integrating Internet search and news aggregation according to claim 3 is characterized in that: The training recommendation port includes a behavior association module, a dynamic weight module and a comprehensive recommendation module. The behavior association module is used to calculate the fit between trainees' behaviors and knowledge gaps and calculate behavior association scores in different fields. The dynamic weight module is used to calculate the news aggregation weight; The comprehensive recommendation module is used to output a learning plan based on the comprehensive recommendation value formula.

5. The nursing remote training platform integrating Internet search and news aggregation according to claim 4 is characterized in that: The matching degree calculation module calculates the matching degree between the student and the knowledge domain. The matching degree calculation formula is: Among them, M j represents the matching degree between the student and the knowledge domain k, ω i represents the weight of knowledge point i, δ(S i ∈C k ) represents the indicator function. When the students master the knowledge domain S i And S i When it belongs to knowledge domain k, the value is 1, |C k | represents the total number of knowledge points in knowledge domain k.

6. The nursing remote training platform integrating Internet search and news aggregation according to claim 5 is characterized in that: The specific formula for calculating the behavior association score is: Among them, Q b represents the compatibility between behavior b and knowledge gap G, |B b ∩G| represents the number of knowledge points not mastered when behavior b occurs, I(B b ∈H k ) represents the indicator function, which takes the value 1 if and only if behavior b has training value for domain k, R k represents the behavior relevance score of knowledge domain k.

7. The nursing remote training platform integrating Internet search and news aggregation according to claim 6 is characterized in that: The dynamic weight module calculates the news aggregation weight, and the calculation formula for calculating the news aggregation weight is: Among them, f(N n ,k) represents the frequency of news n and field k, recency(N n ) represents the number of days of news freshness, and λ represents the decay factor.

8. The nursing remote training platform integrating Internet search and news aggregation according to claim 7 is characterized in that: The comprehensive recommendation module outputs a learning plan according to the comprehensive recommendation value formula. The calculation formula of the comprehensive recommendation value is: F k =αM k +βR k +γT k ; Among them, F k Indicates the comprehensive recommended value.

Images