Teacher intelligent service system based on multi-source academic information

Abstract

The invention discloses a teacher intelligent service system based on multi-source academic information, and the system comprises a data collection module which is used for achieving the multi-source information integration, data screening and duplicate removal, and data sharing of teachers; wherein the multi-source information comprises personal information and academic information of teachers; the data processing module is used for realizing text preprocessing and multi-dimensional knowledge graph construction based on a large language model; wherein the text preprocessing comprises word segmentation and part-of-speech tagging processing, named entity recognition and relation extraction processing; the intelligent analysis module is used for realizing personalized academic resource recommendation and scientific research cooperation opportunity prediction based on a graph neural network; and the front-end interaction module is used for realizing visual display and interactive operation of the teacher data. The embodiment of the invention can ensure the timeliness and integrity of personal information and academic information of teachers, and can be widely applied to the technical field of computers.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to a teacher intelligent service system based on multi-source academic information. Background Art

[0002] In today's globalized and information-based world, higher education institutions are facing increasingly fierce competition and higher academic requirements. Universities not only need to improve teaching quality on the basis of multidisciplinary coordinated development, but also need to demonstrate strong comprehensive strength in scientific research innovation, international cooperation and social services. With the continuous expansion of the teaching staff and the increasing abundance of academic achievements, universities are in urgent need of an intelligent system that can centrally display the teaching and scientific research achievements of teachers, so as to improve the efficiency of academic exchanges, promote interdisciplinary cooperation, and enhance their academic reputation and competitiveness at home and abroad. Summary of the invention

[0003] The main purpose of the embodiment of the present invention is to propose a teacher intelligent service system based on multi-source academic information, which can ensure the timeliness and integrity of teacher personal information and academic information.

[0004] To achieve the above objectives, one aspect of an embodiment of the present invention proposes a teacher intelligent service system based on multi-source academic information, including:

[0005] A data collection module is used to realize the integration of multi-source information about teachers, data screening and deduplication, and data sharing; wherein the multi-source information includes personal information and academic information of teachers;

[0006] A data processing module, used to implement text preprocessing and multi-dimensional knowledge graph construction based on a large language model; wherein the text preprocessing includes word segmentation and part-of-speech tagging, named entity recognition and relationship extraction;

[0007] Intelligent analysis module, which is used to realize personalized academic resource recommendation and scientific research cooperation opportunity prediction based on graph neural network;

[0008] The front-end interactive module is used to realize the visual display and interactive operation of teacher data.

[0009] In some embodiments, the multi-source information integration in the data acquisition module specifically includes: customizing a Web crawler program to automatically capture and parse teacher information disclosed on the public platform; establishing an API interface connection with a public information source to obtain or regularly update teacher information in real time, wherein the teacher information includes at least one of the following: personal profile, educational background, research direction, academic achievements, teaching experience, and social activities;

[0010] The data collection module screens and removes duplicate data, specifically including: screening out teacher information with target value according to the release time and update frequency of the information; removing duplicate information by means of hash algorithm and text similarity calculation;

[0011] The data sharing of the data acquisition module specifically includes: converting the integrated multi-source data into a unified data format; sharing the processed data with other modules within the system and external users through the API interface of the scholar network, thereby realizing open sharing of academic information data.

[0012] In some embodiments, the text preprocessing of the data processing module specifically includes: using a large language model to segment and tag teacher-related texts; identifying and extracting key entities in the text, the key entities including teacher names, institution names, research directions, and academic achievements; based on the pre-trained language model, extracting the association relationships between entities, the association relationships including teacher-student relationships and cooperative research relationships;

[0013] The multi-dimensional knowledge graph construction of the data processing module specifically includes: determining the nodes of the knowledge graph and using the associations between them as edges; the nodes of the knowledge graph include individual teachers, academic achievements, and research directions; adding detailed attribute information to each node, the attribute information includes the teacher's educational background, the specific content of the academic achievements, and a detailed description of the research direction; and optimizing the structure of the knowledge graph according to the target algorithm.

[0014] In some embodiments, the personalized academic resource recommendation in the intelligent analysis module specifically includes:

[0015] Convert each node in the knowledge graph into a feature vector, which contains the node's attribute information and its position information in the graph;

[0016] Using the graph neural network model of graph convolutional network and graph attention network, the feature vector of the teacher node is iteratively propagated, the feature information of the neighboring nodes of the teacher node is absorbed, and the feature representation of the teacher node is formed;

[0017] According to the characteristic representation of the teacher node, the similarity between the teacher node and the potential recommended resource node is calculated, and the most relevant academic resources are recommended to the teacher according to the similarity sorting; the potential resource nodes include academic papers, patents, and experts.

[0018] In some embodiments, the scientific research cooperation opportunity prediction in the intelligent analysis module specifically includes:

[0019] Use graph neural networks to embed teacher nodes into low-dimensional space to form a feature embedding representation of teachers;

[0020] Calculate the similarity between two teacher nodes;

[0021] Recommend potential partners for each teacher based on similarity ranking and predict the possibility of future cooperation;

[0022] The calculation formula of similarity is:

[0023] Among them, V teacher and V resource They represent the feature vectors of the teacher node and the recommended resource node respectively; similarity represents the cosine similarity calculation function.

[0024] In some embodiments, the scientific research cooperation opportunity prediction in the intelligent analysis module further includes:

[0025] Extract key features from the feature vector of the teacher node, wherein the key features include research direction, number of published papers, and academic influence;

[0026] Encode the selected key features, specifically: encode the research direction into a one-hot code or embedding vector;

[0027] The encoded features are fused to form a richer teacher feature vector; the fusion calculation formula is: V teacher_fused =αV direction +βV papers +γV impact , where α, β, and γ are weight coefficients; V teacher_fused Represents the fused teacher feature vector; V direction Represents the characteristic vector of research direction; V papers Represents the feature vector of the number of published papers; V impact Represents the characteristic vector of academic influence.

[0028] In some embodiments, the visualization display in the front-end interaction module specifically includes: displaying detailed information on the teacher's personal profile, educational background, research direction, and academic achievements through a visualization interface; providing an interactive browsing function of the knowledge graph, and viewing and exploring the association relationship between teachers in response to the user's click operation on the nodes and edges;

[0029] The interactive operations in the front-end interactive module specifically include: the display system recommends academic papers, patents, and academic resources of experts to teachers, and provides interactive operations, including screening, sorting, and viewing details;

[0030] The system displays potential partners recommended for teachers, and provides in-depth viewing of partner information, personalized cooperation suggestions and path analysis functions to help teachers plan their own academic research and cooperation plans.

[0031] To achieve the above objective, another aspect of an embodiment of the present invention provides an electronic device, the electronic device comprising a memory and a processor, the memory storing a computer program, and the processor implementing the above-mentioned method when executing the computer program.

[0032] To achieve the above objective, another aspect of an embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the above-mentioned method is implemented.

[0033] The embodiment of the present invention also discloses a computer program product or a computer program, which includes a computer instruction stored in a computer-readable storage medium. A processor of a computer device can read the computer instruction from the computer-readable storage medium, and the processor executes the computer instruction, so that the computer device executes the above method.

[0034] The embodiments of the present invention include at least the following beneficial effects: the present invention provides a teacher intelligent service system based on multi-source academic information, the system includes a data acquisition module, which is used to realize the integration of multi-source information about teachers, data screening and deduplication, and data sharing; wherein the multi-source information includes personal information and academic information of teachers; a data processing module, which is used to realize text preprocessing and multi-dimensional knowledge graph construction based on a large language model; wherein the text preprocessing includes word segmentation and part-of-speech tagging processing, named entity recognition and relationship extraction processing; an intelligent analysis module, which is used to realize personalized academic resource recommendation and scientific research cooperation opportunity prediction based on graph neural network; a front-end interaction module, which is used to realize the visualization display and interactive operation of teacher data. The embodiments of the present invention can ensure the timeliness and integrity of teacher personal information and academic information. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 It is a schematic diagram of an implementation environment provided by an embodiment of the present invention;

[0036] Figure 2 It is a flowchart of a specific implementation process provided by an embodiment of the present invention;

[0037] Figure 3 It is an actual deployment diagram of a certain unit provided by an embodiment of the present invention;

[0038] Figure 4 This is an example diagram of a teacher's homepage in a certain unit provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0039] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the embodiments of the present invention. They are only examples of devices and methods consistent with some aspects of the embodiments of the present invention as detailed in the attached claims.

[0040] It is understood that the terms "first", "second", etc. used in the present invention may be used herein to describe various concepts, but unless otherwise specified, these concepts are not limited by these terms. These terms are only used to distinguish one concept from another. For example, without departing from the scope of the embodiments of the present invention, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the words "if" and "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining".

[0041] The terms "at least one", "multiple", "each", "any", etc. used in the present invention, at least one includes one, two or more, multiple includes two or more, each refers to each of the corresponding multiple, and any refers to any one of the multiple.

[0042] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which the present invention belongs. The terms used herein are only for the purpose of describing the embodiments of the present invention and are not intended to limit the present invention.

[0043] The teacher intelligent service system based on multi-source academic information provided by the embodiment of the present invention relates to a technical field related to computers or data processing. The teacher intelligent service system based on multi-source academic information provided by the embodiment of the present invention can be applied to a terminal, can also be applied to a server, and can also be software running in a terminal or a server. In some embodiments, the terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, a smart speaker, a smart watch, and a car terminal, etc., but is not limited to this; the server side can be configured as an independent physical server, or a server cluster or distributed system composed of multiple physical servers, and can also be configured to provide cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms and other basic cloud computing services. The cloud server, the server can also be a node server in a blockchain network; the software can be an application that implements a teacher intelligent service system based on multi-source academic information, etc., but is not limited to the above forms.

[0044] The present invention can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronic devices, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc. The present invention can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present invention can also be practiced in distributed computing environments, in which tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0045] like Figure 1 FIG. 1 is a schematic diagram of an implementation environment provided by an embodiment of the present invention. Figure 1 The implementation environment includes at least one terminal 102 and a server 101. The terminal 102 and the server 101 can be connected to a network wirelessly or wired to complete data transmission and exchange.

[0046] Server 101 can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers. It can also be a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN (Content Delivery Network), as well as big data and artificial intelligence platforms.

[0047] In addition, the server 101 can also be a node server in the blockchain network. Blockchain is a new application model of computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanism, and encryption algorithm.

[0048] The terminal 102 may be a smart phone, a tablet computer, a laptop computer, a desktop computer, a smart speaker, a smart watch, etc. The terminal 102 may also be a vehicle-mounted terminal of various device types described above, but is not limited thereto. The terminal 102 and the server 101 may be directly or indirectly connected via wired or wireless communication, which is not limited in this embodiment of the present invention.

[0049] Based on the example Figure 1 In the implementation environment shown, an embodiment of the present invention provides a teacher intelligent service system based on multi-source academic information. The following is an example of the teacher intelligent service system based on multi-source academic information being applied in the server 101. It can be understood that the system can also be applied in the terminal 102.

[0050] Specifically, one aspect of an embodiment of the present invention proposes a teacher intelligent service system based on multi-source academic information, including:

[0051] A data collection module is used to realize the integration of multi-source information about teachers, data screening and deduplication, and data sharing; wherein the multi-source information includes personal information and academic information of teachers;

[0052] A data processing module, used to implement text preprocessing and multi-dimensional knowledge graph construction based on a large language model; wherein the text preprocessing includes word segmentation and part-of-speech tagging, named entity recognition and relationship extraction;

[0053] Intelligent analysis module, which is used to realize personalized academic resource recommendation and scientific research cooperation opportunity prediction based on graph neural network;

[0054] The front-end interactive module is used to realize the visual display and interactive operation of teacher data.

[0055] In some embodiments, the multi-source information integration in the data acquisition module specifically includes: customizing a Web crawler program to automatically capture and parse teacher information disclosed on the public platform; establishing an API interface connection with a public information source to obtain or regularly update teacher information in real time, wherein the teacher information includes at least one of the following: personal profile, educational background, research direction, academic achievements, teaching experience, and social activities;

[0056] The data collection module screens and removes duplicate data, specifically including: screening out teacher information with target value according to the release time and update frequency of the information; removing duplicate information by means of hash algorithm and text similarity calculation;

[0057] The data sharing of the data acquisition module specifically includes: converting the integrated multi-source data into a unified data format; sharing the processed data with other modules within the system and external users through the API interface of the scholar network, thereby realizing open sharing of academic information data.

[0058] In some embodiments, the text preprocessing of the data processing module specifically includes: using a large language model to segment and tag teacher-related texts; identifying and extracting key entities in the text, the key entities including teacher names, institution names, research directions, and academic achievements; based on the pre-trained language model, extracting the association relationships between entities, the association relationships including teacher-student relationships and cooperative research relationships;

[0059] The multi-dimensional knowledge graph construction of the data processing module specifically includes: determining the nodes of the knowledge graph and using the associations between them as edges; the nodes of the knowledge graph include individual teachers, academic achievements, and research directions; adding detailed attribute information to each node, the attribute information includes the teacher's educational background, the specific content of the academic achievements, and a detailed description of the research direction; and optimizing the structure of the knowledge graph according to the target algorithm.

[0060] In some embodiments, the personalized academic resource recommendation in the intelligent analysis module specifically includes:

[0061] Convert each node in the knowledge graph into a feature vector, which contains the node's attribute information and its position information in the graph;

[0062] Using the graph neural network model of graph convolutional network and graph attention network, the feature vector of the teacher node is iteratively propagated, the feature information of the neighboring nodes of the teacher node is absorbed, and the feature representation of the teacher node is formed;

[0063] According to the characteristic representation of the teacher node, the similarity between the teacher node and the potential recommended resource node is calculated, and the most relevant academic resources are recommended to the teacher according to the similarity sorting; the potential resource nodes include academic papers, patents, and experts.

[0064] In some embodiments, the scientific research cooperation opportunity prediction in the intelligent analysis module specifically includes:

[0065] Use graph neural networks to embed teacher nodes into low-dimensional space to form a feature embedding representation of teachers;

[0066] Calculate the similarity between two teacher nodes;

[0067] Recommend potential partners for each teacher based on similarity ranking and predict the possibility of future cooperation;

[0068] The calculation formula of similarity is:

[0069] Among them, V teacher and V resource They represent the feature vectors of the teacher node and the recommended resource node respectively; similarity represents the cosine similarity calculation function.

[0070] In some embodiments, the scientific research cooperation opportunity prediction in the intelligent analysis module further includes:

[0071] Extract key features from the feature vector of the teacher node, wherein the key features include research direction, number of published papers, and academic influence;

[0072] Encode the selected key features, specifically: encode the research direction into a one-hot code or embedding vector;

[0073] The encoded features are fused to form a richer teacher feature vector; the fusion calculation formula is: V teacher_fused =αV direction +βV papers +γV impact , where α, β, and γ are weight coefficients; V teacher_fused Represents the fused teacher feature vector; V direction Represents the characteristic vector of research direction; V papers Represents the feature vector of the number of published papers; V impact Represents the characteristic vector of academic influence.

[0074] In some embodiments, the visualization display in the front-end interaction module specifically includes: displaying detailed information on the teacher's personal profile, educational background, research direction, and academic achievements through a visualization interface; providing an interactive browsing function of the knowledge graph, and viewing and exploring the association relationship between teachers in response to the user's click operation on the nodes and edges;

[0075] The interactive operations in the front-end interactive module specifically include: the display system recommends academic papers, patents, and academic resources of experts to teachers, and provides interactive operations, including screening, sorting, and viewing details;

[0076] The system displays potential partners recommended for teachers, and provides in-depth viewing of partner information, personalized cooperation suggestions and path analysis functions to help teachers plan their own academic research and cooperation plans.

[0077] The following describes the specific implementation process of the present invention in detail by taking a specific application scenario as an example in conjunction with the accompanying drawings of the specification:

[0078] like Figure 2 As shown, a teacher intelligent service system based on multi-source academic information includes a data acquisition module, a data processing module, an intelligent analysis module and a front-end interaction module.

[0079] like Figure 3 As shown, the present invention has been deployed with Guangdong University of Technology as a pilot, with the School of Computer Science as the key testing department.

[0080] like Figure 4 As shown, the present invention has designed a teacher personal homepage for the computer teachers of Guangdong University of Technology, showing the personal homepage of the dean as an example.

[0081] In this embodiment, the data collection module uses Web crawler technology to write a customized crawler program and set a URL list of target websites (such as the official website of the school, Baidu Encyclopedia, Scholars Network, etc.);

[0082] Configure the parsing rules of the crawler program to extract teacher information from the target web page, including name, title, educational background, research direction, academic achievements, etc.;

[0083] The extracted information is stored in the database to form a preliminary teacher information data set;

[0084] Write data screening scripts to filter out the latest and most valuable teacher information based on information release time, update frequency and other conditions;

[0085] Use hashing algorithms or text similarity calculation techniques to detect and remove duplicate information in data sets;

[0086] Design and develop API interfaces to provide processed teacher information datasets to the public in a unified format;

[0087] Write interface documentation to clarify the interface's functions, parameters, return values, etc., so that external users can call it.

[0088] In this embodiment, the data processing module uses natural language processing technology (such as BERT, GPT, etc.) to perform word segmentation, part-of-speech tagging, and named entity recognition on teacher-related texts;

[0089] Write a relationship extraction script to extract entity relationships in texts, such as teacher-student relationships, collaborative research relationships, etc., based on a pre-trained language model;

[0090] Define the nodes and edges of the knowledge graph, taking individual teachers, academic achievements, research directions, etc. as nodes and the relationships between them as edges;

[0091] Use graph databases (such as Neo4j) to store knowledge graph data and build indexes to improve query efficiency;

[0092] Write graph optimization algorithms to optimize the structure of knowledge graphs and improve the accuracy and usability of graphs.

[0093] In this embodiment, the intelligent analysis module uses advanced graph neural network models such as GraphSAGE or GAT, which can process large-scale graph data and effectively learn low-dimensional feature representations of nodes;

[0094] Take the teacher nodes, academic achievement nodes, research direction nodes, etc. in the knowledge graph as input data, and consider the association relationship between nodes (such as published papers, research directions, etc.);

[0095] Use deep learning frameworks such as PyTorch or TensorFlow to train the model and learn the feature representation of nodes by optimizing the loss function (such as cross entropy loss);

[0096] After the training is completed, the feature vectors of the teacher node are extracted from the model and recorded as Vteacher. These vectors will be used for subsequent similarity calculations;

[0097] The cosine similarity formula is used to calculate the similarity between the teacher node and the potential recommended resource node. The specific formula is:

[0098]

[0099] Among them, Vteacher and Vresource represent the feature vectors of teacher nodes and recommended resource nodes respectively;

[0100] According to the feature vector of the teacher node, similar candidate resources are screened out from the academic achievement library or patent library;

[0101] Calculate the similarity between the teacher node and each candidate resource, and sort them according to the similarity;

[0102] Select the top N resources with the highest similarity as recommendation results and display them to users;

[0103] Extract key features from the feature vectors of teacher nodes, such as research direction, number of published papers, academic influence, etc.

[0104] Encode the selected features, such as encoding the research direction as a one-hot code or embedding vector;

[0105] The encoded features are fused to form a richer teacher feature vector. The fusion method can be splicing, weighted summation, etc.

[0106] Assume that the feature vector of the teacher node is composed of multiple sub-feature vectors, such as the research direction feature vector Vdirection, the number of published papers feature vector Vpapers, the academic influence feature vector Vimpact, etc. Then the fused feature vector can be expressed as:

[0107] V teacher_fused =αV direction +βV papers +γV impact

[0108] Among them, α, β, and γ are weight coefficients, which can be obtained through training;

[0109] The cosine similarity formula is also used to calculate the similarity between two teacher nodes. The specific formula is:

[0110]

[0111] Based on the similarity calculation results, a cooperation prediction model is constructed. A similarity threshold is set (such as 0.8). When the similarity between two teacher nodes exceeds the threshold, it is considered that there is a potential cooperation opportunity between them.

[0112] For teacher pairs that are predicted to have collaboration opportunities, further evaluate the possibility of collaboration. Evaluation methods may include statistical analysis based on historical collaboration data, matching analysis based on common research interests, etc.

[0113] Based on the evaluation results, potential partners are recommended for each teacher, and the probability value of the cooperation opportunity is given. The probability value of the cooperation opportunity can be predicted by machine learning models (such as logistic regression, support vector machine, etc.). The specific formula is:

[0114] P(cooperation)=σ(W·V teacher_pair +b)

[0115] Among them, W is the weight matrix, Vteacher_pair is the concatenation of the feature vectors after the fusion of the two teacher nodes, σ is the sigmoid function, and b is the bias term.

[0116] In this embodiment, the front-end interaction module uses the Vue.js framework to design a responsive interface to support smooth browsing on different devices;

[0117] Use the ECharts library to draw visual charts of teachers' academic archives, such as bar charts showing the number of papers published by teachers and pie charts showing the distribution of research directions;

[0118] Use D3.js or Three.js library to realize 3D visualization of knowledge graph, supporting interactive operations such as node dragging and zooming;

[0119] Provide filtering conditions (such as time range, research direction, etc.) on the interface, and users can filter recommended resources according to the conditions;

[0120] A list of recommended partners is displayed for each teacher. Click on a partner to view detailed information (such as research direction, published papers, etc.);

[0121] For recommended partners, a path analysis function is provided to show the connection path between the two teachers (such as common research directions, collaborative projects, etc.);

[0122] Set up a user feedback mechanism to allow users to evaluate and score recommendation results and cooperation suggestions in order to continuously optimize the algorithm.

[0123] Compared with the prior art, the present invention has the following advantages and beneficial effects: The present invention realizes the comprehensive and real-time collection of teachers' personal information and academic information through customized Web crawler programs and API interface connections established with authoritative information sources, thereby ensuring the timeliness and integrity of the information. The use of large language models for text preprocessing, including word segmentation, part-of-speech tagging, named entity recognition and relationship extraction, as well as the construction of multidimensional knowledge graphs, improves the efficiency and accuracy of data processing. Personalized academic resource recommendations and scientific research cooperation opportunity predictions are achieved through graph neural networks, providing teachers with accurate academic resource matching and cooperation opportunities, and enhancing the practicality and value of the system. The front-end interactive module provides a visual display and interactive operation of teacher data, allowing teachers to intuitively understand their academic background and research results, and conveniently plan academic research and cooperation plans.

[0124] The embodiment of the present invention designs and develops an online display system tailored for university teachers. The system will provide teachers with a personalized and professional display space through modern digital technology, supporting them to display their teaching achievements, scientific research projects, academic papers, awards and personal development dynamics in detail. The platform not only helps teachers manage and promote their academic resources more efficiently, but also provides convenient access channels for other educational institutions, research organizations and industry partners, becoming a bridge to strengthen communication and cooperation between universities and the outside world.

[0125] In addition, the system will have intelligent data analysis functions, and provide academic influence assessment, cooperation potential analysis and other services through the systematic organization and display of teachers' scientific research projects and academic achievements. This will not only help improve the academic image of individual teachers, but also establish a good reputation for universities in the national and even international academic fields, and enhance their academic discourse power and competitiveness. At the same time, the construction of the platform will also promote internal resource integration and interdisciplinary collaborative innovation, and promote the further improvement of the overall scientific research level of universities.

[0126] The embodiment of the present invention further provides an electronic device, the electronic device includes a memory and a processor, the memory stores a computer program, and the processor implements the above-mentioned method for implementing the teacher intelligent service system based on multi-source academic information when executing the computer program. The electronic device can be any intelligent terminal including a tablet computer, a car computer, etc.

[0127] It can be understood that the contents of the above method embodiments are all applicable to the present device embodiments, the functions specifically implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0128] An embodiment of the present invention also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the above-mentioned method for implementing the teacher intelligent service system based on multi-source academic information.

[0129] It can be understood that the contents of the above method embodiments are all applicable to the present storage medium embodiments, the functions specifically implemented by the present storage medium embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0130] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely disposed relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0131] It should be noted that in various specific embodiments of the present invention, when it comes to the need to perform relevant processing based on data related to user identity or characteristics such as user information, user behavior data, user historical data, and user location information, the user's permission or consent will be obtained first, and the collection, use, and processing of these data will comply with relevant laws, regulations, and standards. In addition, when the embodiment of the present invention needs to obtain the user's sensitive personal information, it will obtain the user's separate permission or consent through a pop-up window or jump to a confirmation page, and after clearly obtaining the user's separate permission or consent, it will obtain the necessary user-related data for the normal operation of the embodiment of the present invention.

[0132] The embodiments described in the embodiments of the present invention are intended to more clearly illustrate the technical solutions of the embodiments of the present invention, and do not constitute a limitation on the technical solutions provided by the embodiments of the present invention. Those skilled in the art can appreciate that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of the present invention are also applicable to similar technical problems.

[0133] Those skilled in the art will appreciate that the technical solutions shown in the figures do not limit the embodiments of the present invention and may include more or fewer steps than shown in the figures, or a combination of certain steps, or different steps.

[0134] The device embodiments described above are merely illustrative, and the units described as separate components may or may not be physically separated, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0135] Those skilled in the art will appreciate that all or some of the steps in the methods disclosed above, and the functional modules / units in the systems and devices may be implemented as software, firmware, hardware, or a suitable combination thereof.

[0136] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0137] It should be understood that in the present invention, "at least one (item)" refers to one or more, and "plurality" refers to two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can represent: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following items" or similar expressions refers to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0138] In the several embodiments provided by the present invention, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the above units is only a logical function division. There may be other division methods in actual implementation, 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.

[0139] The units described above as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0140] 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. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0141] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including multiple instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods of various embodiments of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, etc., various media that can store programs.

[0142] The preferred embodiments of the present invention are described above with reference to the accompanying drawings, but the scope of the rights of the embodiments of the present invention is not limited thereby. Any modification, equivalent substitution and improvement made by those skilled in the art without departing from the scope and essence of the embodiments of the present invention shall be within the scope of the rights of the embodiments of the present invention.

Claims

1. A teacher intelligent service system based on multi-source academic information, characterized in that: include: A data collection module is used to realize the integration of multi-source information about teachers, data screening and deduplication, and data sharing; wherein the multi-source information includes personal information and academic information of teachers; A data processing module, used to implement text preprocessing and multi-dimensional knowledge graph construction based on a large language model; wherein the text preprocessing includes word segmentation and part-of-speech tagging, named entity recognition and relationship extraction; Intelligent analysis module, which is used to realize personalized academic resource recommendation and scientific research cooperation opportunity prediction based on graph neural network; The front-end interactive module is used to realize the visual display and interactive operation of teacher data.

2. According to claim 1, a teacher intelligent service system based on multi-source academic information is characterized in that: The multi-source information integration in the data acquisition module specifically includes: customizing a Web crawler program to automatically capture and parse teacher information disclosed on the public platform; establishing an API interface connection with a public information source to obtain or regularly update teacher information in real time, wherein the teacher information includes at least one of the following: personal profile, educational background, research direction, academic achievements, teaching experience, and social activities; The data collection module screens and removes duplicate data, specifically including: screening out teacher information with target value according to the release time and update frequency of the information; removing duplicate information by means of hash algorithm and text similarity calculation; The data sharing of the data acquisition module specifically includes: converting the integrated multi-source data into a unified data format; sharing the processed data with other modules within the system and external users through the API interface of the scholar network, thereby realizing open sharing of academic information data.

3. According to claim 1, a teacher intelligent service system based on multi-source academic information is characterized in that: The text preprocessing of the data processing module specifically includes: using a large language model to segment and tag teacher-related texts; identifying and extracting key entities in the text, the key entities include teacher names, institution names, research directions, and academic achievements; based on the pre-trained language model, extracting the association relationships between entities, the association relationships include teacher-student relationships and cooperative research relationships; The multi-dimensional knowledge graph construction of the data processing module specifically includes: determining the nodes of the knowledge graph and using the associations between them as edges; the nodes of the knowledge graph include individual teachers, academic achievements, and research directions; adding detailed attribute information to each node, the attribute information includes the teacher's educational background, the specific content of the academic achievements, and a detailed description of the research direction; and optimizing the structure of the knowledge graph according to the target algorithm.

4. According to claim 1, a teacher intelligent service system based on multi-source academic information is characterized in that: The personalized academic resource recommendation in the intelligent analysis module specifically includes: Convert each node in the knowledge graph into a feature vector, which contains the node's attribute information and its position information in the graph; Using the graph neural network model of graph convolutional network and graph attention network, the feature vector of the teacher node is iteratively propagated, the feature information of the neighboring nodes of the teacher node is absorbed, and the feature representation of the teacher node is formed; According to the characteristic representation of the teacher node, the similarity between the teacher node and the potential recommended resource node is calculated, and the most relevant academic resources are recommended to the teacher according to the similarity sorting; the potential resource nodes include academic papers, patents, and experts.

5. According to claim 4, a teacher intelligent service system based on multi-source academic information is characterized in that: The prediction of scientific research cooperation opportunities in the intelligent analysis module specifically includes: Use graph neural networks to embed teacher nodes into low-dimensional space to form a feature embedding representation of teachers; Calculate the similarity between two teacher nodes; Recommend potential partners for each teacher based on similarity ranking and predict the possibility of future cooperation; The calculation formula of similarity is: Among them, V teacher and V resource They represent the feature vectors of the teacher node and the recommended resource node respectively; similarity represents the cosine similarity calculation function.

6. The teacher intelligent service system based on multi-source academic information according to claim 5 is characterized in that: The prediction of scientific research cooperation opportunities in the intelligent analysis module also includes: Extract key features from the feature vector of the teacher node, wherein the key features include research direction, number of published papers, and academic influence; Encode the selected key features, specifically: encode the research direction into a one-hot code or embedding vector; The encoded features are fused to form a richer teacher feature vector; the fusion calculation formula is: V teacher_fused =αV direction +βV papers +γV impact , where α, β, and γ are weight coefficients; V teacher_fused Represents the fused teacher feature vector; V direction Represents the characteristic vector of research direction; V papers Represents the feature vector of the number of published papers; V impact Represents the characteristic vector of academic influence.

7. The teacher intelligent service system based on multi-source academic information according to claim 1 is characterized in that: The visualization display in the front-end interactive module specifically includes: displaying detailed information on the teacher's personal profile, educational background, research direction, and academic achievements through a visualization interface; providing an interactive browsing function of the knowledge graph, and viewing and exploring the association relationship between teachers in response to the user's click operation on the nodes and edges; The interactive operations in the front-end interactive module specifically include: the display system recommends academic papers, patents, and academic resources of experts to teachers, and provides interactive operations, including screening, sorting, and viewing details; The system displays potential partners recommended for teachers, and provides in-depth viewing of partner information, personalized cooperation suggestions and path analysis functions to help teachers plan their own academic research and cooperation plans.

8. An electronic device, characterized in that: including a processor and a memory; The memory is used to store programs; The processor executes the program to implement the implementation method of the teacher intelligent service system as described in any one of claims 1 to 7.

9. A computer-readable storage medium, characterized in that: The storage medium stores a program, and the program is executed by a processor to implement the implementation method of the teacher intelligent service system as described in any one of claims 1 to 7.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method for implementing the teacher intelligent service system as described in any one of claims 1 to 7 is implemented.

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