A general enterprise knowledge intelligent learning and education platform

Abstract

The utility model belongs to the technical field of education and training, disclose a general enterprise knowledge intelligent learning education platform, include: central server, a plurality of terminal equipment, multi -source data acquisition module, practical training simulation module, intelligent recommendation module and learning analysis feedback module, central server is used for storing enterprise knowledge base, external data interface and user learning behavior database, a plurality of terminal equipment is connected with central server through communication network, is used for user login and interactive learning. The scheme integrates the comprehensive learning platform of enterprise knowledge base, multi -source data acquisition, intelligent recommendation and business process simulation, realizes the seamless connection of knowledge management, individualized learning and business practice, solves the pain points such as theory and practice disconnection, individualization deficiency in traditional enterprise training, finally reduces training cost, promotes knowledge conversion rate, helps enterprise to build efficient personnel training system.

Description

Technical Field

[0001] This utility model relates to the field of education and training technology, specifically, to a general-purpose intelligent learning and education platform for enterprise knowledge. Background Technology

[0002] As enterprises accelerate their digital transformation, traditional employee training models mainly rely on offline lectures, standardized online learning management systems, or simple electronic courseware. However, these methods suffer from the following technological bottlenecks.

[0003] 1. Data silo problem: Enterprise training systems are often independent of business systems, and the learning content cannot be linked to actual business data in real time, resulting in the training content being outdated and unable to adapt to rapidly changing business needs;

[0004] 2. Lack of personalization: Traditional training models adopt a "one-size-fits-all" curriculum system, which lacks an intelligent recommendation mechanism based on employees' job skills, learning habits and business needs, resulting in low learning efficiency.

[0005] 3. Theory and practice are disconnected: training often remains at the theoretical level and lacks an immersive hands-on environment, making it difficult for employees to quickly transform the knowledge they have learned into practical business skills;

[0006] 4. Limited assessment methods: Existing systems typically assess learning outcomes only through exams or simple quizzes, which cannot accurately measure employees' actual operational abilities and lack a dynamic feedback mechanism, making it difficult to provide targeted improvement suggestions. Utility Model Content

[0007] The purpose of this utility model is to address the shortcomings of existing technologies by proposing a general-purpose intelligent learning and education platform for enterprise knowledge.

[0008] To solve the above problems, the present invention adopts the following technical solution:

[0009] A general-purpose intelligent learning and education platform for enterprise knowledge includes: a central server, multiple terminal devices, a multi-source data acquisition module, a training simulation module, an intelligent recommendation module, and a learning analysis and feedback module;

[0010] The central server is used to store the enterprise knowledge base, external data interfaces, and user learning behavior database;

[0011] Multiple terminal devices are connected to the central server via a communication network for user login and interactive learning.

[0012] The multi-source data acquisition module is connected to the central server and is used to collect data from the enterprise's internal ERP system, external industry databases, and real-time operation data from terminal devices.

[0013] The training simulation module connects to the central server, providing a virtual operation interface and simulating real business scenarios of enterprises, while recording user operation trajectories.

[0014] The intelligent recommendation module is integrated into the central server and is used to generate personalized learning paths based on the user's job position, learning progress and test results, and push them to the terminal device.

[0015] The learning analysis and feedback module generates personalized improvement suggestion reports based on user training data, test data, and behavioral data.

[0016] As a further description of the above technical solution: the central server is also configured with an enterprise knowledge base storage unit, a user behavior database, a data cleaning unit, and an automatic update unit; the enterprise knowledge base storage unit stores the enterprise knowledge base and external data; the user behavior database stores user learning behavior data; the data cleaning unit performs noise reduction and normalization processing on the collected internal and external data, and marks it as structured training data; the automatic update unit periodically retrieves the latest policy documents from the enterprise ERP system and updates the knowledge base.

[0017] As a further description of the above technical solution: the multi-source data acquisition module includes an internal data interface and an external data crawler unit. The internal data interface is connected to the enterprise's OA and CRM systems to obtain real-time business data; the external data crawler unit periodically crawls and categorizes updated industry policy and regulation data.

[0018] As a further description of the above technical solution: the learning analysis and feedback module includes a capability assessment unit and a prediction unit. The capability assessment unit generates a skill radar chart based on practical training data and theoretical test data. The prediction unit predicts the user's future knowledge forgetting cycle through a machine learning model and prompts for review.

[0019] As a further description of the above technical solution: the training simulation module includes a business process simulation unit and an error correction unit. The business process simulation unit simulates interactive operations of sales, production, or customer service processes. The error correction unit automatically triggers the playback of a demonstration video when the user makes a mistake in the simulated operation.

[0020] As a further description of the above technical solution: the terminal device includes an identity recognition unit, a voice interaction unit, and a display unit;

[0021] The identity recognition unit supports login via facial recognition or employee card scanning and synchronizes data with the enterprise's human resources system; the voice interaction unit supports voice questioning and voice answering functions; the display unit is used to display the user's learning progress, training score trends, and departmental knowledge mastery heatmap.

[0022] As a further description of the above technical solution: the intelligent recommendation module includes a knowledge graph unit, which is used to establish the relationship between knowledge points and generate a reinforcement path for weak knowledge points based on the user's wrong answer records.

[0023] Compared with existing technologies, the advantages of this utility model are:

[0024] This solution constructs an intelligent learning system through multi-dimensional technological innovation, possessing the following core advantages:

[0025] First, by integrating ERP systems, industry databases and real-time operational data through multi-source data collection and automatic update units, a dynamic knowledge graph is constructed to ensure that training content is highly synchronized with the actual business of enterprises.

[0026] Secondly, by simulating real business scenarios, an immersive training environment is created to verify learning effectiveness and shorten the transformation cycle from theory to practice; based on knowledge graphs and user behavior data, the intelligent recommendation module accurately matches job requirements with individual ability gaps, improving training efficiency.

[0027] Finally, the learning analytics feedback module provides companies with employee competency assessment reports to assist in developing training plans and talent development strategies.

[0028] In summary, this solution integrates an enterprise knowledge base, multi-source data collection, intelligent recommendation, and business process simulation into a comprehensive learning platform. It achieves seamless integration of knowledge management, personalized learning, and business practice, addressing pain points in traditional enterprise training such as the disconnect between theory and practice and insufficient personalization. Ultimately, it reduces training costs, improves knowledge conversion rates, and helps enterprises build an efficient talent development system. Attached Figure Description

[0029] Figure 1 This is a system diagram of the present invention;

[0030] Figure 2 This is a system diagram of the central server of this utility model;

[0031] Figure 3 This is a system diagram of the terminal device of this utility model.

[0032] Explanation of the labels in the diagram:

[0033] 1. Central server; 11. Enterprise knowledge base storage unit; 12. User behavior database; 13. Data cleaning unit; 14. Automatic update unit;

[0034] 2. Terminal equipment; 21. Identity recognition unit; 22. Voice interaction unit; 23. Display unit;

[0035] 3. Multi-source data acquisition module; 31. Internal data interface; 32. External data crawler unit;

[0036] 4. Training simulation module; 41. Business process simulation unit; 42. Error correction unit;

[0037] 5. Intelligent recommendation module; 51. Knowledge graph unit;

[0038] 6. Learning analysis and feedback module; 61. Ability assessment unit; 62. Prediction unit. Detailed Implementation

[0039] The technical solutions of the present utility model will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present utility model. Obviously, the described embodiments are only some embodiments of the present utility model, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present utility model without creative effort are within the protection scope of the present utility model.

[0040] Please see Figure 1-3 A general-purpose intelligent learning and education platform for enterprise knowledge includes:

[0041] Central Server 1: Used to store the enterprise knowledge base, external data interfaces, and user learning behavior database, facilitating centralized data management and efficient scheduling. Central Server 1 is configured with an enterprise knowledge base storage unit 11, a user behavior database 12, a data cleaning unit 13, and an automatic update unit 14. The enterprise knowledge base storage unit 11 stores the enterprise knowledge base (policy documents, operation manuals, etc.) and external data. The user behavior database 12 stores user learning behavior data, such as user logins, learning duration, test scores, and operational behavior data. The data cleaning unit 13 uses hardware acceleration chips (such as FPGAs) to denoise and normalize the collected internal and external data, and marks it as structured training data. The automatic update unit 14 periodically retrieves the latest policy documents from the enterprise ERP system and updates the knowledge base to ensure its timeliness.

[0042] Multiple terminal devices 2 are connected to the central server 1 via a communication network for user login and interactive learning. Terminal devices 2 include an identity recognition unit 21, a voice interaction unit 22, and a display unit 23. The identity recognition unit 21 supports facial recognition or employee card scanning login and synchronizes job information with the enterprise's human resources system in real time. The voice interaction unit 22 supports voice questioning and answering functions, integrating a natural language processing engine to recognize user voice questions and match them with standard answers from the knowledge base. The display unit 23 displays user learning progress, training score trends, and a heatmap of departmental knowledge mastery. The heatmap uses 3D rendering technology to visually display departmental knowledge weaknesses.

[0043] Multi-source data acquisition module 3: Connected to the central server 1, it collects data from the enterprise's internal ERP system, external industry databases, and real-time operational data from terminal devices 2, ensuring comprehensive data integration capabilities and the ability to fuse multi-dimensional information. Multi-source data acquisition module 3 includes an internal data interface 31 and an external data crawler unit 32. The internal data interface 31 interfaces with the enterprise's OA and CRM systems to obtain real-time business data. The external data crawler unit 32 periodically crawls and categorizes updated industry policies and regulations. The external data crawler unit 32 is equipped with a data encryption chip (such as an HSM) to perform hardware-level encryption on the crawled external industry data before transmitting it to the central server 1. Through the collaboration of the internal data interface 31 and the external crawler unit 32, multi-source data acquisition module 3 transforms unstructured data such as ERP and industry policies into labeled training data, solving the problem of knowledge fragmentation.

[0044] Training Simulation Module 4: Connected to the central server 1, it provides a virtual operating interface and simulates real-world business scenarios, recording user operation trajectories. Training Simulation Module 4 includes a business process simulation unit 41 and an error correction unit 42. The business process simulation unit 41 simulates interactive operations in sales, production, or customer service processes; the error correction unit 42 automatically triggers demonstration video playback when a user makes a simulated operational error. The business process simulation unit 41 provides interactive operations for nine high-frequency scenarios such as sales and production, reducing the user's error rate in practical operations. The error correction unit 42 achieves real-time correction through video demonstrations, shortening error handling time compared to traditional text prompts.

[0045] Intelligent Recommendation Module 5: Integrated into the heterogeneous computing platform (CPU+GPU) of the central server 1, it generates personalized learning paths based on the user's job position, learning progress, and test results, and pushes them to the terminal device 2. Intelligent Recommendation Module 5 includes a knowledge graph unit 51, which establishes relationships between knowledge points and generates reinforcement paths for weak knowledge points based on the user's incorrect answer records.

[0046] Learning Analysis and Feedback Module 6: Generates personalized improvement suggestion reports based on user training data, test data, and behavioral data. Learning Analysis and Feedback Module 6 includes a capability assessment unit 61 and a prediction unit 62. The capability assessment unit 61 generates a skill radar chart based on training data and theoretical test data; the prediction unit 62 uses a machine learning model to predict the user's future knowledge forgetting cycle and provides review prompts.

[0047] Example 1: A manufacturing company uses this platform for new employee training.

[0048] 1. Data integration: The multi-source data acquisition module 3 captures production process specifications, equipment operation manuals and industry safety standards and stores them in the central server 1;

[0049] 2. Identity verification: New employees log in to terminal device 2 by scanning their work cards, and the system automatically synchronizes their job position (such as production line operator) and training plan;

[0050] 3. Intelligent Recommendation: The platform recommends courses such as "Equipment Operation Specifications" and "Safety Protection Procedures" based on job requirements, and inserts practical training tasks that simulate production line fault handling;

[0051] 4. Practical training and feedback: Employees simulate equipment debugging and operation in a virtual interface. Error correction unit 42 provides real-time prompts for the correct steps, and the learning analysis module generates a skill attainment rate report.

[0052] The above description is merely a preferred embodiment of this utility model; however, the protection scope of this utility model is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the technical scope disclosed in this utility model, based on the technical solution and its improved concept, should be included within the protection scope of this utility model.

Claims

1. A general-purpose intelligent learning and education platform for enterprise knowledge, characterized in that: include: The central server (1) is internally configured with an enterprise knowledge base storage unit (11), a user behavior database (12), a data cleaning unit (13), and an automatic update unit (14); the data cleaning unit (13) is a hardware acceleration chip used to denoise and normalize the collected data; the automatic update unit (14) is used to periodically retrieve the latest policy documents from the enterprise ERP system and update the knowledge base. Multiple terminal devices (2) are connected to the central server (1) through a communication network. Each terminal device (2) includes an identity recognition unit (21), a voice interaction unit (22), and a display unit (23). The identity recognition unit (21) is used to support face recognition or work card scanning login. The voice interaction unit (22) is used to support voice questioning and voice answering functions. The display unit (23) is used to display the user's learning progress, training score trend, and departmental knowledge mastery heat map. The multi-source data acquisition module (3) is connected to the central server (1) through a hardware interface. The multi-source data acquisition module (3) includes an internal data interface (31) and an external data crawler unit (32). The internal data interface (31) is used to interface with the enterprise OA and CRM systems. The external data crawler unit (32) is equipped with a data encryption chip for hardware-level encryption of the crawled external industry data. The training simulation module (4) is connected to the central server (1) through a hardware interface. The training simulation module (4) includes a business process simulation unit (41) and an error correction unit (42). The error correction unit (42) is used to automatically trigger the playback of a demonstration video when the user makes a mistake in the simulated operation. The intelligent recommendation module (5) is integrated on the heterogeneous computing platform of the central server (1). The heterogeneous computing platform includes CPU and GPU, which is used to generate personalized learning paths based on user job, learning progress and test results, and push them to terminal devices (2). The learning analysis feedback module (6) is connected to the central server (1) and is used to generate personalized improvement suggestion reports based on user training data, test data and behavioral data.

2. The general-purpose intelligent learning and education platform for enterprise knowledge according to claim 1, characterized in that: The data cleaning unit (13) of the central server (1) uses an FPGA chip.

3. The general-purpose intelligent learning and education platform for enterprise knowledge according to claim 1, characterized in that: The data encryption chip configured in the external data crawler unit (32) is an independent encryption chip.

4. The general-purpose intelligent learning and education platform for enterprise knowledge according to claim 1, characterized in that: The intelligent recommendation module (5) also includes a knowledge graph unit (51), which is accelerated by the GPU in the heterogeneous computing platform to establish the relationship between knowledge points.

5. The general-purpose intelligent learning and education platform for enterprise knowledge according to claim 1, characterized in that: The learning analysis feedback module (6) includes a capability assessment unit (61) and a prediction unit (62). The capability assessment unit (61) generates a skill radar chart based on practical training data and theoretical test data. The prediction unit (62) predicts the user's future knowledge forgetting cycle through a machine learning model and prompts for review.

6. The general-purpose intelligent learning and education platform for enterprise knowledge according to claim 1, characterized in that: The business process simulation unit (41) of the training simulation module (4) simulates the interactive operation of sales, production or customer service processes.

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