Autonomous learning system and method

Abstract

The invention discloses an autonomous learning system and method, and relates to the technical field of artificial intelligence, and the technical scheme is that the autonomous learning system comprises an autonomous learning system module which comprises an infrastructure service module, a data collection module, a knowledge and model management module, a learning interaction module, a feedback management module and a knowledge base updating module. The autonomous learning system module is in data connection with the basic equipment service module, the data acquisition module, the knowledge and model management module, the learning interaction module, the feedback management module and the knowledge base updating module, and the data acquisition module comprises a data source module and a data processing module. The system has the beneficial effects that knowledge reasoning and prediction are carried out by utilizing an AI model, the learning efficiency is improved, a learning strategy can be automatically adjusted according to user feedback and a model performance evaluation result, and continuous learning and evolution are realized.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence technology, and particularly relates to an autonomous learning system and method. Background Art

[0002] The background art of the autonomous learning system involves multiple aspects, mainly including the comprehensive application of fields such as artificial intelligence, machine learning, big data processing, user behavior analysis, and educational psychology.

[0003] Most of the existing learning systems rely on preset rules and models and are difficult to cope with complex and changeable application scenarios. These systems lack flexibility and adaptability and cannot be adjusted and optimized in a timely manner according to new data and user feedback, resulting in low learning efficiency, difficulty in meeting the rapidly changing market demands, and inability to obtain user feedback in a timely manner and make corresponding adjustments. Summary of the Invention

[0004] Therefore, the present invention provides an autonomous learning system and method to solve the problem that most learning systems rely on preset rules and models and are difficult to cope with complex and changeable application scenarios. These systems lack flexibility and adaptability and cannot be adjusted and optimized in a timely manner according to new data and user feedback, resulting in low learning efficiency, difficulty in meeting the rapidly changing market demands, and inability to obtain user feedback in a timely manner and make corresponding adjustments.

[0005] To achieve the above object, the present invention provides the following technical solution: An autonomous learning system includes an autonomous learning system module. The autonomous learning system module includes a basic device service module, a data acquisition module, a knowledge and model management module, a learning interaction module, a feedback management module, and a knowledge base update module. The autonomous learning system module is respectively connected to the basic device service module, the data acquisition module, the knowledge and model management module, the learning interaction module, the feedback management module, and the knowledge base update module for data connection. The data acquisition module includes a data source module and a data processing module. The data acquisition module is respectively connected to the data source module and the data processing module for data connection.

[0006] Preferably, the knowledge and model management module includes a knowledge base creation module and an AI model selection module. The knowledge base creation module includes a knowledge feature extraction module, a knowledge data display module, and a knowledge fusion module. The knowledge and model management module is respectively connected to the knowledge base creation module and the AI model selection module for data connection. The knowledge base creation module is respectively connected to the knowledge feature extraction module, the knowledge data display module, and the knowledge fusion module for data connection.

[0007] Preferably, the AI model selection module includes an AI model training and evaluation module and an AI learning algorithm management module. The AI model selection module is respectively connected to the AI model training and evaluation module and the AI learning algorithm management module for data connection. The AI learning algorithm management module includes an AI integration module, an automated AI process module, and an edge computing module. The AI learning algorithm management module is respectively connected to the AI integration module, the automated AI process module, and the edge computing module for data connection.

[0008] Preferably, the basic equipment service module includes a learning engine module, a monitoring and evaluation module, an adaptive learning module, a data storage module, and a pan-tilt management module. The basic equipment service module is respectively connected to the learning engine module, the monitoring and evaluation module, the adaptive learning module, the data storage module, and the pan-tilt management module for data connection. An autonomous learning integration and testing module is provided at the connection end of the adaptive learning module, and the adaptive learning module is connected to the autonomous learning integration and testing module for data connection.

[0009] Preferably, the learning interaction module includes a workbench. A frustum is provided inside the workbench, and the workbench is rotationally connected to the frustum. A cavity is formed at the top of the frustum. Both sides inside the cavity are connected to a rotating disk through bearings. A chute is formed on one side of each of the two rotating disks. A connecting shell is provided between the two chutes. An interactive touch screen is fixedly provided at the top of the connecting shell. Side grooves are formed on both sides inside the cavity, and a plurality of limiting grooves are formed on one side of the side grooves. An installation frame is fixedly provided at the bottom of the workbench, and a first motor is fixedly provided at the bottom of the installation frame. The output end of the first motor is fixedly connected to a transmission shaft, and one end of the transmission shaft is fixedly connected to the frustum.

[0010] Preferably, a knob is provided on one side of the connecting shell. A connecting shaft is fixedly provided on one side of the knob, and the connection between the connecting shaft and the connecting shell is connected through a bearing. A gear is fixedly sleeved outside the connecting shaft. Tooth plates are provided on both sides of the gear, and the tooth plates are meshed with the gear. A sliding plate is fixedly provided on one side of each of the two tooth plates. A clamping plate is fixedly provided on one side of the sliding plate. The clamping plate penetrates through the connecting shell and is slidably connected to the connecting shell. A clamping groove is formed on one side of the chute, and the clamping plate extends into the clamping groove. A fixing plate is fixedly provided inside the connecting shell. A sliding column is fixedly provided on one side of the sliding plate. The sliding column penetrates through the fixing plate and is slidably connected to the fixing plate. A spring is sleeved outside the sliding column.

[0011] Preferably, a through groove is provided on one side of the frustum. A rotating rod is connected to the inside of the through groove through a bearing. The rotating rod is fixedly connected to one of the rotating disks. The bottom of the frustum is fixedly provided with a bottom plate. A second motor is fixedly provided at the bottom of the frustum. The output end of the second motor is fixedly connected to a transmission rod. The transmission rod is connected to the bottom plate through a bearing. Synchronous wheels are fixedly sleeved on the outer parts of the rotating rod and the transmission rod. A synchronous belt is sleeved on the outer parts of the two synchronous wheels.

[0012] Preferably, support columns are fixedly provided at the four corners of the bottom of the workbench.

[0013] An autonomous learning method comprises the following specific steps:

[0014] S1. Data collection and processing: Collect data from multiple data sources (such as user behavior data, software logs, external data sources, etc.), clean, format, and standardize the collected data to ensure data quality, and remove noise and outliers;

[0015] S2. Knowledge and model management:

[0016] Knowledge base creation: Construct and maintain a knowledge base through modules such as knowledge feature extraction, knowledge data display, and knowledge fusion. The knowledge base includes domain knowledge, learning rules, user models, etc., and supports the structured storage and efficient retrieval of knowledge;

[0017] AI model selection: Select a suitable AI model according to task requirements, train the model using the preprocessed data, and optimize the model performance through methods such as cross-validation. Integrate multiple AI algorithm models such as machine learning and deep learning to achieve automated process management and edge computing optimization.

[0018] S3. Learning interaction and feedback management: Through the learning interaction module, operate on the user interface to support the information exchange and feedback between the user and the system. Collect user feedback by means of questionnaires, user behavior analysis, etc. According to the feedback and the model performance evaluation results, fine-tune or retrain the model, and adjust the learning strategy and recommendation strategy.

[0019] S4. Basic equipment services and support: Use the basic equipment service module to provide support for hardware resources, operating systems, databases, and network communication facilities. Implement functions such as logical reasoning and semantic reasoning through the learning engine module to support complex queries and decision-making support. The monitoring and evaluation module continuously monitors the system performance and user behavior, evaluates the model effect and the satisfaction of business requirements. The adaptive learning module automatically adjusts the model parameters, selects new algorithms, or updates the data set according to the evaluation results to achieve the continuous learning and evolution of the system. The data storage module uses a database or a data warehouse to store data to support fast access and efficient retrieval. The cloud platform management module uses the cloud platform to achieve rapid deployment and elastic expansion;

[0020] S5, Knowledge Base Update and Version Management: Continuously receive new data, automatically update the knowledge base and model, regularly evaluate the validity of the knowledge in the knowledge base, eliminate obsolete or incorrect knowledge, manage the versions of the knowledge base and model, and ensure the traceability and recoverability of historical data.

[0021] The embodiments of the present invention have the following advantages:

[0022] 1. Through data collection and feedback management, the system can understand the learning needs and interest preferences of users, provide personalized learning resources and recommendations, use the AI model for knowledge reasoning and prediction, provide accurate learning paths and practice questions, improve learning efficiency, and can automatically adjust learning strategies according to user feedback and model performance evaluation results to achieve continuous learning and evolution;

[0023] 2. The unique workbench design enables the interactive touch screen to be flipped and rotated to meet the learning needs in different scenarios and provides users with a flexible learning interaction experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only exemplary, and for those of ordinary skill in the art, without creative efforts, other implementation drawings can be obtained according to the provided drawings.

[0025] The structures, ratios, sizes, etc. illustrated in this specification are only used to cooperate with the content disclosed in the specification for those familiar with this technology to understand and read, and are not used to limit the limited conditions under which the present invention can be implemented. Therefore, they do not have technical essence. Any modification of the structure, change of the proportional relationship, or adjustment of the size should still fall within the scope that can be covered by the technical content disclosed in the present invention without affecting the effects that the present invention can produce and the purposes that can be achieved.

[0026] Figure 1 It is the module structure diagram of the autonomous learning system provided by the present invention;

[0027] Figure 2 It is the module structure diagram of the knowledge and model management provided by the present invention;

[0028] Figure 3 It is the module structure diagram of the basic equipment service provided by the present invention;

[0029] Figure 4 It is the three-dimensional view of the workbench provided by the present invention;

[0030] Figure 5Top-down sectional view of the workbench provided by the present invention;

[0031] Figure 6 Side view sectional view of the workbench provided by the present invention;

[0032] Figure 7 Front view sectional view of the workbench provided by the present invention;

[0033] Figure 8 Provided by the present invention Figure 5 Enlarged view of the structure of part A;

[0034] Figure 9 Provided by the present invention Figure 7 Enlarged view of the structure of part B;

[0035] Figure 10 Provided by the present invention Figure 7 Enlarged view of the structure of part C.

[0036] In the figure: 1, autonomous learning system module; 2, basic equipment service module; 3, data acquisition module; 4, knowledge and model management module; 5, learning interaction module; 6, feedback management module; 7, knowledge base update module; 8, data source module; 9, data processing module; 10, knowledge base creation module; 11, AI model selection module; 12, knowledge feature extraction module; 13, knowledge data display module; 14, knowledge fusion module; 15, AI model training and evaluation module; 16, AI learning algorithm management module; 17, AI integration module; 18, automated AI process module; 19, edge computing module; 20, learning engine module; 21, monitoring and evaluation module; 22, adaptive learning module; 23, data storage module; 24, pan-tilt management module; 25, autonomous learning integration test module; 26, workbench; 27, turntable; 28, cavity; 29, interactive touch screen; 30, knob; 31, mounting bracket; 32, first motor; 33, transmission shaft; 34, support column; 35, side groove; 36, limit groove; 37, connecting shell; 38, through groove; 39, rotating rod; 40, synchronous pulley; 41, synchronous belt; 42, rotating disk; 43, chute; 44, sliding plate; 45, sliding column; 46, fixing plate; 47, spring; 48, transmission rod; 49, second motor; 50, bottom plate; 51, clamping plate; 52, toothed plate; 53, connecting shaft; 54, gear. Detailed implementation manners

[0037] The following specific embodiments illustrate the implementation manners of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0038] Referring to the attached Figure 1 - attached Figure 10 , an autonomous learning system provided by the present invention includes an autonomous learning system module 1. The autonomous learning system module 1 includes a basic device service module 2, a data collection module 3, a knowledge and model management module 4, a learning interaction module 5, a feedback management module 6, and a knowledge base update module 7. The autonomous learning system module 1 is respectively connected to the basic device service module 2, the data collection module 3, the knowledge and model management module 4, the learning interaction module 5, the feedback management module 6, and the knowledge base update module 7 for data connection. The data collection module 3 includes a data source module 8 and a data processing module 9. The data collection module 3 is respectively connected to the data source module 8 and the data processing module 9 for data connection;

[0039] In this implementation scheme, the basic device service module 2 includes hardware resources, an operating system, a database, and network communication facilities. Through the data collection module 3, data is collected, cleaned, transformed, and stored to ensure data quality and provide a basis for subsequent analysis. Through the data source module 8, data is collected from multiple channels, including user behavior data, software logs, external data sources, etc. Through the data processing module 9, data is cleaned, formatted, and standardized to remove noise and outliers to ensure data quality. Through the knowledge and model management module 4, a knowledge base is constructed and maintained, including domain knowledge, learning rules, and user models, to support the structured storage and efficient retrieval of knowledge. Through the learning interaction module 5, operations are performed on the user interface to support the information exchange and feedback between the user and the system. Through the feedback management module 6, user feedback is collected through methods such as questionnaires and user behavior analysis. According to the user feedback and the model performance evaluation results, the model is fine-tuned or retrained, and the learning strategy and recommendation strategy are adjusted. Through the knowledge base update module 7, new data is continuously received, the knowledge base and the model are automatically updated, the effectiveness of the knowledge in the knowledge base is regularly evaluated, outdated or incorrect knowledge is eliminated, and version management is performed on the knowledge base and the model to ensure the traceability and recoverability of historical data;

[0040] Among them, for the purpose of realizing knowledge base management, the present device is implemented by the following technical solutions: The knowledge and model management module 4 includes a knowledge base creation module 10 and an AI model selection module 11. The knowledge base creation module 10 includes a knowledge feature extraction module 12, a knowledge data display module 13, and a knowledge fusion module 14. The knowledge and model management module 4 is respectively connected to the knowledge base creation module 10 and the AI model selection module 11 for data connection. The knowledge base creation module 10 is respectively connected to the knowledge feature extraction module 12, the knowledge data display module 13, and the knowledge fusion module 14 for data connection. The knowledge base is created through the knowledge base creation module 10. The domain-related features are extracted from the text data through the knowledge feature extraction module 12. The knowledge is represented in the form of a graph structure, ontology, etc. through the knowledge data display module 13 to facilitate reasoning and query. The newly obtained knowledge is fused with the existing knowledge base through the knowledge fusion module 14 to eliminate conflicts and form a unified knowledge system;

[0041] Among them, for the purpose of realizing model selection, the present device is implemented by the following technical solutions: The AI model selection module 11 includes an AI model training and evaluation module 15 and an AI learning algorithm management module 16. The AI model selection module 11 is respectively connected to the AI model training and evaluation module 15 and the AI learning algorithm management module 16 for data connection. The AI learning algorithm management module 16 includes an AI integration module 17, an automated AI process module 18, and an edge computing module 19. The AI learning algorithm management module 16 is respectively connected to the AI integration module 17, the automated AI process module 18, and the edge computing module 19 for data connection. The appropriate AI model is selected according to the task requirements through the AI model selection module 11. The AI model training and evaluation module 15 uses the preprocessed data to train the model, optimizes the model performance through methods such as cross-validation, and evaluates the model performance using indicators such as accuracy, recall rate, and F1 score to ensure the model quality. Multiple AI algorithm models such as machine learning and deep learning are integrated through the AI learning algorithm management module 16. The AI integration module 17 uses a deep learning framework to build the model and processes specific types of data in combination with technologies such as natural language processing and computer vision. The automated AI process module 18 realizes the automated process of model training, evaluation, and deployment through continuous integration / continuous deployment tools. Part of the computing tasks are deployed to edge devices through the edge computing module 19 to reduce data transmission latency and risk;

[0042] Among them, in order to achieve the purpose of basic equipment services, the present device is implemented by the following technical solutions: The basic equipment service module 2 includes a learning engine module 20, a monitoring and evaluation module 21, an adaptive learning module 22, a data storage module 23, and a cloud platform management module 24. The basic equipment service module 2 is respectively connected to the learning engine module 20, the monitoring and evaluation module 21, the adaptive learning module 22, the data storage module 23, and the cloud platform management module 24 for data connection. An autonomous learning integration test module 25 is provided at the connection end of the adaptive learning module 22, and the adaptive learning module 22 is connected to the autonomous learning integration test module 25 for data connection. Through the learning engine module 20 based on the knowledge graph, functions such as logical reasoning and semantic reasoning are realized, supporting complex queries and decision-making support. Through the monitoring and evaluation module 21, the system performance and user behavior are continuously monitored, and the model effect and the satisfaction degree of business requirements are evaluated. Through the adaptive learning module 22, according to the evaluation results, the model parameters are automatically adjusted, new algorithms are selected, or the data set is updated to achieve the continuous learning and evolution of the system. Through the data storage module 23, a database or a data warehouse is used to store structured and unstructured data, supporting fast access and efficient retrieval. The cloud platform management module 24 uses the cloud platform to achieve rapid deployment and elastic expansion. Through the autonomous learning integration test module 25, the processes of automated construction, testing, and deployment are carried out, unit tests are performed on each module to ensure the correctness of functions, and the overall performance of the system is verified by simulating the collaborative work of multiple modules. The performance of the system under high load is evaluated through stress testing;

[0043] Among them, in order to achieve the purpose of controlling the display angle, the present device is implemented by the following technical solutions: The learning and interaction module 5 includes a workbench 26. A frustum 27 is provided inside the workbench 26. The workbench 26 is rotationally connected to the frustum 27. A cavity 28 is opened at the top of the frustum 27. Both sides inside the cavity 28 are connected to a rotating disk 42 through bearings. A sliding groove 43 is opened on one side of each of the two rotating disks 42. A connecting shell 37 is provided between the two sliding grooves 43. An interactive touch screen 29 is fixedly provided at the top of the connecting shell 37. Side grooves 35 are opened on both sides inside the cavity 28. A plurality of limiting grooves 36 are opened on one side of the side groove 35. An installation frame 31 is fixedly provided at the bottom of the workbench 26. A first motor 32 is fixedly provided at the bottom of the installation frame 31. The output end of the first motor 32 is fixedly connected to a transmission shaft 33. One end of the transmission shaft 33 is fixedly connected to the frustum 27. When the first motor 32 is started, the first motor 32 drives the transmission shaft 33 to rotate. The transmission shaft 33 drives the frustum 27 to rotate. The frustum 27 drives the interactive touch screen 29 to rotate, facilitating the display of the interactive touch screen 29 in different directions and facilitating operation;

[0044] Among them, in order to achieve the purpose of adjusting the display distance, the present device is implemented by the following technical solution: One side of the connection shell 37 is provided with a knob 30. One side of the knob 30 is fixedly provided with a connection shaft 53. The connection part of the connection shaft 53 and the connection shell 37 is connected by a bearing. The outer part of the connection shaft 53 is fixedly sleeved with a gear 54. Both sides of the gear 54 are provided with toothed plates 52. The toothed plates 52 are meshed with the gear 54. One side of each of the two toothed plates 52 is fixedly provided with a sliding plate 44. One side of the sliding plate 44 is fixedly provided with a clamping plate 51. The clamping plate 51 penetrates through the connection shell 37 and is slidably connected with the connection shell 37. One side of the sliding groove 43 is provided with a clamping groove. The clamping plate 51 extends into the clamping groove. The inner part of the connection shell 37 is fixedly provided with a fixing plate 46. One side of the sliding plate 44 is fixedly provided with a sliding column 45. The sliding column 45 penetrates through the fixing plate 46 and is slidably connected with the fixing plate 46. The outer part of the sliding column 45 is sleeved with a spring 47. Rotate the knob 30. The knob 30 drives the connection shaft 53 to rotate. The connection shaft 53 drives the gear 54 to rotate. The gear 54 drives the toothed plate 52 to move. The toothed plate 52 drives the sliding plate 44 to move. The sliding plate 44 drives the sliding column 45 to move. The spring 47 outside the sliding column 45 is compressed. At the same time, the sliding plate 44 drives the clamping plate 51 to move. The clamping plate 51 leaves the clamping groove. Thus, it is convenient for the connection shell 37 to slide from the sliding groove 43 to the side groove 35. When the control of the knob 30 is released, then the spring 47 rebounds and pushes the sliding plate 44 to move, so that the sliding plate 44 drives the clamping plate 51 to move. The clamping plate 51 enters the limiting groove 36. Thus, the purpose of adjusting the display distance of the interactive touch screen 29 is achieved;

[0045] Among them, in order to achieve the purpose of flipping, the present device is implemented by the following technical solution: One side of the frustum 27 is provided with a through groove 38. The inner part of the through groove 38 is connected by a bearing with a rotating rod 39. The rotating rod 39 is fixedly connected with one of the rotating disks 42. The bottom of the frustum 27 is fixedly provided with a bottom plate 50. The bottom of the frustum 27 is fixedly provided with a second motor 49. The output end of the second motor 49 is fixedly connected with a transmission rod 48. The transmission rod 48 and the bottom plate 50 are connected by a bearing. The outer parts of the rotating rod 39 and the transmission rod 48 are fixedly sleeved with synchronous wheels 40. A synchronous belt 41 is sleeved outside the two synchronous wheels 40. By starting the second motor 49, the second motor 49 controls the transmission rod 48 to rotate. The transmission rod 48 drives the synchronous wheel 40 to rotate. The synchronous wheel 40 drives the synchronous belt 41 to rotate. The synchronous belt 41 drives the synchronous wheel 40 outside the rotating rod 39 to rotate, so that the rotating rod 39 drives the rotating disk 42 to rotate. Since the clamping plate 51 is stuck in the clamping groove, the rotating disk 42 drives the connection shell 37 to rotate. The connection shell 37 drives the interactive touch screen 29 to rotate, so that the interactive touch screen 29 is flipped by ninety degrees and then unfolded, which is convenient for learning and interactive use;

[0046] Among them, in order to achieve the purpose of support, the present device is implemented by the following technical solutions: Support columns 34 are fixedly provided at the four corners of the bottom of the workbench 26, and the support columns 34 have the function of supporting the workbench 26.

[0047] An autonomous learning method, the specific steps are as follows:

[0048] S1. Data collection and processing: Collect data from multiple data sources (such as user behavior data, software logs, external data sources, etc.), clean, format, and standardize the collected data to ensure data quality, and remove noise and outliers;

[0049] S2. Knowledge and model management:

[0050] Knowledge base creation: Build and maintain a knowledge base through modules such as knowledge feature extraction, knowledge data display, and knowledge fusion. The knowledge base contains domain knowledge, learning rules, user models, etc., and supports the structured storage and efficient retrieval of knowledge;

[0051] AI model selection: Select a suitable AI model according to task requirements, train the model using the preprocessed data, and optimize the model performance through methods such as cross-validation. Integrate multiple AI algorithm models such as machine learning and deep learning to achieve automated process management and edge computing optimization.

[0052] S3. Learning interaction and feedback management: Through the learning interaction module, operate on the user interface to support the information exchange and feedback between the user and the system. Collect user feedback by means of questionnaires, user behavior analysis, etc. According to the feedback and the model performance evaluation results, fine-tune or retrain the model, and adjust the learning strategy and recommendation strategy.

[0053] S4. Basic equipment services and support: Use the basic equipment service module to provide hardware resources, operating systems, databases, and network communication facilities support. Implement functions such as logical reasoning and semantic reasoning through the learning engine module to support complex queries and decision-making support. The monitoring and evaluation module continuously monitors the system performance and user behavior, evaluates the model effect and the satisfaction of business requirements. The adaptive learning module automatically adjusts the model parameters, selects new algorithms or updates the data set according to the evaluation results to achieve the continuous learning and evolution of the system. The data storage module uses a database or data warehouse to store data, supporting fast access and efficient retrieval. The cloud platform management module uses the cloud platform to achieve rapid deployment and elastic expansion;

[0054] S5. Knowledge base update and version management: Continuously receive new data, automatically update the knowledge base and model, regularly evaluate the effectiveness of the knowledge in the knowledge base, eliminate outdated or incorrect knowledge, and perform version management on the knowledge base and model to ensure the traceability and recoverability of historical data.

[0055] The usage process of the present invention is as follows: When using the present invention, by starting the second motor 49, the second motor 49 controls the rotation of the transmission rod 48. The transmission rod 48 drives the synchronous pulley 40 to rotate. The synchronous pulley 40 drives the synchronous belt 41 to rotate. The synchronous belt 41 drives the synchronous pulley 40 outside the rotating rod 39 to rotate, causing the rotating rod 39 to drive the rotating disk 42 to rotate. Since the clamping plate 51 is stuck in the card slot, the rotating disk 42 drives the connection shell 37 to rotate. The connection shell 37 drives the interactive touch screen 29 to rotate, so that the interactive touch screen 29 flips 90 degrees and unfolds, facilitating learning and interactive use. According to needs, the first motor 32 can be started. The first motor 32 drives the transmission shaft 33 to rotate. The transmission shaft 33 drives the frustum 27 to rotate. The frustum 27 drives the interactive touch screen 29 to rotate, facilitating the display of the interactive touch screen 29 in different directions for easy operation. Then, rotate the knob 30. The knob 30 drives the connecting shaft 53 to rotate. The connecting shaft 53 drives the gear 54 to rotate. The gear 54 drives the toothed plate 52 to move. The toothed plate 52 drives the sliding plate 44 to move. The sliding plate 44 drives the sliding column 45 to move. The spring 47 outside the sliding column 45 is compressed. At the same time, the sliding plate 44 drives the clamping plate 51 to move. The clamping plate 51 leaves the card slot, thus facilitating the connection shell 37 to slide from the chute 43 to the side slot 35. When the control of the knob 30 is released, then the spring 47 rebounds and pushes the sliding plate 44 to move, causing the sliding plate 44 to drive the clamping plate 51 to move. The clamping plate 51 enters the limiting slot 36, thereby achieving the purpose of adjusting the display distance of the interactive touch screen 29. The interactive touch screen 29 flipped and buckled inside the cavity 28 is hidden and protected.

[0056] The above are only the preferred embodiments of the present invention. Any person skilled in the art may modify the present invention by using the technical solutions described above or modify it into an equivalent technical solution. Therefore, any simple modification or equivalent replacement made according to the technical solutions of the present invention falls within the scope of protection required by the present invention.

Claims

1. An autonomous learning system, comprising an autonomous learning system module (1), characterized in that: The autonomous learning system module (1) comprises a basic equipment service module (2), a data acquisition module (3), a knowledge and model management module (4), a learning interaction module (5), a feedback management module (6) and a knowledge base update module (7); the autonomous learning system module (1) is respectively connected to the basic equipment service module (2), the data acquisition module (3), the knowledge and model management module (4), the learning interaction module (5), the feedback management module (6) and the knowledge base update module (7); the data acquisition module (3) comprises a data source module (8) and a data processing module (9); the data acquisition module (3) is respectively connected to the data source module (8) and the data processing module (9).

2. An autonomous learning system according to claim 1, characterized in that: The knowledge and model management module (4) comprises a knowledge base creation module (10) and an AI model selection module (11); the knowledge base creation module (10) comprises a knowledge feature extraction module (12), a knowledge data display module (13) and a knowledge fusion module (14); the knowledge and model management module (4) is respectively data-connected to the knowledge base creation module (10) and the AI ​​model selection module (11); the knowledge base creation module (10) is respectively data-connected to the knowledge feature extraction module (12), the knowledge data display module (13) and the knowledge fusion module (14).

3. An autonomous learning system according to claim 2, characterized in that: The AI ​​model selection module (11) includes an AI model training evaluation module (15) and an AI learning algorithm management module (16), and the AI ​​model selection module (11) is respectively connected to the AI ​​model training evaluation module (15) and the AI ​​learning algorithm management module (16) in data connection, and the AI ​​learning algorithm management module (16) includes an AI integration module (17), an automated AI process module (18) and an edge computing module (19), and the AI ​​learning algorithm management module (16) is respectively connected to the AI ​​integration module (17), the automated AI process module (18) and the edge computing module (19) in data connection.

4. The autonomous learning system according to claim 1, characterized in that: The basic equipment service module (2) comprises a learning engine module (20), a monitoring and evaluation module (21), an adaptive learning module (22), a data storage module (23) and a pan-tilt management module (24); the basic equipment service module (2) is data-connected to the learning engine module (20), the monitoring and evaluation module (21), the adaptive learning module (22), the data storage module (23) and the pan-tilt management module (24), respectively; the connection end of the adaptive learning module (22) is provided with an autonomous learning integrated test module (25); the adaptive learning module (22) is data-connected to the autonomous learning integrated test module (25).

5. The autonomous learning system according to claim 1, characterized in that: The learning interaction module (5) comprises a workbench (26), a truncated platform (27) is provided inside the workbench (26), the workbench (26) is rotatably connected to the truncated platform (27), a cavity (28) is provided on the top of the truncated platform (27), rotating disks (42) are connected to the two sides of the cavity (28) via bearings, a slide groove (43) is provided on one side of the two rotating disks (42), a connecting shell (37) is provided between the two slide grooves (43), and the connecting shell An interactive touch screen (29) is fixedly provided on the top (37), side grooves (35) are provided on both sides of the cavity (28), a plurality of limit grooves (36) are provided on one side of the side grooves (35), a mounting frame (31) is fixedly provided on the bottom of the workbench (26), a first motor (32) is fixedly provided on the bottom of the mounting frame (31), a transmission shaft (33) is fixedly connected to the output end of the first motor (32), and a round table (27) is fixedly connected to one end of the transmission shaft (33).

6. An autonomous learning system according to claim 5, characterized in that: A knob (30) is provided on one side of the connection shell (37), a connection shaft (53) is fixedly provided on one side of the knob (30), the connection portion of the connection shaft (53) and the connection shell (37) is connected via a bearing, a gear (54) is fixedly sleeved on the outside of the connection shaft (53), tooth plates (52) are provided on both sides of the gear (54), the tooth plates (52) mesh with the gear (54), a sliding plate (44) is fixedly provided on one side of the two tooth plates (52), and one side of the sliding plate (44) is fixedly provided. A card plate (51) is fixedly provided, and the card plate (51) passes through the connecting shell (37) and is slidably connected to the connecting shell (37). A card slot is opened on one side of the slide groove (43), and the card plate (51) extends into the inside of the card slot. A fixed plate (46) is fixedly provided inside the connecting shell (37). A sliding column (45) is fixedly provided on one side of the sliding plate (44). The sliding column (45) passes through the fixed plate (46) and is slidably connected to the fixed plate (46). A spring (47) is sleeved on the outside of the sliding column (45).

7. An autonomous learning system according to claim 5, characterized in that: A through slot (38) is provided on one side of the truncated table (27). A rotating rod (39) is connected to the inside of the through slot (38) via a bearing. The rotating rod (39) is fixedly connected to one of the rotating disks (42). A bottom plate (50) is fixedly provided at the bottom of the truncated table (27). A second motor (49) is fixedly provided at the bottom of the truncated table (27). A transmission rod (48) is fixedly connected to the output end of the second motor (49). The transmission rod (48) is connected to the bottom plate (50) via a bearing. Synchronous wheels (40) are fixedly sleeved on the outside of the rotating rod (39) and the transmission rod (48). Synchronous belts (41) are sleeved on the outside of the two synchronous wheels (40).

8. An autonomous learning system according to claim 5, characterized in that: Support columns (34) are fixedly provided at the four corners of the bottom of the workbench (26).

9. An autonomous learning method, based on an autonomous learning system according to any one of claims 1 to 8, characterized in that: The specific steps are as follows: S1. Data collection and processing: Collect data from multiple data sources (such as user behavior data, software logs, external data sources, etc.), clean, format, and standardize the collected data to ensure data quality and remove noise and outliers; S2. Knowledge and Model Management: Knowledge base creation: Build and maintain a knowledge base through knowledge feature extraction, knowledge data display, and knowledge fusion modules. The knowledge base contains domain knowledge, learning rules, user models, etc., and supports structured storage and efficient retrieval of knowledge. AI model selection: Select the appropriate AI model according to task requirements, use preprocessed data to train the model, and optimize model performance through methods such as cross-validation. Integrate multiple AI algorithm models such as machine learning and deep learning to achieve automated process management and edge computing optimization. S3, learning interaction and feedback management: through the learning interaction module, operations are performed on the user interface to support information exchange and feedback between users and the system. User feedback is collected through questionnaires and user behavior analysis. Based on the feedback and model performance evaluation results, the model is fine-tuned or retrained, and the learning strategy and recommendation strategy are adjusted. S4. Basic equipment services and support: The basic equipment service module provides hardware resources, operating system, database and network communication facilities support. The learning engine module implements logical reasoning, semantic reasoning and other functions to support complex queries and decision support. The monitoring and evaluation module continuously monitors system performance and user behavior, evaluates model effects and business demand satisfaction. The adaptive learning module automatically adjusts model parameters, selects new algorithms or updates data sets based on the evaluation results to achieve continuous learning and evolution of the system. The data storage module uses a database or data warehouse to store data to support fast access and efficient retrieval. The PTZ management module uses the cloud platform to achieve rapid deployment and elastic expansion. S5. Knowledge base update and version management: Continuously receive new data, automatically update the knowledge base and models, regularly evaluate the validity of knowledge in the knowledge base, eliminate outdated or erroneous knowledge, perform version management on the knowledge base and models, and ensure the traceability and recoverability of historical data.

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