Hub system oriented to large model and data set management

Abstract

According to the Hub system oriented to large model and data set management, rapid and accurate data retrieval is achieved through a hierarchical indexing mechanism and a detailed metadata labeling system; model management, data set management and version control of large model application codes are integrated on a single platform, so that a user can complete the whole process from data preparation to model training to deployment in a unified interface; through a graphical user interface and a visual data model management mode, the availability and interactivity of the system are greatly improved, and a user can complete complex data operation. Meanwhile, the system disclosed by the invention can be seamlessly integrated with an existing research and development tool and a version control system, and supports expansion of third-party plug-ins and tools, so that the platform can flexibly adapt to different development environments and requirements, and a sufficient space is reserved for future technical development.

Description

Technical Field

[0001] The present invention belongs to the field of data management, and in particular relates to a Hub system for large model and data set management. Background Art

[0002] In the field of big data and machine learning, model management has become a crucial link. With the continuous expansion of data scale and the increase of model complexity, how to efficiently store, retrieve and manage model files, data sets and related application codes has become a hot issue in research and application. At present, most traditional management methods still rely on file systems or simple database systems to store and call data. However, these methods often encounter performance bottlenecks when facing large-scale, high-dimensional data, and it is difficult to meet the needs of modern machine learning and large model applications.

[0003] The existing technology has obvious defects and deficiencies in the following aspects. First, the traditional storage structure lacks optimization for the characteristics of big data and model files, resulting in low efficiency in data retrieval and management. When processing high-dimensional, multi-format data, conventional storage solutions are difficult to support efficient indexing and query operations, affecting the speed of model training and reasoning. In addition, existing storage methods generally lack support for model version management, which leads to confusion during model iteration and increases maintenance costs.

[0004] Secondly, the current management system lacks one-stop functional integration and is difficult to meet the diverse needs of large model applications. Take prompt word management as an example. With the rise of large models, prompt words, as an important factor affecting model output, are gradually becoming a key management object. However, the current prompt words are mostly scattered in code files in the form of text, lacking systematic management. When the same set of code needs to call different prompt words for different models, the existing management method is cumbersome and not intuitive enough. At the same time, existing solutions rarely integrate model management, data set management, and application code version control into the same system, and users often need to rely on multiple tools to manage different resources separately. This decentralized management method not only reduces work efficiency, but also makes it difficult to establish the association between models, data sets, codes, and prompt words, thereby increasing the complexity of project management.

[0005] In addition, the existing management system still has deficiencies in interactivity. Many tools rely on command lines for operation, which is not friendly to non-technical users and difficult to meet the needs of corporate users and ordinary researchers. At the same time, there is a lack of visual management interface, and users have a poor operating experience in data retrieval, model calling, version comparison, etc., lacking intuitive feedback and interaction methods.

[0006] In summary, the current large model and data management systems have many limitations in data storage structure, functional integration, and user interaction experience. In order to solve these problems, an efficient, unified, and user-friendly management solution is urgently needed that can optimize the storage structure, provide one-stop management capabilities, and improve the system's visualization and interactive experience to meet the model management needs in the era of big data and artificial intelligence. Summary of the invention

[0007] The present invention provides a Hub system for large model and data set management to solve the problem that existing large model and data management systems have many limitations in data storage structure, function integration and user interaction experience.

[0008] In order to solve the above technical problems, the embodiments of the present invention disclose the following technical solutions:

[0009] One aspect of the present invention provides a Hub system for large model and data set management, including:

[0010] A basic management module is configured to perform resource uploading, downloading, searching, sorting, filtering, version management, and establishing and managing associations between different resources, wherein the resources include at least models, data, and codes;

[0011] The model management module is configured to perform inference, fine-tuning, evaluation, distillation, pruning, and format conversion on the model;

[0012] The data management module is configured to preview, clean, evaluate, annotate, collect and convert the data into different formats;

[0013] An application management module is configured to display, search and filter applications, compile and deploy applications, and manage application versions;

[0014] A prompt word library module is configured to preview and optimize prompt words, compare and analyze output results generated by prompt words, and provide standardized rule templates;

[0015] The code management module is configured to perform code review and security scanning, and to perform security analysis and management on the code;

[0016] The intelligent agent module has at least a technical support intelligent agent, a data processing intelligent agent, a personalized recommendation intelligent agent and an experimental optimization intelligent agent, and is configured to realize technical support, automated data processing, personalized content recommendation and experimental optimization functions.

[0017] Optionally, the basic management module includes:

[0018] The hierarchical index submodule is configured to establish a top-level index according to resource types and to establish a lower-level index according to resource characteristics;

[0019] A metadata tagging submodule, configured to construct a metadata structure of all resources, wherein the metadata structure includes at least resource type, license type, language, and application industry;

[0020] A data card submodule is configured to generate data cards for all resources, wherein the data cards include at least basic information of the resources, usage methods, usage scenarios, practical cases, resource features, visualization information and operation procedures;

[0021] The resource association submodule is configured to determine the relationship between different resources and build an association network.

[0022] Optionally, the basic management module further includes:

[0023] The version control submodule is configured to integrate version control tools to track and manage resource versions.

[0024] Optionally, the system further includes:

[0025] The visualization module is configured to use visualization tools to display data structure, model architecture, and training progress.

[0026] Optionally, the system further includes:

[0027] The intelligent search and recommendation module is configured to provide search results and recommended resources based on the user's historical search logs.

[0028] Optionally, the system has an API interface, allowing it to be called by other systems.

[0029] The system according to claim 1, wherein the model management module comprises:

[0030] A model compression submodule is configured to compress the model using a model compression technique, wherein the model compression technique includes at least knowledge distillation, pruning, and parameter sharing;

[0031] A model quantization submodule, configured to convert the parameters of the model from floating point numbers to low bit width representation;

[0032] The model distillation submodule is configured to transfer the knowledge of the large model to other models using the model distillation method;

[0033] The dynamic data preprocessing submodule is configured to automatically perform feature extraction, cleaning, and enhancement of the model.

[0034] Optionally, the system further includes:

[0035] An encryption module, configured to encrypt stored and transmitted resources;

[0036] The access control module is configured to configure access rights according to user roles.

[0037] Optionally, the system further includes:

[0038] The user management module is configured to perform identity authentication, permission management and personalization settings.

[0039] The present invention provides a Hub system for large model and data set management, which realizes fast and accurate data retrieval through a hierarchical index mechanism and a detailed metadata annotation system. Compared with the traditional linear search method or simple database index, the data management structure of the present invention greatly improves the efficiency of data retrieval and screening, especially when dealing with large-scale data sets and model files. In addition, the present invention provides users with a seamless workflow by integrating model management, data set management and version control of large model application code on a single platform. Users can complete the whole process from data preparation, model training to deployment in a unified interface, which significantly improves work efficiency and reduces the error rate of operation. The present invention also greatly improves the usability and interactivity of the system through a graphical user interface and a visual data model management method, so that users can complete complex data operations. At the same time, the present invention can be seamlessly integrated with existing R&D tools and version control systems, and supports the expansion of third-party plug-ins and tools, which not only enables the platform to flexibly adapt to different development environments and needs, but also leaves sufficient space for future technological development. In addition, taking into account the special needs of large model processing, the present invention provides advanced support functions for large models through technologies such as model sharding, distributed processing, dynamic data preprocessing and model format conversion, ensuring that the system can still maintain efficient operation when processing extremely large-scale models and data, and meet the complex needs of high-end users.

[0040] This Summary is provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description. This Summary is not intended to identify key features or essential features of the disclosure, nor is it intended to limit the scope of the disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] The above and other objects, features and advantages of the present disclosure will become more apparent through a more detailed description of exemplary embodiments of the present disclosure in conjunction with the accompanying drawings, wherein like reference numerals generally represent like components throughout the exemplary embodiments of the present disclosure.

[0042] Figure 1 A schematic diagram of the structure of a Hub system for large model and data set management provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0043] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to make the present disclosure more thorough and complete, and to fully convey the scope of the present disclosure to those skilled in the art.

[0044] As used herein, the term "including" and its variations mean open inclusion, i.e., "including but not limited to". Unless otherwise stated, the term "or" means "and / or". The term "based on" means "based at least in part on". The terms "an example embodiment" and "an embodiment" mean "at least one example embodiment". The term "another embodiment" means "at least one additional embodiment". The terms "first", "second", etc. may refer to different or the same objects. Other explicit and implicit definitions may also be included below.

[0045] Figure 1 This is a schematic diagram of the structure of a Hub system for large model and data set management. Figure 1 As shown, it includes the following modules:

[0046] The basic management module 1 is configured to perform resource uploading, downloading, searching, sorting, filtering, version management, and to establish and manage associations between different resources.

[0047] In the disclosed embodiment of the present invention, resources include at least models, data and codes.

[0048] The basic management module 1 includes the following submodules:

[0049] (1) A hierarchical indexing submodule is configured to establish a top-level index according to resource types, and to establish a lower-level index according to resource characteristics.

[0050] The hierarchical index submodule aims to provide efficient resource management and retrieval capabilities, using a multi-level indexing strategy to quickly locate required resources. The core mechanism of this module is to classify according to different levels to ensure that the index structure has both breadth and depth, thereby optimizing query performance and improving data access efficiency.

[0051] At the top level of the index, we first establish a basic index based on the type of resource, such as classifying it by model, dataset, code, etc., to ensure that the initial screening of resources can be completed quickly, greatly reducing the search scope and improving query speed.

[0052] At the lower level of the index, the system further subdivides resources based on their characteristics. For example, in the index of model resources, they can be differentiated according to model algorithm categories (such as deep learning models and traditional machine learning models); dataset resources can be divided based on dimensions such as data size and format (such as CSV, JSON, Parquet). This hierarchical index structure is not only convenient for storage management, but also allows for quick matching of targets during retrieval, improving the system's response speed.

[0053] In addition, the hierarchical indexing mechanism can adapt to the dynamic changes of resources. When new resource types or features appear, flexible scalability can be achieved by expanding the indexing rules.

[0054] (2) A metadata tagging submodule, configured to construct a metadata structure for all resources, where the metadata structure includes at least resource type, license type, language, and application industry.

[0055] The metadata tagging submodule builds a standardized metadata structure for all resources, thereby improving the discoverability, retrieval efficiency, and management capabilities of resources. This module supports detailed attribute descriptions of different types of resources (such as models, datasets, and codes), ensuring that users can quickly filter and match required resources based on key features.

[0056] In the design of metadata structure, this module covers multiple core fields to provide clear and systematic resource classification. For example, the resource type field is used to distinguish different categories such as models, data sets, codes, etc., to ensure that resources have clear ownership when classified at the top level. The license type field is used to identify the use rights of resources, such as open source protocols such as MIT, Apache 2.0, GPL, or commercial licenses, to help users understand the legal scope of application of resources. For language-related resources, this module supports language tags, whether it is a programming language (such as Python, Java, C++) or a natural language, it can be used as one of the conditions for retrieval and screening.

[0057] In addition, the metadata structure is further refined to adapt to different application scenarios. For example, for machine learning models, you can add applicable task types (such as image classification, text generation, speech recognition) and supported hardware (such as CPU, GPU, TPU); for data sets, you can annotate data formats (such as CSV, JSON, Parquet) and data scale (such as sample number, storage size).

[0058] (3) A data card submodule is configured to generate data cards for all resources. The data cards include at least basic information of the resources, usage methods, usage scenarios, practical cases, resource features, visualization information and operation procedures.

[0059] The data card submodule can generate standardized data cards for all resources to improve data management efficiency and enhance the discoverability and accessibility of resources. Through structured information display, this module enables users to quickly understand the key characteristics of resources and easily obtain their usage methods and application scenarios, thereby optimizing the operating experience and improving work efficiency.

[0060] The core content of the data card covers basic information, usage methods, usage scenarios, practical cases, resource features, visualization information and operation procedures to ensure that the display of each resource is complete and intuitive. In terms of specific implementation, the module designs customized data card templates according to different resource types (such as models, data sets, codes, prompt words, applications) to meet the specific needs of various types of resources. For example, for the data card of the machine learning model, the system will provide information such as the model name, version, training data overview, performance indicators, dependent environment, and come with quick usage examples to facilitate users to fine-tune, integrate and evaluate in different application scenarios. For dataset resources, the data card will contain basic information such as the format, size, and annotation type of the dataset, and provide data distribution visualization, sample preview and quality analysis to improve the transparency of data management.

[0061] In terms of code management, data cards not only display basic information about the code base, such as language type, contributors, and usage profiles, but also list in detail the operating environment configuration, dependency library requirements, and specific execution steps to help developers get started quickly and integrate them into the project efficiently. In addition, data cards also provide targeted content for non-traditional data resources such as prompt words and applications. For example, the prompt word data card will display its applicable scenarios, recommended models, and expected outputs, and guide users to optimize their use based on actual cases. The application data card helps users understand its functional design, target user groups, and best practices through detailed operation process descriptions and screenshots.

[0062] The data card submodule supports visual information display, such as data distribution diagrams, model performance curves, code execution results, etc., in the form of charts or examples to enhance user understanding. Combined with standardized structural design, this module ensures that data cards of different resources remain consistent in format, while providing personalized expansion options to adapt to complex business needs.

[0063] (4) Resource association submodule, configured to determine the relationship between different resources and build an association network.

[0064] The resource association submodule can establish logical associations between different resources and build a dynamic resource association network to improve data discoverability and reuse efficiency. Through this mechanism, users can quickly locate related data sets, models, codes or prompt words based on a certain resource to form a complete resource chain, thereby supporting more complex analysis, management and application tasks.

[0065] In actual applications, this module ensures that all data, models, codes, and applications within the platform can be interconnected by defining the associations between resources. For example, for machine learning models, this module will clearly define the data set used for training and list the appropriate prompt words so that users can quickly obtain relevant resources and reduce the time for repeated searches. Similarly, prompt word data can be associated with the model to which it applies, allowing users to quickly match the appropriate model for reasoning or fine-tuning. In addition, this module can record the model version that a certain section of code depends on, ensuring that users can obtain compatible model versions when integrating code, avoiding runtime errors caused by mismatches.

[0066] In order to improve the traceability of resources, the resource association submodule also supports multi-level relationship mapping, which is not limited to direct associations, but can also be extended to deeper resource chains. For example, a data set may be related to multiple different versions of models, and these models may be used by different applications. By establishing a clear association network, users can track forward or backward along the resource relationship tree, quickly understand the dependencies between resources, and perform reasonable resource management and optimization.

[0067] In addition, the module also has dynamic update capabilities, which can automatically adjust the relationship between resources when they change. For example, when a model is upgraded, the system will check the datasets, prompt words, and code libraries it depends on, and automatically update the related information to ensure that all resources are always consistent. This feature not only improves the automation level of resource management, but also reduces problems caused by version mismatches or missing dependencies.

[0068] Combined with visualization technology, the resource association submodule can intuitively display the relationship between resources through a knowledge graph, allowing users to clearly view the association paths of various resources, thereby improving search efficiency and data utilization.

[0069] In an embodiment disclosed in the present invention, the basic management module 1 also includes a version control submodule, which is configured to integrate a version control tool to achieve tracking and management of resource versions.

[0070] The version control submodule can provide complete version tracking and management capabilities for various resources on the platform (such as models, data sets, codes, prompt words, etc.). By integrating mainstream version control tools such as Git, this module can ensure that every change to the resource is accurately recorded and provide an efficient version management process, allowing users to trace back, compare and restore different versions of resources at any time, thereby improving the reliability and traceability of resource management.

[0071] In actual applications, this module supports automatic version tracking, that is, every time a user modifies a resource (such as adjusting model parameters, updating data sets, optimizing code, etc.), the system will automatically create a new version and record the change details, including the modification time, the modifier, the specific content of the modification, etc. This mechanism not only facilitates team collaboration, but also ensures that the previous version can be quickly traced back when needed, avoiding losses caused by misoperation or data corruption.

[0072] Users can intuitively view the historical versions and change records of resources on the GUI interface, and perform common version management operations, such as submitting changes, viewing version differences, rolling back to a specified version, downloading historical versions, etc. In addition, users can also edit and submit files online through this interface, without the need to install and configure complex version control tools, thus lowering the threshold for use.

[0073] In terms of resource sharing and collaboration, for example, during the model development process, different users may perform different optimization experiments on the same model. This module can automatically manage the parallel existence of multiple versions and support version branching, so that different experimental directions can develop independently, and can also merge versions when necessary. In addition, for the management of data sets, this module can ensure that each data update has a clear version record, which makes it easier for users to track data sources and change history, and prevent data pollution or version confusion.

[0074] The model management module 2 is configured to perform inference, fine-tuning, evaluation, distillation, pruning and format conversion on the model.

[0075] In one embodiment disclosed in the present invention, the model management module 2 includes the following submodules:

[0076] (1) A model compression submodule, configured to compress the model using a model compression technique, where the model compression technique includes at least knowledge distillation, pruning, and parameter sharing.

[0077] The model compression submodule optimizes the computational efficiency and storage usage of deep learning models, enabling them to run efficiently with limited computing resources. As AI models continue to grow, their computational overhead and storage requirements are also increasing significantly, especially in resource-constrained environments such as mobile devices, edge computing, and embedded systems. Efficient model compression technology has become particularly important. To this end, this module uses a variety of model compression methods, including knowledge distillation, pruning, and parameter sharing, to reduce the model size while maintaining the performance and accuracy of the model as much as possible.

[0078] Knowledge distillation is a method to compress models through a teacher-student model training strategy. In this process, a complex and powerful teacher model is first trained to generate prediction outputs, and then a smaller student model is trained by learning the prediction distribution of the teacher model. In this way, the student model is able to inherit the knowledge structure of the teacher model and maintain a similar performance level at a lower computational cost. Knowledge distillation is particularly suitable for scenarios with high requirements for inference speed but limited computing resources, such as mobile AI applications, speech recognition, and natural language processing tasks.

[0079] Pruning reduces computational complexity by removing weights in the model that contribute little to the final prediction or are redundant, making the model more lightweight. Pruning methods include weight pruning, neuron pruning, and structured pruning. Among them, weight pruning reduces computational overhead by removing weights with small values, while neuron pruning removes less active neurons to reduce redundant calculations. In addition, structured pruning can be optimized by channel or layer so that the pruned model can still efficiently adapt to the current hardware architecture. Pruning technology is usually combined with a retraining step to compress the model while restoring its accuracy as much as possible. It is suitable for devices with limited computing resources, such as IoT devices and edge servers.

[0080] Parameter sharing reduces the number of independent parameters by allowing multiple neurons or layers to share the same parameters, thereby reducing storage requirements and improving the generalization ability of the model. For example, in natural language processing tasks, the input embedding layer and the output embedding layer can share the same word vector representation to reduce storage usage. In addition, in the CNN structure, different layers can share convolution kernels to reduce redundant calculations. Parameter sharing not only reduces storage costs, but also improves the operating efficiency of the model in different hardware environments.

[0081] (2) A model quantization submodule, configured to convert the parameters of the model from floating point numbers to low bit width representation.

[0082] The model quantization submodule can reduce the numerical precision of model parameters, reduce storage usage and computational complexity, and enable the model to run efficiently in a resource-constrained environment. Traditional deep learning models usually use 32-bit floating point numbers (FP32) for calculations, and model quantization converts these floating point numbers into low-bit width (such as 16-bit, 8-bit, or even 4-bit or 2-bit) representations, thereby reducing the memory bandwidth and power consumption required for calculations and improving the inference speed. This module is particularly suitable for application scenarios with high requirements for computing resources, such as edge devices, mobile AI, and embedded systems.

[0083] Model quantization methods are mainly divided into fixed-point quantization and dynamic quantization. Fixed-point quantization usually involves converting all model parameters and calculations into lower-precision integer formats (such as INT8) to reduce computational complexity. Dynamic quantization allows some weights or activation values ​​to maintain high precision during the calculation process and only reduces the precision during storage, thereby achieving a balance between computational efficiency and accuracy. In addition, mixed-precision quantization technology can use different quantization bit widths at different levels to minimize information loss.

[0084] After the model training is completed, the weights and activation values ​​are approximately converted through the quantization strategy, so that the model can maintain high accuracy while reducing the computational complexity. Alternatively, quantization simulation is introduced during the model training process to adapt to low-precision calculations, thereby improving the final performance of the quantized model.

[0085] The quantized model can use integer (such as INT8) operations instead of floating-point operations, making the calculation more efficient, especially on hardware that supports low-precision calculations (such as ARM processors, TPUs, and NPUs), with significant acceleration effects.

[0086] In the neural network model, different layers have different sensitivities to accuracy. This module supports adaptive quantization strategies, that is, different bit widths are used for different layers. For example, convolutional layers and fully connected layers can be quantized using INT8, while certain key layers (such as normalization layers) remain in FP16 or FP32 to reduce information loss. In addition, this module also incorporates an error compensation mechanism to restore accuracy by fine-tuning the quantized weights, ensuring that the prediction performance of the quantized model is close to the original FP32 model.

[0087] The model quantization submodule can not only significantly reduce the model storage size, making AI models easier to deploy in resource-constrained environments, but also reduce the computational overhead during inference and improve processing speed.

[0088] (3) The model distillation submodule is configured to transfer the knowledge of the large model to other models using the model distillation method.

[0089] The model distillation submodule is used to transfer the knowledge of large models to smaller and more efficient models through knowledge distillation technology, so that it can significantly reduce the consumption of computing resources while maintaining similar performance. Large-scale deep learning models often have powerful learning capabilities and high accuracy, but due to the large number of parameters, a large amount of computing resources are required during the reasoning process, making it difficult to run efficiently in a resource-constrained environment. The introduction of model distillation technology allows these large models to refine their own knowledge and transfer it to student models with smaller computing requirements without significantly reducing the effect, thereby improving reasoning efficiency and reducing storage usage. It is suitable for a variety of practical application scenarios such as edge computing and mobile devices.

[0090] In the process of model distillation, the teacher model is usually a high-capacity deep neural network with sufficient training and superior performance, while the student model is a model with smaller structure and lower computational complexity. Unlike directly using the true labels of the data to train the student model, the distillation method captures the potential relationship between the data by allowing the student model to learn the "soft labels" (Soft Targets) output by the teacher model. For example, in a classification task, the teacher model not only outputs the prediction result of a category, but also generates a complete probability distribution, showing the possibility of the data belonging to different categories. This probabilistic information can provide more feature information than hard labels, making the student model more efficient in the learning process, thereby improving the generalization ability.

[0091] In practical applications, model distillation can be optimized in different ways. For example, the feature distillation method not only allows the student model to learn the final output results, but also requires it to imitate the feature representation of the teacher model at the intermediate level, so that the internal representation of the model is closer and the overall effect is improved. In addition, attention distillation technology is particularly important in natural language processing and computer vision tasks. It allows the student model to learn the attention distribution of the teacher model, and then focus on key information more accurately and improve reasoning ability. In contrastive distillation, the system will enhance the student model's ability to understand the data structure through contrastive learning, so that it can maintain stable performance in different task environments.

[0092] The model distillation submodule has an adaptive distillation strategy that can select appropriate distillation methods according to different application scenarios. For example, in text generation tasks, the student model can learn the hidden layer representation of the teacher model, while in computer vision tasks, the student model can imitate the feature extraction method of the teacher model.

[0093] (4) Dynamic data preprocessing submodule, configured to automatically perform feature extraction, cleaning and enhancement of the model.

[0094] The dynamic data preprocessing submodule can provide an efficient and intelligent data preparation mechanism for machine learning and deep learning models. By automatically performing key steps such as feature extraction, data cleaning, and data enhancement, the data can better match the needs of the model and improve the accuracy and stability of training and reasoning. Data preprocessing is usually an important part of the AI ​​training process. Different tasks have different requirements for data. For example, computer vision tasks may require image normalization and data enhancement, natural language processing tasks may require text segmentation and denoising, and structured data analysis may require feature selection and missing value filling. Therefore, traditional static preprocessing methods are often difficult to meet the needs of complex scenarios, while dynamic data preprocessing can adjust the preprocessing strategy in real time according to the specific characteristics of the model and data to ensure that the data enters the model in the best state.

[0095] In the feature extraction stage, the module can automatically identify and extract key features in the data, reduce redundant information, and improve the efficiency of model learning. For text data, the system can perform tasks such as word segmentation, part-of-speech tagging, named entity recognition, and automatically select feature representation methods suitable for the current task, such as TF-IDF, word vectors, or sentence vectors. In image data processing, the system can perform edge detection, color feature extraction, texture analysis, and adjust the input format for different model architectures so that the data can be optimally adapted to the deep learning network. For structured data, the module can analyze the correlation between fields, automatically build high-value feature combinations, and improve the expressiveness of the data.

[0096] Data cleaning is an indispensable step in data preprocessing. The system needs to ensure the quality of input data to avoid outliers, missing values ​​or duplicate data affecting model performance. This module can automatically detect and repair missing values ​​in the data, and use different strategies such as mean filling, median filling, and interpolation to intelligently complete them. At the same time, the system can also automatically identify and remove outliers, using statistical methods (such as Z-score, IQR method) or machine learning-based anomaly detection algorithms (such as isolation forests, local anomaly factors) to identify abnormal samples in the data, ensuring that the model will not learn the wrong pattern due to dirty data. In addition, data deduplication is also a key link to avoid the same data appearing multiple times, resulting in model overfitting or waste of computing resources.

[0097] When data is limited, this module can automatically generate diverse training samples to help the model learn more robust features. For example, in computer vision tasks, the system can automatically perform operations such as image rotation, cropping, flipping, and color adjustment to expand the diversity of the data set. In natural language processing tasks, the system can generate more varied text data through synonym replacement, random deletion, back translation, etc. to enhance the model's understanding ability. For structured data, this module can enrich the data representation and improve the model's adaptability through methods such as synthetic data, perturbed values, and data transformation.

[0098] In addition, the module uses a dynamic adjustment mechanism that can not only perform static preprocessing operations, but also adjust data processing strategies based on performance indicators fed back during model training. For example, if the model performs poorly on a certain type of sample, the system can automatically add data enhancement operations for that category, or adjust feature selection methods to make the model more adaptable to that type of data. In addition, the module can also combine online learning technology to achieve dynamic preprocessing of real-time data streams, allowing the model to maintain its optimal state as data is constantly updated.

[0099] The data management module 3 is configured to preview, clean, evaluate, annotate, collect and convert the format of the data.

[0100] Data management module 3 ensures that data maintains high quality and high availability throughout the entire AI training and application process. Through comprehensive data management capabilities, this module enables users to quickly understand data characteristics, improve data quality, optimize data structure, and ultimately improve the application value of data.

[0101] First, the data preview function allows users to quickly browse and analyze the basic information of the data set before the data enters the formal processing flow. The system can automatically display key statistical indicators of the data, such as data size, format, field distribution, sample examples, etc., and supports users to visualize data, such as histograms, scatter plots, correlation matrices, etc., so that users can intuitively understand the distribution of data. In addition, for structured data, the system can also provide field descriptions, data type distribution and missing value statistics, and for text or image data, it can provide sample previews to help users understand the data content more comprehensively.

[0102] This module can automatically identify and fix problems in the data, such as missing values, duplicate data, outliers, and data noise. Missing value filling can select the optimal strategy according to the data type, such as mean filling, median filling, interpolation, or model-based prediction filling. For duplicate data, the system can automatically detect and merge duplicate samples to avoid model bias caused by data redundancy. At the same time, the module can also automatically detect outliers using statistical methods or machine learning algorithms (such as isolation forests, local anomaly factors), and provide intelligent denoising functions to ensure the neatness and consistency of the data.

[0103] The data evaluation function is used to comprehensively evaluate the quality of the data set and help users determine whether the data is suitable for model training. The system can score the data set based on indicators such as data integrity, balance, diversity, and annotation quality, and generate a detailed analysis report. For example, in classification tasks, the system can evaluate whether the distribution of data categories is balanced to avoid model deviations due to data imbalance. In time series analysis tasks, the system can detect whether the data has uneven time spans and provide corresponding optimization suggestions. In addition, the module can also predict potential problems with the data set based on the correlation between historical data and model performance, and provide optimization solutions.

[0104] The data annotation function supports users to annotate data efficiently and accurately to meet the needs of supervised learning tasks. This module supports multiple annotation modes, such as manual annotation, automatic annotation, and semi-automatic annotation. Manual annotation is suitable for small-scale data sets or tasks with high precision requirements. Users can use an interactive interface to annotate text, images, or audio data one by one. Automatic annotation is based on pre-trained models or rule engines, which can quickly batch annotate large-scale data and improve annotation efficiency. Semi-automatic annotation combines user feedback and model prediction, and uses active learning technology to make the annotation process more intelligent and efficient. In addition, the system also provides a data consistency check function to ensure the accuracy and consistency of the labeled data and reduce the impact of human errors on model training.

[0105] The data collection function is used to collect and integrate data from different sources to build rich and diverse training data sets. This module supports multiple data collection methods, including API data acquisition, web crawling, database import, sensor data collection, etc. For example, in natural language processing tasks, the system can automatically crawl public data sources such as news and social media texts to build a large-scale corpus. In computer vision tasks, the system can collect image or video data from sources such as cameras and satellite images. In addition, the data collection module also provides data deduplication and format standardization functions to ensure that the collected data can be seamlessly integrated with existing data sets.

[0106] The format conversion function ensures that the data can be adapted to different application scenarios, making it easier to be used by AI models or data analysis tools. This module supports conversion between multiple data formats, such as CSV, JSON, XML, Parquet, TFRecord, etc., which are suitable for different data storage and processing frameworks. At the same time, for text data, the system can perform encoding conversion, word segmentation, stop word removal and other processing to make it more suitable for NLP tasks. For image data, the module can perform operations such as resolution adjustment, channel conversion, format compression, etc. to adapt to different deep learning frameworks. In addition, the format conversion function also supports batch processing to ensure efficient conversion of large-scale data sets.

[0107] The application management module 4 is configured to display, search and filter, compile and deploy applications and manage application versions.

[0108] The application management module 4 ensures that users can efficiently browse, manage and use various application resources. It not only supports the full display of applications, but also provides convenient search and screening capabilities, allowing users to quickly locate target applications. At the same time, through automated compilation and deployment functions, the complexity of application launch and update is reduced, and the system availability and maintenance efficiency are improved.

[0109] First, the application display function allows users to browse all available applications in the form of lists or cards, and provides detailed application information, including application name, version number, function introduction, applicable scenarios, developer information, dependent environment, release time and update log, etc. Users can directly view the detailed page of the application to understand its core features and actual usage effects, and judge the quality and applicability of the application through information such as application ratings and user reviews. In addition, this module supports the classified display of applications, such as grouping by industry field (such as finance, medical care, education), application type (such as machine learning, natural language processing, computer vision) or technology stack (such as TensorFlow, PyTorch, ONNX), so that users can more intuitively find applications that meet their needs.

[0110] In order to improve the efficiency of application search, this module provides powerful search and filtering functions. Users can quickly find the target application through keyword search. The system supports fuzzy matching and intelligent association to improve the accuracy of search. At the same time, the filtering function allows users to filter based on multiple dimensions (such as application category, developer, update time, rating, etc.) to accurately locate applications that meet their needs. For example, users can choose to display only the latest released applications, or filter popular applications with a rating higher than 4.5. In addition, the system also supports sorting functions, such as sorting by release time, popularity, user rating, etc., to ensure that users can efficiently obtain the most relevant application resources.

[0111] This module integrates an automated compilation and deployment mechanism to support users to quickly build and publish applications. For code-based applications, the system provides a one-click compilation function that automatically detects code dependencies, executes the compilation process, and checks code quality and security to ensure that the application can run normally. In addition, it supports multiple deployment methods, including local deployment, cloud deployment, and containerized deployment (such as Docker, Kubernetes) to meet the needs of different environments. Users can select deployment parameters such as computing resource allocation, operating environment configuration, etc. through a visual interface. The system will automatically complete the deployment process and provide real-time logs and status monitoring to ensure stable operation of the application.

[0112] Application version management can help users maintain different versions of applications and provide version tracking and rollback functions. The system supports application version control, and users can view the application's historical versions, change logs, and switch to a specific version freely. For example, when users find that the new version of the application has compatibility issues or performance degradation, they can quickly roll back to the stable version. In addition, the system also supports grayscale release and A / B testing functions, allowing users to conduct small-scale tests on different versions of applications before they are officially launched to ensure that the update does not affect the stability of the overall system.

[0113] The prompt word library module 5 is configured to preview and optimize the prompt words, compare and analyze the output results generated by the prompt words, and provide standardized rule templates.

[0114] The prompt word library module 5 can provide users with a complete prompt word management system, including preview, optimization, output result comparison and analysis of prompt words, and support for standardized rule templates. With the widespread application of artificial intelligence technologies such as large language models and image generation models, the quality of prompt words directly affects the output effect of the model. This module helps users improve the expression clarity, context relevance and consistency of the generated effect of prompt words by providing a systematic prompt word management function, thereby optimizing the interactive experience of the artificial intelligence system.

[0115] First, the prompt word preview function allows users to intuitively view the possible generation effect of the prompt word before using it. This function provides an interactive interface that allows users to enter the prompt word and view the real-time response of the model in combination with the preview mode. For example, for text generation tasks, the system will display multiple example outputs so that users can understand the different answers that the prompt word may produce; for image generation tasks, users can use the preview mode to view the image generation effects under different styles and parameters. With this function, users can adjust the description of the prompt word before actual use to ensure that the model can correctly understand and generate content that meets expectations.

[0116] In terms of prompt word optimization, this module provides a series of optimization tools to help users improve the expression of prompt words and improve the accuracy and controllability of model responses. The optimization process involves language simplification, grammar adjustment, context supplementation, and parameter adjustment. For example, users can use the optimization function to rewrite lengthy or vague prompt words into concise and clear expressions, or adjust the word order of prompt words to make them more in line with the model's preferences. In addition, this function also supports automatic optimization. The system can recommend a better prompt word structure based on historical usage data and best practices to help users quickly improve the quality of prompt words.

[0117] The output result comparison and analysis function of prompt words is a core capability of this module, which aims to help users evaluate the impact of different prompt words on the content generated by the model. This function supports multiple rounds of prompt word testing. Users can enter multiple similar prompt words and compare the output results of the model under different input conditions. For example, in the text generation scenario, users can compare the impact of two different prompt words on the style, content completeness and coherence of the generated text; in the image generation task, users can view the impact of different prompt words on the composition, color, details and other aspects of the generated image. The system also provides an automatic scoring mechanism, which can give quantitative evaluation results based on factors such as the coherence, information richness and consistency of the model output, helping users quickly select the optimal prompt word.

[0118] In addition, the module also provides standardized rule templates to facilitate users to quickly build high-quality prompt words. Standardized rule templates cover multiple fields and application scenarios, such as code generation, copywriting, question-and-answer systems, text summarization, translation tasks, etc. Users can directly select a suitable template and fill in the necessary information to generate professional-level prompt words, greatly reducing the time cost of manual adjustment. For example, in code generation tasks, templates can provide standard instruction formats; in copywriting tasks, templates can provide formatted prompts. With these preset templates, users can more easily construct prompt words that meet their needs and improve the quality and consistency of AI-generated content.

[0119] The code management module 6 is configured to perform code review and security scanning, and to perform security analysis and management on the code.

[0120] Code Management Module 6 can provide users with comprehensive code management and security control capabilities, mainly including two core functions: code review and security scanning. With the expansion of software development scale and the increase of complexity, code quality and security have become key factors affecting software reliability and stability. This module ensures that the code complies with best practices and security standards through automated review and vulnerability detection, reduces potential risks, and improves development efficiency and code quality.

[0121] The code review function uses automated analysis tools to perform static and dynamic detection of the code to ensure that the code complies with coding standards, is readable, and avoids common programming errors. Static analysis mainly checks the code style, structure, and potential logical errors, such as whether it complies with naming rules, whether there are unused variables, whether there are potential null pointer references, etc. Dynamic analysis executes the code in the actual running environment to detect possible abnormal situations, such as runtime errors, performance bottlenecks, and resource leaks. This function supports multiple programming languages ​​and can customize review rules according to project requirements to ensure that different development teams can perform code quality control according to unified standards. In addition, the system provides a visual review report, marks the line number of the problem code, and provides optimization suggestions, so that developers can quickly locate and fix problems and improve the maintainability and readability of the code.

[0122] The security scanning function mainly detects security vulnerabilities and hidden dangers in the code to ensure that the software is not threatened by malicious attacks or data leaks. This function uses technologies such as static code analysis (SAST) and dynamic application security testing (DAST) to scan potential security risks in the code base. For example, it can detect SQL injection, cross-site scripting attacks (XSS), unsafe API calls, use of weak encryption algorithms, improper permission control and other problems. In addition, the system also supports the integration of third-party security rule libraries, such as OWASP Top 10 and CVE (Common Vulnerabilities and Exposures) databases to ensure that the latest security vulnerabilities are identified and fixed in a timely manner. For the security vulnerabilities found, the system will provide a detailed analysis report, including the scope of the vulnerability, possible attack methods and repair suggestions, to help developers quickly take measures to eliminate security risks.

[0123] The intelligent agent module 7 has at least a technical support intelligent agent, a data processing intelligent agent, a personalized recommendation intelligent agent and an experiment optimization intelligent agent, and is configured to realize technical support, automated data processing, personalized content recommendation and experiment optimization functions.

[0124] Agent module 7 can improve the automation capability of the system through various types of agents, optimize user experience, and improve the efficiency of data and model management. This module introduces single agent and multi-agent mechanisms, enabling the system to intelligently execute tasks, process complex logic, and automatically adjust strategies according to environmental changes in different scenarios. Through this mechanism, the platform can effectively reduce the manual operation costs of users while enhancing the intelligence level of task execution.

[0125] First, the technical support and operation automation agent is mainly used to provide immediate technical support and user operation guidance. The agent can parse the user's query content, understand their needs, and provide accurate technical answers. At the same time, it can also interact with the API to automatically perform related operations, such as resource management, task scheduling, service deployment, etc., thereby simplifying the user's workflow and improving the system's availability and usability. For example, users can input questions in natural language, and the agent can quickly retrieve relevant documents, or directly execute specific configuration commands, reducing the user's reliance on manual operations.

[0126] Secondly, the personalized recommendation agent relies on big data analysis and machine learning algorithms to conduct in-depth analysis of user behaviors, preferences, and historical operation records to provide accurate resource recommendations. The agent can learn users' usage habits, identify their areas of concern, and recommend appropriate models, data sets, code libraries and other resources based on the behavior patterns of similar users. For example, if a user frequently uses a certain type of deep learning model, the system can automatically recommend similar or optimized models to help users quickly find the right tools and improve work efficiency. In addition, the agent can also continuously optimize the recommendation algorithm based on user feedback to improve the accuracy of personalized recommendations.

[0127] The experimental automation and optimization agent is mainly used to help users optimize experiments efficiently and improve the effects of model training and inference. The agent can automatically generate optimization solutions based on the user's experimental goals, such as model selection, data set selection, hyperparameter setting, etc., and automatically submit experimental tasks. During the execution of the experiment, it can monitor the progress of the experiment in real time, analyze the experimental results, and provide optimization suggestions. For example, if the convergence speed of an experiment is slow, the agent can automatically adjust parameters such as learning rate and batch size to improve training efficiency. In addition, it also supports automated experiment logging and result visualization, allowing users to intuitively understand the experimental process and its optimization effect.

[0128] The data processing agent focuses on automating data processing tasks, ensuring data quality while reducing the workload of users. The agent can automatically generate data processing templates based on user needs and perform operations such as data cleaning, format conversion, and feature extraction. For example, when a user uploads a new data set, it can automatically check data integrity, detect missing values ​​or outliers, and provide correction solutions. In addition, the agent also supports automatic review of the quality of the model or data set to ensure that it meets the predetermined standards and avoid low-quality data affecting the training effect of the model.

[0129] In one embodiment disclosed in the present invention, the system also includes a visualization module configured to use visualization tools to display data structure, model architecture and training progress.

[0130] The system also includes a visualization module, which integrates advanced data visualization tools to enable users to intuitively view key information such as data structure, model architecture, and training progress. Visualization not only helps users understand complex data relationships more quickly, but also improves the efficiency of debugging, optimization, and decision-making. The module uses a variety of visualization techniques, such as dynamic charts, interactive graphics, and 3D model displays, to ensure that data presentation is both intuitive and easy to understand, and has sufficient depth to support the analysis needs of professional users.

[0131] In terms of data structure visualization, the system provides a variety of chart forms, including bar charts, line charts, scatter plots, and heat maps, to display the distribution, feature statistics, and quality analysis of data sets. For example, users can view the numerical distribution of data through histograms, or use correlation matrix heat maps to analyze the correlation between variables. In addition, the data flow visualization function can intuitively display the entire process of data collection, cleaning, and annotation, allowing users to easily understand the various stages of data processing and quickly discover possible problems.

[0132] In terms of model architecture visualization, the system provides an intuitive neural network structure diagram to show the hierarchical relationship, number of parameters, and computational complexity of the model. Users can view the details of each layer through an interactive interface, including convolutional layers, fully connected layers, normalization layers, etc., to better understand the internal structure of the model. In addition, the system supports visualization at different depths. Beginners can choose a brief view to view only the key structure, while professional users can deeply analyze every detail of the model, such as weight distribution, activation function output, etc. For users who need to customize the model, the system also supports a drag-and-drop model building interface, allowing users to adjust the model structure in a visual way to improve development efficiency.

[0133] In terms of training progress visualization, the system provides real-time training curves, including key indicators such as loss function changes, accuracy improvement, and learning rate adjustment, to help users monitor whether the training process is running normally. For example, users can observe the convergence of the model through the curve to determine whether overfitting or underfitting problems occur. In addition, the system supports graphical display of training logs, so users can view the comparison results of different experiments and quickly find the optimal parameter settings. For distributed training tasks, the system also provides cluster status monitoring functions to display the utilization of computing resources and help users optimize computing resource allocation.

[0134] In order to meet the needs of different users, the system's visualization module provides a multi-level interactive experience. For beginners, the system can present key information in a simple interface to avoid information overload. For advanced users, the system provides detailed analysis options, including model level viewing, data feature correlation analysis, etc., to support more complex decision-making and optimization processes. In addition, the system supports the export of visualization reports, and users can convert key visualization results into chart reports for sharing or further analysis.

[0135] In an embodiment disclosed in the present invention, the system also includes an intelligent search and recommendation module, which is configured to provide search results and recommended resources based on the user's historical search log.

[0136] The system also includes an intelligent search and recommendation module, which integrates an AI-driven search engine and recommendation system to improve resource discoverability and user experience. Traditional search engines are usually based on keyword matching, while this system uses more advanced semantic search, context analysis and personalized recommendation technologies, which can not only accurately match user queries, but also actively push the most relevant resources based on user usage habits, interest preferences and historical operation records.

[0137] In terms of intelligent search, this module uses natural language processing (NLP) technology to achieve semantic-level search understanding. Traditional keyword matching methods may lead to inaccurate retrieval results due to changes in word meanings, while this system analyzes query intent through deep learning models (such as BERT, GPT, etc.) to ensure more accurate search results. For example, when a user searches for "models suitable for image classification", the system will not only return models containing the keyword, but also intelligently recommend well-known image classification models such as ResNet and EfficientNet, rather than just literal matching. In addition, the search engine also supports fuzzy search, spelling correction, related query expansion and other functions. Even if the user input is incomplete or contains spelling errors, the system can still provide high-quality retrieval results.

[0138] In terms of the recommendation system, this module analyzes the user's historical behavior, predicts their potential interests, and provides personalized resource recommendations based on collaborative filtering and deep learning recommendation algorithms. For example, the system will record the models, data sets, prompt words, and other information that users browse, download, and collect, and automatically generate a recommendation list based on these behaviors. For new users, the system uses content-based recommendations to push relevant resources to them based on their initial queries and preferences. For long-term users, the system will combine behavior-based recommendations with cluster analysis, neural networks, and other methods to predict users' potential needs and provide more accurate personalized recommendations.

[0139] In addition, the module also supports context-aware recommendations, that is, providing the most relevant resources based on the user's current usage scenario. For example, on the model fine-tuning page, the system will intelligently recommend optimization methods and related data sets suitable for the model; in the code management module, the system will provide relevant code examples, dependency libraries, and best practice documents; in the prompt word optimization module, the system will recommend similar and efficient prompt word templates based on the prompt word that the user is optimizing. This scenario-based dynamic recommendation enables users to obtain the most valuable information at the right time and improve work efficiency.

[0140] The intelligent search and recommendation module also provides interactive search suggestions and dynamic filtering functions. When users enter queries, the system will provide real-time search suggestions, including popular searches, related searches, historical searches, etc., to help users quickly find target content. At the same time, the search results page provides multi-dimensional filtering options, such as filtering by resource type (model, dataset, code), release time, frequency of use, user ratings, etc., so that users can quickly locate the best resources according to their needs.

[0141] In addition, the system supports personalized learning functions, and users can manually adjust search and recommendation preferences, such as choosing a recommendation strategy that is more inclined to "high-performance model" or "computational efficiency optimization model", or adjusting the sorting method of search results (such as latest first, score first, etc.). This highly customizable search and recommendation system ensures that the needs of different types of users can be met.

[0142] In one embodiment disclosed in the present invention, the system has an API interface, allowing it to be called by other systems.

[0143] The system has an API interface that allows external systems to call its core functions and achieve seamless integration with the current R&D ecosystem. The openness and flexibility of the API allows the system to not only run independently, but also be easily integrated into the company's existing technical architecture, and work with various development tools, model training environments, and automated deployment processes, thereby strengthening the collaborative process between models and codes and improving R&D efficiency.

[0144] In terms of open API design, the system provides a set of APIs based on RESTful or GraphQL architecture, allowing third-party developers to call the platform's functions in their own applications. These APIs cover key modules such as model management, data set operations, code review, model fine-tuning, version control, etc. Developers can upload, download, query, and update resources through simple API requests. In addition, the system provides complete API documentation, including usage examples, parameter descriptions, error handling mechanisms, etc., to ensure that developers can quickly understand and use the API and improve integration efficiency. In order to support long-term maintenance, the API also has a version compatibility strategy, that is, when updating the API interface, the availability of the old version is still guaranteed to avoid compatibility issues caused by system upgrades.

[0145] In addition, the system adopts a flexible adapter design and supports multiple network protocols (such as HTTPS, SSH, WebSocket, etc.) to seamlessly connect with external systems (such as code repositories, CI / CD tools, cloud storage services, etc.). For example, in the code management scenario, the system can synchronize with mainstream code hosting platforms (such as GitHub, GitLab, Bitbucket) through the Git protocol to achieve automated code pulling, submission, and version management. In the model training scenario, the system supports integration with environments such as Kubernetes, Docker, and TensorFlow Serving. Developers can directly call the API to trigger model training, deploy inference services, and obtain real-time status feedback. In addition, the API can also be combined with CI / CD pipelines, such as Jenkins, GitHub Actions, GitLab CI, etc., to automate model building, testing, and deployment tasks to ensure the efficient operation of the entire development process.

[0146] In terms of security and permission management, the system's API provides a multi-level access control mechanism, including OAuth 2.0, API Key, JWT token and other authentication methods, to ensure that only authorized users or systems can access sensitive resources. In addition, the system also supports role-based permission management (RBAC), and administrators can assign different levels of API permissions based on the responsibilities of team members to prevent unauthorized operations. At the same time, the API request record and audit log functions make all access behaviors traceable, which helps with security management and compliance review.

[0147] In addition, the API system also supports asynchronous processing and callback mechanisms. For long-running tasks (such as large-scale data processing, model training, batch analysis, etc.), the API can use task queues (Message Queues) for asynchronous execution and provide Webhooks or WebSocket mechanisms, allowing external systems to monitor task status changes in real time and obtain execution results in a timely manner. This design avoids the problem of API request timeouts and improves the system's responsiveness and scalability.

[0148] In one embodiment disclosed in the present invention, the system further includes the following modules:

[0149] (1) An encryption module, configured to encrypt stored and transmitted resources.

[0150] The encryption module is mainly used to encrypt and protect stored data and transmitted data to prevent data from being stolen, tampered with, or unauthorized access. At the storage level, the system uses advanced symmetric encryption algorithms such as AES-256 to encrypt and store data, and combines the key management system (KMS) to securely store and regularly rotate keys to ensure that the data cannot be decrypted even if it is obtained externally. At the transmission level, the system uses the TLS1.3 protocol to encrypt all data transmission to prevent security risks such as man-in-the-middle attacks and traffic monitoring. In addition, the system also supports end-to-end encryption (E2EE), ensuring that data is encrypted on the user side before being transmitted, and only authorized users can decrypt and view it, further improving security.

[0151] (2) An access control module configured to configure access rights based on user roles.

[0152] The access control module provides fine-grained permission management based on the role-based access control (RBAC) mechanism to ensure that different users can only access resources within their permission scope. Administrators can assign different access rights based on user roles (such as ordinary users, developers, administrators, etc.), for example:

[0153] Ordinary users can only view public resources and cannot modify or delete data; developers can upload and manage their own models and datasets, but cannot access other users' private resources; administrators have global management permissions and can create user groups, set access policies, and audit user operation records.

[0154] In addition, the system supports multi-factor authentication (MFA), and users need to provide authentication methods other than passwords (such as mobile phone verification codes, hardware security keys, fingerprints, etc.) when logging in to prevent accounts from being cracked by brute force or phishing attacks. For enterprise and team collaboration scenarios, the system also supports attribute-based access control (ABAC), which can adjust permissions based on dynamic factors such as the user's department, device, geographic location, time, etc., to achieve more flexible security policies. For example, access to sensitive data can be restricted to the internal network of the enterprise, or the execution of high-privilege operations can be restricted during non-working hours.

[0155] To further enhance security, the system also provides access auditing and logging functions, recording all security events such as access requests, permission changes, abnormal logins, and supports real-time alert mechanisms. When abnormal access behaviors (such as a large number of requests in a short period of time, access from suspicious IP addresses, etc.) are detected, administrators can receive notifications and take countermeasures. In addition, the system can be integrated with SIEM (Security Information and Event Management) systems to perform log analysis, threat detection, and security compliance reporting to meet corporate and regulatory requirements.

[0156] In an embodiment disclosed in the present invention, the system further includes a user management module configured to perform identity authentication, authority management and personalized settings.

[0157] The system also includes a user management module, which is responsible for user identity authentication, permission management, and personalized settings to ensure that the system can provide a safe, flexible, and personalized user experience. This module not only supports refined management of different types of users, but also provides convenient custom settings to meet the personalized needs of users when using the platform.

[0158] In terms of identity authentication, the system adopts a multi-level authentication mechanism, supports the basic authentication method of username + password, and can be combined with multi-factor authentication (MFA) (such as SMS verification code, email verification, hardware token, etc.) to enhance security. In addition, in order to improve the convenience of user access, the system supports single sign-on (SSO), and users can log in directly using corporate accounts or third-party identities (such as Google, GitHub, Microsoft Azure AD, etc.), reducing the trouble of frequently entering passwords while ensuring the security and compliance of identity authentication.

[0159] In terms of permission management, the system adopts a role-based access control (RBAC) mechanism to support fine-grained permission allocation. Administrators can assign different roles to users (such as ordinary users, developers, administrators, auditors, etc.) and set corresponding access permissions for different resources, such as data reading permissions, model training permissions, application deployment permissions, etc. For enterprise and team collaboration scenarios, the system also supports attribute-based access control (ABAC), which can adjust permissions based on dynamic factors such as the user's department, project, geographic location, time, etc. For example, certain sensitive data can be accessed only by specific team members during working hours, further improving security and flexibility. In addition, the system provides access logs and permission change records, and administrators can view user operations at any time to ensure that permission allocation is reasonable and prevent potential security risks.

[0160] In terms of personalized settings, the system allows users to customize personal information, interface style, notification preferences, quick operations, etc. Users can adjust their dashboard layout, efficiently manage commonly used resources, and customize recommended content according to their interests and usage habits. In addition, the system provides a notification center that supports multiple notification methods such as email, SMS, and in-app push. Users can choose the receiving method independently, such as whether to receive system updates, permission change reminders, data analysis results push, etc. For developer users, the system also supports customized API access rights and key management, which facilitates the integration of external tools or automated tasks. In addition, the system provides team management functions, and team administrators can create user groups, manage member permissions in batches, and assign different collaboration tasks.

[0161] In addition, in one embodiment disclosed in the present invention, the graphical user interface (GUI) of the system is carefully designed to provide an intuitive, efficient, and customizable user experience, enabling users to complete complex tasks through simple interactions. The interface adopts modular and visual design, combined with modern UI / UX principles, to ensure clear operation logic and smooth interaction, suitable for users of different backgrounds and skill levels, including developers, researchers, and business personnel.

[0162] In terms of interactive experience, the system supports intuitive gestures such as drag-and-drop operation, one-click execution, and shortcut menus. Users can complete tasks such as data uploading, model training, and resource management through simple clicks, drags, and slides. For example, in the data processing module, users can directly drag and drop data files to the system interface for automatic parsing and preprocessing; in the model training interface, users can select hyperparameter optimization solutions with one click without manually entering complex parameter configurations. In addition, intelligent operation prompts and interactive feedback are also widely used. For example, when users perform high-risk operations (such as deleting critical data), the system will provide visual warning prompts and require secondary confirmation to prevent misoperation.

[0163] In terms of information display, the GUI uses data visualization and dynamic charts to enhance the intuitiveness of data analysis and result presentation. For example, during model training, the system can draw loss function change curves, accuracy trend charts, etc. in real time, allowing users to intuitively understand the progress of training and adjust parameter optimization directions in a timely manner. In addition, in the data management module, the system provides interactive data visualization tools, and users can easily analyze data distribution, identify abnormal points, and improve the efficiency of data quality control through operations such as filtering, sorting, and zooming.

[0164] In order to meet the needs of different users, the system also provides customization functions, allowing users to adjust the interface layout and functional modules according to their work habits and needs. Users can choose to enable or hide specific toolbars, quick operation buttons, and even adjust the layout of the workspace to create an environment that best suits their workflow. For example, developers may prefer code editing and log monitoring, while data analysts may pay more attention to data preview and chart display, so the system provides pluggable tool components that allow users to freely combine interface elements to create a personalized workspace. In addition, the system supports multi-theme interfaces, and users can switch between different color styles and layout modes (dark mode, light mode, etc.) according to their preferences to adapt to different usage scenarios.

[0165] In terms of functional extensibility, the GUI design supports a plug-in architecture, allowing users to integrate third-party tools or develop custom plug-ins. For example, users can add custom data processing scripts, model monitoring panels, personalized recommendation algorithms, etc. to expand the capabilities of the platform. This modular design allows the platform to flexibly adapt to different business needs, while reducing learning costs and improving user work efficiency.

[0166] The embodiments of the present disclosure have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The selection of terms used herein is intended to best explain the principles of the embodiments, practical applications, or technical improvements to the technology in the market, or to enable other persons of ordinary skill in the art to understand the embodiments disclosed herein.

Claims

1. A Hub system for large model and data set management, characterized by: include: A basic management module is configured to perform resource uploading, downloading, searching, sorting, filtering, version management, and establishing and managing associations between different resources, wherein the resources include at least models, data, and codes; The model management module is configured to perform inference, fine-tuning, evaluation, distillation, pruning, and format conversion on the model; The data management module is configured to preview, clean, evaluate, annotate, collect and convert the data into different formats; An application management module is configured to display, search and filter applications, compile and deploy applications, and manage application versions; A prompt word library module is configured to preview and optimize prompt words, compare and analyze output results generated by prompt words, and provide standardized rule templates; The code management module is configured to perform code review and security scanning, and to perform security analysis and management on the code; The intelligent agent module has at least a technical support intelligent agent, a data processing intelligent agent, a personalized recommendation intelligent agent and an experimental optimization intelligent agent, and is configured to realize technical support, automated data processing, personalized content recommendation and experimental optimization functions.

2. The system according to claim 1, characterized in that The basic management module includes: The hierarchical index submodule is configured to establish a top-level index according to resource types and to establish a lower-level index according to resource characteristics; A metadata tagging submodule, configured to construct a metadata structure of all resources, wherein the metadata structure includes at least resource type, license type, language, and application industry; A data card submodule is configured to generate data cards for all resources, wherein the data cards include at least basic information of the resources, usage methods, usage scenarios, practical cases, resource features, visualization information and operation procedures; The resource association submodule is configured to determine the relationship between different resources and build an association network.

3. The system according to claim 1 or 2, characterized in that: The basic management module also includes: The version control submodule is configured to integrate version control tools to track and manage resource versions.

4. The system according to claim 1, characterized in that The system further comprises: The visualization module is configured to use visualization tools to display data structure, model architecture, and training progress.

5. The system according to claim 1, characterized in that The system further comprises: The intelligent search and recommendation module is configured to provide search results and recommended resources based on the user's historical search logs.

6. The system according to claim 1, characterized in that The system has an API interface, allowing it to be called by other systems.

7. The system according to claim 1, characterized in that The model management module includes: A model compression submodule is configured to compress the model using a model compression technique, wherein the model compression technique includes at least knowledge distillation, pruning, and parameter sharing; A model quantization submodule, configured to convert the parameters of the model from floating point numbers to low bit width representation; The model distillation submodule is configured to transfer the knowledge of the large model to other models using the model distillation method; The dynamic data preprocessing submodule is configured to automatically perform feature extraction, cleaning, and enhancement of the model.

8. The system according to claim 1, characterized in that The system further comprises: An encryption module, configured to encrypt stored and transmitted resources; The access control module is configured to configure access rights according to user roles.

9. The system according to claim 1, characterized in that The system further comprises: The user management module is configured to perform identity authentication, permission management and personalization settings.

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