Equipment cloud management system based on RAG technology

Abstract

The invention discloses an equipment cloud management system based on an RAG technology, which comprises a natural language processing module, a knowledge base management module, a machine learning module, a multi-user cooperation module and a system architecture, and is characterized in that the natural language processing module analyzes natural language query of a user and converts the natural language query into structured query; the knowledge base management module is responsible for maintaining and managing a knowledge base related to equipment; the machine learning module continuously trains and optimizes a question and answer model by using historical query data; the multi-user cooperation module supports multiple users to use the system at the same time, and sharing and updating of knowledge are achieved. The method has the following beneficial effects: 1, the natural language processing capability can understand the natural language query of the user; 2, integrating a plurality of equipment management knowledge bases, including an equipment manual, a maintenance guide, a maintenance record and the like; 3, the accuracy of question answering is continuously optimized by utilizing a machine learning technology; and 4, multi-user cooperation is supported, and knowledge sharing and updating are realized.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence technology, and in particular to a device cloud management system based on RAG technology. Background Art

[0002] In modern industrial environments, equipment cloud management systems have become the core tools for enterprise equipment management. These systems usually include functions such as equipment monitoring, maintenance, fault diagnosis and reporting. However, traditional equipment cloud management systems rely on manual query of large amounts of documents when dealing with equipment-related issues, which is inefficient and error-prone. In addition, due to the wide variety of equipment, it is difficult for maintenance personnel to grasp the detailed information of all equipment in a short period of time, which increases the difficulty of maintenance work. Summary of the invention

[0003] The purpose of the present invention is to provide a device cloud management system based on RAG technology to solve the problems raised in the above background technology.

[0004] To achieve the above-mentioned object, the present invention provides the following technical solutions: a device cloud management system based on RAG technology, comprising a natural language processing module, a knowledge base management module, a machine learning module, a multi-user collaboration module and a system architecture, wherein the natural language processing module parses the user's natural language query and converts it into a structured query; The knowledge base management module is responsible for maintaining and managing the knowledge base related to the device; The machine learning module uses historical query data to continuously train and optimize the question-answering model; The multi-user collaboration module supports multiple users to use the system simultaneously, realizing knowledge sharing and updating; The described system architecture supports user authentication and document-level access management, and leverages the latest Large Language Models (LLMs) for intelligent document retrieval, providing answers supported by citations and source links.

[0005] Preferably, the natural language processing (NLP) module is a key part of the machine learning system for understanding and generating natural language. It uses word embedding technology to convert text into machine-understandable vector representations and captures complex language patterns through pre-trained language models such as BERT or GPT. The module can perform tasks such as word sense disambiguation, semantic role labeling, sentiment analysis, and supports applications such as machine translation, question-answering systems, and text summarization. In addition, the NLP module can also identify user intent through contextual understanding, thereby providing more accurate information retrieval and interactive experience.

[0006] Preferably, the knowledge base module is responsible for storing and managing structured and unstructured data, integrating information from multiple sources, such as text files, databases, and Internet resources, using ontologies and semantic web technologies to organize knowledge, ensuring data consistency and scalability, supporting efficient data retrieval, accelerating the query process through indexing and metadata annotation, and providing version control and access permission management to protect the security and integrity of knowledge assets. In addition, it can work in conjunction with the natural language processing module to support complex queries and intelligent question-and-answer functions.

[0007] Preferably, the machine learning module is a power source, focusing on mining patterns and insights from massive data, using advanced algorithms such as random forests, neural networks and deep learning, and continuously optimizing model performance through training and validation steps. It can handle classification, regression, clustering and anomaly detection tasks, and supports automated feature engineering and hyperparameter tuning. In addition, it also integrates model evaluation tools such as cross-validation and performance indicator calculation to ensure the accuracy and generalization ability of the model, which is the key to achieving personalized recommendations, predictive analysis and intelligent decision-making.

[0008] Preferably, the multi-user collaboration module supports multiple users to edit the same data set simultaneously through real-time data synchronization and conflict resolution algorithms, such as operational transformation (OT) and conflict-free replicated data types (CRDT), adopts a mutex mechanism to ensure data consistency, and tracks changes through version control. In addition, it also emphasizes the intuitive design of the user interface and the communication efficiency achieved through the WebSocket protocol, while paying attention to the security and scalability of the system.

[0009] Preferably, the system architecture includes a front-end user interface, a back-end server and a database. The front-end user interface is used to receive user query requests, the back-end server processes the query and returns the answer, and the database stores the device-related knowledge base.

[0010] Compared with the prior art, the present invention has the following beneficial effects: 1. Natural language processing capabilities, which can understand users' natural language queries, including the retrieval module: The retrieval module is one of the core components of RAG technology. Its function is to retrieve information related to user queries from a pre-established knowledge base. This process involves text embedding technology, which uses pre-trained text embedding models (such as BERT or GLM) to convert queries and documents into vector representations for similarity calculations in the vector space. Vector search technologies such as FAISS, Milvus and other vector databases play a key role in this process. They can efficiently retrieve documents or paragraphs that are most similar to the query vector in the vector space. In addition, the dual-encoder model is a technology commonly used in the retrieval module. It consists of two independent encoders, one for encoding queries and the other for encoding documents. These two encoders map queries and documents to the same vector space for similarity calculations, including the generation module: The generation module is another core component of RAG technology. It is usually used 1. Generative models pre-trained on large-scale data, such as the GPT series or LLama series, which perform well in generating natural language text. The generation module merges the retrieved relevant documents with the original query to form richer contextual information as the input of the generative model. The generation process involves inputting this rich contextual information into the large language model. The model generates coherent, accurate and informative answers or texts based on the input contextual information; 2. Integrate multiple equipment management knowledge bases, including equipment manuals, maintenance guides, maintenance records, etc.; 3. Use machine learning technology to continuously optimize the accuracy of answering questions; 4. Support multi-user collaboration to achieve knowledge sharing and updating. RAG technology supports multi-user collaboration, allowing different users to access and update the knowledge base at the same time to achieve knowledge sharing and updating. This collaborative mechanism not only improves the efficiency of knowledge utilization, but also promotes communication and collaboration between team members. Through this mechanism, maintenance personnel can quickly acquire the experience and knowledge of other team members and jointly improve the quality of equipment management. DETAILED DESCRIPTION

[0011] The following will be combined with the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0012] In the description of this application, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "provided with", "connected", etc. should be understood in a broad sense. For example, "connected" can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0013] 1. Import equipment knowledge base: Import equipment-related knowledge base into the system, including equipment manuals, maintenance guides, maintenance records, etc.; 2. Configure connectors: Configure connectors between the system and enterprise tools, such as Slack, GitHub, Google Drive, etc., to achieve seamless integration of knowledge.

[0014] 1. User input question: The user inputs a device-related question through the front-end user interface, such as "How to change the filter of device A"; 2. Query processing: The natural language processing module parses the user's query and converts it into a structured query; 3. Knowledge retrieval: The system retrieves information related to the query in the knowledge base and generates answers using machine learning models; 4. Result display: The system will display the generated answers to the user through the front-end user interface, including the steps for changing the filter, the required tools and precautions, etc.

[0015] 1. Knowledge update: After replacing the filter, the maintenance personnel will update the maintenance record of the equipment and add the new knowledge to the knowledge base; 2. Knowledge sharing: Other maintenance personnel can view the equipment’s maintenance records, learn how to change filters, and provide assistance when needed.

[0016] 1. User authentication: The system supports user authentication to ensure that only authorized users can access sensitive device information; 2. File-level access management: The system provides file-level access management, allowing maintenance personnel to access different knowledge base files based on their permissions.

[0017] 1. Machine learning model training: The system uses historical query data to continuously train and optimize the question-answering model to improve the accuracy of answers; 2. User feedback collection: The system collects user feedback on answers to further optimize the question-answering model.

[0018] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A device cloud management system based on RAG technology, including a natural language processing module, a knowledge base management module, a machine learning module, a multi-user collaboration module and a system architecture, characterized in that: The natural language processing module parses the user's natural language query and converts it into a structured query; The knowledge base management module is responsible for maintaining and managing the knowledge base related to the device; The machine learning module uses historical query data to continuously train and optimize the question-answering model; The multi-user collaboration module supports multiple users to use the system simultaneously, realizing knowledge sharing and updating; The described system architecture supports user authentication and document-level access management, and leverages state-of-the-art large-scale language models for intelligent document retrieval, providing answers supported by citations and source links.

2. According to claim 1, a device cloud management system based on RAG technology is characterized in that: The natural language processing module is a key part of the machine learning system for understanding and generating natural language. It uses word embedding technology to convert text into machine-understandable vector representations and captures complex language patterns through pre-trained language models such as BERT or GPT.

3. According to claim 1, a device cloud management system based on RAG technology is characterized in that: The knowledge base module is responsible for storing and managing structured and unstructured data, integrating information from multiple sources such as text files, databases, and Internet resources, using ontologies and semantic web technologies to organize knowledge, ensuring data consistency and scalability, supporting efficient data retrieval, accelerating the query process through indexing and metadata annotation, and providing version control and access permission management to protect the security and integrity of knowledge assets. In addition, it can work in conjunction with the natural language processing module to support complex queries and intelligent question-and-answer functions.

4. The device cloud management system based on RAG technology according to claim 1, characterized in that: The machine learning module is the powerhouse, focusing on mining patterns and insights from massive data. It uses advanced algorithms such as random forests, neural networks, and deep learning to continuously optimize model performance through training and validation steps. It can handle classification, regression, clustering, and anomaly detection tasks, and supports automated feature engineering and hyperparameter tuning. In addition, it also integrates model evaluation tools such as cross-validation and performance indicator calculation to ensure the accuracy and generalization ability of the model, which is the key to achieving personalized recommendations, predictive analysis, and intelligent decision-making.

5. According to claim 1, a device cloud management system based on RAG technology is characterized in that: The multi-user collaboration module supports multiple users to edit the same data set simultaneously through real-time data synchronization and conflict resolution algorithms, such as operation conversion and conflict-independent replication of data types, adopts a mutex mechanism to ensure data consistency, and tracks changes through version control. In addition, it also emphasizes the intuitive design of the user interface and the communication efficiency achieved through the WebSocket protocol, while paying attention to the security and scalability of the system.

6. The device cloud management system based on RAG technology according to claim 1, characterized in that: The system architecture includes a front-end user interface, a back-end server and a database. The front-end user interface is used to receive user query requests, the back-end server processes the query and returns the answer, and the database stores the device-related knowledge base.