Dynamic knowledge updating method and system based on industry large model

Abstract

The invention discloses a dynamic knowledge updating method and system based on an industry large model, and belongs to the technical field of natural language processing and machine learning, and the method comprises the steps: building a comprehensive knowledge base through systematic data collection and analysis, and precisely extracting key knowledge points through a natural language processing technology; through a dynamic and flexible knowledge updating system, industry dynamics and market trends are monitored in real time, and new knowledge is automatically identified and integrated by using a pre-trained industry large model; a knowledge fusion algorithm is adopted to intelligently identify and integrate association points between new and old knowledge, and conflict detection and multi-level verification are carried out; optimizing an industry large model by using the latest knowledge base data, including retraining and performance evaluation, and managing the updated model through a version control system; integrating and deploying the system; and continuous monitoring and maintenance are realized. According to the invention, when the user uses the industry large model, the latest industry knowledge information can be quickly and accurately obtained, so that the use experience of the user is greatly improved.

Description

Technical Field

[0001] The present invention relates to the technical fields of natural language processing and machine learning, and specifically to a dynamic knowledge update method and system based on an industry large model. Background Art

[0002] In the broad and in-depth technical field of natural language processing (NLP), the dynamic knowledge update technology occupies an important position and is one of the key sub-fields driving the progress of NLP applications and performance optimization. Dynamic knowledge update generally refers to the process of real-time collecting and integrating the latest industry knowledge based on an industry large model to adapt to the changing dataset and task requirements. This method makes full use of the powerful generalization ability of the pre-trained industry large model, and at the same time captures the latest industry knowledge and trends through dynamic updates, thereby significantly improving the performance of the model in specific application scenarios. Although the technology is constantly developing, there are also related technical methods, such as an industry large model update method based on a static knowledge base. However, this method still needs to be strengthened in terms of the timeliness and comprehensiveness of knowledge. It may lag in dealing with newly emerging industry knowledge and data, resulting in the answers generated by the model not being able to reflect the latest information and requirements in a timely manner, especially in rapidly developing industry fields. Summary of the Invention

[0003] The technical task of the present invention is to address the above deficiencies and provide a dynamic knowledge update method and system based on an industry large model, enabling users to quickly and accurately obtain the latest industry knowledge information when using the industry large model, thereby significantly improving their usage experience.

[0004] The technical solution adopted by the present invention to solve its technical problems is as follows:

[0005] A dynamic knowledge update method based on an industry large model, the implementation of this method includes:

[0006] (1) Knowledge base initialization: Through systematic data collection and analysis, a comprehensive knowledge base is established, including a wide range of data sources such as academic literature, industry reports, databases, and user feedback, and key knowledge points are accurately extracted using advanced natural language processing (NLP) technology;

[0007] (2) Dynamic knowledge update mechanism: Through a dynamic and flexible knowledge update system, monitor industry dynamics and market trends in real time, and automatically identify and integrate new knowledge using the pre-trained industry large model;

[0008] (3) Knowledge fusion and verification: Adopt a knowledge fusion algorithm to intelligently identify and integrate the correlation points between new and old knowledge, and perform conflict detection and multi-level verification to ensure the consistency and accuracy of the knowledge base;

[0009] (4) Model Update and Optimization: Optimize the industry large model using the latest knowledge base data, including retraining and performance evaluation, and manage the updated model through a version control system;

[0010] (5) System Integration and Deployment: Integrate the dynamic knowledge update system into intelligent question - answering systems, recommendation systems, and prediction systems, and develop standardized API interfaces to ensure reliable information access and prediction services;

[0011] (6) Continuous Monitoring and Maintenance: Keep the system running steadily through continuous monitoring and system maintenance, regularly optimize the update strategy, improve the automation and intelligence level of the system, and provide users with a continuously optimized information experience.

[0012] By combining knowledge extraction, fusion technology, and machine learning technology, the knowledge base in the industry large model is updated in real - time, improving the model's ability to understand and apply the latest industry data, thereby enhancing the accuracy and efficiency of the model in practical applications to meet the dynamic needs of customers and achieve the continuous optimization and delivery of the industry large model.

[0013] Furthermore, for the initialization of the knowledge base, key knowledge points are organized into a structured form, including clearly defined entities, explicit relationships, and operation rules.

[0014] Furthermore, to efficiently manage and query this information, it is stored in the form of a graph database (such as Neo4j) to ensure that each knowledge point can be quickly accessed and effectively utilized.

[0015] Furthermore, for the dynamic knowledge update mechanism, the pre - trained industry large model automatically identifies and extracts key points in new information, and at the same time compares and integrates them with the existing knowledge framework.

[0016] Furthermore, for the knowledge fusion and verification,

[0017] The knowledge fusion algorithm is based on graph theory and semantic web theory, and can intelligently identify the connection points between new knowledge and the existing knowledge base. By establishing semantic links and concept mappings, it realizes the smooth integration of knowledge and ensures the internal consistency of the knowledge structure;

[0018] The conflict detection mechanism can automatically identify knowledge conflict points, including inconsistent concept definitions, overlapping data, or contradictory statements, etc. Once a conflict is found, the priority setting and version control process are started, and according to the preset rules and expert judgment, it decides to retain, merge, or correct the information to ensure the harmony and unity of the knowledge base;

[0019] The multi-level verification, the verification of the validity and practicality of new knowledge is a multi-faceted process. The multi-level verification includes: First, relying on the professional review of an expert team to carefully examine new knowledge to ensure it conforms to industry standards and scientific principles; Second, using automated methods, including logical reasoning engines and pattern matching technologies, to deeply analyze new knowledge and examine its logical self-consistency and compatibility with existing knowledge; These methods can not only verify the correctness of information but also reveal the potential value of knowledge, providing users with more reliable and practical information services.

[0020] In summary, the knowledge fusion and verification work, by adopting advanced fusion algorithms, conflict detection mechanisms, and multi-level verification means, constructs an efficient and rigorous knowledge update process, ensuring the integrity, accuracy, and practicality of the knowledge base, and providing users with a trustworthy knowledge resource platform.

[0021] Furthermore, for the model update and optimization, the latest knowledge base data is used to generate a new training set for comprehensively retraining the model; This process includes optimizing the parameters and hyperparameters of the model and covers multi-dimensional analysis of model performance evaluation, including indicators such as accuracy, recall rate, and F1 score;

[0022] By using an advanced version control system, it is possible to effectively manage the updated model and knowledge base, ensuring that the system can continuously track and respond to industry changes and technological advancements.

[0023] Furthermore, a user feedback mechanism is established to continuously collect and integrate user feedback opinions to continuously improve the functions and performance of the system;

[0024] The continuous monitoring and maintenance involve real-time monitoring of the knowledge base update frequency, model prediction accuracy, and user feedback, and timely response and handling of system anomalies; Regular system maintenance includes vulnerability repair, performance optimization, and system upgrades to keep the system in sync with technological advancements and market demands.

[0025] Based on the monitoring data and user feedback, continuously optimize the knowledge update strategy, enhance the automation and intelligence level of the system, and at the same time provide personalized knowledge recommendations and prediction services to ensure that the system maintains a competitive advantage in a constantly changing environment.

[0026] The present invention also claims protection for a dynamic knowledge update system based on an industry large model, including:

[0027] A knowledge base initialization module, used to establish a comprehensive knowledge base through systematic data collection and analysis, including a wide range of data sources such as academic literature, industry reports, databases, and user feedback, and precisely extract key knowledge points using advanced natural language processing (NLP) technologies;

[0028] A dynamic knowledge update module, which is used to build a dynamic and flexible knowledge update system, monitor industry dynamics and market trends in real time, and automatically identify and integrate new knowledge using a pre-trained large industry model;

[0029] A knowledge fusion and verification module, which is used to intelligently identify and integrate the correlation points between new and old knowledge using a knowledge fusion algorithm, perform conflict detection and multi-level verification to ensure the consistency and accuracy of the knowledge base;

[0030] A model update and optimization module, which is used to optimize the large industry model using the latest knowledge base data, including re-training and performance evaluation, and manage the updated model through a version control system;

[0031] A system integration and deployment module, which is used to integrate the dynamic knowledge update system into an intelligent question-answering system, a recommendation system, and a prediction system, develop standardized API interfaces, and ensure reliable information access and prediction services;

[0032] A continuous monitoring and maintenance module, which is used to keep the system running steadily through continuous monitoring and system maintenance, regularly optimize the update strategy, improve the automation and intelligence level of the system, and provide users with a continuously optimized information experience;

[0033] This system realizes dynamic knowledge update based on the large industry model through the above methods.

[0034] The present invention also claims a device for implementing dynamic knowledge update based on a large industry model, including: at least one memory and at least one processor;

[0035] The at least one memory is used to store machine-readable programs;

[0036] The at least one processor is used to call the machine-readable program to implement the above method.

[0037] The present invention also claims a computer-readable medium, on which computer instructions are stored, and when the computer instructions are executed by a processor, the above method is implemented.

[0038] Compared with the prior art, the dynamic knowledge update method and system based on a large industry model of the present invention have the following beneficial effects:

[0039] 1. Improve knowledge update efficiency: The present invention uses a pre-trained large industry model and real-time data monitoring technology, greatly reducing the need for manual operations and significantly improving the speed and efficiency of dynamically updating the knowledge base according to multi-source data.

[0040] 2. Ensure the quality of knowledge update: Through advanced knowledge fusion algorithms and conflict detection mechanisms, the present invention can ensure the seamless integration of new knowledge and verify the correctness and effectiveness of new knowledge through expert review or automatic verification means, thereby improving the accuracy and reliability of knowledge base update.

[0041] 3. Optimize the cost-effectiveness of model performance: The present invention significantly reduces the dependence on manual intervention through automated knowledge extraction and model retraining processes, effectively reducing the costs and time overheads for updating the model and knowledge base, thereby optimizing the cost-effectiveness of the overall system.

[0042] 4. Strengthen the real-time monitoring ability: The system of the present invention has a real-time monitoring module that can continuously monitor changes in industry data and literature. Once important new information is detected, the system immediately triggers the knowledge update process to ensure that the knowledge base and model can promptly reflect the latest industry trends, improving the response speed and real-time performance of the system.

[0043] 5. Enhance the adaptability and flexibility of the model: After adopting the method of the present invention, the industry large model can more quickly and accurately adapt to different data formats and changing industry needs, significantly enhancing the adaptability and flexibility of the model and improving its universality and practicality in diverse application scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 is a flowchart showing the dynamic knowledge update method based on the industry large model provided by the embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0045] The following further describes the present invention with reference to the drawings and specific embodiments.

[0046] The embodiment of the present invention provides a dynamic knowledge update method based on the industry large model, and the implementation of this method includes:

[0047] 1. Knowledge base initialization: Through systematic data collection and analysis, a comprehensive knowledge base is established, including extensive data sources such as academic literature, industry reports, databases, and user feedback, and key knowledge points are accurately extracted using advanced natural language processing (NLP) technologies.

[0048] 2. Dynamic knowledge update mechanism: Through a dynamic and flexible knowledge update system, industry dynamics and market trends are monitored in real time, and a pre-trained industry large model is used to automatically identify and integrate new knowledge.

[0049] 3. Knowledge fusion and verification: Knowledge fusion algorithms are used to intelligently identify and integrate the connection points between new and old knowledge, and conflict detection and multi-level verification are performed to ensure the consistency and accuracy of the knowledge base.

[0050] 4. Model Update and Optimization: Optimize the industry large model using the latest knowledge base data, including retraining and performance evaluation, and manage the updated model through a version control system.

[0051] 5. System Integration and Deployment: Integrate the dynamic knowledge update system into intelligent question - answering systems, recommendation systems, and prediction systems, and develop standardized API interfaces to ensure reliable information access and prediction services.

[0052] 6. Continuous Monitoring and Maintenance: Keep the system running stably through continuous monitoring and system maintenance, regularly optimize the update strategy, improve the automation and intelligence level of the system, and provide users with a continuously optimized information experience.

[0053] According to the dynamic update mechanism of the industry large model, it can monitor and respond to new industry trends and research progress in real - time, maintaining the forefront and accuracy of the knowledge base content. Through the automated knowledge fusion and verification process, ensure the smooth integration and internal consistency between new and old knowledge, avoiding information redundancy and contradictions. Through model update and optimization, continuously improve the performance and applicability of the industry large model in specific fields, maintaining the system's competitive advantage and user satisfaction. Combine with an intelligent continuous monitoring and maintenance mechanism to achieve real - time monitoring of the system operation status and exception handling, ensuring system stability and reliability. Through the development of standardized API interfaces, enable external applications to safely and efficiently access the updated knowledge base and model prediction results of the system. Utilize advanced natural language processing technologies to achieve precise information extraction and semantic analysis of diverse data sources, thereby effectively managing and organizing the large amount of information in the knowledge base. Adopt a pre - trained industry large model during the knowledge update process to automatically identify the key points of new knowledge and compare and integrate them with the existing knowledge framework, ensuring that the unique value of new knowledge is fully explored. Through the combination of machine learning and artificial intelligence, achieve intelligent management of the knowledge update process, improve the update efficiency and response speed, and ensure that the system can quickly adapt to new industry challenges and opportunities. Combine with efficient management tools such as graph databases to ensure that each knowledge point can be quickly accessed and effectively utilized, enhancing the system's response speed and accuracy to user queries and demands.

[0054] With the rapid advancement of artificial intelligence technology in recent years, industry large models, as outstanding representatives in this field, have occupied a crucial position in both academic research and industrial practice. The reason these models stand out is that they have made breakthrough progress in the deep representation learning of natural language and the generalization ability across contexts, enabling them to demonstrate unprecedented efficiency and flexibility when facing a series of natural language processing tasks ranging from simple to complex. Whether it is understanding text context, generating coherent responses, or performing high-order functions such as language reasoning and translation, industry large models have shown great potential and practical effects. However, as the application scope of these advanced models continues to expand in the real world, a challenge that cannot be ignored has gradually emerged, that is, the limitations they show when dealing with the latest industry knowledge and specific domain data. Specifically, although these models perform well in general language understanding and generation, when the questions raised by users involve the latest developments or in-depth professional knowledge in a certain field, the answers of the models often lack sufficient depth, accuracy, or pertinence. This limitation partly stems from the extensiveness and diversity of the model training data. Although they cover a wide range of knowledge categories, they may lack timeliness and professional details, making it difficult to fully meet the needs of timely understanding and in-depth exploration of the latest developments in a certain industry field. To solve this problem, the method of reinforcement learning (RL) is innovatively adopted. This method can optimize the decision-making process of the model through continuous exploration and feedback, making it more accurate and effective when facing problems in professional fields. The specific implementation steps can be summarized as follows:

[0055] (1) Prepare the dataset: Obtain data in the professional field from reliable sources according to the task requirements to ensure the diversity and representativeness of the data. Then, adapt the data to the input of the industry large model through preprocessing steps such as cleaning, annotation, and format unification. Reasonably split the data into training set, validation set, and test set to evaluate the model performance and generalization ability. Handle data imbalance, implement feature engineering, and ensure that the data is stored in an efficient format.

[0056] (2) Design the reward mechanism: In reinforcement learning, the reward mechanism is crucial. Set a reasonable reward function according to the problems in the specific field to ensure that the model receives positive rewards when correctly answering questions in the professional field and is punished when answering incorrectly. The reward mechanism should be able to effectively guide the model to optimize towards the expected goal.

[0057] (3) Model Training: In the reinforcement learning framework, the model learns by continuously interacting with the environment. Using the configured optimizer and loss function, the model parameters are gradually adjusted. By feeding data into the model in batches, the model parameters are updated by backpropagation based on the reward signal. Meanwhile, the learning progress is monitored with the validation set, and the learning strategy is adjusted timely to prevent overfitting until the model performance meets the standards on the target task. During training, key metrics and parameter settings are recorded in detail to support performance tracking and tuning decisions. Finally, the optimized model weights are saved to lay the foundation for subsequent testing and deployment.

[0058] (4) Model Validation and Adjustment: Continuously monitor the generalization ability of large models in the field to prevent overfitting and continuously improve the model's performance. This process includes regularly evaluating the prediction accuracy of the model on the validation set, using performance metrics to track changes during training; flexibly adjusting the learning rate as needed to address performance plateaus, and applying early stopping strategies to avoid unnecessary training; actively exploring and optimizing the model's hyperparameter settings to ensure that the model version with the best performance on the validation set is saved.

[0059] (5) Model Testing and Evaluation: Input the reserved separate test dataset into the model for prediction, and use multiple evaluation metrics such as confusion matrix, accuracy, recall, and F1-score to comprehensively evaluate the classification or regression performance of the model and its errors; examine the model's performance under different metrics to identify potential bias or variance issues; ensure that the model can not only be optimized on the validation set but also maintain stable performance on a wider range of data. This serves as the last quality control step before model deployment, providing strong support for the reliability and applicability of the model.

[0060] In the process of model optimization, this reinforcement learning method enables large industry models to better understand and handle problems in professional fields through continuous adjustment and feedback. However, it cannot handle the large amount of training data and computational resource requirements, and the reward mechanism design is too complex. To solve these problems, the dynamic knowledge update method based on large industry models proposed in this method can improve the model's understanding and application ability of the latest industry data by real-time updating the knowledge base and the model, further enhancing its performance in professional fields.

[0061] The specific implementation process of this method is as follows:

[0062] 1. Knowledge Base Initialization.

[0063] In the first stage of the design, through systematic data collection and analysis, efforts are made to establish a comprehensive knowledge base. This process covers a wide range of data sources, including academic literature, industry reports, databases, and user feedback. Using advanced natural language processing (NLP) techniques, valuable information and key knowledge points can be accurately extracted from these diverse data. These knowledge points are carefully organized into a structured form, including well-defined entities, clear relationships, and operating rules. To efficiently manage and query this information, a graph database (such as Neo4j) is selected to ensure that every knowledge point can be quickly accessed and effectively utilized.

[0064] 2. Dynamic knowledge update mechanism.

[0065] In the second stage, by carefully building a dynamic and flexible knowledge update system, the content of the knowledge base is ensured to always stay at the forefront of the industry while guaranteeing the accuracy of the information. The core competitiveness of this mechanism lies in its real-time monitoring function, which uses advanced data analysis techniques and algorithms to continuously collect, screen, and analyze the latest industry trends, research breakthroughs, and market trends from a wide range of data sources. The pre-trained large industry model plays a key role in this process. It can automatically identify and extract the key points in the new information, and at the same time compare and integrate them with the existing knowledge framework. This process is not just a simple data overlay, but through a set of carefully designed rules and algorithms, ensuring that the unique value of the new knowledge is fully explored, while avoiding the accumulation of duplicate and redundant information, thus maintaining the refinement and efficiency of the knowledge base.

[0066] In addition, an automated update process, combining the power of machine learning with the insights of artificial intelligence, ensures the timeliness and relevance of knowledge updates. This mechanism not only accelerates the iteration speed of the knowledge base but also significantly enhances its adaptability to the changing environment, enabling the system to quickly respond to new challenges and opportunities, providing users with the freshest and most authoritative information resources. Through this intelligent update mode, a self-evolving and continuously learning knowledge ecosystem is constructed, providing strong support for decision-makers and creating a richer and more valuable information experience for users.

[0067] 3. Knowledge fusion and verification.

[0068] During the knowledge integration and verification process in the third stage, the focus shifts to ensuring the seamless integration and comprehensive verification of new knowledge to maintain the overall coherence of the knowledge base and the accuracy of the data. Advanced knowledge fusion algorithms are adopted, which are based on graph theory and semantic web theory. These algorithms can intelligently identify the connection points between new knowledge and the existing knowledge base. By establishing semantic links and concept mappings, the smooth integration of knowledge is achieved, ensuring the internal consistency of the knowledge structure. Conflict detection is a key aspect of this stage, which involves identifying and handling potential contradictions between new and old knowledge. By designing a sophisticated conflict detection mechanism, knowledge conflict points can be automatically identified, such as inconsistent concept definitions, overlapping data, or contradictory statements. Once a conflict is detected, the system will initiate a priority setting and version control process. According to the preset rules and expert judgments, decisions will be made to retain, merge, or correct the information to ensure the harmony and unity of the knowledge base.

[0069] The verification of the validity and practicality of new knowledge is a multi-faceted process: Firstly, relying on the professional review of an expert team, based on their profound domain knowledge and experience, a detailed examination of the new knowledge is carried out to ensure its compliance with industry standards and scientific principles. Secondly, automated methods such as logical reasoning engines and pattern matching techniques are used to conduct in-depth analysis of the new knowledge, testing its logical self-consistency and compatibility with existing knowledge. These methods can not only verify the correctness of the information but also reveal the potential value of the knowledge, providing users with more reliable and practical information services. In summary, the knowledge integration and verification work in the third stage, through the adoption of advanced fusion algorithms, conflict detection mechanisms, and multi-level verification means, constructs an efficient and rigorous knowledge update process, ensuring the integrity, accuracy, and practicality of the knowledge base, and providing users with a trustworthy knowledge resource platform.

[0070] 4. Model update and optimization.

[0071] The fourth stage focuses on the continuous optimization and update of the industry large model. The latest knowledge base data is used to generate a new training set for a comprehensive retraining of the model. This process not only includes optimizing the model's parameters and hyperparameters but also covers multi-dimensional analysis of model performance evaluation, such as indicators like accuracy, recall, and F1 score. By using an advanced version control system, the updated model and knowledge base can be effectively managed, ensuring that the system can continuously track and respond to industry changes and technological advancements.

[0072] 5. System integration and deployment

[0073] In the fifth stage, the dynamic knowledge update system is organically integrated into existing industrial applications, such as intelligent question - answering systems, recommendation systems, and prediction systems. In terms of design, emphasis is placed on building an efficient system architecture to ensure that each module can cooperate and communicate efficiently. By developing standardized API interfaces, external systems can access the latest industrial knowledge and model prediction results safely and reliably. And by establishing a sound user feedback mechanism, user feedback opinions are continuously collected and integrated to continuously improve the system's functions and performance.

[0074] 6. Continuous monitoring and maintenance.

[0075] In the final stage, through continuous monitoring and system maintenance, the system is ensured to always maintain a stable operating state. The update frequency of the knowledge base, the accuracy of model prediction, and user feedback are monitored in real - time, and system anomalies are responded to and processed in a timely manner. Regular system maintenance includes vulnerability repair, performance optimization, and system upgrades to keep the system in sync with technological progress and market demands. Based on the monitoring data and user feedback, the knowledge update strategy is continuously optimized to improve the automation and intelligence level of the system. At the same time, personalized knowledge recommendations and prediction services are provided to ensure that the system maintains a competitive advantage in a changing environment.

[0076] This method ensures the real - time update of the knowledge base and the model through steps such as real - time data monitoring, automatic knowledge extraction and fusion, conflict detection and resolution, and model retraining. By integrating an automated knowledge extraction algorithm, the latest industrial knowledge can be extracted from multiple data sources, fused with the existing knowledge base, and verified to ensure the consistency and accuracy of the knowledge. Through regular model retraining and parameter tuning, the model performance is optimized so that it can quickly adapt to industry changes. This method can achieve the continuous knowledge update of the industrial large - model, meet the knowledge needs of different fields, enhance the industrial large - model's understanding and application ability of the latest industrial data, and ensure the efficient application of the model in various projects.

[0077] An embodiment of the present invention also provides a dynamic knowledge update system based on an industrial large - model. This system realizes dynamic knowledge update based on the industrial large - model through the dynamic knowledge update method described in the above - mentioned embodiment.

[0078] The system includes:

[0079] 1. Knowledge base initialization module:

[0080] Build a comprehensive knowledge base through systematic data collection and analysis. This process covers a wide range of data sources, including academic literature, industry reports, databases, and user feedback. Utilize advanced natural language processing (NLP) techniques to accurately extract valuable information and key knowledge points from this diverse data. These knowledge points are carefully organized into a structured form, including well-defined entities, clear relationships, and operating rules. To efficiently manage and query this information, use a graph database (such as Neo4j) to ensure that every knowledge point can be quickly accessed and effectively utilized.

[0081] 2. Dynamic Knowledge Update Module:

[0082] Create a dynamic and flexible knowledge update system to ensure that the content of the knowledge base always remains at the forefront of the industry while ensuring the accuracy of the information. The core competitiveness of this mechanism lies in its real-time monitoring function, which uses advanced data analysis techniques and algorithms to continuously collect, screen, and analyze the latest industry trends, research breakthroughs, and market trends from a wide range of data sources. The pre-trained industry large model plays a key role in this process. It can automatically identify and extract the key points in the new information and at the same time compare and integrate them with the existing knowledge framework. This process is not just a simple data overlay, but through a set of carefully designed rules and algorithms, it ensures that the unique value of the new knowledge is fully explored while avoiding the accumulation of duplicate and redundant information, thus maintaining the refinement and efficiency of the knowledge base.

[0083] Adopt an automated update process that combines the power of machine learning with the insights of artificial intelligence to ensure the timeliness and relevance of knowledge updates. This mechanism not only accelerates the iteration speed of the knowledge base but also significantly enhances its adaptability to the changing environment, enabling the system to quickly respond to new challenges and opportunities and provide users with the freshest and most authoritative information resources. Through this intelligent update mode, a self-evolving and continuously learning knowledge ecosystem is constructed, providing strong support for decision-makers and creating a richer and more valuable information experience for users.

[0084] 3. Knowledge Fusion and Verification Module:

[0085] Use knowledge fusion algorithms to intelligently identify and integrate the connection points between new and old knowledge, conduct conflict detection and multi-level verification to ensure the consistency and accuracy of the knowledge base.

[0086] Adopt cutting-edge knowledge fusion algorithms, which are based on graph theory and semantic web theory, and can intelligently identify the connection points between new knowledge and the existing knowledge base. By establishing semantic links and concept mappings, the smooth integration of knowledge is achieved, ensuring the internal consistency of the knowledge structure. Conflict detection is a key link in this stage, which involves identifying and handling potential contradictions between new and old knowledge. By designing a complex conflict detection mechanism, knowledge conflict points can be automatically identified, such as inconsistent concept definitions, overlapping data, or contradictory statements, etc. Once a conflict is detected, the system will initiate a priority setting and version control process, and according to the preset rules and expert judgments, decide to retain, merge, or correct the information to ensure the harmony and unity of the knowledge base.

[0087] The verification of the validity and practicality of new knowledge is a multi-faceted process. First, rely on the professional review of the expert team. Based on in-depth domain knowledge and experience, conduct a detailed inspection of new knowledge to ensure that it conforms to industry standards and scientific principles. Second, use automated methods, such as logical reasoning engines and pattern matching techniques, to conduct in-depth analysis of new knowledge and test its logical self-consistency and compatibility with existing knowledge. These methods can not only verify the correctness of information but also reveal the potential value of knowledge, providing users with more reliable and practical information services.

[0088] The knowledge fusion and verification module constructs an efficient and rigorous knowledge update process by adopting advanced fusion algorithms, conflict detection mechanisms, and multi-level verification means, ensuring the integrity, accuracy, and practicality of the knowledge base, and providing users with a trustworthy knowledge resource platform.

[0089] 4. Model Update and Optimization Module:

[0090] The model update and optimization module focuses on the continuous optimization and update of the industry large model. Use the latest knowledge base data to generate a new training set for a comprehensive retraining of the model. This process not only includes optimizing the parameters and hyperparameters of the model but also covers multi-dimensional analysis of model performance evaluation, such as indicators like accuracy, recall rate, and F1 score, etc. By using an advanced version control system, the updated model and knowledge base can be effectively managed to ensure that the system can continuously track and respond to industry changes and technological progress.

[0091] 5. System Integration and Deployment Module:

[0092] Organically integrate the dynamic knowledge update system into existing industry applications, such as intelligent question - answering systems, recommendation systems, and prediction systems. In system design, focus on building an efficient system architecture to ensure that each module can cooperate and communicate efficiently. By developing standardized API interfaces, external systems can access the latest industry knowledge and model prediction results safely and reliably. Also, by establishing a sound user feedback mechanism, continuously collect and integrate user feedback to continuously improve the system's functions and performance.

[0093] 6. Continuous monitoring and maintenance module:

[0094] Through continuous monitoring and system maintenance, ensure that the system always maintains a stable operating state. Real - time monitor the knowledge base update frequency, model prediction accuracy, and user feedback, and respond to and handle system anomalies in a timely manner. Regular system maintenance includes vulnerability repair, performance optimization, and system upgrade to keep the system in sync with technological progress and market demands. Based on the monitoring data and user feedback, continuously optimize the knowledge update strategy, improve the automation and intelligence level of the system, and at the same time provide personalized knowledge recommendations and prediction services to ensure that the system maintains a competitive advantage in a changing environment.

[0095] This system can solve the limitations of traditional industry large models in terms of knowledge timeliness and comprehensiveness. Through real - time monitoring and automatic knowledge extraction, quickly integrate key information in new data into the existing knowledge base, and update and optimize the model accordingly to ensure that the industry large model can timely reflect the latest industry trends and knowledge. The answers output are not only closely related to user needs but also more accurate and error - free in content. By designing a dynamic knowledge update mechanism and integrating automatic knowledge extraction algorithms and knowledge fusion technologies, the system can continuously collect and process the latest information from multiple data sources and solve the conflict and fusion problems of old and new knowledge. Facing the performance bottleneck of the industry large model, utilize real - time data and automatic processing technologies to effectively improve the model's ability to analyze specific domain information and give accurate answers. It not only significantly improves the timeliness of the knowledge base and the adaptability of the model but also ensures the response quality and efficiency of the industry large model when processing industry - specific data, achieving the goal of dynamic knowledge update and continuous model optimization. Through this innovative knowledge update strategy, the industry large model can be given stronger adaptability. Whether facing rapidly changing industry information or diverse application scenarios, it can quickly respond and provide accurate answers. In this way, users will enjoy a more personalized interaction experience that is closer to their individual needs and has a higher satisfaction level, and the personalization level of model services and the overall user satisfaction are significantly improved.

[0096] An embodiment of the present invention also provides a device for implementing dynamic knowledge update based on an industry large model, including: at least one memory and at least one processor;

[0097] The at least one memory for storing a machine-readable program;

[0098] The at least one processor for invoking the machine-readable program to implement the dynamic knowledge update method based on the industry large model described in the above embodiments.

[0099] An embodiment of the present invention also provides a computer-readable medium, on which computer instructions are stored. When the computer instructions are executed by a processor, the processor executes the dynamic knowledge update method based on the industry large model described in the above embodiments. Specifically, a system or device equipped with a storage medium can be provided, on which software program code for implementing the functions of any one of the above embodiments is stored, and the computer (or CPU or MPU) of the system or device reads and executes the program code stored in the storage medium.

[0100] In this case, the program code read from the storage medium itself can implement the functions of any one of the above embodiments. Therefore, the program code and the storage medium storing the program code constitute a part of the present invention.

[0101] Embodiments of the storage medium for providing program code include floppy disks, hard disks, magneto-optical disks, optical disks (such as CD-ROM, CD-R, CD-RW, DVD-ROM, DVD-RAM, DVD-RW, DVD+RW), magnetic tapes, non-volatile memory cards, and ROMs. Optionally, the program code can be downloaded from a server computer via a communication network.

[0102] In addition, it should be clear that not only can the functions of any one of the above embodiments be implemented by executing the program code read by the computer, but also by the operating system and the like operating on the computer based on the instructions of the program code to complete part or all of the actual operations.

[0103] In addition, it can be understood that the program code read from the storage medium is written into the memory provided in the expansion board inserted into the computer or the memory provided in the expansion unit connected to the computer, and then based on the instructions of the program code, the CPU and the like installed on the expansion board or the expansion unit execute part and all of the actual operations, so as to implement the functions of any one of the above embodiments.

[0104] The present invention has been shown and described in detail above through the accompanying drawings and preferred embodiments. However, the present invention is not limited to these disclosed embodiments. Based on the above multiple embodiments, those skilled in the art can know that the code review means in the above different embodiments can be combined to obtain more embodiments of the present invention, and these embodiments are also within the protection scope of the present invention.

Claims

1. A dynamic knowledge updating method based on industry big model, characterized in that: The implementation of this method includes: (1) Knowledge base initialization: Through systematic data collection and analysis, a comprehensive knowledge base is established, including a wide range of data sources such as academic literature, industry reports, databases, and user feedback, and natural language processing technology is used to accurately extract key knowledge points; (2) Dynamic knowledge update mechanism: Through a dynamic and flexible knowledge update system, industry dynamics and market trends are monitored in real time, and new knowledge is automatically identified and integrated using pre-trained industry big models; (3) Knowledge fusion and verification: Use knowledge fusion algorithms to intelligently identify and integrate the connections between new and old knowledge, perform conflict detection and multi-level verification to ensure the consistency and accuracy of the knowledge base; (4) Model update and optimization: Use the latest knowledge base data to optimize industry large models, including retraining and performance evaluation, and manage updated models through a version control system; (5) System integration and deployment: Integrate the dynamic knowledge update system into the intelligent question-answering system, recommendation system, and prediction system, and develop standardized API interfaces to ensure reliable information access and prediction services; (6) Continuous monitoring and maintenance: Maintain the stable operation of the system through continuous monitoring and system maintenance, regularly optimize and update strategies, improve the automation and intelligence level of the system, and provide users with a continuously optimized information experience.

2. According to claim 1, a dynamic knowledge updating method based on industry big model is characterized in that: The knowledge base initialization organizes key knowledge points into a structured form, including clearly defined entities, clear relationships and operation rules.

3. A dynamic knowledge updating method based on industry big model according to claim 2, characterized in that: Using a graph database ensures that every knowledge point can be quickly accessed and effectively utilized.

4. The method for dynamic knowledge updating based on industry big model according to claim 1 is characterized in that: The dynamic knowledge updating mechanism and the pre-trained industry big model automatically identify and extract key points in new information, and compare and integrate it with the existing knowledge framework.

5. The method for dynamic knowledge updating based on industry big model according to claim 1 is characterized in that: The knowledge fusion and verification, The knowledge fusion algorithm is based on graph theory and semantic web theory, and can intelligently identify the connection points between new knowledge and existing knowledge bases. By establishing semantic links and concept mapping, it can achieve smooth integration of knowledge and ensure the internal consistency of the knowledge structure. Conflict detection mechanism that can automatically identify knowledge conflict points, including inconsistent concept definitions, overlapping data, or contradictory statements; Once a conflict is found, the priority setting and version control process is initiated. Based on preset rules and expert judgment, it is decided to retain, merge or modify the information to ensure the harmony and unity of the knowledge base. The multi-level verification includes: first, relying on the professional review of the expert team to carefully check the new knowledge to ensure that it complies with industry standards and scientific principles; Secondly, use automated methods, including logical reasoning engines and pattern matching techniques, to conduct in-depth analysis of new knowledge to test its logical consistency and compatibility with existing knowledge.

6. The method for dynamic knowledge updating based on industry big model according to claim 1 is characterized in that: The model update and optimization uses the latest knowledge base data to generate a new training set for comprehensive retraining of the model. This process includes optimizing the parameters and hyperparameters of the model and covers multi-dimensional analysis of model performance evaluation, including accuracy, recall, and F1 score indicators. By using a version control system to manage updated models and knowledge bases, we ensure that the system can continuously track and respond to industry changes and technological advances.

7. The method for dynamic knowledge updating based on industry big model according to claim 1 is characterized in that: Establish a user feedback mechanism to continuously collect and integrate user feedback to continuously improve the system's functions and performance; The continuous monitoring and maintenance includes real-time monitoring of the knowledge base update frequency, model prediction accuracy and user feedback, and timely response and handling of system abnormalities; regular system maintenance includes vulnerability repairs, performance optimization and system upgrades to keep the system synchronized with technological advances and market demands.

8. A dynamic knowledge updating system based on industry big model, characterized in that: include: The knowledge base initialization module is used to build a comprehensive knowledge base through systematic data collection and analysis, including a wide range of data sources such as academic literature, industry reports, databases, and user feedback, and accurately extract key knowledge points using natural language processing technology; Dynamic knowledge update module, used to create a dynamic and flexible knowledge update system, monitor industry dynamics and market trends in real time, and automatically identify and integrate new knowledge using pre-trained industry big models; The knowledge fusion and verification module is used to intelligently identify and integrate the connection points between new and old knowledge using knowledge fusion algorithms, perform conflict detection and multi-level verification to ensure the consistency and accuracy of the knowledge base; Model update and optimization module, which is used to optimize large industry models using the latest knowledge base data, including retraining and performance evaluation, and manage updated models through a version control system; System integration and deployment module, which is used to integrate the dynamic knowledge update system into the intelligent question-answering system, recommendation system and prediction system, develop standardized API interfaces, and ensure reliable information access and prediction services; Continuous monitoring and maintenance module, which is used to maintain the robust operation of the system through continuous monitoring and system maintenance, regularly optimize and update strategies, improve the automation and intelligence level of the system, and provide users with a continuously optimized information experience; The system realizes dynamic knowledge updating based on the industry big model through any method described in claims 1 to 7.

9. A dynamic knowledge update implementation device based on an industry big model, characterized in that: include: at least one memory and at least one processor; The at least one memory is used to store a machine-readable program; The at least one processor is used to call the machine-readable program to implement the method described in any one of claims 1 to 7.

10. A computer-readable medium, characterized in that The computer readable medium stores computer instructions, and when the computer instructions are executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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