A power network security intelligent consulting system based on a generative large model

Abstract

The application discloses a power network security intelligent consultation system based on a generative large model, and relates to the technical field of network security consultation systems, comprising: a consultation question and answer function module, including a technical consultation unit, an attack and defense research and judgment unit and a protection treatment suggestion unit; a generative large model discovers statistical relationships and patterns in text data based on deep learning methods and large-scale corpus training, and learns to generate new data samples to establish a network security knowledge base during the training process, which provides data support for the technical consultation unit, the attack and defense research and judgment unit and the protection treatment suggestion unit. The entire question and answer consultation process of the system relies on advanced deep learning models and algorithms in the generative large model, ensuring that the system can automatically and accurately answer various network security field questions raised by users and provide timely and professional consultation services.

Description

Technical Field

[0001] This invention relates to the field of cybersecurity consulting system technology, specifically to an intelligent consulting system for power network security based on a generative large model. Background Technology

[0002] With the deepening of the digital transformation of power systems, cybersecurity threats are characterized by diversified attack methods, more complex attack chains, and more precise attack targets. Traditional rule-based cybersecurity protection methods mainly rely on preset security policies and signature databases, which suffer from problems such as detection lag and high false positive rates when facing new types of attacks. Although large-scale model-based cybersecurity solutions have improved security analysis capabilities by introducing deep learning technology, achieving accurate understanding and rapid response to security knowledge in the complex industrial environment of power networks remains a challenge.

[0003] Currently, power network security faces the following technical challenges: First, the efficiency of security event correlation analysis is low; existing systems struggle to effectively capture potential correlations between cross-domain security events, leading to insufficient accuracy in threat tracing and impact assessment. Second, the level of intelligence in security analysis is low, lacking the ability to deeply mine massive amounts of security logs and historical data, thus failing to promptly identify potential security risks. Third, the application efficiency of security knowledge is low; existing systems fail to provide precise security consulting services based on the specific needs of different user roles, impacting the overall effectiveness of security operations. Therefore, building a system that can improve the efficiency of network security monitoring and analysis and achieve intelligent security management has become a critical issue that urgently needs to be addressed. Summary of the Invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the present invention provides an intelligent consulting system for power network security based on a generative large model, which can solve the problems mentioned in the background art.

[0006] To address the aforementioned technical problems, the present invention provides the following technical solution: a power network security intelligent consultation system based on a generative large model, comprising: a consultation and Q&A function module, wherein the consultation and Q&A function module includes a technical consultation unit, an attack and defense analysis unit, and a protection and handling suggestion unit;

[0007] The generative large model is trained using deep learning methods and a large-scale corpus to discover statistical relationships and patterns in text data. During training, the model learns to generate new data samples to build a cybersecurity knowledge base. This cybersecurity knowledge base provides data support for the technical consultation unit, the attack and defense analysis unit, and the protection and handling suggestion unit. The consultation and question-and-answer function module is based on the cybersecurity knowledge base in the generative large model to understand and answer power network security questions raised by users.

[0008] As a preferred embodiment of the intelligent consulting system for power network security based on a generative large model as described in this invention, the generative large model includes:

[0009] Deep neural network architecture units are used to capture semantic relationships in input text through a self-attention mechanism;

[0010] Pre-training units are used for pre-training on corpora in the field of power network security;

[0011] The fine-tuning unit is used to adjust the parameters of the pre-trained model for specific security consulting tasks;

[0012] Vector representation unit, used to convert security-related technical terms into representations in a high-dimensional vector space.

[0013] As a preferred embodiment of the intelligent consulting system for power network security based on generative large models described in this invention, the dialogue processing of the generative large model includes:

[0014] Retrieve the history of conversations in the current session;

[0015] Extract key information and context from the dialogue;

[0016] Perform semantic understanding of the current input based on contextual information;

[0017] If a contextual relationship is detected, historical information is incorporated into the response generation process; otherwise, a response is generated solely based on the current input.

[0018] As a preferred embodiment of the intelligent consulting system for power network security based on generative large models described in this invention, the response generation process of the generative large model includes:

[0019] Recognize security-related technical terms in the input questions;

[0020] Select the appropriate knowledge base retrieval strategy based on the question type;

[0021] Extract relevant information from the knowledge base;

[0022] If the completeness of the extracted information exceeds a preset threshold, a response is generated directly; otherwise, a response is generated after supplementing the necessary information through knowledge reasoning.

[0023] As a preferred embodiment of the intelligent consulting system for power network security based on generative large models described in this invention, the consulting and Q&A function module covers users including security decision-makers, security operations personnel, and general personnel, and the processing of different user roles includes:

[0024] If identified as a security decision-maker, the security situation dashboard data is obtained and updated at preset time intervals. At the same time, a risk warning index is generated based on statistical analysis of historical security events.

[0025] If identified as a security operations personnel, real-time security logs are received, and event tracing analysis is performed based on preset association rules;

[0026] If identified as a regular user, their historical access records are retrieved, and personalized security knowledge content is recommended based on the user profile.

[0027] As a preferred embodiment of the intelligent consulting system for power network security based on generative large models described in this invention, the data processing process for security decision-makers includes:

[0028] If an abnormal risk warning is received, relevant security event information will be automatically aggregated and an emergency response process will be triggered.

[0029] If the security posture score is detected to be lower than the first preset threshold, a security hardening strategy will be initiated and a special protection recommendation report will be generated.

[0030] If the frequency of a certain type of security incident exceeds the second preset threshold, the protection strategy weight for that type of incident will be automatically adjusted, and the detection rules will be optimized.

[0031] As a preferred embodiment of the intelligent consulting system for power network security based on generative large models described in this invention, the event analysis process of the security operations personnel includes:

[0032] If a security event with unknown features is detected, its feature vector is compared with the similarity of the event to historical events in the knowledge base.

[0033] If the similarity exceeds the third preset threshold, the event is classified into the most similar known event type, and the feature library is updated.

[0034] If the similarity is lower than the fourth preset threshold, a new event type is created, and a deep analysis process is triggered to expand the feature library through multi-dimensional feature extraction.

[0035] As a preferred embodiment of the intelligent consulting system for power network security based on generative large models described in this invention, the knowledge push process for ordinary personnel includes:

[0036] If a user queries a specific security knowledge point, the system analyzes the prerequisite knowledge dependencies for that knowledge point.

[0037] If necessary prerequisite knowledge exists, learning content for that prerequisite knowledge will be pushed out first.

[0038] If a user's query frequency in a specific knowledge domain exceeds the fifth preset threshold, a deep learning path for that domain will be automatically generated.

[0039] As a preferred embodiment of the intelligent consulting system for power network security based on generative large models described in this invention, it further includes an intelligent homepage module, wherein the personalization process of the intelligent homepage module includes:

[0040] If the security knowledge points recently accessed by the user are related, a knowledge graph will be automatically constructed and displayed according to the strength of the relationship between the knowledge points;

[0041] If it is detected that a user's attention to a certain type of security incident exceeds the sixth preset threshold, the latest updates and protection recommendations for that type of incident will be pushed to the user first.

[0042] If a user's access pattern is regular within a specific time period, relevant content will be preloaded to improve response speed.

[0043] As a preferred embodiment of the intelligent consulting system for power network security based on generative large models described in this invention, it further includes an intelligent homepage module, wherein the personalization process of the intelligent homepage module includes:

[0044] If the security knowledge points recently accessed by the user are related, a knowledge graph will be automatically constructed and displayed according to the strength of the relationship between the knowledge points;

[0045] If it is detected that a user's attention to a certain type of security incident exceeds the sixth preset threshold, the latest updates and protection recommendations for that type of incident will be pushed to the user first.

[0046] If a user's access pattern is regular within a specific time period, relevant content will be preloaded to improve response speed.

[0047] The beneficial effects of this invention are as follows: This invention relies on advanced deep learning models and algorithms within a generative large-scale model to ensure the system can automatically and accurately answer various cybersecurity-related questions raised by users, providing timely and professional consulting services. This technical approach integrates multiple advanced technologies such as deep learning, knowledge reasoning, and correlation analysis, aiming to build an efficient and intelligent cybersecurity question-and-answer consulting system to meet users' needs in the cybersecurity field. Attached Figure Description

[0048] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0049] Figure 1 This is a schematic diagram of the overall structure of an intelligent consulting system for power network security based on a generative large model proposed in this invention. Detailed Implementation

[0050] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0051] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0052] Example 1, referring to Figure 1 As an embodiment of the present invention, a power network security intelligent consulting system based on a generative large model is provided.

[0053] Generative large-scale models refer to generative models based on large-scale corpora. They are typically trained and generated using methods similar to autoregressive models, enabling end-to-end generation, understanding, and reasoning of natural language. Common models include Transformer, GPT, GPT-2, and GPT-3. The intelligent consulting system for power network security is an intelligent solution designed to address information security issues in power system networks. Applying generative large-scale models to this system allows for the construction of a highly intelligent and adaptive consulting system. This system can leverage the natural language processing capabilities of generative large-scale models to intelligently analyze and diagnose security issues in power networks, while providing targeted solutions and recommendations.

[0054] Cybersecurity is of paramount importance in power systems. Because power systems involve vast amounts of sensitive data and critical information, cyberattacks or data breaches can have severe consequences. Therefore, intelligent consulting systems for power system cybersecurity, employing generative large-scale models, can provide robust protection and support for power system cybersecurity.

[0055] This invention provides an intelligent consulting system for power network security based on a generative large model, including a consulting and question-and-answer function module and a generative large model.

[0056] The consultation and Q&A module includes a technical consultation unit, an attack and defense analysis unit, and a protection and handling suggestion unit. The generative large-scale model, trained using deep learning methods and a large-scale corpus, discovers statistical relationships and patterns in text data. During training, the model learns to generate new data samples to build a cybersecurity knowledge base. This knowledge base provides data support for the technical consultation, attack and defense analysis, and protection and handling suggestion units. The consultation and Q&A module, based on the cybersecurity knowledge base within the generative large-scale model, understands and answers user-submitted questions about power network security.

[0057] This intelligent online power network security consultation system, based on a generative large model, relies on advanced deep learning models and algorithms within the generative large model to ensure that the system can automatically and accurately answer various network security questions raised by users, providing timely and professional consultation services. This technical approach integrates multiple advanced technologies such as deep learning, knowledge reasoning, and correlation analysis, aiming to build an efficient and intelligent network security question-and-answer consultation system to meet users' needs in the field of network security.

[0058] Furthermore, the generative large model includes a deep neural network architecture unit for capturing semantic relationships in the input text through a self-attention mechanism; a pre-training unit for pre-training on a corpus in the field of power network security; a fine-tuning unit for adjusting the parameters of the pre-trained model for specific security consulting tasks; and a vector representation unit for converting security domain terminology into representations in a high-dimensional vector space.

[0059] This intelligent consulting system for power network security, based on a generative large model, possesses the following functions: Intelligent identification and diagnosis: The system can automatically identify security vulnerabilities and potential threats in the power network, and conduct in-depth analysis and diagnosis of security events through the generative large model to determine the root cause and nature of the problem. Intelligent consultation and advice: Based on the analysis results, the system can provide targeted security consultation and advice to help users understand the severity and scope of impact of security issues, while providing effective solutions and measures. Intelligent early warning and prevention: Through real-time monitoring and analysis of security data in the power network, the system can promptly detect potential security risks and issue early warning signals to help users take preventive measures and avoid security incidents. Continuous learning and optimization: The generative large model has the ability to continuously learn and optimize, constantly learning new knowledge and patterns from new security data to improve the accuracy and reliability of the system.

[0060] The dialogue processing of generative large models includes: obtaining the historical dialogue records of the current session; extracting key information and context from the dialogue; performing semantic understanding of the current input based on contextual information; if a contextual relevance is detected, incorporating historical information into the response generation process; otherwise, generating a response based solely on the current input.

[0061] The response generation process of the generative large model includes: identifying security domain terminology for the input question; selecting the appropriate knowledge base retrieval strategy based on the question type; extracting relevant information from the knowledge base; if the completeness of the extracted information exceeds a preset threshold, generating the response directly; otherwise, generating the response after supplementing the necessary information through knowledge reasoning.

[0062] The consultation and Q&A module covers users including security decision-makers, security operations personnel, and general staff. The processing for different user roles includes: if identified as a security decision-maker, it retrieves security posture dashboard data and updates it at preset time intervals, while generating a risk warning index based on statistical analysis of historical security events. If identified as a security operations personnel, it receives real-time security logs and performs event tracing analysis according to preset association rules. If identified as a general staff member, it retrieves their historical access records and recommends personalized security knowledge content based on their user profile.

[0063] The data processing procedure for security decision-makers includes: if an abnormal risk warning is received, relevant security event information is automatically aggregated and an emergency response process is triggered; if the security posture score is detected to be lower than the first preset threshold, a security hardening strategy is initiated and a special protection recommendation report is generated; if the frequency of a certain type of security event is found to exceed the second preset threshold, the protection strategy weight for that type of event is automatically adjusted and the detection rules are optimized.

[0064] The event analysis process for security operations personnel includes: if a security event with unknown characteristics is detected, its feature vector is compared with the historical events in the knowledge base for similarity calculation; if the similarity exceeds the third preset threshold, the event is classified into the most similar known event type and the feature base is updated; if the similarity is below the fourth preset threshold, a new event type is created and a deep analysis process is triggered to expand the feature base through multi-dimensional feature extraction.

[0065] The knowledge delivery process for ordinary users includes analyzing the prerequisite knowledge dependencies of a user's query for a certain security knowledge point; if necessary prerequisite knowledge exists, prioritizing the delivery of learning content for that prerequisite knowledge; and automatically generating a deep learning path for that knowledge area if the user's query frequency in a specific knowledge area exceeds a fifth preset threshold.

[0066] The personalized processing of the intelligent homepage module includes: if the security knowledge points recently visited by the user are related, a knowledge graph is automatically constructed and displayed according to the strength of the relationship between the knowledge; if the user's attention to a certain type of security event exceeds the sixth preset threshold, the latest developments and protection suggestions for that type of event are pushed first; if the user's access pattern in a specific time period is regular, relevant content is preloaded to improve response speed.

[0067] The collaborative processing of the technical consultation unit, the attack and defense analysis unit, and the defense and response suggestion unit includes:

[0068] If the technical consultation unit receives a security incident alarm, it will call the attack and defense analysis unit to conduct a risk level assessment.

[0069] If the risk level assessed by the attack and defense assessment unit exceeds the first preset threshold, the protection and handling suggestion unit will be triggered to generate an emergency response plan.

[0070] If the emergency response plan involves multiple security domains, the response steps are prioritized and conflict detection is performed based on the knowledge reasoning capabilities of the generative large model.

[0071] In the above specific implementation, generative large-scale deep learning models capture complex patterns and semantic relationships in natural language, enabling a more accurate understanding of user-generated questions. Through the comprehensive application of deep neural network architecture, pre-training and fine-tuning, core algorithms and technologies, and massive amounts of parameters and data, generative large-scale models successfully capture complex patterns and semantic relationships in natural language.

[0072] Specifically: 1. Deep Neural Network Architecture: Generative large models typically employ deep neural network structures, such as Transformer and BERT. These architectures use self-attention mechanisms to capture long-range dependencies and semantic information in the input text. Each Encoder and Decoder consists of multiple identical layers stacked together, each layer containing a self-attention sublayer and a feedforward neural network sublayer. 2. Pre-training and Fine-tuning: Pre-training involves pre-training the generative large model on a large amount of text data to learn the basic structure and semantics of language. For example, BERT learned a large amount of language knowledge through pre-training tasks and broke records on multiple NLP tasks. Fine-tuning involves fine-tuning the pre-trained model for specific tasks to adapt to the needs of different scenarios. During fine-tuning, the model parameters are adjusted appropriately according to the task objectives, thereby improving performance on specific tasks. 3. Core Algorithms and Technologies: Word Embedding: Deep learning models use word embedding techniques to convert words into vectors in a high-dimensional space. These vectors can capture the semantic and contextual information of words. In this way, models can understand semantic relationships between words, such as similarity ("cat" and "dog" might be closer in vector space) and antonyms. Sequence models, such as recurrent neural networks (RNNs) and long short-term memory networks (LSTMs), can handle the sequential nature of natural language. These models can capture long-distance dependencies in sentences, thus better understanding the semantics of the entire sentence. Attention mechanisms enable models to focus on the most important parts when processing input sequences. This mechanism is particularly important in Transformer models, helping them to capture key information more effectively when processing long texts, thereby improving the accuracy of semantic understanding. Multi-task learning allows some models to improve their semantic understanding by learning multiple related tasks simultaneously. This approach enables models to learn general and specific language features from different tasks, thereby improving their generalization ability. Graph neural networks, when dealing with complex semantic relationships, can more effectively capture semantic dependencies by representing words and the relationships between them as graph structures. 4. Massive parameters and data: Generative large models typically have massive parameters, achieving deep understanding and efficient processing of language through deep learning and training on large-scale datasets. These models are able to store and process more information, thus capturing more accurately the complex patterns and features in language.

[0073] Specifically, generative large models improve the accuracy of question answers by maintaining dialogue context and employing association analysis. Based on the dialogue context, generative large models analyze and utilize historical dialogue information to better understand the user's current question, thus providing more accurate answers. Attention mechanisms are crucial tools for generative large models when processing sequential data. They allow the model to focus on different parts of the input sequence when generating the current output, especially those most relevant to the current output. In dialogue systems, this means the model can generate the current response based on specific information from historical dialogues, maintaining the coherence and consistency of the conversation. To more effectively utilize historical dialogue information, generative large models employ hierarchical structures or memory networks. The model can capture multi-layered information in the dialogue, such as the topic, sentiment, and style, and comprehensively consider these factors when generating responses. During training, generative large models learn how to extract useful information from large amounts of dialogue data and generate new dialogue samples similar to the training data. To improve the model's ability to utilize historical dialogue information, various strategies are typically employed during training, such as data augmentation, regularization, and pre-training. In addition, the performance of the model can be optimized by adjusting its parameters and structure, such as increasing the number of layers or changing the implementation of the attention mechanism.

[0074] In the above specific implementation, the users covered by the consultation and Q&A function module include security decision-makers, security operations personnel, and general personnel.

[0075] Furthermore, security decision-makers can gain an intuitive understanding of the security posture displayed on a large screen and quickly obtain key security data through simple query-based queries, assisting decision-makers in grasping the overall security situation. The cybersecurity auxiliary analysis and decision-making system based on a generative large model will provide comprehensive decision support for security decision-makers, offering the following benefits: Overall Security Overview: Through deep learning and analysis of the network environment, the system can comprehensively grasp and present the overall security status of the enterprise network. It provides real-time information on network security risk levels, the frequency of various security incidents, and their handling, helping decision-makers to keep abreast of the network security situation and facilitate timely detection and response to security risks. Security Incident Overview: The system can collect, analyze, and organize various security incidents in real time, including attack incidents, risk incidents, and security threats, and intelligently classify and sort these incidents, providing decision-makers with detailed security incident reports, helping them to fully understand and handle security incidents. Attacker Profile: Through deep learning and analysis of attacker behavior patterns, the system can clearly depict the characteristics and behavioral patterns of attackers, helping decision-makers understand the attackers' strategies and methods, and providing strong decision support for attack prevention. Security Operations Report: Based on various collected data, the system can automatically generate detailed security operations reports, including security threat analysis, security incident handling reports, and security risk warnings, providing decision-makers with comprehensive and in-depth security operations data support, helping them make accurate and effective security decisions. In summary, the network security auxiliary analysis and decision-making system based on generative large-scale models can provide decision-makers with comprehensive, accurate, and real-time network security situation reports and decision recommendations through deep learning and big data analysis, effectively improving the accuracy and efficiency of network security decision-making.

[0076] Furthermore, security operations personnel can utilize this module to conduct in-depth queries on security incidents and security logs. By inputting a query, the system can intelligently analyze and return specific results, improving the accuracy and timeliness of security analysis. This generative large-scale model-based cybersecurity auxiliary analysis and decision-making system provides security operations personnel with a series of functions, enhancing the efficiency and accuracy of cybersecurity defense.

[0077] First, the system features intelligent analysis capabilities. Through large-scale modeling technology, it can automatically identify and analyze cybersecurity incidents, including correlation analysis of risk assets, attackers, and security events, providing operations personnel with comprehensive and in-depth information to help them understand and predict cybersecurity threats. Second, the system offers auxiliary judgment functions. For submitted security samples, the system can automatically analyze them to determine whether they are attack payloads or malicious code samples, and provide relevant security recommendations. This significantly improves the work efficiency of operations personnel and enhances the accuracy of cybersecurity protection. Third, the system implements intelligent response functions. Based on the intelligent analysis and judgment results of the large-scale model, the system can automatically transmit security commands to boundary network security devices, achieving coordinated blocking or trapping of network attacks, improving the real-time nature and effectiveness of cybersecurity protection. Finally, the system provides summary analysis and intelligent Q&A functions. Operations personnel can obtain information such as overall security overview, security incident overview, and attacker overview through the system, as well as intelligent Q&A content on laws and regulations, company rules and regulations, and cybersecurity knowledge, improving the professional knowledge and operational efficiency of operations personnel. In summary, the cybersecurity assisted analysis and decision-making system based on generative large models provides security operations personnel with powerful intelligent tools, which not only improve the efficiency and accuracy of cybersecurity protection, but also support the professional development of operations personnel, and has great practical value.

[0078] Furthermore, for ordinary users, the system provides comprehensive answers to cybersecurity-related questions, including legal regulations, rules and regulations, cybersecurity knowledge, and security product knowledge. This module also offers an interactive consultation platform for ordinary users. Users can input cybersecurity-related questions, and the system will provide comprehensive answers, including legal regulations, company rules and regulations, cybersecurity knowledge, and security product knowledge, thus popularizing security knowledge and enhancing the security awareness of all employees. The cybersecurity auxiliary analysis and decision-making system based on a generative large model primarily involves functions related to querying legal regulations, company rules and regulations, cybersecurity knowledge, and security product knowledge. First, the system possesses professional cybersecurity consulting functions, providing ordinary users with access to and answers to basic cybersecurity knowledge and technical knowledge such as computer networks and cryptography. Users can acquire professional cybersecurity knowledge through the system, enhancing their cybersecurity awareness and skills. Second, the system also deeply integrates with relevant company cybersecurity management systems and technical specifications, allowing ordinary users to easily and quickly access and understand the company's cybersecurity policies and processes, improving the efficiency and effectiveness of the company's cybersecurity management. In addition, the system provides users with a rich knowledge base through the large model's knowledge base function, including content on legal regulations, company rules and regulations, and cybersecurity knowledge, meeting users' diverse knowledge needs and assisting them in learning and researching cybersecurity. In summary, the generative large-scale model-based cybersecurity auxiliary analysis and decision-making system provides ordinary users with a one-stop cybersecurity consulting and learning platform. It not only improves users' cybersecurity knowledge but also enhances the efficiency and effectiveness of companies' cybersecurity management. Through the application of the system, ordinary users can more easily access and understand cybersecurity-related knowledge and information, thereby raising their cybersecurity awareness and improving their cybersecurity protection capabilities.

[0079] Specifically, the generative large model also includes an intelligent homepage function. This function can customize the display of content based on the user's recent questions, providing personalized cybersecurity information services. The intelligent homepage module is a crucial component of the generative large model-based cybersecurity assisted analysis and decision-making system, its core function being to provide personalized cybersecurity information services. This module automatically records and analyzes the cybersecurity questions recently raised by users, intelligently inferring the user's concerns and information needs based on these questions. Then, it customizes these concerns into an intuitive page display, allowing users to easily grasp the cybersecurity dynamics and information they care about. In this way, the intelligent homepage not only improves the efficiency of users obtaining information but also enhances the system's usability, providing strong support for users' cybersecurity management efforts.

[0080] In summary, the entire question-and-answer consultation process of this intelligent power network security consultation system based on generative large models relies on advanced deep learning models and algorithms within the generative large model to ensure that the system can automatically and accurately answer various network security questions raised by users, providing timely and professional consultation services. This technical approach integrates multiple advanced technologies such as deep learning, knowledge reasoning, and correlation analysis, aiming to build an efficient and intelligent network security question-and-answer consultation system to meet users' needs in the field of network security.

[0081] In summary, this invention relies on advanced deep learning models and algorithms within a generative large-scale model to ensure the system can automatically and accurately answer various cybersecurity-related questions raised by users, providing timely and professional consulting services. This technical approach integrates multiple advanced technologies such as deep learning, knowledge reasoning, and correlation analysis, aiming to build an efficient and intelligent cybersecurity question-and-answer consulting system to meet users' needs in the cybersecurity field.

[0082] Example 2 is an embodiment of the present invention, which provides an intelligent consulting system for power network security based on a generative large model. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0083] This embodiment conducted a three-month system performance verification experiment in a power network environment with 100 nodes, selecting 10 typical security scenarios including malicious code detection, abnormal traffic identification, and intrusion detection. During the experiment, a distributed deployment architecture was adopted, with each node configured with dual Intel Xeon processors and 128GB of memory. By simulating real-world network attack scenarios, over one million security event data points were collected. The system of this invention employs a generative large-scale model based on the Transformer architecture, with the model parameters reaching the billion-level. It was pre-trained and fine-tuned on a specific corpus of the power industry, and the knowledge base contains over 500,000 professional knowledge entries in the field of power security.

[0084] The experiment employed a tiered evaluation method, focusing on the system's performance in key indicators such as security incident detection rate, analysis response time, and false alarm rate. Comparative analysis revealed that the system, supported by a deep learning model, can effectively identify complex attack patterns, demonstrating significant advantages, particularly in handling zero-day vulnerabilities and unknown threats. Through continuous learning and knowledge accumulation, the system's detection accuracy steadily improved over time, reaching a high precision of 98.5% in the later stages of the experiment. Furthermore, leveraging the parallel computing capabilities of the generative large model, the system's average response time was reduced to below 200ms, an improvement of approximately 65% ​​compared to traditional solutions, providing strong support for the timely detection and handling of security threats.

[0085] Table 1 Comparison of Security System Performance Data

[0086]

[0087] In-depth analysis of the experimental data reveals that this invention significantly outperforms existing technologies in several key performance indicators. From a time perspective, the system performance shows a continuous optimization trend, with the detection rate increasing from 95.8% initially to 98.5%, and the false alarm rate decreasing from 1.8% to 0.9%, demonstrating the system's self-learning capability. From an efficiency perspective, the average response time of this invention is only 180ms, a 69% improvement over the 580ms of traditional rule-matching systems, and the concurrent processing capacity reaches 5200 times / second, 4.3 times that of traditional systems. From an accuracy perspective, the system's knowledge base coverage reaches 95.8%, a 20 percentage point improvement over traditional systems, thanks to the powerful knowledge acquisition and reasoning capabilities of the generative large model. From a user experience perspective, the system achieved a high satisfaction score of 9.6, a 33.3% improvement over traditional systems, fully demonstrating the superiority of this invention in practical applications. These data fully demonstrate the significant technical advantages and application value of this invention in the field of power network security protection.

[0088] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A power network security intelligent consulting system based on a generative large model, characterized in that, The method comprises the following steps: The consultation and question-answering function module comprises a technical consultation unit, an attack and defense research and judgment unit, and a protection treatment suggestion unit; The generative large model discovers statistical relationships and patterns in text data based on deep learning methods and large-scale corpus training. During the training process, the model learns to generate new data samples to establish a network security knowledge base. The network security knowledge base provides data support for the technical consultation unit, the attack and defense research and judgment unit, and the protection treatment suggestion unit. The consultation and question-answering function module is based on the network security knowledge base in the generative large model to understand and answer the power network security questions raised by the user; The users covered by the consultation and question-answering function module include security decision makers, security operators, and ordinary personnel. The processing of different user roles includes: If it is identified as a security decision maker, the security situation large screen data is obtained and updated at a preset time interval, and a risk warning index is generated based on statistical analysis of historical security events; If it is identified as a security operator, real-time security logs are received, and event traceability analysis is performed according to the preset correlation rules; If it is identified as an ordinary personnel, the historical access records of the user are obtained, and personalized security knowledge content is recommended based on user portrait; The data processing process of the security decision maker includes: If an abnormal risk warning is received, the relevant security event information is automatically aggregated, and an emergency response process is triggered; If the security situation score is detected to be lower than a first preset threshold, a security reinforcement strategy is started, and a special protection suggestion report is generated; If the frequency of a certain type of security event is found to exceed a second preset threshold, the protection strategy weight of the event is automatically adjusted, and the detection rule is optimized; The event analysis process of the security operator includes: If a security event with unknown characteristics is detected, the feature vector of the event is calculated for similarity with the historical events in the knowledge base; If the similarity exceeds a third preset threshold, the event is classified into the most similar known event type, and the feature library is updated; If the similarity is lower than a fourth preset threshold, a new event type is created, and a deep analysis process is triggered to expand the feature library through multi-dimensional feature extraction.

2. The power network security intelligent consulting system based on a generative large model of claim 1, wherein: The generative large model comprises: A deep neural network architecture unit for capturing semantic relationships in input text through self-attention mechanisms; A pre-training unit for pre-training on a corpus in the field of power network security; A fine-tuning unit for adjusting the parameters of the pre-trained model for specific security consultation tasks; A vector representation unit for converting security field professional vocabulary into representations in a high-dimensional vector space.

3. The power network security intelligent consulting system based on a generative large model of claim 2, wherein: The dialogue processing process of the generative large model includes: Obtaining the historical dialogue records of the current session; Extracting key information and contextual context from the dialogue; Performing semantic understanding on the current input based on the contextual information; If it is detected that the context has relevance, the historical information is included in the response generation process; otherwise, only the current input is used to generate a response.

4. The power network security intelligent consulting system based on a generative large model of claim 3, wherein: The response generation process of the generative large model includes: Performing security field professional term recognition on the input question; Selecting a corresponding knowledge base retrieval strategy based on the question type; Extracting relevant information from the knowledge base; If the completeness of the extracted information exceeds the preset threshold, a response is directly generated; otherwise, the necessary information is supplemented through knowledge reasoning, and then a response is generated.

5. The power network security intelligent consulting system based on a generative large model of claim 4, wherein: The knowledge pushing process of the general personnel includes: If a user queries a certain security knowledge point, the prerequisite knowledge dependency relationship of the knowledge point is analyzed; If there is necessary prerequisite knowledge, the learning content of the prerequisite knowledge is preferentially pushed; If the query frequency of a user in a specific knowledge field exceeds the fifth preset threshold, a deep learning path of the field is automatically generated.

6. The power network security intelligent consulting system based on a generative large model of claim 5, wherein: Further comprising an intelligent homepage module, the individualization processing process of the intelligent homepage module includes: If the security knowledge points recently accessed by a user have relevance, a knowledge graph is automatically constructed, and the knowledge graph is displayed according to the correlation strength between the knowledge; If it is detected that the attention degree of a user to a certain type of security event exceeds the sixth preset threshold, the latest dynamics and protection suggestions of the type of event are preferentially pushed; If the access mode of a user in a specific time period presents regularity, related content is preloaded to improve the response speed.

7. The power network security intelligent consulting system based on a generative large model of claim 6, wherein: The collaborative processing process of the technical consultation unit, the attack and defense research and judgment unit, and the protection treatment suggestion unit includes: If the technical consultation unit receives a security event alarm, the attack and defense research and judgment unit is called to perform risk level evaluation; If the risk level evaluated by the attack and defense research and judgment unit exceeds the first preset threshold, the protection treatment suggestion unit is triggered to generate an emergency treatment scheme; If the emergency treatment scheme involves multiple security domains, based on the knowledge reasoning capability of the generative large model, the treatment steps are prioritized and conflict detected.

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