System and method for networked knowledge question and answer and security detection based on large model
Through the networked knowledge acquisition and security detection module based on large models, the problems of lagging knowledge updates and weak security are solved, and a real-time and secure knowledge question-and-answer system is realized, which enhances user trust.
Patent Information
- Application Number
- CN202510528277.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-04-25
- Publication Date
- 2025-08-01
AI Technical Summary
The existing knowledge Q&A system has lagged knowledge updates, weak security and insufficient user trust, making it difficult for users to meet users' needs for real-time information and effectively resist network security threats.
The networked knowledge acquisition module and security detection module based on large models are adopted to realize dynamic knowledge updates and multi-level security detection, including networked knowledge acquisition, multi-dimensional knowledge graph construction, and multi-level security detection mechanisms. Combined with deep learning and transfer learning technology, semantic understanding and real-time security monitoring are carried out.
It realizes the real-time and security of the knowledge Q&A system, can provide the latest information and effectively resist network attacks, and improve user trust.
Smart Images
Figure CN120409692A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of artificial intelligence, and specifically provides an internet-connected knowledge Q&A and security detection system and method based on large models. Background Art
[0002] With the rapid development of artificial intelligence technology, knowledge Q&A systems based on large models have gradually become important tools for information acquisition and knowledge services. By integrating massive amounts of data and advanced natural language processing technologies, such systems can provide users with efficient and convenient knowledge query services. However, existing knowledge Q&A systems still face many challenges in practical applications, mainly in the following aspects:
[0003] Lag in knowledge update and understanding deviation: Traditional knowledge Q&A systems usually rely on static knowledge bases and are difficult to obtain or update the latest internet-connected knowledge in real time, resulting in insufficient timeliness of the knowledge provided by the system and inability to meet users' needs for real-time information. Especially when dealing with complex contexts or polysemous words, inaccurate or irrelevant answers are likely to be generated.
[0004] Security hazard problems: Internet-connected knowledge Q&A systems operate in an open network environment and are prone to security threats such as malicious attacks and data leakage. Existing systems are generally weak in network security protection and lack a comprehensive detection mechanism for network traffic, data content, and user input, making it difficult to effectively resist potential security risks.
[0005] User trust issues: Users have doubts about the accuracy, security, and privacy protection capabilities of the system, which limits the widespread application of the system. Summary of the Invention
[0006] The purpose of the present invention is to provide an internet-connected knowledge Q&A and security detection system and method based on large models, to achieve the intelligence, security, and efficiency of the knowledge Q&A system, and to provide users with more reliable knowledge services, so as to solve the problems raised in the above background art.
[0007] To achieve the above purpose, the present invention provides the following technical solution: An internet-connected knowledge Q&A and security detection system based on large models, including an internet-connected knowledge acquisition module, a large model knowledge Q&A module, and a security detection module;
[0008] The internet-connected knowledge acquisition module is used to obtain the latest knowledge data from authoritative internet knowledge sources and establish a dynamically updated multi-source knowledge base;
[0009] The large model knowledge Q&A module uses large model technology to perform semantic understanding, knowledge extraction, and intelligent integration on the acquired knowledge, and constructs a multi-dimensional knowledge graph to achieve in-depth understanding of knowledge;
[0010] The security detection module integrates multi-level and all-round security detection mechanisms, covering the data content level, network transmission level, and data storage level.
[0011] Preferably, the specific implementation method of the networked knowledge acquisition module includes: establishing high-speed and stable connections with multiple authoritative knowledge sources, and assigning different weight coefficients according to the API interfaces and authorities provided by each knowledge source; calling the API interfaces of each knowledge source to collect raw data, and conducting in-depth preprocessing on the collected raw data, using text cleaning algorithms to remove noise information, unifying the data format with the help of character encoding conversion technology, and using lexical analysis tools to convert unstructured text data into structured forms.
[0012] Preferably, the specific implementation method of the large model knowledge Q&A module includes: introducing an advanced large model base and performing precise parameter configuration; organizing the knowledge data obtained from multiple source knowledge bases and preprocessed into a training data set, using transfer learning technology, and promoting the model to accurately understand and apply new knowledge through fine-tuning training, and flexibly adjusting hyperparameters during the training process to optimize the model's understanding and processing ability of professional domain knowledge; when the user inputs a question, using natural language processing technology to conduct in-depth semantic parsing on the question, extracting key entities, relationships, and semantic intentions, and inputting the parsed and security-detected question information into the large model after fusion training to generate accurate, detailed, and user-demand-compliant answers.
[0013] Preferably, the specific implementation method of the security detection module at the network transmission level includes: deploying a high-performance intrusion detection system, based on real-time monitoring of network traffic data, using pattern matching and anomaly detection technical means to identify abnormal behaviors in network traffic; when abnormal behaviors are detected, it is determined that there may be an attack behavior and an alarm is issued in a timely manner, and at the same time, a network connection blocking measure of blocking the IP address is taken to prevent the attack from spreading further.
[0014] Preferably, the specific implementation method of the security detection module at the data storage and user interaction levels includes: establishing a role-based access control mechanism, defining different user roles in the system and assigning corresponding permissions to each role, verifying the user roles and permissions when the user accesses data, and regularly reviewing and updating the user roles and permissions; during the process of user-system interaction, conducting real-time security detection on user inputs, using regular expression matching and blacklist filtering technologies to detect malicious character sequences and special instructions, and once malicious inputs are detected, refusing to execute relevant operations and issuing a security prompt to the user; for the obtained data content, constructing a deep learning-based text classification model, training the model with a large number of malicious text samples and normal text samples, and detecting through this text classification model before the data enters the knowledge Q&A system. If malicious information is detected, the data is immediately isolated.
[0015] A method for an online knowledge Q&A and security detection system based on a large model, comprising the following steps:
[0016] Establish multi-source knowledge source connections: Build high-speed and stable connections with numerous authoritative knowledge sources such as Baidu Encyclopedia, Bing, Moji Weather, Bocha Search, mainstream news websites, and some authoritative knowledge bases in professional fields; According to the characteristics of each knowledge source, based on the API interfaces they provide respectively, assign corresponding weight coefficients to different websites according to their authority; Specifically, it includes calling the Baidu Encyclopedia knowledge retrieval API to obtain encyclopedia entry information, using the Moji Weather weather data API to achieve real-time collection of meteorological information, relying on the Bocha Search search API to accurately locate information, and scraping key data such as news content and release time for mainstream news websites with open news data API;
[0017] Deep preprocessing of raw data: Carry out deep preprocessing on the collected raw data, use text cleaning algorithms to remove noise information in the data, such as advertising codes, irrelevant HTML tags, and garbled characters; Use character encoding conversion technology to unify the character encoding format of the data; Use lexical analysis tools in the field of natural language processing to perform word segmentation, part-of-speech tagging, and named entity recognition on the text, and convert unstructured text data into a structured form.
[0018] Preferably, it further includes the following steps:
[0019] Selection and deployment of large models: Introduce advanced large model bases and perform precise parameter configuration according to the specific requirements of the selected models;
[0020] Knowledge preprocessing and fusion training: Organize the knowledge data obtained from multi-source knowledge bases and preprocessed into a training data set according to a specific format; Use transfer learning technology to transfer the language understanding ability accumulated by the large model during pre-training on a large-scale general corpus to the learning of specific fields and the latest knowledge of this system; Through fine-tuning training, prompt the model to accurately understand and apply new knowledge, and flexibly adjust hyperparameters such as the learning rate and the number of iterations of the model during the training process;
[0021] Semantic understanding and knowledge extraction: When the user inputs a question, use syntactic analysis and semantic role labeling methods in natural language processing technology to conduct in-depth semantic analysis on the question, and accurately extract key entities, relationships, and semantic intentions in the question; Input the question information after parsing and passing through the security detection system into the large model that has undergone fusion training. Based on its powerful semantic understanding and reasoning ability, the model performs efficient retrieval and in-depth reasoning by integrating the information obtained online and the knowledge reserve of the large model itself, and generates accurate, detailed, and user-demand-compliant answers.
[0022] Preferably, the following steps are further included: at the network traffic level, deploy high-performance intrusion detection systems such as Snort and Suricata; the system monitors network traffic data in real time and identifies abnormal behaviors in network traffic through pattern matching and anomaly detection techniques; when it detects that a certain IP address initiates a large number of port scan requests within a short period of time, or when there is a sudden sharp increase in traffic that does not conform to the normal business model, the system immediately determines that there may be an attack behavior, issues an alarm in a timely manner, and takes corresponding blocking measures, such as blocking the network connection of the IP address to prevent the attack from spreading further.
[0023] Preferably, the following steps are further included: establish a role-based access control mechanism, define different user roles in the system, and assign corresponding permissions to each role; when a user accesses data, the system first verifies the user's role and permissions, and only users with the corresponding permissions can access specific data to prevent data leakage and illegal access; regularly review and update user roles and permissions to ensure the effectiveness of access control.
[0024] Preferably, the following steps are further included:
[0025] Real-time security detection of user input: During the interaction between the user and the system, perform real-time security detection on user input; use regular expression matching and blacklist filtering techniques to detect whether the user input contains malicious character sequences and special instructions; once malicious input is detected, the system refuses to execute the relevant operation and issues a security prompt to the user, informing them that the input is risky;
[0026] Deep security detection of data content: For the obtained data content, build a text classification model based on deep learning; use a large number of malicious text samples and normal text samples to train the model so that it can accurately identify malicious content in the data; before the data enters the knowledge Q&A system, first detect it through this text classification model; if malicious information is detected in the data, immediately isolate the data and prevent it from entering the system to avoid harm to the system and users.
[0027] Compared with the prior art, the beneficial effects of the present invention are:
[0028] The networked knowledge Q&A and security detection system and method based on large models proposed by the present invention can obtain the latest knowledge in real time through networking and conduct integrated training with large models. The system can provide users with accurate answers regarding the latest events, technological progress, etc., meeting users' needs for timely and accurate information. It comprehensively ensures the safe and stable operation of the system. The multi-level security detection mechanism protects the system from multiple dimensions such as network traffic, data content, and user input, effectively resisting various malicious attacks, preventing data leakage, protecting user privacy, improving the reliability and availability of the system, enhancing users' trust in the system, and having good promotion value. Brief Description of the Drawings
[0029] Figure 1 It is a block diagram of the system of the present invention. Detailed Embodiments
[0030] In order to clearly and completely describe the objectives, technical solutions of the present invention and make the advantages more clearly understood, the following further details the embodiments of the present invention with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are some but not all embodiments of the present invention, and are only used to explain the embodiments of the present invention, not to limit the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of the present invention.
[0031] Embodiment 1, the present invention provides a technical solution: a networked knowledge Q&A and security detection system based on large models, which adopts a modular architecture design and has good scalability and maintainability. The networked knowledge acquisition module obtains the latest knowledge data from authoritative knowledge sources on the Internet (such as well-known academic databases, mainstream news media websites, and authoritative knowledge bases in professional fields) through API interfaces and establishes a dynamically updated multi-source knowledge base. The large model knowledge Q&A module uses advanced large model technologies to conduct semantic understanding, knowledge extraction, and intelligent integration of the acquired knowledge, constructs a multi-dimensional knowledge graph, and realizes in-depth understanding of knowledge. In terms of security protection, the system integrates a multi-level and all-round security detection mechanism: (1) At the data content level, it adopts deep learning-based sensitive information recognition technology and semantic analysis algorithms to conduct dual security detection on user input and system output; (2) At the network transmission level, by real-time monitoring of network traffic and combining machine learning abnormal behavior detection algorithms, it effectively identifies and prevents various network threats such as DDoS attacks and SQL injections; (3) At the data storage level, it adopts AES encrypted storage and role-based access control mechanisms to ensure user data security. The details of the present invention are as follows:
[0032] The First Step: The Networked Knowledge Acquisition Module
[0033] Build high-speed and stable connections with numerous authoritative knowledge sources such as Baidu Encyclopedia, Bing, Moji Weather, Bocha Search, mainstream news websites, and authoritative knowledge bases in some professional fields. According to the characteristics of each knowledge source, based on the API interfaces they provide respectively, and assign corresponding weight coefficients to different websites according to their authority. Specifically, for Baidu Encyclopedia, by calling its knowledge retrieval API, various encyclopedia entry information can be accurately obtained; for Moji Weather, with the help of its weather data API, real-time collection of meteorological information can be achieved to ensure the timeliness and accuracy of the data; relying on the search API of Bocha Search, relevant information can be accurately located in a vast amount of network information; and for mainstream news websites, if they open news data APIs, these will be used as interfaces to capture key data such as news content and release time, providing rich and real-time news for the system.
[0034] Conduct in-depth preprocessing on the collected raw data. Use text cleaning algorithms to remove noise information in the data, such as advertising codes, irrelevant HTML tags, garbled characters, etc. With the help of character encoding conversion technology, unify the character encoding format of the data to ensure the consistency and readability of the data. Adopt lexical analysis tools in the field of natural language processing (such as NLTK, Stanford CoreNLP) to perform word segmentation, part-of-speech tagging, and named entity recognition on the text, converting unstructured text data into a structured form so that the subsequent large model can better understand and process it.
[0035] Step 2: Large model knowledge Q&A module
[0036] Large model selection and deployment: The system introduces an advanced large model base and performs precise parameter configuration according to the specific requirements of the selected model to provide strong guarantee for the efficient operation of the model.
[0037] Knowledge preprocessing and fusion training: Organize the knowledge data obtained from multi-source knowledge bases and preprocessed into a training data set in a specific format. Use transfer learning technology to fully absorb the language understanding ability accumulated by the large model during pre-training on a large-scale general corpus and transfer it to the learning of specific fields and the latest knowledge of this system. Through fine-tuning training, enable the model to accurately understand and apply this new knowledge. During the training process, flexibly adjust hyperparameters such as the learning rate and the number of iterations of the model to optimize the model's understanding and processing ability of professional field knowledge.
[0038] Semantic Understanding and Knowledge Extraction: When the user enters a question, the system first uses methods such as syntactic analysis and semantic role labeling in natural language processing technology to conduct in-depth semantic analysis of the question, accurately extracting the key entities, relationships, and semantic intentions in the question. Subsequently, the question information after parsing and passing through the security detection system is input into the large model trained through integration. Based on its powerful semantic understanding and reasoning capabilities, the model conducts efficient retrieval and in-depth reasoning by integrating the information obtained from the network and the knowledge reserve of the large model itself, generating accurate, detailed, and user-demand-compliant answers.
[0039] Step 3: Security Detection Module
[0040] At the network traffic level, deploy high-performance intrusion detection systems (IDS), such as Snort, Suricata, etc. These systems are based on real-time monitoring of network traffic data and use technical means such as pattern matching and anomaly detection to identify abnormal behaviors in network traffic. For example, when it detects that a certain IP address initiates a large number of port scanning requests within a short period of time, or when there is a sudden sharp increase in traffic that does not conform to the normal business model, the system immediately determines that there may be an attack behavior, issues an alarm in a timely manner, and takes corresponding blocking measures, such as blocking the network connection of that IP address, to prevent the attack from spreading further.
[0041] Establish a role-based access control (RBAC) mechanism, define different user roles in the system, and assign corresponding permissions to each role. When a user accesses data, the system first verifies the user's role and permissions. Only users with the corresponding permissions can access specific data, preventing data leakage and illegal access. Regularly review and update user roles and permissions to ensure the effectiveness of access control.
[0042] During the interaction between the user and the system, conduct real-time security detection on the user input. Use technologies such as regular expression matching and blacklist filtering to detect whether the user input contains malicious character sequences, special instructions, etc. Once malicious input is detected, the system refuses to execute the relevant operation and issues a security prompt to the user, informing them that there is a risk in the input. For the obtained data content, construct a text classification model based on deep learning. Use a large number of malicious text samples (such as texts containing virus code, sensitive political remarks, fraud information, etc.) and normal text samples to train the model so that it can accurately identify malicious content in the data. Before the data enters the knowledge Q&A system, first detect it through this text classification model. If malicious information is detected in the data, immediately isolate the data and prevent it from entering the system to avoid harm to the system and users.
[0043] Embodiment 2, based on Embodiment 1, proposes a method for the networked knowledge Q&A and security detection system based on a large model according to claim 5, which is characterized by the following steps: Establishing multi-source knowledge source connections: Building high-speed and stable connections with numerous authoritative knowledge sources such as Baidu Encyclopedia, Bing, Moji Weather, Bocha Search, mainstream news websites, and some authoritative knowledge bases in professional fields; According to the characteristics of each knowledge source, and based on the API interfaces they provide respectively, assign corresponding weight coefficients to different websites according to their authority; Specifically, it includes calling the Baidu Encyclopedia knowledge retrieval API to obtain encyclopedia entry information, using the Moji Weather weather data API to achieve real-time collection of meteorological information, relying on the Bocha Search search API to accurately locate information, and scraping key data such as news content and release time for mainstream news websites with open news data APIs; Deep preprocessing of raw data: Conducting deep preprocessing on the collected raw data, using text cleaning algorithms to remove noise information in the data, such as advertising codes, irrelevant HTML tags, and garbled characters; Using character encoding conversion technology to unify the character encoding format of the data; Using lexical analysis tools in the field of natural language processing to perform word segmentation, part-of-speech tagging, and named entity recognition on the text, and converting unstructured text data into a structured form.
[0044] It also includes the following steps: Large model selection and deployment: Introducing an advanced large model base and making precise parameter configurations according to the specific requirements of the selected model; Knowledge preprocessing and fusion training: Organizing the knowledge data obtained from multi-source knowledge bases and preprocessed into a training data set in a specific format; Using transfer learning technology to transfer the language understanding ability accumulated during the pre-training of the large model on a large-scale general corpus to the learning of specific fields and the latest knowledge of this system; Through fine-tuning training, prompting the model to accurately understand and apply new knowledge, and flexibly adjusting hyperparameters such as the learning rate and the number of iterations of the model during the training process; Semantic understanding and knowledge extraction: When the user inputs a question, use syntactic analysis and semantic role annotation methods in natural language processing technology to conduct in-depth semantic analysis of the question, and accurately extract key entities, relationships, and semantic intentions in the question; Input the question information after parsing and passing through the security detection system into the large model that has undergone fusion training. Based on its powerful semantic understanding and reasoning ability, the model conducts efficient retrieval and in-depth reasoning by integrating the information obtained after networking and the knowledge reserve of the large model itself, and generates an accurate, detailed, and user-demand-compliant answer.
[0045] It also includes the following steps: At the network traffic level, deploy high-performance intrusion detection systems such as Snort and Suricata; the system, based on real-time monitoring of network traffic data, uses pattern matching and anomaly detection techniques to identify abnormal behaviors in network traffic; when it detects that a certain IP address initiates a large number of port scan requests within a short period of time, or when there is a sudden sharp increase in traffic and it does not conform to the normal business model, the system immediately determines that there may be an attack behavior, issues an alarm in a timely manner, and at the same time takes corresponding blocking measures, such as blocking the network connection of the IP address to prevent the attack from spreading further.
[0046] It also includes the following steps: Establish a role-based access control mechanism, define different user roles in the system, and assign corresponding permissions to each role; when a user accesses data, the system first verifies the user's role and permissions, and only users with the corresponding permissions can access specific data to prevent data leakage and illegal access; regularly review and update user roles and permissions to ensure the effectiveness of access control.
[0047] It also includes the following steps: Real-time security detection of user input: During the interaction between the user and the system, conduct real-time security detection of user input; use regular expression matching and blacklist filtering techniques to detect whether the user input contains malicious character sequences and special instructions; once malicious input is detected, the system refuses to execute the relevant operation and issues a security prompt to the user, informing them that the input is risky; Deep security detection of data content: For the obtained data content, build a text classification model based on deep learning; use a large number of malicious text samples and normal text samples to train the model so that it can accurately identify malicious content in the data; before the data enters the knowledge Q&A system, first detect it through this text classification model; if malicious information is detected in the data, immediately isolate the data and prevent it from entering the system to avoid harm to the system and users.
[0048] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention, and the scope of the present invention is defined by the appended claims and their equivalents.
Claims
1. A large model-based networked knowledge Q&A and security detection system, characterized in that: It includes an Internet-connected knowledge acquisition module, a large model knowledge Q&A module, and a security detection module; The Internet-connected knowledge acquisition module is used to obtain the latest knowledge data from authoritative Internet knowledge sources and establish a dynamically updated multi-source knowledge base; The large model knowledge Q&A module uses large model technology to perform semantic understanding, knowledge extraction, and intelligent integration of the acquired knowledge, and constructs a multi-dimensional knowledge graph to achieve in-depth understanding of knowledge; The security detection module integrates multi-level and all-round security detection mechanisms, covering the data content level, network transmission level, and data storage level.
2. The networked knowledge Q&A and security detection system based on a large model according to claim 1, wherein: The specific implementation method of the Internet-connected knowledge acquisition module includes: building a high-speed and stable connection with multiple authoritative knowledge sources, assigning different weight coefficients according to the API interfaces provided by each knowledge source and their authority; calling the API interfaces of each knowledge source to collect raw data, performing in-depth preprocessing on the collected raw data, using text cleaning algorithms to remove noise information, unifying the data format with the help of character encoding conversion technology, and using lexical analysis tools to convert unstructured text data into a structured form.
3. The networked knowledge Q&A and security detection system based on a large model according to claim 2, wherein: The specific implementation method of the large model knowledge Q&A module includes: introducing an advanced large model base and performing precise parameter configuration; organizing the knowledge data obtained from the multi-source knowledge base and preprocessed into a training data set, using transfer learning technology, and promoting the model to accurately understand and apply new knowledge through fine-tuning training, and flexibly adjusting hyperparameters during the training process to optimize the model's understanding and processing ability of professional domain knowledge; when the user inputs a question, using natural language processing technology to perform in-depth semantic analysis on the question, extracting key entities, relationships, and semantic intentions, and inputting the parsed and security-detected question information into the large model that has undergone fusion training to generate accurate, detailed, and user-demand-compliant answers.
4. A large model-based networked knowledge Q&A and security detection system according to claim 4, characterized in that: The specific implementation method of the security detection module at the network transmission level includes: deploying a high-performance intrusion detection system, based on real-time monitoring of network traffic data, using pattern matching and anomaly detection techniques to identify abnormal behaviors in network traffic; when abnormal behaviors are detected, it is determined that there may be an attack behavior and an alarm is issued in a timely manner, and at the same time, a network connection blocking measure to block the IP address is taken to prevent the attack from spreading further.
5. The networked knowledge Q&A and security detection system based on a large model according to claim 4, characterized in that: The specific implementation method of the security detection module at the data storage and user interaction levels includes: establishing a role-based access control mechanism, defining different user roles in the system and assigning corresponding permissions to each role, verifying the user role and permissions when the user accesses data, and regularly reviewing and updating the user roles and permissions; during the process of user interaction with the system, performing real-time security detection on user inputs, using regular expression matching and blacklist filtering technologies to detect malicious character sequences and special instructions, and once malicious inputs are detected, rejecting the execution of relevant operations and issuing a security prompt to the user; for the obtained data content, constructing a deep learning-based text classification model, training the model with a large number of malicious text samples and normal text samples, and detecting through this text classification model before the data enters the knowledge Q&A system. If malicious information is detected, the data is immediately isolated.
6. A method for a large model-based networked knowledge Q&A and security detection system according to claim 5, characterized in that: It includes the following steps: Establish multi-source knowledge connections: Build high-speed, stable connections with numerous authoritative knowledge sources, including Baidu Encyclopedia, Bing, Moji Weather, Bocha Search, mainstream news websites, and authoritative knowledge bases in some professional fields. Based on the characteristics of each knowledge source and the API interfaces it provides, assign corresponding weight coefficients to different websites according to their authority. This includes calling the Baidu Encyclopedia Knowledge Retrieval API to obtain encyclopedia entry information, leveraging the Moji Weather weather data API to achieve real-time meteorological information collection, relying on the Bocha Search API to accurately locate information, and capturing key data on news content and release time from mainstream news websites that open news data APIs. Deep preprocessing of raw data: Deep preprocessing of the collected raw data, using text cleaning algorithms to remove noise information in the data, such as advertising codes, irrelevant HTML tags, and garbled characters; using character encoding conversion technology to unify the character encoding format of the data; using lexical analysis tools in the field of natural language processing to perform word segmentation, part-of-speech tagging, and named entity recognition on the text, converting unstructured text data into a structured form.
7. A method according to claim 6, characterized in that: The following steps are also included: Large model selection and deployment: Introducing advanced large model bases and performing precise parameter configuration based on the specific requirements of the selected model; Knowledge preprocessing and fusion training: The knowledge data obtained from multi-source knowledge bases and preprocessed are organized into training data sets according to a specific format; Using transfer learning technology, the language understanding capabilities accumulated by the large model during pre-training on a large-scale general corpus are transferred to learning specific domains and the latest knowledge for this system; Through meticulous fine-tuning training, the model is encouraged to accurately understand and apply new knowledge, and the model's learning rate and number of iterations hyperparameters are flexibly adjusted during training; Semantic understanding and knowledge extraction: When a user enters a question, the system uses natural language processing techniques such as syntactic analysis and semantic role labeling to conduct in-depth semantic analysis of the question, accurately extracting key entities, relationships, and semantic intent in the question. The question information that has been parsed and passed the security inspection system is input into the large model that has been integrated and trained. Based on its powerful semantic understanding and reasoning capabilities, the model integrates the information obtained through the Internet and the knowledge reserves of the large model itself to perform efficient retrieval and deep reasoning to generate accurate, detailed and user-friendly answers.
8. A method according to claim 7, characterized in that: The following steps are also included: At the network traffic level, deploy high-performance intrusion detection systems, such as Snort and Suricata. These systems monitor network traffic data in real time and use pattern matching and anomaly detection techniques to identify abnormal behavior in network traffic. When it is detected that a certain IP address initiates a large number of port scan requests in a short period of time, or there is a sudden surge in traffic that is inconsistent with the normal business pattern, the system immediately determines that there may be an attack behavior, issues an alarm in time, and takes corresponding blocking measures, such as blocking the network connection of the IP address to prevent the attack from spreading further.
9. A method according to claim 8, characterized in that: The following steps are also included: Establish a role-based access control mechanism, define different user roles in the system, and assign corresponding permissions to each role; When a user accesses data, the system first verifies the user's role and permissions. Only users with the corresponding permissions can access specific data, preventing data leakage and illegal access; regularly review and update the user roles and permissions to ensure the effectiveness of access control.
10. A method according to claim 9, characterized in that: It also includes the following steps: Real-time security detection of user input: During the interaction between the user and the system, conduct real-time security detection of the user input; use regular expression matching and blacklist filtering technologies to detect whether the user input contains malicious character sequences and special instructions; Once malicious input is detected, the system refuses to execute the relevant operation and issues a security prompt to the user, informing them that there is a risk in their input; Deep security detection of data content: For the obtained data content, build a text classification model based on deep learning; use a large number of malicious text samples and normal text samples to train the model so that it can accurately identify malicious content in the data; before the data enters the knowledge Q&A system, first detect it through this text classification model; if malicious information is detected in the data, immediately isolate the data and prevent it from entering the system to avoid harm to the system and users.