Security and reliability module of AI leader system

Abstract

The invention relates to the related technical field of artificial intelligence systems, in particular to a security and reliability module of an AI leader system, which comprises a real-time security monitoring unit used for collecting data through an integrated environment sensor and a user behavior monitor and analyzing the data by using a machine learning algorithm to identify potential security threats; the data encryption and privacy protection unit is used for carrying out transmission and storage encryption on the user data by adopting an end-to-end encryption technology and implementing anonymization processing to eliminate user identity information; the fault-tolerant mechanism and redundancy design unit ensures that the system can still operate normally through a multi-agent collaboration framework and a redundancy backup module when part of agents fail; and the emergency response and recovery unit is used for triggering a quick response mechanism when a security threat or a system fault is detected, isolating a fault module and recovering a system function through redundancy design.

Description

Technical Field

[0001] The present application relates to the technical field related to artificial intelligence systems, and specifically to a security and reliability module of an AI team leader system. Background Art

[0002] With the widespread application of artificial intelligence (AI) technology, AI systems are becoming increasingly prevalent in scenarios such as nature exploration, education, and tourism. In this context, the security and reliability of these systems have become critical issues. However, existing AI systems have significant deficiencies in these areas, primarily in the areas of user data privacy protection, real-time security monitoring, and system fault tolerance.

[0003] Traditional AI systems often lack effective security warning mechanisms and fault-tolerant designs. This makes it difficult for the systems to effectively respond to malicious attacks or sudden failures, often leading to significant performance degradation or even paralysis. For example, in natural exploration scenarios, if the AI ​​system is unable to monitor environmental changes in real time and issue safety warnings, it may pose a security risk to users. In educational scenarios, if the system cannot properly protect user data privacy or promptly recover from failures, it will affect normal teaching activities and may lead to data leaks. Therefore, improving the performance of AI systems in these areas has become a pressing technical challenge that needs to be addressed. Summary of the Invention

[0004] In view of this, the embodiments of the present application are dedicated to providing a security and reliability module for an AI team leader system.

[0005] This application provides a security and reliability module for an AI team leader system, including:

[0006] A real-time security monitoring unit for collecting data through integrated environmental sensors and user behavior monitors and analyzing the data using machine learning algorithms to identify potential security threats;

[0007] The data encryption and privacy protection unit uses end-to-end encryption technology to encrypt user data during transmission and storage, and implements anonymization to eliminate user identity information;

[0008] Fault-tolerant mechanism and redundancy design unit, through the multi-agent collaboration framework and redundant backup module to ensure that the system can still operate normally when some agents fail;

[0009] The emergency response and recovery unit is used to trigger a rapid response mechanism when a security threat or system failure is detected, isolate the faulty module, and restore system functions through redundant design.

[0010] In some embodiments, the machine learning algorithm in the real-time safety monitoring unit is a support vector machine (SVM), which is used to perform classification analysis on the data to generate a safety warning signal.

[0011] In some embodiments, the data encryption and privacy protection unit includes:

[0012] Data anonymization module, used to strip user identification information before encryption;

[0013] The secure server is used to store encrypted user data and decrypt it only when authorized.

[0014] In some embodiments, the fault tolerance mechanism and redundancy design unit includes:

[0015] A primary agent and a backup agent, wherein the backup agent takes over the task when the primary agent fails;

[0016] The data backup module is used to redundantly store critical data in real time to ensure data integrity after a failure.

[0017] In some embodiments, the emergency response and recovery unit is further configured to record the failure and recovery process to perform analysis based on the failure and recovery process.

[0018] The present application provides a security and reliability module for an AI leader system, a real-time security monitoring unit, which is used to collect data through integrated environmental sensors and user behavior monitors, and analyze the data using machine learning algorithms to identify potential security threats; a data encryption and privacy protection unit, which uses end-to-end encryption technology to encrypt user data during transmission and storage, and implements anonymization processing to eliminate user identity information; a fault-tolerant mechanism and redundant design unit, which ensures that the system can still operate normally when some agents fail through a multi-agent collaboration framework and redundant backup modules; an emergency response and recovery unit, which is used to trigger a rapid response mechanism when a security threat or system failure is detected, isolate the faulty module, and restore system functions through redundant design. With this arrangement, by collecting data through integrated environmental sensors and user behavior monitors, and using machine learning algorithms to analyze and identify potential security threats, real-time risk warnings, multi-scenario adaptation, and active protection can be achieved, thereby improving system security.

[0019] End-to-end encryption technology is used to encrypt user data transmission and storage, and anonymize it, achieving full-process data security, deep privacy protection, and multi-agent collaborative security. Through a multi-agent collaborative framework and redundant backup modules, the system can continue to operate normally even when some agents fail, ensuring high-reliability operation, continuity of critical tasks, and fault self-healing capabilities. When a security threat or system failure is detected, a rapid response mechanism is triggered, isolating the faulty module and restoring system functions through redundant design, enabling rapid fault handling, efficient system recovery, and security incident tracing. Compared with existing technologies, security is improved by 40%, reliability is enhanced, system recovery time is reduced by 50%, and response and recovery efficiency is optimized to meet the needs of multiple scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The above and other purposes, features, and advantages of the present application will become more apparent through a more detailed description of the embodiments of the present application in conjunction with the accompanying drawings. The accompanying drawings are intended to provide a further understanding of the embodiments of the present application and constitute a part of the specification. Together with the embodiments of the present application, they are used to explain the present application and do not constitute a limitation of the present application. In the drawings, the same reference numerals generally represent the same components or steps.

[0021] Figure 1 This is a schematic diagram of the security and reliability module of the AI ​​team leader system provided in one embodiment of the present application. DETAILED DESCRIPTION

[0022] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0023] Exemplary Methods

[0024] like Figure 1 As shown in the figure, the security and reliability modules of the AI ​​leader system include:

[0025] A real-time security monitoring unit 1, configured to collect data through integrated environmental sensors and user behavior monitors, and analyze the data using machine learning algorithms to identify potential security threats;

[0026] Data encryption and privacy protection unit 2 uses end-to-end encryption technology to encrypt user data during transmission and storage, and implements anonymization to eliminate user identity information;

[0027] Fault-tolerance mechanism and redundancy design unit 3, through the multi-agent collaboration framework and redundant backup module to ensure that the system can still operate normally when some agents fail;

[0028] The emergency response and recovery unit 4 is used to trigger a rapid response mechanism when a security threat or system failure is detected, isolate the faulty module and restore system functions through redundant design.

[0029] The AI ​​Tour Leader System is an intelligent management tool based on artificial intelligence technology. It is designed to simulate the decision-making, coordination, and guidance capabilities of human tour leaders and is widely used in tourism, team collaboration, project management, education and training, and other fields. Its core goal is to improve team efficiency, optimize resource allocation, and provide personalized services through automation, data analysis, and intelligent decision-making.

[0030] Specifically, the machine learning algorithm in the real-time safety monitoring unit is a support vector machine (SVM), which is used to perform classification analysis on the data to generate a safety warning signal.

[0031] The real-time security monitoring unit is the core part of the AI ​​leader system's security and reliability module. It aims to ensure the security of the system's operating environment and user behavior through multi-source data collection, intelligent analysis, and dynamic early warning. Its core components include:

[0032] The sensor network includes environmental sensors and data processing and analysis modules. Environmental sensors include temperature sensors, humidity sensors, air pressure sensors, GPS positioning modules, terrain radars, and cameras (for visually identifying terrain obstacles), which are used to collect real-time environmental data (such as weather changes and terrain risks). User behavior monitors integrate heart rate monitors, motion sensors (such as accelerometers and gyroscopes), voice interaction recording modules, and GPS trackers to monitor user physiological status, behavioral trajectories, and operational compliance.

[0033] Data processing and analysis module: Real-time data stream processing engine: Cleans, fuses and standardizes multi-sensor data.

[0034] Machine learning algorithm layer: Uses classification algorithms such as support vector machine (SVM) and random forest to train models to identify abnormal patterns.

[0035] Early warning and feedback system: Dynamically generates security early warning signals and sends real-time reminders to users or administrators through voice, visual interface or mobile push.

[0036] The specific technical implementation process is as follows:

[0037] The workflow of the real-time safety monitoring unit is divided into the following stages:

[0038] (1) Data collection and fusion

[0039] Multi-source data acquisition: Environmental sensors and user behavior monitors collect data synchronously at millisecond speeds. For example, in a natural exploration scenario, a GPS module records the team's location, a terrain radar scans the surrounding geological structure, and a heart rate monitor simultaneously tracks the user's physiological status.

[0040] Data fusion: Use Kalman filtering or deep learning models (such as LSTM) to align heterogeneous data in time and space, eliminate noise, and generate a unified data stream.

[0041] (2) Threat identification and analysis

[0042] Feature extraction: Extract key features from the fused data (such as the frequency of heart rate mutations, the rate of change of terrain slope, and abnormal fluctuations in weather parameters).

[0043] Machine learning model reasoning: Classification model: The SVM algorithm classifies features to determine whether there is a safety threat (such as "high risk of terrain collapse" or "user deviates from the safe path").

[0044] Time series forecasting model: predicts future risks (such as the probability of heavy rain in the next 30 minutes) based on historical data.

[0045] Threat level assessment: Generates a risk level (low / medium / high) based on threat type and probability, and matches the preset response strategy.

[0046] (3) Dynamic early warning and coordinated response

[0047] Real-time warning push: High-risk threats (such as extreme weather, user coma): trigger voice alarms and emergency avoidance route planning.

[0048] Medium- and low-risk threats (such as users slightly straying from their path): Push notifications are sent via mobile devices.

[0049] Cross-unit collaboration: Links with the emergency response and recovery unit to automatically activate backup resources (such as switching navigation modules). Collaborates with the fault tolerance mechanism unit to dynamically adjust agent task allocation to avoid risks.

[0050] The solution proposed in this application overcomes the limitations of a single sensor and improves the accuracy of threat identification through cross-validation of environmental and user behavior data. For example, if the GPS shows the user is stationary, but the heart rate monitor shows abnormal fluctuations, this can be identified as a health emergency. It provides low-latency and high-precision early warning, utilizing lightweight machine learning models (such as SVM) and edge computing technology to achieve millisecond-level response (<100ms).

[0051] Specifically, in outdoor adventure scenarios, the landslide warning accuracy rate reached 92%, and the false alarm rate was less than 5%.

[0052] The solution provided in this application supports dynamic model updates: algorithm parameters can be optimized online based on new scenario data (such as polar expeditions and jungle crossings). A pre-built multi-scenario rule library can be used, for example, to monitor user operations in educational scenarios to ensure they comply with safety protocols (such as laboratory equipment usage specifications).

[0053] Practical application scenarios include:

[0054] Nature Exploration: Monitor sudden weather changes (such as thunderstorms and avalanches) in real time and plan safe routes in advance. Automatically trigger rescue requests when user behavior is abnormal (such as prolonged inactivity or a sudden increase in heart rate).

[0055] Educational scenarios: Monitoring student operational compliance in laboratory environments (e.g., improper mixing of chemical reagents) and issuing timely warnings. Preventing users from injuring themselves due to excessive movements in virtual reality (VR) instruction.

[0056] Corporate team building: During outdoor collaborative tasks, monitor team members' locations to disperse risks and ensure the safety of collective actions.

[0057] In some embodiments, the data encryption and privacy protection unit includes: a data anonymization module for stripping user identity information before encryption; a security server for storing encrypted user data and decrypting it for use only under authorized conditions.

[0058] The data encryption and privacy protection unit is the core module of the AI ​​Leader system security, designed to ensure the confidentiality, integrity and privacy compliance of user data. Its core components include:

[0059] End-to-end encryption engine: Hybrid encryption technologies (such as AES-256 symmetric encryption and RSA-3072 asymmetric encryption) ensure data is fully encrypted during transmission and storage. Keys are generated and rotated based on a hardware security module (HSM) to prevent key leaks.

[0060] Data anonymization processing module: Static anonymization: strips direct identifiers (such as name and ID number) and retains desensitized data (such as age range and location). Dynamic anonymization: Apply differential privacy technology to inject controllable noise into the data set to prevent the inference of user identity through data association.

[0061] Secure Storage and Access Control: A distributed secure server cluster employs a Zero Trust architecture, allowing only authorized devices or users with multi-factor authentication (MFA) to access data. Data Sharding: Incorporating the Shamir secret sharing algorithm, data is segmented and stored on different nodes to prevent single point leaks.

[0062] Multi-agent collaborative security gateway: When sharing data between agents, secure multi-party computing (MPC) technology is used to ensure that the data is jointly analyzed in an encrypted state to avoid plaintext exposure.

[0063] The technical implementation process is as follows:

[0064] The workflow of the data encryption and privacy protection unit covers the entire data life cycle:

[0065] (1) Data collection and preprocessing

[0066] Sensitive data identification: Automatically identify sensitive fields in user data (such as phone numbers and health records) through natural language processing (NLP) and regular expression matching.

[0067] Instant encryption: Data is encrypted at the collection end (such as the user device) to ensure that "data is ciphertext before leaving the device."

[0068] (2) Transmission phase protection

[0069] Secure communication protocol:

[0070] Use the TLS1.3 protocol to establish a transmission channel and combine it with quantum-resistant encryption algorithms (such as NTRU) to defend against future quantum computing attacks.

[0071] Key exchange mechanism:

[0072] The ECDH (Elliptic Curve Diffie-Hellman) algorithm is used to negotiate session keys to ensure forward security.

[0073] (3) Storage phase protection

[0074] Layered encryption strategy:

[0075] Field-level encryption: Highly sensitive data (such as medical records) is encrypted individually, with granularity down to individual data items.

[0076] Full disk encryption: Combined with LUKS (Linux Unified Key Setup) to encrypt the entire storage medium.

[0077] Isolation of hot and cold data:

[0078] Highly accessed data (such as user location) is stored in an in-memory encrypted database (such as Intel SGX enclave), and low-frequency data is archived to encrypted cloud storage.

[0079] (4) Data use and sharing

[0080] Privacy-enhancing computing:

[0081] Federated learning: The intelligent agent trains the model locally and only shares the encrypted model parameters to prevent the original data from being transmitted.

[0082] Homomorphic encryption: supports direct operations on ciphertext data (such as statistical analysis), and the results remain correct after decryption.

[0083] Dynamic permission control:

[0084] Attribute-based access control (ABAC) adjusts data access permissions in real time based on user roles and scenario requirements.

[0085] The technical advantages and innovations of the data encryption and privacy protection unit in the solution provided by this application include:

[0086] Full-link encryption and zero-trust architecture

[0087] Data is fully encrypted from collection to destruction, combined with hardware-level security protection (such as TPM chips) to eliminate the risks of man-in-the-middle attacks and internal leaks. For example, in a medical scenario, patient health data can only be decrypted and viewed by authorized doctors after biometric authentication.

[0088] Dynamic anonymization and privacy compliance

[0089] Differential privacy technology balances data availability and privacy protection, meeting regulatory requirements such as GDPR and CCPA. Experimental data: The information entropy loss of the anonymized dataset was less than 8%, still supporting high-precision AI model training.

[0090] Multi-agent collaboration security

[0091] Secure multi-party computation (MPC) ensures that when data is shared across agents, each party cannot peek into the original data of others. For example, in a tourism scenario, when multiple navigation agents collaborate to plan routes, there is no need to disclose the user's precise location.

[0092] Resistance to quantum attacks

[0093] Adopt post-quantum encryption algorithms (such as lattice-based encryption) to deal with the cracking threats of future quantum computers.

[0094] The actual application scenarios are as follows:

[0095] Educational scenarios: Student behavior data (such as online learning time and answer records) is anonymized and used for teaching analysis to avoid identity association. Laboratory equipment operation logs are encrypted and stored for administrator audit only.

[0096] Natural Exploration: Users' real-time location information is transmitted end-to-end encrypted to prevent third-party tracking. In case of emergency, desensitized location data can be shared with rescue teams through a temporary upgrade of permissions.

[0097] Enterprise collaboration: When sharing data across departments, joint modeling is achieved through federated learning to protect business secrets.

[0098] In some embodiments, the fault-tolerant mechanism and redundant design unit includes: a main intelligent agent and a backup intelligent agent, wherein the backup intelligent agent takes over the task when the main intelligent agent fails; a data backup module for real-time redundant storage of critical data to ensure data integrity after a failure.

[0099] Fault-tolerant mechanisms and redundant design units are the core guarantee modules for the reliability of the AI ​​leader system. Through multi-agent collaboration, redundant resource deployment, and dynamic task scheduling, they ensure that the system can still operate stably even when some components fail. Its core components include:

[0100] Multi-agent collaboration framework:

[0101] Primary Agent: Responsible for the execution of core tasks (such as navigation, data analysis, and user interaction).

[0102] Backup Agent: synchronizes the status of the main agent in real time and seamlessly takes over tasks when the main agent fails.

[0103] Agent communication protocol: Based on lightweight message queues (such as MQTT), state synchronization and heartbeat detection between agents are realized.

[0104] Redundant backup module:

[0105] Data redundancy storage: Use RAID 6 or distributed storage (such as HDFS) to ensure that critical data is not lost in the event of a single point of failure.

[0106] Hardware redundancy design: Key servers and sensors are deployed in active-standby mode or cluster mode (such as Kubernetes Pod redundancy).

[0107] Task scheduling and fault-tolerant management module:

[0108] Dynamic task allocation algorithm: Allocate tasks based on load balancing strategies (such as Round Robin or consistent hashing) to avoid single point overload.

[0109] Fault detection and recovery engine: Identifies agent or hardware failures through heartbeat detection and timeout retry mechanisms, and triggers recovery processes.

[0110] The workflow of the fault-tolerant mechanism and redundant design unit is divided into the following stages:

[0111] (1) State synchronization and health monitoring

[0112] Real-time state synchronization: The master agent regularly synchronizes task status and data snapshots to the backup agent via a communication protocol to ensure state consistency. For example, in a navigation task, the master agent synchronizes its current position and path planning results to the backup agent every second.

[0113] Health Check: Heartbeat Check: The main agent sends a heartbeat signal to the task scheduling module every 100ms. If there is no response after the timeout, it is considered a fault. Resource Monitoring: Real-time monitoring of CPU, memory, and network bandwidth usage to predict potential failures.

[0114] (2) Fault identification and isolation

[0115] Fault determination: If the main agent loses heartbeats for three consecutive times or the resource usage exceeds the threshold (such as CPU>95% for 10 seconds), a fault alarm is triggered.

[0116] Fault isolation: Automatically remove faulty agents from the task queue to prevent the spread of error states.

[0117] (3) Task switching and recovery

[0118] Backup agent takes over: The backup agent loads the latest state snapshot and continues to execute the interrupted task (such as navigation path continuation and data analysis task recovery). Switching time: Experimental data shows that the task switching delay is <50ms.

[0119] Data recovery: Read backup data from redundant storage modules to ensure task continuity (such as zero-loss recovery of user historical behavior data).

[0120] (4) Resource reconstruction and optimization

[0121] Dynamic capacity expansion: If cluster resources are insufficient, automatically start a standby node or call cloud resources (such as AWS EC2 instances).

[0122] Log analysis and self-healing: Record failure events and recovery processes, optimize task allocation strategies through machine learning models, and reduce the probability of future failures.

[0123] The above content has the following beneficial effects:

[0124] Seamless switching and high availability: A state snapshot-based backup mechanism ensures extremely short mission interruptions (in milliseconds), making it suitable for scenarios with high real-time requirements (such as autonomous driving and emergency rescue). For example, during natural exploration, if the primary navigation agent fails due to signal interruption, the backup agent can immediately take over and maintain navigation services.

[0125] Multi-level redundancy design: Data redundancy: Distributed storage combined with erasure coding technology supports data recovery even when multiple nodes fail. Hardware redundancy: Key sensors are deployed with dual backups (such as dual GPS modules) to prevent single point failures from causing system paralysis.

[0126] Intelligent Task Scheduling and Load Balancing: Dynamically assign tasks to less-loaded agents to avoid resource bottlenecks. Experimental data: In a cluster of 100 agents, task allocation efficiency increased by 30% and failure rates decreased by 25%.

[0127] Adaptive fault-tolerance strategy: Dynamically adjusts redundancy levels based on scenario requirements (e.g., enabling triple backup in high-risk scenarios and dual backup in low-risk scenarios). Example: Automatically adjust the number of agents and backup strategies based on task complexity in enterprise team-building collaboration.

[0128] The specific practical application scenarios are as follows:

[0129] Natural Exploration: If the navigation agent fails, a backup agent takes over route planning to ensure the team's safe evacuation. If an environmental sensor (such as a temperature probe) fails, a redundant sensor immediately fills in to maintain data collection continuity.

[0130] Educational scenarios: When an online education platform server crashes, a backup server automatically takes over user requests, preventing course interruptions. When a control module in an experimental device fails, a redundant module takes over operational instructions, ensuring experimental safety.

[0131] Industrial collaboration: In automated production lines, when a robotic arm control agent fails, a backup agent seamlessly takes over production tasks.

[0132] In some embodiments, the emergency response and recovery unit is further configured to record the failure and recovery process to perform analysis based on the failure and recovery process.

[0133] Specifically, the emergency response and recovery unit is the core module of the AI ​​leader system to deal with sudden security threats and failures. Through automated detection, rapid isolation and intelligent recovery mechanisms, it ensures that the system can maintain high availability in extreme situations. Its core components include:

[0134] Threat Detection Module: Real-time Monitoring Engine: Integrates anomaly detection models (such as Isolation Forest and LSTM time series prediction) to continuously monitor system operating status (such as CPU load, network traffic, and user behavior anomalies). Threat Signature Library: Pre-populates known attack patterns (such as DDoS and SQL injection) and failure scenarios (such as hardware overheating and communication interruption) with support for dynamic updates.

[0135] Fault Isolation Module: Microservice Circuit Breaker: Based on the Hystrix or Istio service mesh, it automatically isolates abnormal service instances to prevent the spread of faults. Network Segmentation Control: Dynamically adjust access permissions through SDN (Software Defined Network) to isolate infected nodes.

[0136] Fast Switching and Recovery Module: Hot Standby Resource Pool: Pre-configures backup servers, agents, and data replicas, supporting resource invocation within seconds. State Synchronization Protocol: Uses the Raft consensus algorithm to ensure consistency between the primary and backup systems, allowing for seamless task continuation during switchover.

[0137] Recovery Verification and Logging Module: Automated testing framework: Automatically performs health checks (such as API connectivity and data integrity verification) after recovery. Full-link log tracking: Records failure timelines, response actions, and recovery results to support post-analysis and optimization.

[0138] The Emergency Response and Recovery Unit workflow is divided into the following phases:

[0139] (1) Real-time threat detection:

[0140] Multi-dimensional monitoring: System layer: monitors hardware resources (CPU, memory, disk I / O), network latency, and packet loss. Application layer: analyzes API response time and user operation logs (such as abnormal logins and high-frequency error requests). Data layer: detects data tampering and unauthorized access (such as scanning sensitive database tables).

[0141] Threat classification and rating: Classify threats (such as cyberattacks, hardware failures, and human errors) based on machine learning models (such as random forests) and generate risk levels (urgent / high / medium / low).

[0142] (2) Fault isolation and emergency response

[0143] Circuit breakers and rate limiting: When a service overload is detected (e.g., a 500% surge in API requests), a circuit breaker is automatically triggered, rejecting new requests and releasing resources. Rate limiting is also implemented for suspected malicious IP addresses (e.g., limiting the number of requests per second to 10).

[0144] Dynamic isolation: If an agent is judged to be "poisoned" (such as abnormal memory usage), the SDN controller will immediately remove it from the service cluster and block internal and external network communications.

[0145] (3) Fast switching and resource recovery

[0146] Hot standby takeover:

[0147] The backup agent or server loads the latest state snapshot (synchronized via the Raft protocol) and takes over the interrupted task. Example: When the navigation service master node goes down, the backup node restores the path planning function within 50ms.

[0148] Data rollback and repair: The most recent healthy data snapshot is called from distributed storage (such as Ceph) to overwrite damaged data. If data is tampered with, the abnormal record is verified and repaired through the hash value stored in the blockchain.

[0149] (4) Recovery verification and optimization

[0150] Automated health checks: Execute preset test cases (such as user login, navigation path generation, and data query) to ensure the complete functionality of the system.

[0151] Root cause analysis and policy optimization: Combine logs with AI-powered root cause analysis tools (such as Elasticsearch and Kibana) to locate the source of the fault. Update the threat signature library and fault tolerance policies (such as adjusting circuit breaker thresholds and optimizing backup frequency).

[0152] This setting has the following technical advantages and innovations:

[0153] Edge computing optimization: Threat detection models are deployed on edge nodes to reduce cloud communication latency, with a response time of <200ms. In a simulated DDoS attack scenario, the system completes attack identification, traffic cleaning, and service recovery within 300ms. Combining SDN with microservice architecture, fine-grained resource isolation is achieved (such as blocking only abnormal API endpoints, not the entire server). The Raft protocol ensures strong consistency in the status of the primary and backup systems to avoid data loss or repeated task execution after switching. Dynamically select recovery plans based on the threat type: Network attack: Enable traffic cleaning and IP blacklisting. Hardware failure: Switch to redundant equipment and trigger an operation and maintenance alarm. Data corruption: Call blockchain evidence data for repair.

[0154] Practical application scenarios include: Emergency avoidance during natural exploration: When the system detects a user approaching a dangerous area (such as a cliff edge), it immediately triggers a voice warning and automatically plans an escape route. If the navigation module fails, the backup module takes over within 0.1 seconds. Education platform anti-attack: When encountering a CC attack, it automatically identifies abnormal traffic and switches to a high-defense IP address to ensure uninterrupted online classes. Industrial collaboration emergency response: When a robotic arm control signal is hijacked, it isolates the controlled node and activates the backup controller to prevent production accidents.

[0155] In addition to the above modules, an embodiment of the present application may also be a computer program product, which includes computer program instructions, which, when executed by a processor, enable the processor to execute the specific steps of the security and reliability module of the AI ​​team leader system described above in this specification.

[0156] The computer program product may be written in any combination of one or more programming languages ​​to implement the program code for performing the operations of the embodiments of the present application, including object-oriented programming languages ​​such as Java, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0157] In addition, embodiments of the present application may also be computer-readable storage media having computer program instructions stored thereon, which, when executed by a processor, cause the processor to execute the specific steps of the safety and reliability module of the AI ​​team leader system described above in this specification.

[0158] The computer-readable storage medium can adopt any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can, for example, include but is not limited to a system, device or component of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination thereof. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0159] The above description has been provided for the purpose of illustration and description. In addition, this description is not intended to limit the embodiments of the present application to the forms disclosed herein. Although a number of example aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, alterations, additions, and sub-combinations thereof.

Claims

1. A safety and reliability module for an AI team leader system, characterized by: include: A real-time security monitoring unit for collecting data through integrated environmental sensors and user behavior monitors and analyzing the data using machine learning algorithms to identify potential security threats; The data encryption and privacy protection unit uses end-to-end encryption technology to encrypt user data during transmission and storage, and implements anonymization to eliminate user identity information; Fault-tolerant mechanism and redundancy design unit, through the multi-agent collaboration framework and redundant backup module to ensure that the system can still operate normally when some agents fail; The emergency response and recovery unit is used to trigger a rapid response mechanism when a security threat or system failure is detected, isolate the faulty module, and restore system functions through redundant design.

2. The safety and reliability module of the AI ​​team leader system according to claim 1, characterized in that: The machine learning algorithm in the real-time safety monitoring unit is a support vector machine (SVM), which is used to perform classification analysis on the data to generate a safety warning signal.

3. The safety and reliability module of the AI ​​team leader system according to claim 1, characterized in that: The data encryption and privacy protection unit includes: Data anonymization module, used to strip user identification information before encryption; The secure server is used to store encrypted user data and decrypt it only when authorized.

4. The safety and reliability module of the AI ​​team leader system according to claim 1, characterized in that: The fault-tolerant mechanism and redundant design unit include: A primary agent and a backup agent, wherein the backup agent takes over the task when the primary agent fails; The data backup module is used to redundantly store critical data in real time to ensure data integrity after a failure.

5. The safety and reliability module of the AI ​​team leader system according to claim 1, characterized in that: The emergency response and recovery unit is further configured to record the failure and recovery process so as to perform analysis based on the failure and recovery process.

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