A python-based hierarchical server security intelligent operation and maintenance system and method

Abstract

The application discloses a kind of server cluster intelligent operation and maintenance system, belong to server operation and maintenance technical field.System uses C / S layered architecture, include system layered architecture module, server security management module, server group intelligent operation and maintenance module, server health state intelligent monitoring module, middleware one-button automation installation module, intelligent alarm configuration module, optimization expansion core function module.Realize server asset account key security control, cluster batch automation operation and maintenance, server resource state monitoring, unattended deployment of middleware, hierarchical multi-channel alarm, log audit, fault self-recovery, timing scheduling and backup recovery, improve cluster operation and maintenance efficiency and security level.

Description

Technical Field

[0001] This invention relates to the field of server operation and maintenance technology, and in particular to an intelligent operation and maintenance system for server clusters. Background Technology

[0002] With the rapid popularization of cloud computing and distributed cluster technologies, the number of enterprise servers is growing at a large scale, and hybrid deployment modes of single clusters and multi-clusters have become mainstream. Traditional manual server operation and maintenance methods have exposed many drawbacks. At present, the mainstream server operation and maintenance methods are mostly manual operation of single machines or simple script-assisted operation and maintenance, which have problems such as dispersed architecture, limited functionality, and lack of security control.

[0003] Existing operation and maintenance technologies suffer from the following core defects: First, the operation and maintenance architecture lacks a layered design, making centralized management impossible. Multi-server operation and maintenance tasks are highly repetitive and extremely inefficient. Second, server account, password, and key management is chaotic, lacking a unified mechanism for permission verification, expiration updates, and destruction, making it highly susceptible to security risks such as key leakage, weak passwords, and permission abuse. Third, server group management capabilities are weak; access within the cluster lacks permission isolation, and traditional passwordless login configuration and file directory copying require individual operation on each server, which is time-consuming and labor-intensive, and unsuitable for clustered operation and maintenance scenarios. Fourth, server health status monitoring has a single dimension, only allowing for simple viewing of individual server health status. First, the system cannot collect, analyze, and predict anomalies in real time across all dimensions of resources, including CPU, memory, disk, and network traffic, leading to server failures being discovered only after the fact. Second, middleware deployment relies on manual configuration, resulting in complex processes for environment adaptation, parameter debugging, and dependency installation, leading to low deployment success rates and poor consistency. Third, the operation and maintenance alarm mechanism is incomplete, lacking tiered alarms and multi-channel notification capabilities, resulting in delayed handling of anomalies. Furthermore, existing technologies generally lack operation and maintenance log auditing, operation tracing, and automatic fault repair functions, making operation and maintenance operations untraceable. Minor faults can easily accumulate and lead to major incidents such as server crashes and cluster paralysis.

[0004] Python, with its advantages of cross-platform compatibility, lightweight nature, high development efficiency, and strong script portability, is compatible with mainstream server systems such as Linux and Windows, making it one of the optimal development languages ​​for automated operations and maintenance. Based on this, this invention designs a client-server layered architecture Python server security intelligent operations and maintenance system, integrating multi-dimensional security management, automated operations and maintenance, and intelligent monitoring and alarm functions, thus addressing many shortcomings of existing technologies. Summary of the Invention

[0005] The purpose of this invention is to address the problems of low automation, weak security control, insufficient cluster operation and maintenance capabilities, delayed monitoring and alarms, and low deployment efficiency in existing server operation and maintenance technologies. It provides a layered intelligent server security operation and maintenance system and method based on Python, which realizes centralized, secure, automated, and intelligent operation and maintenance of servers, and comprehensively improves the stability and security of server clusters.

[0006] A layered server security intelligent operation and maintenance system and method based on Python according to an embodiment of the present invention includes the following steps: S1. System layered architecture module; This system adopts a C / S (client / server) layered architecture, which is the core infrastructure of the system.

[0007] S2. Server security management module; This module implements full lifecycle security management of server assets, accounts, passwords, and keys, and is a core module of system security, specifically including...

[0008] S3. Intelligent Operation and Maintenance Module for Server Groups; Designed for server cluster scenarios, it enables fine-grained cluster management and automated batch operation and maintenance, optimizing the inefficiency of traditional cluster operation and maintenance.

[0009] S4. Server health status intelligent monitoring module; Based on the Python psutil resource monitoring library, this system enables real-time collection, statistics, and analysis of server operating status across all dimensions, covering core hardware and network resources.

[0010] S5. Middleware one-click automated installation module; It includes built-in automated deployment scripts for mainstream service middleware, enabling fully unattended installation based on Python. It supports environment adaptation, automatic dependency installation, and automatic parameter configuration, covering commonly used middleware for operations and maintenance, such as Nginx, Tomcat, Redis, MySQL, Docker, JDK, and Maven. Operations personnel only need to select the target server group and the required middleware on the server side. The system automatically completes system environment detection, dependency package installation, version matching, configuration file generation, service start / stop, and automatic startup configuration. After installation, it automatically verifies service availability and outputs a deployment report, completely solving the problems of poor adaptability, cumbersome steps, and inconsistent configurations associated with manual deployment.

[0011] S6. Intelligent alarm configuration module; It supports custom alarm thresholds, tiered alarms, and multi-channel notifications, enabling early detection and handling of server anomalies.

[0012] S7. Optimize and expand core functions; To enhance system security and intelligence, this invention adds several differentiated and innovative functions to address the shortcomings of existing technologies. Attached Figure Description

[0013] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings: Figure 1 This invention proposes a layered server security intelligent operation and maintenance system and method based on Python. Detailed Implementation

[0014] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, illustrating only the basic structure of the invention, and therefore only show the components relevant to the invention.

[0015] refer to Figure 1 A layered intelligent server security operation and maintenance system and method based on Python, comprising the following steps: S1. System layered architecture module; This system adopts a C / S (client / server) layered architecture, which is the core infrastructure of the system.

[0016] S2. Server security management module; This module implements full lifecycle security management of server assets, accounts, passwords, and keys, and is a core module of system security, specifically including...

[0017] S3. Intelligent Operation and Maintenance Module for Server Groups; Designed for server cluster scenarios, it enables fine-grained cluster management and automated batch operation and maintenance, optimizing the inefficiency of traditional cluster operation and maintenance.

[0018] S4. Server health status intelligent monitoring module; Based on the Python psutil resource monitoring library, this system enables real-time collection, statistics, and analysis of server operating status across all dimensions, covering core hardware and network resources.

[0019] S5. Middleware one-click automated installation module; It includes built-in automated deployment scripts for mainstream service middleware, enabling fully unattended installation based on Python. It supports environment adaptation, automatic dependency installation, and automatic parameter configuration, covering commonly used middleware for operations and maintenance, such as Nginx, Tomcat, Redis, MySQL, Docker, JDK, and Maven. Operations personnel only need to select the target server group and the required middleware on the server side. The system automatically completes system environment detection, dependency package installation, version matching, configuration file generation, service start / stop, and automatic startup configuration. After installation, it automatically verifies service availability and outputs a deployment report, completely solving the problems of poor adaptability, cumbersome steps, and inconsistent configurations associated with manual deployment.

[0020] S6. Intelligent alarm configuration module; It supports custom alarm thresholds, tiered alarms, and multi-channel notifications, enabling early detection and handling of server anomalies.

[0021] S7. Optimize and expand core functions; To enhance system security and intelligence, this invention adds several differentiated and innovative functions to address the shortcomings of existing technologies.

[0022] Optionally, step S1 includes the following steps: S11. Server-side module: As the core control hub, it is built on the Python Flask framework to provide backend services. It integrates core capabilities such as data storage, command distribution, permission management, data statistics, alarm push, and task scheduling. It is responsible for uniformly receiving operation instructions from operation and maintenance personnel, issuing operation and maintenance tasks, summarizing server data reported by clients, storing operation and maintenance logs and configuration information, and providing a visual operation and maintenance management backend. S12. Client Module: Developed using lightweight Python scripts, with no redundant dependencies, deployed on various business servers, and running continuously in the background without manual intervention. Its main functions include collecting local server running status, receiving maintenance instructions from the server, executing passwordless login configuration, file copying, middleware installation, and other operations, and transmitting the execution results and monitoring data back to the server in real time.

[0023] Optionally, step S2 includes the following steps: S21. Server Asset Management: Automatically inputs asset information such as IP, port, system version, hardware configuration, and deployed services of all servers in the cluster, supports asset classification, grouping, and status marking, and realizes unified ledger management of cluster servers; S22. Account Permission Management: Supports batch creation, disabling, deletion, and hierarchical configuration of server accounts, distinguishing between three levels of permissions: super administrator, operation and maintenance administrator, and ordinary operator, limiting the operation and maintenance permissions of different accounts, and preventing unauthorized operations; automatically detects unfamiliar and redundant accounts on the server and provides timely warnings of account security risks; S23. Password Security Management: Based on Python encryption algorithms, server login passwords are stored uniformly, updated periodically, and weak passwords are detected. Simple and expired passwords are automatically identified, and modification reminders are pushed. All password data is stored in encrypted form to prevent plaintext leakage. S24. Key Security Management: Supports batch generation, distribution, binding, and invalidation of SSH keys; automatically detects leaked and expired keys; supports fine-grained control of key permissions; records key usage logs to ensure full traceability of key operations.

[0024] Optionally, step S3 includes the following steps: S31. Cluster Internal Access Restriction: Configure server firewall policies and IP whitelists based on Python scripts, support custom server access permissions within the cluster, limit communication ports and access permissions between specified servers, prohibit unauthorized cross-node access, and avoid the risk of cluster intranet penetration. S32. One-click cluster passwordless login: Operation and maintenance personnel initiate commands on the server side, and the client automatically configures SSH passwordless authentication for each server in batches. There is no need to manually configure each server individually. It supports passwordless login deployment for single server, group, and full cluster. It also supports one-click revocation of passwordless permissions to ensure login security. S33. One-click batch directory copy: Supports batch upload, download, and synchronous copy of file directories across servers and clusters, supports incremental copy and resume interrupted download, automatically verifies file integrity, and solves the problem of cumbersome cluster file synchronization. S34. Batch Operation and Maintenance Extension: Added batch command execution, batch service start and stop, and batch system patch update functions, and supports batch distribution and execution of custom operation and maintenance task scripts, which greatly improves cluster operation and maintenance efficiency.

[0025] Optionally, step S4 includes the following steps: S41. CPU Status Monitoring: Real-time collection of CPU utilization, core utilization, average load, and operating temperature; statistics of peak usage time; and prediction of CPU overload risk. S42. Memory Status Monitoring: Real-time collection of total memory capacity, used capacity, remaining capacity, and cache usage; monitoring for memory overflow and memory leak abnormal states. S43. Disk Status Monitoring: Collects the usage rate of each disk partition, remaining space, disk read / write speed, and disk I / O load, and detects disk abnormalities such as bad sectors and insufficient space; S44. Network Traffic Monitoring: Real-time statistics of server uplink and downlink traffic, monitoring of port connection count, network latency, packet loss rate, and identification of network congestion, abnormal access, and port attack risks; S45. Data Visualization and Trend Analysis: The server aggregates all monitoring data and generates hourly / daily / weekly operational trend reports, supports historical data backtracking, and provides data support for server expansion and performance optimization.

[0026] Optionally, step S6 includes the following steps: S61. Customizable threshold configuration: Operation and maintenance personnel can customize high and low alarm thresholds for CPU, memory, disk, network, and middleware operating status to adapt to the operating load requirements of different business servers; S62. Three-level alarm classification: divided into three levels of alarms: prompt, warning and emergency. Minor abnormalities are prompted and recorded, moderate abnormalities are reminded by pop-up window, and serious faults immediately trigger emergency alarms; S63. Multi-channel alarm push: Supports pushing alarm information via system background pop-ups, SMS, email, and WeChat / DingTalk robots, along with abnormal server information, abnormal indicators, and risk levels, facilitating quick handling by operations and maintenance personnel.

[0027] Optionally, step S7 includes the following steps: S71. Operation and maintenance log audit and traceability: Record all operation and maintenance operations, server status changes, alarm records, and middleware deployment records throughout the entire process. The logs are encrypted and retained, and cannot be tampered with. They support keyword search and time period query, enabling full traceability of operation and maintenance operations and meeting the requirements of information security compliance. S72. Intelligent self-healing: For common minor faults such as memory overload, network congestion, middleware anomalies, and service crashes, the system automatically performs repair operations such as restarting services, clearing cache, resetting the network, and releasing memory, without manual intervention, reducing the duration of fault impact; S73. Hierarchical audit of operation permissions: A secondary audit mechanism is added to high-risk operation and maintenance operations (key deletion, permission modification, disk formatting), which can only be executed after administrator authorization, to prevent high-risk misoperations; S74. Scheduled Operation and Maintenance Task Scheduling: Based on the Python scheduled task framework, it supports custom scheduled inspections, scheduled backups, scheduled patch updates, and scheduled password updates, realizing the normalization and automation of operation and maintenance work; S75. Data Backup and Recovery: Supports one-click backup and targeted recovery of server configuration files, business data, and operation and maintenance logs, ensuring the security of operation and maintenance data.

[0028] Example 1: This example provides a specific implementation of a layered server security intelligent operation and maintenance system based on Python. The system is developed based on Python 3.9, the server adopts a Flask+MySQL architecture, and the client is a lightweight independent script, compatible with mainstream server systems such as CentOS, Ubuntu, and Windows Server. 1. Architecture Implementation: The server is deployed on the operation and maintenance management server and exposes the management interface to the outside world; the client communicates with the server through encrypted TCP protocol, runs in the background by default, with CPU utilization of less than 3%, extremely low resource consumption, and does not affect the normal operation of the business server.

[0029] 2. Security Management Implementation: The system uses the AES encryption algorithm to encrypt and store password and key data. It automatically scans the server for weak passwords and redundant accounts daily and automatically reminds users to update passwords monthly. It supports manual batch import and export of key ledgers and records key usage records throughout the entire process.

[0030] 3. Cluster Operation and Maintenance Implementation: For a cluster of 100 servers, passwordless login configuration for the entire cluster can be completed with one click on the server side, taking no more than 5 minutes; it supports incremental synchronization of directories across nodes, with a 100% file copy success rate; and it allows customization of cluster IP access permissions to block unauthorized cross-node access requests.

[0031] 4. Status monitoring implementation: Server resource data is collected every 5 seconds to display the real-time status of CPU, memory, disk, and network. When the disk utilization exceeds 85%, the CPU is continuously occupied at 100%, or the network packet loss rate exceeds 1%, the corresponding alarm level is triggered.

[0032] 5. Middleware Deployment and Implementation: Supports one-click deployment of 8 commonly used middleware such as Nginx, Redis, and MySQL, automatically adapts to the server system version, and automatically detects port listening and service running status after installation, with a deployment success rate of over 99%.

[0033] 6. Alarm and self-healing implementation: Configure a three-level alarm mechanism. Emergency alarms are pushed to maintenance personnel within 3 seconds. For common faults such as memory overflow and Redis service crash, the system automatically performs cache cleanup and service restart operations, and the fault self-healing rate can reach 90%.

[0034] Compared with the prior art, the present invention has the following advantages: 1. Architectural advantages: It adopts a client + server layered architecture to achieve centralized management and control of multiple server clusters. It is developed based on Python with lightweight technology, which has strong cross-platform adaptability, simple deployment, low resource consumption, and is suitable for various large-scale server operation and maintenance scenarios.

[0035] 2. Comprehensive security management: Enables full lifecycle management of server assets, accounts, passwords, and keys, coupled with permission leveling, high-risk operation review, log auditing, and anomaly monitoring functions, comprehensively avoiding server operation and maintenance security risks and meeting network security level protection compliance requirements.

[0036] 3. Highly efficient cluster operation and maintenance: It is the first to have one-click passwordless login, batch directory copying and batch operation and maintenance functions, which solves the problems of cumbersome and inefficient traditional cluster operation and maintenance, and significantly reduces the cost of manual operation and maintenance.

[0037] 4. Intelligent monitoring: Monitor server hardware, network, and service status in all dimensions, and combine trend analysis and anomaly prediction capabilities to achieve pre-fault warning, in-process alarm, and post-fault traceability.

[0038] 5. High degree of automation: Enables unattended one-click deployment of middleware, scheduled operation and maintenance, and intelligent self-healing of faults, reducing manual intervention and improving operation and maintenance stability and efficiency.

[0039] 6. High scalability: The system is developed based on Python scripts and can be customized to extend operation and maintenance scripts, monitoring indicators, and middleware types to adapt to the enterprise's subsequent business expansion and operation and maintenance needs.

[0040] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A layered server security intelligent operation and maintenance system and method based on Python, characterized in that, Includes the following steps: S1. System Layered Architecture Module; This system adopts a C / S (client / server) layered architecture, which is the core infrastructure of the system; S2. Server Security Management Module; This module implements full lifecycle security management of server assets, accounts, passwords, and keys, and is the core module of system security; S3. Intelligent Operation and Maintenance Module for Server Clusters: Designed for server cluster scenarios, this module enables refined cluster management and automated batch operation and maintenance, optimizing the inefficiency of traditional cluster operation and maintenance. S4. Server health status intelligent monitoring module, based on Python psutil resource monitoring library, realizes real-time collection, statistics and analysis of server operation status in all dimensions, covering core hardware and network resources; S5. One-click automated middleware installation module; Includes built-in automated deployment scripts for mainstream service middleware, enabling fully unattended installation based on Python. Supports environment adaptation, automatic dependency installation, and automatic parameter configuration, covering commonly used middleware for operations and maintenance: Nginx, Tomcat, Redis, MySQL, Docker, JDK, Maven, etc. Operations and maintenance personnel only need to select the target server group and the required middleware on the server side. The system automatically completes system environment detection, dependency package installation, version matching, configuration file generation, service start / stop, and automatic startup configuration. After installation, it automatically verifies service availability and outputs a deployment report, completely solving the problems of poor adaptability, cumbersome steps, and inconsistent configurations associated with manual deployment. S6. Intelligent alarm configuration module, supports custom alarm thresholds, hierarchical alarms, and multi-channel notifications, enabling early detection and handling of server anomalies; S7. Optimize and expand core functions; To enhance system security and intelligence, this invention adds several differentiated and innovative functions to address the shortcomings of existing technologies.

2. The layered server security intelligent operation and maintenance system and method based on Python according to claim 1, characterized in that, Step S1 includes the following steps: S11. Server-side module: As the core control hub, it is built on the Python Flask framework to provide backend services. It integrates core capabilities such as data storage, command distribution, permission management, data statistics, alarm push, and task scheduling. It is responsible for uniformly receiving operation instructions from operation and maintenance personnel, issuing operation and maintenance tasks, summarizing server data reported by clients, storing operation and maintenance logs and configuration information, and providing a visual operation and maintenance management backend. S12. Client Module: Developed using lightweight Python scripts, with no redundant dependencies, deployed on various business servers, and running continuously in the background without manual intervention. Its main functions include collecting local server running status, receiving maintenance instructions from the server, executing passwordless login configuration, file copying, middleware installation, and other operations, and transmitting the execution results and monitoring data back to the server in real time.

3. The layered server security intelligent operation and maintenance system and method based on Python according to claim 1, characterized in that, Step S2 includes the following steps: S21. Server Asset Management: Automatically inputs asset information such as IP, port, system version, hardware configuration, and deployed services of all servers in the cluster, supports asset classification, grouping, and status marking, and realizes unified ledger management of cluster servers; S22. Account Permission Management: Supports batch creation, disabling, deletion, and hierarchical configuration of server accounts, distinguishing between three levels of permissions: super administrator, operation and maintenance administrator, and ordinary operator, limiting the operation and maintenance permissions of different accounts, and preventing unauthorized operations; automatically detects unfamiliar and redundant accounts on the server and provides timely warnings of account security risks; S23. Password Security Management: Based on Python encryption algorithms, server login passwords are stored uniformly, periodically and forcibly updated, and weak passwords are detected. Simple and expired passwords are automatically identified, and modification reminders are pushed. All password data is stored in encrypted form to prevent plaintext leakage. S24. Key Security Management: Supports batch generation, distribution, binding, and invalidation of SSH keys; automatically detects leaked and expired keys; supports fine-grained control of key permissions; records key usage logs to ensure full traceability of key operations.

4. The layered server security intelligent operation and maintenance system and method based on Python according to claim 1, characterized in that, Step S3 includes the following steps: S31. Cluster Internal Access Restriction: Configure server firewall policies and IP whitelists based on Python scripts, support custom server access permissions within the cluster, limit communication ports and access permissions between specified servers, prohibit unauthorized cross-node access, and avoid the risk of cluster intranet penetration. S32. One-click cluster passwordless login: Operation and maintenance personnel initiate commands on the server side, and the client automatically configures SSH passwordless authentication for each server in batches without the need for manual configuration on each machine. It supports passwordless login deployment for single machines, groups, and the entire cluster, and also supports one-click revocation of passwordless permissions to ensure login security. S33. One-click batch directory copy: Supports batch upload, download, and synchronous copy of file directories across servers and clusters, supports incremental copy and resume interrupted download, automatically verifies file integrity, and solves the problem of cumbersome cluster file synchronization. S34. Batch Operation and Maintenance Extension: Added batch command execution, batch service start and stop, and batch system patch update functions, and supports batch distribution and execution of custom operation and maintenance task scripts, which greatly improves cluster operation and maintenance efficiency.

5. The layered server security intelligent operation and maintenance system and method based on Python according to claim 1, wherein step S4 includes the following steps: S41. CPU Status Monitoring: Real-time collection of CPU utilization, core utilization, average load, and operating temperature; statistics of peak usage time; and prediction of CPU overload risk. S42. Memory Status Monitoring: Real-time collection of total memory capacity, used capacity, remaining capacity, and cache usage; monitoring for memory overflow and memory leak abnormal states. S43. Disk Status Monitoring: Collects the usage rate of each disk partition, remaining space, disk read / write speed, and disk I / O load, and detects disk abnormalities such as bad sectors and insufficient space; S44. Network Traffic Monitoring: Real-time statistics of server uplink and downlink traffic, monitoring of port connection count, network latency, packet loss rate, and identification of network congestion, abnormal access, and port attack risks; S45. Data Visualization and Trend Analysis: The server aggregates all monitoring data and generates hourly / daily / weekly operational trend reports, supports historical data backtracking, and provides data support for server expansion and performance optimization.

6. The layered server security intelligent operation and maintenance system and method based on Python according to claim 1, wherein step S6 includes the following steps: S61. Customizable threshold configuration: Operation and maintenance personnel can customize high and low alarm thresholds for CPU, memory, disk, network, and middleware operating status to adapt to the operating load requirements of different business servers; S62. Three-level alarm classification: divided into three levels of alarms: prompt, warning and emergency. Minor abnormalities are prompted and recorded, moderate abnormalities are reminded by pop-up window, and serious faults immediately trigger emergency alarms; S63. Multi-channel alarm push: Supports pushing alarm information via system background pop-ups, SMS, email, and WeChat / DingTalk robots, along with abnormal server information, abnormal indicators, and risk levels, facilitating quick handling by operations and maintenance personnel.

7. The layered server security intelligent operation and maintenance system and method based on Python according to claim 1, wherein step S7 includes the following steps: S71. Operation and maintenance log audit and traceability: Record all operation and maintenance operations, server status changes, alarm records, and middleware deployment records throughout the entire process. The logs are encrypted and retained, and cannot be tampered with. They support keyword search and time period query, enabling full traceability of operation and maintenance operations and meeting the requirements of information security compliance. S72. Intelligent self-healing: For common minor faults such as memory overload, network congestion, middleware anomalies, and service crashes, the system automatically performs repair operations such as restarting services, clearing cache, resetting the network, and releasing memory, without manual intervention, reducing the duration of fault impact; S73. Hierarchical audit of operation permissions: A secondary audit mechanism is added to high-risk operation and maintenance operations (key deletion, permission modification, disk formatting), which can only be executed after administrator authorization, to prevent high-risk misoperations; S74. Scheduled Operation and Maintenance Task Scheduling: Based on the Python scheduled task framework, it supports custom scheduled inspections, scheduled backups, scheduled patch updates, and scheduled password updates, realizing the normalization and automation of operation and maintenance work; S75. Data Backup and Recovery: Supports one-click backup and targeted recovery of server configuration files, business data, and operation and maintenance logs, ensuring the security of operation and maintenance data.

8. A layered intelligent server security operation and maintenance system and method based on Python, characterized in that, The method for implementing claim 1 is characterized by comprising: a system layered architecture module, a server security management module, a server group intelligent operation and maintenance module, a server health status intelligent monitoring module, a middleware one-click automated installation module, an intelligent alarm configuration module, and optimized and extended core functions. It has a low operating threshold, allowing operation and maintenance personnel to easily manage multiple servers, significantly improving the security, automation, and efficiency of server operation and maintenance.

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