Data center control method and system realized based on large model

Abstract

The invention discloses a data center control method and system realized based on a large model, and belongs to the technical field of large models and intelligent operation and maintenance, and the method comprises the following steps: a data center realizes the unified control of a network system through the large model; automatic and intelligent management is realized through a specially trained model; an SDN controller is integrated: the SDN controller provides a capability interface for monitoring and controlling network equipment for the large model, and the large model realizes intelligent decision making and automatic management by combining the capability of the SDN controller; other devices of the data center comprise a firewall and a load balancing device, and are connected with the SDN controller to be combined with the large model control center to form an intelligent control system; and the intelligent operation and maintenance platform is responsible for operation, maintenance and monitoring of the large model control center, management, control and maintenance work, and provides a configuration interface at the same time. According to the invention, the automation and intelligence level of the data center can be improved, and quick response and self-repairing capability can be provided.

Description

Technical Field

[0001] The present invention relates to the technical fields of large models and intelligent operation and maintenance, and specifically to a data center control method and system implemented based on a large model. Background Art

[0002] During the current deployment process of data centers, to ensure the security of data centers and improve the reliability and performance of services, a large number of devices such as DDoS protection, Web application firewalls (WAFs), firewalls, and load balancing devices are deployed.

[0003] On the one hand, these devices enhance security, can resist large-scale traffic-based attacks, protect data centers from service interruptions, defend against specific attacks on Web applications such as SQL injection and cross-site scripting (XSS), provide security control at the network boundary, and prevent unauthorized access and malicious traffic. The deployment of load balancing devices and high reliability improves the high availability and performance of the system. By distributing requests to multiple servers, it ensures that a single server will not be overloaded, thereby improving system availability and response speed. In a multi-server environment, load balancing can also improve overall performance by optimizing resource usage to reduce latency and increase throughput. At the same time, these devices allow IT teams to implement complex traffic management and security policies, such as access control based on IP, protocol, port, or application, to achieve flexible traffic management and policy implementation. Multiple devices provide the possibility of failover. If one device or path fails, traffic can be automatically redirected to other available resources, reducing the risk of single points of failure.

[0004] However, a large number of security and high-reliability devices also bring a large number of problems. A large number of devices means a more complex network architecture, increasing the difficulty of configuration, monitoring, and troubleshooting. It may also lead to configuration errors, becoming a source of security vulnerabilities. In addition, firewalls, WAFs, and DDoS protection devices may introduce additional latency when processing traffic, especially when performing in-depth inspection and filtering, resulting in a large performance overhead. The update and maintenance burden is also increased. As threats evolve, software and devices need to be updated and upgraded regularly, which may require downtime and affect the continuity of services. Summary of the Invention

[0005] The technical task of the present invention is to address the above deficiencies and provide a data center control method and system implemented based on a large model, which can improve the automation and intelligence levels of data centers, provide fast response and self-repair capabilities, can significantly improve the efficiency, security, and cost-effectiveness of data center devices, and at the same time reduce the complexity of management and operation and maintenance.

[0006] The technical solution adopted by the present invention to solve its technical problems is:

[0007] A data center control method implemented based on a large model, and the implementation of this method includes:

[0008] 1) Large model control center: The data center realizes the unified control of the network system through the large model; realizes automated and intelligent management through a specially trained model;

[0009] 2) Integrated SDN controller: The large model can analyze network traffic, application requirements, and user behavior, provide more accurate network resource allocation suggestions for the SDN controller, and can also pre-adjust network configurations through predictive analysis to optimize bandwidth usage and reduce latency; the SDN controller provides the large model with an interface for monitoring and controlling network devices, and the large model combines the capabilities of the SDN controller to achieve intelligent decision-making and automated management;

[0010] 3) Other network devices in the data center: Other devices in the data center include firewalls, load balancers, etc., which are connected to the SDN controller and combined with the large model control center to form an intelligent control system;

[0011] 4) Intelligent operation and maintenance platform: Responsible for the operation and maintenance and monitoring of the large model control center, conducts management and maintenance work, and at the same time provides a friendly configuration interface, and can configure various network devices through scenario-based natural language.

[0012] This method uses a large model to achieve automatic configuration and operation and maintenance in the data center, replacing a large amount of manual configuration, monitoring, and operation and maintenance work. By combining the large model with the SDN controller, the large model can realize functions such as intelligent monitoring and configuration of traditional network devices, which can improve the intelligent level of the data center, simplify the difficulty of configuration and operation and maintenance, and improve the response speed to emergencies.

[0013] Furthermore, the large model control center can automatically analyze network traffic, and after identifying anomalies, automatically adjust protection strategies including firewalls, DDOS, etc., reduce manual intervention, and improve the response speed.

[0014] Furthermore, the specific implementation of the large model control center includes:

[0015] Deploy the pre-trained large model in the cloud data center and register various devices with the large model control center for unified management by the large model;

[0016] Based on the configuration information, the large model control center detects the status of various devices, calls the interface to obtain configuration-related capabilities, or dynamically adjusts the configuration;

[0017] For operation and maintenance users, the large model provides an intelligent operation and maintenance platform interaction interface; for various network devices and security devices, the large model uses the integrated SDN controller to interact with various devices using the capabilities of the SDN controller.

[0018] Furthermore, the intelligent operation and maintenance platform includes data collection and analysis, visualization and monitoring, intelligent decision-making, event management, fault recovery strategy, and scenario operation and maintenance functions.

[0019] Furthermore, the intelligent operation and maintenance platform is specifically implemented as follows:

[0020] Data collection and analysis: Collect various data from the environment, including system logs, performance metrics, events, transaction data, network traffic, user behavior, etc.; perform data cleaning, transformation, and integration to ensure data quality for further analysis;

[0021] Visualization and monitoring: Real-time monitor the status of the environment and display key metrics through a graphical interface; perform data visualization to facilitate the operation and maintenance team's understanding and analysis of complex data sets;

[0022] Intelligent decision-making: Provide decision support, give optimization suggestions and strategies based on data analysis, and present key operation and maintenance metrics through dashboards and reports to enable operation and maintenance personnel to quickly understand the system status;

[0023] Fault recovery: Automatically isolate and recover from faults, reduce the need for manual intervention, and shorten the mean time to repair by quickly detecting and responding to events;

[0024] Event management: If the user defines business monitoring logic, the data collection module will perform monitoring regularly, and based on the acquired data, use the large model to judge the business status;

[0025] Automated tasks: Automatically execute predefined operations.

[0026] Furthermore, the predefined operations of the automated tasks include restarting services, adjusting resource allocation, sending alerts, etc.

[0027] Furthermore, the SDN controller supports multiple communication protocols, including Netconf, RESTFulApi, gRPC, Openflow, SNMP, etc., and can control network devices through these standard protocols and proprietary protocols to achieve centralized management and dynamic adjustment of network policies.

[0028] The present invention also claims to protect a data center control system implemented based on a large model. The data center combines the large model and the SDN controller to implement an intelligent control center, and improves the intelligence level of the data center by integrating the intelligent capabilities of the large model and the network device control and monitoring capabilities of the SDN controller;

[0029] The intelligent operation and maintenance platform based on the large model provides a natural description language configuration method; it liberates operation and maintenance personnel from a large number of professional configuration device configuration instructions and allows them to focus more on the business itself;

[0030] The system specifically realizes data center control through the above method.

[0031] The present invention also claims to protect a data center control device implemented based on a large model, including: at least one memory and at least one processor;

[0032] The at least one memory is used to store machine-readable programs;

[0033] The at least one processor is used to call the machine-readable program to implement the above method.

[0034] The present invention also claims to protect a computer-readable medium, on which computer instructions are stored, and when the computer instructions are executed by a processor, the above method can be implemented.

[0035] Compared with the prior art, a data center control method and system implemented based on a large model of the present invention have the following beneficial effects:

[0036] While the complex network architecture improves the security of the data center and the reliability of services, it also increases the difficulty of configuration, monitoring, and troubleshooting. With the development of technology, various services in the data center have put forward higher requirements for the intelligent and automated level of the network and the ability to quickly respond to events. The method for constructing a data center based on a large model designed in this article can effectively improve these problems:

[0037] First, this method reduces the maintenance difficulty of the data center network, improves the operation and maintenance efficiency, can shorten the deployment time of network devices, especially in large-scale data centers, quickly and accurately configure hundreds or thousands of devices; at the same time, it provides intelligent troubleshooting suggestions, simplifies the operation and maintenance process, and improves the work efficiency of the operation and maintenance team.

[0038] Second, it improves the intelligent and automated level. The large model can automatically infer and generate network configuration strategies based on historical data and the current network state, reduce manual configuration errors and improve efficiency; it can also predict network traffic patterns and take measures in advance to adjust the network.

[0039] Third, security and performance are enhanced. The large model can identify abnormal network behaviors and timely adjust firewall rules or intrusion prevention strategies; it can also timely adjust network configurations, such as bandwidth allocation, routing selection, and QoS policies, to avoid congestion and improve network performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 is a schematic diagram of the basic architecture of a data center control method implemented based on a large model provided by an embodiment of the present invention;

[0041] Figure 2 This is a functional diagram of the intelligent operation and maintenance platform provided by an embodiment of the present invention. Detailed implementation manners

[0042] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments.

[0043] An embodiment of the present invention provides a data center control method implemented based on a large model. The implementation of this method includes:

[0044] 1. Large model control center.

[0045] The data center realizes unified control of the network system through a large model. Through a specially trained model, automated and intelligent management is achieved. It can automatically analyze network traffic, and after identifying anomalies, automatically adjust the protection strategies of firewalls, DDOS, etc., reduce manual intervention, and improve the response speed.

[0046] 2. Integrated SDN controller.

[0047] The large model can analyze network traffic, application requirements, and user behavior, provide more accurate network resource allocation suggestions for the SDN controller, and can also pre-adjust network configurations through predictive analysis to optimize bandwidth usage and reduce latency; the SDN controller provides an ability interface for monitoring and controlling network devices for the large model, and the large model combines the capabilities of the SDN controller to achieve intelligent decision-making and automated management.

[0048] 3. Other network devices in the data center.

[0049] Other devices in the data center include devices such as firewalls and load balancers. By connecting to the SDN controller and combining with the large model control center, an intelligent control system is formed.

[0050] 4. Intelligent operation and maintenance platform.

[0051] The intelligent operation and maintenance platform is mainly responsible for the operation and maintenance and monitoring of the large model control center, conducts management and maintenance work, and at the same time provides a friendly configuration interface, and can configure various network devices through scenario-based natural language.

[0052] Combined with the attached Figure 1-2 As shown, the specific implementation of this method is as follows:

[0053] The specific implementation of the large model control center is as follows:

[0054] (1) The control center based on the large model is the core part of the architecture of this method. The pre-trained large model is deployed in the cloud data center, and various devices are registered in the large model control center for unified management by the large model.

[0055] (2) The control center will detect the status of various devices based on the configuration information, call the interface to obtain configuration-related capabilities, or dynamically adjust the configuration.

[0056] (3) For operation and maintenance users, the large model provides an intelligent operation and maintenance platform interaction interface; for various network devices and security devices, the large model integrates the SDN controller and uses the controller's capabilities to interact with various devices.

[0057] The detailed description of the intelligent operation and maintenance platform is as follows:

[0058] The intelligent operation and maintenance platform is an interface provided by the large model control center to operation and maintenance personnel. After the large model pre-configures various device information in the data center, etc., natural language can be used through this interface to handle operation and maintenance matters, which can further reduce the complexity of operation and maintenance.

[0059] Such as Figure 2 As shown, the intelligent operation and maintenance platform includes functional modules such as data collection and analysis, visualization and monitoring, intelligent decision-making, event management, fault recovery strategy, and scenario operation and maintenance.

[0060] The specific implementation of each module is as follows:

[0061] Data collection and analysis: Collect various data from the environment, including system logs, performance metrics, events, transaction data, network traffic, user behavior, etc. Clean, transform, and integrate the data to ensure data quality for further analysis.

[0062] Visualization and monitoring: Real-time monitor the status of the environment and display key metrics through a graphical interface. Data visualization facilitates the operation and maintenance team to understand and analyze complex data sets.

[0063] Intelligent decision-making: Provide decision support, give optimization suggestions and strategies based on data analysis, and present key operation and maintenance metrics on dashboards and reports to help operation and maintenance personnel quickly understand the system status.

[0064] Fault recovery: Automatically isolate and recover from faults, reduce the need for manual intervention, quickly detect and respond to events, and shorten the mean time to repair.

[0065] Event management: If the user defines business monitoring logic, the data collection module will perform regular monitoring, and based on the obtained data, use the large model to judge the business status.

[0066] Automated tasks: Automatically execute predefined operations, such as restarting services, adjusting resource allocation, sending alerts, etc.

[0067] The integrated SDN controller mentioned above:

[0068] Integrating large models into SDN controllers can significantly enhance the intelligence and automation level of the network. This integration can provide SDN controllers with higher-level analysis, prediction, and decision-making capabilities, enabling the network to more autonomously respond to complex and dynamic network conditions. The SDN controller supports multiple communication protocols such as Netconf, RESTFulApi, gRPC, Openflow, and SNMP, and can control network devices through these standard and proprietary protocols to achieve centralized management and dynamic adjustment of network policies.

[0069] To ensure data security, service reliability, and regulatory compliance, data centers usually deploy a large number of security devices such as firewalls, load balancers, web application firewalls, and DDoS protection. While these devices enhance the security of the data center and improve the reliability and performance of services, they may also bring some burdens. For example, the complexity increases. These devices require complex configurations to achieve optimal performance and security effects, and incorrect configurations may lead to security vulnerabilities. Continuous monitoring of device status and network traffic, as well as analysis of potential threats and performance bottlenecks, require high-level skills and tools. Moreover, maintaining the operation, upgrade, and management of these devices requires professional technical personnel, which also incurs labor costs. To address this issue, the method proposed in this paper is a data center construction method based on large model technology and software-defined network (SDN) that can simplify data center configuration and operation and maintenance. By collaborating with traditional devices through SDN, it enhances decision-making, detection, and response capabilities, and can build a more intelligent and efficient data center security protection system, simplify configuration complexity, and improve and optimize the user experience.

[0070] The embodiment of the present invention also provides a data center control system implemented based on a large model. The data center combines a large model and an SDN controller to implement an intelligent control center, and combines the intelligent capabilities of the large model and the network device control and monitoring capabilities of the SDN controller to improve the intelligence level of the data center;

[0071] The intelligent operation and maintenance platform based on a large model provides a natural description language configuration method; it frees operation and maintenance personnel from a large number of professional configuration device configuration instructions and allows them to focus more on the business itself.

[0072] This system integrates large pre-trained models (large models) with software-defined network (SDN) technology, aiming to achieve intelligent management and automated operation and maintenance of data center networks. Utilizing the powerful data processing and prediction capabilities of large models and combining the centralized control characteristics of SDN, it provides unprecedented flexibility, efficiency, and security for data center networks.

[0073] The core mechanism of the system lies in its intelligent network configuration and optimization module. Based on the deep learning of the large model on network traffic patterns, device performance, and network topology, this module can automatically adjust the strategies of the SDN controller to achieve dynamic allocation and optimization of network resources. For example, the large model can predict network congestion points, adjust routing strategies in advance to avoid network bottlenecks; at the same time, it can also intelligently allocate bandwidth resources according to real-time workloads to ensure high-performance transmission of critical services.

[0074] In addition, the system also has intelligent fault prediction and recovery functions. By analyzing historical fault data and the current network state, the large model can identify potential fault risks and trigger the SDN controller to take preventive measures, such as automatically isolating faulty devices and reconfiguring network paths, thereby significantly reducing network downtime and operation and maintenance costs.

[0075] In terms of security, an advanced anomaly detection model is integrated, which can monitor network behavior in real time, quickly identify and respond to potential network security threats, such as DDoS attacks and unauthorized access attempts. Once an anomaly is detected, the system can automatically adjust firewall rules and intrusion prevention strategies to build a dynamic security protection barrier to protect the data center from network attacks.

[0076] The system specifically realizes data center control through the data center control method based on the large model described in the above embodiments. It includes:

[0077] 1. Large model control center.

[0078] The data center realizes unified control of the network system through the large model. Through specially trained models, it realizes automated and intelligent management. It can automatically analyze network traffic, and after identifying anomalies, automatically adjust protection strategies such as firewalls and DDOS, reducing manual intervention and improving response speed.

[0079] 2. Integrated SDN controller.

[0080] The large model can analyze network traffic, application requirements, and user behavior, provide more accurate network resource allocation suggestions for the SDN controller, and can also pre-adjust network configurations through predictive analysis to optimize bandwidth usage and reduce latency; the SDN controller provides the large model with an interface for the ability to monitor and control network devices, and the large model combines the capabilities of the SDN controller to achieve intelligent decision-making and automated management.

[0081] 3. Other network devices in the data center.

[0082] Other devices in the data center include devices such as firewalls and load balancers. By connecting to the SDN controller and combining with the large model control center, they form an intelligent control system.

[0083] 4. Intelligent Operation and Maintenance Platform

[0084] The intelligent operation and maintenance platform is mainly responsible for the operation and maintenance and monitoring of the large model control center, conducts management and maintenance work, and at the same time provides a friendly configuration interface, through which various network devices can be configured in a scenario-based natural language.

[0085] Among them, the specific implementation of the large model control center is as follows:

[0086] (1) The control center based on the large model is the core part of this method architecture. The pre-trained large model is deployed in the cloud data center, and various devices are registered in the large model control center for unified management by the large model.

[0087] (2) The control center will detect the status of various devices based on the configuration information, call the interface to obtain the configuration-related capabilities, or dynamically adjust the configuration.

[0088] (3) For operation and maintenance users, the large model provides an intelligent operation and maintenance platform interaction interface; for various network devices and security devices, the large model integrates the SDN controller and uses the capabilities of the controller to interact with various devices.

[0089] The intelligent operation and maintenance platform is the interface provided by the large model control center to operation and maintenance personnel. After the large model pre-configures various device information in the data center, etc., operation and maintenance affairs can be processed using natural language through this interface, which can further reduce the complexity of operation and maintenance. The intelligent operation and maintenance platform includes function modules such as data collection and analysis, visualization and monitoring, intelligent decision-making, event management, fault recovery strategy, and scenario-based operation and maintenance. The specific implementation of each module is as follows:

[0090] Data collection and analysis: Collect various data from the environment, including system logs, performance metrics, events, transaction data, network traffic, user behavior, etc. Clean, transform, and integrate the data to ensure data quality for further analysis.

[0091] Visualization and monitoring: Real-time monitor the status of the environment and display key metrics through a graphical interface. Data visualization facilitates the operation and maintenance team to understand and analyze complex data sets.

[0092] Intelligent decision-making: Provide decision support, give optimization suggestions and strategies based on data analysis, and present key operation and maintenance metrics through dashboards and reports to help operation and maintenance personnel quickly understand the system status.

[0093] Fault recovery: Automatically isolate and recover from faults, reduce the need for manual intervention, quickly detect and respond to events, and shorten the mean time to repair.

[0094] Event Management: If the user defines business monitoring logic, the data collection module will perform monitoring regularly, judge the business status using the large model based on the acquired data.

[0095] Automated Tasks: Automatically execute predefined operations, such as restarting services, adjusting resource allocation, sending alerts, etc.

[0096] The integrated SDN controller:

[0097] Integrating a large model into the SDN controller can significantly enhance the intelligence and automation level of the network. This integration can provide the SDN controller with higher-level analysis, prediction, and decision-making capabilities, enabling the network to more autonomously respond to complex and dynamic network conditions. The SDN controller supports multiple communication protocols such as Netconf, RESTFulApi, gRPC, Openflow, SNMP, etc., and can control network devices through these standard protocols and proprietary protocols to achieve centralized management and dynamic adjustment of network policies.

[0098] An embodiment of the present invention also provides a data center control device implemented based on a large model, including: at least one memory and at least one processor;

[0099] The at least one memory is used to store machine-readable programs;

[0100] The at least one processor is used to call the machine-readable program to implement the data center control method based on the large model described in the above embodiment.

[0101] An embodiment of the present invention also provides a computer-readable medium, on which computer instructions are stored. When the computer instructions are executed by a processor, they can implement the data center control method based on the large model described in the above embodiment. Specifically, a system or device equipped with a storage medium can be provided, on which software program codes for implementing the functions of any one of the above embodiments are stored, and the computer (or CPU or MPU) of the system or device is made to read and execute the program codes stored in the storage medium.

[0102] In this case, the program code read from the storage medium itself can implement the functions of any one of the above embodiments, so the program code and the storage medium storing the program code constitute a part of the present invention.

[0103] Examples of storage media for providing program code include floppy disks, hard disks, magneto-optical disks, optical disks (such as CD-ROM, CD-R, CD-RW, DVD-ROM, DVD-RAM, DVD-RW, DVD+RW), magnetic tapes, non-volatile memory cards, and ROMs. Optionally, the program code can be downloaded from a server computer via a communication network.

[0104] In addition, it should be clear that not only can the functions of any one of the above embodiments be realized by executing the program code read by the computer, but also by causing an operating system or the like operating on the computer based on the instructions of the program code to complete part or all of the actual operations.

[0105] Furthermore, it can be understood that the program code read from the storage medium is written into the memory provided in the expansion board inserted into the computer or into the memory provided in the expansion unit connected to the computer, and then based on the instructions of the program code, the CPU or the like installed on the expansion board or the expansion unit is caused to execute part and all of the actual operations, thereby realizing the functions of any one of the above embodiments.

[0106] The present invention has been described in detail above with reference to the accompanying drawings and preferred embodiments. However, the present invention is not limited to these disclosed embodiments. Based on the above-mentioned multiple embodiments, those skilled in the art can know that more embodiments of the present invention can be obtained by combining the code review means in the above different embodiments, and these embodiments are also within the protection scope of the present invention.

Claims

1. A data center control method based on a large model, characterized in that: The implementation of this method includes: 1) Large model control center: The data center uses large models to achieve unified control of the network system; it uses specially trained models to achieve automated and intelligent management; 2) Integration with SDN controller: The big model provides network resource allocation suggestions to the SDN controller and can pre-adjust the network configuration through predictive analysis. The SDN controller provides the big model with an interface for monitoring and controlling network devices. The big model combines the capabilities of the SDN controller to achieve intelligent decision-making and automated management. 3) Other network devices in the data center: Other devices in the data center include firewalls and load balancers, which are connected to the SDN controller and combined with the large model control center to form an intelligent control system; 4) Intelligent operation and maintenance platform: responsible for the operation and monitoring of the large model control center, performing management and maintenance work, and providing a configuration interface that can configure various network devices through scenario-based natural language.

2. A data center control method based on a large model according to claim 1, characterized in that: The large model control center can automatically analyze network traffic, and after identifying anomalies, automatically adjust protection strategies including firewalls and DDOS.

3. A data center control method based on a large model according to claim 1 or 2, characterized in that: The large model control center is specifically implemented as follows: Deploy the pre-trained big model in the cloud data center and register various devices to the big model control center; The large model control center detects the status of various devices based on configuration information, calls interfaces to obtain configuration-related capabilities, or dynamically adjusts configurations; For operation and maintenance users, the big model provides an interactive interface for the intelligent operation and maintenance platform; for various network devices and security devices, the big model integrates the SDN controller and uses the capabilities of the SDN controller to interact with various devices.

4. The data center control method based on a large model according to claim 1 is characterized in that: The intelligent operation and maintenance platform includes data collection and analysis, visualization and monitoring, intelligent decision-making, event management, fault recovery strategy, and scenario operation and maintenance functions.

5. A data center control method based on a large model according to claim 4, characterized in that: The intelligent operation and maintenance platform is specifically implemented as follows: Data collection and analysis: Collect various data from the environment, including system logs, performance indicators, events, transaction data, network traffic, and user behavior data; Perform data cleaning, conversion and integration to ensure data quality for further analysis; Visualization and monitoring: monitor the status of the environment in real time and display key indicators through a graphical interface; Perform data visualization to help operations teams understand and analyze complex data sets; Intelligent decision-making: Provides decision support, gives optimization suggestions and strategies based on data analysis, and displays key operation and maintenance indicators in dashboards and reports, so that operation and maintenance personnel can quickly understand the system status; Fault recovery: Automatic fault isolation and recovery, shortening the mean time to repair by quickly detecting and responding to events; Event management: If the user defines the business monitoring logic, the data collection module will monitor regularly and use the big model to determine the business status based on the acquired data; Automated tasks: Automated execution of predefined actions.

6. A data center control method based on a large model according to claim 5, characterized in that: The predefined operations of the automated tasks include restarting services, adjusting resource allocation, and sending alerts.

7. The data center control method based on a large model according to claim 1 is characterized in that: The SDN controller supports multiple communication protocols, including Netconf, RESTFulApi, gRPC, Openflow, and SNMP. It controls network devices through these standard protocols and proprietary protocols to achieve centralized management and dynamic adjustment of network policies.

8. A data center control system based on a large model, characterized in that: The data center uses a combination of big models and SDN controllers to realize an intelligent control center. The intelligence level of the data center is improved by combining the intelligent capabilities of the big model and the network equipment control and monitoring capabilities of the SDN controller. An intelligent operation and maintenance platform based on a large model, providing a natural description language configuration method; The system specifically implements data center control through any method described in claims 1 to 7.

9. A data center control device based on a large model, characterized in that: include: at least one memory and at least one processor; The at least one memory is used to store a machine-readable program; The at least one processor is used to call the machine-readable program to implement the method described in any one of claims 1 to 7.

10. A computer-readable medium, characterized in that The computer readable medium stores computer instructions, which, when executed by a processor, can implement the method described in any one of claims 1 to 7.

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