Method, system and server for data center asset management based on out-of-band network

Abstract

The application relates to the field of operation and maintenance service management, and discloses a data center asset management method and system based on an out-of-band network and a server. In the application, at least two types of devices are imported into a data center based on an out-of-band network according to a multi-protocol adaptation template and / or a collection plug-in, and configuration information and hardware information of the at least two types of devices are acquired; the acquired hardware information is preprocessed, and the preprocessed hardware information is predicted and abnormally judged; alarm data is generated according to a prediction result and an abnormal judgment result, and an association between the generated alarm data and historical alarm data is extracted to perform alarm analysis and fault prediction; and in response to a device configuration instruction, the configuration information of the at least two types of devices is set through the out-of-band network, and a control script is generated to control the at least two types of devices to perform corresponding operations.

Description

Technical Field

[0001] This application relates to the field of operation and maintenance service management, and in particular to a data center asset management method, system and server based on out-of-band network. Background Technology

[0002] Data centers are the core carriers of cloud computing and IT operations, housing a large number of heterogeneous hardware assets such as servers, network equipment, and security devices. Their reliable operation and maintenance depend on efficient asset management. Currently, cloud platforms are continuously expanding in scale, and the underlying hardware exhibits heterogeneity in terms of vendors, models, and architectures, gradually revealing the shortcomings of existing technologies.

[0003] Because a unified multi-protocol adaptation system has not been built, the asset management system is difficult to be compatible with multiple types of devices and hardware from multiple manufacturers, and is prone to incomplete component information collection and alarm subscription failures. In high-concurrency management and monitoring scenarios, it lacks efficient batch processing capabilities, and the asset management system is prone to performance bottlenecks and response delays. Functionally, it only supports basic device-level monitoring and configuration viewing, and lacks component-level fine-grained management and batch operation and maintenance capabilities. The data analysis form is singular and lacks multi-dimensional correlation analysis methods, which cannot adapt to the intelligent management needs of large-scale heterogeneous assets. Summary of the Invention

[0004] The purpose of this application is to provide a data center asset management method, system, and server based on out-of-band networks, enabling unified management of heterogeneous devices, device monitoring, automated configuration and maintenance, alarm generation and analysis, and fault prediction, thereby effectively improving asset management capabilities, operational efficiency, and system security.

[0005] To address the aforementioned technical problems, this application provides a data center asset management method based on an out-of-band network, comprising: importing at least two types of devices into a data center based on an out-of-band network according to a multi-protocol adaptation template and / or acquisition plugin, and obtaining configuration information and hardware information of the at least two types of devices; performing data preprocessing on the acquired hardware information, and predicting and judging anomalies in the preprocessed hardware information; generating alarm data based on the prediction results and anomaly judgment results, and extracting the correlation between the generated alarm data and historical alarm data to perform alarm analysis and fault prediction; and, in response to device configuration instructions, setting the configuration information of the at least two types of devices through the out-of-band network, and generating control scripts to control the at least two types of devices to perform corresponding operations.

[0006] This application also provides a data center asset management system based on an out-of-band network for executing the data center asset management method described above, including: an asset management module, a monitoring management module, an alarm management module, and a configuration management module; wherein, the asset management module is used to import at least two types of devices into the data center based on the out-of-band network, according to a multi-protocol adaptation template and / or acquisition plugin, and obtain the configuration information and hardware information of the at least two types of devices; the monitoring management module is used to perform data preprocessing on the hardware information collected by the asset management module, and to predict and determine anomalies in the preprocessed hardware information; the alarm management module is used to generate alarm data based on the prediction results and anomaly determination results output by the monitoring management module, and to extract the correlation between the generated alarm data and historical alarm data for alarm analysis and fault prediction; the configuration management module is used to respond to device configuration instructions, set the configuration information of the at least two types of devices through the out-of-band network, and generate control scripts to control the at least two types of devices to perform corresponding operations.

[0007] Embodiments of this application also provide a server, including: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the data center asset management method based on out-of-band network as described above.

[0008] Compared to related technologies, this application's implementation method is based on out-of-band networks, employs multi-protocol adaptation templates and acquisition plugins to import at least two types of devices and uniformly acquire configuration and hardware information. This enables unified management of heterogeneous devices of multiple types, manufacturers, and architectures, resolving device compatibility bottlenecks. After preprocessing, predicting, and judging anomalies in the hardware information, alarm data is generated. Alarm correlations are extracted to achieve alarm analysis and fault prediction. By combining real-time and historical alarms for correlation analysis and fault prediction, intelligent alarm judgment and fault root cause location are achieved, improving data analysis and fault handling efficiency. Simultaneously, in response to configuration commands, device configuration settings are completed through out-of-band networks, and control scripts are generated to drive devices to execute corresponding operations, ensuring the stability of high-concurrency systems and allowing independent management of devices even during business failures. The above process as a whole realizes unified management of heterogeneous devices, device monitoring, automated configuration and maintenance, alarm generation and analysis, and fault prediction, effectively improving asset management capabilities, maintenance efficiency, and system security. Attached Figure Description

[0009] One or more embodiments are illustrated by way of example with reference numerals in the accompanying drawings. These illustrations do not constitute a limitation on the embodiments. Elements with the same reference numerals in the drawings are denoted as similar elements. Unless otherwise stated, the figures in the drawings are not to be limited by scale.

[0010] Figure 1 This is a schematic diagram of a data center asset management system based on an out-of-band network, according to some embodiments of this application; Figure 2 This is a flowchart illustrating a data center asset management method based on out-of-band networks according to some embodiments of this application; Figure 3 This is a schematic diagram of the server structure according to some embodiments of this application. Detailed Implementation

[0011] To more clearly illustrate the technical solutions of the embodiments in this specification, the embodiments will be described in detail below with reference to the accompanying drawings. Obviously, the content described below are some examples or embodiments of this specification. For those skilled in the art, without creative effort, the technical solutions or means disclosed in this specification can be applied to other scenarios based on this technical content.

[0012] It should be understood that the terms "system," "device," "unit," and / or "module" used in this specification are a method of distinguishing different components, elements, parts, sections, or assemblies at different levels. However, if other words can achieve the same purpose, they may be replaced by other expressions.

[0013] Unless otherwise specified, the technical terms used to describe components, elements, etc. in this specification are not singular but may include plural. Generally speaking, terms such as "comprising" or "including" only indicate that explicitly identified steps, elements, or components are included, and these steps, elements, and components do not constitute an exclusive list, as the described method or apparatus may also include other steps or components.

[0014] This specification uses flowcharts to illustrate the operational steps performed by the apparatus or system of related embodiments. However, unless otherwise specified, the order in which these steps are described should not be construed as a limitation on the order of execution. Those skilled in the art can adjust the order of these steps based on the knowledge and information conveyed by the embodiments in this specification. Adjustments include, but are not limited to, reversing the order of steps, merging multiple steps, and splitting a step.

[0015] As the background technology shows, due to the lack of a unified multi-protocol adaptation system, the asset management system is difficult to be compatible with multiple types of devices and hardware from multiple manufacturers, and is prone to incomplete component information collection and alarm subscription failures; in high-concurrency management and monitoring scenarios, it lacks efficient batch processing capabilities, and the asset management system is prone to performance bottlenecks and response delays; in terms of functionality, it only supports basic device-level monitoring and configuration viewing, and lacks component-level fine-grained management and batch operation and maintenance capabilities; the data analysis form is single and lacks multi-dimensional correlation analysis methods, which cannot adapt to the intelligent management needs of large-scale heterogeneous assets.

[0016] In view of this, embodiments of this application provide a data center asset management system based on out-of-band networks, such as... Figure 1 As shown, the system includes: an asset management module, a monitoring management module, an alarm management module, a configuration management module, and a system management module.

[0017] The system comprises the following modules: Asset Management Module, which imports at least two types of devices into the data center via out-of-band network, using multi-protocol adaptation templates and / or acquisition plugins, and acquires configuration and hardware information for these devices; Monitoring Management Module, which preprocesses the hardware information acquired by Asset Management Module and predicts and identifies anomalies in the preprocessed hardware information; Alarm Management Module, which generates alarm data based on the prediction and anomaly identification results output by Monitoring Management Module, extracts the correlation between the generated alarm data and historical alarm data, and performs alarm analysis and fault prediction; Configuration Management Module, which responds to device configuration commands by setting configuration information for at least two types of devices via out-of-band network and generates control scripts to control the devices to perform corresponding operations; and System Management Module, which responds to user configuration commands by configuring user information in the data center via out-of-band network and exporting configuration logs.

[0018] It is worth mentioning that all modules involved in this embodiment are logical modules. In practical applications, a logical unit can be a physical unit, a part of a physical unit, or a combination of multiple physical units. Furthermore, to highlight the innovative aspects of this application, this embodiment does not introduce units that are not closely related to solving the technical problem proposed in this application; however, this does not mean that other units are absent in this embodiment.

[0019] Through the above-described method described in this application, a data center asset management system and server based on out-of-band network are established. This system or device enables unified management of heterogeneous devices, device monitoring, automated configuration and maintenance, alarm generation and analysis, and fault prediction, effectively improving asset management capabilities, maintenance efficiency, and system security.

[0020] In the aforementioned hardware and software operating environment, this application provides a data center asset management method based on out-of-band networks. This method, based on out-of-band networks, employs multi-protocol adaptation templates and acquisition plugins to import at least two types of devices and uniformly acquire configuration and hardware information. This enables unified management of heterogeneous devices of multiple types, manufacturers, and architectures, resolving device compatibility bottlenecks. After preprocessing, predicting, and judging anomalies in the hardware information, alarm data is generated. Alarm correlations are extracted to achieve alarm analysis and fault prediction. By combining real-time and historical alarms for correlation analysis and fault prediction, intelligent alarm judgment and fault root cause location are achieved, improving data analysis and fault handling efficiency. Simultaneously, in response to configuration commands, device configuration settings are completed through the out-of-band network, and control scripts are generated to drive devices to execute corresponding operations, ensuring the stability of high-concurrency systems and allowing independent device management even during business failures. The entire process achieves unified management of heterogeneous devices, device monitoring, automated configuration and maintenance, alarm generation and analysis, and fault prediction, effectively improving asset management capabilities, operational efficiency, and system security.

[0021] Figure 2 This is a flowchart illustrating a data center asset management method based on out-of-band networks, according to some embodiments of this application. Figure 2 As shown, in some embodiments, the data center asset management method based on out-of-band networks may include the following steps.

[0022] Step 210: Based on the out-of-band network, import at least two types of devices into the data center according to the multi-protocol adaptation template and / or acquisition plugin, and obtain the configuration information and hardware information of at least two types of devices.

[0023] In step 210, the asset management module in the data center asset management, based on the isolation characteristics of out-of-band networks, uses multi-protocol adaptation templates and dedicated acquisition plugins to achieve unified import and information collection of at least two types of devices, thus solving the problems of poor compatibility of heterogeneous devices and incomplete information collection. Specifically, this application first establishes an independent communication channel between the out-of-band network and the data center equipment to ensure stable device import and information collection operations even in the event of a business network failure. Then, it pre-configures multi-protocol adaptation templates and dedicated collection plugins. These templates are compatible with mainstream device management protocols such as Intelligent Platform Management Interface (IPMI), Simple Network Management Protocol (SNMP), and Redfish, loading corresponding protocol templates and collection plugins for devices from different manufacturers and with different processor architectures. Next, it performs multi-type device import operations, using batch template import to manage rack servers and network switches into the system, and completes device authentication and access verification. Finally, it initiates information collection requests based on the out-of-band network, synchronously acquiring configuration and hardware information for both types of devices using the adapted protocol templates and collection plugins. The configuration information includes firmware version, power policy, baseline configuration, and network parameters, while the hardware information includes the model, serial number, health status, and performance parameters of components such as the Central Processing Unit (CPU), memory, disk, fan, and power supply.

[0024] In this way, by combining out-of-band networks with multi-protocol adaptation templates and data acquisition plugins, the unified import and management of two or more heterogeneous devices can be successfully achieved. The device configuration information and hardware information can be fully obtained, and the device is compatible with multiple manufacturers, multiple protocols and multiple types of devices. This solves the problems of limited device support, poor platform compatibility and missing component information in existing technologies, and provides data support for subsequent monitoring, alarm and configuration management.

[0025] In some embodiments, configuration and hardware information of at least two types of devices in the data center are collected based on a multi-protocol adaptation template and / or acquisition plugin, including: importing at least two types of devices into the data center based on the out-of-band network according to the multi-protocol adaptation template and / or acquisition plugin, wherein the types of devices include at least servers, network devices, security devices, mainframes, and minicomputers; and collecting configuration and hardware information of the at least two types of devices through a device management protocol; wherein the configuration information includes at least the device firmware version and Internet Protocol (IP) address, and the hardware information includes at least the hardware status data of the device's CPU, memory, disk, power supply, fan, and Redundant Array of Independent Disks (RAID).

[0026] In one example, the data center asset management system includes a device discovery submodule. Leveraging the independent isolation of out-of-band networks and their continued accessibility even during business network failures, this submodule uses pre-built multi-protocol adaptation templates and dedicated data acquisition plugins within the system's asset management module to import and manage at least two types of hardware devices within the data center. These device types include, at a minimum, servers (rack, edge, multi-node servers), network devices, security devices, mainframes, and minicomputers. Device import supports three methods: manual import, batch import using Excel templates, and automatic discovery. The system utilizes dedicated plugins and protocol templates to ensure compatibility with heterogeneous devices from different manufacturers, models, and processor architectures, guaranteeing the integrity and compatibility of managing large volumes of heterogeneous assets.

[0027] Furthermore, it provides dedicated data collection plugins and various device management protocol templates for mainstream servers and network devices, ensuring excellent compatibility with devices from different manufacturers, models, and with different processor architectures. It also enables in-depth data collection of information on key components such as CPU, memory, and disk. For example, this out-of-band network-based data center asset management system uses mainstream device management protocols such as IPMI, SNMP (v1 / v2c / v3), and Redfish to collect configuration information from various managed devices. In addition, the system simultaneously employs the Apache Spark big data processing engine to perform timed batch processing of large-scale device scan data, ensuring the stability and reliability of high-concurrency, large-volume device data transmission. Through the above data collection process, the collected configuration information includes at least the device firmware version and device IP address. At the same time, core configuration data such as device unique identifier, network configuration, and maintenance configuration can be collected simultaneously to achieve unified collection of configuration information of heterogeneous devices. The collected hardware information includes at least the hardware status data of the device's CPU, memory, disk, power supply, fan, and RAID card. Specifically, it covers the number of CPU cores and health status, memory capacity, serial number and running status, disk presence and health status, power supply working status, fan speed and health status, RAID card array configuration and status, etc., to achieve multi-dimensional hardware data collection.

[0028] In one example, different types of devices employ different device management service submodules for data collection. For instance, the server management service submodule periodically collects information on server components such as CPU, memory, and disk; the network device management service submodule, after importing network devices, allows viewing and displaying the managed network device information in a network device list, where detailed information is available, including basic information, component information, procurement and maintenance, performance, alarms, web terminal, and settings; the security device management service submodule allows viewing and managing basic information, component information, performance, alarms, and settings of security devices; and the mainframe / minicomputer management service submodule allows viewing and managing basic information, node information, procurement and maintenance, performance, monitoring, alarms, change management, and settings of mainframes / minicomputers. This data center asset management system also provides an IP management service submodule, through which the IP usage status of the aforementioned types of devices can be viewed.

[0029] In this way, by using multi-protocol adaptation templates and data collection plugins, unified access can be achieved for various types, manufacturers, and architectures of devices such as servers, network devices, security devices, and mainframes / minicomputers, solving the problems of poor compatibility and limited management types in existing systems. In addition, this application example can standardize the collection of configuration information such as firmware version and IP address, as well as component-level hardware status such as CPU, memory, disk, power supply, fan, and RAID, ensuring comprehensive and complete information collection. At the same time, in conjunction with the Spark big data processing engine, it supports batch scanning and data processing of large numbers of devices, maintaining stability under high load and improving the efficiency of large-scale asset management.

[0030] Step 220: Perform data preprocessing on the collected hardware information, and predict and judge the anomalies of the preprocessed hardware information.

[0031] Specifically, the monitoring and management module in the data center asset management system relies on the big data processing engine Apache Spark to perform scheduled batch data preprocessing on hardware information (including component model, capacity, serial number, health status, operating parameters, and performance indicators) of key components such as CPU, memory, disk, power supply, fans, and RAID cards, collected through dedicated acquisition plugins and multi-protocol adaptation templates. This preprocessing includes data cleaning, redundancy removal, outlier filtering, format standardization, and unified field mapping to eliminate problems such as inconsistent data formats, missing information, and noise interference caused by devices from multiple manufacturers, models, and architectures. This ensures that hardware information is accurate, standardized, and complete, providing a high-quality data foundation for subsequent performance prediction and anomaly detection, while also guaranteeing the stability and reliability of data transmission and processing in high-concurrency acquisition scenarios involving large-scale devices.

[0032] Following the preprocessing operation, this embodiment, based on the standardized hardware information after preprocessing, uses the performance prediction and multi-granular fine monitoring capabilities of the system monitoring and management module to quantitatively predict the operating trends, performance degradation, load changes, and health status evolution of equipment and key components. This allows for the early prediction of potential component failure risks, equipment performance bottlenecks, and operating overload trends, achieving a shift from "passive monitoring" to "proactive prediction" and providing data support for preventive maintenance.

[0033] In addition to predicting hardware information, this embodiment also uses preprocessed hardware information as the analysis object, combining the system's built-in component health monitoring rules, multi-dimensional alarm judgment strategies, and the analysis capabilities of the OpsBrain large-scale model in the operation and maintenance field to conduct anomaly judgment. Specifically, based on preset thresholds and health standards, it determines in real time whether components such as CPU, memory, and disk have abnormal states, performance exceeding standards, or hardware failures; and it extracts the temporal and correlation features of hardware information through the large-scale model to identify hidden anomalies and cascading failures.

[0034] In some embodiments, the data center asset management system also includes a health monitoring service submodule, a performance monitoring service submodule, an inspection management service submodule, and a utility tool service submodule. The health monitoring service submodule is used to monitor the health status of various types of assets under management, such as servers, network devices, security devices, and mainframes / minicomputers, from multiple dimensions. For example, the monitoring scope covers the overall health status at the device level and the in-depth health status at the component level. Component-level monitoring objects include key hardware such as CPUs, memory, disks, power supplies, fans, network cards, and RAID cards. It also collects component health status, operating parameters, and anomaly information in real time, generating an overview of device health status to provide basic data for anomaly detection and fault warning. The performance monitoring service submodule continuously collects and presents full-dimensional performance indicator data of devices to support performance analysis and trend prediction. For example, it monitors core performance indicators of devices in real time to form a visualized view of device performance; it performs performance prediction based on historical and real-time performance data to identify performance bottlenecks and overload risks in advance; and it provides quantitative basis for performance anomaly detection, capacity planning, and operation and maintenance optimization. The Inspection Management Service submodule is used to conduct automated and customized inspection operations based on real-time equipment monitoring results. Specifically, it supports the creation, editing, and deletion of inspection tasks, and the targeted configuration of inspection objects and cycles. It allows for the execution of inspection operations, viewing of inspection records and results, forming a standardized inspection closed loop. Furthermore, it integrates health and performance monitoring data to achieve targeted inspections, improving the efficiency of potential hazard detection. The Practical Tools Service submodule provides a set of operation and maintenance tools to assist in monitoring verification, network troubleshooting, and anomaly location. It includes built-in tools such as Ping, device network testing, SNMP Trap testing, and general diagnostic tools, enabling different testing processes based on these built-in tools.

[0035] Step 230: Generate alarm data based on the prediction results and anomaly judgment results, and extract the correlation between the generated alarm data and historical alarm data to perform alarm analysis and fault prediction.

[0036] In step 230, the alarm management module in the data center asset management system, based on the performance prediction results and hardware information anomaly judgment results generated by the monitoring management module, combined with the equipment health status, component operating parameters, and performance index thresholds, triggers and generates alarm data. This embodiment categorizes alarm data into types such as business processing, power environment, equipment performance, security alarms, and system alarms, and marks them with five alarm levels: urgent, severe, moderate, minor, and event.

[0037] In some embodiments, alarm data is generated based on the prediction results and anomaly judgment results. Specifically, this is implemented according to the monitoring management module and alarm management module of the present invention, including the following steps: obtaining the prediction result of any one of the hardware status data of CPU, memory, disk, power supply, fan and RAID, and determining whether the prediction result exceeds the corresponding threshold; if the prediction result exceeds the corresponding threshold, determining the alarm type based on the hardware information, and determining the alarm level based on the prediction results and anomaly judgment results; generating alarm data based on the alarm level, alarm type and anomaly judgment results.

[0038] Specifically, the system acquires hardware status data for CPU, memory, disk, power supply, fans, and RAID through the health monitoring and performance monitoring submodules. Based on this preprocessed hardware status data, it performs performance and operational trend predictions. The prediction results are compared with preset health and performance thresholds to determine if the predicted values ​​exceed the corresponding thresholds. Next, based on the hardware information triggering the alarm (component type, anomaly, device category), the alarms are categorized into business processing, power environment, device performance, security alarms, and system alarms. Combining the prediction results (risk level) and anomaly judgment results (fault severity), the alarm data is marked into five alarm levels: urgent, severe, moderate, minor, and event. Finally, the alarm level, alarm type, and anomaly judgment conclusion are integrated, along with device information, component information, occurrence time, threshold deviation, etc., to generate standardized, structured alarm data. This alarm data is stored in the database using a combination of active and passive alarm collection methods for subsequent alarm display, correlation analysis, and fault prediction. Furthermore, the alarm data also includes the alarm source, occurrence time, device information, and hardware name.

[0039] In some embodiments, the alarm management module further includes a business view submodule, an alarm console module, and an alarm analysis submodule. The business view submodule provides multi-dimensional centralized display and categorized viewing of alarm data, enabling visualized management of alarm information. It displays four core views: current alarms, historical alarms, alarm events, and masked alarms. This allows operations and maintenance personnel to quickly grasp the overall alarm status, distinguish between valid alarms and masked / processed alarms, and improve alarm viewing and tracing efficiency. The alarm console module enables real-time alarm reception, centralized management, and closed-loop handling, supporting 24 / 7 device monitoring. It uses protocols such as IPMI, SNMP, and Redfish, employing a combination of active and passive methods, to acquire device alarm information in real time, achieving 24 / 7 uninterrupted device monitoring and rapid fault response, reducing potential operational risks. The alarm analysis submodule is used to realize intelligent correlation analysis and fault prediction of alarms, and improve the ability of fault location and prediction. This submodule extracts the correlation features and time dimension features of current alarms and historical alarms, identifies the causal, chain, and concurrent relationships between alarms, locates the root cause alarms, and further performs in-depth analysis of alarm data based on the large-scale operation and maintenance model OpsBrain (fine-tuned to adapt to operation and maintenance scenarios) to realize intelligent fault analysis and potential fault prediction.

[0040] In some embodiments, the correlation between the generated alarm data and historical alarm data is extracted for alarm analysis and fault prediction, including: extracting the dependency features between the generated alarm data and each historical alarm data; extracting the time features of the generated alarm data and the historical alarm data respectively; generating an alarm correlation graph based on the dependency features and the time features; and inputting the dependency features and the time features into an alarm analysis model to generate alarm causes and alarm predictions.

[0041] Specifically, this alarm analysis submodule extracts the dependency features between current alarm data and each historical alarm data based on the device information, component type, fault phenomenon, and anomaly type corresponding to the alarm data. These dependency features include causal relationships, chain triggering relationships, concurrent relationships, and component-level dependencies among the alarm data, identifying which alarm is the root cause and which are derivative alarms, and eliminating redundant and duplicate alarms. Furthermore, it extracts the time-dimensional features of the alarms, including alarm occurrence time, sequence, time interval, duration, frequency, and periodicity, to reconstruct the timeline of fault occurrence and evolution. Finally, the above dependency features and time features are visually integrated to generate an alarm correlation graph. The graph uses lines or arrows to indicate the correlation and triggering direction between alarms, intuitively presenting the causal chain, concurrent relationships, and time evolution logic of the alarms, helping maintenance personnel quickly understand the fault propagation path. Furthermore, the extracted dependency features and time features are input into the OpsBrain (i.e. alarm analysis model) large model in this application for fine-tuning and adaptation in the field of operation and maintenance. The model outputs alarm causes and locates the root cause of the failure. Based on historical alarm patterns and current features, the system completes fault prediction, predicts potential fault risks, alarm storm trends and component failure probabilities, and realizes proactive operation and maintenance early warning.

[0042] Step 240: In response to the device configuration command, configure the configuration information of at least two types of devices through the out-of-band network, and generate control scripts to control the at least two types of devices to perform corresponding operations.

[0043] In some embodiments, step 240 is implemented based on the configuration management module and specifically includes: responding to device configuration instructions to perform firmware upgrades and firmware configurations on the firmware of at least two types of devices; responding to device configuration instructions to select a corresponding script from a preset script library and send the selected script to at least two types of devices to control the at least two types of devices to perform corresponding operations; and responding to device configuration instructions to generate a corresponding script and store the generated script in the script library.

[0044] Specifically, upon receiving device configuration instructions, the firmware upgrade and configuration functions of the configuration management module are invoked to perform automated firmware-level operation and maintenance on various target device types via out-of-band network. Specifically, the firmware upgrade submodule performs the following operations: based on preset firmware upgrade templates, it performs batch firmware version upgrades on target devices, supporting the addition, editing, application, and deletion of firmware upgrade templates, adapting to firmware upgrade processes for devices from different manufacturers, models, and processor architectures. The firmware configuration submodule performs the following operations: it performs batch configuration and distribution of firmware parameters for target devices through firmware configuration templates, supporting the addition, editing, application, copying, and deletion of firmware configuration templates, achieving unified and standardized configuration of firmware parameters across multiple devices. This step does not rely on the business network and can be stably executed even during business interruptions, ensuring the continuity of device firmware operation and maintenance.

[0045] The script execution submodule within the configuration management module responds to device configuration commands. Based on the device type, maintenance scenario, and operational requirements corresponding to the command, it selects archived standardized scripts from the configuration management module's pre-set script library (warehouse management). Through script execution and file distribution functions, the selected scripts are distributed to at least two types of target devices, controlling the devices to automatically execute the corresponding maintenance operations. This enables batch and automated execution of operations such as device configuration, parameter adjustment, and initialization, reducing manual maintenance costs. Furthermore, when no matching maintenance script is found in the pre-set script library, the system responds to device configuration commands and generates custom scripts adapted to the current requirements through the custom template submodule and script execution submodule. It supports configuring script names, script types, executors, execution paths, and script content. After script generation, it is automatically and synchronously archived in the configuration management module's script library, achieving unified management, repeated use, and continuous accumulation of script resources, adapting to diverse and personalized device maintenance scenarios. The above script can perform operations including firmware upgrade, firmware configuration, hard disk erasure, custom template, file distribution, script execution, common functions, power on / off, power supply unit (PSU) working mode, version baseline management, snapshot management, and repository management, enabling batch deployment and configuration of servers, facilitating rapid server deployment and improving operation and maintenance efficiency.

[0046] Based on step 240, firmware upgrades and configurations are templated and executed in batches, solving the problem of limited configuration management capabilities in existing technologies. Relying on the script library and automated script execution, equipment operation and maintenance operations are standardized, improving the efficiency of rapid server deployment and batch deployment. Custom script generation and database management are supported, improving configuration and maintenance capabilities and adapting to the diverse operation and maintenance needs of multi-source heterogeneous devices.

[0047] Please see Figure 2In some embodiments, the data center asset management method further includes step 250. In step 250, in response to a user configuration instruction, user information of the data center is configured via an out-of-band network, and configuration logs are exported.

[0048] Specifically, the system management module of this data center asset management system supports user management, including five parts: user, role, user group, organization, and scope management. It can perform operations such as adding, editing, and deleting users, roles, user groups, and organization / scope management; it supports log management, allowing users to view system operation logs with information such as operation time, operator, and operation IP, and perform operations such as deleting and exporting logs; it supports notification management, allowing users to send notification information to pre-configured email addresses; and it supports security control, including security policies, policy request forms, and policy approval forms, allowing users to perform operations such as viewing security policies and requesting security policies.

[0049] In one example, the system management module also includes a user management service submodule. The system calls the user management submodule to complete user information configuration, including configuring five core types of information related to data center operations and maintenance: users, roles, user groups, organizations, and scopes; and supporting operations such as adding, editing, and deleting users, roles, user groups, organizations, and scopes. Parameters such as username, password, role permissions, full name, email, phone number, affiliated organization, and scope can be configured. In this way, fine-grained, role-based access control is achieved through user configuration, accurately allocating system operation permissions for different users and roles, and ensuring the security of system and asset data access.

[0050] In one example, the system management module also includes a log management service submodule. After user information configuration is completed, the data center asset management system automatically generates and records the operation log (i.e., configuration log) for this configuration through the log management submodule of the system management module, and supports exporting the configuration log. The configuration log records detailed information such as the configuration operation time, operator information, operation IP address, operation type, and configuration object. Maintenance personnel can use the exported configuration log for operation and maintenance traceability, security auditing, and compliance verification, achieving full-process traceability and auditability of user configuration operations.

[0051] In one or more of the above embodiments, the data center asset management system based on out-of-band networks of this application can specifically solve the problem of difficulty in managing data center assets due to their large number and variety. By integrating the out-of-band interfaces of multiple vendors and types of devices through a unified management platform, it provides centralized status monitoring, firmware upgrades, remote power on / off, log collection, alarm analysis and other functions, realizing real-time monitoring, remote operation and centralized management of underlying hardware resources, ensuring the integrity of infrastructure and improving operation and maintenance efficiency.

[0052] The steps of the various methods described above are only for clarity. In practice, they can be combined into one step or some steps can be split into multiple steps. As long as they include the same logical relationship, they are all within the scope of protection of this application. Adding insignificant modifications or introducing insignificant designs to the algorithm or process, but without changing the core design of the algorithm and process, are also within the scope of protection of this patent.

[0053] Additionally, this application also provides a server, such as... Figure 3 As shown, it includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the out-of-band network-based data center asset management method described in one or more embodiments above.

[0054] The memory and processor are connected via a bus, which can include any number of interconnecting buses and bridges, connecting various circuits of one or more processors and the memory. The bus can also connect various other circuits, such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and therefore will not be described further herein. The bus interface provides an interface between the bus and the transceiver. The transceiver can be a single element or multiple elements, such as multiple receivers and transmitters, providing a unit for communicating with various other devices over a transmission medium. Data processed by the processor is transmitted over the wireless medium via an antenna, which also receives and transmits data to the processor. The processor manages the bus and general processing, and can also provide various functions, including timing, peripheral interface, voltage regulation, power management, and other control functions. The memory can be used to store data used by the processor during operation.

[0055] Those skilled in the art will understand that the above embodiments are specific examples of implementing this application, and in practical applications, various changes can be made in form and detail without departing from the spirit and scope of this application.

Claims

1. An out-of-band network based data center asset management method, characterized by, include: Based on out-of-band networks, at least two types of devices are imported into the data center according to multi-protocol adaptation templates and / or acquisition plugins, and the configuration information and hardware information of the at least two types of devices are obtained. The collected hardware information is preprocessed, and the preprocessed hardware information is then used for prediction and anomaly detection. Alarm data is generated based on the prediction results and anomaly judgment results, and the correlation between the generated alarm data and historical alarm data is extracted to perform alarm analysis and fault prediction. In response to a device configuration command, the configuration information of the at least two types of devices is set through the out-of-band network, and a control script is generated to control the at least two types of devices to perform corresponding operations.

2. The data center asset management method according to claim 1, characterized in that, The process of collecting configuration and hardware information of at least two types of devices in the data center based on multi-protocol adaptation templates and / or acquisition plugins includes: Based on the out-of-band network, at least two types of devices are imported into the data center according to the multi-protocol adaptation template and / or acquisition plugin, wherein the types of devices include at least servers, network devices, security devices, mainframes and minicomputers; The device management protocol collects configuration information and hardware information of at least two types of devices; wherein the configuration information includes at least the device firmware version and IP address, and the hardware information includes at least the hardware status data of the device's CPU, memory, disk, power supply, fan, and RAID.

3. The data center asset management method according to claim 2, characterized in that, The generation of alarm data based on the prediction results and anomaly detection results includes: Obtain the prediction result of any one of the hardware status data of the CPU, the memory, the disk, the power supply, the fan, and the RAID, and determine whether the prediction result exceeds the corresponding threshold. If the prediction result exceeds the corresponding threshold, the alarm type is determined based on the hardware information, and the alarm level is determined based on the prediction result and the anomaly judgment result, wherein the alarm level includes: emergency, severe, moderate, minor and event. The alarm data is generated based on the alarm level, the alarm type, and the anomaly determination result.

4. The data center asset management method according to claim 3, characterized in that, The step of extracting the correlation between the generated alarm data and historical alarm data to perform alarm analysis and fault prediction includes: Extract the dependency features between the generated alarm data and each historical alarm data; Extract the time features of the generated alarm data and historical alarm data respectively; Based on the dependency features and the time features, an alarm correlation graph is generated; and the dependency features and the time features are input into an alarm analysis model to generate alarm causes and alarm predictions.

5. The data center asset management method according to claim 1, characterized in that, The step of responding to a device configuration command by configuring the firmware of the at least two types of devices through the out-of-band network and generating control scripts to control the at least two types of devices to perform corresponding operations includes: In response to the device configuration command, firmware upgrades and firmware configurations are performed on the firmware of the at least two types of devices. In response to the device configuration command, a corresponding script is selected from a preset script library, and the selected script is sent to the at least two types of devices to control the at least two types of devices to perform corresponding operations; and, In response to the device configuration command, a corresponding script is generated and stored in the script library.

6. The data center asset management method according to claim 5, characterized in that, The control of the at least two types of devices to perform corresponding operations includes: Control the at least two types of devices to perform hard disk erasure, power-on / off control, power consumption strategy adjustment, and version baseline management operations.

7. The data center asset management method according to claim 1, characterized in that, The method further includes: configuring user information of the data center through the out-of-band network in response to user configuration instructions, and exporting configuration logs.

8. A data center asset management system based on out-of-band networks, characterized in that, The data center asset management method for executing any one of claims 1-7 includes: an asset management module, a monitoring management module, an alarm management module, and a configuration management module; wherein, The asset management module is used to import at least two types of devices into the data center based on out-of-band network, according to multi-protocol adaptation templates and / or acquisition plugins, and to obtain the configuration information and hardware information of the at least two types of devices. The monitoring and management module is used to preprocess the hardware information collected by the asset management module, and to predict and judge the anomalies of the preprocessed hardware information. The alarm management module is used to generate alarm data based on the prediction results and anomaly judgment results output by the monitoring management module, and to extract the correlation between the generated alarm data and historical alarm data for alarm analysis and fault prediction. The configuration management module is used to respond to device configuration instructions by setting the configuration information of the at least two types of devices through the out-of-band network, and to generate control scripts to control the at least two types of devices to perform corresponding operations.

9. The data center asset management system according to claim 8, characterized in that, The system also includes a system management module, which, in response to user configuration commands, configures user information in the data center through the out-of-band network and exports configuration logs.

10. A server, characterized in that, include: At least one processor; as well as, A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the out-of-band network-based data center asset management method as described in any one of claims 1 to 7.

Images