Machine room equipment management method and system

Abstract

The application provides a kind of machine room equipment management method and system, it is related to equipment management technical field;The application realizes the optimization upgrade of equipment management whole process by constructing data-driven intelligent operation and maintenance system: based on unique identification and associated topological graph, realize the fine tracking of equipment whole life cycle, dynamically identify the strong correlation between devices and core group in combination with historical operation data, form a multi-dimensional load fluctuation analysis model;Through real-time state monitoring and weighted safety scoring mechanism, accurately quantify the risk of equipment operation and generate priority ranking, drive operation and maintenance resources to intelligent allocation of critical path;With the aid of visual platform, the load characteristics of equipment, risk weight and security posture are presented, forming a closed-loop management chain of "data collection-correlation analysis-risk quantification-resource optimization".

Description

Technical Field

[0001] This invention relates to the field of equipment management technology, and in particular to a method and system for managing computer room equipment. Background Technology

[0002] The devices within a data center have complex interrelationships, extending beyond physical connections to include interdependencies at the business logic level. Traditional device management relies heavily on the experience of operations and maintenance personnel to identify these relationships. This approach is not only inefficient but also prone to overlooking potentially strong dependencies. As data centers expand and business operations become increasingly complex, the number of devices grows exponentially, and the relationships between them become even more intricate, rendering manual identification insufficient. Furthermore, in terms of load analysis, traditional methods often focus only on the load of individual devices, lacking a comprehensive analysis of multi-dimensional load fluctuations. They fail to consider the mutual influence between devices and the dynamic changes in business traffic over different time periods, resulting in an inability to accurately assess the overall load of the device cluster and hindering the early detection of potential load bottlenecks.

[0003] Traditional data center equipment management lacks an effective risk quantification mechanism, relying primarily on the subjective judgment and experience of operations and maintenance (O&M) personnel to assess equipment risks. This approach lacks scientific rigor and objectivity, easily leading to inaccurate risk assessment results. Differences in risk perception and judgment standards among O&M personnel further complicate the consistency and comparability of risk assessment results, making it difficult to provide a reliable basis for O&M decision-making.

[0004] Therefore, it is necessary to provide a data center equipment management method and system to solve the above-mentioned technical problems. Summary of the Invention

[0005] To address the aforementioned technical problems, this invention provides a data center equipment management method and system to solve the problems of existing technologies lacking comprehensive analysis of multi-dimensional load fluctuations, which makes it difficult to accurately assess the overall load status of equipment clusters and lacks an effective risk quantification mechanism.

[0006] The present invention provides a method for managing computer room equipment, comprising the following steps:

[0007] S1. Create digital archives for each device in the computer room, including generating a unique ID QR code for each device, entering the device model, configuration, and maintenance information, and establishing a topology diagram of device relationships.

[0008] S2. Obtain historical operating status data of each device through monitoring logs, and analyze the correlation between each device based on the historical operating status data. Through the correlation between each device, identify and identify the core device group.

[0009] S3. Based on historical operating status data, the load fluctuation of each device is divided into multiple stages from multiple dimensions, and the weight of each stage is determined by combining the core device group in each device. The multiple dimensions include time dimension and business dimension.

[0010] S4. Collect the current operating status data of each device in real time according to the preset cycle, and determine the specific stage of the current device based on the phased load fluctuation. At the same time, combine the weight of each stage and use the weighted scoring method to sort the devices for safety and obtain the device safety sorting results.

[0011] S5. Based on the device security ranking results, implement dynamic operation and maintenance resource allocation, and display the management data through a visualization platform. The management data includes: the load fluctuation stage of each device, the stage weight, and the device security ranking results.

[0012] Preferably, step S1 specifically includes:

[0013] S101. Generate a unique ID QR code for each device in the computer room as a unique identifier for the device;

[0014] S102. Input and store the model, configuration, and maintenance information of each device;

[0015] S103. Analyze the network connection relationships and service dependencies between devices in the computer room to determine the device interaction links, and establish a device relationship topology diagram in a graphical way.

[0016] Preferably, step S2 specifically includes:

[0017] S201. By collecting the monitoring logs of each device in the computer room, historical operating status data of each device is extracted from them.

[0018] S202. Based on the equipment relationship topology diagram and historical operating status data, use data analysis methods to analyze the relationship between each piece of equipment and obtain the relationship analysis results between the equipment.

[0019] S203. Based on the correlation analysis results between devices, identify the related devices whose correlation exceeds a preset threshold in the correlation analysis results as strongly correlated device groups, and identify the core device groups in the strongly correlated device groups. The identification method of the core device groups specifically includes: if any failure in a strongly correlated device group will cause service interruption, then it is identified as a core device group.

[0020] Preferably, step S3 specifically includes:

[0021] S301. Based on historical operating status data, the load fluctuation of the equipment is divided into stages according to the daily cycle. Specifically, the stage division of the daily cycle includes analyzing the daily load curve to identify the peak period, low period and normal period of the day, and defining a corresponding stage label for each period.

[0022] S302. Obtain the different service types carried by the computer room, analyze the load of each service type on the equipment, and associate each service type with the equipment load to divide the high-load service stage and the low-load service stage.

[0023] S303. Combining the core equipment groups in each device, analyze the impact of different load fluctuation stages on the core equipment groups and the operation of the entire data center, and determine the corresponding weight for each load fluctuation stage according to the magnitude of the impact. The higher the weight, the greater the importance of the load fluctuation stage to the operation of the data center.

[0024] Preferably, step S4 specifically includes:

[0025] S401. Collect the current operating status data of each device in the computer room in real time according to the preset cycle, including the real-time load, operating temperature and network traffic of the device;

[0026] S402. Compare and analyze the real-time collected data on the current operating status of the equipment with the divided load fluctuation stages, and determine the current load fluctuation stage of each equipment through comparison.

[0027] S403. Based on the current load fluctuation stage of each device and the corresponding weight of each load fluctuation stage, a weighted scoring method is used to score the safety of each device, and the devices are ranked according to their safety scores to obtain the device safety ranking results. The specific calculation method of the safety score is as follows: the corresponding weight of the load fluctuation stage of the device is combined with the safety index of the device in the current load fluctuation stage to obtain the safety score of each device.

[0028] Preferably, step S5 specifically includes:

[0029] S501. Based on the obtained equipment security ranking results, multi-level threshold segments are set as trigger conditions for different operation and maintenance resource adjustments, and the allocation of operation and maintenance resources is dynamically adjusted by identifying the trigger conditions.

[0030] S502. Collect data on load fluctuation stages, stage weights, and equipment safety ranking results for each device, and display them in an intuitive chart or graph form through a visualization platform.

[0031] A data center equipment management system, the management system comprising:

[0032] The data storage module is used to create data archives for each device in the computer room, including generating a unique ID QR code for each device, entering the device model, configuration, maintenance information, and establishing a topology diagram of device relationships.

[0033] The correlation analysis module is used to obtain historical operating status data of each device through monitoring logs, and analyze the correlation between each device based on the historical operating status data. Through the correlation between each device, the core device group is identified and identified.

[0034] The weighting module is used to divide the load fluctuation of each device into multiple stages based on historical operating status data from multiple dimensions, and to determine the weight of each stage by combining the core device group in each device. The multiple dimensions include time dimension and business dimension.

[0035] The identification and sorting module is used to collect the current operating status data of each device in real time according to a preset cycle, and determine the specific stage of the current device based on the phased load fluctuation. At the same time, it combines the weight of each stage and uses a weighted scoring method to sort the devices for safety and obtain the device safety sorting results.

[0036] The allocation and display module is used to dynamically allocate operation and maintenance resources based on the device security ranking results, and to display the management data through a visualization platform. The management data includes: the load fluctuation stage of each device, the stage weight, and the device security ranking results.

[0037] Compared with related technologies, the data center equipment management method and system provided by this invention have the following beneficial effects:

[0038] This invention optimizes and upgrades the entire equipment management process by constructing a data-driven intelligent operation and maintenance system: It achieves refined tracking of the entire equipment lifecycle based on unique identifiers and associated topology maps; it dynamically identifies strong correlations and core connections between equipment by combining historical operating data, forming a multi-dimensional load fluctuation analysis model; through real-time status monitoring and a weighted security scoring mechanism, it accurately quantifies equipment operating risks and generates priority rankings, driving intelligent allocation of operation and maintenance resources to critical paths; supplemented by a visualization platform that centrally presents equipment load characteristics, risk weights, and security status, forming a closed-loop management chain of "data collection - correlation analysis - risk quantification - resource optimization". This invention effectively improves the scientific and forward-looking nature of operation and maintenance decisions, significantly enhances the stability and resource utilization efficiency of data center equipment, and provides a scalable and traceable intelligent management paradigm for digital infrastructure. Attached Figure Description

[0039] Figure 1 This is a flowchart of a data center equipment management method according to the present invention;

[0040] Figure 2 This is a system block diagram of a computer room equipment management system according to the present invention. Detailed Implementation

[0041] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0042] Example 1

[0043] like Figure 1 As shown, a method for managing computer room equipment includes the following steps:

[0044] S1. Create digital archives for each device in the computer room, including generating a unique ID QR code for each device, entering the device model, configuration, and maintenance information, and establishing a topology diagram of device relationships.

[0045] S2. Obtain historical operating status data of each device through monitoring logs, and analyze the correlation between each device based on the historical operating status data. Through the correlation between each device, identify and identify the core device group.

[0046] S3. Based on historical operating status data, the load fluctuation of each device is divided into multiple stages from multiple dimensions, and the weight of each stage is determined by combining the core device group in each device. The multiple dimensions include time dimension and business dimension.

[0047] S4. Collect the current operating status data of each device in real time according to the preset cycle, and determine the specific stage of the current device based on the phased load fluctuation. At the same time, combine the weight of each stage and use the weighted scoring method to sort the devices for safety and obtain the device safety sorting results.

[0048] S5. Based on the device security ranking results, implement dynamic operation and maintenance resource allocation, and display the management data through a visualization platform. The management data includes: the load fluctuation stage of each device, the stage weight, and the device security ranking results.

[0049] In the specific implementation process, step S1 includes the following steps:

[0050] S101. Generate a unique ID QR code for each device in the computer room as a unique identifier for the device.

[0051] Specifically, each device is assigned a unique identifier code, and a QR code generation tool or software is selected. The assigned unique identifier code is then input into the QR code generation tool to generate the corresponding QR code image.

[0052] S102. Input and store the model, configuration, and maintenance information of each device.

[0053] Specifically, the model and configuration information of the equipment are obtained by searching the equipment manuals, product labels, system information, etc. The collected equipment model, configuration and maintenance information are entered into the database of the computer room equipment management system to facilitate equipment traceability and provide convenience for precise equipment management.

[0054] S103. Analyze the network connection relationships and service dependencies between devices in the computer room to determine the device interaction links, and establish a device relationship topology diagram in a graphical way.

[0055] Specifically, network scanning tools such as Nmap are used to scan the network within the data center, obtaining IP addresses, port information, and network connectivity details between devices. By analyzing these details, the physical and logical connections between devices are determined, such as which devices are connected via switches and which devices communicate with each other via network services. The dependencies of various business systems and applications running within the data center on different devices are analyzed. For example, a web application might depend on database servers, application servers, and load balancers. A graphical tool such as Visio is selected to create a device relationship topology diagram. Based on the relationship analysis results, device nodes are created in the graphical tool, each node representing a data center device and labeled with its name, model, and other basic information. Lines or arrows are used to represent the connections and dependencies between devices, and the connection type can be labeled on the lines, such as Ethernet or fiber optic connections.

[0056] In the specific implementation process, step S2 includes the following steps:

[0057] S201. By collecting the monitoring logs of each device in the computer room, historical operating status data of each device is extracted.

[0058] Specifically, for servers using Linux systems in the data center, system logs are collected using built-in logging services such as rsyslog. For network devices, SNMP (Simple Network Management Protocol) tools are used to collect device operational status logs and monitoring logs, storing these logs in designated storage locations, such as a log server or a specific directory on the local disk. Data related to the historical operational status of the devices is extracted from the stored logs. Specifically, to extract data such as CPU utilization and memory utilization of the server, relevant fields in the system logs are parsed; for bandwidth usage of network devices, corresponding data is extracted from the logs collected by SNMP. The extracted historical operational status data should include device identifiers, timestamps, and monitoring metric values ​​for subsequent analysis and processing.

[0059] S202. Based on the equipment relationship topology diagram and historical operating status data, use data analysis methods to analyze the relationship between each piece of equipment and obtain the relationship analysis results between the equipment.

[0060] Specifically, the extracted historical operating status data undergoes preprocessing, including data cleaning, data transformation, and data normalization. After preprocessing, the preprocessed historical operating status data is combined with the device relationship topology diagram for analysis. The Pearson correlation coefficient, a data analysis method, is used to analyze the linear correlation between the operating statuses of the devices. For example, if the topology diagram shows that server A and server B are connected via a network, and server B depends on the data services provided by server A, then the impact of server A's operating status (e.g., CPU utilization, response time) on server B's operating status (e.g., error rate) is analyzed. The correlation index value between the devices is calculated using the Pearson correlation coefficient, and the strength of the correlation between the devices is determined based on these index values. The formula for calculating the correlation index value using the Pearson correlation coefficient is: Correlation index value = Covariance / Standard deviation.

[0061] S203. Based on the correlation analysis results between devices, identify the related devices whose correlation exceeds a preset threshold in the correlation analysis results as strongly correlated device groups, and identify the core device groups in the strongly correlated device groups. The identification method of the core device groups specifically includes: if any failure in a strongly correlated device group will cause service interruption, then it is identified as a core device group.

[0062] Specifically, a threshold for correlation is set. In this embodiment, the preset threshold is set to 0.7, indicating that when the correlation index value between devices is greater than 0.7, the operating states of the devices have a significant mutual influence. Then, the correlation analysis results between devices are traversed, and related devices with a correlation exceeding the preset threshold are identified as strongly correlated device groups. For each strongly correlated device group, a business impact analysis is performed to determine whether a failure of any device in the group would lead to business interruption. If a failure of any device in a strongly correlated device group would lead to business interruption, the group is identified as a core device group.

[0063] In the specific implementation process, step S3 specifically includes:

[0064] S301. Based on historical operating status data, the load fluctuation of the equipment is divided into stages according to the daily cycle. Specifically, the stage division of the daily cycle includes analyzing the daily load curve to identify the peak period, low period and normal period of the day, and defining a corresponding stage label for each period.

[0065] Specifically, from the historical operating status data extracted in step S201, data related to device load is filtered out, including timestamps and corresponding load values. For each device, a daily load curve is plotted with time (accurate to the hour or even minute, depending on the data collection frequency) on the horizontal axis and load value on the vertical axis. Then, by identifying the trend of the load curve, peak, off-peak, and normal periods are identified, including: peak periods are times when the load value is consistently high and stable; off-peak periods are times when the load value is consistently low; and normal periods are when the load value falls between the peak and off-peak periods. Specifically, a moving average of the load value is calculated. When the load value is continuously higher than a preset percentage of the moving average for a period of time, it is determined to be a peak period; when the load value is continuously lower than a preset percentage of the moving average for a period of time, it is determined to be an off-peak period. Corresponding stage labels are defined for the identified peak, off-peak, and normal periods, such as "Peak Stage," "Off-peak Stage," and "Normal Stage."

[0066] S302. Obtain the different service types carried by the data center, analyze the load of each service type on the equipment, and associate each service type with the equipment load to divide the high-load service stage and the low-load service stage.

[0067] Specifically, data on business volume for each business type at different time periods is collected. Simultaneously, combined with the equipment load data compiled in step S301, the impact of business volume on equipment load for different business types is analyzed. Specifically, the correlation between business volume and equipment load can be calculated using the Pearson correlation coefficient in correlation analysis to quantify the degree of influence and obtain the correlation analysis results between business volume and equipment load. When business volume causes equipment load to exceed a preset threshold, the corresponding business type for that time period is determined as a high-load business phase; conversely, it is a low-load business phase. Corresponding high-load and low-load business phases are defined for each business type, and the time range and judgment criteria for each division are recorded.

[0068] S303. Combining the core equipment groups in each device, analyze the impact of different load fluctuation stages on the core equipment groups and the operation of the entire data center, and determine the corresponding weight for each load fluctuation stage according to the magnitude of the impact. The higher the weight, the greater the importance of the load fluctuation stage to the operation of the data center.

[0069] Specifically, for each load fluctuation phase, including the daily cycle phase defined in step S301 and the high-load and low-load business phases defined in step S302, the impact of this phase on the core equipment interconnection and the overall data center operation is analyzed. Specifically, the impact of different load fluctuation phases on the core equipment interconnection and the overall data center operation is analyzed from two aspects: equipment performance indicators and service continuity indicators. For equipment performance indicators: CPU utilization and memory utilization are mainly selected, and the average of these two indicators is calculated for different business phases to assess equipment load. For service continuity indicators: the number of service interruptions and the number of user complaints are selected, and the number of service interruptions and user complaints for different business phases is directly counted. Then, based on the results of the impact analysis, a proportional assignment method is used to determine the weights. In this embodiment, the weights for the high-load and low-load business phases are determined to be 0.7 and 0.3, respectively.

[0070] In the specific implementation process, step S4 includes the following steps:

[0071] S401. Collect the current operating status data of each device in the computer room in real time according to the preset cycle, including the real-time load, operating temperature and network traffic of the device.

[0072] Specifically, a data collection cycle is pre-set based on the characteristics of the equipment in the data center and management needs. For example, for critical business servers with high real-time requirements, the data collection cycle can be set to 1 minute; while for some auxiliary equipment, such as infrequently used surveillance cameras, the data collection cycle can be set to 10 minutes. Based on the set data collection cycle, the current operating status data of each device in the data center is collected in real time, including the real-time load, operating temperature, and network traffic of the device.

[0073] S402. Compare and analyze the real-time collected data on the current operating status of the equipment with the divided load fluctuation stages, and determine the current load fluctuation stage of each equipment through comparison.

[0074] Specifically, for example, for daily periodic segmentation, the current time is compared with predefined peak, off-peak, and normal time ranges; for business-level segmentation, the current business type and volume are matched with the criteria for high-load and low-load business stages. Based on the matching results, the current load fluctuation stage of each device is determined. It should be noted that if the data simultaneously meets the conditions of multiple stages, a preset priority rule can be used for judgment, for example, prioritizing the segmentation based on the business dimension.

[0075] S403. Based on the current load fluctuation stage of each device and the corresponding weight of each load fluctuation stage, a weighted scoring method is used to score the safety of each device, and the devices are ranked according to their safety scores to obtain the device safety ranking results. The specific calculation method of the safety score is as follows: the corresponding weight of the load fluctuation stage of the device is combined with the safety index of the device in the current load fluctuation stage to obtain the safety score of each device.

[0076] Specifically, based on the type and characteristics of the equipment, security indicators are determined for evaluating equipment security during the current load fluctuation phase. In this embodiment, for server equipment, security indicators include CPU utilization and memory utilization; for network equipment, security indicators include interface packet loss rate, packet error rate, and bandwidth utilization. A weighted scoring method is used to comprehensively calculate the security score for each device by combining the weight corresponding to the load fluctuation phase of the device with the security indicators of the device during the current load fluctuation phase. The specific calculation formula is: Security Score = Security Indicator Value × Business Phase Weight.

[0077] In the specific implementation process, step S5 includes the following steps:

[0078] S501. Based on the obtained device security ranking results, multi-level threshold segments are set as trigger conditions for different operation and maintenance resource adjustments, and the allocation of operation and maintenance resources is dynamically adjusted by identifying the trigger conditions.

[0079] Specifically, the equipment safety score is divided into several different threshold ranges. In this embodiment, the safety score is divided into three ranges: high-risk range (safety score below 30), medium-risk range (safety score between 30 and 70), and low-risk range (safety score above 70). When equipment is identified in the high-risk range, a notification is generated: add professional maintenance personnel to monitor the equipment in real time and troubleshoot faults. When equipment is identified in the medium-risk range, a notification is generated: implement regular inspections and preventative maintenance measures, and arrange maintenance personnel to inspect the equipment at regular intervals. When equipment is identified in the low-risk range, a notification is generated: adopt the conventional maintenance management model and perform regular maintenance according to the established maintenance plan.

[0080] S502. Collect data on load fluctuation stages, stage weights, and equipment safety ranking results for each device, and display them in an intuitive chart or graph form through a visualization platform.

[0081] Example 2

[0082] like Figure 2 As shown, a data center equipment management system applied to a data center equipment management method includes:

[0083] The data storage module is used to create data archives for each device in the computer room, including generating a unique ID QR code for each device, entering the device model, configuration, maintenance information, and establishing a topology diagram of device relationships.

[0084] The correlation analysis module is used to obtain historical operating status data of each device through monitoring logs, and analyze the correlation between each device based on the historical operating status data. Through the correlation between each device, the core device group is identified and identified.

[0085] The weighting module is used to divide the load fluctuation of each device into multiple stages based on historical operating status data from multiple dimensions, and to determine the weight of each stage by combining the core device group in each device. The multiple dimensions include time dimension and business dimension.

[0086] The identification and sorting module is used to collect the current operating status data of each device in real time according to a preset cycle, and determine the specific stage of the current device based on the phased load fluctuation. At the same time, it combines the weight of each stage and uses a weighted scoring method to sort the devices for safety and obtain the device safety sorting results.

[0087] The allocation and display module is used to dynamically allocate operation and maintenance resources based on the device security ranking results, and to display the management data through a visualization platform. The management data includes: the load fluctuation stage of each device, the stage weight, and the device security ranking results.

[0088] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0089] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, including read-only memory (ROM), random access memory (RAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), one-time programmable read-only memory (OTPROM), electrically-Erasable Programmable Read-Only Memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, disk storage, magnetic tape storage, or any other computer-readable medium capable of carrying or storing data.

[0090] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

Claims

1. A method for managing computer room equipment, characterized in that, The management method includes the following steps: S1. Create digital archives for each device in the computer room, including generating a unique ID QR code for each device, entering the device model, configuration, and maintenance information, and establishing a topology diagram of device relationships. S2. Obtain historical operating status data of each device through monitoring logs, and analyze the correlation between each device based on the historical operating status data. Through the correlation between each device, identify and identify the core device group. S3. Based on historical operating status data, the load fluctuation of each device is divided into multiple stages from multiple dimensions, and the weight of each stage is determined by combining the core device group in each device. The multiple dimensions include time dimension and business dimension. S4. Collect the current operating status data of each device in real time according to the preset cycle, and determine the specific stage of the current device based on the phased load fluctuation. At the same time, combine the weight of each stage and use the weighted scoring method to sort the devices for safety and obtain the device safety sorting results. S5. Based on the device security ranking results, implement dynamic operation and maintenance resource allocation, and display the management data through a visualization platform. The management data includes: the load fluctuation stage of each device, the stage weight, and the device security ranking results. The steps in S2 specifically include: S201. By collecting the monitoring logs of each device in the computer room, historical operating status data of each device is extracted from them. S202. Based on the equipment relationship topology diagram and historical operating status data, use data analysis methods to analyze the relationship between each piece of equipment and obtain the relationship analysis results between the equipment. S203. Based on the correlation analysis results between devices, identify the related devices with a correlation degree exceeding a preset threshold in the correlation analysis results as strongly correlated device groups, and identify the core device groups in the strongly correlated device groups. The identification method of the core device groups specifically includes: if any failure in a strongly correlated device group will cause service interruption, then it will be identified as a core device group. The steps in S3 specifically include: S301. Based on historical operating status data, the load fluctuation of the equipment is divided into stages according to the daily cycle. Specifically, the stage division of the daily cycle includes analyzing the daily load curve to identify the peak period, low period and normal period of the day, and defining a corresponding stage label for each period. S302. Obtain the different service types carried by the computer room, analyze the load of each service type on the equipment, and associate each service type with the equipment load to divide the high-load service stage and the low-load service stage. S303. Combining the core equipment groups in each device, analyze the impact of different load fluctuation stages on the core equipment groups and the operation of the entire data center, and determine the corresponding weight for each load fluctuation stage according to the magnitude of the impact. The higher the weight, the greater the importance of the load fluctuation stage to the operation of the data center.

2. The method for managing computer room equipment according to claim 1, characterized in that, The steps in step S1 specifically include: S101. Generate a unique ID QR code for each device in the computer room as a unique identifier for the device; S102. Input and store the model, configuration, and maintenance information of each device; S103. Analyze the network connection relationships and service dependencies between devices in the computer room to determine the device interaction links, and establish a device relationship topology diagram in a graphical way.

3. The method for managing computer room equipment according to claim 1, characterized in that, The specific steps of step S4 include: S401. Collect the current operating status data of each device in the computer room in real time according to the preset cycle, including the real-time load, operating temperature and network traffic of the device; S402. Compare and analyze the real-time collected data on the current operating status of the equipment with the divided load fluctuation stages, and determine the current load fluctuation stage of each equipment through comparison. S403. Based on the current load fluctuation stage of each device and the corresponding weight of each load fluctuation stage, a weighted scoring method is used to score the safety of each device, and the devices are ranked according to their safety scores to obtain the device safety ranking results. The specific calculation method of the safety score is as follows: the corresponding weight of the load fluctuation stage of the device is combined with the safety index of the device in the current load fluctuation stage to obtain the safety score of each device.

4. The method for managing computer room equipment according to claim 1, characterized in that, The specific steps of step S5 include: S501. Based on the obtained equipment security ranking results, multi-level threshold segments are set as trigger conditions for different operation and maintenance resource adjustments, and the allocation of operation and maintenance resources is dynamically adjusted by identifying the trigger conditions. S502. Collect data on load fluctuation stages, stage weights, and equipment safety ranking results for each device, and display them in an intuitive chart or graph form through a visualization platform.

5. A data center equipment management system, applied to the data center equipment management method as described in any one of claims 1-4, characterized in that, The management system includes: The data storage module is used to create data archives for each device in the computer room, including generating a unique ID QR code for each device, entering the device model, configuration, maintenance information, and establishing a topology diagram of device relationships. The correlation analysis module is used to obtain historical operating status data of each device through monitoring logs, and analyze the correlation between each device based on the historical operating status data. Through the correlation between each device, the core device group is identified and identified. The weighting module is used to divide the load fluctuation of each device into multiple stages based on historical operating status data from multiple dimensions, and to determine the weight of each stage by combining the core device group in each device. The multiple dimensions include time dimension and business dimension. The identification and sorting module is used to collect the current operating status data of each device in real time according to a preset cycle, and determine the specific stage of the current device based on the phased load fluctuation. At the same time, it combines the weight of each stage and uses a weighted scoring method to sort the devices for safety and obtain the device safety sorting results. The allocation and display module is used to dynamically allocate operation and maintenance resources based on the device security ranking results, and to display the management data through a visualization platform. The management data includes: the load fluctuation stage of each device, the stage weight, and the device security ranking results.

Images