Intelligent operation and maintenance monitoring method and system for data center

Abstract

The invention relates to the technical field of operation and maintenance of data centers, and discloses an intelligent operation and maintenance monitoring method and system of a data center, and the method comprises the steps: building a hierarchical monitoring frame, carrying out the hierarchical data collection and standardization processing of three layers, namely, physical equipment, virtual resources and application services, and constructing an interlayer data sharing model; performing anomaly scanning on the running state of each layer by using a preset fault detection algorithm, triggering an alarm once an anomaly is found, and determining a propagation path influenced by a fault through data backtracking; and then analyzing a causal relationship between anomaly and cross-layer data by using an association rule mining algorithm, performing multi-dimensional verification in combination with a decision support rule base, and finally generating a fault positioning conclusion and a repair priority. In addition, response scripts can be automatically called for parameter adjustment, rapid positioning and automatic repair of faults are achieved, and the operation and maintenance efficiency and reliability of the data center are effectively improved.

Description

Technical Field

[0001] The present invention relates to the field of data center operation and maintenance technology, and in particular to an intelligent operation and maintenance monitoring method and system for a data center. Background Art

[0002] Whether it is cloud computing services or internal enterprise systems, the operating status of the data center must be accurately controlled to avoid huge economic losses and social impacts caused by failures.

[0003] Current data center operations and maintenance face numerous challenges. Due to a lack of clear hierarchical delineation, operations and maintenance monitoring often presents a fragmented state. Equipment and system data at different levels cannot be effectively correlated, making it difficult to quickly trace the root cause of a problem. This fragmentation further exacerbates the difficulty of collaborative analysis across layers. Even if a problem is discovered at one layer, it's difficult to quickly connect with other layers for comprehensive analysis, ultimately leading to delayed and inaccurate operations and maintenance decisions.

[0004] Therefore, the current data center operation and maintenance methods lack systematic hierarchical division and coordination mechanisms, resulting in difficulty in problem location, slow response speed, and low overall management efficiency. Summary of the Invention

[0005] The present invention provides an intelligent operation and maintenance monitoring method and system for a data center to realize a reliable operation and maintenance system.

[0006] In a first aspect, in order to solve the above technical problems, the present invention provides an intelligent operation and maintenance monitoring method for a data center, comprising: Collect data from the data center's physical device layer, virtual resource layer, and application service layer, obtain the original data set of each layer's operating status, obtain a structured hierarchical monitoring information set and perform format conversion, and obtain the correlation identifiers between the data at each layer; Building an inter-layer data sharing model based on the correlation identifier, obtaining a real-time mapping table for cross-layer data interaction, and obtaining a basic data set for inter-layer collaborative analysis; Scanning the operating status of each layer for abnormal values ​​based on the basic data set; if a monitoring indicator of one layer exceeds a preset indicator threshold, obtaining location information of the fault point; calling the real-time mapping table based on the location information; and obtaining a propagation path of the fault point based on the real-time mapping table; Generating a preliminary positioning report based on the propagation path, performing multi-dimensional verification on the preliminary positioning report, and if the verification is successful, generating a final positioning basis and determining a repair priority sequence based on the final positioning basis; Parameters of the layer and device type corresponding to the fault point are adjusted according to the repair priority sequence, and the adjusted device is determined to be restored to a normal operating range.

[0007] Preferably, the data collected from the physical device layer, virtual resource layer, and application service layer of the data center, and the original data sets of the operating status of each layer are obtained to obtain a structured hierarchical monitoring information set, including: Setting corresponding monitoring indicators and indicator ranges for the physical device layer, the virtual resource layer, and the application service layer, respectively, and obtaining initial data of the operating status from each layer to obtain the original data set; De-noising and formatting the original data set to obtain a structured information set; If the monitoring indicator in the structured information set exceeds the indicator range, it is marked as abnormal data; The operating status of each layer is adjusted in real time according to the abnormal data to obtain a set of hierarchical monitoring information that meets the monitoring requirements of each layer.

[0008] Preferably, performing format conversion to obtain association identifiers between data at each layer includes: According to a pre-established hierarchical information specification, the hierarchical monitoring information set is formatted to obtain an initial set of unified data; Matching data fields at different levels in the initial set to determine identification information of connections between the levels; If the identification information does not match the preset identification threshold, a check is performed to obtain a corrected correlation identification.

[0009] Preferably, the constructing of an inter-layer data sharing model based on the correlation identifier, obtaining a real-time mapping table for cross-layer data interaction, and obtaining a basic data set for inter-layer collaborative analysis includes: The data dependency of each layer is obtained from the pre-established interaction mapping rules, and the data flow of each layer is obtained. After obtaining the initial cross-layer data interaction set, a layer-by-layer comparison is performed. If the data flow of one layer does not meet the preset flow threshold, the corresponding data flow is adjusted; Classify and process the adjusted inter-layer data streams, obtain the classified inter-layer collaborative analysis basis, and obtain a structured real-time mapping table; The data dependency relationships of the layers are archived according to the structured real-time mapping table, and it is determined whether the archived data conforms to the inter-layer collaborative analysis basis. If so, the final basic data set is obtained.

[0010] Preferably, performing abnormal value scanning on the operating status of each layer according to the basic data set, and obtaining the location information of the fault point if the monitoring indicator of one layer exceeds a preset indicator threshold, includes: Obtain monitoring indicators for each layer, determine whether the monitoring indicators deviate from the preset indicator threshold, and obtain a preliminary status monitoring data set; If the monitoring indicator of one layer exceeds the indicator threshold, it is marked as abnormal and determined as the key object of abnormal value scanning; If the abnormality of the key object persists, an alarm signal is generated, preliminary location information of the fault point is determined, the preliminary location information is reconfirmed, and the final fault point distribution is determined.

[0011] Preferably, calling the real-time mapping table according to the location information and obtaining the propagation path of the fault point according to the real-time mapping table includes: Acquire the hierarchical distribution information corresponding to the fault point from the real-time mapping table to obtain the preliminary associated layer list; Performing an in-depth query on the historical operating status of each layer through the preliminary associated layer list, obtaining the status monitoring records related to the fault point, and determining the starting point of the propagation path of the fault point; If there is a deviation between the starting point of the propagation path and the preset position, the interaction record of the inter-layer data is verified layer by layer to obtain the diffusion trajectory of the fault point in the layer distribution and determine the direction of the propagation path; According to the direction of the propagation path, the status monitoring record is compared and analyzed with the historical operating status of the associated layer to obtain the distribution of the fault impact of the fault point among various layers and determine the propagation path of the fault point.

[0012] Preferably, generating a preliminary positioning report according to the propagation path includes: Obtaining layer-level interaction information corresponding to the propagation path from a pre-established repository of cross-layer data to determine a preliminary path direction; Perform layer-by-layer screening based on the preliminary path direction. If the state fluctuation exceeds a preset fluctuation threshold, mark the corresponding cross-layer data as a key focus object. Analyze the key focus objects and determine the boundaries of the impact range; Each level node of the propagation path is checked one by one according to the boundary, and a preliminary location report of the root cause of the fault is generated.

[0013] Preferably, the multi-dimensional verification of the preliminary positioning report is performed, and if the verification is successful, a final positioning basis is generated and a repair priority sequence is determined based on the final positioning basis, including: Verifying the verification dimensions of the preliminary positioning report one by one according to pre-established decision rules, obtaining status tracking data associated with the hierarchy, and marking the status tracking data as valid if it meets pre-defined conditions, thereby obtaining a preliminary verification result set; Comparing the preliminary verification result set with the decision rule to obtain key matching items corresponding to the fault point and verify the consistency distribution; If the key matching item meets the predefined condition, obtaining the final positioning basis of the fault point; The repair priority sequence is dynamically adjusted according to the final positioning basis to determine a final repair priority sequence.

[0014] Preferably, the step of adjusting parameters of the layer and device type corresponding to the fault point according to the repair priority sequence, and determining that the adjusted device is restored to a normal operating range, includes: Call the corresponding automation script library according to the pre-established repair priority list, obtain the script execution instructions associated with the level, determine the specific order of script calls and target devices, and obtain the initial execution plan; Adjust parameters of the target device using the initial execution schedule, obtain real-time operating status data, and determine whether it meets the expected range; If it meets the requirements, adjustment basis data is generated to determine the equipment parameter list that needs to be optimized. The content in the equipment parameter list is adjusted according to the adjustment basis data. The latest feedback data is obtained from the adjusted equipment operation log and it is determined that the adjusted equipment has returned to the normal operating range.

[0015] In a second aspect, the present invention further provides an intelligent operation and maintenance monitoring system for a data center, the system comprising: The detection end is used to collect data from the physical device layer, virtual resource layer, and application service layer of the data center, obtain the original data set of the operating status of each layer, obtain the structured hierarchical monitoring information set and perform format conversion, and obtain the correlation identification between the data of each layer; The processing end is used to build an inter-layer data sharing model based on the correlation identifier, obtain a real-time mapping table for cross-layer data interaction, and obtain a basic data set for inter-layer collaborative analysis; perform an outlier scan on the operating status of each layer based on the basic data set, and if the monitoring indicator of one layer exceeds a preset indicator threshold, obtain the location information of the fault point, call the real-time mapping table based on the location information, and obtain the propagation path of the fault point based on the real-time mapping table; generate a preliminary positioning report based on the propagation path, perform multi-dimensional verification on the preliminary positioning report, and if the verification is successful, generate a final positioning basis and determine a repair priority sequence based on the final positioning basis; The adjustment end is used to adjust the parameters of the layer and device type corresponding to the fault point according to the repair priority sequence, and determine whether the adjusted device is restored to the normal operating range.

[0016] Compared with the existing technology, the present invention provides an intelligent operation and maintenance monitoring method and system for a data center. The method establishes a hierarchical monitoring framework, collects and standardizes layered data on the three levels of physical equipment, virtual resources and application services, and constructs an inter-layer data sharing model. A preset fault detection algorithm is used to scan the operating status of each layer for abnormalities. Once an abnormality is found, an alarm is triggered, and the propagation path of the fault impact is determined through data backtracking. Subsequently, an association rule mining algorithm is used to analyze the causal relationship between the abnormality and cross-layer data, and a multi-dimensional verification is performed in combination with a decision support rule library, and finally a fault location conclusion and repair priority are generated. The present invention can also automatically call the response script to adjust parameters, realize rapid fault location and automatic repair, and effectively improve the operation and maintenance efficiency and reliability of the data center. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 This is a flow chart of an intelligent operation and maintenance monitoring method for a data center provided by an embodiment of the present invention; Figure 2 This is a flow chart of another intelligent operation and maintenance monitoring method for a data center provided by an embodiment of the present invention; Figure 3 This is a flow chart of another intelligent operation and maintenance monitoring method for a data center provided by an embodiment of the present invention; Figure 4 This is a flow chart of another intelligent operation and maintenance monitoring method for a data center provided by an embodiment of the present invention; Figure 5 This is a flow chart of another intelligent operation and maintenance monitoring method for a data center provided by an embodiment of the present invention; Figure 6 This is a flow chart of another intelligent operation and maintenance monitoring method for a data center provided by an embodiment of the present invention; Figure 7 This is a flow chart of another intelligent operation and maintenance monitoring method for a data center provided by an embodiment of the present invention; Figure 8 This is a flow chart of another intelligent operation and maintenance monitoring method for a data center provided by an embodiment of the present invention; Figure 9 This is a flow chart of another intelligent operation and maintenance monitoring method for a data center provided by an embodiment of the present invention; Figure 10 This is a structural diagram of an intelligent operation and maintenance monitoring system for a data center provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0018] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0019] Reference Figure 1 The first embodiment of the present invention provides a flow chart of an intelligent operation and maintenance monitoring method for a data center, comprising the following steps: S1, collects data from the physical device layer, virtual resource layer, and application service layer of the data center, obtains the original data set of the operating status of each layer, obtains a structured hierarchical monitoring information set and performs format conversion, and obtains the correlation identifier between the data of each layer; S2, constructing an inter-layer data sharing model based on the correlation identifier, obtaining a real-time mapping table for cross-layer data interaction, and obtaining a basic data set for inter-layer collaborative analysis; S3, scanning the operating status of each layer for abnormal values ​​based on the basic data set. If a monitoring indicator of one layer exceeds a preset indicator threshold, obtaining the location information of the fault point, calling the real-time mapping table based on the location information, and obtaining the propagation path of the fault point based on the real-time mapping table; S4: generating a preliminary fault root cause location report based on the propagation path, performing multi-dimensional verification on the preliminary fault root cause location report, and if the verification is successful, generating a final location basis and determining a repair priority sequence based on the final location basis; S5: Adjust the parameters of the layer and device type corresponding to the fault point according to the repair priority sequence, and determine whether the adjusted device is restored to a normal operating range.

[0020] In step S1, data from the physical device layer, virtual resource layer, and application service layer of the data center are collected to obtain the original data set of the operating status of each layer, a structured hierarchical monitoring information set is obtained and formatted, and the correlation identifiers between the data of each layer are obtained.

[0021] In the data center sector, hierarchical monitoring information collection refers to the collection, integration, and management of monitoring data from devices, systems, and environmental parameters at different levels within the data center, divided into logical or physical layers. Its core purpose is to achieve full-stack visualization, fault warning, and performance optimization, from infrastructure to application services.

[0022] A structured hierarchical monitoring information collection is a unified data system formed by classifying, standardizing, and integrating various types of monitoring data within the data center according to predefined logical or physical hierarchical models. Its core is to transform dispersed monitoring information into standardized data that can be efficiently analyzed and automatically processed through a layered and modular architecture to support operations and maintenance decision-making and intelligent management.

[0023] refer to Figure 2 , S1 includes: S11, setting corresponding monitoring indicators and indicator ranges for the physical device layer, the virtual resource layer, and the application service layer, respectively, and obtaining initial data of the operating status from each layer to obtain the original data set; S12, performing denoising and format unification processing on the original data set to obtain a structured information set; S13, if the monitoring indicator in the structured information set exceeds the indicator range, it is marked as abnormal data; S14: Adjust the operating status of each layer in real time according to the abnormal data to obtain a set of hierarchical monitoring information that meets the monitoring requirements of each layer.

[0024] For example, setting monitoring indicators and ranges for the three levels of physical devices, virtual resources, and application services is key to ensuring comprehensive system coverage. At the physical device level, hardware temperature, CPU utilization, and disk space can be monitored, with indicator ranges such as temperature controlled between 20-50 degrees Celsius and CPU utilization not exceeding 80%. At the virtual resource level, virtual machine memory usage and network bandwidth are monitored, with a memory usage range set between 30% and 70%. At the application service level, response time and error rate are monitored, with response time expected to be less than 200 milliseconds. These indicators enable the initial collection of operational status data to form a raw data set. The significance of this process lies in laying the foundation for subsequent analysis and ensuring the comprehensiveness and accuracy of the data source.

[0025] Specifically, data cleaning is a crucial step in processing raw data sets. For example, suppose the collected temperature data for physical devices contains outliers—for example, if a device displays a temperature of 100 degrees Celsius, clearly outside the acceptable range—then it can be marked as noise and removed. Furthermore, data formats are standardized, such as by adjusting timestamps to a standard format, to facilitate subsequent analysis. This processing not only improves data quality but also provides a reliable basis for generating structured information sets, helping to reduce the risk of misjudgment.

[0026] In one possible implementation, if a monitoring metric in a structured information set exceeds a preset range, such as a VM memory usage reaching 85%, the anomaly is flagged and classified as a virtual resource-level issue. The detailed logging helps quickly locate the root cause of the problem, such as recording the time of the anomaly and the specific VM ID, providing precise guidance for subsequent adjustments. This step improves problem tracing efficiency and reduces manual troubleshooting costs.

[0027] For example, if the application service response time reaches 300 milliseconds, exceeding the preset range, the script can automatically increase the number of service instances or optimize the load balancing strategy. After adjustment, the response time drops to 150 milliseconds, meeting the requirement. The adjusted status data is then updated to the information collection, ensuring the dynamic adaptability of the monitoring system. This automated approach significantly improves system stability and reduces the delays associated with manual intervention.

[0028] It's important to note that the updated information collected through hierarchical monitoring isn't just a data aggregation; it also serves as a basis for system optimization. Through continuous monitoring and adjustments, such as automatically reducing the load when physical equipment experiences temperature anomalies, hardware failures can be effectively prevented. This mechanism is crucial in ensuring system continuity and provides managers with clear decision-making support, achieving a complete closed-loop management process from data collection to problem resolution.

[0029] refer to Figure 3 , S1 also includes: S15, adjusting the format of the hierarchical monitoring information set according to a pre-established hierarchical information specification to obtain an initial set of unified data; S16, matching data fields at different levels in the initial set to determine identification information of inter-layer connections; S17: If the identification information does not match the preset identification threshold, a check is performed to obtain a checksum of the correlation identification set.

[0030] For example, when processing heterogeneous formats in monitoring data, standardize the format of raw records for the physical layer, virtual resources, and application services. Physical layer records may be stored as text logs containing device numbers and operating parameters; virtual resource data may be in a table format, recording memory and bandwidth information; and application service information may be structured database entries containing fields such as response time. Adjusting these different formats to a unified structured format, such as adjusting all time fields to a standard "year-month-day hour:minute:second" format, ensures consistency in subsequent processing.

[0031] For example, applying field mapping rules to the content in the initial collection is a critical step. For example, suppose the device ID field at the physical layer is "Device ID," the corresponding field at the virtual resource layer is "Host ID," and the field at the application service layer is "Service ID." Using field mapping rules, these three fields can be linked to form unified identification information. This mapping helps clarify dependencies between layers. For example, a device ID corresponds to multiple host numbers, and each host number is associated with a specific service ID, thus clarifying the complete hierarchical relationship.

[0032] Suppose the preset threshold requires a 1:3 ratio for device IDs to host numbers, but the actual mapping result shows a ratio of 1:5, clearly exceeding the range. In this case, the mapping results are reviewed to check for duplicate associations or mismatches. For example, if a device ID is mistakenly associated with an unrelated host number, the invalid data is removed after review to generate an accurate set of association identifiers. This approach improves data reliability and lays the foundation for subsequent integration.

[0033] For example, monitoring data at different levels can be categorized and stored based on the associated identifier set. Physical-level temperature and CPU usage data can be stored in the hardware monitoring library, virtual resource memory usage data can be classified in the resource allocation library, and application service response time data can be stored in the service quality library. By associating identifier sets, a clear correspondence can be established between the data in these libraries. For example, the corresponding host number can be queried through the device ID, and then further traced back to the specific service identifier, forming a complete hierarchical information association structure. This structured storage method facilitates rapid retrieval and analysis.

[0034] For example, in a real-world scenario, suppose a physical device's data shows a temperature of 45 degrees Celsius, the corresponding virtual resource memory usage is 60%, and the application service response time is 180 milliseconds. Using the hierarchical information association structure, it's possible to quickly determine whether the device and its associated resources and services are functioning normally. If an anomaly is detected, administrators can quickly locate the source of the problem based on the association structure, for example, tracing the device ID back to the specific service identifier, and then taking targeted measures. This approach significantly improves troubleshooting efficiency.

[0035] For example, the construction of hierarchical information association structures can be extended to dynamic update scenarios. For example, if the memory usage of a virtual resource increases from 60% to 75%, the record in the repository can be updated in real time, and the associated identifier set can be adjusted simultaneously to ensure the timeliness of the hierarchical structure. This dynamic adjustment capability helps maintain the accuracy of monitoring data and provides continuous support for system management.

[0036] In step S2, an inter-layer data sharing model is constructed according to the correlation identifier, a real-time mapping table of cross-layer data interaction is obtained, and a basic data set for inter-layer collaborative analysis is obtained.

[0037] In the data center sector, the Inter-layer Data Sharing Model (IDS) is a standardized data exchange framework designed to enable efficient circulation and collaborative utilization of monitoring, management, and operations data across different layers of the data center (e.g., infrastructure, network, server, and application layers). Its core goal is to break down data silos and support cross-layer comprehensive analysis, automated decision-making, and unified operations through standardized interfaces and protocols.

[0038] refer to Figure 4 , S2 includes: S21: Obtain the data dependency of each layer from the pre-established interaction mapping rules, and obtain the data flow of each layer. After obtaining the initial cross-layer data interaction set, perform layer-by-layer comparison. If the data flow of one layer does not meet the preset flow threshold, adjust the corresponding data flow. S22, classifying the adjusted inter-layer data stream, obtaining the classified inter-layer collaborative analysis basis, and obtaining a structured real-time mapping table; S23, archiving the data dependency relationships of the layers according to the structured real-time mapping table, and determining whether the archived data meets the inter-layer collaborative analysis criteria, and if so, obtaining the final basic data set.

[0039] For example, in the field of data center operation and maintenance technology, interaction mapping rules refer to predefined logic or policies used to describe the data interaction relationships (such as calls, transmissions, and dependencies) between the various layers of the data center (such as the physical layer, virtualization layer, application layer, and service layer). Data dependencies refer to the information transmission and influence chains formed between layers during operation. For example, the operating status of physical devices will directly affect the allocation of virtual resources, and the performance of virtual resources will affect the performance of application services. Key data flow information is extracted from each layer to form an initial cross-layer data interaction set. Assuming that the data flow at the physical device layer contains device operating time and temperature information, the virtual resource layer records the CPU allocation ratio, and the application service layer contains the request processing time, this information can be aggregated to form a set containing multiple layers of data flows.

[0040] For example, for the initial set of cross-layer data interactions, we can start by comparing dynamic link relationships. Dynamic link relationships refer to the real-time changing correlations between layers. For example, a temperature increase on a device may lead to an adjustment in virtual resource allocation. Suppose the preset threshold stipulates that the correlation between device temperature and CPU allocation ratio should not change by more than 20%, but the actual data flow shows that the CPU allocation ratio changes by 30% when the temperature increases by 10 degrees. This anomaly will be marked. The cause of the anomaly is then analyzed, which may be due to data entry errors or failure to update device status in a timely manner. The data flow is then adjusted to ensure the accuracy of the interactive mapping dataset.

[0041] For example, when classifying and processing inter-layer data flows, a specific approach might be to categorize data flows by layer characteristics. Data flows at the physical device layer could be categorized as hardware status, data flows at the virtual resource layer as resource scheduling, and data flows at the application service layer as service performance. Assuming a device temperature of 50°C, a corresponding virtual resource CPU allocation of 70%, and an application service request processing time of 200 milliseconds, a structured real-time mapping table could be generated to clearly display the corresponding relationships between data at each layer. This categorization facilitates subsequent analysis of inter-layer collaboration.

[0042] In the field of data center operations and maintenance technology, collaborative analysis requirements (CARs) refer to the standardization and interactivity requirements for data integration, process collaboration, and tool interoperability to achieve efficient, cross-team or cross-system operations and maintenance management. The core goal is to break down information silos and improve the efficiency of fault diagnosis, performance optimization, and resource scheduling through multi-party collaboration.

[0043] For example, a structured real-time mapping table can be archived by hierarchical dependency. If, after archiving, it is discovered that device data and virtual resource data do not effectively correspond, this could be due to missing data flows or incorrect links. In this case, it is necessary to determine whether the data meets the requirements for inter-layer collaborative analysis. If not, the missing data needs to be supplemented or the links need to be corrected to ultimately form a basic dataset. This archiving method helps ensure data integrity and consistency, providing a reliable basis for subsequent analysis.

[0044] In step S3, the operating status of each layer is scanned for abnormal values ​​based on the basic data set. If the monitoring indicators of one layer exceed the preset indicator threshold, the location information of the fault point is obtained, the real-time mapping table is called according to the location information, and the propagation path of the fault point is obtained according to the real-time mapping table.

[0045] refer to Figure 5 , S3 includes: S31, obtaining monitoring indicators of each layer, determining whether the monitoring indicators deviate from a preset indicator threshold, and obtaining a preliminary status monitoring data set; S32, if the monitoring indicator of one layer exceeds the indicator threshold, it is marked as abnormal and determined as a key object for abnormal value scanning; S33: If the abnormality of the key object persists, an alarm signal is generated, preliminary location information of the fault point is determined, the preliminary location information is reconfirmed, and the final fault point distribution is determined.

[0046] For example, in a scenario where an inter-layer data sharing model is being built, real-time metrics can be obtained using pre-defined rules for monitoring the operational status of the physical device layer, virtual resource layer, and application service layer. Monitoring rules are developed based on the operational characteristics of each layer. For example, the physical device layer focuses on hardware temperature and uptime, the virtual resource layer focuses on resource allocation ratios, and the application service layer focuses on service response time. Assuming the monitoring rules specify a device temperature threshold of 60 degrees Celsius, a resource allocation ratio fluctuation range of plus or minus 10%, and a response time of no more than 300 milliseconds, these metrics are captured in real time to form a preliminary status monitoring dataset.

[0047] For example, for a preliminary status monitoring dataset, you can start with layer-by-layer verification. For example, if the temperature of a device in the physical device layer reaches 65°C, exceeding the threshold by 5°C, it will be immediately marked as an outlier and designated as a priority for scanning. Similarly, if the allocation ratio of a node in the virtual resource layer fluctuates by more than 15%, it will also be flagged. This layer-by-layer verification approach can quickly identify potential problem areas and provide clear guidance for subsequent analysis.

[0048] For example, after identifying the key targets for outlier scanning, in-depth analysis becomes crucial. For example, if a device's temperature consistently exceeds a threshold and is accompanied by abnormal fluctuations in the allocation ratio of the virtual resource layer, the system analyzes the correlation between the two to determine whether hardware overheating is causing resource scheduling issues and generates an alarm. Preliminary location information may point to a server at the physical device layer, but the specific point of failure still requires further confirmation. This in-depth analysis helps narrow the scope of the problem and avoids blind investigation.

[0049] For example, secondary confirmation of preliminary location information can be achieved by comparing historical and real-time data. If historical data indicates that the server experienced no resource fluctuations under similar temperature conditions, but current data indicates an anomaly, further investigation will be conducted to determine whether the issue is caused by sensor data deviation or resource scheduling policy adjustments. Ultimately, if the fault is confirmed to be the server cooling module, the hardware component can be precisely located. This secondary confirmation method improves location accuracy and provides a reliable basis for subsequent processing.

[0050] For example, throughout the entire process, collaborative work demonstrates the advantages of the inter-layer data sharing model. This ensures comprehensive indicator information, allows for rapid screening of anomalies, in-depth exploration of the root causes of problems, and further pinpoints the problem location. For example, the above process reveals that a device heat dissipation issue is causing uneven virtual resource allocation, impacting application service response time. After promptly locating and addressing the fault point, normal operation of all layers is restored. This complete chain, from monitoring to location, not only improves problem discovery efficiency but also provides solid data support for inter-layer collaborative analysis.

[0051] refer to Figure 6 , S3 also includes: S34, acquiring the hierarchical distribution information corresponding to the fault point from the real-time mapping table to obtain the preliminary associated layer list; S35, performing an in-depth query on the historical operating status of each layer through the preliminary associated layer list, obtaining status monitoring records related to the fault point, and determining the starting point of the propagation path of the fault point; S36, if the starting point of the propagation path deviates from the preset position, verify the interaction records of the inter-layer data layer by layer, obtain the diffusion trajectory of the fault point in the layer distribution, and determine the direction of the propagation path; S37, comparing and analyzing the status monitoring record with the historical operating status of the associated layer according to the direction of the propagation path, obtaining the distribution of the fault impact of the fault point among various layers, and determining the propagation path of the fault point.

[0052] For example, in the scenario of inter-layer data analysis, the hierarchical distribution information of abnormal signals is extracted by obtaining relevant data from the real-time mapping table, focusing on the relationship between the physical device layer, the virtual resource layer, and the application service layer. Assuming that the real-time mapping table records the data interaction relationship between each layer, the distribution information of the layers that may be affected will be filtered out based on the initial alarm scope of the abnormal signal. For example, an abnormal temperature signal in a physical device layer may be mapped to the scheduling record of the virtual resource layer, and then associated with the response delay data of the application service layer. This layer-by-layer extraction method can quickly sort out the initial impact range of the abnormal signal, laying the foundation for subsequent analysis.

[0053] For example, after generating a preliminary list of associated layers, in-depth queries are particularly important. Assuming the list shows that a server at the physical device layer is associated with a scheduling anomaly at the virtual resource layer, the system will look back at the operating status records for the past 24 hours to check whether the server temperature has been consistently high and whether resource scheduling has been frequently adjusted. If the temperature has reached 62 degrees Celsius multiple times over the past six hours, exceeding the threshold by 2 degrees, and resource scheduling records show a 12% fluctuation in allocation ratios, it can be preliminarily determined that the origin of the abnormal signal may be at the physical device layer. This retrospective query helps identify the initial point of the fault's impact and provides a basis for subsequent path tracing.

[0054] For example, when there's a discrepancy between the starting point of a propagation path and the location information, layer-by-layer verification becomes crucial. Assuming the initial positioning points to a specific server, but data from the virtual resource layer indicates the anomaly has spread to multiple nodes, inter-layer data interaction records will be used to analyze how the anomaly signal spread from the physical device layer to the virtual resource layer. For example, if records show that after a server temperature anomaly, resource scheduling instructions were frequently issued within 10 minutes, resulting in an imbalance in the allocation ratios across multiple nodes, the diffusion trajectory will be outlined, determining the direction of propagation to be from the lower layer to the upper layer. This verification method clearly demonstrates the propagation path of the anomaly signal and avoids positioning deviations.

[0055] For example, further analysis of the propagation path direction involves comparing status monitoring records with the operating status of associated layers. If the path direction indicates that the anomaly has spread from the physical device layer to the application service layer, data from each layer will be integrated to reveal that a temperature anomaly at the physical device layer caused an imbalance in resource scheduling, which in turn increased the application service layer response time from 200 milliseconds to 350 milliseconds. This comparative analysis can determine the distribution of the fault's impact at each layer, ultimately forming a complete picture of the propagation path. This integrated analysis can fully reveal the layer-by-layer distribution of the anomaly's impact, providing precise guidance for subsequent processing.

[0056] For example, throughout the entire process, collaborative work demonstrates the advantages of inter-layer data analysis. This process helps identify the initial scope, traces back to identify the starting point, clarifies the direction, and integrates the overall picture. This collaborative approach effectively improves the comprehensiveness and accuracy of abnormal signal analysis, providing strong support for rapid response and resolution of fault impacts.

[0057] In step S4, a preliminary location report of the fault root cause is generated based on the propagation path, and the preliminary location report is verified in multiple dimensions. If the verification is successful, a final location basis is generated and a repair priority sequence is determined based on the final location basis.

[0058] refer to Figure 7 , S4 includes: S41, obtaining layer interaction information corresponding to the propagation path from a pre-established cross-layer data repository, and determining a preliminary path direction; S42, screening layer by layer according to the preliminary path direction, if the state fluctuation exceeds a preset fluctuation threshold, marking the corresponding cross-layer data as a key focus object; S43, analyzing the key focus objects to determine the boundaries of the impact range; S44: Verify each level node of the propagation path one by one according to the boundary, and generate a preliminary location report of the root cause of the fault.

[0059] For example, in the scenario of abnormal signal analysis based on a cross-layer data repository, relevant methods can be understood and implemented from multiple perspectives. Regarding the step of obtaining hierarchical interaction information from the repository, assuming that the repository stores historical interaction data of the physical device layer, virtual resource layer, and application service layer, the interaction records between related layers will be screened out based on the alarm range of the abnormal signal. For example, an abnormal power signal of a physical device layer may be associated with the allocation adjustment record of the virtual resource layer. The relevant data within the past 48 hours will be extracted, and the preliminary judgment of the path direction is that it spreads from the lower layer to the upper layer. This extraction method helps to quickly focus on key information.

[0060] For example, the process of filtering historical records layer by layer can be considered a hierarchical verification mechanism. For example, if the allocation ratio of the virtual resource layer in the status fluctuation data fluctuates by 15%, exceeding the preset threshold of 10%, that layer will be marked as a key concern. This screening method can effectively narrow the scope of analysis and identify potential problem nodes. For example, if the response time fluctuation of the application service layer is found to be within the standard, it can be temporarily eliminated as a major influencing factor.

[0061] For example, when analyzing cross-layer data and the distribution of abnormal signals, the focus is on identifying key data distribution characteristics. For example, if analysis reveals that voltage instability at a node in the physical device layer is highly correlated with frequent scheduling adjustments at the virtual resource layer, the node's operation logs will be further extracted to identify possible root causes. For example, if the logs show that voltage has fallen below the standard value by 18% multiple times over the past 12 hours, combined with a 20% increase in the frequency of scheduling adjustments, the impact area can be initially delineated. This matching analysis provides precise guidance for subsequent location determination.

[0062] For example, layer-by-layer verification of the propagation path can be considered a refinement of the initial path direction. Assuming the impact range boundary points to a physical device node, historical records will be combined to analyze whether its status fluctuations are consistent with anomalies at other levels. For example, if, within 10 minutes of discovering the voltage anomaly at that node, scheduling imbalances occur across multiple nodes in the virtual resource layer, and the response time at the application service layer increases to 300 milliseconds, this can be confirmed as the root cause of the fault. This layer-by-layer verification approach ensures reliable positioning. Through the above multi-faceted implementation method, each link, from data extraction to path verification, is closely integrated to support the complete process of abnormal signal analysis. The detailed operations at each link effectively improve the targeted analysis and provide a solid foundation for subsequent troubleshooting.

[0063] refer to Figure 8 , S4 also includes: S45, verifying the verification dimensions of the preliminary positioning report one by one according to pre-established decision rules, obtaining status tracking data associated with the hierarchy, and marking the status tracking data as valid if it meets pre-defined conditions, thereby obtaining a preliminary verification result set; S46, comparing the preliminary verification result set with the decision rule to obtain key matching items corresponding to the fault point and verify the consistency distribution; S47, if the key matching item meets the predefined condition, obtaining the final location basis of the fault point; S48: Dynamically adjust the repair priority sequence according to the final positioning basis to determine a final repair priority sequence.

[0064] For example, in a fault location scenario based on cross-layer data analysis, a pre-established decision rule base can be understood in principle as a knowledge base containing multi-level association rules, which is used to guide the verification and location of the root cause of the fault. Assuming that the rule base stores the status thresholds and association conditions of the physical device layer, virtual resource layer, and application service layer, the status tracking data of the relevant layers will be extracted based on the preliminary location report. For example, if the report indicates that the temperature of a physical device node is abnormal, the temperature data of the node over the past 24 hours will be verified. If it is found that the temperature has exceeded the preset threshold of 45 degrees for more than 6 hours, it will be marked as valid evidence, forming a preliminary verification result set.

[0065] For example, data integration can be understood as a process of deeply matching the initial verification results set with the evidence in the decision rule base. For example, if the verification results show an abnormal 12% fluctuation in resource allocation at the virtual resource layer, while the threshold defined in the rule base is 10%, this item will be marked as a critical match. Combined with multi-dimensional verification requirements, its correlation with temperature anomalies at the physical device layer will be analyzed to determine the final verification consistency distribution. This approach helps focus on the core issue.

[0066] For example, the focus is on combining verification consistency distribution and status tracking data to generate conclusions. For example, if a key match shows a high correlation between temperature anomalies at the physical device layer and fluctuations in virtual resource allocation, with the fluctuations occurring only two hours apart, the predefined threshold conditions will be determined, providing the final basis for locating the root cause of the fault. This layer-by-layer verification approach effectively improves location accuracy.

[0067] For example, priority sorting can be viewed as a process that dynamically adjusts the order of repairs. For example, if the conclusion generation data indicates that abnormal temperature at the physical device layer is the primary root cause of a fault, the repair priority will be adjusted based on the scope of its impact, with that node being addressed first, followed by issues related to the virtual resource layer. This ultimately generates a prioritized repair list. This dynamic adjustment ensures efficient resource allocation and prioritizes resolving critical issues.

[0068] For example, in a specific business scenario, if a temperature anomaly on the physical device layer of a data center causes frequent scheduling adjustments at the virtual resource layer, the system will prioritize cooling measures based on the location of the issue while simultaneously monitoring resource allocation. This logic, which prioritizes the core issue and its associated impacts, effectively prevents the problem from spreading and ensures system stability.

[0069] It should be noted that the above-mentioned links are closely connected, from data verification to priority adjustment, forming a complete closed loop to ensure the efficiency and reliability of fault handling.

[0070] In step S5, the parameters of the layer and device type corresponding to the fault point are adjusted according to the repair priority sequence, and it is determined that the adjusted device is restored to the normal operating range.

[0071] refer to Figure 9 , S5 includes: S51, calling the corresponding automation script library according to the pre-established repair priority list, obtaining the script execution instructions associated with the level, determining the specific order of script calls and the target device, and obtaining an initial execution schedule; S52, adjusting parameters of the target device according to the initial execution plan, obtaining real-time operating status data and determining whether it meets the expected range; S53, if it meets the requirements, then generate adjustment basis data, determine the equipment parameter list that needs to be optimized, adjust the content in the equipment parameter list according to the adjustment basis data, obtain the latest feedback data from the adjusted equipment operation log and determine whether the adjusted equipment has returned to the normal operating range.

[0072] For example, in a data center troubleshooting scenario, a pre-established repair priority list can be conceptualized as a task allocation framework based on tier and device type. For example, if the list prioritizes temperature control for physical devices, the automated script library will prioritize these instructions for specific server nodes, forming an initial execution schedule. This approach ensures targeted task allocation.

[0073] For example, script execution applications can be understood as a process driven by a schedule to adjust device parameters. For example, if the initial execution schedule specifies increasing the speed of a server node's cooling fan, real-time data on the fan's operating status will be collected. For example, if the speed reaches the preset 8,000 rpm, the data will be compared against the preset threshold of 7,000-9,000 rpm to determine whether it meets the expected range and generate preliminary feedback. This real-time data collection and comparison helps quickly identify deviations in parameter adjustments.

[0074] The key is to verify the initial feedback against the recovery range requirements. For example, if the feedback shows that the fan speed is within the expected range, but the device temperature remains at 48 degrees Celsius, exceeding the upper limit of the recovery range of 45 degrees Celsius, adjustment data will be generated to clearly indicate the need for further optimization of the fan control strategy or inspection of other cooling components. This verification process can accurately identify the parameters that do not meet the requirements and provide a basis for subsequent optimization.

[0075] This analysis can be conducted from the perspective of secondary adjustments. Assuming that the adjustment data indicates that the fan speed needs to be further increased and the coolant circulation status needs to be checked, the speed will be prioritized to 8500 rpm based on the equipment parameter list, and coolant flow monitoring will be enabled. The latest feedback data will be obtained from the post-adjustment operation log, such as the temperature dropping to 44 degrees Celsius, entering the recovery range, and ultimately forming a status monitoring conclusion. This secondary adjustment mechanism can effectively address situations where the initial adjustment does not meet expectations.

[0076] For example, the generation of condition monitoring conclusions can be understood as a comprehensive assessment of the system's overall recovery status. If the latest feedback data indicates that the temperature has stabilized at 44 degrees Celsius and other related parameters, such as resource allocation ratios, have returned to 8% of the normal range, the system will be confirmed to have entered the recovery range. This multi-dimensional verification approach ensures the comprehensiveness and reliability of the repair process, reducing the risk of recurrence.

[0077] From an overall process perspective, the aforementioned steps, from script invocation to final conclusion, form a closed-loop mechanism. For example, in a data center, if temperature anomalies at the physical device layer are prioritized, scheduling stability at the virtual resource layer will also improve, boosting overall system efficiency. This closed-loop mechanism ensures efficient repair tasks while providing data support and reference for subsequent maintenance.

[0078] In summary, the present invention discloses an intelligent operation and maintenance monitoring method for a data center. The method establishes a hierarchical monitoring framework, collects and standardizes layered data on the three levels of physical equipment, virtual resources, and application services, and constructs an inter-layer data sharing model. A preset fault detection algorithm is used to perform an abnormality scan on the operating status of each layer. Once an abnormality is found, an alarm is triggered, and the propagation path of the fault impact is determined through data backtracking. Subsequently, an association rule mining algorithm is used to analyze the causal relationship between the abnormality and the cross-layer data, and a multi-dimensional verification is performed in combination with a decision support rule library, and finally a fault location conclusion and repair priority are generated. The present invention can also automatically call the response script to adjust parameters, realize rapid fault location and automatic repair, and effectively improve the operation and maintenance efficiency and reliability of the data center.

[0079] like Figure 10 As shown, an embodiment of the present invention provides an intelligent operation and maintenance monitoring system for a data center, including: The detection end is used to collect data from the physical device layer, virtual resource layer, and application service layer of the data center, obtain the original data set of the operating status of each layer, obtain the structured hierarchical monitoring information set and perform format conversion, and obtain the correlation identification between the data of each layer; The processing end is used to build an inter-layer data sharing model based on the correlation identifier, obtain a real-time mapping table for cross-layer data interaction, and obtain a basic data set for inter-layer collaborative analysis; perform an outlier scan on the operating status of each layer based on the basic data set, and if the monitoring indicator of one layer exceeds a preset indicator threshold, obtain the location information of the fault point, call the real-time mapping table based on the location information, and obtain the propagation path of the fault point based on the real-time mapping table; generate a preliminary positioning report based on the propagation path, perform multi-dimensional verification on the preliminary positioning report, and if the verification is successful, generate a final positioning basis and determine a repair priority sequence based on the final positioning basis; The adjustment end is used to adjust the parameters of the layer and device type corresponding to the fault point according to the repair priority sequence, and determine whether the adjusted device is restored to the normal operating range.

[0080] It should be noted that the intelligent operation and maintenance monitoring system for a data center provided in an embodiment of the present invention is used to execute all the process steps of the intelligent operation and maintenance monitoring method for a data center in the above embodiment. The working principles and beneficial effects of the two correspond one to one, so they will not be repeated here.

[0081] The embodiment of the present invention further provides a terminal device. The terminal device includes: a processor, a memory, and a computer program stored in the memory and executable on the processor, such as a data center intelligent operation and maintenance monitoring program. When the processor executes the computer program, the steps of the above-mentioned intelligent operation and maintenance monitoring method embodiments of each data center are implemented, such as Figure 1 Alternatively, the processor implements the functions of the modules / units in the above-mentioned system embodiments when executing the computer program.

[0082] Exemplarily, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to implement the present invention. The one or more modules / units may be a series of computer program instruction segments capable of performing specific functions, and the instruction segments are used to describe the execution process of the computer program in the terminal device.

[0083] The terminal device may be a computing device such as a desktop computer, laptop, PDA, or smart tablet. The terminal device may include, but is not limited to, a processor and memory. Those skilled in the art will appreciate that the aforementioned components are merely examples of terminal devices and do not constitute a limitation of the terminal device. The terminal device may include more or fewer components than those described above, or a combination of certain components or different components. For example, the terminal device may also include input / output devices, network access devices, buses, and the like.

[0084] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor. The processor is the control center of the terminal device and connects various parts of the entire terminal device using various interfaces and lines.

[0085] The memory can be used to store the computer programs and / or modules. The processor implements the various functions of the terminal device by running or executing the computer programs and / or modules stored in the memory and accessing the data stored in the memory. The memory may primarily include a program storage area and a data storage area. The program storage area may store an operating system and at least one application required for a function (such as a sound playback function or an image playback function); the data storage area may store data generated based on the use of the mobile phone (such as audio data, a phone book, etc.). Furthermore, the memory may include high-speed random access memory and non-volatile memory, such as a hard disk, internal memory, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, at least one disk storage device, a flash memory device, or other volatile solid-state storage device.

[0086] If the module / unit integrated into the terminal device is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention can implement all or part of the process steps in the above-mentioned method embodiments by using a computer program to instruct the relevant hardware. The computer program can be stored in a computer-readable storage medium. When executed by a processor, the computer program can implement the steps of each of the above-mentioned method embodiments. The computer program includes computer program code, which can be in source code form, object code form, executable file, or some intermediate form. The computer-readable medium can include: any entity or system capable of carrying the computer program code, recording medium, USB flash drive, mobile hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal, and software distribution medium. It should be noted that the content of the computer-readable medium can be appropriately increased or decreased based on the requirements of legislation and patent practice in a jurisdiction. For example, in some jurisdictions, based on legislation and patent practice, computer-readable media does not include electric carrier signals and telecommunication signals.

[0087] It should be noted that the system embodiment described above is merely illustrative, wherein the units described as separate components may or may not be physically separate, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed across multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present embodiment. In addition, in the drawings of the system embodiment provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which can be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art can understand and implement the present invention without inventive work.

[0088] The specific embodiments described above further illustrate the objectives, technical solutions, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. In particular, it should be noted that any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included within the scope of protection of the present invention for those skilled in the art.

Claims

1. A data center intelligent operation and maintenance monitoring method, characterized in that: include: Collect data from the data center's physical device layer, virtual resource layer, and application service layer, obtain the original data set of each layer's operating status, obtain a structured hierarchical monitoring information set and perform format conversion, and obtain the correlation identifiers between the data at each layer; Building an inter-layer data sharing model based on the correlation identifier, obtaining a real-time mapping table for cross-layer data interaction, and obtaining a basic data set for inter-layer collaborative analysis; Scanning the operating status of each layer for abnormal values ​​based on the basic data set; if a monitoring indicator of one layer exceeds a preset indicator threshold, obtaining location information of the fault point; calling the real-time mapping table based on the location information; and obtaining a propagation path of the fault point based on the real-time mapping table; Generating a preliminary positioning report based on the propagation path, performing multi-dimensional verification on the preliminary positioning report, and if the verification is successful, generating a final positioning basis and determining a repair priority sequence based on the final positioning basis; Parameters of the layer and device type corresponding to the fault point are adjusted according to the repair priority sequence, and the adjusted device is determined to be restored to a normal operating range.

2. The intelligent operation and maintenance monitoring method of a data center according to claim 1, characterized in that: The data collected from the physical device layer, virtual resource layer, and application service layer of the data center is used to obtain the original data set of the operating status of each layer, and a structured hierarchical monitoring information set is obtained, including: Setting corresponding monitoring indicators and indicator ranges for the physical device layer, the virtual resource layer, and the application service layer, respectively, and obtaining initial data of the operating status from each layer to obtain the original data set; De-noising and formatting the original data set to obtain a structured information set; If the monitoring indicator in the structured information set exceeds the indicator range, it is marked as abnormal data; The operating status of each layer is adjusted in real time according to the abnormal data to obtain a set of hierarchical monitoring information that meets the monitoring requirements of each layer.

3. The intelligent operation and maintenance monitoring method of a data center according to claim 1, characterized in that: The format conversion to obtain the association identifiers between the data layers includes: According to a pre-established hierarchical information specification, the hierarchical monitoring information set is formatted to obtain an initial set of unified data; Matching data fields at different levels in the initial set to determine identification information of connections between the levels; If the identification information does not match the preset identification threshold, a check is performed to obtain a corrected correlation identification.

4. The intelligent operation and maintenance monitoring method of a data center according to claim 1, characterized in that: The step of constructing an inter-layer data sharing model based on the correlation identifier, obtaining a real-time mapping table for cross-layer data interaction, and obtaining a basic data set for inter-layer collaborative analysis includes: The data dependency of each layer is obtained from the pre-established interaction mapping rules, and the data flow of each layer is obtained. After obtaining the initial cross-layer data interaction set, a layer-by-layer comparison is performed. If the data flow of one layer does not meet the preset flow threshold, the corresponding data flow is adjusted; Classify and process the adjusted inter-layer data streams, obtain the classified inter-layer collaborative analysis basis, and obtain a structured real-time mapping table; The data dependency relationships of the layers are archived according to the structured real-time mapping table, and it is determined whether the archived data conforms to the inter-layer collaborative analysis basis. If so, the final basic data set is obtained.

5. The intelligent operation and maintenance monitoring method of a data center according to claim 1, characterized in that: In the process of scanning the operating status of each layer for abnormal values ​​based on the basic data set, if the monitoring indicator of one layer exceeds a preset indicator threshold, obtaining the location information of the fault point includes: Obtain monitoring indicators for each layer, determine whether the monitoring indicators deviate from the preset indicator threshold, and obtain a preliminary status monitoring data set; If the monitoring indicator of one layer exceeds the indicator threshold, it is marked as abnormal and determined as the key object of abnormal value scanning; If the abnormality of the key object persists, an alarm signal is generated, preliminary location information of the fault point is determined, the preliminary location information is reconfirmed, and the final fault point distribution is determined.

6. The intelligent operation and maintenance monitoring method for a data center according to any one of claims 1 to 5, characterized in that: The calling the real-time mapping table according to the location information and obtaining the propagation path of the fault point according to the real-time mapping table includes: Acquire the hierarchical distribution information corresponding to the fault point from the real-time mapping table to obtain the preliminary associated layer list; Performing an in-depth query on the historical operating status of each layer through the preliminary associated layer list, obtaining the status monitoring records related to the fault point, and determining the starting point of the propagation path of the fault point; If there is a deviation between the starting point of the propagation path and the preset position, the interaction record of the inter-layer data is verified layer by layer to obtain the diffusion trajectory of the fault point in the layer distribution and determine the direction of the propagation path; According to the direction of the propagation path, the status monitoring record is compared and analyzed with the historical operating status of the associated layer to obtain the distribution of the fault impact of the fault point among various layers and determine the propagation path of the fault point.

7. The intelligent operation and maintenance monitoring method for a data center according to any one of claims 1 to 5, characterized in that: Generating a preliminary positioning report according to the propagation path includes: Obtaining layer-level interaction information corresponding to the propagation path from a pre-established repository of cross-layer data to determine a preliminary path direction; Perform layer-by-layer screening based on the preliminary path direction. If the state fluctuation exceeds a preset fluctuation threshold, mark the corresponding cross-layer data as a key focus object. Analyze the key focus objects and determine the boundaries of the impact range; Each level node of the propagation path is checked one by one according to the boundary, and a preliminary location report of the root cause of the fault is generated.

8. The intelligent operation and maintenance monitoring method for a data center according to any one of claims 1 to 5, characterized in that: The multi-dimensional verification of the preliminary positioning report is performed, and if the verification is successful, a final positioning basis is generated and a repair priority sequence is determined based on the final positioning basis, including: Verifying the verification dimensions of the preliminary positioning report one by one according to pre-established decision rules, obtaining status tracking data associated with the hierarchy, and marking the status tracking data as valid if it meets pre-defined conditions, thereby obtaining a preliminary verification result set; Comparing the preliminary verification result set with the decision rule to obtain key matching items corresponding to the fault point and determine the verification consistency distribution; If the key matching item meets the predefined condition, obtaining the final positioning basis of the fault point; The repair priority sequence is dynamically adjusted according to the final positioning basis to determine a final repair priority sequence.

9. The intelligent operation and maintenance monitoring method for a data center according to any one of claims 1 to 5, characterized in that: The step of adjusting parameters of the layer and device type corresponding to the fault point according to the repair priority sequence, and determining that the adjusted device is restored to a normal operating range, includes: Call the corresponding automation script library according to the pre-established repair priority list, obtain the script execution instructions associated with the level, determine the specific order of script calls and target devices, and obtain the initial execution plan; Adjust parameters of the target device using the initial execution schedule, obtain real-time operating status data, and determine whether it meets the expected range; If it meets the requirements, adjustment basis data is generated to determine the equipment parameter list that needs to be optimized. The content in the equipment parameter list is adjusted according to the adjustment basis data. The latest feedback data is obtained from the adjusted equipment operation log and it is determined that the adjusted equipment has returned to the normal operating range.

10. An intelligent operation and maintenance monitoring system for a data center, characterized in that: The system comprises: The detection end is used to collect data from the physical device layer, virtual resource layer, and application service layer of the data center, obtain the original data set of the operating status of each layer, obtain the structured hierarchical monitoring information set and perform format conversion, and obtain the correlation identification between the data of each layer; The processing end is used to build an inter-layer data sharing model based on the correlation identifier, obtain a real-time mapping table for cross-layer data interaction, and obtain a basic data set for inter-layer collaborative analysis; perform an outlier scan on the operating status of each layer based on the basic data set, and if the monitoring indicator of one layer exceeds a preset indicator threshold, obtain the location information of the fault point, call the real-time mapping table based on the location information, and obtain the propagation path of the fault point based on the real-time mapping table; generate a preliminary positioning report based on the propagation path, perform multi-dimensional verification on the preliminary positioning report, and if the verification is successful, generate a final positioning basis and determine a repair priority sequence based on the final positioning basis; The adjustment end is used to adjust the parameters of the layer and device type corresponding to the fault point according to the repair priority sequence, and determine whether the adjusted device is restored to the normal operating range.

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