A data center equipment monitoring method and system, electronic equipment and storage medium

Abstract

The application discloses a data center equipment monitoring system and device, electronic equipment and a storage medium. When the equipment is monitored, the data collection protocol used for monitoring different collection indexes is constructed in advance. Thus, when used, the corresponding protocol is selected according to the to-be-monitored indexes, the collection of the data corresponding to the to-be-monitored indexes is realized, and therefore, compared with the prior art, the application does not need the user to perform function configuration, reduces the use threshold and personnel cost, and is convenient for promotion. Meanwhile, the application can analyze and process the collected data, can obtain real-time monitoring analysis data and operation prediction data of the equipment, and based on this, can realize real-time alarm of the equipment operation and early warning of the equipment operation state, so that the abnormality and failure of the equipment can be found in time, so as to perform rapid response and processing. Thus, the risk of equipment downtime and failure can be reduced, and the reliability and stability of the equipment operation are improved.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of data monitoring, and particularly relates to a data center equipment monitoring method and system, an electronic device and a storage medium. BACKGROUND

[0002] At present, the operation monitoring of various network devices is mostly based on Zabbix and uses a client / server architecture for monitoring, wherein Zabbix is an open-source network monitoring software, Zabbix supports multiple monitoring methods and can monitor various indicators (such as CPU usage, memory usage, network bandwidth usage, etc.), therefore, a user can install the software on a device to be monitored to collect the operation data of the device to be monitored in real time based on the software and send the data to a server periodically, so as to realize the operation monitoring of the device.

[0003] However, the use of Zabbix for device operation monitoring has the following disadvantages: (1) the user needs to configure the functions of Zabbix based on the monitored functions, thus requiring the user to have certain professional technical level and experience, which leads to high use threshold, increases personnel cost and is not convenient for promotion; (2) Zabbix can only realize the data collection function, but cannot realize the data analysis and alarm functions, thus the real-time alarm of the abnormality of the data center equipment and the prediction of the operation state of the equipment cannot be realized; based on this, how to provide a monitoring method with low use threshold and data analysis and alarm functions has become a problem to be solved. SUMMARY

[0004] The application aims to provide a data center equipment monitoring method, system, electronic device and storage medium, to solve the problems of high use threshold and inability to realize data analysis and alarm functions in the prior art.

[0005] In order to achieve the above-mentioned purpose, the application adopts the following technical solutions:

[0006] In a first aspect, a data center equipment monitoring method is provided, comprising:

[0007] an equipment collection indicator protocol abstract class is acquired, wherein the equipment collection indicator protocol abstract class contains a plurality of collection protocols of collection indicators and indicator attributes of each collection indicator, and the indicator attribute of any collection indicator includes the name of the any collection indicator, the data type corresponding to the any collection indicator and the data value range;

[0008] a data collection protocol for acquiring different collection indicators is constructed based on the equipment collection indicator protocol abstract class;

[0009] The data collection protocols are encapsulated to obtain the device monitoring configuration files corresponding to the data collection protocols;

[0010] At least one device monitoring task is acquired, wherein each device monitoring task in the at least one device monitoring task includes a to-be-monitored index, and each device monitoring task corresponds to a to-be-monitored device;

[0011] According to the to-be-monitored indexes in each device monitoring task, the device monitoring configuration files corresponding to each device monitoring task are determined, and the to-be-monitored devices are subjected to data collection by using the determined device monitoring configuration files, so as to obtain the monitoring data corresponding to each device monitoring task.

[0012] The monitoring data corresponding to each device monitoring task is subjected to data analysis and processing to obtain the monitoring analysis data and the operation prediction data of each to-be-monitored device, and when it is identified that there is abnormal data in the monitoring analysis data and / or the prediction data of any to-be-monitored device, an alarm prompt is generated.

[0013] Based on the above disclosure, the device collection index protocol abstract class is pre-constructed, wherein the device collection index protocol abstract class stores the collection protocols used by each collection index and the index attributes corresponding to each collection index; in this way, a database containing the protocols and collection contents used for data collection is provided for device operation monitoring; based on this, in specific application, the data collection protocols used when collecting different indexes can be constructed according to the device collection index protocol abstract class; then, each data collection protocol is encapsulated to obtain each device monitoring configuration file; in this way, when the device monitoring task is acquired, the device monitoring configuration file corresponding to the to-be-monitored index of the device monitoring task can be determined according to the to-be-monitored index; then, the operation data corresponding to the to-be-monitored index on the to-be-monitored device can be collected based on the determined device monitoring configuration file, so as to obtain the monitoring data; then, the monitoring analysis data and the operation prediction data of the device can be obtained by analyzing and processing the collected monitoring data; finally, the real-time alarm of the device operation can be realized according to the monitoring analysis data, and the early warning of the abnormal operation of the device can be realized based on the operation prediction data.

[0014] Through the above design, this invention pre-builds data acquisition protocols for monitoring different indicators when performing equipment monitoring. Thus, during use, the corresponding protocol is selected based on the indicator to be monitored, enabling the acquisition of the corresponding data. Therefore, compared to traditional technologies, this invention eliminates the need for user configuration, lowering the barrier to entry and personnel costs, and facilitating wider adoption. Furthermore, this invention can analyze and process the collected data to obtain real-time monitoring and analysis data and operational prediction data. Based on this, real-time alarms and early warnings of equipment operation status can be provided, enabling timely detection of equipment anomalies and malfunctions for rapid response and handling. This reduces the risk of equipment downtime and failures, thereby improving the reliability and stability of equipment operation.

[0015] In one possible design, the various data acquisition protocols are encapsulated to obtain the corresponding device monitoring configuration files for each data acquisition protocol, including:

[0016] Each data acquisition protocol is packaged to obtain the corresponding jar package for each data acquisition protocol.

[0017] By using the Java SPI mechanism, the JAR packages corresponding to each data acquisition protocol are registered to a preset configuration path file, so that after registration, the device monitoring configuration file corresponding to each data acquisition protocol can be obtained.

[0018] In one possible design, any device monitoring task also includes the device attributes of the device to be monitored, and the device attributes include the IP address of the device to be monitored corresponding to the device monitoring task.

[0019] This involves using the determined device monitoring configuration files to collect data from each device to be monitored, thereby obtaining the monitoring data corresponding to each device's monitoring task, including:

[0020] For any device monitoring task in at least one device monitoring task, based on the device monitoring configuration file of the device monitoring task, determine the collection protocol, name, data type and data value range of the monitoring indicator corresponding to the device monitoring task.

[0021] A collection request is generated based on the collection protocol, name, data type, and data value range of the monitored indicator corresponding to any of the device monitoring tasks.

[0022] Based on the IP address of the target device, the collection request is sent to the target device. Upon receiving the collection request, the target device uses its internal target protocol proxy component to search for the device operation data corresponding to the monitored indicator in the collection request in the target device's management information database. The target device encapsulates the device operation data according to the target protocol and sends the encapsulated device operation data to the data center device monitoring system. Here, the target device is the device to be monitored corresponding to any device monitoring task, the target protocol is the collection protocol in the collection request, and the target protocol proxy component is the proxy service component corresponding to the collection protocol in the collection request.

[0023] The system receives encapsulated device operation data sent by the target device and parses the encapsulated device operation data to obtain monitoring data corresponding to any device monitoring task after parsing.

[0024] In one possible design, the monitoring task for any device also includes: monitoring interval time;

[0025] Before determining the device monitoring configuration file corresponding to each device monitoring task based on the monitored indicators in each device monitoring task, the method further includes:

[0026] Construct a time poller, wherein the time poller is used to periodically trigger the execution of monitoring tasks for each device;

[0027] Each device monitoring task is added to the time poller so that the monitoring interval time in each device monitoring task can be read based on the time poller. According to the monitoring interval time in each device monitoring task, the device monitoring configuration file corresponding to each device monitoring task is determined periodically based on the monitoring indicators in each device monitoring task. The determined device monitoring configuration file is then used to collect data from each device to obtain the monitoring data corresponding to each device monitoring task.

[0028] In one possible design, the monitoring data corresponding to the monitoring tasks of each device is analyzed and processed to obtain the monitoring analysis data and operation prediction data of each device to be monitored, including:

[0029] Preprocess the monitoring data corresponding to each device monitoring task to obtain the preprocessed monitoring data corresponding to each device monitoring task.

[0030] An initial operational data prediction model is obtained, and the initial operational data prediction model is trained using various preprocessed monitoring data to obtain a device operational data prediction model after training.

[0031] For any device monitoring task, the preprocessed monitoring data corresponding to the device monitoring task is statistically analyzed to determine the operation statistics of the device to be monitored corresponding to the device monitoring task. The operation statistics include the number, name and abnormal monitoring indicators of the device to be monitored corresponding to the device monitoring task.

[0032] Based on the operational statistics, monitoring reports and / or data charts are generated, and these reports and / or charts are used to compose monitoring analysis data for the device to be monitored corresponding to any device monitoring task; and

[0033] Using the device operation data prediction model, the status prediction processing is performed on the device to be monitored corresponding to any device monitoring task to obtain the operation prediction data of the device to be monitored corresponding to any device monitoring task.

[0034] In one possible design, the monitoring data corresponding to each device monitoring task is preprocessed to obtain the preprocessed monitoring data corresponding to each device monitoring task, including:

[0035] For any monitoring data corresponding to a device monitoring task, data type detection, data anomaly detection, data deduplication, and data formatting are performed sequentially on the monitoring data corresponding to the device monitoring task, so that pre-cleaned data is obtained after processing.

[0036] The pre-cleaned data is smoothed using the following formula (1) to obtain smoothed data;

[0037] S t =α×y t +(1-α)×S t-1 ,t=1,2,...,T (1)

[0038] In the above formula (1), y t S represents the data corresponding to time t in the pre-cleaned data. t Indicates y t The corresponding smoothed data, S t-1 Let S represent the smoothed data at time t-1, α represent the smoothing coefficient, and T represent the total number of data collection times in the pre-cleaned data. When t is 1, S... t-1 Initial value;

[0039] Determine whether there is missing data in the smoothed data;

[0040] If so, the missing data in the smoothed data is filled in using the following formula (2) so that the preprocessed monitoring data can be obtained after the filling in process.

[0041] F t+1 =2S t -S t-1 +α×(S t -S t-1 (2)

[0042] In the above formula (2), F t+1 This represents the padded value at time t+1 in the smoothed data.

[0043] In one possible design, the initial operational data prediction model is trained using the various preprocessed monitoring data to obtain a device operational data prediction model after training, including:

[0044] Feature extraction is performed on each preprocessed monitoring data to obtain several feature vectors.

[0045] Using the aforementioned eigenvectors, a feature matrix is ​​constructed, and principal component analysis is used to reduce the dimensionality of the feature matrix to obtain several feature data.

[0046] Several feature data are used as training data, and the initial operation data prediction model is trained using the training data to obtain the device operation data prediction model after training is completed. The initial operation data prediction model is a linear regression model.

[0047] Secondly, a data center equipment monitoring system is provided. This system employs a distributed system, deployed across multiple monitoring nodes, and includes:

[0048] The acquisition unit is used to acquire the device acquisition indicator protocol abstract class, wherein the device acquisition indicator protocol abstract class contains acquisition protocols for multiple acquisition indicators and indicator attributes for each acquisition indicator, and the indicator attributes of any acquisition indicator include the name of the acquisition indicator, the data type corresponding to the acquisition indicator, and the data value range.

[0049] The protocol construction unit is used to construct a data acquisition protocol for acquiring different acquisition indicators based on the device acquisition indicator protocol abstract class.

[0050] The protocol encapsulation unit is used to encapsulate various data acquisition protocols to obtain the device monitoring configuration files corresponding to each data acquisition protocol.

[0051] A task creation unit is used to obtain at least one device monitoring task, wherein each device monitoring task includes indicators to be monitored, and each device monitoring task corresponds to a device to be monitored.

[0052] The data acquisition unit is used to determine the device monitoring configuration file corresponding to each device monitoring task based on the monitoring indicators in each device monitoring task, and to collect data from each device to obtain the monitoring data corresponding to each device monitoring task using the determined device monitoring configuration file.

[0053] The data analysis unit is used to perform data analysis and processing on the monitoring data corresponding to the monitoring tasks of each device, to obtain the monitoring analysis data and operation prediction data of each device to be monitored, and to generate an alarm prompt when abnormal data is identified in the monitoring analysis data and / or prediction data of any device to be monitored.

[0054] Thirdly, a data center equipment monitoring device is provided. Taking the device as an electronic device as an example, it includes a memory, a processor, and a transceiver that are connected in sequence. The memory is used to store computer programs, the transceiver is used to send and receive messages, and the processor is used to read the computer programs and execute the data center equipment monitoring method as described in the first aspect or any possible design of the first aspect.

[0055] Fourthly, a storage medium is provided, on which instructions are stored, which, when executed on a computer, perform the data center equipment monitoring method as described in the first aspect or any possible design of the first aspect.

[0056] Fifthly, a computer program product containing instructions is provided, which, when executed on a computer, cause the computer to perform the data center equipment monitoring method as described in the first aspect or any possible design of the first aspect.

[0057] Beneficial effects:

[0058] (1) When monitoring equipment, this invention pre-constructs data acquisition protocols for monitoring different acquisition indicators. Thus, when using it, the corresponding protocol can be selected according to the indicator to be monitored, and the corresponding data of the indicator to be monitored can be collected. Therefore, compared with traditional technology, this invention does not require users to configure the functions themselves, which not only reduces the threshold for use and personnel costs, but also facilitates promotion. At the same time, this invention can also analyze and process the collected data to obtain real-time monitoring and analysis data and operation prediction data of equipment operation. Based on this, real-time alarms of equipment operation and early warning of equipment operation status can be realized, so as to detect equipment abnormalities and faults in a timely manner, so as to respond and deal with them quickly. As a result, the risk of equipment downtime and faults can be reduced, thereby improving the reliability and stability of equipment operation.

[0059] (2) The present invention can realize automated alarms, and the system can promptly notify relevant personnel to handle the issues, reducing the workload of maintenance personnel and improving maintenance efficiency and effectiveness; at the same time, it can also improve user experience and satisfaction, thereby increasing user trust and loyalty.

[0060] (3) This invention loads the registration protocol through the Java SPI service discovery mechanism and creates a time poller, adds the monitoring task to the time poller, and executes the task by polling at regular intervals based on the time poller. This implementation method has high flexibility and can add, modify or delete the protocol and collection strategy at any time as needed, so as to meet the actual needs of different data centers.

[0061] (4) The system provided by the present invention adopts distributed technology, which can support data sharing and transmission between different servers and data centers, thereby expanding the scale and capabilities of the system and improving its scalability and availability. Attached Figure Description

[0062] Figure 1 This is a flowchart illustrating the steps of a data center equipment monitoring method provided in an embodiment of the present invention.

[0063] Figure 2 This is a schematic diagram of the structure of a data center equipment monitoring system provided in an embodiment of the present invention;

[0064] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0065] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the present invention will be briefly introduced below in conjunction with the accompanying drawings and descriptions of the embodiments or the prior art. Obviously, the following description of the structure of the accompanying drawings is only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. It should be noted that the description of these embodiments is for the purpose of helping to understand the present invention, but does not constitute a limitation of the present invention.

[0066] It should be understood that although the terms first, second, etc., may be used herein to describe various units, these units should not be limited by these terms. These terms are only used to distinguish one unit from another. For example, a first unit may be referred to as a second unit, and similarly, a second unit may be referred to as a first unit, without departing from the scope of the exemplary embodiments of the invention.

[0067] It should be understood that the term "and / or" that may appear in this document is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can mean: A exists alone, B exists alone, and A and B exist simultaneously. The term " / and" that may appear in this document describes another relationship between related objects, indicating that two relationships can exist. For example, A / and B can mean: A exists alone, and A and B exist alone. In addition, the character " / " that may appear in this document generally indicates that the related objects before and after it are in an "or" relationship.

[0068] Example:

[0069] See Figure 1 As shown, the data center equipment monitoring method provided in this embodiment does not require users to configure functions themselves when monitoring equipment, which not only reduces the threshold for use and personnel costs, but also facilitates promotion. At the same time, this method can also analyze and process the collected data to obtain real-time monitoring and analysis data and operation prediction data of equipment operation. Based on this, real-time alarms of equipment operation and early warnings of equipment operation status can be realized, thereby enabling timely detection of equipment anomalies and faults. This allows maintenance personnel to respond and handle quickly, ensuring the stability and reliability of equipment operation, and is suitable for large-scale application and promotion in the field of equipment data monitoring. In this embodiment, the method can be run on, but is not limited to, the data center equipment monitoring system side. It is understood that the aforementioned execution subject does not constitute a limitation on the embodiment of this application. Accordingly, the operation steps of this method can be, but are not limited to, the steps S1 to S6 below.

[0070] S1. Obtain the device acquisition indicator protocol abstract class, wherein the device acquisition indicator protocol abstract class contains acquisition protocols for multiple acquisition indicators and indicator attributes for each acquisition indicator, and the indicator attributes of any acquisition indicator include the name of the acquisition indicator, the data type corresponding to the acquisition indicator, and the data value range; in this embodiment, the device acquisition indicator protocol abstract class defines the general acquisition attributes and methods for the data corresponding to each acquisition indicator in different devices, while different acquisition protocols implement specific acquisition methods and data classification. By pre-constructing the aforementioned device acquisition indicator protocol abstract class, the entire system can support data acquisition from multiple devices and can classify and... Processing; meanwhile, the name, description, data type, and value range of the collected metrics can help determine the content of the collected data. Specifically, the name of the collected metric describes the data type, the data type describes the format of the collected data, and the value range describes the range of values ​​for the collected data. These attributes are key factors in determining the content of the collected data. For example, metrics such as CPU utilization, memory utilization, and interface bandwidth utilization of a device can be collected using the SNMP protocol. Similarly, metrics such as file system usage, process status, and network connection status of a device can be collected using the SSH protocol. Of course, the collection protocols used for other metrics of different devices will not be elaborated on here.

[0071] After constructing the aforementioned abstract class of device acquisition indicator protocol, the protocol used to acquire different acquisition indicators can be constructed based on the aforementioned abstract class of device acquisition indicator protocol. In this way, when monitoring the device in the future, the constructed protocol can be directly called to complete the acquisition of the corresponding operating data of the device. The construction process of the protocol used for different acquisition indicators can be, but is not limited to, as shown in step S2 below.

[0072] S2. Based on the device acquisition indicator protocol abstract class, a data acquisition protocol for obtaining different acquisition indicators is constructed. In this embodiment, the device acquisition indicator protocol abstract class is essentially classified according to the acquisition protocol used by the acquisition indicator, thereby obtaining the acquisition protocol corresponding to each acquisition indicator. Then, by combining the indicator attributes of each acquisition indicator, a data acquisition protocol for obtaining different acquisition indicators can be generated. For example, if the device's CPU utilization, memory utilization, and interface bandwidth utilization are all collected using the SNMP protocol, then the generated SNMP data acquisition protocol includes the acquisition indicators of CPU utilization, memory utilization, and interface bandwidth utilization, as well as the data type and data value range of the aforementioned three acquisition indicators. Of course, the construction principle of other data acquisition protocols is the same as the above example, and will not be repeated here.

[0073] Based on the aforementioned step S2, a data acquisition protocol for obtaining different acquisition indicators can be constructed. Then, by encapsulating it, a configuration file used to monitor different acquisition indicators can be obtained. In this way, when monitoring the device, calling different configuration files can realize the acquisition of running data corresponding to different acquisition indicators. In this embodiment, the encapsulation process of the data acquisition protocol can be, but is not limited to, the following step S3.

[0074] S3. Encapsulate each data acquisition protocol to obtain the device monitoring configuration file corresponding to each data acquisition protocol. In this embodiment, the encapsulation of the data acquisition protocol can be based on, but is not limited to, the Java SPI mechanism. Optionally, the specific encapsulation process can be, but is not limited to, the steps S31 and S32 below.

[0075] S31. Package each data acquisition protocol to obtain the corresponding jar package for each data acquisition protocol.

[0076] S32. Using the Java SPI mechanism, the JAR packages corresponding to each data acquisition protocol are registered to a preset configuration path file. After registration, the device monitoring configuration file corresponding to each data acquisition protocol is obtained. In this embodiment, the registration protocol is loaded through the Java SPI (Service Provider Interface) service discovery mechanism. Essentially, the data acquisition protocols in the JAR packages in step S31 are registered so that they can be automatically loaded during actual use to achieve the collection of running data from different devices. Java SPI is a service discovery mechanism provided by Java SE, which can dynamically register interface implementation classes to the system without modifying the code. Thus, when a new type of data needs to be collected, the corresponding data acquisition protocol can be written and packaged into a JAR package. When the system is running, it can be added to the classpath (i.e., the aforementioned configuration path file) to complete the automatic collection of the new type of data. That is, the system will automatically scan and load the data acquisition protocol in the JAR package and register it to the system, thereby realizing the collection of the new type of data. Based on this, this method can effectively avoid downtime maintenance during system upgrades or expansions, improving the availability and flexibility of the system.

[0077] After encapsulating each data acquisition protocol based on the aforementioned steps S31 and S32, a monitoring configuration file for collecting data corresponding to different acquisition indicators can be obtained. Then, when a device monitoring task is obtained, the corresponding monitoring configuration file can be determined according to the indicators to be monitored by the monitoring task, so as to realize the automatic collection of data corresponding to the indicators to be monitored in the device based on the determined monitoring configuration file. The data acquisition process can be, but is not limited to, as shown in steps S4 and S5 below.

[0078] S4. Obtain at least one device monitoring task, wherein each device monitoring task includes a monitoring indicator and corresponds to a monitoring device; in specific applications, for example, any device monitoring task may also include the device attributes and monitoring interval of the corresponding monitoring device, and the device attributes include the IP address of the monitoring device corresponding to the device monitoring task; at the same time, this embodiment also constructs a time poller to initiate device monitoring tasks periodically, thereby ensuring the real-time nature of monitoring.

[0079] Optionally, the process of periodically triggering the execution of device monitoring tasks may be, but is not limited to, the steps S41 and S42 below.

[0080] S31. Construct a time poller, wherein the time poller is used to periodically trigger the execution of monitoring tasks for each device; in this embodiment, the time poller is essentially a timer that can trigger tasks periodically according to a preset time interval.

[0081] After the time poller is built, the device monitoring tasks can be added to the time poller to enable the timed triggering of each device monitoring task. The triggering process is shown in step S42 below.

[0082] S42. Add each device monitoring task to the time poller so that the monitoring interval time in each device monitoring task can be read based on the time poller, and execute each device monitoring task periodically according to the monitoring interval time in each device monitoring task. In this embodiment, the monitoring interval time is an important parameter that determines the execution frequency of the device monitoring task. It can be set according to the specific device type, acquisition protocol, and user needs. In this embodiment, for some important devices or indicators, a shorter monitoring interval time can be set to achieve more real-time data acquisition and monitoring, while for some less important devices or indicators, a longer monitoring interval time can be set to reduce system load and resource consumption.

[0083] Therefore, by using a time poller to initiate device monitoring tasks periodically, it is possible to effectively collect, process, and gain real-time insights into real-time data streams, thereby improving the efficiency and real-time performance of data processing.

[0084] Furthermore, in this embodiment, for example, when a device monitoring task in the time poller expires, the data collection strategy factory can be invoked to perform a collection action. That is, the data collection strategy factory determines the monitoring configuration file corresponding to the monitoring indicator in the device monitoring task, and collects data based on the data collection protocol and other information in the configuration file. The data collection process can be, but is not limited to, the steps described in step S5.

[0085] S5. Based on the monitored indicators in each device monitoring task, determine the device monitoring configuration file corresponding to each device monitoring task, and use the determined device monitoring configuration file to collect data from each device to obtain the monitoring data corresponding to each device monitoring task. In this embodiment, taking any device monitoring task as an example, the process is as follows: based on the monitored indicators in any device monitoring task, select the device monitoring configuration file corresponding to the same number of data collections as the monitored indicators from the various device monitoring configuration files, and use it as the device configuration file for that device monitoring task. Then, the data corresponding to the monitored indicators can be collected based on the selected device monitoring configuration file. For example, assuming the monitored indicator is CPU utilization, the device monitoring configuration file corresponding to the SNMP data collection protocol can be used as the backup monitoring configuration file for that device monitoring task, that is, the SNMP protocol can be used to collect the CPU utilization of the monitored device. Of course, the process of determining the device monitoring configuration files for other device monitoring tasks is the same as the example above, and will not be described in detail here.

[0086] After obtaining the device monitoring configuration file for data collection, data corresponding to the monitored indicators can be collected from the device to be monitored. The specific collection process can be, but is not limited to, the steps S51 to S54 below.

[0087] S51. For any device monitoring task in at least one device monitoring task, based on the device monitoring configuration file of the device monitoring task, determine the collection protocol, name, data type, and data value range of the monitored metric corresponding to the device monitoring task. In this embodiment, the above example is used as a basis for further explanation. If the monitored metric is CPU utilization, then the following are determined: the collection protocol used by CPU utilization (i.e., SNMP protocol), the data type of CPU utilization, and the data value range. Similarly, if the monitored metric is memory utilization, then the following are determined: the collection protocol used by memory utilization (also SNMP protocol), the data type of memory utilization, and the data value range. Of course, the above examples are only illustrative, and the determination process of the collection protocol and attributes of more monitored metrics will not be described in detail.

[0088] After obtaining the acquisition protocol and attribute data of the monitored indicators corresponding to any device monitoring task, an acquisition request can be generated so as to obtain the operating data on the monitored device based on the acquisition request; wherein, the process of generating the acquisition request may be, but is not limited to, as shown in step S52 below.

[0089] S52. Generate a collection request based on the collection protocol, name, data type, and data value range of the monitored indicator corresponding to any device monitoring task. In this embodiment, after generating the collection request, the collection request can be sent to the monitored device corresponding to any device monitoring task so that the monitored device can query the data corresponding to the monitored indicator in the collection request. The query process is as shown in step S53 below.

[0090] S53. Based on the IP address of the target device, the collection request is sent to the target device, so that after receiving the collection request, the target device uses its internal target protocol proxy component to find the device operation data corresponding to the monitored indicator in the collection request in the target device's management information database, and encapsulates the device operation data according to the target protocol, so as to send the encapsulated device operation data to the data center device monitoring system. Here, the target device is the device to be monitored corresponding to any device monitoring task, the target protocol is the collection protocol in the collection request, and the target protocol proxy component is the proxy service component corresponding to the collection protocol in the collection request.

[0091] In this embodiment, the target device sends a collection request to another target device, requesting the value of a specific OID (Object Identifier). Then, the target protocol proxy component inside the target device, based on the collection request, looks up the value corresponding to the OID in the MIB (Management Information Base), which is to say, it looks up the operating data corresponding to the metric to be monitored. Then, it encapsulates the collected operating data and returns it to the data center equipment monitoring system, thus completing one data collection. For example, assuming the metric to be monitored is CPU utilization, then the value corresponding to CPU utilization is looked up in the MIB as the device operating data, then encapsulated, and returned to the data center equipment monitoring system. Of course, the collection process for the operating data corresponding to other different metrics to be monitored is the same as the example above, and will not be described again here.

[0092] After the target device obtains the device operation data of the monitoring indicators corresponding to any device monitoring task based on the collection request, it can send the data to the data center device monitoring system. The data center device monitoring system can parse the data to obtain the monitoring data corresponding to any device monitoring task. The parsing process is as shown in step S54 below.

[0093] S54. Receive the encapsulated device operation data sent by the target device, and parse the encapsulated device operation data to obtain the monitoring data corresponding to any device monitoring task after parsing; in this embodiment, after obtaining the monitoring data, the monitoring data can be saved to the database to achieve data retention.

[0094] In this embodiment, the execution of the aforementioned device monitoring tasks is based on a time poller. That is, the time poller reads the monitoring interval time in each device monitoring task, and determines the device monitoring configuration file corresponding to each device monitoring task according to the monitoring interval time in each device monitoring task based on the indicators to be monitored in each device monitoring task. Then, it uses the determined device monitoring configuration file to collect data from each device to be monitored in order to obtain the monitoring data corresponding to each device monitoring task.

[0095] Therefore, this embodiment can quickly collect equipment data corresponding to the monitored indicators in different equipment monitoring tasks based on the equipment monitoring configuration file constructed in step S3 above, thereby realizing real-time monitoring of the operation of each monitored device; after collecting the monitoring data of each monitored device during operation, real-time analysis and prediction of equipment operation can be performed to realize real-time alarm of equipment operation and early warning of operation. The data analysis process can be, but is not limited to, as shown in step S6 below.

[0096] S6. Perform data analysis and processing on the monitoring data corresponding to each device monitoring task to obtain the monitoring analysis data and operation prediction data of each device to be monitored. When abnormal data is identified in the monitoring analysis data and / or prediction data of any device to be monitored, an alarm prompt is generated. In this embodiment, for example, but not limited to, preprocessing each monitoring data first, then using the preprocessed monitoring data to perform device operation analysis, and using the preprocessed monitoring data to train a model to obtain a device operation data prediction model. In this way, real-time analysis of device operation and prediction of future operation status can be realized.

[0097] Optionally, the aforementioned data analysis and processing procedures may be, but are not limited to, as shown in steps S61 to S65 below.

[0098] S61. Preprocess the monitoring data corresponding to each device monitoring task to obtain the preprocessed monitoring data corresponding to each device monitoring task; In this embodiment, the preprocessing process is specifically illustrated using any monitoring data as an example. This process may be, but is not limited to, the steps S61a to S61d below.

[0099] S61a. For the monitoring data corresponding to any device monitoring task, the monitoring data corresponding to the device monitoring task is sequentially processed by data type detection, data anomaly detection, data deduplication, and data formatting to obtain pre-cleaned data after processing. In this embodiment, data type detection checks the collected data type to ensure the correctness and integrity of the data; data anomaly detection checks the collected data range, data boundaries, and data distribution to determine whether there are any anomalies based on the aforementioned detection results; data deduplication avoids duplicate data from interfering with subsequent analysis; and data deduplication converts the collected data into a unified format for easier subsequent processing. Additionally, in this example, unnecessary spaces, line breaks, and other formatting characters can be removed from the data to ensure accuracy. Optionally, the data processing in step S61 can be implemented using, but is not limited to, a Hive table, i.e., using data cleaning functions in the Hive table (such as the REGEXP_REPLACE function for string matching and replacement, CASE). The aforementioned data processing can be achieved through WHEN statements for conditional judgment and processing, clean expressions, and query statements. Of course, using Hive tables for data processing is a common data preprocessing technique, and its principles will not be elaborated here.

[0100] After obtaining the pre-cleaned data, it can be normalized. Then, the pre-cleaned data can be smoothed and data completion processed to improve the accuracy of the data. The smoothing process can be, but is not limited to, the steps shown in step S61b below.

[0101] S61b. The pre-cleaned data is smoothed using the following formula (1) to obtain smoothed data.

[0102] S t =α×y t +(1-α)×S t-1 ,t=1,2,...,T (1)

[0103] In the above formula (1), y t S represents the data corresponding to time t in the pre-cleaned data. t Indicates y t The corresponding smoothed data, S t-1 Let S represent the smoothed data at time t-1, α represent the smoothing coefficient, and T represent the total number of data collection times in the pre-cleaned data. When t is 1, S... t-1 This is the initial value.

[0104] Therefore, by using the aforementioned formula (1), the data at different times in the pre-cleaned data can be smoothed to obtain smoothed data. At the same time, data loss may occur during the acquisition process. Therefore, after smoothing, the missing data needs to be supplemented to ensure the integrity of the data. The data supplementation process is shown in steps S61c and S61d below.

[0105] S61c. Determine whether there is missing data in the smoothed data; In this embodiment, when the data in the smoothed data is empty at a certain moment, it can be determined that there is missing data at that moment; The process of completing the missing data is as shown in step S61d below.

[0106] S61d. If so, the following formula (2) is used to complete the missing data in the smoothed data so that the preprocessed monitoring data can be obtained after the completion process.

[0107] F t+1 =2S t -S t-1 +α×(S t -S t-1 (2)

[0108] In the above formula (2), F t+1 This represents the padded value at time t+1 in the smoothed data.

[0109] Based on the aforementioned formula (2), the smoothed data at time t and the smoothed data at time t-1 can be used to predict the data value at time t+1, and the predicted data value is used as the complete value at time t+1. Thus, based on the aforementioned formula (2), the missing data in the smoothed data can be completed to obtain the preprocessed monitoring data. Of course, the preprocessing process of the monitoring data of the other monitoring tasks is the same as the aforementioned steps S61a to S61d, and its principle will not be repeated.

[0110] After completing the preprocessing of the monitoring data corresponding to each device monitoring task, the preprocessed monitoring data can be used to train the model, thereby obtaining the device operation data prediction model. The model training process can be, but is not limited to, the steps shown in S62 below.

[0111] S62. Obtain the initial operation data prediction model, and train the initial operation data prediction model using the preprocessed monitoring data to obtain the equipment operation data prediction model after training. In this embodiment, the initial operation data prediction model may be, but is not limited to, a linear regression model, and may, but is not limited to, first extracting features from the preprocessed monitoring data to obtain several feature vectors; then, using the several feature vectors, constructing a feature matrix, and using principal component analysis to reduce the dimensionality of the feature matrix to obtain several feature data; finally, using the several feature data as training data, and using the training data to train the initial operation data prediction model to obtain the equipment operation data prediction model after training; further, for example, the feature vector of any preprocessed monitoring data is composed of data from each time point within that preprocessed monitoring data, and each row of the feature matrix represents a feature vector; in addition, a loss function can be defined during model training. The loss function measures the difference between the predicted and actual values. Then, gradient descent or normal equation methods are used to minimize the loss function. If gradient descent is used, multiple iterations are performed, and the gradient of the loss function is calculated and the model parameters are updated in each iteration until the loss function value reaches a threshold or no longer changes, thus obtaining the equipment operation data prediction model. Specifically, linear regression is performed on the aforementioned feature data to obtain a linear regression model, which serves as the equipment operation data prediction model.

[0112] Thus, after obtaining the preprocessed monitoring data corresponding to each device monitoring task based on the aforementioned step S61, and obtaining the device operation data prediction model based on step S62, real-time analysis of the operation status of each device to be monitored and prediction of the operation status can be performed. The real-time analysis process is shown in steps S63 and S64 below, while the operation status prediction is shown in step S65 below.

[0113] S63. For any device monitoring task, perform statistical analysis on the preprocessed monitoring data corresponding to the device monitoring task to determine the operational statistics of the device to be monitored corresponding to the device monitoring task. The operational statistics include the number, name, and abnormal monitoring indicators of the device to be monitored corresponding to the device monitoring task. In this embodiment, alarm rules can be preset first, so as to determine the abnormal data in the preprocessed monitoring data corresponding to the device monitoring task, such as data threshold, collection frequency, and abnormal duration, based on the alarm rules. Then, the abnormal data and normal data can be statistically analyzed to obtain the operational statistics. Furthermore, corresponding reports and charts can be generated based on the aforementioned operational statistics to intuitively display the operational status of the device to be monitored corresponding to the device monitoring task. The process of generating reports and charts can be, but is not limited to, as shown in step S64 below.

[0114] S64. Generate monitoring reports and / or data charts based on the operational statistics, and use the monitoring reports and / or data charts to compose monitoring analysis data for the monitoring device corresponding to any device monitoring task; In this embodiment, monitoring reports can be generated based on the aforementioned operational statistics. The report content may include indicators of monitoring data, the number of abnormal information, etc., and of course, it may also include fault handling information, etc.; At the same time, for example, software such as D3.js can be used to visualize the data as line graphs, scatter plots, etc., so that users can more intuitively understand the data change trend.

[0115] After completing the real-time analysis of the equipment's operating status, the status prediction of the equipment to be monitored corresponding to any equipment monitoring task can be performed based on the aforementioned trained equipment operating data prediction module. The prediction process is as shown in step S65 below.

[0116] S65. Using the device operation data prediction model, perform state prediction processing on the device to be monitored corresponding to any device monitoring task to obtain the operation prediction data of the device to be monitored corresponding to any device monitoring task; in this embodiment, the feature to be predicted (i.e. the prediction time) is input into the model to obtain the operation prediction data of the device at future times.

[0117] In this embodiment, based on the prediction results, warning lines can be used to alert users to indicate whether the data points exceed the expected range; for example, if some data points in the prediction results exceed the expected range, a warning notification can be automatically generated to notify the relevant users to perform necessary follow-up operations.

[0118] Meanwhile, the aforementioned alarm notifications can be sent to relevant personnel via email, SMS, WeChat, etc., and alarm levels can be set and classified according to alarm level and urgency to ensure the timeliness and accuracy of alarm information. In this way, real-time alarms for equipment operation and early warnings of equipment operating status can be realized, thereby enabling timely detection of equipment anomalies and faults, and improving the reliability and stability of equipment operation.

[0119] Therefore, through the data center equipment monitoring method described in detail in steps S1 to S6 above, this invention eliminates the need for users to configure functions themselves when monitoring equipment, which not only lowers the barrier to entry and personnel costs but also facilitates promotion. Furthermore, this method can analyze and process the collected data to obtain real-time monitoring and analysis data and operational prediction data for the equipment. Based on this, real-time alarms and early warnings of equipment operation status can be achieved, enabling timely detection of equipment anomalies and faults. This allows maintenance personnel to respond and handle issues quickly, ensuring the stability and reliability of equipment operation, and making it suitable for large-scale application and promotion in the field of equipment data monitoring.

[0120] like Figure 2 As shown, the second aspect of this embodiment provides a hardware system for implementing the data center equipment monitoring method described in the first aspect of the embodiment. For example, this data center equipment monitoring system employs a distributed system deployed across multiple monitoring nodes, and the system may include, but is not limited to, the following:

[0121] The acquisition unit is used to acquire the device acquisition indicator protocol abstract class, wherein the device acquisition indicator protocol abstract class contains acquisition protocols for multiple acquisition indicators and indicator attributes for each acquisition indicator, and the indicator attributes of any acquisition indicator include the name of the acquisition indicator, the data type corresponding to the acquisition indicator, and the data value range.

[0122] The protocol construction unit is used to construct a data acquisition protocol for obtaining different acquisition indicators based on the device acquisition indicator protocol abstract class.

[0123] The protocol encapsulation unit is used to encapsulate various data acquisition protocols to obtain the device monitoring configuration files corresponding to each data acquisition protocol.

[0124] A task creation unit is used to obtain at least one device monitoring task, wherein each device monitoring task includes indicators to be monitored, and each device monitoring task corresponds to a device to be monitored.

[0125] The data acquisition unit is used to determine the device monitoring configuration file corresponding to each device monitoring task based on the monitoring indicators in each device monitoring task, and to collect data from each device to obtain the monitoring data corresponding to each device monitoring task using the determined device monitoring configuration file.

[0126] The data analysis unit is used to perform data analysis and processing on the monitoring data corresponding to the monitoring tasks of each device, to obtain the monitoring analysis data and operation prediction data of each device to be monitored, and to generate an alarm prompt when abnormal data is identified in the monitoring analysis data and / or prediction data of any device to be monitored.

[0127] In addition, by employing distributed technology, the system can support data sharing and transmission between different servers and data centers, thereby expanding the scale and capabilities of the system and improving its scalability and availability.

[0128] The working process, working details and technical effects of the device provided in this embodiment can be found in the first aspect of the embodiment, and will not be repeated here.

[0129] like Figure 3 As shown, the third aspect of this embodiment provides a data center equipment monitoring device. Taking the device as an electronic device as an example, it includes: a memory, a processor, and a transceiver that are connected in sequence. The memory is used to store a computer program, the transceiver is used to send and receive messages, and the processor is used to read the computer program and execute the data center equipment monitoring method as described in the first aspect of the embodiment.

[0130] For specific examples, the memory may include, but is not limited to, random access memory (RAM), read-only memory (ROM), flash memory, first-in-first-out (FIFO) memory, and / or first-in-last-out (FILO) memory, etc.; specifically, the processor may include one or more processing cores, such as a 4-core processor, an 8-core processor, etc. The processor may be implemented using at least one hardware form of DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), PLA (Programmable Logic Array). The processor may also include a main processor and a coprocessor. The main processor, also known as the CPU (Central Processing Unit), is used to process data in the wake-up state; the coprocessor is a low-power processor used to process data in the standby state.

[0131] In some embodiments, the processor may integrate a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content to be displayed on the screen. For example, the processor may not be limited to microprocessors of the STM32F105 series, reduced instruction set computer (RISC) microprocessors, x86 architecture processors, or processors with integrated neural network processing units (NPUs). The transceiver may be, but is not limited to, a Wi-Fi transceiver, a Bluetooth transceiver, a General Packet Radio Service (GPRS) transceiver, a ZigBee (a low-power LAN protocol based on the IEEE 802.15.4 standard) transceiver, a 3G transceiver, a 4G transceiver, and / or a 5G transceiver. Furthermore, the device may also include, but is not limited to, a power module, a display screen, and other necessary components.

[0132] The working process, working details and technical effects of the electronic device provided in this embodiment can be found in the first aspect of the embodiment, and will not be repeated here.

[0133] The fourth aspect of this embodiment provides a storage medium for storing instructions containing the data center equipment monitoring method described in the first aspect of the embodiment. That is, the storage medium stores instructions that, when executed on a computer, perform the data center equipment monitoring method as described in the first aspect of the embodiment.

[0134] The storage medium refers to a carrier for storing data, which may include, but is not limited to, floppy disks, optical disks, hard disks, flash memory, USB flash drives, and / or memory sticks. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices.

[0135] The working process, working details and technical effects of the storage medium provided in this embodiment can be found in the first aspect of the embodiment, and will not be repeated here.

[0136] The fifth aspect of this embodiment provides a computer program product containing instructions that, when executed on a computer, cause the computer to perform the data center equipment monitoring method as described in the first aspect of this embodiment, wherein the computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device.

[0137] Finally, it should be noted that the above description is merely a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention. 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.

Claims

1. A method for monitoring data center equipment, characterized in that, include: Obtain an abstract class for device data acquisition indicator protocols, wherein the abstract class for device data acquisition indicator protocols contains multiple data acquisition indicators and indicator attributes of each data acquisition indicator, and the indicator attributes of any data acquisition indicator include the name of the data acquisition indicator, the data type corresponding to the data acquisition indicator, and the range of data values. Based on the aforementioned abstract class of device acquisition index protocol, a data acquisition protocol for obtaining different acquisition indexes is constructed. Each data acquisition protocol is encapsulated to obtain the device monitoring configuration file corresponding to each data acquisition protocol. At least one device monitoring task is obtained, wherein each device monitoring task includes a monitoring indicator and each device monitoring task corresponds to a monitoring device. Based on the monitoring indicators in each device monitoring task, determine the device monitoring configuration file corresponding to each device monitoring task, and use the determined device monitoring configuration file to collect data from each device to obtain the monitoring data corresponding to each device monitoring task. The system performs data analysis and processing on the monitoring data corresponding to each device monitoring task to obtain the monitoring analysis data and operation prediction data of each device to be monitored. When abnormal data is identified in the monitoring analysis data and / or prediction data of any device to be monitored, an alarm prompt is generated.

2. The method according to claim 1, characterized in that, Each data acquisition protocol is encapsulated to obtain the corresponding device monitoring configuration file, including: Each data acquisition protocol is packaged to obtain the corresponding jar package for each data acquisition protocol. By using the Java SPI mechanism, the JAR packages corresponding to each data acquisition protocol are registered to a preset configuration path file, so that after registration, the device monitoring configuration file corresponding to each data acquisition protocol can be obtained.

3. The method according to claim 1, characterized in that, Any device monitoring task also includes the device attributes of the corresponding device to be monitored, and the device attributes include the IP address of the device to be monitored corresponding to the device monitoring task. This involves using the determined device monitoring configuration files to collect data from each device to be monitored, thereby obtaining the monitoring data corresponding to each device's monitoring task, including: For any device monitoring task in at least one device monitoring task, based on the device monitoring configuration file of the device monitoring task, determine the collection protocol, name, data type and data value range of the monitoring indicator corresponding to the device monitoring task. A collection request is generated based on the collection protocol, name, data type, and data value range of the monitored indicator corresponding to any of the device monitoring tasks. Based on the IP address of the target device, the collection request is sent to the target device. Upon receiving the collection request, the target device uses its internal target protocol proxy component to search for the device operation data corresponding to the monitored indicator in the collection request in the target device's management information database. The target device encapsulates the device operation data according to the target protocol and sends the encapsulated device operation data to the data center device monitoring system. Here, the target device is the device to be monitored corresponding to any device monitoring task, the target protocol is the collection protocol in the collection request, and the target protocol proxy component is the proxy service component corresponding to the collection protocol in the collection request. The system receives encapsulated device operation data sent by the target device and parses the encapsulated device operation data to obtain monitoring data corresponding to any device monitoring task after parsing.

4. The method according to claim 1, characterized in that, The monitoring task for any device also includes: monitoring interval time; Before determining the device monitoring configuration file corresponding to each device monitoring task based on the monitored indicators in each device monitoring task, the method further includes: Construct a time poller, wherein the time poller is used to periodically trigger the execution of monitoring tasks for each device; Each device monitoring task is added to the time poller so that the monitoring interval time in each device monitoring task can be read based on the time poller. According to the monitoring interval time in each device monitoring task, the device monitoring configuration file corresponding to each device monitoring task is determined periodically based on the monitoring indicators in each device monitoring task. The determined device monitoring configuration file is then used to collect data from each device to obtain the monitoring data corresponding to each device monitoring task.

5. The method according to claim 1, characterized in that, Data analysis and processing are performed on the monitoring data corresponding to each device monitoring task to obtain monitoring analysis data and operational prediction data for each device to be monitored, including: Preprocess the monitoring data corresponding to each device monitoring task to obtain the preprocessed monitoring data corresponding to each device monitoring task. An initial operational data prediction model is obtained, and the initial operational data prediction model is trained using various preprocessed monitoring data to obtain a device operational data prediction model after training. For any device monitoring task, the preprocessed monitoring data corresponding to the device monitoring task is statistically analyzed to determine the operation statistics of the device to be monitored corresponding to the device monitoring task. The operation statistics include the number, name and abnormal monitoring indicators of the device to be monitored corresponding to the device monitoring task. Based on the operational statistics, monitoring reports and / or data charts are generated, and these reports and / or charts are used to compose monitoring analysis data for the device to be monitored corresponding to any device monitoring task; and Using the device operation data prediction model, the status prediction processing is performed on the device to be monitored corresponding to any device monitoring task to obtain the operation prediction data of the device to be monitored corresponding to any device monitoring task.

6. The method according to claim 5, characterized in that, The monitoring data corresponding to each device monitoring task is preprocessed to obtain the preprocessed monitoring data corresponding to each device monitoring task, including: For any monitoring data corresponding to a device monitoring task, data type detection, data anomaly detection, data deduplication, and data formatting are performed sequentially on the monitoring data corresponding to the device monitoring task, so that pre-cleaned data is obtained after processing. The pre-cleaned data is smoothed using the following formula (1) to obtain smoothed data; S t =α×y t +(1-α)×S t-1 ,t=1,2,...,T (1) In the above formula (1), y t S represents the data corresponding to time t in the pre-cleaned data. t Indicates y t The corresponding smoothed data, S t-1 Let S represent the smoothed data at time t-1, α represent the smoothing coefficient, and T represent the total number of data collection times in the pre-cleaned data. When t is 1, S... t-1 Initial value; Determine whether there is missing data in the smoothed data; If so, the missing data in the smoothed data is filled in using the following formula (2) so that the preprocessed monitoring data can be obtained after the filling in process. F t+1 =2S t -S t-1 +α×(S t -S t-1 ) (2) In the above formula (2), F t+1 This represents the padded value at time t+1 in the smoothed data.

7. The method according to claim 5, characterized in that, Using the preprocessed monitoring data, the initial operational data prediction model is trained to obtain the equipment operational data prediction model after training, including: Feature extraction is performed on each preprocessed monitoring data to obtain several feature vectors. Using the aforementioned eigenvectors, a feature matrix is ​​constructed, and principal component analysis is used to reduce the dimensionality of the feature matrix to obtain several feature data. Several feature data are used as training data, and the initial operation data prediction model is trained using the training data to obtain the device operation data prediction model after training is completed. The initial operation data prediction model is a linear regression model.

8. A data center equipment monitoring system, characterized in that, The data center equipment monitoring system adopts a distributed system, deployed on multiple monitoring nodes, and includes: The acquisition unit is used to acquire the device acquisition indicator protocol abstract class, wherein the device acquisition indicator protocol abstract class contains acquisition protocols for multiple acquisition indicators and indicator attributes for each acquisition indicator, and the indicator attributes of any acquisition indicator include the name of the acquisition indicator, the data type corresponding to the acquisition indicator, and the data value range. The protocol construction unit is used to construct a data acquisition protocol for acquiring different acquisition indicators based on the device acquisition indicator protocol abstract class. The protocol encapsulation unit is used to encapsulate various data acquisition protocols to obtain the device monitoring configuration files corresponding to each data acquisition protocol. A task creation unit is used to obtain at least one device monitoring task, wherein each device monitoring task includes indicators to be monitored, and each device monitoring task corresponds to a device to be monitored. The data acquisition unit is used to determine the device monitoring configuration file corresponding to each device monitoring task based on the monitoring indicators in each device monitoring task, and to collect data from each device to obtain the monitoring data corresponding to each device monitoring task using the determined device monitoring configuration file. The data analysis unit is used to perform data analysis and processing on the monitoring data corresponding to the monitoring tasks of each device, to obtain the monitoring analysis data and operation prediction data of each device to be monitored, and to generate an alarm prompt when abnormal data is identified in the monitoring analysis data and / or prediction data of any device to be monitored.

9. An electronic device, characterized in that, include: A memory, a processor, and a transceiver are sequentially connected in communication, wherein the memory is used to store computer programs, the transceiver is used to send and receive messages, and the processor is used to read the computer programs and execute the data center equipment monitoring method as described in any one of claims 1 to 7.

10. A storage medium, characterized in that, The storage medium stores instructions that, when executed on a computer, perform the data center equipment monitoring method as described in any one of claims 1 to 7.

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