Node classification method and apparatus, related device and computer program product

Abstract

The application discloses a node classification method and device, related equipment and computer program product, and relates to the technical field of operation and maintenance system management. The node classification method comprises the following steps: obtaining the first node configuration information of each node stored in the cluster management node, and automatically obtaining the related information of all nodes, thereby reducing the workload of manual data collection and the possibility of errors. By using a preset configuration data model, the node type to which each node belongs is determined based on the first node attribute information of each node. Based on the node type, the first IP address and the first cluster name of each node, the first label information corresponding to each node is generated. The first label information comprises first location information, node type and first cluster name. The node classification method simplifies the identification and management of nodes through an automatic label generation mechanism, improves the efficiency of operation and maintenance, supports automatic operation and maintenance, and further improves the accuracy of node classification.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of operation and maintenance system management, and more particularly, to a node classification method and device, related equipment and computer program product. BACKGROUND

[0002] With the rapid development of information technology, enterprise-level data centers are expanding in size, and the operation and maintenance management of tens of thousands of servers has become a challenge. In traditional operation and maintenance management systems, due to the lack of efficient automated classification means, it is an extremely tedious task to manage and maintain hundreds of thousands of machines. In this case, operation and maintenance personnel often need to manually assign labels such as location, node type, and cluster name to each machine, which not only consumes time and effort, but also is prone to errors, resulting in low management efficiency and failing to meet the rapidly changing business needs. SUMMARY

[0003] In view of the above problems, the present application is proposed to provide a node classification method, device, related equipment and computer program product to improve the efficiency of node classification in the operation and maintenance system. The specific scheme is as follows:

[0004] In a first aspect, a node classification method is provided, comprising:

[0005] Obtaining first node configuration information of each node stored in a cluster management node, the first node configuration information comprising a first IP address, a first cluster name to which the node belongs, and first node attribute information;

[0006] Using a preset configuration data model, determining the node type to which each node belongs based on the first node attribute information of each node;

[0007] Based on the node type, the first IP address and the first cluster name of each node, generating first label information corresponding to each node, the first label information comprising first location information, the node type and the first cluster name, wherein the first location information is obtained based on the first IP address.

[0008] In a possible design, in another implementation manner of the first aspect of the embodiments of the present application, the first node attribute information comprises any one or more of hardware configuration, network status and service running status.

[0009] In a possible design, in another implementation manner of the first aspect of the embodiments of the present application, the second node configuration information of each node is collected by communicating with the nodes in the cluster, the second node configuration information comprising a second IP address, a second cluster name to which the node belongs, and second node attribute information;

[0010] The configured node classification model is invoked to process the second node attribute information, and a node type classification result output by the node classification model is obtained; wherein the node classification model is trained by using node attribute information training data labeled with a node type label;

[0011] According to the node type classification result, the second IP address and the second cluster name, second label information of each node is generated, the second label information including second location information, the node type classification result and the second cluster name, wherein the second location information is obtained based on the second IP address;

[0012] The first label information and the second label information are compared, and a verification report is generated, the verification report recording differences between the first label information and the second label information.

[0013] In a possible design, in another implementation manner of the first aspect of the embodiment of the present application, the second node attribute information includes any one or more of hardware configuration, software environment, directory structure and process activity.

[0014] In a possible design, in another implementation manner of the first aspect of the embodiment of the present application, based on the first node attribute information of each node, the process of determining the node type to which the node belongs by using the preset configuration data model includes:

[0015] The first node attribute information of each node is matched with attribute requirements corresponding to each node type in the configuration data model, and a matched node type of each node is obtained.

[0016] The second aspect provides a node classification device, including:

[0017] An information acquisition unit is configured to acquire first node configuration information of each node stored in a cluster management node, the first node configuration information including a first IP address, a first cluster name to which the node belongs and first node attribute information;

[0018] A type determination unit is configured to determine, by using a preset configuration data model, a node type to which each node belongs based on first node attribute information of each node.

[0019] A label generation unit is configured to generate first label information corresponding to each node based on the node type, the first IP address and the first cluster name of each node, the first label information including first location information, the node type and the first cluster name, wherein the first location information is obtained based on the first IP address.

[0020] In a possible design, in another implementation manner of the second aspect of the embodiment of the present application, the method further includes the following steps.

[0021] a node communication unit, configured to communicate with the nodes in the cluster, and collect second node configuration information of the nodes, the second node configuration information including a second IP address, a second cluster name to which the node belongs, and second node attribute information;

[0022] a result obtaining unit, configured to invoke a configured node classification model to process the second node attribute information, to obtain a node type classification result output by the node classification model; the node classification model is trained by using node attribute information training data labeled with a node type label;

[0023] a label determining unit, configured to generate second label information of each node according to the node type classification result and the second IP address and the second cluster name, the second label information including second location information, the node type classification result, and the second cluster name, wherein the second location information is obtained based on the second IP address;

[0024] a report generating unit, configured to compare the first label information and the second label information, to generate a verification report, the verification report recording differences between the first label information and the second label information.

[0025] In a third aspect, an electronic device is provided, including a memory and a processor.

[0026] The memory is configured to store a program.

[0027] The processor is configured to execute the program, to implement each step of the node classification method described in any one of the preceding first aspects of the present application.

[0028] In a fourth aspect, a readable storage medium is provided, having a computer program stored thereon, the computer program being executed by a processor to implement each step of the node classification method described in any one of the preceding first aspects of the present application.

[0029] In a fifth aspect, a computer program product is provided, including a computer program, the computer program being executed by a processor to implement each step of the node classification method described in any one of the preceding first aspects of the present application.

[0030] By employing the above technical solution, the node classification method proposed in this application automatically obtains relevant information for all nodes by acquiring the first node configuration information of each node stored in the cluster management node, reducing the workload and possibility of errors from manual data collection. Utilizing a preset configuration data model, based on the first node attribute information of each node, the node type to which the node belongs is determined. Based on the node type, first IP address, and first cluster name of each node, first tag information corresponding to each node is generated. The first tag information includes first location information, node type, and first cluster name. The node classification method of this application simplifies node identification and management through an automated tag generation mechanism, improves operational efficiency, supports automated operation and maintenance, and further enhances the accuracy of node classification. Attached Figure Description

[0031] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the scope of this application. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0032] Figure 1 This is a schematic flowchart of a node classification method provided in an embodiment of this application;

[0033] Figure 2 This is a schematic diagram of another node classification method provided in an embodiment of this application;

[0034] Figure 3 This is a schematic diagram of a node classification device provided in an embodiment of this application;

[0035] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0036] Before introducing the proposed solution, let's first explain the English terms used in this document:

[0037] CMDB, or Configuration Management Database, is a core component of IT service management. It's a specialized database used to store and manage an organization's IT infrastructure components and their interrelationships. Key functions of CMDB include IT asset management, change management, relationship management, issue management, service level agreement (SLA) management, and compliance and auditing. By centrally recording and tracking all configuration items, CMDB improves the efficiency and reliability of IT operations, helps enterprises promptly identify and resolve issues in the IT environment, optimizes change management processes, and ensures that IT services meet established service level agreements. Furthermore, CMDB's openness allows for seamless integration with other systems, expanding its functionality and scope to support more comprehensive IT management and decision-making.

[0038] Configuration Data Model: The configuration data model is a structural framework within the Configuration Management Database (CMDB) used to define and organize IT infrastructure components and their interrelationships. It provides a clear data organization method for the CMDB by defining various elements in the IT environment (such as servers, network devices, software applications, etc.) as configuration items (CIs) and clarifying the relationships and dependencies between these CIs. The main functions of the configuration data model include ensuring data consistency and accuracy in the CMDB, supporting a comprehensive view of IT assets, facilitating cross-departmental and cross-system communication and collaboration, and providing a solid data foundation for change management, problem resolution, and service delivery. Furthermore, the configuration data model helps automate IT service management processes, improve the efficiency and effectiveness of IT operations, and meet compliance and audit requirements.

[0039] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0040] The node classification method proposed in this application automatically obtains relevant information for all nodes by acquiring the first node configuration information of each node stored in the cluster management node, reducing the workload and possibility of errors from manual data collection. Utilizing a pre-defined configuration data model, the node type is determined based on the first node attribute information of each node. Based on the node type, first IP address, and first cluster name of each node, first tag information is generated for each node. The first tag information includes first location information, node type, and first cluster name. This node classification method simplifies node identification and management through an automated tag generation mechanism, improves operational efficiency, supports automated operation and maintenance, and further enhances the accuracy of node classification.

[0041] The proposed solution can be implemented based on a terminal with data processing capabilities, such as a mobile phone, computer, or server.

[0042] The terms "first IP address," "second IP address," "first cluster name," and "second cluster name" used in this application do not refer to two completely different entities, but are used to distinguish differences in node information collected through different methods. Specifically, this application covers various embodiments. In some embodiments, node configuration information is obtained directly through the cluster management node, while in others it is collected by traversing each node in the network. Due to the different methods of information collection, the obtained node configuration information may be exactly the same or different. To clearly distinguish these information obtained through different means, descriptions such as "first" and "second" are used to identify them. This identification method helps to distinguish and reference data collected in different ways in subsequent processing and analysis.

[0043] Next, see Figure 1 , Figure 1 The flowchart illustrates a node classification method provided in this application. This node classification method can be implemented using a node classification system deployed on a terminal, as described below, and specifically includes the following steps:

[0044] Step S100: Obtain the first node configuration information of each node stored in the cluster management node.

[0045] Specifically, the first node configuration information of each node stored in the cluster management node can be obtained by accessing the cloud platform's API or by directly querying the cluster management node's database. The first node configuration information can include the following aspects:

[0046] First IP address: The network identifier of each node, that is, its unique address in the network, used for network communication and device identification, and can be used to determine the network location of the node and to manage the network.

[0047] The name of the first cluster to which the node belongs: Retrieving the cluster or group to which the node belongs helps in grouping and managing devices in large-scale IT infrastructures, as well as quickly locating and addressing issues in specific clusters when needed.

[0048] First node attribute information: The first node attribute information is used to understand the node's functional role, performance status, and maintenance requirements.

[0049] The collection of configuration information for the first node is fundamental to ensuring the effectiveness and accuracy of the entire automatic classification method. In this way, this embodiment can achieve efficient and accurate node classification, reducing the workload of maintenance personnel and improving the accuracy and efficiency of resource management.

[0050] Step S110: Using the preset configuration data model, determine the node type of each node based on the first node attribute information of each node.

[0051] Specifically, by analyzing the primary node attribute information of each node using a pre-defined configuration data model, the node type of each node can be determined. This automatically assigns each node to the correct node type without manual intervention, significantly improving the efficiency and accuracy of classification. This automated classification method based on a pre-defined configuration data model not only reduces the burden on operations and maintenance personnel but also improves the efficiency and effectiveness of overall IT infrastructure management.

[0052] Step S120: Generate first tag information corresponding to each node based on the node type, the first IP address and the first cluster name of each node.

[0053] Specifically, the node type, the first IP address, and the first cluster name are combined to form a comprehensive tag, namely the first tag information. The first tag information can include the following three parts:

[0054] First location information: Location information derived from a node's first IP address identifies the node's position within the network. By resolving the IP address, the subnet, data center, or other physical location of the node can be determined, which is crucial for network management and fault diagnosis.

[0055] Node type: Determined based on the first node attribute information, it describes the functional role of the node, such as whether it is a database server, web server, application server, etc. Node type is key to identifying the purpose and performance requirements of a node.

[0056] First cluster name: Used to identify the name of the cluster to which the node belongs. The cluster name helps to quickly locate and handle problems of a specific cluster when needed.

[0057] Alternatively, during the generation of the first tag information, these information elements can be integrated to form a structured tag. For example, if a node's first IP address is 192.168.1.1, the node type is a database server, and it belongs to a cluster named "Financial System", then the generated first tag information might be "Location: 192.168.1.1, Type: Database Server, Cluster: Financial System".

[0058] By assigning a unique primary tag to each node, not only is the node's identification information included, but it also facilitates subsequent automated operation and maintenance, such as high availability checks, alarm response, problem localization, and daily inspections, making IT infrastructure management more intuitive and efficient.

[0059] The node classification method proposed in this embodiment automatically obtains relevant information for all nodes by acquiring the first node configuration information of each node stored in the cluster management node, reducing the workload and possibility of errors from manual data collection. Using a preset configuration data model, the node type of each node is determined based on its first node attribute information. Based on each node's node type, first IP address, and first cluster name, first tag information is generated for each node. The first tag information includes first location information, node type, and first cluster name. This node classification method simplifies node identification and management through an automated tag generation mechanism, improves operational efficiency, supports automated operation and maintenance, and further enhances the accuracy of node classification.

[0060] Furthermore, in some embodiments of this application, another implementation of the node classification method is described. See also Figure 2 , Figure 2 This is a schematic diagram of another node classification method provided in an embodiment of this application.

[0061] Step S200: Obtain the first node configuration information of each node stored in the cluster management node.

[0062] Step S210: Using a preset configuration data model, determine the node type of each node based on the first node attribute information of each node.

[0063] Step S220: Generate first tag information corresponding to each node based on the node type, the first IP address and the first cluster name of each node.

[0064] Specifically, steps S200-S220 correspond one-to-one with steps S100-S120 in the aforementioned embodiments, as detailed above, and will not be repeated here.

[0065] Step S230: Communicate with each node in the cluster to collect the second node configuration information of each node.

[0066] Specifically, by communicating with each node in the cluster to collect second-node configuration information, secure remote access protocols such as SSH or other network communication protocols can be used to establish connections and communicate with each node in the cluster. Direct communication allows for real-time acquisition of the node's current status and the latest data, i.e., the second-node configuration information, ensuring the comprehensiveness and accuracy of the data. The second-node configuration information can include the following aspects:

[0067] Second IP address: This IP address information is obtained through direct communication with the node. It may be the same as or different from the IP address in the first node's information. Collecting the second IP address helps to gain a more comprehensive understanding of the node's network configuration, especially in environments with multiple network interfaces or multiple networks, which is crucial for network management and troubleshooting.

[0068] The second cluster name to which the node belongs: This cluster name information is obtained by directly communicating with the node. It may be the same as the cluster name in the first node's information, or it may differ depending on the node's affiliation in different contexts. The second cluster name helps to further confirm the node's organizational structure and business affiliation, especially in complex IT environments where a node may belong to multiple logical clusters simultaneously.

[0069] Second node attribute information: The collection of second node attribute information is to obtain the latest status and configuration of the node, which helps in the real-time management and maintenance of the dynamic environment.

[0070] By communicating with each node in the cluster, the latest configuration information of the second node can be obtained directly from the source. This information can be used to verify and supplement the information of the first node in the CMDB, ensuring data consistency and accuracy throughout the system. This dual verification mechanism enhances the robustness of the system, ensuring the continuous provision of accurate and reliable data support in a rapidly changing IT environment.

[0071] Step S240: Call the configured node classification model to process the second node attribute information and obtain the node type classification result output by the node classification model.

[0072] Specifically, a pre-trained node classification model can be invoked to process the collected second-node attribute information, thereby automatically determining the type of each node. The configured node classification model is a tool used to analyze and classify node attribute information. It analyzes the input second-node attribute information, evaluating and classifying the attribute information of each node by applying pattern recognition and classification rules learned during training, ultimately outputting a node type classification result for each node. For example, the node classification model might classify a node as a database server, file server, application server, etc.

[0073] The configured node classification model can be built based on machine learning techniques. During the training phase, the model uses a large amount of node attribute information labeled with node type tags as training data. This training data contains attribute information for various types of nodes, along with corresponding node type labels, to teach the node classification model how to identify and classify different nodes. This enables automated classification of a large number of nodes without manual intervention, significantly improving the efficiency and accuracy of operation and maintenance management.

[0074] Step S250: Generate second tag information for each node based on the node type classification result, the second IP address, and the second cluster name.

[0075] Specifically, based on the node type classification result, the second IP address, and the second cluster name, the second tag information corresponding to the node is formed. The second tag information may include the following parts:

[0076] Second location information: The second location information is derived based on the second IP address of each node. By analyzing the second IP address, the node's location in the network can be determined, which may include its subnet, data center, or other physical location.

[0077] Node type classification results: After processing the second node attribute information through the configured node classification model, the node type classification results are obtained, representing the functional role or classification of the node. For example, if a node is classified as a database server, this classification result will be directly reflected in the second tag information.

[0078] Second cluster name: This is the cluster to which the node belongs. It can identify the node's position or business affiliation in the organizational structure, which helps to group and manage devices and quickly locate them in a large-scale IT infrastructure.

[0079] Step S260: Compare the first tag information and the second tag information to generate a verification report.

[0080] Specifically, the first tag information generated from the first node information obtained from the cluster management node is compared one by one with the second tag information generated from the second node information collected through direct communication with each node.

[0081] It can automatically compare corresponding fields in two tag information sets, including location information, node type, and cluster name, to identify any inconsistencies or differences in IP address, node type identification, or the cluster to which a node belongs. Once a difference is identified, a verification report is automatically generated, which can record all differences between the first and second tag information sets. The verification report may also provide guidance on how to further investigate and resolve these differences, helping the operations team respond quickly and take necessary corrective actions to ensure that IT infrastructure configuration data remains up-to-date and accurate.

[0082] By comparing and verifying the data, we can ensure that the configuration data in the CMDB is consistent with the real-time status of the actual nodes, thereby improving the reliability and efficiency of the entire IT infrastructure management.

[0083] This embodiment collects second node configuration information, including the second IP address, the name of the second cluster to which the node belongs, and second node attribute information, by directly communicating with each node in the cluster, ensuring the timeliness and accuracy of the data. Using a trained node classification model, the second node attribute information is automatically processed, outputting the corresponding node type classification result. Further, based on the classification result, the second IP address, and the second cluster name, second tag information containing second location information, node type, and the second cluster name is generated. The second location information is obtained by parsing the second IP address, ensuring the accuracy of the location information. Finally, by comparing the first tag information and the second tag information, a verification report is generated to record the differences between them. This helps identify and resolve data inconsistencies, thereby verifying the accuracy of the first tag information, improving the accuracy and reliability of IT infrastructure management, reducing manual intervention, improving operational efficiency, and enhancing the system's adaptability and flexibility to different IT environments.

[0084] Furthermore, in some embodiments of this application, the attribute information of the first node and the attribute information of the second node can be further described in detail below.

[0085] The first-node attribute information encompasses information collected from the cluster management node, which may include any one or more of the following: hardware configuration, network status, and service operation status. Specifically, hardware configuration involves the node's physical specifications and capabilities, such as CPU model, memory capacity, and hard drive specifications; network status includes the node's network connectivity information, such as IP address, subnet mask, and routing information; and service operation status focuses on the status of services and applications running on the node, such as whether services are started, performance metrics, and error logs. First-node attribute information can be passively collected through database queries or API calls.

[0086] Second-node attribute information is obtained through direct communication with each node in the cluster. This information can include one or more of the following: hardware configuration, software environment, open ports, directory structure, and process activity. The hardware configuration section describes the node's physical components, but may contain more detailed information depending on the data collection method. The software environment records all software installed on the node and its versions, including the operating system, middleware, and applications. The directory structure provides a detailed view of the node's file system, including file and folder layout, permission settings, and the presence of critical configuration files. Process activity reflects all currently running processes on the node in real time, including process IDs, resource consumption, and running status.

[0087] Because the second node attribute information is collected through direct communication with each node, it can provide a more comprehensive data view than the first node attribute information, especially regarding directory structure and process activity. This provides crucial clues for a deeper understanding of the node's operational status and security. This differentiated data collection method allows the second node attribute information to provide richer and more detailed node operational information in certain situations, helping to verify the accuracy of the first tag information.

[0088] In this embodiment, the first node attribute information includes hardware configuration, network status, and service operation information collected from the cluster management node. This helps to understand the basic functions and network connections of the node, thereby enabling the CMDB to classify the node. The second node attribute information, obtained through direct communication with each node, includes not only hardware configuration and software environment information, but also open ports, directory structure, and process activity. This allows the second node attribute information to capture the real-time operating status and security condition of the node. The directory structure and process activity information, in particular, facilitate a deeper understanding of the node's operating and security status, leading to more accurate node classification.

[0089] Furthermore, in some embodiments of this application, the process of determining the node type of a node based on the first node attribute information of each node using a preset configuration data model can be described in detail below.

[0090] First, the system collects primary node attribute information for each node from the cluster management node. This primary node attribute information may include hardware configuration, network status, and service operation status. Next, it invokes a pre-defined configuration data model, which defines the attribute characteristics and requirements that different node types should possess.

[0091] The collected first-node attribute information is compared and matched one by one with the predefined node type attribute requirements in the configuration data model. This matching process may involve complex logical judgments and conditional filtering to ensure that the attribute information of each node corresponds to the correct node type. Through matching, it is possible to identify which predefined node type the attribute information of each node best matches, thereby determining the type of each node.

[0092] After matching is completed, the node type matched by each node will be recorded. These results will be used for subsequent tag generation and data verification steps. The first node configuration information of a node can be stored as a configuration item in the corresponding CMDB node type configuration item table.

[0093] If, during the matching process, the attribute information of certain nodes is found to be inconsistent with any predefined node type, these anomalies can be logged and may trigger further investigation or manual classification. The configuration data model is dynamically adjustable, and the attribute requirements of node types can be updated according to new business needs or technological changes to adapt to the ever-changing IT environment.

[0094] This embodiment automatically classifies a large number of nodes by matching the attribute information of the first node with the attribute requirements corresponding to each node type in the configuration data model, thereby improving the efficiency and accuracy of operation and maintenance management, and providing a solid data foundation for subsequent operation and maintenance.

[0095] The node classification device provided in the embodiments of this application is described below. The node classification device described below and the node classification method described above can be referred to each other.

[0096] See Figure 3 , Figure 3 This is a schematic diagram of a node classification device disclosed in an embodiment of this application.

[0097] like Figure 3 As shown, the device may include:

[0098] The information acquisition unit 11 is used to acquire the first node configuration information of each node stored in the cluster management node. The first node configuration information includes the first IP address, the name of the first cluster to which the node belongs, and the first node attribute information.

[0099] The type determination unit 12 is used to determine the node type of a node based on the first node attribute information of each node using a preset configuration data model.

[0100] The tag generation unit 13 is used to generate first tag information corresponding to each node based on the node type, the first IP address and the first cluster name of each node. The first tag information includes first location information, the node type and the first cluster name, wherein the first location information is obtained based on the first IP address.

[0101] In one possible implementation, the first node attribute information includes any one or more of the following: hardware configuration, network status, and service operation status.

[0102] In one possible implementation, a node classification device in this application embodiment further includes:

[0103] A node communication unit is used to communicate with each node in the cluster and collect the second node configuration information of each node. The second node configuration information includes a second IP address, the name of the second cluster to which the node belongs, and second node attribute information.

[0104] The result acquisition unit is used to call the configured node classification model to process the second node attribute information and obtain the node type classification result output by the node classification model; wherein, the node classification model is trained using node attribute information training data labeled with node type labels;

[0105] The tag determination unit is used to generate second tag information for each node based on the node type classification result, the second IP address, and the second cluster name. The second tag information includes second location information, the node type classification result, and the second cluster name, wherein the second location information is obtained based on the second IP address.

[0106] The report generation unit is used to compare the first label information and the second label information to generate a verification report, which records the differences between the first label information and the second label information.

[0107] In one possible implementation, the second node attribute information includes any one or more of the following: hardware configuration, software environment, directory structure, and process activity.

[0108] In one possible implementation, the type determination unit 12 uses a preset configuration data model to determine the node type of each node based on the first node attribute information of each node, including:

[0109] The first node attribute information of each node is matched with the attribute requirements corresponding to each node type in the configuration data model to obtain the matching node type for each node.

[0110] This application also provides an electronic device in its embodiments. (See reference...) Figure 4 The diagram illustrates a structural schematic suitable for implementing the electronic device in the embodiments of this application. The electronic device in the embodiments of this application may include, but is not limited to, fixed terminals such as mobile phones, tablet computers, etc. Figure 4 The electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0111] like Figure 4 As shown, the electronic device may include a processing unit (e.g., a central processing unit, a graphics processing unit, etc.) 601, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 602 or a program loaded from a storage device 608 into a random access memory (RAM) 603 to implement the node classification method of the foregoing embodiments of this application. When the electronic device is powered on, the RAM 603 also stores various programs and data required for the operation of the electronic device. The processing unit 601, ROM 602, and RAM 603 are interconnected via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0112] Typically, the following devices can be connected to I / O interface 605: input devices 606 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 607 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 608 including, for example, memory cards, hard drives, etc.; and communication devices 609. Communication device 609 allows electronic devices to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 4 Electronic devices with various devices are shown, but it should be understood that it is not required to implement or have all of the devices shown. More or fewer devices may be implemented or have alternatively.

[0113] This application also provides a computer-readable storage medium that carries one or more computer programs. When the one or more computer programs are executed by an electronic device, the electronic device is able to implement the node classification method of the foregoing embodiments of this application.

[0114] This application also provides a computer program product comprising one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in this application are generated.

[0115] It should also be noted that the device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. In addition, in the device embodiment drawings provided in this application, the connection relationship between modules indicates that they have a communication connection, which can be implemented as one or more communication buses or signal lines.

[0116] Through the above description of the embodiments, those skilled in the art can clearly understand that this application can be implemented by means of software plus necessary general-purpose hardware, or it can be implemented by special-purpose hardware including application-specific integrated circuits, special-purpose CPUs, special-purpose memory, special-purpose components, etc. Generally, any function performed by a computer program can be easily implemented by corresponding hardware, and the specific hardware structure used to implement the same function can also be diverse, such as analog circuits, digital circuits, or special-purpose circuits. However, for this application, software program implementation is more often the preferred implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a readable storage medium, such as a computer floppy disk, USB flash drive, mobile hard disk, ROM, RAM, magnetic disk, or optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, training equipment, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0117] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product.

[0118] The various embodiments in this specification are described in a progressive manner. Each embodiment focuses on the differences from other embodiments. The various embodiments can be combined as needed, and the same or similar parts can be referred to each other.

Claims

1. A node classification method, characterized in that, include: Obtain the first node configuration information of each node stored in the cluster management node. The first node configuration information includes the first IP address, the first cluster name to which the node belongs, and the first node attribute information. Using a pre-defined configuration data model, the node type of each node is determined based on the first node attribute information of each node. Based on the node type, the first IP address, and the first cluster name of each node, first tag information corresponding to each node is generated. The first tag information includes first location information, the node type, and the first cluster name, wherein the first location information is obtained based on the first IP address. Also includes: Communicate with each node in the cluster to collect the second node configuration information of each node, the second node configuration information including the second IP address, the name of the second cluster to which the node belongs, and the second node attribute information; The configured node classification model is invoked to process the second node attribute information to obtain the node type classification result output by the node classification model; wherein, the node classification model is trained using node attribute information training data labeled with node type labels; Based on the node type classification result, the second IP address, and the second cluster name, second tag information is generated for each node. The second tag information includes second location information, the node type classification result, and the second cluster name, wherein the second location information is obtained based on the second IP address. The first label information and the second label information are compared to generate a verification report, which records the differences between the first label information and the second label information.

2. The method according to claim 1, characterized in that, The first node attribute information includes any one or more of the following: hardware configuration, network status, and service operation status.

3. The method according to claim 1, characterized in that, The second node attribute information includes any one or more of the following: hardware configuration, software environment, directory structure, and process activity.

4. The method according to claim 1, characterized in that, The process of determining the node type of a node based on the first node attribute information of each node using a preset configuration data model includes: The first node attribute information of each node is matched with the attribute requirements corresponding to each node type in the configuration data model to obtain the matching node type for each node.

5. A node classification device, characterized in that, include: The information acquisition unit is used to acquire the first node configuration information of each node stored in the cluster management node. The first node configuration information includes the first IP address, the name of the first cluster to which the node belongs, and the first node attribute information. The type determination unit is used to determine the node type of a node based on the first node attribute information of each node using a preset configuration data model. The tag generation unit is used to generate first tag information corresponding to each node based on the node type, the first IP address and the first cluster name of each node. The first tag information includes first location information, the node type and the first cluster name, wherein the first location information is obtained based on the first IP address. Also includes: A node communication unit is used to communicate with each node in the cluster and collect the second node configuration information of each node. The second node configuration information includes a second IP address, the name of the second cluster to which the node belongs, and second node attribute information. The result acquisition unit is used to call the configured node classification model to process the second node attribute information and obtain the node type classification result output by the node classification model; wherein, the node classification model is trained using node attribute information training data labeled with node type labels; The tag determination unit is used to generate second tag information for each node based on the node type classification result, the second IP address, and the second cluster name. The second tag information includes second location information, the node type classification result, and the second cluster name, wherein the second location information is obtained based on the second IP address. The report generation unit is used to compare the first label information and the second label information to generate a verification report, which records the differences between the first label information and the second label information.

6. An electronic device, characterized in that, include: Memory and processor; The memory is used to store programs; The processor is used to execute the program to implement the various steps of the node classification method as described in any one of claims 1 to 4.

7. A readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements each step of the node classification method as described in any one of claims 1 to 4.

8. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements each step of the node classification method as described in any one of claims 1 to 4.

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