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

Abstract

The invention discloses a node classification method and device, related equipment and a computer program product, and relates to the technical field of operation and maintenance system management. The node classification method comprises the following steps of: automatically acquiring related information of all nodes by acquiring first node configuration information of each node stored in a cluster management node, so that the workload of manually collecting data and the possibility of errors are reduced. A preset configuration data model is utilized, the node type of the node is determined based on the first node attribute information of each node, first label information corresponding to each node is generated based on the node type of each node, the first IP address and the first cluster name, and the first label information comprises the first position information, the node type and the first cluster name. According to the node classification method, node identification and management are simplified through an automatic label generation mechanism, the operation and maintenance efficiency is improved, automatic operation and maintenance operation is supported, and the node classification accuracy is further improved.

Description

Technical Field

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

[0002] With the rapid development of information technology, the scale of enterprise-level data centers continues to expand, 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 methods, managing and maintaining hundreds of thousands of machines is an extremely tedious task. 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 is not only time-consuming and labor-intensive, but also prone to errors, resulting in low management efficiency and failure to meet rapidly changing business needs. Summary of the invention

[0003] In view of the above problems, this application is proposed to provide a node classification method, apparatus, 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: In a first aspect, a node classification method is provided, comprising: Acquire first node configuration information of each node stored in the cluster management node, where the first node configuration information includes a first IP address, a name of a first cluster to which the node belongs, and first node attribute information; Using a preset configuration data model, based on the first node attribute information of each node, determining the node type to which the node belongs; Based on the node type, the first IP address and the first cluster name of each node, first label information corresponding to each node is generated, 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.

[0004] In one possible design, in another implementation of the first aspect of the embodiment of the present application, the first node attribute information includes: any one or more of hardware configuration, network status, and service operation status.

[0005] In one possible design, in another implementation of the first aspect of the embodiment of the present application, communicating with each node in the cluster to collect second node configuration information of each node, where the second node configuration information includes a second IP address, a name of a second cluster to which the node belongs, and second node attribute information; Calling the configured node classification model to process the second node attribute information to obtain a node type classification result output by the node classification model; wherein the node classification model is trained using node attribute information training data annotated with node type labels; Generate second label information for each node according to the node type classification result, the second IP address, and the second cluster name, where the second label 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, wherein the verification report records the difference between the first label information and the second label information.

[0006] In one possible design, in another implementation 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.

[0007] In a possible design, in another implementation of the first aspect of the embodiment of the present application, a process of determining a node type to which a node belongs based on 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 node type matched by each node.

[0008] In a second aspect, a node classification device is provided, comprising: An information acquisition unit, used to acquire first node configuration information of each node stored in the cluster management node, wherein the first node configuration information includes a first IP address, a name of a first cluster to which the node belongs, and first node attribute information; A type determination unit, configured to determine the node type to which the node belongs based on the first node attribute information of each node by using a preset configuration data model; A label generation unit is used 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, wherein the first label 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.

[0009] In a possible design, in another implementation of the second aspect of the embodiment of the present application, the method further includes: A node communication unit, used to communicate with each node in the cluster and collect second node configuration information of each node, wherein the second node configuration information includes a second IP address, a name of a second cluster to which the node belongs, and second node attribute information; A result acquisition unit, used for calling the configured node classification model to process the second node attribute information, and obtaining a node type classification result output by the node classification model; wherein the node classification model is trained using node attribute information training data annotated with node type labels; a label determination unit, configured to generate second label information of each node according to the node type classification result, the second IP address, and the second cluster name, wherein the second label 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; A report generating unit is used to compare the first label information with the second label information to generate a verification report, wherein the verification report records the difference between the first label information and the second label information.

[0010] In a third aspect, an electronic device is provided, comprising: a memory and a processor; The memory is used to store programs; The processor is used to execute the program to implement each step of the node classification method described in any one of the first aspects of the present application.

[0011] In a fourth aspect, a readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the various steps of the node classification method described in any one of the first aspects of the present application are implemented.

[0012] In a fifth aspect, a computer program product is provided, including a computer program, which, when executed by a processor, implements the various steps of the node classification method described in any one of the aforementioned first aspects of the present application.

[0013] By means of the above technical solution, the node classification method proposed in the present application automatically obtains the relevant information of all nodes by obtaining the first node configuration information of each node stored in the cluster management node, thereby reducing the workload of manual data collection and the possibility of errors. Using the preset configuration data model, based on the first node attribute information of each node, the node type to which the node belongs is determined, and based on the node type, first IP address and first cluster name of each node, the first label information corresponding to each node is generated, and the first label information includes the first location information, node type and first cluster name. The node classification method of the present application simplifies the identification and management of nodes through an automated label generation mechanism, improves the efficiency of operation and maintenance, supports automated operation and maintenance operations, and further improves the accuracy of node classification. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present application. Also, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings: Figure 1 A schematic diagram of a node classification method flow chart provided in an embodiment of the present application; Figure 2 A schematic diagram of another node classification method flow chart provided in an embodiment of the present application; Figure 3 A schematic diagram of the structure of a node classification device provided in an embodiment of the present application; Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0015] Before introducing this application, the English terms involved in this article are explained first: CMDB: Configuration Management Database. CMDB is a core component in IT service management. It is a specialized database used to store and manage an organization's IT infrastructure components and their relationships. The main functions of CMDB include IT asset management, change management, relationship management, problem management, service level agreement (SLA) management, and compliance and auditing. By centrally recording and tracking all configuration items, CMDB can improve the efficiency and reliability of IT operations, help enterprises promptly discover and solve problems in the IT environment, optimize change management processes, and ensure that IT services meet established service level agreements. In addition, the openness of CMDB enables it to be seamlessly integrated with other systems, expanding its functions and application scope, thereby supporting more comprehensive IT management and decision-making.

[0016] Configuration data model: The configuration data model is a structural framework used to define and organize IT infrastructure components and their relationships in the configuration management database (CMDB). It provides a clear way to organize data 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 associations and dependencies between these configuration items. The main functions of the configuration data model include ensuring the consistency and accuracy of data in the CMDB, supporting a comprehensive view of IT assets, facilitating communication and collaboration across departments and systems, and providing a solid data foundation for change management, problem solving, and service delivery. In addition, the configuration data model also helps to automate IT service management processes, improve the efficiency and effectiveness of IT operations, and meet compliance and audit requirements.

[0017] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0018] The node classification method proposed in the present application automatically obtains relevant information of all nodes by obtaining the first node configuration information of each node stored in the cluster management node, thereby reducing the workload of manual data collection and the possibility of errors. Using the preset configuration data model, based on the first node attribute information of each node, the node type to which the node belongs is determined, and based on the node type, first IP address and first cluster name of each node, the first label information corresponding to each node is generated, and the first label information includes first location information, node type and first cluster name. The node classification method of the present application simplifies the identification and management of nodes through an automated label generation mechanism, improves the efficiency of operation and maintenance, supports automated operation and maintenance operations, and further improves the accuracy of node classification.

[0019] The present application solution can be implemented based on a terminal with data processing capabilities, which can be a mobile phone, computer, server, etc.

[0020] The terms such as the first IP address, the second IP address, the first cluster name, the second cluster name, etc. mentioned in this application do not refer to two completely different entities, but are used to distinguish the differences in node information collected by different methods. Specifically, the present application covers a variety of embodiments, in some of which, the node configuration information is directly obtained through the cluster management node, while in other embodiments, it is collected by traversing each node in the network. Due to the different methods of collecting information, the obtained node configuration information may be exactly the same or there may be differences. In order to clearly distinguish the information obtained through different channels, descriptions such as "first" and "second" are used to identify them. Such an identification method helps to distinguish and reference data collected in different ways in subsequent processing and analysis.

[0021] Next, see Figure 1 , Figure 1 A schematic diagram of a process flow of a node classification method provided by the present application. The node classification method of the present application can be implemented by a node classification system deployed on a terminal, as described below, specifically including the following steps: Step S100: Acquire first node configuration information of each node stored in the cluster management node.

[0022] Specifically, the first node configuration information of each node stored in the cluster management node is obtained by accessing the API of the cloud platform or directly querying the cluster management node such as the database. The first node configuration information may include the following aspects: First IP address: The network identification 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 perform network management.

[0023] Name of the first cluster to which the node belongs: Obtaining the cluster or group to which the node belongs helps to group and manage devices in large-scale IT infrastructure, and quickly locate and handle problems in a specific cluster when necessary.

[0024] First node attribute information: The first node attribute information is used to understand the functional role, performance status and maintenance requirements of the node.

[0025] The collection of the first node configuration information is the basis for ensuring the effectiveness and accuracy of the entire automatic classification method. In this way, the present embodiment can achieve efficient and accurate classification of nodes, reduce the workload of operation and maintenance personnel, and improve the accuracy and efficiency of resource management.

[0026] Step S110: using a preset configuration data model, based on the first node attribute information of each node, determine the node type to which the node belongs.

[0027] Specifically, by using the preset configuration data model to analyze the first node attribute information of each node, the node type of each node can be determined. Each node is automatically assigned to the correct node type without manual intervention, which greatly improves the efficiency and accuracy of classification. This automated classification method based on the preset configuration data model not only reduces the burden on operation and maintenance personnel, but also improves the efficiency and effectiveness of the entire IT infrastructure management.

[0028] Step S120: 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.

[0029] Specifically, the three information elements of the node type, the first IP address and the first cluster name are combined to form a comprehensive label, namely, the first label information. The first label information may include the following three parts: First location information: The location information obtained based on the first IP address of the node, which identifies the location of the node in the network. By resolving the IP address, the subnet, computer room or other physical location of the node can be determined, which is very important for network management and fault diagnosis.

[0030] Node type: Determined based on the first node attribute information of the node. It describes the functional role of the node, such as whether it is a database server, Web server, application server, etc. The node type is the key to identifying the purpose and performance requirements of the node.

[0031] First cluster name: It is used to identify the name of the cluster to which the node belongs. The cluster name helps to quickly locate and handle problems with a specific cluster when necessary.

[0032] In an optional manner, in the process of generating the first label information, these information elements can be integrated together to form a structured label. For example, if the first IP address of a node 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 label information may be "location: 192.168.1.1, type: database server, cluster: financial system".

[0033] By assigning a unique first tag information to each node, it not only contains the node's identification information, but also facilitates subsequent automated operation and maintenance operations, such as high availability checks, alarm responses, problem location, and daily inspections, making IT infrastructure management more intuitive and efficient.

[0034] The node classification method proposed in this embodiment automatically obtains relevant information of all nodes by obtaining the first node configuration information of each node stored in the cluster management node, thereby reducing the workload of manual data collection and the possibility of errors. Using the preset configuration data model, based on the first node attribute information of each node, the node type to which the node belongs is determined, and based on the node type, first IP address and first cluster name of each node, the first label information corresponding to each node is generated, and the first label information includes first location information, node type and first cluster name. The node classification method of the present application simplifies the identification and management of nodes through an automated label generation mechanism, improves the efficiency of operation and maintenance, supports automated operation and maintenance operations, and further improves the accuracy of node classification.

[0035] Furthermore, in some embodiments of the present application, another implementation of the node classification method is introduced. Figure 2 , Figure 2 A flowchart of another node classification method provided in an embodiment of the present application.

[0036] Step S200: Acquire first node configuration information of each node stored in the cluster management node.

[0037] Step S210: using a preset configuration data model, based on the first node attribute information of each node, determine the node type to which the node belongs.

[0038] Step S220: 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.

[0039] Specifically, the above steps S200-S220 correspond one-to-one to the steps S100-S120 in the aforementioned embodiment, and please refer to the above introduction for details, which will not be repeated here.

[0040] Step S230: Communicate with each node in the cluster to collect second node configuration information of each node.

[0041] Specifically, by communicating with each node in the cluster to collect the second node configuration information, a secure remote access protocol, such as SSH or other network communication protocols, can be used to establish a connection and communicate with each node in the cluster. The current state of the node is obtained in real time through direct communication, and the latest data, i.e., the second node configuration information, is obtained, thereby ensuring the comprehensiveness and accuracy of the data. Among them, the second node configuration information may include the following aspects: Second IP address: The IP address information is obtained by directly communicating with the node. It may be the same as the IP address in the first node information, or it may be different. The collection of the second IP address helps to more fully understand the network configuration of the node, especially in a multi-network interface or multi-network environment, which is crucial for network management and troubleshooting.

[0042] The name of the second cluster to which the node belongs: The cluster name information is obtained by directly communicating with the node. It may be consistent with the cluster name in the first node information, or it may be different due to the node belonging to different contexts. The second cluster name helps to further confirm the organizational structure and business ownership of the node, especially in a complex IT environment where the node may belong to multiple logical clusters at the same time.

[0043] Second node attribute information: The collection of second node attribute information is to obtain the latest status and configuration of the node, which helps to manage and maintain the dynamic environment in real time.

[0044] By communicating with each node in the cluster, the latest second node configuration information can be obtained directly from the source, which can be used to verify and supplement the first node information in the CMDB to ensure the data consistency and accuracy of the entire system. This double verification mechanism enhances the robustness of the system and ensures that accurate and reliable data support can be continuously provided in a rapidly changing IT environment.

[0045] Step S240: Call the configured node classification model to process the second node attribute information to obtain a node type classification result output by the node classification model.

[0046] Specifically, a pre-trained node classification model can be called to process the collected second node attribute information so as to automatically determine the type of each node. The configured node classification model is a tool for analyzing and classifying node attribute information. The node classification model will analyze the input second node attribute information and can evaluate and classify the attribute information of each node by applying the pattern recognition and classification rules learned in the training phase, and finally output the node type classification result of each node. For example, the node classification model may classify a node as a database server, a file server, an application server, etc.

[0047] The configured node classification model can be built based on machine learning technology. During the training phase, the node classification model uses a large amount of node attribute information labeled with node type labels as training data. These training data contain attribute information of various types of nodes and corresponding node type labels, which are used to teach the node classification model how to identify and classify different nodes. This enables automatic classification of a large number of nodes without manual intervention, greatly improving the efficiency and accuracy of operation and maintenance management.

[0048] Step S250: Generate second label information for each node according to the node type classification result, the second IP address, and the second cluster name.

[0049] Specifically, based on the node type classification result, the second IP address and the second cluster name, the second label information corresponding to the node is formed. The second label information may include the following parts: 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 location of the node in the network can be determined, which may include its subnet, computer room or other physical location.

[0050] Node type classification result: The node type classification result is obtained by processing the second node attribute information through the configured node classification model, which represents 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 label information.

[0051] Second cluster name: This is the cluster to which the node belongs. It can identify the node's position in the organizational structure or business affiliation, and helps to group and manage devices and quickly locate them in large-scale IT infrastructure.

[0052] Step S260: Compare the first label information and the second label information to generate a verification report.

[0053] Specifically, the first label information generated by the first node information obtained at the cluster management node is compared one by one with the second label information generated by the second node information collected by directly communicating with each node.

[0054] The corresponding fields in the two tag information can be automatically compared, including location information, node type, and cluster name, to identify any inconsistencies or differences in IP addresses, node type identification, or the cluster to which the node belongs. Once the difference is identified, a verification report will be automatically generated, which can record all the differences between the first tag information and the second tag information. The verification report may also provide guidance on how to further investigate and resolve these differences, helping the operation and maintenance team to respond quickly and take necessary corrective measures to ensure that the configuration data of the IT infrastructure remains up to date and accurate.

[0055] Through the comparison and verification process, it is possible to ensure that the configuration data in the CMDB is consistent with the real-time status of the actual node, thereby improving the reliability and efficiency of the entire IT infrastructure management.

[0056] This embodiment collects 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 by directly communicating with each node in the cluster, thereby ensuring the latest and accuracy of the data. The second node attribute information is automatically processed using the trained node classification model, and the corresponding node type classification result is output. Furthermore, based on the classification result and the second IP address and the second cluster name, second label information including the second location information, the node type, and the second cluster name is generated, wherein the second location information is obtained by parsing the second IP address, thereby ensuring the accuracy of the location information. Finally, by comparing the first label information and the second label information, a verification report is generated to record the difference between the two, which helps to identify and resolve data inconsistencies, thereby verifying the accuracy of the first label information, and improving the accuracy and reliability of IT infrastructure management, reducing manual intervention, improving operation and maintenance efficiency, and enhancing the system's adaptability and flexibility to different IT environments.

[0057] Furthermore, in some embodiments of the present application, the first node attribute information and the second node attribute information may be described in detail, and this part of the content is described in detail below.

[0058] The first node attribute information covers the information collected from the cluster management node, which can include any one or more of the hardware configuration, network status, and service operation status. Specifically, the hardware configuration involves the physical specifications and capabilities of the node, such as CPU model, memory capacity, hard disk specifications, etc.; the network status includes the network connection information of the node, such as IP address, subnet mask, routing information, etc.; the service operation status focuses on the status of services and applications running on the node, such as whether the service is started, performance indicators, error logs, etc. The first node attribute information can be passively collected through database queries or API calls.

[0059] The second node attribute information is obtained by directly communicating with each node in the cluster. The second node attribute information may include any one or more of the hardware configuration, software environment, open ports, directory structure, and process activity. The hardware configuration in the second node attribute information also describes the physical components of the node, but may contain more detailed information due to different collection methods; the software environment records all software installed on the node and its version, including operating systems, middleware, applications, etc.; the directory structure provides a detailed view of the node file system, including the layout of files and folders, permission settings, the existence of key configuration files, etc.; the process activity reflects all processes currently running on the node in real time, including process ID, resource consumption, running status, etc.

[0060] Since the second node attribute information is collected by directly communicating with each node, it can provide a more comprehensive data view than the first node attribute information, especially the directory structure and process activity information, which provides important clues for in-depth understanding of the node's operating status and security status. This differentiated data collection method enables the second node attribute information to provide richer and more detailed node operation details in some cases, which helps to verify the accuracy of the first tag information.

[0061] In this embodiment, the first node attribute information includes attribute information such as hardware configuration, network status, and service operation status collected from the cluster management node, which helps to understand the basic functions and network connections of the node, so as to classify the node using CMDB. The second node attribute information, by directly communicating with each node, not only includes information such as hardware configuration and software environment, but also includes open ports, directory structure, and process activities, so that the second node attribute information can capture the real-time operation status and security status of the node, especially the two information of directory structure and process activity, which facilitates in-depth understanding of the node operation status and security status, so as to more accurately classify the node by type.

[0062] Furthermore, in some embodiments of the present application, the process of determining the node type to which a node belongs based on the first node attribute information of each node using a preset configuration data model may be described in detail. This part of the content is described in detail below.

[0063] First, the first node attribute information of each node is collected from the cluster management node, and the first node attribute information may include attribute information such as hardware configuration, network status, and service operation status. Then, the preset configuration data model is called, which defines the attribute characteristics and requirements that different node types should have.

[0064] The collected first node attribute information is compared and matched one by one with the node type attribute requirements predefined in the configuration data model. This matching process may involve complex logical judgments and conditional screening 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.

[0065] After the matching is completed, the node type matched by each node will be recorded. These results will be used for subsequent label generation and data verification steps. The first node configuration information of the node can be stored as a configuration item in the node type configuration item table of the corresponding CMDB.

[0066] If the attribute information of some nodes is found to not match any predefined node type during the matching process, these anomalies can be recorded and may trigger further investigation or manual classification. The configuration data model is dynamically adjustable and can update the attribute requirements of node types according to new business needs or technology changes to adapt to the ever-changing IT environment.

[0067] This embodiment automatically classifies a large number of nodes by matching the first node attribute information 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 operations.

[0068] The node classification device provided in an embodiment of the present application is described below. The node classification device described below and the node classification method described above can be referenced to each other.

[0069] See also Figure 3 , Figure 3 A schematic diagram of the structure of a node classification device disclosed in an embodiment of the present application.

[0070] like Figure 3 As shown, the device may include: The information acquisition unit 11 is used to acquire first node configuration information of each node stored in the cluster management node, where the first node configuration information includes a first IP address, a name of a first cluster to which the node belongs, and first node attribute information; A type determination unit 12, configured to determine the node type to which the node belongs based on the first node attribute information of each node by using a preset configuration data model; The label generation unit 13 is used 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, wherein the first label 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.

[0071] In a possible implementation, the first node attribute information includes: any one or more of hardware configuration, network status, and service operation status.

[0072] In a possible implementation, a node classification device in an embodiment of the present application further includes: A node communication unit, used to communicate with each node in the cluster and collect second node configuration information of each node, wherein the second node configuration information includes a second IP address, a name of a second cluster to which the node belongs, and second node attribute information; A result acquisition unit, used for calling the configured node classification model to process the second node attribute information, and obtaining a node type classification result output by the node classification model; wherein the node classification model is trained using node attribute information training data annotated with node type labels; a label determination unit, configured to generate second label information of each node according to the node type classification result, the second IP address, and the second cluster name, wherein the second label 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; A report generating unit is used to compare the first label information with the second label information to generate a verification report, wherein the verification report records the difference between the first label information and the second label information.

[0073] In a possible implementation, the second node attribute information includes any one or more of hardware configuration, software environment, directory structure, and process activity.

[0074] In a possible implementation, the type determination unit 12 determines the node type to which the node belongs based on the first node attribute information of each node using a preset configuration data model, including: 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 node type matched by each node.

[0075] The present application also provides an electronic device in an embodiment. Figure 4 As shown, it shows a schematic diagram of the structure of an electronic device suitable for implementing the embodiment of the present application. The electronic device in the embodiment of the present 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 bring any limitation to the functions and scope of use of the embodiments of the present application.

[0076] like Figure 4 As shown, the electronic device may include a processing device (such as 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 to a random access memory (RAM) 603 to implement the node classification method of the aforementioned embodiment of the present application. When the electronic device is powered on, various programs and data required for the operation of the electronic device are also stored in the RAM 603. The processing device 601, the ROM 602, and the RAM 603 are connected to each other via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0077] Typically, the following devices may be connected to the I / O interface 605: an input device 606 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 607 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 608 including, for example, a memory card, a hard disk, etc.; and a communication device 609. The communication device 609 may allow the electronic device to communicate with other devices wirelessly or by wire to exchange data. Although Figure 4 An electronic device having various devices is shown, but it should be understood that it is not required to implement or possess all the devices shown. More or fewer devices may be implemented or possessed instead.

[0078] A computer-readable storage medium is also provided in an embodiment of the present application. The storage medium carries one or more computer programs. When the one or more computer programs are executed by an electronic device, the electronic device can implement the node classification method of the aforementioned embodiment of the present application.

[0079] The present application also provides a computer program product, which includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the present application is generated in whole or in part.

[0080] It should also be noted that the device embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed over multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. In addition, in the drawings of the device embodiments provided by the present application, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines.

[0081] Through the description of the above implementation mode, the technicians in the field can clearly understand that the present application can be implemented by means of software plus necessary general hardware, and of course, it can also be implemented by special hardware including special integrated circuits, special CPUs, special memories, special components, etc. In general, all functions completed by computer programs can be easily implemented by corresponding hardware, and the specific hardware structure used to implement the same function can also be various, such as analog circuits, digital circuits or special circuits. However, for the present application, software program implementation is a better implementation mode in more cases. Based on such an understanding, the technical solution of the present application is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a readable storage medium, such as a computer floppy disk, a U disk, a mobile hard disk, a ROM, a RAM, a disk or an optical disk, etc., including a number of instructions to enable a computer device (which can be a personal computer, a training device, or a network device, etc.) to execute the methods described in each embodiment of the present application.

[0082] In the above embodiments, all or part of the embodiments may be implemented by software, hardware, firmware or any combination thereof. When implemented by software, all or part of the embodiments may be implemented in the form of a computer program product.

[0083] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The various embodiments can be combined as needed, and the same or similar parts can refer to each other.

Claims

1. A node classification method, characterized in that: include: Acquire first node configuration information of each node stored in the cluster management node, where the first node configuration information includes a first IP address, a name of a first cluster to which the node belongs, and first node attribute information; Using a preset configuration data model, based on the first node attribute information of each node, determining the node type to which the node belongs; Based on the node type, the first IP address and the first cluster name of each node, first label information corresponding to each node is generated, 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.

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

3. The method according to claim 1, characterized in that Also includes: Communicate with each node in the cluster to collect second node configuration information of each node, where the second node configuration information includes a second IP address, a name of a second cluster to which the node belongs, and second node attribute information; Calling the configured node classification model to process the second node attribute information to obtain a node type classification result output by the node classification model; wherein the node classification model is trained using node attribute information training data annotated with node type labels; Generate second label information for each node according to the node type classification result, the second IP address, and the second cluster name, where the second label 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, wherein the verification report records the difference between the first label information and the second label information.

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

5. The method according to claim 1, characterized in that The process of determining the node type to which the node belongs based on the first node attribute information of each node by using the 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 node type matched by each node.

6. A node classification device, characterized in that: include: An information acquisition unit, used to acquire first node configuration information of each node stored in the cluster management node, wherein the first node configuration information includes a first IP address, a name of a first cluster to which the node belongs, and first node attribute information; A type determination unit, configured to determine the node type to which the node belongs based on the first node attribute information of each node by using a preset configuration data model; A label generation unit is used 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, wherein the first label 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.

7. The node classification device according to claim 6, characterized in that: Also includes: A node communication unit, used to communicate with each node in the cluster and collect second node configuration information of each node, wherein the second node configuration information includes a second IP address, a name of a second cluster to which the node belongs, and second node attribute information; A result acquisition unit, used for calling the configured node classification model to process the second node attribute information, and obtaining a node type classification result output by the node classification model; wherein the node classification model is trained using node attribute information training data annotated with node type labels; a label determination unit, configured to generate second label information of each node according to the node type classification result, the second IP address, and the second cluster name, wherein the second label 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; A report generating unit is used to compare the first label information with the second label information to generate a verification report, wherein the verification report records the difference between the first label information and the second label information.

8. 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 each step of the node classification method as described in any one of claims 1 to 5.

9. A readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, each step of the node classification method according to any one of claims 1 to 5 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, each step in the node classification method according to any one of claims 1 to 5 is implemented.

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