Methods, devices, equipment, media, and procedures for determining fault types.

Abstract

This application discloses a method, apparatus, device, medium, and program product for determining fault types, belonging to the field of big data technology. The method involves: acquiring key node status information from a first data packet sent by a target network node; analyzing the key node status information using a preset fault analysis model to obtain analysis results; if the analysis result indicates an operational fault, acquiring key node status information and basic node status information from a second data packet sent by the target network node; and determining the fault type of the target network node based on a comparison between the key node status information and basic node status information in the second data packet and corresponding preset thresholds. According to the embodiments of this application, faults can be quickly identified and the specific fault type of the target network node can be accurately determined, thereby significantly improving the efficiency and accuracy of fault detection and enhancing the stability and reliability of network operation.

Description

Technical Field

[0001] This application belongs to the field of big data technology, and in particular relates to a method, apparatus, equipment, medium and program product for determining fault types. Background Technology

[0002] As networks continue to grow in size and complexity, rapid detection and recovery from network failures become crucial.

[0003] However, traditional network fault detection methods, such as those relying on periodic polling mechanisms or post-incident log analysis, are inadequate in the face of rapidly changing network environments. These methods not only fail to provide immediate responses to network faults but also lead to significant delays in fault detection and handling. This delay directly reduces the overall quality of network services, impairing user experience and potentially causing business continuity disruptions, posing a serious threat to the stability and reliability of network operations. Furthermore, after a fault occurs, existing network fault detection methods struggle to quickly and accurately pinpoint the specific fault type, hindering the improvement of network fault detection and recovery capabilities and often leaving network operators in a reactive position when facing faults.

[0004] Therefore, existing network fault detection technologies suffer from low fault detection efficiency, untimely response, and difficulty in accurately and efficiently determining the specific fault type of a network node. Summary of the Invention

[0005] This application provides a method, apparatus, device, medium, and program product for determining fault types, which can quickly identify faults and accurately determine the specific fault type of a target network node, thereby improving the efficiency and accuracy of fault detection and enhancing the stability and reliability of network operation.

[0006] In a first aspect, embodiments of this application provide a method for determining a fault type, including:

[0007] When the target network node sends the first data packet, obtain the key node status information in the first data packet;

[0008] Using a pre-defined fault analysis model, the status information of key nodes in the first data packet is analyzed to obtain the analysis results;

[0009] In the case where the analysis result indicates an operational failure, obtain the key node status information and basic node status information from the second data packet sent by the target network node.

[0010] The fault type of the target network node is determined by comparing the key node status information and basic node status information in the second data packet with the corresponding preset threshold.

[0011] In one possible embodiment of the first aspect, determining the fault type of the target network node based on a comparison between the key node status information and basic node status information in the second data packet and the corresponding preset threshold includes:

[0012] The key node status information and basic node status information in the second data packet are compared with the corresponding preset thresholds to obtain the target comparison result.

[0013] In the preset correspondence between comparison results and fault types, the target fault type corresponding to the target comparison result is obtained, and the target fault type is used as the fault type of the target network node.

[0014] In one possible embodiment of the first aspect, before obtaining the key node status information in the first data packet when the target network node sends the first data packet, the method further includes:

[0015] Target network nodes are monitored at the kernel level using a pre-defined packet filter;

[0016] When the target network node sends the first data packet, obtain the key node status information in the first data packet, including:

[0017] When the preset packet filter detects that the target network node has sent the first packet, the preset packet filter is used to obtain the key node status information in the first packet.

[0018] In the case of an operational failure, the key node status information and basic node status information from the second data packet sent by the target network node are obtained, including:

[0019] If the analysis result indicates an operational failure and the preset data packet filter detects that the target network node has sent a second data packet, the preset data packet filter is used to obtain the key node status information and basic node status information in the second data packet.

[0020] In one possible embodiment of the first aspect, a preset fault analysis model is used to analyze the critical node status information in the first data packet to obtain analysis results, including:

[0021] The key node status information in the first data packet is stored according to a preset structure;

[0022] According to the preset cycle, the key node status information in the first data packet stored is sent to the user layer;

[0023] Using a pre-set fault analysis model in the user layer, the status information of key nodes in the first data packet is analyzed to obtain the analysis results.

[0024] In one possible embodiment of the first aspect, a fault analysis model preset in the user layer is used to analyze the key node status information in the first data packet to obtain the analysis results, including:

[0025] Calculate the average value of the status information of key nodes of the same type in the first data packet to obtain the target key node information;

[0026] By using the pre-set fault analysis model in the user layer, the status information of the target key nodes is analyzed to obtain the analysis results.

[0027] In one possible embodiment of the first aspect, before analyzing the key node status information in the first data packet using a preset fault analysis model to obtain the analysis result, the method further includes:

[0028] Within a preset time period, acquire all historical node status information and historical fault type information for each network node.

[0029] The information on historical fault types is used as the supervision result, and the state information of all historical nodes is used as the training sample to conduct supervised training on the pre-set original fault analysis model, so as to obtain the fault analysis model.

[0030] In one possible embodiment of the first aspect, the fault analysis model includes weight coefficients corresponding to the state information of each node; the weight coefficients corresponding to the state information of key nodes are greater than or equal to a preset weight coefficient threshold; and the weight coefficients corresponding to the state information of basic nodes are less than the preset weight coefficient threshold.

[0031] In one possible embodiment of the first aspect, it further includes:

[0032] Based on the preset correspondence between fault types and solutions, obtain the target solution corresponding to the fault type of the target network node;

[0033] Implement the target solution.

[0034] In one possible embodiment of the first aspect, it further includes:

[0035] Obtain the execution results of the target solution;

[0036] The target page displays information about the fault types of the target network nodes and the execution results of the target solution.

[0037] Based on the same inventive concept, in a second aspect, embodiments of this application also provide a fault type determination device, comprising:

[0038] The acquisition module is used to acquire key node status information in the first data packet when the target network node sends the first data packet;

[0039] The analysis module is used to analyze the status information of key nodes in the first data packet using a preset fault analysis model, and obtain the analysis results.

[0040] The acquisition module is also used to acquire key node status information and basic node status information from the second data packet sent by the target network node when the analysis result indicates an operational failure.

[0041] The determination module is used to determine the fault type of the target network node based on the comparison results between the key node status information and basic node status information in the second data packet and the corresponding preset threshold.

[0042] Based on the same inventive concept, in a third aspect, embodiments of this application also provide a fault type determination device, the device including a processor and a memory storing computer program instructions; when the processor executes the computer program instructions, it implements the first aspect, or the fault type determination method in any embodiment of the first aspect.

[0043] Based on the same inventive concept, in a fourth aspect, embodiments of this application also provide a computer storage medium, on which computer program instructions are stored, which, when executed by a processor, implement the first aspect, or the method for determining the fault type in any embodiment of the first aspect.

[0044] Based on the same inventive concept, in a fifth aspect, embodiments of this application also provide a computer program product, wherein instructions in the computer program product, when executed by a processor of a device, enable the device to perform the method for determining the fault type in the first aspect or any embodiment of the first aspect.

[0045] The method, apparatus, device, medium, and program product for determining fault types according to embodiments of this application, when a target network node sends a first data packet, can quickly extract key node status information from the full node status information carried in the first data packet. Then, using a preset, pre-trained fault analysis model, the key node status information extracted from the first data packet is analyzed to obtain the analysis result. If the analysis result indicates an operational fault, when the target network node sends a second data packet, the full node status information in the second data packet, including key node status information and basic node status information, can be obtained. Then, based on the comparison between the key node status information and basic node status information in the second data packet and the corresponding preset thresholds, the fault type of the target network node can be determined. By real-time monitoring and immediate extraction of key node status information from data packets sent by the target network node, using a preset fault analysis model to quickly determine whether an operational fault exists, and further obtaining detailed full node status information of the target network node after confirming the fault, and comparing it with preset thresholds, the specific fault type of the target network node can be determined efficiently and accurately, thereby significantly improving the efficiency and accuracy of fault detection and diagnosis, and enhancing the stability and reliability of network operation. Attached Figure Description

[0046] Other features, objects, and advantages of this application will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings, in which the same or similar reference numerals denote the same or similar features, and the drawings are not drawn to scale.

[0047] Figure 1 This is a flowchart illustrating a method for determining fault types provided in an embodiment of this application;

[0048] Figure 2 This is another flowchart illustrating the method for determining fault types provided in the embodiments of this application;

[0049] Figure 3 This is another flowchart illustrating the method for determining fault types provided in the embodiments of this application;

[0050] Figure 4 This is another flowchart illustrating the method for determining fault types provided in the embodiments of this application;

[0051] Figure 5 This is another flowchart illustrating the method for determining fault types provided in the embodiments of this application;

[0052] Figure 6 This is another flowchart illustrating the method for determining fault types provided in the embodiments of this application;

[0053] Figure 7 This is another flowchart illustrating the method for determining fault types provided in the embodiments of this application;

[0054] Figure 8 This is another flowchart illustrating the method for determining fault types provided in the embodiments of this application;

[0055] Figure 9 This is a schematic diagram of a fault type determination device provided in an embodiment of this application;

[0056] Figure 10 This is a schematic diagram of a fault type determination device provided in an embodiment of this application. Detailed Implementation

[0057] The features and exemplary embodiments of various aspects of this application will now be described in detail. To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only configured to explain this application and are not configured to limit this application. For those skilled in the art, this application can be implemented without some of these specific details. The following description of the embodiments is merely to provide a better understanding of this application by illustrating examples of this application.

[0058] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus that includes said element.

[0059] It should be understood that the term "and / or" used in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0060] Various modifications and variations can be made to this application without departing from its spirit or scope, which will be apparent to those skilled in the art. Therefore, this application is intended to cover modifications and variations falling within the scope of the corresponding claims (the claimed technical solutions) and their equivalents. It should be noted that the embodiments provided in this application can be combined with each other without contradiction.

[0061] It should be noted that the acquisition, storage, use, and processing of data in this application embodiment all comply with the relevant provisions of national laws and regulations.

[0062] It should also be noted that in the embodiments of this application, certain software, components, models and other existing solutions in the industry may be mentioned. These should be regarded as exemplary and are only intended to illustrate the feasibility of implementing the technical solution of this application. However, it does not mean that the applicant has used or necessarily used the solution.

[0063] Before describing the technical solutions provided in the embodiments of this application, in order to facilitate understanding of the embodiments of this application, this application first specifically explains the problems existing in the related technologies:

[0064] As networks continue to grow in size and complexity, rapid detection and recovery from network failures become crucial.

[0065] However, existing network fault detection methods, such as those relying on periodic polling mechanisms or post-incident log analysis, are inadequate in the face of rapidly changing network environments. These methods not only fail to provide immediate responses to network faults but also lead to significant delays in fault detection and handling. This delay directly reduces the overall quality of network services, impairing user experience and potentially causing business continuity interruptions, posing a serious threat to the stability and reliability of network operations. Furthermore, after a fault occurs, these methods struggle to quickly and accurately pinpoint the specific fault type, hindering the improvement of network fault detection and recovery capabilities and often leaving network operators in a reactive position when facing faults. Therefore, existing network fault detection technologies suffer from low detection efficiency, untimely response, and difficulty in accurately and efficiently determining the specific fault type of network nodes.

[0066] The related technologies not only suffer from low fault detection efficiency and untimely response, but also from insufficient flexibility, low recovery efficiency, high resource consumption, and high false alarm rate.

[0067] Among these issues, the fault detection efficiency is low and the response is not timely: relevant network fault detection methods usually adopt a periodic polling approach, which results in a large delay in fault detection and affects the timeliness of fault response.

[0068] Insufficient flexibility: Most relevant network fault detection methods lack sufficient flexibility and are difficult to adapt to changes in the network environment, especially when faced with complex and dynamic network architectures.

[0069] Low recovery efficiency: Once a fault is detected, existing recovery mechanisms often require manual intervention and have a low degree of automation, resulting in low recovery efficiency.

[0070] High resource consumption: Some software-based fault detection technologies may consume a lot of computing resources, affecting the overall performance of the system.

[0071] High false alarm rate: Related network fault detection technologies sometimes produce a high false alarm rate, leading to unnecessary alarms and wasted resources.

[0072] Based on this, embodiments of this application provide a method, apparatus, device, medium, and program product for determining fault types, which can quickly identify faults and accurately determine the specific fault type of a target network node, thereby significantly improving the efficiency and accuracy of fault detection and diagnosis, enhancing the stability and reliability of network operation, and solving problems such as detection delay, insufficient flexibility, low recovery efficiency, high resource consumption, and high false alarm rate.

[0073] The method for determining the fault type provided in the embodiments of this application will be described in detail below with reference to the accompanying drawings.

[0074] Figure 1 This is a schematic flowchart of a method for determining a fault type provided in an embodiment of this application, such as... Figure 1 As shown, the method may include steps S110 to S140.

[0075] S110: When the target network node sends the first data packet, obtain the key node status information in the first data packet.

[0076] A network node is a network device or component with an independent address and the ability to transmit and receive data. Examples include network interface cards and network bridges.

[0077] A data packet is the basic unit of data in network communication. In the absence of detected operational failures at the target network node, the data packet sent by the target network node in real-time can be called the first data packet. The data packet can include node status information of the network node, such as packet loss rate, latency, and error rate.

[0078] Critical node status information is node status information that can accurately reflect the network's operating status and has a direct impact on network performance or stability.

[0079] Specifically, it can monitor target network nodes in real time, and when the target network node sends out the first data packet, it can immediately obtain the representative key node status information in the first data packet from the full node status information in the first data packet.

[0080] S120: Using a preset fault analysis model, the status information of key nodes in the first data packet is analyzed to obtain the analysis results.

[0081] The preset fault analysis model is a predefined and configured fault analysis model used to identify potential faults by analyzing the status information of network nodes. The fault analysis model can be trained based on machine learning, statistics, rule engines, or other technologies. It can extract features from the input data and determine whether the network node is in a normal or faulty state based on these features.

[0082] Specifically, by using a pre-defined fault analysis model, the key status information contained in the first data packet sent by the target network node is analyzed to obtain the analysis results.

[0083] S130, if the analysis result indicates an operational failure, obtain the key node status information and basic node status information from the second data packet sent by the target network node.

[0084] In this process, when the analysis result obtained from the pre-set fault analysis model indicates that the target network node is malfunctioning, the data packets sent by the target network node are monitored in real time. These data packets can be called second data packets. The second data packets are obtained when the target network node malfunctions and are used to further diagnose and confirm the specific fault type.

[0085] Among them, basic node status information is the node status information other than critical node status information in the total node status information of network nodes.

[0086] It should be noted that the distinction between critical node status information and basic node status information in the total node status information of a network node can be determined through a trained fault analysis model.

[0087] Specifically, when the preset fault analysis model determines that the target network node has an operational fault, it can further extract full node status information from the second data packet sent by the target network node, including key node status information and basic node status information, in order to conduct more in-depth diagnosis and analysis.

[0088] S140, Based on the comparison results between the key node status information and basic node status information in the second data packet and the corresponding preset threshold, determine the fault type of the target network node.

[0089] The preset threshold is a pre-defined critical value used to assess whether the network node status is normal. The preset threshold can be set as needed, and this embodiment does not limit it.

[0090] Specifically, by comparing the full node status information (such as critical node status information and basic node status information) contained in the second data packet with the preset thresholds, it is possible to determine which node status information of the target network node is abnormal, thereby determining its specific fault type.

[0091] The fault type determination method provided in this application involves immediately extracting key node status information from the full node status information carried in the first data packet when the target network node sends a first data packet in real time. Then, a pre-trained fault analysis model is used to analyze the key node status information extracted from the first data packet to obtain the analysis result. If the analysis result indicates an operational fault, the full node status information in the second data packet, including key node status information and basic node status information, can be obtained when the target network node sends a second data packet. The fault type of the target network node can then be determined by comparing the key node status information and basic node status information in the second data packet with corresponding pre-set thresholds. By real-time monitoring and immediate extraction of key node status information from data packets sent by the target network node, quickly determining the existence of an operational fault using a pre-set fault analysis model, and further obtaining detailed full node status information of the target network node after confirming the fault, and comparing it with pre-set thresholds, the specific fault type of the target network node can be determined efficiently and accurately. This significantly improves the efficiency and accuracy of fault detection and diagnosis, and enhances the stability and reliability of network operation.

[0092] It should be noted that the embodiments of this application monitor and extract key node status information from data packets sent by the target network node in real time, and quickly determine whether there is an operational fault using a preset fault analysis model. Once a fault is confirmed, the full node status information of the target network node (including key node status information and basic node status information) is further obtained and compared with a preset threshold, thereby efficiently and accurately determining the specific fault type of the target network node. This method significantly improves the efficiency and accuracy of fault detection and diagnosis, and enhances the stability and reliability of network operation. Related technologies lack this ability to monitor in real time, quickly extract key information, and use a preset model for judgment. Furthermore, after confirming a fault, related technologies fail to further obtain detailed full node status information and accurately compare it with a preset threshold. Therefore, the embodiments of this application can solve the problem of low efficiency and accuracy in fault detection and diagnosis in related technologies.

[0093] Figure 2 This is another flowchart illustrating the method for determining fault types provided in the embodiments of this application.

[0094] In some embodiments, such as Figure 2 As shown, step S140 determines the fault type of the target network node based on the comparison results between the key node status information and basic node status information in the second data packet and the corresponding preset threshold, and may include steps S141 and S142.

[0095] S141, compare the key node status information and basic node status information in the second data packet with the corresponding preset thresholds to obtain the target comparison result.

[0096] Specifically, after the failure occurs and the key node status information and basic node status information in the second data packet sent by the target network node are obtained, the key node status information in the second data packet can be compared with its corresponding preset threshold, and the basic node status information in the second data packet can be compared with its corresponding preset threshold to obtain the final target comparison result.

[0097] S142, in the preset correspondence between comparison results and fault types, obtain the target fault type corresponding to the target comparison result, and use the target fault type as the fault type of the target network node.

[0098] The preset correspondence between comparison results and fault types is a predefined mapping relationship that specifies which fault type corresponds to different comparison results.

[0099] Specifically, by querying the correspondence between preset comparison results and fault types, the fault type corresponding to the target comparison result obtained by comparing the key node status information and basic node status information with preset thresholds can be determined. This fault type can be called the target fault type.

[0100] The embodiments of this application can compare the key and basic node status information in the second data packet with a preset threshold to quickly obtain the target comparison result. By querying the predefined mapping relationship between the comparison result and the fault type, the target fault type of the target network node can be quickly determined, thereby significantly improving the efficiency and accuracy of network fault diagnosis.

[0101] Figure 3 This is another flowchart illustrating the method for determining fault types provided in the embodiments of this application.

[0102] In some embodiments, such as Figure 3As shown, before obtaining the key node status information in the first data packet when the target network node sends the first data packet, the method may further include step S150.

[0103] The S150 uses a preset packet filter to monitor target network nodes at the kernel level.

[0104] The preset packet filter can be used to filter network packets. It can be used to monitor packets sent by target network nodes at the kernel level.

[0105] For example, the default packet filter can be the Extended Berkeley Packet Filter (eBPF). As an emerging kernel programming framework, eBPF enables efficient and flexible network packet processing and system event tracing at the kernel level. eBPF allows user-space programs running in a secure sandbox within the kernel, thereby enabling high-performance network filtering, performance monitoring, and system tracing.

[0106] It should be noted that while eBPF technology provides powerful basic capabilities, how to utilize eBPF to achieve efficient network fault detection and recovery remains a problem worthy of further research.

[0107] Specifically, by using a preset packet filter, the packet transmission status of the target network node can be monitored in real time at the kernel level of the operating system, enabling immediate response to network failures.

[0108] Step S110, when the target network node sends the first data packet, involves obtaining the key node status information in the first data packet, which may include step S111.

[0109] S111, when the preset data packet filter detects that the target network node has sent a first data packet, the preset data packet filter is used to obtain the key node status information in the first data packet.

[0110] Specifically, when the preset data packet filter detects that the target network node has sent the first data packet, the filter can be used to extract the key node status information in the first data packet in real time.

[0111] If the analysis result indicates a failure, step S130 involves obtaining the key node status information and basic node status information from the second data packet sent by the target network node, which may include step S131.

[0112] S131, if the analysis result indicates an operational failure and the preset data packet filter detects that the target network node has sent a second data packet, the preset data packet filter is used to obtain the key node status information and basic node status information in the second data packet.

[0113] Specifically, when the analysis result indicates an operational failure, the data packets (second data packets) sent by the target network node can be monitored in real time through a preset data packet filter. The preset data packet filter can be used to extract and obtain the full node status information contained in the second data packet, such as the status information of key nodes and the status information of basic nodes.

[0114] This application embodiment utilizes a preset packet filter (such as eBPF) to monitor the packet transmission status of target network nodes in real time at the kernel level. It can send the monitored first packet to the fault analysis model at the user layer for fault analysis, and in the case that the analysis result is a running fault, it can immediately obtain the key node status information and basic node status information in the second packet, thereby realizing the immediate response to network faults and efficient detection and location, improving the efficiency and accuracy of network management.

[0115] Figure 4 This is another flowchart illustrating the method for determining fault types provided in the embodiments of this application.

[0116] In some embodiments, such as Figure 4 As shown, step S120 uses a preset fault analysis model to analyze the status information of key nodes in the first data packet and obtain the analysis results, which may include steps S121 to S123.

[0117] S121, store the key node status information in the first data packet according to a preset structure.

[0118] Specifically, after obtaining the key node status information in the first data packet using a preset data packet filter, the key node status information in the first data packet can be stored according to a preset structure (such as the map data structure of eBPF).

[0119] S122, according to the preset period, send the key node status information in the stored first data packet to the user layer.

[0120] Specifically, according to a pre-set time period, the key node status information in the first data packet that has been stored is transmitted from the kernel layer to the user layer for further processing and analysis by the user layer.

[0121] S123, using the pre-set fault analysis model in the user layer, analyze the key node status information in the first data packet to obtain the analysis results.

[0122] Specifically, at the user layer, the key node status information extracted from the first data packet is analyzed using a pre-set fault analysis model to determine whether the target network node has a network fault. For example, with a preset period of 10ms, the key node status information from the stored first data packet is sent to the user layer every 10ms. Then, the pre-set fault analysis model at the user layer analyzes the key node status information to obtain the analysis result.

[0123] This application embodiment utilizes a preset period to send key node status information from the kernel layer to the user layer for analysis, enabling real-time monitoring of network status. Furthermore, by reasonably setting the time period (such as every 10ms, every 20ms, etc.), frequent data transmission and analysis operations can be avoided, effectively saving computing resources. This allows for the optimization of system resource utilization and improved overall operating efficiency while ensuring the timeliness and accuracy of fault detection.

[0124] Figure 5 This is another flowchart illustrating the method for determining fault types provided in the embodiments of this application.

[0125] In some embodiments, such as Figure 5 As shown, step S123 uses the preset fault analysis model in the user layer to analyze the key node status information in the first data packet and obtain the analysis results, which may include steps S1231 and S1232.

[0126] S1231, calculate the average value of the status information of the same type of key nodes in the first data packet to obtain the target key node information.

[0127] Specifically, the key node status information of the same type in the first data packet is aggregated. For example, the target key node information is obtained by calculating the average value of the key node status information of the same type. For example, if the target network node sends the first data packet at a period of 1ms and the preset period is 10ms, then 10 pieces of key node status information of the same type can be stored every 10ms. For example, 10 pieces of error rate information can be stored. Then, the stored key node status information in the first data packet can be sent to the user layer. The user layer can then calculate the average value of the key node status information of the same type in the first data packet to obtain the target key node information. For example, the average value of 10 error rates can be calculated to obtain the target error rate.

[0128] S1232 uses a pre-set fault analysis model in the user layer to analyze the status information of the target key nodes and obtain the analysis results.

[0129] Specifically, at the user level, by using a pre-configured fault analysis model to analyze the status information of each target key node, the analysis results of whether the target network node is malfunctioning can be obtained.

[0130] This application embodiment uses the average value of the status information of key nodes of the same type as the target key node information, effectively reducing the amount of data to be processed during the analysis process. Compared with directly analyzing the status information of each key node, analyzing the average value can significantly reduce the computational load, thereby saving computing resources. Subsequently, the target key node information is analyzed using a preset fault analysis model, which not only improves the analysis efficiency but also ensures the accuracy and reliability of the analysis results. It can optimize resource utilization while ensuring fault detection accuracy, enabling the system to operate more efficiently.

[0131] Figure 6 This is another flowchart illustrating the method for determining fault types provided in the embodiments of this application.

[0132] In some embodiments, such as Figure 6 As shown, before step S120, which uses a preset fault analysis model to analyze the key node status information in the first data packet and obtain the analysis results, the method may also include steps S161 and S162.

[0133] S161, within a preset time period, acquire all historical node status information and historical fault type information for each network node.

[0134] Specifically, the pre-defined fault analysis model can be obtained through supervised training before analysis is performed using the pre-defined fault analysis model. Before supervised training, information on training samples and supervision results can be obtained, that is, information on all historical node states of all network nodes within a specific historical time period and information on the actual historical fault types that have been determined can be collected.

[0135] S162, using historical fault type information as the supervision result and full historical node state information as the training sample, supervised training is performed on the preset original fault analysis model to obtain the fault analysis model.

[0136] Specifically, historical fault type information is used as the desired output (i.e., the supervised result), while the full set of historical node state information is used as input data (i.e., training samples) to conduct a supervised training process on the initially set original fault analysis model. This process optimizes and calibrates the model's parameters, ultimately resulting in a trained fault analysis model that can accurately analyze fault types.

[0137] This application embodiment significantly improves the accuracy and reliability of the fault analysis model by collecting all historical node state information and historical fault type information before performing analysis using a preset fault analysis model. This information is then used as training samples and supervised training results to perform supervised training on the original fault analysis model. This ensures that the model can more accurately analyze key node state information in practical applications, thereby more effectively determining fault types and improving the efficiency of fault diagnosis and handling.

[0138] In some embodiments, the fault analysis model includes weight coefficients corresponding to the state information of each node; the weight coefficients corresponding to the state information of key nodes are greater than or equal to a preset weight coefficient threshold; and the weight coefficients corresponding to the state information of basic nodes are less than a preset weight coefficient threshold.

[0139] The preset weight coefficient threshold is a pre-defined numerical limit used to distinguish between critical node status information and basic node status information. For example, the preset weight coefficient threshold can be 0.1, or 0.2. The preset weight coefficient threshold can be set according to requirements, and this embodiment does not limit it.

[0140] Specifically, since the trained fault analysis model includes weight coefficients corresponding to the state information of each node, node state information with weight coefficients greater than or equal to a preset weight coefficient threshold can be considered as critical node state information, while node state information with weight coefficients less than the preset weight coefficient threshold can be considered as basic node state information. In other words, by comparing the weight coefficients of each node state information with the preset weight coefficient threshold, it is possible to select which node state information belongs to critical node state information and which belongs to basic node state information from the full set of node state information.

[0141] It should be noted that the weight coefficients corresponding to the key node status information in the first data packet are greater than or equal to a preset weight coefficient threshold. The weight coefficients corresponding to the key node status information in the second data packet are greater than or equal to a preset weight coefficient threshold. The weight coefficients corresponding to the basic node status information in the second data packet are less than a preset weight coefficient threshold.

[0142] This application embodiment, by setting a preset weight coefficient threshold, can accurately filter out key information crucial to fault analysis from massive amounts of node state information, while excluding relatively minor information. This significantly improves data processing speed and analysis efficiency while ensuring accuracy, enabling faster response and handling of network faults. Furthermore, by inputting key node state information with weight coefficients greater than or equal to the preset weight coefficient threshold into a pre-trained fault analysis model for fault analysis, compared to using all node state information, the amount of data processing is reduced. This improves analysis speed, efficiency, and reliability while maintaining accuracy, thus enabling immediate response to network faults.

[0143] Figure 7 This is another flowchart illustrating the method for determining fault types provided in the embodiments of this application.

[0144] In some embodiments, such as Figure 7 As shown, the method for determining the fault type may also include steps S171 and S172.

[0145] S171, in the preset correspondence between fault types and solutions, obtain the target solution corresponding to the fault type of the target network node.

[0146] Specifically, in the pre-set fault type and solution mapping relationship, the solution corresponding to the fault type encountered by the target network node can be found and determined. This solution can be called the target solution.

[0147] S172, Execute the target solution.

[0148] Specifically, the target solution can be implemented to resolve operational failures of the target network nodes.

[0149] This application embodiment can quickly obtain and execute the target solution corresponding to the fault type of the target network node from the preset fault type and solution correspondence, which can automatically and quickly solve the operational fault problem of the target network node, effectively improving the efficiency and accuracy of fault handling.

[0150] Figure 8 This is another flowchart illustrating the method for determining fault types provided in the embodiments of this application.

[0151] In some embodiments, such as Figure 8 As shown, it also includes steps S173 and S174.

[0152] S173, Obtain the execution result of the target solution.

[0153] Specifically, obtaining the execution results of the target solution refers to collecting and evaluating the actual effectiveness or impact of the solution after its implementation, in order to determine whether the problem has been effectively solved or improved. For example, the execution result can be success or failure.

[0154] S174, on the target page, displays information about the fault types of the target network nodes and the execution results of the target solution.

[0155] Specifically, the target page can display detailed information about the fault type of the target network node and the execution results of the target solution implemented for the fault, allowing users to understand the fault status and resolution progress at a glance.

[0156] This application embodiment, by acquiring and displaying the execution results of the target solution, not only ensures that users can understand the fault resolution status of the target network node in a timely manner, but also significantly improves the transparency and efficiency of the fault handling process, enabling users to quickly determine whether the fault has been effectively handled, thereby accelerating the fault recovery process.

[0157] In one embodiment, the fault type determination system may include a data collection module, a data analysis module, a fault location module, a fault recovery module, and a result feedback module. Specifically:

[0158] 1) The data collection module is responsible for collecting node status information of network nodes at the kernel level. It can capture the first data packet of network nodes (such as network interfaces) by writing eBPF programs and record key performance indicators (key node status information).

[0159] For example: 1.1) Write an eBPF program to capture key node status information in data packets of the network interface; 1.2) Use the eBPF map data structure to store the collected key node status information; 1.3) Periodically export the key node status information to the user layer for further analysis.

[0160] The data collection module sends the collected node status information to the data analysis module.

[0161] It should be noted that data collection modules in related technologies use the traditional Simple Network Management Protocol (SNMP) or other network monitoring tools to collect network device status information. SNMP is a mature protocol and easy to integrate into existing network management systems; however, SNMP collects data slowly, which may lead to detection delays. Furthermore, its flexibility and customizability are inferior to eBPF technology. This application employs eBPF technology to collect network node status information at the kernel level, ensuring the real-time nature and accuracy of the information and significantly reducing fault detection latency. Using eBPF also reduces resource consumption, ensuring that the overall system performance is not affected.

[0162] 2) The data analysis module performs real-time analysis of the received key node status information to identify whether any network malfunctions exist at each target network node. Machine learning algorithms can be used to analyze the collected data to identify any network malfunctions.

[0163] For example: Supervised learning methods are used to train a model to identify normal and abnormal network operation. Steps: 2.1) Data preprocessing: Cleaning and standardizing the collected data. 2.2) Feature extraction: Extracting meaningful features from the raw data. 2.3) Model training: Training a fault classification model using historical data. 2.4) Anomaly detection: Applying the trained fault classification model in real time to detect abnormal behavior in the network. 2.5) Result output: After the data analysis module identifies an operational fault, it notifies the fault location module to locate the fault.

[0164] It should be noted that related technologies use traditional threshold comparison methods for direct fault diagnosis and analysis, without requiring a complex model training process. However, the accuracy of using threshold comparison methods alone is low, and it is prone to false alarms or missed alarms. The data analysis module of this application embodiment combines machine learning algorithms for intelligent analysis, enabling intelligent analysis of collected data, quickly locating the cause and type of network faults, improving the accuracy and efficiency of fault detection, and reducing the false alarm rate.

[0165] 3) Fault location module: When an operational fault is detected, the specific type of fault can be quickly located using eBPF's tracking capabilities.

[0166] For example, eBPF's tracing function can be used to obtain full node status information from the second data packet sent by the network node. Similarly, eBPF's probe function can be used to trace specific kernel function calls to obtain detailed information about the target network node (such as full node status information) and determine the specific fault type.

[0167] After the fault location module determines the specific fault type, it transmits the relevant information to the fault recovery module.

[0168] It should be noted that the relevant technologies use log analysis methods to locate faults. Log analysis methods are applicable to various types of faults and are easy to implement; however, they are slow and require a significant amount of manual analysis. This application's embodiments utilize eBPF's tracing function to quickly locate the specific type of fault.

[0169] 4) Fault recovery module: This module can execute corresponding recovery measures based on the fault type. An automatic recovery strategy can be pre-designed, such as redirecting traffic or restarting network components.

[0170] For example, select an appropriate recovery strategy based on the type of failure: update the network routing table using an eBPF program or enable backup links. Then send commands to the network nodes to perform the recovery operation.

[0171] The fault recovery module performs the recovery operation and feeds back the results to the result feedback module.

[0172] It should be noted that the relevant technologies employ a manual recovery mechanism. While this allows administrators to flexibly adjust recovery strategies based on specific circumstances, it is inefficient, time-consuming, and may lead to service interruptions. This application's embodiments employ automatic recovery strategies, such as traffic redirection and network component restarts, enabling immediate action upon fault detection without manual intervention, significantly improving recovery efficiency.

[0173] 5) Results Feedback Module: This module reports the results of fault detection and recovery to users or the management system. It can record the fault detection and recovery process and report it to external systems via the user interface or API.

[0174] For example, log files can be created to record the fault detection and recovery process, and the results can be pushed to external systems via a RESTful API.

[0175] The results feedback module will report the final fault detection and recovery status to the user or management system.

[0176] It should be noted that related technologies notify administrators via email or SMS. While email and SMS notifications are simple, practical, and easy to implement, their real-time performance is poor and may not meet the needs of certain application scenarios. This application's embodiment reports the results of fault detection and recovery to users or the management system, supporting flexible configuration of fault detection rules and recovery strategies, and can be customized according to different network environments and requirements.

[0177] The fault type determination system provided in this application integrates data collection, analysis, location, recovery, and result feedback modules. Utilizing eBPF technology and machine learning algorithms, it achieves real-time detection, rapid location, and automatic recovery of network faults at various target network nodes, significantly improving the efficiency and accuracy of network operation and maintenance. It also supports flexible configuration to meet the customized needs of different network environments. In other words, this application not only solves the problems of low fault detection efficiency and untimely response in related technologies through real-time data collection and analysis, but also provides highly configurable fault detection rules and recovery strategies, enhancing the system's flexibility. It implements an automated fault recovery mechanism, improving recovery efficiency. By optimizing the eBPF program, resource consumption is reduced, improving the overall system performance. The use of machine learning algorithms for intelligent analysis reduces the false alarm rate.

[0178] Based on the same inventive concept, embodiments of this application also provide a fault type determination device, such as... Figure 9 As shown, the device 900 may include an acquisition module 910, an analysis module 920, and a determination module 930.

[0179] The acquisition module 910 is used to acquire key node status information in the first data packet when the target network node sends the first data packet;

[0180] Analysis module 920 is used to analyze the key node status information in the first data packet using a preset fault analysis model to obtain analysis results;

[0181] The acquisition module 910 is also used to acquire key node status information and basic node status information in the second data packet sent by the target network node when the analysis result is an operational failure.

[0182] The determination module 930 is used to determine the fault type of the target network node based on the comparison results between the key node status information and basic node status information in the second data packet and the corresponding preset threshold.

[0183] In some embodiments, the determining module is used to determine the fault type of the target network node based on the comparison result between the key node status information and basic node status information in the second data packet and the corresponding preset threshold, specifically for:

[0184] The key node status information and basic node status information in the second data packet are compared with the corresponding preset thresholds to obtain the target comparison result.

[0185] In the preset correspondence between comparison results and fault types, the target fault type corresponding to the target comparison result is obtained, and the target fault type is used as the fault type of the target network node.

[0186] In some embodiments, before obtaining the key node status information in the first data packet when the target network node sends the first data packet, the device further includes a monitoring module:

[0187] The monitoring module is used to monitor target network nodes at the kernel level using preset packet filters;

[0188] The acquisition module is used to obtain key node status information from the first data packet sent by the target network node. Specifically, it can be used for:

[0189] When the preset packet filter detects that the target network node has sent the first packet, the preset packet filter is used to obtain the key node status information in the first packet.

[0190] The acquisition module is used to obtain key node status information and basic node status information from the second data packet sent by the target network node when the analysis result indicates an operational failure. Specifically, it can be used for:

[0191] If the analysis result indicates an operational failure and the preset data packet filter detects that the target network node has sent a second data packet, the preset data packet filter is used to obtain the key node status information and basic node status information in the second data packet.

[0192] In some embodiments, the analysis module is used to analyze the key node status information in the first data packet using a preset fault analysis model to obtain analysis results, which can be specifically used for:

[0193] The key node status information in the first data packet is stored according to a preset structure;

[0194] According to the preset cycle, the key node status information in the first data packet stored is sent to the user layer;

[0195] Using a pre-set fault analysis model in the user layer, the status information of key nodes in the first data packet is analyzed to obtain the analysis results.

[0196] In some embodiments, the analysis module is used to analyze the key node status information in the first data packet using a preset fault analysis model in the user layer, and obtain analysis results, which can be specifically used for:

[0197] Calculate the average value of the status information of key nodes of the same type in the first data packet to obtain the target key node information;

[0198] By using the pre-set fault analysis model in the user layer, the status information of the target key nodes is analyzed to obtain the analysis results.

[0199] In some embodiments, before analyzing the key node status information in the first data packet using a preset fault analysis model to obtain the analysis results, the device may further include a training module:

[0200] The acquisition module is also used to acquire full historical node status information and historical fault type information of each network node within a preset time period;

[0201] The training module is used to supervise the training of the pre-set original fault analysis model by using information on historical fault types as the supervision result and the full set of historical node state information as the training sample, so as to obtain the fault analysis model.

[0202] In some embodiments, the fault analysis model includes weight coefficients corresponding to the state information of each node; the weight coefficients corresponding to the state information of key nodes are greater than or equal to a preset weight coefficient threshold; and the weight coefficients corresponding to the state information of basic nodes are less than a preset weight coefficient threshold.

[0203] In some embodiments, an execution module is also included:

[0204] The acquisition module is used to acquire the target solution corresponding to the fault type of the target network node from the preset correspondence between fault types and solutions;

[0205] The execution module is used to execute the target solution.

[0206] In some embodiments, a display module is also included:

[0207] The acquisition module is also used to acquire the execution results of the target solution;

[0208] The display module is used to display information about the fault types of the target network nodes and the execution results of the target solution on the target page.

[0209] The various modules in the fault type determination device provided in this application embodiment can achieve... Figures 1 to 8 The functions of each step in the provided fault type determination method, and their corresponding technical effects, will not be elaborated here for the sake of brevity.

[0210] Figure 10 A schematic diagram of the hardware structure of the fault type determination device provided in an embodiment of this application is shown.

[0211] The device for determining the type of fault may include a processor 1001 and a memory 1002 storing computer program instructions.

[0212] Specifically, the processor 1001 may include a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits that can be configured to implement the embodiments of this application.

[0213] Memory 1002 may include mass storage for data or instructions. For example, and not limitingly, memory 1002 may include a hard disk drive (HDD), floppy disk drive, flash memory, optical disk, magneto-optical disk, magnetic tape, or Universal Serial Bus (USB) drive, or a combination of two or more of these. Where appropriate, memory 1002 may include removable or non-removable (or fixed) media. Where appropriate, memory 1002 may be internal or external to a device with a determined failure type. In a particular embodiment, memory 1002 is a non-volatile solid-state memory.

[0214] Memory may include read-only memory (ROM), random access memory (RAM), disk storage media devices, optical storage media devices, flash memory devices, and electrical, optical, or other physical / tangible memory storage devices. Therefore, typically, memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., memory devices) encoded with software including computer-executable instructions, and when the software is executed (e.g., by one or more processors), it is operable to perform the operations described with reference to the method according to one aspect of this disclosure.

[0215] The processor 1001 reads and executes computer program instructions stored in the memory 1002 to implement any of the fault type determination methods in the above embodiments.

[0216] In one example, the fault type determination device may further include a communication interface 1003 and a bus 1004. For example, Figure 10 As shown, the processor 1001, memory 1002, and communication interface 1003 are connected through bus 1004 and complete communication with each other.

[0217] The communication interface 1003 is mainly used to realize communication between various modules, devices, units and / or equipment in the embodiments of this application.

[0218] Bus 1004 includes hardware, software, or both, that couples components of a device with a defined fault type together. For example, and not limitingly, the bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Extended Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a Hyper Transport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an Infinite Bandwidth Interconnect, a Linear Predictive Coding (LPC) bus, a memory bus, a MicroChannel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (Peripheral Component Interconnect-X, PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local Bus (VESA Local Bus, VLB) bus, or other suitable buses, or a combination of two or more of these. Where appropriate, bus 1004 may include one or more buses. Although specific buses are described and illustrated in the embodiments of this application, this application considers any suitable bus or interconnection.

[0219] This device can execute the fault type determination method in the embodiments of this application based on each unit / component in the fault type determination device, thereby achieving a combination Figures 1 to 7 The method for determining the type of fault described.

[0220] Furthermore, in conjunction with the fault type determination method in the above embodiments, this application embodiment can provide a computer storage medium for implementation. This computer storage medium stores computer program instructions; when these computer program instructions are executed by a processor, they implement any of the fault type determination methods in the above embodiments.

[0221] This application also provides a computer program product in which the instructions, when executed by a processor of an electronic device, cause the electronic device to perform various processes implementing the above-described method embodiments for determining any of the fault types.

[0222] It should be clarified that this application is not limited to the specific configurations and processes described above and shown in the figures. For the sake of brevity, detailed descriptions of known methods are omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of this application is not limited to the specific steps described and shown. Those skilled in the art can make various changes, modifications, and additions, or change the order of steps, after understanding the spirit of this application.

[0223] The functional blocks shown in the above-described block diagram can be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, they can be, for example, electronic circuits, application-specific integrated circuits (ASICs), appropriate firmware, plug-ins, function cards, etc. When implemented in software, the elements of this application are programs or code segments used to perform the required tasks. Programs or code segments can be stored on a machine-readable medium or transmitted over a transmission medium or communication link via data signals carried on a carrier wave. "Machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, read-only memory (ROM), flash memory, erasable read-only memory (EROM), floppy disks, compact disc read-only memory (CD-ROM), optical disks, hard disks, fiber optic media, radio frequency (RF) links, etc. Code segments can be downloaded via computer networks such as the Internet, intranets, etc.

[0224] It should also be noted that the exemplary embodiments mentioned in this application describe methods or systems based on a series of steps or apparatus. However, this application is not limited to the order of the above steps; that is, the steps can be performed in the order mentioned in the embodiments, or in a different order, or several steps can be performed simultaneously.

[0225] The aspects of this disclosure have been described above with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It should be understood that each block in the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that these instructions, executable via the processor of the computer or other programmable data processing apparatus, enable the implementation of the functions / actions specified in one or more blocks of the flowchart illustrations and / or block diagrams. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor, or a field-programmable logic circuit. It is also understood that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can also be implemented by special-purpose hardware performing the specified functions or actions, or can be implemented by a combination of special-purpose hardware and computer instructions.

[0226] The above are merely specific embodiments of this application. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, modules, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here. It should be understood that the protection scope of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the protection scope of this application.

Claims

1. A method for determining a fault type, characterized in that, include: When the target network node sends the first data packet, obtain the key node status information in the first data packet; Using a pre-defined fault analysis model, the status information of key nodes in the first data packet is analyzed to obtain the analysis results; If the analysis result indicates a malfunction, obtain the key node status information and basic node status information from the second data packet sent by the target network node. The fault type of the target network node is determined based on the comparison results between the key node status information and basic node status information in the second data packet and the corresponding preset threshold.

2. The method according to claim 1, characterized in that, The step of determining the fault type of the target network node based on the comparison results between the key node status information and basic node status information in the second data packet and the corresponding preset thresholds includes: The key node status information and basic node status information in the second data packet are compared with the corresponding preset thresholds to obtain the target comparison result. In the preset correspondence between comparison results and fault types, the target fault type corresponding to the target comparison result is obtained, and the target fault type is used as the fault type of the target network node.

3. The method according to claim 1, characterized in that, Before obtaining the key node status information in the first data packet when the target network node sends the first data packet, the method further includes: Target network nodes are monitored at the kernel level using a pre-defined packet filter; When the target network node sends a first data packet, obtaining the key node status information in the first data packet includes: When the preset data packet filter detects that the target network node has sent a first data packet, the preset data packet filter is used to obtain the key node status information in the first data packet; In the event that the analysis result indicates an operational failure, the key node status information and basic node status information obtained from the second data packet sent by the target network node include: If the analysis result indicates a malfunction and the preset data packet filter detects that the target network node has sent a second data packet, the preset data packet filter is used to obtain the key node status information and basic node status information in the second data packet.

4. The method according to claim 3, characterized in that, The step involves using a preset fault analysis model to analyze the key node status information in the first data packet, obtaining analysis results, including: The key node status information in the first data packet is stored according to a preset structure; According to a preset period, the key node status information in the first data packet stored is sent to the user layer; Using the pre-set fault analysis model in the user layer, the status information of key nodes in the first data packet is analyzed to obtain the analysis results.

5. The method according to claim 4, characterized in that, The step of using a pre-set fault analysis model in the user layer to analyze the key node status information in the first data packet and obtaining analysis results includes: Calculate the average value of the same type of key node status information in the first data packet to obtain the target key node status information; The status information of the target key node is analyzed using the pre-set fault analysis model in the user layer to obtain the analysis results.

6. The method according to claim 1, characterized in that, Before analyzing the key node status information in the first data packet using a preset fault analysis model to obtain the analysis results, the method further includes: Within a preset time period, acquire all historical node status information and historical fault type information for each network node. The information on the historical fault types is used as the supervision result, and the information on the state of all historical nodes is used as the training sample to conduct supervised training on the preset original fault analysis model to obtain the fault analysis model.

7. The method according to claim 1, characterized in that, The fault analysis model includes weight coefficients corresponding to the status information of each node; the weight coefficients corresponding to the status information of key nodes are greater than or equal to a preset weight coefficient threshold. The weight coefficient corresponding to the basic node status information is less than the preset weight coefficient threshold.

8. The method according to any one of claims 1 to 7, characterized in that, Also includes: Based on the preset correspondence between fault types and solutions, obtain the target solution corresponding to the fault type of the target network node; Execute the target solution.

9. The method according to claim 8, characterized in that, Also includes: Obtain the execution result of the target solution; The target page displays information about the fault type of the target network node and the execution results of the target solution.

10. A fault type determination device, characterized in that, include: The acquisition module is used to acquire key node status information in the first data packet when the target network node sends the first data packet; The analysis module is used to analyze the key node status information in the first data packet using a preset fault analysis model to obtain the analysis results. The acquisition module is also used to acquire key node status information and basic node status information in the second data packet sent by the target network node when the analysis result is an operational failure. The determination module is used to determine the fault type of the target network node based on the comparison results between the key node status information and basic node status information in the second data packet and the corresponding preset threshold.

11. A fault type determination device, characterized in that, The device includes: a processor and a memory storing computer program instructions; the processor, when executing the computer program instructions, implements the method for determining the fault type as described in any one of claims 1 to 9.

12. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer program instructions that, when executed by a processor, implement the method for determining the fault type as described in any one of claims 1 to 9.

13. A computer program product, characterized in that, When the instructions in the computer program product are executed by the processor of the device, the device is able to perform the fault type determination method as described in any one of claims 1 to 9.

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