Fault locating method, device, equipment and storage medium

Abstract

This disclosure provides a fault location method, apparatus, device, and storage medium, relating to the field of computer technology. The method includes: acquiring network parameters of network devices within a preset area; comparing the network parameters with a dynamic threshold to determine if a fault exists within the preset area; inputting the network parameters into a first intelligent model to identify multiple faulty network devices; and locating the target network device causing the fault based on the multiple faulty network devices and a preset fault causal topology diagram. This achieves rapid location of the faulty network device and improves the efficiency of faulty network device location.

Description

Technical Field

[0001] This disclosure relates to the field of communication technology, and in particular to a fault location method, apparatus, device and storage medium. Background Technology

[0002] With the rapid development of network technology and the continuous changes in business needs, users have increasingly higher requirements for network quality. However, with the increase in network devices, the frequency of network device failures is also increasing. How to quickly locate faulty network devices in a large number of devices is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0003] This disclosure provides a fault location method, apparatus, device, and storage medium, which improves fault location efficiency to at least a certain extent.

[0004] Other features and advantages of this disclosure will become apparent from the following detailed description, or may be learned in part from practice of this disclosure.

[0005] According to one aspect of this disclosure, a fault location method is provided, comprising:

[0006] Obtain network parameters of network devices within a preset area;

[0007] By comparing network parameters with dynamic thresholds, it can be determined whether a fault exists within a preset area.

[0008] Input the network parameters into the first intelligent model to identify multiple faulty network devices;

[0009] Based on multiple faulty network devices, a pre-defined fault cause-effect topology map is used to locate the target network device causing the fault.

[0010] In one embodiment of this disclosure, the method further includes:

[0011] The network parameters are standardized based on the minimum and maximum values ​​of historical network parameters and the network parameters themselves.

[0012] By comparing network parameters with dynamic thresholds, faults are identified within a preset area, including:

[0013] The standardized network parameters are compared with dynamic thresholds to determine if a fault exists within a preset area.

[0014] In one embodiment of this disclosure, the method further includes:

[0015] The dynamic threshold is determined based on the mean of the network parameters, the standard deviation of the network parameters, and the sensitivity adjustment coefficient.

[0016] In one embodiment of this disclosure, the method further includes:

[0017] The first intelligent model is trained using historical network parameters and historical fault judgment results as the training set. When the training stopping condition is met, the first intelligent model that has been trained is obtained.

[0018] The network parameters are input into the first intelligent model to identify multiple faulty network devices, including:

[0019] By inputting the network parameters into the first trained intelligent model, multiple faulty network devices are identified.

[0020] In one embodiment of this disclosure, the method further includes:

[0021] In response to user input, the faulty area and the target network device are visualized.

[0022] In one embodiment of this disclosure, the method further includes:

[0023] Input the identifiers of network devices that have failed in the past and the time points of failure into the second preset model to obtain a fault causal topology graph. The fault causal topology graph is used to indicate the probability of other network devices failing when any one network device fails.

[0024] In one embodiment of this disclosure, locating the target network device causing the fault based on multiple faulty network devices and a preset fault cause-effect topology map includes:

[0025] The identification of multiple faulty network devices and the fault cause-and-effect topology diagram are input into the third intelligent model to obtain the model output results, which indicate the target network device that caused the fault.

[0026] According to another aspect of this disclosure, a fault location device is provided, comprising:

[0027] The acquisition module is used to acquire network parameters of network devices within a preset area;

[0028] The first determination module is used to compare network parameters with dynamic thresholds to determine whether a fault exists within a preset area;

[0029] The second determination module is used to input network parameters into the first intelligent model to determine multiple faulty network devices.

[0030] The location module is used to locate the target network device that caused the fault based on multiple faulty network devices and a preset fault cause-effect topology map.

[0031] In one embodiment of this disclosure, the apparatus further includes:

[0032] The standardization module is used to standardize network parameters based on the minimum and maximum values ​​of historical network parameters and the network parameters themselves.

[0033] The first determining module includes:

[0034] The comparison unit is used to compare the standardized network parameters with the dynamic threshold to determine whether a fault exists within the preset area.

[0035] In one embodiment of this disclosure, the apparatus further includes:

[0036] The third determination module is used to determine the dynamic threshold based on the mean of the network parameters, the standard deviation of the network parameters, and the sensitivity adjustment coefficient.

[0037] In one embodiment of this disclosure, the apparatus further includes:

[0038] The training module is used to train the first intelligent model based on historical network parameters and historical fault judgment results as the training set. When the training stopping condition is met, the first intelligent model that has been trained is obtained.

[0039] The second determining module includes:

[0040] The determination unit is used to input network parameters into the first intelligent model after training and to identify multiple faulty network devices.

[0041] In one embodiment of this disclosure, the apparatus further includes:

[0042] The display module is used to visually display the faulty area and the target network device in response to user input.

[0043] In one embodiment of this disclosure, the apparatus further includes:

[0044] The input module is used to input the identifiers of network devices that have failed in the past and the time points of the failures into the second preset model to obtain a fault causal topology diagram. The fault causal topology diagram is used to indicate the probability of other network devices failing when any one network device fails.

[0045] In one embodiment of this disclosure, the positioning module includes:

[0046] The input unit is used to input the identifiers of multiple faulty network devices and the fault cause-effect topology diagram into the third intelligent model to obtain the model output results, which indicate the target network device that caused the fault.

[0047] According to another aspect of this disclosure, an electronic device is provided, comprising: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to perform the above-described fault location method by executing the executable instructions.

[0048] According to another aspect of this disclosure, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the above-described fault location method.

[0049] The fault location method, apparatus, device, and storage medium provided in the embodiments of this disclosure acquire network parameters of network devices within a preset area, compare the network parameters with dynamic thresholds to determine that a fault exists within the preset area, input the network parameters into a first intelligent model to identify multiple faulty network devices, and locate the target network device causing the fault based on the multiple faulty network devices and a preset fault causal topology map, thereby achieving rapid location of faulty network devices and improving the efficiency of faulty network device location.

[0050] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0051] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure. It is obvious that the drawings described below are merely some embodiments of this disclosure, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort.

[0052] Figure 1 This diagram illustrates a fault location system structure according to an embodiment of the present disclosure.

[0053] Figure 2 This diagram illustrates a fault location method according to an embodiment of the present disclosure.

[0054] Figure 3 This diagram illustrates another fault location method according to an embodiment of the present disclosure.

[0055] Figure 4 This diagram illustrates a flowchart of yet another fault location method according to an embodiment of the present disclosure.

[0056] Figure 5 This diagram illustrates a flowchart of yet another fault location method according to an embodiment of the present disclosure;

[0057] Figure 6 This diagram illustrates a flowchart of yet another fault location method according to an embodiment of the present disclosure;

[0058] Figure 7This diagram illustrates a flowchart of yet another fault location method according to an embodiment of the present disclosure;

[0059] Figure 8 This diagram illustrates a structural diagram of a fault location device according to an embodiment of the present disclosure;

[0060] Figure 9 A structural block diagram of an electronic device according to an embodiment of the present disclosure is shown. Detailed Implementation

[0061] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided so that this disclosure will be more comprehensive and complete, and will fully convey the concept of the exemplary embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0062] Furthermore, the accompanying drawings are merely illustrative of this disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0063] It should be understood that the steps described in the method embodiments of this disclosure may be performed in different orders and / or in parallel. Furthermore, the method embodiments may include additional steps and / or omit the steps shown. The scope of this disclosure is not limited in this respect.

[0064] It should be noted that the concepts of "first" and "second" mentioned in this disclosure are used only to distinguish different devices, modules or units, and are not used to limit the order of functions performed by these devices, modules or units or their interdependencies.

[0065] It should be noted that the terms "a" and "a plurality of" used in this disclosure are illustrative rather than restrictive, and those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".

[0066] To address the aforementioned problems, embodiments of this disclosure provide a fault location method, apparatus, system, device, and storage medium.

[0067] To provide a detailed explanation of this disclosure, the fault location system will first be described in detail. Figure 1A schematic diagram of a fault location system according to an embodiment of the present disclosure is shown. This system can be applied to fault location methods and fault location devices in various embodiments of the present disclosure.

[0068] like Figure 1 As shown, the fault location system 10 may include a fault location device 101 and a network device 102;

[0069] Network device 102 is used to send network parameters to fault location device 101.

[0070] In some embodiments, the fault location device 101 and the network device 102 may be located on different devices, and the fault location device 101 and the network device 102 may be modules on electronic devices with data acquisition capabilities. The fault location device 101 and the network device 102 may also be modules on electronic devices with data processing capabilities, such as computer devices and servers.

[0071] The fault location device 101 and the network device 102 are connected by a network, which can be a wired network or a wireless network.

[0072] Optionally, the aforementioned wireless or wired networks use standard communication technologies and / or protocols. The network is typically the Internet, but can also be any network, including but not limited to Local Area Networks (LANs), Metropolitan Area Networks (MANs), Wide Area Networks (WANs), mobile, wired or wireless networks, private networks, or any combination of virtual private networks. In some embodiments, technologies and / or formats including Hyper Text Markup Language (HTML), Extensible Markup Language (XML), etc., are used to represent data exchanged over the network. Furthermore, conventional encryption technologies such as Secure Socket Layer (SSL), Transport Layer Security (TLS), Virtual Private Networks (VPNs), and Internet Protocol Security (IPsec) can be used to encrypt all or some links. In other embodiments, custom and / or dedicated data communication technologies can be used to replace or supplement the aforementioned data communication technologies.

[0073] Network device 102 can be a terminal device, which can be various electronic devices, including but not limited to smartphones, tablets, laptops, desktop computers, wearable devices, augmented reality devices, virtual reality devices, etc.

[0074] Optionally, the client for the application installed on different terminal devices can be the same, or the client for the same type of application based on different operating systems. Depending on the terminal platform, the specific form of the application client can also differ; for example, the application client can be a mobile client, a PC client, etc.

[0075] The fault location device 101 can be a server, which can provide various services, such as a background fault location server that supports the operation of a device by a user using a terminal device. The background fault location server can analyze and process received requests and other data, and feed the processing results back to the terminal device.

[0076] Optionally, the server can be a standalone physical server, a server cluster or distributed system consisting of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN (Content Delivery Network), and big data and artificial intelligence platforms.

[0077] Those skilled in the art will know that Figure 1 The number of fault location devices 101 and network devices 102 shown is merely illustrative; any number of fault location devices 101 and network devices 102 can be used as needed. This disclosure does not limit the number of such devices.

[0078] The fault location system provided in the embodiments of this disclosure acquires network parameters of network devices within a preset area, compares the network parameters with dynamic thresholds to determine that a fault exists within the preset area, inputs the network parameters into a first intelligent model to identify multiple faulty network devices, and locates the target network device causing the fault based on the multiple faulty network devices and a preset fault causal topology map, thereby realizing rapid location of faulty network devices and improving the efficiency of faulty network device location.

[0079] Based on the same inventive concept, this disclosure also provides a fault location method. This fault location method is applied to a fault location device, as shown in the following embodiments. Since the principle by which this method solves the problem is similar to that of the above-described method embodiments, the implementation of this method embodiment can refer to the implementation of the above-described method embodiments, and repeated details will not be elaborated further.

[0080] Figure 2 A schematic flowchart of a fault location method according to an embodiment of this disclosure is shown.

[0081] like Figure 2 As shown, the fault location method in this embodiment may include:

[0082] S210, Obtain network parameters of network devices within the preset area.

[0083] In some embodiments, the preset area may include a user-defined area.

[0084] In some embodiments, a network device may be a hardware device for establishing, managing and maintaining a computer network.

[0085] For example, network devices may include routers, switches, user terminals, hubs, repeaters, bridges, gateways, and network interface cards.

[0086] In some embodiments, network parameters may include data generated by the operation of network devices.

[0087] For example, network parameters may include latency data, IP address, subnet mask, bandwidth, speed, and throughput.

[0088] S220 compares network parameters with dynamic thresholds to determine if a fault exists within a preset area.

[0089] In some embodiments, the dynamic threshold is determined based on the mean of the network parameters, the standard deviation of the network parameters, and the sensitivity adjustment coefficient.

[0090] In some embodiments, there are multiple types of network parameters. The mean value of each type of network parameter, the standard deviation of each type of network parameter, and the sensitivity adjustment coefficient can be obtained to determine multiple sub-dynamic thresholds. Then, the multiple sub-dynamic thresholds are mathematically processed to obtain the final dynamic threshold.

[0091] S230 inputs network parameters into the first intelligent model to identify multiple faulty network devices.

[0092] In some embodiments, inputting network parameters into the first intelligent model may include: inputting the identifier of each network device and the network parameters corresponding to each network device into the first intelligent model.

[0093] S240 locates the target network device causing the fault based on multiple faulty network devices and a preset fault cause-effect topology map.

[0094] The fault location method provided in the embodiments of this disclosure obtains network parameters of network devices within a preset area, compares the network parameters with dynamic thresholds to determine that a fault exists within the preset area, inputs the network parameters into a first intelligent model to identify multiple faulty network devices, and locates the target network device causing the fault based on the multiple faulty network devices and a preset fault causal topology map. This achieves rapid location of faulty network devices and improves the efficiency of faulty network device location.

[0095] Figure 3 This illustration shows a flowchart of another fault location method in an embodiment of the present disclosure.

[0096] like Figure 3 As shown, the fault location method in this embodiment may include:

[0097] S310, Obtain network parameters of network devices within a preset area;

[0098] S320 completes the standardization of network parameters based on the minimum and maximum values ​​of historical network parameters and the network parameters themselves.

[0099] In some embodiments, there are multiple types of network parameters, and the standardization of network parameters can be completed based on obtaining the minimum and maximum values ​​of historical network parameters for each type, as well as the network parameters themselves.

[0100] For example, the network parameters can be standardized based on a first preset formula.

[0101] The first preset formula can be: x′=x-min(X) / (max(X)-min(X)).

[0102] x′ represents the standardized network parameters, x represents the network parameters, min(X) represents the minimum value among the historical network parameters, and max(X) represents the maximum value among the historical network parameters.

[0103] S330 compares the standardized network parameters with dynamic thresholds to determine if a fault exists within a preset area.

[0104] S340 inputs network parameters into the first intelligent model to identify multiple faulty network devices.

[0105] In some embodiments, the training termination condition includes the number of training iterations reaching a preset number or the loss function value reaching a preset threshold.

[0106] S350 locates the target network device causing the fault based on multiple faulty network devices and a preset fault cause-effect topology map.

[0107] The fault location method provided in the embodiments of this disclosure obtains network parameters of network devices within a preset area, compares the network parameters with dynamic thresholds to determine that a fault exists within the preset area, inputs the network parameters into a first intelligent model to identify multiple faulty network devices, and locates the target network device causing the fault based on the multiple faulty network devices and a preset fault causal topology map. This achieves rapid location of faulty network devices and improves the efficiency of faulty network device location.

[0108] Figure 4 A schematic flowchart of another fault location method in an embodiment of this disclosure is shown.

[0109] like Figure 4 As shown, the fault location method in this embodiment may include:

[0110] S410, Obtain network parameters of network devices within a preset area;

[0111] S420 compares network parameters with dynamic thresholds to determine if a fault exists within a preset area;

[0112] S430: The first intelligent model is trained based on historical network parameters and historical fault judgment results as the training set. When the training stopping condition is met, the first intelligent model that has been trained is obtained.

[0113] In some embodiments, the historical fault determination result includes determining whether a network device has failed. S440, network parameters are input into the first intelligent model to identify multiple network devices with faults;

[0114] S450 locates the target network device causing the fault based on multiple faulty network devices and a pre-defined fault cause-effect topology map.

[0115] The fault location method provided in the embodiments of this disclosure obtains network parameters of network devices within a preset area, compares the network parameters with dynamic thresholds to determine that a fault exists within the preset area, inputs the network parameters into a first intelligent model to identify multiple faulty network devices, and locates the target network device causing the fault based on the multiple faulty network devices and a preset fault causal topology map. This achieves rapid location of faulty network devices and improves the efficiency of faulty network device location.

[0116] Figure 5 This illustration shows a flowchart of another fault location method in an embodiment of the present disclosure.

[0117] like Figure 5 As shown, the fault location method in this embodiment may include:

[0118] S510: Obtain network parameters of network devices within a preset area;

[0119] S520 compares network parameters with dynamic thresholds to determine if a fault exists within a preset area;

[0120] S530 inputs network parameters into the first intelligent model to identify multiple faulty network devices;

[0121] S540 locates the target network device causing the fault based on multiple faulty network devices and a preset fault cause-effect topology map.

[0122] The S550, in response to user input, visualizes the faulty area and the target network device.

[0123] In some embodiments, user input includes user-input text information, voice information, and image information.

[0124] In some embodiments, the fault location device can automatically acquire network parameters of network devices within a preset area, compare the network parameters with dynamic thresholds, and determine that a fault exists within the preset area. If a fault is determined to exist within the preset area, an alarm message can be sent.

[0125] In some embodiments, alarm information of different levels can be displayed in different ways.

[0126] For example, different alarm levels can correspond to different colors.

[0127] For example, a heatmap can be used to identify the strength of an alarm.

[0128] In some embodiments, different severity weights can be assigned to faults of different degrees. If the cumulative alarm intensity S = ∑f_alert × w_severity of the alarm frequency f_alert and severity weight w_severity in the area exceeds a preset threshold T, the system will mark it as a high-risk area on the alarm heatmap.

[0129] Users can select specific regions and time ranges through the filter bar to focus on high-priority alarms, thus improving the user experience.

[0130] In some embodiments, the fault location device may generate a root cause analysis report containing fault handling recommendations based on the existence of a fault in a determined area and a determined target network device.

[0131] In some embodiments, the fault location device may also assess the possible causes of the fault based on the root cause analysis report and the similarity of historical fault data.

[0132] The fault location method provided in the embodiments of this disclosure obtains network parameters of network devices within a preset area, compares the network parameters with dynamic thresholds to determine that a fault exists within the preset area, inputs the network parameters into a first intelligent model to identify multiple faulty network devices, and locates the target network device causing the fault based on the multiple faulty network devices and a preset fault causal topology map. This achieves rapid location of faulty network devices and improves the efficiency of faulty network device location.

[0133] Figure 6 This illustration shows a flowchart of another fault location method in an embodiment of the present disclosure.

[0134] like Figure 6 As shown, the fault location method in this embodiment may include:

[0135] S610, obtain network parameters of network devices in a preset area;

[0136] S620 compares network parameters with dynamic thresholds to determine if a fault exists within a preset area;

[0137] The S630 inputs network parameters into the first intelligent model to identify multiple faulty network devices.

[0138] S640: Input the identifiers of network devices that have failed in the past and the time points of failure into the second preset model to obtain a fault causal topology diagram. The fault causal topology diagram is used to indicate the probability of other network devices failing when any one network device fails.

[0139] In some embodiments, prior to S640, the method may further include: using the network device identifiers that have experienced historical failures, the time points of failures, and the historical fault causal topology as a training set to train the second intelligent model, and obtaining the trained second intelligent model when the training stop condition is met.

[0140] In some embodiments, training termination conditions may include reaching a preset number of training iterations and the loss function value reaching a preset threshold.

[0141] S650 locates the target network device causing the fault based on multiple faulty network devices and a preset fault cause-effect topology map.

[0142] In some embodiments, the method further includes:

[0143] The fault location method provided in the embodiments of this disclosure obtains network parameters of network devices within a preset area, compares the network parameters with dynamic thresholds to determine that a fault exists within the preset area, inputs the network parameters into a first intelligent model to identify multiple faulty network devices, and locates the target network device causing the fault based on the multiple faulty network devices and a preset fault causal topology map. This achieves rapid location of faulty network devices and improves the efficiency of faulty network device location.

[0144] Figure 7 This illustration shows a flowchart of another fault location method in an embodiment of the present disclosure.

[0145] like Figure 7 As shown, the fault location method in this embodiment may include:

[0146] S710, obtains network parameters of network devices within a preset area;

[0147] S720 compares network parameters with dynamic thresholds to determine if a fault exists within a preset area;

[0148] S730 inputs network parameters into the first intelligent model to identify multiple faulty network devices;

[0149] The S740 inputs the identifiers of multiple faulty network devices and the fault cause-effect topology diagram into the third intelligent model, and obtains the model output results, which indicate the target network device that caused the fault.

[0150] In some embodiments, the third intelligent model can perform inference based on a Bayesian network model to determine the root cause node leading to the fault, i.e., the target network device. The Bayesian network model is B = (N, A), where node N represents a network device and edge A represents the causal relationship between network devices. Causal inference is performed using conditional probability P(Ni|Nj) to determine the target network device. This improves the fault location accuracy in complex network environments.

[0151] In some embodiments, this disclosure can also perform trend prediction based on network parameters, such as the possible changing trend of lag issues in the next 24 hours, and an overview of the health status of devices within the affected area. The interface also supports continuous dialogue, automatically records historical questions, facilitates cross-device and multi-question correlation analysis, and helps maintenance personnel efficiently locate complex faults from multiple perspectives and carry out preventive handling.

[0152] Based on the same inventive concept, this disclosure also provides a fault location device, as shown in the following embodiment. Since the principle by which this device solves the problem is similar to that of the method embodiment described above, the implementation of this device embodiment can refer to the implementation of the method embodiment described above, and repeated details will not be elaborated further.

[0153] The fault location device provided in the embodiments of this disclosure acquires network parameters of network devices within a preset area, compares the network parameters with dynamic thresholds to determine that a fault exists within the preset area, inputs the network parameters into a first intelligent model to identify multiple faulty network devices, and locates the target network device causing the fault based on the multiple faulty network devices and a preset fault causal topology map, thereby realizing rapid location of faulty network devices and improving the efficiency of faulty network device location.

[0154] Figure 8 A structural diagram of a fault location device according to an embodiment of this disclosure is shown.

[0155] like Figure 8 As shown, the fault location device 800 in this embodiment may include:

[0156] The acquisition module is used to acquire network parameters of network devices within a preset area;

[0157] The first determining module 810 is used to compare network parameters with dynamic thresholds to determine that a fault exists in a preset area;

[0158] The second determination module 820 is used to input network parameters into the first intelligent model to determine multiple faulty network devices;

[0159] The positioning module 830 is used to locate the target network device that caused the fault based on multiple faulty network devices and a preset fault cause-effect topology map.

[0160] The fault location device provided in the embodiments of this disclosure acquires network parameters of network devices within a preset area, compares the network parameters with dynamic thresholds to determine that a fault exists within the preset area, inputs the network parameters into a first intelligent model to identify multiple faulty network devices, and locates the target network device causing the fault based on the multiple faulty network devices and a preset fault causal topology map, thereby realizing rapid location of faulty network devices and improving the efficiency of faulty network device location.

[0161] In one embodiment of this disclosure, the apparatus further includes:

[0162] The standardization module is used to standardize network parameters based on the minimum and maximum values ​​of historical network parameters and the network parameters themselves.

[0163] The first determining module includes:

[0164] The comparison unit is used to compare the standardized network parameters with the dynamic threshold to determine whether a fault exists within the preset area.

[0165] In one embodiment of this disclosure, the apparatus further includes:

[0166] The third determination module is used to determine the dynamic threshold based on the mean of the network parameters, the standard deviation of the network parameters, and the sensitivity adjustment coefficient.

[0167] In one embodiment of this disclosure, the apparatus further includes:

[0168] The training module is used to train the first intelligent model based on historical network parameters and historical fault judgment results as the training set. When the training stopping condition is met, the first intelligent model that has been trained is obtained.

[0169] The second determining module includes:

[0170] The determination unit is used to input network parameters into the first intelligent model after training and to identify multiple faulty network devices.

[0171] In one embodiment of this disclosure, the apparatus further includes:

[0172] The display module is used to visually display the faulty area and the target network device in response to user input.

[0173] In one embodiment of this disclosure, the apparatus further includes:

[0174] The input module is used to input the identifiers of network devices that have failed in the past and the time points of the failures into the second preset model to obtain a fault causal topology diagram. The fault causal topology diagram is used to indicate the probability of other network devices failing when any one network device fails.

[0175] In one embodiment of this disclosure, the positioning module includes:

[0176] The input unit is used to input the identifiers of multiple faulty network devices and the fault cause-effect topology diagram into the third intelligent model to obtain the model output results, which indicate the target network device that caused the fault.

[0177] The fault location device provided in the embodiments of this disclosure acquires network parameters of network devices within a preset area, compares the network parameters with dynamic thresholds to determine that a fault exists within the preset area, inputs the network parameters into a first intelligent model to identify multiple faulty network devices, and locates the target network device causing the fault based on the multiple faulty network devices and a preset fault causal topology map, thereby realizing rapid location of faulty network devices and improving the efficiency of faulty network device location.

[0178] The fault location device provided in this embodiment can be used to execute the fault location methods provided in the above-described method embodiments. The implementation principle and technical effect are similar, and for the sake of simplicity, they will not be described in detail here.

[0179] Those skilled in the art will understand that various aspects of this disclosure can be implemented as a system, method, or program product. Therefore, various aspects of this disclosure can be specifically implemented in the following forms: a completely hardware implementation, a completely software implementation (including firmware, microcode, etc.), or a combination of hardware and software aspects, collectively referred to herein as a "circuit," "module," or "system."

[0180] The following reference Figure 9 To describe an electronic device 900 according to such an embodiment of the present disclosure. Figure 9 The electronic device 900 shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments disclosed herein.

[0181] like Figure 9 As shown, the electronic device 900 is manifested in the form of a general-purpose computing device. The components of the electronic device 900 may include, but are not limited to: at least one processing unit 910, at least one storage unit 920, and a bus 930 connecting different system components (including the storage unit 920 and the processing unit 910).

[0182] The storage unit stores program code, which can be executed by the processing unit 910, causing the processing unit 910 to perform the steps described in the "Exemplary Methods" section of this specification according to various exemplary embodiments of this disclosure. For example, the processing unit 910 can perform the following steps of the above method embodiments:

[0183] Obtain network parameters of network devices within a preset area;

[0184] By comparing network parameters with dynamic thresholds, it can be determined whether a fault exists within a preset area.

[0185] Input the network parameters into the first intelligent model to identify multiple faulty network devices;

[0186] Based on multiple faulty network devices, a pre-defined fault cause-effect topology map is used to locate the target network device causing the fault.

[0187] Storage unit 920 may include readable media in the form of volatile storage units, such as random access memory (RAM) 9201 and / or cache memory 9202, and may further include read-only memory (ROM) 9203.

[0188] The storage unit 920 may also include a program / utility 9204 having a set (at least one) of program modules 9205, including but not limited to: an operating system, one or more application programs, other program modules, and program tasks, each or some combination of these examples may include an implementation of a network environment.

[0189] Bus 930 can represent one or more of several types of bus structures, including a memory cell bus or memory cell controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of the various bus structures.

[0190] Electronic device 900 can also communicate with one or more external devices 940 (e.g., keyboard, pointing device, Bluetooth device, etc.), and with one or more devices that enable a user to interact with electronic device 900, and / or with any device that enables electronic device 900 to communicate with one or more other computing devices (e.g., router, modem, etc.). This communication can be performed via input / output (I / O) interface 950. Furthermore, electronic device 900 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 960. As shown, network adapter 960 communicates with other modules of electronic device 900 via bus 930. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with electronic device 900, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and task backup storage systems.

[0191] From the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, terminal device, or network device, etc.) to execute the methods according to the embodiments of this disclosure.

[0192] In exemplary embodiments of this disclosure, a computer-readable storage medium is also provided, which may be a readable signal medium or a readable storage medium. A program product capable of implementing the methods described above is stored thereon. In some possible implementations, various aspects of this disclosure may also be implemented as a program product including program code, which, when run on a terminal device, causes the terminal device to perform the steps described in the "Exemplary Methods" section of this specification according to various exemplary embodiments of this disclosure.

[0193] More specific examples of computer-readable storage media in this disclosure may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0194] In this disclosure, a computer-readable storage medium may include a task signal propagated in baseband or as part of a carrier wave, wherein readable program code is carried. Such a propagated task signal may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. The readable signal medium may also be any readable medium other than a readable storage medium, capable of transmitting, propagating, or transmitting a program for use by or in connection with an instruction execution system, apparatus, or device.

[0195] Optionally, the program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination thereof.

[0196] In practical implementation, program code for performing the operations of this disclosure can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can execute entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0197] It should be noted that although several modules or units for the device used to perform actions have been mentioned in the detailed description above, this division is not mandatory. In fact, according to embodiments of this disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0198] Furthermore, although the steps of the method in this disclosure are described in a specific order in the accompanying drawings, this does not require or imply that the steps must be performed in that specific order, or that all the steps shown must be performed to achieve the desired result. Additional or alternative steps may be omitted, multiple steps may be combined into one step, and / or a step may be broken down into multiple steps.

[0199] From the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, mobile terminal, or network device, etc.) to execute the methods according to the embodiments of this disclosure.

[0200] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This disclosure is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the appended claims.

Claims

1. A fault location method, characterized in that, include: Obtain network parameters of network devices within a preset area; The network parameters are compared with dynamic thresholds to determine if a fault exists within the preset area; The network parameters are input into the first intelligent model to identify multiple faulty network devices. Based on multiple faulty network devices, a preset fault cause-effect topology map is used to locate the target network device causing the fault; The method further includes: The standardization of network parameters is completed based on the minimum and maximum values ​​of historical network parameters and the network parameters themselves. The step of comparing the network parameters with a dynamic threshold to determine the presence of a fault within the preset area includes: The standardized network parameters are compared with the dynamic threshold to determine if a fault exists within the preset area.

2. The method according to claim 1, characterized in that, The method further includes: The dynamic threshold is determined based on the mean of the network parameters, the standard deviation of the network parameters, and the sensitivity adjustment coefficient.

3. The method according to claim 1, characterized in that, The method further includes: The first intelligent model is trained using historical network parameters and historical fault judgment results as the training set. When the training stopping condition is met, the first intelligent model that has been trained is obtained. The step of inputting the network parameters into the first intelligent model to identify multiple faulty network devices includes: The network parameters are input into the first intelligent model after training to identify multiple faulty network devices.

4. The method according to claim 1, characterized in that, The method further includes: In response to user input, the faulty area and the target network device are visualized.

5. The method according to claim 1, characterized in that, The method further includes: The network device identifiers that have failed in the past and the time points of failure are input into the second preset model to obtain the fault causal topology graph. The fault causal topology graph is used to indicate the probability of other network devices failing when any one network device fails.

6. The method according to claim 1, characterized in that, The method of locating the target network device causing the fault based on multiple faulty network devices and a preset fault cause-effect topology map includes: The identifiers of multiple faulty network devices and the fault cause-effect topology diagram are input into the third intelligent model to obtain the model output results, which indicate the target network device that caused the fault.

7. A fault location device, characterized in that, include: The acquisition module is used to acquire network parameters of network devices within a preset area; The first determining module is used to compare the network parameters with a dynamic threshold to determine that a fault exists in the preset area; The second determining module is used to input the network parameters into the first intelligent model to determine multiple faulty network devices; The location module is used to locate the target network device causing the fault based on multiple faulty network devices and a preset fault cause-effect topology map. The device also includes: The standardization module is used to standardize network parameters based on the minimum and maximum values ​​of historical network parameters and the network parameters themselves. The first determining module includes: The comparison unit is used to compare the standardized network parameters with the dynamic threshold to determine whether a fault exists within the preset area.

8. An electronic device, characterized in that, include: processor; as well as Memory for storing the executable instructions of the processor; The processor is configured to execute the fault location method according to any one of claims 1 to 6 by executing the executable instructions.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the fault location method according to any one of claims 1 to 6.

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