Fault positioning method and device, equipment and storage medium

Abstract

The invention provides a fault positioning method and device, equipment and a storage medium, and relates to the technical field of computers. The method comprises the following steps: acquiring network parameters of network equipment in a preset area, comparing the network parameters with a dynamic threshold value, determining that a fault exists in the preset area, inputting the network parameters into a first intelligent model, determining a plurality of pieces of network equipment with the fault, according to the invention, the target network device causing the fault is positioned based on the plurality of network devices having the fault and the preset fault causal topological graph, so that rapid positioning of the network device having the fault is realized, and the efficiency of positioning the fault network device is improved.

Description

Background Art

[0002] With the rapid development of network technology and the continuous changes in business needs, users are now demanding higher and higher network quality. However, with the increase in network devices, the frequency of network device failures is also increasing. For a large number of network devices, how to quickly locate faulty network devices is an urgent problem to be solved by those skilled in the art. Summary of the invention

[0003] The present disclosure provides a fault location method, apparatus, device and storage medium, which improve the fault location efficiency at least to a certain extent.

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

[0005] According to one aspect of the present disclosure, a fault location method is provided, including:

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

[0007] Compare network parameters with dynamic thresholds to determine if a fault exists in a preset area;

[0008] Inputting network parameters into a first intelligent model to determine a plurality of faulty network devices;

[0009] Based on multiple faulty network devices and a preset fault cause-and-effect topology diagram, the target network device that caused the fault is located.

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

[0011] The network parameter standardization is completed based on the minimum value of the historical network parameter, the maximum value of the historical network parameter and the network parameter;

[0012] Compare network parameters with dynamic thresholds to determine if a fault exists in a preset area, including:

[0013] The standardized network parameters are compared with the dynamic thresholds to determine whether there is a fault in the preset area.

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

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

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

[0017] The first intelligent model is trained based on historical network parameters and historical fault judgment results as a training set, and when a training stop condition is met, a trained first intelligent model is obtained;

[0018] The network parameters are input into the first intelligent model to determine a plurality of faulty network devices, including:

[0019] The network parameters are input into the trained first intelligent model to identify multiple faulty network devices.

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

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

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

[0023] The network device identifiers of historical failures and the time points of the failures are input into a second preset model to obtain a failure causal topology diagram, which is used to indicate the probability of failure of other network devices when any network device fails.

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

[0025] The identifiers of multiple faulty network devices and a fault cause-effect topology diagram are input into a third intelligent model to obtain a model output result, and the model output result indicates a target network device that causes the fault.

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

[0027] An acquisition module, used to acquire network parameters of network devices in a preset area;

[0028] A first determination module is used to compare the network parameters with the dynamic threshold value to determine whether a fault exists in a preset area;

[0029] A second determination module is used to input the network parameters into the first intelligent model to determine a plurality of faulty network devices;

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

[0031] In one embodiment of the present disclosure, the device further includes:

[0032] A standardization module, used for completing the standardization processing of the network parameters based on the minimum value of the historical network parameters, the maximum value of the historical network parameters and the network parameters;

[0033] The first determination module includes:

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

[0035] In one embodiment of the present disclosure, the device further includes:

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

[0037] In one embodiment of the present disclosure, the device further includes:

[0038] A training module, used to train the first intelligent model based on historical network parameters and historical fault judgment results as a training set, and obtain a trained first intelligent model when a training stop condition is met;

[0039] The second determination module includes:

[0040] The determination unit is used to input the network parameters into the trained first intelligent model to determine multiple faulty network devices.

[0041] In one embodiment of the present disclosure, the device 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 the present disclosure, the device further includes:

[0044] The input module is used to input the network device identifier of the historical failure and the time point of the failure into the second preset model to obtain a failure cause and effect topology diagram, which is used to indicate the probability of failure of other network devices when any network device fails.

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

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

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

[0048] According to another aspect of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the above-mentioned fault location method is implemented.

[0049] The fault location method, apparatus, device and storage medium provided in the embodiments of the present disclosure obtain network parameters of network devices in a preset area, compare the network parameters with dynamic thresholds, determine that a fault exists in the preset area, input the network parameters into a first intelligent model, determine multiple network devices with faults, and locate the target network device that causes the fault based on the multiple network devices with faults and a preset fault cause-effect topology map, thereby achieving rapid location of the faulty network device and improving the efficiency of locating the faulty network device.

[0050] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] The accompanying drawings herein are incorporated into the specification and constitute a part of the specification, illustrate embodiments consistent with the present disclosure, and together with the specification are used to explain the principles of the present disclosure. Obviously, the accompanying drawings described below are only some embodiments of the present disclosure, and for ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without creative work.

[0052] Figure 1 A structural diagram of a fault location system in an embodiment of the present disclosure is shown;

[0053] Figure 2 A flow chart of a fault location method in an embodiment of the present disclosure is shown;

[0054] Figure 3 Another flow chart of a fault location method in an embodiment of the present disclosure is shown;

[0055] Figure 4 A flow chart of another fault location method in an embodiment of the present disclosure is shown;

[0056] Figure 5 A flow chart of another fault location method in an embodiment of the present disclosure is shown;

[0057] Figure 6 A flow chart of another fault location method in an embodiment of the present disclosure is shown;

[0058] Figure 7A flow chart of another fault location method in an embodiment of the present disclosure is shown;

[0059] Figure 8 A structural diagram of a fault location device in an embodiment of the present disclosure is shown;

[0060] Fig. 9 A structural block diagram of an electronic device in an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0061] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that the disclosure will be more comprehensive and complete and to fully convey the concepts of the example 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] In addition, the accompanying drawings are only schematic illustrations of the present disclosure and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor devices and / or microcontroller devices.

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

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

[0065] It should be noted that the modifications of "one" and "plurality" mentioned in the present disclosure are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, it should be understood as "one or more".

[0066] In order to solve the above problems, the embodiments of the present disclosure provide a fault location method, apparatus, system, device and storage medium.

[0067] In order to explain the present disclosure in detail, the present disclosure first explains the fault location system in detail. Figure 1A schematic diagram of the structure of a fault location system in an embodiment of the present disclosure is shown. The system can be applied to the fault location method and the fault location device in each embodiment 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] The network device 102 is used to send network parameters to the fault locating apparatus 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 to each other via a network, which may be a wired network or a wireless network.

[0072] Optionally, the wireless network or wired network described above uses standard communication technology and / or protocol. The network is usually the Internet, but it can also be any network, including but not limited to a local area network (LAN), a metropolitan area network (MAN), a wide area network (WAN), a mobile, wired or wireless network, a dedicated network or any combination of a virtual private network). In some embodiments, technologies and / or formats including Hyper Text Mark-up Language (HTML), Extensible Markup Language (XML), etc. are used to represent data exchanged through the network. In addition, conventional encryption technologies such as Secure Socket Layer (SSL), Transport Layer Security (TLS), Virtual Private Network (VPN), Internet Protocol Security (IPsec) can also be used to encrypt all or some links. In other embodiments, customized and / or dedicated data communication technologies can also be used to replace or supplement the above data communication technologies.

[0073] The network device 102 may be a terminal device, and the terminal device may be various electronic devices, including but not limited to smart phones, tablet computers, laptop computers, desktop computers, wearable devices, augmented reality devices, virtual reality devices, etc.

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

[0075] The fault location device 101 may be a server, and the server may be a server that provides various services, such as a background fault location server that provides support for devices operated by users using terminal devices. The background fault location server may analyze and process the received request and other data, and feed back the processing results to the terminal device.

[0076] Optionally, the server can be an independent physical server, a server cluster or distributed system composed 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 communications, middleware services, domain name services, security services, CDN (Content Delivery Network), as well as 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 in the figure is only for illustration, and any number of fault location devices 101 and network devices 102 may be provided according to actual needs. The embodiments of the present disclosure are not limited to this.

[0078] The fault location system provided by the embodiments of the present disclosure obtains network parameters of network devices in a preset area, compares the network parameters with dynamic thresholds, determines that a fault exists in the preset area, inputs the network parameters into a first intelligent model, determines multiple network devices with faults, and locates the target network device that causes the fault based on the multiple network devices with faults and a preset fault cause-effect topology diagram, thereby achieving rapid location of the faulty network device and improving the efficiency of locating the faulty network device.

[0079] Based on the same inventive concept, a fault location method is also provided in the embodiment of the present disclosure. The fault location method in the embodiment of the present disclosure is applied to a fault location device, such as the following embodiment. Since the principle of solving the problem in the embodiment of the method is similar to that in the above embodiment of the method, the implementation of the embodiment of the method can refer to the implementation of the above embodiment of the method, and the repeated parts will not be repeated.

[0080] Figure 2 A schematic flow chart of a fault location method in an embodiment of the present disclosure is shown.

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

[0082] S210, obtaining network parameters of network devices in a 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 used to establish, manage, and maintain a computer network.

[0085] Exemplarily, the 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] Exemplarily, the network parameters may include latency data, IP address, subnet mask, bandwidth, rate, and throughput.

[0088] S220: Compare the network parameters with the dynamic threshold to determine whether a fault exists in a preset area.

[0089] In some embodiments, the dynamic threshold is determined based on a mean value of the network parameter, a standard deviation of the network parameter, and a sensitivity adjustment factor.

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

[0091] S230, inputting network parameters into a first intelligent model to determine a plurality of faulty network devices.

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

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

[0094] The fault location method provided by the embodiments of the present disclosure obtains network parameters of network devices in a preset area, compares the network parameters with dynamic thresholds, determines that a fault exists in the preset area, inputs the network parameters into a first intelligent model, determines multiple network devices with faults, and locates the target network device that causes the fault based on the multiple network devices with faults and a preset fault cause-effect topology diagram, thereby achieving rapid location of the faulty network device and improving the efficiency of locating the faulty network device.

[0095] Figure 3 A schematic diagram of another fault location method flow in an embodiment of the present disclosure is shown.

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

[0097] S310, obtaining network parameters of network devices in a preset area;

[0098] S320, completing standardization processing of the network parameters based on the minimum value of the historical network parameters, the maximum value of the historical network parameters, and the network parameters.

[0099] In some embodiments, there are multiple categories of network parameters, and the standardization of the network parameters can be completed based on obtaining the minimum value of the historical network parameters, the maximum value of the historical network parameters, and the network parameters of each category.

[0100] Exemplarily, the standardization of the network parameters may be completed based on the first preset formula.

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

[0102] x′ is the network parameter after normalization, x is the network parameter, min(X) is the minimum value of the historical network parameter, and max(X) is the maximum value of the historical network parameter.

[0103] S330, comparing the standardized network parameters with the dynamic thresholds to determine whether a fault exists in a preset area.

[0104] S340, inputting the network parameters into the first intelligent model to determine a plurality of faulty network devices.

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

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

[0107] The fault location method provided by the embodiments of the present disclosure obtains network parameters of network devices in a preset area, compares the network parameters with dynamic thresholds, determines that a fault exists in the preset area, inputs the network parameters into a first intelligent model, determines multiple network devices with faults, and locates the target network device that causes the fault based on the multiple network devices with faults and a preset fault cause-effect topology diagram, thereby achieving rapid location of the faulty network device and improving the efficiency of locating the faulty network device.

[0108] Figure 4 A schematic flow chart of another fault location method in an embodiment of the present disclosure is shown.

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

[0110] S410, obtaining network parameters of network devices in a preset area;

[0111] S420, comparing the network parameter with the dynamic threshold to determine whether a fault exists in the preset area;

[0112] S430, training the first intelligent model based on historical network parameters and historical fault judgment results as a training set, and obtaining a trained first intelligent model when a training stop condition is met.

[0113] In some embodiments, the historical fault determination result includes determining whether a network device has a fault. S440, inputting network parameters into a first intelligent model to determine a plurality of network devices with faults;

[0114] S450, locating a target network device causing the fault based on multiple faulty network devices and a preset fault cause-effect topology diagram.

[0115] The fault location method provided by the embodiments of the present disclosure obtains network parameters of network devices in a preset area, compares the network parameters with dynamic thresholds, determines that a fault exists in the preset area, inputs the network parameters into a first intelligent model, determines multiple network devices with faults, and locates the target network device that causes the fault based on the multiple network devices with faults and a preset fault cause-effect topology diagram, thereby achieving rapid location of the faulty network device and improving the efficiency of locating the faulty network device.

[0116] Figure 5 A schematic diagram of a flow chart of another fault location method in an embodiment of the present disclosure is shown.

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

[0118] S510, obtaining network parameters of network devices in a preset area;

[0119] S520, comparing the network parameter with the dynamic threshold to determine whether a fault exists in the preset area;

[0120] S530, inputting the network parameters into the first intelligent model to determine a plurality of faulty network devices;

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

[0122] S550: In response to the user input, visually display the area where the fault occurs and the target network device.

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

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

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

[0126] Exemplarily, different warning levels may correspond to different colors.

[0127] Exemplarily, the severity of the alarm may be indicated by a heat map.

[0128] In some embodiments, different severity weights may be assigned to faults of different degrees. If the accumulated alarm intensity S = ∑f_alert×w_severity of the alarm frequency f_alert and the 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 heat map.

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

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

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

[0132] The fault location method provided by the embodiments of the present disclosure obtains network parameters of network devices in a preset area, compares the network parameters with dynamic thresholds, determines that a fault exists in the preset area, inputs the network parameters into a first intelligent model, determines multiple network devices with faults, and locates the target network device that causes the fault based on the multiple network devices with faults and a preset fault cause-effect topology diagram, thereby achieving rapid location of the faulty network device and improving the efficiency of locating the faulty network device.

[0133] Figure 6 A schematic diagram of a flow chart of another fault location method in an embodiment of the present disclosure is shown.

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

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

[0136] S620, comparing the network parameter with the dynamic threshold to determine whether a fault exists in the preset area;

[0137] S630: Input the network parameters into the first intelligent model to determine a plurality of faulty network devices.

[0138] S640, input the identifier of the network device that has historically failed and the time point of the failure into the second preset model to obtain a failure cause and effect topology map, which is used to indicate the probability of failure of other network devices when any network device fails.

[0139] In some embodiments, before S640, the method may also include: using the network device identifier of a historical failure, the time point of the failure, and the historical failure causal topology diagram as a training set to train the second intelligent model, and when the training stop condition is met, a trained second intelligent model is obtained.

[0140] In some embodiments, the training stop condition may include the number of training times reaching a preset number of times and the loss function value reaching a preset threshold.

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

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

[0143] The fault location method provided by the embodiments of the present disclosure obtains network parameters of network devices in a preset area, compares the network parameters with dynamic thresholds, determines that a fault exists in the preset area, inputs the network parameters into a first intelligent model, determines multiple network devices with faults, and locates the target network device that causes the fault based on the multiple network devices with faults and a preset fault cause-effect topology diagram, thereby achieving rapid location of the faulty network device and improving the efficiency of locating the faulty network device.

[0144] Figure 7 A schematic diagram of a flow chart of another fault location method in an embodiment of the present disclosure is shown.

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

[0146] S710, obtaining network parameters of network devices in a preset area;

[0147] S720, comparing the network parameter with the dynamic threshold, and determining that a fault exists in the preset area;

[0148] S730, inputting the network parameters into the first intelligent model to determine a plurality of faulty network devices;

[0149] S740, input the identifiers of multiple faulty network devices and the fault cause-effect topology diagram into a third intelligent model to obtain a model output result, where the model output result indicates the target network device that caused the fault.

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

[0151] In some embodiments, the present disclosure can also perform trend prediction based on network parameters, such as the possible change trend of the jamming problem in the next 24 hours, and an overview of the health status of the devices within the affected range. The interface also supports continuous dialogue, automatically records historical questions, facilitates correlation analysis across devices and multiple inquiries, and helps operation and maintenance personnel efficiently locate complex faults from multiple angles and perform preventive processing.

[0152] Based on the same inventive concept, the present disclosure also provides a fault location device in the following embodiments. Since the principle of solving the problem in the device embodiment is similar to that in the above method embodiment, the implementation of the device embodiment can refer to the implementation of the above method embodiment, and the repeated parts will not be repeated.

[0153] The fault locating device provided by the embodiments of the present disclosure obtains network parameters of network devices in a preset area, compares the network parameters with dynamic thresholds, determines that a fault exists in the preset area, inputs the network parameters into a first intelligent model, determines multiple network devices with faults, and locates the target network device that causes the fault based on the multiple network devices with faults and a preset fault cause-effect topology diagram, thereby achieving rapid location of the faulty network device and improving the efficiency of locating the faulty network device.

[0154] Figure 8 A structural diagram of a fault location device in an embodiment of the present disclosure is shown.

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

[0156] An acquisition module, used to acquire network parameters of network devices in a preset area;

[0157] A first determination module 810 is used to compare the network parameters with the dynamic threshold value to determine whether a fault exists in a preset area;

[0158] A second determination module 820 is used to input the network parameters into the first intelligent model to determine a plurality of faulty network devices;

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

[0160] The fault locating device provided by the embodiments of the present disclosure obtains network parameters of network devices in a preset area, compares the network parameters with dynamic thresholds, determines that a fault exists in the preset area, inputs the network parameters into a first intelligent model, determines multiple network devices with faults, and locates the target network device that causes the fault based on the multiple network devices with faults and a preset fault cause-effect topology diagram, thereby achieving rapid location of the faulty network device and improving the efficiency of locating the faulty network device.

[0161] In one embodiment of the present disclosure, the device further includes:

[0162] A standardization module, used for completing the standardization processing of the network parameters based on the minimum value of the historical network parameters, the maximum value of the historical network parameters and the network parameters;

[0163] The first determination module includes:

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

[0165] In one embodiment of the present disclosure, the device further includes:

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

[0167] In one embodiment of the present disclosure, the device further includes:

[0168] A training module, used to train the first intelligent model based on historical network parameters and historical fault judgment results as a training set, and obtain a trained first intelligent model when a training stop condition is met;

[0169] The second determination module includes:

[0170] The determination unit is used to input the network parameters into the trained first intelligent model to determine multiple faulty network devices.

[0171] In one embodiment of the present disclosure, the device 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 the present disclosure, the device further includes:

[0174] The input module is used to input the network device identifier of the historical failure and the time point of the failure into the second preset model to obtain a failure cause and effect topology diagram, which is used to indicate the probability of failure of other network devices when any network device fails.

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

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

[0177] The fault locating device provided by the embodiments of the present disclosure obtains network parameters of network devices in a preset area, compares the network parameters with dynamic thresholds, determines that a fault exists in the preset area, inputs the network parameters into a first intelligent model, determines multiple network devices with faults, and locates the target network device that causes the fault based on the multiple network devices with faults and a preset fault cause-effect topology diagram, thereby achieving rapid location of the faulty network device and improving the efficiency of locating the faulty network device.

[0178] The fault location device provided in the embodiment of the present disclosure can be used to execute the fault location methods provided in the above-mentioned method embodiments. The implementation principles and technical effects thereof are similar and will not be described in detail here for the sake of brevity.

[0179] Those skilled in the art will appreciate that various aspects of the present disclosure may be implemented as systems, methods or program products. Therefore, various aspects of the present disclosure may be specifically implemented in the following forms, namely: complete hardware implementation, complete software implementation (including firmware, microcode, etc.), or a combination of hardware and software, which may be collectively referred to herein as "circuits", "modules" or "systems".

[0180] Refer to the following Fig. 9 The electronic device 900 according to this embodiment of the present disclosure is described. Fig. 9 The electronic device 900 shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0181] like Fig. 9 As shown, the electronic device 900 is in the form of a general 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 codes, which can be executed by the processing unit 910, so that the processing unit 910 performs the steps of various exemplary embodiments of the present disclosure described in the above “Exemplary Method” section of this specification. For example, the processing unit 910 can perform the following steps of the above method embodiment:

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

[0184] Compare network parameters with dynamic thresholds to determine if a fault exists in a preset area;

[0185] Inputting network parameters into a first intelligent model to determine a plurality of faulty network devices;

[0186] Based on multiple faulty network devices and a preset fault cause-and-effect topology diagram, the target network device that caused the fault is located.

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

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

[0189] Bus 930 may represent one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0190] The electronic device 900 may also communicate with one or more external devices 940 (e.g., keyboards, pointing devices, Bluetooth devices, etc.), may also communicate with one or more devices that enable a user to interact with the electronic device 900, and / or communicate with any device that enables the electronic device 900 to communicate with one or more other computing devices (e.g., routers, modems, etc.). Such communication may be performed via an input / output (I / O) interface 950. Furthermore, the electronic device 900 may also communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) via a network adapter 960. As shown, the network adapter 960 communicates with other modules of the electronic device 900 via a bus 930. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the 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, etc.

[0191] Through the description of the above implementation, it is easy for those skilled in the art to understand that the example implementation described here can be implemented by software, or by software combined with necessary hardware. Therefore, the technical solution according to the implementation of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which can be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the implementation of the present disclosure.

[0192] In an exemplary embodiment of the present 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 above method of the present disclosure is stored thereon. In some possible implementations, various aspects of the present disclosure may also be implemented in the form of a program product, which includes a program code, and when the program product is run on a terminal device, the program code is used to enable the terminal device to execute the steps according to various exemplary implementations of the present disclosure described in the above “Exemplary Method” section of this specification.

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

[0194] In the present disclosure, a computer readable storage medium may include a task signal propagated in baseband or as part of a carrier wave, wherein a readable program code is carried. Such propagated task signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A readable signal medium may also be any readable medium other than a readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

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

[0196] In a specific implementation, the program code for performing the operations of the present disclosure may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., using an Internet service provider to connect through the Internet).

[0197] It should be noted that, although several modules or units of the device for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiments of the present disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. On the contrary, the features and functions of one module or unit described above can be further divided into multiple modules or units to be embodied.

[0198] In addition, although the steps of the method in the present disclosure are described in a specific order in the drawings, this does not require or imply that the steps must be performed in this specific order, or that all the steps shown must be performed to achieve the desired results. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step, and / or one step may be decomposed into multiple steps, etc.

[0199] Through the description of the above implementation modes, it is easy for those skilled in the art to understand that the example implementation modes described here can be implemented by software or by combining software with necessary hardware. Therefore, the technical solution according to the implementation mode of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which can be a personal computer, a server, a mobile terminal, or a network device, etc.) to execute the method according to the implementation mode of the present disclosure.

[0200] Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the specification and practicing the invention disclosed herein. The present disclosure is intended to cover any variations, uses or adaptations of the present disclosure, which follow the general principles of the present disclosure and include common knowledge or customary techniques in the art that are not disclosed in the present disclosure. The description and examples are intended to be exemplary only, and the true scope and spirit of the present disclosure are indicated by the appended claims.

Claims

1. A fault location method, characterized in that: include: Obtain network parameters of network devices in a preset area; Comparing the network parameter with a dynamic threshold to determine whether a fault exists in the preset area; Inputting the network parameters into a first intelligent model to determine a plurality of faulty network devices; Based on the multiple faulty network devices and a preset fault cause-effect topology diagram, the target network device causing the fault is located.

2. The method according to claim 1, characterized in that The method further comprises: Complete the standardization processing of the network parameters based on the minimum value of the historical network parameters, the maximum value of the historical network parameters and the network parameters; The comparing the network parameter with the dynamic threshold to determine whether a fault exists in the preset area includes: The normalized network parameters are compared with the dynamic threshold to determine whether a fault exists in the preset area.

3. The method according to claim 1, characterized in that The method further comprises: The dynamic threshold is determined based on the mean value of the network parameter, the standard deviation of the network parameter, and a sensitivity adjustment coefficient.

4. The method according to claim 1, characterized in that: The method further comprises: The first intelligent model is trained based on historical network parameters and historical fault judgment results as a training set, and a trained first intelligent model is obtained when a training stop condition is met; The step of inputting the network parameters into a first intelligent model to determine a plurality of faulty network devices includes: The network parameters are input into the trained first intelligent model to determine a plurality of faulty network devices.

5. The method according to claim 1, characterized in that The method further comprises: In response to user input, the area where the fault occurs and the target network device are visualized.

6. The method according to claim 1, characterized in that The method further comprises: The network device identifiers of historical failures and the time points of the failures are input into a second preset model to obtain the failure causal topology diagram, which is used to indicate the probability of failure of other network devices when any one network device fails.

7. The method according to claim 1, characterized in that The method of locating the target network device causing the fault based on the plurality of faulty network devices and a preset fault cause-effect topology graph includes: The identifiers of multiple faulty network devices and the fault cause-effect topology diagram are input into a third intelligent model to obtain a model output result, wherein the model output result indicates a target network device that causes the fault.

8. A fault location device, characterized in that: include: An acquisition module, used to acquire network parameters of network devices in a preset area; A first determination module, configured to compare the network parameter with a dynamic threshold value to determine whether a fault exists in the preset area; A second determination module is used to input the network parameters into the first intelligent model to determine a plurality of faulty network devices; The positioning module is used to locate the target network device that causes the fault based on the multiple faulty network devices and a preset fault cause-effect topology diagram.

9. An electronic device, characterized in that: include: processor; as well as A memory, configured to store executable instructions of the processor; The processor is configured to execute the fault location method according to any one of claims 1 to 7 by executing the executable instructions.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the fault location method according to any one of claims 1 to 7 is implemented.

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