Derivative alarm judgment method and device applied to alarm knowledge graph

Abstract

The present application provides a derivative alarm judgment method and device applied to an alarm knowledge graph, the method comprising: obtaining a target network element, if the target network element currently has no fault alarm, then according to the number of sub-network elements currently having fault alarms in each level below the level where the target network element is located, which is obtained in advance, and the level coefficients of each level below the level where the target network element is located, the sum of the influences of the sub-network elements currently having fault alarms on the target network element in all levels below the level where the target network element is located is calculated, and the probability of the target network element having a derivative alarm is judged. The method of the present application obtains the sum of the influences of the sub-network elements currently having fault alarms on the target network element by calculating, and then judges the probability of the target network element having a derivative alarm according to the sum of the influences, without the need for manual participation, thereby improving the efficiency and accuracy of derivative alarm judgment, and thus achieving timely and accurate fault prevention.

Description

Technical Field

[0001] The present application relates to the field of communication technology, and in particular to a derivative alarm judgment method and device applied to an alarm knowledge graph. Background Art

[0002] With the development of network communication technology, the amount of data generated by the network is becoming increasingly huge. Therefore, network maintenance is required. The key to network maintenance is to monitor and collect fault alarm information issued by each network element during network operation, and query the fault alarm information of the target network element and related work orders through the pre-established alarm knowledge graph to troubleshoot. In addition, in order to reduce business losses, in addition to troubleshooting as soon as possible when a fault alarm occurs, fault prevention is also required, that is, derivative alarm judgment is required.

[0003] At present, the common method for judging derivative alarms is to query whether there are any sub-network elements with fault alarms at the lower level of the target network element, count the number of sub-network elements with fault alarms, and then manually judge the probability of the target network element failing based on experience, that is, the probability of the target network element generating derivative alarms.

[0004] However, the above-mentioned derivative alarm judgment method based on manual experience is inefficient and the judgment is not accurate enough, so that timely and accurate fault prevention cannot be achieved. Summary of the invention

[0005] The present application provides a derivative alarm judgment method and device applied to an alarm knowledge graph to achieve timely and accurate fault prevention.

[0006] In a first aspect, the present application provides a derivative alarm judgment method applied to an alarm knowledge graph, comprising:

[0007] Obtain a target network element from a pre-established alarm knowledge graph, and determine whether a fault alarm currently occurs in the target network element;

[0008] If the target network element currently has no fault alarm, then the probability of the target network element generating a derivative alarm is judged according to the sum of the influences of the sub-network elements currently generating fault alarms on the target network element in all levels below the level where the target network element is located; wherein the sum of the influences is calculated based on the pre-acquired number of sub-network elements currently generating fault alarms in each level below the level where the target network element is located, and the pre-calibrated level coefficients of each level below the level where the target network element is located; wherein the pre-calibrated level coefficients of each level below the level where the target network element is located represent the influence of the sub-network elements generating fault alarms on the target network element in each level below the level where the target network element is located.

[0009] Further, in the above method, the determining whether a fault alarm currently occurs in the target network element includes:

[0010] Acquire fault alarm information, where the fault alarm information includes the ID of the network element where the fault alarm currently occurs;

[0011] Determine whether the ID of the target network element is consistent with the ID of the network element in which the fault alarm currently occurs in the fault alarm information;

[0012] If they are consistent, it is determined that a fault alarm currently occurs on the target network element;

[0013] If they are inconsistent, it is determined that no fault alarm occurs on the target network element currently.

[0014] Further, the method as described above, before judging the probability of the target network element generating a derivative alarm according to the sum of the influences of the sub-network elements currently generating fault alarms on the target network element in all levels below the level where the target network element is located, further comprises:

[0015] According to the pre-acquired number of sub-network elements with fault alarms currently occurring in each layer below the layer where the target network element is located, and the pre-calibrated layer coefficients of each layer below the layer where the target network element is located, the sum of the influences of the sub-network elements with fault alarms currently occurring in all layers below the layer where the target network element is located on the target network element is calculated, and the calculation formula is:

[0016]

[0017] Wherein, M is the sum of the influences, z is the level number of the sub-network element farthest from the target network element below the target network element level, m is the level number of each sub-network element below the target network element level, where m=1 is the first level below the target network element level, d m is the number of sub-network elements with fault alarms currently occurring in the mth layer below the layer where the target network element is located, λ m is the level coefficient of the mth level below the level where the target network element is located in advance.

[0018] Further, in the above method, judging the probability of the target network element generating a derivative alarm according to the sum of the influences of the sub-network elements currently generating fault alarms on the target network element in all levels below the level where the target network element is located, includes:

[0019] If the sum of the influence degrees is greater than zero and less than or equal to a preset first threshold, it is determined that the probability of the target network element generating a derivative alarm is zero;

[0020] If the sum of the influence degrees is greater than the preset first threshold and less than or equal to the preset second threshold, then the probability of the target network element generating a derived alarm is determined to be a first probability;

[0021] If the sum of the influence degrees is greater than the preset second threshold and less than or equal to the preset third threshold, the probability of the target network element generating a derived alarm is determined to be a second probability; wherein the second probability is greater than the first probability.

[0022] Furthermore, the method as described above, after determining the probability of the target network element generating a derived alarm, further includes:

[0023] If it is determined that the probability of the target network element generating a derived alarm is the second probability, a derived alarm reminder message is sent to the user; wherein the derived alarm reminder message includes the ID of the target network element.

[0024] Furthermore, the method as described above, after determining whether a fault alarm currently occurs in the target network element, further includes:

[0025] If a fault alarm currently occurs in the target network element, then the probability of determining that a derivative alarm occurs in the target network element is one.

[0026] In a second aspect, the present application provides a derivative alarm judgment device applied to an alarm knowledge graph, comprising:

[0027] A judgment module is used to obtain a target network element from a pre-established alarm knowledge graph and judge whether a fault alarm currently occurs in the target network element;

[0028] The judgment module is also used to judge the probability of the target network element generating a derivative alarm if the target network element currently has no fault alarm, based on the sum of the influences of the sub-network elements currently generating a fault alarm on the target network element in all levels below the level where the target network element is located; wherein the sum of the influences is calculated based on the pre-acquired number of sub-network elements currently generating fault alarms in each level below the level where the target network element is located, and the pre-calibrated level coefficients of each level below the level where the target network element is located; wherein the pre-calibrated level coefficients of each level below the level where the target network element is located represent the influence of the sub-network elements generating fault alarms in each level below the level where the target network element is located on the target network element.

[0029] Furthermore, the device as described above further comprises:

[0030] The sending module is used to send derived alarm reminder information to the user if it is determined that the probability of the derived alarm occurring in the target network element is the second probability; wherein the derived alarm reminder information includes the ID of the target network element.

[0031] In a third aspect, the present application provides a processor, and a memory communicatively connected to the processor;

[0032] The memory stores computer-executable instructions;

[0033] The processor executes the computer-executable instructions stored in the memory to implement the method according to the first aspect.

[0034] In a fourth aspect, the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are executed by a processor, they are used to implement the method described in the first aspect.

[0035] The present application provides a derivative alarm judgment method and device applied to an alarm knowledge graph, which obtains a target network element from a pre-established alarm knowledge graph, and judges whether a fault alarm currently occurs in the target network element. If a fault alarm does not currently occur in the target network element, the sum of the influences of the sub-network elements that currently have fault alarms on the target network element in all levels below the level where the target network element is located is calculated based on the number of sub-network elements that currently have fault alarms in each level below the level where the target network element is located, and the level coefficients of each level below the level where the target network element is located that are pre-calibrated, and the probability of the target network element having a derivative alarm is judged. In other words, the present application obtains the sum of the influences of the sub-network elements that currently have fault alarms on the target network element by calculating, and then judges the probability of the target network element having a derivative alarm based on the sum of the influences, without the need for manual intervention, thereby improving the efficiency and accuracy of derivative alarm judgment, and thus achieving timely and accurate fault prevention. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0037] Figure 1 A schematic diagram of a system architecture provided for an embodiment of the present application;

[0038] Figure 2 A flowchart of a derived alarm judgment method applied to an alarm knowledge graph provided in an embodiment of the present application;

[0039] Figure 3 A flowchart of a derived alarm judgment method applied to an alarm knowledge graph provided in an embodiment of the present application;

[0040] Figure 4 A schematic diagram of the structure of a derived alarm judgment device applied to an alarm knowledge graph provided in an embodiment of the present application;

[0041] Figure 5 This is a schematic diagram of the structure of the electronic device of the present application.

[0042] The above drawings have shown clear embodiments of the present application, which will be described in more detail later. These drawings and text descriptions are not intended to limit the scope of the present application in any way, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0043] Here, exemplary embodiments will be described in detail, and examples thereof are shown in the accompanying drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. Instead, they are only examples of devices and methods consistent with some aspects of the present application.

[0044] During network operation, operational failures occur from time to time. In order to ensure the safe operation of the network, network maintenance is required. The fault alarm information of the target network element and related work orders and other information are queried through the pre-established alarm knowledge graph to troubleshoot the fault. In addition, in order to reduce business losses, in addition to troubleshooting as soon as possible when a fault alarm occurs, fault prevention is also required, that is, derivative alarm judgment is required.

[0045] At present, the common derivative alarm judgment method is to query whether there is a sub-network element with a fault alarm at the lower level of the target network element, count the number of sub-network elements with fault alarms, and then manually judge the probability of the target network element failing based on experience, that is, the probability of the target network element generating a derivative alarm. However, the derivative alarm judgment method based on manual experience is not accurate enough, and thus cannot achieve accurate fault prevention.

[0046] The derived alarm judgment method and device applied to the alarm knowledge graph provided in this application are intended to solve the above technical problems of the prior art.

[0047] The technical solution of the present application and how the technical solution of the present application solves the above-mentioned technical problems are described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described below in conjunction with the accompanying drawings.

[0048] Figure 1 A schematic diagram of a system architecture provided in an embodiment of the present application is shown as follows: Figure 1 As shown, in Figure 1The system architecture shown may specifically include a pre-established alarm knowledge graph 1 and a server 2, wherein the server 2 is provided with a derived alarm judgment device applied to the alarm knowledge graph.

[0049] Among them, the derivative alarm judgment device applied to the alarm knowledge graph can be installed on the server 2, and is used to obtain the target network element from the pre-established alarm knowledge graph, and judge whether the target network element currently has a fault alarm. If the target network element currently has no fault alarm, then the probability of a derivative alarm occurring in the target network element is judged based on the calculated sum of the influence of the sub-network elements that currently have fault alarms on the target network element in all levels below the level where the target network element is located.

[0050] Embodiment 1

[0051] Figure 2 A flowchart of a derived alarm judgment method applied to an alarm knowledge graph provided in an embodiment of the present application, such as Figure 2 As shown, the derived alarm judgment method applied to the alarm knowledge graph provided in this embodiment includes the following steps:

[0052] Step 101: Obtain a target network element from a pre-established alarm knowledge graph, and determine whether a fault alarm currently occurs in the target network element.

[0053] Step 102: If the target network element currently has no fault alarm, determine the probability of a derivative alarm occurring in the target network element based on the sum of the influences of the sub-network elements currently having fault alarms on the target network element in all levels below the level where the target network element is located; wherein the sum of the influences is calculated based on the pre-acquired number of sub-network elements currently having fault alarms in each level below the level where the target network element is located, and the pre-calibrated level coefficients of each level below the level where the target network element is located; wherein the pre-calibrated level coefficients of each level below the level where the target network element is located represent the influence of the sub-network elements having fault alarms on the target network element in each level below the level where the target network element is located.

[0054] It should be noted that the execution subject of the derived alarm judgment method applied to the alarm knowledge graph provided in this embodiment can be a derived alarm judgment device applied to the alarm knowledge graph. In practical applications, the derived alarm judgment device applied to the alarm knowledge graph can be implemented by a computer program, such as application software, computer program, etc., or by a medium storing relevant computer programs, such as a USB flash drive, a CD, etc.; or, it can also be implemented by a physical device integrated or installed with relevant computer programs, such as a chip, etc.

[0055] In this embodiment, in order to prevent faults, it is necessary to judge the probability of each network element failing, that is, to make a derivative alarm judgment. Specifically, the derivative alarm judgment device can first obtain the target network element from the pre-established alarm knowledge graph, and judge whether the target network element currently has a fault alarm.

[0056] In actual applications, in one example, the derived alarm judgment device can obtain each network element stored in a pre-established alarm knowledge graph in sequence at preset time intervals, and use the currently obtained network element as the target network element to determine whether a fault alarm currently occurs in the target network element.

[0057] In another example, the derived alarm judgment device can receive a network element ID input by a user, and obtain a network element consistent with the network element ID input by the user as a target network element from a pre-established alarm knowledge graph, and judge whether a fault alarm currently occurs in the target network element.

[0058] Figure 3 A flowchart of a derived alarm judgment method applied to an alarm knowledge graph provided in an embodiment of the present application, such as Figure 3 As shown, based on the above embodiment 1, in step 101, the determination of whether a fault alarm currently occurs in the target network element specifically includes the following steps:

[0059] Step 201: Obtain fault alarm information, where the fault alarm information includes the ID of the network element where the fault alarm currently occurs.

[0060] Step 202: Determine whether the ID of the target network element is consistent with the ID of the network element in which the fault alarm currently occurs in the fault alarm information.

[0061] Step 203: If they are consistent, it is determined that a fault alarm currently occurs in the target network element.

[0062] Step 204: If they are inconsistent, it is determined that no fault alarm occurs in the target network element currently.

[0063] In this embodiment, in order to determine whether a fault alarm currently occurs in the target network element, the derived alarm determination device can obtain each fault alarm information currently monitored by the monitoring device, wherein each fault alarm information includes the ID of the network element where the fault alarm currently occurs.

[0064] Next, the derived alarm judgment device can judge whether the ID of the target network element is consistent with the ID of the network element where the fault alarm currently occurs in the fault alarm information. If they are consistent, the derived alarm judgment device can judge that a fault alarm currently occurs in the target network element. If they are inconsistent, the derived alarm judgment device can judge that no fault alarm currently occurs in the target network element.

[0065] Next, if the derivative alarm judgment device judges that the target network element currently has no fault alarm, it indicates that the target network element currently has no fault, and the probability of the target network element possibly failing can be further judged, that is, the probability of the derivative alarm occurring. Specifically, the derivative alarm judgment device can judge the probability of the target network element having a derivative alarm according to the sum of the influence of the sub-network elements that currently have fault alarms on the target network element in all levels below the level where the target network element is located. The specific judgment method will be described in detail in Example 3 of the present application, and will not be described in detail here.

[0066] Among them, the sum of the influence degrees is calculated based on the number of sub-network elements that currently have fault alarms in each layer below the layer where the target network element is located, which is obtained in advance, and the layer coefficients of each layer below the layer where the target network element is located, which are pre-calibrated. Among them, the pre-calibrated layer coefficients of each layer below the layer where the target network element is located can characterize the influence of the sub-network elements that have fault alarms in each layer below the layer where the target network element is located on the target network element. The specific calculation method will be described in detail in Example 2 of this application and will not be repeated here.

[0067] The derivative alarm judgment method applied to the alarm knowledge graph provided in this embodiment obtains the target network element from the pre-established alarm knowledge graph, and judges whether the target network element currently has a fault alarm. If the target network element currently has no fault alarm, then the sum of the influences of the sub-network elements currently having fault alarms on the target network element in all levels below the level where the target network element is located is calculated based on the number of sub-network elements currently having fault alarms in each level below the level where the target network element is located, and the level coefficients of each level below the level where the target network element is located, which are pre-calibrated, is used to judge the probability of the target network element having a derivative alarm. That is, in the embodiment of the present application, the sum of the influences of the sub-network elements currently having fault alarms on the target network element is obtained by calculating, and then judging the probability of the target network element having a derivative alarm based on the sum of the influences, without the need for manual intervention, thereby improving the efficiency and accuracy of derivative alarm judgment, and thus achieving timely and accurate fault prevention.

[0068] Embodiment 2

[0069] On the basis of the above-mentioned embodiment 1, the derivative alarm judgment method applied to the alarm knowledge graph provided in this embodiment, in step 102, according to the sum of the influences of the sub-network elements currently generating fault alarms on the target network element in all levels below the level where the target network element is located, before judging the probability of the derivative alarm of the target network element, it also includes:

[0070] According to the pre-acquired number of sub-network elements with fault alarms currently occurring in each layer below the layer where the target network element is located, and the pre-calibrated layer coefficients of each layer below the layer where the target network element is located, the sum of the influences of the sub-network elements with fault alarms currently occurring in all layers below the layer where the target network element is located on the target network element is calculated, and the calculation formula is:

[0071]

[0072] Wherein, M is the sum of the influences, z is the level number of the sub-network element farthest from the target network element below the target network element level, m is the level number of each sub-network element below the target network element level, where m=1 is the first level below the target network element level, d m is the number of sub-network elements with fault alarms currently occurring in the mth layer below the layer where the target network element is located, λ m is the level coefficient of the mth level below the level where the target network element is located in advance.

[0073] In this embodiment, in order to calculate the total influence of the sub-network elements that are currently generating fault alarms on the target network element in all levels below the level where the target network element is located, and then judge the probability of a derivative alarm occurring in the target network element, the derivative alarm judgment device can first obtain the number of sub-network elements that are currently generating fault alarms in each level below the level where the target network element is located, and the pre-calibrated level coefficients of each level below the level where the target network element is located.

[0074] In one example, the derived alarm judgment device can query the sub-network elements in each level below the level where the target network element is located through a pre-established alarm knowledge graph. The specific query method is not limited in this embodiment, and then judge whether each sub-network element in each level below the level where the target network element is located currently has a fault alarm. The specific judgment method can be the same as the method for judging whether a fault alarm currently occurs in the target network element in Example 1 of the present application, and will not be repeated here. After judging whether each sub-network element in each level below the level where the target network element is located currently has a fault alarm, the number of sub-network elements in each level below the level where the target network element is located that currently have a fault alarm can be obtained by counting.

[0075] Next, the derived alarm judgment device can use the following calculation formula to calculate the sum of the influence of the sub-network elements that currently have fault alarms on the target network element in all levels below the level where the target network element is located. The specific formula is:

[0076]

[0077] Where M is the total influence, z is the level number of the sub-network element farthest from the target network element below the target network element level, m is the level number of each sub-network element below the target network element level, where m=1 is the first level below the target network element level, d m is the number of sub-network elements with fault alarms currently occurring in the mth layer below the layer where the target network element is located, λ m is the level coefficient of the mth level below the level where the target network element is located in advance.

[0078] For example, if the number of levels below the level where the target network element is located and the sub-network element farthest from the level where the target network element is located is 5, that is, z=5, and in the first level below the level where the target network element is located, the number of sub-network elements that currently have fault alarms is 6, that is, d1=6, and the pre-calibrated level coefficient of the first level below the level where the target network element is located is 0.9, that is, λ1=0.8, that is, in the first level below the level where the target network element is located, the influence of the sub-network element that has a fault alarm on the target network element is 0.8. Similarly, d2=10, d3=3, d4=8, d5=0, λ2=0.6, λ3=0.4, λ4=0.2, λ5=0.1, then in all levels below the level where the target network element is located, the sum of the influences of the sub-network elements that currently have fault alarms on the target network element is

[0079] The derivative alarm judgment method applied to the alarm knowledge graph provided in this embodiment calculates the total influence of the sub-network elements that currently have fault alarms on the target network element in all levels below the level where the target network element is located by pre-acquired numbers of sub-network elements that currently have fault alarms in each level below the level where the target network element is located, and pre-calibrated level coefficients of each level below the level where the target network element is located, thereby laying a foundation for accurately judging the probability of a derivative alarm occurring in the target network element.

[0080] Embodiment 3

[0081] On the basis of the above-mentioned embodiment 1, the derivative alarm judgment method applied to the alarm knowledge graph provided in this embodiment, in step 102, the probability of the target network element generating a derivative alarm is judged according to the sum of the influences of the sub-network elements that currently generate fault alarms on the target network element in all levels below the level where the target network element is located, including: if the sum of the influences is greater than zero and less than or equal to a preset first threshold, then the probability of the target network element generating a derivative alarm is judged to be zero; if the sum of the influences is greater than the preset first threshold and less than or equal to a preset second threshold, then the probability of the target network element generating a derivative alarm is judged to be a first probability; if the sum of the influences is greater than the preset second threshold and less than or equal to a preset third threshold, then the probability of the target network element generating a derivative alarm is judged to be a second probability; wherein, the second probability is greater than the first probability.

[0082] In this embodiment, after the derivative alarm judgment device calculates the sum of the influences of the sub-network elements currently generating fault alarms on the target network element in all levels below the level where the target network element is located, the probability of a derivative alarm occurring in the target network element can be judged based on the sum of the influences.

[0083] Specifically, in one example, if the sum of the impact degrees is greater than zero and less than or equal to a preset first threshold, it indicates that among all levels below the level where the target network element is located, the sub-network element that currently has a fault alarm has a very small impact on the target network element, that is, the possibility of a failure in the target network element is very small, then the derivative alarm judgment device can determine that the probability of a derivative alarm occurring in the target network element is zero.

[0084] The preset first threshold may be 1, or other suitable thresholds, which is not limited in this embodiment.

[0085] In another example, if the sum of the impact degrees is greater than a preset first threshold and less than or equal to a preset second threshold, it indicates that among all levels below the level where the target network element is located, the sub-network element that currently has a fault alarm has a greater impact on the target network element, that is, the target network element may have a fault. The derivative alarm judgment device can judge that the probability of a derivative alarm occurring in the target network element is the first probability.

[0086] The preset second threshold value may be 5, or other appropriate threshold values, which are not limited in this embodiment. The first probability may be 0.5, or other appropriate probability, which are not limited in this embodiment.

[0087] In another example, if the sum of the influence degrees is greater than a preset second threshold and less than or equal to a preset third threshold, it indicates that among all levels below the level where the target network element is located, the sub-network element that currently has a fault alarm has a great influence on the target network element, that is, the possibility of failure of the target network element is very high. Then, the derivative alarm judgment device can judge that the probability of a derivative alarm occurring in the target network element is the second probability, where the second probability is greater than the first probability.

[0088] The preset third threshold value may be 10, or other appropriate threshold values, which are not limited in this embodiment. The second probability may be 0.9, or other appropriate probability, which are not limited in this embodiment.

[0089] Based on the above-mentioned embodiment three, in an optional implementation manner, after determining the probability of the derivative alarm occurring in the target network element, it also includes: if it is determined that the probability of the derivative alarm occurring in the target network element is a second probability, a derivative alarm reminder message is sent to the user; wherein the derivative alarm reminder message includes the ID of the target network element.

[0090] In this embodiment, if the derivative alarm judgment device judges that the probability of the target network element generating a derivative alarm is the second probability, it indicates that the target network element is likely to fail. In order to prevent the failure as early as possible, the derivative alarm judgment device can send a derivative alarm reminder message to the user to remind the user to perform corresponding processing on the target network element, thereby preventing the failure from occurring. Specifically, the sent derivative alarm reminder message can include the ID of the target network element.

[0091] Based on any other embodiment, in an optional implementation, in step 101, after determining whether a fault alarm currently occurs in the target network element, it also includes: if a fault alarm currently occurs in the target network element, determining that the probability of a derivative alarm occurring in the target network element is one.

[0092] In this embodiment, if the derived alarm judgment device judges that the target network element currently has a fault alarm, it indicates that the target network element currently has a fault, so the derived alarm judgment device can judge that the probability of the target network element having a derived alarm is one.

[0093] The derivative alarm judgment method applied to the alarm knowledge graph provided in this embodiment judges the probability of a derivative alarm occurring in the target network element based on the total influence of the sub-network element that currently has a fault alarm on the target network element in all levels below the level where the target network element is located, and when it is judged that the probability of a derivative alarm occurring in the target network element is the second probability, a derivative alarm reminder message is sent to the user to remind the user to perform corresponding processing on the target network element, thereby preventing the occurrence of a fault.

[0094] Embodiment 4

[0095] Figure 4 A schematic diagram of the structure of a derived alarm judgment device applied to an alarm knowledge graph provided in an embodiment of the present application, such as Figure 4 As shown, the derived alarm judgment device applied to the alarm knowledge graph provided in this embodiment includes: a judgment module 41. The judgment module 41 is used to obtain the target network element from the pre-established alarm knowledge graph, and judge whether the target network element currently has a fault alarm. The judgment module 41 is also used to judge the probability of the target network element having a derived alarm according to the sum of the influence of the sub-network elements currently having a fault alarm on the target network element in all levels below the level where the target network element is located if the target network element currently has no fault alarm; wherein the sum of the influence is calculated based on the number of sub-network elements currently having a fault alarm in each level below the level where the target network element is located, and the level coefficients of each level below the level where the target network element is located, which are pre-calibrated; wherein the pre-calibrated level coefficients of each level below the level where the target network element is located represent the influence of the sub-network elements having a fault alarm in each level below the level where the target network element is located on the target network element.

[0096] The derivative alarm judgment device applied to the alarm knowledge graph provided in this embodiment obtains the target network element from the pre-established alarm knowledge graph, and judges whether the target network element currently has a fault alarm. If the target network element currently has no fault alarm, then the total influence of the sub-network elements currently having a fault alarm on the target network element in all levels below the level where the target network element is located is calculated based on the number of sub-network elements currently having a fault alarm in each level below the level where the target network element is located, and the level coefficients of each level below the level where the target network element is located, which are pre-calibrated, is used to judge the probability of the target network element having a derivative alarm. That is to say, in the embodiment of the present application, the total influence of the sub-network elements currently having a fault alarm on the target network element is obtained by calculating, and then judging the probability of the target network element having a derivative alarm based on the total influence, without the need for manual participation, thereby improving the efficiency and accuracy of the derivative alarm judgment, and thus achieving timely and accurate fault prevention.

[0097] In an optional implementation manner, the judgment module 41 is also used to obtain fault alarm information, and the fault alarm information includes the ID of the network element where the fault alarm currently occurs; judge whether the ID of the target network element is consistent with the ID of the network element where the fault alarm currently occurs in the fault alarm information; if they are consistent, judge that the target network element currently has a fault alarm; if they are inconsistent, judge that the target network element currently has no fault alarm.

[0098] In an optional implementation manner, the derived alarm judgment device applied to the alarm knowledge graph further includes: a calculation module 42, which is used to calculate the sum of the influence of the sub-network elements that currently have fault alarms on the target network element in all levels below the level where the target network element is located, according to the number of sub-network elements that currently have fault alarms in each level below the level where the target network element is located, which is obtained in advance, and the level coefficients of each level below the level where the target network element is located, which are pre-calibrated, The calculation formula is:

[0099]

[0100] Wherein, M is the sum of the influences, z is the level number of the sub-network element farthest from the target network element below the target network element level, m is the level number of each sub-network element below the target network element level, where m=1 is the first level below the target network element level, d m is the number of sub-network elements with fault alarms currently occurring in the mth layer below the layer where the target network element is located, λ m is the level coefficient of the mth level below the level where the target network element is located in advance.

[0101] In an optional implementation manner, the judgment module 41 is also used to judge that the probability of the target network element generating a derived alarm is zero if the total impact is greater than zero and less than or equal to a preset first threshold; if the total impact is greater than the preset first threshold and less than or equal to a preset second threshold, then the probability of the target network element generating a derived alarm is judged to be a first probability; if the total impact is greater than the preset second threshold and less than or equal to a preset third threshold, then the probability of the target network element generating a derived alarm is judged to be a second probability; wherein the second probability is greater than the first probability.

[0102] In an optional implementation, the derivative alarm judgment device applied to the alarm knowledge graph further includes: a sending module 43, which is used to send a derivative alarm reminder information to the user if it is judged that the probability of a derivative alarm occurring in the target network element is a second probability; wherein the derivative alarm reminder information includes the ID of the target network element.

[0103] In an optional implementation manner, the judgment module 41 is further configured to judge that the probability of the target network element generating a derivative alarm is one if the target network element currently generates a fault alarm.

[0104] It should be noted that the technical solution and effects of the derived alarm judgment device applied to the alarm knowledge graph provided in this embodiment can be found in the relevant content of the aforementioned method embodiment, and will not be repeated here.

[0105] Embodiment 5

[0106] Figure 5 A schematic diagram of the structure of the electronic device of this application, such as Figure 5 As shown, the present application also provides an electronic device 500 , including: a memory 501 and a processor 502 .

[0107] The memory 501 is used to store programs. Specifically, the program may include program codes, and the program codes include computer-executable instructions. The memory 501 may include a high-speed RAM memory, and may also include a non-volatile memory, such as at least one disk memory.

[0108] The processor 502 is used to execute the program stored in the memory 501 .

[0109] The computer program is stored in the memory 501 and is configured to be executed by the processor 502 to implement the derived alarm judgment method applied to the alarm knowledge graph provided in any embodiment of the present application. The relevant description can be understood by referring to the relevant descriptions and effects corresponding to the steps in the accompanying drawings, and no further details will be given here.

[0110] In this embodiment, the memory 501 and the processor 502 are connected via a bus. The bus may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 5 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0111] Embodiment 6

[0112] An embodiment of the present application also provides a computer-readable storage medium on which a computer program is stored. The computer program is executed by a processor to implement a derived alarm judgment method applied to an alarm knowledge graph provided in any embodiment of the present application.

[0113] Embodiment 7

[0114] An embodiment of the present application also provides a computer program product, including a computer program, which, when executed by a processor, implements a derived alarm judgment method applied to an alarm knowledge graph provided by any embodiment of the present application.

[0115] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of modules is only a logical function division. There may be other division methods in actual implementation, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or modules, which can be electrical, mechanical or other forms.

[0116] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, that is, they may be located in one place or distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0117] In addition, each functional module in each embodiment of the present application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module. The above integrated modules can be implemented in the form of hardware or in the form of hardware plus software functional modules.

[0118] The program code for implementing the method of the present application can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable alarm judgment device applied to the alarm knowledge graph, so that when the program code is executed by the processor or controller, the functions / operations specified in the flow chart and / or block diagram are implemented. The program code can be executed entirely on the machine, partially on the machine, partially on the machine as a stand-alone software package and partially on a remote machine, or entirely on a remote machine or server.

[0119] In the context of the present application, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, 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), an 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.

[0120] In addition, although each operation is described in a specific order, this should be understood as requiring such operation to be performed in the specific order shown or in a sequential order, or requiring that all illustrated operations should be performed to obtain desired results. Under certain circumstances, multitasking and parallel processing may be advantageous. Similarly, although some specific implementation details are included in the above discussion, these should not be interpreted as limiting the scope of the application. Some features described in the context of a separate embodiment can also be implemented in a single implementation in combination. On the contrary, the various features described in the context of a single implementation can also be implemented in multiple implementations individually or in any suitable sub-combination mode.

[0121] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the invention disclosed herein. The present application is intended to cover any modification, use or adaptation of the present application, which follows the general principles of the present application and includes common knowledge or customary techniques in the art that are not disclosed in the present application. The specification and examples are intended to be exemplary only, and the true scope and spirit of the present application are indicated by the following claims.

[0122] It should be understood that the present application is not limited to the precise structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present application is limited only by the appended claims.

Claims

1. A derivative alarm judgment method applied to an alarm knowledge graph, characterized in that: include: Obtain a target network element from a pre-established alarm knowledge graph, and determine whether a fault alarm currently occurs in the target network element; If the target network element currently has no fault alarm, then according to the pre-acquired number of sub-network elements currently having fault alarms in each layer below the layer where the target network element is located, and the pre-calibrated layer coefficients of each layer below the layer where the target network element is located, the sum of the influences of the sub-network elements currently having fault alarms on the target network element in all layers below the layer where the target network element is located is calculated, and the calculation formula is: Wherein, M is the sum of the influences, z is the level number of the sub-network element farthest from the level where the target network element is located below the level where the target network element is located, m is the level number of each sub-network element below the level where the target network element is located, wherein m=1 is the first level below the level where the target network element is located, is the number of sub-network elements with fault alarms currently occurring in the mth layer below the layer where the target network element is located. is the level coefficient of the mth level below the level where the target network element is located; The probability of the target network element generating a derivative alarm is judged according to the sum of the influences of the sub-network elements currently generating fault alarms on the target network element in all the layers below the layer where the target network element is located; wherein the sum of the influences is calculated based on the pre-acquired number of sub-network elements currently generating fault alarms in each layer below the layer where the target network element is located, and the pre-calibrated layer coefficients of each layer below the layer where the target network element is located; wherein the pre-calibrated layer coefficients of each layer below the layer where the target network element is located represent the influences of the sub-network elements generating fault alarms on the target network element in each layer below the layer where the target network element is located.

2. The method according to claim 1, characterized in that The determining whether a fault alarm currently occurs in the target network element includes: Acquire fault alarm information, where the fault alarm information includes the ID of the network element where the fault alarm currently occurs; Determine whether the ID of the target network element is consistent with the ID of the network element in which the fault alarm currently occurs in the fault alarm information; If they are consistent, it is determined that a fault alarm currently occurs on the target network element; If they are inconsistent, it is determined that no fault alarm occurs on the target network element currently.

3. The method according to claim 1, characterized in that The determining the probability of the target network element generating a derivative alarm according to the sum of the influences of the sub-network elements currently generating fault alarms on the target network element in all the layers below the layer where the target network element is located, comprises: If the sum of the influence degrees is greater than zero and less than or equal to a preset first threshold, it is determined that the probability of the target network element generating a derivative alarm is zero; If the sum of the influence degrees is greater than the preset first threshold and less than or equal to the preset second threshold, then the probability of the target network element generating a derived alarm is determined to be a first probability; If the sum of the influence degrees is greater than the preset second threshold and less than or equal to the preset third threshold, the probability of the target network element generating a derived alarm is determined to be a second probability; wherein the second probability is greater than the first probability.

4. The method according to claim 3, characterized in that After determining the probability of the target network element generating a derivative alarm, the method further includes: If it is determined that the probability of the target network element generating a derived alarm is the second probability, a derived alarm reminder message is sent to the user; wherein the derived alarm reminder message includes the ID of the target network element.

5. The method according to any one of claims 1 to 4, characterized in that: After determining whether a fault alarm currently occurs in the target network element, the method further includes: If a fault alarm currently occurs in the target network element, then the probability of determining that a derivative alarm occurs in the target network element is one.

6. A derivative alarm judgment device applied to an alarm knowledge graph, characterized in that: include: A judgment module is used to obtain a target network element from a pre-established alarm knowledge graph and judge whether a fault alarm currently occurs in the target network element; The judgment module is further configured to calculate, if no fault alarm currently occurs in the target network element, the sum of the influences of the sub-network elements currently having fault alarms on the target network element in all levels below the level where the target network element is located, according to the pre-acquired number of sub-network elements currently having fault alarms in each level below the level where the target network element is located, and the pre-calibrated level coefficients of each level below the level where the target network element is located, and the calculation formula is: Wherein, M is the sum of the influences, z is the level number of the sub-network element farthest from the level where the target network element is located below the level where the target network element is located, m is the level number of each sub-network element below the level where the target network element is located, wherein m=1 is the first level below the level where the target network element is located, is the number of sub-network elements with fault alarms currently occurring in the mth layer below the layer where the target network element is located. is the level coefficient of the mth level below the level where the target network element is located; The judgment module is also used to judge the probability of the target network element generating a derivative alarm based on the sum of the influences of the sub-network elements currently generating fault alarms on the target network element in all levels below the level where the target network element is located; wherein the sum of the influences is calculated based on the pre-acquired number of sub-network elements currently generating fault alarms in each level below the level where the target network element is located, and the pre-calibrated level coefficients of each level below the level where the target network element is located; wherein the pre-calibrated level coefficients of each level below the level where the target network element is located represent the influence of the sub-network elements generating fault alarms on the target network element in each level below the level where the target network element is located.

7. The device according to claim 6, characterized in that The device also includes: The sending module is used to send derived alarm reminder information to the user if it is determined that the probability of the derived alarm occurring in the target network element is the second probability; wherein the derived alarm reminder information includes the ID of the target network element.

8. An electronic device comprising: A processor, and a memory communicatively connected to the processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory to implement the method according to any one of claims 1 to 5.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to any one of claims 1 to 5 when executed by a processor.

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