CN109995561B

CN109995561B - Method, device, equipment and medium for positioning communication network fault

Info

Publication number: CN109995561B
Application number: CN201711492276.6A
Authority: CN
Inventors: 方博文; 罗卫鸿
Original assignee: China Mobile Communications Group Co Ltd; China Mobile Group Fujian Co Ltd
Current assignee: China Mobile Communications Group Co Ltd; China Mobile Group Fujian Co Ltd
Priority date: 2017-12-30
Filing date: 2017-12-30
Publication date: 2022-03-29
Anticipated expiration: 2037-12-30
Also published as: CN109995561A

Abstract

The invention provides a communication network fault positioning method, a device, equipment and a medium. The method comprises the following steps: acquiring at least two alarm data, wherein the two alarm data comprise time, network elements and titles; based on the two alarm data, obtaining the time distance, the network element distance and the header distance of the two alarm data through a time distance function, a network element distance function and a header distance function; obtaining the alarm distance of the two alarm data through an alarm distance function based on the time distance, the network element distance and the header distance; and analyzing the fault reason based on the alarm distance of the two alarm data. Based on the technical scheme of the invention, the positioning accuracy and the positioning cost of the communication network fault can be improved.

Description

Method, device, equipment and medium for positioning communication network fault

Technical Field

The present invention relates to the field of communications technologies, and in particular, to a method, an apparatus, a device, and a medium for locating a communication network fault.

Background

With the development of communication technology (especially the popularization of 4G services), operator networks are larger and larger, and network scenes are more and more complex. The range of specialties covered in daily maintenance of communication networks is gradually widened, for example, five major specialties including wireless, transmission, exchange, data, internet and the like are involved, and meanwhile, more secondary sub-specialties are further subdivided by the five major specialties. Each specialty corresponds to different equipment, and when a fault occurs, alarms associated with multiple specialties are often generated.

The popularization of alarm standardization enables monitoring alarms generated by the devices to be centralized to a unified alarm platform, and the full-scale alarm management is realized. However, it is not easy to precisely manage these vast alarms, and the complex scenario and the ever-emerging alarms make it impossible for the people currently engaged in alarm monitoring to be able to take over. Taking the daily average alarm amount of a certain operator as an example, the daily average alarm amount in 2009 is 500 ten thousand, and the daily average alarm amount in 2016 is 4300 ten thousand, and the average rate increases by about 50% every year. Once a large-area alarm occurs, often only experienced maintenance personnel can be used for prejudging possible problem network elements, then different network management systems are logged in to check the alarm one by combining a topological graph of the current network, the alarm is checked step by step from top to bottom, and finally a fault is positioned to a specific network element. Such a process often requires a lot of labor and time, and has a low probability of being able to accurately judge.

In summary, how to accurately locate the alarm of the large-scale network element is a technical problem that needs to be solved urgently in the communication field.

Disclosure of Invention

The embodiment of the invention provides a method, a device, equipment and a medium for positioning a communication network fault, which can accurately position the position of a fault network element and the influence range of the fault.

In a first aspect, an embodiment of the present invention provides a method for locating a communication network fault, where the method includes: acquiring at least two alarms, wherein the two alarm data comprise time, network elements and titles; based on the two alarm data, obtaining the time distance, the network element distance and the header distance of the two alarm data through a time distance function, a network element distance function and a header distance function; obtaining the alarm distance of the two alarm data through an alarm distance function based on the time distance, the network element distance and the header distance; and analyzing the fault reason based on the alarm distance of the two alarm data.

In a second aspect, an embodiment of the present invention provides a communication network fault location apparatus, where the apparatus includes: the system comprises an acquisition module, a processing module and a display module, wherein the acquisition module is configured to acquire at least two alarms, and the two alarm data comprise time, network elements and titles; the processing module is configured to obtain the time distance, the network element distance and the title distance of the two alarm data through a time distance function, a network element distance function and a title distance function based on the two alarm data; obtaining the alarm distance of the two alarm data through an alarm distance function based on the time distance, the network element distance and the header distance; and the analysis module is configured to analyze the fault reason based on the alarm distance of the two alarm data.

The embodiment of the invention provides a communication network fault positioning device, which comprises: at least one processor, at least one memory, and computer program instructions stored in the memory, which when executed by the processor, implement the method of the first aspect of the embodiments described above.

In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, on which computer program instructions are stored, which, when executed by a processor, implement the method of the first aspect in the foregoing embodiments.

According to the communication network fault positioning method, device, equipment and medium provided by the embodiment of the invention, the internal relations of different alarms are associated through machine learning of alarm big data, and meanwhile, the association degree between the alarms is quantized through dimension decomposition and a defined distance function, so that the position of the fault and the influence range of the fault are accurately found, and the positioning cost is saved.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required to be used in the embodiments of the present invention will be briefly described below, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.

FIG. 1 illustrates a flow diagram of communication network fault location in accordance with an exemplary embodiment of the present invention;

fig. 2 is a schematic flow chart illustrating network element distance calculation according to an exemplary embodiment of the present invention;

fig. 3 is a flowchart illustrating a network element distance calculation according to an exemplary embodiment of the present invention;

FIG. 4 is a flow diagram illustrating alarm data clustering in accordance with an exemplary embodiment of the present invention;

FIG. 5 illustrates a flow diagram of a failure cause analysis in accordance with an exemplary embodiment of the present invention;

fig. 6 shows a schematic structural diagram of a communication network fault locating device according to an exemplary embodiment of the present invention;

FIG. 7 is a block diagram illustrating a processing module according to an exemplary embodiment of the present invention;

FIG. 8 shows a schematic diagram of the structure of an analysis module of an exemplary embodiment of the present invention; and

fig. 9 shows a schematic diagram of a hardware device for communication network fault location in an exemplary embodiment of the invention.

Detailed Description

Features and exemplary embodiments of various aspects of the present invention will be described in detail below, and in order to make objects, technical solutions and advantages of the present invention more apparent, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not to be construed as limiting the invention. It will be apparent to one skilled in the art that the present invention may be practiced without some of these specific details. The following description of the embodiments is merely intended to provide a better understanding of the present invention by illustrating examples of the present invention.

It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.

Fig. 1 shows a flow diagram of communication network fault location according to an exemplary embodiment of the present invention. The communication network fault location method 100 may be implemented by one or more components. For example, the communication network fault location method 100 may be implemented by the communication network fault location apparatus 600 (see fig. 6 and the description thereof for details).

In step 101, the communication network fault location apparatus 600 obtains two alarm data, where the two alarm data include time, network element, and title.

The two alarm data are from the communication network and are generated based on the fault of the communication network. The communication network may employ any of a variety of technologies including first generation (1G), second generation (2G), third generation (3G), fourth generation (4G) and other mobile communication technologies currently existing in the art or hereafter available. For example, the 3G communication network may comprise CDMA 2000, WCDMA, TD-SCDMA, etc. types, and the 4G communication network may comprise TD-LTE, FDD-LTE, etc. types.

The communication network comprises one or more network elements. The network element may be composed of one or more machine disks or machine frames, and can independently complete a certain transmission function. Taking TD-LTE as an example, the network elements mainly include a core network EPC and an access network E-UTRAN. The core network EPC mainly comprises a core professional MME, an HSS, a Serving GW, a PDN GW and the like. The network interfaces mainly comprise an X2 interface, an S1 interface, an S1-MME, an S1-U and the like.

In the event of a failure of the above-mentioned communication network, a plurality of alarms of the associated specialties may be generated. For example, if transmission interruption occurs in network communication, an alarm of base station outage may be accompanied. For another example, an exchange in one location (e.g., province a) goes down, and an alarm in another location (e.g., province B) may be generated. In the invention, the alarm data can be collected and stored in the database through the alarm management platform. The communication network fault location device 600 may retrieve alarm data in the alarm network platform database. In other embodiments, the alarm data may also be directly obtained by the communication network fault location device 600.

The alarm data may include time, network elements, titles or other data. Specifically, the time data may include data such as time of occurrence, alarm time, duration, etc. The network element data may include network element name, alarm province, alarm city, equipment type, equipment manufacturer, network type, etc. The header data may include an alarm header, an alarm criteria header, an alarm interpretation, an alarm category, an alarm level, etc. Other data may include data on the number of alarms, impact on equipment, impact on business, etc.

In certain embodiments, the communication network fault location device 600 may preprocess the alarm data. The preprocessing includes one or more of data formatting, data sampling, denoising, data quantization, attribute decomposition, attribute merging, and the like. After the alarm data is preprocessed, an intermediate table can be made. For example, assuming that the communication network fault location apparatus 600 runs on a Hadoop platform, an intermediate table (e.g., table I) may be made for the target field in the alarm data by the MR program to facilitate the subsequent processing of the alarm data.

TABLE I

In table I, the number of rows in the table may be adjusted according to the number of alarms, and the number of alarms may include two or more. The occurrence time refers to the occurrence time of the alarm, and the recording format of the occurrence time can be 'year, month, day, hour, minute and second', and can also be specific to the order of milliseconds and the like according to the requirement. The device type may indicate the type of device involved in the failure, e.g., EnodeB, etc. The alarm header refers to a standardized description of the fault, and may include, for example, "base station retirement", "communication failure", "temperature alarm", "S1 interface connection failure", and the like. The alarm level is a description of the severity of the fault, for example, 5 levels are divided according to the sequence of the severity from high to low, and are respectively "first level alarm", "second level alarm", "third level alarm", "fourth level alarm" and "fifth level alarm". It should be noted that the above description of the alarm data is merely for convenience in understanding the invention. Those skilled in the art can modify the alarm data, such as adjusting the sequence of the alarm data, changing the expression of the alarm data, increasing or decreasing the category of the alarm data, and the like, all of which are within the protection scope of the present invention.

In step 103, the communication network fault location apparatus 600 obtains the time distance, the network element distance, and the header distance of the two alarm data through the time distance function, the network element distance function, and the header distance function based on the two alarm data.

Wherein the time distance function is based on time data of the alarm data. In particular, the time distance function may be related to the difference between the occurrence times of the two alarm data. In an embodiment, the time distance function may be a step function. For ease of understanding, the following is exemplified.

Assuming that the occurrence Time of the alarm data 001 is t1 and the occurrence Time of the alarm data 002 is t2, the Time distance function (denoted as Dist _ Time) of the alarm data 001 and 002 can be defined as a function of the Time difference between the two, and the function value is between 0 and 1. The smaller the function value is, the closer the time distance is, and the greater the correlation between the two alarm data is. The larger the function value is, the longer the time distance is, the smaller the correlation between the two alarm data is. In order to more finely distinguish the time distance between two alarm data, the time distance function may be further made a phase function. The additional distance of the time distance function is related to the time difference value of the two alarm data, and the additional distance is specifically represented by the following formula:

if t1-t2| ≦ 1800 seconds, the time distance function may be formula (1)

Dist _ Time ═ t1-t2|/6000 formula (1)

If 1800 seconds is less than | t1-t2| ≦ 3600 seconds, the time distance function may be the equation (2)

Dist _ Time ═ t1-t2|/18000+0.2 equation (2)

If 3600 seconds < | t1-t2| ≦ 7200 seconds, the time-distance function may be formula (3)

Dist _ Time ═ (| t1-t2| -3600)/36000+0.4 equation (3)

If t1-t2| is greater than 7200 seconds, the value of Dist _ Time is 1.

The Time distance function is recorded as Dist _ Time, so that the Time distance of two alarm data can be obtained.

It should be noted that the above equations (1) - (3) are only exemplary time-distance function embodiments, and those skilled in the art can make modifications according to the above equations, such as changing the function type, changing the parameter size and/or number, and the like, and all such modifications are within the scope of the present invention.

The network element distance function (denoted Dist _ Net) is constructed based on network element data, which is determined by the network element attributes to which the two alarm data relate. For a detailed description of the network element distance function, refer to fig. 2 and fig. 3 and the description thereof. The network element distance of the two alarm data can be obtained through the network element distance function Dist _ Net.

The Title distance function (denoted Dist _ Title) is a function related to the alert Title. In one embodiment, the communication network fault locator 600 may calculate the matching degree of the alarm headers in the two alarm data. For example, it is determined whether both are the same, and if they are the same, let Dist _ Title be 0, which means that the distance correlation between the titles of the two alarm data is large. If the two warning data are different, let Dist _ Title be 1, and the distance correlation between the titles representing the two warning data is small. In other embodiments, fuzzy matching can be performed on the alarm titles, for example, the similarity of two alarm titles is scored, and the score is between 0 and 1. The header distance of the two alarm data can be obtained by the header distance function Dist _ Title.

In step 105, the communication network fault location apparatus 600 obtains the alarm distance of the two alarm data through an alarm distance function based on the time distance, the network element distance, and the header distance.

The Alarm distance function (denoted Dist _ Alarm) is related to parameters such as time distance, network element distance, and header distance. In certain embodiments, the alert distance function may be as shown in equation (4):

in a certain embodiment, the quadratic sum of the time distance, the network element distance and the header distance in formula (4) may be expanded to an m-th quadratic sum of the three, wherein m and n are positive integers not less than 2.

In certain embodiments, the alert distance function may be as shown in equation (5):

wherein F1, F2 and F2 are weighted values of time distance, network element distance and title distance, respectively, and the weighted values can be adjusted according to actual situations, for example, F1 is 0.2, F2 is 0.5, and F3 is 0.3.

According to the Alarm distance function Dist _ Alarm, the Alarm distance of two Alarm data can be obtained. Other conceivable variations of the alert distance function, which may be made by those skilled in the art, are within the scope of the present invention.

In step 107, the communication network fault location device 600 analyzes the fault cause based on the alarm distance of the two alarm data.

In an embodiment, for the obtained alarm data, the communication network fault location apparatus 600 may perform pairwise calculation on the alarm data therein to obtain an alarm distance of the two alarm data. The alarm distances can be further clustered, so as to analyze the fault causes, and detailed descriptions thereof are shown in fig. 4 and fig. 5.

In one embodiment, the alarm data and the fault analysis result can be displayed to the user through a display function, so that a communication network maintenance worker can conveniently view the fault unit and corresponding information, or forward the fault unit and corresponding information to other users.

It is noted that for the communication network fault location method 100, a person skilled in the art may omit certain steps, add certain steps, and adjust the order of certain steps. Such variations are within the scope of the invention.

Fig. 2 is a flowchart illustrating a network element distance calculation according to an exemplary embodiment of the present invention. The network element distance calculation method 200 may be implemented by one or more of the components. For example, the network element distance calculation method 200 may be implemented by the communication network fault location apparatus 600 (see fig. 6 and the description thereof for details).

In step 201, the communication network fault locating device 600 obtains network element data. The network element data comprises a network element name, an alarm province, an alarm city, an equipment type, an equipment manufacturer, a network type and the like.

In step 203, the communication network fault location apparatus 600 determines whether the network elements involved in the two alarms are the same. For example, whether the network elements of the two alarms are the same can be determined by judging whether the network element names are consistent. And if the network element names are consistent and the representative network elements are the same, making Dist _ Net be a, and making the value range of a be 0-1. In one embodiment, a may take a value of 0 to 0.2, for example, a is 0. If the network element names are not consistent, the method 200 for calculating the network element distance proceeds to step 205.

In step 205, the communication network fault location apparatus 600 determines whether there is a link between the network elements involved in the two alarms. The link determination method can be seen in fig. 3 and the description thereof. If a link exists between the network elements, let Dist _ Net be b, and the value range of b is 0-1. In one embodiment, b may take a value of 0 to 0.3, for example, 0.1. If there is no link between the network elements, the method 200 for calculating the distance between the network elements proceeds to step 207.

In step 207, the communication network fault location apparatus 600 determines whether the network elements involved in the two alarms are in the same province. And if the network elements are not in the same province, making Dist _ Net equal to c, wherein the value range of c is 0-1. In one embodiment, c may be 0.5 to 1, for example, c is 0.9. If the network element is in the same province, the method 200 for calculating the distance between network elements proceeds to step 209.

In step 209, the communication network fault locator 600 determines whether there is an interface between the network elements involved in the two alarms. And if no interface exists between the network elements, making Dist _ Net equal to d, wherein the value range of d is 0-1. In one embodiment, d may be 0.5 to 1, for example, d is 1. If there is an interface between the network elements, the method 200 for calculating the distance between the network elements proceeds to step 211.

In step 211, the communication network fault location apparatus 600 determines whether the network elements involved in the two alarms are wireless devices. And if the network element is not the wireless equipment, making Dist _ Net equal to e, wherein the value range of e is 0-1. In one embodiment, c may take a value of 0 to 0.5, for example, e is 0.2. If the network element is a wireless device, the method 200 for calculating the distance to the network element proceeds to step 213.

In step 213, the communication network fault locator 600 determines whether the network elements involved in the two alarms are in the same city. And if the network elements are not in the same province, making Dist _ Net equal to f, wherein the value range of f is 0-1. In one embodiment, f may be 0.5 to 1, for example, f is 1. And if the network elements are in the same city, making Dist _ Net equal to g, wherein the value range of g is 0-1. In one embodiment, g may be 0 to 0.5, for example, 0.2.

The network element distance between two alarms may be obtained by the exemplary network element distance calculation method 200 in fig. 2. It should be noted that, for the network element distance calculation method 200, a person skilled in the art may omit some steps, add some steps, and adjust the order of some steps. Such variations are within the scope of the invention.

Fig. 3 is a flowchart illustrating a network element distance calculation according to an exemplary embodiment of the present invention. The network element distance calculation method 300 may be implemented by one or more components. For example, the network element distance calculation method 300 may be implemented by the communication network fault location apparatus 600 (see fig. 6 and the description thereof for details).

In step 301, the communication network fault location apparatus 600 groups network element interfaces based on network element types to obtain a group table. The packet table contains network element types and interface information. In an embodiment, the network element types may include Cell, UtranCell, EutranCell, MME, SGSN, SGW, RCRF, HSS _ FE, Mscserver, MSC, DRA, SCP, and the like. According to the interface information of the network elements, if interfaces exist between different network element types, the two network elements can be put into one group. For example, if there is an interface between the wireless professional BTS and the BSC, both may be placed in the same packet. As another example, if the interface exists in the core specialties for the MME and HSS, both are put in the same packet.

In step 303, the communication network fault locating apparatus 600 may determine whether there is a link between the network elements related to the two warning data according to the grouping table by the communication network fault locating apparatus 600. For example, whether a link exists may be determined by determining whether there is a same interface between network elements in the packet table.

FIG. 4 is a flow chart illustrating alarm data clustering according to an exemplary embodiment of the present invention. The alarm data clustering method 400 may be implemented by one or more components therein. For example, the alarm data clustering method 400 may be implemented by the communication network fault location apparatus 600 (see fig. 6 and the description thereof for details).

In step 401, the communication network fault location device 600 compares the alert distance to an associated threshold.

The method for calculating the Alarm distance Dist _ Alarm is shown in fig. 1 to 3. The correlation threshold (denoted as r) can be set according to actual conditions, for example, the value range of r is 0-1. Preferably, the value range of r is 0-0.5; more preferably, the value range of r is 0-0.3; more preferably, r is in the range of 0 to 0.2.

If Dist _ Alarm is smaller than r, go to step 403, determine that two Alarm data have correlation, and then cluster the two Alarm data in step 405.

Those skilled in the art can also use other clustering methods to complete the analysis of the cause of the failure in the communication network based on the three dimensions (i.e., time distance, network element distance, and head distance) or any one or two dimensions of the alarm distance disclosed in the present application. The other clustering methods may include a longest distance method, a middle distance method, a class averaging method, a kNN algorithm, a k-means algorithm, a Density-based algorithm (Density-base methods), a Grid-based algorithm (Grid-based methods), a Model-based algorithm (Model-based methods), and the like.

Fig. 5 is a flow chart illustrating a failure cause analysis according to an exemplary embodiment of the present invention. The failure cause analysis method 500 may be implemented by one or more components therein. For example, the fault cause analysis method 500 may be implemented by the communication network fault location apparatus 600 (see fig. 6 and the description thereof for details).

In step 501, the communication network fault location apparatus 600 determines important alarm data in the alarm data of the same cluster based on the alarm titles.

As shown in fig. 1 and described herein, the alert header is taken from the header data. In addition to the alarm title, the title data may include an alarm criteria title, an alarm interpretation, an alarm category, an alarm level, and the like. According to the alarm level, important alarm data can be judged.

In step 503, the communication network fault location device 600 analyzes the fault cause based on the important alarm data.

For example, for the important alarm data, the information in the important alarm data may be analyzed and examined in a focused manner, so as to locate a specific faulty network element.

Fig. 6 shows a schematic structural diagram of a communication network fault location device according to an exemplary embodiment of the present invention. As shown in fig. 6, the communication network fault location apparatus 600 includes an acquisition module 602, a processing module 604, an analysis module 606, and a display module 608.

The obtaining module 602 is configured to obtain at least two alarm data, where the two alarm data include time, a network element, and a title. The processing module 604 is configured to obtain a time distance, a network element distance, and a header distance of the two alarm data through a time distance function, a network element distance function, and a header distance function based on the two alarm data; and obtaining the alarm distance of the two alarm data through an alarm distance function based on the time distance, the network element distance and the header distance. The analysis module 606 is configured to analyze a fault cause based on the alarm distance between the two alarm data. The display module 608 is used for displaying the failure reason.

Fig. 7 shows a schematic structural diagram of a processing module according to an exemplary embodiment of the present invention. As shown in fig. 7, the processing module 604 includes a time distance calculating unit 702, a network element distance calculating unit 704, a title distance calculating unit 706, and an alert distance calculating unit 708. The time distance calculating unit 702 is configured to calculate a time distance according to time data of the two alarm data. The network element distance calculating unit 704 is configured to calculate the network element distance according to the network element data of the two alarm data. The header distance calculating unit 706 is configured to calculate a header distance according to the header data of the two alert data. The alarm distance calculation unit 708 is configured to calculate an alarm distance according to the time distance, the network element distance, and the header distance of the two alarms.

Fig. 8 shows a schematic structural diagram of an analysis module according to an exemplary embodiment of the present invention. As shown in fig. 8, the analysis module 606 includes a judgment unit 802, a clustering unit 804, and a failure analysis unit 806. The determining unit 802 is configured to compare the alert distance with an associated threshold. The clustering unit 804 is configured to cluster the alarm data. The fault analysis unit 806 is configured to analyze a fault cause based on the alarm data after the distance.

It should be noted that, for the structural diagrams in fig. 6 to 8, a person skilled in the art may omit some modules/units, add some modules/units, and integrate some modules/units. Such variations are within the scope of the invention.

The communication network fault location method, the network element distance calculation method, the alarm data clustering method and the fault cause analysis method described in fig. 1 to fig. 5 in the embodiments of the present invention can be implemented by a communication network fault location device. Fig. 9 is a schematic diagram illustrating a hardware structure of a network fault location device according to an embodiment of the present invention.

As shown in fig. 9, the communication network fault location device 900 may include a processor 902 and a memory 904 having stored thereon computer program instructions.

In particular, the processor 904 may include a Central Processing Unit (CPU), or an Application Specific Integrated Circuit (ASIC), or may be configured as one or more Integrated circuits implementing embodiments of the present invention.

Memory 904 may include mass storage for data or instructions. By way of example, and not limitation, memory 904 may include a Hard Disk Drive (HDD), a floppy Disk Drive, flash memory, an optical Disk, a magneto-optical Disk, tape, or a Universal Serial Bus (USB) Drive or a combination of two or more of these. Memory 904 may include removable or non-removable (or fixed) media, where appropriate. The memory 904 may be internal or external to the data processing apparatus, where appropriate. In a particular embodiment, the memory 904 is a non-volatile solid-state memory. In a particular embodiment, the memory 904 includes Read Only Memory (ROM). Where appropriate, the ROM may be mask-programmed ROM, Programmable ROM (PROM), Erasable PROM (EPROM), Electrically Erasable PROM (EEPROM), electrically rewritable ROM (EAROM), or flash memory or a combination of two or more of these.

The processor 902 can read and execute the computer program instructions stored in the memory 904 to implement any one of the source code analysis method, the model analysis method, and the intelligent analysis method in the above embodiments.

In one example, the communication network fault location device 900 may also include a communication interface 906 and a bus 908. As shown in fig. 9, the processor 902, the memory 904, and the communication interface 906 are connected via a bus 908 to complete communication therebetween.

Communication interface 906 is used for implementing communication among modules, apparatuses, units and/or devices in the embodiments of the present invention.

Bus 908 comprises hardware, software, or both to couple the components of communication network fault locating device 900 to each other. By way of example, and not limitation, the bus 908 may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a Hyper Transport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an infiniband interconnect, a Low Pin Count (LPC) bus, a memory bus, a Micro Channel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a video electronics standards association local (VLB) bus, or other suitable bus, or a combination of two or more of these. Bus 908 may include one or more buses, where appropriate. Although specific buses have been described and shown in the embodiments of the invention, any suitable buses or interconnects are contemplated by the invention.

In addition, in combination with the source code analysis method, the model analysis method, and the intelligent analysis method in the foregoing embodiments, embodiments of the present invention may provide a computer-readable storage medium to implement. The computer readable storage medium having stored thereon computer program instructions; the computer program instructions, when executed by a processor, implement any one of the source code analysis method, the model analysis method, and the intelligent analysis method of the embodiments described above.

It is to be understood that the invention is not limited to the specific arrangements and instrumentality described above and shown in the drawings. A detailed description of known methods is omitted herein for the sake of brevity. In the above embodiments, several specific steps are described and shown as examples. However, the method processes of the present invention are not limited to the specific steps described and illustrated, and those skilled in the art can make various changes, modifications and additions or change the order between the steps after comprehending the spirit of the present invention.

The functional blocks shown in the above-described structural block diagrams may be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, it may be, for example, an electronic circuit, an Application Specific Integrated Circuit (ASIC), suitable firmware, plug-in, function card, or the like. When implemented in software, the elements of the invention are the programs or code segments used to perform the required tasks. The program or code segments may be stored in a machine-readable medium or transmitted by a data signal carried in a carrier wave over a transmission medium or a communication link. A "machine-readable medium" may include any medium that can store or transfer information. Examples of a machine-readable medium include electronic circuits, semiconductor memory devices, ROM, flash memory, Erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, Radio Frequency (RF) links, and so forth. The code segments may be downloaded via computer networks such as the internet, intranet, etc.

It should also be noted that the exemplary embodiments mentioned in this patent describe some methods or systems based on a series of steps or devices. However, the present invention is not limited to the order of the above-described steps, that is, the steps may be performed in the order mentioned in the embodiments, may be performed in an order different from the order in the embodiments, or may be performed simultaneously.

As described above, only the specific embodiments of the present invention are provided, and it can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes of the system, the module and the unit described above may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again. It should be understood that the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive various equivalent modifications or substitutions within the technical scope of the present invention, and these modifications or substitutions should be covered within the scope of the present invention.

Claims

1. A method for locating a fault in a communication network, the method comprising:

acquiring at least two alarm data, wherein the two alarm data comprise time, network elements and titles;

based on the two alarm data, obtaining the time distance, the network element distance and the header distance of the two alarm data through a time distance function, a network element distance function and a header distance function;

obtaining the alarm distance of the two alarm data through an alarm distance function based on the time distance, the network element distance and the header distance; and

analyzing the fault reason based on the alarm distance of the two alarm data;

wherein, obtaining the network element distance through the network element distance function includes:

s1, judging whether the network elements involved in the two alarm data are the same, if so, making the distance of the network element a, and the value range of a is 0-1; if not, go to S2;

s2, judging whether a link exists between network elements related to the two alarm data, if so, setting the distance of the network elements as b, and setting the value range of the b as 0-1; if not, go to S3;

s3, judging whether the network elements related to the two alarm data are in the same province, if not, making the distance of the network elements be c, and the value range of c is 0-1; if yes, go to S4;

s4, judging whether an interface exists between the network elements related to the two alarm data, if not, making the distance of the network elements d, and the value range of d is 0-1; if yes, go to S5;

s5, judging whether the network elements involved by the two alarm data are wireless devices, if not, making the distance of the network elements be e, and the value range of b be 0-1; if yes, go to S6;

s6, judging whether the network elements related to the two alarm data are in the same city, if not, making the distance of the network elements f, and the value range of f is 0-1; and if so, setting the network element distance as g, wherein the value range of g is 0-1.

2. The method of claim 1, wherein the time distance function is a phase function and is related to the time of two alarm data.

3. The method of claim 1, further comprising:

based on the network element type, grouping the network element interfaces to obtain a grouping table; and

and judging whether a link exists between the network elements based on the grouping table.

4. The method of claim 1, wherein the header distance function is related to a header match of two alarm data.

5. The method of claim 1, wherein the alarm distance function is the square root of the sum of the squares of the time distance, the network element distance, and the header distance.

6. The method of claim 1, wherein analyzing the cause of the fault based on the alarm distance of the two alarm data further comprises:

comparing the alert distance to an associated threshold;

if the alarm distance is smaller than the association threshold, the two alarm data have association; and

and clustering the two alarm data.

7. The method of claim 6, wherein analyzing the fault cause based on the alarm distance of the two alarm data further comprises:

determining important alarms based on the alarm titles in the same cluster of alarms; and

and analyzing the fault reason based on the important alarm.

8. A communication network fault location apparatus, the apparatus comprising:

the system comprises an acquisition module, a processing module and a processing module, wherein the acquisition module is configured to acquire at least two alarms, and the two alarm data comprise alarm data such as time, network elements, titles and the like;

the processing module is configured to obtain the time distance, the network element distance and the title distance of the two alarm data through a time distance function, a network element distance function and a title distance function based on the two alarm data; and

obtaining the alarm distance of the two alarm data through an alarm distance function based on the time distance, the network element distance and the header distance; and an analysis module configured to analyze the failure source based on the alarm distance of the two alarm data

Thus;

wherein, the processing module is specifically configured to:

9. A communication network fault location device, comprising: at least one processor, at least one memory, and computer program instructions stored in the memory that, when executed by the processor, implement the method of any of claims 1-7.

10. A computer-readable storage medium having computer program instructions stored thereon, which when executed by a processor implement the method of any one of claims 1-7.