CN113923096B - Network element fault early warning method and device, electronic equipment and storage medium - Google Patents
Network element fault early warning method and device, electronic equipment and storage medium Download PDFInfo
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Abstract
The embodiment of the invention provides a network element fault early warning method, a device, electronic equipment and a storage medium, which are used for acquiring target general service data of a target network element according to a preset strategy; acquiring historical general service data of a target network element from the historical service data of the target network element, wherein the historical service data comprises at least one type of service data; according to the historical general service data, the target general service data is subjected to early warning analysis to obtain an early warning result, and because the target general service data and the historical general service data are used when the network element faults are subjected to early warning analysis, independent analysis on network elements of different manufacturers is not needed, the service efficiency of the network element data is improved, the automatic early warning of the network element faults is realized, and the timeliness and the accuracy of the network fault early warning are improved.
Description
Technical Field
The present invention relates to the field of communications technologies, and in particular, to a network element fault early warning method, a device, an electronic device, and a storage medium.
Background
With the development of network technology and the high-speed growth of mobile network users, the role played by the core network of a telecom service operator is more and more important, and the core network is timely and reasonably maintained, so that the key of ensuring that the core network of the operator can provide high-quality telecom service is realized.
In core network maintenance, network failure discovery generally relies on network management alarms and user complaints. The network management alarms are generally generated and reported by each network element, and then analyzed and judged by engineers to obtain fault diagnosis conclusion, meanwhile, the fault positioning means rely on engineers to comprehensively analyze and judge according to the alarm information, the configuration information of the communication network element, the signaling and other information, and finally position fault points.
Then, as the core network becomes more and more complex, the types of network elements are more and more, the alarm information of the network elements of different manufacturers are inconsistent, the manual analysis difficulty is great, and the network is often discovered to have faults only after the user complaints are waited, so that the problems of early warning lag, poor timeliness and low accuracy of the network faults exist in the prior art.
Disclosure of Invention
The invention provides a network element fault early warning method, a network element fault early warning device, electronic equipment and a storage medium, which are used for solving the problems of network fault early warning lag, poor timeliness and low accuracy.
According to a first aspect of an embodiment of the present disclosure, the present invention provides a network element fault early warning method, where the method includes:
acquiring target general service data of a target network element according to a preset strategy;
acquiring historical general service data of the target network element from the historical service data of the target network element, wherein the historical service data comprises at least one type of service data;
and carrying out early warning analysis on the target general service data according to the historical general service data to obtain an early warning result.
Optionally, performing early warning analysis on the target general service data according to the historical general service data to obtain an early warning result, including:
according to the historical general service data, processing and analyzing the target general service data to obtain an early warning result, wherein the processing and analyzing comprises at least one of the following steps: threshold analysis, mutation analysis, bias analysis.
Optionally, the performing threshold analysis on the target general service data according to the historical general service data to obtain an early warning result includes:
determining a threshold range according to the historical general service data;
and determining an early warning result according to whether the threshold value of the general service data is in the threshold value range.
Optionally, the determining a threshold range according to the historical general service data includes:
acquiring target historical data meeting preset conditions in the historical general service data;
machine learning based on the fbppropset algorithm is carried out by utilizing the target historical data, and predicted universal service data in a second preset time length in the future is predicted;
and determining a threshold range according to the threshold value of the predicted universal service data.
Optionally, the step of performing mutation analysis on the target general service data according to the historical general service data to determine an early warning result includes:
acquiring one or more target historical data corresponding to the target universal service data in the historical universal service data;
calculating the change rate of the general service data relative to the target historical data;
and determining an early warning result according to the change rate.
Optionally, the performing bias analysis on the target general service data according to the historical general service data to determine an early warning result includes:
determining a target traffic duty ratio corresponding to the target network element according to the target general service data;
acquiring a historical traffic duty ratio corresponding to the target network element according to the historical general service data;
calculating a deviation value of the target traffic duty cycle and the historical traffic duty cycle;
and determining an early warning result according to the deviation value.
Optionally, the target general service data includes an online user number, and/or flow data; the obtaining the target general service data of the target network element according to the preset strategy comprises the following steps:
acquiring the network element type of the target network element;
determining a general service item corresponding to the network element type according to a preset mapping relation, wherein the mapping relation is a mapping relation between the network element type and the general service item;
and acquiring the number of online users and/or flow data corresponding to the general service items of the target network element at each preset time interval.
According to a second aspect of the embodiments of the present disclosure, the present invention provides a network element fault early-warning device, including:
the first acquisition module is used for acquiring target general service data of a target network element according to a preset strategy;
a second obtaining module, configured to obtain historical generic service data of the target network element from historical service data of the target network element, where the historical service data includes at least one type of service data;
and the analysis module is used for carrying out early warning analysis on the target general service data according to the historical general service data to obtain an early warning result.
Optionally, the analysis module is specifically configured to:
according to the historical general service data, processing and analyzing the target general service data to obtain an early warning result, wherein the processing and analyzing comprises at least one of the following steps: threshold analysis, mutation analysis, bias analysis.
Optionally, the analysis module is specifically configured to, when performing threshold analysis on the target general service data according to the historical general service data to obtain an early warning result:
determining a threshold range according to the historical general service data;
and determining an early warning result according to whether the threshold value of the general service data is in the threshold value range.
Optionally, the analysis module is specifically configured to, when determining a threshold range according to the historical generic service data:
acquiring target historical data meeting preset conditions in the historical general service data;
machine learning based on the fbppropset algorithm is carried out by utilizing the target historical data, and predicted universal service data in a second preset time length in the future is predicted;
and determining a threshold range according to the threshold value of the predicted universal service data.
Optionally, the analysis module is specifically configured to, when performing mutation analysis on the target general service data according to the historical general service data and determining an early warning result:
acquiring one or more target historical data corresponding to the target universal service data in the historical universal service data;
calculating the change rate of the general service data relative to the target historical data;
and determining an early warning result according to the change rate.
Optionally, the analysis module is specifically configured to, when performing bias analysis on the target general service data according to the historical general service data and determining an early warning result:
determining a target traffic duty ratio corresponding to the target network element according to the target general service data;
acquiring a historical traffic duty ratio corresponding to the target network element according to the historical general service data;
calculating a deviation value of the target traffic duty cycle and the historical traffic duty cycle;
and determining an early warning result according to the deviation value.
Optionally, the target general service data includes an online user number, and/or flow data; the first obtaining module is specifically configured to:
acquiring the network element type of the target network element;
determining a general service item corresponding to the network element type according to a preset mapping relation, wherein the mapping relation is a mapping relation between the network element type and the general service item;
and acquiring the number of online users and/or flow data corresponding to the general service items of the target network element at each preset time interval.
According to a third aspect of the embodiments of the present disclosure, there is provided an electronic device, including: a memory, a processor, and a computer program;
wherein the computer program is stored in the memory and configured to be executed by the processor for performing the network element failure warning method according to any one of the first aspect of the embodiments of the present disclosure.
According to a fourth aspect of the embodiments of the present disclosure, the present disclosure provides a computer-readable storage medium, where computer-executable instructions are stored, where the computer-executable instructions are used to implement the network element failure early-warning method according to any one of the first aspects of the embodiments of the present disclosure when the computer-executable instructions are executed by a processor.
According to the network element fault early warning method, the network element fault early warning device, the electronic equipment and the storage medium, target general service data of a target network element are obtained according to a preset strategy; acquiring historical general service data of the target network element from the historical service data of the target network element, wherein the historical service data comprises at least one type of service data; and according to the historical general service data, carrying out early warning analysis on the target general service data to obtain an early warning result, wherein the target general service data and the historical general service data are used when the early warning analysis is carried out on network element faults, so that independent analysis is not required to be carried out on network elements of different manufacturers, the service efficiency of the network element data is improved, the automatic early warning of the network element faults is realized, and the timeliness and the accuracy of the early warning of the network faults are improved.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the disclosure and together with the description, serve to explain the principles of the disclosure.
Fig. 1 is an application scenario diagram of a network element fault early warning method provided by an embodiment of the present invention;
fig. 2 is a flowchart of a network element fault early warning method according to an embodiment of the present invention;
fig. 3 is a flowchart of a network element fault early warning method according to another embodiment of the present invention;
FIG. 4 is a flowchart of step S204 in the embodiment shown in FIG. 3;
FIG. 5 is a flowchart of step S205 in the embodiment shown in FIG. 3;
FIG. 6 is a flowchart of step S206 in the embodiment shown in FIG. 3;
fig. 7 is a schematic structural diagram of a network element fault early warning device according to an embodiment of the present invention;
fig. 8 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Specific embodiments of the present disclosure have been shown by way of the above drawings and will be described in more detail below. These drawings and the written description are not intended to limit the scope of the disclosed concepts in any way, but rather to illustrate the disclosed concepts to those skilled in the art by reference to specific embodiments.
Detailed Description
Reference will now be made in detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The implementations described in the following exemplary examples are not representative of all implementations consistent with the present disclosure. Rather, they are merely examples of apparatus and methods consistent with some aspects of the present disclosure as detailed in the accompanying claims.
First, the terms involved in the present invention will be explained:
network element: the network element is the minimum unit monitored and managed in the network management, and the definition of the network element can be different according to different network architectures. For example, the network elements may include base stations, mobile management node functions (Mobility Management Entity, MME), serving GateWay (SGW), public data network (Public Data Network, PDN), etc., and for network elements in the core network, different network elements carry different services, traffic.
Fig. 1 is an application scenario diagram of a network element fault early warning method provided by the embodiment of the present invention, as shown in fig. 1, where the network element fault early warning method provided by the embodiment is applied to an electronic device, for example, a network management device 11, where the network management device 11 is connected to a core network, the core network includes a plurality of network elements 12 used for a core switching or call routing function, different network elements 12 bear different functions, so as to play a role in supporting different telecommunication network services, and the network management device 11 may obtain alarm information uploaded by the network elements 12 through the core network, and send the network element alarm information to a terminal device 13 used by an operation and maintenance personnel as required.
In the prior art, in the process of maintaining a core network, network fault discovery generally depends on network management alarm and user complaints. The network management alarms are generally generated and reported by each network element, so that the timeliness is good, the fault problem can be reflected at the first time, and the user complaints are often caused by the situation that the network has serious problems, so that the use of the user is influenced for a long time, so that the timeliness is poor.
However, in the mode of realizing fault early warning through network management alarm, after the network element reports information, the information such as alarm information, communication network element configuration information, signaling and the like is comprehensively analyzed and judged by engineers, and finally, a fault point is positioned. As the core network becomes more and more complex, the types of network elements become more and more, the alarm information of the network elements of different factories are inconsistent, engineers need to be familiar with the network elements of all factories to realize accurate identification and early warning of network faults, and the operation difficulty is high and the accuracy is low. Therefore, the user complaints are often waited to discover that the network fails, and the problems of early warning lag, poor timeliness and low accuracy of the network failure are caused.
The following describes the technical scheme of the present invention and how the technical scheme of the present application solves the above technical problems in detail with specific embodiments. The following embodiments may be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments. Embodiments of the present invention will be described below with reference to the accompanying drawings.
Fig. 2 is a flowchart of a network element fault early-warning method according to an embodiment of the present invention, as shown in fig. 2, where the network element fault early-warning method provided in the embodiment includes the following steps:
step S101, obtaining target general service data of a target network element according to a preset strategy.
The target network element refers to a network element to be pre-warned, and the target network element can be one network element or a group of network elements for realizing the same or similar functions. The method for determining the target network element is various, for example, the network element is actively maintained according to a preset maintenance plan, and the network element to be maintained in the maintenance plan is taken as the target network element; or the network element sending the request information can be used as the network element to be pre-warned after the request information of the network element is passively received, and the network element can be designed according to specific requirements and use scenes, and the network element is not particularly limited.
The target general service data refers to general service data of the target network element. The general service data refers to basic service data generated by the network element in the process of bearing traffic or service. For network elements produced by different manufacturers, the general service data are used for representing the basic operation state of the network elements, so that unified early warning of the network elements of different manufacturers can be realized according to the general service data, independent configuration of early warning strategies for different manufacturers is not needed, and the early warning efficiency is effectively improved. The implementation manner of the general service data may be various, for example, the online user quantity, the traffic, etc., and the specific content of the general service data may be set according to the usage scenario and the requirement, that is, determined according to a preset policy, which is not specifically limited herein.
Step S102, obtaining historical general service data of the target network element from the historical service data of the target network element, wherein the historical service data comprises at least one type of service data.
Historical traffic data refers to traffic data that network elements have generated over a period of time and stored in a particular storage medium. The historical service data may characterize the operation characteristics of the network element during a historical period, and for the same network element or a class of network elements, there is a certain correlation between the historical service data and the current service data. However, as the time of the historical data up to date is gradually long, the correlation between the historical data and the current running state of the gateway is also gradually weakened, so that the time of the historical data corresponding to the current running state of the gateway is not easy to be too long. The historical data can be selected from data in one year, and of course, it should be noted that, for different network elements and different usage scenarios, the time length corresponding to the historical data can be different and can be determined according to requirements.
Specifically, the historical general service data refers to historical service data of general service data, and the historical service data includes at least one type of service data, for example, general service data and non-general service data, wherein the non-general data is specific service data generated by network elements of various manufacturers.
And step S103, performing early warning analysis on the target general service data according to the historical general service data to obtain an early warning result.
For the same or same type of network element, namely the target network element, because certain correlation exists between the historical general service data and the current target general service data, when the historical general service data is inconsistent with the target general service data, the network element running state corresponding to the historical general service data is described, and the network element running state corresponding to the target general service data is inconsistent, namely the running state of the network element is changed, so that the current running state of the gateway can be judged according to the relation between the historical general service data and the target service data, and the early warning of the running state of the network element is realized.
In this embodiment, target general service data of a target network element is obtained according to a preset policy; acquiring historical general service data of a target network element from the historical service data of the target network element, wherein the historical service data comprises at least one type of service data; according to the historical general service data, the target general service data is subjected to early warning analysis to obtain an early warning result, and because the target general service data and the historical general service data are used when the network element faults are subjected to early warning analysis, independent analysis on network elements of different manufacturers is not needed, the service efficiency of the network element data is improved, the automatic early warning of the network element faults is realized, and the timeliness and the accuracy of the network fault early warning are improved.
Fig. 3 is a flowchart of a network element fault early-warning method according to another embodiment of the present invention, where, as shown in fig. 3, the network element fault early-warning method according to this embodiment further refines steps S101 and S103 on the basis of the network element fault early-warning method according to the embodiment shown in fig. 2, and the target general service data includes an online user number and/or flow data, and then the network element fault early-warning method according to this embodiment includes the following steps:
step S201, obtaining the network element type of the target network element.
Specifically, according to the function of the target network element, the network element may be divided into different network element types, for example, a mobility management node, a service gateway, a public data network, etc., for different network elements, the service and the service carried by the network element are different, and the corresponding running state features are different.
Step S202, determining a general service item corresponding to the network element type according to a preset mapping relation, wherein the mapping relation is the mapping relation between the network element type and the general service item.
For different network element types, the general service items are different, for example, the general service items corresponding to the class A network element are call connection, the general service items corresponding to the class B network element are mobility management, the mapping strategies of the different network elements corresponding to the different general service items are determined by a preset mapping relation, that is, the general service items corresponding to the different network elements can be determined according to the preset mapping relation.
Step S203, each time interval is preset, the number of online users corresponding to the general service item of the target network element and/or the flow data are collected.
Specifically, the preset time length is the data acquisition frequency, for example, the preset time length is 5 minutes, that is, the data of the target network element is acquired every 5 minutes; the preset time length is 1 hour, that is, data of the target network element is collected every 1 hour, and specific values of the preset time length can be determined according to different requirements, and are not particularly limited herein.
When the target network element bears the service corresponding to the general service item, service information such as the number of online users and flow data can be generated. These data belong to basic data, which are generated by different vendors in time. The online user number and/or the flow data are used as the target general service data, so that the running state of the target network element can be better reflected, unified early warning of network elements of different manufacturers can be realized, and the early warning accuracy and early warning efficiency are improved.
And step S204, carrying out threshold analysis on the target general service data according to the historical general service data, and determining an early warning result.
When the historical general service data of the target network element and the specific threshold value of the target general service data change, the early warning result of the target network element can be obtained by analyzing the change.
Optionally, as shown in fig. 4, step S204 includes two specific implementation steps of steps S2041 and S2042:
step S2041, determining a threshold range according to the historical general service data.
Specifically, the threshold range may be a numerical interval in which all the historical generic service data is located, for example, a maximum value and a minimum value interval, and more specifically, for example, a maximum value and a minimum value of the historical traffic of the target network element. The threshold range may be a numerical value interval in which a part of the historical general service data is located, for example, an effective value interval of the historical general service data in a 95% confidence interval, and the determining method of the threshold range may be various, and may be determined according to specific needs, which is not limited herein.
Optionally, determining the threshold range according to the historical general service data includes:
and acquiring target historical data meeting preset conditions in the historical general service data. Specifically, the method comprises the steps of screening historical general service data, removing deviation points, screening out data which can obviously represent the running state characteristics, and acting on sample data, namely target historical data.
And performing machine learning based on the fbarophet algorithm by utilizing the target historical data, and predicting predicted universal service data in a second preset time length in the future. Predicting data within a second preset time period, for example, 1 week or 10 days, can be achieved by performing machine learning based on the fbppropset algorithm using the target history data as sample data.
And determining a threshold range according to the threshold of the predicted universal service data.
And determining a threshold range by taking the predicted universal service data output through machine learning as a standard. Because the predicted universal service data output through machine learning contains a change rule of trend compared with the historical universal service data, the threshold range determined according to the threshold of the predicted universal service data is more close to the numerical value interval of the data generated when the real target network element operates in the normal state, and further, the accuracy of fault early warning on the target network element can be improved.
Step S2042, determining an early warning result according to whether the threshold value of the general service data is within the threshold value range.
And processing the general service data according to the same calculation method as the calculation history general service data, so as to obtain the threshold value of the general service data. Judging whether the threshold value falls into a threshold value range, judging whether the general service data is normal, further judging whether the running state of a target network element corresponding to the general service data is normal, and judging whether the running state of the target network element is normal, namely, the early warning result.
For example, according to the historical general service data, the valid value interval of the historical general service data in the 95% confidence interval of the target network element is determined to be [100,120], wherein the historical general service data is flow data. Correspondingly, according to the same calculation method, namely calculating the effective value of the universal service data in the 95% confidence interval, obtaining the threshold value of the universal service data as 110, and judging that the data is normal if the threshold value is not exceeded, namely the running state of the target network element is normal; correspondingly, if the threshold value of the obtained general service data is 130 and exceeds the threshold value range, the abnormal data can be judged, namely the abnormal running state of the target network element.
Of course, the fluctuation correction value can be added on the basis of the threshold range, for example, the fluctuation correction value is 5, that is, the threshold value is within the range of [100,120] ±5, and the operation state of the target network element is considered to be normal, so that the stability of the algorithm is improved, and the false alarm probability is reduced.
Step S205, according to the historical general service data, mutation analysis is carried out on the target general service data, and an early warning result is determined.
Specifically, the abrupt change refers to that the service data is suddenly changed, and for the network element, the service data which is suddenly changed cannot be generated during normal operation, so that the abrupt change analysis is performed on the target general service data, and the fault early warning of the target network element can be realized.
Optionally, as shown in fig. 5, step S205 includes three specific implementation steps of steps S2051, S2052, S2053:
step S2051, obtaining one or more target history data corresponding to the target general service data in the history general service data.
And acquiring target historical data meeting preset conditions in the historical general service data. Specifically, the method comprises the steps of screening historical general service data, removing deviation points, screening out data which can obviously represent the running state characteristics, and acting on sample data, namely target historical data.
Step S2052, calculating the change rate of the general service data with respect to the target history data.
As the service carried by the network element runs, the target historical data generated by the network element may generate a certain change, including rising, falling or fluctuation change within a certain range, and the change rate represents the degree of fluctuation change. From the extent of the change, the rate of change of the change can be determined.
Specifically, the rate of change may be based on different units, such as a rate of change per minute, a rate of change per 15 minutes, a rate of change per hour, a rate of change per day, and the like. The unit of change rate may be determined according to different requirements, and is not particularly limited herein. The rate of change may be implemented as an absolute value, e.g., the rate of change of the online user quantity is: the daily fluctuations of the amount of 1000 people/day, i.e. on-line users, are within 1000 people. The rate of change may also be implemented as a relative value, e.g. 1% for the online user amount, i.e. the daily fluctuation of the online user amount is within 1%.
Illustratively, a method of calculating a rate of change includes:
assume that the index of the general service data is x 1 The index of the target history data is x 2 The change rate d is calculated as d= (x) 1 -x 2 ) And/x 2, further, when d is greater than a preset value d 0 And if so, the network element index is abnormal.
Step S2053, determining an early warning result according to the change rate.
When the target network element works normally, the fluctuation condition of the generated target general service data is similar to the fluctuation condition of the target historical data, for example, the daily online user number in the target general service data and the daily online user number in the target historical data are maintained within 1%, and the change rate indicates that the target network element works normally. When the change rate of the number of daily online users in the target general service data is 10%, the change rate may be caused by the connection failure of the target network element, so that the failure of the target network element can be judged according to the change rate.
In the step of the embodiment, the state of the target network element is judged according to the change rate of the service data relative to the target historical data, so that the state change condition of the target network element can be better represented, the accurate change degree of the target network element can be determined according to the change condition, and the accuracy of fault early warning is improved.
And S206, performing deviation analysis on the target general service data according to the historical general service data, and determining an early warning result.
Specifically, the deviation analysis refers to analyzing the deviation of the traffic volume duty ratio carried by the target network element through the target general service data. For example, for a normally operating network element, the amount of traffic it assumes, the proportion of which in the group of network elements it is in, is fixed, for example 10%. Therefore, by uniformly distributing the service volume, the pressure can be uniformly distributed among different network elements, and the service processing efficiency is improved. When a problem occurs in a network element in the network element group, the amount of the traffic born by the network element group is reduced, and the fault condition of the target network element can be judged according to the deviation of the occurrence of the traffic.
Alternatively, as shown in fig. 6, step S206 includes four specific implementation steps of steps S2061, S2062, S2063, S2064:
step S2061, determining the target traffic duty ratio corresponding to the target network element according to the target general service data.
Specifically, the target general service data includes characterization information of the data value, and according to the characterization information, the target data value corresponding to the target general service data can be determined. The ratio of the target data value to the preset total value is calculated to determine the target traffic duty ratio corresponding to the target network element. For example, the traffic ratio of the information forwarding service corresponding to the target network element is 10%, i.e. the target network element bears 10% of the traffic.
Step S2062, according to the history general service data, the history service volume duty ratio corresponding to the target network element is obtained.
Similarly, according to the historical general service data of the target network element, the historical service volume ratio corresponding to the target network element can be determined by calculating the ratio of the historical data magnitude to the historical total magnitude. Wherein the historical traffic ratio and the corresponding generic traffic term of the historical traffic ratio should be the same.
In step S2063, a deviation value of the target traffic ratio from the history traffic ratio is calculated.
Calculating the difference between the target traffic duty ratio and the historical traffic duty ratio to obtain a signed deviation value, wherein the target traffic duty ratio is increased when the deviation value is positive, and the target traffic duty ratio is decreased when the deviation value is negative. For example, the target traffic is 10% and the history traffic is 8%, the bias value is 2%, indicating that the target traffic is 2% higher.
Step S2064, determining the early warning result according to the deviation value.
When the deviation value of the target traffic duty ratio is larger than the preset deviation threshold value, the target network element can be determined to be in an abnormal state.
Illustratively, a method of bias analysis includes:
for a group of target network elements for load sharing, according to preset proportion and deviation degree, n target network elements are assumed, and the indexes are x respectively 1 、x 2 ……x n The traffic setting ratio of each network element is a 1 、a 2 ……a n Wherein the index of the network element i is x i The service proportion is a i When the preset deviation ratio threshold is d
And when the network element index is abnormal.
In the step of this embodiment, the operation state of the target network element is judged by the deviation value of the target traffic duty ratio, so that the accuracy of judging the operation state of the target network element can be further increased, and the application scenario and the flexibility of use of the method of this embodiment are increased.
It should be noted that in this embodiment, according to the historical general service data corresponding to the steps S204, S205, and S206, the step of processing and analyzing the target general service data to obtain the early warning result may use any one of the methods in the steps S204, S205, and S206 alone as required to obtain the early warning result; the methods in S204, S205, S206 may also be used in different orders, and the early warning result may be obtained according to multiple ways according to the multiple results obtained correspondingly, for example, the early warning result may be determined according to the weight coefficient corresponding to the multiple results; for another example, the early warning result is determined based on the most degraded result among the plurality of results. The specific limitation is herein set forth.
Optionally, after any one of steps S204, S205, S206, the method further includes:
step S207, pushing the early warning result to the terminal equipment.
In order to enable maintenance personnel to determine a network element with a fault at the first time after the network has the fault, the electronic equipment applying the network element fault early warning method provided by the embodiment pushes the early warning result to terminal equipment used by operation and maintenance personnel, for example, the terminal equipment is pushed to mobile phones of the operation and maintenance personnel in a way of WeChat, APP and the like, so that the operation and maintenance personnel can position the network element fault in time, and the timeliness of network element fault early warning is improved.
Fig. 7 is a schematic structural diagram of a network element fault early-warning device according to an embodiment of the present invention, as shown in fig. 7, the network element fault early-warning device 7 provided in this embodiment includes:
a first obtaining module 71, configured to obtain target general service data of a target network element according to a preset policy;
a second obtaining module 72, configured to obtain historical generic service data of the target network element from the historical service data of the target network element, where the historical service data includes at least one type of service data;
and the analysis module 73 is used for performing early warning analysis on the target general service data according to the historical general service data to obtain an early warning result.
Wherein the first acquisition module 71, the second acquisition module 72 and the analysis module 73 are connected in sequence. The network element fault early warning device 7 provided in this embodiment may execute the technical scheme of the method embodiment shown in any one of fig. 2 to 6, and its implementation principle and technical effect are similar, and will not be described herein again.
Fig. 8 is a schematic diagram of an electronic device according to an embodiment of the present invention, as shown in fig. 8, where the electronic device provided in the embodiment includes: memory 81, processor 82 and computer programs.
The computer program is stored in the memory 81 and is configured to be executed by the processor 82 to implement the network element failure warning device provided in any of the embodiments corresponding to fig. 2-6 of the present invention.
Wherein the memory 81 and the processor 82 are connected by a bus 83.
The relevant descriptions and effects corresponding to the steps in the embodiments corresponding to fig. 2 to fig. 6 may be correspondingly understood, and are not repeated herein.
An embodiment of the present invention provides a computer readable storage medium, on which a computer program is stored, where the computer program is executed by a processor to implement the network element fault early warning device provided in any one of the embodiments corresponding to fig. 2 to 6 of the present invention.
The computer readable storage medium may be, among other things, ROM, random Access Memory (RAM), CD-ROM, magnetic tape, floppy disk, optical data storage device, etc.
In the several embodiments provided by the present invention, it should be understood that the disclosed apparatus and method may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of modules is merely a logical function division, and there may be additional divisions of actual implementation, e.g., multiple modules or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or modules, which may be in electrical, mechanical, or other forms.
Other embodiments of the invention will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. This invention is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the invention and including such departures from the present disclosure as come within known or customary practice within the art to which the invention pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the invention being indicated by the following claims.
It is to be understood that the invention is not limited to the precise arrangements and instrumentalities shown in the drawings, which have been described above, and that various modifications and changes may be effected without departing from the scope thereof. The scope of the invention is limited only by the appended claims.
Claims (5)
1. A network element failure early warning method, the method comprising:
acquiring target general service data of a target network element according to a preset strategy;
acquiring historical general service data of the target network element from the historical service data of the target network element, wherein the historical service data comprises at least one type of service data;
performing early warning analysis on the target general service data according to the historical general service data to obtain an early warning result;
performing early warning analysis on the target general service data according to the historical general service data to obtain an early warning result, wherein the early warning method comprises the following steps:
processing and analyzing the target general service data according to the historical general service data to obtain an early warning result, wherein the processing and analyzing comprises deviation analysis;
and performing deviation analysis on the target general service data according to the historical general service data to determine an early warning result, wherein the method comprises the following steps:
determining a target traffic duty ratio corresponding to the target network element according to the target general service data; wherein, the target traffic corresponding to the target network element accounts for the proportion of the traffic born by the target network element in the network element group where the target network element is positioned;
acquiring a historical traffic duty ratio corresponding to the target network element according to the historical general service data;
calculating a deviation value of the target traffic duty cycle and the historical traffic duty cycle;
and determining an early warning result according to the deviation value.
2. The method according to claim 1, wherein the target general service data comprises an online user number, and/or traffic data; the obtaining the target general service data of the target network element according to the preset strategy comprises the following steps:
acquiring the network element type of the target network element;
determining a general service item corresponding to the network element type according to a preset mapping relation, wherein the mapping relation is a mapping relation between the network element type and the general service item;
and acquiring the number of online users and/or flow data corresponding to the general service items of the target network element at each preset time interval.
3. A network element failure early warning device, characterized in that the device comprises:
the first acquisition module acquires target general service data of a target network element according to a preset strategy;
a second obtaining module, configured to obtain historical general service data of the target network element from the historical service data of the target network element, where the historical service data includes at least one type of service data;
the analysis module performs early warning analysis on the target general service data according to the historical general service data to obtain an early warning result;
the analysis module is further used for processing and analyzing the target general service data according to the historical general service data to obtain an early warning result, wherein the processing and analyzing comprises deviation analysis;
the analysis module is further used for determining a target traffic duty ratio corresponding to the target network element according to the target general service data; acquiring a historical traffic duty ratio corresponding to the target network element according to the historical general service data; calculating a deviation value of the target traffic duty cycle and the historical traffic duty cycle; determining an early warning result according to the deviation value; wherein, the target traffic corresponding to the target network element is the proportion of the traffic borne by the target network element in the network element group where the target network element is located.
4. An electronic device, comprising: a memory, a processor, and a computer program;
wherein the computer program is stored in the memory and configured to be executed by the processor to implement the network element failure warning method according to claim 1 or 2.
5. A computer readable storage medium, wherein computer executable instructions are stored in the computer readable storage medium, and when executed by a processor, the computer executable instructions are configured to implement the network element failure warning method according to claim 1 or 2.
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