CN113923096B - Network element fault early warning method and device, electronic equipment and storage medium - Google Patents

Abstract

Embodiments of the present invention provide a network element failure early warning method, device, electronic equipment, and storage medium, by obtaining the target general service data of the target network element according to a preset strategy; and obtaining the target network element from the historical service data of the target network element The historical general service data, wherein, the historical service data includes at least one type of service data; according to the historical general service data, early warning analysis is performed on the target general service data, and the early warning result is obtained. The target general service data and historical general service data do not need to be analyzed separately for network elements of different manufacturers, which improves the use efficiency of network element data, realizes automatic early warning of network element faults, and improves the timeliness of network fault early warning and accuracy.

Description

Network element failure early warning method, device, electronic equipment and storage medium

technical field

The present invention relates to the field of communication technology, in particular to a network element failure early warning method, device, electronic equipment and storage medium.

Background technique

With the development of network technology and the rapid growth of mobile network users, the core network of telecom service operators is playing an increasingly important role, and timely and reasonable maintenance of the core network is to ensure that the core network of operators can provide high-quality services. Key to quality telecommunication services.

In core network maintenance, the discovery of network faults generally relies on network management alarms and user complaints. Network management alarms are generally generated and reported by each network element itself, and then analyzed and judged by engineers to draw fault diagnosis conclusions. At the same time, fault location means rely on engineers to conduct comprehensive analysis and judgment based on alarm information, communication network element configuration information, signaling and other information , and finally locate the fault point.

However, as the core network network becomes more and more complex, and there are more and more types of network elements, the alarm information of network elements from different manufacturers is inconsistent, and manual analysis is very difficult. It is often not until the user complains that the network is found to be faulty. Therefore, the existing technology There are problems such as network failure early warning lag, poor timeliness, and low accuracy.

Contents of the invention

The invention provides a network element fault early warning method, device, electronic equipment and storage medium, which are used to solve the problems of network fault early warning lag, poor timeliness and low accuracy.

According to the first aspect of the embodiments of the present disclosure, the present invention provides a network element fault early warning method, the method comprising:

Obtain target general service data of target network elements according to preset policies;

Obtain historical general service data of the target network element from historical service data of the target network element, wherein the historical service data includes at least one type of service data;

According to the historical general business data, an early warning analysis is performed on the target general business data to obtain an early warning result.

Optionally, according to the historical general business data, an early warning analysis is performed on the target general business data, and an early warning result is obtained, including:

According to the historical general business data, the target general business data is processed and analyzed to obtain an early warning result, wherein the processing and analysis includes at least one of the following: threshold analysis, mutation analysis, and deviation analysis.

Optionally, performing threshold analysis on the target general business data according to the historical general business data to obtain an early warning result includes:

Determine a threshold range according to the historical general business data;

An early warning result is determined according to whether the threshold of the general service data is within the threshold range.

Optionally, the determining the threshold range according to the historical general service data includes:

Acquiring target historical data that meets preset conditions in the historical general business data;

Use the target historical data to carry out machine learning based on the fbprophet algorithm to predict the predicted general business data within the second preset time period in the future;

A threshold range is determined according to the threshold for predicting general service data.

Optionally, the performing mutation analysis on the target general business data according to the historical general business data to determine the warning result includes:

Acquiring one or more target historical data corresponding to the target general business data in the historical general business data;

calculating the rate of change of the general business data relative to the target historical data;

An early warning result is determined according to the rate of change.

Optionally, the performing deviation analysis on the target general business data according to the historical general business data, and determining the early warning result include:

determining the proportion of target traffic corresponding to the target network element according to the target general service data;

Acquiring the proportion of historical traffic corresponding to the target network element according to the historical general traffic data;

calculating the deviation value between the target business volume ratio and the historical traffic volume ratio;

According to the deviation value, an early warning result is determined.

Optionally, the target general service data includes the number of online users, and/or, traffic data; the acquisition of the target general service data of the target network element according to a preset strategy includes:

Obtaining the network element type of the target network element;

Determine a general service item corresponding to the network element type according to a preset mapping relationship, where the mapping relationship is a mapping relationship between a network element type and a general service item;

The number of online users and/or traffic data corresponding to the common service items of the target network element are collected at intervals of preset time intervals.

According to the second aspect of the embodiments of the present disclosure, the present invention provides a network element failure early warning device, including:

The first obtaining module is used to obtain the target general service data of the target network element according to a preset strategy;

A second acquiring module, configured to acquire historical general service data of the target network element from historical service data of the target network element, wherein the historical service data includes at least one type of service data;

An analysis module, configured to perform early warning analysis on the target general business data according to the historical general business data, and obtain an early warning result.

Optionally, the analysis module is specifically used for:

According to the historical general business data, the target general business data is processed and analyzed to obtain an early warning result, wherein the processing and analysis includes at least one of the following: threshold analysis, mutation analysis, and deviation analysis.

Optionally, the analysis module is specifically used to:

Determine a threshold range according to the historical general business data;

An early warning result is determined according to whether the threshold of the general service data is within the threshold range.

Optionally, when the analysis module determines the threshold range according to the historical general business data, it is specifically used to:

Acquiring target historical data that meets preset conditions in the historical general business data;

Use the target historical data to carry out machine learning based on the fbprophet algorithm to predict the predicted general business data within the second preset time period in the future;

A threshold range is determined according to the threshold for predicting general service data.

Optionally, the analysis module is specifically used to:

Acquiring one or more target historical data corresponding to the target general business data in the historical general business data;

calculating the rate of change of the general business data relative to the target historical data;

An early warning result is determined according to the rate of change.

Optionally, the analysis module is specifically used to:

determining the proportion of target traffic corresponding to the target network element according to the target general service data;

Acquiring the proportion of historical traffic corresponding to the target network element according to the historical general traffic data;

calculating the deviation value between the target business volume ratio and the historical traffic volume ratio;

According to the deviation value, an early warning result is determined.

Optionally, the target general service data includes the number of online users, and/or, traffic data; the first acquisition module is specifically used for:

Obtaining the network element type of the target network element;

Determine a general service item corresponding to the network element type according to a preset mapping relationship, where the mapping relationship is a mapping relationship between a network element type and a general service item;

The number of online users and/or traffic data corresponding to the common service items of the target network element are collected at intervals of preset time intervals.

According to a third aspect of the embodiments of the present disclosure, the present invention provides an electronic device, including: a memory, a processor, and a computer program;

Wherein, the computer program is stored in the memory, and is configured to execute, by the processor, the network element fault early warning method according to any one of the first aspect of the embodiments of the present disclosure.

According to the fourth aspect of the embodiments of the present disclosure, the present invention provides a computer-readable storage medium, where computer-executable instructions are stored in the computer-readable storage medium, and when executed by a processor, the computer-executable instructions are used to implement the following: The network element fault early warning method described in any one of the first aspects of the embodiments of the present disclosure.

The network element failure early warning method, device, electronic equipment and storage medium provided by the present invention obtain the target general service data of the target network element according to the preset strategy; obtain the target network element from the historical service data of the target network element. Yuan’s historical general business data, wherein, the historical business data includes at least one type of business data; according to the historical general business data, carry out early warning analysis on the target general business data, and obtain early warning results, due to network The target general service data and historical general service data are used in the early warning analysis of element failures, without separate analysis for network elements of different manufacturers, which improves the use efficiency of network element data, realizes automatic early warning of network element failures, and improves It improves the timeliness and accuracy of network failure early warning.

Description of 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 failure early warning method provided by an embodiment of the present invention;

FIG. 2 is a flowchart of a network element failure early warning method provided by an embodiment of the present invention;

FIG. 3 is a flowchart of a network element failure early warning method provided by another embodiment of the present invention;

Fig. 4 is the flowchart of step S204 in the embodiment shown in Fig. 3;

Fig. 5 is the flowchart of step S205 in the embodiment shown in Fig. 3;

Fig. 6 is the flowchart of step S206 in the embodiment shown in Fig. 3;

FIG. 7 is a schematic structural diagram of a network element failure early warning device provided by an embodiment of the present invention;

FIG. 8 is a schematic structural diagram of an electronic device provided by an embodiment of the present invention.

By means of the above-mentioned drawings, certain embodiments of the present disclosure have been shown and will be described in more detail hereinafter. These drawings and written description are not intended to limit the scope of the disclosed concept in any way, but to illustrate the disclosed concept for those skilled in the art by referring to specific embodiments.

Detailed ways

Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numerals in different drawings refer to the same or similar elements unless otherwise indicated. The implementations described in the following exemplary examples do not represent all implementations consistent with the present disclosure. Rather, they are merely examples of apparatuses and methods consistent with aspects of the present disclosure as recited in the appended claims.

At first the terms involved in the present invention are explained:

Network element: refers to the elements and devices in the network that can independently complete certain transmission functions. The network element is the smallest unit that is monitored and managed in network management. According to different network architectures, the definition of network elements can be different. For example, a network element may include a base station, and may also include a mobility management node function (Mobility Management Entity, MME), a serving gateway (Serving GateWay, SGW), a public data network (Public Data Network, PDN), etc., for the core network In terms of network elements, different network elements carry different services and services.

FIG. 1 is an application scene diagram of the network element failure early warning method provided by the embodiment of the present invention. As shown in FIG. 1, the network element failure early warning method provided by this embodiment is applied to electronic equipment, such as network management equipment 11, Connected to the core network, the core network includes a plurality of network elements 12 for core switching or call routing functions, and different network elements 12 bear different functions, and then play a role in supporting different telecommunication network services. The network management device 11 can pass The core network obtains the alarm information uploaded by the network element 12, and at the same time, sends the network element alarm information to the terminal device 13 used by the operation and maintenance personnel as required.

In the prior art, during the maintenance process of the core network, the discovery of network faults generally relies on network management alarms and user complaints. Among them, the network management alarm is generally generated and reported by each network element itself, and the timeliness is good, and the fault problem can be reflected in the first time, while the user complaint is often a serious problem in the network, which affects the user's use for a long time As a result, the timeliness is poor.

However, to implement fault early warning through network management alarms, it is necessary for engineers to comprehensively analyze and judge the alarm information, communication network element configuration information, signaling and other information after the network elements report the information, and finally locate the fault point. As the core network network becomes more and more complex, there are more and more types of network elements, and the alarm information of network elements from different manufacturers is inconsistent. Engineers need to be familiar with network elements from all manufacturers in order to realize accurate identification and early warning of network faults, and operate Difficulty, low accuracy. Therefore, it is often not until the user complains that the network failure is discovered, resulting in the problems of delayed early warning of network failure, poor timeliness, and low accuracy.

The technical solution of the present invention and how the technical solution of the present application solves the above technical problems will be described in detail below with specific embodiments. The following specific embodiments may be combined with each other, and the same or similar concepts or processes may not be repeated 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 failure early warning method provided by an embodiment of the present invention. As shown in Fig. 2, the network element failure early warning method provided in this embodiment includes the following steps:

Step S101, acquiring target general service data of a target network element according to a preset policy.

Wherein, the target network element refers to the network element to be alerted, and the target network element may be a network element, or a group of network elements that realize the same or similar functions. There are many ways to determine the target network element. For example, according to the preset maintenance plan, the network element is actively maintained, and the network element that needs to be maintained in the maintenance plan is used as the target network element; it can also be passively received by the network element. After requesting information, the network element that sends the requested information is used as the network element to be alerted, which can be designed according to specific requirements and usage scenarios, and is not specifically limited here.

The target general service data refers to the general service data of the target network element. Wherein, general service data refers to basic service data generated by network elements during the process of carrying traffic or services. For network elements produced by different manufacturers, common service data will be used to represent the basic operating status of network elements. Therefore, based on general service data, unified early warning for network elements of different manufacturers can be realized, without the need for separate configurations for different manufacturers Early warning strategy to effectively improve the efficiency of early warning. The general business data can be realized in many ways, for example, the number of online users, traffic, etc. The specific content of the general business data can be set according to the usage scenarios and requirements, that is, determined according to the preset strategy, and will not be detailed here limited.

Step S102, acquiring historical general service data of the target network element from historical service data of the target network element, wherein the historical service data includes at least one type of service data.

Historical service data refers to the service data generated by network elements and stored in a specific storage medium in the past period of time. The historical service data can represent the operating characteristics of a network element in a historical period, and, for the same or a type of network element, there is a certain correlation between the historical service data and the current service data. However, as the time from the historical data becomes longer, its correlation with the current operating status of the gateway will gradually weaken. Therefore, the corresponding time from the historical data should not be too long. The historical data can be selected within one year. Of course, it should be pointed out that for different network elements and different usage scenarios, the corresponding time length of historical data can be different and can be determined according to needs.

Specifically, historical general service data refers to historical service data of general service data, and historical service data includes at least one type of service data, for example, general service data and non-general service data, and non-general service data is the network element of each manufacturer Generated unique business data.

In step S103 , according to the historical general business data, an early warning analysis is performed on the target general business data, and an early warning result is obtained.

For the same or the same type of network element, that is, the target network element, since there is a certain correlation 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 history The running state of the network element corresponding to the general service data is inconsistent with the running state of the network element corresponding to the target general service data, that is, the running state of the network element has changed. Therefore, according to the relationship between the historical general service data and the target service data, the The current operating status of the gateway is judged to realize the early warning of the operating status of network elements.

In this embodiment, the target general service data of the target network element is obtained according to a preset strategy; the historical general service data of the target network element is obtained from the historical service data of the target network element, wherein the historical service data includes at least one type of service data; according to the historical general service data, carry out early warning analysis on the target general service data, and obtain the early warning results. The individual network elements are analyzed separately, which improves the efficiency of network element data usage, realizes automatic early warning of network element faults, and improves the timeliness and accuracy of network fault early warning.

Figure 3 is a flowchart of a network element failure early warning method provided by another embodiment of the present invention. As shown in Figure 3, the network element failure early warning method provided by this embodiment is different from the network element failure early warning method provided by the embodiment shown in Figure 2 On the basis of further refinement of steps S101 and S103, the target general service data includes the number of online users, and/or, traffic data, then the network element failure early warning method provided in this embodiment includes the following steps:

Step S201, acquiring the network element type of the target network element.

Specifically, according to the role played by the target network element, the network element can be divided into different network element types, for example, mobility management node, serving gateway, public data network, etc. For different types of network elements, the services and The services are different, and the corresponding operating state characteristics are also different. In the steps of this embodiment, by first determining the network element type of the target network element, and then classifying and processing, the accuracy of fault early warning can be improved, and a better early warning effect can be achieved.

Step S202: Determine the general service item corresponding to the network element type according to the preset mapping relationship, wherein the mapping relationship is the mapping relationship between the network element type and the general service item.

Different types of network elements have different general service items. For example, the general service item corresponding to a class A network element is call connection, and the general service item corresponding to a class B network element is mobility management. Different network elements correspond to different The mapping policy of the general service item is determined by the preset mapping relationship, that is, the general service item corresponding to different network elements can be determined according to the preset mapping relationship.

Step S203, collecting the number of online users and/or traffic data corresponding to the general service items of the target network element at intervals of preset time intervals.

Specifically, the data collection frequency is the preset time length, for example, the preset time length is 5 minutes, that is, the data of the target network element is collected every 5 minutes; the preset time length is 1 hour, that is, the target network element is collected every 1 hour The data and the specific value of the preset time length can be determined according to different needs, and are not specifically limited here.

When the target network element undertakes the service corresponding to the general service item, service information, such as the number of online users and traffic data, will be generated. These data are basic data, and network elements generated by different vendors will also generate these basic data. Taking the number of online users and/or traffic data as the target general service data can better reflect the operating status of the target network element, and can realize unified early warning for network elements of different manufacturers, improving the accuracy and efficiency of early warning.

Step S204, according to the historical general business data, threshold value analysis is performed on the target general business data, and an early warning result is determined.

When the historical general service data of the target network element and the specific threshold 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, determine the threshold range according to the historical general business data.

Specifically, the threshold range may be a value range of all historical general service data, for example, a maximum value and a minimum value range, and more specifically, for example, a maximum value and a minimum value of historical traffic of the target network element. The threshold range can be the value interval of some historical general business data, such as the effective value range of historical general business data within the 95% confidence interval. There are many methods for determining the threshold range, which can be determined according to specific needs. There is no specific limit.

Optionally, determine the threshold range based on historical general business data, including:

Obtain the target historical data that meets the preset conditions in the historical general business data. Specifically, it includes screening historical general business data, removing deviation points, and screening out data that can clearly characterize the characteristics of the operating state, and using it as sample data, that is, target historical data.

Use the target historical data to carry out machine learning based on the fbprophet algorithm to predict the general business data within the second preset time period in the future. By using the target historical data as sample data to perform machine learning based on the fbprophet algorithm, the data within the second preset time period can be predicted, for example, 1 week or 10 days.

Determine the threshold range based on the threshold for predicting general business data.

The predicted general business data output by machine learning is used as a standard to determine the threshold range. Compared with the historical general service data, the predicted general service data output by machine learning contains trend changes. Therefore, the threshold range determined according to the threshold of the predicted general service data is closer to the real target network element in the normal state. The value range of the data generated during operation can further improve the accuracy of fault warning for the target network element.

Step S2042, determine the warning result according to whether the threshold of the general service data is within the threshold range.

According to the same calculation method as calculating the historical general business data, the general business data threshold can be obtained by processing the general business data. Judging whether the threshold falls within the threshold range, judging whether the general service data is normal, and then judging whether the operation status of the target network element corresponding to the general service data is normal, and whether the operation status of the target network element is normal, is the early warning result.

For example, according to the historical general service data, it is determined that the effective value interval of the historical general service data within the 95% confidence interval of the target network element is [100,120], wherein the historical general service data is traffic data. Correspondingly, according to the same calculation method, that is, calculating the effective value of the general service data within the 95% confidence interval, the threshold value of the general service data is 110, and if it does not exceed the threshold range, it can be judged that the data is normal, that is, the operation of the target network element The state is normal; correspondingly, the threshold for obtaining general service data is 130, and if it exceeds the threshold range, it can be judged that the data is abnormal, that is, the operating state of the target network element is abnormal.

Of course, the fluctuation correction value can also be increased on the basis of the threshold range. For example, the fluctuation correction value is 5, that is, the threshold falls within the range of [100,120]±5, and the operation status of the target network element is considered to be normal, so as to increase the stability of the algorithm. and reduce the chance of false positives.

In step S205, according to the historical general business data, a sudden change analysis is performed on the target general business data, and an early warning result is determined.

Specifically, a sudden change refers to a sudden change in service data. For a network element, when it is running in a normal state, there will be no sudden change in service data. Therefore, performing mutation analysis on target general service data can realize Element failure warning.

Optionally, as shown in FIG. 5, step S205 includes three specific implementation steps of steps S2051, S2052, and S2053:

Step S2051, acquiring one or more target historical data corresponding to the target general business data in the historical general business data.

Obtain the target historical data that meets the preset conditions in the historical general business data. Specifically, it includes screening historical general business data, removing deviation points, and screening out data that can clearly characterize the characteristics of the operating state, and using it as sample data, that is, target historical data.

Step S2052, calculating the rate of change of the general business data relative to the target historical data.

With the operation of the service carried by the network element, the target historical data generated by the network element may change to a certain extent, including rise, fall or fluctuation within a certain range, and the change rate represents the degree of its fluctuation. 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, for example, the rate of change per minute, the rate of change every 15 minutes, the rate of change per hour, the rate of change per day, and the like. The change rate unit can be determined according to different requirements, and is not specifically limited here. The realization method of the change rate can be an absolute value, for example, the change rate of the number of online users is: 1000 people/day, that is, the daily fluctuation of the number of online users is within 1000 people. The implementation manner of the change rate may also be a relative value, for example, the change rate of the number of online users is 1%, that is, the daily fluctuation of the number of online users is within 1%.

Exemplarily, a method for calculating the rate of change includes:

Assuming that the index of general business data is x ₁ and the index of target historical data is x ₂ , the formula for calculating the rate of change d is d=(x ₁ -x ₂ )/x2, further, when d is greater than the preset value d ₀ , the NE indicator is abnormal.

Step S2053, determine the warning result according to the rate of change.

When the target network element is operating normally, the fluctuation of the target general service data generated by it should be similar to the fluctuation of the target historical data, for example, the number of daily online users in the target general service data and the target historical data, The rate of change is maintained within 1%, indicating that the target network element is operating normally. When the change rate of the number of daily online users in the target general service data is 10%, it may be caused by a connection failure of the target network element. Therefore, it can be judged that the target network element is faulty based on this.

In the steps of this embodiment, by judging the state of the target network element based on the change rate of the service data relative to the target historical data, the state change of the target network element can be better represented, and the accuracy of the target network element can be determined according to the change situation. The degree of change improves the accuracy of fault warning.

Step S206, according to the historical general business data, analyze the deviation of the target general business data, and determine the early warning result.

Specifically, the deviation analysis refers to analyzing the deviation of the proportion of the service volume carried by the target network element through the target general service data. For example, for a network element operating normally, the proportion of the traffic it undertakes in the network element group it belongs to is fixed, for example, 10%. In this way, by evenly distributing the service volume, the pressure can be shared among different network elements, and the service processing efficiency can be improved. When there is a problem with a network element in the network element group, the service volume it undertakes will decrease, and according to the deviation, the fault condition of the target network element can be judged.

Optionally, as shown in FIG. 6, step S206 includes four specific implementation steps of steps S2061, S2062, S2063, and S2064:

Step S2061, according to the target general service data, determine the target service volume proportion corresponding to the target network element.

Specifically, the target general service data includes characterization information on the data volume value, and according to the characterization information, the target data volume value corresponding to the target general service data can be determined. By calculating the ratio of the target data volume value to the preset total value, the target service volume proportion corresponding to the target network element can be determined. For example, the service volume of the information forwarding service corresponding to the target network element accounts for 10%, that is, the target network element undertakes 10% of the service volume.

Step S2062, according to the historical general service data, obtain the historical service volume proportion corresponding to the target network element.

Similarly, according to the historical general service data of the target network element, by calculating the ratio of the historical data value to the historical total value, the historical service volume proportion corresponding to the target network element can be determined. Wherein, the proportion of historical business volume should be the same as the general business item corresponding to the proportion of historical business volume.

Step S2063, calculating the deviation value between the target business volume proportion and the historical business volume proportion.

Calculate the difference between the proportion of target business volume and the proportion of historical business volume, and a signed deviation value can be obtained. When the deviation value is positive, it means that the proportion of target business volume has increased. When the deviation value is negative, It is said that the proportion of target business volume has decreased. For example, if the proportion of target business volume is 10%, and the proportion of historical business volume is 8%, then the deviation value is 2%, indicating that the proportion of target business volume increases by 2%.

Step S2064, determine the warning result according to the deviation value.

When the deviation value of the target traffic proportion is greater than the preset deviation threshold, it can be determined that the target network element is in an abnormal state.

Exemplarily, a method for deviation analysis includes:

时，网元指标异常。For a group of target network elements for load sharing, according to the preset ratio and deviation degree, assuming that there are n target network elements, the indicators are x ₁ , x ₂ ... x _n , and the proportion of the service volume of each network element is a ₁ , a ₂ ... a _n , where the index of network element i is x _i , the service ratio is a _i , and the preset deviation ratio threshold is d, then when

, the NE indicators are abnormal.

In the steps of this embodiment, the operating state of the target network element is judged by the deviation value of the proportion of the target traffic, which can further increase the accuracy of judging the operating state of the target network element, and increase the applicable scenarios and applications of the method in this embodiment. Use flexibility.

It should be noted that, in this embodiment, steps S204, S205, and S206 correspond to processing and analyzing target general business data based on historical general business data, and obtaining early warning results can be performed separately as required by steps S204, S205, and S206 Any one of the methods in S204, S205, and S206 can also be used in different orders to obtain early warning results in multiple ways, for example, according to multiple The weight coefficient corresponding to the item result and multiple results is used to determine the early warning result; another example is to determine the early warning result according to the worst result among the multiple results. Specific restrictions are made here.

Optionally, after any one of steps S204, S205, and S206, it also includes:

Step S207, pushing the warning result to the terminal device.

In order to enable maintenance personnel to determine the faulty network element immediately after a network failure occurs, the electronic device applying the network element failure early warning method provided in this embodiment pushes the early warning result to the terminal equipment used by the operation and maintenance personnel, such as through WeChat, APP etc., and push it to the mobile phone of the operation and maintenance personnel, so that the operation and maintenance personnel can locate the fault of the network element in time, and improve the timeliness of the early warning of the fault of the network element.

FIG. 7 is a schematic structural diagram of a network element failure early warning device provided by an embodiment of the present invention. As shown in FIG. 7 , the network element failure early warning device 7 provided in this embodiment includes:

The first acquisition module 71 is configured to acquire the target general service data of the target network element according to a preset policy;

The second acquiring module 72 is configured to acquire the historical general service data of the target network element from the historical service data of the target network element, wherein the historical service data includes at least one type of service data;

The analysis module 73 is configured to perform early warning analysis on target general business data according to historical general business data, and obtain early warning results.

Wherein, the first acquisition module 71 , the second acquisition module 72 and the analysis module 73 are connected in sequence. The network element failure early warning device 7 provided in this embodiment can implement the technical solution of the method embodiment shown in any one of Figures 2-6, and its implementation principle and technical effect are similar, and will not be repeated here.

FIG. 8 is a schematic diagram of an electronic device provided by an embodiment of the present invention. As shown in FIG. 8 , the electronic device provided by this embodiment includes: a memory 81 , a processor 82 and a computer program.

Wherein, 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 early warning device provided in any one of the embodiments corresponding to FIG. 2 to FIG. 6 of the present invention.

Wherein, the memory 81 and the processor 82 are connected through a bus 83 .

Relevant descriptions can be understood by referring to the relevant descriptions and effects corresponding to the steps in the embodiments corresponding to FIG. 2 to FIG. 6 , and details are not repeated here.

An embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored, and the computer program is executed by a processor to implement the network element provided by any of the embodiments corresponding to FIGS. 2-6 of the present invention. Fault warning device.

Among them, the computer-readable storage medium may be ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, optical data storage device and the like.

In the several embodiments provided by the present invention, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only illustrative. For example, the division of modules is only a logical function division. In actual implementation, there may be other division methods. For example, multiple modules or components can be combined or integrated. to another system, or some features may be ignored, or not implemented. In another point, the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces, and the indirect coupling or communication connection of devices or modules may be in electrical, mechanical or other forms.

Other embodiments of the invention will be readily apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. The present invention is intended to cover any modification, use or adaptation of the present invention. These modifications, uses or adaptations follow the general principles of the present invention and include common knowledge or conventional technical means in the technical field not disclosed in the present invention . The specification and examples are to be considered exemplary only, with a true scope and spirit of the invention being indicated by the following claims.

It should be understood that the present invention is not limited to the precise constructions which have been described above and shown in the accompanying drawings, and various modifications and changes may be made without departing from the scope thereof. The scope of the present 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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