CN115334560B - Base station abnormality monitoring method, device, equipment and computer readable storage medium - Google Patents

Base station abnormality monitoring method, device, equipment and computer readable storage medium Download PDF

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Publication number
CN115334560B
CN115334560B CN202211001605.3A CN202211001605A CN115334560B CN 115334560 B CN115334560 B CN 115334560B CN 202211001605 A CN202211001605 A CN 202211001605A CN 115334560 B CN115334560 B CN 115334560B
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base station
monitored
data
historical
flow
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CN115334560A (en
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吴争光
苗岩
郑夏妍
贾东霖
蔡勇
吕政辉
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China United Network Communications Group Co Ltd
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China United Network Communications Group Co Ltd
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/04—Arrangements for maintaining operational condition
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
    • H04W16/22—Traffic simulation tools or models

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Abstract

The application provides a method, a device, equipment and a computer readable storage medium for monitoring base station abnormality. The method comprises the following steps: acquiring predicted flow data of a base station to be monitored when busy and idle in a preset time period and historical flow data of the base station to be monitored when historical busy and idle; determining the flow weight of the base station to be monitored according to the predicted flow data of the base station to be monitored when busy and idle in a preset time period and the historical flow data of the base station to be monitored when the historical busy and idle; if the flow weight is greater than a preset weight threshold, determining that the base station to be monitored has invisible abnormality. According to the method, the predicted flow data and the historical flow data in idle and busy time are obtained, so that the flow weight is determined; and determining whether the base station to be monitored has invisible abnormality or not through the flow weight, and solving the problem of low efficiency of detecting the abnormality of the base station in the prior art.

Description

Base station abnormality monitoring method, device, equipment and computer readable storage medium
Technical Field
The present application relates to communications technologies, and in particular, to a method, an apparatus, a device, and a computer readable storage medium for monitoring an anomaly of a base station.
Background
With the development of network technology and the increasing of network scale, network topology and network equipment become more complex, and the variety of loaded services is also increasing. These have all created a significant increase in the chances of failure or performance problems in the network, with network monitoring facing greater challenges. The network monitoring aims to timely discover abnormal conditions in the network through continuous monitoring of network equipment and network operation conditions, and can timely send out alarm notification when the network is abnormal so as to remind network management personnel to take necessary measures to keep the network operating normally.
At present, there are two types of traffic base station abnormality detection: one is anomaly detection for sudden increases in network traffic, and the other is anomaly detection for a drop in network traffic to zero.
However, when the flow of the base station is suddenly reduced in a certain time period and is lower than the flow value in the normal state, the flow is larger than zero flow at the moment, and the flow is restored to the normal state in a later period; also, when the base station traffic increases suddenly in a certain period of time, the user traffic is normal in a certain period of time, but there is a potential abnormality, and the existing technology cannot detect such network abnormality, so that the existing base station abnormality detection efficiency is low.
Disclosure of Invention
The application provides a method, a device, equipment and a computer readable storage medium for monitoring base station abnormality, which are used for solving the problem of low base station abnormality detection efficiency in the prior art.
An embodiment of the present application provides a method for monitoring an abnormality of a base station, including:
acquiring predicted flow data of a base station to be monitored when busy and idle in a preset time period and historical flow data of the base station to be monitored when historical busy and idle;
Determining the flow weight of the base station to be monitored according to the predicted flow data of the base station to be monitored when busy and idle in a preset time period and the historical flow data of the base station to be monitored when the historical busy and idle;
and responding to the fact that the flow weight is larger than a preset weight threshold, and determining that the base station to be monitored has the invisible abnormality.
In an embodiment, obtaining predicted traffic data when a base station to be monitored is busy and idle in a preset time period specifically includes:
predicting historical flow data of the base station to be monitored by using a trained flow prediction model to obtain predicted flow data when the base station to be monitored is busy and idle in a preset time period;
wherein the trained traffic prediction model is obtained using coverage scenarios of the base station and traffic data training.
In an embodiment, before predicting the historical traffic data of the base station to be monitored by using the trained traffic prediction model to obtain the predicted traffic data when the base station to be monitored is busy for a preset period of time, the method further includes:
acquiring a coverage scene of a base station and service data of the base station at each moment, and dividing the coverage scene and the service data of the base station at each moment according to the service data of each base station at each moment during busy hours to acquire time characteristics of the base station; wherein the service data comprises total flow data;
and taking the coverage scene and the time characteristic of the base station as training samples, taking the total flow data of the base station at each moment as the label data of the training samples, and training a flow prediction model by using the training samples and the label data to obtain a trained flow prediction model.
In an embodiment, the time characteristics of the base stations are obtained by dividing the busy hours and the idle hours according to the service data of each base station at each time, which specifically includes:
When the total flow data of the base station in a certain period is the maximum total flow data in the total period, determining that the period is busy; when the total flow data of the base station in a certain period is the minimum total flow data in the total period, determining that the period is idle;
Or alternatively
The service data also comprises user access numbers, and when the user access number of the base station in a certain period is the maximum user access number in the total period, the period is determined to be busy; and when the user access number of the base station in a certain period is the minimum user access number in the total period, determining that the period is idle.
In an embodiment, determining the traffic weight of the base station to be monitored according to the predicted traffic data of the base station to be monitored when busy and idle in a preset time period and the historical traffic data of the base station to be monitored when the historical busy and idle comprises:
obtaining first flow weight according to predicted flow data of a base station to be monitored when busy in a preset time period and historical flow data of a historical busy time;
Obtaining second flow weight according to predicted flow data of the base station to be monitored when idle in a preset time period and historical flow data of the base station to be monitored when historical idle;
And processing the first flow weight and the second flow weight to obtain the flow weight of the base station to be monitored.
In an embodiment, the obtaining the first traffic weight according to the predicted traffic data of the base station to be monitored when busy and the historical traffic data when the historical busy in the preset time period specifically includes:
Calculating according to a first formula to obtain a first flow weight, wherein the first formula specifically comprises:
wherein ω 1 is a first traffic weight, a pre represents predicted traffic data of the base station to be monitored when busy in a preset time period, min (a) minimum historical traffic data of the base station to be monitored when historical busy, and max (a) maximum historical traffic data of the base station to be monitored when historical busy;
Obtaining a second traffic weight according to predicted traffic data of the base station to be monitored when idle in a preset time period and historical traffic data when historical idle, wherein the method specifically comprises the following steps:
calculating according to a second formula to obtain a second flow weight, wherein the second formula specifically comprises:
Wherein ω 2 is a second traffic weight, B pre represents predicted traffic data when the base station to be monitored is idle in a preset time period, min (B) minimum historical traffic data when the base station to be monitored is historically idle, and max (B) maximum historical traffic data when the base station to be monitored is historically idle.
In an embodiment, the processing the first traffic weight and the second traffic weight to obtain the traffic weight of the base station to be monitored specifically includes:
calculating according to a third formula to obtain the flow weight of the base station to be monitored, wherein the third formula specifically comprises:
ω＝αω1+βω2
ω is the traffic weight of the base station to be monitored, α is the duty cycle of the first traffic weight, and β is the duty cycle of the second traffic weight.
Another embodiment of the present application provides a device for monitoring an abnormality of a base station, including:
The acquisition module is used for acquiring predicted flow data of the base station to be monitored when busy and idle in a preset time period and historical flow data of the base station to be monitored when the base station is busy and idle in a historical mode;
The processing module is used for determining the flow weight of the base station to be monitored according to the predicted flow data of the base station to be monitored when busy and idle in a preset time period and the historical flow data of the base station to be monitored when the historical busy and idle;
the processing module is further used for determining that the base station to be monitored has a stealth abnormality in response to the fact that the flow weight is greater than a preset weight threshold.
Yet another embodiment of the present application provides a monitoring device, comprising: a memory and a processor;
Storing computer-executable instructions in a memory;
A processor executing computer-executable instructions stored in memory to implement the method of any one of claims 1 to 7.
A further embodiment of the application provides a computer-readable storage medium having stored therein computer-executable instructions which, when executed by a processor, are adapted to carry out the method of any one of claims 1 to 7.
A further embodiment of the application provides a computer program product comprising a computer program which, when executed by a processor, implements the method as claimed in any one of claims 1 to 7.
The application provides a method, a device, equipment and a computer readable storage medium for monitoring base station abnormality, which are used for acquiring predicted flow data when a base station to be monitored is busy and idle in a preset time period and historical flow data when the base station to be monitored is historically busy and idle; determining the flow weight of the base station to be monitored according to the predicted flow data of the base station to be monitored when busy and idle in a preset time period and the historical flow data of the base station to be monitored when the historical busy and idle; if the flow weight is greater than a preset weight threshold, determining that the base station to be monitored has invisible abnormality. The flow weight is determined by acquiring predicted flow data and historical flow data in idle and busy states; and determining whether the base station to be monitored has invisible abnormality or not through the flow weight, and solving the problem of low efficiency of detecting the abnormality of the base station in the prior art.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the application and together with the description, serve to explain the principles of the application.
Fig. 1 is a flowchart of a method for monitoring base station abnormality according to an embodiment of the present application;
fig. 2 is a flowchart of a method for monitoring base station abnormality according to another embodiment of the present application;
Fig. 3 is a flowchart of a method for monitoring base station abnormality according to still another embodiment of the present application;
Fig. 4 is a flowchart of a method for determining a traffic weight of a base station to be monitored according to another embodiment of the present application;
Fig. 5 is a schematic structural diagram of a base station abnormality monitoring device according to another embodiment of the present application;
fig. 6 is a schematic structural diagram of a monitoring device according to another embodiment of the present application.
Specific embodiments of the present application have been shown by way of the above drawings and will be described in more detail below. The drawings and the written description are not intended to limit the scope of the inventive concepts in any way, but rather to illustrate the inventive concepts to those skilled in the art by reference to the 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 do not represent all implementations consistent with the application. Rather, they are merely examples of apparatus and methods consistent with aspects of the application as detailed in the accompanying claims.
With the development of network technology and the increasing of network scale, network topology and network equipment become more complex, and the variety of loaded services is also increasing. These have all created a significant increase in the chances of failure or performance problems in the network, with network monitoring facing greater challenges. The network monitoring aims to timely discover abnormal conditions in the network through continuous monitoring of network equipment and network operation conditions, and can timely send out alarm notification when the network is abnormal so as to remind network management personnel to take necessary measures to keep the network operating normally.
At present, there are two types of traffic base station abnormality detection: one is anomaly detection for sudden increases in network traffic, and the other is anomaly detection for a drop in network traffic to zero.
However, when the flow of the base station is suddenly reduced in a certain time period and is lower than the flow value in the normal state, the flow is larger than zero flow at the moment, and the flow is restored to the normal state in a later period; also, when the base station traffic increases suddenly in a certain period of time, the user traffic is normal in a certain period of time, but there is a potential abnormality, and the existing technology cannot detect such network abnormality, so that the existing base station abnormality detection efficiency is low.
In view of the above problems, embodiments of the present application provide a method, an apparatus, a device, and a computer readable storage medium for monitoring base station anomalies, which aim to solve the problem of low efficiency of detecting base station anomalies in the prior art. The technical conception of the application is as follows: the method comprises the steps of obtaining predicted flow data of a base station to be monitored when busy and idle in a preset time period and historical flow data of the base station to be monitored when historical busy and idle; determining the flow weight of the base station to be monitored according to the predicted flow data of the base station to be monitored when busy and idle in a preset time period and the historical flow data of the base station to be monitored when the historical busy and idle; if the flow weight is greater than a preset weight threshold, determining that the base station to be monitored has invisible abnormality. According to the method, the predicted flow data and the historical flow data in idle and busy time are obtained, so that the flow weight is determined; and determining whether the base station to be monitored has invisible abnormality or not through the flow weight, and solving the problem of low efficiency of detecting the abnormality of the base station in the prior art.
As shown in fig. 1, an embodiment of the present application provides a method for monitoring an abnormality of a base station, including the following steps:
s101, obtaining predicted flow data of a base station to be monitored when busy and idle in a preset time period and historical flow data of the base station to be monitored when the base station is busy and idle in a historical mode.
In this step, the busy hours are determined according to the size of the traffic data from the time period corresponding to one whole day of the operation of the base station.
The predicted traffic data of the base station to be monitored when busy and idle in a preset time period can be obtained through a trained base station traffic prediction model and based on a long-short-period memory neural network model.
The historical traffic data of the base station to be monitored during the historical busy hour can be obtained through a corresponding base station control platform and OSSIM open source safety information management system (OPEN SOURCE SECURITY INFORMATION MANAGEMENT) platform.
It may be understood that the obtaining of the predicted traffic data of the base station to be monitored when busy and idle in the preset time period and the obtaining of the historical traffic data when the historical busy and idle are not limited to the above embodiments, but may be other obtaining manners according to the actual working conditions, which is not limited to this embodiment.
S102, determining the flow weight of the base station to be monitored according to the predicted flow data of the base station to be monitored when busy and idle in a preset time period and the historical flow data of the base station to be monitored when the historical busy and idle.
In the step, according to the predicted traffic data of the base station to be monitored when idle in a preset time period and the minimum traffic data when history is idle, obtaining the difference value between the predicted traffic data when idle in the preset time period and the minimum traffic data when history is idle; and obtaining the difference value of the maximum traffic data in idle time and the minimum traffic data in historical idle time according to the maximum traffic data in historical idle time and the minimum traffic data in historical idle time of the base station to be monitored. And calculates the two differences.
According to the predicted flow data of the base station to be monitored in busy hours in a preset time period and the minimum flow data in the historical busy hours, obtaining the difference value between the predicted flow data of the base station in busy hours in the preset time period and the minimum flow data in the historical busy hours; and obtaining the difference value of the maximum traffic data in busy hours and the minimum traffic data in the historical busy hours according to the maximum traffic data in the historical busy hours and the minimum traffic data in the historical busy hours of the base station to be monitored. And calculates the two differences.
And carrying out certain weight assignment on the difference value operation results in idle time and busy time respectively, thereby determining the flow weight of the base station to be monitored.
It can be understood that a certain weight assignment is performed on the difference value operation results of the idle time and the busy time respectively, and the assignment can be performed according to the actual working condition, which is not limited in this embodiment.
S103, if the flow weight is greater than a preset weight threshold.
In this step, the preset weight threshold is a value preset in advance according to the situation of the base station, the preset flow weight threshold is a flow weight standard value set by the operator according to the coverage scene, the threshold values of different coverage scenes are different, and after the flow weight is obtained in the above step, it is determined whether the flow weight is greater than the preset weight threshold.
It may be appreciated that the preset weight threshold may be assigned according to an actual working condition, which is not limited in this embodiment.
S104, determining that the base station to be monitored has invisible abnormality.
In this step, the invisible anomaly is not a base station failure in the conventional sense, and the invisible anomaly in the present invention is a phenomenon that the actual operation of the base station deviates from the normal operation range, and this phenomenon is defined as an invisible anomaly phenomenon of the base station.
And determining that the base station to be monitored has invisible abnormality when judging that the flow weight is greater than a preset weight threshold.
In the technical scheme, the predicted traffic data of the base station to be monitored when busy and idle in a preset time period and the historical traffic data of the base station to be monitored when the base station is busy and idle in a historical mode are obtained; determining the flow weight of the base station to be monitored according to the predicted flow data of the base station to be monitored when busy and idle in a preset time period and the historical flow data of the base station to be monitored when the historical busy and idle; if the flow weight is greater than a preset weight threshold, determining that the base station to be monitored has invisible abnormality. According to the method, the predicted flow data and the historical flow data in idle and busy time are obtained, so that the flow weight is determined; and determining whether the base station to be monitored has invisible abnormality or not through the flow weight, and solving the problem of low efficiency of detecting the abnormality of the base station in the prior art. Therefore, the anomaly is optimized in a targeted manner, and the network perception of the user is actively improved.
As shown in fig. 2, another embodiment of the present application provides a method for monitoring an abnormality of a base station, including the following steps:
S201, predicting historical flow data of a base station to be monitored by using a trained flow prediction model to obtain predicted flow data when the base station to be monitored is busy and idle in a preset time period; wherein the trained traffic prediction model is obtained using coverage scenarios of the base station and traffic data training.
In the step, the coverage scene type is an area scene where an access user under the base station is located, and the coverage scene corresponding to the base station is further determined according to the area scene. Exemplary area scenarios include, but are not limited to: residential areas, business offices, industrial parks, metropolitan villages, commercial shopping areas, transportation hubs, urban thoroughfares, schools, and the like. When the residential area where the access user under the base station is located is most, the coverage scene of the base station can be determined to be the residential area.
The traffic data is service traffic usage data recorded by the base station in the operation process, and the data can be divided into minute data, hour data and day data according to different corresponding time granularity. The historical traffic data of the base station is illustratively minute-level traffic data of the base station cell, the traffic unit being MB.
Training the time series model by using coverage scenes and flow data of the base station, thereby obtaining a flow prediction model. And predicting the historical flow data of the base station to be monitored by using the trained flow prediction model to obtain the predicted flow data of the base station to be monitored when busy and idle in a preset time period.
S202, determining the flow weight of the base station to be monitored according to the predicted flow data of the base station to be monitored when busy and idle in a preset time period and the historical flow data of the base station to be monitored when the historical busy and idle.
This step is already described in detail in S102, and will not be described here again.
S203, if the flow weight is greater than a preset weight threshold.
This step is already described in detail in S103, and will not be described here again.
S204, determining that the base station to be monitored has invisible abnormality.
This step is already described in detail in S104, and will not be described here again.
In the technical scheme, the historical flow data of the base station to be monitored is predicted by using the trained flow prediction model, so that the predicted flow data of the base station to be monitored when busy and idle in a preset time period is obtained; the trained traffic prediction model is obtained by using coverage scenes of the base station and traffic data training; determining the flow weight of the base station to be monitored according to the predicted flow data of the base station to be monitored when busy and idle in a preset time period and the historical flow data of the base station to be monitored when the historical busy and idle; if the flow weight is greater than a preset weight threshold, determining that the base station to be monitored has invisible abnormality. The method comprises the steps of establishing a prediction model, acquiring predicted flow data and historical flow data in idle and busy time, and further determining flow weight; and determining whether the base station to be monitored has invisible abnormality or not through the flow weight, and solving the problem of low efficiency of detecting the abnormality of the base station in the prior art.
As shown in fig. 3, a method for monitoring an abnormality of a base station according to still another embodiment of the present application includes the following steps:
S301, acquiring a coverage scene of a base station and service data of the base station at each moment, and dividing busy hours according to the service data of each base station at each moment to obtain time characteristics of the base station; wherein the traffic data comprises total traffic data.
In the step, all base stations in a certain period of area are acquired through a control platform, and the basic construction data and service data of all base stations are acquired; wherein the traffic data comprises total traffic data. The infrastructure data are engineering parameter data determined by the base station in the planning and construction process, and also comprise engineering parameter adjustment data and the like of the base station in the operation and maintenance process. The traffic data is service traffic usage data recorded by the base station in the operation process, and the data can be divided into minute data, hour data and day data according to different corresponding time granularity.
Illustratively, the base station cell infrastructure data is base station height, longitude and latitude, azimuth angle, downtilt angle, cell identification, frequency band, bandwidth, belonging grid and the like, the base station historical traffic data is the base station cell minute-level traffic data, and the traffic unit is MB.
And according to the coverage characteristics in the base station infrastructure data, obtaining a specific coverage scene of the base station. Exemplary area scenarios include, but are not limited to: residential areas, business offices, industrial parks, metropolitan villages, commercial shopping areas, transportation hubs, urban thoroughfares, schools, and the like. When the residential area where the access user under the base station is located is most, the coverage scene of the base station can be determined to be the residential area.
According to the traffic data of the base station, the traffic data time period is divided into working days, weekends and holidays. Furthermore, the working days, the weekends and the holidays are divided into idle time and busy time based on the time dimension, so that the time characteristics of the base station are obtained.
Illustratively, weekdays, weekends and holidays can be distinguished according to date attributes of the base station location, and idle busy hours can be divided according to idle busy hour attributes of the base station location. For example, 18 to 24 hours of a certain workday of a certain area are workday busy time periods, and 0 to 18 hours of a certain workday of a certain area are workday busy time periods.
It can be understood that the coverage scenario of the base station and the acquisition of the service data of the base station at each time are not limited to the foregoing embodiments, but may be other acquisition modes according to the actual working conditions, which is not limited to this embodiment.
In a specific embodiment, the time characteristics of the base stations are obtained by dividing busy and idle time according to service data of each base station at each moment, which specifically includes:
S301a, when the total flow data of a base station in a certain period is the maximum total flow data in the total period, determining that the period is busy; and when the total flow data of the base station in a certain period is the minimum total flow data in the total period, determining that the period is idle.
In the step, according to the hour granularity flow data of the base station, the hour with the largest flow is selected as the busy hour of the base station, and the hour with the smallest flow is selected as the idle hour of the base station.
In another specific embodiment, the obtaining the time characteristics of the base station according to the busy/idle time division of the service data of each base station at each time may specifically further include:
s301b, the service data also comprises user access numbers, and when the user access number of the base station in a certain period is the maximum user access number in the total period, the period is determined to be busy; and when the user access number of the base station in a certain period is the minimum user access number in the total period, determining that the period is idle.
In this step, the determination of the idle time of the base station may also select the time with the most user connection as the busy time and the time with the least user connection as the idle time according to the number of user connections with the granularity of the base station hours.
S302, taking coverage scenes and time characteristics of the base station as training samples, taking total flow data of the base station at each moment as label data of the training samples, and training a flow prediction model by using the training samples and the label data to obtain a trained flow prediction model.
In this step, after the coverage scene and the time feature of the base station are obtained in the step S301, a base station flow prediction model based on a time sequence is established by taking the total flow of each hour of the base station history as a training sample label according to the flow data corresponding to the coverage scene and the time feature of the base station as a training sample, wherein the time sequence model is a supervised algorithm commonly applicable in the current machine learning field. According to the real flow data of the historical hour granularity of the base station, the historical flow data of the base station to be monitored is predicted through a flow prediction model, and the predicted flow data of the base station to be monitored when busy and idle in a preset time period is obtained.
The time sequence model may be an LSTM (Long Short-Term Memory) model, the scene type and the traffic of each time of the base station are input into the unit of the model according to the time sequence characteristics, and the traffic use condition of each base station in the next time period is finally obtained through the extraction of the time sequence characteristics and the fusion of the Memory information of each unit.
It will be appreciated that the time series model is not limited to the above embodiment, but may be other models according to actual requirements, and the present embodiment is not limited thereto.
S303, predicting historical flow data of the base station to be monitored by using a trained flow prediction model to obtain predicted flow data of the base station to be monitored when busy and idle in a preset time period; wherein the trained traffic prediction model is obtained using coverage scenarios of the base station and traffic data training.
This step is already described in detail in S201, and will not be described here again.
S304, determining the flow weight of the base station to be monitored according to the predicted flow data of the base station to be monitored when busy and idle in a preset time period and the historical flow data of the base station to be monitored when the historical busy and idle.
This step is already described in detail in S102, and will not be described here again.
S305, if the flow weight is greater than a preset weight threshold.
This step is already described in detail in S103, and will not be described here again.
S306, determining that the base station to be monitored has invisible abnormality.
This step is already described in detail in S104, and will not be described here again.
In the technical scheme, the coverage scene of the base station and the service data of the base station at each moment are acquired, and when the total flow data of the base station in a certain period is the maximum total flow data in the total period, the period is determined to be busy; when the total flow data of the base station in a certain period is the minimum total flow data in the total period, determining that the period is idle, and obtaining the time characteristic of the base station; or, the service data also comprises the user access number, and when the user access number of the base station in a certain period is the maximum user access number in the total period, the period is determined to be busy; and when the user access number of the base station in a certain period is the minimum user access number in the total period, determining that the period is idle. And taking the coverage scene and the time characteristic of the base station as training samples, taking the total flow data of the base station at each moment as the label data of the training samples, and training a flow prediction model by using the training samples and the label data to obtain a trained flow prediction model. Predicting historical flow data of the base station to be monitored by using a trained flow prediction model to obtain predicted flow data when the base station to be monitored is busy and idle in a preset time period; further determining the flow weight of the base station to be monitored; if the flow weight is greater than a preset weight threshold, automatically identifying that the base station to be monitored has invisible abnormality. The problem of the inefficiency of prior art basic station anomaly detection is solved to the targeted optimization of this anomaly, initiatively improves user's network perception.
As shown in fig. 4, a method for determining a traffic weight of a base station to be monitored according to still another embodiment of the present application includes the following steps:
s401, obtaining first flow weight according to predicted flow data of a base station to be monitored when busy and historical flow data of a historical busy in a preset time period.
In this step, a first flow weight is obtained by calculation according to a first formula, which specifically includes:
Wherein ω 1 is a first traffic weight, A pre represents predicted traffic data of the base station to be monitored when busy, min (A) minimum historical traffic data of the base station to be monitored when historical busy, and max (A) maximum historical traffic data of the base station to be monitored when historical busy
S402, obtaining second flow weight according to predicted flow data of the base station to be monitored when idle in a preset time period and historical flow data of the base station to be monitored when historical idle.
In this step, a second flow weight is obtained by calculation according to a second formula, which specifically includes:
Wherein ω 2 is the second traffic weight, B pre represents the predicted traffic data of the base station to be monitored when idle, min (B) the minimum historical traffic data of the base station to be monitored when historically idle, and max (B) the maximum historical traffic data of the base station to be monitored when historically idle.
S403, processing the first flow weight and the second flow weight to obtain the flow weight of the base station to be monitored.
In the step, the flow weight of the base station to be monitored is obtained through calculation according to a third formula, wherein the third formula specifically comprises:
ω＝αω1+βω2
ω is the traffic weight of the base station to be monitored, α is the duty cycle of the first traffic weight, and β is the duty cycle of the second traffic weight.
It is to be understood that α and β are set according to actual conditions, and this embodiment is not limited thereto.
As shown in fig. 5, a base station abnormality monitoring apparatus 500 according to still another embodiment of the present application includes:
An obtaining module 501, configured to obtain predicted traffic data when a base station to be monitored is busy and idle in a preset period of time and historical traffic data when the base station to be monitored is historically busy and idle;
The processing module 502 is configured to determine a traffic weight of the base station to be monitored according to predicted traffic data when the base station to be monitored is busy and idle in a preset time period and historical traffic data when the base station to be monitored is busy and idle in a historical time period;
The processing module 502 is further configured to determine that the base station to be monitored has a stealth anomaly if the traffic weight is greater than a preset weight threshold.
In an embodiment, obtaining predicted traffic data when a base station to be monitored is busy and idle in a preset time period specifically includes:
the processing module 502 is further configured to predict historical traffic data of the base station to be monitored by using the trained traffic prediction model, so as to obtain predicted traffic data when the base station to be monitored is busy and idle in a preset time period;
wherein the trained traffic prediction model is obtained using coverage scenarios of the base station and traffic data training.
In an embodiment, before predicting the historical traffic data of the base station to be monitored by using the trained traffic prediction model to obtain the predicted traffic data when the base station to be monitored is busy for a preset period of time, the method further includes:
The processing module 502 is further configured to obtain a coverage scenario of the base station and service data of the base station at each time, and divide the coverage scenario and the service data of the base station at each time according to busy hours and idle hours of the service data of the base station at each time, so as to obtain a time characteristic of the base station; wherein the service data comprises total flow data;
The processing module 502 is further configured to use the coverage scene and the time feature of the base station as training samples, use total traffic data of the base station at each moment as tag data of the training samples, and train the traffic prediction model by using the training samples and the tag data to obtain a trained traffic prediction model.
In an embodiment, the time characteristics of the base stations are obtained by dividing the busy hours and the idle hours according to the service data of each base station at each time, which specifically includes:
the processing module 502 is further configured to determine that the period is busy when the total traffic data of the base station in a certain period is the maximum total traffic data in the total period; when the total flow data of the base station in a certain period is the minimum total flow data in the total period, determining that the period is idle;
Or alternatively
The processing module 502 is further configured to determine that the time period is busy when the number of user accesses of the base station in a certain time period is the maximum number of user accesses in the total time period; and when the user access number of the base station in a certain period is the minimum user access number in the total period, determining that the period is idle.
In an embodiment, determining the traffic weight of the base station to be monitored according to the predicted traffic data of the base station to be monitored when busy and idle in a preset time period and the historical traffic data of the base station to be monitored when the historical busy and idle comprises:
The processing module 502 is further configured to obtain a first traffic weight according to predicted traffic data of the base station to be monitored when busy in a preset time period and historical traffic data of the base station to be monitored when historical busy;
the processing module 502 is further configured to obtain a second traffic weight according to the predicted traffic data of the base station to be monitored when idle in the preset time period and the historical traffic data of the base station to be monitored when historical idle;
The processing module 502 is further configured to process the first traffic weight and the second traffic weight to obtain a traffic weight of the base station to be monitored.
In an embodiment, the obtaining the first traffic weight according to the predicted traffic data of the base station to be monitored when busy and the historical traffic data when the historical busy in the preset time period specifically includes:
the processing module 502 is further configured to calculate and obtain a first flow weight according to a first formula, where the first formula specifically includes:
Wherein ω 1 is a first traffic weight, a pre represents predicted traffic data of the base station to be monitored when busy, min (a) minimum historical traffic data of the base station to be monitored when historical busy, and max (a) maximum historical traffic data of the base station to be monitored when historical busy;
The processing module 502 is further configured to obtain a second traffic weight according to predicted traffic data of the base station to be monitored when idle in a preset time period and historical traffic data of the base station to be monitored when historical idle, and specifically includes:
The processing module 502 is further configured to calculate and obtain a second flow weight according to a second formula, where the second formula specifically includes:
Wherein ω 2 is the second traffic weight, B pre represents the predicted traffic data of the base station to be monitored when idle, min (B) the minimum historical traffic data of the base station to be monitored when historically idle, and max (B) the maximum historical traffic data of the base station to be monitored when historically idle.
In an embodiment, the processing the first traffic weight and the second traffic weight to obtain the traffic weight of the base station to be monitored specifically includes:
The processing module 502 is further configured to calculate and obtain a traffic weight of the base station to be monitored according to a third formula, where the third formula specifically includes:
ω＝αω1+βω2
ω is the traffic weight of the base station to be monitored, α is the duty cycle of the first traffic weight, and β is the duty cycle of the second traffic weight.
As shown in fig. 6, a further embodiment of the present application provides a monitoring device 600, the monitoring device 600 comprising a memory 601 and a processor 602.
Wherein the memory 601 is for storing computer instructions executable by the processor;
The processor 602, when executing computer instructions, implements the steps of the methods of the embodiments described above. Reference may be made in particular to the relevant description of the embodiments of the method described above.
Alternatively, the memory 601 may be separate or integrated with the processor 602. When the memory 601 is provided separately, the electronic device further comprises a bus for connecting the memory 601 and the processor 602.
The embodiment of the application also provides a computer readable storage medium, wherein computer instructions are stored in the computer readable storage medium, and when the processor executes the computer instructions, the steps of the method in the embodiment are realized.
Embodiments of the present application also provide a computer program product comprising computer instructions which, when executed by a processor, implement the steps of the method of the above embodiments.
Other embodiments of the application will be apparent to those skilled in the art from consideration of the specification and practice of the application disclosed herein. This application is intended to cover any variations, uses, or adaptations of the application following, in general, the principles of the application and including such departures from the present disclosure as come within known or customary practice within the art to which the application pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the application being indicated by the following claims.
It is to be understood that the application 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 application is limited only by the appended claims.

Claims (8)

1. A method for monitoring anomalies in a base station, the method comprising:
acquiring predicted flow data of a base station to be monitored when busy and idle in a preset time period and historical flow data of the base station to be monitored when historical busy and idle;
Obtaining first flow weight according to predicted flow data of the base station to be monitored when busy and historical flow data of the base station to be monitored when busy in a preset time period;
obtaining second flow weight according to predicted flow data of the base station to be monitored when idle in a preset time period and historical flow data of the base station to be monitored when historical idle;
Processing the first flow weight and the second flow weight to obtain the flow weight of the base station to be monitored;
Determining that the base station to be monitored has a stealth abnormality in response to the flow weight being greater than a preset weight threshold;
obtaining a first traffic weight according to the predicted traffic data of the base station to be monitored when busy and the historical traffic data of the base station to be monitored when busy in a preset time period, wherein the method specifically comprises the following steps:
Calculating according to a first formula to obtain the first flow weight, wherein the first formula specifically comprises:
Wherein ω 1 is the first traffic weight, a pre represents predicted traffic data of the base station to be monitored when busy in a preset time period, min (a) minimum historical traffic data of the base station to be monitored when historical busy, and max (a) maximum historical traffic data of the base station to be monitored when historical busy;
Obtaining a second traffic weight according to the predicted traffic data of the base station to be monitored when idle in a preset time period and the historical traffic data when historical idle, wherein the method specifically comprises the following steps:
calculating according to a second formula to obtain the second flow weight, wherein the second formula specifically comprises:
Wherein ω 2 is the second traffic weight, B pre represents predicted traffic data when the base station to be monitored is idle in a preset time period, min (B) minimum historical traffic data when the base station to be monitored is historically idle, and max (B) maximum historical traffic data when the base station to be monitored is historically idle.
2. The method for monitoring according to claim 1, wherein the step of obtaining the predicted traffic data when the base station to be monitored is busy for a preset period of time specifically comprises:
Predicting historical flow data of a base station to be monitored by using a trained flow prediction model to obtain predicted flow data of the base station to be monitored when busy and idle in a preset time period;
the trained traffic prediction model is obtained by using coverage scenes of the base station and traffic data training.
3. The monitoring method according to claim 2, wherein before predicting historical traffic data of a base station to be monitored using a trained traffic prediction model to obtain predicted traffic data of the base station to be monitored when busy for a preset period of time within the preset period of time, the method further comprises:
acquiring a coverage scene of a base station and service data of the base station at each moment, and dividing the coverage scene and the service data of the base station at each moment according to the service data of each base station at each moment when busy and idle, so as to acquire the time characteristics of the base station; wherein the service data comprises total flow data;
And taking the coverage scene and the time characteristic of the base station as training samples, taking the total flow data of the base station at each moment as the label data of the training samples, and training a flow prediction model by using the training samples and the label data to obtain the trained flow prediction model.
4. The method for monitoring abnormal base station according to claim 3, wherein the time characteristics of the base stations are obtained by dividing busy hours and idle hours according to service data of each base station at each moment, and the method specifically comprises the following steps:
When the total flow data of the base station in a certain period is the maximum total flow data in the total period, determining that the period is busy; when the total flow data of the base station in a certain period is the minimum total flow data in the total period, determining that the period is idle;
Or alternatively
The service data also comprises user access numbers, and when the user access number of the base station in a certain period is the maximum user access number in the total period, the period is determined to be busy; and when the user access number of the base station in a certain period is the minimum user access number in the total period, determining that the period is idle.
5. The method for monitoring the base station abnormality according to claim 1, wherein processing the first traffic weight and the second traffic weight to obtain the traffic weight of the base station to be monitored specifically comprises:
Calculating according to a third formula to obtain the flow weight of the base station to be monitored, wherein the third formula specifically comprises:
ω＝αω1+βω2
omega is the flow weight of the base station to be monitored, alpha is the duty ratio coefficient of the first flow weight, and beta is the duty ratio coefficient of the second flow weight.
6. A monitoring apparatus for base station anomalies, comprising:
The acquisition module is used for acquiring predicted flow data of the base station to be monitored when busy and idle in a preset time period and historical flow data of the base station to be monitored when the base station is busy and idle in a historical mode;
The processing module is used for obtaining first flow weight according to predicted flow data of the base station to be monitored when busy in a preset time period and historical flow data of the base station to be monitored when historical busy; obtaining second flow weight according to predicted flow data of the base station to be monitored when idle in a preset time period and historical flow data of the base station to be monitored when historical idle; processing the first flow weight and the second flow weight to obtain the flow weight of the base station to be monitored;
The response processing module is further used for determining that the base station to be monitored has a stealth abnormality if the flow weight is greater than a preset weight threshold;
the processing module is specifically configured to, when obtaining a first traffic weight according to predicted traffic data of the base station to be monitored when busy in a preset time period and historical traffic data of the base station to be monitored when historical busy:
Calculating according to a first formula to obtain the first flow weight, wherein the first formula specifically comprises:
Wherein ω 1 is the first traffic weight, a pre represents predicted traffic data of the base station to be monitored when busy in a preset time period, min (a) minimum historical traffic data of the base station to be monitored when historical busy, and max (a) maximum historical traffic data of the base station to be monitored when historical busy;
the processing module is specifically configured to, when obtaining the second traffic weight according to the predicted traffic data of the base station to be monitored when idle in a preset time period and the historical traffic data of the base station to be monitored when historical idle:
calculating according to a second formula to obtain the second flow weight, wherein the second formula specifically comprises:
Wherein ω 2 is the second traffic weight, B pre represents predicted traffic data when the base station to be monitored is idle in a preset time period, min (B) minimum historical traffic data when the base station to be monitored is historically idle, and max (B) maximum historical traffic data when the base station to be monitored is historically idle.
7. A monitoring device, comprising: a memory and a processor;
Storing computer-executed instructions in the memory;
the processor executes computer-executable instructions stored in the memory to implement the method of any one of claims 1 to 5.
8. A computer readable storage medium having stored therein computer executable instructions which when executed by a processor are adapted to carry out the method of any one of claims 1 to 5.
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