CN114286370B - Method and device for determining influence of base station alarm on user perception service - Google Patents
Method and device for determining influence of base station alarm on user perception service Download PDFInfo
- Publication number
- CN114286370B CN114286370B CN202010986380.6A CN202010986380A CN114286370B CN 114286370 B CN114286370 B CN 114286370B CN 202010986380 A CN202010986380 A CN 202010986380A CN 114286370 B CN114286370 B CN 114286370B
- Authority
- CN
- China
- Prior art keywords
- base station
- station alarm
- kpi
- type
- user perception
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Classifications
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D30/00—Reducing energy consumption in communication networks
- Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks
Landscapes
- Telephonic Communication Services (AREA)
- Mobile Radio Communication Systems (AREA)
Abstract
The embodiment of the invention provides a method and a device for determining influence of base station alarm on user perceived business, in the embodiment of the method, base station alarm data are acquired, based on a base station alarm on user perceived business influence probability reference table, the probability value of influence of base station alarm types corresponding to the base station alarm data on each business type is determined through the base station alarm on user perceived business influence probability reference table, the probability value is determined based on the number of time periods included in a preset duration and the correlation calculation result of each base station alarm type and corresponding target user perceived KPI indexes in each time period, the business types and influence probabilities of base station alarm influence can be accurately positioned, and the target user perceived KPI indexes corresponding to each base station alarm type are one or more of primary user perceived KPI indexes, so that the workload of data processing is reduced.
Description
Technical Field
The present invention relates to the field of wireless communications, and in particular, to a method and apparatus for determining an influence of a base station alarm on a user perceived service.
Background
The base station is a basic unit of the cellular network, the running stability and reliability of the base station are directly related to the quality of the wireless network and the user perception, the alarm is the most visual data reflecting the stability of the base station, the prior mode for counting the relation between the base station alarm and the user perception is mainly through experience judgment, and whether the user perception is influenced is determined through alarm information and judgment of a main equipment manufacturer on whether the main equipment manufacturer influences the service. The existing assessment method has the following two problems:
1. the assessment means are not sufficiently comprehensive and accurate
The existing evaluation method mainly comprises the steps of obtaining the base station alarm information and corresponding the base station alarm information through alarm reasons, alarm levels and KPI (Key Performance Indicator ) indexes when alarms occur, but the specific association relation between the alarms and service types cannot be found, so that the type of service affected when the base station alarms occurs cannot be evaluated, the association result is not comprehensive and accurate enough, the real condition cannot be reflected, and accurate positioning is difficult.
2. Great workload
The existing evaluation method mainly processes multidimensional data manually, and obtains a result through data association, and the data processing and screening workload is large, time and labor are consumed due to the fact that data sources are more.
Disclosure of Invention
The embodiment of the invention provides a method and a device for determining influence of base station alarm on user perception service, which are used for solving the problems that the result in the prior art is not comprehensive and accurate enough, the real condition cannot be reflected, accurate positioning is difficult and the workload is large.
In a first aspect, an embodiment of the present invention provides a method for determining an influence of a base station alarm on a user perceived service, including:
acquiring base station alarm data;
determining a probability value of influence of a base station alarm type corresponding to base station alarm data on each service type based on a base station alarm influence probability reference table on a user perceived service;
the base station alarm influence probability reference table comprises probability values of influence of each base station alarm type on each service type, each service type is characterized by a plurality of primary user perception KPI indexes, the probability values are determined based on the number of time periods included in a preset time period and the correlation calculation result of each base station alarm type and the corresponding target user perception KPI indexes in each time period, and the target user perception KPI indexes corresponding to each base station alarm type are one or more of the primary user perception KPI indexes.
Optionally, the method for determining the influence of the base station alarm on the user perceived service according to one embodiment of the present invention, where the probability value is determined based on the number of time periods included in a preset duration and the correlation calculation result between each base station alarm type and the corresponding target user perceived KPI index in each time period, specifically includes:
in each time period, if the correlation calculation result of any one of all target user perception KPI indexes included in each service type and the base station alarm type is in strong correlation, adding 1 to the molecule of the probability value of the influence of the base station alarm type on the service type; the denominator of the probability value is the number of the time periods;
the correlation calculation result of each base station alarm type and the corresponding target user perception KPI is obtained based on the correlation coefficient of each base station alarm type and the corresponding target user perception KPI; and when the absolute value of the correlation coefficient is larger than a preset threshold value, the correlation calculation result is a strong correlation.
Optionally, according to the method for determining the influence of the base station alarm on the user perceived service according to an embodiment of the present invention, the correlation coefficient of each base station alarm type and the corresponding target user perceived KPI index is obtained by pearson correlation analysis based on each base station alarm type and the corresponding target user perceived KPI index.
Optionally, a method for determining an influence of a base station alarm on a user perceived service according to an embodiment of the present invention further includes:
determining a correlation calculation result of each base station alarm type and each of a plurality of primary user perception KPI indexes, and screening out a plurality of target user perception KPI indexes corresponding to each base station alarm type based on the correlation calculation result; the correlation calculation result of the target user perception KPI and the base station alarm type is strong correlation;
classifying the multiple primary user perception KPI indexes, determining multiple service types, wherein each service type is characterized by the multiple primary user perception KPI indexes.
Optionally, according to a method for determining an influence of a base station alarm on a user perceived service according to an embodiment of the present invention, the plurality of initially selected user perceived KPI indexes are determined based on network management KPI indexes and SOC (Service Operations Center, service operation center) perceived indexes.
Optionally, a method for determining an influence of a base station alarm on a user-aware service according to an embodiment of the present invention, where the service type includes: a surfing sensing class I, a surfing sensing class II, a voice sensing class I and a voice sensing class II;
The user perception KPI index corresponding to the Internet surfing perception class I comprises: application store download rate, video download average rate and video play success rate;
the user perception KPI index corresponding to the Internet surfing perception class II comprises: HTTP (HyperText Transfer Protocol ) response success rate, packet uplink average delay and packet downlink average delay;
the user perception KPI index corresponding to the voice perception class I comprises: VOLTE (Voice over Long Term Evolution-Term Evolution) call completing rate, VOLTE call dropping rate and average time delay of call establishment;
the user perception KPI index corresponding to the voice perception class II comprises: upstream average MOS (Mean Opinion Score, mean opinion value), downstream average MOS.
In a second aspect, an embodiment of the present invention further provides a device for determining an influence of a base station alarm on a user perceived service, including:
the base station alarm data acquisition module is used for acquiring base station alarm data;
the base station alarm to user perception service influence determining module is used for determining a probability value of influence of a base station alarm type corresponding to the base station alarm data on each service type based on a base station alarm to user perception service influence probability reference table;
The base station alarm influence probability reference table comprises probability values of influence of each base station alarm type on each service type, each service type is characterized by a plurality of primary user perception KPI indexes, the probability values are determined based on the number of time periods included in a preset time period and the correlation calculation result of each base station alarm type and the corresponding target user perception KPI indexes in each time period, and the target user perception KPI indexes corresponding to each base station alarm type are one or more of the primary user perception KPI indexes.
Optionally, the determining device for determining the influence of the base station alarm on the user perceived service according to one embodiment of the present invention, where the probability value is determined based on the number of time periods included in a preset duration and the correlation calculation result between each base station alarm type and the corresponding target user perceived KPI index in each time period, specifically includes:
in each time period, if the correlation calculation result of any one of all target user perception KPI indexes included in each service type and the base station alarm type is in strong correlation, adding 1 to the molecule of the probability value of the influence of the base station alarm type on the service type; the denominator of the probability value is the number of the time periods;
The correlation calculation result of each base station alarm type and the corresponding target user perception KPI is obtained based on the correlation coefficient of each base station alarm type and the corresponding target user perception KPI; and when the absolute value of the correlation coefficient is larger than a preset threshold value, the correlation calculation result is a strong correlation.
In a third aspect, an embodiment of the present invention further provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor implementing the steps of the method as provided in the first aspect above when the program is executed.
In a fourth aspect, embodiments of the present invention also provide a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the method as provided in the first aspect described above.
According to the method and the device for determining the influence of the base station alarm on the user-perceived service, the probability value of the influence of the base station alarm type corresponding to the base station alarm data on each service type is determined based on the base station alarm on-user-perceived service influence probability reference table, wherein each service type is characterized by a plurality of primary user-perceived KPI indexes, the probability value is determined based on the number of time periods included in a preset time period and the correlation calculation result of each base station alarm type and the corresponding target user-perceived KPI index in each time period, the service type and the influence probability of the influence of the base station alarm can be accurately positioned, the equipment maintenance plan is conveniently formulated in a targeted manner, the target user-perceived KPI index corresponding to each base station alarm type is one or more of the primary user-perceived KPI indexes, and the workload of data processing can be reduced.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flow chart of a method for determining influence of a base station alarm on a user perceived service according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of a device for determining influence of a base station alarm on a user perceived service according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The prior art cannot evaluate which type of service is affected when the base station alarms, the associated result is not comprehensive and accurate enough, the real condition cannot be reflected, and the accurate positioning is difficult, and meanwhile, the processing and screening workload of data is large due to the fact that the data sources are more, time and labor are consumed. Fig. 1 is a flow chart of a method for determining influence of a base station alarm on a user perceived service according to an embodiment of the present invention, where, as shown in fig. 1, the method includes:
step 110, acquiring base station alarm data.
Specifically, the base station alarm data are the most intuitive data reflecting the stability of the base station, and analyzing the influence of the base station alarm on the user perception service can facilitate optimizing maintenance personnel to process the base station fault in a targeted manner, thereby improving the user satisfaction degree rapidly and avoiding the waste of maintenance resources. Therefore, in order to determine the influence of the base station alarm on the user-perceived service, the determining device of the influence of the base station alarm on the user-perceived service needs to acquire the base station alarm data first, and the means for acquiring the base station alarm data is the content of the prior art, and the implementation of the invention is not particularly limited herein;
Step 120, determining probability values of influence of base station alarm types corresponding to the base station alarm data on each service type based on a base station alarm influence probability reference table on user perception services;
the base station alarm influence probability reference table comprises probability values of influence of each base station alarm type on each service type, each service type is characterized by a plurality of primary user perception KPI indexes, the probability values are determined based on the number of time periods included in a preset time period and the correlation calculation result of each base station alarm type and the corresponding target user perception KPI indexes in each time period, and the target user perception KPI indexes corresponding to each base station alarm type are one or more of the primary user perception KPI indexes.
Specifically, the base station alarm influence probability reference table includes probability values of influence of each base station alarm type on each service type, so that the device for determining influence of the base station alarm on the user perception service is based on the obtained base station alarm data, and can determine the probability value of influence of the base station alarm type corresponding to the base station alarm data on each service type through table lookup.
The base station alarms are performance alarms which are defined according to the existing rules, different types of base station alarms correspond to different base station faults, each user perception service type corresponds to a plurality of user perception KPI indexes, therefore, a plurality of primary user perception KPI indexes corresponding to the user perception service type are determined in advance, and each service type can be represented by the plurality of primary user perception KPI indexes. The base station alarm influence probability reference table for the user perception business is obtained based on a large number of base station alarms and user perception KPI index sample data analysis. To obtain the association relation between the base station alarms and the service types, the association relation between each base station alarm and the user perceived KPI index corresponding to the service type needs to be obtained first, so that the device for determining the influence of the base station alarms on the user perceived KPI index needs to determine the probability value of the influence of each base station alarm type on each service type based on the number of time periods included in a preset time period and the correlation calculation result of each base station alarm type and the corresponding target user perceived KPI index in each time period in advance. The preset time period may be one year, each time period may correspond to one day, the preset time period may also be one month, each time period may correspond to one hour, and specific preset time period and a division manner of the time periods may be freely set according to actual needs, which is not particularly limited in the embodiment of the present invention.
Because the number of the base station alarm types and the primary user perception KPI indexes is large, the workload of correlation calculation is huge, one or more of the primary user perception KPI indexes are selected for the target user perception KPI indexes corresponding to each base station alarm type, so that the workload of data processing is reduced, for example, 30 primary user perception KPI indexes are adopted, but for the base station alarm type I, only 5 of the base station alarm type I indexes are influenced, and then the 5 primary user perception KPI indexes are adopted as the target user perception KPI indexes of the base station alarm type I.
According to the method provided by the embodiment of the invention, the probability value of the influence of the base station alarm type corresponding to the base station alarm data on each service type is determined based on the base station alarm on user perception service influence probability reference table, wherein each service type is characterized by a plurality of primary user perception KPI indexes, the probability value is determined based on the number of time periods included in a preset time period and the correlation calculation result of each base station alarm type and the corresponding target user perception KPI index in each time period, the service type and the influence probability of the base station alarm influence can be accurately positioned, the equipment maintenance plan is conveniently formulated in a targeted manner, and the target user perception KPI index corresponding to each base station alarm type is one or more of the primary user perception KPI indexes, so that the workload of data processing can be reduced.
Based on the above embodiment, the probability value is determined based on the number of time periods included in a preset duration and the correlation calculation result between each base station alarm type and the corresponding target user perception KPI index in each time period, and specifically includes:
in each time period, if the correlation calculation result of any one of all target user perception KPI indexes included in each service type and the base station alarm type is in strong correlation, adding 1 to the molecule of the probability value of the influence of the base station alarm type on the service type; the denominator of the probability value is the number of the time periods;
the correlation calculation result of each base station alarm type and the corresponding target user perception KPI is obtained based on the correlation coefficient of each base station alarm type and the corresponding target user perception KPI; and when the absolute value of the correlation coefficient is larger than a preset threshold value, the correlation calculation result is a strong correlation.
Specifically, for each time period, calculating a correlation coefficient of each base station alarm type and a corresponding target user perception KPI index, when the absolute value of the correlation coefficient is larger than a preset threshold, the correlation calculation result is strong correlation, and if the correlation calculation result of the current base station alarm type and the corresponding target user perception KPI index is strong correlation, the current base station alarm type is considered to influence the target user perception KPI index, so that the current base station alarm type is judged to influence the service type corresponding to the target user perception KPI index, the number of influence times of the current base station alarm type on the service type corresponding to the target user perception KPI index is increased by 1, namely, the numerator of a probability value of the influence of the base station alarm type on the service type is increased by 1, and if the correlation calculation result is weak correlation, the numerator is unchanged, therefore, for each time period, the number of influence times of one base station alarm type on one service type is only 1 or 0, the number of the corresponding probability value of the influence of the corresponding base station alarm type on the service type is increased by 1 or 0, the number of the influence times of the base station alarm type on the service type in each time period is taken as a denominator, and the number of influence times of the base station alarm type on the service type on the same service type can be increased by the numerator. For example, the preset duration is 1 year, each time period is 1 day, the number of time periods is 365, the correlation coefficient of each base station alarm type and the corresponding target user perceived KPI index is calculated for the base station alarm type and the target user perceived KPI index data corresponding to each 1 day, if the correlation calculation result of the base station alarm type I and the corresponding target user perceived KPI index is a strong correlation, the molecular addition of 1 of the probability value of the base station alarm type I influencing the service type corresponding to the target user perceived KPI index is assumed, if the correlation calculation result of the base station alarm type I and the corresponding target user perceived KPI index in 300 time periods in 365 time periods is a strong correlation, the probability value of the base station alarm type I influencing the service type corresponding to the target user perceived KPI index is 300/365 ≡82%.
It should be noted that, since the target user perceived KPI indicators are one or more of the primary user perceived KPI indicators, there may be a plurality of target user perceived KPI indicators in all primary user perceived KPI indicators included in each service type, and as long as the correlation calculation result of any one of all target user perceived KPI indicators included in each service type and the base station alarm type is strongly correlated, the molecule of the probability value that the base station alarm type affects the service type is added with 1, and even if a plurality of target user perceived KPI indicators included in the same service type are strongly correlated with the base station alarm type, the molecule of the probability value is still added with 1, because it is determined whether the service type is affected by the base station alarm type, the result is only 1 or 0, and even if a plurality of user perceived KPI indicators are affected, the service type is considered to be affected only 1 time.
In the method provided by the embodiment of the invention, in each time period, if the correlation calculation result of any one of all target user perception KPI indexes included in each service type and the base station alarm type is strong correlation, the numerator of the probability value of the influence of the base station alarm type on the service type is added with 1, the denominator of the probability value is the number of the time period, a large amount of sample data can be analyzed to obtain the probability value of the influence of each base station alarm type on each service type, and the accuracy of analyzing the influence of the base station alarm on the user perception service is ensured.
Based on the above embodiment, the correlation coefficient between each base station alarm type and the corresponding target user perceived KPI index is obtained by pearson correlation analysis based on each base station alarm type and the corresponding target user perceived KPI index.
Specifically, the calculation formula of the pearson correlation coefficient in pearson correlation analysis is as follows:
ρ X,Y the pearson correlation coefficient, which is the pearson correlation coefficient for two consecutive variables X and Y, is used to measure the degree of linear correlation. cov (X, Y) is the covariance of X and Y, σ X Standard deviation of X, sigma Y Is the standard deviation of Y. Mu (mu) X Sum mu Y The expectations of X and Y, E (X), E (Y) and E (XY), respectively, are the variances of X, Y, XY, respectively.
The pearson correlation coefficient always takes values between-1.0 and 1.0, and a variable close to 0 is referred to as no correlation, and a variable close to 1 or-1 is referred to as having a strong correlation. For the embodiment of the invention, X is the value of each base station alarm type corresponding to each time period, when the base station alarm occurs, the corresponding value is 1, and when the base station alarm does not occur, the corresponding value is 0; y is the KPI index value perceived by each target user corresponding to each time period. It should be noted that, the target user perceived KPI index value is obtained by counting corresponding indexes of tens of millions of users, and the specific statistical manner is the content of the prior art, which is not specifically limited in the embodiment of the present invention.
According to the method provided by the embodiment of the invention, the pearson correlation analysis is performed on the basis of the alarm types of each base station and the corresponding target user perception KPI indexes to obtain the correlation coefficient of the alarm types of each base station and the corresponding target user perception KPI indexes, so that the accuracy of the correlation analysis result is ensured, and the accuracy of the influence analysis of the base station alarm on the user perception business is further ensured.
Based on the above embodiment, the method further includes:
determining a correlation calculation result of each base station alarm type and each of a plurality of primary user perception KPI indexes, and screening out a plurality of target user perception KPI indexes corresponding to each base station alarm type based on the correlation calculation result; the correlation calculation result of the target user perception KPI and the base station alarm type is strong correlation;
classifying the multiple primary user perception KPI indexes, determining multiple service types, wherein each service type is characterized by the multiple primary user perception KPI indexes.
Specifically, because the number of primary user perceived KPI indexes is large, if the correlation coefficient of each base station alarm type and each primary user perceived KPI index is calculated for each time period, the calculation workload is huge, so that the subsequent workload of calculating the correlation of each base station alarm type and each corresponding target user perceived KPI index in each time period is greatly reduced because of the reduction of the calculation workload and the improvement of the calculation efficiency, the correlation calculation result of each base station alarm type and each of a plurality of primary user perceived KPI indexes is determined based on one sample data, and the primary user perceived KPI index which is strongly correlated with the correlation calculation result of the base station alarm type is taken as the target user perceived KPI index corresponding to each base station alarm type, namely, the corresponding relation between the base station alarm with strong correlation and the user perceived KPI index is reserved, and the non-strongly correlated user perceived KPI index is removed.
Meanwhile, the determining device of the influence of the base station alarm on the user perception service classifies the plurality of primary user perception KPI indexes to determine a plurality of service types, and each service type is characterized by the plurality of primary user perception KPI indexes so as to perform subsequent probability value calculation.
According to the method provided by the embodiment of the invention, the correlation calculation result of each base station alarm type and each of the multiple primary user perception KPI indexes is determined, the multiple target user perception KPI indexes corresponding to each base station alarm type are screened out based on the correlation calculation result, the correlation calculation result of the target user perception KPI indexes and the base station alarm types is strongly correlated, the workload of probability value calculation is greatly reduced, meanwhile, the multiple primary user perception KPI indexes are classified, multiple service types are determined, each service type is characterized by the multiple primary user perception KPI indexes, and the probability value of influence of each base station alarm type on each service type can be accurately obtained.
Based on the above embodiment, the plurality of primary user perceived KPI indicators are determined based on the network management KPI indicator and the SOC perceived indicator.
Specifically, the network management KPI and the SOC perception index comprise a plurality of user perception KPI indexes, and in practical application, a plurality of primary user perception KPI indexes are selected according to the importance degree of the indexes.
According to the method provided by the embodiment of the invention, the network management KPI and the SOC perception index are used for determining the plurality of primary selection user perception KPI indexes, so that the affected condition of the corresponding service type can be accurately reflected according to the plurality of primary selection user perception KPI indexes, and the accuracy of analyzing the influence of the base station alarm on the user perception service is ensured.
Based on the above embodiment, the service types include: a surfing sensing class I, a surfing sensing class II, a voice sensing class I and a voice sensing class II;
the user perception KPI index corresponding to the Internet surfing perception class I comprises: application store download rate, video download average rate and video play success rate;
the user perception KPI index corresponding to the Internet surfing perception class II comprises: HTTP response success rate, small packet uplink average time delay and small packet downlink average time delay;
the user perception KPI index corresponding to the voice perception class I comprises: VOLTE call completing rate, VOLTE call dropping rate and average time delay of call establishment;
the user perception KPI index corresponding to the voice perception class II comprises: upstream average MOS and downstream average MOS.
Specifically, the user perceived KPI indicators are classified based on their correspondence with the service types. Of course, the service types are not limited to the above 4 types, and the number of the user perceived KPI indexes included in each service type and the corresponding service type may be changed according to the difference of the actually selected primary user perceived KPI indexes, which is not particularly limited in the embodiment of the present invention.
According to the method provided by the embodiment of the invention, the user perceived KPI is classified based on the corresponding relation between the user perceived KPI and the service type, so that the influence of the base station alarm on the user perceived service can be accurately obtained.
The following describes, with a specific example, a process and a method for generating the reference table of the probability of influence of the base station alarm on the user perceived service in any of the foregoing embodiments:
(1) And (3) data extraction: and acquiring base station alarm data and user perception KPI index data corresponding to a preset time length.
The base station alarm data is an influence performance alarm defined according to a group, taking a certain main equipment manufacturer as an example, 309 total pieces are shown in table 1:
TABLE 1
The user perception KPI index data is 36 indexes which are selected according to the network management KPI index and the SOC perception index and are relevant to user perception, namely, the initially selected user perception KPI index is shown in the table 2:
TABLE 2
(2) The pearson correlation coefficients for each of the 309 base station alert types and the 36 primary user perceived KPI indicators are determined as shown in table 3:
TABLE 3 Table 3
| Index 1 | Index 2 | Index 3 | Index 4 | Index 5 | ... | |
| Alarm type 1 | 0.96 | 0.32 | -0.97 | 0.56 | -0.32 | 0.75 |
| Alarm type 2 | 0.61 | 0.75 | -0.13 | 0.08 | -0.21 | 0.86 |
| Alarm type 3 | -0.71 | 0.81 | 0.23 | 0.15 | -0.53 | -0.70 |
| Alarm type 4 | 0.11 | -0.27 | 0.31 | -0.29 | 0.06 | 0.95 |
| Alarm type 5 | 0.81 | -0.13 | 0.19 | 0.78 | -0.55 | -0.78 |
| Alarm type 6 | 0.74 | 0.07 | 0.57 | -0.70 | -0.25 | 0.16 |
| Alarm type 7 | -0.52 | 0.57 | -0.18 | 0.64 | -0.25 | 0.25 |
| ... | 0.95 | 0.52 | -0.41 | 0.43 | 0.61 | -0.91 |
And when the absolute value of the correlation coefficient is larger than 0.8, the correlation calculation result is strong correlation, and an index which is strong correlation with each base station alarm type in the 36 primary user perception KPI indexes is used as a target user perception KPI index corresponding to the base station alarm type.
(3) Classifying the 36 primary user perception KPI indexes, determining a plurality of service types, wherein each service type is characterized by a plurality of primary user perception KPI indexes. As shown in table 4:
TABLE 4 Table 4
(4) Assuming that an alarm type X occurs, the possible affected traffic types are Y, each possible Y corresponds to a conditional probability of occurrence of an alarm type X: p (Y|X).
Dividing the base station alarm data and the user perception KPI index data corresponding to the preset duration into a plurality of time periods, and adding 1 to the numerator of the probability value (namely the conditional probability) that the base station alarm type affects the service type if the correlation calculation result of any one of all initially selected user perception KPI indexes included in the service type and the base station alarm type is strongly correlated in each time period; the denominator of the probability value is the number of the time periods.
Taking the accumulated value of the influence times of the same base station alarm type on the same service type in each time period as a molecule, the probability value of the influence of each base station alarm type on each service type can be obtained, and a probability reference table of the influence of the base station alarm on the user perception service is generated based on the probability value, as shown in the table 5:
TABLE 5
After the base station alarm influence probability reference table for the user perception service is obtained, the probability value of influence of the base station alarm type corresponding to the base station alarm data on each service type can be determined through table lookup based on the obtained base station alarm data. For example, after acquiring a piece of base station alarm information, based on the manufacturer alarm ID of the alarm information, searching the matched manufacturer alarm ID in the reference table, so as to acquire the influence probability of the alarm type corresponding to the alarm information on each service type.
Based on any of the foregoing embodiments, fig. 2 is a schematic diagram of a device for determining an influence of a base station alarm on a user perceived service according to an embodiment of the present invention, where, as shown in fig. 2, the device includes:
the base station alarm data acquisition module 210 is configured to acquire base station alarm data.
Specifically, in order to determine the influence of the base station alarm on the user perceived service, the base station alarm data acquisition module 210 needs to acquire the base station alarm data first, and the means for acquiring the base station alarm data is the content of the prior art, and the implementation of the present invention is not specifically limited herein.
The base station alarm to user perception service influence determining module 220 is configured to determine a probability value that a base station alarm type corresponding to the base station alarm data affects each service type based on a base station alarm to user perception service influence probability reference table;
the base station alarm influence probability reference table comprises probability values of influence of each base station alarm type on each service type, each service type is characterized by a plurality of primary user perception KPI indexes, the probability values are determined based on the number of time periods included in a preset time period and the correlation calculation result of each base station alarm type and the corresponding target user perception KPI indexes in each time period, and the target user perception KPI indexes corresponding to each base station alarm type are one or more of the primary user perception KPI indexes.
Specifically, the base station alarm to user perception service influence probability reference table includes probability values of influence of each base station alarm type on each service type, so that the base station alarm to user perception service influence determining module 220 can determine, based on the obtained base station alarm data, the probability value of influence of the base station alarm type corresponding to the base station alarm data on each service type through table lookup.
To obtain the association relationship between the base station alarms and the service types, the association relationship between each base station alarm and the user perceived KPI index corresponding to the service type needs to be obtained first, so that the base station alarm to user perceived KPI influence determining module 220 needs to determine the probability value of influence of each base station alarm type on each service type based on the number of time periods included in a preset duration and the correlation calculation result of each base station alarm type and the corresponding target user perceived KPI index in each time period in advance.
According to the device provided by the embodiment of the invention, the base station alarm to user perception service influence determining module is used for determining the probability value of the influence of the base station alarm type corresponding to the base station alarm data on each service type based on the base station alarm to user perception service influence probability reference table, wherein each service type is characterized by a plurality of primary user perception KPI indexes, the probability value is determined based on the number of time periods included in a preset duration and the correlation calculation result of each base station alarm type and the corresponding target user perception KPI index in each time period, the service type and the influence probability of the base station alarm influence can be accurately positioned, the equipment maintenance plan is conveniently formulated in a targeted manner, the target user perception KPI index corresponding to each base station alarm type is one or more of the primary user perception KPI indexes, and the workload of data processing can be reduced.
Based on the above embodiment, the probability value is determined based on the number of time periods included in a preset duration and the correlation calculation result between each base station alarm type and the corresponding target user perception KPI index in each time period, and specifically includes:
in each time period, if the correlation calculation result of any one of all target user perception KPI indexes included in each service type and the base station alarm type is in strong correlation, adding 1 to the molecule of the probability value of the influence of the base station alarm type on the service type; the denominator of the probability value is the number of the time periods;
the correlation calculation result of each base station alarm type and the corresponding target user perception KPI is obtained based on the correlation coefficient of each base station alarm type and the corresponding target user perception KPI; and when the correlation coefficient is larger than a preset threshold value, the correlation calculation result is a strong correlation.
Based on the above embodiment, the correlation coefficient between each base station alarm type and the corresponding target user perceived KPI index is obtained by pearson correlation analysis based on each base station alarm type and the corresponding target user perceived KPI index.
Based on the above embodiment, the device for determining the influence of the base station alarm on the user perceived service is further configured to:
determining a correlation calculation result of each base station alarm type and each of a plurality of primary user perception KPI indexes, and screening out a plurality of target user perception KPI indexes corresponding to each base station alarm type based on the correlation calculation result; the correlation calculation result of the target user perception KPI and the base station alarm type is strong correlation;
classifying the multiple primary user perception KPI indexes, determining multiple service types, wherein each service type is characterized by the multiple primary user perception KPI indexes.
Based on the above embodiment, the plurality of primary user perceived KPI indicators are determined based on the network management KPI indicator and the SOC perceived indicator.
Based on the above embodiment, the service types include: a surfing sensing class I, a surfing sensing class II, a voice sensing class I and a voice sensing class II;
the user perception KPI index corresponding to the Internet surfing perception class I comprises: application store download rate, video download average rate and video play success rate;
the user perception KPI index corresponding to the Internet surfing perception class II comprises: HTTP response success rate, small packet uplink average time delay and small packet downlink average time delay;
The user perception KPI index corresponding to the voice perception class I comprises: VOLTE call completing rate, VOLTE call dropping rate and average time delay of call establishment;
the user perception KPI index corresponding to the voice perception class II comprises: upstream average MOS and downstream average MOS.
The device for determining the influence of the base station alarm on the user perceived service provided by the embodiment of the invention can execute the method for determining the influence of the base station alarm on the user perceived service, and the specific working principle and the corresponding technical effects are the same as those of the embodiment of the method and are not repeated herein.
Fig. 3 illustrates a physical schematic diagram of an electronic device, as shown in fig. 3, where the electronic device may include: processor 310, communication interface (Communications Interface) 320, memory 330 and communication bus 340, wherein processor 310, communication interface 320, memory 330 accomplish communication with each other through communication bus 340. Processor 310 may invoke logic instructions in memory 330 to perform the flow of steps provided by the method embodiments described above.
Further, the logic instructions in the memory 330 described above may be implemented in the form of software functional units and may be stored in a computer-readable storage medium when sold or used as a stand-alone product. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
In another aspect, the present invention further provides a non-transitory computer readable storage medium, on which a computer program is stored, where the computer program when executed by a processor implements the step flow provided by the above method embodiment.
The apparatus embodiments described above are merely illustrative, wherein the elements illustrated as separate elements may or may not be physically separate, and the elements shown as elements may or may not be physical elements, may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art will understand and implement the present invention without undue burden.
From the above description of the embodiments, it will be apparent to those skilled in the art that the embodiments may be implemented by means of software plus necessary general hardware platforms, or of course may be implemented by means of hardware. Based on this understanding, the foregoing technical solution may be embodied essentially or in a part contributing to the prior art in the form of a software product, which may be stored in a computer readable storage medium, such as ROM/RAM, a magnetic disk, an optical disk, etc., including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method described in the respective embodiments or some parts of the embodiments.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (8)
1. A method for determining the impact of a base station alert on a user perceived service, comprising:
acquiring base station alarm data;
determining a probability value of influence of a base station alarm type corresponding to base station alarm data on each service type based on a base station alarm influence probability reference table on a user perceived service;
the base station alarm influence probability reference table comprises probability values of influence of each base station alarm type on each service type, each service type is characterized by a plurality of primary user perception KPI indexes, the probability values are determined based on the number of time periods included by a preset time period and the correlation calculation result of each base station alarm type and the corresponding target user perception KPI index in each time period, and the target user perception KPI index corresponding to each base station alarm type is one or more of the primary user perception KPI indexes;
The probability value is determined based on the number of time periods included in a preset time period and the correlation calculation result of each base station alarm type and the corresponding target user perception KPI in each time period, and specifically comprises the following steps:
in each time period, if the correlation calculation result of any one of all target user perception KPI indexes included in each service type and the base station alarm type is in strong correlation, adding 1 to the molecule of the probability value of the influence of the base station alarm type on the service type; the denominator of the probability value is the number of the time periods;
the correlation calculation result of each base station alarm type and the corresponding target user perception KPI is obtained based on the correlation coefficient of each base station alarm type and the corresponding target user perception KPI; and when the absolute value of the correlation coefficient is larger than a preset threshold value, the correlation calculation result is a strong correlation.
2. The method for determining influence of base station alarms on user-perceived traffic according to claim 1, wherein the correlation coefficient of each base station alarm type and the corresponding target user-perceived KPI index is obtained by pearson correlation analysis based on each base station alarm type and the corresponding target user-perceived KPI index.
3. The method for determining the impact of a base station alert on a user-perceived service of claim 1, further comprising:
determining a correlation calculation result of each base station alarm type and each of a plurality of primary user perception KPI indexes, and screening out a plurality of target user perception KPI indexes corresponding to each base station alarm type based on the correlation calculation result; the correlation calculation result of the target user perception KPI and the base station alarm type is strong correlation;
classifying the multiple primary user perception KPI indexes, determining multiple service types, wherein each service type is characterized by the multiple primary user perception KPI indexes.
4. The method of claim 3, wherein the plurality of primary user perceived KPI indicators are determined based on network management KPI indicators and SOC perceived indicators.
5. The method for determining the impact of a base station alert on a user-perceived service according to claim 4, wherein the service type comprises: a surfing sensing class I, a surfing sensing class II, a voice sensing class I and a voice sensing class II;
the user perception KPI index corresponding to the Internet surfing perception class I comprises: application store download rate, video download average rate and video play success rate;
The user perception KPI index corresponding to the Internet surfing perception class II comprises: HTTP response success rate, small packet uplink average time delay and small packet downlink average time delay;
the user perception KPI index corresponding to the voice perception class I comprises: VOLTE call completing rate, VOLTE call dropping rate and average time delay of call establishment;
the user perception KPI index corresponding to the voice perception class II comprises: upstream average MOS and downstream average MOS.
6. A device for determining the impact of a base station alert on a user perceived service, comprising:
the base station alarm data acquisition module is used for acquiring base station alarm data;
the base station alarm to user perception service influence determining module is used for determining a probability value of influence of a base station alarm type corresponding to the base station alarm data on each service type based on a base station alarm to user perception service influence probability reference table;
the base station alarm influence probability reference table comprises probability values of influence of each base station alarm type on each service type, each service type is characterized by a plurality of primary user perception KPI indexes, the probability values are determined based on the number of time periods included by a preset time period and the correlation calculation result of each base station alarm type and the corresponding target user perception KPI index in each time period, and the target user perception KPI index corresponding to each base station alarm type is one or more of the primary user perception KPI indexes;
The probability value is determined based on the number of time periods included in a preset time period and the correlation calculation result of each base station alarm type and the corresponding target user perception KPI in each time period, and specifically comprises the following steps:
in each time period, if the correlation calculation result of any one of all target user perception KPI indexes included in each service type and the base station alarm type is in strong correlation, adding 1 to the molecule of the probability value of the influence of the base station alarm type on the service type; the denominator of the probability value is the number of the time periods;
the correlation calculation result of each base station alarm type and the corresponding target user perception KPI is obtained based on the correlation coefficient of each base station alarm type and the corresponding target user perception KPI; and when the absolute value of the correlation coefficient is larger than a preset threshold value, the correlation calculation result is a strong correlation.
7. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the steps of the method for determining the impact of a base station alert on user perceived traffic as claimed in any one of claims 1 to 5 when said program is executed by said processor.
8. A non-transitory computer readable storage medium having stored thereon a computer program, which when executed by a processor performs the steps of a method of determining the impact of a base station alert on user perceived traffic as recited in any one of claims 1 to 5.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010986380.6A CN114286370B (en) | 2020-09-18 | 2020-09-18 | Method and device for determining influence of base station alarm on user perception service |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010986380.6A CN114286370B (en) | 2020-09-18 | 2020-09-18 | Method and device for determining influence of base station alarm on user perception service |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114286370A CN114286370A (en) | 2022-04-05 |
| CN114286370B true CN114286370B (en) | 2023-08-01 |
Family
ID=80867392
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010986380.6A Active CN114286370B (en) | 2020-09-18 | 2020-09-18 | Method and device for determining influence of base station alarm on user perception service |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114286370B (en) |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102447577A (en) * | 2011-10-31 | 2012-05-09 | 浪潮通信信息***有限公司 | Alarming treatment method of communication network for client orientation |
| CN107105447A (en) * | 2016-02-22 | 2017-08-29 | 大唐移动通信设备有限公司 | A kind of recognition methods of base station alarm information and device |
| CN108848515A (en) * | 2018-05-31 | 2018-11-20 | 武汉虹信技术服务有限责任公司 | A kind of internet of things service quality-monitoring platform and method based on big data |
| CN109005556A (en) * | 2018-07-24 | 2018-12-14 | 武汉虹信技术服务有限责任公司 | A kind of 4G network quality optimization method and system based on user bill |
| CN111200515A (en) * | 2018-11-20 | 2020-05-26 | ***通信集团内蒙古有限公司 | Alarm processing method and device |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6690411B2 (en) * | 1999-07-20 | 2004-02-10 | @Security Broadband Corp. | Security system |
| EP2894813A1 (en) * | 2014-01-08 | 2015-07-15 | Telefonaktiebolaget L M Ericsson (publ) | Technique for creating a knowledge base for alarm management in a communications network |
-
2020
- 2020-09-18 CN CN202010986380.6A patent/CN114286370B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102447577A (en) * | 2011-10-31 | 2012-05-09 | 浪潮通信信息***有限公司 | Alarming treatment method of communication network for client orientation |
| CN107105447A (en) * | 2016-02-22 | 2017-08-29 | 大唐移动通信设备有限公司 | A kind of recognition methods of base station alarm information and device |
| CN108848515A (en) * | 2018-05-31 | 2018-11-20 | 武汉虹信技术服务有限责任公司 | A kind of internet of things service quality-monitoring platform and method based on big data |
| CN109005556A (en) * | 2018-07-24 | 2018-12-14 | 武汉虹信技术服务有限责任公司 | A kind of 4G network quality optimization method and system based on user bill |
| CN111200515A (en) * | 2018-11-20 | 2020-05-26 | ***通信集团内蒙古有限公司 | Alarm processing method and device |
Non-Patent Citations (1)
| Title |
|---|
| EXCEL VBA在移动基站告警分析中的应用;何南 等;《大众科技》;全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114286370A (en) | 2022-04-05 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109921941B (en) | Network service quality evaluation and optimization method, device, medium and electronic equipment | |
| US9769190B2 (en) | Methods and apparatus to identify malicious activity in a network | |
| CN108075925B (en) | Improving performance of a communication network based on end-to-end performance observation and evaluation | |
| CN104396188A (en) | System and method for root cause analysis of mobile network performance problems | |
| CN111294819A (en) | Network optimization method and device | |
| CN109256148B (en) | Voice quality assessment method and device | |
| CN111158926B (en) | Service request analysis method, device and equipment | |
| CN112269937B (en) | Method, system and device for calculating user similarity | |
| CN110348717A (en) | Base station value methods of marking and device based on grid granularity | |
| CN114286370B (en) | Method and device for determining influence of base station alarm on user perception service | |
| CN107222319B (en) | Communication operation analysis method and device | |
| CN111368858B (en) | User satisfaction evaluation method and device | |
| CN112465565A (en) | User portrait prediction method and device based on machine learning | |
| CN112598326A (en) | Model iteration method and device, electronic equipment and storage medium | |
| CN111277451B (en) | Service evaluation method, device, terminal equipment and medium | |
| CN113517990B (en) | Method and device for predicting net recommendation value NPS (network performance indicator) | |
| CN114828055A (en) | User service perception evaluation method, device, equipment, medium and program product | |
| CN114358548A (en) | Method and device for determining evaluation index and electronic equipment | |
| CN113722199B (en) | Abnormal behavior detection method, device, computer equipment and storage medium | |
| CN111222897B (en) | Client Internet surfing satisfaction prediction method and device | |
| CN112312445B (en) | Voice call processing method and device, storage medium and server | |
| CN113905400B (en) | Network optimization processing method and device, electronic equipment and storage medium | |
| CN118134425A (en) | Intelligent management method, system and medium for travel scene | |
| CN117596621A (en) | Internet surfing satisfaction prediction method and device, electronic equipment and computer storage medium | |
| CN117609039A (en) | Policy evaluation method, device, terminal equipment and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |