CN108134703B - Network cell hidden danger fault prediction analysis method and device - Google Patents
Network cell hidden danger fault prediction analysis method and device Download PDFInfo
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/06—Management of faults, events, alarms or notifications
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/06—Management of faults, events, alarms or notifications
- H04L41/0677—Localisation of faults
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/14—Network analysis or design
- H04L41/147—Network analysis or design for predicting network behaviour
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- H—ELECTRICITY
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- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L43/00—Arrangements for monitoring or testing data switching networks
- H04L43/08—Monitoring or testing based on specific metrics, e.g. QoS, energy consumption or environmental parameters
Abstract
The embodiment of the invention provides a method and a device for predicting and analyzing hidden danger faults of a network cell, wherein the method comprises the following steps: the method comprises the steps of obtaining values of various characteristic indexes of a target cell within a preset time period, generating a characteristic distribution map according to the value of each characteristic index, comparing the characteristic distribution map of each characteristic index with a corresponding abnormal distribution map to obtain the number of abnormal characteristic distribution maps, obtaining an abnormal ratio according to the number of the abnormal characteristic distribution maps and the number of the characteristic indexes, determining the fault state of the target cell according to the abnormal ratio, achieving rapid analysis and judgment of hidden danger faults of the wireless network cell, and improving network quality.
Description
Technical Field
The embodiment of the invention relates to the technical field of network communication, in particular to a method and a device for predicting and analyzing hidden danger faults of a network cell.
Background
In recent years, mobile communication services have been rapidly developed, communication networks have become advanced and increasingly large in scale, but it is impossible to satisfy user demands by endlessly enlarging network scale, and with the increasing of network user volume, telephone traffic rapidly increases, which causes shortage and congestion of network resources, and therefore, hidden dangers are caused in each network cell, which affects the communication quality of users and the satisfaction degree of networks. Currently, fault detection for a cell is mostly based on complaints of users, the complaints are utilized to position a hidden danger cell, and then detection and processing are arranged.
Disclosure of Invention
The embodiment of the invention provides a method and a device for predicting and analyzing hidden danger faults of network cells, which are used for solving the problem that hidden danger fault cells cannot be located in time in the prior art.
In a first aspect, an embodiment of the present invention provides a method for predicting and analyzing a network cell hidden danger fault, including:
acquiring values of various characteristic indexes of a target cell in a preset time period;
generating a characteristic distribution map according to the value of each characteristic index;
comparing the characteristic distribution map of each characteristic index with the corresponding abnormal distribution map to obtain the number of the abnormal characteristic distribution maps;
obtaining an abnormal ratio according to the number of the abnormal feature distribution maps and the number of the feature indexes;
and determining the fault state of the target cell according to the abnormal ratio.
Optionally, the generating a feature distribution map according to the value of each feature index includes:
classifying the values of the target characteristic indexes according to a preset segmentation range;
acquiring the number of values in each segmentation range;
and generating a characteristic distribution map by adopting a preset graphic template according to the segmentation range and the corresponding number.
Optionally, comparing the feature distribution map of each feature index with the corresponding abnormal distribution map to obtain the number of the abnormal feature distribution maps, including:
acquiring the number of values in each segmentation range in the target characteristic distribution map and the corresponding abnormal distribution map;
acquiring the number ratio of the values of the target characteristic distribution map and the corresponding abnormal distribution map in a target segmentation range, and taking the number ratio as the matching ratio of the target segmentation range;
if the matching ratio is within the corresponding matching threshold range, matching the feature distribution map with the corresponding abnormal distribution map within the target segmentation range;
and if the matching number of the feature distribution map and the corresponding abnormal distribution map in all the segmentation ranges is smaller than the preset number, the feature distribution map is not matched with the corresponding abnormal distribution map, and the feature distribution map is used as the abnormal feature distribution map.
Optionally, the determining the fault state of the target cell according to the abnormal ratio includes:
if the abnormal ratio is larger than the preset ratio, determining that the target cell is a hidden trouble fault cell; otherwise, the target cell is determined to be a normal cell.
Optionally, the characteristic indicator includes a transmission margin, a coverage rate, an interference power, an average interference level, a handover success rate, an uplink packet loss rate, an uplink signal-to-noise ratio, a wireless call drop rate, a wireless call completing rate, and a wireless utilization rate.
In a second aspect, an embodiment of the present invention provides a network cell hidden danger fault prediction analysis apparatus, including:
the acquisition module is used for acquiring values of various characteristic indexes of the target cell in a preset time period;
the generating module is used for generating a characteristic distribution map according to the value of each characteristic index;
the comparison module is used for comparing the characteristic distribution map of each characteristic index with the corresponding abnormal distribution map to obtain the number of the abnormal characteristic distribution maps;
the calculation module is used for obtaining an abnormal ratio according to the number of the abnormal feature distribution maps and the number of the feature indexes;
and the determining module is used for determining the fault state of the target cell according to the abnormal ratio.
Optionally, the generating module is specifically configured to:
classifying the values of the target characteristic indexes according to a preset segmentation range;
acquiring the number of values in each segmentation range;
and generating a characteristic distribution map by adopting a preset graphic template according to the segmentation range and the corresponding number.
Optionally, the alignment module is specifically configured to:
acquiring the number of values in each segmentation range in the target characteristic distribution map and the corresponding abnormal distribution map;
acquiring the number ratio of the values of the target characteristic distribution map and the corresponding abnormal distribution map in a target segmentation range, and taking the number ratio as the matching ratio of the target segmentation range;
if the matching ratio is within the corresponding matching threshold range, matching the feature distribution map with the corresponding abnormal distribution map within the target segmentation range;
and if the matching number of the feature distribution map and the corresponding abnormal distribution map in all the segmentation ranges is smaller than the preset number, the feature distribution map is not matched with the corresponding abnormal distribution map, and the feature distribution map is used as the abnormal feature distribution map.
Optionally, the determining module is specifically configured to: if the abnormal ratio is larger than the preset ratio, determining that the target cell is a hidden trouble fault cell; otherwise, the target cell is determined to be a normal cell.
Optionally, the characteristic indicator includes a transmission margin, a coverage rate, an interference power, an average interference level, a handover success rate, an uplink packet loss rate, an uplink signal-to-noise ratio, a wireless call drop rate, a wireless call completing rate, and a wireless utilization rate.
As can be seen from the foregoing technical solutions, in the method and the device for predicting and analyzing a hidden danger fault in a network cell provided in the embodiments of the present invention, values of multiple feature indexes of a target cell in a preset time period are obtained, a feature distribution map is generated according to the value of each feature index, the feature distribution map of each feature index is compared with a corresponding abnormal distribution map to obtain the number of abnormal feature distribution maps, an abnormal ratio is obtained according to the number of the abnormal feature distribution maps and the number of the feature indexes, and a fault state of the target cell is determined according to the abnormal ratio, so that a quick analysis and judgment on a hidden danger fault in a wireless network cell are achieved, and network quality is improved.
Drawings
Fig. 1 is a schematic flowchart of a method for predicting and analyzing a hidden danger fault of a network cell according to an embodiment of the present invention;
fig. 2 is a schematic diagram of a generation flow of a feature distribution diagram according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a feature distribution map according to an embodiment of the present invention;
fig. 4 is a schematic flow chart illustrating a comparison between a feature distribution map and a corresponding abnormal distribution map according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of a network cell hidden danger fault prediction analysis apparatus according to an embodiment of the present invention.
Detailed Description
The following detailed description of embodiments of the present invention is provided in connection with the accompanying drawings and examples. The following examples are intended to illustrate the invention but are not intended to limit the scope of the invention.
Fig. 1 shows that an embodiment of the present invention provides a method for predicting and analyzing a network cell hidden danger fault, including:
and S11, acquiring the values of the various characteristic indexes of the target cell in a preset time period.
In this step, it should be noted that, in the embodiment of the present invention, the characteristic indicators include a transmission margin, a coverage rate, an interference power, an average interference level, a handover success rate, an uplink packet loss rate, an uplink signal-to-noise ratio, a wireless call drop rate, a wireless call completing rate, and a wireless utilization rate.
In the embodiment of the present invention, the preset time period may be, but is not limited to, a day, a week, or a month.
In the embodiment of the invention, the staff can monitor each characteristic index at the acquisition point by adopting equipment in the target cell to obtain the value of each characteristic index. A large number of values are collected for each of the characteristic indicators within a predetermined time period.
And S12, generating a characteristic distribution diagram according to the value of each characteristic index.
In this step, it should be noted that, in the embodiment of the present invention, as shown in fig. 2, this step may specifically include the following steps:
s121, classifying the values of the target characteristic indexes according to a preset segmentation range;
s122, acquiring the number of values in each segmentation range;
and S123, generating a feature distribution map by adopting a preset graphic template according to the segmentation range and the corresponding number.
With respect to steps S121 to S123, it should be noted that, the numerical values of each feature index may be uniformly segmented, and then the number of values in each segmentation range is counted. And finally, generating a characteristic distribution diagram by adopting a preset graphic template (such as column, cake and the like) according to the segmentation range and the corresponding number. As shown in fig. 3, taking "emission margin" as an example, it can be seen that the abscissa is the range of segments and the ordinate is the number of values in each segment.
In step S12, it is necessary to generate a feature distribution map for each feature index in each of steps S121 to S123.
S13, comparing the characteristic distribution map of each characteristic index with the corresponding abnormal distribution map to obtain the number of the abnormal characteristic distribution maps.
In this step, it should be noted that, in the embodiment of the present invention, as shown in fig. 4, this step may specifically include the following steps:
s131, acquiring the number of values in each segmentation range in the target feature distribution map and the corresponding abnormal distribution map;
s132, acquiring the number ratio of the values of the target characteristic distribution map and the corresponding abnormal distribution map in the target segmentation range as a matching ratio of the target segmentation range;
s133, if the matching ratio is within the corresponding matching threshold range, matching the feature distribution map with the corresponding abnormal distribution map within a target segmentation range;
and S134, if the matching number of the feature distribution map and the corresponding abnormal distribution map in all the segmentation ranges is smaller than the preset number, the feature distribution map is not matched with the corresponding abnormal distribution map, and the feature distribution map is used as the abnormal feature distribution map.
Regarding step S131 to step S134, it should be noted that the "emission margin" is taken as an example, and is the target feature distribution map. The system can prestore an abnormal distribution map of 'emission margin', the generation of the abnormal distribution map is based on the collection of index values of a large number of fault cells and normal cells as training data, and the distribution map is finally generated. The abnormal distribution map and the characteristic distribution map have the same segmentation range, so that the system acquires the number of values in each segmentation range in the target characteristic distribution map and the corresponding abnormal distribution map, and then acquires the number ratio of the values of the target characteristic distribution map and the corresponding abnormal distribution map in the target segmentation range as the matching ratio of the target segmentation range. If the segmentation range is [10-20], the segmentation range is the target segmentation range. The system would take 10 for the values in [10-20] in the target feature profile and would also take 15 for the values in [10-20] in the anomaly profile. At this time, the matching ratio is 10/15.
And if the matching ratio is within the corresponding matching threshold range, matching the characteristic distribution map with the corresponding abnormal distribution map within the target segmentation range. By way of example, the feature profile matches the corresponding anomaly profile within [10-20 ].
And if the matching number of the feature distribution map and the corresponding abnormal distribution map in all the segmentation ranges is smaller than the preset number, the feature distribution map is not matched with the corresponding abnormal distribution map, and the feature distribution map is used as the abnormal feature distribution map. If the segmentation range is 100, if the matching number is 79, and if the preset number is 80, the feature distribution map is not matched with the corresponding abnormal distribution map, and the feature distribution map is used as the abnormal feature distribution map.
And processing each feature distribution map according to the steps S131 to S134, and recording the number of the abnormal feature distribution maps.
S14, obtaining an abnormal ratio according to the number of the abnormal feature distribution maps and the number of the feature indexes;
and S15, determining the fault state of the target cell according to the abnormal ratio.
With respect to step S14 and step S15, it should be noted that, in the embodiment of the present invention, the number of the abnormal feature distribution maps is compared with the number of the feature indexes to obtain an abnormal ratio. If the abnormal ratio is larger than the preset ratio, determining that the target cell is a hidden trouble fault cell; otherwise, the target cell is determined to be a normal cell.
According to the method for predicting and analyzing the hidden danger faults of the network cell, the values of various characteristic indexes of a target cell in a preset time period are obtained, a characteristic distribution diagram is generated according to the values of the characteristic indexes, the characteristic distribution diagram of each characteristic index is compared with a corresponding abnormal distribution diagram to obtain the number of abnormal characteristic distribution diagrams, an abnormal ratio is obtained according to the number of the abnormal characteristic distribution diagrams and the number of the characteristic indexes, the fault state of the target cell is determined according to the abnormal ratio, the hidden danger faults of the wireless network cell are rapidly analyzed and judged, and the network quality is improved.
Fig. 5 shows a network cell hidden danger fault prediction analysis apparatus provided in an embodiment of the present invention, which includes an obtaining module 21, a generating module 22, a comparing module 23, a calculating module 24, and a determining module 25, where:
an obtaining module 21, configured to obtain values of multiple feature indexes of a target cell within a preset time period;
a generating module 22, configured to generate a feature distribution map according to the value of each feature index;
the comparison module 23 is configured to compare the feature distribution map of each feature index with the corresponding abnormal distribution map to obtain the number of abnormal feature distribution maps;
a calculating module 24, configured to obtain an abnormal ratio according to the number of the abnormal feature distribution maps and the number of the feature indexes;
and a determining module 25, configured to determine a fault state of the target cell according to the abnormal ratio.
Since the principle of the apparatus according to the embodiment of the present invention is the same as that of the method according to the above embodiment, further details are not described herein for further explanation.
It should be noted that, in the embodiment of the present invention, the relevant functional module may be implemented by a hardware processor (hardware processor).
According to the device for predicting and analyzing the hidden danger faults of the network cell, the values of various characteristic indexes of the target cell in a preset time period are obtained, the characteristic distribution map is generated according to the value of each characteristic index, the characteristic distribution map of each characteristic index is compared with the corresponding abnormal distribution map to obtain the number of the abnormal characteristic distribution maps, the abnormal ratio is obtained according to the number of the abnormal characteristic distribution maps and the number of the characteristic indexes, the fault state of the target cell is determined according to the abnormal ratio, the hidden danger faults of the wireless network cell are rapidly analyzed and judged, and the network quality is improved.
Furthermore, those skilled in the art will appreciate that while some embodiments described herein include some features included in other embodiments, rather than other features, combinations of features of different embodiments are meant to be within the scope of the invention and form different embodiments. For example, in the following claims, any of the claimed embodiments may be used in any combination.
It should be noted that the above-mentioned embodiments illustrate rather than limit the invention, and that those skilled in the art will be able to design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The usage of the words first, second and third, etcetera do not indicate any ordering. These words may be interpreted as names.
Those of ordinary skill in the art will understand that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; such modifications and substitutions do not depart from the spirit of the corresponding technical solutions and scope of the present invention as defined in the appended claims.
Claims (6)
1. A method for predicting and analyzing hidden danger faults of a network cell is characterized by comprising the following steps:
acquiring values of various characteristic indexes of a target cell in a preset time period, wherein the characteristic indexes comprise transmission allowance, coverage rate, received interference power, average interference level, switching success rate, uplink packet loss rate, uplink signal-to-noise ratio, wireless call drop rate, wireless call completing rate and wireless utilization rate;
generating a characteristic distribution map according to the value of each characteristic index;
comparing the characteristic distribution map of each characteristic index with the corresponding abnormal distribution map to obtain the number of the abnormal characteristic distribution maps;
obtaining an abnormal ratio according to the number of the abnormal feature distribution maps and the number of the feature indexes;
determining the fault state of the target cell according to the abnormal ratio;
wherein the generating a feature distribution map according to the value of each feature index includes:
classifying the values of the target characteristic indexes according to a preset segmentation range;
acquiring the number of values in each segmentation range;
and generating a characteristic distribution map by adopting a preset graphic template according to the segmentation range and the corresponding number.
2. The method of claim 1, wherein comparing the feature distribution map of each feature index with the corresponding abnormal distribution map to obtain the number of abnormal feature distribution maps comprises:
acquiring the number of values in each segmentation range in the target characteristic distribution map and the corresponding abnormal distribution map;
acquiring the number ratio of the values of the target characteristic distribution map and the corresponding abnormal distribution map in a target segmentation range, and taking the number ratio as the matching ratio of the target segmentation range;
if the matching ratio is within the corresponding matching threshold range, matching the feature distribution map with the corresponding abnormal distribution map within the target segmentation range;
and if the matching number of the feature distribution map and the corresponding abnormal distribution map in all the segmentation ranges is smaller than the preset number, the feature distribution map is not matched with the corresponding abnormal distribution map, and the feature distribution map is used as the abnormal feature distribution map.
3. The method of claim 2, wherein the determining the fault status of the target cell according to the abnormal ratio comprises:
if the abnormal ratio is larger than the preset ratio, determining that the target cell is a hidden trouble fault cell; otherwise, the target cell is determined to be a normal cell.
4. A network cell hidden danger fault prediction analysis device is characterized by comprising:
the device comprises an acquisition module, a processing module and a processing module, wherein the acquisition module is used for acquiring values of various characteristic indexes of a target cell in a preset time period, and the characteristic indexes comprise transmission allowance, coverage rate, received interference power, average interference level, switching success rate, uplink packet loss rate, uplink signal-to-noise ratio, wireless call drop rate, wireless call completing rate and wireless utilization rate;
the generating module is used for generating a characteristic distribution map according to the value of each characteristic index;
the comparison module is used for comparing the characteristic distribution map of each characteristic index with the corresponding abnormal distribution map to obtain the number of the abnormal characteristic distribution maps;
the calculation module is used for obtaining an abnormal ratio according to the number of the abnormal feature distribution maps and the number of the feature indexes;
a determining module, configured to determine a fault state of the target cell according to the abnormal ratio;
the generation module is specifically configured to:
classifying the values of the target characteristic indexes according to a preset segmentation range;
acquiring the number of values in each segmentation range;
and generating a characteristic distribution map by adopting a preset graphic template according to the segmentation range and the corresponding number.
5. The apparatus of claim 4, wherein the alignment module is specifically configured to:
acquiring the number of values in each segmentation range in the target characteristic distribution map and the corresponding abnormal distribution map;
acquiring the number ratio of the values of the target characteristic distribution map and the corresponding abnormal distribution map in a target segmentation range, and taking the number ratio as the matching ratio of the target segmentation range;
if the matching ratio is within the corresponding matching threshold range, matching the feature distribution map with the corresponding abnormal distribution map within the target segmentation range;
and if the matching number of the feature distribution map and the corresponding abnormal distribution map in all the segmentation ranges is smaller than the preset number, the feature distribution map is not matched with the corresponding abnormal distribution map, and the feature distribution map is used as the abnormal feature distribution map.
6. The apparatus of claim 5, wherein the determining module is specifically configured to: if the abnormal ratio is larger than the preset ratio, determining that the target cell is a hidden trouble fault cell; otherwise, the target cell is determined to be a normal cell.
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