CN107147521B - Early warning and monitoring method for complaint service - Google Patents
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- 238000012544 monitoring process Methods 0.000 title claims abstract description 25
- 238000000034 method Methods 0.000 title claims abstract description 24
- 230000011664 signaling Effects 0.000 claims abstract description 92
- 238000004458 analytical method Methods 0.000 claims abstract description 17
- 238000007405 data analysis Methods 0.000 claims abstract description 12
- 230000002159 abnormal effect Effects 0.000 claims description 20
- 230000004888 barrier function Effects 0.000 claims description 3
- 238000005516 engineering process Methods 0.000 claims description 3
- 238000012545 processing Methods 0.000 claims description 3
- 238000004141 dimensional analysis Methods 0.000 claims description 2
- 230000000007 visual effect Effects 0.000 claims description 2
- 230000006399 behavior Effects 0.000 claims 1
- 239000000872 buffer Substances 0.000 claims 1
- 230000003139 buffering effect Effects 0.000 description 3
- 238000004364 calculation method Methods 0.000 description 3
- 238000005457 optimization Methods 0.000 description 3
- 208000027744 congestion Diseases 0.000 description 2
- 230000007547 defect Effects 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
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- 238000011161 development Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
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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
- H04L41/0631—Management of faults, events, alarms or notifications using root cause analysis; using analysis of correlation between notifications, alarms or events based on decision criteria, e.g. hierarchy, tree or time analysis
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/27—Replication, distribution or synchronisation of data between databases or within a distributed database system; Distributed database system architectures therefor
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/28—Databases characterised by their database models, e.g. relational or object models
- G06F16/284—Relational databases
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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
- H04L41/06—Management of faults, events, alarms or notifications
- H04L41/0681—Configuration of triggering conditions
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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
- H04L41/50—Network service management, e.g. ensuring proper service fulfilment according to agreements
- H04L41/5061—Network service management, e.g. ensuring proper service fulfilment according to agreements characterised by the interaction between service providers and their network customers, e.g. customer relationship management
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/10—Protocols in which an application is distributed across nodes in the network
Abstract
The invention discloses a forewarning and monitoring method for complaint service, which comprises the following steps: firstly, acquiring signaling data; analyzing the signaling data; setting rules for signaling service data to be monitored by configuring an early warning rule template, and early warning the signaling subjected to data analysis according to the rules. Compared with the prior art, the early warning monitoring method for the complaint service provided by the invention has the advantages that the analysis and positioning of the complaint reasons in the complaint system are obviously improved, the early warning capability on equipment faults and user complaints is increased, the technical guarantee is provided for improving the service quality of the mobile customer service support, the practicability is high, the application range is wide, and the popularization and application values are good.
Description
Technical Field
The invention relates to the technical field of computer data service, in particular to a forewarning and monitoring method for complaint service.
Background
Over the past two decades, businesses have collected large amounts of data, but to date, most of the data has not been sent to the market, or at least not fully utilized. However, this situation will change because the large data systems and cloud services provided to large, medium, and small enterprises are bringing the availability of data to unprecedented levels. Now, enterprises can obtain real-time information about business operations through big data, so that big data and cloud computing become one of the biggest trends in business development.
The prior mobile network complaint handling work has certain defects in the links of in-service positioning, after-event analysis and early warning, and is mainly reflected in that:
the positioning in the middle is difficult: the complaint sheet only contains basic information of a user, lacks related network data when the complaint of the user occurs, is not enough to support quick positioning of the complaint reason, and needs to position the complaint position of the user and analyze whether the cell equipment has problems or not through a plurality of platforms and tools. When the existing tool cannot judge the problem, field testing is required by field personnel, and the positioning treatment period is long.
The post analysis is difficult: because signaling data is huge and the equipment abnormity reason and the environmental reason are complicated, the summary analysis is not carried out on the high-incidence areas, time periods and equipment operation conditions of network complaints, and the network construction and optimization are difficult to be guided through complaint analysis results.
The prior pre-judgment is difficult: complaints are dealt together, factors which possibly cause the complaints of the users are not analyzed and judged, optimization is difficult to carry out in advance, avoidance is achieved, and the effect of improving the perception of the users is limited.
Based on the above, the invention provides a complaint service early warning monitoring method.
Disclosure of Invention
The technical task of the invention is to provide a forewarning and monitoring method for complaint service aiming at the defects.
A forewarning and monitoring method for complaint service comprises the following implementation processes:
firstly, acquiring signaling data;
analyzing the signaling data;
setting rules for signaling service data to be monitored by configuring an early warning rule template, and early warning the signaling subjected to data analysis according to the rules.
The signaling data acquisition refers to that a signaling platform is connected through a server cluster, then the server cluster is used as a signaling cache to receive real-time signaling data from the signaling platform, and cache service is provided for the signaling data.
And after receiving the signaling data, the server cluster caches the signaling data through the message middleware.
The signaling data analysis means: firstly, a distributed cluster is used as a computing node, and a NoSql database is used as distributed storage; and then the computing node acquires the signaling data from the server cluster, performs big data analysis and stores the analysis result in a relational database, and when abnormal signaling data are analyzed, the abnormal signaling data are stored in a Nosql database, so that problem tracing at the later stage is facilitated.
The specific process of the signaling data analysis is as follows:
firstly, analyzing and modeling a signaling data rule, and determining a signaling field and data description related to an exception;
and then analyzing, sorting and summarizing the received signaling data through a big data computing platform, and storing the sorted problem data into a Nosql database so as to trace the problem at a later stage.
The exception signaling data includes the following description data: CM service rejection, encryption rejection, assignment failure RR, handover failure RR reason, first hang-up, Relcause release, clear.
When an early warning rule template is set, the early warning period, namely a service early warning monitoring period, is configured, the early warning threshold, namely the threshold of abnormal data in signaling data is configured, upgrading early warning, first-stage early warning, second-stage early warning and third-stage early warning are performed according to the range of the threshold, and the early warning type is configured finally, namely an alarm mode and a notification object are determined.
The early warning period comprises minutes, hours and days; the early warning threshold value comprises upgrade early warning, first-stage early warning, second-stage early warning and third-stage early warning; the early warning types comprise call quality, network coverage and roaming barriers.
Compared with the prior art, the early warning and monitoring method for the complaint service has the following beneficial effects that:
the early warning and monitoring method for the complaint service adopts big data calculation and signaling analysis as the basis of a monitoring model, and provides a method for realizing dynamic early warning by monitoring signaling abnormal information in real time; the invention is suitable for monitoring and early warning the network abnormal fault by the complaint customer service system; the method can obviously improve the analysis and positioning of the complaint reasons in the complaint system, increase the early warning capability of the complaint of the equipment and the user, provide technical guarantee for improving the service quality of the mobile customer service support, and has strong practicability, wide application range and good popularization and application values.
Drawings
Fig. 1 is a schematic diagram of the implementation of the present invention.
Detailed Description
The invention is further described with reference to the following figures and specific embodiments.
As shown in fig. 1, the invention relates to a method for early warning and monitoring complaint service, which has the following innovation points and design reasons:
the buffering of real-time signaling data is not capable of rapidly receiving and buffering signaling data due to huge signaling data and traditional interface and processing mode, and the problem of rapid buffering of signaling data is firstly solved when the signaling data is analyzed.
For real-time analysis and calculation of signaling data, the traditional calculation model and architecture cannot meet the requirement of real-time summary of the signaling data.
And (3) analyzing the abnormity of the signaling data, and analyzing and summarizing the change and the reason of the signaling data caused by the abnormity of the environment and the equipment.
The realization process is as follows:
firstly, acquiring signaling data: because the signaling data information is huge, taking Ningxia voice data as an example, the signaling data generated by the mobile phone every day is more than 4T, and a message server cluster is adopted for receiving the real-time signaling as signaling cache, so that the signaling data is quickly received and cached.
Analyzing the signaling data: distributed clusters are used as computing nodes, and the NoSql database is used as distributed storage. The computing node acquires the signaling data from the message server to perform big data analysis, stores the analysis result in the relational database, and stores the related abnormal signaling data in the Nosql database, so that problem tracing in the later period is facilitated.
Setting rules for signaling service data to be monitored by configuring an early warning rule template, and early warning the signaling subjected to data analysis according to the rules.
The signaling data acquisition refers to that a signaling platform is connected through a server cluster, then the server cluster is used as a signaling cache to receive real-time signaling data from the signaling platform, and cache service is provided for the signaling data.
And after receiving the signaling data, the server cluster caches the signaling data through the message middleware.
The specific process of the signaling data analysis is as follows:
firstly, analyzing and modeling a signaling data rule, and determining a signaling field and data description related to an exception;
and then analyzing, sorting and summarizing the received signaling data through a big data computing platform, and storing the sorted problem data into a Nosql database so as to trace the problem at a later stage.
The exception signaling data includes the following description data: more specifically, the signaling fields and reasons are classified as follows, that is, when the following fields are involved, the signaling data is abnormal data:
cmcause (CM service reject cause, luccause location update reject cause):
2 IMSI unknown in HLR ；
3 Illegal MS ；
4 IMSI unknown in VLR ；
5 IMEI not accepted ；
6 Illegal ME ；
11 PLMN not allowed ；
12 Location Area not allowed ；
13 Roaming not allowed in this location area ；
17 Network failure ；
22 Congestion ；
32 Service option not supported ；
33 Requested service option not subscribed ；
34 Service option temporarily out of order ；
38 Call cannot be identified ；
48-63 retry upon entry into a new cell；
95 Semantically incorrect message；
96 Invalid mandatory information；
97 Message type non-existent or not implemented；
98 Message type not compatible with the protocol state；
99 Information element non-existent or not implemented；
100 Conditional IE error；
101 Message not compatible with the protocol state；
113 Protocol error, unspecified。
(encryption rejection reason):
0 IMEISV shall not be included；
1 IMEISV shall be included。
(assignment failure RR cause, handover failure RR cause):
0 Normal event；
1 Abnormal release, unspecified；
2 Abnormal release, channel unacceptable；
3 Abnormal release, timer expired；
4 Abnormal release, no activity on the radio path；
5 Preemptive release；
8 Handover impossible, timing advance out of range；
9 Channel mode unacceptable；
10 Frequency not implemented；
65 Call already cleared；
95 Semantically incorrect message；
96 Invalid mandatory information；
97 Message type non-existent or not implemented；
98 Message type not compatible with protocol state；
100 Conditional IE error；
101 No cell allocation available；
111 Protocol error unspecified。
(first on-hook cause, relclean release cause, clear clearance cause):
1. Unassigned (unallocated)；
3. No route to destination；
6. Channel unacceptable；
8. Operator determined barring；
16. Normal call clearing；
17. User busy；
18. No user responding；
19. User alerting, no answer；
21. Call rejected；
22. Number changed；
25 Pre-emption；
26. Non selected user clearing；
27. Destination out of order；
28. Invalid number format (in- complete number)；
29. Facility rejected；
30. Response to STATUS ENQUIRY；
31. Normal, unspecified；
34. No circuit/channel available Note 1；
38. Network out of order；
41. Temporary failure；
42. Switching equipment conges-tion；
43. Access information discarded；
44. requested circuit/channel；
47. Resources unavailable, un- specified；
49. Quality of service；
50. Requested facility not subscribed；
55. Incoming calls barred with in the CUG；
57. Bearer capability not authorized；
58. Bearer capability not presently available；
63. Service or option not available, unspecified；
65. Bearer service not implemented；
68. ACM equal to or greater than ACMmax；
69. Requested facility not implemented；
70. Only restricted digital information bearer capability isavailable；
79. Service or option not implemented, unspecified；
81. Invalid transaction iden tifier value -；
87. User not member of CUG；
88. Incompatible destination；
91. Invalid transit network selection；
95. Semantically incorrect message；
96. Invalid mandatory information；
97. Message type non-existent or not implemented；
98. Message type not compatible with protocol state；
99. Information element non-existent or not implemented；
100. Conditional IE error；
101. Message not compatible with protocol state；
102. Recovery on timer expiry；
111. Protocol error, unspecified；
127. Interworking, unspecified。
when an early warning rule template is set, the early warning period, namely a service early warning monitoring period, is configured, the early warning threshold, namely the threshold of abnormal data in signaling data is configured, upgrading early warning, first-stage early warning, second-stage early warning and third-stage early warning are performed according to the range of the threshold, and the early warning type is configured finally, namely an alarm mode and a notification object are determined.
The early warning period comprises minutes, hours and days; the early warning threshold value comprises upgrade early warning, first-stage early warning, second-stage early warning and third-stage early warning; the early warning types comprise call quality, network coverage and roaming barriers.
In the invention, data modeling analysis is carried out aiming at signaling data and problem reasons, and key indexes and threshold values causing user complaints are deduced. And early warning the behavior that the user complaints possibly occur. Therefore, the early warning is carried out in advance before the complaint of the user occurs. And performing rapid problem analysis and positioning on the area which has the complaint of the user, thereby further improving the user feeling.
In the invention, the received signaling data is analyzed, sorted and summarized by depending on a big data computing platform, and the sorted problem data is stored in a distributed database so as to trace the problem at a later stage.
And configuring a service early warning monitoring period and an abnormal data threshold value through a rule template configuration module, and defining an alarm and information notification object.
And the service scheduling module monitors and manages the signaling analysis result according to the configuration of the rule template, and performs related processing operation according to the alarm level when the abnormal data exceeds the threshold value.
The early warning management module carries out multi-dimensional analysis on the warning data information and combines with GIS and other visual display technologies to present time intervals, regions, crowds and other comprehensive dimensions.
The invention can provide effective technical support means for network complaints in the complaint treatment of the mobile customer service, improve the complaint interception rate of the first-line customer service and improve the accuracy and real-time performance of problem analysis; the invention can provide early warning and problem tracing analysis capability for communication abnormity caused by mobile equipment and environment, and provides data basis for network optimization and equipment updating.
The present invention can be easily implemented by those skilled in the art from the above detailed description. It should be understood, however, that the intention is not to limit the invention to the particular embodiments described. On the basis of the disclosed embodiments, a person skilled in the art can combine different technical features at will, thereby implementing different technical solutions.
In addition to the technical features described in the specification, the technology is known to those skilled in the art.
Claims (5)
1. A complaint service early warning monitoring method is characterized by comprising the following implementation processes:
firstly, acquiring signaling data;
secondly, analyzing the signaling data, wherein the signaling data analysis means: firstly, a distributed cluster is used as a computing node, and a NoSql database is used as distributed storage; then, the computing node acquires signaling data from the server cluster, performs big data analysis and stores the analysis result in a relational database, and when abnormal signaling data are analyzed, the abnormal signaling data are stored in a Nosql database, so that problem tracing in the later period is facilitated;
the specific process of the signaling data analysis is as follows:
1) firstly, analyzing and modeling a signaling data rule, and determining a signaling field and data description related to an exception;
namely: carrying out data modeling analysis aiming at signaling data and problem reasons, deducing indexes and threshold values for causing user complaints, and carrying out early warning on behaviors which are likely to cause the user complaints;
2) then, analyzing, sorting and summarizing the received signaling data through a big data computing platform, and storing the sorted problem data into a Nosql database so as to trace the problem at a later stage;
namely: configuring a service early warning monitoring period and an abnormal data threshold, and defining an alarm and information notification object;
monitoring and managing a signaling analysis result according to the configuration of the rule template, and performing related processing operation according to the alarm level when abnormal data exceeds a threshold value;
carrying out multi-dimensional analysis on the alarm data information, and presenting the comprehensive dimensions of time intervals, regions, crowds and the like by combining with a GIS and other visual presentation technologies;
setting an early warning rule for signaling service data to be monitored by configuring an early warning rule template, and early warning the signaling subjected to data analysis according to the early warning rule; when an early warning rule template is set, the early warning period, namely a service early warning monitoring period, is configured, the early warning threshold, namely the threshold of abnormal data in signaling data is configured, upgrading early warning, first-stage early warning, second-stage early warning and third-stage early warning are performed according to the range of the threshold, and the early warning type is configured finally, namely an alarm mode and a notification object are determined.
2. The method for early warning and monitoring of complaint services according to claim 1, wherein the signaling data acquisition means that a signaling platform is connected through a server cluster, and then the server cluster is used as a signaling cache to receive real-time signaling data from the signaling platform and provide a cache service for the signaling data.
3. The complaint service early warning and monitoring method of claim 2, wherein the server cluster buffers the signaling data through message middleware after receiving the signaling data.
4. The complaint service early warning and monitoring method of claim 1, wherein the abnormal signaling data comprises the following description data: CM service rejection, encryption rejection, assignment failure RR, handover failure RR reason, first hang-up, Relcause release, clear.
5. The complaint service early warning and monitoring method of claim 4, wherein the early warning period comprises minutes, hours, days; the early warning threshold value comprises upgrade early warning, first-stage early warning, second-stage early warning and third-stage early warning; the early warning types comprise call quality, network coverage and roaming barriers.
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| CN108847955A (en) * | 2018-05-04 | 2018-11-20 | 郑州祺石信息技术有限公司 | A kind of fault pre-alarming monitoring method of equipment and business |
| CN108961696A (en) * | 2018-06-20 | 2018-12-07 | 中国船舶重工集团公司第七〇九研究所 | A kind of early warning system and method for early warning of ocean nuclear power platform |
| CN111368859B (en) * | 2018-12-25 | 2023-08-15 | 中国移动通信集团浙江有限公司 | Complaint early warning processing method and device |
| CN110378712A (en) * | 2019-07-26 | 2019-10-25 | 上海秒针网络科技有限公司 | A kind of complaint handling method and device |
| CN110713088B (en) * | 2019-10-25 | 2021-06-01 | 日立楼宇技术（广州）有限公司 | Early warning method, device, equipment and medium for elevator complaints |
| CN113660380A (en) * | 2021-08-16 | 2021-11-16 | 西安京迅递供应链科技有限公司 | Information processing method and device |
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