CN103906145B - A kind of SLA threshold generation method and devices of speech business - Google Patents
A kind of SLA threshold generation method and devices of speech business Download PDFInfo
- Publication number
- CN103906145B CN103906145B CN201210571542.5A CN201210571542A CN103906145B CN 103906145 B CN103906145 B CN 103906145B CN 201210571542 A CN201210571542 A CN 201210571542A CN 103906145 B CN103906145 B CN 103906145B
- Authority
- CN
- China
- Prior art keywords
- time scale
- complaint
- sla
- customer
- short
- 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
Landscapes
- Monitoring And Testing Of Exchanges (AREA)
- Data Exchanges In Wide-Area Networks (AREA)
Abstract
The invention discloses a kind of SLA threshold generation method and apparatus of speech business, the signaling data for complaining client is analyzed, judge that the index of the complaint client has abnormal time window, generate corresponding time scale, the time scale includes short-term time scale and long time scale；Judge described in the short-term time scale or long time scale complain client index exceptional sample, generation in short-term SLA threshold values or it is long when SLA threshold values.The present invention has a wide range of application while is adapted to 2G/3G networks, and when procotol changes in monitoring range, it is not necessary to carry out modification of program.
Description
Technical Field
The present invention relates to the field of mobile communications, and in particular, to a method and an apparatus for generating an SLA threshold for a voice service.
Background
Under the circumstances that the wireless communication market is growing at a high speed, the competition is increasingly fierce, and the market evolves to be more and more complex, mature telecommunication operators concern about stable operation of the own network, and how to improve the user satisfaction, reduce the off-network rate, and mine the potential value and profit growth point of the user becomes the key for protecting the competitive advantage and competing for the future market leadership.
The SLA (Service-Level Agreement) is an Agreement approved by both parties, defined between a Service provider and a user, for guaranteeing the performance and reliability of a Service at a certain cost. After the agreement is signed, the operator can guarantee the SLA so as to improve the satisfaction degree of the user. When the SLA is signed, how to balance user satisfaction and network operation and maintenance expenditure is a major concern for operators.
The current SLA research is mainly focused on data services, and the customer has higher and higher requirements for the service quality of voice services, so that the customer perception is guaranteed and improved through the voice service SLA research, and the method is a worthy direction. Considering the huge customer base of voice services and the complexity of mobile networks, it is not feasible to subscribe to SLAs for voice services for each general customer.
The publication numbers in the application are: CN1859227, a method and system for monitoring communication service quality according to service level agreement SLA is disclosed in the patent "method and system for monitoring service quality according to service level agreement". The system sets a quality of service manager in a service provider, SP, network and a performance policy handler in a service delivery network. The service quality manager formulates a monitoring strategy according to SLA (service level agreement) to be guaranteed and issues the monitoring strategy to a performance strategy processor in a service transmission network; the performance strategy processor monitors and collects performance parameters in the transmission network and reports the performance parameters to the service quality manager, and the service quality management server determines the service quality corresponding to the SLA according to the reported performance parameters.
The publication numbers are: CN1859427, a method and system for guaranteeing service quality in a communication network is disclosed in the patent "method and system for guaranteeing service quality in a communication network". The system comprises: service/service-related servers, user systems, service level agreement databases, and SLA servers. When the service/service related server receives a user service request, the SLA server is informed, determines the current service level by inquiring SLA information of the user service and informs the service/service related server of the current service level; and the service/service related server guarantees the service quality of the user according to the current service level.
The two prior arts are both researched for monitoring and managing SLAs, and are both researched for data services, and do not consider the particularity of voice services and research for SLAs of voice services; in addition, both SLAs in the prior art consider average performance requirements, do not consider short-term specificity of voice traffic incidents, and do not consider the impact of consecutive abnormal events on customers on a short time scale.
Disclosure of Invention
The invention provides a method and a device for generating an SLA threshold of a voice service, which can be suitable for the voice service.
The SLA threshold generation method of the voice service provided by the invention comprises the following steps:
analyzing signaling data of a complaint customer, judging a time window with abnormal indexes of the complaint customer, and generating a corresponding time scale, wherein the time scale comprises a short time scale and a long time scale;
and judging an abnormal sample of the indexes of the complaint customers in the short time scale or the long time scale, and generating a short-term SLA threshold value or a long-term SLA threshold value.
The SLA threshold generation device for voice service provided by the invention comprises:
the system comprises a time scale generation module, a time scale generation module and a time scale generation module, wherein the time scale generation module is used for analyzing signaling data of a complaint customer, judging a time window with abnormal indexes of the complaint customer and generating a corresponding time scale, and the time scale comprises a short time scale and a long time scale;
and the SLA threshold value generation module is used for judging the abnormal sample of the indexes of the complaint customers in the short time scale or the long time scale and generating a short-term SLA threshold value or a long-term SLA threshold value.
The invention analyzes the signaling data of the user with complaint to obtain the quantitative definitions of different time scales in short time and long time, and further analyzes to obtain the SLA threshold values under different time scales.
Because the invention is completely automatically completed by the system, the manual maintenance is not needed, the workload of system maintenance personnel is reduced, and the risks of management and manual operation do not exist;
the SLA based on the signaling monitoring is automatically generated, objectivity is improved, customer expectation and network performance can be accurately reflected, the target SLA is generated more accurately, practicability is higher, and better performance is achieved.
Drawings
FIG. 1 is a schematic illustration of complaint analysis;
FIG. 2 is a flowchart illustrating a method for generating an SLA threshold for voice services according to an embodiment;
FIG. 3 is a flow diagram for short timescale generation in one embodiment;
FIG. 4 is a flowchart illustrating the generation of a short-term SLA threshold in one embodiment;
fig. 5 is a schematic block diagram of an SLA threshold generation apparatus for voice traffic in an embodiment.
Detailed Description
The invention provides a concept of voice service target SLA, automatically generates a target SLA aiming at a class of customer groups, further distinguishes SLA performance requirements on different time scales on the basis, and defines a short-term SLA threshold and a long-term SLA threshold. The voice service target SLA of the customer group is obtained by automatically analyzing the signaling data of the user who complains, so that the defects that the current manual formulation method is unscientific and unobtrusive are overcome. The invention focuses on the automatic generation of SLA, and the generated SLA can be used for monitoring in a network background and used as a target for guaranteeing the service performance of the network; and the method can also be used as a reference for signing agreements between operators and large group customers in the future.
The invention relates to complaint information and signaling monitoring information, automatically generates threshold time scale and threshold value, and completes the automatic generation of voice service target SLA double-threshold. Analysis of a complaint customer can see where their expected lower SLA limit is, and the customer will make a corresponding complaint only when the target SLA is violated.
Suppose that the user is at time T as shown in FIG. 1 0 Complaints are made, possibly in a short time frame (T) 0 -S，T 0 ) A large number of abnormal events are generated, for example, within one hour, the user continuously drops the call, and then corresponding complaints are generated; possibly also over a long time range (T) 0 -L，T 0 ) A number of exceptions are generated, for example, within a month, the user frequently drops the call repeatedly. The invention can analyze the detailed information of the abnormal events generated by the complaint clients on the time axis by combining with a signaling monitoring system.
In general, when a target SLA is violated, a customer may incur complaints. And the generation of the target SLA can be understood as: and determining what abnormal index exists under what quantization time scale the complaint user has, namely, using the abnormal index as a time scale quantization value of the target SLA and a specific SLA threshold value. The method can be obtained by carrying out comparative analysis on samples of the complaint users and samples of normal non-complaint users and completing detection of corresponding abnormal samples.
The SLA threshold generation method for voice service disclosed in the present invention is shown in fig. 2: analyzing signaling data of a complaint customer, judging a time window with abnormal indexes of the complaint customer, and generating a corresponding time scale (S201), wherein the time scale comprises a short time scale and a long time scale;
and judging an abnormal sample of the indexes of the complaint customers in the short time scale or the long time scale, and generating a short-term SLA threshold value or a long-term SLA threshold value (S202).
As a preferred embodiment, the present invention may also update the threshold value of the SLA periodically (S203).
Step S201 is an automatic quantization process of the short time scale S and the long time scale L. The method is characterized in that the method is combined with signaling information of complaining customers to analyze, and the indexes of the complaining customers are different from those of non-complaining customers within a certain time range. In the invention, the search is simultaneously carried out from two directions, and the short time scale S and the long time scale L are simultaneously found out. And if the client information and the client information meet each other in the searching process, the searching is considered to be failed, and the client information is not taken into consideration. Searching in a short time scale to gradually increase a monitored index time window from an initial value according to step length; the long time scale search reduces the monitored indicator time window step by step from the initial value. For the search of a short time scale, every time the time window is increased, the index in the time period is compared with the index sample of a normal non-complaint customer to see whether the index is in the normal confidence range or not, and the index is determined as the short time scale of the customer when the performance index is abnormal at a certain time point. And averaging the short timescales of the whole complaint customer to obtain the quantized value of the short timescale S. The calculation of the long timescale L is similar to the calculation of the short timescale S, except that the search is performed in a direction from a large time window to a small time window. The specific quantization process is shown in fig. 3:
s2011, collecting complaint data and signaling monitoring data of a complaint client;
s2012, the collected complaint data and the signaling monitoring data are created into a complaint client list; monitoring the monitored normal non-complaint customer signaling monitoring index information into a database (with the monitored customer group as a sample of the whole customer group, and with a sufficient amount of samples to complete the analysis of the whole sample space, as a preferred embodiment, the monitoring can be initiated for the VIP customer, on one hand, the data can be used for system analysis, on the other hand, the VIP customer information is more important, and can be used for other system purposes)
S2013, taking out a complaint customer for specific analysis. Collecting signaling monitoring indexes of the system and putting the signaling monitoring indexes into a database;
s2014, giving an initial short time scale S;
s2015, collecting a complaint customer index value X' in the time window; collecting index values of normal clients in a time window to form a sample of a random variable X (X) 1 ，x 2 。。。x n )；
S2016, determine whether X' is within a confidence interval where the confidence level of the random variable X is α (as an embodiment, the value of α may be 95%), if not, in the time window, an index anomaly occurs, which leads to a customer complaint, and then determine that the time scale is a short time scale (2017). Otherwise, the process advances to step S2018.
VII, solving the mean value of XStandard deviation of X(N is the number of samples of the random scalar N);
VIII ofConstructing normal distribution by using sigma as standard deviation, and calculating P (X & gt X ') under the normal distribution (if the index is smaller, the better, otherwise, P (X & lt X'));
IX, if P (X > X ') <0.05, X' is not within the confidence interval of random variable X confidence α, otherwise.
S2018, re-executing S2014, where S = S + λ (λ is a step length of window increase, and may be a minimum time granularity of signaling monitoring to ensure system realizability);
s2019, the steps are executed in a circulating mode until the complaint client set is empty, and S2020 is executed;
s2020, averaging the short time scales of all complaint customers to obtain a short time scale S of the voice service of the whole network;
the long time scale L of the voice service SLA is solved in a similar way. The difference is that the solution process will start from a large time window and gradually decrease. The process is as follows: step 101, collecting the complaint customer index value Y' in a preset initial long-time scale L; collecting samples of index value forming random variables Y of normal clients in the initial long-time scale L: (y) 1 ，y 2 。。。y n )；
102, judging whether the complaint customer index value Y' is in a confidence interval of preset confidence level of the random variable Y, if not, determining the initial long-time scale L as the long-time scale, otherwise, entering the step 3;
step 103, re-executing step 102 with L = L- λ and λ as the step length of window reduction;
and 104, averaging the long-time scales of each complaint customer to obtain the long-time scale of the voice service of the whole network.
Step S201 is a process of the short-term SLA threshold and the long-term SLA threshold. After the time scale is determined, the threshold value of the SLA needs to be further determined, so that the whole target SLA can be completely generated. The generation of the threshold value is to determine a value, and the samples above/below (depending on the type of the specific indicator, whether larger or smaller is better) the threshold value are different from the sample values of the non-complaint customers. Starting with a certain initial value, gradually increasing/decreasing the target value, taking the index against the target value as a sample space, and then taking the index of a normal non-complaint customer as a sample space. And judging whether the two sample spaces are significantly different, if so, judging that the samples which violate the target value have abnormity relative to the normal samples, namely, the SLA is violated, and then taking the target value as the threshold value of the SLA. The basic idea is to determine how much the indexes of the complaint customers exceed in the corresponding time window, which is an abnormal sample, so as to generate a corresponding SLA threshold value. The detailed flow chart is shown in fig. 4.
S401, complaint data and signaling monitoring data are collected;
s402, creating a complaint client list, and putting the monitored normal non-complaint client signaling monitoring index information into a database;
s403, collecting signaling monitoring index information of the complaint customer and putting the signaling monitoring index information into a database;
s404, setting an initial SLA short-time threshold X 0 Is a target value in the calculation;
s405, collecting complaint customer indexes in a short time window, wherein the complaint customer indexes are larger/smaller (the indexes are larger or smaller, the better is) than a threshold X 0 The index value of (2) forms a sample space X'; acquiring index values of normal clients in a time window to form a sample space X;
s406, it is determined whether or not the sample space X' and the sample space X are significantly different with the confidence level α. If there is a significant difference, there is an anomaly in the metrics generated at the threshold, resulting in customer complaints, and the target value is determined to be the voice service target SLA short-term threshold (S407). Otherwise, entering S408;
i. solving for the mean of XMean and standard deviation of X
ii. calculating
Calculating μ α So that
If μ | > μ α There is a significant difference between the two samples, otherwise none.
S408, mixing X 0 ＝X 0 λ (± λ is a step size of the increase/decrease of the index, and whether the increase or decrease is determined as the index is larger or smaller, the step is executed again S404;
and solving the long-term threshold value of the voice service target SLA according to a similar method. The only difference is that the time windows used in the solution process are different.
Step S203 is a periodic dynamic adjustment of the SLA threshold value. After the SLA threshold value is automatically generated, the threshold value needs to be dynamically adjusted according to information such as real-time complaints, so as to achieve more accurate and optimized effects. The basic idea is to periodically calculate the real-time threshold value of the target SLA according to the following steps and correct the existing threshold value.
1) Calculating a current real-time threshold value theta' of the target SLA;
2) And updating the threshold value of the SLA according to a certain proportionality coefficient eta, wherein theta = (1-eta) theta + eta theta' (wherein eta is usually 0.1).
Corresponding to the method for generating the SLA threshold of the voice service, the invention also provides a device for generating the SLA threshold, which comprises the following steps: the system comprises a time scale generation module, an SLA threshold value generation module and a time scale generation module, wherein the time scale generation module is used for generating a short time scale and a long time scale; the SLA threshold value generation module is used for generating a short-term SLA threshold value or a long-term SLA threshold value. The specific generation method corresponds to the above SLA threshold generation method for voice service, and is not described again.
As one embodiment, the time scale generation module includes: the collection module is used for collecting the complaint customer index value X' in a preset initial short/long time scale S; and the index values of normal customers within the initial short/long time scale S form a sample of a random variable X: (x) 1 ，x 2 。。。x n )；
The judging module is used for judging whether the complaint customer index value X' is in a confidence interval of the preset confidence level of the random variable X, and if not, determining the initial short/long time scale S as the short/long time scale;
the circulating module is used for increasing the step length of the initial short time scale S or decreasing the step length of the initial long time scale S and then inputting the increased step length or decreased step length of the initial long time scale S into the judging module;
and the calculation module is used for averaging the short/long time scales of each complaint customer to obtain the short/long time scales of the voice service of the whole network.
It should be noted that: the above embodiments are only used to illustrate the present invention and not to limit the present invention, and the present invention is not limited to the above examples, and all technical solutions and modifications thereof without departing from the spirit and scope of the present invention should be covered by the claims of the present invention.
Claims (18)
1. A method for generating SLA threshold of voice service is characterized by comprising the following steps:
analyzing signaling data of a complaint customer, judging a time window with abnormal indexes of the complaint customer, and generating a corresponding time scale, wherein the time scale comprises a short time scale and a long time scale;
judging an abnormal sample of the indexes of the complaint customers in the short time scale or the long time scale, and generating a short-term SLA threshold value or a long-term SLA threshold value;
the generation process of the short timescale comprises the following steps:
step 1, collecting an index value Xp of the complaint customer within a preset initial short time scale S; collecting a sample of index value forming random variables X of normal customers in the initial short time scale S: (x) 1 ，x 2 …x n )；
Step 2, judging whether the index value Xp of the complaint customer is in a confidence interval with the random variable X reliability being alpha, if not, determining the initial short time scale S as the short time scale, otherwise, entering step 3;
step 3, re-executing the step 2 by taking S = S + lambda and lambda as a step length;
and 4, averaging the short time scales of each complaint customer to obtain the short time scale of the voice service of the whole network.
2. The method for SLA threshold generation for voice service according to claim 1, wherein said steps further include:
and updating the threshold value of the SLA periodically.
3. The SLA threshold generation method for voice services according to claim 2,
periodically updating the SLA threshold value according to the following formula:
θ＝(1-η)θ+ηθ′
wherein θ' is the current SLA threshold; η is a predetermined scaling factor.
4. The SLA threshold generation method for voice traffic according to claim 3,
the value of eta is 0.1.
5. The SLA threshold generation method for voice services according to claim 1,
alpha in step 2 was 95%.
6. The SLA threshold generation method for voice service according to claim 5, wherein the process of determining whether the index value Xp of the complaint customer is within the confidence interval of the random variable X confidence level α includes:
I. solving for the mean of XStandard deviation of XWhere N is the number of samples of a random scalar X, X i Is the ith sample value of the random variable X;
II. To be provided withConstructing a normal distribution for mean and sigma for standard deviation, and calculating P (X) under the normal distribution>Xp):
III, if P (X > Xp) <0.05, then Xp is not within a confidence interval of alpha of the confidence level of the random variable X, otherwise, the Xp is within the confidence interval.
7. The SLA threshold generation method for voice service according to claim 5, wherein the process of determining whether the index value Xp of the complaint customer is within the confidence interval of the random variable X confidence degree α includes:
I. solving for the mean of XStandard deviation of XWhere N is the number of samples of a random scalar X, X i Is the ith sample value of the random variable X;
II. To be provided withA normal distribution is constructed for mean and σ is the standard deviation, and P (X) under the normal distribution is calculated<Xp)；
And III, if P (X < Xp) <0.05, the Xp is not in a confidence interval of the random variable X with the confidence degree of alpha, otherwise, the Xp is in the confidence interval.
8. The SLA threshold generation method for voice service according to claim 1, characterized in that, before step 1, it further includes the steps of:
1) Obtaining a complaint client list according to the signaling data of the complaint client;
2) Storing the monitored normal non-complaint client signaling monitoring index information in the client group;
3) And collecting and storing signaling monitoring indexes of the complaint client.
9. The SLA threshold generation method for voice traffic as set forth in claim 8,
the customer base is a VIP customer.
10. The SLA threshold generation method for voice service according to claim 1, characterized in that the generation process of the long time scale includes:
step 101, collecting said projection within a predetermined initial long time scale LAn index value Y' for the customer; collecting samples of index value forming random variables Y of normal clients in the initial long-time scale L: (y) 1 ，y 2 …y n )；
102, judging whether the index value Y' of the complaint customer is in a confidence interval of preset confidence level of the random variable Y, if not, determining the initial long-time scale L as the long-time scale, otherwise, entering the step 3;
step 103, re-executing step 102 with L = L- λ and λ as the step length of window reduction;
and 104, averaging the long-time scales of each complaint customer to obtain the long-time scale of the voice service of the whole network.
11. The SLA threshold generation method for voice service according to claim 1, characterized in that the step of generating the short term SLA threshold/long term SLA threshold comprises:
1) Collecting the complaint customer indexes in the short time scale/long time scale which are more than or less than a preset initial SLA short time/long time threshold value X 0 The index value of (a) forms a sample space X'; acquiring index values of normal clients in the short time scale/long time scale to form a sample space X;
2) Judging whether the sample space X' is significantly different from the sample space X under the condition that the reliability is alpha, if so, determining the initial short-time/long-time SLA threshold value X 0 Is SLA short time threshold value/long time SLA threshold value, otherwise step 3) is entered;
3) X is to be 0 ＝X 0 And +/-lambda is the step size of the increase/decrease of the index, and the step 2) is executed again.
12. The method according to claim 11, wherein the step of determining whether the sample space X' and the sample space X are significantly different with a confidence level α in step 2) comprises:
I. solving for the mean of XMean and standard deviation of Xσ；
II. Calculating outWhere μ represents the dispersion statistic of the spatial means of the two samples;
III, calculating mu α So thatWherein, mu α A variable representing normal distribution with a confidence level of α;
IV, if | μ | > μ α There is a significant difference between the two samples, otherwise there is no significant difference.
13. An apparatus for generating SLA threshold for voice service, comprising:
the system comprises a time scale generation module, a time scale generation module and a time scale generation module, wherein the time scale generation module is used for analyzing signaling data of a complaint customer, judging a time window with abnormal indexes of the complaint customer and generating a corresponding time scale, and the time scale comprises a short time scale and a long time scale;
an SLA threshold value generation module, configured to determine an abnormal sample of the indicators of the complaint customers in the short time scale or the long time scale, and generate a short-term SLA threshold value or a long-term SLA threshold value;
the time scale generation module comprises:
the acquisition module is used for acquiring an index value Xp of the complaint customer within a preset initial short/long time scale S; and the index values of normal customers within the initial short/long time scale S form a sample of a random variable X: (x) 1 ，x 2 …x n )；
The judging module is used for judging whether the index value Xp of the complaint customer is in a confidence interval with the confidence level of the random variable X being alpha or not, and if not, determining the initial short/long time scale S as the short/long time scale;
the circulating module is used for inputting the initial short time scale S increased step length or the initial long time scale decreased step length into the judging module;
and the calculation module is used for averaging the short/long time scales of each complaint customer to obtain the short/long time scales of the voice service of the whole network.
14. The apparatus of claim 13, wherein the SLA threshold generation module is further configured to update the SLA threshold periodically.
15. The apparatus of claim 14, wherein the SLA threshold generation module periodically updates the SLA threshold according to the following formula:
θ＝(1-η)θ+ηθ′
wherein θ' is the current SLA threshold; eta is a predetermined scaling factor.
16. The apparatus for generating SLA threshold for voice service according to claim 13, wherein the process of determining whether the index value Xp of the complaint customer is within the confidence interval of the random variable X confidence level α by the determining module includes:
IV, solving the mean value of XStandard deviation of XWhere N is the number of samples of a random scalar X, X i Is the ith sample value of the random variable X;
v, toA normal distribution is constructed for mean and σ is the standard deviation, and P (X) under the normal distribution is calculated>Xp):
VI, if P (X > Xp) <0.05, then Xp is not within the confidence interval for random variable X confidence α, otherwise.
17. The apparatus for generating SLA threshold for voice service according to claim 13, wherein the process of determining whether the index value Xp of the complaint customer is within the confidence interval of the random variable X confidence level α by the determining module includes:
IV, solving the mean value of XStandard deviation of XWhere N is the number of samples of a random scalar X, X i Is the ith sample value of the random variable X;
v to VA normal distribution is constructed for mean and σ is the standard deviation, and P (X) under the normal distribution is calculated<Xp)；
VI, if P (X < Xp) <0.05, then Xp is not within the confidence interval for random variable X confidence α, otherwise.
18. The SLA threshold generation device according to claim 13, further comprising a storage module, configured to store a list of complaint customers that are obtained based on the signaling data of the complaint customers, and monitoring indicator information of normal non-complaint customers in a customer group that is a VIP customer, and the signaling monitoring indicators of the complaint customers.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201210571542.5A CN103906145B (en) | 2012-12-25 | 2012-12-25 | A kind of SLA threshold generation method and devices of speech business |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201210571542.5A CN103906145B (en) | 2012-12-25 | 2012-12-25 | A kind of SLA threshold generation method and devices of speech business |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN103906145A CN103906145A (en) | 2014-07-02 |
| CN103906145B true CN103906145B (en) | 2018-01-30 |
Family
ID=50997244
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201210571542.5A Active CN103906145B (en) | 2012-12-25 | 2012-12-25 | A kind of SLA threshold generation method and devices of speech business |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN103906145B (en) |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101299863A (en) * | 2008-06-11 | 2008-11-05 | 中国移动通信集团湖北有限公司 | Complaining method, complaint processing method, terminal, complaint processing server and system |
| CN102487523A (en) * | 2010-12-01 | 2012-06-06 | 中国移动通信集团公司 | User compliant analysis method and device |
Family Cites Families (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP4709827B2 (en) * | 2005-02-28 | 2011-06-29 | 東芝ストレージデバイス株式会社 | Track pitch inspection method for storage device, program, and storage device |
| CN100531066C (en) * | 2007-04-02 | 2009-08-19 | 北京亿阳信通软件研究院有限公司 | Method and device for determining business parameter grade quantizing range of business service |
| CN101262648A (en) * | 2008-04-24 | 2008-09-10 | 中兴通讯股份有限公司 | A method and system for processing spam |
| CN102623009B (en) * | 2012-03-02 | 2013-11-20 | 安徽科大讯飞信息科技股份有限公司 | Abnormal emotion automatic detection and extraction method and system on basis of short-time analysis |
-
2012
- 2012-12-25 CN CN201210571542.5A patent/CN103906145B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101299863A (en) * | 2008-06-11 | 2008-11-05 | 中国移动通信集团湖北有限公司 | Complaining method, complaint processing method, terminal, complaint processing server and system |
| CN102487523A (en) * | 2010-12-01 | 2012-06-06 | 中国移动通信集团公司 | User compliant analysis method and device |
Also Published As
| Publication number | Publication date |
|---|---|
| CN103906145A (en) | 2014-07-02 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107943809B (en) | Data quality monitoring method and device and big data computing platform | |
| US9491285B2 (en) | Technique for performance management in a mobile communications network | |
| RU2507704C2 (en) | Method and apparatus for queue routing | |
| US9258200B2 (en) | Method and apparatus for acquiring quality of experience and method and apparatus for ensuring quality of experience | |
| CN103493433A (en) | Method and apparatus for quality of service monitoring of services in a communication network | |
| CN107026750B (en) | User Internet QoE evaluation method and device | |
| CN103024761A (en) | Establishing method for energy consumption model of base station, and energy consumption predicating method and device | |
| WO2012113279A1 (en) | Data processing method and device for essential factor lost score | |
| US20120060198A1 (en) | Method and system for generating metrics representative of policy and charging control rules | |
| CN111010426A (en) | Message pushing method and device | |
| JP2015207795A (en) | Communication traffic prediction device, method and program | |
| CN102487523A (en) | User compliant analysis method and device | |
| CN102149113A (en) | Mobile user perception quantification method | |
| CN113947260A (en) | User satisfaction prediction method and device and electronic equipment | |
| CN109963292B (en) | Complaint prediction method, complaint prediction device, electronic apparatus, and storage medium | |
| CN103906145B (en) | A kind of SLA threshold generation method and devices of speech business | |
| CN102547789B (en) | Early warning method, device and system for quality of peer-to-peer service | |
| CN102281549A (en) | Mobile Ad-hoc NETwork (MANET) resource admission control method based on game theory | |
| WO2021171063A1 (en) | Adaptive method for measuring service level consistency | |
| CN114727309A (en) | Network optimization method and equipment | |
| US9736704B1 (en) | Providing an overlay network using multiple underlying networks | |
| CN101605339A (en) | Monitoring of network bandwidth resources operating position and prompt system and method | |
| CN116366436B (en) | Method for providing various telecom value-added services based on wide area networking | |
| EP4391473A1 (en) | System and method for capacity planning | |
| CN117391644B (en) | Parameter adjustment method, device, equipment and medium in contract management process |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |