CN103034733A - Data monitoring statistical method for call center - Google Patents
Data monitoring statistical method for call center Download PDFInfo
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- CN103034733A CN103034733A CN2012105730001A CN201210573000A CN103034733A CN 103034733 A CN103034733 A CN 103034733A CN 2012105730001 A CN2012105730001 A CN 2012105730001A CN 201210573000 A CN201210573000 A CN 201210573000A CN 103034733 A CN103034733 A CN 103034733A
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- 238000012544 monitoring process Methods 0.000 title claims abstract description 27
- 238000007619 statistical method Methods 0.000 title claims abstract description 23
- 238000012545 processing Methods 0.000 claims abstract description 7
- 241000533950 Leucojum Species 0.000 claims description 5
- 230000005055 memory storage Effects 0.000 claims description 2
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- 238000004458 analytical method Methods 0.000 abstract description 6
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Abstract
The invention discloses a data monitoring statistical method for a call center. The data monitoring statistical method comprises the following steps of sending business data to a message queue; distributing the business data to a processing progress; obtaining a business data metering value according to calculation model processing business data; putting the business data metering value in a to-be-stored internal storage queue; and storing the to-be-stored internal storage queue in a persistent database. According to the data monitoring statistical method disclosed by the invention, the accuracy of data search is increased through the effective analysis of the business data; and during a data monitoring statistical process, the whole calculating process is carried out in the internal storage, the data throughout of a magnetic disk is reduced, and the data searching speed is greatly quickened.
Description
Technical field
The present invention relates to a kind of data monitoring statistical method, relate in particular to a kind of data Real Time Monitoring statistical method for the call center, belong to communication technical field.
Background technology
In recent years, along with the rapid expanding of telecommunications industry at aspects such as user, business and networks, cause the portfolio rapid growth.When portfolio increased, statistics and allotment existing resource improved resource utilization effectively, were that each enterprise keeps service and professional leading inevitable choice.For tackling competitive environment complicated and changeable, how do not increase existing hardware resource and human resources, not increase operation cost in, reasonable arrangement allotment existing resource for the user provides higher-quality service, becomes a present enterprise problem in the urgent need to address more.
For addressing this problem, need to analyze the call data of call center, and then adopt suitable improvement measure.Existing Customer Service Center obtains service indication by call monitoring and statistical report form, carries out statistics by manpower and collects and compare of analysis, and the problem that exists in the discovery customer service takes to improve accordingly measure.But this method need to spend a large amount of manpowers and time, even even sometimes spend a large amount of manpowers and time and also can't in time find problem in the customer service, and suffering to know just in the situation of customer complaint that problem has appearred in customer service.For example: adopt the association analysis technology, customer churn pattern in the research network, can't be to comprising KPI Key Performance Indicator (the Key Performance Indicator of key business figureofmerit, quality of service index, performance index etc., referred to as KPI) carry out comprehensive analysis, reach the target to network performance and service quality comprehensive assessment, satisfy the needs that the service operation situation is weighed by each enterprise.Therefore, existing analytical approach is the network analysis system take report data as core, can not satisfy the needs of user's macro-management, can't instruct effectively and accurately the network analysis work of every aspect.
At present, comparatively advanced data monitoring statistical method is: set up timed task for every monitoring form, regularly go inquiry, store Query Result into database, statistical report form data are afterwards obtained from this database.Then according to various querying condition dynamic queries results.But also there are following two problems in the method:
1. can only directly realize from querying service database the statistical query of call data, can't represent in real time fast Query Result.
2. the data volume that can cause the call center that increases of call data increases, and is very large on the answer speed impact of call center.When concurrency is excessive, may cause the call center normally to use.
Be in the Chinese invention patent of CN101141759B at notification number, disclose a kind of calling behavioral statistics and analytical approach and device.The method may further comprise the steps: step S102, carry out data acquisition to signaling network, and generate original call detailed record data; Step S104 reads the original call detailed record data of objective time interval; And step S106, the calling behavior in the statistics original call detailed data record.Therefore, this invention can provide the user customer-action analysis data aspect telecommunication service, thereby has helped operator to improve the service quality level of self, and giving the business decision of operator and dealing with problems provides Data support.
Summary of the invention
For the existing deficiency of prior art, technical matters to be solved by this invention is to be provided for the data monitoring statistical method of call center.Use the method and can greatly accelerate inquiry velocity, realize the real-time query of business datum.
For realizing above-mentioned goal of the invention, the present invention adopts following technical scheme:
A kind of data monitoring statistical method for the call center comprises the steps:
Send business datum in message queue;
Distribute described business datum to treatment progress;
Obtain the business datum metric by the computation model processing service data;
Described business datum metric is put into memory queue to be preserved;
Memory queue to be preserved is deposited in the perdurable data storehouse.
Wherein more preferably, the business datum metric also comprises the steps: before putting into memory queue to be preserved
Compare with the historical data in the call center;
If described business datum metric Already in the historical data, is then revised according to the metric in historical data corresponding to described business datum metric, amended described business datum metric is put into memory queue to be preserved;
If described business datum metric does not exist, then described business datum metric is directly put into memory queue to be preserved in historical data.
Wherein more preferably, described storage of history data P is in internal memory.
Wherein more preferably, described computation model is one or more in snowflake model, Star Model, the StarNet's model.
Wherein more preferably, described business datum is distributed by task dispatch.
Wherein more preferably, described task dispatch is Gearman.
Wherein more preferably, memory queue described to be preserved is kept in the memory database.
Wherein more preferably, described perdurable data storehouse is the distributed memory storage system.
Data monitoring statistical method provided by the present invention is by the effective analysis to business datum, the accuracy that has improved data query.In the data monitoring statistic processes, whole computation process is all carried out in internal memory, has reduced the data throughout of disk, has greatly accelerated the speed of data query.
Description of drawings
Fig. 1 is the schematic flow sheet that is used for the data monitoring statistical method of call center;
Fig. 2 is employed data warehouse exemplary plot among the present invention.
Embodiment
The present invention is described in further detail below in conjunction with the drawings and specific embodiments.
As shown in Figure 1, the invention provides a kind of data monitoring statistical method for the call center, comprise following step: send business datum in message queue; The distribution services data are to treatment progress; Obtain metric by the computation model processing service data; Metric is put into memory queue to be preserved; Deposit the state of preparing against user's real time inspection call data in the perdurable data storehouse in.The below launches to describe in detail to this data monitoring statistical method.
When there is call business the call center, all can produce business datum.The call center directly sends to message queue with the form of message with this business datum; Or gather this business datum by capture program, send to message queue with form of message.These message are deposited business datum with the form of " mode of operation ‖ business datum " in message queue.Wherein, this business datum comprises all business datums (Data) that this operation affects.For example, insert ‖ agent_no:1001; User_name: Zhang San; Talkingtime:188; Wherein, agent_no, user_name are the dimension fields, 1001, and Zhang San is dimension values, talkingtime is the metric title, the 188th, metric.
The task dispatch of call center is given different data treatment progress according to the distribution algorithms of self with these message distributions that contain business datum.In one embodiment of the invention, task dispatch preferably uses the distributed tasks program frame Gearman that increases income.Gearman self has based on the distributed message of internal memory distribution formation, can according to self the distribution algorithms parallel processing and distribute these message.
After the data treatment progress is taken the message that contains business datum of task dispatch distribution, calculate and process according to the computation model that pre-defines, obtain the business datum metric.Computation model is the model of data warehouse.The model of data warehouse comprises snowflake model, Star Model, StarNet's model etc.As shown in Figure 2, in one embodiment of the invention, the model of data warehouse preferably adopts snowflake model, has comprised the definition of parameter and the relation of dimension in the snowflake model.
The business datum metric that treatment progress will obtain after will processing is put into formation to be preserved.In order to guarantee the accuracy of business datum, before these business datum metrics are put into this locality formation to be preserved, also need with the call center in historical data compare.In order to improve comparison efficiency, historical data all adopts the storage mode based on internal memory to be kept in the internal memory.Judge at first whether this business datum metric calculated, and if calculated with historical data comparison, if do not calculate then this business datum metric put into memory queue to be preserved.Next is compared with the historical data in the call center.If the business datum metric that treatment progress will be processed is Already in the historical data, then make amendment according to last business datum metric corresponding with this business datum metric in the historical data, and amended business datum metric is put into memory queue to be preserved.Because the business datum of per call all is stored in the historical data.When needs are made amendment, find last business datum corresponding in the historical data according to the business datum of this calling, make amendment according to this business datum metric.If the business datum metric that treatment progress will be processed does not exist, then this business datum metric is directly put into memory queue to be preserved in historical data.For example, the duration of call of certain business datum is 0, after comparing with the historical data in the call center, these data that lead to errors is for some reason corrected.In one embodiment of the invention, memory queue to be preserved preferably deposits in the memory database.
Because memory database is not suitable for the long-term storage data, in order to guarantee the security of data, these memory queues to be preserved is deposited in the perdurable data storehouse.For guarantee the user can be in time query traffic data efficiently, central distribution formula storage system is preferably used in the perdurable data storehouse among the present invention, such as Redis collection etc.When the Data Concurrent amount of user's inquiry business is large, also can satisfy user's query demand like this.
In sum, the present invention is by gathering the business datum of call center, and in conjunction with distributed memory and computing technique, these business datums carried out comprehensive, instant analysis, and increases the Inspection to historical data, Effective Raise the accuracy of data query.Whole computation process can be carried out in internal memory, directly obtains the result during inquiry from internal memory, has reduced the data throughout of disk, has greatly accelerated inquiry velocity.
The above has been described in detail the data monitoring statistical method for the call center provided by the present invention.For one of ordinary skill in the art, any apparent change of under the prerequisite that does not deviate from connotation of the present invention it being done all will consist of infringement of patent right of the present invention, will bear corresponding legal liabilities.
Claims (8)
1. a data monitoring statistical method that is used for the call center is characterized in that comprising the steps:
Send business datum in message queue;
Distribute described business datum to treatment progress;
Obtain the business datum metric by the computation model processing service data;
Described business datum metric is put into memory queue to be preserved;
Memory queue to be preserved is deposited in the perdurable data storehouse.
2. data monitoring statistical method as claimed in claim 1 is characterized in that the business datum metric also comprises the steps: before putting into memory queue to be preserved
Compare with the historical data in the call center;
If described business datum metric Already in the historical data, is then revised according to the metric in historical data corresponding to described business datum metric, amended described business datum metric is put into memory queue to be preserved;
If described business datum metric does not exist, then described business datum metric is directly put into memory queue to be preserved in historical data.
3. data monitoring statistical method as claimed in claim 1 is characterized in that:
Described storage of history data P is in internal memory.
4. data monitoring statistical method as claimed in claim 1 is characterized in that:
Described computation model is one or more in snowflake model, Star Model, the StarNet's model.
5. data monitoring statistical method as claimed in claim 1 is characterized in that:
Described business datum is distributed by task dispatch.
6. data monitoring statistical method as claimed in claim 4 is characterized in that:
Described task dispatch is Gearman.
7. data monitoring statistical method as claimed in claim 1 is characterized in that:
Memory queue described to be preserved is kept in the memory database.
8. data monitoring statistical method as claimed in claim 1 is characterized in that:
Described perdurable data storehouse is the distributed memory storage system.
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| CN2012105730001A CN103034733A (en) | 2012-12-25 | 2012-12-25 | Data monitoring statistical method for call center |
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| CN2012105730001A CN103034733A (en) | 2012-12-25 | 2012-12-25 | Data monitoring statistical method for call center |
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104363073A (en) * | 2014-10-31 | 2015-02-18 | 上海大唐移动通信设备有限公司 | Method and device for processing CDL (call detail log) data of signaling subprocess |
| CN106533752A (en) * | 2016-11-04 | 2017-03-22 | 东北大学 | Network big data analysis method for call modes and resource utilization in cellular network |
| CN108769435A (en) * | 2018-05-30 | 2018-11-06 | 平安科技（深圳）有限公司 | Monitoring method, electronic device, computer equipment and the storage medium of call center |
| CN108881651A (en) * | 2018-05-30 | 2018-11-23 | 平安科技（深圳）有限公司 | Data processing method, device, equipment and the storage medium of call platform |
| CN109165223A (en) * | 2018-08-24 | 2019-01-08 | 张宇 | A kind of information data link method |
| CN112416648A (en) * | 2020-06-03 | 2021-02-26 | 上海哔哩哔哩科技有限公司 | Data verification method and device |
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| CN1787588A (en) * | 2005-12-01 | 2006-06-14 | 大唐软件技术有限责任公司 | Method for processing multiprogress message and method for processing multiprogress talk ticket |
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2012
- 2012-12-25 CN CN2012105730001A patent/CN103034733A/en active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
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| US20060004995A1 (en) * | 2004-06-30 | 2006-01-05 | Sun Microsystems, Inc. | Apparatus and method for fine-grained multithreading in a multipipelined processor core |
| CN1787588A (en) * | 2005-12-01 | 2006-06-14 | 大唐软件技术有限责任公司 | Method for processing multiprogress message and method for processing multiprogress talk ticket |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104363073A (en) * | 2014-10-31 | 2015-02-18 | 上海大唐移动通信设备有限公司 | Method and device for processing CDL (call detail log) data of signaling subprocess |
| CN104363073B (en) * | 2014-10-31 | 2017-12-01 | 上海大唐移动通信设备有限公司 | The method and device that a kind of CDL data to signaling sub-process are handled |
| CN106533752A (en) * | 2016-11-04 | 2017-03-22 | 东北大学 | Network big data analysis method for call modes and resource utilization in cellular network |
| CN106533752B (en) * | 2016-11-04 | 2019-05-21 | 东北大学 | The network big data analysis method of call model and the utilization of resources in cellular network |
| CN108769435A (en) * | 2018-05-30 | 2018-11-06 | 平安科技（深圳）有限公司 | Monitoring method, electronic device, computer equipment and the storage medium of call center |
| CN108881651A (en) * | 2018-05-30 | 2018-11-23 | 平安科技（深圳）有限公司 | Data processing method, device, equipment and the storage medium of call platform |
| CN109165223A (en) * | 2018-08-24 | 2019-01-08 | 张宇 | A kind of information data link method |
| CN112416648A (en) * | 2020-06-03 | 2021-02-26 | 上海哔哩哔哩科技有限公司 | Data verification method and device |
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Application publication date: 20130410 |