CN103581976A - Cell identifying method and device - Google Patents

Abstract

The invention discloses a cell identifying method and device. The cell identifying method includes the steps of obtaining call logs corresponding to user calls, obtaining abnormal information from the call logs, identifying abnormal events recorded in the call logs according to the abnormal information, locating abnormal happening cells according to the types of the identified abnormal events, and selecting the abnormal happening cells in the preset number from the abnormal happening cells as key cells. By obtaining the abnormal information and identifying the types of the abnormal events recorded in the call logs according to the abnormal information, the abnormal happening cells with the abnormal events can be located according to the types of the abnormal events, and the key cells can be further identified according to the cell identifying method. Due to the fact that the methods for locating the abnormal happening cells of the same type of abnormal events are the same, classification is carried out first on the abnormal events before the abnormal happening cells are located, then batch locating is carried out on the same type of abnormal events, the efficiency for identifying the key cells is improved, time is saved, and the precision and accuracy for identifying the key cells can be improved greatly.

Description

The recognition methods of community and device

Technical field

The present invention relates to mobile communication technology field, particularly relate to a kind of recognition methods and device of community.

Background technology

Mobile subscriber is when using communication network, tend to because the reasons such as poor signal quality, Internet resources deficiency cause in off-grid, access failure, communication process call drop or the anomalous event such as speech quality is poor, therefore have a strong impact on user's normal use, and may cause that customer complaint even changes operator.In order to reduce above-mentioned call abnormal problem, at present mainly by communication network is constantly optimized, but because the community of a certain LAN of composition is often thousands of, it is almost impossible for each community, being optimized, and operator is only to causing user's normal call Shuai Di community to be optimized conventionally.These cause user's normal call Shuai Di community to be known as crucial community, therefore, to the prerequisite of network implementation optimization, are effectively to identify the crucial community that causes user's normal call rate low.

The method that prior art is identified crucial community is: calculate the traffic statistics index of all communities, according to traffic statistics index, descending sort is carried out in community, selecting the forward community of sequence is crucial community.The traffic statistics index of the community abnormal call number of times that to be a period of time occur in community, divided by the value of the call count gained of this community, comprises cutting off rate, access failure rate etc.The cutting off rate value that to be abnormal call number of times that call drop occurs in a period of time community draw divided by the call count of this community, the call drop class of ratio Yue Gao， community that the abnormal call number of times that call drop occurs in community during this period of time in the larger explanation of cutting off rate of community accounts for total number of calls is extremely more serious; The access failure rate value that to be the number of calls that access failure occurs in a period of time community draw divided by the call count of this community, the access failure class of ratio Yue Gao， community that the abnormal call number of times that call drop occurs in community during this period of time in the larger explanation of access failure rate of community accounts for total number of calls is extremely more serious.

In realizing process of the present invention, inventor finds that above-mentioned prior art at least exists following problem:

Prior art is according to the crucial community of the traffic statistics index sequencing selection of community, although can be crucial community by the cell identification that severely subnormal event occurs, but can not determine that this key community is to cause user's normal call Shuai Di community, crucial community and the user-association identified are poor, even this key community is optimized, also cannot effectively improve user's call perception, reduce the rate of complaints of user.

For making the problems referred to above easy to understand, now suppose that a period of time user U1 and user U2 community C1He community C2 call out, and by prior art to the processing of C1, community, community C2, from the two, filter out crucial community.Referring to table 1, suppose that C1He community, community C2 is from not far, user U1 is everlasting movable under the C2 of C1He community, community, and user U2 is everlasting movable under the C2 of community.In a period of time community C1, have 10 users, user U1 is one of them, and these 10 users have respectively called out 1 time and call drop has occurred in the C1 of community, and the cutting off rate that calculates known community C1 is 100%.In this time period user U1Hai community C2, called out 5 times and call drop has occurred 3 times, another user U2 in the C2 of community called out 5 times and call drop has occurred 2 times in this time period, and the cutting off rate that calculates known community C2 is 50%.

Table 1

Referring to table 1, according to prior art, C1, community, community C2 is carried out to descending sort according to cutting off rate, selecting the forward community of sequence is crucial community, known C1 community is crucial community.In the C1 of community, occurred altogether 10 times and called out, and call drop has all occurred in these 10 times calling, occurred altogether 10 times and call out in the C2 of community, wherein called out generation call drops for 5 times, the call drop that contrasts known community C1 is extremely the most serious.But for user U1, this time period user U1 has called out altogether 6 Ci， community C1 3 call drops of 1 call drop ， community C2 generation has occurred, obviously causing the too low community of user U1 normal call rate is community C2, for the crucial community of user U1, is namely community C2.Referring to table 1, if the Zhi Dui of operator community C1 is optimized, obviously user U1 cutting off rate is reduced not obvious, namely not obvious to the normal call rate raising of user U1.

Summary of the invention

In order to solve the problem of prior art, the embodiment of the present invention provides a kind of recognition methods and device of community, can identify and cause the low crucial community of user's normal call rate.Described technical scheme is as follows:

First aspect, provides a kind of recognition methods of community, and described method comprises:

Obtain user and call out corresponding call log, and obtain abnormal information from described call log;

According to described abnormal information, identify the type of the anomalous event recording in described call log;

According to the Sort positioning of the anomalous event identifying, extremely there is community, from described abnormal generation community, select the abnormal generation community of preset number as crucial community.

In conjunction with first aspect, in the possible implementation of the first of first aspect, the described abnormal generation community of preset number of selecting from described abnormal generation community, as crucial community, comprising:

Obtain the abnormal index of described abnormal generation community;

By described abnormal index, to the sequence of described abnormal generation community, obtain extremely occurring sequence of cells, and from described abnormal generation sequence of cells, select the abnormal generation community of preset number as crucial community.

In conjunction with first aspect, in the possible implementation of the second of first aspect, the described type of identifying the anomalous event recording in described call log according to described abnormal information, comprising:

Mate described abnormal information and caller access failure or called access failure, if coupling, the type of identifying the anomalous event recording in described call log is access class anomalous event.

In conjunction with first aspect, in the third possible implementation of first aspect, the described type of identifying the anomalous event recording in described call log according to described abnormal information, comprising:

Call drop or the poor on-hook of matter after call drop, connection before mating described abnormal information and connecting, if coupling is identified the type of the anomalous event recording in described call log for keeping class anomalous event.

In conjunction with first aspect, in the 4th kind of possible implementation of first aspect, the described type of identifying the anomalous event recording in described call log according to described abnormal information, comprising:

Mate that described abnormal information and up covering are abnormal or descending covering is abnormal, if coupling is identified the type of the anomalous event recording in described call log for covering class anomalous event.

In conjunction with first aspect, in the 5th kind of possible implementation of first aspect, the described type of identifying the anomalous event recording in described call log according to described abnormal information, comprising:

Mate that described abnormal information and up/down row high-quality index HQI are abnormal, up/down lang sound quality index VQI abnormal, up/down row single-pass, the cross-talk of up/down row or frequent switch abnormal, if coupling, the type of identifying the anomalous event recording in described call log is voice quality class anomalous event.

In conjunction with the possible implementation of the second of first aspect, in the 6th kind of possible implementation of first aspect, there is community in the Sort positioning of the anomalous event that described basis identifies, comprising extremely:

If the type of the anomalous event identifying is access class anomalous event, the abnormal community that occurs, location is for access community.

In conjunction with the third possible implementation of first aspect, in the 7th kind of possible implementation of first aspect, there is community in the Sort positioning of the anomalous event that described basis identifies, comprising extremely:

If the type of the anomalous event identifying is to keep class anomalous event, the abnormal community that occurs, location is for discharging community.

In conjunction with the 5th kind of possible implementation of first aspect, in the 8th kind of possible implementation of first aspect, there is community in the Sort positioning of the anomalous event that described basis identifies, comprising extremely:

If the type of the anomalous event identifying is up/down row single-pass or the cross-talk of up/down row in voice quality class anomalous event, according to the abnormal community that occurs of the log information of base station, described anomalous event place and base station controller BSC warning file location.

In conjunction with the 4th kind or the 5th kind of possible implementation of first aspect, in the 9th kind of possible implementation of first aspect, there is community in the Sort positioning of the anomalous event that described basis identifies, comprising extremely:

If the type of the anomalous event identifying is that the up HQI in covering class anomalous event and/or voice quality class anomalous event is abnormal and/or descending HQI is abnormal and/or up VQI is abnormal and/or the abnormal community that occurs, abnormal measurement report MR contribution rate location of the abnormal ，Ze An of descending VQI community.

In conjunction with the 9th kind of possible implementation of first aspect, in the tenth kind of possible implementation of first aspect, described by the abnormal community that occurs, abnormal MR contribution rate location of community, comprising:

Obtain the MR record that described user calls out;

Add up described user's calling and count N at the abnormal MR through cell i _{celli_abnormal}mR sum with cell i;

Calculate the abnormal MR ratio of described cell i

Judge the abnormal MR ratio of described cell i whether exceed threshold value, if exceed, described cell i is abnormal community, by abnormal coefficient Abnormal _cellibe set to 1; Otherwise described cell i is normal cell, by abnormal coefficient Abnormal _cellibe set to 0;

Calculate the abnormal MR contribution rate of described cell i $D_{\mathrm{Celli}\_\mathrm{abnormal}}=\frac{N_{\mathrm{Celli}\_\mathrm{abnormal}}*{\mathrm{Abnormal}}_{\mathrm{Celli}}}{\underset{i=1}{\overset{n}{\mathrm{\Σ}}}(N_{\mathrm{Celli}\_\mathrm{abnormal}}*{\mathrm{Abnormal}}_{\mathrm{Celli}})};$

According to the abnormal MR contribution rate D of community _{celli_abnormal}to described set of cells Zhong Ge community descending sort, according to ranking results, determine the abnormal community that occurs;

Wherein, described cell i is all set of cells that described user calls out process, and wherein, i is more than or equal to 1 and be less than or equal to n, and described n is more than or equal to 1 integer.

In conjunction with the possible implementation of the first of first aspect, in the 11 kind of possible implementation of first aspect, the abnormal index of described abnormal generation community is abnormal events corresponding to described abnormal generation community or abnormal call number or abnormal call number of users or anomalous concentration number of users.

In conjunction with the 11 kind of possible implementation of first aspect, in the 12 kind of possible implementation of first aspect, when described abnormal index is anomalous concentration number of users, described in obtain the abnormal index of abnormal generation community, comprising:

If it is a plurality of users' repeatedly calling that described user calls out, obtain successively described a plurality of users' anomalous concentration community, the anomalous concentration number of users of adding up described anomalous concentration community is abnormal index.

Second aspect, provides a kind of recognition device of community, and described device comprises:

The first acquisition module, calls out corresponding call log for obtaining user;

The second acquisition module, obtains abnormal information for the call log obtaining from described the first acquisition module;

The first identification module, identifies the type of the anomalous event that call log that described the first acquisition module obtains records for the abnormal information of obtaining according to described the second acquisition module;

, for locating the Sort positioning of the anomalous event that described the first identification module identifies, extremely there is community in locating module;

The second identification module, for selecting the abnormal generation community of preset number as crucial community from the abnormal generation community of described locating module location.

In conjunction with second aspect, in the possible implementation of the first of second aspect, described the second identification module, comprising:

Abnormal index acquiring unit, for obtaining the abnormal index of the abnormal generation community of described locating module location;

Crucial cell identification unit, for the abnormal index obtaining by described abnormal index acquiring unit, sorted in the abnormal generation community of described locating module location, obtain extremely occurring sequence of cells, and from described abnormal generation sequence of cells, select the abnormal generation community of preset number as crucial community.

In conjunction with second aspect, in the possible implementation of the second of second aspect, described the first identification module, for mating abnormal information and caller access failure or the called access failure obtaining from described the second acquisition module, if coupling, the anomalous event recording the call log obtaining from the first acquisition module described in identification is access class anomalous event.

In conjunction with second aspect, in the third possible implementation of second aspect, described the first identification module, be used for mating the abnormal information and the front call drop of connection, the rear call drop of connection or the poor on-hook of matter that described the second acquisition module obtains, if coupling, identifies the anomalous event that records in the call log that described the first acquisition module obtains for keeping class anomalous event.

In conjunction with second aspect; in the 4th kind of possible implementation of second aspect; described the first identification module; for mating the abnormal information that described the second acquisition module obtains and up covering is abnormal or descending covering is abnormal; if coupling, identifies the anomalous event that records in the call log that described the first acquisition module obtains for covering class anomalous event.

In conjunction with second aspect, in the 5th kind of possible implementation of second aspect, described the first identification module, for mating, the abnormal information that described the second acquisition module obtains is abnormal with up/down row high-quality index HQI, up/down lang sound quality index VQI is abnormal, up/down row single-pass, the cross-talk of up/down row or frequent switching extremely, if coupling, identifying the anomalous event recording in the call log that described the first acquisition module obtains is voice quality class anomalous event.

In conjunction with the possible implementation of the second of second aspect, in the 6th kind of possible implementation of second aspect, described locating module, is access community for locating the abnormal generation community of the access class anomalous event of described the first identification module identification.

In conjunction with the third possible implementation of second aspect, in the 7th kind of possible implementation of second aspect, described locating module, is release community for locating the abnormal generation community of the maintenance class anomalous event of described the first identification module identification.

The 4th kind of possible implementation in conjunction with second aspect, in the 8th kind of possible implementation of second aspect,, for extremely there is community according to the log information of base station, described anomalous event place and base station controller BSC warning file location in described locating module.

In conjunction with the third or the 4th kind of possible implementation of second aspect, in the 9th kind of possible implementation of second aspect, described locating module, for the abnormal community that occurs, abnormal measurement report MR contribution rate location by community.

In conjunction with the 9th kind of possible implementation of second aspect, in the tenth kind of possible implementation of second aspect, described locating module, comprising:

Acquiring unit, for obtaining the MR record of described calling;

Statistic unit, counts N for adding up described calling at the abnormal MR through cell i _{celli_abnormal}mR sum with cell i;

The first computing unit, for calculating the abnormal MR ratio of described cell i

Judging unit, for judging the abnormal MR ratio of described cell i

whether exceed threshold value, if exceed, described cell i is abnormal community, by abnormal coefficient Abnormal _cellibe set to 1; Otherwise described cell i is normal cell, by abnormal coefficient Abnormal _cellibe set to 0;

The second computing unit, for calculating the abnormal MR contribution rate of described cell i $D_{\mathrm{Celli}\_\mathrm{abnormal}}=\frac{N_{\mathrm{Celli}\_\mathrm{abnormal}}*{\mathrm{Abnormal}}_{\mathrm{Celli}}}{\underset{i=1}{\overset{n}{\mathrm{\Σ}}}(N_{\mathrm{Celli}\_\mathrm{abnormal}}*{\mathrm{Abnormal}}_{\mathrm{Celli}})};$

Sequencing unit, for according to the abnormal MR contribution rate D of community _{celli_abnormal}to described set of cells Zhong Ge community descending sort;

Determining unit, for determining the abnormal community that occurs according to ranking results;

Wherein, described cell i is all set of cells of described calling process, and wherein, i is more than or equal to 1 and be less than or equal to n, and described n is more than or equal to 1 integer.

In conjunction with the possible implementation of the first of second aspect, in the 11 kind of possible implementation of second aspect, the abnormal index of the abnormal generation community that described abnormal index acquiring unit obtains is abnormal events corresponding to described abnormal generation community or abnormal call number or abnormal call number of users or anomalous concentration number of users.

The 11 kind of possible implementation in conjunction with second aspect, in the 12 kind of possible implementation of second aspect, described abnormal index acquiring unit, while being a plurality of users' repeatedly calling for calling out as described user, obtain successively described a plurality of users' anomalous concentration community, the anomalous concentration number of users of adding up described anomalous concentration community is abnormal index.

The beneficial effect that the technical scheme that the embodiment of the present invention provides is brought is:

By the abnormal information occurring in analysis user calling behavior, identify anomalous event corresponding to this abnormal information, then locate the abnormal generation community at this anomalous event place, finally from abnormal generation community, select crucial community.Because abnormal generation community is the community, immediate cause place that causes user's call exception, therefore, based on selected crucial community, abnormal generation community and user, there is relevance closely, with respect to prior art, all communities are pressed the method for the crucial community of traffic statistics index sequencing selection, the technical scheme that the embodiment of the present invention provides can significantly improve accuracy of identification and the accuracy of crucial community.

Secondly, owing to identifying anomalous event corresponding to this abnormal information by the abnormal information occurring in analysis user calling behavior, then locate the abnormal generation community at this anomalous event place, finally from abnormal generation community, select crucial community, therefore can cause this user to call out the poor crucial community of impression for specific user's rapid analyses such as report user, VIP users.

Accompanying drawing explanation

In order to be illustrated more clearly in the technical scheme in the embodiment of the present invention, below the accompanying drawing of required use during embodiment is described is briefly described, apparently, accompanying drawing in the following describes is only some embodiments of the present invention, for those of ordinary skills, do not paying under the prerequisite of creative work, can also obtain according to these accompanying drawings other accompanying drawing.

Fig. 1 is the recognition methods flow chart of a kind of community of providing of the embodiment of the present invention one;

Fig. 2 is the recognition methods flow chart of a kind of community of providing of the embodiment of the present invention two;

Fig. 3 is the recognition device structural representation of a kind of community of providing of the embodiment of the present invention three;

Fig. 4 is the recognition device structural representation of a kind of community of providing of the embodiment of the present invention four.

Embodiment

For making the object, technical solutions and advantages of the present invention clearer, below in conjunction with accompanying drawing, embodiment of the present invention is described further in detail.

In the embodiment of the present invention, call out the behavior that refers to that user utilizes mobile terminal and another user to set up call, converse, keep call or finish to converse.Wherein, mobile terminal can include but not limited to it is mobile phone, intelligent computer or other any equipment that can carry out mobile communication.The regional classification that mobile communications network will cover is several communities, the covering radius of each community is in 1～10km left and right, each community sets up a base station for the user's service in this cell range, therefore, community is the elementary cell that mobile communications network carries out the network optimization to the elementary cell ，Ye Shi operator of user's service.

Embodiment mono-

The embodiment of the present invention provides a kind of recognition methods of community, and referring to Fig. 1, described method idiographic flow is as follows:

Step 101, obtains user and calls out corresponding call log.

In the embodiment of the present invention, call log is the data that the carrying user that obtains from communication equipment calls out behavior, and communication equipment includes but not limited to mobile communication equipment, base station, base station controller, network management platform etc.Communication equipment obtains by the mode of monitoring, collection or storage networking corresponding interface data flow and recording carrying user calls out the data of behavior.Wherein, the data that user calls out behavior include but not limited to the data relevant with user's normal call, and the data of anomalous event while occurring etc., such as call number, access code, abnormal information etc.

Step 102 is obtained abnormal information from call log.

In the embodiment of the present invention, abnormal information will be added call log to by communication equipment for characterizing the information of this calling behavior outcome or process for calling behavior, the format and content of this abnormal information can include but not limited to it is digital coding, English label, exception procedure information or other any information that can identify anomalous event, for example, the abnormal coding 10010 of caller access failure; The abnormal label of called access failure: In-Call access failure.

Step 103, according to the type of the anomalous event recording in the daily record of abnormal information call identifying.

In the embodiment of the present invention, anomalous event refers to and causes calling out the event that can not normally carry out, generally by due to non-user behavior reason.Due to the mobile network signals of user's environment of living in may occur intensity or second-rate, mobile network resource is not enough or the outside factors such as interference that mobile network is existed of mobile network, and these factors all may cause user to call out, cannot normally carry out, produce anomalous event.

Calling for the present embodiment, anomalous event may occur in moment or a moment point, also may occur in the calling procedure continuing for some time, for example, caller access failure, called access failure, connect before call drop, connect after call drop, the poor on-hook of speech quality, up HQI abnormal etc.After anomalous event occurs, the abnormal information relevant with this anomalous event can be recorded in call log.

, there is community according to the Sort positioning of the anomalous event identifying in step 104, selects the abnormal generation community of preset number as crucial community extremely from abnormal generation community.

For this step, according to the anomalous event identifying, judge and locate the abnormal generation community of this anomalous event.Specifically, abnormal for the abnormal or called access failure of caller access failure, there is the access community that community is this calling in it extremely; For anomalous event, being to connect front call drop, the rear call drop of connection or the poor on-hook of matter, there is the release community that community is this calling in it extremely.

The abnormal community that occurs is the generation community that causes the anomalous event that user can not normal call, any one in abnormal generation community is optimized to the frequency that can reduce anomalous event, raising user's normal call rate, therefore abnormal any one abnormal generation community occurring in community can be as crucial community.In real operation, can, according to actual conditions, select the abnormal generation community of preset number to be optimized as crucial community.

The method that the embodiment of the present invention provides, by the abnormal information occurring in analysis user calling behavior, identify anomalous event corresponding to this abnormal information, then locate the abnormal generation community at this anomalous event place, finally from abnormal generation community, select crucial community.Because abnormal generation community is the community, immediate cause place that causes user's call exception, therefore, based on selected crucial community, abnormal generation community and user, there is relevance closely, in terms of existing technologies, can significantly improve accuracy of identification and the accuracy of crucial community.

Further, the recognition methods of the community providing based on the embodiment of the present invention, can cause this user's communication to experience poor crucial community for specific user's rapid analyses such as report user, VIP users, by in time optimization of network performance being carried out in this key community, can solve in time the abnormal problem occurring in user's communication behavior, improve rapidly user's call successful rate, improve user's communication impression.

Embodiment bis-

Content in conjunction with above-described embodiment one, the embodiment of the present invention provides a kind of recognition methods of community, for convenience of explanation, it is example that the present embodiment be take the executive agent of the method that communication equipment provides as the present embodiment, and the method that the present embodiment is provided is illustrated.Referring to Fig. 2, method idiographic flow is as follows:

Step 201, obtains user and calls out corresponding call log.

In the embodiment of the present invention, call log is the data that the carrying user that obtains from communication equipment calls out behavior, and communication equipment includes but not limited to mobile communication equipment, base station, base station controller, network management platform etc.Communication equipment obtains by the mode of monitoring, collection or storage networking corresponding interface data flow and recording carrying user calls out the data of behavior.Wherein, the data that user calls out behavior include but not limited to the data relevant with user's normal call, and the data of anomalous event while occurring etc., such as call number, access code, abnormal information etc.

Step 202 is obtained abnormal information from call log.

In the embodiment of the present invention, abnormal information will be added call log to by communication equipment for characterizing the information of this calling behavior outcome or process for calling behavior, the format and content of this abnormal information can include but not limited to it is digital coding, English label, exception procedure information or other any information that can identify anomalous event, for example, the abnormal coding 10010 of caller access failure; The abnormal label of called access failure: In-Call access failure.By resolving call log, can from call log, obtain abnormal information.

Step 203, according to the type of the anomalous event recording in the daily record of abnormal information call identifying.

In the embodiment of the present invention, the Exception Type of anomalous event can be divided according to Different Rule, such as: time occurring according to anomalous event, moment sexual abnormality, Process Character can be divided into abnormal etc.

As a kind of preferred embodiment, anomalous event type comprises: access class is abnormal and/or covering class is abnormal and/or maintenance class is abnormal and/or speech quality class is abnormal.

It should be noted that, those skilled in the art can also divide anomalous event type according to custom rule when enforcement is of the present invention, and the embodiment of the present invention is not restricted this.

In the present embodiment, whether communication equipment occurs extremely by the current calling of rule judgment, if occur, coding, abnormal marking or other corresponding abnormal informations is extremely recorded in the call log of current calling, and particular content is as follows:

1) access class is abnormal

If be that the abnormal or called access of caller access is abnormal extremely, current abnormal abnormal for accessing class.Particularly: if do not receive after calling, ALERTING message and non-user behavior or opposite end reason cause, and this calling behavior is caller access failure; If do not receive after called calling, ALERTING message and non-user behavior or opposite end reason cause, and this calling behavior is called access failure.When meeting above condition, communication equipment just by caller/called access abnormal marking or extremely coding or other corresponding abnormal informations be recorded in corresponding call log.

2) keep class abnormal

If be call drop before connecting extremely, connect rear call drop or the poor on-hook of matter, current abnormal for keeping class abnormal.Particularly:

If call setup result for " disconnect before connect acknowledge " and call setup unsuccessfully non-user behavior extremely cause, this calling behavior is call drop before connecting;

If call setup success but has called out result be receive DISCONNECT message and unsuccessfully non-user behavior or opposite end reason cause, this calling behavior is to connect call drop afterwards;

If on signaling plane, call out normal termination but before channel discharges N in second without MR(Measure Report, measurement report) or the last M quality of reception threshold value (such as being set to 6 or 7 to GSM) that in second, up/down row average quality is greater than setting cause user cannot stand poor voice quality and initiatively on-hook, this calling behavior is the poor on-hook of matter.

3) covering class is abnormal

If be extremely the calling that up covering is abnormal or descending covering is abnormal, current abnormal abnormal for covering class.Particularly:

If the access level of calling out is less than the up level threshold value (such as to can be set to-100dBm of GSM) of setting or the up level threshold value (such as to can be set to-100dBm of GSM) that average uplink receiving level is less than setting, this calling behavior is that up covering is abnormal;

If the average descending incoming level of calling out in call log is less than the downlink electrical jib door limit value (such as to can be set to-90dBm of GSM) of setting, this calling behavior is that descending covering is abnormal;

4) speech quality class is abnormal

If call out, there is up/down row high-quality index (High Quality Index, HQI) abnormal, up/down lang sound quality index (Voice Quality Index, VQI) abnormal, up/down row single-pass, the cross-talk of up/down row, frequently switch that abnormal etc. any one or more is abnormal in abnormal, be that speech quality class is abnormal.Particularly:

If call out, meet up/down row and receive the MR(Measurement Report that is less than or equal to " up/down row high-quality thresholding " (such as " up/down row high-quality thresholding " in GSM can be set to 5), original measurement report) ratio is less than " the good ratio thresholding of the quality of reception " (such as being set to 70% to GSM) of setting, and this calling behavior is that up/down row HQI is abnormal;

If the ratio that the average VQI of up/down row calling out accounts for total call duration lower than " low VQI ratio thresholding " (such as being set to 2.7 to GSM) that arrange or too low time of up/down row VQI is greater than " low VQI continues duration thresholding " (such as being set to 10% to GSM) of setting, this calling behavior is that up/down row VOI is abnormal;

If call out, have up/down row single-pass record, this calling behavior is up/down row single-pass;

If call out, have up/down row cross-talk record, this calling behavior is the cross-talk of up/down row;

If call out switching times in once call surpass " the frequent switching times thresholding " set and average switching time interval be less than setting " frequently switching minimum interval thresholding ", this calling behavior is frequent switching.

For this step 203, the type according to the anomalous event recording in the daily record of abnormal information call identifying, specifically comprises:

Coupling abnormal information and caller access failure or called access failure, if coupling, the type of the anomalous event recording in call identifying daily record is access class anomalous event.

Alternatively, the type according to the anomalous event recording in the daily record of abnormal information call identifying, specifically comprises:

Call drop or the poor on-hook of matter after call drop, connection before coupling abnormal information and connection, if mate, the type of the anomalous event recording in call identifying daily record is to keep class anomalous event.Alternatively, the type according to the anomalous event recording in the daily record of abnormal information call identifying, specifically comprises:

Coupling abnormal information is with up covering is abnormal or descending covering is abnormal, if coupling, the type of the anomalous event recording in call identifying daily record is covering class anomalous event.

Alternatively, the type according to the anomalous event recording in the daily record of abnormal information call identifying, specifically comprises:

Coupling abnormal information and up/down row high-quality index HQI are abnormal, up/down lang sound quality index VQI is abnormal, up/down row single-pass, the cross-talk of up/down row or frequent switching extremely, if coupling, the type of the anomalous event recording in call identifying daily record is voice quality class anomalous event.

, according to the Sort positioning of the anomalous event identifying, extremely there is community in step 204.

Wherein, according to the Sort positioning of the anomalous event identifying, extremely there is community, specifically comprise:

If the type of the anomalous event identifying is access class anomalous event, the abnormal community that occurs, location is for access community.

Access community refers to that caller makes a call or shared community when called response is called out, take mobile phone as example be exactly that calling subscriber inputs to press after called number and dials the community that key or called subscriber hear mobile phone place while pressing turn-on key after ring.

Alternatively, according to the Sort positioning of the anomalous event identifying, extremely there is community, specifically comprise:

If the type of the anomalous event identifying is to keep class anomalous event, the abnormal community that occurs, location is for discharging community.

Discharge community and refer to community shared when mobile communication equipment discharges the unlimited link of call of setting up with base station, take mobile phone as example be exactly the community at mobile phone place while receiving DISCONNECT message.

Alternatively, according to the Sort positioning of the anomalous event identifying, extremely there is community, specifically comprise:

If the type of the anomalous event identifying is up/down row single-pass or the cross-talk of up/down row in voice quality class anomalous event, according to the abnormal community that occurs of the log information of base station, anomalous event place and base station controller BSC warning file location.

Alternatively, according to the Sort positioning of the anomalous event identifying, extremely there is community, specifically comprise:

If the type of the anomalous event identifying is that the up HQI in covering class anomalous event and/or voice quality class anomalous event is abnormal and/or descending HQI is abnormal and/or up VQI is abnormal and/or the abnormal community that occurs, abnormal measurement report MR contribution rate location of the abnormal ，Ze An of descending VQI community.Extremely there is community in the abnormal MR contribution rate location of ，An community wherein, specifically comprises:

Obtain the MR record that user calls out;

Statistics is called out at the abnormal MR through cell i and is counted N _{celli_abnormal}mR sum with cell i;

The abnormal MR ratio of calculation plot i

The abnormal MR ratio of judgement cell i

whether exceed threshold value, if exceed, cell i is abnormal community, by abnormal coefficient Abnormal _cellibe set to 1; Otherwise cell i is normal cell, by abnormal coefficient Abnormal _cellibe set to 0;

The abnormal MR contribution rate of calculation plot i $D_{\mathrm{Celli}\_\mathrm{abnormal}}=\frac{N_{\mathrm{Celli}\_\mathrm{abnormal}}*{\mathrm{Abnormal}}_{\mathrm{Celli}}}{\underset{i=1}{\overset{n}{\mathrm{\Σ}}}(N_{\mathrm{Celli}\_\mathrm{abnormal}}*{\mathrm{Abnormal}}_{\mathrm{Celli}})};$

According to the abnormal MR contribution rate D of community _{celli_abnormal}to set of cells Zhong Ge community descending sort, according to ranking results, determine the abnormal community that occurs;

Wherein, cell i is called out all set of cells of process for user, and wherein, i is more than or equal to 1 and be less than or equal to n, and n is more than or equal to 1 integer.

If current calling occurred, up HQI was abnormal and/or descending HQI is abnormal and/or up VQI is abnormal and/or two classes or the multiclass of descending VQI extremely and/or in covering is extremely abnormal, calculate respectively the abnormal corresponding abnormal MR contribution rate of each class, and obtain such abnormal corresponding abnormal generation community.The up HQI of take is below example extremely, and the process of locating the abnormal generation community that up HQI is abnormal is as follows:

Obtain the MR record that user calls out;

Statistics single call is counted N at the abnormal MR of up HQI through cell i _{celli_UL_HQI_abnormal}, and the MR of cell i sum;

The abnormal MR ratio of up HQI of calculation plot i

The abnormal MR ratio of up HQI of judgement cell i

whether exceed threshold value, if exceed, cell i is the abnormal community of up HQI, by abnormal coefficient Abnormal _cellibe set to 1; Otherwise cell i is the abnormal community of non-up HQI, by abnormal coefficient Abnormal _cellibe set to 0;

The abnormal MR contribution rate of up HQI of calculation plot i $D_{\mathrm{Celli}\_\mathrm{UL}\_\mathrm{HQI}\_\mathrm{abnormal}}=\frac{N_{\mathrm{Celli}\_\mathrm{UL}\_\mathrm{HQI}\_\mathrm{abnormal}}*{\mathrm{Abnormal}}_{\mathrm{Celli}}}{\underset{i=1}{\overset{n}{\mathrm{\Σ}}}(N_{\mathrm{Celli}\_\mathrm{UL}\_\mathrm{HQI}\_\mathrm{abnormal}}*{\mathrm{Abnormal}}_{\mathrm{Celli}})};$

According to the abnormal MR contribution rate of the up HQI D of community _{celli_UL_HQI_abnormal}to set of cells Zhong Ge community descending sort, by community, the community of getting sequence first is here the abnormal generation community of up HQI anomalous event;

Cell i is called out all set of cells of process for user, and wherein, i is more than or equal to 1 and be less than or equal to n, and n is more than or equal to 1 integer.

Step 205, obtains the abnormal index of abnormal generation community.

The abnormal index of this step S205 refers to the indication information that community intensity of anomaly can characterize abnormalities occurs.

Alternatively, the abnormal index that community occurs this step S205 extremely comprises abnormal events or abnormal call number or abnormal call number of users or the anomalous concentration number of users that abnormal generation community is corresponding.

Wherein, when abnormal index is anomalous concentration number of users, obtain the abnormal index of abnormal generation community, specifically comprise:

If it is a plurality of users' repeatedly calling that user calls out, obtain successively a plurality of users' anomalous concentration community, the anomalous concentration number of users of statistics anomalous concentration community is abnormal index.Wherein, the anomalous concentration number of users of statistics anomalous concentration community, specifically comprises:

First the descending sort of anomalous event number of times is pressed in the corresponding abnormal generation community of repeatedly calling out of unique user, get front w the anomalous concentration community that community is this user occurs extremely, this user is called the anomalous concentration user of this w anomalous concentration community; W is greater than 1 integer; Then obtain successively all the other users' anomalous concentration community; The abnormal quantity that the anomalous concentration user of community occurs of statistics is anomalous concentration number of users.

Step 206,, obtains extremely occurring sequence of cells, and from abnormal generation sequence of cells, selects the abnormal generation community of preset number as crucial community the sequence of abnormal generation community by abnormal index.

By abnormal index, to the sequence of abnormal generation community, can be by abnormal index descending sort, can be also ascending sort, also can be by other rule compositors.Take descending as example, and extremely occurring in sequence of cells, according to priority to select the abnormal generation community of preset number is crucial community, and wherein, the priority of the abnormal generation community of sorting more forward is higher.

Further, for covering, the up HQI of class extremely and in voice quality class anomalous event is abnormal, descending HQI is abnormal, up VQI is abnormal and descending VQI is abnormal, due to this class anomalous event be in calling procedure occur on all communities of process, the related community of this process may have hundreds of even thousands of, wherein, each community may be the community that causes above-mentioned abnormal generation, difference is only that it causes the shared weighted of this abnormal effect, therefore, directly using the community of quantity like this as abnormal generation community and crucial community be optimized and be difficult to realize.For addressing this problem, the embodiment of the present invention proposes the abnormal community that occurs, abnormal MR contribution rate location by community.Because abnormal generation community shared weight in likely causing above-mentioned abnormal community of selecting by abnormal MR contribution rate is the highest, therefore, as long as selecting some communities by abnormal MR contribution rate is the abnormal community that occurs, make each community abnormal MR contribution rate sum exceed threshold value, by optimizing this abnormal community that occurs, just can solve covering class abnormal, up/down HQI is abnormal or up/down VQI is abnormal, so, greatly reduce and need abnormal generation number of cells to be processed, make crucial community corresponding to the abnormal generation community of said process sexual abnormality event enter to optimize to become feasible.

Further, the embodiment of the present invention adopts anomalous concentration number of users as the abnormal index of the crucial community of identification, to can further improve accuracy and the accuracy of identification of the crucial community of identification.Obtaining anomalous concentration number of users comprises: first in a plurality of abnormal generations community, select anomalous event frequency Gao community as anomalous concentration community, the abnormal call number of users of then adding up in anomalous concentration community is anomalous concentration number of users.

Analyzing examples below, adopts anomalous concentration number of users as abnormal index, with respect to usining anomalous event frequency, abnormal call number of users, as abnormal index, can improve precision and the accuracy of the crucial community of identification:

Suppose, by identification before, locate, get tri-abnormal communities that occur of C1, C2 and C3, due to limited conditions, can only select one of them as crucial community, to be optimized.These three the abnormal situations that community occurs are: there are U1 to U3 totally three users C1 community, and each has been called out ten times, and wherein call drop has occurred 9 times U1 user, other user's no exceptions events; There are S1 to S3 totally three users C2 community, and each has been called out ten times, and call drop has occurred 1 time each user; There are W1 to W3 totally three users C3 community, and each has been called out ten times, and wherein call drop has respectively occurred 4 times for W1 and W2 user, W3 user's no exceptions event.3 abnormal there is community and user as shown in table 2:

Table 2

Using anomalous event frequency as abnormal index, C1, C2 and C3 are carried out to descending sort: C1,9 anomalous events; C3,8 anomalous events; C2,3 anomalous events.Select the C1 of sequence first as crucial community.

Using abnormal call number of users as abnormal index, C1, C2 and C3 are carried out to descending sort: C2,3 abnormal call users; C3,2 abnormal call users; C1,1 abnormal call user.Select the C2 of sequence first as crucial community.

The anomalous concentration number of users of usining carries out descending sort as abnormal index to C1, C2 and C3.First select C1, C3 that anomalous event frequency is high as anomalous concentration community, user in anomalous concentration community C1 or C3 generation anomalous event is exactly anomalous concentration user, by anomalous concentration number of users, abnormal community C1, C3 are sorted again: C3,2 anomalous concentration users; C1,1 anomalous concentration user.Select the C3 of sequence first as crucial community.

Table 3 is effects of optimizing behind HuoC3 community, ,C2 community, C1 community:

Table 3

In table 3, the summation of abnormal call number of users Shi Sange community abnormal call number of users.The comprehensive cutting off rate of ,C2 community, cutting off rate ShiC1 community and C3 community user, computing formula: number of dropped calls/total calls * 100%.Cutting off rate is one of important indicator of branch company of examination operator of telecom operators.

By the contrast of upper table, can draw: optimize and take anomalous event frequency as the crucial community that abnormal index draws, cutting off rate declines by a big margin, but that abnormal user is counted fall is less; Optimization be take abnormal call number of users as the crucial community that abnormal index draws, abnormal call number of users declines by a big margin, but cutting off rate fall is less; Optimization be take anomalous concentration number of users as the crucial community that abnormal index draws, abnormal call number of users declines to a great extent, and cutting off rate also declines to a great extent simultaneously.Therefore, the embodiment of the present invention adopts anomalous concentration number of users to identify crucial community as abnormal index, more crucial community is optimized, and can obtain maximum technique effect.

The method that the present embodiment provides, by obtaining abnormal information, and according to the type of the anomalous event recording in the daily record of abnormal information call identifying, according to the abnormal generation community of the Sort positioning anomalous event of anomalous event, identifies crucial community then accordingly.Because the localization method of the abnormal generation community of same class anomalous event is identical, therefore abnormal generation the in location first classified to anomalous event before community, then anomalous event of the same type is located in batches, effectively improved the recognition efficiency of crucial community, saved the time.

In addition, because abnormal generation community is the community, immediate cause place that causes user's call exception, therefore, based on selected crucial community, abnormal generation community and user, there is relevance closely, in terms of existing technologies, can significantly improve accuracy of identification and the accuracy of crucial community.

Further, the method providing based on the embodiment of the present invention, can cause this user's communication to experience poor crucial community for specific user's rapid analyses such as report user, VIP users, by in time optimization of network performance being carried out in this key community, can solve in time the abnormal problem occurring in user's communication behavior, improve rapidly user's call successful rate, improve user's communication impression.

Embodiment tri-

The embodiment of the present invention provides a kind of recognition device of community, and referring to Fig. 3, device comprises:

The first acquisition module 301, calls out corresponding call log for obtaining user;

The second acquisition module 302, obtains abnormal information for the call log obtaining from the first acquisition module 301;

The first identification module 303, identifies the type of the anomalous event that call log that the first acquisition module obtains records for the abnormal information of obtaining according to the second acquisition module 302;

, for the Sort positioning of the anomalous event that identifies according to the first identification module 303, extremely there is community in locating module 304;

The second identification module 305, for selecting the abnormal generation community of preset number as crucial community from the abnormal generation community of locating module 304 location.

The device that the embodiment of the present invention provides, by obtaining abnormal information, and according to the type of the anomalous event recording in the daily record of abnormal information call identifying, according to the abnormal generation community of the Sort positioning anomalous event of anomalous event, identifies crucial community then accordingly.Because the localization method of the abnormal generation community of same class anomalous event is identical, therefore abnormal generation the in location first classified to anomalous event before community, then anomalous event of the same type is located in batches, effectively improved the recognition efficiency of crucial community, saved the time.

Embodiment tetra-

The embodiment of the present invention provides a kind of recognition device of community, and referring to Fig. 4, this device comprises:

The first acquisition module 401, calls out corresponding call log for obtaining user;

The second acquisition module 402, obtains abnormal information for the call log obtaining from the first acquisition module 401;

The first identification module 403, identifies the type of the anomalous event that call log that the first acquisition module obtains records for the abnormal information of obtaining according to the second acquisition module 402;

, for the Sort positioning of the anomalous event that identifies according to the first identification module 403, extremely there is community in locating module 404;

The second identification module 405, for selecting the abnormal generation community of preset number as crucial community from the abnormal generation community of locating module 404 location.

As a kind of preferred embodiment, referring to Fig. 4, the second identification module 405, comprising:

Abnormal index acquiring unit 4051, for obtaining the abnormal index of the abnormal generation community of locating module 404 location;

Crucial cell identification unit 4052, for the abnormal index obtaining by abnormal index acquiring unit 4051, sorted in the abnormal generation community of locating module location, obtain extremely occurring sequence of cells, and from abnormal generation sequence of cells, select the abnormal generation community of preset number as crucial community.

As a kind of preferred embodiment, the first identification module 403, for mating abnormal information and caller access failure or the called access failure that the second acquisition module 402 obtains, if coupling is identified the anomalous event that records in the call log that the first acquisition module obtains for accessing class anomalous event.

Alternatively, the first identification module 403, for call drop before mating abnormal information that the second acquisition module 402 obtains and connecting, connect after call drop or the poor on-hook of matter, if coupling is identified the anomalous event that records in the call log that the first acquisition module obtains for keeping class anomalous event.

Alternatively; the first identification module 403; for mating abnormal information that the second acquisition module 402 obtains with up covering is abnormal or descending covering is abnormal, if coupling, identifying the anomalous event recording in the call log that the first acquisition module obtains is covering class anomalous event.

Alternatively, the first identification module 403, for mating, the abnormal information that the second acquisition module 402 obtains is abnormal with up/down row high-quality index HQI, up/down lang sound quality index VQI is abnormal, up/down row single-pass, the cross-talk of up/down row or frequent switching extremely, if coupling, identifying the anomalous event recording in the call log that the first acquisition module obtains is voice quality class anomalous event.

As a kind of preferred embodiment, locating module 404, is access community for locating the abnormal generation community of the access class anomalous event of the first identification module 403 identifications.

Alternatively, locating module 404, is release community for locating the abnormal generation community of the maintenance class anomalous event of the first identification module 403 identifications.

Alternatively,, for extremely there is community according to the log information of base station, anomalous event place and base station controller BSC warning file location in locating module 404.

Alternatively, locating module 404, for the abnormal community that occurs, abnormal measurement report MR contribution rate location by community.

Further, referring to Fig. 4, locating module 404, specifically comprises:

Acquiring unit 4041, for obtaining the MR record of calling;

Statistic unit 4042, counts N for adding up the abnormal MR calling out through cell i _{celli_abnormal}mR sum with cell i;

The first computing unit 4043, for the abnormal MR ratio of calculation plot i

Judging unit 4044, for judging the abnormal MR ratio of cell i

whether exceed threshold value, if exceed, cell i is abnormal community, by abnormal coefficient Abnormal _cellibe set to 1; Otherwise cell i is normal cell, by abnormal coefficient Abnormal _cellibe set to 0;

The second computing unit 4045, for the abnormal MR contribution rate of calculation plot i $D_{\mathrm{Celli}\_\mathrm{abnormal}}=\frac{N_{\mathrm{Celli}\_\mathrm{abnormal}}*{\mathrm{Abnormal}}_{\mathrm{Celli}}}{\underset{i=1}{\overset{n}{\mathrm{\Σ}}}(N_{\mathrm{Celli}\_\mathrm{abnormal}}*{\mathrm{Abnormal}}_{\mathrm{Celli}})};$

Sequencing unit 4046, for according to the abnormal MR contribution rate D of community _{celli_abnormal}to set of cells Zhong Ge community descending sort;

Determining unit 4047, for determining the abnormal community that occurs according to ranking results;

Wherein, cell i is for calling out all set of cells of process, and wherein, i is more than or equal to 1 and be less than or equal to n, and n is more than or equal to 1 integer.

The abnormal index of the abnormal generation community that further, abnormal index acquiring unit 4045 obtains is abnormal corresponding abnormal events or abnormal call number or abnormal call number of users or the anomalous concentration number of users in community that occur.

Alternatively, abnormal index acquiring unit 4045, while being a plurality of users' repeatedly calling for calling out as user, the anomalous concentration community that obtains successively a plurality of users, the anomalous concentration number of users of statistics anomalous concentration community is abnormal index.

The device that the embodiment of the present invention provides, by obtaining abnormal information, and according to the type of the anomalous event recording in the daily record of abnormal information call identifying, according to the abnormal generation community of the Sort positioning anomalous event of anomalous event, identifies crucial community then accordingly.Because the localization method of the abnormal generation community of same class anomalous event is identical, therefore abnormal generation the in location first classified to anomalous event before community, then anomalous event of the same type is located in batches, effectively improved the recognition efficiency of crucial community, saved the time.

In addition, because abnormal generation community is the community, immediate cause place that causes user's call exception, therefore, based on selected crucial community, abnormal generation community and user, there is relevance closely, in terms of existing technologies, can significantly improve accuracy of identification and the accuracy of crucial community.

Further, the method providing based on the embodiment of the present invention, can cause this user's communication to experience poor crucial community for specific user's rapid analyses such as report user, VIP users, by in time optimization of network performance being carried out in this key community, can solve in time the abnormal problem occurring in user's communication behavior, improve rapidly user's call successful rate, improve user's communication impression.

Embodiment six

The present embodiment provides a kind of terminal, and this terminal comprises: processor.

This processor, calls out corresponding call log for obtaining user, and from call log, obtains abnormal information; According to the type of the anomalous event recording in the daily record of abnormal information call identifying; According to the Sort positioning of the anomalous event identifying, extremely there is community, from abnormal generation community, select the abnormal generation community of preset number as crucial community.

Further, processor selects the abnormal generation community of preset number as crucial community, for obtaining the abnormal index of abnormal generation community from abnormal generation community; Press abnormal index to the sequence of abnormal generation community, obtain extremely occurring sequence of cells, and from abnormal generation sequence of cells, select the abnormal generation community of preset number as crucial community.

Further, processor is during according to the type of the anomalous event recording in the daily record of abnormal information call identifying, be used for mating abnormal information and caller access failure or called access failure, if coupling, the type of the anomalous event recording in call identifying daily record is access class anomalous event.

Alternatively, processor is during according to the type of the anomalous event recording in the daily record of abnormal information call identifying, call drop or the poor on-hook of matter after call drop, connection before being used for mating abnormal information and connecting, if coupling, the type of the anomalous event recording in call identifying daily record is for keeping class anomalous event.

Alternatively; processor is during according to the type of the anomalous event recording in the daily record of abnormal information call identifying; be used for mating that abnormal information and up covering are abnormal or descending covering is abnormal, if coupling, the type of the anomalous event recording in call identifying daily record is for covering class anomalous event.

Alternatively, processor is during according to the type of the anomalous event recording in the daily record of abnormal information call identifying, be used for mating that abnormal information and up/down row high-quality index HQI are abnormal, up/down lang sound quality index VQI abnormal, up/down row single-pass, the cross-talk of up/down row or frequent switch abnormal, if coupling, the type of the anomalous event recording in call identifying daily record is voice quality class anomalous event.

Further, when processor extremely community occurs according to the Sort positioning of the anomalous event that identifies, if be access class anomalous event for the type of the anomalous event that identifies, the abnormal community that occurs, location is for access community.

Alternatively, when processor extremely community occurs according to the Sort positioning of the anomalous event that identifies, if be to keep class anomalous event for the type of the anomalous event that identifies, the abnormal community that occurs, location is for discharging community.

Alternatively, while extremely there is community according to the Sort positioning of the anomalous event identifying in processor, the type of the anomalous event identifying if be used for is up/down row single-pass or the cross-talk of up/down row of voice quality class anomalous event, according to the abnormal community that occurs of the log information of base station, anomalous event place and base station controller BSC warning file location.

Alternatively, while extremely there is community according to the Sort positioning of the anomalous event identifying in processor, the type of the anomalous event identifying if be used for is that the up HQI of covering class anomalous event and/or voice quality class anomalous event is abnormal and/or descending HQI is abnormal and/or up VQI is abnormal and/or the abnormal community that occurs, abnormal measurement report MR contribution rate location of the abnormal ，Ze An of descending VQI community.

Further, while there is community by the abnormal MR contribution rate location of community is abnormal in processor, the MR record of calling out for obtaining user; Counting user is called out at the abnormal MR through cell i and is counted N _{celli_abnormal}mR sum with cell i; The abnormal MR ratio of calculation plot i

the abnormal MR ratio of judgement cell i

whether exceed threshold value, if exceed, cell i is abnormal community, by abnormal coefficient Abnormal _cellibe set to 1; Otherwise cell i is normal cell, by abnormal coefficient Abnormal _cellibe set to 0; The abnormal MR contribution rate of calculation plot i $D_{\mathrm{Celli}\_\mathrm{abnormal}}=\frac{N_{\mathrm{Celli}\_\mathrm{abnormal}}*{\mathrm{Abnormal}}_{\mathrm{Celli}}}{\underset{i=1}{\overset{n}{\mathrm{\Σ}}}(N_{\mathrm{Celli}\_\mathrm{abnormal}}*{\mathrm{Abnormal}}_{\mathrm{Celli}})};$ According to the abnormal MR contribution rate D of community _{celli_abnormal}to set of cells Zhong Ge community descending sort, according to ranking results, determine the abnormal community that occurs; Wherein, cell i is called out all set of cells of process for user, and wherein, i is more than or equal to 1 and be less than or equal to n, and n is more than or equal to 1 integer.

For extremely there is corresponding abnormal events or abnormal call number or abnormal call number of users or the anomalous concentration number of users in community in the abnormal index that wherein, community occurs extremely.

Further, when abnormal index is anomalous concentration number of users, when this processor obtains the abnormal index of abnormal generation community, if call out as a plurality of users' repeatedly calling for user, obtain successively a plurality of users' anomalous concentration community, the anomalous concentration number of users of statistics anomalous concentration community is abnormal index.

The terminal that the embodiment of the present invention provides, by obtaining abnormal information, and according to the type of the anomalous event recording in the daily record of abnormal information call identifying, according to the abnormal generation community of the Sort positioning anomalous event of anomalous event, identifies crucial community then accordingly.Because the localization method of the abnormal generation community of same class anomalous event is identical, therefore abnormal generation the in location first classified to anomalous event before community, then anomalous event of the same type is located in batches, effectively improved the recognition efficiency of crucial community, saved the time.

In addition, because abnormal generation community is the community, immediate cause place that causes user's call exception, therefore, based on selected crucial community, abnormal generation community and user, there is relevance closely, in terms of existing technologies, can significantly improve accuracy of identification and the accuracy of crucial community.

Further, the method providing based on the embodiment of the present invention, can cause this user's communication to experience poor crucial community for specific user's rapid analyses such as report user, VIP users, by in time optimization of network performance being carried out in this key community, can solve in time the abnormal problem occurring in user's communication behavior, improve rapidly user's call successful rate, improve user's communication impression.

It should be noted that: the recognition device of the community that above-described embodiment provides is when the crucial community of identification, only the division with above-mentioned each functional module is illustrated, in practical application, can above-mentioned functions be distributed and by different functional modules, completed as required, the internal structure that is about to device is divided into different functional modules, to complete all or part of function described above.In addition, the recognition methods embodiment of the recognition device of the community that above-described embodiment provides and community belongs to same design, and its specific implementation process refers to embodiment of the method, repeats no more here.

The invention described above embodiment sequence number, just to describing, does not represent the quality of embodiment.

All or part of step in the embodiment of the present invention, can utilize software to realize, and corresponding software program can be stored in the storage medium can read, as CD or hard disk etc.

The foregoing is only preferred embodiment of the present invention, in order to limit the present invention, within the spirit and principles in the present invention not all, any modification of doing, be equal to replacement, improvement etc., within all should being included in protection scope of the present invention.

Claims (26)

1. The recognition methods of 1.Yi Zhong community, is characterized in that, described method comprises:

   Obtain user and call out corresponding call log, and obtain abnormal information from described call log;

   According to described abnormal information, identify the type of the anomalous event recording in described call log;

   According to the Sort positioning of the anomalous event identifying, extremely there is community, from described abnormal generation community, select the abnormal generation community of preset number as crucial community.
2. 2. the method for claim 1, is characterized in that, the described abnormal generation community of preset number of selecting from described abnormal generation community, as crucial community, comprising:

   Obtain the abnormal index of described abnormal generation community;

   By described abnormal index, to the sequence of described abnormal generation community, obtain extremely occurring sequence of cells, and from described abnormal generation sequence of cells, select the abnormal generation community of preset number as crucial community.
3. 3. the method for claim 1, is characterized in that, the described type of identifying the anomalous event recording in described call log according to described abnormal information, comprising:

   Mate described abnormal information and caller access failure or called access failure, if coupling, the type of identifying the anomalous event recording in described call log is access class anomalous event.
4. 4. the method for claim 1, is characterized in that, the described type of identifying the anomalous event recording in described call log according to described abnormal information, comprising:

   Call drop or the poor on-hook of matter after call drop, connection before mating described abnormal information and connecting, if coupling is identified the type of the anomalous event recording in described call log for keeping class anomalous event.
5. 5. the method for claim 1, is characterized in that, the described type of identifying the anomalous event recording in described call log according to described abnormal information, comprising:

   Mate that described abnormal information and up covering are abnormal or descending covering is abnormal, if coupling is identified the type of the anomalous event recording in described call log for covering class anomalous event.
6. 6. the method for claim 1, is characterized in that, the described type of identifying the anomalous event recording in described call log according to described abnormal information, comprising:

   Mate that described abnormal information and up/down row high-quality index HQI are abnormal, up/down lang sound quality index VQI abnormal, up/down row single-pass, the cross-talk of up/down row or frequent switch abnormal, if coupling, the type of identifying the anomalous event recording in described call log is voice quality class anomalous event.
7. 7. method as claimed in claim 3, is characterized in that, community occurs the Sort positioning of the anomalous event that described basis identifies extremely, comprising:

   If the type of the anomalous event identifying is access class anomalous event, the abnormal community that occurs, location is for access community.
8. 8. method as claimed in claim 4, is characterized in that, community occurs the Sort positioning of the anomalous event that described basis identifies extremely, comprising:

   If the type of the anomalous event identifying is to keep class anomalous event, the abnormal community that occurs, location is for discharging community.
9. 9. method as claimed in claim 6, is characterized in that, community occurs the Sort positioning of the anomalous event that described basis identifies extremely, comprising:

   If the type of the anomalous event identifying is up/down row single-pass or the cross-talk of up/down row in voice quality class anomalous event, according to the abnormal community that occurs of the log information of base station, described anomalous event place and base station controller BSC warning file location.
10. 10. the method as described in claim 5 or 6, is characterized in that, community occurs the Sort positioning of the anomalous event that described basis identifies extremely, comprising:

    If the type of the anomalous event identifying is that the up HQI in covering class anomalous event and/or voice quality class anomalous event is abnormal and/or descending HQI is abnormal and/or up VQI is abnormal and/or the abnormal community that occurs, abnormal measurement report MR contribution rate location of the abnormal ，Ze An of descending VQI community.
11. 11. methods as claimed in claim 10, is characterized in that, described by the abnormal community that occurs, abnormal MR contribution rate location of community, comprising:

    Obtain the MR record that described user calls out;

    Add up described user's calling and count N at the abnormal MR through cell i _{celli_abnormal}mR sum with cell i;

    Calculate the abnormal MR ratio of described cell i

    

    Judge the abnormal MR ratio of described cell i

    

    whether exceed threshold value, if exceed, described cell i is abnormal community, by abnormal coefficient Abnormal _cellibe set to 1; Otherwise described cell i is normal cell, by abnormal coefficient Abnormal _cellibe set to 0;

    Calculate the abnormal MR contribution rate of described cell i $D_{\mathrm{Celli}\_\mathrm{abnormal}}=\frac{N_{\mathrm{Celli}\_\mathrm{abnormal}}*{\mathrm{Abnormal}}_{\mathrm{Celli}}}{\underset{i=1}{\overset{n}{\mathrm{\Σ}}}(N_{\mathrm{Celli}\_\mathrm{abnormal}}*{\mathrm{Abnormal}}_{\mathrm{Celli}})};$

    According to the abnormal MR contribution rate D of community _{celli_abnormal}to described set of cells Zhong Ge community descending sort, according to ranking results, determine the abnormal community that occurs;

    Wherein, described cell i is all set of cells that described user calls out process, and wherein, i is more than or equal to 1 and be less than or equal to n, and described n is more than or equal to 1 integer.
12. 12. methods as claimed in claim 2, is characterized in that, the abnormal index of described abnormal generation community is abnormal events corresponding to described abnormal generation community or abnormal call number or abnormal call number of users or anomalous concentration number of users.
13. 13. methods as claimed in claim 12, is characterized in that, when described abnormal index is anomalous concentration number of users, described in obtain the abnormal index of abnormal generation community, comprising:

    If it is a plurality of users' repeatedly calling that described user calls out, obtain successively described a plurality of users' anomalous concentration community, the anomalous concentration number of users of adding up described anomalous concentration community is abnormal index.
14. The recognition device of 14.Yi Zhong community, is characterized in that, described device comprises:

    The first acquisition module, calls out corresponding call log for obtaining user;

    The second acquisition module, obtains abnormal information for the call log obtaining from described the first acquisition module;

    The first identification module, identifies the type of the anomalous event that call log that described the first acquisition module obtains records for the abnormal information of obtaining according to described the second acquisition module;

    , for the Sort positioning of the anomalous event that identifies according to described the first identification module, extremely there is community in locating module;

    The second identification module, for selecting the abnormal generation community of preset number as crucial community from the abnormal generation community of described locating module location.
15. 15. devices as claimed in claim 14, is characterized in that, described the second identification module, comprising:

    Abnormal index acquiring unit, for obtaining the abnormal index of the abnormal generation community of described locating module location;

    Crucial cell identification unit, for the abnormal index obtaining by described abnormal index acquiring unit, sorted in the abnormal generation community of described locating module location, obtain extremely occurring sequence of cells, and from described abnormal generation sequence of cells, select the abnormal generation community of preset number as crucial community.
16. 16. devices as claimed in claim 14, it is characterized in that, described the first identification module, for mating abnormal information and caller access failure or the called access failure that described the second acquisition module obtains, if coupling, identifying the anomalous event recording in the call log that described the first acquisition module obtains is access class anomalous event.
17. 17. devices as claimed in claim 14, it is characterized in that, described the first identification module, be used for mating the abnormal information and the front call drop of connection, the rear call drop of connection or the poor on-hook of matter that described the second acquisition module obtains, if coupling, identifies the anomalous event that records in the call log that described the first acquisition module obtains for keeping class anomalous event.
18. 18. devices as claimed in claim 14; it is characterized in that; described the first identification module; for mating the abnormal information that described the second acquisition module obtains and up covering is abnormal or descending covering is abnormal; if coupling, identifies the anomalous event that records in the call log that described the first acquisition module obtains for covering class anomalous event.
19. 19. devices as claimed in claim 14, it is characterized in that, described the first identification module, for mating, the abnormal information that described the second acquisition module obtains is abnormal with up/down row high-quality index HQI, up/down lang sound quality index VQI is abnormal, up/down row single-pass, the cross-talk of up/down row or frequent switching extremely, if coupling, identifying the anomalous event recording in the call log that described the first acquisition module obtains is voice quality class anomalous event.
20. 20. devices as claimed in claim 16, is characterized in that, described locating module is access community for locating the abnormal generation community of the access class anomalous event of described the first identification module identification.
21. 21. devices as claimed in claim 17, is characterized in that, described locating module is release community for locating the abnormal generation community of the maintenance class anomalous event of described the first identification module identification.
22. 22. devices as claimed in claim 18, is characterized in that, described locating module, for community occurring extremely according to the log information of base station, described anomalous event place and base station controller BSC warning file location.
23. 23. devices as described in claim 17 or 18, is characterized in that, described locating module, for the abnormal community that occurs, abnormal measurement report MR contribution rate location by community.
24. 24. devices as claimed in claim 23, is characterized in that, described locating module, specifically comprises:

    Acquiring unit, for obtaining the MR record of described calling;

    Statistic unit, counts N for adding up described calling at the abnormal MR through cell i _{celli_abnormal}mR sum with cell i;

    The first computing unit, for calculating the abnormal MR ratio of described cell i

    

    Judging unit, for judging the abnormal MR ratio of described cell i

    

    whether exceed threshold value, if exceed, described cell i is abnormal community, by abnormal coefficient Abnormal _cellibe set to 1; Otherwise described cell i is normal cell, by abnormal coefficient Abnormal _cellibe set to 0;

    The second computing unit, for calculating the abnormal MR contribution rate of described cell i $D_{\mathrm{Celli}\_\mathrm{abnormal}}=\frac{N_{\mathrm{Celli}\_\mathrm{abnormal}}*{\mathrm{Abnormal}}_{\mathrm{Celli}}}{\underset{i=1}{\overset{n}{\mathrm{\Σ}}}(N_{\mathrm{Celli}\_\mathrm{abnormal}}*{\mathrm{Abnormal}}_{\mathrm{Celli}})};$

    Sequencing unit, for according to the abnormal MR contribution rate D of community _{celli_abnormal}to described set of cells Zhong Ge community descending sort;

    Determining unit, for determining the abnormal community that occurs according to ranking results;

    Wherein, described cell i is all set of cells of described calling process, and wherein, i is more than or equal to 1 and be less than or equal to n, and described n is more than or equal to 1 integer.
25. 25. devices as claimed in claim 15, it is characterized in that, the abnormal index of the abnormal generation community that described abnormal index acquiring unit obtains is abnormal events corresponding to described abnormal generation community or abnormal call number or abnormal call number of users or anomalous concentration number of users.
26. 26. devices as claimed in claim 25, it is characterized in that, described abnormal index acquiring unit, while being a plurality of users' repeatedly calling for calling out as described user, obtain successively described a plurality of users' anomalous concentration community, the anomalous concentration number of users of adding up described anomalous concentration community is abnormal index.

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