CN111246498A

CN111246498A - eSRVCC abnormity analysis method and device

Info

Publication number: CN111246498A
Application number: CN201811443173.5A
Authority: CN
Inventors: 刘毅; 郭宝; 张阳; 李言兵; 刘立洋; 刘亚; 公维伟; 吴德胜; 吴颢
Original assignee: China Mobile Communications Group Co Ltd; China Mobile Group Shandong Co Ltd
Current assignee: China Mobile Communications Group Co Ltd; China Mobile Group Shandong Co Ltd
Priority date: 2018-11-29
Filing date: 2018-11-29
Publication date: 2020-06-05
Anticipated expiration: 2038-11-29
Also published as: CN111246498B

Abstract

The embodiment of the invention provides an eSRVCC abnormity analysis method and device, which are characterized in that the position of the eSRVCC and the overlapping characteristic of wireless signals are positioned by means of MR data, the time-intensity characteristic before the eSRVCC execution and the signal intensity distribution characteristic of an LTE cell are judged by combining with CTR big data analysis, and a 60-grid characteristic grid space is combined by the three characteristics. Each grid cell represents: what the type of coverage of the LTE cell the eSRVCC takes place is, how the target handover cell is in relation to the coverage of LTE, what the characteristics of the measurement and execution phases are. In a 60-grid 'feature grid space', comparing the 'feature grid' where the current eSRVCC occurs with the optimal 'feature grid' calculated by combining the previous three, determining different eSRVCC failure cause values and distinguishing different cause adjustment optimization.

Description

eSRVCC abnormity analysis method and device

Technical Field

The embodiment of the invention relates to the technical field of mobile communication, in particular to an eSRVCC abnormality analysis method and device.

Background

With the network construction of LTE (Long Term Evolution), one of the main technologies for Voice service interoperability is eSRVCC (Enhanced Single Radio Voice Call Continuity). The eSRVCC mainly solves the problem of seamless Switching between VoIP (Voice over internet protocol) Voice and CS (Circuit Switching) Voice controlled by IMS (IP Multimedia Subsystem) by a single radio frequency UE (User Experience) to maintain continuity of Voice service. The traditional eSRVCC parameter configuration is derived from a configuration manual issued by a group, adopts 'one-time cutting', does not consider different scene characteristics, and is difficult to meet the requirement of differentiation. And the later optimization is correspondingly adjusted according to the test statistical results of the network management side and the DT (drive test) and the personal experience of a network optimization engineer, and a proper set value is sought through continuous testing and adjustment.

Chinese patent application publication No. CN 108337696 a discloses a method, an apparatus, a storage medium, and an electronic device for processing an SRVCC exception, and provides a method, an apparatus, and a product for processing an SRVCC exception. In one aspect, an event of reporting a measurement report to a network side is detected, the event triggers execution of an operation of adjusting a switching threshold of VoWifi, a target Wifi hotspot is searched according to the adjusted switching threshold, and the VoWifi operation or the SRVCC operation is executed according to a set switching strategy according to a search result. In the process that the UE detects the switching command of the SRVCC, the switching threshold of the VoWifi is reduced, so that when the UE does not receive the switching command of the SRVCC, the UE can be switched to the VoWifi first, and the continuity of the call is ensured. In another aspect, a computer-readable storage medium storing various threshold and timer data and integrated into an electronic device, such as a UE, is provided. The method needs to integrate the used threshold and timer data and related programs into the electronic equipment in advance, and increases the difficulty of process implementation.

In the chinese invention patent with application publication No. CN 103404107 a, disclosed "an information transmission method, an information change method, and an apparatus", when a terminal UE is in a connected state, it is determined whether a single radio frequency voice service continuity SRVCC capability of the UE is changed from a first capability to a second capability different from the first capability; when the SRVCC capability is changed from the first capability to the second capability, first information is sent to a base station to which the UE belongs, the first information carries first state information, the first state information is used for representing that the SRVCC capability of the UE is the second capability, so that the base station can update the local SRVCC capability information of the UE according to the first information, when the SRVCC capability of the UE changes, the base station can determine whether to initiate an SRVCC process according to actual conditions, the SRVCC process can be ensured to be successfully carried out as much as possible, and phenomena such as voice call drop are avoided. The method requires the UE to report the SRVCC capability to the base station in real time, and occupies signaling and base station resources.

In summary, the eSRVCC optimization testing process in the prior art is time-consuming, labor-consuming and error-prone, the testing result is easily affected by the external environment and some emergencies, the optimization effect is affected, and the efficiency is low.

Disclosure of Invention

Embodiments of the present invention provide an eSRVCC anomaly analysis method and apparatus that overcome the above-mentioned problems, or at least partially solve the above-mentioned problems.

In a first aspect, an embodiment of the present invention provides an eSRVCC anomaly analysis method, including:

acquiring the position and wireless signal overlapping characteristics of the eSRVCs, time-intensity characteristics before the eSRVCs are executed and signal intensity distribution characteristics corresponding to Long Term Evolution (LTE) cells, and constructing a characteristic grid space; each feature grid in the feature grid space represents the coverage type of an LTE cell where eSRVCC occurs, the coverage relation between a target switching cell and the LTE, and the switching parameter characteristics of a switching area between an LTE weak field and a GSM strong field of a global system for mobile communication;

and acquiring an actual feature lattice in the feature lattice space when the current eSRVCC occurs and a standard optimal feature lattice in the feature lattice space, and adjusting parameters of the eSRVCC based on switching parameters of the actual feature lattice and the standard optimal feature lattice in the feature lattice space.

Optionally, the obtaining of the location of the eSRVCC and the overlapping characteristics of the wireless signals specifically includes:

based on the coverage difference of the 2G cell and the 4G cell, the coverage overlapping area is divided into four LTE and GSM overlapping combination scenes: dividing an area with good 2G coverage and poor 4G coverage into a first scene; dividing the areas covered by 2G and 4G into a second scene; dividing the areas with poor 2G coverage and 4G coverage into a third scene; dividing the area with poor 2G coverage and good 4G coverage into a fourth scene; the 2G cell and the 4G cell are respectively provided with a signal coverage threshold, if the signal coverage is lower than the corresponding signal coverage threshold, the coverage is poor, and if the signal coverage is higher than the corresponding signal coverage threshold, the coverage is good.

Optionally, after dividing the coverage overlapping area into four LTE and GSM overlapping combination scenarios based on the coverage difference between the 2G cell and the 4G cell, the method further includes:

location and radio signal overlap characteristics of the eSRVCC, coverage of the problematic cell located by sample data MRO or statistical data MRE, and eSRVCC location or range associated with the B2 event are obtained based on the measurement report MR data.

Optionally, the coverage of the problematic cell located by the sample data MRO specifically includes:

acquiring sampling point distribution of GSM m cells and LTE i cells based on MRO, and acquiring LTE j interference areas between adjacent LTE i cells;

intensity SRVCC of GSM m cell and LTE j interference cell at each point when eSRVCC is calculated_(j，m)(X_i|Y_i)；

And obtaining an area range S ═ tone of the eSRVCC<X|Y>}∈{SRVCC_(j,m)<X_i|Y_i>＞0}。

Optionally, the obtaining of the time-intensity characteristic of the eSRVCC before execution specifically includes:

positioning a handover area of the LTE weak field and the GSM strong field based on the LTE measurement report and the peripheral GSM measurement report;

obtaining the number of sampling points of the MR from the cell telephone traffic record CTR, calculating the mean value and the variance of the number of the sampling points, and based on the sectional variance of the reaction time;

comparing the variance waveform of the cross-connection area with the variance waveform of an eSRVCC trigger starting point to acquire three switching characteristics of the cross-connection area; setting a region in which the switching time is greater than a preset time threshold, the strong signal is switched earlier than the weak signal, and the weak signal is switched more easily than the strong signal as an aggressive region; setting a region in which switching is limited when the switching time is not more than a preset time threshold and the signal intensity is more than a set first intensity threshold or the signal intensity is less than a second intensity threshold as an equilibrium region, and setting a region in which switching is not performed when the switching time is not more than the preset time threshold and the signal intensity is more than a third intensity threshold and in which a weak signal is easy to switch than a strong signal as a conservative region.

Optionally, the obtaining of the signal strength distribution characteristic of the corresponding LTE cell specifically includes:

based on the differentiation of MR data waveforms, five MR distribution map characteristics are constructed: a reverse slide type, a bone taper type, a flat type, a bimodal type, and an attenuation type;

if the signal intensity of the LTE cell user is smaller than a-110 dBm proportion and is lower than a preset first proportion threshold value, and the signal intensity of the LTE cell user is smaller than a-115 dBm proportion and is lower than a preset second proportion threshold value, and the proportion is gradually increased when the signal intensity is larger than-105 dBm, the LTE cell user is divided into a reverse sliding ladder type; the first proportional threshold is greater than the second proportional threshold, and the first proportional threshold is less than 10%;

if the signal intensity of the user in the LTE cell is less than-110 dBm and is greater than 70%, the ratio of the signal intensity of the user in the LTE cell which is less than-115 dBm in the ratio of the user in the LTE cell which is less than-110 dBm is greater than 50%, the ratio is reduced after the signal intensity of the user in the LTE cell which is greater than-105 dBm, and the ratio of the signal intensity of the user in the LTE cell which is greater than-100 dBm;

if the signal intensity of the LTE cell user is smaller than-110 dBm by less than 30%, and the ratio of the LTE cell user signal intensity smaller than-115 dBm in the ratio of-110 dBm is smaller than 30%, and the ratio is unchanged after the LTE cell user signal intensity is larger than-105 dBm, the LTE cell user is divided into a flat type;

if the signal intensity of the LTE cell user is smaller than-105 dBm and is larger than 40%, and the signal intensity of the LTE cell user is larger than-90 dBm and is larger than 40%, the LTE cell user is divided into a double-peak type;

if the signal intensity of the user in the LTE cell is smaller than-110 dBm by more than 70%, the signal intensity is larger than-105 dBm and gradually reduced, and the ratio of the signal intensity smaller than-115 dBm in the ratio of-110 dBm is smaller than 30%, the LTE cell is classified as attenuation type.

Optionally, the constructing the feature lattice space specifically includes:

and taking four LTE and GSM overlapped combined scenes as 2G and 4G scene overlapped independent surfaces, taking three switching characteristics of an intersection area as 2G and 4G signal conversion independent surfaces, and taking five MR distribution diagram characteristics as multi-dimensional waveform difference independent surfaces to construct a 60-grid characteristic grid space.

In a second aspect, an embodiment of the present invention provides an eSRVCC abnormality analysis apparatus, including:

the system comprises a first module, a second module and a third module, wherein the first module is used for acquiring the position and wireless signal overlapping characteristics of eSRVCC, time-intensity characteristics before execution of eSRVCC and signal intensity distribution characteristics of a corresponding Long Term Evolution (LTE) cell and constructing a characteristic grid space; each feature grid in the feature grid space represents the coverage type of an LTE cell where eSRVCC occurs, the coverage relation between a target switching cell and the LTE, and the switching parameter characteristics of a switching area between an LTE weak field and a GSM strong field of a global system for mobile communication;

and the second module is used for acquiring an actual feature lattice in the feature lattice space when the current eSRVCC occurs, and a standard optimal feature lattice in the feature lattice space, and adjusting the parameters of the eSRVCC based on the switching parameters of the actual feature lattice and the standard optimal feature lattice in the feature lattice space.

In a third aspect, an embodiment of the present invention provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the steps of the method provided in the first aspect when executing the program.

In a fourth aspect, an embodiment of the present invention provides a non-transitory computer readable storage medium, on which a computer program is stored, which when executed by a processor, implements the steps of the method as provided in the first aspect.

The embodiment of the invention provides an eSRVCC abnormity analysis method and device, which are characterized in that the position of the eSRVCC and the overlapping characteristic of wireless signals are positioned by means of MR data, the time-intensity characteristic before the eSRVCC execution and the signal intensity distribution characteristic of an LTE cell are judged by combining with CTR big data analysis, and a 60-cell characteristic cell space is combined by the three characteristics. Each grid cell represents: what the type of coverage of the LTE cell the eSRVCC takes place is, how the target handover cell is in relation to the coverage of LTE, what the characteristics of the measurement and execution phases are. In a 60-grid 'feature grid space', comparing the 'feature grid' where the current eSRVCC occurs with the optimal 'feature grid' calculated by combining the previous three, determining different eSRVCC failure cause values and distinguishing different cause adjustment optimization. And determining the selection of the adjacent cell pair, the setting of the threshold and the corresponding measures based on the CTR big data analysis and combining the MR distribution and the switching judgment mode. Aiming at different scenes and environment construction differential gene combinations, an optimization means gene library is established, and the optimization thought is solidified to form a 'flow' optimization method. By accurately optimizing the voice service, the quality of the service is improved, and the user perception is guaranteed.

Drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and those skilled in the art can also obtain other drawings according to the drawings without creative efforts.

Fig. 1 is a schematic diagram of an eSRVCC anomaly analysis method according to an embodiment of the invention;

FIG. 2 is a schematic diagram of a 2G and 4G coverage overlap region according to an embodiment of the invention;

fig. 3 is a schematic diagram of a region scope of an eSRVCC according to an embodiment of the invention;

FIG. 4 is a diagram illustrating three performance relationships of mean and variance in accordance with an embodiment of the present invention;

FIG. 5 is a schematic diagram of a 60-cell "feature cell space" according to an embodiment of the present invention;

fig. 6 is a diagram illustrating RSRP distribution of all UEs in a cell according to an embodiment of the present invention;

fig. 7 is a schematic diagram illustrating B2 test start and decision values when sampling points of esrvccs are dotted according to an embodiment of the present invention;

fig. 8 is a schematic physical structure diagram of an electronic device according to an embodiment of the present invention.

Detailed Description

In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

Because the traditional eSRVCC optimization test process is time-consuming, labor-consuming and error-prone, the test result is easily affected by the external environment and some emergencies, the optimization effect is affected, and the efficiency is low. Therefore, the embodiments of the present invention determine the selection of the neighboring cell pair, the setting of the threshold, and the countermeasure based on the CTR big data analysis and in combination with the MR distribution and the handover decision manner. Aiming at different scenes and environment construction differential gene combinations, an optimization means gene library is established, and the optimization thought is solidified to form a 'flow' optimization method. By accurately optimizing the voice service, the quality of the service is improved, and the user perception is guaranteed. The following description and description will proceed with reference being made to various embodiments.

Fig. 1 is a method for analyzing eSRVCC abnormality according to an embodiment of the present invention, including:

In the embodiment, the location of the eSRVCC and the wireless signal overlapping characteristics are located by means of MR data in the LTE system, the time-intensity characteristics before the eSRVCC is executed and the signal intensity distribution characteristics of the LTE cell are determined by combining the CTR big data analysis, and the "feature lattice space" is combined by the three types of characteristics. Each grid cell represents: what the coverage type of the LTE cell occurs by the eSRVCC is, how the coverage relationship of the target handover cell and the LTE is, and handover parameter characteristics of the handover area of the LTE weak field and the GSM strong field of the global system for mobile communications.

In the feature lattice space, comparing the feature lattice where the current eSRVCC occurs with the optimal feature lattice calculated by combining the previous three, determining different eSRVCC failure cause values and distinguishing different cause adjustment optimization.

And determining the selection of the adjacent cell pair, the setting of the threshold and the corresponding measures based on the CTR big data analysis and combining the MR distribution and the switching judgment mode. Aiming at different scenes and environment construction differential gene combinations, an optimization means gene library is established, and the optimization thought is solidified to form a 'flow' optimization method. By accurately optimizing the voice service, the quality of the service is improved, and the user perception is guaranteed.

On the basis of the above embodiments, acquiring the location of the eSRVCC and the overlapping characteristics of the wireless signals specifically includes:

In the present embodiment, as shown in fig. 2, the coverage overlap area is divided into four different scenarios according to the 2G, 4G cell coverage difference, and the coverage of the problem cell and the eSRVCC location/range associated with B2 are located by MRO \ MRE data.

Based on the coverage difference between the 2G cell and the 4G cell, the coverage overlapping area is divided into four LTE and GSM overlapping combination scenarios, as shown in table 1 below:

TABLE 1 overlapping combination scenarios

Scene	2G overlay		4G covering
				First scene	Is covered well	Poor coverage
Second scenario	Is covered well	Is covered well
			Third scenario	Poor coverage	Poor coverage
Fourth scenario	Poor coverage	Is covered well

On the basis of the above embodiments, after dividing the coverage overlapping area into four LTE and GSM overlapping combination scenarios based on the coverage difference between the 2G cell and the 4G cell, the method further includes:

On the basis of the foregoing embodiments, the coverage area of the problematic cell located by the sample data MRO specifically includes:

In this embodiment, as shown in fig. 3, the distribution of sampling points of cells can be obtained according to MRO data, and it is assumed that different ellipses in the figure represent the distribution of sampling points of different GSM cells and LTE cells, regions a1 and B1 in fig. 3 represent eSRVCC generation regions between LTE i and GSM m, and region C1 represents interference regions between LTE i and LTE j.

For a point within the region<X_i|Y_i>We can obtain the RSRP (Reference Signal Receiving Power) strength LTE of the j cell and the m cell at the coordinate point respectively_j<X_i|Y_i>And GSM_m<X_i|Y_i>Then the intensity of eSRVCC at the coordinate point of j cell and m cell can be expressed as SRVCC_(j,m)<X_i|Y_i>The average level of the adjacent j cells within the eSRVCC area is calculated as:

the region range for eSRVCC can be finally derived as:

S＝{<X|Y>}∈{SRVCC_(j,m)<X_i|Y_i>＞0}。

on the basis of the foregoing embodiments, obtaining a time-intensity feature of the eSRVCC before execution specifically includes:

Specifically, three different decision results are also shown in the following table 2 corresponding to different handover features:

TABLE 2 three handover characteristics

Region of agitation	Early strong signal	Weak signal is easier	Faster switching away
				Equalization region	Good signal limiting	Difference signal limiting	Switch to slow
Conserved regions	Good signal is not tangent	The difference signal is easier	Switch to slow

In this embodiment, the LTE MR measurement report and the peripheral GSM MR measurement report are projected onto a geographical map layer in a geographical presentation form, so as to visually locate a handover area or a critical line between the LTE weak field and the GSM strong field, compare the handover area or the critical line with the original eSRVCC trigger start point, compare variance waveforms as shown in fig. 4, evaluate a decision in three forms of aggressive, balanced and conservative, and use the decision as a decision basis for next adjustment.

In this embodiment, the number of sampling points of the event MR is obtained from CTR (Cell Traffic Recording):

calculating to obtain the mean value mu and the variance sigma of the number of the sampling points²：

The variance of the segments is

Reaction time t₁∈{σ²(T|t₁)＞＞σ²(T|t₂)}。

Three expressions of mean and variance are reflected in fig. 4, and these three different decision results also correspond to different handover characteristics.

On the basis of the foregoing embodiments, acquiring a signal strength distribution characteristic corresponding to a long term evolution LTE cell specifically includes:

based on the differentiation of MR data waveforms, five MR distribution map characteristics are constructed: inverted slide, bone-cone, flat, bimodal, and attenuated.

The judgment method is shown in the following table 3:

TABLE 3 judgment basis for characteristics of five MR distribution maps

On the basis of the above embodiments, the constructing the feature lattice space specifically includes:

taking four LTE and GSM overlapped combination scenes as 2G and 4G scene overlapped independent planes, taking three switching features of an interface area as 2G and 4G signal conversion independent planes, taking five MR profile features as multi-dimensional waveform difference independent planes, and constructing a 60-grid feature grid space, specifically, taking the three switching features of the four LTE and GSM overlapped combination scenes and the interface area and the five MR profile features as three coordinate axes, and constructing a 4 × 5 × 3 60-grid feature grid space, as shown in fig. 5.

After a 60-grid feature grid space is constructed, a recommended feature grid optimization direction table is further constructed, as shown in the following table 4;

TABLE 4 recommended feature lattice optimization Direction

On the basis of the above embodiments, in specific application, a station A3 is selected for experiment, original data of the station MRO \ MRE \ CTR in busy hours of 7 consecutive days is extracted, and MRR data of 2G stations within 2km around the station is extracted for operation.

(1)2G, 4G scenes overlap: performing rasterization analysis on the MR data of the site A3 and peripheral LTE and GSM sites to obtain positioning points of eSRVCC, coverage grid distribution of 4G and 2G and overlapping types of eSRVCC areas;

(2) multi-dimensional waveform difference: the type reflects the RSRP distribution characteristics of all UE in the cell, and the signal intensity of the user is lower than-110 dBm; the cell has no signal dip region; as shown in fig. 6 (where the abscissa is the interval distribution of signal strength RSRP, the left signal strength is low, the right signal strength is high; the ordinate is the number of sample points in the corresponding signal interval), the RSRP decreases gradually as the user moves away; the signal of the UE is of a slow-down type;

(3)2G and 4G signal conversion: a decision result diagram is obtained by extracting the B2 event starting test and the decision value when the cell performs sampling point dotting of the eSRVCC each time in a week, as shown in fig. 7.

In fig. 7, point a 3: an upper threshold appears in the measurement report, which indicates the best signal intensity distribution of the VOLTE call; b3 point: the earliest time limit of the measurement report shows the time length of the call establishment of the cell; point C3: a threshold appears in the measurement report, which indicates the worst signal intensity distribution experienced by VOLTE call; point D3: best baseline, low noise case of eSRVCC area.

Positioning the 2G and 4G signal conversion regions according to an optimal reference interval standard (MR sampling points in the eSRVCC region are as few as possible, a measurement report sampling point distribution model is balanced, a 4G boundary +2G good region) and a measurement report sampling point distribution balance model standard (a sampling point distribution central line is close to or slightly better than an optimal reference line; an upper threshold appears in a measurement report-a lower threshold appears in the measurement report is less than 6) to determine the optimal threshold range of the eSRVCC:

B2.1∈{40,38,…20}

B2.2∈{24,22,…14}

TTT∈{320,640,…5120}。

(4) adjusting verification effect comparison: adjusting and verifying according to the step value, and finally determining that the eSRVCC index is optimal and other indexes have minimum influence when the following threshold is reached: b2.1 ∈ {32}, B2.2 ∈ {20}, and TTT ∈ {1280 }.

The embodiment of the present invention further provides an eSRVCC anomaly analysis device, based on the eSRVCC anomaly analysis method in the foregoing embodiments, including:

Fig. 8 is a schematic entity structure diagram of an electronic device according to an embodiment of the present invention, and as shown in fig. 8, the electronic device may include: a processor (processor)810, a communication Interface 820, a memory 830 and a communication bus 840, wherein the processor 810, the communication Interface 820 and the memory 830 communicate with each other via the communication bus 840. Processor 810 may invoke a computer program stored on memory 830 and executable on processor 810 to perform the eSRVCC anomaly analysis methods provided by the various embodiments described above, including, for example:

In addition, the logic instructions in the memory 830 may be implemented in software functional units and stored in a computer readable storage medium when the logic instructions are sold or used as independent products. Based on such understanding, the technical solutions of the embodiments of the present invention may be essentially implemented or make a contribution to the prior art, or may be implemented in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the methods described in the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.

An embodiment of the present invention further provides a non-transitory computer-readable storage medium, on which a computer program is stored, where the computer program is implemented to perform the eSRVCC anomaly analysis method provided in the foregoing embodiments when executed by a processor, for example, the method includes:

An embodiment of the present invention further provides a computer program product, where the computer program product includes a computer program stored on a non-transitory computer readable storage medium, where the computer program includes program instructions, and when the program instructions are executed by a computer, the computer can execute the eSRVCC anomaly analysis method as described above, for example, including:

In summary, the embodiments of the present invention provide an eSRVCC anomaly analysis method and apparatus, which locate the eSRVCC position and the wireless signal overlapping characteristic by means of MR data, determine the "time-intensity" characteristic before the eSRVCC is executed and the signal intensity distribution characteristic of the LTE cell by combining with CTR big data analysis, and combine these three types of characteristics to form a "characteristic cell space" of 60 cells. Each grid cell represents: what the type of coverage of the LTE cell the eSRVCC takes place is, how the target handover cell is in relation to the coverage of LTE, what the characteristics of the measurement and execution phases are. In a 60-grid 'feature grid space', comparing the 'feature grid' where the current eSRVCC occurs with the optimal 'feature grid' calculated by combining the previous three, determining different eSRVCC failure cause values and distinguishing different cause adjustment optimization. And determining the selection of the adjacent cell pair, the setting of the threshold and the corresponding measures based on the CTR big data analysis and combining the MR distribution and the switching judgment mode. Aiming at different scenes and environment construction differential gene combinations, an optimization means gene library is established, and the optimization thought is solidified to form a 'flow' optimization method. By accurately optimizing the voice service, the quality of the service is improved, and the user perception is guaranteed.

The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.

Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments.

Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.

Claims

1. An eSRVCC abnormality analysis method is characterized by comprising the following steps:

2. The eSRVCC anomaly analysis method of claim 1, wherein obtaining the eSRVCC location and the radio signal overlap characteristics comprises:

3. The eSRVCC anomaly analysis method according to claim 2, wherein after dividing the coverage overlap area into four LTE and GSM overlapping combination scenarios based on the coverage difference of the 2G cell and the 4G cell, the method further comprises:

4. The eSRVCC anomaly analysis method according to claim 3, wherein the coverage of the problematic cell located by the MRO sample data specifically comprises:

And obtaining an area range S ═ tone of the eSRVCC<X|Y>}∈{SRVCC_(j,_m)<X_i|Y_i>＞0}。

5. The eSRVCC anomaly analysis method of claim 2, wherein obtaining the time-intensity characteristics of the eSRVCC before execution specifically comprises:

6. The eSRVCC anomaly analysis method according to claim 5, wherein obtaining the signal strength distribution characteristics of the corresponding LTE cell specifically comprises:

7. The eSRVCC anomaly analysis method according to claim 6, wherein the constructing the feature lattice space specifically comprises:

8. An eSRVCC abnormality analysis device, comprising:

9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the steps of the method according to any of claims 1 to 7 are implemented when the processor executes the program.

10. A non-transitory computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 7.