CN112887991B

CN112887991B - Network quality analysis method and device

Info

Publication number: CN112887991B
Application number: CN202011637279.6A
Authority: CN
Inventors: 董军社; 刘忠江
Original assignee: Beijing Dongtu Tuoming Technology Co ltd
Current assignee: Beijing Dongtu Tuoming Technology Co ltd
Priority date: 2020-12-31
Filing date: 2020-12-31
Publication date: 2023-01-24
Anticipated expiration: 2040-12-31
Also published as: CN112887991A

Abstract

The invention discloses a network quality analysis method and a device, wherein the method comprises the following steps: presetting a plurality of resident types, and performing scene analysis on each user to obtain cell numbers corresponding to the various preset resident types of the user; establishing a data mapping table containing mapping relations among user numbers, resident types and cell numbers; counting the data mapping table to obtain various resident user sets corresponding to the cell numbers; and analyzing the user set data corresponding to the selected resident type of the selected cell number to obtain the network quality analysis result of the selected cell number. The invention can improve the network quality analysis effect.

Description

Network quality analysis method and device

Technical Field

The invention relates to the technical field of wireless communication, in particular to a network quality analysis method and a network quality analysis device.

Background

At present, with the gradual development of networks and the gradual change of life styles of people, the corresponding relation between a resident user and a network scene becomes more and more complex.

For example, buildings of different scenes may exhibit different traffic distribution and quality conditions during different periods of time, such as subways during rush hours, office buildings during work hours, residential areas at night and night, and the like. The traditional method only simply analyzes the network performance in a period of time or analyzes the performance of a certain area, and the problem of poor network quality analysis effect exists.

Disclosure of Invention

In view of the defects in the prior art, an object of the present invention is to provide a method and an apparatus for network quality analysis, so as to at least improve the network quality analysis effect.

In order to achieve the purpose, the technical scheme adopted by the invention is as follows:

a network quality analysis method, comprising: presetting a plurality of resident types, and performing scene analysis on each user to obtain cell numbers corresponding to the various preset resident types of the user; establishing a data mapping table containing mapping relations among user numbers, resident types and cell numbers; counting the data mapping table to obtain various resident user sets corresponding to the cell numbers; and analyzing the user set data corresponding to the selected resident type of the selected cell number to obtain a network quality analysis result of the selected cell number.

Further, the method further comprises: acquiring the corresponding relation among the cell number, the scene name and the scene type; and summarizing and analyzing the user set data of the selected resident type corresponding to all the cell numbers under the selected scene name or scene type to obtain the network quality analysis result of the selected scene name or scene type.

Further, the obtaining of the correspondence between the cell number, the scene name, and the scene type includes: and acquiring the corresponding relation among the cell number, the scene name and the scene type according to the cell parameter data.

Further, the resident types described above include combinations of more than one of the following options: whole day, sleep, daytime, evening, workday and weekend.

Further, the step of performing scene analysis on each user to obtain the cell numbers corresponding to the various preset resident types of the user includes: paying attention to the time period from 0 to 00; if the result can not be output or more than one result can be output, selecting the cell number with the most service establishment times of at least Z1 day under the same cell number in the time of X1 day as the cell number corresponding to the whole-day resident service of the user, wherein X1, Y1, N1 and Z1 are positive integers; focusing on a time period from 0 to 00-6 of the user, if at least Y2 days within X2 days have the most route updates or location updates under the same cell and at least N2 times, determining the cell number as the cell number corresponding to the sleep-time residence of the user; if the result can not be output or more than one result can be output, selecting the cell number with the most service establishment times under the same cell number in at least Z2 days in X2 days as the cell number corresponding to the sleeping time of the user, wherein X2, Y2, N2 and Z2 are positive integers; paying attention to the 10-00-17 time period of the user, if the route update or the position update is the most and at least N3 times in the same cell in at least Y3 days within X3 days, determining the cell number as the cell number corresponding to the resident daytime of the user; if the result cannot be output or more than one result is output, selecting the cell number with the largest service establishment frequency under the same cell number in at least Z3 days in X3 days as the cell number corresponding to the user resident in daytime, wherein X3, Y3, N3 and Z3 are positive integers; paying attention to the 20; if the result cannot be output or more than one result is output, selecting the cell number with the largest service establishment times under the same cell number for at least Z4 days in X4 days as the cell number corresponding to the evening resident of the user, wherein X4, Y4, N4 and Z4 are positive integers; paying attention to a time period from 0 00 to 00 of Monday to Friday of the user, if the route update or the position update is the most and at least N5 times in the same cell in at least Y5 days within X5 days, determining the cell number as the cell number corresponding to the daily work residence of the user; if the result can not be output or more than one result can be output, selecting the cell number with the most service establishment times under the same cell number in at least Z5 days in X5 days as the cell number corresponding to the work daily residence of the user, wherein X5, Y5, N5 and Z5 are positive integers; paying attention to the time period of 0-00-6 of the weekend of the user, if the route update or the position update is maximum and at least N6 times in the same cell for at least Y6 days in X6 days, determining the cell number as the cell number corresponding to the resident weekend of the user; and if the result cannot be output or more than one result is output, selecting the cell number with the most service establishment times under the same cell number in at least Z6 days in X6 days as the cell number corresponding to the weekend resident of the user, wherein X6, Y6, N6 and Z6 are positive integers.

According to another aspect of the embodiments of the present invention, there is also provided a network quality analysis apparatus, including: the scene analysis unit is configured to preset a plurality of resident types, and performs scene analysis on each user to obtain cell numbers corresponding to the various preset resident types of the user; a mapping table establishing unit configured to establish a data mapping table including mapping relationships among the user numbers, the resident types, and the cell numbers; a set determining unit configured to count the data mapping table to obtain various resident user sets corresponding to the cell numbers; and the first data analysis unit is configured to analyze the user set data corresponding to the selected resident type of the selected cell number to obtain a network quality analysis result of the selected cell number.

Further, the above apparatus further comprises: a correspondence obtaining unit configured to obtain a correspondence between a cell number, a scene name, and a scene type; and the second data analysis unit is configured to summarize and analyze the user set data of the selected resident type corresponding to all the cell numbers under the selected scene name or scene type to obtain a network quality analysis result of the selected scene name or scene type.

Further, the correspondence obtaining unit is further configured to: and acquiring the corresponding relation among the cell number, the scene name and the scene type according to the cell engineering parameter data.

Further, the scene analysis unit is further configured to: paying attention to the time period from 0; if the result can not be output or more than one result can be output, selecting the cell number with the most service establishment times of at least Z1 day under the same cell number in the time of X1 day as the cell number corresponding to the whole-day resident service of the user, wherein X1, Y1, N1 and Z1 are positive integers; focusing on a time period from 0 to 00-6 of the user, if at least Y2 days within X2 days have the most route updates or location updates under the same cell and at least N2 times, determining the cell number as the cell number corresponding to the sleep-time residence of the user; if the result can not be output or more than one result can be output, selecting the cell number with the largest service establishment times under the same cell number for at least Z2 days in X2 days as the cell number corresponding to the sleeping time of the user, wherein X2, Y2, N2 and Z2 are positive integers; paying attention to the 10-00-17 time period of the user, if the route update or the position update is maximum and at least N3 times in the same cell for at least Y3 days within X3 days, determining the cell number as the cell number corresponding to the resident daytime of the user; if the result can not be output or more than one result can be output, selecting the cell number with the largest service establishment times under the same cell number for at least Z3 days in X3 days as the cell number corresponding to the daytime resident service of the user, wherein X3, Y3, N3 and Z3 are positive integers; paying attention to the 20; if the result cannot be output or more than one result is output, selecting the cell number with the largest service establishment times under the same cell number for at least Z4 days in X4 days as the cell number corresponding to the evening resident of the user, wherein X4, Y4, N4 and Z4 are positive integers; paying attention to a time period from 0 00 to 00 of Monday to Friday of the user, if the route update or the position update is the most and at least N5 times in the same cell in at least Y5 days within X5 days, determining the cell number as the cell number corresponding to the daily work residence of the user; if the result cannot be output or more than one result is output, selecting the cell number with the largest service establishment times under the same cell number for at least Z5 days in X5 days as the cell number corresponding to the daily work residence of the user, wherein X5, Y5, N5 and Z5 are positive integers; paying attention to a time period of 0-00-6; and if the result cannot be output or more than one result is output, selecting the cell number with the largest service establishment times under the same cell number for at least Z6 days in X6 days as the cell number corresponding to the weekend resident service of the user, wherein X6, Y6, N6 and Z6 are positive integers.

According to another aspect of the embodiments of the present invention, there is also provided a network quality analysis device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the network quality analysis method through the computer program.

According to a further aspect of the embodiments of the present invention, there is also provided a computer-readable storage medium, in which a computer program is stored, wherein the computer program is configured to execute the above network quality analysis method when running.

The invention has the following effects: by adopting the method of the invention, the resident type can be subdivided, and a data mapping table containing the mapping relation among the user number, the resident type and the cell number is established. Through the abundant and meticulous data mapping table of division of data, can be therefrom selectively to the pertinence confirm user's set, realize carrying out network quality analysis to specific user's set, can satisfy multiple network quality analysis demands, network quality analysis effect is better.

Drawings

Fig. 1 is a flow diagram of an alternative network quality analysis method according to an embodiment of the invention;

FIG. 2 is a schematic diagram of an alternative indicator according to an embodiment of the invention;

fig. 3 is a schematic diagram of an implementation architecture of an alternative network quality analysis method according to an embodiment of the present invention;

fig. 4 is a schematic structural diagram of an alternative network quality analysis apparatus according to an embodiment of the present invention;

fig. 5 is a schematic structural diagram of an alternative network quality analysis device according to an embodiment of the present invention.

Detailed Description

In order to make the technical problems solved, the technical solutions adopted, and the technical effects achieved by the present invention clearer, the technical solutions of the embodiments of the present invention will be described in further detail below with reference to the accompanying drawings. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the invention. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention.

An embodiment of the present invention provides a selectable network quality analysis method, as shown in fig. 1, where the network quality analysis includes:

s101, presetting a plurality of resident types, and performing scene analysis on each user to obtain cell numbers corresponding to the various preset resident types of the user.

As an alternative embodiment, the resident types include combinations of more than one of the following options: whole day, sleep, daytime, evening, workday and weekend.

For example, for the specific manner of the above-mentioned step of performing scene analysis on each user to obtain the cell numbers corresponding to the various preset resident types of the user, please refer to table one:

watch 1

Wherein, CELL ID in table one refers to the above CELL label, RAU/TAU in table one refers to the above route update or location update, and HTTP/HTTPs in table one refers to the number of times of service establishment.

S102, establishing a data mapping table containing mapping relations among the user numbers, the resident types and the cell numbers.

In the embodiment of the present invention, please refer to table two, which is an example of the above data mapping table, as shown in table two:

watch two

User number	Type of permanent residence	Cell numbering
			U000001	All day long standing	C354812
U000001	Often staying at sleep	C182663
			U000001	Resident in daytime	C447845
U000001	Resident at night	C288712
			U000001	Daily work	C862445
U000001	Weekend resident	C558865
			U000002	All day resident	C685881
U000002	Often staying at sleep	C655874
			U000002	Resident in daytime	C887100
U000002	Resident at night	C715820
			U000002	Daily work	C954134
U000002	Weekend resident	C587410

As shown in table two, the permanent type is one of the above six permanent types.

S103, counting the data mapping table to obtain various resident user sets corresponding to the cell numbers.

In the embodiment of the present invention, please refer to table three for the table after the statistics is performed on the data mapping table, where table three is an example of the table after the statistics is performed on the data mapping table, and is shown in table three:

watch III

As shown in table three, after the statistics is performed on the data mapping table, various resident types of user sets corresponding to the cell numbers can be clearly obtained. It should be noted that the user sets U158745, U578932, and U177814 in table three are user numbers corresponding to respective users, and since the user numbers may indicate the identities of the users, the user sets U158745, U578932, and U177814 may correspond to the corresponding users to obtain the user sets.

And S104, analyzing the user set data corresponding to the selected resident type of the selected cell number to obtain a network quality analysis result of the selected cell number.

In the embodiment of the invention, the network quality analysis result can be used as the index of the cell corresponding to the selected cell number based on the index corresponding to each user in the summarized user set data, so as to carry out network analysis and diagnosis. For example, if the selected cell number is 12345678, the selected resident type is daytime resident, and if the number of users corresponding to the user set data in which the resident type is daytime resident in the selected cell number is 12345678 is 222, the user-level indexes of the 222 users are subjected to cell-level summarization to obtain the cell dimension index of the cell with the cell number of 12345678, and based on the analysis and diagnosis of the cell dimension index, a network quality analysis result can be obtained. Or, if the selected cell number indicates that the cell is an office building, because the office building emphasizes workday residence, the selected residence type of the office building is determined as workday residence, and at this time, the user set data resident in the workday of the office building can be analyzed to obtain the network quality analysis result of the office building. Alternatively, if the selected cell number indicates that the cell is a residential cell, the residential cell is resident when sleeping heavily, and therefore, the selected resident type of the residential cell is determined as sleeping resident, and at this time, the user set data resident when the residential cell is sleeping can be analyzed to obtain the network quality analysis result of the residential cell. Or, if the selected cell number indicates that the cell is a hospital or a high-speed rail, because the hospital or the high-speed rail is mainly resident all day long, the selected resident type of the hospital or the high-speed rail is determined to be resident all day long, and at this time, the user set data resident all day long in the hospital or the high-speed rail can be analyzed to obtain the network quality analysis result of the hospital or the high-speed rail.

As an alternative implementation, the following steps may also be performed: acquiring the corresponding relation among the cell number, the scene name and the scene type; and summarizing and analyzing the user set data of the selected resident type corresponding to all the cell numbers under the selected scene name or scene type according to the corresponding relation to obtain the network quality analysis result of the selected scene name or scene type.

In the embodiment of the invention, the scene name and the scene category corresponding to each cell number can be obtained by using the cell engineering parameter data. By analogy, the user groups of each scene name and each scene category dimension can be obtained in a gathering mode, then statistical analysis is carried out, and network quality diagnosis is carried out. Optionally, the cell parameters may be preset, obtained by manually tagging, and may be updated periodically to ensure real-time performance of data. The correspondence relationship may refer to table four, as shown in table four:

watch four

As shown in table four, according to the corresponding relationship, the user set data of the target resident type corresponding to each cell number under the selected scene name is summarized and analyzed, so as to obtain the network quality analysis result of the selected scene name, and the user set data of the target resident type corresponding to all the cell numbers under the selected scene type is summarized and analyzed, so as to obtain the network quality analysis result of the selected scene type. Wherein the target resident type can be any combination of the above six resident types.

It should be noted that, in the above process of determining the network quality analysis result, the layer granularity is selected based on the cell number, the scene name, and the scene type, then the user group of the concerned resident type is determined based on the selected layer granularity, and then the data of the user group is analyzed to obtain the network quality analysis result. The level granularity may include different granularities, such as cell level, scene level, and scene type level. As a further alternative, the layer granularity may be ordered from microscopic to macroscopic as: user- > cell- > scene name- > scene type. In practical use, different layers can be selected to evaluate network problems based on different requirements.

Referring to fig. 2, fig. 2 is a schematic diagram of an optional index according to an embodiment of the present invention, where the index is a specific data index under each level granularity, and the user dimension index, the cell dimension index, the scene dimension index, and the scene category dimension index in fig. 2 correspond to the user-level granularity, the cell-level granularity, the scene-level granularity, and the scene-category granularity, respectively. The indexes in the same column as the indexes of each dimension are specific indexes belonging to the dimension, and in practical application, part or all of the indexes can be selected to be used for evaluating network problems.

Further, the indexes are provided with corresponding thresholds and judgment conditions, and when the indexes are actually used for evaluating network problems, network quality analysis results can be obtained according to the thresholds and the judgment conditions corresponding to the indexes. For example, the following criteria are selected for illustration: the VoLTE call completing rate, the S1 handover success rate, the VoLTE call drop rate, the downlink coverage, the high latency, and the traffic impression, please refer to table five, as shown in table five:

watch five

As can be seen from table five, different scene types, corresponding index thresholds and determination conditions may be different. And substituting the real data into the index threshold and judgment conditions corresponding to each index to judge the condition of the index. Taking the scene type as a high-speed rail as an example, for the VoLTE call-on rate, the index threshold and the judgment condition are that the call-on rate is less than 92% and lasts for 3 days or more, and if the real data meets the judgment condition, the VoLTE call-on rate is poor. Aiming at the S1 switching success rate, the index threshold and the judgment condition are that the switching success rate is less than 90% and lasts for 3 days or more, and if the real data meets the judgment condition, the S1 is switched into the power difference. And aiming at the VoLTE call drop rate, the index threshold and the judgment condition are that the call drop rate is greater than 0.1% and lasts for 3 days or more, and if the real data meet the judgment condition, the VoLTE call drop rate is high. For high time delay, the index threshold and the judgment condition are that the average time delay of 5G on the wireless side of the Tcp is more than 1ms and appears more than 5 times (not containing 5 times) in 24 hours, the time lasts for 3 days or more, and if the real data meets the judgment condition, the real data belongs to a high time delay cell.

Further referring to table six, table six is an example of a network quality analysis result obtained based on the threshold corresponding to each index and the determination condition, as shown in table six:

watch six

Cell numbering	Scene name	Scene categories	Network quality analysis results
				Cell number
1	Beijing university teaching area	College	Difference of cell access rate
				Cell number
2	Dormitory area of Beijing university	College	The cell switching success rate is poor
				Cell number
3	Beijing coordination hospital	Hospital	The downlink weak coverage of the cell
				Cell number
4	Mansion of Wanhe	Commercial district	The cell belongs to a traffic suppression cell
				Cell number n	Omit	Omit	Omit

As shown in table six, the scene names and the scene categories are introduced, and based on the analysis of the statistical data, the network quality analysis result of the selected scene name or the network quality analysis result of the selected scene type can be obtained.

In addition, referring to fig. 3, fig. 3 is a schematic diagram of an implementation architecture of an optional network quality analysis method according to an embodiment of the present invention, and the network quality analysis method can be implemented through the architecture. As shown in fig. 3, in the present implementation architecture, the accessed data source may include 5G MR data, 5G signaling and user plane data, other data (such as wireless indexes), cell parameter data, and the like, and may further include signaling plane data, user plane HTTP/HTTPs service data, 4G network S1-MME data, voLTE interface data, and 2G network Mc interface signaling data. When the data sources are input, the data collected according to the specifications can be stored in a file according to a corresponding storage mode of a directory and a date, and the data are input into the directory required by the scene perception analysis and evaluation system of the resident user. Then, data processing is performed on these data. The data processing process may include a classification process by decompression, decoding, time granularity, a packet reassembly process, a fingerprint location process, a data association process, and the like. Specifically, for the data size, the data is subjected to IMSI shunt operation and time sorting, service data of different IMSI users are distinguished and then subjected to multi-thread simultaneous processing, and the data is sorted according to time, so that parallel computing of multiple processors can be achieved, and the data processing efficiency is improved. And calculating and screening results of each type of resident users through big data analysis by the established modeling rule. Furthermore, data aggregation operation can be performed, day-level summarization or week/month summarization can be performed on multi-scene multi-dimensional basic data and resident user modeling data, and the multi-scene multi-dimensional basic data and the resident user modeling data are stored in a relational database according to the day-level summarization or week/month summarization, so that multi-dimensional multi-scene analysis display and GIS display based on 5G resident users can be achieved. Specifically, index summarizing statistics is carried out on the residential area occupied by each type of resident users, scene dimensions and scene category dimensions are respectively counted, summarized and key indexes of each dimension are output, and problem delimitation is carried out through key index threshold rules of each important scene. And finally, reasonably judging and outputting the problem delimiting result of each cell through the cell threshold values of different scenes, namely outputting the network quality analysis result.

By adopting the method of the invention, the resident type can be subdivided, and a data mapping table containing the mapping relation among the user number, the resident type and the cell number is established. Through the data-rich and finely divided data mapping table, the user set can be selectively determined in a targeted manner, the network quality analysis of the specific user set is realized, various network quality analysis requirements can be met, and the network quality analysis effect is better.

It should be noted that, for simplicity of description, the above-mentioned method embodiments are described as a series of acts or combination of acts, but those skilled in the art will recognize that the present invention is not limited by the order of acts, as some steps may occur in other orders or concurrently in accordance with the invention. Further, those skilled in the art should also appreciate that the embodiments described in the specification are preferred embodiments and that the acts and modules referred to are not necessarily required by the invention.

According to another aspect of the embodiments of the present invention, there is also provided a network quality analysis apparatus for implementing the network quality analysis method, as shown in fig. 4, including:

the scene analysis unit 401 is configured to preset multiple types of residences, and performs scene analysis on each single user to obtain a cell number corresponding to each preset type of residences of the user.

A mapping table establishing unit 402 configured to establish a data mapping table containing mapping relationships among the user numbers, the resident types, and the cell numbers.

A set determining unit 403, configured to count the data mapping table to obtain various resident type user sets corresponding to each cell number.

The first data analysis unit 404 is configured to analyze the user set data corresponding to the selected resident type of the selected cell number to obtain a network quality analysis result of the selected cell number.

Further, the above apparatus further comprises: a correspondence obtaining unit configured to obtain a correspondence between a cell number, a scene name, and a scene type according to the cell parameter data; and the second data analysis unit is configured to collect and analyze the user set data of the selected resident type corresponding to all the cell numbers under the selected scene name or scene type to obtain a network quality analysis result of the selected scene name or scene type.

Further, the correspondence obtaining unit is further configured to: and acquiring the corresponding relation among the cell number, the scene name and the scene type according to the cell parameter data.

Further, the resident types described above include: whole day resident, sleep resident, day resident, night resident, workday resident and weekend resident.

For example, please refer to table one above for a specific manner of the step of performing scene analysis on each user to obtain the cell numbers corresponding to the various preset resident types of the user. It should be noted that the network quality analysis apparatus corresponds to the network quality analysis method, and for the detailed description of the network quality analysis apparatus, reference is made to the detailed description of the network quality analysis method, which is not repeated herein.

The device of the invention can subdivide the resident type and establish a data mapping table containing the mapping relation among the user number, the resident type and the cell number. Through the data-rich and finely divided data mapping table, the user set can be selectively determined in a targeted manner, the network quality analysis of the specific user set is realized, various network quality analysis requirements can be met, and the network quality analysis effect is better.

According to yet another aspect of the embodiments of the present invention, there is also provided a network quality analysis device for implementing the network quality analysis method, as shown in fig. 5, the network quality analysis device includes a memory 502 and a processor 504, the memory 502 stores a computer program therein, and the processor 504 is configured to execute the steps in any one of the method embodiments by the computer program.

Optionally, in this embodiment, the network quality analysis device may be located in at least one network device of a plurality of network devices of a computer network.

Alternatively, in this embodiment, the processor may be configured to execute the network quality analysis method through a computer program

Alternatively, it can be understood by those skilled in the art that the structure shown in fig. 5 is only an illustration, and the network quality analysis device may also be a terminal device such as a smart phone (e.g., an Android phone, an iOS phone, etc.), a tablet computer, a palm computer, a Mobile Internet Device (MID), a PAD, and the like. Fig. 5 is not intended to limit the structure of the network quality analysis device.

The memory 502 may be used to store software programs and modules, and the processor 504 executes various functional applications and data processing by running the software programs and modules stored in the memory 502, so as to implement the network quality analysis method. The memory 502 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. The memory 502 may further include a memory remotely located from the processor 504, wherein the memory 502 may be particularly, but not exclusively, used to store information such as operational instructions. As an example, as shown in fig. 5, the above-mentioned memory 502 may include, but is not limited to, the scene analysis unit 401, the mapping table creation unit 402, the set determination unit 403, and the first data analysis unit 404 in the above-mentioned network quality analysis apparatus.

Optionally, the transmission device 506 is used for receiving or sending data via a network. In addition, the network quality analyzing apparatus further includes: a display 508 for displaying the display content; and a connection bus 510 for connecting the respective module components in the network quality analysis apparatus.

According to a further aspect of embodiments of the present invention, there is also provided a storage medium having a computer program stored therein, wherein the computer program is arranged to perform the steps of any of the above-mentioned method embodiments when executed.

Optionally, in this embodiment, all or part of the steps in the methods of the foregoing embodiments may be implemented by a program instructing hardware associated with the terminal device, where the program may be stored in a computer-readable storage medium, and the storage medium may include: flash disks, read-Only memories (ROMs), random Access Memories (RAMs), magnetic or optical disks, and the like.

The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.

The integrated unit in the above embodiments, if implemented in the form of a software functional unit and sold or used as a separate product, may be stored in the above computer-readable storage medium. Based on such understanding, the technical solution of the present invention may be substantially or partially implemented in the prior art, or all or part of the technical solution may be embodied in the form of a software product stored in a storage medium, and including instructions for causing one or more computer devices (which may be personal computers, servers, or network devices) to execute all or part of the steps of the method according to the embodiments of the present invention.

In the above embodiments of the present invention, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.

It will be appreciated by those skilled in the art that the system and method of the present invention is not limited to the embodiments described in the detailed description, and the detailed description is for the purpose of explanation and not limitation. Other embodiments will be apparent to those skilled in the art from the following detailed description, which is intended to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the invention as defined by the appended claims.

Claims

1. A method for network quality analysis, comprising:

presetting a plurality of resident types, and performing scene analysis on each user to obtain cell numbers corresponding to the various preset resident types of the user;

establishing a data mapping table containing mapping relations among user numbers, resident types and cell numbers;

counting the data mapping table to obtain various resident user sets corresponding to the cell numbers;

analyzing the user set data corresponding to the selected resident type of the selected cell number to obtain a network quality analysis result of the selected cell number;

acquiring the corresponding relation among the cell number, the scene name and the scene type;

and summarizing and analyzing the user set data of the selected resident type corresponding to all the cell numbers under the selected scene name or scene type to obtain the network quality analysis result of the selected scene name or scene type.

2. The method according to claim 1, wherein the obtaining the correspondence between the cell number, the scene name, and the scene type comprises:

and acquiring the corresponding relation among the cell number, the scene name and the scene type according to the cell parameter data.

3. The method of claim 1, wherein the resident type comprises a combination of more than one of the following options: whole day resident, sleep resident, day resident, night resident, workday resident and weekend resident.

4. The method according to claim 3, wherein the step of performing scene analysis on each user to obtain the cell numbers corresponding to the various preset resident types of the user comprises:

paying attention to the time period from 0 to 00; if the result can not be output or more than one result can be output, selecting the cell number with the most service establishment times of at least Z1 day under the same cell number in the time of X1 day as the cell number corresponding to the whole-day resident service of the user, wherein X1, Y1, N1 and Z1 are positive integers;

focusing on a 0-00-6 time period of the user, if at least Y2 days within X2 days have the most routing updates or location updates under the same cell and at least N2 times, determining the cell number as the cell number corresponding to the sleeping time dwell of the user; if the result can not be output or more than one result can be output, selecting the cell number with the largest service establishment times under the same cell number for at least Z2 days in X2 days as the cell number corresponding to the sleeping time of the user, wherein X2, Y2, N2 and Z2 are positive integers;

paying attention to the 10-00-17 time period of the user, if the route update or the position update is the most and at least N3 times in the same cell in at least Y3 days within X3 days, determining the cell number as the cell number corresponding to the resident daytime of the user; if the result cannot be output or more than one result is output, selecting the cell number with the largest service establishment frequency under the same cell number in at least Z3 days in X3 days as the cell number corresponding to the user resident in daytime, wherein X3, Y3, N3 and Z3 are positive integers;

paying attention to the 20; if the result cannot be output or more than one result is output, selecting the cell number with the most service establishment times under the same cell number in at least Z4 days in X4 days as the cell number corresponding to the evening resident of the user, wherein X4, Y4, N4 and Z4 are positive integers;

paying attention to a time period of 0 00-6 of Monday to Friday of the user, if the route update or the position update is maximum and at least N5 times in the same cell for at least Y5 days within X5 days, determining the cell number as the cell number corresponding to the daily work residence of the user; if the result can not be output or more than one result can be output, selecting the cell number with the most service establishment times under the same cell number in at least Z5 days in X5 days as the cell number corresponding to the work daily residence of the user, wherein X5, Y5, N5 and Z5 are positive integers;

paying attention to the time period of 0-00-6 of the weekend of the user, if the route update or the position update is maximum and at least N6 times in the same cell for at least Y6 days in X6 days, determining the cell number as the cell number corresponding to the resident weekend of the user; and if the result cannot be output or more than one result is output, selecting the cell number with the largest service establishment times under the same cell number for at least Z6 days in X6 days as the cell number corresponding to the weekend resident service of the user, wherein X6, Y6, N6 and Z6 are positive integers.

5. A network quality analysis apparatus, comprising:

the scene analysis unit is configured to preset a plurality of resident types, and performs scene analysis on each user to obtain cell numbers corresponding to the various preset resident types of the user;

a mapping table establishing unit configured to establish a data mapping table including mapping relationships among the user numbers, the resident types, and the cell numbers;

the set determining unit is configured to count the data mapping table to obtain various resident user sets corresponding to the cell numbers;

and the first data analysis unit is configured to analyze the user set data corresponding to the selected resident type of the selected cell number to obtain a network quality analysis result of the selected cell number.

6. The network quality analysis apparatus of claim 5, wherein the apparatus further comprises:

a correspondence obtaining unit configured to obtain a correspondence between a cell number, a scene name, and a scene type;

and the second data analysis unit is configured to summarize and analyze the user set data of the selected resident type corresponding to all the cell numbers under the selected scene name or scene type to obtain a network quality analysis result of the selected scene name or scene type.

7. The network quality analysis apparatus according to claim 6, wherein the correspondence relation acquisition unit is further configured to:

8. The network quality analysis device of claim 5, wherein the resident type comprises a combination of more than one of the following options: whole day, sleep, daytime, evening, workday and weekend.

9. The network quality analysis device of claim 8, wherein the scenario analysis unit is further configured to:

paying attention to the time period from 0; if the result can not be output or more than one result can be output, selecting the cell number with the most service establishment times under the same cell number in at least Z1 day in the X1 day as the cell number corresponding to the whole-day resident of the user, wherein X1, Y1, N1 and Z1 are positive integers;

focusing on a time period from 0 to 00-6 of the user, if at least Y2 days within X2 days have the most route updates or location updates under the same cell and at least N2 times, determining the cell number as the cell number corresponding to the sleep-time residence of the user; if the result can not be output or more than one result can be output, selecting the cell number with the largest service establishment times under the same cell number for at least Z2 days in X2 days as the cell number corresponding to the sleeping time of the user, wherein X2, Y2, N2 and Z2 are positive integers;

paying attention to the 10-00-17 time period of the user, if the route update or the position update is maximum and at least N3 times in the same cell for at least Y3 days within X3 days, determining the cell number as the cell number corresponding to the resident daytime of the user; if the result can not be output or more than one result can be output, selecting the cell number with the largest service establishment times under the same cell number for at least Z3 days in X3 days as the cell number corresponding to the daytime resident service of the user, wherein X3, Y3, N3 and Z3 are positive integers;

paying attention to a time period of 0 00-6 of Monday to Friday of the user, if the route update or the position update is maximum and at least N5 times in the same cell for at least Y5 days within X5 days, determining the cell number as the cell number corresponding to the daily work residence of the user; if the result cannot be output or more than one result is output, selecting the cell number with the largest service establishment times under the same cell number for at least Z5 days in X5 days as the cell number corresponding to the daily work residence of the user, wherein X5, Y5, N5 and Z5 are positive integers;

paying attention to the time period of 0-00-6 of the weekend of the user, if the route update or the position update is maximum and at least N6 times in the same cell for at least Y6 days in X6 days, determining the cell number as the cell number corresponding to the resident weekend of the user; and if the result cannot be output or more than one result is output, selecting the cell number with the most service establishment times under the same cell number in at least Z6 days in X6 days as the cell number corresponding to the weekend resident of the user, wherein X6, Y6, N6 and Z6 are positive integers.