CN114173356A

CN114173356A - Network quality detection method, device, equipment and storage medium

Info

Publication number: CN114173356A
Application number: CN202111301109.5A
Authority: CN
Inventors: 杨飞虎; 刘贤松; 欧大春; 石旭荣; 佘士钊; 张硕伟; 许国平; 陈旻; 李万方; 闻铭
Original assignee: China United Network Communications Group Co Ltd
Current assignee: China United Network Communications Group Co Ltd
Priority date: 2021-11-04
Filing date: 2021-11-04
Publication date: 2022-03-11
Anticipated expiration: 2041-11-04
Also published as: CN114173356B

Abstract

The application provides a method, a device, equipment and a storage medium for detecting network quality. The method comprises the following steps: acquiring MR data in a region to be detected, rasterizing the region to be detected according to the MR data to determine a road in the region to be detected, segmenting the road to obtain a plurality of road segments, associating the XDR data with the MR data corresponding to each of the plurality of road segments to obtain a network problem type of each road segment, wherein the network problem type comprises at least one of a network coverage problem, an abnormal event problem, a user perception problem or a network quality problem, determining the severity of the network problem according to the network problem type of the road segments, and finally displaying the severity of the network problem of the road in the region to be detected. According to the method and the device, the network problem type and the network problem severity in the region to be detected can be accurately determined, so that the staff can pertinently perform network optimization, and the efficiency of network optimization is improved.

Description

Network quality detection method, device, equipment and storage medium

Technical Field

The present application relates to communications technologies, and in particular, to a method, an apparatus, a device, and a storage medium for detecting network quality.

Background

With the rapid development of mobile communication services, the types of services, the number of users, and the like are increasing, and higher requirements are put forward on network optimization and service quality. Drive Test (DT) is an indispensable part of network optimization work, and the efficiency and accuracy of the virtual Drive Test method bring great convenience to users.

At present, a virtual Drive test method is mainly a Minimization Of Drive Tests (MDT), that is, by collecting mass Measurement Report (MR) data reported by a terminal user, and accurately and efficiently obtaining parameter Information Of network coverage analysis according to latitude Information in the MR data, and then fitting the data to a road Of a Geographic Information System (GIS) for display.

However, the existing MDT-based virtual drive test method can only acquire basic wireless acquisition data information to perform network coverage analysis, and cannot accurately acquire specific network problem types and severity, so that network optimization personnel can quickly locate the problem types and pertinently provide an optimization scheme, and network optimization efficiency is low.

Disclosure of Invention

The application provides a method, a device, equipment and a storage medium for detecting network quality, which are used for solving the problems that the existing MDT-based virtual drive test method can only carry out network coverage analysis and cannot accurately acquire specific network problem types and position phenomena, and the method can accurately determine the network problem types and the network problem severity in a region to be detected, so that a worker can pertinently carry out network optimization, and the network optimization efficiency is improved.

In a first aspect, the present application provides a method for detecting network quality, including:

measurement report MR data is acquired within the region to be examined.

And rasterizing the area to be detected according to the MR data to determine the road in the area to be detected, and performing segmentation processing on the road to obtain a plurality of road segments.

And correlating the XDR data with the MR data corresponding to each road segment in the plurality of road segments to obtain the network problem type of each road segment, wherein the network problem type comprises at least one of a network coverage problem, an abnormal event problem, a user perception problem or a network quality problem.

And determining the severity of the network problem according to the type of the network problem of the road section.

And displaying the network problem severity of the road in the area to be detected.

In one possible implementation, rasterizing the MR data to determine roads within the area to be detected includes:

the MR data is rasterized to obtain a plurality of grids and respective grid identifications of the plurality of grids.

And determining the road attribute of each grid according to the vector information and the grid identification in the high-precision map.

And classifying the grids with the same road attribute to obtain the road in the area to be detected.

In one possible implementation, associating the XDR data with the MR data corresponding to each of the plurality of road segments to obtain a network problem type of each road segment, includes:

a plurality of grids within each road segment is obtained.

And determining network parameters of each grid according to associated data obtained by associating the XDR and MR data corresponding to each grid, wherein the network parameters comprise at least one of network coverage parameters, abnormal event parameters, user perception parameters or network quality parameters.

And determining the network problem type of the road section according to the network parameters of each grid.

In one possible implementation, the network parameters include network coverage parameters, which include RSRP;

determining the network problem type of the road segment according to the network parameters of each grid, wherein the method comprises the following steps:

and determining a plurality of sampling points in each grid according to the associated data, and acquiring the RSRP of the plurality of sampling points in each grid.

For each grid, the fraction of sample points whose RSRP is less than a first preset value is determined.

And determining the grid with the occupation ratio larger than the second preset value as a first target grid.

And if the ratio of the first target grid to all grids in the road segment is greater than a third preset value, determining that the road segment has a network coverage problem.

In one possible implementation, the network parameters include abnormal event parameters, and the abnormal event parameters include a volt drop rate and a volt make rate;

and determining a second target grid with the Volte call drop rate larger than a fourth preset value and the Volte call-on rate smaller than a fifth preset value according to the Volte call drop rate and the Volte call-on rate of each grid.

And if the ratio of the second target grid to all grids in the road segment is greater than a sixth preset value, determining that the road segment has an abnormal event problem.

In one possible implementation, the network parameters include user-aware parameters, which include an average transmission rate of data packets;

and determining a third target grid with the average transmission rate smaller than a seventh preset value according to the average transmission rate of each grid.

And if the ratio of the third target grid to all grids in the road segment is greater than the eighth preset value, determining that the road segment has the user perception problem.

In one possible implementation, the network parameters include network quality parameters, which include RSRP and MOD3 interference rate;

and for each grid, determining that the RSRP of the master control cell in the grid is greater than a ninth preset value, the level difference between the RSRP and the adjacent cell is less than a tenth preset value, and the MOD3 interference rate is the ratio of sampling points of an eleventh preset value.

And determining the grid with the occupation ratio larger than the twelfth preset value as a fourth target grid.

And if the ratio of the fourth target grid to all grids in the road segment is greater than the thirteenth preset value, determining that the road segment has the network quality problem.

In a second aspect, the present application provides an apparatus for detecting network quality, including:

and the acquisition module is used for acquiring measurement report MR data in the region to be detected.

And the processing module is used for rasterizing the area to be detected according to the MR data so as to determine the road in the area to be detected and carrying out segmentation processing on the road to obtain a plurality of road segments.

And the association module is used for associating the XDR data with the MR data corresponding to each road segment in the plurality of road segments to obtain the network problem type of each road segment, wherein the network problem type comprises at least one of a network coverage problem, an abnormal event problem, a user perception problem or a network quality problem.

And the determining module is used for determining the severity of the network problem according to the type of the network problem of the road section.

And the display module is used for displaying the network problem severity of the road in the area to be detected.

In a possible implementation manner, the processing module is specifically configured to:

and rasterizing the area to be detected according to the MR data to obtain a plurality of grids and grid marks of the grids.

In a possible implementation manner, the association module is specifically configured to:

a plurality of grids within each road segment is obtained.

a determination module specifically configured to: and determining a plurality of sampling points in each grid according to the associated data, and acquiring the RSRP of the plurality of sampling points in each grid.

a determination module specifically configured to:

a determination module specifically configured to: and determining a third target grid with the average transmission rate smaller than a seventh preset value according to the average transmission rate of each grid.

a determination module specifically configured to: and for each grid, determining that the RSRP of the master control cell in the grid is greater than a ninth preset value, the level difference between the RSRP and the adjacent cell is less than a tenth preset value, and the MOD3 interference rate is the ratio of sampling points of an eleventh preset value.

In a third aspect, the present application provides an electronic device that may include a processor, and a memory communicatively coupled to the processor; wherein the content of the first and second substances,

a memory for storing a computer program.

And a processor configured to read the computer program stored in the memory, and execute the method for detecting network quality in any one of the possible implementations of the first aspect according to the computer program in the memory.

In a fourth aspect, the present application provides a computer-readable storage medium, where computer-executable instructions are stored, and when the processor executes the computer-executable instructions, the method for detecting network quality in any one of the possible implementation manners of the first aspect is implemented.

The method for detecting the network quality includes acquiring measurement report MR data in a region to be detected, rasterizing the MR data to determine a road in the region to be detected, performing segmentation processing on the road to obtain a plurality of road segments, associating XDR data with the MR data corresponding to each of the plurality of road segments to obtain a network problem type of each road segment, wherein the network problem type includes at least one of a network coverage problem, an abnormal event problem, a user perception problem or a network quality problem, determining the severity of the network problem according to the network problem type of the road segments, and finally displaying the severity of the network problem of the road in the region to be detected. The method obtains the network parameters by dividing the road into a plurality of road segments and then counting and analyzing associated data formed by associating XDR data with MR data in each road segment. The network problem type of the road segment can be determined through a plurality of network parameters, the phenomenon that in the prior art, only the network parameters in the MR data can be obtained to realize network coverage analysis of one area, and the road segment with the network problem cannot be quickly positioned can be avoided, so that the position of the road segment with the problem and the network problem type which specifically exists can be quickly positioned, network optimization personnel can be facilitated to provide a network optimization scheme through the specific position of the road segment with the network problem and the network problem type which exists, and the network optimization efficiency is improved.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and together with the description, serve to explain the principles of the application. Specific embodiments of the present application have been shown by way of example in the drawings and will be described in more detail below. These drawings and written description are not intended to limit the scope of the inventive concepts in any manner, but rather to illustrate the inventive concepts to those skilled in the art by reference to specific embodiments. 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.

Fig. 1 is a system architecture diagram of a network quality detection method provided in an embodiment of the present application;

fig. 2 is a schematic flowchart of a method for detecting network quality according to an embodiment of the present disclosure;

fig. 3 is a schematic process diagram of a rasterization method provided in an embodiment of the present application;

fig. 4 is a schematic diagram of a data association process provided in an embodiment of the present application;

fig. 5 is a schematic structural diagram of an apparatus for detecting network quality according to an embodiment of the present disclosure;

fig. 6 is a schematic structural diagram of a terminal device according to an embodiment of the present application.

Detailed Description

Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.

The terms "first," "second," "third," and "fourth," if any, in the description and claims of the invention and in the above-described figures are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.

In order to help those skilled in the art to better understand the technical solutions of the present application, the related contents related to the technical solutions of the present application will be described below. (1) Voice Over Long Term Evolution (Volet): high definition voice services are provided over LTE networks. (2) Physical Cell Identity (PCI): for differentiating radio signals of different cells to ensure that there is no same physical cell identity within the coverage area of the relevant cell.

(3) Signaling data: the (X Data Recording, XDR) signaling Data is evolved from Call Data Recording (CDR), and is a Call detail record and a transaction detail record extracted after analyzing, processing and associating the original signaling Data according to the application layer requirements. Wherein the signaling is control information transferred on an interface between different communication devices.

The method for detecting the network quality provided by the embodiment of the application can be applied to an application scene that network optimization personnel acquire and analyze wireless network data in an area to be evaluated and realize network problem optimization and management and control according to the acquired wireless network data. For example, in order to obtain the current network situation in the area to be evaluated, a network optimizer needs to obtain and analyze wireless network data required by the network situation in the area to be evaluated, then analyze the type of a network problem existing in the area to be evaluated according to the obtained data, and finally provide a targeted scheme for the type of the network problem, so as to quickly optimize and control the network problem.

In the prior art, the method of drive test can be divided into a traditional drive test method and a virtual drive test method. In the traditional drive test method, a professional tester drives a vehicle, then a professional test instrument is used for testing the whole road section passing the vehicle, and the field test is carried out to obtain network coverage data so as to analyze the network quality. However, the conventional drive test method has high test cost and low network optimization efficiency. In order to solve the problem, in the prior art, the network condition of the area to be detected may also be evaluated by an MDT virtual drive test method, which is specifically implemented by acquiring massive MR data transmitted by User Equipment (UE) to a base station, acquiring longitude and latitude information and related network parameters included in the MR data, and analyzing the network coverage condition of the area to be detected according to the network parameters. However, the existing MDT-based virtual drive test method is limited to the information reported by the MR data, that is, the MR data only contains basic network parameters, such as: reference Signal Receiving Power (RSRP), Reference Signal Receiving Quality (RSRQ), Power Headroom Report (PHR), Angle Of Arrival ranging (AOA), maximum Time Advance (TA), neighbor serving cell, and the like. Network coverage analysis can only be carried out on the area to be detected through the network parameters, specifically, the weak coverage area or the over coverage area and the like in the area to be detected are confirmed, and important network parameters such as network event analysis and throughput rate analysis on the area to be detected are lacked, so that the actual network problem type of the area to be detected cannot be accurately judged, network optimization personnel cannot rapidly position the network problem type and the problem severity, an optimization scheme is pertinently provided, and the network optimization efficiency is low. Therefore, the traditional drive test method cannot be completely replaced, and network optimization work needs to be completed by combining the traditional drive test method and the MDT virtual drive test method during drive test.

In view of the above problems, an embodiment of the present application provides a method for detecting network quality, where a road in a region to be detected is segmented into a plurality of road segments for processing, and MR data and XDR data are associated to obtain a plurality of network parameters of each road segment, so as to analyze a network problem type existing in each road segment from a plurality of dimensions, such as coverage problems, quality problems, perception problems, abnormal event problems, and the like, and determine the severity of a network problem, thereby helping network optimizers to quickly make a network optimization scheme according to the severity of the network problem and the network problem type in time, and improving efficiency of network optimization. The method can completely replace the traditional drive test method, thereby creating a better network with lower cost and higher efficiency.

Fig. 1 is a system architecture diagram of a network quality detection method according to an embodiment of the present invention, as shown in fig. 1, the system includes a user equipment 101, a base station 102, a server 103 and a core network (not shown), and a database 104 is deployed in the server 103. The base station 102 may collect, in real time, MR data reported by the user equipment 101 in the coverage area, send the MR data to the server 103, and store the MR data in the database 104.

Specifically, the user equipment 101 automatically uploads MR data to the base station 102 periodically, and meanwhile, the user equipment 101 may also send a service request, such as a video or voice call service request. After receiving the service request, the base station 102 forwards the service request to the core network 105, and further establishes a service connection with an external network, and can obtain XDR data at each interface of the core network 105. The acquired MR data and XDR data are transmitted to a server 103, the server 103 associates the XDR data with the MR data, so that the determined position information in the MR data is filled in a stored XDR perception data table, a plurality of network parameters are acquired, the analysis of the network problem type and the problem severity of the area to be detected is completed, network optimization personnel are helped to quickly position the network problem type and the problem severity, a targeted scheme is provided, and the network optimization efficiency is improved.

It should be understood that the number of user equipments 101, base stations 102, servers 103 and databases 104 in the system architecture shown in fig. 1 is merely exemplary, and that a greater or lesser number is within the scope of protection of the present application. Also, in the above example operational scenario, the user device 101 may be, for example, a Personal computer, a server, a Personal Digital Assistant (PDA), a notebook, or any other computing device with networking functionality. The server 103 may be a single server, or may be a server cluster formed by a plurality of servers. The database 104 may be a Redis database, a Structured Query Language (SQL) database, or other type of database. The communication network between user equipment 101, base station 102, server 103 and database 104 may include various types of wired and wireless networks, such as, but not limited to: the internet, a Local Area network, Wireless Fidelity (WIFI), a Wireless Local Area Network (WLAN), a cellular communication network (General Packet Radio Service (GPRS), Code Division Multiple Access (CDMA), 2G/3G/4G/5G cellular network), a satellite communication network, and so on.

With the system architecture of the present application understood, the scheme of the network quality detection method of the present application is described in detail with reference to fig. 2.

Fig. 2 is a flowchart illustrating a method for detecting network quality according to an embodiment of the present disclosure, where the method may be performed by any device that performs the method for detecting network quality, and the device may be implemented by software and/or hardware. In this embodiment, the apparatus may be integrated in the user equipment 101 shown in fig. 1. As shown in fig. 2, the method for detecting network quality provided in the embodiment of the present application includes the following steps:

step 201: measurement report MR data is acquired within the region to be examined.

The MR data is a measurement report generated by the UE during the wireless asset management process, and the MR data includes longitude and latitude information of the UE at the time of uploading the MR data, and network parameters for performing network coverage analysis, such as: RSRP, RSRQ, PHR, AOA, TA values, neighbor serving cells, etc.

Optionally, the base station collects MR data uploaded by all UEs in the coverage area, and transmits the MR data to the server, and the server stores the MR data transmitted by all the base stations in the area to be tested in a database in the form of sample data. The terminal device may obtain MR data in the area to be detected from the database.

It should be understood that according to the 3rd Generation Partnership Project (3 GPP) protocol specification, only active UEs report MR data and upload MR data at certain periods. The acquisition of MR data may select all active UEs for reporting, or select some active UEs for sampling for reporting, which is not limited herein.

Step 202: and rasterizing the area to be detected according to the MR data to determine the road in the area to be detected, and performing segmentation processing on the road to obtain a plurality of road segments.

Specifically, the rasterization is to use a regular pattern grid with a set side length to divide the region to be detected into a combination of multiple grids, for example: a square grid. Then reserving grids of the road part in the area to be detected for drive test analysis

For example, when determining a road in a region to be detected, rasterization processing may be performed on the region to be detected according to MR data to obtain a plurality of grids and respective grid identifiers of the plurality of grids, then road attributes of each grid are determined according to vector information and the grid identifiers in a high-precision map, and finally the grids with the same road attributes are classified to obtain the road in the region to be detected.

Wherein, the high accuracy map is that positioning accuracy is high, vector information is abundanter map, for example: 5m high accuracy map. The vector information refers to a ground object vector, for example: the system comprises a plurality of types of roads, urban areas, high-rise buildings, factories, forests, greenbelts, wetlands and the like, and the ground object vector also comprises specific information of each type, such as the grade, the width, the lane line position and fixed object information of the periphery of a lane of the road type, wherein the fixed object information comprises traffic lights, traffic signs, obstacles, roadside landmarks and the like.

Next, a process of rasterizing the region to be measured will be described in detail with reference to fig. 3. Fig. 3 is a schematic process diagram of a rasterization method according to an embodiment of the present application.

As shown in fig. 3, the region to be detected is generally an irregularly shaped region, and as shown in fig. 3(a), a rectangular block diagram including the entire region to be detected inside is selected and then subjected to rasterization. The specific implementation method comprises the steps of taking a point of the north extension outermost side of the area to be detected, which is tangent to the weft, as a first tangent point, and determining a point of the west extension outermost side of the area to be detected, which is tangent to the warp, as a second tangent point. And then, taking the point where the latitude lines passing through the first tangent point on the north face and the longitude lines passing through the second tangent point on the west face intersect as the starting point of rasterization treatment. Wherein the order of determining the first and second tangent points is not limited. For example: the area to be detected is in an irregular shape, the point with the most prominent northern outline is used as a first tangent point, the point with the most prominent western outline is used as a second tangent point, then the latitude line of the first tangent point extends to the west, the longitude line of the second tangent point extends to the north until the point is intersected, and the point is determined as the starting point. The longitude of the center of the grid where the starting point is located is determined to be (X, Y), wherein the abscissa X is the latitude, and the ordinate Y is the longitude.

After the center longitude of the starting point is determined, a square grid with fixed side length can be generated through software, and the side length of the grid can be set according to the requirements of the grid. And then, according to the size of the area to be detected and the side length of the square grid, calculating the central longitude and latitude (Xm, Ym) of the M-th grid on the outermost side of the diagonal in the southward and eastward directions of the starting point.

After determining the central longitude and latitude of the starting point and the mth grid on the outermost side of the diagonal, starting from the starting point grid and keeping the latitude of the starting point grid unchanged as shown in fig. 3(b), expanding a row of grids south in the direction of the longitude until the central longitude of the grids is less than or equal to Ym, and stopping continuing to expand south. And then keeping the longitude of a row of grids unchanged, expanding the grids eastward by using the latitude lines until the central latitude of the grids is less than or equal to Xm, and stopping continuously expanding eastward. The area to be examined is now divided into a combination of a plurality of grids. The order of expanding the grids outwards is not particularly limited, and a row of grids can be expanded south and then east in the warp direction preferentially; it is also possible to expand the grid in the direction of the dimension line to the east and then to the south.

It should be understood that the equidistant expansion grids may calculate the center longitude and latitude of each newly added grid, and the center longitude or center latitude of the newly added grid may be offset from the center longitude or center latitude of the last adjacent grid. For example: in the process of expanding the new grid to the south in the meridian direction, the central latitudes of the expanded new grid and the grid where the starting point is located are kept to be X. When the grid 1 is expanded by taking the grid side length as k, the center longitude of the grid 1 is Y + k + P, wherein Y is the center longitude value of the starting point, and P is the longitude offset of the starting point grid and the center longitude of the grid 1 when the distance is converted. Similarly, when a new grid is expanded to the east in the direction of the dimension line, latitude bias is generated when the central longitude and latitude of two adjacent grids are converted into distance.

In this example, by selecting a square grid with a determined side length and rasterizing the region to be detected, an irregular region to be detected can be converted into a set of a plurality of regular shapes. The road positioning problem can be converted into grid processing, the efficiency of positioning the road object is improved, and the efficiency of analyzing the road network problem is further improved.

Furthermore, after the area to be detected is divided into a plurality of grids, unique identification information can be added to each grid. And finally, associating the vector information with the identification information of the grids, and determining the road attribute of each grid.

As shown in table 1, the vector information in the high-precision map can be divided into a plurality of vector types. Since the research object of the drive test is the road in the area to be detected, the multiple vector types are divided into two attribute categories, namely road and non-road, and the multiple grids can be correspondingly divided into road matching grids and non-road matching grids. Specifically, the vector types corresponding to the road matching grids are 5 types of first, second, third and fourth grade roads and expressways, and the vector types corresponding to the non-road matching grids are 14 types such as urban areas, high-rise buildings, factories, markets, villages and the like.

TABLE 1

Optionally, the unique identifier assigned to each grid is associated with vector information in the high-precision map, so as to determine the attribute of each grid in the region to be detected. If the attribute of the current grid is the attribute of the road grid, the current grid is reserved, and if the attribute of the current grid is the attribute of the non-road grid, the current grid is deleted. Then, different grids are classified according to the vector type and the road grid attribute, finally a closed-loop frame is formed to generate a plurality of different roads, and marks are added to the different grids in each road. For example: there are 5 adjacent grids numbered 001, 002, 003, 004 and 005, respectively, and if the grid attribute of 001, 002, 003 and 004 is a road grid and the grid attribute of 005 is a non-road grid, the 001, 002, 003 and 004 grids are reserved and the 005 grid is deleted. If the vector types of 001 and 002 are first-class roads, and the vector types of 003 and 004 are expressways. Then, 001 and 002 are classified into one road, 003 and 004 are classified into the other road, and identification is carried out by respectively combining the road information of 001, 002, 003 and 004. It will be appreciated that deleting a grid that is not a road grid attribute, the MR data within that grid is also deleted.

Optionally, the following rules may be used to add identifiers to different grids in each road:

road ID number of primary road: Pro-City-level 1-000001-N (N is maximum 6 digits, 999999), wherein Pro is province, City is City province, and level 1 is a first-level road.

Road ID number of secondary road: Pro-City-level 2-000001-N (N is maximum 6 digits, 999999), wherein Pro is province, City is City attribution province, and level 2 is a secondary road.

Road ID number of the third-level road: Pro-City-level 3-000001-N (N is maximum 6 digits, 999999), wherein Pro is province, City is City province, and level 3 is a three-level road.

Road ID number of the four-level road: Pro-City-level 4-000001-N (N is maximum 6 digits, 999999), wherein Pro is province, City is City attribution province, and level 4 is a four-level road.

Expressway ID number: Pro-City-Expressway-000001-N (N is maximum 4 digits, 9999), wherein Pro is province, City is City home province, and Expressway is Expressway.

In the method, the area to be detected is subjected to rasterization processing, and the grid identification is combined with the vector information in the high-precision map, so that the road part in the area to be detected can be quickly determined, the road test can be conveniently carried out, the network parameters can be obtained, and the network problem can be further analyzed.

After all roads in the area to be detected are obtained, each road can be cut according to different standard lengths by taking the grid length as a reference from the starting point of each road for each closed-loop area of the road, so that segmentation processing is realized, a plurality of road segments are obtained, and identification information is distributed for each road segment.

Optionally, considering that the more areas users are, the more urgent the network optimization work is, so different standard lengths may be selected for road segmentation processing in combination with different scenes such as dense urban areas, general urban areas, suburban areas, and the like, as well as the grid length and the road length. For example, if the road length is 600 meters and the length of one grid is 10 meters by 10 meters, then for a dense urban area, a standard length is selected to be cut by 50 meters to obtain 12 road segments, generally, the standard length is selected to be cut by 100 meters to obtain 6 road segments, and for a suburban area, a standard length is selected to be cut by 200 meters to obtain 3 road segments.

After the road segmentation is completed, each segmented road segment can be numbered, and the numbering rule can use the road name, the standard length road segment identification and the raster road segment ID number (000001-999999). For example: in a dense area, the standard length of 50 meters can be selected for segmentation processing, and the road name is A, then the road segment can be identified as A-50-000001.

Step 203: and correlating the XDR data with the MR data corresponding to each road segment in the plurality of road segments to obtain the network problem type of each road segment, wherein the network problem type comprises at least one of a network coverage problem, an abnormal event problem, a user perception problem or a network quality problem.

The XDR data is signaling data, and when the UE sends a service request, the service request is sent to the core network through the base station, so that the XDR data can be obtained at each interface of the core network, for example: each interface of the core network may be S1_ MME, S1_ U, etc. The XDR data includes user service information, user identity information, and ticket information, which is specifically information such as call completing rate, call dropping rate, and call duration.

Optionally, when determining the network problem type of each road segment, the network parameter of each grid may be determined by obtaining a plurality of grids in each road segment, then associating the obtained associated data with the XDR data and the MR data corresponding to the plurality of grids, where the network parameter includes at least one of a network coverage parameter, an abnormal event parameter, a user perception parameter, or a network quality parameter, and finally determining the network problem type of each road segment according to the network parameter of each grid.

Specifically, by acquiring MR data and corresponding XDR data falling into each grid and correlating the XDR and MR data, network parameters of each grid can be determined through the correlated data. Next, a process of obtaining the associated data after associating the XDR data with the MR data will be described in detail with reference to fig. 4.

Fig. 4 is a schematic diagram of a data association process according to an embodiment of the present application. The XDR data and the MR data are correlated, the XDR data can be divided into 3 correlation modes according to the XDR data sources of MME data, HTTP data and S1_ COMMON data, the correlation modes are MME _ MR correlation, HTTP _ MR correlation and COMMON _ MR correlation respectively, and the processes of the 3 correlation modes are the same. Fig. 4 illustrates the association between XDR data and MR data by using MME _ MR as an example.

Before the XDR data and the MR data are associated, data cleaning can be performed to clean invalid data so as to keep the valid data for association. The invalid data may be data with a Cell Global Identifier (ECGI) of null, unreasonable start TIME (TIME), unreasonable end TIME (TIME), and the like.

When performing association of XDR data with MR data, the conditions that MME _ Code, MME _ Group ID and long Code MME _ UE _ S1AP _ ID are the same and TIME of XDR data is between the start TIME and the end TIME of MR data need to be satisfied. Two processes are required to achieve association: the first procedure is to associate the XDR data and the MR data using an association field MME _ UE _ S1AP _ ID, and the second procedure is to associate the XDR data and the MR data, which are not associated in the first procedure, for a second time. The first process specifically includes the steps of selecting the XDR data and the MR data which meet the association conditions, then calculating the time difference between the XDR data and the MR data, sequencing the XDR data and the MR data from small to large according to the time difference, and finally selecting the XDR data with the minimum time difference as a credible association record to achieve first association of the XDR data and the MR data. In the second process, sliding window search is respectively carried out in a period of time before and after the starting time and the ending time of the MR data, and the forward sliding window search result and the backward sliding window search result are merged and deduplicated to complete the association of the XDR data which is not associated between the starting time and the ending time of the MR data and the corresponding MR data so as to realize second association recording. Finally, the first time of associated record and the second time of associated record can be compared and the duplicate of associated record can be removed, so as to obtain the associated record of XDR data and MR data.

After the association between the XDR data and the MR data is completed, the first network parameters and the position information in the MR data can be filled in a table for storing the XDR data through backfill operation, and then the whole association process of the XDR data and the MR data is completed. The second network parameters in the XDR data may then be combined with the first network parameters, the location information in the MR data, to obtain the network parameters for each trellis. And finally, determining the type of the network problem existing in each road section according to the network parameters of each grid. The network parameters of each grid comprise a first network parameter and a second network parameter.

In the method, the second network parameter in the XDR data can be combined with the first network parameter and the position information in the MR data by correlating the XDR data with the MR data, so that the first network parameter and the second network parameter are simultaneously obtained in each grid, the road segmentation is subjected to multi-dimensional analysis to determine the type of the network problem, network optimization personnel can be helped to quickly make an optimization scheme aiming at the type of the network problem, and the network optimization efficiency is improved.

Optionally, the network parameter includes at least one of a network coverage parameter, an abnormal event parameter, a user perception parameter, or a network quality parameter, so that it may be determined whether a network coverage problem exists in the road segment through the network coverage parameter, where the network coverage parameter includes RSRP. For example, according to the associated data, a plurality of sampling points in each grid can be determined, RSRPs of the plurality of sampling points in each grid can be obtained, then for each grid, an occupation ratio of the sampling points with the RSRPs smaller than a first preset value is determined, and the grid with the occupation ratio larger than a second preset value is determined as a first target grid. And if the ratio of the first target grid to all grids in the road segment is greater than a third preset value, determining that the road segment has a network coverage problem.

When the UE transmits MR data to the base station once at a certain coordinate position, the position coordinate of the UE is a sampling point. It can be understood that there may be multiple UEs uploading MR data in the same location coordinate, and different sampling points may be distinguished according to the time of uploading MR data and the user identification information of the UEs. And the associated data includes the RSRP value of each sample point.

Specifically, a grid with sampling points all larger than a fixed numerical value in a period of time is selected as an effective grid, then the total sampling points in the effective grid and the sampling points with the RSRP smaller than a first preset value are counted to determine the proportion of the sampling points with the RSRP smaller than the first preset value, if the proportion is larger than a second preset value, the grid is confirmed to have a network coverage problem, and the grid is determined to be a first target grid. And then counting that the occupation ratio of the first target grid in the road segment is greater than a third preset value, and determining that the road segment has a network coverage problem. For example: in the road segment a, the total number of the grids is 50, and the number of the effective grids is 20, and the effective grid ratio is 20/50 × 100%, namely 40%, and the road segment is determined to be an effective road segment when the requirement is basically met. In the active grid, the grid with RSRP less than-100 dBm and a duty ratio greater than 30% is identified as the grid with network coverage problem, i.e., the first target grid. Assuming that the number of the first target grids is 5, the grid occupancy of the road segment with the network coverage problem is 5/20 × 100% — 25%, which is greater than the third preset value of 20%, so that the road segment has the network coverage problem.

The first preset value, the second preset value, and the third preset value may be set according to actual conditions or experience, for example, may be-100 dBm, 30%, and 20%, respectively, and specific values of the first preset value, the second preset value, and the third preset value are not limited herein.

In this example, whether the network coverage problem exists is determined by counting the grid ratio of the network coverage problem existing in the road segment, so that the road segment with the network coverage problem is quickly positioned, network optimization personnel can be helped to quickly make a network optimization scheme, and the network optimization efficiency is improved.

Optionally, it may be determined whether a network abnormal event problem exists in the road segment through an abnormal event parameter, where the abnormal event parameter includes a voltage drop rate and a voltage call completing rate. For example, a second target grid with a voltage drop rate greater than a fourth preset value and a voltage call-on rate less than a fifth preset value may be determined according to the voltage drop rate and the voltage call-on rate of each grid, and if the ratio of the second target grid to all the grids in the road segment is greater than a sixth preset value, it is determined that the road segment has an abnormal event problem.

The voice call drop rate is call drop times/call successfully established times 100%, the voice call completion rate is voice service Radio Resource Control (RRC) establishment success times or Evolved Radio Access Bearer (E-RAB) establishment success times/voice service RRC establishment request times 100%, and the voltage non-call completion rate can be calculated according to the voltage completion rate, that is, the voltage non-call completion rate is 1-voltage call completion rate. And the correlation data comprises the number of times of the Volte call drop rate and the Volte call completing rate.

Specifically, a grid with sampling points all larger than a fixed value in one week is selected as an effective grid, then the number of Volte dropped calls and the number of Volte connected calls in the effective grid are counted, and if the Volte dropped call rate is larger than a fourth preset value and the Volte connected call rate is smaller than a fifth preset value, it is determined that the grid has an abnormal event problem. And then counting that the ratio of the grid with the abnormal event problem to all grids in the road segment is greater than a sixth preset value, and determining that the road segment has the abnormal event problem. For example: in the road segment b, the total number of the grids is 60, and the number of the effective grids is 40, and the effective grid ratio is 40/60 × 100%, which is about 67%, and the road segment b is determined to be an effective road segment. In the effective grids, a Volte call drop rate is determined according to the Volte call drop times, a Volte call-on rate is determined according to the Volte call-on times, and then the grids with the Volte call drop rate of more than 56% and the Volte call-on rate of less than 80% are determined as the grids with the abnormal event problem, i.e. the second target grid. Assuming that the number of the second target grids is 15, the grid occupancy of the road segment in which the abnormal event problem exists is 15/40 × 100% — 37.5%, which is greater than the sixth preset value of 20%, so that the road segment in which the abnormal event problem exists.

The fourth preset value, the fifth preset value, and the sixth preset value may be set according to actual conditions or experience, for example, the fourth preset value, the fifth preset value, and the sixth preset value may be respectively 20%, 90%, and 20%, and specific values of the fourth preset value, the fifth preset value, and the sixth preset value are not limited herein.

In the example, whether the abnormal event problem exists is determined by counting the grid ratio of the abnormal event problem existing in the road segment, so that the road segment with the abnormal event problem is quickly positioned, network optimization personnel can be helped to quickly make a network optimization scheme, and the network optimization efficiency is improved.

Optionally, it may be determined whether the road segment has a network awareness problem through a user awareness parameter, where the user awareness parameter includes an average transmission rate of the data packet. For example, a third target grid with an average transmission rate smaller than a seventh preset value may be determined according to the average transmission rate of each grid, and if the ratio of the third target grid to all grids in the road segment is greater than an eighth preset value, it is determined that the road segment has a user perception problem.

The user perception problem is embodied by an average transmission rate when large data packets are transmitted, the average transmission rate of each grid is the sum of network rates of each large data sample transmitted/the number of the large data samples in the grid, and the associated data includes the average transmission rate.

Specifically, the grid with the number of the large data samples transmitted in a continuous period of time greater than a fixed value is counted as an effective grid. And in the effective grids, determining the effective grids with the average transmission rate of each grid smaller than a seventh preset value as third target grids, and if the ratio of the third target grids to all the grids in the road segment is larger than an eighth preset value, determining that the road segment has the user perception problem. For example: and finally, judging whether the road segmentation has the user perception problem or not by judging whether the ratio of the grids with the user perception problem to the effective grids exceeds 20% or not.

The seventh preset value and the eighth preset value may be set according to actual conditions or experience, for example, the values may be 5Mbps and 20%, and specific values of the seventh preset value and the eighth preset value are not limited herein.

In this example, by counting the number of low-rate transmission problem data packets in a road segment and determining the ratio of the number of all large data sample packets, it is determined whether the grid has a user perception problem, and thus, whether the road segment has the user perception problem is determined. Therefore, the road segments with the user perception problem are quickly positioned, network optimization personnel can be helped to quickly make a network optimization scheme, and the network optimization efficiency is improved.

Optionally, it may be determined whether the road segment has a network quality problem through network quality parameters, where the network quality parameters include RSRP and MOD3 interference rate. For example, for each grid, it may be determined that the RSRP of the master cell in the grid is greater than a ninth preset value, the RSRP difference with the RSRP of the neighboring cell is less than a tenth preset value, and the MOD3 interference rate of the PCI is the proportion of sampling points of an eleventh preset value, then the grid with the proportion greater than a twelfth preset value is determined as a fourth target grid, and if the proportion of the fourth target grid to all the grids in the road segment is greater than a thirteenth preset value, it is determined that the road segment has a network quality problem.

The RSRP and the interference rate parameters of the master control cell and the neighboring cell need to be obtained when the network quality problem is judged, and the associated data includes the RSRP and the MOD3 interference rate of the PCI. The PCI is used to distinguish each cell, and when the MOD3 of the PCI of the master cell and the PCI of the neighbor cell are controlled, interference is caused.

Specifically, the grids with the sampling points being larger than the fixed numerical value in one week are selected as effective grids, and if the effective grid occupation ratio is reasonable, the road section can be determined as an effective road section. And then, counting that the RSRP of the main control cell in the effective grid is greater than a ninth preset value, and if the difference between the RSRP of the main control cell and the RSRP of the adjacent cell is less than a tenth preset value, determining that the main control cell and the adjacent cell have larger repeated coverage rate. If the sampling points of which the PCI meets the MOD3 interference rate is the eleventh preset value account for the grid of which the total sampling points are greater than the twelfth preset value, the grid with the network quality problem, that is, the fourth target grid, can be determined. And if the ratio of the fourth target grid to all grids in the road segment is greater than the thirteenth preset value, determining that the road segment has the network quality problem. For example: in the road segment c, 60 grids are counted in total, 40 effective grids are counted, the RSRP of the main control cell is larger than or equal to-115 dBm, the difference between the RSRP of the main control cell and the RSRP of the adjacent cell is smaller than 6dB, meanwhile, a grid with the PCI meeting the condition that the ratio of sampling points of MOD3 equal to 1 to the total sampling points is larger than 5% is determined as a grid with quality problems, the grid with the quality problems is determined as a fourth target grid, and if the ratio of the effective grid occupied by the fourth target grid is larger than 20%, the road segment is determined to have network quality problems.

It should be understood that the proportion of the effective grid in the total grid of the road segment is larger than the set proportion, so that the road segment can be used as a research object for analyzing the network problem type.

The ninth preset value, the tenth preset value, the eleventh preset value, the twelfth preset value, and the thirteenth preset value may be set according to actual conditions or experience, for example, may be-115 dBm, 6dB, 1, 5%, and 20%, respectively, and specific values of the ninth preset value, the tenth preset value, the eleventh preset value, the twelfth preset value, and the thirteenth preset value are not limited herein.

In this example, whether the road segment has the network quality problem is determined by counting the occupation ratio of the grids having the network quality problem in all the grids in the road segment. Therefore, the road segments with the network quality problem are quickly positioned, network optimization personnel can be helped to quickly make a network optimization scheme, and the network optimization efficiency is improved.

In a possible implementation manner, MOS values and SINR values may also be obtained from the associated data, and the occupation ratios of sampling points in which the MOS values and the Signal-to-Interference-plus-Noise Ratio (SINR) values in the effective grid are respectively smaller than a fourteenth preset value and a fifteenth preset value at all the sampling points are counted to determine a grid with a low MOS problem and a low Signal-to-Noise Ratio problem, and then whether the grid with the low MOS problem and the low Signal-to-Noise Ratio problem is a road segment with the low MOS problem or a road segment with the low Signal-to-Noise Ratio problem is determined by obtaining the occupation ratios of the grid with the low MOS problem and the low Signal-to-Noise Ratio problem at the road segment total grid.

Step 204: and determining the severity of the network problem according to the type of the network problem of the road section.

After the network problem types of each road segment are determined, the severity of the network problems of the road segment can be determined by integrating multiple problem types, and automatic sequencing is performed according to the severity of the network problems. And each road segment may have at least one of a network coverage problem, an abnormal event problem, a user perception problem, or a network quality problem.

Optionally, the severity of the network problem can be represented by combining labeling and occupied weight of different network problem types in the road section to score the network problem, and then the severity of the network problem is determined according to the degree of the score, wherein the higher the score is, the more serious the network problem is. The specific step of marking different network problem types is to mark a 1 behind the unique identification number of the corresponding road grid road section if any problem road section definition is met, and mark a 0 if the problem road section definition is not met. And the problem road section score can be coverage problem weight coefficient + quality problem weight coefficient + perception problem weight coefficient + abnormal event problem weight coefficient. For example: the total score is 100, if there are only coverage problems and abnormal event problems in a certain problem road segment, the coverage problems and the abnormal event problems are marked as 1, the quality problems and the perception problems are marked as 0, and the weighting coefficients of the types of the network problems are respectively 25, then the score of the road segment is 1 × 25+0 + 25+1 × 25, namely 50.

Optionally, before scoring the network problems, the network problems can be displayed according to the dimensions of focusing, non-focusing, province, city, county, administrative district and unit, a user can select a specific network problem type existing in the section of the road segment of each road in the area to be detected, and the network condition of the whole area to be detected or the network problem type of a certain dimension can be macroscopically checked. For example: the user can check the network problem types of all roads in province A and can also check the network problem types of the roads in district B and district C.

Step 205: and displaying the network problem severity of the road in the area to be detected.

Specifically, the network problem type and the score condition of the road segment can be updated in real time according to the data of the XDR data and the MR data every week, and whether the network problem type existing in the road segment history is normal or not can be automatically judged, so that a network problem type management and control table of the road segment is formed. The network problem type management and control table supports display according to regions, and visually presents network problem solution conditions, unsolved network problem distribution, ranking and other conditions so as to check the result of network optimization at any time.

The network problem type management and control table can be displayed according to different network problem types, namely, network coverage problems, abnormal event problems, user perception problems, network quality problems, low MOS problems, low signal to noise ratio problems and the like, and can automatically count the number of all the road sections, the number of the problem road sections, the number of the road sections with a certain problem type solved, the number of the road sections with a certain problem type newly added and the like, so that a road section management and control table with a certain problem type is formed, and the problem type road section management and control table can be checked in a multi-dimension mode. And the display can also be carried out according to a plurality of network parameters, namely the PCI of the master cell, the MOD3 interference, the average MOS, the Volt call-on rate, the Volt call-off rate, the SINR and the like. For example, the number of problem road segments, the number of solved problem road segments, the number of newly added problem road segments and the like caused by automatically counting the selected network parameters are calculated, so that a problem type road segment management and control table is formed. And the multi-dimensional display of the whole to-be-detected region, the city, the county, the administrative district, the unit and the like is supported according to the focusing of a certain road section. Meanwhile, a certain problem type road section is newly added and a certain problem type road section is solved, the conditions of the certain problem type road section in the current period and the certain problem type road section in the previous period can be automatically counted, and whether the certain problem type road section is the same problem type road section can be identified based on the road segmentation identification, specifically, if the current period exists and the previous period does not exist, the new problem type road section is newly added and covered. If the current period is none, but the last period is yes, the road section is the type of the solved coverage problem.

In the embodiment, the severity of the network problem of the road in the area to be detected is displayed according to different network parameters and the types of the network problems of the road segments, so that network optimization personnel can be helped to know the network type problem of the specific road segment according to the requirements of the network optimization personnel, and then a network optimization scheme is provided according to the type of the network problem, so that the efficiency of network optimization is improved.

Fig. 5 is a schematic structural diagram of a network quality detection apparatus 50 according to an embodiment of the present application, for example, please refer to fig. 5, where the network quality detection apparatus 50 may include:

the acquisition module 501 is configured to acquire measurement report MR data in a region to be detected.

The processing module 502 is configured to perform rasterization processing on the area to be detected according to the MR data to determine a road in the area to be detected, and perform segmentation processing on the road to obtain a plurality of road segments.

The associating module 503 is configured to associate the XDR data with the MR data corresponding to each of the plurality of road segments to obtain a network problem type of each road segment, where the network problem type includes at least one of a network coverage problem, an abnormal event problem, a user perception problem, or a network quality problem.

And the determining module 504 is configured to determine the severity of the network problem according to the type of the network problem of the road segment.

And the display module 505 is used for displaying the network problem severity of the road in the area to be detected.

Optionally, the processing module 502 is specifically configured to:

Optionally, the association module 503 is specifically configured to:

a plurality of grids within each road segment is obtained.

Optionally, the network parameter includes a network coverage parameter, and the network coverage parameter includes RSRP;

the determining module 504 is specifically configured to: and determining a plurality of sampling points in each grid according to the associated data, and acquiring the RSRP of the plurality of sampling points in each grid.

Optionally, the network parameter includes an abnormal event parameter, and the abnormal event parameter includes a voltage drop rate and a voltage call completing rate;

the determining module 504 is specifically configured to: determining the network problem type of the road segment according to the network parameters of each grid,

Optionally, the network parameter includes a user perception parameter, and the user perception parameter includes an average transmission rate of the data packet;

the determining module 504 is specifically configured to: and determining a third target grid with the average transmission rate smaller than a seventh preset value according to the average transmission rate of each grid.

Optionally, the network parameter includes a network quality parameter, and the network quality parameter includes RSRP and MOD3 interference rate;

the determining module 504 is specifically configured to: and for each grid, determining that the RSRP of the master control cell in the grid is greater than a ninth preset value, the level difference between the RSRP and the adjacent cell is less than a tenth preset value, and the MOD3 interference rate is the ratio of sampling points of an eleventh preset value.

The network quality detection apparatus 50 provided in this embodiment of the present application can execute the technical solution of the network quality detection method in any embodiment, and its implementation principle and beneficial effect are similar to those of the network quality detection method, and reference may be made to the implementation principle and beneficial effect of the network quality detection method, which are not described herein again.

Fig. 6 is a schematic structural diagram of a terminal device 60 according to an embodiment of the present application, for example, please refer to fig. 6, where the terminal device may include a processor 601 and a memory 602; wherein the content of the first and second substances,

a memory 602 for storing a computer program.

The processor 601 is configured to read the computer program stored in the memory 602, and execute the technical solution of the network quality detection method in any of the embodiments according to the computer program in the memory 602.

Alternatively, the memory 602 may be separate or integrated with the processor 601. When the memory 602 is a device separate from the processor 601, the terminal device may further include: a bus for connecting the memory 602 and the processor 601.

Optionally, this embodiment further includes: a communication interface, which may be connected to the processor 601 through a bus. The processor 601 may control the communication interface to implement the above-described functions of acquisition and transmission of the terminal device.

The terminal device shown in the embodiment of the present application can execute the technical solution of the network quality detection method in any embodiment, and the implementation principle and the beneficial effect thereof are similar to those of the network quality detection method, and reference may be made to the implementation principle and the beneficial effect of the network quality detection method, which is not described herein again.

An embodiment of the present application further provides a computer-readable storage medium, where a computer execution instruction is stored in the computer-readable storage medium, and when a processor executes the computer execution instruction, the technical solution for implementing the network quality detection method in any of the above embodiments is implemented, and implementation principles and beneficial effects of the method are similar to those of the network quality detection method, and reference may be made to the implementation principles and beneficial effects of the network quality detection method, which is not described herein again.

The embodiment of the present application further provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, the technical solution of the method for detecting network quality in any of the above embodiments is implemented, and the implementation principle and the beneficial effect of the method for detecting network quality are similar to those of the method for detecting network quality, which can be referred to as the implementation principle and the beneficial effect of the method for detecting network quality, and are not described herein again.

In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.

Units described as separate parts may or may not be physically separate, and parts shown 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 units can be selected according to actual needs to achieve the purpose of the solution of the embodiment. In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional unit.

The integrated module implemented in the form of a software functional module may be stored in a computer-readable storage medium. The software functional module is stored in a storage medium and includes several instructions to enable a computer device (which may be a personal computer, a server, or a network device) or a processor (processor) to execute some steps of the methods according to the embodiments of the present application.

It should be understood that the Processor may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of a method disclosed in connection with the present invention may be embodied directly in a hardware processor, or in a combination of the hardware and software modules within the processor.

The memory may comprise a high-speed RAM memory, and may further comprise a non-volatile storage NVM, such as at least one disk memory, and may also be a usb disk, a removable hard disk, a read-only memory, a magnetic or optical disk, etc.

The bus may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an Extended ISA (EISA) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, the buses in the figures of the present application are not limited to only one bus or one type of bus.

The computer-readable storage medium may be implemented by any type or combination of volatile or non-volatile memory devices, such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disks. A storage media may be any available media that can be accessed by a general purpose or special purpose computer.

Finally, it should be noted that: the above embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should 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 or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for detecting network quality is characterized by comprising the following steps:

acquiring measurement report MR data in a region to be detected;

rasterizing the to-be-detected region according to the MR data to determine a road in the to-be-detected region, and performing segmentation processing on the road to obtain a plurality of road segments;

correlating the XDR data with MR data corresponding to each road segment in a plurality of road segments to obtain a network problem type of each road segment, wherein the network problem type comprises at least one of a network coverage problem, an abnormal event problem, a user perception problem or a network quality problem;

determining the severity of the network problem according to the type of the network problem of the road section;

2. The method according to claim 1, wherein the rasterizing the region to be detected according to the MR data to determine the road in the region to be detected comprises:

rasterizing the area to be detected according to the MR data to obtain a plurality of grids and respective grid marks of the plurality of grids;

determining the road attribute of each grid according to the vector information in the high-precision map and the grid identification;

3. The method according to claim 1 or 2, wherein the associating the XDR data with the MR data corresponding to each of the plurality of road segments to obtain the network problem type of each road segment comprises:

obtaining a plurality of grids in each road segment;

determining network parameters of each grid according to associated data obtained by associating the XDR data and the MR data corresponding to the grids respectively, wherein the network parameters comprise at least one of network coverage parameters, abnormal event parameters, user perception parameters or network quality parameters;

and determining the network problem type of the road section according to the network parameters of the grids.

4. The method of claim 3, wherein the network parameters comprise the network coverage parameters, wherein the network coverage parameters comprise RSRP;

the determining the network problem type of the road segment according to the network parameters of the grids comprises the following steps:

determining a plurality of sampling points in each grid according to the associated data, and acquiring RSRP of the sampling points in each grid;

for each grid, determining the proportion of sampling points with the RSRP smaller than a first preset value;

determining the grid with the proportion larger than a second preset value as a first target grid;

and if the ratio of the first target grid to all grids in the road segment is greater than a third preset value, determining that the road segment has the network coverage problem.

5. The method of claim 3, wherein the network parameters comprise the exceptional event parameters, wherein the exceptional event parameters comprise a Volt call drop rate and a Volt call completion rate;

determining a second target grid with the Volte call drop rate larger than a fourth preset value and the Volte call-on rate smaller than a fifth preset value according to the Volte call drop rate and the Volte call-on rate of each grid;

and if the ratio of the second target grid to all grids in the road segment is greater than a sixth preset value, determining that the road segment has the abnormal event problem.

6. The method of claim 3, wherein the network parameters comprise the user-awareness parameters, and wherein the user-awareness parameters comprise an average transmission rate of data packets;

determining a third target grid with the average transmission rate smaller than a seventh preset value according to the average transmission rate of each grid;

and if the ratio of the third target grid to all grids in the road segment is greater than an eighth preset value, determining that the road segment has the user perception problem.

7. The method of claim 3, wherein the network parameters comprise the network quality parameters, wherein the network quality parameters comprise RSRP and MOD3 interference rates;

for each grid, determining the ratio of sampling points, of which the RSRP of the master control cell in the grid is greater than a ninth preset value, the level difference with the adjacent cell is less than a tenth preset value, and the MOD3 interference rate is an eleventh preset value;

determining the grid with the proportion larger than a twelfth preset value as a fourth target grid;

and if the ratio of the fourth target grid to all grids in the road segment is greater than a thirteenth preset value, determining that the road segment has the network quality problem.

8. An apparatus for detecting network quality, comprising:

the acquisition module is used for acquiring measurement report MR data in a region to be detected;

the determining module is used for rasterizing the MR data to determine a road in the area to be detected and carrying out segmentation processing on the road to obtain a plurality of road segments;

the system comprises a correlation module, a data processing module and a data processing module, wherein the correlation module is used for correlating the XDR data with MR data corresponding to each road section in a plurality of road sections to obtain a network problem type of each road section, and the network problem type comprises at least one of a network coverage problem, an abnormal event problem, a user perception problem or a network quality problem;

the determining module is used for determining the severity of the network problem according to the type of the network problem of the road section;

9. An electronic device, comprising: a processor, and a memory communicatively coupled to the processor;

the memory stores computer execution instructions;

the processor executes the computer-executable instructions stored in the memory to implement the method of detecting network quality as claimed in any one of claims 1 to 7.

10. A computer-readable storage medium, wherein computer-executable instructions are stored in the computer-readable storage medium, and when executed by a processor, the computer-executable instructions are used for implementing the method for detecting network quality according to any one of claims 1 to 7.