CN115767582A

CN115767582A - Road network problem processing method, device, server and storage medium

Info

Publication number: CN115767582A
Application number: CN202211164460.9A
Authority: CN
Inventors: 杨飞虎; 刘贤松; 欧大春; 张忠平; 许国平; 董建; 刘斌; 周杰华; 张硕伟; 蒋波
Original assignee: China United Network Communications Group Co Ltd
Current assignee: China United Network Communications Group Co Ltd
Priority date: 2022-09-23
Filing date: 2022-09-23
Publication date: 2023-03-07

Abstract

The application provides a road network problem processing method, a device, a server and a storage medium, wherein the server acquires a target area on a map, divides the target area into a plurality of grid areas, carries out segmentation processing on roads in the target area, acquires MR data of sampling points in each road segment after the plurality of road segments are acquired, determines whether the road segment is a problem road section according to the MR data, and determines the network problem type of the problem road section according to a root cause algorithm and the MR data of each sampling point in the problem road section in a targeted manner aiming at each problem road section, wherein each road comprises a plurality of road segments, each road segment comprises a plurality of grid areas, so that a worker can directly acquire the reasons causing the road network problem, network optimization is carried out based on the reasons, and the efficiency and the accuracy of the network optimization are improved.

Description

Road network problem processing method, device, server and storage medium

Technical Field

The present application relates to the field of communications technologies, and in particular, to a method, an apparatus, a server, and a storage medium for processing a road network problem.

Background

With the rapid development of communication services, the network scale is continuously enlarged, the service types are continuously increased, the number of users is continuously increased, and the network optimization work is more and more complicated. In the process of network optimization, road network data needs to be tested, and problems existing in the network are determined according to the road network data, so that workers can repair the network problems.

In the prior art, network data tests include traditional drive tests and virtual drive tests. Traditional drive test needs professional tester to drive through the target route, utilizes drive test instrument to gather on the spot and test network coverage data, and drive test cost accounts for 45% of network optimization project cost, and the test cost is high, and traditional drive test only can test limited road in addition, and not only efficiency is comparatively low, still easily to not report problem omission, the processing problem that the highway section exists untimely. The virtual drive test reflects the network coverage situation by acquiring and analyzing massive Measurement Report (MR) data with longitude and latitude information of a wireless network, associating Call Detail Trace (CDT) Call tickets and fitting the data onto a defined road, and cannot analyze the problems existing in the network, and needs to manually analyze the acquired data to locate the problems existing in the network, so that the technical problems of inaccurate network optimization and low efficiency exist.

Disclosure of Invention

The application provides a road network problem processing method, a road network problem processing device, a road network problem processing server and a storage medium, which are used for solving the technical problems of inaccurate network optimization and low efficiency in the prior art.

In a first aspect, the present application provides a method for processing a road network problem, the method comprising:

acquiring a target area on a map;

dividing the target area into a plurality of grid areas;

carrying out segmentation processing on the road in the target area to obtain a plurality of road segments; each road comprises a plurality of road segments, and each road segment comprises a plurality of grid areas;

acquiring measurement report MR data of sampling points in each road segment, and determining whether the road segment is a problem road segment according to the MR data;

and aiming at each problem road section, determining the network problem type of the problem road section according to a root cause algorithm and MR data of each sampling point in the problem road section.

In the technical scheme, the server performs grid division on the target area to obtain a plurality of grid areas, performs segmentation processing on the road in the target area to obtain a road segment comprising the plurality of grid areas, and determines the problem state of the road segment by analyzing the MR data of the sampling points in the grid, so that the root cause algorithm processing is adopted for each problem road section in a targeted manner to determine the network problem type of each problem road section, a worker can directly obtain the causes of the road network problem, network optimization is performed based on the reasons, and the efficiency and accuracy of network optimization are improved.

Optionally, the acquiring MR data of sampling points located in each road segment, and determining whether the road segment is a problem road segment according to the MR data specifically includes:

acquiring the number of sampling points in each grid in each road segment;

determining an effective grid in each road segment according to the number of sampling points in each grid and an effective grid sampling point threshold;

determining the index to be tested of each effective sampling point according to the MR data of the effective sampling point in each effective grid; the effective sampling points are sampling points positioned in the effective grid;

counting the number of problem sampling points of which the indexes to be tested are in a first preset range in each effective grid, and determining the effective grid containing the problem sampling points as a problem grid when the ratio of the number of the problem sampling points to the number of all the sampling points in the effective grid in which the problem sampling points are located is in a second preset range;

determining the ratio of the number of the problem grids to the number of the effective grids in each road segment as a problem grid ratio aiming at each road segment;

and when the problem grid proportion is larger than a problem road section threshold value, determining the road section as the problem road section.

In the technical scheme, the server determines the effective grids in the road segments according to the number of the sampling points, determines the to-be-tested indexes of the sampling points according to the MR data of the effective sampling points in the effective grids so as to determine whether the effective grids have problems, and determines whether the road segments where the effective grids are located are problem road segments according to the number of the problem grids, so that the server can analyze the road segments in a more targeted manner, and the network optimization is more accurate.

Optionally, the to-be-tested indicator includes a downlink signal to interference and noise ratio; determining the to-be-tested index of each effective sampling point according to the MR data of the effective sampling point in each effective grid, which specifically comprises the following steps:

processing the MR data by using a downlink signal-to-interference-and-noise ratio estimation model, and estimating a downlink signal-to-interference-and-noise ratio corresponding to each effective sampling point;

relatively, counting the number of the problem sampling points of which the indexes to be tested are in a first preset range in each effective grid, and determining the effective grid containing the problem sampling points as a problem grid when the ratio of the number of the problem sampling points to the number of all the sampling points in the effective grid in which the problem sampling points are located is in a second preset range, wherein the method specifically comprises the following steps:

counting the number of the problem sampling points with the downlink signal to interference plus noise ratio smaller than a preset downlink signal to interference plus noise ratio threshold value in each effective grid, and determining the effective grid containing the problem sampling points as a problem grid when the ratio of the number of the problem sampling points to the number of all the sampling points in the effective grid is larger than the threshold value of the problem sampling points.

Optionally, for each problem road segment, determining a network problem type of the problem road segment according to a root cause algorithm and MR data of each sampling point in the problem road segment, specifically including:

obtaining at least one network characteristic data associated with each sampling point according to the MR data of the sampling point positioned in each problem grid in the problem road section and at least one network characteristic index associated with the root cause algorithm;

counting the number of target sampling points of network characteristic data corresponding to the network characteristic indexes in each problem grid within a preset data range corresponding to the network characteristic indexes aiming at each network characteristic index;

calculating root factor evaluation indexes corresponding to the network characteristic indexes according to the number of the target sampling points and the total number of the sampling points in the problem grid where the target sampling points are located;

and comparing each root cause evaluation index with a corresponding root cause evaluation preset range, determining a network characteristic index corresponding to the root cause evaluation index in the root cause evaluation preset range as a problem network index, and determining the network problem type of the problem road section according to the problem network index.

Optionally, the network characteristic indicator includes: reference signal receiving power, time advance, overlapping coverage and downlink signal-to-interference-and-noise ratio; for each network characteristic index, counting the number of target sampling points of the network characteristic data corresponding to the network characteristic index in each problem grid within a preset data range corresponding to the network characteristic index, specifically including:

counting the number of first target sampling points meeting weak coverage conditions in each problem grid; the weak coverage condition is that the reference signal receiving power of the first target sampling point is smaller than a weak coverage area signal receiving power threshold;

counting the number of second target sampling points meeting the over-coverage condition in each problem grid; the over-coverage condition is that the coverage distance corresponding to the time advance of the second target sampling point is greater than an over-coverage distance threshold;

counting the number of third target sampling points meeting the overlapping coverage condition in each problem grid; the overlapping coverage condition is that the overlapping coverage of the third target sampling point is greater than or equal to an overlapping coverage threshold;

counting the number of fourth target sampling points meeting the downlink quality difference condition in each problem grid; and the downlink quality difference condition is that the fourth target sampling point does not meet the weak coverage condition, the over-coverage condition and the overlapping coverage condition, and the downlink signal-to-interference-and-noise ratio of the fourth target sampling point is less than the downlink quality difference signal-to-interference-and-noise ratio threshold.

Optionally, calculating a root cause evaluation index corresponding to each network characteristic index according to the number of the target sampling points and the total number of sampling points in the problem grid where the target sampling points are located, specifically including:

determining the ratio of the number of the first target sampling points to the number of all sampling points in the problem grid where the first target sampling points are located as a weak coverage evaluation index of the problem grid;

determining the ratio of the number of the second target sampling points to the number of all sampling points in the problem grid where the second target sampling points are located as an over-coverage evaluation index of the problem grid;

determining the ratio of the number of the third target sampling points to the number of all sampling points in the problem grid where the third target sampling points are located as a repeated coverage evaluation index of the problem grid;

and determining the ratio of the number of the fourth target sampling points to the number of all sampling points in the problem grid where the fourth target sampling points are located as a downlink quality difference evaluation index of the problem grid.

Optionally, comparing each root cause evaluation index with a preset root cause evaluation range corresponding to the root cause evaluation index, determining a network characteristic index corresponding to the root cause evaluation index within the preset root cause evaluation range as a problem network index, and determining a network problem type of the problem road section according to the problem network index, specifically including:

when the weak coverage evaluation index of the problem grid is larger than or equal to a weak coverage evaluation threshold value, determining the reference signal receiving power as the problem network index, and determining the network quality problem type of the problem road section as the weak coverage type;

when the over-coverage evaluation index of the problem grid is greater than or equal to an over-coverage evaluation threshold, determining the time lead as the problem network index, and determining the network quality problem of the problem road section as an over-coverage type;

when the repeated coverage evaluation index of the problem grid is larger than or equal to the repeated coverage evaluation threshold, determining the overlapping coverage as the problem network index, and determining the network quality problem of the problem road section as the repeated coverage type;

and when the downlink quality difference evaluation index of the problem grid is greater than or equal to a downlink quality difference evaluation threshold value, determining the downlink signal-to-interference-and-noise ratio as the problem network index, and determining the network quality problem of the problem road section as a downlink quality difference type.

Optionally, after determining, for each problem road segment, a network problem type of the problem road segment according to a root cause algorithm and MR data of each sampling point in the problem road segment, the method further includes:

and aiming at each road in the target area, sorting problem grid proportions corresponding to each road segment contained in the road, and determining the processing priority of the problem road section according to a preset sequence.

Optionally, the method further comprises:

and displaying the network quality problem types and the processing priorities corresponding to the problem road sections in the geographic position corresponding to the target area.

In a second aspect, the present application provides a road network problem processing apparatus, including:

the acquisition module is used for acquiring a target area on a map;

the processing module is used for dividing the target area into a plurality of grid areas;

the processing module is further used for carrying out segmentation processing on the road in the target area to obtain a plurality of road segments; each road comprises a plurality of road segments, and each road segment comprises a plurality of grid areas;

the processing module is further used for acquiring measurement report MR data of sampling points in each road segment and determining whether the road segment is a problem road segment according to the MR data;

and the processing module is also used for determining the network problem type of the problem road section according to a root cause algorithm and the MR data of each sampling point in the problem road section aiming at each problem road section.

Optionally, the processing module is specifically configured to:

acquiring the number of sampling points in each grid in each road segment;

determining the index to be tested of each effective sampling point according to the measurement report MR data of the effective sampling point in each effective grid; the effective sampling points are sampling points positioned in the effective grid;

counting the number of problem sampling points of which the indexes to be tested are in a first preset range in each effective grid, and determining the effective grid containing the problem sampling points as a problem grid when the ratio of the number of the problem sampling points to the number of all the sampling points in the effective grid in which the problem sampling points are positioned is in a second preset range;

Optionally, the processing module is specifically configured to:

processing the MR data by using a downlink signal-to-interference-and-noise ratio estimation model, and estimating downlink signal-to-interference-and-noise ratios corresponding to the effective sampling points; the to-be-tested index comprises a downlink signal to interference plus noise ratio;

Optionally, the processing module is specifically configured to:

counting the number of first target sampling points meeting the weak coverage condition in each problem grid; the weak coverage condition is that the reference signal receiving power of the first target sampling point is smaller than a weak coverage area signal receiving power threshold;

counting the number of fourth target sampling points meeting the downlink quality difference condition in each problem grid; the downlink quality difference condition is that the fourth target sampling point does not meet the weak coverage condition, the over-coverage condition and the overlapping coverage condition, and the downlink signal-to-interference-and-noise ratio of the fourth target sampling point is smaller than a downlink quality difference signal-to-interference-and-noise ratio threshold; the network characteristic indicators include: reference signal received power, timing advance, overlap coverage and downlink signal-to-interference-and-noise ratio.

Optionally, the processing module is specifically configured to:

when the repeated coverage evaluation index of the problem grid is greater than or equal to a repeated coverage evaluation threshold, determining the overlapping coverage as the problem network index, and determining the network quality problem of the problem road section as a repeated coverage type;

and when the downlink quality difference evaluation index of the problem grid is greater than or equal to a downlink quality difference evaluation threshold, determining the downlink signal-to-interference-and-noise ratio as the problem network index, and determining the network quality problem of the problem road section as a downlink quality difference type.

Optionally, the processing module is specifically configured to:

and aiming at each road in the target area, sequencing the problem grid proportion corresponding to each road segment contained in the road, and determining the processing priority of the problem road section according to a preset sequence.

Optionally, the processing module is specifically configured to:

In the technical scheme, the server determines the number of target sampling points in each problem grid within a preset data range by using the associated network characteristic indexes in the root cause algorithm, and determines the reason causing the network problem according to the corresponding root cause evaluation preset range, so that the process of determining the problem by the server is more targeted, and the efficiency of the optimization process is improved.

In a third aspect, the present application provides a server, comprising: a processor and a memory communicatively coupled to the processor;

the memory stores computer instructions;

the processor, when executing the computer instructions, is configured to implement the road network problem handling method as referred to in the first aspect.

In a fourth aspect, the present application provides a computer-readable storage medium, in which computer instructions are stored, and the computer instructions are executed by a processor to implement the road network problem processing method according to the first aspect.

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.

FIG. 1 is a schematic view of a scenario of a road network problem processing method provided by the present application according to an exemplary embodiment;

FIG. 2 is a schematic flow chart diagram illustrating a method for processing a road network problem provided by the present application according to an exemplary embodiment;

FIG. 3 is a schematic diagram of a target area rasterization method provided herein in accordance with an exemplary embodiment;

FIG. 4A is a schematic illustration of a road segment provided herein according to an exemplary embodiment;

FIG. 4B is a schematic illustration of a road segment provided herein according to another exemplary embodiment;

FIG. 4C is a schematic illustration of a road segment provided by the present application in accordance with another exemplary embodiment;

FIG. 4D is a schematic illustration of a road segment provided by the present application in accordance with another exemplary embodiment;

fig. 5 is a flowchart illustrating a problem road segment determining method according to an exemplary embodiment of the present application;

fig. 6 is a flowchart illustrating a method for determining a network problem type of a problem road segment according to an exemplary embodiment of the present application;

FIG. 7 is a schematic structural diagram of a road network problem processing device according to an embodiment of the present application;

fig. 8 is a schematic structural diagram of a server according to an embodiment of the present application.

With the above figures, there are shown specific embodiments of the present application, which 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.

Detailed Description

Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. The following description refers to the accompanying drawings in which the same 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 application provides a road network problem processing method, a road network problem processing device, a road network problem processing server and a storage medium, and aims to solve the technical problems that network optimization is not accurate and efficiency is low in the prior art. The technical idea of the application is as follows: the method comprises the steps that after a server acquires a target area on a map, the target area is rasterized to generate a plurality of rasterized areas, a road is segmented to obtain road segments containing a plurality of raster areas, the server acquires MR data of sampling points in all the road segments, whether the road segments are problem road sections or not is determined according to the MR data, and according to a root cause algorithm and the MR data of the sampling points in the problem road sections, the network problem type of the problem road sections is determined, so that a worker can directly determine the network problems of all the problem road sections to maintain a base station, manual analysis of the network problems existing in the base station is not needed, the accuracy of network optimization is improved, and the analysis efficiency can be improved.

Fig. 1 is an application scenario diagram of a road network problem processing method according to an exemplary embodiment of the present application, as shown in fig. 1, the scenario diagram includes a target area 10, an MR server 11, and a server 12, where the target area 10 includes at least one road and at least one base station. In an embodiment, the target area 10 includes two roads, and 3 base stations are disposed near the two roads, and are respectively a first base station 101, a second base station 102, and a third base station 103, and each base station provides network service for its surrounding area. The three base stations are connected to the MR server 11 in communication, and the MR server 11 is connected to the server 12 in communication.

When the server 12 obtains the network state of the road in the target area 10 and determines the corresponding network problem, the server 12 obtains the target area on the map, and divides the target area into a plurality of grid areas, and performs segmentation processing on the road in the target area to obtain a plurality of road segments, each road segment comprises a plurality of grid areas, and when a sampling point exists in a grid area and is in the coverage range of a base station and establishes a connection relationship with the base station, the sampling point tests the currently connected base station and an adjacent base station to generate MR data, and sends the MR data to the MR server 11. The server 12 acquires MR data of sampling points in each grid region within a preset time range from the MR server 11, determines whether a road segment is a problem road segment according to the MR data, and determines a network problem type of the problem road segment according to a root cause algorithm and the MR data of each sampling point in the problem road segment for each problem road segment. In one embodiment, the sampling point is a terminal.

Fig. 2 is a flowchart illustrating a road network problem processing method according to an exemplary embodiment of the present application. As shown in fig. 2, the method includes:

s201, the server acquires a target area on the map.

The target area is an area of the network state to be detected preset by the server. For example: city a, district B, prefecture C, etc.

After the server determines the target area, the area of the device on the electronic map is acquired. In one embodiment, the electronic map is a 5-meter high-precision map.

S202, the server divides the target area into a plurality of grid areas.

The server performs rasterization division on the target area on the map.

More specifically, the server divides the target area according to the longitude and latitude directions to obtain a plurality of grid areas, and the grid areas are the same in size. In an embodiment, the grid has sides of 5 meters, 10 meters, 20 meters, or 50 meters.

The following explains a process of dividing the target area into a plurality of grid areas by the server through fig. 3, where, as shown in fig. 3, an area drawn by a solid line is the target area, the server takes a point where the outermost side extending from the north side of the target area is tangent to a weft as a first tangent point 301, a point where the outermost side extending from the west side of the target area is tangent to a meridian as a second tangent point 302, and the server determines a point where the weft where the first tangent point 301 is located and the meridian where the second tangent point 302 is located intersect as a rasterization starting point 303. The server determines the central longitude and latitude data (X) of the grid where the initial point 303 is according to the preset grid side length and the initial point 303 ₁ ,Y ₁ ) And determining the position of the end point 304 of rasterization and the central longitude and latitude data (X) of the grid where the end point 304 is positioned according to the size of the target area and the preset grid side length _m ,Y _k ) The end points are the outermost diagonal points that start point 303 extends southward and out-eastward. In one embodiment, the grid is a square grid.

When the server performs rasterization on the target area, the server respectively extends to south along the longitude lines to generate grids and extends to east along the latitude lines to generate grids, wherein when the grids are generated to south, the central latitude data of each grid is less than or equal to X _m When the central latitude data is equal to X _m When the grid is generated southward, the server stops generating the grid; similarly, when the grids are generated eastward, the central longitude data of each grid is less than or equal to Y _k When the central longitude data is equal to Y _k At that time, the server stops generating the grid eastward. It should be understood that, in the above process, the order of determining the first tangent point and the second tangent point is not specifically limited, and the second tangent point may be determined first and then the first tangent point may be determined; the specific order of generating the grids southward and eastward is not particularly limited. In addition, in the grid expansion process, the longitude and latitude of the central coordinate of the newly added grid are offset compared with those of the adjacent grid. Therefore, in an actual amplification process, the difference in longitude or latitude between adjacent grids is not equal to the side length of the grid.

After the server rasterizes the target area, the electronic device assigns a unique identifier to each raster area.

S203, the server carries out segmentation processing on the road in the target area to obtain a plurality of road segments.

When the target area contains the road, the server performs segmentation processing on the road. In the same road, each road comprises a plurality of road segments, each road segment comprises a plurality of grid areas, and the number of the grid areas contained in each road segment is the same.

More specifically, the server determines the feature attribute information of each grid according to the vector information in the map where the target area is located. The feature attribute information can be divided into two types of attributes: a road matching grid attribute and a non-road matching grid attribute. In one embodiment, the feature attribute information related to the road matching grid attribute comprises: the non-road matching grid attribute related ground feature attribute information comprises the following information: urban areas, high-rise buildings, factories, shopping malls, villages and the like. It is noted that when the central longitude and latitude position of the grid is located in the area covered by the road, the attribute of the grid is the road matching grid attribute.

The server divides the road matching grids in the target area, forms a closed-loop area by combining road names, and determines the area as an area related to the road. The server assigns corresponding identifications to closed-loop areas associated with roads within the target area. In one embodiment, the rules for road identification are as follows: first-level road marking: pro-City-level1-A, secondary road marking: pro-City-level2-A, three-level road identification: pro-City-level3-A, four-level road sign: pro-City-level4-A, expressway road marking: pro-City-Expressway-A, where A is 6 digits, A is an e (000001, 999999), pro is province, and City is a City belonging to province.

The server segments the closed-loop area formed by the road, and the process of segmenting the road will be explained with reference to fig. 4A to 4D.

Fig. 4A is a road of 600 meters, and the server divides the road according to the feature attribute information of the road. When the feature attribute information is in the dense city area, the length of the divided road segments in the road is 50 meters, then the road of 600 meters is divided into 12 road segments, each road segment is 50 meters in length, and each road segment includes 5 columns of grids, taking a 10 × 10 grid as an example. And after the server divides the road segments, distributing corresponding identifiers to the road segments. The road segment identification naming rules are as follows: road name-road length segment length-B, where B is a 6-digit number, and B e (000001, 999999). The identification of the road segment in fig. 4A is: road name-50 m-B.

Fig. 4B is a schematic diagram of a road with feature attribute information of dense urban areas or general urban areas segmented by a server, wherein the road of 600 meters can be divided into 6 road segments, each road segment has a length of 100 meters, and each road segment has 10 columns of grids. The naming convention for this road segment is the same as that described in fig. 4A. The identification of the road segment in fig. 4B is: road name-100 m-B.

Fig. 4C is a schematic diagram of a road with feature attribute information of a general urban area segmented by a server, wherein the road of 600 meters can be divided into 3 road segments, each road segment has a length of 200 meters, and each road segment has 20 columns of grids. The naming convention for this road segment is the same as that described in fig. 4A. The identification of the road segment in fig. 4C is: road name-200 m-B.

Fig. 4D is a schematic diagram of a server segmenting a road with feature attribute information of suburban or rural areas, wherein the road of 600 meters can be divided into 2 road segments, each road segment has a length of 300 meters, and each road segment has 30 grids. The naming rule of the road segment is the same as that described in fig. 4A. The identification of the road segment in fig. 4D is: road name-300 m-B.

S204, the server acquires MR data of sampling points in each road segment, and determines whether the road segment is a problem road segment according to the MR data.

The sampling points refer to the points when the user terminal device reports the MR data to the base station. In a possible implementation manner, the UE may be a User Equipment (UE) mobile phone. The MR data is a measurement report generated by a UE mobile phone in the process of wireless asset management, and comprises the following steps: reference Signal Receiving Power (RSRP), reference Signal Receiving Quality (RSRQ), power Headroom Report (PHR), angle Of Arrival ranging (AOA), maximum Time Advance (TA), and the like.

After acquiring the MR data of the sampling points, the server allocates corresponding longitudes and latitudes to the MR data in the target area, namely position information corresponding to the MR data, through MR convergence, user AOA estimation, ray intersection based on topological relation, confidence correction and map matching, so that the server can still determine the position information of the sampling points corresponding to the MR data when acquiring the MR data without Auxiliary Global Positioning System (AGPS).

And after the server obtains the position information of the MR data, determining sampling points in each grid and the MR data of the sampling points according to the position information and the position information covered by the grid area.

The server determines whether the network data related to the road segment is in a normal range according to the MR data, and determines the road segment as a problem road segment when the network data is not in the normal range.

S205, the server determines the network problem type of the problem road section according to the root cause algorithm and the MR data of each sampling point in the problem road section aiming at each problem road section.

The root cause algorithm is an algorithm for determining the reason for the network data bits being in the normal range according to the MR data of each sampling point in the grid.

In addition, after the server determines the network problem type of the problem road section according to the root cause algorithm and the MR data of each sampling point in the problem road section aiming at each problem road section, the method further comprises the following steps:

the method comprises the steps of aiming at each road in a target area, sequencing problem grid proportions corresponding to each road segment contained in the road, determining the processing priority of a problem road section according to a preset sequence, and displaying the network quality problem type and the processing priority corresponding to each problem road section in a geographic position corresponding to the target area.

More specifically, the server ranks all road segments in the road according to the problem grid scale. When the problem grid is a poor quality grid, namely the ratio of the number of the sampling points with the downlink signal to interference plus noise ratio smaller than 0 in the problem grid to all the sampling points in the grid is larger than a preset threshold value, sorting the problem sampling points with the downlink signal to interference plus noise ratio smaller than 0 in the problem grid from small to large, scoring the poor quality state of each road segment according to quantiles, determining the high priority of the problem road segment with serious poor quality, namely high score, and displaying the problem road segment at the corresponding geographic position on a map so that a worker can solve the problem preferentially.

And after the staff determines that the network problem is solved, the MR data of the sampling point in the region is obtained again, downlink signal-to-interference-and-noise ratio fitting is carried out according to the MR data, the downlink signal-to-interference-and-noise ratio is compared with the MR data and the corresponding index thereof, and whether the problem of the road segmentation is solved or not is judged. In addition, the server also counts the road sections with problems and performs geographic rasterization presentation on the electronic map, so that the management and control of the existing states and the solution conditions of the problems in the target area are realized, and the efficiency of network optimization in the target area is promoted.

Fig. 5 is a flowchart illustrating a problem road segment determining method according to an exemplary embodiment of the present application, and as shown in fig. 5, the method includes:

s401, the server obtains the number of sampling points in each grid in each road segment.

S402, the server determines effective grids in each road segment according to the number of sampling points in each grid and the threshold value of the sampling points of the effective grids.

More specifically, within the preset time range, when the number of the sampling points in the grid is greater than the threshold value of the sampling points of the effective grid, the grid is determined as the effective grid.

For example, when the number of sampling points in the same grid is greater than 50 in a consecutive circle, the grid is an effective grid.

S403, the server determines the to-be-tested indexes of the effective sampling points according to the MR data of the effective sampling points in the effective grids.

Wherein the effective sampling points are sampling points located in an effective grid.

The index to be tested is an index describing the network performance of the base station covering the grid.

In an embodiment, the to-be-tested indicator includes a downlink signal-to-interference-and-noise ratio, where the downlink signal-to-interference-and-noise ratio is estimated at each effective sampling point after the server processes the MR data by using a downlink signal-to-interference-and-noise ratio estimation model. The MR data includes periodic table-based data (measurement Report information, abbreviated as MRO).

The downlink signal-to-interference-and-noise ratio estimation model is a trained model stored locally by a server. And when the server trains the model, the server obtains the drive test data acquired when the model is subjected to the road test from the test terminal. The drive test data includes: a terminal reports sampling point time, and tests a terminal number, namely, an international ISDN number (Mobile Station international ISDN number, abbreviated: MSISDN) and an international Mobile subscriber identity (international Mobile subscriber identity, abbreviation: IMSI) Identifier, longitude and latitude of a reported sampling point, E-UTRAN Cell Identifier (ECI) of a main control Cell to which the terminal reports the sampling point, frequency point number of a main control Cell to which the terminal reports the sampling point, physical Cell Identifier (PCI) of a main control Cell to which the terminal reports the sampling point, RSRP (Signal to Interference plus Noise Ratio) of a main control Cell to which the terminal reports the sampling point, RSRP (Received Signal Strength Indicator) of the total Received bandwidth of the main control Cell to which the terminal reports the sampling point belongs, RSSI (Received Signal to which the terminal reports the sampling point), RSRQ (Reference Signal Quality Indicator) of the Received Quality of the main control Cell to which the terminal reports the sampling point belongs, RSRQ 2 of the main control Cell to which the terminal reports the sampling point, PCI 2 of the sampling point, and PCI 2 of the sampling point, … …, the frequency point number of the nth neighbor cell of the home master control cell of the sampling point reported by the terminal, the PCI of the nth neighbor cell of the home master control cell of the sampling point reported by the terminal, the signal strength RSRP of the nth neighbor cell of the home master control cell of the sampling point reported by the terminal, and the receiving quality RSRQ of the nth neighbor cell of the home master control cell of the sampling point reported by the terminal.

The test terminal uses the designated international mobile subscriber identity, and actively reports the generated MRO data to the MRO server in the process of performing the drive test. The server obtains MRO data from the MRO server and associates the drive test data with the MRO data through the international mobile subscriber identity. The test terminal can acquire the drive test data for multiple times in 1s, so that multiple downlink signal-to-interference-and-noise ratios can be obtained in 1s, and when the test terminal reports the MRO, the time interval between two adjacent reports is greater than 1s, so that the drive test data and the MRO data cannot correspond one to one, seconds are taken as a time unit, the time stamp of the drive test data at the same time is matched with the time stamp of the MRO data, the time stamp of the drive test data is matched with the MRO data with the nearest time distance, and the data corresponding to the same index in the drive test data corresponding to the time stamp are subjected to mean processing.

The server screens the characteristic data from the MRO, and the method comprises the following steps: the method comprises the steps of main control service cell level strength, main control service cell received signal quality, main control service cell physical identification, main control service cell function allowance, main control service cell Time Advance (TA), adjacent cell quantity, average difference value of service cell and adjacent cell level strength, cell MR overlapping quantity and modulo three quantity. In addition, the server obtains the downlink signal-to-interference-and-noise ratio from the drive test data. And the server takes the mean value of the downlink signal-to-interference-and-noise ratio as a characteristic label of the characteristic data screened in the MRO to make training data.

The server divides the training data into a training set and a test set, inputs the training set into an AI model for training, and verifies the training result by using the test set to obtain a downlink signal-to-interference-and-noise ratio estimation model. In one embodiment, the AI model employs the XGBOST algorithm. In one embodiment, the result of verifying the SINR estimation model by the test set is R ² ＝0.72。

The server can directly estimate the downlink signal-to-interference-and-noise ratio corresponding to the MRO data by utilizing the MRO data and the downlink signal-to-interference-and-noise ratio estimation model, so that the downlink signal-to-interference-and-noise ratio can be obtained without passing through the circuit measurement.

S404, the server counts the number of the problem sampling points of each effective grid, of which the indexes to be tested are in a first preset range, and determines the effective grid containing the problem sampling points as the problem grid when the ratio of the number of the problem sampling points to the number of all the sampling points in the effective grid is in a second preset range.

The first preset range is a value range of the sampling point in which the index to be tested is in an abnormal state.

The second preset range is a value range which represents that the effective grid contains problem sampling points when the effective grid has problems.

The server obtains an index to be tested of a sampling point in the effective grid, compares the index to be tested with a first preset range, determines that the index to be tested of the sampling point is abnormal when the index to be tested is within the first preset range, and determines the sampling point as a problem sampling point.

And the server counts the number of the problem sampling points in each effective grid, calculates a quotient of the number of the problem sampling points divided by the number of all the sampling points in the effective grid, and determines the effective grid as the problem grid when the quotient is within a second preset range.

When the index to be tested is the downlink signal to interference plus noise ratio, the server counts the number of problem sampling points with the downlink signal to interference plus noise ratio smaller than a preset downlink signal to interference plus noise ratio threshold value in each effective grid, and when the ratio of the number of the problem sampling points to the number of all the sampling points in the effective grid is larger than the problem sampling point threshold value, the effective grid containing the problem sampling points is determined as a problem grid. For example: the server determines the sampling points with the downlink signal-to-interference-and-noise ratio smaller than 0 as problem sampling points, the server counts the number of the problem sampling points in each grid, and when the ratio of the number to the total sampling points in the grid is larger than or equal to 50%, the grid is determined as a problem grid.

S405, the server determines the ratio of the number of the problem grids to the number of the effective grids in each road segment as a problem grid ratio according to each road segment.

And S406, when the problem grid proportion is larger than the problem road section threshold value, the server determines the road section as the problem road section.

The problem link threshold is a threshold that defines a problem state for a road segment.

In one embodiment, in the road segment shown in fig. 4A, if the problem grid ratio is equal to 100%, the road segment is determined to be the problem road segment; in the road segment shown in fig. 4B, if the problem grid ratio is equal to 100%, the road segment is determined to be a problem road segment; in the road segment shown in fig. 4C, if the problem grid ratio is greater than or equal to 80%, the road segment is determined to be a problem road segment; in the road segment shown in fig. 4D, if the problem grid ratio is greater than or equal to 60%, the road segment is determined to be the problem link.

Fig. 6 is a flowchart illustrating a method for determining a network problem type of a problem road segment according to an exemplary embodiment of the present application, where as shown in fig. 6, the method includes:

s501, the server obtains at least one network characteristic data associated with each sampling point according to the MR data of the sampling point located in each problem grid in the problem road section and at least one network characteristic index associated with the root cause algorithm.

In one embodiment, the network characteristic metrics include: reference signal received power, timing advance, overlap coverage and downlink signal-to-interference-and-noise ratio.

S502, the server counts the number of target sampling points of network characteristic data corresponding to the network characteristic indexes in each problem grid in a preset data range corresponding to the network characteristic indexes according to each network characteristic index.

The problem of poor quality of roads is basically caused by unreasonable coverage of a wireless network, and generally, the problem of poor quality of the wireless network of the roads under the scene of a macro station is mainly divided into three categories, namely weak coverage, over coverage and overlapping coverage. The server jointly positions the road quality difference problems through the main service cell fitting prediction downlink SINR in the MR data related with the longitude and latitude and the overlapping coverage rate constructed by the main service cell TA, the main service cell RSRP and the adjacent cell RSRP in the MR data.

More specifically, the server counts the number of first target sampling points satisfying the weak coverage condition in each problem grid. And the weak coverage condition is that the reference signal received power of the first target sampling point is less than the signal received power threshold of the weak coverage area. In one embodiment, the weak coverage condition is that the reference signal received power is less than-100 dBm.

And the server counts the number of second target sampling points meeting the over-coverage condition in each problem grid. And the over-coverage condition is that the coverage distance corresponding to the time advance of the second target sampling point is greater than an over-coverage distance threshold. In an embodiment, the over-coverage condition is that the coverage distance corresponding to the time advance is greater than 2km.

And the server counts the number of the third target sampling points meeting the overlapping coverage condition in each problem grid. The overlapping coverage condition is that the overlapping coverage of the third target sampling point is greater than or equal to the overlapping coverage threshold. In one embodiment, the overlap coverage condition is that the overlap coverage is greater than or equal to 1.

And the server counts the number of the fourth target sampling points meeting the downlink quality difference condition in each problem grid. And the downlink quality difference condition is that the fourth target sampling point does not meet the weak coverage condition, the over-coverage condition or the overlapping coverage condition, and the downlink signal-to-interference-and-noise ratio of the fourth target sampling point is less than the downlink quality difference signal-to-interference-and-noise ratio threshold. In one embodiment, the downlink quality difference signal to interference plus noise ratio threshold is 0.

S503, the server calculates root factor evaluation indexes corresponding to the network characteristic indexes according to the number of the target sampling points and the total number of the sampling points in the problem grid where the target sampling points are located.

More specifically, the server determines the ratio of the number of the first target sampling points to the number of all sampling points in the problem grid where the first target sampling points are located as a weak coverage evaluation index of the problem grid, determines the ratio of the number of the second target sampling points to the number of all sampling points in the problem grid where the second target sampling points are located as an over coverage evaluation index of the problem grid, determines the ratio of the number of the third target sampling points to the number of all sampling points in the problem grid where the third target sampling points are located as a repeated coverage evaluation index of the problem grid, and determines the ratio of the number of the fourth target sampling points to the number of all sampling points in the problem grid where the fourth target sampling points are located as a downlink quality difference evaluation index of the problem grid.

S504, the server compares each root cause evaluation index with a corresponding root cause evaluation preset range, determines a network characteristic index corresponding to the root cause evaluation index in the root cause evaluation preset range as a problem network index, and determines a network problem type of the problem road section according to the problem network index.

And when the weak coverage evaluation index of the problem grid is greater than or equal to the weak coverage evaluation threshold, the server determines the reference signal received power as a problem network index and determines the network quality problem type of the problem road section as a weak coverage type. In one embodiment, the weak coverage evaluation threshold is 30%.

And when the over-coverage evaluation index of the problem grid is greater than or equal to the over-coverage evaluation threshold, determining the time lead as a problem network index, and determining the network quality problem of the problem road section as an over-coverage type. In one embodiment, the over-coverage evaluation threshold is 70%.

And when the repeated coverage evaluation index of the problem grid is greater than or equal to the repeated coverage evaluation threshold, determining the overlapping coverage as a problem network index, and determining the network quality problem of the problem road section as a repeated coverage type. In one embodiment, the overlap coverage evaluation threshold is 70%.

When the downlink quality difference evaluation index of the problem grid is greater than or equal to the downlink quality difference evaluation threshold, determining the downlink signal-to-interference-and-noise ratio as a problem network index, and determining the network quality problem of the problem road section as a downlink quality difference type, wherein in one embodiment, the downlink quality difference evaluation threshold is 70%.

In the technical scheme, the server determines the number of target sampling points in the preset data range in each problem grid by using the associated network characteristic indexes in the root cause algorithm, and determines the reason causing the network problem according to the corresponding root cause evaluation preset range, so that the problem determining process of the server is more targeted, and the efficiency of the optimization process is improved.

Fig. 7 is a schematic structural diagram of a road network problem processing device 600 provided by the present application according to an embodiment, where the road network problem processing device 600 includes an obtaining module 601 and a processing module 602, where,

the obtaining module 601 is configured to obtain a target area located on a map.

A processing module 602, configured to divide the target area into a plurality of grid areas.

The processing module 602 is further configured to perform segmentation processing on the road in the target area to obtain a plurality of road segments; each road includes a plurality of road segments, each road segment including a plurality of raster regions.

The processing module 602 is further configured to obtain MR data of the measurement report of the sampling points located in each road segment, and determine whether the road segment is a problem road segment according to the MR data.

The processing module 602 is further configured to determine, for each problem road segment, a network problem type of the problem road segment according to the root cause algorithm and the MR data of each sampling point in the problem road segment.

In an embodiment, the processing module 602 is specifically configured to:

acquiring the number of sampling points in each grid in each road section;

determining effective grids in each road section according to the number of sampling points in each grid and the threshold value of the effective grid sampling points;

determining the index to be tested of each effective sampling point according to the MR data of the effective sampling points in each effective grid; the effective sampling points are sampling points positioned in the effective grids;

and when the problem grid proportion is larger than the problem road section threshold value, determining the road section as the problem road section.

In an embodiment, the processing module 602 is specifically configured to:

processing the MR data by using a downlink signal-to-interference-and-noise ratio estimation model, and estimating downlink signal-to-interference-and-noise ratios corresponding to the effective sampling points;

relatively, count the quantity of the problem sampling point that the index of awaiting measuring is in first preset within range in each effective grid, when the ratio of the quantity of problem sampling point and all sampling point quantity in the effective grid that it was located is in the second preset within range, confirm the effective grid that contains the problem sampling point as the problem grid, specifically include:

and counting the number of the problem sampling points with the downlink signal to interference plus noise ratios smaller than a preset downlink signal to interference plus noise ratio threshold value in each effective grid, and determining the effective grid containing the problem sampling points as the problem grid when the ratio of the number of the problem sampling points to the number of all the sampling points in the effective grid where the problem sampling points are located is larger than the problem sampling point threshold value.

In an embodiment, the processing module 602 is specifically configured to:

obtaining at least one network characteristic data associated with each sampling point according to the MR data of the sampling point positioned in each problem grid in the problem road section and at least one network characteristic index associated with a root cause algorithm;

and comparing each root cause evaluation index with a corresponding root cause evaluation preset range, determining the network characteristic index corresponding to the root cause evaluation index in the root cause evaluation preset range as a problem network index, and determining the network problem type of the problem road section according to the problem network index.

In an embodiment, the processing module 602 is specifically configured to:

counting the number of first target sampling points meeting the weak coverage condition in each problem grid; the weak coverage condition is that the reference signal receiving power of the first target sampling point is smaller than the signal receiving power threshold of the weak coverage area;

counting the number of third target sampling points meeting the overlapping coverage condition in each problem grid; the overlapping coverage condition is that the overlapping coverage of the third target sampling point is greater than or equal to the overlapping coverage threshold;

counting the number of fourth target sampling points meeting the downlink quality difference condition in each problem grid; and the downlink quality difference condition is that the fourth target sampling point does not meet the weak coverage condition, the over-coverage condition or the overlapping coverage condition, and the downlink signal-to-interference-and-noise ratio of the fourth target sampling point is smaller than the downlink quality difference signal-to-interference-and-noise ratio threshold.

In an embodiment, the processing module 602 is specifically configured to:

when the weak coverage evaluation index of the problem grid is larger than or equal to the weak coverage evaluation threshold, determining the reference signal receiving power as a problem network index, and determining the network quality problem type of the problem road section as a weak coverage type;

when the over-coverage evaluation index of the problem grid is greater than or equal to the over-coverage evaluation threshold, determining the time lead as a problem network index, and determining the network quality problem of the problem road section as an over-coverage type;

when the repeated coverage evaluation index of the problem grid is larger than or equal to the repeated coverage evaluation threshold, determining the overlapping coverage as a problem network index, and determining the network quality problem of the problem road section as a repeated coverage type;

and when the downlink quality difference evaluation index of the problem grid is greater than or equal to the downlink quality difference evaluation threshold, determining the downlink signal-to-interference-and-noise ratio as a problem network index, and determining the network quality problem of the problem road section as a downlink quality difference type.

In an embodiment, the processing module 602 is specifically configured to:

and aiming at each road in the target area, sorting the problem grid proportions corresponding to each road segment contained in the road, and determining the processing priority of the problem road section according to a preset sequence.

In an embodiment, the processing module 602 is specifically configured to:

Fig. 8 is a schematic structural diagram of a server according to an embodiment of the present application. The server 700 includes a memory 701 and a processor 702, wherein the memory 701 is used for storing computer instructions executable by the processor. The Memory 701 may include a Random Access Memory (RAM), a Non-Volatile Memory (NVM), at least one disk Memory, a usb disk, a removable hard disk, a read-only Memory, a magnetic disk or an optical disk.

The processor 702, when executing the computer instructions, performs the steps of the method for processing a road network problem, which is mainly performed by the server in the above-described embodiments. Reference may be made in particular to the description relating to the method embodiments described above. The Processor 702 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), or the like. 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 hardware and software modules.

Alternatively, the memory 701 may be separate or integrated with the processor 702. When the memory 701 is separately provided, the electronic device 700 further includes a bus for connecting the memory 701 and the processor 702.

The embodiment of the present application further provides a computer-readable storage medium, in which computer instructions are stored, and when a processor executes the computer instructions, the steps in the method for processing a road network problem according to the above embodiment are implemented.

The embodiment of the present application further provides a computer program product, which includes computer instructions, and the computer instructions, when executed by a processor, implement the steps in the road network problem processing method according to the above embodiment.

Other embodiments of the present application will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the application and including such departures from the present disclosure as come within known or customary practice within the art to which the invention pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the application being indicated by the following claims.

It will be understood that the present application is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the application is limited only by the appended claims.

Claims

1. A method for processing a road network problem, the method comprising:

acquiring a target area on a map;

dividing the target area into a plurality of grid areas;

2. The method according to claim 1, wherein the step of obtaining MR data of sampling points located in each road segment and determining whether the road segment is a problem road segment according to the MR data comprises:

acquiring the number of sampling points in each grid in each road segment;

determining the ratio of the number of problem grids in each road segment to the number of effective grids as a problem grid ratio aiming at each road segment;

3. The method of claim 2, wherein the index to be tested comprises a downlink signal to interference plus noise ratio; determining the to-be-tested index of each effective sampling point according to the MR data of the effective sampling point in each effective grid, which specifically comprises the following steps:

4. The method according to claim 2, wherein determining, for each of the problem road segments, a network problem type of the problem road segment according to a root cause algorithm and MR data of each of the sampling points in the problem road segment specifically comprises:

5. The method of claim 4, wherein the network characteristic metric comprises: reference signal receiving power, time advance, overlapping coverage and downlink signal-to-interference-and-noise ratio; for each network characteristic index, counting the number of target sampling points of the network characteristic data corresponding to the network characteristic index in each problem grid within a preset data range corresponding to the network characteristic index, specifically including:

6. The method according to claim 5, wherein calculating root cause evaluation indexes corresponding to each network characteristic index according to the number of the target sampling points and the total number of sampling points in the problem grid where the target sampling points are located specifically comprises:

7. The method according to claim 6, wherein comparing each of the root cause evaluation indexes with a corresponding root cause evaluation preset range, determining a network characteristic index corresponding to the root cause evaluation index within the root cause evaluation preset range as a problem network index, and determining a network problem type of the problem road section according to the problem network index specifically comprises:

8. The method of claim 2, wherein after determining, for each of the problem segments, a network problem type for the problem segment based on a root cause algorithm and the MR data for each of the sample points in the problem segment, the method further comprises:

9. The method of claim 8, further comprising:

10. A road network problem processing device, comprising:

the acquisition module is used for acquiring a target area on a map;

11. The device for processing road network problems of claim 10, wherein the processing module is specifically configured to:

acquiring the number of sampling points in each grid in each road segment;

determining the index to be tested of each effective sampling point according to the MR data of the effective sampling point in each effective grid; the effective sampling points are sampling points positioned in the effective grids;

12. The device for processing the road network problem according to claim 11, wherein the processing module is specifically configured to:

13. The device for processing the road network problem according to claim 11, wherein the processing module is specifically configured to:

14. The device for processing the road network problem according to claim 13, wherein the processing module is specifically configured to:

counting the number of third target sampling points meeting the overlapping coverage condition in each problem grid; the overlapping coverage condition is that the overlapping coverage degree of the third target sampling point is greater than or equal to an overlapping coverage threshold value;

counting the number of fourth target sampling points meeting the downlink quality difference condition in each problem grid; the downlink quality difference condition is that the fourth target sampling point does not meet the weak coverage condition, the over coverage condition and the overlapping coverage condition, and the downlink signal to interference plus noise ratio of the fourth target sampling point is smaller than a downlink quality difference signal to interference plus noise ratio threshold;

the network characteristic indicators include: reference signal received power, timing advance, overlap coverage and downlink signal-to-interference-and-noise ratio.

15. The device for processing the road network problem according to claim 14, wherein the processing module is specifically configured to:

16. The device for processing the road network problem according to claim 15, wherein the processing module is specifically configured to:

17. The device for processing the road network problem according to claim 11, wherein the processing module is specifically configured to:

18. The device for processing the road network problem according to claim 17, wherein the processing module is specifically configured to:

19. A server, comprising: a processor and a memory communicatively coupled to the processor;

the memory stores computer instructions;

the processor, when executing the computer instructions, is configured to implement a road network problem handling method as claimed in any one of claims 1 to 9.

20. A computer-readable storage medium, in which computer instructions are stored, which, when executed by a processor, are configured to implement the road network problem processing method according to any one of claims 1 to 9.