CN115835261A

CN115835261A - Road coverage assessment method, device, server and medium

Info

Publication number: CN115835261A
Application number: CN202211376187.6A
Authority: CN
Inventors: 杨飞虎; 刘贤松; 欧大春; 许国平; 张忠平; 刘斌; 董建; 石旭荣; 佘士钊; 姜志恒
Original assignee: China United Network Communications Group Co Ltd
Current assignee: China United Network Communications Group Co Ltd
Priority date: 2022-11-04
Filing date: 2022-11-04
Publication date: 2023-03-21

Abstract

The application provides a road coverage assessment method, a device, a server and a medium, comprising the following steps: rasterizing a first geographical area to be evaluated; acquiring related MR data and performing matching processing on the MR data and the grids to acquire a matched second geographical area to be evaluated; performing outward expansion grid processing on the second geographical area to be evaluated by adopting a frame generation principle to obtain an outward expanded third geographical area to be evaluated; determining attribute information of each grid in a third geographical area to be evaluated; acquiring closed-loop areas of a plurality of roads from a third geographical area to be evaluated; determining a plurality of road grid road sections in a closed loop area of a road, acquiring related XDR data, performing correlation processing on the XDR data and corresponding MR data, analyzing road indexes, and determining road sections with coverage problems. Compared with the prior art, the method and the device improve the drive test efficiency and reduce the cost.

Description

Road coverage assessment method, device, server and medium

Technical Field

The present application relates to the field of wireless drive tests, and in particular, to a method, an apparatus, a server, and a medium for evaluating road coverage.

Background

With the rapid development of mobile communication services, the quality of wireless network coverage directly affects the user experience, so wireless network coverage testing and problem analysis are one of the critical tasks.

In the prior art, network optimization is usually completed by adopting a traditional drive test mode. Specifically, in the conventional drive test mode, a professional tester drives through a target route to obtain network coverage data through a field test.

However, the traditional drive test has low efficiency and high cost, the sampling of data has certain limitation, the problems in the network cannot be accurately positioned, so that partial network problems cannot be found, the problems cannot be found before users, the customer perception cannot be comprehensively reflected, the problems cannot be quickly positioned after the users complain, and the user perception is influenced.

Disclosure of Invention

The application provides an assessment method, an assessment device, a server and a medium for road coverage, and aims to solve the problems that in the prior art, road measurement efficiency is low, cost is high, accurate management and control are difficult to be performed on a weak coverage serious area, and an analysis process and a positioning means for a road coverage problem are lacked.

In one aspect, the present application provides a method for evaluating road coverage, including:

and rasterizing the first geographical area to be evaluated to obtain a plurality of grids.

And acquiring MR data related to the first geographical area to be evaluated, and respectively matching each MR data with a grid according to the longitude and latitude information in each MR data to acquire a matched second geographical area to be evaluated.

And performing outward expansion grid processing on the second geographical area to be evaluated by adopting a frame generation principle to obtain a third geographical area to be evaluated after outward expansion.

And associating the ground object vector in the 5-meter high-precision map with the third geographical area to be evaluated, and respectively determining the attribute information of each grid in the third geographical area to be evaluated.

And acquiring closed loop areas of a plurality of roads from the third geographical area to be evaluated by adopting the frame generation principle and taking the attribute information as the grids of the road grid attribute information.

For the closed loop area of each road, a plurality of road grid segments under the closed loop area of the road are determined.

For the closed-loop area of each road, obtaining XDR data related to the closed-loop area of the road, and performing association processing on the XDR data and MR data corresponding to the closed-loop area of the road so as to respectively perform matching processing on a plurality of obtained association data and road raster sections of the closed-loop area of the road.

And for the closed-loop area of each road, analyzing and processing the road indexes of the closed-loop area of the road according to the associated data corresponding to each road grid road section in the closed-loop area of the road so as to determine the road sections with coverage problems in the closed-loop area of the road.

In one embodiment, the method further comprises:

and for the closed loop area of each road, determining the priority corresponding to the road sections with coverage problems in the closed loop area of the road, and sequentially carrying out root cause analysis processing on the road sections with coverage problems according to the sequence of the priority from high to low so as to obtain a corresponding optimization scheme.

In a specific embodiment, the associating the feature vector in the 5-meter high-accuracy map with the third geographic area to be evaluated and determining the attribute information of each grid in the third geographic area to be evaluated respectively includes:

and associating the ground object vector in the 5-meter high-precision map with the third geographical area to be evaluated.

And if the corresponding physical vector associated with the grid in the third geographical area to be evaluated is the road attribute, determining that the attribute information of the grid is the road grid attribute information.

And if the corresponding physical vector associated with the grid in the third geographical area to be evaluated is the non-road attribute, determining that the attribute information of the grid is the non-road grid attribute information.

In a specific embodiment, the road attribute includes one or a combination of the following: first-level roads, second-level roads, third-level roads, fourth-level roads and expressways.

The non-road attribute comprises one or a combination of the following: urban areas, high-rise buildings, factories, shopping malls, and villages.

In a specific embodiment, the method further comprises the following steps:

determining a scene type of the third geographic area to be evaluated.

Then, for the closed-loop area of each road, determining a plurality of road grid segments under the closed-loop area of the road includes:

and for the closed loop area of each road, dividing the closed loop area of the road according to the length of the road grid section matched with the scene type to obtain a plurality of road grid sections under the closed loop area of the road.

In a specific embodiment, the scene type includes one or a combination of the following: dense urban areas, general urban areas, suburban areas, and rural areas.

In one embodiment, the associating the XDR data with MR data corresponding to a closed-loop region of the road includes:

and combining the position information in the MR data and the perception information in the XDR data.

In a second aspect, the present application provides an apparatus for assessing road coverage, comprising:

and the grid processing module is used for carrying out rasterization processing on the first geographical area to be evaluated so as to obtain a plurality of grids.

The grid processing module is further configured to acquire MR data related to the first geographical area to be evaluated, and perform matching processing on each MR data and a grid according to longitude and latitude information in each MR data, so as to acquire a matched second geographical area to be evaluated.

The grid processing module is further configured to perform outward expansion grid processing on the second geographical area to be evaluated by using a frame generation principle to obtain an outward expanded third geographical area to be evaluated.

And the association processing module is used for associating the ground object vector in the 5-meter high-precision map with the third geographical area to be evaluated and respectively determining the attribute information of each grid in the third geographical area to be evaluated.

And the association processing module is further configured to acquire a closed-loop area of the multiple roads from the third geographical area to be evaluated by using the frame generation principle and according to the attribute information as the grid of the road grid attribute information.

The association processing module is further configured to determine, for the closed-loop area of each road, a plurality of road grid segments under the closed-loop area of the road.

The association processing module is further configured to, for the closed-loop region of each road, acquire XDR data related to the closed-loop region of the road, and perform association processing on the XDR data and MR data corresponding to the closed-loop region of the road, so as to perform matching processing on the acquired plurality of association data and road raster segments of the closed-loop region of the road, respectively.

And the analysis processing module is used for analyzing and processing the road indexes of the closed-loop area of the road according to the associated data corresponding to each road grid road section in the closed-loop area of the road so as to determine the road sections with coverage problems in the closed-loop area of the road.

In a specific embodiment, the analysis processing module is further configured to determine, for the closed-loop area of each road, a priority corresponding to a road segment with a coverage problem in the closed-loop area of the road, and sequentially perform root cause analysis processing on the road segments with the coverage problem according to a sequence from high to low in the priority, so as to obtain a corresponding optimization scheme.

In a specific embodiment, the association processing module is specifically configured to:

In a specific embodiment, the method further comprises the following steps:

a determining module, configured to determine a scene type of the third geographic area to be evaluated.

The association processing module is specifically configured to:

In a specific embodiment, the scene type includes one or a combination of the following: dense urban areas, general urban areas, suburban areas and rural areas.

In a third aspect, the present application provides a server, comprising:

a processor, a memory, a communication interface;

the memory is used for storing executable instructions executable by the processor;

wherein the processor is configured to perform the method of assessing road coverage as described in the first aspect via execution of the executable instructions.

In a fourth aspect, the present application provides a readable storage medium having stored thereon a computer program, in which the computer program, when being executed by a processor, implements the method for assessing road coverage of the first aspect.

The application provides a road coverage assessment method, a road coverage assessment device, a server and a medium, wherein a first geographical area to be assessed is subjected to rasterization processing to obtain a plurality of grids; acquiring MR data related to the first geographical area to be evaluated, and respectively matching each MR data with a grid according to longitude and latitude information in each MR data to acquire a matched second geographical area to be evaluated; performing outward expansion grid processing on the second geographical area to be evaluated by adopting a frame generation principle to obtain a third geographical area to be evaluated after outward expansion; associating the ground object vector in the 5-meter high-precision map with the third geographical area to be evaluated, and respectively determining the attribute information of each grid in the third geographical area to be evaluated; acquiring closed-loop areas of a plurality of roads from the third geographical area to be evaluated by adopting the frame generation principle and taking the attribute information as the grids of the road grid attribute information; for the closed-loop area of each road, determining a plurality of road grid road segments under the closed-loop area of the road; for the closed-loop area of each road, obtaining XDR data related to the closed-loop area of the road, and performing association processing on the XDR data and MR data corresponding to the closed-loop area of the road so as to respectively perform matching processing on a plurality of obtained association data and road raster sections of the closed-loop area of the road; and for the closed-loop area of each road, analyzing and processing the road indexes of the closed-loop area of the road according to the associated data corresponding to each road grid road section in the closed-loop area of the road so as to determine the road sections with coverage problems in the closed-loop area of the road. Compared with the prior art that the network coverage data is acquired in the traditional drive test mode, the efficiency is low, the cost is high, and the positioning is inaccurate and untimely, the method and the device divide the road attribute grid area into the road grid road sections, match the associated XDR data and the MR data, determine the road section with the coverage problem according to the associated data, improve the drive test efficiency and the positioning accuracy, and simultaneously realize the cost reduction.

Drawings

In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to these drawings without inventive exercise.

Fig. 1 is a schematic flow chart of a first embodiment of a road coverage evaluation method provided in the present application;

FIG. 2 is a graph of MR data correlating XDR data;

fig. 3 is a schematic flowchart illustrating a second embodiment of a road coverage assessment method provided in the present application;

FIG. 4 is a plurality of road raster segments under a closed loop area of a road;

FIG. 5 is a schematic structural diagram of an embodiment of an apparatus for evaluating road coverage provided in the present application;

fig. 6 is a schematic structural diagram of a server according to 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

In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by persons skilled in the art based on the embodiments in the present application in light of the present disclosure, are within the scope of protection of the present application.

The terms "first," "second," "third," "fourth," and the like in the description and in the claims of the present application and in the above-described drawings (if any) 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 application described herein are capable of operation in sequences other than those illustrated or described herein. Moreover, 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 server 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 server.

The terms referred to in the present application are explained first:

measurement Report (MR) data: the measurement report carries the relevant information of the uplink and downlink wireless links. The deep analysis based on MR is one of effective means for evaluating and optimizing network performance, such as network problem positioning, network coverage analysis, neighbor optimization and the like.

External Data Representation (XDR) Data: is a function of an open network computing environment. XDR provides an architecture-independent way to represent data, accounting for differences in data byte ordering, data byte size, data representation, and data alignment. Data may be exchanged over heterogeneous hardware systems using application programs of the XDR.

Reference Signal Receiving Power (Reference Signal Receiving Power; RSRP for short): one of the key parameters that can represent radio signal strength in LTE networks and physical layer measurement requirements is the average of the received signal power over all REs (resource elements) that carry reference signals within a certain symbol.

Maximum Time Advanced (TA): is the difference between the actual time the mobile station signal arrives at the base station and the time the mobile station signal arrives at the base station assuming the mobile station is 0 away from the base station.

Geographic Information System (GIS): is a special and very important spatial information system. The system is a technical system for collecting, storing, managing, operating, analyzing, displaying and describing relevant geographic distribution data in the whole or partial earth surface (including the atmosphere) space under the support of a computer hardware and software system.

In the prior art, network optimization is usually completed by adopting a traditional drive test mode. Specifically, in the traditional drive test mode, professional testers drive through a target route, network coverage data are obtained through field test, and the problems of low drive test efficiency and high cost exist.

The technical solution of the present application will be described in detail below with reference to specific examples. It should be noted that the following specific embodiments may be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments.

Fig. 1 is a schematic flow chart of a first embodiment of a road coverage assessment method provided in the present application; as shown in fig. 1, the method for evaluating road coverage specifically includes the following steps:

step S101: and rasterizing the first geographical area to be evaluated to obtain a plurality of grids.

In the present embodiment, the MR rasterization mainly uses the GIS technology to build road grids, and the grid size can be set to 10 meters by 10 meters, or 20 meters by 20 meters, or even 50 meters by 50 meters through a high-precision positioning algorithm.

Step S102: and acquiring MR data related to the first geographical area to be evaluated, and respectively matching each MR data with the grid according to the longitude and latitude information in each MR data to acquire a second geographical area to be evaluated after matching.

In this embodiment, the MR data is collected by the base station, and the network manager is responsible for generating the data file, and meanwhile, performs related rule configuration in the wireless network manager. Reporting the active user MR according to the third GeneraTIon Partnership Project (3 rd 3 GPP) specification, wherein usually, part of the users are selected to be reported in a sampling mode in order to reduce the influence on the users. The MR sampling rule mainly comprises the number of MR sampling users, a data reporting period, data reporting times and the like.

Specifically, converting an electronic map of a target area into GIS text information, converting the electronic map MAPINFO-TAB into a KML format, and converting the edited format into a jsp format; JOSN is used for making platform GIS text information, josn is a javascript object representation method and can be used for carrying out text data exchange format, format conversion is carried out through a wisdomamanalysis program developed based on Net, and the converted text information is integrated into a 4G platform function module; the rasterized slice information correlates MR mass data.

In this embodiment, a base station acquires MR data Of mobile phone users in an area, estimates an Angle Of Arrival (AOA for short) Of the users through MR convergence, performs intersection based on a topological relation ray, and matches the MR data with longitude and latitude information Of a grid to which the MR data belongs based on four algorithms Of confidence correction and map matching, thereby acquiring a second geographical area to be evaluated.

Step S103: and carrying out outward expansion grid processing on the second geographical area to be evaluated by adopting a frame generation principle so as to obtain an outward expanded third geographical area to be evaluated.

In this embodiment, a square grid block diagram with a length of side capable of being set by itself is generated through software, a unique number is given to each grid, a point where the north extension outermost side of the second geographical area to be evaluated is tangent to the latitude line is determined as a first tangent point, a point where the west extension outermost side of the second geographical area to be evaluated is tangent to the longitude line is determined as a second tangent point, and a point where the latitude line of the first tangent point is intersected with the longitude line of the second tangent point is determined as a starting point of a third geographical area to be evaluated. And determining the central longitude (X, Y) of the grid to which the starting point belongs, and acquiring the central longitude and latitude (Xm, ym) of the M-th grid on the outermost side of the southward and eastward extended diagonal of the starting point according to the area of the third geographical area to be evaluated and the side length of the square grid. The latitude of the grid at the starting point is unchanged, a fixed square side length (k x k) outward-extending grid is arranged from the longitude direction to the south, the central longitude and latitude of the next grid are calculated according to the equidistant outward-extending side lengths, and the central longitude Yn (n is equal to 1,2, \ 8230; \ 8230;, M-1) of the next grid is compared with the central longitude Ym of the Mth grid at the outermost side of the outward-extending diagonal. If Yn is less than or equal to Ym, continue to scale out to the next grid, and center longitude Yn1= Yn + P (P is the longitude offset of the distance between the center longitudes of two adjacent grids, n1 range (2, 3 8230; M), continue to compare Yn1 with Ym until Yn > Ym is satisfied. The method comprises the steps that the grids at the starting point are outwards expanded from weft to east, the central latitude Xg1= Xg + Q (Q is the latitude offset of distance conversion between the central latitudes and the latitudes of the adjacent grids), the g range (1, 2, \ 8230; \ 8230;, M-1) and the g1 range (2, 3, \ 8230;. M) are obtained, if the central dimensionality of the grids after the outward expansion meets Xg or is less than Xm, the outward expansion is continued, if not, the outward expansion is finished, and the grids after the outward expansion are endowed with unique identifiers.

Step S104: and associating the ground object vector in the 5-meter high-precision map with a third geographical area to be evaluated, and respectively determining the attribute information of each grid in the third geographical area to be evaluated.

Step S105: and acquiring closed-loop areas of a plurality of roads from the third geographical area to be evaluated by adopting a frame generation principle and taking the attribute information as the grids of the road grid attribute information.

Step S106: for each closed-loop area of the road, a plurality of road raster segments under the closed-loop area of the road is determined.

Step S107: for the closed-loop area of each road, obtaining XDR data related to the closed-loop area of the road, and performing association processing on the XDR data and MR data corresponding to the closed-loop area of the road so as to respectively perform matching processing on the obtained associated data and a road raster section of the closed-loop area of the road.

In the embodiment, the XDR data is associated with the MR data, the position information in the MR data is filled in the storage XDR perception data table, and the key parameters of the MR data and the key parameters in the XDR data in the road closed-loop area are counted. XDR data and MR data of the same time, the same place and the same user are mainly related through an associated field MME _ UE _ S1AP _ ID, a signaling field and a service field to be analyzed are backfilled, and statistics and analysis of related indexes are carried out on the basis. There are 3 methods for associating XDR with MR data, which are MME _ MR association, HTTP _ MR association, and COMMON _ MR association, respectively. Each association is distinguished by XDR data, which is MME data, HTTP data, and S1_ COMMON data, respectively. As shown in fig. 2, the key parameters in the XDR data include an International Mobile Subscriber Identity (IMSI) Identifier, an E-UTRAN Cell Global Identifier (ECGI), an MME Group ID, MME data, an MME UE S1AP ID, and time; the key parameters in the MR data comprise MME Group ID, MME data, MME UE S1AP ID, AOA angle, TA value, time, longitude and latitude; the XDR data and the MR data can be associated through 4 types of key fields, the 4 types of key fields comprise MME Group ID, MME data, MME UE S1AP ID and time, and TA value, AOA angle, MME UE S1AP ID, MME data, MME Group ID, E-UTRAN cell global identifier, international mobile subscriber identity, latitude and longitude can be acquired after the XDR data is associated with the MR data.

Specifically, before data association, invalid data with a null cell number, unreasonable starting time, unreasonable ending time and the like are cleaned, and effective data are reserved for association, so that the data association efficiency is improved, and the validity of the data obtained by association is ensured; using an association field MME _ UE _ S1AP _ ID to associate XDR data and MR data, wherein the data association condition is that the station numbers are equal, the long code MME _ UE _ S1AP _ ID is the same, selecting XDR signaling data with the starting time between the starting time and the ending time of the MR at the same time, calculating the time difference between the XDR and the MR, sorting according to the time difference, selecting the XDR data with the minimum time difference as a credible association record, respectively performing sliding window search on the part without association for 20 minutes before and after the starting time and the ending time of the MR data, and merging and de-duplicating the forward sliding window search result and the backward sliding window search result to obtain the association record of the XDR data and the MR data; and filling the measurement information and the position information in the MR data which are in one-to-one correspondence to a table for storing XDR perception data according to the association record obtained by the association part.

In this embodiment, the XDR data and the MR data are correlated, and the position information in the MR data and the key parameter in the XDR data are combined, so as to perform rasterization on the key quality indicator or the key performance indicator according to the position information in the correlated data. Therefore, the statistical analysis of key quality indexes or key performance indexes and the like in the road closed-loop frame area is obtained.

Obtaining related coverage indexes after correlating XDR data, wherein the average RSRP is defined as the average value of RSRP level values of main service cells of all sampling points in a grid contained in a road closed-loop frame area; the good coverage rate is defined as the ratio of RSRP (reference signal received power) more than or equal to-100 dBm sampling points to total sampling points in grids contained in the road closed-loop frame area; the weak coverage rate is defined as the ratio of the number of grids with the sampling point ratio of RSRP less than or equal to-100 dBm greater than 20% to the number of effective grids in the grids contained in the road closed-loop frame area; road grid segments that satisfy weak coverage are defined as segments that are coverage problems. Specifically, as shown in the first coverage index:

table-coverage index

In this embodiment, the index statistical dimension may be displayed according to focus, unfocused, province, city, county, administrative district, and unit.

Step S108: and for the closed-loop area of each road, analyzing and processing the road indexes of the closed-loop area of the road according to the associated data corresponding to each road grid road section in the closed-loop area of the road so as to determine the road sections with coverage problems in the closed-loop area of the road.

In this embodiment, whether the raster road segment is a road segment with coverage problem is determined according to the evaluation result of the raster contained in the road closed-loop frame area. Specifically, sampling point indexes in the grid are converged to the grid, whether the grid belongs to a coverage problem grid or not is judged through grid definitions of different indexes, then the grid is converged to a grid road section according to the coverage problem, road problem objects are marked through definitions of different types of road sections with coverage problems, a traditional single event problem point is converted into a road section identification network problem based on geographic rasterization and having the coverage problems, and the problem serious road section with concentrated events is conveniently focused and processed.

In the embodiment, the road sections with coverage problems support statistics according to different areas, so that a grid manager and a network optimizer can intuitively master the problem distribution conditions of different attributes in the area in which the network manager and the network optimizer are responsible for, and a targeted solution is formulated.

In the present embodiment, the effective grid is defined as a grid satisfying a cycle of sampling points greater than 300 (the number of sampling points is adjustable); the coverage problem grid is defined as a grid with the sampling point occupation ratio of RSRP < -100 larger than 30% in an effective grid (the sampling point occupation ratio of RSRP interval range and interval range is adjustable); the road section with coverage problem is defined as the grid road section with coverage problem grid occupation ratio (number of coverage problem grids/all effective grids in the grid road section) more than 20% in the grid road section. For example: the road section with the width of 50 meters is 100 meters, wherein 50 geographic grids are adopted, 20 effective grids are adopted (the grid with the sampling number of more than 300 in one continuous week is satisfied), 5 weak coverage grids are adopted (the grid with the weak coverage proportion of more than 30% in the effective grids), the premise that 20/50=40% is satisfied, the grid with the weak coverage proportion of more than 20% is adopted as the object of the analysis road section, the weak coverage grid proportion of the road section reaches 25%, the threshold of more than 20% is satisfied, and therefore the road section with the coverage problem is identified.

In the embodiment, a plurality of grids are obtained by rasterizing a first geographical area to be evaluated; acquiring MR data related to a first geographical area to be evaluated, and respectively matching each MR data with a grid according to longitude and latitude information in each MR data to acquire a matched second geographical area to be evaluated; performing outward expansion grid processing on the second geographical area to be evaluated by adopting a frame generation principle to obtain a third geographical area to be evaluated after outward expansion; associating the ground object vector in the 5-meter high-precision map with a third geographical area to be evaluated, and respectively determining attribute information of each grid in the third geographical area to be evaluated; acquiring closed-loop areas of a plurality of roads from a third geographical area to be evaluated by adopting a frame generation principle and taking the attribute information as a grid of the road grid attribute information; for the closed-loop area of each road, determining a plurality of road grid road sections under the closed-loop area of the road; for the closed-loop area of each road, obtaining XDR data related to the closed-loop area of the road, and performing correlation processing on the XDR data and MR data corresponding to the closed-loop area of the road so as to respectively perform matching processing on the obtained correlation data and a road raster section of the closed-loop area of the road; and for the closed-loop area of each road, analyzing and processing the road indexes of the closed-loop area of the road according to the associated data corresponding to each road grid road section in the closed-loop area of the road so as to determine the road sections with coverage problems in the closed-loop area of the road. Compared with the prior art, the network optimization is usually completed by adopting a traditional drive test mode. Specifically, in a traditional drive test mode, a professional tester drives a vehicle to pass through a target route, network coverage data are obtained through field test, the road attribute grid area is divided into road grid road sections, associated XDR data and MR data are matched, road sections with coverage problems are determined according to the associated data, drive test efficiency is improved, cost is reduced, limitation of sampled data is avoided, problems in a network are accurately positioned, problems are analyzed and diagnosed according to relevant indexes, user perception is comprehensively reflected, an event problem focusing area of the whole network is effectively mastered, relevant problems can be found earlier than users, the relevant problems are solved in time, and user use experience is improved.

Fig. 3 is a schematic flow chart of a second embodiment of the road coverage evaluation method provided in the present application, and as shown in fig. 2, the road coverage evaluation method specifically includes the following steps:

step S201: and rasterizing the first geographical area to be evaluated to obtain a plurality of grids.

Step S202: and acquiring MR data related to the first geographical area to be evaluated, and respectively matching each MR data with the grid according to the longitude and latitude information in each MR data to acquire a second geographical area to be evaluated after matching.

Step S203: and carrying out outward expansion grid processing on the second geographical area to be evaluated by adopting a frame generation principle so as to obtain an outward expanded third geographical area to be evaluated.

Step S204: and associating the ground object vector in the 5-meter high-precision map with a third geographical area to be evaluated, and respectively determining the attribute information of each grid in the third geographical area to be evaluated.

In the embodiment, the ground object vector in the 5-meter high-precision map is associated with the third geographical area to be evaluated; if the corresponding physical vector associated with the grid in the third geographical area to be evaluated is the road attribute, determining that the attribute information of the grid is the road grid attribute information; and if the corresponding physical vector associated with the grid in the third geographical area to be evaluated is the non-road attribute, determining that the attribute information of the grid is the non-road grid attribute information. The grids of which the attribute information is the attribute information of the road grids are used as analysis objects of road index statistics and problem point management and control.

Wherein, the road attribute comprises one or a combination of the following: a first-level road, a second-level road, a third-level road, a fourth-level road and an expressway; the non-road attribute comprises one or a combination of the following: urban areas, high-rise buildings, factories, shopping malls and villages. Specifically, for example, the corresponding physical vector associated with the attribute information and the grid is shown as the following two attribute information and physical vector:

table two attribute information and physical vector

Step S205: and acquiring closed loop areas of a plurality of roads from the third geographical area to be evaluated by adopting a frame generation principle and taking the attribute information as the grids of the road grid attribute information.

In this embodiment, a unique ID number is assigned to the closed-loop area of the road for identification, and the ID rule assigned to the closed-loop area of the road is as follows:

primary road closed loop area ID number: pro-City-level 1-000001-N (N is maximum 6 digits, 999999), pro is province, city is City attribution province; and (3) numbering the secondary road closed-loop area ID: pro-City-level 2-000001-N (N is maximum 6 digits, 999999), pro is province, city is City attribution province; third-level road closed-loop area ID number: pro-City-level 3-000001-N (N is maximum 6 digits, 999999), pro is province, city is City attribution province; four-level road closed-loop area ID number: pro-City-level 4-000001-N (the maximum N is 6 digits, 999999), pro is province, city is province of City attribution; expressway ID number: pro-City-Expressway-000001-N (N is maximum 4 digits, 9999), pro is province, city is City attribution province. For example, as shown in the following table three road closed loop area ID number and attribute information:

TABLE TRI-ROAD CLOSED LOOP REGION ID NUMBER AND Attribute INFORMATION

Step S206: for each closed-loop area of the road, a plurality of road raster segments under the closed-loop area of the road is determined.

In this embodiment, a scene type of a third geographical area to be evaluated is determined; and for the closed loop area of each road, dividing the closed loop area of the road according to the length of the road grid sections matched with the scene type to obtain a plurality of road grid sections in the closed loop area of the road.

The scene type comprises one or a combination of the following types: dense urban areas, general urban areas, suburban areas and rural areas.

Specifically, the lengths of the road grid sections are determined according to the lengths of the grids (5 meters by 5 meters, 10 meters by 10 meters, 20 meters by 20 meters and 50 meters by 50 meters) from the starting point of the road according to the closed loop area of the road, and in combination with different scenes such as dense urban areas, general urban areas, suburban areas, rural areas and the like, wherein the lengths of the road grid sections can be set to be 50 meters, 100 meters, 200 meters and 300 meters. The traditional single event problem point is converted into the network problem identification based on the geography grid road section, so that the problem serious road section in the event set can be conveniently focused and processed.

Generally, for a closed-loop area of a road with a scene type of dense urban area, dividing the closed-loop area according to a grid link length of 50 meters or 100 meters; for a closed loop area of a road with a scene type of a common urban area, dividing the closed loop area according to the length of a grid road section of 100 meters or 200 meters; for a closed-loop area of a road with a scene type of suburb or rural area, the closed-loop area is divided according to the length of a grid road section of 300 meters.

In this embodiment, a plurality of road raster segments under the closed loop area are given unique identifiers. For example, as shown in fig. 4, the number of road grid links in the closed-loop area of the road is as follows, where the closed-loop area of the road is divided into the closed-loop areas according to the lengths of the grid links of 50 meters from the start point to the end point of the road in the figure, and the total length of the closed-loop areas of the road is 600 meters, and the grid length is 10 meters by 10 meters, for example, for a dense urban area, 12 road grid links can be obtained, as shown in fig. 4 (a) where the length of the grid link is 50 meters, each grid link is 10 meters by 10 meters, and the length of the grid link is 50 meters, where the length of the grid link can be regarded as a standard-length link, and the road grid links are numbered according to the following rules: road name-standard length road section identification (50 m) -road grid section ID number (000001-999999), then from the road starting point to the road ending point direction in (a), the first road grid section number is road grid section number: the middle mountain road-50-000001, the second road grid road section number is the road grid road section number: the number of the third road raster road section is the road raster road section number: the number of the eleventh road raster road section is the number of the road raster road section: the number of the twelfth road raster road section is the number of the road raster road section: zhongshan road-50-000012; for a dense urban area or a general urban area, the closed-loop area is divided according to the length of the road grid section of 100 meters, and 6 road grid sections can be obtained, as shown in fig. 4 (b), where the length of the road grid section is 100 meters, each grid is 10 meters by 10 meters, and the length of the road grid section is 1000 meters, where the length of the road grid section can be regarded as a road section with a standard length, and the road grid section numbering rules are as follows: road name-standard length road section identification (100 meters) -road grid section ID number (000001-999999), then from the road starting point to the road ending point direction in (b), the first road grid section number is road grid section number: the number of the second road raster road section is the number of the road raster road section: the middle mountain road-100-000002, and so on, the number of the fifth road raster road section is the number of the road raster road section: the number of the sixth road raster road section is the number of the road raster road section: middle mountain road-100-000006; for a general urban area, the closed-loop area is divided according to the length of the road grid link of 200 meters, and 3 road grid links can be obtained, as shown in fig. 4 (c), where the length of the road grid link is 200 meters, each grid is 10 meters by 10 meters, and the length of the road grid link is 200 meters, where the length of the road grid link can be regarded as a standard-length link, and the road grid link numbering rules are as follows: road name-standard length road section identification (200 m) -road grid section ID number (000001-999999), then in (c), from the road starting point to the road ending point direction, the first road grid section number is road grid section number: the middle mountain road-200-000001, and so on, the number of the third road raster road section is the number of the road raster road section: middle mountain road-200-000003; for suburbs or rural areas, the closed-loop area is divided according to the length of the road grid link of 300 meters, and 2 road grid links can be obtained, as shown in fig. 4 (d), where the length of the road grid link is 300 meters, each grid is 10 meters by 10 meters, and the length of the road grid link is 300 meters, where the length of the road grid link can be regarded as a standard-length link, and the road grid link numbering rule is as follows: road name-standard length road section identification (300 m) -road grid section ID number (000001-999999), then from the road starting point to the road ending point direction in (d), the first road grid section number is road grid section number: the middle mountain road-300-000001, the second road grid road section number is the road grid road section number: zhongshan road-300-000002.

Step S207: for the closed-loop area of each road, obtaining XDR data related to the closed-loop area of the road, and performing association processing on the XDR data and MR data corresponding to the closed-loop area of the road so as to respectively perform matching processing on the obtained associated data and a road raster section of the closed-loop area of the road.

In this embodiment, the position information in the MR data and the perception information in the XDR data are subjected to a combination process.

Step S208: and for the closed loop area of each road, analyzing and processing the road indexes of the closed loop area of the road according to the associated data corresponding to each road grid road section in the closed loop area of the road so as to determine the road sections with coverage problems in the closed loop area of the road.

Step S209: and for the closed loop area of each road, determining the priority corresponding to the road sections with coverage problems in the closed loop area of the road, and sequentially carrying out root cause analysis processing on the road sections with coverage problems according to the sequence of the priority from high to low so as to obtain a corresponding optimization scheme.

In the embodiment, road grid road sections with different lengths are sorted according to coverage definition sampling point proportion according to the condition that sampling points are met, road sections with coverage problems are scored based on the quantile principle, and automatic sorting of processing priorities of road sections with coverage problems is completed.

After the classification of the road problems is completed, the unique identification numbers of road grid road sections can be utilized, sampling point indexes in grids are converged to the grids, whether the grids belong to coverage problem grids is judged through the grid definitions of different indexes, then the grids are converged to the grid road sections with the unique identification numbers by means of the coverage problem grids, clustering evaluation can be carried out from 3 dimensions of time, space and problem classification, the scoring of the coverage problem road sections is realized on the basis of the quantile principle, the automatic sequencing of the processing priority of the coverage problem road sections is completed, a grid manager and a net optimization worker are helped to quickly master the road sections and the lengths with the most serious road problems, the coverage problem road sections with the highest priority and the most serious problems are preferentially processed by concentrated resources, and the problem solving efficiency is improved, so that the user perception is timely guaranteed.

Specifically, the score of the road section index covering the problem is obtained by using the existing historical data based on the unique identification number of the road grid road section of each road and the quantile algorithm of probability distribution. Specifically, the historical data of each road grid road section in the closed-loop area of a selected road are sorted from small to large, the quantiles of the historical data are calculated, and specific thresholds corresponding to different thresholds are determined. The coverage problem road section indexes are scored by adopting percentage calculation, each index defines 6 thresholds which are respectively a zero-point threshold, a 20-point threshold, a 40-point threshold, a 60-point threshold, an 80-point threshold and a 100-point threshold, and the coverage problem road section indexes corresponding to different thresholds are obtained based on a probability distribution quantile algorithm. The index is equal to zero score through a threshold, is equal to 100 scores through a threshold better than 100 scores, and other intervals are subjected to linear calculation on the basis of the front threshold value and the rear threshold value. For example, if a certain road has 44 grid road segment IDs, and the rank of the coverage problem road segment index Q1 = (44 + 1) × 20% =45 × 20% =9, the index corresponding to the coverage problem road segment ranked 9 is taken as the 20-point threshold, and it is assumed that the index corresponding to the coverage problem road segment ranked 9 is 3.2%; if the rank of the coverage problem road section index Q2 = (44 + 1) =40% =45 =40% =18, the index corresponding to the 18 th ranked coverage problem road section is taken as the threshold of 40 points, and the index corresponding to the 18 th ranked coverage problem road section is assumed to be 15.58%; if the rank of the coverage problem road section index Q3 is = (44 + 1) = 60% =45 = 60% =27, the corresponding index of the 27 th coverage problem road section is taken as the threshold of 60 points, and the corresponding index of the 27 th coverage problem road section is assumed to be 32.65%; if the rank of the coverage problem section indicator Q4 = (44 + 1) = 80% =45 × 80% =36, the coverage problem section corresponding indicator of the rank 36 is taken as an 80-point threshold, and it is assumed that the corresponding indicator of the coverage problem section of the rank 36 is 47.92%, specifically, as shown in the coverage problem section corresponding threshold in the example of table four:

table four example coverage problem road section corresponding threshold

For the linear calculation of scores between different thresholds, in this example, when the coverage problem road segment index of a certain grid road segment ID is 12.62%, since 12.62% is between threshold 1 and threshold 2, then

Therefore, the coverage problem link index for the grid link ID is 12.62% and the threshold score is 35.22.

The coverage problem road section score range corresponding to the closed loop area road grid road section ID of each road is 0-100, the higher the score is, the more serious the coverage problem exists in the grid road section ID, the important priority attention processing is needed, for example, as shown by the score corresponding to the coverage problem road section in the table five:

score corresponding to road section covered with problems in table five

The score of the middle mountain road 50-000001 is the highest among the scores corresponding to the problem links covered by the second table, and the attention should be paid to the processing.

In this embodiment, the classification and aggregation result of the coverage problem road segments of each road in the third geographical area to be evaluated may be displayed according to dimensions such as focus, non-focus, province, city, county, administrative district, unit, and the like.

In the embodiment, the root cause positioning is carried out on the road section with the coverage problem according to the expert experience, the root cause analysis conclusion of the road section with the coverage problem is extracted by introducing an AI algorithm, the positioning rule of the root cause is optimized, whether the existing root cause algorithm is reasonable or not is verified according to the feedback result of the field processing of the front-line staff, the iterative optimization of the root cause positioning algorithm of the road wireless coverage problem is realized through the AI algorithm, the automatic output accuracy of the solution of the road coverage problem is improved, and the output solution of the road coverage problem can be used as the reference analysis suggestion provided for the field processing of the front-line staff.

The reason for the road coverage problem is that the coverage area of the wireless network is unreasonable, and the wireless network coverage problem under the macro station scene is mainly divided into four categories, namely weak coverage, over coverage, near coverage and overlapping coverage. And jointly positioning the four road coverage problems according to indexes such as overlapping coverage rate of structures of a main service cell TA, a main service cell RSRP and an adjacent cell RSRP in the MR data associated with the longitude and the latitude.

The TA represents the distance between a user and a base station, can realize the coverage analysis of a cell based on a TA value, judges whether the cell antenna needs to be adjusted, investigates whether the coverage area of the base station is reasonable, and whether the problems of over-coverage, coverage shadow area and the like exist, and can also provide position service by using TA assistance.

The specific calculation method comprises the following steps: in the random access process, a base station determines a time advance value by measuring a received pilot signal, wherein the value range of the time advance value is (0, 1,2, \8230;, 1282) × 16Ts; in a Radio Resource Control (RRC) connection state, a base station determines a TA (timing advance) adjustment value of each UE (user equipment) based on measuring uplink transmission of a corresponding user, wherein the adjustment value ranges from (0, 1,2, \ 8230; \ 8230;, 63) × 16Ts; the latest time advance obtained this time is the sum of the time advance recorded last time and the adjustment value measured by the base station this time. The time advance distance corresponding to 1Ts is 4.89m, which can be obtained by the following formula:

distance = propagation speed (speed of light) [1Ts/2 (sum of up and down paths) ]

Calculating the distance corresponding to the TA command value by referring to 1Ts, specifically, the TA value range and the converted coverage distance range are shown in table six TA value ranges and converted coverage distance ranges: from 0 to 192Ts every 16Ts for one interval, corresponding to mr.tadv.00 to mr.tadv.11; an interval from 192Ts to 1024Ts every 32Ts, corresponding to MR.Tadv.12 to MR.Tadv.37; every 256Ts from 1024Ts to 2048Ts is an interval corresponding to MR.Tadv.38 to MR.Tadv.41; each 1048Ts is an interval from 2048Ts to 4096Ts, corresponding to mr.tadv.42 and mr.tadv.43; ts greater than 4096 is an interval, corresponding to mr. Tadv.44.

Table six TA value range and conversion coverage range

The coverage condition of the cell can be analyzed through the TA value, whether the cell antenna needs to be adjusted or not is judged, whether the coverage area of the base station is reasonable or not is investigated, and the TA can be used for providing location service in an auxiliary mode.

The MR data includes a TA value, specifically, as shown in table seven, part of the detailed contents of the MR data related to the longitude and latitude:

part of concrete contents of longitude and latitude related to seven MR data of table

Wherein enb _ ID represents a base station ID, eci represents an evolved universal terrestrial radio access network cell identity, orig _ times represents time, lte _ scrsrp represents an RSRP value, ltesctadv represents a TA value, longitude represents longitude, and latitude represents latitude.

And according to the TA values of the sampling points in the grid, the TA of the grid is obtained through aggregation, and the proportion of different coverage ranges in the grid is calculated by utilizing the corresponding conversion relation between the distribution of the measurement data intervals and the distribution of the conversion coverage distance intervals.

In this embodiment, the overlapping coverage sampling point is defined as a sampling point whose number of the neighboring cell level difference from the current main serving cell to which the MR sampling point belongs is less than 6dB is greater than or equal to 3 on the premise that the RSRP of the sampling point of the covered road is greater than-100 dBm, if the number of the neighboring cell of the MR sampling point whose current main serving cell level difference from the neighboring cell level difference is less than 6dB is equal to 3, the overlapping coverage is defined as 1, if the number of the neighboring cell of the MR sampling point whose current main serving cell level difference from the neighboring cell level difference is less than 6dB is equal to 4, the overlapping coverage is defined as 2, and so on, if the number of the neighboring cell of the MR sampling point whose current main serving cell level difference from the neighboring cell level difference is less than 6dB is = n, the overlapping coverage is defined as (n-2).

Specifically, as shown in the table eight MR data table, the MR data table includes the level strength of the primary serving cell and the number of neighboring cells whose level strength difference is within 6 db.

TABLE eight MR data sheet

And calculating the overlapping coverage of the sampling points, converging and acquiring the overlapping coverage proportion of the grid according to the overlapping coverage of each sampling point in the grid, and counting the occupation ratios of different overlapping coverage in the grid.

In this embodiment, a root cause positioning algorithm such as indoor coverage, over-coverage-near coverage, and overlapping coverage of a road coverage problem is formulated according to indexes such as overlapping coverage of a primary serving cell TA, a primary serving cell RSRP, and a neighboring cell RSRP structure in the MR, and by combining a station interval of a macro station coverage.

In all coverage problem grids covering a problem road section, the proportion of MR sampling points belonging to the attributes of the indoor main service cells is greater than or equal to 60%, and then the root cause of the coverage problem road section is judged to be positioned as indoor coverage; in all coverage problem grids covering the problem road section, if the proportion of MR sampling points with TA larger than 2 kilometers is larger than 40%, judging that the root cause of the coverage problem road section is positioned to be over-coverage; in all coverage problem grids covering the problem road section, if the proportion of MR sampling points with TA less than 0.078 kilometer is more than 40%, judging that the root cause of the coverage problem road section is positioned too close to coverage; and in all coverage problem grids of the coverage problem road section, the proportion of the MR sampling points with the overlapping coverage degree of more than or equal to 1 is more than 40%, and the root cause of the coverage problem road section is judged to be positioned as overlapping coverage. As shown in the nine root cause algorithm judgment rules in the table:

rule for judging table nine root cause algorithm

Specifically, a road section with coverage problems is defined as a grid road section in which the coverage problem grid occupancy (the number of coverage problem grids/all effective grids in the grid road section) is greater than 20%, wherein the coverage problem grid is defined as a grid in which the sampling point occupancy of RSRP < -100 is greater than 30% in the effective grids (the sampling point occupancy of the RSRP interval range and the sampling point occupancy of the interval range are adjustable), and the effective grid is defined as a grid which meets the requirement that the number of sampling points in a continuous circle is greater than 300 (the number of sampling points is adjustable). For the root cause analysis of the Cell coverage, the service Cell unique Identifier (E-UTRAN Cell Identifier; ECI for short) of the MR sampling points in all coverage problem grids in the coverage problem road section and the ECI of the Cell engineering parameter are associated to obtain the Cell site type corresponding to the service Cell ECI in the Cell engineering parameter table, and whether the MR sampling points are the Cell of the Cell is judged; and judging whether the ratio is greater than or equal to 60% according to the ratio of the sum of the number of the MR sampling points of the indoor sub-main service cell judged in all the coverage problem grids to the total number of the MR sampling points of all the coverage problem grids, thereby judging whether the coverage problem road section is a weak coverage problem caused by indoor sub-coverage, specifically, the service cell ECI corresponding table is 'sc _ lcd' in the eight MR data table. For the root cause analysis of the over coverage, whether the ratio is greater than or equal to 40% or not can be judged by acquiring the TA values of the service cells of the MR sampling points in all coverage problem grids in the coverage problem road section and according to the ratio of the sum of the number of the MR sampling points with TA greater than 2 kilometers in all the coverage problem grids to the total number of the MR sampling points of all the coverage problem grids, so as to judge whether the coverage problem road section is a weak coverage problem caused by the over coverage, wherein the TA >2 corresponds to MR _ Tadv greater than or equal to 19 according to the value range of six TAs in a table and the converted coverage distance range, and the corresponding coverage distance range is 2034 m < TADV <2190 m. For the root cause analysis of the too close coverage, whether the ratio is greater than 40% or not can be judged by acquiring the TA values of the service cells of the MR sampling points in all coverage problem grids in the coverage problem road section and according to the ratio of the sum of the number of the MR sampling points of which the TA is less than 0.078 kilometer in all the coverage problem grids to the total number of the MR sampling points of all the coverage problem grids, so as to judge whether the coverage problem road section is a weak coverage problem caused by the too close coverage, wherein the MR _ Tadv corresponding to TA <78 m is less than or equal to 0 and the corresponding coverage distance range TADV is less than 78 m according to the value range of the six TAs in the table and the converted coverage distance range. For the root cause analysis of the overlapping coverage, whether the ratio is more than 40% or not can be judged by acquiring the overlapping coverage of the MR sampling points in all the coverage problem grids in the coverage problem road section and according to the ratio of the sum of the MR sampling points with the overlapping coverage of more than or equal to 1 in all the coverage problem grids to the total number of the MR sampling points of all the coverage problem grids, so that whether the coverage problem road section is a weak coverage problem caused by the overlapping coverage or not can be judged.

In the embodiment, for the root cause positioning algorithm set by the expert experience, sorting of the road covering problem is realized by introducing an Extreme Gradient Boosting algorithm (XGboost for short) in the AI algorithm, the judgment rule of the root cause is optimized, whether the existing root cause algorithm is reasonable or not is verified according to the feedback result of the field processing of the front-line personnel, iterative optimization of the root cause positioning algorithm of the road wireless covering problem is realized by the AI algorithm, and automatic output of the solution of the road covering problem is gradually realized and is used as a reference analysis suggestion provided for the field processing of the front-line personnel.

The XGboost algorithm is compared with a traditional Boosting algorithm Boosting such as a Gradient Boosting Tree algorithm (GBDT) on the basis of most regression and classification problems, and has the advantages that: GBDT only utilizes information of a first-order derivative, XGboost performs second-order Taylor expansion on a loss function, and a regular term is added into a target function to balance the complexity of the target function and a model and prevent overfitting; the Boost is a serial process, can not be parallelized, has high computational complexity, and is not suitable for high-dimensional sparse features, while the XGboost can be used for parallelizing computation on feature granularity and considering the condition that training data are sparse values.

Specifically, the MR data and the XDR data are used as input data of an XGboost model; cleaning and sorting invalid data, null data and error data beyond the range in the MR and XDR original data; screening all characteristic values which affect the positioning of the road coverage problem root, and performing characteristic processing on the characteristic values to construct a required data set; acquiring a training set and a test set according to a data set, wherein the training set is used for XGboost model training, the test set is used for verifying an XGboost model which completes training, and the output of the XGboost model is a root cause positioning result of a road coverage problem; the method comprises the steps of providing a road coverage problem root cause positioning result output based on an AI algorithm for a front-line worker to process, verifying whether the existing root cause algorithm is reasonable or not according to a front-line worker field processing feedback result, if the existing root cause algorithm is basically consistent with the feedback result of the field worker, proving that the output root cause positioning result is accurate, and if the existing root cause algorithm is not consistent with the feedback result of the field worker, adjusting parameters and characteristic value ranges in an XGboost algorithm to realize iterative optimization of the road wireless coverage problem root cause positioning algorithm and improve automatic output accuracy of a road coverage problem solution.

The characteristic values comprise a service cell ECI used for judging the indoor coverage root cause algorithm, a service cell TA used for judging the over coverage and the over-coverage near root cause algorithm, the quantity of adjacent cells within 6db of the level intensity difference value used for judging the overlapping coverage root cause algorithm, and the associated backfill of XDR and MR data. The service cell ECI corresponds to sc _ ECI in the MR data, the service cell TA corresponds to sc _ tadv in the MR data, the number of adjacent cells within 6db of the level intensity difference corresponds to cell _ cnt _ in6db _ count in the MR data, the XDR and the MR data are correlated with backfill data, and the backfill part fills measurement information and position information in the MR data which correspond one to one into a table for storing XDR perception data according to correlation records obtained by the correlation part.

In this embodiment, after a crew member automatically outputs a road section with a coverage problem according to a positioning reason of the positioned road coverage problem based on an AI algorithm and completes the road section processing of the coverage problem, whether the road section with the coverage problem is normal or not is automatically judged according to an aggregation result of the XDR data associated with the MR data, a problem point management and control table of a road raster section is formed, statistics according to regions is supported, problem solution conditions, unsolved problem distribution, serious problems, and left-over problems can be visually presented, and the solution progress of the road coverage problem can be known at any time. And according to the weekly index statistics, automatically updating the number of newly-increased problem road sections, the number of closed-loop problem road sections and all problem road sections in the whole period of the previous week of the ring ratio, and forming a whole coverage problem road section management and control table. The problem road section management and control table covered by the total quantity can be displayed in a partitioned mode according to the dimensions of a focus, a non-focus, a city, a district and a county, an administrative district and a unit. The method comprises the steps of automatically counting the loop ratio of the current period to the previous period to cover the problem road sections, wherein the loop ratio of the problem road sections to cover is the ratio of the number of the closed loops of the problem road sections to the number of various problem road sections; whether the road section is the same problem road section or not is identified based on the unique identification of the road grid road section, the current period exists, the previous period does not exist, the road section is newly covered, the current period does not exist, the previous period exists, the road section is covered in a closed loop mode, and the period time can be set according to needs. As shown in table ten road section management and control table with full coverage problem:

ten-table full coverage problem road section management and control table

In this embodiment, different map layers, different regions, different types of scenes of the road block and the length of the road block can be selected to present the coverage characteristic index and the coverage problem road block, so that virtual road survey geography, physics and chemistry rapid presentation of road coverage problem evaluation is realized, and the original traditional manual analysis mode which consumes time and labor is replaced.

In the embodiment, the grids with the MR data are matched with a 5-meter accurate map, the grids are converged to form a grid road section, whether the road section belongs to the coverage problem grid is judged to be a coverage problem road section or not is judged according to the coverage problem grid, root cause positioning is carried out on the coverage problem road section with high priority, specifically, the root cause positioning of the coverage problem road section is obtained according to the coverage problem grid in the coverage problem road section, an AI algorithm is introduced, extraction of root cause analysis conclusion of the road coverage problem is achieved, the positioning rule of the root cause is optimized, whether the existing root cause algorithm is reasonable or not is verified according to a front-line staff field processing feedback result, iterative optimization of the root cause positioning algorithm of the road wireless coverage problem is achieved through the AI algorithm, and the automatic output accuracy of the road coverage problem solution is improved. Compared with the prior art, the method is generally completed by adopting a traditional drive test mode through network optimization. Specifically, in traditional drive test mode, professional tester drives through the target route, obtains network coverage data through testing on the spot this application promotes drive test efficiency, reduce cost, it has the limitation to avoid the sample data, problem in the accurate positioning network, and carry out analysis and diagnosis to the problem according to relevant index, comprehensively reflect the user perception, effectively master the incident problem focus area of whole net, can be earlier than the user discovery to relevant coverage problem, in time solve relevant problem, promote the user and use the impression.

Fig. 5 is a schematic structural diagram of an embodiment of the road coverage evaluation device provided in the present application, and as shown in fig. 5, the road coverage evaluation device 30 includes: a grid processing module 31, a correlation processing module 32, and an analysis processing module 33; the grid processing module 31 is configured to perform rasterization processing on a first geographic area to be evaluated to obtain a plurality of grids; the grid processing module 31 is further configured to acquire MR data related to the first geographical area to be evaluated, and perform matching processing on each MR data and the grid according to longitude and latitude information in each MR data, so as to acquire a matched second geographical area to be evaluated; the grid processing module 31 is further configured to perform outward expansion grid processing on the second geographical area to be evaluated by using a frame generation principle to obtain an outward expanded third geographical area to be evaluated; the association processing module 32 is configured to associate the ground feature vector in the 5-meter high-accuracy map with a third geographic area to be evaluated, and determine attribute information of each grid in the third geographic area to be evaluated respectively; the association processing module 32 is further configured to obtain a closed-loop area of the multiple roads from the third geographical area to be evaluated by using a frame generation principle and using the attribute information as a grid of the road grid attribute information; the association processing module 32 is further configured to determine, for the closed-loop area of each road, a plurality of road grid segments under the closed-loop area of the road; the association processing module 32 is further configured to, for the closed-loop region of each road, acquire XDR data related to the closed-loop region of the road, and perform association processing on the XDR data and MR data corresponding to the closed-loop region of the road, so as to perform matching processing on the acquired multiple association data and road raster segments of the closed-loop region of the road, respectively; and the analysis processing module 33 is configured to, for the closed-loop area of each road, perform analysis processing on the road indexes of the closed-loop area of the road according to the associated data corresponding to each road grid road segment in the closed-loop area of the road, so as to determine a road segment with a coverage problem in the closed-loop area of the road.

The processing apparatus for water pit attack in this embodiment may execute the method example shown in fig. 1, and the implementation principle and the technical effect are similar, which are not described herein again.

In a possible embodiment, the analysis processing module 33 is further configured to determine, for the closed-loop area of each road, a priority corresponding to a road segment with a coverage problem in the closed-loop area of the road, and sequentially perform root cause analysis processing on the road segments with the coverage problem according to a sequence from high to low in the priority to obtain a corresponding optimization scheme.

In a possible embodiment, the association processing module is specifically configured to:

and associating the ground object vector in the high-precision map of 5 meters with the third geographic area to be evaluated.

In one possible embodiment, the road attribute includes one or more of the following combinations: first-level roads, second-level roads, third-level roads, fourth-level roads and expressways.

The non-road attribute comprises one or a combination of the following: urban areas, high-rise buildings, factories, shopping malls and villages.

In one possible embodiment, the method further comprises: a determining module 34, configured to determine a scene type of a third geographic area to be evaluated;

the association processing module 32 is specifically configured to:

and for the closed loop area of each road, dividing the closed loop area of the road according to the length of the road grid sections matched with the scene type to obtain a plurality of road grid sections in the closed loop area of the road.

In a possible embodiment, the scene type includes one or several combinations of the following: dense urban areas, general urban areas, suburban areas and rural areas.

In a possible implementation, the association processing module 32 is specifically configured to:

the position information in the MR data and the perception information in the XDR data are combined.

Fig. 6 is a schematic structural diagram of a server provided in the present application, and as shown in fig. 6, the server 40 includes: a processor 41, a memory 42, and a communication interface 43; wherein, the memory 42 is used for storing executable instructions executable by the processor 41; processor 41 is configured to perform the solution of any of the method embodiments described above via execution of executable instructions.

Alternatively, the memory 42 may be separate or integrated with the processor 41.

Optionally, when the memory 42 is a device independent from the processor 41, the server 40 may further include: and the bus is used for connecting the devices.

The server is configured to execute the technical solution in any of the foregoing method embodiments, and the implementation principle and the technical effect are similar, which are not described herein again.

The embodiment of the present application further provides a readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the computer program implements the technical solutions provided by any of the foregoing embodiments.

Those of ordinary skill in the art will understand that: all or a portion of the steps of implementing the above-described method embodiments may be performed by hardware associated with program instructions. The program may be stored in a computer-readable storage medium. When executed, the program performs steps comprising the method embodiments described above; and the aforementioned storage medium includes: various media that can store program codes, such as ROM, RAM, magnetic or optical disks.

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 assessing road coverage, comprising:

rasterizing a first geographical area to be evaluated to obtain a plurality of grids;

acquiring MR data related to the first geographical area to be evaluated, and respectively matching each MR data with a grid according to longitude and latitude information in each MR data to acquire a matched second geographical area to be evaluated;

performing outward expansion grid processing on the second geographical area to be evaluated by adopting a frame generation principle to obtain a third geographical area to be evaluated after outward expansion;

associating the ground object vector in the 5-meter high-precision map with the third geographical area to be evaluated, and respectively determining the attribute information of each grid in the third geographical area to be evaluated;

acquiring closed-loop areas of a plurality of roads from the third geographical area to be evaluated by adopting the frame generation principle and taking the attribute information as the grids of the road grid attribute information;

for the closed-loop area of each road, determining a plurality of road grid road segments under the closed-loop area of the road;

for the closed-loop area of each road, obtaining XDR data related to the closed-loop area of the road, and performing association processing on the XDR data and MR data corresponding to the closed-loop area of the road so as to respectively perform matching processing on a plurality of obtained association data and road raster sections of the closed-loop area of the road;

and for the closed loop area of each road, analyzing and processing the road indexes of the closed loop area of the road according to the associated data corresponding to each road grid road section in the closed loop area of the road so as to determine the road sections with coverage problems in the closed loop area of the road.

2. The method of assessing road coverage according to claim 1, further comprising:

3. The method according to claim 1, wherein the associating the feature vector in the 5-meter high-accuracy map with the third geographical area to be evaluated and determining the attribute information of each grid in the third geographical area to be evaluated respectively comprises:

associating the ground object vector in the 5-meter high-precision map with the third geographic area to be evaluated;

if the corresponding physical vector associated with the grid in the third geographical area to be evaluated is the road attribute, determining that the attribute information of the grid is the road grid attribute information;

4. The method for evaluating road coverage according to claim 3, wherein the road attribute comprises one or more of the following combinations: a first-level road, a second-level road, a third-level road, a fourth-level road and an expressway;

5. The method for evaluating road coverage according to any one of claims 1 to 4, further comprising:

determining a scene type of the third geographical area to be evaluated;

then said determining, for the closed loop area of each road, a plurality of road raster segments under the closed loop area of the road comprises:

and for the closed loop area of each road, dividing the closed loop area of the road according to the length of the road grid section matched with the scene type to obtain a plurality of road grid sections in the closed loop area of the road.

6. The method for evaluating road coverage according to claim 5, wherein the scene type comprises one or a combination of the following: dense urban areas, general urban areas, suburban areas and rural areas.

7. The method for evaluating road coverage according to any one of claims 1 to 4, wherein the associating the XDR data with the MR data corresponding to the closed loop area of the road comprises:

8. An apparatus for evaluating road coverage, comprising:

the grid processing module is used for carrying out rasterization processing on a first geographical area to be evaluated so as to obtain a plurality of grids;

the grid processing module is further configured to acquire MR data related to the first geographical area to be evaluated, and perform matching processing on each MR data and a grid according to longitude and latitude information in each MR data, so as to acquire a matched second geographical area to be evaluated;

the grid processing module is further configured to perform outward expansion grid processing on the second geographical area to be evaluated by using a frame generation principle to obtain an outward expanded third geographical area to be evaluated;

the association processing module is used for associating the ground feature vector in the 5-meter high-precision map with the third geographical area to be evaluated and respectively determining the attribute information of each grid in the third geographical area to be evaluated;

the association processing module is further configured to acquire a closed-loop area of the multiple roads from the third geographical area to be evaluated by using the frame generation principle and according to the attribute information as a road grid of the road grid attribute information;

the association processing module is further used for determining a plurality of road grid road sections under the closed-loop area of each road for the closed-loop area of each road;

the association processing module is further configured to, for a closed-loop region of each road, acquire XDR data related to the closed-loop region of the road, and perform association processing on the XDR data and MR data corresponding to the closed-loop region of the road, so as to perform matching processing on a plurality of acquired association data and road raster segments of the closed-loop region of the road, respectively;

9. The device according to claim 8, wherein the analysis processing module is further configured to determine, for the closed-loop area of each road, a priority corresponding to a segment with coverage problems in the closed-loop area of the road, and sequentially perform root cause analysis processing on the segments with coverage problems according to a sequence from high to low in the priority to obtain a corresponding optimization scheme.

10. The device according to claim 8, characterized in that the correlation processing module is specifically configured to:

11. The device for assessing road coverage according to claim 10, wherein said road attributes include one or a combination of: a first-level road, a second-level road, a third-level road, a fourth-level road and an expressway;

12. The apparatus for evaluating road coverage according to any one of claims 8 to 11, further comprising:

a determining module, configured to determine a scene type of the third geographic area to be evaluated;

the association processing module is specifically configured to:

13. The device for evaluating road coverage according to claim 12, wherein the scene type comprises one or a combination of the following: dense urban areas, general urban areas, suburban areas and rural areas.

14. The device for assessing road coverage according to any one of claims 8 to 11, wherein the correlation processing module is specifically configured to:

15. A server, comprising:

a processor, a memory, a communication interface;

the memory is to store executable instructions executable by the processor;

wherein the processor is configured to perform the method of assessing road coverage of any one of claims 1 to 7 via execution of the executable instructions.

16. A readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the method of assessing road coverage according to any one of claims 1 to 7.