Network quality problem processing method, device, server and storage medium

Abstract

The present application provides a network quality problem processing method, device, server and storage medium, including: obtaining measurement data of a to-be-processed area; grid-dividing the to-be-processed area to obtain a first grid, and matching it with the measurement data to obtain a second grid; associating the second grid with vector information in an electronic map, and dividing the second grid into a road matching grid and a non-road matching grid according to the vector information; aggregating the road matching grids belonging to each road in the to-be-evaluated area into a closed-loop area of ​​each road, and dividing the closed-loop area of ​​each road to obtain grid sections; determining whether the grid sections corresponding to each road are sections with network quality problems; clustering the sections with network quality problems using a DBSCAN algorithm to form a network quality problem cluster; and sending the location of the network quality problem cluster to a terminal of a maintenance personnel for display. The cost of handling network quality problems is reduced and efficiency is improved.

Description

Technical Field

[0001] The present application relates to the field of wireless communication technology, and in particular to a method, device, server and storage medium for processing network quality problems. Background Art

[0002] With the rapid development of wireless communication technology, the scale of networks continues to expand, and the handling of network quality issues has become an important part of communication services.

[0003] At present, in the prior art, the processing of network quality problems mainly relies on traditional road testing, that is, professional testers drive through the target route and use road testing instruments to test network quality problems on the spot to achieve network quality problem processing.

[0004] However, the inventors have found that the prior art has at least the following technical problems: the traditional drive test technology is inefficient and costly, and therefore it is urgent to propose a new drive test method to solve the problems existing in the traditional drive test. Summary of the invention

[0005] The present application provides a network quality problem processing method, device, server and storage medium, which realizes the processing of network quality problems by providing a method of dividing the area to be processed into grids and aggregating the grids into grid sections to determine whether the grid sections are sections with network quality problems, and aggregating the sections with network quality problems into network quality problem clusters and sending them to the maintenance personnel terminal for processing.

[0006] In a first aspect, the present application provides a method for handling network quality issues, comprising:

[0007] Obtaining measurement data of the area to be processed;

[0008] Dividing the area to be processed into grids to obtain a first grid, and matching the first grid with the measurement data according to position information of the first grid and position information in the measurement data to obtain a second grid matching the measurement data;

[0009] According to the position information of the second grid, the second grid is associated with the vector information in the electronic map, and the second grid is divided into a road matching grid and a non-road matching grid according to the vector information;

[0010] The road matching grids belonging to each road in the area to be processed are aggregated into closed-loop areas of each road, and the closed-loop areas of each road are divided to obtain grid sections corresponding to each road;

[0011] Determine whether the grid road section corresponding to each road is a road section with network quality problems, where network quality problems include: coverage problems, poor quality problems, interference problems, rate problems, voice communication quality problems, and abnormal event problems;

[0012] For the road sections with network quality problems corresponding to various types of network quality problems, the DBSCAN algorithm is used to cluster the road sections with network quality problems to form network quality problem clusters;

[0013] The location of the network quality problem cluster is sent to the terminal of the maintenance personnel for display, so that the maintenance personnel can handle the road section with network quality problems according to the display results.

[0014] In one possible design, the DBSCAN algorithm is used to cluster the sections with network quality problems to form network quality problem clusters, including: taking the benchmark points of the sections with network quality problems as the problem points of the sections with network quality problems; dividing the problem points of the sections with network quality problems into core problem points, boundary problem points and noise problem points; filtering the noise problem points; clustering the core problem points and boundary problem points to form network quality problem clusters.

[0015] In one possible design, core problem points and boundary problem points are clustered to form network quality problem clusters, including: connecting the core problem points within a preset interval in pairs, and each group of connected core problem points forms an initial network quality problem cluster; and dividing the boundary problem points within the initial network quality problem cluster into the final network quality problem cluster.

[0016] In one possible design, determining whether a grid section corresponding to each road is a section with network quality problems includes: determining whether each road matching grid is a grid with network quality problems, and determining whether the grid section corresponding to each road is a section with network quality problems based on the proportion of grids with network quality problems in the grid sections corresponding to each road.

[0017] In a possible design, it is determined whether each road matching grid is a grid with network quality problems, and whether the grid section corresponding to each road is a section with network quality problems is determined according to the proportion of grids with network quality problems in the grid sections corresponding to each road, including: judging a road matching grid whose number of sampling points in a preset time period is greater than a first preset value as a valid grid; judging a grid whose proportion of sampling points with a network quality problem index lower than a second preset value in a valid grid is greater than a third preset value as a grid with network quality problems; and judging a grid section whose proportion of grids with network quality problems in the grid sections corresponding to each road is greater than a fourth preset value as a section with network quality problems.

[0018] In a possible design, for network quality problem sections corresponding to various types of network quality problems, the DBSCAN algorithm is used to cluster the network quality problem sections to form network quality problem clusters, and the design also includes: sorting the number of core problem points in the network quality problem cluster to obtain the processing priority of the network quality problem section, wherein the core problem point is the center point of the network quality problem cluster; and sending the processing priority to the maintenance personnel's terminal for display, so that the maintenance personnel can process the network quality problem cluster according to the processing priority.

[0019] In one possible design, the location of the network quality problem cluster is sent to the maintenance personnel's terminal for display, so that the maintenance personnel can process the network quality problem section according to the displayed results. It also includes: obtaining the measurement data processed by the maintenance personnel, and generating a management and control table for the network quality problem section according to the processed measurement data; judging whether the network quality problem cluster has returned to normal according to the management and control table of the network quality problem section.

[0020] In one possible design, a control table for sections with network quality problems is generated based on processed measurement data, including: judging whether a network quality problem grid is a closed-loop grid with network quality problems based on the processed measurement data; judging closed-loop network quality problem sections based on the proportion of closed-loop grids with network quality problems; clustering sections with network quality problems into network quality problem clusters; judging whether network quality problem clusters are closed-loop clusters; and generating a control table for sections with network quality problems so that staff can understand the progress of solving network quality problems.

[0021] In one possible design, the location of the network quality problem cluster is sent to the maintenance personnel's terminal for display, so that the maintenance personnel can process the network quality problem section according to the display results. It also includes: displaying the network quality problem cluster in the area to be processed and displaying the corresponding indicator information of the network quality problem cluster.

[0022] In a second aspect, the present application provides a network quality problem processing device, comprising:

[0023] An acquisition module, used for acquiring measurement data of the area to be processed;

[0024] A matching module is used to divide the area to be processed into grids to obtain a first grid, and to match the first grid with the measurement data according to the position information of the first grid and the position information in the measurement data to obtain a second grid matching the measurement data;

[0025] an associating module, for associating the second grid with the vector information in the electronic map according to the position information of the second grid, and dividing the second grid into a road matching grid and a non-road matching grid according to the vector information;

[0026] A convergence module is used to converge the road matching grids belonging to each road in the area to be processed into a closed loop area of ​​each road, and divide the closed loop area of ​​each road to obtain grid sections corresponding to each road;

[0027] A judgment module is used to judge whether the grid road section corresponding to each road is a road section with network quality problems, wherein network quality problems include: coverage problems, poor quality problems, interference problems, rate problems, voice communication quality problems and abnormal event problems;

[0028] The clustering module is used to cluster the network quality problem sections corresponding to various network quality problems using the DBSCAN algorithm to form network quality problem clusters;

[0029] The sending module is used to send the location of the network quality problem cluster to the terminal of the maintenance personnel for display, so that the maintenance personnel can handle the road section with network quality problems according to the display results.

[0030] In a third aspect, the present application provides a server, comprising: at least one processor and a memory;

[0031] Memory stores computer-executable instructions;

[0032] At least one processor executes the computer-executable instructions stored in the memory, so that the at least one processor executes the network quality problem processing method as described in the first aspect and various possible designs of the first aspect.

[0033] In a fourth aspect, the present application provides a computer storage medium storing computer execution instructions. When a processor executes the computer execution instructions, the network quality problem processing method as described in the first aspect and various possible designs of the first aspect is implemented.

[0034] The network quality problem processing method provided in this embodiment is to obtain a first grid by dividing the area to be processed into grids, and to obtain a second grid matching the measurement data according to the location information of the first grid and the location information in the acquired measurement data, and to associate the second grid with the vector information in the electronic map, and to divide the second grid into a road matching grid and a non-road matching grid according to the vector information, and to gather the road matching grid into a closed-loop area, and to divide the closed-loop area into grid sections, and to judge whether the grid sections corresponding to each road are network quality problem sections according to the proportion of network quality problem grids in the grid sections. The DBSCAN algorithm is used to cluster the network quality problem sections to form network quality problem clusters, and the locations of the network quality problem clusters are sent to the terminal of the maintenance personnel for display, so that the maintenance personnel can process the network quality problem sections according to the display results. There is no need to spend a lot of manpower and material resources to collect measurement data, which reduces maintenance costs and improves maintenance efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0036] Figure 1 It is a schematic diagram of the scenario of the traditional drive test provided by the prior art;

[0037] Figure 2 A schematic diagram of a scenario of a method for handling network quality issues provided in an embodiment of the present application;

[0038] Figure 3 A schematic diagram of a method flow for handling network quality issues provided in an embodiment of the present application;

[0039] Figure 4 A specific field diagram for associating valid XDR data with valid MR data provided in an embodiment of the present application;

[0040] Figure 5 A segment identification diagram of a road segment provided in an embodiment of the present application;

[0041] Figure 6 A schematic diagram of clustering results provided in an embodiment of the present application;

[0042] Figure 7 A flowchart of a method for handling network quality issues provided in another embodiment of the present application;

[0043] Figure 8A schematic diagram of the structure of a network quality problem processing device provided in an embodiment of the present application;

[0044] Fig. 9 A schematic diagram of the hardware structure of the server provided in an embodiment of the present application. DETAILED DESCRIPTION

[0045] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0046] Figure 1 This is a schematic diagram of the traditional drive test scenario provided by the prior art. Figure 1 As shown, it includes: a road test vehicle 101. Among them, professional testers drive the road test vehicle carrying the road test instrument along the target route to conduct field tests to obtain measurement data. However, this road test method has certain requirements on the skills of professional testers, and also requires professional equipment and road test vehicles to collect data on site. The data sampling has certain limitations, and the test efficiency is low and the cost is high.

[0047] In order to solve the above-mentioned problems of the prior art, the embodiments of the present application provide the following technical concepts: based on the measurement report data of the area to be processed, the area to be processed is divided into a grid, the road matching grids are aggregated into grid sections, and the measurement report data is used to determine whether the grid section is a section with network quality problems, and then the sections with network quality problems in the area to be processed are aggregated into network quality problem clusters, and the locations of the network quality problem clusters are provided to maintenance personnel so that the maintenance personnel can process the sections with network quality problems.

[0048] Figure 2 A schematic diagram of a scenario of a method for handling network quality problems provided in an embodiment of the present application includes: a communication base station 201 and a server 202. The user transmits a measurement report to the communication base station 201 through a user terminal, and the communication base station 201 transmits the measurement report to the server 202. The execution subject of this embodiment can be Figure 2 The server 202 shown may also be other computer storage devices, which is not particularly limited in this embodiment.

[0049] The technical solution of the present application and how the technical solution of the present application solves the above-mentioned technical problems are described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described below in conjunction with the accompanying drawings.

[0050] Figure 3 A flow chart of a method for handling network quality issues provided in an embodiment of the present application. In the embodiment of the present application, the implementation subject is a server. Figure 3 As shown, the method includes:

[0051] S301: Acquire measurement data of the area to be processed.

[0052] In this embodiment, the area to be processed may be a province or a prefecture-level city.

[0053] In this embodiment, the measurement data may be measurement report (MR) data.

[0054] Specifically, the user terminal transmits the measurement report to the communication base station, and the communication base station transmits the measurement report to the server, and obtains the measurement data from the measurement report by collecting the measurement reports uploaded by the user terminals in the area to be processed.

[0055] In this embodiment, the measurement data may also be associated data associated with the MR data, wherein the associated data may be associated data between the MR data and signaling monitoring data (X Data Recording, XDR).

[0056] Specifically, XDR data is evolved from Call Data Recording (CDR) uploaded by user terminals.

[0057] Specifically, the process of associating MR data with XDR data includes:

[0058] 1) Preprocessing

[0059] The unreasonable invalid data in the MR data and XDR data are eliminated, and the valid MR data and valid XDR data are retained.

[0060] In this embodiment, the preprocessing part includes data cleaning and data processing. Before data association, invalid data such as empty cell number, unreasonable start time, unreasonable end time, etc. are cleaned, and only valid MR data and valid XDR data are retained for association, so as to improve the efficiency of data association and ensure the validity of the data obtained by association.

[0061] 2) Association

[0062] An association process is performed on the valid MR data and the valid XDR data according to the association field.

[0063] In this embodiment, the association process includes: a first association process and a second association process.

[0064] Among them, the first association process: associate the valid MR data and the valid XDR data according to the association field.

[0065] In this embodiment, the associated fields may be MME data, HTTP data, and S1_COMMON data, wherein the MME data may be MME_UE_S1AP_ID.

[0066] Exemplarily, the specific fields of the association between valid XDR data and valid MR data are as follows: Figure 4 shown.

[0067] Specifically, the valid MR data and valid XDR data with equal station numbers and the same associated fields are screened out, and the valid XDR data with a start time between the start time and end time of the valid MR data is selected, and the time difference between the valid XDR data and the valid MR data is calculated, and then sorted according to the time difference, and finally the valid XDR data with the smallest time difference is selected as the credible associated record to complete the first association process.

[0068] The second association process is to associate the valid MR data and the valid XDR data that are not associated in the first association process according to the sliding window search.

[0069] Specifically, for the unassociated parts, a sliding window search will be performed on the preset time lengths before and after the start and end time of the valid MR data, and the forward sliding window search results and the backward sliding window search results will be merged and deduplicated to complete the second association process.

[0070] The results of the first association process and the second association process are deduplicated to obtain association records of valid XDR data and valid MR data.

[0071] 3) Backfill

[0072] Backfill according to the associated records of the association process to obtain associated data.

[0073] In this embodiment, the backfill part is based on the association records obtained by the association part, and the measurement information and position information in the valid MR data corresponding to each other are filled into the table storing the valid XDR data. After all the backfilling is completed, the association work between the valid XDR data and the valid MR data is completed.

[0074] S302: Divide the area to be processed into grids to obtain a first grid, and match the first grid with the measurement data according to position information of the first grid and position information in the measurement data to obtain a second grid that matches the measurement data.

[0075] Specifically, a square grid frame diagram with self-determinable side lengths is generated through grid division software, and a unique number value is assigned to each grid presented geographically.

[0076] In this embodiment, the region to be processed is divided into grids to obtain the first grid, which specifically includes:

[0077] S3021: Determine the point at which the outermost northern extension of the area to be processed is tangent to the latitude line as the first tangent point.

[0078] S3022: Determine the point at which the outermost western extension of the area to be processed is tangent to the meridian as the second tangent point.

[0079] S3023: The point where the latitude line passing through the first tangent point intersects with the meridian line passing through the second tangent point is taken as the starting point.

[0080] S3024: Expand the grids one by one from the starting point toward the south or east according to the preset grid side length until it reaches the outermost grid on the diagonal of the starting point of the area to be evaluated.

[0081] Specifically, step S3024 includes the following steps a to e.

[0082] Step a: Determine the center longitude (X, Y) of the starting point grid. According to the area size and the side length of the square grid, calculate the center longitude and latitude (Xm, Ym) of the outermost Mth grid of the diagonal line extending southward and eastward from the starting point. After this step is completed, proceed to step b.

[0083] Step b: The starting point grid expands southward along the longitude to determine whether the central longitudes of the adjacent 1st, 2nd, ..., Nth grids satisfy the value range of Yn≤Ym, n (1, 2..., M-1).

[0084] Specifically, starting from the starting point grid, first keep the latitude of the starting point grid unchanged, set a fixed square side length (k*k) to expand the grid southward in the longitude direction, calculate the central longitude and latitude of the next grid according to the equal distance of the expansion side length, and compare the central longitude Yn (n∈1,2,...,M-1) of the next grid with the central longitude Ym of the outermost Mth grid of the expansion diagonal. If Yn≤Ym, go to step c.

[0085] Step c: Continue to expand to the next grid, and the center longitude Yn1 = Yn + P, P is the longitude offset converted from the distance between the center longitudes and longitudes of two adjacent grids, and n1 ranges from (2, 3..., M).

[0086] Specifically, when Yn≤Ym, continue to expand the next grid Yn1 (n1∈2,3,...,M), and keep the center latitude equal to the starting point grid, and the center longitude Yn1=Yn+P. If Yn≤Ym is still satisfied, n range (1,2...,M-1), continue to loop to step b, if Yn>Ym is satisfied, go to step d.

[0087] Step d: The starting point grid expands eastward along the latitude line to determine whether the central longitudes of the adjacent 1st, 2nd, ..., Gth grids satisfy Xg≤Xm, and the value range of g is (1, 2..., M-1).

[0088] Specifically, when the grid longitude Yn>Ym of the grid that expands southward along the longitude of the starting point grid, it is necessary to expand eastward along the latitude, calculate the central longitude and latitude of the next grid based on the length of the equally spaced expansion side, and compare the central latitude Xg (g∈1,2,…,M-1) of the next grid with the central latitude Xm of the outermost Mth grid of the expansion diagonal. If Xg≤Xm, proceed to step e.

[0089] Step e: Continue to expand to the next grid, and the central longitude Xg1 = Xg + Q, Q is the latitude offset converted from the distance between the central longitudes and latitudes of adjacent grids, and the range of g1 is (2, 3..., M).

[0090] Specifically, when Xg≤Xm, continue to expand to the next grid Xn1 (n1∈2,3,...,M), and the central longitude Xg1=Xg+Q. If Xg≤Xm is still satisfied, g range (1,2...,M-1), continue to loop to step d, and if Xg>Xm is satisfied, go to step f.

[0091] Step f: After completing the outward expansion grids for all the areas to be processed, a unique identifier is assigned to each first grid.

[0092] Specifically, according to the position information of the first grid and the position information in the measurement data, the first grid and the measurement data are matched to obtain a second grid matching the measurement data.

[0093] The location information in the measurement data includes longitude and latitude information.

[0094] In this embodiment, the measurement data may be associated data obtained after the XDR data and the MR data are associated. The measurement data is matched with the first grid through the position information in the associated data to obtain the second grid.

[0095] S303: Associating the second grid with the vector information in the electronic map according to the position information of the second grid, and dividing the second grid into a road matching grid and a non-road matching grid according to the vector information.

[0096] Specifically, the longitude and latitude of the second grid are associated with the vector information in the electronic map.

[0097] Among them, the vector information is the feature vectors in the precise map, including primary roads, secondary roads, tertiary roads, quaternary roads and expressways, as well as urban areas, urban areas, high-rise buildings, factories and shopping malls, villages, etc.

[0098] Specifically, the second grid corresponding to five types of vector information, namely, primary road, secondary road, tertiary road, quaternary road and expressway, is assigned with a road matching grid attribute to obtain a road matching grid.

[0099] Specifically, the second grid corresponding to 14 types of vector information, such as urban area, city area, high-rise building, factory and shopping mall, village, etc., is assigned with non-road matching grid attribute to obtain the non-road matching grid.

[0100] Exemplarily, the road attributes matched by each vector information are shown in the following table (Table 1):

[0101] Table 1

[0102]

[0103]

[0104] S304: Gather the road matching grids belonging to each road in the area to be processed into a closed loop area of ​​each road, and divide the closed loop area of ​​each road to obtain grid sections corresponding to each road.

[0105] Specifically, S304 includes S3041 to S3042, as follows:

[0106] S3041: Generate a frame according to the area where the road matching grid of each road is located through the preset software to obtain a closed loop area of ​​each road.

[0107] Specifically, a frame is generated by software, and a road matching grid is combined with the name of the road to form a closed loop area.

[0108] Exemplarily, the rules for assigning identification to road borders are as follows:

[0109] ① First-level road sign number: Pro-City-level 1-000001~N (N is a maximum of 6 digits, 999999), Pro is the province, and City is the province to which the city belongs.

[0110] ② Secondary road sign number: Pro-City-level 2-000001~N (N is a maximum of 6 digits, 999999), Pro is the province, and City is the province to which the city belongs.

[0111] ③Third-level road sign number: Pro-City-level 3-000001~N (N is a maximum of 6 digits, 999999), Pro is the province, and City is the province to which the city belongs.

[0112] ④Level 4 road sign number: Pro-City-level 4-000001~N (N is a maximum of 6 digits, 999999), Pro is the province, and City is the province to which the city belongs.

[0113] ⑤Expressway sign number: Pro-City-Expressway-000001~N (N is a maximum of 4 digits, 9999), Pro is the province, and City is the province to which the city belongs.

[0114] For example, as shown in the following table (Table 2):

[0115] Table 2

[0116]

[0117] S3042: For the closed-loop area of ​​each road, starting from the starting point of the road, the entire road is cut into different lengths based on the road matching grid length and different applicable scenarios, and divided into grid sections.

[0118] In this embodiment, the road matching grid length may be 5 meters, 10 meters, 20 meters, and 50 meters.

[0119] In this embodiment, the applicable scenarios may be: densely populated urban areas, general urban areas, suburban areas, etc.

[0120] In this embodiment, the length of the grid section can be 50 meters, 100 meters, 200 meters and 300 meters.

[0121] Specifically, the closed-loop area of ​​each road is divided according to the length of the grid sections, and each grid section is assigned a unique section identifier.

[0122] For example, taking a road matching grid length of 10 meters as an example, a road with a total length of 600 meters in a dense urban area is cut into different standard lengths.

[0123] Figure 5 This is a segmentation identification diagram of a road segment provided in an embodiment of the present application. As shown in the figure:

[0124] 1) 50-meter standard length road section cutting (applicable to densely populated urban areas)

[0125] The road with a total length of 600 meters is divided into 12 grid sections according to the standard length of 50 meters. Each section is assigned a unique number. The grid section numbering rules are as follows:

[0126] Road name-standard length section mark (50 meters)-grid section mark number (000001 to 999999)

[0127] 2) 100-meter standard length road section cutting (applicable to densely populated urban areas or general urban areas)

[0128] The road with a total length of 600 meters is divided into 6 grid sections according to the standard length of 100 meters, and each section is assigned a unique number. The grid section numbering rules are as follows:

[0129] Road name-standard length section mark (100 meters)-grid section mark number (000001 to 999999)

[0130] 3) 200m standard length road section cutting (applicable to general urban areas)

[0131] The road with a total length of 600 meters is divided into three grid sections according to the standard length of 200 meters, and each section is assigned a unique number. The grid section numbering rules are as follows:

[0132] Road name-standard length section mark (200 meters)-grid section mark number (000001 to 999999)

[0133] 4) 300m standard length road section cutting (suitable for suburbs or rural areas)

[0134] The road with a total length of 600 meters is divided into two grid sections according to the standard length of 300 meters, and each section is assigned a unique number. The grid section numbering rules are as follows:

[0135] Road name - standard length section identification (300 meters) - grid section identification number (000001~999999).

[0136] S305: Determine whether the grid road section corresponding to each road is a road section with network quality problems, where network quality problems include: coverage problems, poor quality problems, interference problems, rate problems, voice communication quality problems, and abnormal event problems.

[0137] Specifically, it is determined whether each road matching grid is a grid with network quality problems, and whether the grid section corresponding to each road is a section with network quality problems is determined according to the proportion of grids with network quality problems in the grid section corresponding to each road.

[0138] Specifically, S305 includes S3051 to S3053, as follows:

[0139] S3051: Determine a road matching grid whose number of sampling points within a preset time period is greater than a first preset value as a valid grid.

[0140] In this embodiment, the sampling point may be the location of the user terminal.

[0141] Exemplarily, a road matching grid having more than 50 sampling points for a week is determined to be a valid grid.

[0142] S3052: Determine a grid with a network quality problem grid whose proportion of sampling points whose network quality problem index is lower than the second preset value in the valid grid is greater than a third preset value.

[0143] When the network quality problem is a coverage problem, the network quality problem indicator may be a reference signal receiving power (RSRP) in the signal strength, the second preset value is an RSRP interval range value, and the third preset value is a sampling point ratio in the interval range.

[0144] Exemplarily, a valid grid is a grid in which the proportion of sampling points with RSRP<-100dBm in the valid grid is greater than 50% and is determined to be a grid with coverage problems, wherein the RSRP interval range and the proportion of sampling points in the interval range are adjustable.

[0145] When the network quality problem is a poor quality problem, the network quality problem indicator may be a signal to interference plus noise ratio (SINR), the second preset value is an SINR interval value, and the third preset value is a proportion of sampling points in the interval.

[0146] Exemplarily, a valid grid is a grid in which the proportion of sampling points of the fitted predicted SINR<0 is greater than 50%, and is determined to be a grid with poor quality problems, wherein the SINR interval range and the proportion of sampling points in the interval range are adjustable.

[0147] When the network quality problem is an interference problem, the network quality problem indicator can be the level strength and modulus 3 of the co-frequency neighboring cell and the main service cell. The second preset value is the range value and modulus 3 value of the level strength difference between the co-frequency neighboring cell and the main service cell. The third preset value is the proportion of sampling points that meet the conditions.

[0148] Exemplarily, a valid grid is a grid in which the difference in level strength between the same-frequency neighboring cell and the primary serving cell is less than 6 dB and the proportion of sampling points with modulus 3 equal to 1 is greater than 50%, wherein the modulus 3 range and the proportion of sampling points are adjustable.

[0149] When the network quality problem is a rate problem, the network quality problem indicator may be an average rate of a large packet sample.

[0150] Among them, large packet samples are samples with business traffic or HTTP transmission rate greater than or equal to 500KB.

[0151] Average rate of large packet samples = total traffic of large packet samples / total duration

[0152] The second preset value is a range value of an average rate of large packet samples associated with the XDR call record, and the third preset value is a proportion of sampling points that meet the conditions.

[0153] Exemplarily, the proportion of sampling points in the valid grid whose average rate of large packet samples associated with the XDR call record is lower than 500 Kbps is greater than or equal to 50%, wherein the average rate and proportion of large packet samples are adjustable.

[0154] When the network quality problem is a voice communication quality problem, the network quality problem indicator may be a voice communication quality indicator, the second preset value is a range value of voice communication quality, and the third preset value is the proportion of sampling points that meet the conditions.

[0155] Exemplarily, a valid grid is a grid in which the proportion of sampling points with voice communication quality <3.0 is greater than 30%, wherein the voice communication quality interval range and the proportion of sampling points in the interval range are adjustable.

[0156] When the network quality problem is an abnormal event problem, the network quality problem indicator may be the number of abnormal events, the second preset value is the range value of the number of abnormal events, and the third preset value is the proportion of sampling points that meet the conditions.

[0157] Among them, abnormal events include dropped or unconnected wireless calls.

[0158] Exemplarily, the effective grid is a grid in which the number of dropped calls exceeds the number of times according to different section lengths within the set section length range (the number of dropped calls for a 50-meter section length is greater than or equal to 5 times, the number of dropped calls for a 100-meter section length is greater than or equal to 10 times, the number of dropped calls for a 200-meter section length is greater than or equal to 20 times, and the number of dropped calls for a 300-meter section length is greater than or equal to 30 times) and the dropped call rate satisfies a grid greater than or equal to 20%, or the number of failed calls exceeds the number of times according to different section lengths (the number of failed calls for a 50-meter section length is greater than or equal to 10 times, the number of failed calls for a 100-meter section length is greater than or equal to 20 times, the number of failed calls for a 200-meter section length is greater than or equal to 40 times, and the number of failed calls for a 300-meter section length is greater than or equal to 60 times) and the failed call rate satisfies a grid greater than or equal to 15%, wherein the number of abnormal events and the proportion interval range are adjustable.

[0159] S3053: Determine the grid sections corresponding to each road, in which the proportion of grids with network quality problems is greater than a fourth preset value, as sections with network quality problems.

[0160] Specifically, the proportion of valid 10-meter sections is obtained according to the number of grids with network quality problems, and whether a section is a section with network quality problems is determined according to the number of grids with network quality problems and the proportion of valid 10-meter sections.

[0161] Among them, the effective 10-meter section ratio is the ratio of effective grids to the entire section length.

[0162] In this embodiment, the effective 10-meter sections of 50-meter sections account for >= 80% (4), the effective 10-meter sections of 100-meter sections account for >= 80% (8), the effective 10-meter sections of 200-meter sections account for >= 60% (12), and the effective 10-meter sections of 300-meter sections account for >= 60% (18).

[0163] For example, if the proportion of network quality problem grids in a 50-meter section is equal to 100%, the section is determined to be a section with network quality problems; if the proportion of network quality problem grids in a 100-meter section is equal to 100%, the section is determined to be a section with network quality problems; if the proportion of network quality problem grids in a 200-meter section is greater than or equal to 80%, the section is determined to be a section with network quality problems; if the proportion of network quality problem grids in a 300-meter section is greater than or equal to 60%, the section is determined to be a section with network quality problems.

[0164] S306: For the network quality problem sections corresponding to various types of network quality problems, a DBSCAN algorithm is used to cluster the network quality problem sections to form network quality problem clusters.

[0165] Specifically, S306 includes S3061 to S3064, as follows:

[0166] S3061: Take the reference point of the road section with network quality problems as the problem point of the road section with network quality problems.

[0167] Among them, the reference point of the section with network quality problems can be the center point of the section with network quality problems, or it can be the end point of the section with network quality problems.

[0168] S3062: Divide the problem points of the road section with network quality problems into core problem points, boundary problem points and noise problem points.

[0169] Specifically, the problem points of the road section with network quality problems are divided into core problem points, boundary problem points and noise problem points according to the preset radius and the preset number of points.

[0170] Exemplarily, the preset radius is 5 road section lengths, and the preset number of points MinPts is 3.

[0171] If a problem point is taken as the center and an area with a radius of 5 contains more than 3 problem points, then this problem point is determined to be the core problem point.

[0172] If a problem point is taken as the center of the circle and the area with a radius of 5 contains no more than 3 problem points, and the location of this problem point falls within the vicinity of the core problem point, then this problem point is determined to be a boundary problem point.

[0173] If a problem point is neither a core problem point nor a boundary problem point, it is determined to be a noise problem point.

[0174] S3063: Filter noise problem points.

[0175] S3064: Cluster the core problem points and the boundary problem points to form a network quality problem cluster.

[0176] Specifically, the problem core points within the preset interval are connected in pairs, and each group of connected problem core points forms an initial network quality problem cluster; the boundary problem points within the initial network quality problem cluster are divided into the final network quality problem cluster.

[0177] For example, the following 13 50-meter problem sections are clustered as problem points, and the coordinates of the problem points P1-P13 are shown in the following table:

[0178] P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11 P12 P13 X 1 2 2 4 5 6 6 7 9 1 3 5 3 Y 2 1 4 3 8 7 9 9 5 12 12 12 3

[0179] The DBSCAN algorithm is used to cluster the above 13 problem points. The preset radius is 3 road section lengths, MinPts = 3. For each point, the set of points in its neighborhood is calculated. Points with more than 3 points in the set are core points. Check whether the remaining points are in the neighborhood of the core point. If so, they are boundary points, otherwise they are noise points. Connect the points whose distance does not exceed the preset radius to form a cluster. Add the points in the neighborhood of the core point to this cluster, and three clusters are formed. Figure 6 shown.

[0180] S307: Send the location of the network quality problem cluster to the terminal of the maintenance personnel for display, so that the maintenance personnel can process the road section with the network quality problem according to the display result.

[0181] In this embodiment, the terminal of the maintenance personnel can be a mobile phone, a tablet, or a computer monitor.

[0182] In summary, the network quality problem processing method provided in this embodiment is to obtain a first grid by dividing the area to be processed into grids, and to obtain a second grid matching the measurement data according to the location information of the first grid and the location information in the acquired measurement data, and to associate the second grid with the vector information in the electronic map, and to divide the second grid into a road matching grid and a non-road matching grid according to the vector information, and to gather the road matching grid into a closed-loop area, and to divide the closed-loop area into grid sections, and to determine whether the grid sections corresponding to each road are network quality problem sections according to the proportion of network quality problem grids in the grid sections. The DBSCAN algorithm is used to cluster the network quality problem sections to form network quality problem clusters, and the locations of the network quality problem clusters are sent to the terminal of the maintenance personnel for display, so that the maintenance personnel can process the network quality problem sections according to the display results. There is no need to spend a lot of manpower and material resources to collect measurement data, which reduces maintenance costs and improves maintenance efficiency.

[0183] This embodiment Figure 3 Based on the embodiment, the process of sorting the severity of the coverage problem sections after S306 is mainly introduced as follows:

[0184] The number of core problem points in the network quality problem cluster is sorted to obtain the processing priority of the network quality problem section, where the core problem point is the center point of the network quality problem cluster; the processing priority is sent to the maintenance personnel's terminal for display, so that the maintenance personnel can process the network quality problem cluster according to the processing priority.

[0185] Specifically, the more core problem points there are in the network quality problem cluster, the higher the processing priority of the corresponding network quality problem section.

[0186] In summary, the network quality problem processing method provided in this embodiment determines the processing order of the network quality problem sections by the maintenance personnel by sorting the processing priority of the network quality problem sections according to the number of core problem points in the network quality problem cluster.

[0187] This embodiment Figure 3 Based on the embodiment, the processing process of classifying and aggregating the sections with coverage problems after S305 is mainly introduced as follows:

[0188] The length of the coverage problem sections is divided based on different applicable scenarios, and the coverage problem sections divided according to the corresponding length in any scenario are displayed. Alternatively, the coverage problem sections in the assessment area are classified and aggregated based on the regional dimension, and the classification and aggregation results are displayed, where the regional dimension includes focus, non-focus, province, city, district, county, administrative district, and unit dimension.

[0189] Specifically, the length of the problematic road section is determined according to different applicable scenarios.

[0190] Exemplarily, the grid sections in dense urban areas are divided into 50 meters, the grid sections in general urban areas or county towns are divided into 100 meters, the grid sections in suburban areas are divided into 200 meters, and the grid sections in towns or rural areas are divided into 300 meters.

[0191] In summary, the network quality problem processing method provided in this embodiment, by dividing the grid road section lengths for different adaptation scenarios in the processing area and classifying different regional dimensions, can enable maintenance personnel to calculate the indicators of coverage problem sections according to the category classification.

[0192] Figure 7 A flowchart of a method for handling network quality issues provided in another embodiment of the present application. Figure 3 Based on the embodiment, the process of generating a control table of a road section with network quality problems after S307 and sending it to a maintenance personnel terminal is described as follows:

[0193] S701: Obtain measurement data processed by maintenance personnel, and generate a control table for sections with network quality problems based on the processed measurement data.

[0194] Specifically, including S7011~S7014:

[0195] S7011: Determine whether the network quality problem grid is a network quality problem closed-loop grid based on the processed measurement data.

[0196] In this embodiment, when the network quality problem is a coverage problem, the network quality problem closed-loop grid is a grid in which the sampling points with RSRP<-100dB account for less than 40% of the effective grids that meet the definition of the coverage problem grid last week; when the network quality problem is a poor quality problem, the network quality problem closed-loop grid is a grid in which the sampling points with fitted predicted SINR<0 account for less than 30% of the effective grids that meet the definition of the poor quality problem grid last week; when the network quality problem is an interference problem, the network quality problem closed-loop grid is a grid in which the difference in level strength between the co-frequency neighboring area and the main service area is less than 6dB and the sampling points with modulus three equal to 1 account for less than 40% of the effective grids that meet the definition of the interference problem grid last week; when the network quality problem is a low-rate problem, the network quality problem closed-loop grid is a grid in which the sampling points with an average rate of large packet samples associated with the XDR call list lower than 500Kbps account for less than or equal to 30% of the effective grids that meet the definition of the low-rate problem grid last week; when the network quality problem is a low-rate problem, the network quality problem closed-loop grid is a grid in which the average rate of large packet samples associated with the XDR call list lower than 500Kbps account for less than 30% of the effective grids that meet the definition of the low-rate problem grid last week; When the problem is a voice communication quality problem, the network quality problem closed-loop grid is the grid where the proportion of sampling points with voice communication quality <3.0 is less than 20% among the valid grids that meet the definition of the low voice communication quality problem grid compared with the previous week; when the network quality problem is an abnormal event problem, the network quality problem closed-loop grid is the number of dropped calls within the set section length according to the different section lengths (the number of dropped calls is less than or equal to 2 times for a 50-meter section length, less than or equal to 5 times for a 100-meter section length, and less than or equal to 5 times for a 200-meter section length). The number of dropped calls for a certain length of section is less than or equal to 10 times, and the number of dropped calls for a 300-meter section is less than or equal to 20 times) and the dropped call rate meets the grid of less than or equal to 10%, or the number of failed connections is less than the number of times according to different section lengths (the number of failed connections for a 50-meter section is less than or equal to 5 times, the number of failed connections for a 100-meter section is less than or equal to 10 times, the number of failed connections for a 200-meter section is less than or equal to 20 times, and the number of failed connections for a 300-meter section is less than or equal to 30 times) and the failed connection rate meets the grid of less than or equal to 10%.

[0197] S7012: Determine the section with closed-loop network quality problems based on the proportion of closed-loop grids with network quality problems.

[0198] Specifically, whether a closed-loop network quality section is a section with closed-loop network quality problems is determined based on the proportion of valid 10-meter section grids with closed-loop grids with network quality problems within a set section length.

[0199] For example, a 50-meter section where the closed-loop grid of network quality problems accounts for 100% is a section with closed-loop network quality problems, a 100-meter section where the closed-loop grid of network quality problems accounts for 100% is a section with closed-loop network quality problems, a 200-meter section where the closed-loop grid of network quality problems accounts for 80% or more is a section with closed-loop network quality problems, and a 300-meter section where the closed-loop grid of network quality problems accounts for 60% or more is a section with closed-loop network quality problems.

[0200] S7013: Cluster the sections with network quality problems into network quality problem clusters.

[0201] Specifically, the sections with network quality problems are clustered into network quality problem clusters according to the number of sections with network quality problems within a preset interval.

[0202] For example, if there are two or more road sections with network quality problems within 50 meters, they can be clustered into a cluster.

[0203] S7014: Determine whether the network quality problem cluster is a closed-loop cluster.

[0204] Specifically, at every preset time interval, if the closed-loop resolution rate of the number of problem sections within a set section length range in the network quality problem cluster is greater than or equal to a preset threshold, the network quality problem cluster is determined to be a closed-loop cluster.

[0205] For example, if a certain network quality problem cluster is determined to be unclosed in the third week, and the closed-loop resolution rate of the problem road sections is greater than or equal to 70% in the fourth week, then starting from the fourth week, this network quality problem cluster is a closed-loop network quality problem cluster.

[0206] S7015: Generate a control table for sections with network quality issues so that staff can understand the progress of solving network quality issues.

[0207] Among them, the control table of network quality problem sections includes one or more of the following contents: date of occurrence, administrative area, network quality problem cluster ID, road name, problem classification, problem type, total number of problem sections, original problem section ID, number of unclosed-loop problem sections, unclosed-loop problem section ID, number of closed-loop problem sections and number of weeks the problem lasted.

[0208] In summary, the network quality problem processing method provided in this embodiment generates a control table of the network quality problem cluster by updating the network quality problem cluster, so as to manage the network quality problem more efficiently.

[0209] This embodiment Figure 3 Based on the embodiment, the processing process of classifying and aggregating the sections with coverage problems after S307 is mainly introduced as follows:

[0210] The network quality problem clusters in the area to be processed are displayed, and the corresponding indicator information of the network quality problem clusters is displayed.

[0211] Specifically, different layers, different areas, different road section categories and road section lengths are selected to present network quality problem clusters and network quality problem indicators.

[0212] Among them, network quality problem indicators include: coverage indicators, quality indicators, VoLTE voice indicators and perception indicators.

[0213] In this embodiment, coverage indicators include average level, master cell scrambling code and weak coverage rate; quality indicators include downlink SINR, Mod3 interference and overlapping coverage rate; VoLTE voice indicators include average MOS, low MOS ratio and call drop rate; perception indicators include average rate and low rate ratio.

[0214] In summary, the network quality problem processing method provided in this embodiment displays the indicator information corresponding to the network quality problem cluster so as to more intuitively display the network quality problem.

[0215] Figure 8 This is a schematic diagram of the structure of the network quality problem processing device provided in the embodiment of the present application. Figure 8 As shown, the network quality problem processing device includes: an acquisition module 801, a matching module 802, an association module 803, an aggregation module 804, a judgment module 805, a clustering module 806 and a sending module 807.

[0216] An acquisition module 801 is used to acquire measurement data of the area to be processed;

[0217] The matching module 802 is used to divide the area to be processed into grids to obtain a first grid, and match the first grid with the measurement data according to the position information of the first grid and the position information in the measurement data to obtain a second grid matching the measurement data;

[0218] An association module 803 is used to associate the second grid with the vector information in the electronic map according to the position information of the second grid, and divide the second grid into a road matching grid and a non-road matching grid according to the vector information;

[0219] The aggregation module 804 is used to aggregate the road matching grids belonging to each road in the area to be processed into a closed loop area of ​​each road, and divide the closed loop area of ​​each road to obtain grid sections corresponding to each road;

[0220] The judgment module 805 is used to judge whether the grid road section corresponding to each road is a road section with network quality problems, wherein the network quality problems include: coverage problems, poor quality problems, interference problems, rate problems, voice communication quality problems and abnormal event problems;

[0221] A clustering module 806 is used to cluster the network quality problem sections corresponding to various network quality problems using a DBSCAN algorithm to form network quality problem clusters;

[0222] The sending module 807 is used to send the location of the network quality problem cluster to the terminal of the maintenance personnel for display, so that the maintenance personnel can process the road section with network quality problems according to the display result.

[0223] In one or more embodiments of the present application, the clustering module 806 is specifically used to: take the reference point of the network quality problem section as the problem point of the network quality problem section; divide the problem point of the network quality problem section into core problem points, boundary problem points and noise problem points; filter the noise problem points; cluster the core problem points and boundary problem points to form a network quality problem cluster.

[0224] In one or more embodiments of the present application, the clustering module 806 is also used to: connect the problem core points within a preset interval in pairs, and each group of connected problem core points forms an initial network quality problem cluster; and divide the boundary problem points within the initial network quality problem cluster into the final network quality problem cluster.

[0225] In one or more embodiments of the present application, the judgment module 805 is used to: judge whether each road matching grid is a grid with network quality problems, and determine whether the grid section corresponding to each road is a section with network quality problems based on the proportion of grids with network quality problems in the grid sections corresponding to each road.

[0226] In one or more embodiments of the present application, the judgment module 805 is also used to: determine a road matching grid whose number of sampling points within a preset time period is greater than a first preset value as a valid grid; determine a grid whose proportion of sampling points with a network quality problem index lower than a second preset value in a valid grid is greater than a third preset value as a network quality problem grid; determine a grid section corresponding to each road whose proportion of network quality problem grids is greater than a fourth preset value as a network quality problem section.

[0227] In one or more embodiments of the present application, continue to refer to Figure 8The device also includes a sorting module 808, which is specifically used to: sort the number of core problem points in the network quality problem cluster to obtain the processing priority of the network quality problem section, wherein the core problem point is the center point of the network quality problem cluster; send the processing priority to the maintenance personnel's terminal for display, so that the maintenance personnel can process the network quality problem cluster according to the processing priority.

[0228] In one or more embodiments of the present application, continue to refer to Figure 8 The device also includes a generation module 809, which is specifically used to: obtain the measurement data processed by the maintenance personnel, generate a control table for the network quality problem section according to the processed measurement data; and determine whether the network quality problem cluster has returned to normal according to the control table for the network quality problem section.

[0229] In one or more embodiments of the present application, the generation module 809 is also used to: determine whether the network quality problem grid is a network quality problem closed-loop grid based on the processed measurement data; determine the closed-loop network quality problem section based on the proportion of the network quality problem closed-loop grid; cluster the network quality problem sections into network quality problem clusters; determine whether the network quality problem clusters are closed-loop clusters; generate a management and control table for the network quality problem sections so that the staff can understand the progress of solving the network quality problems.

[0230] In one or more embodiments of the present application, continue to refer to Figure 8 The device also includes a display 810, which is specifically used to: display the network quality problem cluster of the area to be processed and display the corresponding indicator information of the network quality problem cluster.

[0231] The device provided in this embodiment can be used to execute the technical solution of the above method embodiment. Its implementation principle and technical effect are similar, and this embodiment will not be repeated here.

[0232] Fig. 9 The hardware structure diagram of the server provided in the embodiment of the present application is shown in FIG. Fig. 9 As shown, the server 90 of this embodiment includes: a processor 901 and a memory 902;

[0233] Memory 902, used to store computer-executable instructions;

[0234] The processor 901 is used to execute the computer-executable instructions stored in the memory to implement the various steps executed by the server in the above embodiment. For details, please refer to the relevant description in the above method embodiment.

[0235] Optionally, the memory 902 may be independent or integrated with the processor 901 .

[0236] When the memory 902 is independently configured, the server further includes a bus 903 for connecting the memory 902 and the processor 901 .

[0237] The embodiment of the present application also provides a computer storage medium, in which computer execution instructions are stored. When a processor executes the computer execution instructions, the above-mentioned network quality problem processing method is implemented.

[0238] The embodiment of the present application also provides a computer program product, including a computer program, which implements the above network quality problem processing method when executed by a processor.

[0239] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of modules is only a logical function division, and there may be other division methods in actual implementation, such as multiple modules can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or modules, which can be electrical, mechanical or other forms.

[0240] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to implement the solution of this embodiment.

[0241] In addition, each functional module in each embodiment of the present application can be integrated into one processing unit, or each module can exist physically separately, or two or more modules can be integrated into one unit. The above-mentioned module-composed unit can be implemented in the form of hardware or in the form of hardware plus software functional units.

[0242] The above-mentioned integrated module implemented in the form of a software function module can be stored in a computer-readable storage medium. The above-mentioned software function module is stored in a storage medium, including a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) or a processor to perform some steps of the methods of various embodiments of the present application.

[0243] It should be understood that the processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), etc. A general-purpose processor may be a microprocessor or any conventional processor. The steps of the method disclosed in the invention may be directly implemented as being executed by a hardware processor, or may be implemented by a combination of hardware and software modules in the processor.

[0244] The memory may include a high-speed RAM memory, and may also include a non-volatile storage NVM, such as at least one disk memory, and may also be a USB flash drive, a mobile hard disk, a read-only memory, a magnetic disk or an optical disk, etc.

[0245] The bus may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, the bus in the drawings of the present application is not limited to only one bus or one type of bus.

[0246] The above storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk. The storage medium can be any available medium that can be accessed by a general or special purpose computer.

[0247] An exemplary storage medium is coupled to a processor so that the processor can read information from the storage medium and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and the storage medium can be located in an application specific integrated circuit (ASIC). Of course, the processor and the storage medium can also exist as discrete components in an electronic device or a main control device.

[0248] Those skilled in the art can understand that all or part of the steps of implementing the above-mentioned method embodiments can be completed by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, the steps of the above-mentioned method embodiments are executed; and the aforementioned storage medium includes: ROM, RAM, disk or optical disk and other media that can store program codes.

[0249] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for handling network quality problems, characterized in that: include: Obtaining measurement data of the area to be processed; Dividing the area to be processed into a grid to obtain a first grid, and matching the first grid with the measurement data according to position information of the first grid and position information in the measurement data to obtain a second grid matching the measurement data; Associating the second grid with vector information in the electronic map according to the position information of the second grid, and dividing the second grid into a road matching grid and a non-road matching grid according to the vector information; Aggregating the road matching grids belonging to each road in the area to be processed into a closed loop area of ​​each road, and dividing the closed loop area of ​​each road to obtain grid sections corresponding to each road; Determine whether the grid road section corresponding to each road is a road section with network quality problems, where network quality problems include: coverage problems, poor quality problems, interference problems, rate problems, voice communication quality problems, and abnormal event problems; For the network quality problem sections corresponding to various types of network quality problems, the DBSCAN algorithm is used to cluster the network quality problem sections to form network quality problem clusters; Sending the location of the network quality problem cluster to a maintenance personnel's terminal for display, so that the maintenance personnel can process the network quality problem section according to the display result; Acquire measurement data processed by maintenance personnel, and determine whether the network quality problem grid is a network quality problem closed-loop grid according to the processed measurement data; According to the closed-loop grid ratio of the network quality problem and a preset threshold, determining the road section with closed-loop network quality problem; the preset threshold is related to the length of the grid road section; Clustering the closed-loop network quality problem sections into network quality problem clusters; Determining whether the network quality problem cluster is a closed-loop cluster; Generate a control table for sections with network quality issues so that staff can understand the progress of solving network quality issues; According to the control table of the network quality problem section, determine whether the network quality problem cluster has returned to normal; Wherein, determining whether the network quality problem cluster is a closed-loop cluster includes: At every preset time interval, if the closed-loop resolution rate of the number of problem sections within a set section length range in the network quality problem cluster is greater than or equal to a preset threshold, the network quality problem cluster is determined to be a closed-loop cluster.

2. The method according to claim 1, characterized in that The DBSCAN algorithm is used to cluster the network quality problem sections to form network quality problem clusters, including: Taking the reference point of the road section with network quality problems as the problem point of the road section with network quality problems; The problem points of the network quality problem section are divided into core problem points, boundary problem points and noise problem points; Filter out noise problem points; The core problem points and the boundary problem points are clustered to form a network quality problem cluster.

3. The method according to claim 2, characterized in that The clustering of the core problem points and the boundary problem points to form a network quality problem cluster includes: Connect the problem core points in pairs within a preset interval, and each group of connected problem core points forms an initial network quality problem cluster; The boundary problem points within the initial network quality problem cluster are divided into the final network quality problem cluster.

4. The method according to claim 1, characterized in that The step of judging whether the grid road section corresponding to each road is a road section with network quality problems includes: It is determined whether each road matching grid is a grid with network quality problems, and whether the grid section corresponding to each road is a section with network quality problems according to the proportion of grids with network quality problems in the grid section corresponding to each road.

5. The method according to claim 4, characterized in that The step of judging whether each road matching grid is a grid with network quality problems, and determining whether the grid sections corresponding to each road are sections with network quality problems according to the proportion of grid sections with network quality problems in the grid sections corresponding to each road, comprises: Determine a road matching grid whose number of sampling points within a preset time period is greater than a first preset value as a valid grid; Determine as a grid with network quality problem a grid whose proportion of sampling points whose network quality problem index is lower than the second preset value in the valid grid is greater than a third preset value; The grid sections corresponding to each road, in which the proportion of grids with network quality problems is greater than a fourth preset value, are determined as sections with network quality problems.

6. The method according to any one of claims 1 to 5, characterized in that: After clustering the network quality problem sections corresponding to various types of network quality problems using the DBSCAN algorithm to form network quality problem clusters, the method further includes: Sorting the number of core problem points in the network quality problem cluster to obtain the processing priority of the network quality problem section, wherein the core problem point is the center point of the network quality problem cluster; The processing priority is sent to a terminal of a maintenance personnel for display, so that the maintenance personnel can process the network quality problem cluster according to the processing priority.

7. The method according to any one of claims 1 to 5, characterized in that: After sending the location of the network quality problem cluster to the terminal of the maintenance personnel for display so that the maintenance personnel can process the network quality problem section according to the display result, the method further includes: The network quality problem cluster of the area to be processed is displayed, and the corresponding indicator information of the network quality problem cluster is displayed.

8. A network quality problem processing device, characterized in that: include: An acquisition module, used for acquiring measurement data of the area to be processed; A matching module, configured to divide the area to be processed into grids to obtain a first grid, and to match the first grid with the measurement data according to position information of the first grid and position information in the measurement data to obtain a second grid matching the measurement data; an associating module, configured to associate the second grid with vector information in an electronic map according to the position information of the second grid, and divide the second grid into a road matching grid and a non-road matching grid according to the vector information; A convergence module, used to converge the road matching grids belonging to each road in the area to be processed into a closed-loop area of ​​each road, and divide the closed-loop area of ​​each road to obtain grid sections corresponding to each road; A judgment module is used to judge whether the grid road section corresponding to each road is a road section with network quality problems, wherein network quality problems include: coverage problems, poor quality problems, interference problems, rate problems, voice communication quality problems and abnormal event problems; A clustering module, for clustering the network quality problem sections corresponding to various types of network quality problems using a DBSCAN algorithm to form network quality problem clusters; A sending module, used for sending the location of the network quality problem cluster to a terminal of a maintenance personnel for display, so that the maintenance personnel can process the road section with the network quality problem according to the display result; A generation module is used to obtain measurement data processed by maintenance personnel, and determine whether a network quality problem grid is a network quality problem closed-loop grid according to the processed measurement data; determine a closed-loop network quality problem section according to the proportion of the network quality problem closed-loop grid and a preset threshold; the preset threshold is related to the length of the grid section; cluster the closed-loop network quality problem section into a network quality problem cluster; determine whether the network quality problem cluster is a closed-loop cluster; generate a control table for the network quality problem section so that the staff can grasp the progress of solving the network quality problem; determine whether the network quality problem cluster has returned to normal according to the control table for the network quality problem section; The generation module is used to determine whether the network quality problem cluster is a closed-loop cluster. Specifically, at every preset time interval, if the closed-loop solution rate of the number of problem sections within the section length range set in the network quality problem cluster is greater than or equal to a preset threshold, then the network quality problem cluster is determined to be a closed-loop cluster.

9. A server, characterized in that: include: at least one processor and memory; The memory stores computer-executable instructions; The at least one processor executes the computer-executable instructions stored in the memory, so that the at least one processor executes the network quality problem processing method according to any one of claims 1 to 7.

10. A computer storage medium, characterized in that: The computer storage medium stores computer execution instructions, and when the processor executes the computer execution instructions, the network quality problem processing method according to any one of claims 1 to 7 is implemented.

11. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method for processing network quality problems according to any one of claims 1 to 7 is implemented.

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