Method, device and equipment for evaluating 5G network quality and storage medium

Abstract

The application discloses a 5G network quality evaluation method and device, equipment and a storage medium, relates to the technical field of communication, and is used for improving the rationality of 5G network quality evaluation. Including: based on the plurality of 5G network data sent by the plurality of base stations in the target area, determining the plurality of network parameters corresponding to each base station in the plurality of base stations, at least one 5G network data is sent by each base station in the plurality of base stations, the 5G network data includes: network element configuration parameters, scene parameters, and the plurality of network parameters corresponding to each base station are used to indicate the network state corresponding to each base station; based on the plurality of network parameters corresponding to each base station in the plurality of base stations, determining the first evaluation parameter corresponding to the target area, and the first evaluation parameter is used to indicate the network quality corresponding to the target area.

Description

Technical Field

[0001] This application relates to the field of communication technology, and in particular to a 5G network quality assessment method, apparatus, device and storage medium. Background Technology

[0002] Currently, to improve the accuracy of investment in 5G wireless network construction, it is generally necessary to analyze network coverage information, continuously determine the importance of different network performance parameters based on the analysis results, and manage and control these parameters. At present, the main method used by various operators to manage wireless parameters is a combination of baseline management and characteristic parameter optimization. Recommended baseline values ​​are specified for key parameters for unified management, while optimized values ​​are used for some performance-optimized characteristic parameters.

[0003] However, the above methods have problems such as the inability to distinguish between scenarios, difficulty in reflecting the importance of different parameters, and lack of effect reference for performance parameters. The efficiency of network parameter control and evaluation is poor, resulting in poor rationality of the current 5G network quality assessment. Summary of the Invention

[0004] This application provides a 5G network quality assessment method, apparatus, device, and storage medium to improve the rationality of 5G network quality assessment.

[0005] To achieve the above objectives, this application adopts the following technical solution:

[0006] Firstly, a 5G network quality assessment method is provided. The method includes: determining multiple network parameters corresponding to each of the multiple base stations based on multiple 5G network data transmitted by multiple base stations within a target area; each of the multiple base stations transmits at least one 5G network data, and the 5G network data includes: network element configuration parameters and scenario parameters; the multiple network parameters corresponding to each base station are used to indicate the network status corresponding to each base station; and determining a first assessment parameter corresponding to the target area based on the multiple network parameters corresponding to each base station within the target area, the first assessment parameter being used to indicate the network quality corresponding to the target area.

[0007] In one design, based on multiple 5G network data transmitted by multiple base stations within a target area, multiple network parameters corresponding to each of the multiple base stations are determined, including: based on the multiple 5G network data transmitted by multiple base stations, determining a target identifier corresponding to each of the multiple 5G network data, wherein the target identifier includes at least one of the following: base station identifier and cell identifier; and based on the target identifier corresponding to each of the multiple 5G network data, performing data fitting processing on the multiple 5G network data to obtain multiple network parameters corresponding to each of the multiple base stations.

[0008] In one design, each of a plurality of base stations corresponds to at least one cell, and each cell corresponds to multiple network parameters. Based on the multiple network parameters corresponding to each of the plurality of base stations, a first evaluation parameter corresponding to a target area is determined, including: for any one of the multiple network parameters corresponding to the target area, determining a target cell list corresponding to any one of the network parameters from the at least one cell corresponding to each of the plurality of base stations, the target cell list including multiple cells, wherein any one of the network parameters corresponding to the multiple cells does not meet a preset condition; and determining the first evaluation parameter corresponding to the target area based on the target cell list corresponding to each of the multiple network parameters corresponding to the target area.

[0009] In one design, the target area includes multiple scenarios, each scenario corresponds to at least one base station among multiple base stations, each base station corresponds to one vendor, multiple base stations correspond to multiple vendors, and one vendor corresponds to at least one base station. The method further includes: determining the weight corresponding to each scenario among multiple scenarios and the weight corresponding to each vendor among multiple vendors based on the scenario to which each base station belongs and the vendor to which each base station belongs; and determining a second evaluation parameter corresponding to each of the multiple network parameters corresponding to each scenario among multiple scenarios based on the weight corresponding to each scenario, the weight corresponding to each vendor among multiple vendors, and the multiple network parameters corresponding to each base station among multiple base stations. The second evaluation parameter is used to indicate the network quality corresponding to each scenario among multiple scenarios.

[0010] In one design, based on the weights corresponding to each scenario in multiple scenarios, the weights corresponding to each vendor in multiple scenarios, and the multiple network parameters corresponding to each base station in multiple scenarios, a second evaluation parameter is determined for each of the multiple network parameters corresponding to each scenario in multiple scenarios. This includes: for any one of the multiple network parameters corresponding to any scenario in multiple scenarios, based on any one of the network parameters corresponding to each base station in at least one of the multiple vendors, determining a target cell list corresponding to the any one network parameter; and determining the second evaluation parameter corresponding to the any one of the multiple network parameters corresponding to any scenario based on the target cell list.

[0011] In one design, the method further includes: determining the parameter type corresponding to each of the multiple network parameters, and determining the weight corresponding to each parameter type among the multiple parameter types, including: basic configuration parameters, co-construction and sharing parameters, and performance optimization parameters; determining the total score corresponding to the target region based on the score corresponding to each of the multiple network parameters corresponding to the target region, including: determining the first evaluation parameter corresponding to the target region based on the score corresponding to each of the multiple network parameters corresponding to the target region and the weight corresponding to each network parameter.

[0012] Secondly, a 5G network quality assessment device is provided, comprising: a determining unit; the determining unit being configured to determine multiple network parameters corresponding to each of the multiple base stations based on multiple 5G network data transmitted by multiple base stations within a target area, wherein each of the multiple base stations transmits at least one 5G network data, and the 5G network data includes: network element configuration parameters and scenario parameters, and the multiple network parameters corresponding to each base station are used to indicate the network status corresponding to each base station; the determining unit being configured to determine a first assessment parameter corresponding to the target area based on the multiple network parameters corresponding to each of the multiple base stations, the first assessment parameter being used to indicate the network quality corresponding to the target area.

[0013] In one design, the 5G network quality assessment device further includes: a processing unit; a determining unit, configured to determine a target identifier corresponding to each 5G network data in the multiple 5G network data based on multiple 5G network data transmitted by multiple base stations, wherein the target identifier includes at least one of the following: base station identifier and cell identifier; and a processing unit, configured to perform data fitting processing on the multiple 5G network data based on the target identifier corresponding to each 5G network data in the multiple 5G network data to obtain multiple network parameters corresponding to each of the multiple base stations.

[0014] In one design, each of a plurality of base stations corresponds to at least one cell, and each cell corresponds to multiple network parameters; a determining unit is used to determine a target cell list corresponding to any one of the multiple network parameters corresponding to a target area from the at least one cell corresponding to each of the plurality of base stations, the target cell list including multiple cells, and any one of the network parameters corresponding to the multiple cells does not meet a preset condition; the determining unit is used to determine a first evaluation parameter corresponding to the target area based on the target cell list corresponding to each of the multiple network parameters corresponding to the target area.

[0015] In one design, the target area includes multiple scenarios, each scenario corresponds to at least one base station among multiple base stations, each base station corresponds to one vendor, multiple base stations correspond to multiple vendors, and one vendor corresponds to at least one base station; a determining unit is used to determine the weight corresponding to each scenario among multiple scenarios and the weight corresponding to each vendor among multiple vendors based on the scenario to which each base station belongs and the vendor corresponding to each base station; a determining unit is used to determine a second evaluation parameter corresponding to each of the multiple network parameters corresponding to each scenario among multiple scenarios based on the weight corresponding to each scenario, the weight corresponding to each vendor among multiple vendors, and the multiple network parameters corresponding to each base station among multiple base stations, the second evaluation parameter is used to indicate the network quality corresponding to each scenario among multiple scenarios.

[0016] In one design, a determining unit is used to determine a target cell list corresponding to any one of the multiple network parameters corresponding to any one of the multiple scenarios, based on any one of the network parameters corresponding to each of at least one base station of any one of the multiple vendors; the determining unit is used to determine a second evaluation parameter corresponding to any one of the multiple network parameters corresponding to any one of the multiple network parameters for any one scenario based on the target cell list.

[0017] In one design, a determining unit is used to determine the parameter type corresponding to each of the multiple network parameters and the weight corresponding to each parameter type among the multiple parameter types, including: basic configuration parameters, co-construction and sharing parameters, and performance optimization parameters; the determining unit is used to determine the first evaluation parameter corresponding to the target area based on the score corresponding to each of the multiple network parameters corresponding to the target area and the weight corresponding to each network parameter.

[0018] Thirdly, an electronic device is provided, including a processor and a memory; wherein the memory is used to store one or more programs, the one or more programs including computer execution instructions, and when the electronic device is running, the processor executes the computer execution instructions stored in the memory to cause the electronic device to perform a 5G network quality assessment method as described in the first aspect.

[0019] Fourthly, a computer-readable storage medium is provided for storing one or more programs, the one or more programs including instructions that, when executed by a computer, cause the computer to perform a 5G network quality assessment method as described in the first aspect.

[0020] This application provides a 5G network quality assessment method, apparatus, device, and storage medium, applied to scenarios for assessing 5G network quality. First, based on at least one piece of 5G network data, including network element configuration parameters and scenario parameters, transmitted by each of multiple base stations within a target area, multiple network parameters corresponding to each base station are determined, indicating the network status of each base station. Then, based on the multiple network parameters corresponding to each base station, a first assessment parameter corresponding to the target area is determined, indicating the network quality of the target area. According to the above method, multiple network parameters corresponding to each base station can be determined based on the 5G network parameters transmitted by each base station within the target area, and then the first assessment parameter corresponding to the target area can be determined. By controlling and assessing the multiple network parameters corresponding to each base station, the network quality of the target area can be assessed, fully considering the importance of different network parameters, improving the efficiency of network parameter control and assessment, and thus enhancing the rationality of 5G network quality assessment. Attached Figure Description

[0021] Figure 1 A schematic diagram of a 5G network quality assessment system provided for an embodiment of this application;

[0022] Figure 2 A flowchart illustrating a 5G network quality assessment method provided for embodiments of this application. Figure 1 ;

[0023] Figure 3 A flowchart illustrating a 5G network quality assessment method provided for embodiments of this application. Figure 2 ;

[0024] Figure 4 A flowchart illustrating a 5G network quality assessment method provided for embodiments of this application. Figure 3 ;

[0025] Figure 5 A flowchart illustrating a 5G network quality assessment method provided for embodiments of this application. Figure 4;

[0026] Figure 6 A flowchart illustrating a 5G network quality assessment method provided for embodiments of this application. Figure 5 ;

[0027] Figure 7 A flowchart illustrating a 5G network quality assessment method provided for embodiments of this application. Figure 6 ;

[0028] Figure 8 A flowchart illustrating a 5G network quality assessment method provided for embodiments of this application. Figure 7 ;

[0029] Figure 9 A flowchart illustrating a 5G network quality assessment method provided for embodiments of this application. Figure 8 ;

[0030] Figure 10 A flowchart illustrating a 5G network quality assessment method provided for embodiments of this application. Figure 9 ;

[0031] Figure 11 A schematic diagram of network parameter classification provided for an embodiment of this application;

[0032] Figure 12 A flowchart illustrating a 5G network quality assessment method provided for embodiments of this application. Figure 10 ;

[0033] Figure 13 A schematic diagram of a 5G network quality assessment device provided for an embodiment of this application;

[0034] Figure 14 This is a schematic diagram of an electronic device structure provided for an embodiment of this application. Detailed Implementation

[0035] The technical solutions in the embodiments of this application will now be described with reference to the accompanying drawings.

[0036] In the description of this application, unless otherwise stated, " / " means "or". For example, A / B can mean A or B. "And / or" in this document is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, and B alone. Furthermore, "at least one" and "multiple" refer to two or more. The terms "first," "second," etc., do not limit the quantity or order of execution, and "first," "second," etc., do not necessarily imply differences.

[0037] The 5G network quality assessment method provided in this application embodiment can be applied to a 5G network quality assessment system. Figure 1 A schematic diagram of one structure of the 5G network quality assessment system is shown. Figure 1 As shown, the 5G network quality assessment system 20 includes: an electronic device 21, a server 22, and multiple base stations 23. The server 22 is connected to the electronic device 21 and the multiple base stations 23.

[0038] The 5G network quality assessment system 20 can be used for the Internet of Things. The 5G network quality assessment system 20 may include hardware such as multiple central processing units (CPUs), multiple memories, and storage devices storing multiple operating systems.

[0039] Electronic device 21 can be used in the Internet of Things to process data. For example, electronic device 21 can interact with server 22 to obtain a list of target cells corresponding to each of the multiple network parameters in the target area based on at least one 5G network data sent by each base station, thereby obtaining the first evaluation parameter corresponding to the target area.

[0040] Server 22 is used to store data. For example, server 22 can be a database server used to store at least one piece of 5G network data sent by each base station.

[0041] Optionally, server 22 may also store other data required to determine the total score corresponding to the target area, such as manufacturer rule tables, city rule tables, etc.

[0042] Base station 23 is a base station in the target area, used to transmit 5G network data and store the 5G network data in server 22.

[0043] The following describes a 5G network quality assessment method provided by an embodiment of this application, with reference to the accompanying drawings.

[0044] like Figure 2 As shown in the figure, an embodiment of this application provides a 5G network quality assessment method, including steps S201-S203:

[0045] S201. Based on multiple 5G network data transmitted by multiple base stations within the target area, determine multiple network parameters corresponding to each of the multiple base stations.

[0046] Each of the multiple base stations sends at least one 5G network data message. The 5G network data includes network element configuration parameters and scenario parameters. Multiple network parameters corresponding to each base station are used to indicate the network status of each base station.

[0047] Optionally, configuration can be extracted through gateway tasks to periodically transmit 5G network data sent by each of the multiple base stations to a specified path, thereby obtaining multiple 5G network data sent by multiple base stations within the target area.

[0048] Optionally, the uploading of 5G network data sent by each of the multiple base stations can also be completed manually.

[0049] For example, network data can be manually uploaded to a File Transfer Protocol (FTP) server.

[0050] Optionally, after acquiring multiple 5G network data messages sent by multiple base stations, the data can be preprocessed according to business requirements.

[0051] Specifically, data can be parsed, cleaned, and stored in the database through the data processing module of a process automation tool.

[0052] Optionally, the network configuration parameters can be 5G network configuration parameters, such as 5G site configuration parameters, 5G cell configuration parameters, etc.

[0053] Optionally, scenario parameters can be understood as scenario parameters corresponding to each base station, i.e. scenario information, such as network element scenario affiliation information.

[0054] Optionally, scene parameters can be obtained by manually uploading them to a preset server path within a preset time period.

[0055] It should be noted that because the scenario information needs to be aggregated from multiple data sources, and may require cross-carrier acquisition, and the site nature still needs to be determined manually, automation is difficult to achieve, resulting in poor timeliness of information acquisition. Scenario parameters can be acquired through collaboration between multiple carriers.

[0056] Optionally, network configuration parameters can be selected based on specific business requirements (such as different network parameters).

[0057] For example, taking the determination of the network parameters of the periodic adaptive switch of the Sounding Reference Signal (SRS) corresponding to each of the multiple base stations as an example, the 5G network element configuration parameters corresponding to each of the multiple base stations can be obtained, such as 5G site configuration parameters (gNodeBfunction) and 5G cell configuration parameters (NRDUCell); and scenario parameters, such as 5G network element scenario affiliation parameters.

[0058] Table 1

[0059]

[0060] Specifically, 5G site configuration information can be found in the fields shown in Table 1, 5G cell configuration information can be found in the fields shown in Table 2, and 5G network element scenario affiliation information can be found in the fields shown in Table 3.

[0061] Table 2

[0062]

[0063] Table 3

[0064]

[0065] Optionally, process automation tools can be used to automatically determine multiple network parameters corresponding to each of the multiple 5G network data sent by multiple base stations in the target area, so as to complete the automated verification of multiple network parameters corresponding to each base station.

[0066] S202. Based on multiple network parameters corresponding to each of the multiple base stations, determine the first evaluation parameter corresponding to the target area.

[0067] The first evaluation parameter is used to indicate the network quality corresponding to the target area.

[0068] Optionally, the first evaluation parameter corresponding to the target area can be determined by performing data processing (such as data parsing, data integration, and data fitting) on ​​multiple network parameters corresponding to each of the multiple base stations.

[0069] For example, the score of each network parameter can be determined based on whether the network parameters corresponding to each of the multiple base stations meet the preset conditions, and then the first evaluation parameter corresponding to the target area can be determined.

[0070] Optionally, after determining the score of each network parameter among the multiple network parameters corresponding to the target area, the total score of the target area can be obtained by combining different business needs (such as different evaluation directions, different business scenarios, etc.) based on the score of each network parameter among the multiple network parameters corresponding to the target area.

[0071] Optionally, after completing the automatic parameter verification (after determining the first evaluation parameter corresponding to the target area), a list of items to be modified and a network management script can be automatically generated based on the parameter rule table and gateway command rules. The list of items to be modified and the network management script can be sent to the prefecture-level city in the form of a work order through the internal work order platform. The prefecture-level city will then confirm and modify the work order to achieve the evaluation and optimization of 5G network quality.

[0072] It should be noted that, in this embodiment of the application, the quality of the 5G network can be assessed by evaluating the 5G network parameters.

[0073] In this embodiment, firstly, based on at least one piece of 5G network data, including network element configuration parameters and scenario parameters, sent by each of multiple base stations within the target area, multiple network parameters corresponding to each of the multiple base stations are determined, which indicate the network status of each base station. Then, based on the multiple network parameters corresponding to each of the multiple base stations, a first evaluation parameter corresponding to the target area, which indicates the network quality of the target area, is determined. According to the above method, multiple network parameters corresponding to each base station can be determined based on the 5G network parameters sent by each base station within the target area, and then the first evaluation parameter corresponding to the target area can be determined. By controlling and evaluating the multiple network parameters corresponding to each base station, the network quality of the target area can be evaluated, fully considering the importance of different network parameters, improving the efficiency of network parameter control and evaluation, and thus enhancing the rationality of 5G network quality evaluation.

[0074] In a design, such as Figure 3 As shown in the embodiment of this application, in a 5G network quality assessment method, the above-mentioned S201 includes S301-S302:

[0075] S301. Based on multiple 5G network data transmitted by multiple base stations, determine the target identifier corresponding to each 5G network data in the multiple 5G network data.

[0076] The target identifier includes at least one of the following: base station identifier and cell identifier.

[0077] S302. Based on the target identifier corresponding to each 5G network data in multiple 5G network data, perform data fitting processing on multiple 5G network data to obtain multiple network parameters corresponding to each base station in multiple base stations.

[0078] For example, to determine the network parameters of the SRS periodic adaptive switch corresponding to each of multiple base stations, the data fitting process can be as follows a1-a2:

[0079] a1. Generate manufacturer and city data.

[0080] like Figure 4 As shown, combined with Figure 1 The system extracts 5G site configuration data from server 22 and uses the network management address of the extracted file to distinguish manufacturers through the vendor rule table or manufacturer rule table defined within the process automation tool, generating a "vendor" field; and uses the city rule table to process the network element name field according to the naming rules to generate a "city" field.

[0081] Optionally, the city rule table can be a table showing the correspondence between some fields in the network element name and the city. For example, if the network element name contains the abbreviation of the city's pinyin, the corresponding city can be determined through the city rule table; the vendor rule table can determine the vendor corresponding to the site based on the network management address configured for the site.

[0082] a2. Configure data fitting.

[0083] like Figure 5 As shown, combined with Figure 1 The required fields for 5G sites, 5G cells, and 5G cell SRS configuration are extracted from server 22. Data set 1 is then merged using the common network element name field through site configuration and cell configuration. Furthermore, based on data set 1 and the 5G cell SRS configuration field, data set 2 is merged using the network element name, gNB identifier, and NR DU cell identifier as indexes, thus completing the data fitting.

[0084] In this embodiment, the target identifier of multiple network data sent by the base station is used to fit the multiple network data to obtain the network parameters corresponding to the base station. This enables the automatic generation of network parameters to improve the efficiency of 5G network quality assessment. Furthermore, the network parameters are used to assess the 5G network quality, thereby improving the rationality of the 5G network quality assessment.

[0085] In one design, each of the multiple base stations corresponds to at least one cell, and each cell corresponds to multiple network parameters, such as... Figure 6 As shown in the embodiment of this application, in a 5G network quality assessment method, the above-mentioned S202 includes S401-S402:

[0086] S401. For any one of the multiple network parameters corresponding to the target area, determine the target cell list corresponding to the any one network parameter from at least one cell corresponding to each of the multiple base stations.

[0087] The target cell list includes multiple cells, and any network parameter corresponding to any of these cells does not meet the preset conditions.

[0088] S402. Based on the list of target cells corresponding to each of the multiple network parameters corresponding to the target area, determine the first evaluation parameter corresponding to the target area.

[0089] Optional, the target cell list can be a target cell list file.

[0090] Optionally, for any one of the multiple network parameters corresponding to the target area, parameter verification can be performed on all cells corresponding to all base stations for that one network parameter, thereby identifying cells whose values ​​of that one network parameter do not meet the preset conditions, and determining the target cell list corresponding to that one network parameter.

[0091] For example, taking the determination of network parameters for the SRS periodic adaptive switch corresponding to each of multiple base stations as an example, such as... Figure 7 As shown, from the dataset 2 obtained above a2, according to preset rules (preset conditions), cells that have not turned off the "SRS periodic adaptive switch" (non-compliant cells) can be filtered out to obtain a list of non-compliant cells and determine the target cell list.

[0092] Optional, such as Figure 7 As shown by the dotted line, after filtering out cells where the "SRS periodic adaptive switch" is not turned off (non-compliant cells), a reverse connection can be made with the whitelist in the server to remove whitelisted cells from the non-compliant cells, thereby determining the target cell list.

[0093] Optionally, the whitelist is a list of communities that meet preset conditions but do not need to be filtered, which is set in advance according to business needs.

[0094] Optionally, after obtaining the target cell list corresponding to any one of the network parameters, the score corresponding to any one of the network parameters can be determined based on the number of cells or the proportion of cells in the target cell list (the ratio of the number of cells in the target cell list to the total number of cells). Then, based on the score corresponding to each of the multiple network parameters corresponding to the target area, the first evaluation parameter corresponding to the target area can be determined.

[0095] It should be noted that for each of the multiple network parameters corresponding to the target area, there is a corresponding list of target cells.

[0096] Optionally, for each of the multiple network parameters corresponding to the target area, multiple compliance thresholds can be set according to "excellent, good, medium, poor", and the proportion of non-compliant configurations (non-compliant cells, i.e. cells in the target cell list) in the preset area to the total number of configurations can be determined.

[0097] It should be noted that compliance can be understood as whether the network parameter corresponding to each of the multiple base stations meets the preset conditions such as the preset value or preset format of the network parameter.

[0098] Optionally, a maximum score of one hundred can be preset, and the deduction coefficient corresponding to any network parameter corresponding to the target area can be determined according to the compliance threshold, thereby determining the score corresponding to any network parameter corresponding to the target area.

[0099] Specifically, for items exceeding the poor compliance threshold, points will be deducted according to the deduction coefficient of that parameter item. For example, the deduction coefficient for exceeding the poor threshold is 10%, for items exceeding the medium to poor threshold it is 60%, for good it is 90%, and for excellent it is no deduction.

[0100] For example, the compliance threshold for "poor" can be 10%, the compliance threshold for "medium" can be 60%, the compliance threshold for "good" can be 80%, and the compliance threshold for "excellent" can be 90%.

[0101] Specifically, for any network parameter, when the cells in the corresponding target cell list account for more than 90% (including 90%) of the total cells, the deduction coefficient is 10%, and the corresponding evaluation parameter is 100*10%=10; when the cells in the corresponding target cell list account for between 40% and 90% (including 40%) of the total cells, the deduction coefficient is 60%, and the corresponding evaluation parameter is 100*60%=60.

[0102] It should be noted that the compliance ratio threshold and the corresponding deduction coefficients for different compliance ratio thresholds can be adjusted according to specific business needs.

[0103] Optionally, after obtaining the target cell list corresponding to each network parameter, the summation can be performed for each city based on the target cell list to generate city-wide aggregated statistical data. Then, based on the city-wide aggregated statistical data, the first evaluation parameter corresponding to the target area can be determined.

[0104] Optionally, each city refers to any city within the target area.

[0105] In this embodiment, by determining the proportion of compliant base stations corresponding to each network parameter among all base stations, the score of each network parameter corresponding to the target area is determined. The score of each network parameter is determined from the perspective of the target area as a whole, which improves the rationality of the 5G network quality assessment.

[0106] In one design, the target area includes multiple scenarios, each scenario corresponds to at least one base station from a plurality of base stations, each base station corresponds to one vendor, the multiple base stations correspond to multiple vendors, and one vendor corresponds to at least one base station, such as... Figure 8 As shown in the embodiment of this application, a 5G network quality assessment method further includes S501-S502:

[0107] S501. Based on the scenario to which each base station belongs and the manufacturer corresponding to each base station, determine the weight corresponding to each scenario in the multiple scenarios, and the weight corresponding to each manufacturer in the multiple manufacturers.

[0108] S502. Based on the weights corresponding to each scenario in multiple scenarios, the weights corresponding to each vendor in multiple vendors, and the multiple network parameters corresponding to each base station in multiple base stations, determine the second evaluation parameter corresponding to each of the multiple network parameters corresponding to each scenario in multiple scenarios.

[0109] The second evaluation parameter is used to indicate the network quality corresponding to each of the multiple scenarios.

[0110] Optionally, the scenarios included in the target area can be understood as dividing the target area into multiple sub-regions, thereby obtaining multiple scenarios included in the target area.

[0111] Optionally, the type of scenario can be determined by combining different business needs (such as different geographical locations of the target area, different economic development, and different evaluation indicator requirements).

[0112] For example, the target area can be divided into rural areas, border areas, urban areas, universities, and subways, resulting in multiple scenarios.

[0113] Optionally, scenarios can be divided into important scenarios and secondary scenarios based on their importance, and then a weight value can be set for each scenario according to the importance and secondary scenarios.

[0114] For example, the weight of a secondary scene can be set to 0.3 (that is, the sum of the weights of all scenes in the secondary scene is 0.3), and the weight of an important scene can be set to 0.7 (that is, the sum of the weights of all scenes in the important scene is 0.7).

[0115] Optionally, the weight for each scenario can be understood as the scenario coefficient for each scenario, which can be determined in combination with different business needs (such as the importance of different scenarios, user perception, etc.).

[0116] For example, the weight values ​​(scenario coefficients) corresponding to each scenario are shown in Table 4.

[0117] Optionally, the manufacturer corresponding to each base station can be understood as the manufacturer of each base station. The weight of each manufacturer can be determined based on specific needs, such as the ratio of the number of configurations (base stations) of each manufacturer in the scenario.

[0118] Table 4

[0119]

[0120] For example, if there are three manufacturers corresponding to base stations in the target area, with manufacturer 1 accounting for 45% of all base stations, manufacturer 2 accounting for 35% of all base stations, and manufacturer 3 accounting for 20% of all base stations, then the weight of manufacturer 1 can be set to 0.45, the weight of manufacturer 2 to 0.35, and the weight of manufacturer 3 to 0.2.

[0121] Optionally, after determining the second evaluation parameter corresponding to each of the multiple network parameters for each scenario in multiple scenarios, the third evaluation parameter corresponding to each of the multiple network parameters for the target region can be further determined based on the second evaluation parameter corresponding to each of the multiple network parameters for each scenario in multiple scenarios, so as to determine the first evaluation parameter corresponding to the target region based on the third evaluation parameter corresponding to each of the multiple network parameters for the target region.

[0122] Optionally, based on the weights of each vendor in multiple vendors, the weights of each scenario in multiple scenarios, and the multiple network parameters of each base station in multiple base stations, a fourth evaluation parameter can be determined for each of the multiple network parameters of each vendor in multiple vendors. Then, based on the fourth evaluation parameter, a third evaluation parameter can be determined for each of the multiple network parameters of the target area. Finally, based on the third evaluation parameter, a first evaluation parameter can be determined for the target area.

[0123] In this embodiment of the application, the target area is divided into multiple scenarios, and different weights are determined for different scenarios according to their importance. In combination with the manufacturers corresponding to the base stations, different weights are determined for different manufacturers. Then, by combining the weights corresponding to different scenarios and the weights corresponding to different manufacturers, the second evaluation parameter corresponding to each of the multiple network parameters for each scenario is determined, so as to improve the rationality of 5G network quality assessment.

[0124] In a design, such as Figure 9 As shown in the embodiment of this application, in a 5G network quality assessment method, the above-mentioned S502 includes S601-S602:

[0125] S601. For any one of the multiple network parameters corresponding to any one of the multiple scenarios, and based on any one of the network parameters corresponding to each base station of at least one base station of any one of the multiple vendors, determine the target cell list corresponding to any one network parameter.

[0126] S602. Based on the target cell list, determine the second evaluation parameter corresponding to any one of the multiple network parameters for any given scenario.

[0127] It should be noted that the number of target cells in the list is the same as the number of vendors corresponding to base stations in any given scenario; one vendor corresponds to one target cell list in one scenario.

[0128] Optionally, for any one of the multiple network parameters corresponding to any one of the multiple scenarios, the vendors corresponding to all base stations in that scenario can be determined, and the cells corresponding to each base station can be determined. Then, based on whether the network parameter corresponding to each cell meets the preset conditions, a list of target cells corresponding to each vendor can be generated.

[0129] Furthermore, based on the target cell list corresponding to each vendor in any given scenario, and the weight corresponding to each vendor, a second evaluation parameter is determined for any one of the multiple network parameters corresponding to that given scenario.

[0130] For example, in a subway scenario, there are two vendors for the corresponding base stations. For a certain network parameter, the deduction coefficient for the target cell list of vendor 1 is 90%, and the deduction coefficient for the target cell list of vendor 2 is 85%. Vendor 1 has a weight of 65%, and vendor 2 has a weight of 35%. Therefore, the score for this certain network parameter in the subway scenario is 100*90%*65%+100*85%*35%=88.25.

[0131] In this embodiment of the application, the second evaluation parameter corresponding to any one of the multiple network parameters corresponding to any one of the multiple network parameters in any one of the multiple scenarios is determined by the target cell list corresponding to each manufacturer, so as to fully consider the impact of each manufacturer on the network quality evaluation and improve the rationality of 5G network quality evaluation.

[0132] In a design, such as Figure 10 As shown, the 5G network quality assessment method provided in this application embodiment further includes S701, and the above-mentioned S203 includes S702:

[0133] S701. Determine the parameter type corresponding to each network parameter in the multiple network parameters, and determine the weight corresponding to each parameter type in the multiple parameter types.

[0134] Among them, the parameter types include: basic configuration parameters, co-construction and sharing parameters, and performance optimization parameters.

[0135] Table 5

[0136] type parameter Recommended value 5G latitude and longitude Not empty / Correct format 5G Time slot allocation Dual-cycle 7:3 time slot ratio 5G Time slot structure SS104 5G External definition Correct format 5G Community activation status normal

[0137] Table 6

[0138] type Parameter name Recommended value 5G gNodeB Multi-Carrier Sharing Mode SHARED_FREQ 5G RRC connection user ratio Not configured / All 255 / All 100 5G Wireless Bearer (RB) Dynamic Shared Switch 0 5G Minimum RB protection switch 0 5G Maximum RB limit switch 0 5G EUTRAN redirection switch On 5G EPSFB switch On

[0139] Optional, such as Figure 11 As shown, various network parameters are categorized. Referring to Table 5, basic configuration parameters mainly include basic parameters of base stations and cells, such as planning parameters, external definitions (definition information of the external environment for base stations and cells), geographical location information (such as latitude and longitude), and cell activation status. Referring to Table 6, co-construction and sharing parameters mainly include parameters of co-construction and sharing sites, such as shared switches, transmission route configuration, and resource limitations. Referring to Tables 7 to 10, performance optimization parameters mainly include parameters that affect performance indicators, such as Packet Data Convergence Protocol (PDCP) parameters (downlink PDCP sequence number length, uplink PDCP sequence number length) and Power Headroom Report (PHR) parameters (PHR periodic timer, PHR disable timer).

[0140] Table 7

[0141]

[0142] Table 8

[0143] type Parameter name Recommended value Optimize special projects 5G Uplink 256QAM switch 0 Disconnection 5G Uplink UE-AMBR speed limiting function switch 0 rate 5G Downlink UE-AMBR speed limiting function switch 0 rate 5G Bandwidth index Bsrs of periodic SRS 1 Speed / Disconnection 5G Bandwidth index Csrs of periodic SRS 63 Speed / Disconnection

[0144] Optionally, performance optimization parameters can be divided into two categories: basic performance and specific optimization. Basic performance can be classified according to the basic performance indicators it affects, such as access, handover, disconnection, quality, load, and interference, as shown in Tables 7 and 8. Specific optimization can be distinguished according to the specific optimization project implemented, such as the Voice over Long-Term Evolution (VOLTE) voice interruption project and the Maximum Bit Rate (MBR) control project, as shown in Tables 9 and 10.

[0145] Table 9

[0146]

[0147] Table 10

[0148]

[0149] It should be noted that, in conjunction with S401 above, the recommended value can be understood as the preset condition corresponding to each network parameter.

[0150] S702. Based on the score of each network parameter in the multiple network parameters corresponding to the target region, and the weight of each network parameter, determine the total score corresponding to the target region.

[0151] For example, such as Figure 12 As shown, combined with Formula 1, the total score for the target region can be determined by the score corresponding to each network parameter.

[0152] City Score = Σ Parameter Item Score = 100 * Deduction Coefficient * Category Coefficient * Item Coefficient * Manufacturer Coefficient

[0153] Scene coefficient formula 1

[0154] Optionally, the class coefficient can be understood as scoring each city based on the category to which each parameter belongs and the weight coefficient set for each subcategory.

[0155] For example, the class coefficient can be 15% for basic configuration parameters; 35% for co-construction and sharing parameters; and 50% for performance optimization parameters.

[0156] Optionally, the weight value (parameter coefficient) corresponding to each coefficient can be determined based on expert experience.

[0157] Optional, such as Figure 12 As shown, the weight values ​​(parameter coefficients) corresponding to performance optimization parameters can be dynamically adjusted after being determined by expert experience. For example, the influence factor can be determined based on the ratio of the improvement before and after parameter modification under this type of index. That is, for performance optimization parameters, parameter coefficient = index coefficient * influence factor.

[0158] Table 11

[0159]

[0160] Optionally, for the basic performance parameters in the performance optimization parameters, the index coefficient is equal to the index class coefficient multiplied by the index item coefficient.

[0161] Specifically, the index coefficients of the basic performance parameters, the corresponding index category coefficients and index item coefficients are shown in Table 11.

[0162] Optionally, for specific optimization parameters in the performance optimization parameters, their index coefficients can be adjusted according to the importance of the index and the current business needs (such as the current focus of network optimization).

[0163] Specifically, the index coefficients for the special optimization parameters are shown in Table 12.

[0164] Table 12

[0165] Special Project Name Index coefficient VONR 0.15 5G dwell time 0.15 5G high reverse flow 0.2 Low data perception 0.2 ……

[0166] The foregoing mainly describes the solutions provided by the embodiments of this application from a methodological perspective. To achieve the above functions, it includes corresponding hardware structures and / or software modules for executing each function. Those skilled in the art should readily recognize that, in conjunction with the units and algorithm steps of the various examples described in the embodiments disclosed herein, the embodiments of this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed in hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0167] This application embodiment can divide a 5G network quality assessment device into functional modules based on the above method example. For example, each function can be divided into its own functional modules, or two or more functions can be integrated into one processing module. The integrated module can be implemented in hardware or as a software functional module. Optionally, the module division in this application embodiment is illustrative and only represents one logical functional division; other division methods may be used in actual implementation.

[0168] Figure 13 This is a schematic diagram of the structure of a 5G network quality assessment device provided in an embodiment of this application. Figure 13 As shown, the 5G network quality assessment device 40 is used to improve the rationality of 5G network quality assessment, for example, for performing... Figure 2 A 5G network quality assessment method is shown. The 5G network quality assessment device 40 includes: a determination unit 401.

[0169] The determining unit is used to determine multiple network parameters corresponding to each of the multiple base stations based on multiple 5G network data sent by multiple base stations in the target area. Each of the multiple base stations sends at least one 5G network data, which includes network element configuration parameters and scenario parameters. The multiple network parameters corresponding to each base station are used to indicate the network status of each base station.

[0170] The determining unit is used to determine a first evaluation parameter corresponding to the target area based on multiple network parameters corresponding to each of the multiple base stations. The first evaluation parameter is used to indicate the network quality corresponding to the target area.

[0171] In a design, such as Figure 13 As shown, the 5G network quality assessment device also includes a processing unit 402.

[0172] The determining unit 401 is used to determine the target identifier corresponding to each 5G network data in the multiple 5G network data sent by multiple base stations. The target identifier includes at least one of the following: base station identifier and cell identifier.

[0173] The processing unit 402 is used to perform data fitting processing on multiple 5G network data based on the target identifier corresponding to each 5G network data in multiple 5G network data, to obtain multiple network parameters corresponding to each of the multiple base stations.

[0174] In one design, each of the multiple base stations corresponds to at least one cell, and each cell corresponds to multiple network parameters.

[0175] The determining unit 401 is used to determine a target cell list corresponding to any one of the multiple network parameters corresponding to the target area, from at least one cell corresponding to each of the multiple base stations. The target cell list includes multiple cells, and the network parameter corresponding to the multiple cells does not meet the preset conditions.

[0176] The determining unit 401 is used to determine the first evaluation parameter corresponding to the target area based on the target cell list corresponding to each of the multiple network parameters corresponding to the target area.

[0177] In one design, the target area includes multiple scenarios, each scenario corresponds to at least one base station among multiple base stations, each base station corresponds to one vendor, multiple base stations correspond to multiple vendors, and one vendor corresponds to at least one base station.

[0178] The determining unit 401 is used to determine the weight of each scenario in the multiple scenarios and the weight of each manufacturer in the multiple manufacturers based on the scenario to which each base station belongs and the manufacturer corresponding to each base station.

[0179] The determining unit 401 is used to determine a second evaluation parameter for each of the multiple network parameters corresponding to each of the multiple scenarios based on the weights corresponding to each scenario in the multiple scenarios, the weights corresponding to each vendor in the multiple vendors, and the multiple network parameters corresponding to each base station in the multiple base stations. The second evaluation parameter is used to indicate the network quality corresponding to each of the multiple scenarios.

[0180] In one design, a determining unit 401 is used to determine a target cell list corresponding to any one of the multiple network parameters corresponding to any one of the multiple network parameters corresponding to any one of the multiple scenarios, based on any one of the network parameters corresponding to each of the at least one base station of any one of the multiple vendors.

[0181] The determining unit 401 is used to determine the second evaluation parameter corresponding to any one of the multiple network parameters corresponding to any scenario, based on the target cell list.

[0182] In one design, a determining unit 401 is used to determine the parameter type corresponding to each of the multiple network parameters and to determine the weight corresponding to each parameter type among the multiple parameter types. The multiple parameter types include: basic configuration parameters, co-construction and sharing parameters, and performance optimization parameters.

[0183] The determining unit 401 is used to determine the first evaluation parameter corresponding to the target region based on the score of each network parameter in the multiple network parameters corresponding to the target region and the weight of each network parameter.

[0184] In the case where the functions of the integrated modules described above are implemented in hardware, this application provides a possible structural schematic diagram of the electronic device involved in the above embodiments. For example... Figure 14 As shown, an electronic device 70 is used to improve the rationality of 5G network quality assessment, for example, for performing... Figure 2 This illustrates a 5G network quality assessment method. The electronic device 70 includes a processor 701, a memory 702, and a bus 703. The processor 701 and the memory 702 are connected via the bus 703.

[0185] Processor 701 is the control center of the communication device. It can be a single processor or a collective term for multiple processing elements. For example, processor 701 can be a general-purpose central processing unit (CPU) or other general-purpose processors. Among them, the general-purpose processor can be a microprocessor or any conventional processor.

[0186] As one embodiment, processor 701 may include one or more CPUs, for example Figure 14 CPU 0 and CPU 1 are shown in the diagram.

[0187] The memory 702 may be a read-only memory (ROM) or other type of static storage device capable of storing static information and instructions, random access memory (RAM) or other type of dynamic storage device capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), disk storage medium or other magnetic storage device, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but is not limited thereto.

[0188] As one possible implementation, the memory 702 can exist independently of the processor 701. The memory 702 can be connected to the processor 701 via a bus 703 and is used to store instructions or program code. When the processor 701 calls and executes the instructions or program code stored in the memory 702, it can implement the 5G network quality assessment method provided in this application embodiment.

[0189] In another possible implementation, the memory 702 can also be integrated with the processor 701.

[0190] Bus 703 can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus. This bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 14 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0191] It should be pointed out that, Figure 14 The structure shown does not constitute a limitation on the electronic device 70. Except... Figure 14 In addition to the components shown, the electronic device 70 may include more or fewer components than illustrated, or combine certain components, or have different component arrangements.

[0192] As an example, combined Figure 13 The functions implemented by the determining unit 401 and the processing unit 402 in the 5G network quality assessment device 40 are the same as those of the other two units. Figure 14 The processor 701 in it has the same function.

[0193] Optional, such as Figure 14 As shown, the electronic device 70 provided in this application embodiment may further include a communication interface 704.

[0194] Communication interface 704 is used to connect with other devices via a communication network. This communication network can be Ethernet, a wireless access network, a wireless local area network (WLAN), etc. Communication interface 704 may include a receiving unit for receiving data and a transmitting unit for transmitting data.

[0195] In one design, the communication interface in the electronic device provided in this application embodiment can also be integrated into the processor.

[0196] Through the above description of the embodiments, those skilled in the art will clearly understand that, for the sake of convenience and brevity, only the division of the above functional units is used as an example. In practical applications, the above functions can be assigned to different functional units as needed, that is, the internal structure of the device can be divided into different functional units to complete all or part of the functions described above. The specific working process of the system, device, and unit described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0197] This application also provides a computer-readable storage medium storing instructions. When a computer executes these instructions, the computer performs each step of the method flow shown in the above-described method embodiments.

[0198] Embodiments of this application provide a computer program product containing instructions that, when executed on a computer, cause the computer to perform a 5G network quality assessment method as described in the above method embodiments.

[0199] The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), registers, hard disks, optical fibers, compact disc read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof, or any other form of computer-readable storage medium in the art. An exemplary storage medium is coupled to a processor, enabling the processor to read information from and write information to the storage medium. Of course, the storage medium may also be a component of the processor. The processor and the storage medium may reside in an application-specific integrated circuit (ASIC). In the embodiments of this application, the computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0200] Since the electronic devices, computer-readable storage media, and computer program products in the embodiments of this application can be applied to the above methods, the technical effects they can achieve can also be referred to the above method embodiments. The embodiments of this application will not be repeated here.

[0201] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions within the technical scope disclosed in this application should be covered within the scope of protection of this application.

Claims

1. A method for 5G network quality evaluation, characterized in that, The method comprises: determining, based on a plurality of pieces of 5G network data transmitted by a plurality of base stations in a target area, a plurality of network parameters corresponding to each of the plurality of base stations, each of the plurality of base stations transmitting at least one piece of 5G network data, the 5G network data comprising: network element configuration parameters, scene parameters, the plurality of network parameters corresponding to each of the plurality of base stations being used to indicate a network state corresponding to each of the plurality of base stations, each of the plurality of base stations corresponding to at least one cell, each cell corresponding to the plurality of network parameters; determining, based on the plurality of network parameters corresponding to each of the plurality of base stations, a first evaluation parameter corresponding to the target area, the first evaluation parameter being used to indicate a network quality corresponding to the target area; the determining, based on the plurality of network parameters corresponding to each of the plurality of base stations, the first evaluation parameter corresponding to the target area, comprises: for any one of the plurality of network parameters corresponding to the target area, determining, from the at least one cell corresponding to each of the plurality of base stations, a target cell list corresponding to the any one of the plurality of network parameters, the target cell list comprising a plurality of cells, the any one of the plurality of network parameters corresponding to the plurality of cells not satisfying a preset condition; determining, based on the target cell list corresponding to each of the plurality of network parameters corresponding to the target area, the first evaluation parameter corresponding to the target area.

2. The method of claim 1, wherein, the determining, based on a plurality of pieces of 5G network data transmitted by a plurality of base stations in a target area, a plurality of network parameters corresponding to each of the plurality of base stations, comprises: determining, based on the plurality of pieces of 5G network data transmitted by the plurality of base stations, a target identifier corresponding to each piece of 5G network data in the plurality of pieces of 5G network data, the target identifier comprising at least one of: a base station identifier, a cell identifier; performing data fitting processing on the plurality of pieces of 5G network data based on the target identifier corresponding to each piece of 5G network data in the plurality of pieces of 5G network data, to obtain the plurality of network parameters corresponding to each of the plurality of base stations.

3. The method according to claim 1 or 2, characterized in that, The target area comprises a plurality of scenes, each of the plurality of scenes corresponding to at least one of the plurality of base stations, each of the plurality of base stations corresponding to a manufacturer, the plurality of base stations corresponding to a plurality of manufacturers, one manufacturer corresponding to at least one base station, the method further comprising: determining, based on the scene to which each of the plurality of base stations belongs and the manufacturer corresponding to each of the plurality of base stations, a weight corresponding to each of the plurality of scenes and a weight corresponding to each of the plurality of manufacturers; determining, based on the weight corresponding to each of the plurality of scenes, the weight corresponding to each of the plurality of manufacturers, and the plurality of network parameters corresponding to each of the plurality of base stations, a second evaluation parameter corresponding to each of the plurality of network parameters corresponding to each of the plurality of scenes, the second evaluation parameter being used to indicate a network quality corresponding to each of the plurality of scenes.

4. The method of claim 3, wherein, The determining the second evaluation parameter corresponding to each network parameter in the multiple network parameters corresponding to each scene in the multiple scenes comprises: For any network parameter in the multiple network parameters corresponding to any scene in the multiple scenes, determining a target cell list corresponding to the any network parameter based on the any network parameter corresponding to each base station in the at least one base station corresponding to any manufacturer in the multiple manufacturers; According to the target cell list, determining the second evaluation parameter corresponding to the any network parameter in the multiple network parameters corresponding to the any scene.

5. The method according to claim 1 or 2, characterized in that, The method further comprises: Determining a parameter type corresponding to each network parameter in the multiple network parameters, and determining a weight corresponding to each parameter type in the multiple parameter types, the multiple parameter types comprising: a basic configuration parameter, a co-construction and sharing parameter, and a performance optimization parameter; The determining the total score corresponding to the target area based on the score corresponding to each network parameter in the multiple network parameters corresponding to the target area comprises: Determining the first evaluation parameter corresponding to the target area based on the score corresponding to each network parameter in the multiple network parameters corresponding to the target area and the weight corresponding to each network parameter. 6.A 5G network quality evaluation device, characterized in that, The apparatus comprises a determination unit. The determination unit is configured to determine multiple network parameters corresponding to each base station in the multiple base stations based on multiple pieces of 5G network data transmitted by the multiple base stations, each base station in the multiple base stations transmits at least one piece of 5G network data, the 5G network data comprises network element configuration parameters and scene parameters, and the multiple network parameters corresponding to each base station are used to indicate a network state corresponding to each base station; each base station in the multiple base stations corresponds to at least one cell, and each cell corresponds to the multiple network parameters. The determination unit is configured to determine a first evaluation parameter corresponding to the target area based on the multiple network parameters corresponding to each base station in the multiple base stations, and the first evaluation parameter is used to indicate a network quality corresponding to the target area. The determination unit is configured to, for any network parameter in the multiple network parameters corresponding to the target area, determine a target cell list corresponding to the any network parameter from the at least one cell corresponding to each base station in the multiple base stations, the target cell list comprises multiple cells, and the any network parameter corresponding to the multiple cells does not meet a preset condition. The determination unit is configured to determine the first evaluation parameter corresponding to the target area based on the target cell list corresponding to each network parameter in the multiple network parameters corresponding to the target area.

7. The 5G network quality assessment apparatus of claim 6, wherein, The 5G network quality evaluation apparatus further comprises a processing unit. The determination unit is configured to determine, based on the multiple pieces of 5G network data sent by the multiple base stations, a target identifier corresponding to each piece of 5G network data in the multiple pieces of 5G network data, the target identifier including at least one of a base station identifier and a cell identifier. The processing unit is configured to perform data fitting processing on the multiple pieces of 5G network data based on the target identifier corresponding to each piece of 5G network data in the multiple pieces of 5G network data, to obtain multiple network parameters corresponding to each base station in the multiple base stations.

8. The 5G network quality assessment apparatus of claim 6 or 7, wherein, The target area includes multiple scenes, each scene in the multiple scenes corresponding to at least one base station in the multiple base stations, each base station in the multiple base stations corresponding to one vendor, the multiple base stations corresponding to multiple vendors, and one vendor corresponding to at least one base station. The determination unit is configured to determine a weight corresponding to each scene in the multiple scenes and a weight corresponding to each vendor in the multiple vendors based on a scene to which each base station in the multiple base stations belongs and a vendor corresponding to each base station. The determination unit is configured to determine, based on the weight corresponding to each scene in the multiple scenes, the weight corresponding to each vendor in the multiple vendors, and the multiple network parameters corresponding to each base station in the multiple base stations, a second evaluation parameter corresponding to each network parameter in the multiple network parameters corresponding to each scene in the multiple scenes, the second evaluation parameter being used to indicate a network quality corresponding to each scene in the multiple scenes.

9. The 5G network quality assessment apparatus of claim 8, wherein, The determination unit is configured to determine, for any network parameter in the multiple network parameters corresponding to any scene in the multiple scenes, a target cell list corresponding to the any network parameter based on the any network parameter corresponding to each base station in the at least one base station corresponding to any vendor in the multiple vendors. The determination unit is configured to determine, according to the target cell list, the second evaluation parameter corresponding to the any network parameter in the multiple network parameters corresponding to the any scene.

10. The 5G network quality assessment apparatus of claim 6 or 7, wherein, The determination unit is configured to determine a parameter type corresponding to each network parameter in the multiple network parameters and a weight corresponding to each parameter type in multiple parameter types, the multiple parameter types including a basic configuration parameter, a co-construction and sharing parameter, and a performance optimization parameter. The determination unit is configured to determine the first evaluation parameter corresponding to the target area based on a score corresponding to each network parameter in the multiple network parameters corresponding to the target area and the weight corresponding to each network parameter.

11. An electronic device, comprising: The electronic device comprises a processor and a memory, wherein the memory is configured to store one or more programs including computer execution instructions, and the processor is configured to execute the computer execution instructions stored in the memory to enable the electronic device to perform the 5G network quality evaluation method in any one of claims 1-5 when the electronic device is running. The one or more programs include instructions that, when executed by a computer, cause the computer to perform the 5G network quality evaluation method in any one of claims 1-5.

12. A computer-readable storage medium storing one or more programs, the one or more programs comprising instructions for: ​

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