4G network quality assessment methods, devices, equipment and storage media

Abstract

This application discloses a 4G network quality assessment method, apparatus, device, and storage medium, relating to the field of communication technology, and is used to improve the rationality of 4G network quality assessment. It includes: determining multiple network parameters corresponding to each of the multiple base stations based on multiple 4G network data transmitted by multiple base stations within a target area; each base station transmitting at least one 4G network data transmission, the 4G network data including: network element configuration parameters and scenario parameters; the multiple network parameters corresponding to each base station indicating the network status of each base station; determining a score for each network parameter in the multiple network parameters corresponding to the target area based on the multiple network parameters corresponding to each base station; and determining a total score for the target area based on the scores for each network parameter in the multiple network parameters corresponding to the target area, the total score being used to assess the network quality of the target area.

Description

Technical Field

[0001] This application relates to the field of communication technology, and in particular to a 4G network quality assessment method, apparatus, device and storage medium. Background Technology

[0002] Currently, to improve the accuracy of investment in 4G 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 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 current technical solutions mentioned above 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. As a result, the efficiency of network parameter control and evaluation is poor, leading to the poor rationality of the current 4G network quality assessment. Summary of the Invention

[0004] This application provides a 4G network quality assessment method, apparatus, device, and storage medium to improve the rationality of 4G network quality assessment.

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

[0006] Firstly, a 4G network quality assessment method is provided. This method includes: determining multiple network parameters corresponding to each of the multiple base stations based on multiple 4G network data transmitted by multiple base stations within a target area; each base station transmitting at least one 4G network data transmission; the 4G network data including network element configuration parameters and scenario parameters; and the multiple network parameters corresponding to each base station indicating the network status of each base station. Based on the multiple network parameters corresponding to each base station, a score is determined for each network parameter in the target area. Based on the score for each network parameter in the target area, a total score is determined for the target area, and the total score is used to assess the network quality of the target area.

[0007] In one design, based on multiple 4G 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 4G network data transmitted by multiple base stations, determining a target identifier corresponding to each of the multiple 4G 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 4G network data, performing data fitting processing on the multiple 4G network data to obtain multiple network parameters corresponding to each of the multiple base stations.

[0008] In one design, based on multiple network parameters corresponding to each of the multiple base stations, the score corresponding to each of the multiple network parameters corresponding to the target area is determined, including: for any one of the multiple network parameters corresponding to the target area, based on any one of the multiple base stations, determining the first proportion of base stations whose any one network parameter satisfies a preset condition among the multiple base stations; and determining the score corresponding to any one of the multiple network parameters corresponding to the target area based on the first proportion.

[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 corresponding to each base station; and determining the score 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.

[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, the score corresponding to each of the multiple network parameters corresponding to each scenario in multiple scenarios is determined. 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 base station of any vendor in multiple scenarios, determining a second proportion of base stations whose network parameters satisfy preset conditions in at least one base station; and determining the score corresponding to any one of the multiple network parameters corresponding to any scenario based on the second proportion.

[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; and 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 total score 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 4G 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 4G network data transmitted by multiple base stations within a target area, wherein each base station transmits at least one 4G network data, and the 4G 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 score corresponding to each of the multiple network parameters corresponding to the target area based on the multiple network parameters corresponding to each base station; and the determining unit being configured to determine a total score corresponding to the target area based on the score corresponding to each of the multiple network parameters corresponding to the target area, the total score being used to assess the network quality corresponding to the target area.

[0013] In one design, the 4G network quality assessment device further includes: a processing unit; a determining unit, configured to determine a target identifier corresponding to each of the multiple 4G 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 4G network data based on the target identifier corresponding to each of the multiple 4G network data to obtain multiple network parameters corresponding to each of the multiple base stations.

[0014] In one design, a determining unit is used to determine, for any one of the multiple network parameters corresponding to the target area, a first proportion of the base stations whose network parameters satisfy preset conditions among the multiple base stations, based on any one of the network parameters corresponding to each of the multiple base stations; the determining unit is used to determine the score corresponding to any one of the network parameters corresponding to the target area according to the first proportion.

[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 the score 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.

[0016] In one design, a determining unit is used to determine, 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 of the at least one base station corresponding to any one of the multiple vendors, a second proportion of the base stations whose network parameters satisfy a preset condition among the at least one base station; the determining unit is used to determine the score corresponding to any one of the multiple network parameters corresponding to any one scenario based on the second proportion.

[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 total score 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 4G 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 4G network quality assessment method as described in the first aspect.

[0020] This application provides a 4G network quality assessment method, apparatus, device, and storage medium, applied to scenarios involving 4G network quality assessment. First, based on at least one 4G network data transmission from each of multiple base stations within a target area, including network element configuration parameters and scenario parameters, 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 score is determined for each network parameter within the target area. Finally, based on the scores of each network parameter within the target area, a total score for assessing the network quality of the target area is determined. According to this method, multiple network parameters corresponding to each base station can be determined based on the 4G network parameters transmitted by each base station within the target area, and then the score for each network parameter within the target area can be determined. This allows for the management and assessment of network parameters, fully considering the importance of different network parameters, improving the efficiency of network parameter management and assessment, and thus enhancing the rationality of the 4G network quality assessment. Attached Figure Description

[0021] Figure 1 A schematic diagram of a 4G network quality assessment system provided for an embodiment of this application;

[0022] Figure 2 A flowchart illustrating a 4G network quality assessment method provided for embodiments of this application. Figure 1 ;

[0023] Figure 3 A flowchart illustrating a 4G network quality assessment method provided for embodiments of this application. Figure 2 ;

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

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

[0026] Figure 6 A flowchart illustrating a 4G network quality assessment method provided for embodiments of this application. Figure 5 ;

[0027] Figure 7 A flowchart illustrating a 4G network quality assessment method provided for embodiments of this application. Figure 6 ;

[0028] Figure 8A flowchart illustrating a 4G network quality assessment method provided for embodiments of this application. Figure 7 ;

[0029] Figure 9 A flowchart illustrating a 4G network quality assessment method provided for embodiments of this application. Figure 8 ;

[0030] Figure 10 A flowchart illustrating a 4G network quality assessment method provided for embodiments of this application. Figure 9 ;

[0031] Figure 11 A flowchart illustrating a 4G network quality assessment method provided for embodiments of this application. Figure 10 ;

[0032] Figure 12 A flowchart illustrating a 4G network quality assessment method provided for embodiments of this application. Figure 10 one;

[0033] Figure 13 A flowchart illustrating a 4G network quality assessment method provided for embodiments of this application. Figure 10 two;

[0034] Figure 14 A flowchart illustrating a 4G network quality assessment method provided for embodiments of this application. Figure 10 three;

[0035] Figure 15 A schematic diagram of network parameter classification provided for an embodiment of this application;

[0036] Figure 16 A flowchart illustrating a 4G network quality assessment method provided for embodiments of this application. Figure 10 Four;

[0037] Figure 17 A schematic diagram of a 4G network quality assessment device provided for an embodiment of this application;

[0038] Figure 18 This is a schematic diagram of an electronic device structure provided for an embodiment of this application. Detailed Implementation

[0039] The technical solutions in the embodiments of this application will now be described with reference to the accompanying drawings.

[0040] 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.

[0041] The 4G network quality assessment method provided in this application embodiment can be applied to a 4G network quality assessment system. Figure 1 A schematic diagram of one structure of this 4G network quality assessment system is shown. Figure 1 As shown, the 4G 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.

[0042] The 4G network quality assessment system 20 can be used for the Internet of Things. The 4G network quality assessment system 20 may include hardware such as multiple central processing units (CPUs), multiple memories, and storage devices storing multiple operating systems.

[0043] 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 the score of each network parameter in the multiple network parameters corresponding to the target area based on at least one 4G network data sent by each base station, and then obtain the total score corresponding to the target area.

[0044] Server 22 is used to store data. For example, server 22 can be a database server used to store at least one piece of 4G network data sent by each base station.

[0045] 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.

[0046] Base station 23 is a base station in the target area, used to transmit 4G network data and store the 4G network data in server 22.

[0047] The following describes a 4G network quality assessment method provided by an embodiment of this application, with reference to the accompanying drawings.

[0048] like Figure 2As shown in the figure, an embodiment of this application provides a 4G network quality assessment method, including S201-S203:

[0049] S201. Based on multiple 4G network data transmitted by multiple base stations within the target area, determine multiple network parameters corresponding to each of the multiple base stations.

[0050] Each of the multiple base stations sends at least one 4G network data message. The 4G 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.

[0051] Optionally, configuration can be extracted through gateway tasks to periodically transmit 4G network data sent by each of the multiple base stations to a specified path, thereby obtaining multiple 4G network data sent by multiple base stations within the target area.

[0052] Optionally, the uploading of 4G network data sent by each of the multiple base stations can also be completed manually.

[0053] For example, network data can be manually uploaded to a File Transfer Protocol (FTP) server.

[0054] Optionally, after acquiring multiple 4G network data messages sent by multiple base stations, the data can be preprocessed.

[0055] For example, data can be parsed, cleaned, and stored in a database using the data processing module of a process automation tool.

[0056] Optionally, network configuration parameters can be 4G network element configuration parameters, such as 4G site configuration parameters, 4G cell configuration parameters, etc.

[0057] It should be noted that for certain network parameters involving 5G technology, such as network parameters for configuring 4G and 5G inter-neighbor cells and X2 relationships, it is also necessary to obtain relevant 5G parameters, such as 5G network element configuration parameters (5G site configuration parameters, 5G cell configuration parameters, etc.).

[0058] 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.

[0059] Optionally, scene parameters can be obtained by manually uploading them to a preset server path within a preset time period.

[0060] 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.

[0061] Optionally, network configuration parameters can be selected based on specific business requirements (such as different network parameters).

[0062] For example, taking the determination of network parameters for the 4G and 5G inter-neighbor cell and X2 relationship configurations for each of multiple base stations as an example, the following can be obtained: 4G network element configuration parameters for each of the multiple base stations, such as 4G site configuration (eNodeB function), 4G cell configuration (Cell), 4G-5G neighbor cell relationship (NRrelationship), and 4G X2 configuration parameters (X2interface); 5G network element configuration parameters, such as 5G site configuration parameters (gNodeB function) and 5G cell configuration parameters (NRDUCell); and scenario parameters, such as 4G network element scenario affiliation and 5G network element scenario affiliation parameters.

[0063] Table 1

[0064]

[0065] Table 2

[0066]

[0067] Table 3

[0068]

[0069] Specifically, the 4G site configuration information can be found in the fields shown in Table 1; the 4G cell configuration information can be found in the fields shown in Table 2; the 4-5G neighbor cell relationship information can be found in the fields shown in Table 3; the 4G X2 configuration parameter information can be found in the fields shown in Table 4; the 5G site configuration parameter information can be found in the fields shown in Table 5; the 5G cell configuration parameter information can be found in the fields shown in Table 6; the 4G network element scenario affiliation information can be found in the fields shown in Table 7; and the 5G network element scenario affiliation parameter information can be found in the fields shown in Table 8.

[0070] Table 4

[0071]

[0072] Table 5

[0073]

[0074] Table 6

[0075]

[0076] Table 7

[0077]

[0078] Table 8

[0079]

[0080] Optionally, process automation tools can be used to automatically determine multiple network parameters corresponding to each of the multiple base stations based on multiple 4G network data sent from multiple base stations in the target area, so as to complete the automated verification of multiple network parameters corresponding to each parameter.

[0081] S202. Based on the multiple network parameters corresponding to each of the multiple base stations, determine the score corresponding to each of the multiple network parameters corresponding to the target area.

[0082] Optionally, the score of each network parameter in 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.

[0083] For example, the score of each network parameter can be determined based on whether the multiple network parameters corresponding to each of the multiple base stations meet the preset conditions.

[0084] S203. Based on the score of each network parameter in the multiple network parameters corresponding to the target area, determine the total score corresponding to the target area.

[0085] The total score is used to evaluate the network quality corresponding to the target area.

[0086] 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.

[0087] Optionally, after completing the automatic parameter verification (determining the score corresponding to each network parameter and the total score corresponding to the target area), a list of parameters 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 parameters to be modified and the network management script can then be sent to the city in the form of a work order through the internal work order platform. The city will then confirm and modify the work order to achieve the evaluation and optimization of 4G network quality.

[0088] It should be noted that, in this embodiment of the application, the quality of the 4G network can be assessed by evaluating the parameters of the 4G network.

[0089] In this embodiment, firstly, based on at least one 4G network data transmission from each of multiple base stations within the target area, including network element configuration parameters and scenario parameters, multiple network parameters corresponding to each of the multiple base stations are determined, indicating the network status of each base station. Then, based on the multiple network parameters corresponding to each of the multiple base stations, a score corresponding to each of the multiple network parameters corresponding to the target area is determined. Finally, based on the scores corresponding to each of the multiple network parameters corresponding to the target area, a total score for evaluating 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 4G network parameters transmitted by each base station within the target area, and then the score corresponding to each of the multiple network parameters corresponding to the target area can be determined. This allows for the management and evaluation of network parameters, fully considering the importance of different network parameters, thereby improving the efficiency of network parameter management and evaluation, and ultimately enhancing the rationality of 4G network quality evaluation.

[0090] In a design, such as Figure 3 As shown in the embodiment of this application, in a 4G network quality assessment method, the above-mentioned S201 includes S301-S302:

[0091] S301. Based on multiple 4G network data transmitted by multiple base stations, determine the target identifier corresponding to each 4G network data in the multiple 4G network data.

[0092] The target identifier includes at least one of the following: base station identifier and cell identifier.

[0093] S302. Based on the target identifier corresponding to each of the multiple 4G network data, perform data fitting processing on the multiple 4G network data to obtain multiple network parameters corresponding to each of the multiple base stations.

[0094] For example, taking the determination of network parameters for the 4G / 5G inter-neighbor cell and X2 relationship configuration corresponding to each of multiple base stations as an example, the data processing flow can be as follows a1-a6:

[0095] a1. Generate manufacturer and city data.

[0096] like Figure 4 As shown, combined with Figure 1The system extracts 4G 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.

[0097] 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.

[0098] a2, Fitting neighboring cells to X2 data.

[0099] like Figure 5 As shown, combined with Figure 1 Extract the required 4-5G neighbor cell relationships and related fields of 4G X2 configuration from server 22, and merge two datasets with base station identifiers using the common network element name field through the site configuration dataset generated by a1. Use the base station identifier and the neighbor base station identifier to form "base station identifier - neighbor base station identifier" as the index field to form the required 4-5G neighbor cell dataset 1 and X2 relationship dataset 2.

[0100] It should be noted that since there is no base station identifier in the neighbor cell relationship and X2, the dataset with base station identifier can be merged using the shared network element name field through the site configuration dataset generated by a1.

[0101] a3, X2 and neighboring cells are matched and distinguished.

[0102] like Figure 6 As shown, using the "base station identifier - neighboring base station identifier" obtained from a2 as an index, datasets 1 and 2 are merged.

[0103] Optionally, use the anti-join function to merge datasets 1 and 2.

[0104] Optionally, after merging datasets 1 and 2, records in the main set that do not match the auxiliary set can also be exported.

[0105] Specifically, when using dataset 1 as the main set, export the data in the 4-5G neighbor cell data that does not match the X2 relationship, i.e., dataset 3 with X2 but no neighbor cells; when using dataset 2 as the main set, export the data in the X2 relationship data that does not match the 4-5G neighbor cells, i.e., dataset 4 with neighbor cells but no X2.

[0106] a4. Fitting and cleaning up community configuration data.

[0107] like Figure 7 As shown, different operators (such as...) Figure 7 The 5G site and cell configuration data of operators A and B in the dataset were fitted by network element names and then merged after adding the "Public Land Mobile Network (PLMN) Identifier" field according to the data source to obtain dataset 5.

[0108] Furthermore, such as Figure 8 As shown, dataset 3 (with X2 no neighbor cells) and 4G cell configuration data are indexed together using base station identifiers and local cell identifiers. The resulting datasets are then filtered, such as by network element name (removing indoor distributed antenna system sites), by NB-IoT cell identifier (removing NB-IoT cells), by downlink frequency point, and by operator-shared sites. The filtered data is then further filtered to remove useless fields. After fitting the dataset with dataset 5 using base station identifiers, cell identifiers, and (neighboring) base station PLMN identifiers, LampSite cells are removed by filtering using LampSite cell identifiers. Finally, redundant fields are filtered to obtain the cleaned dataset 6 (with X2 no neighbor cells).

[0109] a5. Scene attribution data fitting.

[0110] like Figure 9 As shown, the 5G network element scenario affiliation of operators A and B is determined respectively. After adding the "base station PLMN identifier" field according to the data table source, the data is merged. At the same time, the longitude and latitude names are changed to NR longitude and NR latitude. Then, the dataset is fitted with dataset 6 through base station identifier, cell identifier and (neighboring) base station PLMN identifier. At the same time, the 4G network element scenario affiliation is also fitted with dataset 6 through base station identifier, local cell identifier and dataset 6 to obtain dataset 7 with X2 and no neighboring cells, which contains the longitude and latitude of source cell and target cell in 4-5 neighboring cells.

[0111] a6. Generation of community list and summary data.

[0112] like Figure 10 As shown, firstly, the latitude and longitude of the source cell and target cell in dataset 7 are substituted into the latitude and longitude distance calculation formula to calculate the latitude and longitude distance. The "station spacing judgment" field is generated and assigned the value "station spacing ≤ 1000m" or "station spacing > 1000m" according to the latitude and longitude distance calculation result. Cells with station spacing ≤ 1000m are set as cells that need to add X2. The list of cells with X2 but no neighboring cells is output and summarized by manufacturer and city, and the neighboring base station type is counted to obtain the summary data of cells with X2 but no neighboring cells.

[0113] Meanwhile, the dataset 4 from step 3 is exported as a list of cells with neighboring cells but no X2. Similarly, by summarizing by manufacturer and city, and counting the types of neighboring base stations, a summary data of cells with neighboring cells but no X2 is obtained. In order to be consistent with the data format of cells with X2 but no neighboring cells, a "cell spacing judgment" field also needs to be generated and the rule is set to "with X2 but no matching".

[0114] Finally, the aggregated data of having neighboring cells but no X2 and having X2 but no neighboring cells are merged to obtain the aggregated data required for the automated verification of X2 and neighboring cell relationship parameters in the co-construction and sharing scenario.

[0115] 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 4G network quality assessment. Furthermore, the network parameters are used to assess the 4G network quality, thereby improving the rationality of the 4G network quality assessment.

[0116] In a design, such as Figure 11 As shown in the embodiment of this application, in a 4G network quality assessment method, the above-mentioned S202 includes S401-S402:

[0117] S401. For any one of the multiple network parameters corresponding to the target area, based on any one of the network parameters corresponding to each of the multiple base stations, determine the first proportion of base stations whose network parameters satisfy the preset conditions among the multiple base stations.

[0118] It should be noted that for each of the multiple network parameters corresponding to the target area, there is a corresponding first proportion.

[0119] Optionally, the preset conditions can be determined according to specific business needs, such as whether the base station is compliant.

[0120] 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.

[0121] For example, if there are 1000 base stations in the target area, and 800 base stations have network parameter A that meets the preset condition, then the first proportion corresponding to network parameter A is 80%.

[0122] S402. Based on the first proportion, determine the score corresponding to any network parameter of the target area.

[0123] 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.

[0124] 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 base stations) in the preset area to the total number of configurations (first proportion) can be determined. Then, based on the range corresponding to the size of the first proportion, the score corresponding to any network parameter of the target area can be determined.

[0125] 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.

[0126] 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%.

[0127] Specifically, for any network parameter, when the corresponding first percentage is 90% or higher (including 90%), the deduction coefficient is 10%, and the score for that network parameter is 100 * 10% = 10; when the corresponding first percentage is between 40% and 90% (including 40%), the deduction coefficient is 60%, and the score for that network parameter is 100 * 60% = 60.

[0128] 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.

[0129] Optionally, the score for any network parameter corresponding to the target area can be determined by identifying the proportion of base stations whose corresponding network parameter does not meet the preset conditions among multiple base stations.

[0130] In this embodiment of the application, 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 4G network quality assessment.

[0131] 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 12As shown in the embodiment of this application, a 4G network quality assessment method further includes S501-S502:

[0132] 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.

[0133] 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 score corresponding to each network parameter in the multiple network parameters corresponding to each scenario in multiple scenarios.

[0134] 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.

[0135] 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).

[0136] For example, the target area can be divided into rural areas, border areas, urban areas, universities, and subways, resulting in multiple scenarios.

[0137] 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.

[0138] 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).

[0139] 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.).

[0140] Table 9

[0141]

[0142] For example, the weight values ​​(scenario coefficients) corresponding to each scenario are shown in Table 9.

[0143] 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.

[0144] 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.

[0145] Optionally, after determining the score of each network parameter in the multiple network parameters corresponding to each scenario in multiple scenarios, the score of each network parameter in the multiple network parameters corresponding to each scenario in multiple scenarios can be further determined to be the score of each network parameter in the multiple network parameters corresponding to the target region.

[0146] 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, the score of each network parameter in the multiple network parameters of each vendor in multiple vendors can be determined, and then the score of each network parameter in the multiple network parameters of the target area can be determined based on the score of each network parameter in the multiple network parameters of each vendor in multiple vendors.

[0147] 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 score corresponding to each network parameter in the multiple network parameters corresponding to each scenario is determined, so as to improve the rationality of 4G network quality assessment.

[0148] In a design, such as Figure 13 As shown in the embodiment of this application, in a 4G network quality assessment method, the above-mentioned S502 includes S601-S602:

[0149] S601. For 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 base station of at least one base station of any one of the multiple vendors, determine the second proportion of base stations whose network parameters satisfy the preset conditions in at least one base station.

[0150] S602. Based on the second proportion, determine the score corresponding to any one of the multiple network parameters for any given scenario.

[0151] It should be noted that the number of second percentages is the same as the number of manufacturers corresponding to the base stations in any given scenario; one manufacturer corresponds to one second percentage in one scenario.

[0152] Optionally, for any one of the multiple network parameters corresponding to any one of the multiple scenarios, the manufacturers corresponding to all base stations in that scenario can be determined. Then, based on whether the network parameter corresponding to each base station meets the preset conditions and the number of base stations corresponding to each manufacturer, the proportion of base stations of each manufacturer that meet the preset conditions in all base stations of that manufacturer in that scenario can be determined, thus obtaining the second proportion corresponding to that manufacturer.

[0153] Furthermore, based on the second proportion of each vendor in any given scenario and the weight of each vendor, the score of any one of the multiple network parameters in that given scenario is determined.

[0154] 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 second percentage of vendor 1 is 90%, and the deduction coefficient for the second percentage of vendor 2 is 85%. Vendor 1 has a weight of 65%, and vendor 2 has a weight of 35%. Therefore, the score for the corresponding network parameter in the subway scenario is 100*90%*65%+100*85%*35%=88.25.

[0155] In this embodiment of the application, the score of 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 second proportion of each manufacturer in any one of the multiple network parameters in any one of the multiple scenarios, and then the score corresponding to any one scenario is obtained. This fully considers the impact of each manufacturer's differences on network quality assessment, so as to improve the rationality of 4G network quality assessment.

[0156] In a design, such as Figure 14 As shown, the 4G network quality assessment method provided in this application embodiment further includes S701, and the above-mentioned S203 includes S702:

[0157] 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.

[0158] Among them, the parameter types include: basic configuration parameters, co-construction and sharing parameters, and performance optimization parameters.

[0159] Optional, such as Figure 15As shown, various network parameters are categorized. Referring to Table 10, basic configuration parameters mainly include base station and cell basic parameters, such as planning parameters, external definitions (external environment definition information for base stations and cells), geographical location information (such as latitude and longitude), and cell activation status. Referring to Table 11, co-construction and sharing parameters mainly include parameters for co-construction and sharing sites, such as shared switches, transmission route configuration, and resource limitations. Referring to Tables 12 to 16, performance optimization parameters mainly include parameters that affect performance indicators, such as parameters related to Radio Resource Control (RRC) (such as enhanced RRC reconstruction protection threshold and repeated RRC reconstruction protection timer) and parameters related to RSRP (Reference Signal Receiving Power) (such as downlink RSRP distribution index threshold 2 and downlink RSRP distribution index threshold 3).

[0160] Table 10

[0161] type parameter Recommended value 4G latitude and longitude Not empty / Correct format 4G Time slot allocation Dual-cycle 7:3 time slot ratio 4G Time slot structure SS104 4G External definition Correct format 4G Community activation status normal

[0162] Table 11

[0163]

[0164] Table 12

[0165]

[0166] Table Thirteen

[0167]

[0168] 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 12 and 13. 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 14, 15, and 16.

[0169] Table 14

[0170]

[0171] Table 15

[0172]

[0173] Table 16

[0174]

[0175] 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.

[0176] 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.

[0177] For example, such as Figure 16 As shown, combined with Formula 1, the total score for the target region can be determined by the score corresponding to each network parameter.

[0178] City Score = Σ Parameter Item Score = 100 * Deduction Coefficient * Category Coefficient * Item Coefficient * Manufacturer Coefficient

[0179] Scene coefficient formula 1

[0180] Optionally, the weight value (parameter coefficient) corresponding to each coefficient can be determined based on expert experience.

[0181] Optional, such as Figure 16 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.

[0182] Table 17

[0183]

[0184] Table 18

[0185]

[0186] 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.

[0187] Specifically, the index coefficients of the basic performance parameters, the corresponding index category coefficients and index item coefficients are shown in Tables 17 and 18.

[0188] 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).

[0189] Specifically, the index coefficients for the special optimization parameters are shown in Table 19.

[0190] Table 19

[0191] Special Project Name Index coefficient Intermittent audio 0.15 4G dwell time 0.15 High backflow 0.2 Inefficient 0.2 ……

[0192] 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.

[0193] This application embodiment can divide a 4G 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 a logical functional division; other division methods may be used in actual implementation.

[0194] Figure 17 This is a schematic diagram of the structure of a 4G network quality assessment device provided in an embodiment of this application. Figure 17 As shown, the 4G network quality assessment device 40 is used to improve the rationality of 4G network quality assessment, for example, for performing... Figure 2 A 4G network quality assessment method is shown. The 4G network quality assessment device 40 includes: a determination unit 401.

[0195] The determining unit 401 is used to determine multiple network parameters corresponding to each of the multiple base stations based on multiple 4G network data sent by multiple base stations in the target area. Each of the multiple base stations sends at least one 4G network data. The 4G 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.

[0196] The determining unit 401 is used to determine the score of each network parameter in the multiple network parameters corresponding to the target area based on the multiple network parameters corresponding to each of the multiple base stations.

[0197] The determining unit 401 is used to determine the total score corresponding to the target area based on the score of each network parameter in the multiple network parameters corresponding to the target area. The total score is used to evaluate the network quality corresponding to the target area.

[0198] In a design, such as Figure 17 As shown, the 4G network quality assessment device also includes a processing unit 402.

[0199] The determining unit 401 is used to determine the target identifier corresponding to each 4G network data in the multiple 4G network data sent by multiple base stations. The target identifier includes at least one of the following: base station identifier and cell identifier.

[0200] The processing unit 402 is used to perform data fitting processing on the multiple 4G network data based on the target identifier corresponding to each of the multiple 4G network data, to obtain multiple network parameters corresponding to each of the multiple base stations.

[0201] In one design, a determining unit 401 is used to determine, for any one of the multiple network parameters corresponding to the target area, the first proportion of base stations whose network parameters satisfy preset conditions among the multiple base stations based on any one of the network parameters corresponding to each base station among the multiple base stations.

[0202] The determining unit 401 is used to determine the score corresponding to any network parameter of the target area based on the first proportion.

[0203] In one design, the target area includes multiple scenarios, each scenario in the multiple scenarios corresponds to at least one base station in the multiple base stations, each base station in the multiple base stations corresponds to one vendor, the multiple base stations correspond to multiple vendors, and one vendor corresponds to at least one base station. The determining unit 401 is used to determine the weight corresponding to each scenario in the multiple scenarios and the weight corresponding to each vendor in the multiple vendors based on the scenario to which each base station in the multiple base stations belongs and the vendor corresponding to each base station.

[0204] The determining unit 401 is used to determine the score of each network parameter in the multiple network parameters corresponding to each scenario in multiple scenarios, the weight of each manufacturer in multiple scenarios, and the multiple network parameters corresponding to each base station in multiple scenarios.

[0205] In one design, determining unit 401 is used to determine, 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 of the at least one base station corresponding to any one of the multiple vendors, a second proportion of the base stations whose network parameters satisfy preset conditions in at least one base station.

[0206] The determining unit 401 is used to determine the score of any one of the multiple network parameters corresponding to any scenario based on the second proportion.

[0207] 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.

[0208] The determining unit 401 is used to determine the total score 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.

[0209] 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 18 As shown, an electronic device 70 is used to improve the rationality of 4G network quality assessment, for example, for performing... Figure 2 This illustrates a 4G 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.

[0210] 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.

[0211] As one embodiment, processor 701 may include one or more CPUs, for example Figure 18 CPU 0 and CPU 1 are shown in the diagram.

[0212] 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.

[0213] 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 4G network quality assessment method provided in this application embodiment.

[0214] In another possible implementation, the memory 702 can also be integrated with the processor 701.

[0215] 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 18 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.

[0216] It should be pointed out that, Figure 18 The structure shown does not constitute a limitation on the electronic device 70. Except... Figure 18 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.

[0217] As an example, combined Figure 17 The functions implemented by the determining unit 401 and the processing unit 402 in the 4G network quality assessment device 40 are the same as those of the other two units. Figure 18 The processor 701 in it has the same function.

[0218] Optional, such as Figure 18 As shown, the electronic device 70 provided in this application embodiment may further include a communication interface 704.

[0219] 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.

[0220] In one design, the communication interface in the electronic device provided in this application embodiment can also be integrated into the processor.

[0221] 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.

[0222] 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.

[0223] Embodiments of this application provide a computer program product containing instructions that, when executed on a computer, cause the computer to perform a 4G network quality assessment method as described in the above method embodiments.

[0224] 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.

[0225] 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.

[0226] 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 evaluating the quality of a 4G network, characterized in that, The method includes: Based on multiple 4G network data transmissions from multiple base stations within a target area, multiple network parameters corresponding to each of the multiple base stations are determined. Each of the multiple base stations transmits at least one 4G network data transmission. The 4G 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 of each base station. The target area includes multiple scenarios, each scenario corresponds to at least one base station among the multiple 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. Based on the scenario to which each of the multiple base stations belongs and the manufacturer corresponding to each base station, determine the weight corresponding to each of the multiple scenarios and the weight corresponding to each of the multiple manufacturers. 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 score corresponding to each network parameter in the multiple network parameters corresponding to each scenario in the multiple scenarios is determined; the type of scenario is determined according to business requirements; the vendor is the base station manufacturer; Based on the multiple network parameters corresponding to each of the multiple base stations, determine the score corresponding to each of the multiple network parameters corresponding to the target area; Based on the score of each network parameter among the multiple network parameters corresponding to the target region, a total score is determined for the target region, and the total score is used to evaluate the network quality corresponding to the target region.

2. The method according to claim 1, characterized in that, The method involves determining multiple network parameters for each of the multiple base stations based on multiple 4G network data transmitted from multiple base stations within the target area, including: Based on the multiple 4G network data transmitted by the multiple base stations, a target identifier corresponding to each 4G network data is determined. The target identifier includes at least one of the following: base station identifier and cell identifier. Based on the target identifier corresponding to each of the multiple 4G network data, data fitting processing is performed on the multiple 4G network data to obtain multiple network parameters corresponding to each of the multiple base stations.

3. The method according to claim 1 or 2, characterized in that, The step of determining the score for each network parameter among the multiple network parameters corresponding to each of the plurality of base stations, including: For any one of the multiple network parameters corresponding to the target area, based on the network parameter corresponding to each of the multiple base stations, determine the first proportion of base stations whose network parameter satisfies the preset conditions among the multiple base stations; Based on the first proportion, determine the score corresponding to any one of the network parameters for the target region.

4. The method according to claim 1, characterized in that, The step of determining the score for each network parameter among the multiple network parameters corresponding to each of the multiple scenarios, based on the weight corresponding to each of the multiple scenarios, the weight corresponding to each of the multiple vendors, and the multiple network parameters corresponding to each of the multiple base stations, includes: For any one of the multiple network parameters corresponding to any one of the multiple scenarios, based on the network parameter corresponding to each of the at least one base station of any one of the multiple vendors, determine the second proportion of base stations whose network parameter satisfies the preset conditions in the at least one base station. Based on the second proportion, determine the score corresponding to any one of the multiple network parameters in any given scenario.

5. The method according to claim 1 or 2, characterized in that, The method further includes: Determine the parameter type corresponding to each of the multiple network parameters, and 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. The process of determining the total score for the target region based on the score of each network parameter among multiple network parameters corresponding to the target region includes: The total score for the target region is determined based on the score of each network parameter and the weight of each network parameter among the multiple network parameters corresponding to the target region.

6. A 4G network quality assessment device, characterized in that, The device includes: a determining unit; The determining unit is configured to determine multiple network parameters corresponding to each of the multiple base stations based on multiple 4G network data transmitted by multiple base stations in the target area. Each of the multiple base stations transmits at least one 4G network data transmission. The 4G 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. The target area includes multiple scenarios. Each scenario in the multiple scenarios corresponds to at least one base station in the multiple base stations. Each base station in the multiple base stations corresponds to one vendor. The multiple base stations correspond to multiple vendors, and one vendor corresponds to at least one base station. The determining unit is configured to determine the weight corresponding to each scenario in the multiple scenarios and the weight corresponding to each vendor in the multiple vendors based on the scenario to which each base station belongs and the vendor corresponding to each base station. The determining unit is further configured to determine the score of each network parameter in the multiple network parameters corresponding to each of the multiple scenarios based on the weight corresponding to each scenario in the multiple scenarios, the weight corresponding to each manufacturer in the multiple manufacturers, and the multiple network parameters corresponding to each base station in the multiple base stations; the type of scenario is determined according to business requirements; the manufacturer is the base station manufacturer; The determining unit is used to determine the score corresponding to each of the multiple network parameters corresponding to the target area based on the multiple network parameters corresponding to each of the multiple base stations. The determining unit is used to determine the total score corresponding to the target region based on the score of each of the multiple network parameters corresponding to the target region, and the total score is used to evaluate the network quality corresponding to the target region.

7. The 4G network quality assessment device according to claim 6, characterized in that, The 4G network quality assessment device further includes: a processing unit; The determining unit is configured to determine a target identifier corresponding to each 4G network data in the plurality of 4G network data based on the plurality of 4G network data sent by the plurality of base stations, wherein the target identifier includes at least one of the following: base station identifier and cell identifier; The processing unit is used to perform data fitting processing on the multiple 4G network data based on the target identifier corresponding to each of the multiple 4G network data, to obtain multiple network parameters corresponding to each of the multiple base stations.

8. The 4G network quality assessment device according to claim 6 or 7, characterized in that, The determining unit is used to determine, for any one of the multiple network parameters corresponding to the target area, a first proportion of the base stations whose network parameters satisfy preset conditions among the multiple base stations, based on the network parameters corresponding to each of the multiple base stations. The determining unit is used to determine the score corresponding to any one of the network parameters corresponding to the target region based on the first proportion.

9. The 4G network quality assessment device according to claim 6, characterized in that, The determining unit is used to determine, for any one of the multiple network parameters corresponding to any one of the multiple scenarios, and based on the network parameter corresponding to each of the at least one base station corresponding to any one of the multiple vendors, the second proportion of base stations whose network parameter satisfies a preset condition in the at least one base station. The determining unit is used to determine the score corresponding to any one of the multiple network parameters in any one scenario based on the second proportion.

10. The 4G network quality assessment device according to claim 6 or 7, characterized in that, The determining unit 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. The determining unit is used to determine the total score 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.

11. An electronic device, characterized in that, include: 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 4G network quality assessment method according to any one of claims 1-5.

12. A computer-readable storage medium for storing one or more programs, characterized in that, The one or more programs include instructions that, when executed by a computer, cause the computer to perform a 4G network quality assessment method according to any one of claims 1-5.

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