Base Station Network Access Quality Inspection Method, System, Device and Storage Medium

Abstract

The embodiments of the present invention provide a method, system, device and storage medium for quality inspection of base station network access, which relate to a quality inspection system for base station network access. The method includes: obtaining base station data of a base station through a data acquisition module; evaluating quantization indicators in different dimensions according to the base station data through a quantization evaluation module to obtain a quality inspection score of the base station; wherein, the quality inspection score of the base station is determined based on a first weight coefficient of quantization indicators in different dimensions, and the first weight coefficient is adaptively adjusted based on the score loss situation of quantization indicators in different dimensions; and displaying the quality inspection score of the network-accessed base station through a quality inspection result output module to obtain the quality inspection result of the base station. Without relying on manual quality inspection of network-accessed base stations, a comprehensive evaluation and quality inspection of network-accessed base stations are carried out from quantization indicators in different dimensions, and specific construction problems of the base station can be presented when displaying the quality inspection result based on the adaptively adjusted first weight coefficient, exposing the short board of the constructed base station and strengthening the targeted control of the base station network access process.

Description

Technical Field

[0001] The present invention relates to the field of communication technology, and in particular to a base station network access quality inspection method, a base station network access quality inspection system, a corresponding electronic device and a corresponding computer storage medium. Background Art

[0002] 5G (5th Generation Mobile Communication Technology) base station network access quality inspection is helpful to avoid network access quality problems caused by 5G base stations during the construction phase during engineering optimization and daily optimization. Therefore, it is usually necessary to conduct network access quality inspection on base stations during the construction phase.

[0003] Among the technologies related to the quality inspection of base station network access, some 5G base station network access quality inspections rely on manual work, mainly through manual inspection of the relevant data of the equipment network management base station to judge the opening of the base station; some use the existing IT (Information Technology) system to achieve convenient statistics of some projects; some propose a base station acceptance system, but its base station acceptance focuses on the base station data collected on site. In general, the technologies related to the quality inspection of base station network access lack systematic and comprehensive quality inspection. Summary of the invention

[0004] In view of the above problems, embodiments of the present invention are proposed to provide a base station network access quality inspection method, a base station network access quality inspection system, a corresponding electronic device and a corresponding computer storage medium that overcome the above problems or at least partially solve the above problems.

[0005] The embodiment of the present invention discloses a base station network access quality inspection method, which relates to a base station network access quality inspection system. The base station network access quality inspection system includes a data acquisition module, a quantitative evaluation module, and a quality inspection result output module. The method includes:

[0006] Acquire base station data of the base station through the data acquisition module;

[0007] The quantitative evaluation module evaluates the quantitative indicators of different dimensions according to the base station data to obtain a quality inspection score of the base station; wherein the quality inspection score of the base station is determined based on a first weight coefficient of the quantitative indicators of different dimensions, and the first weight coefficient is adaptively adjusted based on the loss of the quantitative indicators of different dimensions;

[0008] The quality inspection result output module is used to display the quality inspection score of the base station accessed, and the quality inspection result of the base station is obtained.

[0009] The embodiment of the present invention further discloses a base station network access quality inspection system, the system comprising:

[0010] A data acquisition module, used to acquire base station data of the base station;

[0011] A quantitative evaluation module, used to evaluate the quantitative indicators of different dimensions according to the base station data to obtain the quality inspection score of the base station; wherein the quality inspection score of the base station is determined based on the first weight coefficient of the quantitative indicators of different dimensions, and the first weight coefficient is adaptively adjusted based on the loss of the quantitative indicators of different dimensions;

[0012] The quality inspection result output module is used to display the quality inspection scores of the base stations connected to the network and obtain the quality inspection results of the base stations.

[0013] An embodiment of the present invention also discloses an electronic device, comprising: a processor, a memory, and a computer program stored in the memory and capable of running on the processor, wherein when the computer program is executed by the processor, the steps of any one of the base station network access quality inspection methods are implemented.

[0014] An embodiment of the present invention further discloses a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of any one of the base station network access quality inspection methods are implemented.

[0015] The embodiments of the present invention include the following advantages:

[0016] In an embodiment of the present invention, the base station network access quality inspection system includes a data acquisition module, a quantitative evaluation module and a quality inspection result output module. The base station data of the base station can be mainly acquired through the data acquisition module, and then the quantitative evaluation module is used to evaluate the quantitative indicators of different dimensions based on the acquired base station data, so as to obtain a quality inspection score that can be adaptively adjusted based on the loss of quantitative indicators of different dimensions, so as to display the quality inspection score of the network access base station through the quality inspection result output module, and obtain the quality inspection result of the base station. The base station network access quality inspection system automatically performs quantitative evaluation and quality inspection result output on the acquired base station data, and comprehensively evaluates the network access quality inspection of the base station from quantitative indicators of different dimensions while avoiding reliance on manual network access quality inspection. Based on the weight coefficient that can be adaptively adjusted, the specific construction problems of the base station are presented when the quality inspection results are displayed, exposing the shortcomings of the constructed base station, and strengthening the targeted management and control of the 5G base station network access process. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a schematic diagram of the framework of a base station network access quality inspection system provided by an embodiment of the present invention;

[0018] Figure 2 It is a flowchart of a method for quality inspection of base station network access according to an embodiment of the present invention;

[0019] Figure 3 It is a schematic diagram showing the quality inspection results of a base station provided by an embodiment of the present invention;

[0020] Figure 4 It is a flowchart of the steps of another embodiment of the base station network access quality inspection method of the present invention;

[0021] Figure 5 It is a schematic diagram showing the construction of a triangular network provided by an embodiment of the present invention;

[0022] Figure 6 It is a schematic diagram showing the TAC influence area provided by an embodiment of the present invention;

[0023] Figure 7 It is a schematic diagram showing the division of the neighbor cell set of the "visual angle" provided by an embodiment of the present invention;

[0024] Figure 8 It is a schematic diagram showing the calculation of longitude and latitude coordinates provided by an embodiment of the present invention;

[0025] Figure 9 It is a schematic diagram showing cell benchmarking provided by an embodiment of the present invention;

[0026] Figure 10 It is a schematic diagram showing the application scenario of base station network access quality inspection provided by an embodiment of the present invention;

[0027] Figure 11 It is a structural block diagram of an embodiment of a base station network access quality inspection system of the present invention. Detailed implementation manners

[0028] To make the above objects, features, and advantages of the present invention more obvious and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific implementation manners.

[0029] To facilitate the understanding of those skilled in the art of the present application, the following explains the terms or nouns involved in the following embodiments of the present invention:

[0030] Equipment network management: It can refer to network management equipment, which is the equipment required for network management.

[0031] Northbound interface: It is an interface defined for user access and management of the network. Users usually use the relevant network management programs in the application layer defined and developed by the northbound interface to achieve access and management of the network by using this relevant network management program.

[0032] Big data lake: A centralized repository that allows storing all structured and unstructured data at any scale.

[0033] AAU: Active Antenna Unit, an active antenna unit, which is usually used as a signal transmission device for 5G base stations.

[0034] RRU: Remote Radio Unit, the remote radio unit, is a device that converts the baseband optical signal into a radio frequency signal and amplifies it for transmission at the remote end.

[0035] CPRI: Common Public Radio Interface, a standardized protocol mainly used to define the digital interface between the radio frequency device control and radio frequency devices of a wireless infrastructure base station.

[0036] PCI: Physical Cell Identifier, the physical cell identifier, by which the terminal differentiates the wireless signals of different cells in the 5G network.

[0037] PRACH: Physical Random Access Channel, the physical random access channel.

[0038] TAC: Tracking Area Code, the tracking area code, which is the area code used for terminal location management.

[0039] Delaunay triangulation: Delaunay triangle, a set of connected but non-overlapping triangles, and the circumcircles of these triangles do not contain any other points in this surface area.

[0040] CQI: Channel Quality Indicator, the channel quality indicator, used to represent the quality of the current channel, which usually corresponds to the signal-to-noise ratio of the channel.

[0041] Base Line: The lowest baseline.

[0042] Reference Line: The reference line.

[0043] During the process of engineering optimization and daily optimization, there may be subsequent legacy problems caused by 5G base stations with network access quality problems during the construction stage, which bring troubles to the subsequent network optimization and maintenance work. Especially in the case where the construction period is tight and the base stations are put into the network in order to catch up with the progress, problems such as incomplete opening of base station cells or equipment, equipment failures, improper or incorrect parameter configurations may occur. In severe cases, it may even lead to the need for the already networked base stations to roll back to the engineering commissioning link, which has an adverse impact on network quality and user perception. Therefore, it is necessary to conduct quality inspections on the base stations during network access.

[0044] One of the core ideas of the embodiments of the present invention lies in the base station access quality inspection method for comprehensive quantitative evaluation. It mainly can comprehensively and quantitatively evaluate by constructing a multi-dimensional evaluation model from quantitative indicators in different dimensions such as equipment online, working status, key parameters, service bearing, and key performance, so as to objectively and accurately grasp the quality of newly accessed sites; and based on the dynamic adaptive adjustment of the corresponding weight coefficients of the quantitative indicators in each dimension, when presenting the quality inspection results, the specific construction problems of the base station are presented, the short board of the constructed base station is exposed, the problem is directly revealed, the unnecessary calculation amount is reduced, and the targeted control of the 5G base station access process is strengthened; and, also based on the evaluation perspective of the influence domain and the neighbor area auditing algorithm from the "visual angle", through the coordination with the set baseline and reference line, the newly accessed base station is compared with the surrounding comparable base stations, so as to achieve the purpose of scene-based adaptive auditing of the newly accessed base station, realize the effect of accurate auditing within the influence domain, comprehensively, objectively and accurately evaluate the work of the newly accessed 5G base station, and greatly improve the auditing efficiency of the base station access.

[0045] Referring to Figure 1 , a schematic framework diagram of the base station access quality inspection system provided by the embodiments of the present invention is shown. The base station access quality inspection system 110 includes a data acquisition module 111, a quantitative scoring module 112, and a quality evaluation module 113, and can comprehensively and quantitatively evaluate the newly accessed base station based on the foregoing modules of the base station access quality inspection system.

[0046] Specifically, the data acquisition module 111 is responsible for collecting relevant data of the newly accessed base station. It mainly can collect base station data through the device network management, and the collected data can include relevant base station data such as configuration data, fault alarm data, and performance index data. In addition to collecting relevant data of the base station, it can also be used as a data providing module of the base station access quality inspection system. Specifically, it can parse the collected relevant base station data and store the parsed data in the big data lake. The data stored in the big data lake can be used as the data source for the quantitative scoring module 112 to perform quantitative scoring.

[0047] The quantitative scoring module 112 is responsible for constructing a multi-dimensional evaluation model to comprehensively and quantitatively evaluate based on quantitative indicators in different dimensions. It mainly constructs a multi-dimensional evaluation model for quantitative indicators in different dimensions. The quantitative indicators in different dimensions can include indicators such as equipment online, working status, key parameters, service bearing, and key performance. Among them, the constructed multi-dimensional evaluation model can be given a dynamic adaptive adjustment mechanism for weight coefficients, construct an auditing algorithm for the influence domain based on a triangular network, and a scene-based adaptive auditing algorithm through the coordination of the baseline and the reference line, so that during the access quality inspection, auditing can be carried out within the range of the newly accessed 5G base station and the related base stations affected by it, so as to evaluate the working conditions of the newly accessed 5G base station.

[0048] The quality assessment module 113 can be equivalent to the quality inspection result output module, which is responsible for displaying the quality inspection scores of the base stations obtained by the quantization scoring module 112 to present the quality inspection results of the base stations. When displaying the quality inspection results, it can mainly conduct quality inspection evaluations from two perspectives: individual items and overall.

[0049] Refer to Figure 2 , which shows the step flowchart of an embodiment of the base station network access quality inspection method of the present invention, related to the Figure 1 shown base station network access quality inspection system. The base station network access quality inspection system includes a data acquisition module, a quantization evaluation module, and a quality inspection result output module, and specifically may include the following steps:

[0050] Step 201, obtain the base station data of the base station through the data acquisition module;

[0051] In the embodiment of the present invention, the data acquisition module can mainly obtain various types of data required for subsequent quantization evaluation of newly networked base stations. The various types of data required are usually relevant base station data, mainly including configuration data, fault alarm data, performance index data, session statistics data, etc., to provide data for the base station network access quality inspection system.

[0052] In practical applications, when the data acquisition module obtains relevant base station data, it usually can collect through the northbound interface of the wireless device network management, and then parse the collected relevant base station data according to the data file format, and store the parsed data in the big data lake. The data stored in the big data lake can be used as the data source for the quantization scoring module to conduct quantization scoring.

[0053] Among them, among the obtained relevant base station data, the configuration data can be used to reflect the relevant configuration information of the newly networked base station, such as base station parameter configuration, device configuration, etc.; the fault alarm data can be used to reflect the relevant fault alarm information of the newly networked base station, such as alarm information, alarm type, etc.; the performance index data is mainly used to measure the working performance of the newly networked base station, such as traffic, number of users, key performance, etc.; the session statistics data can be used to reflect the working conditions of the newly networked base station when conducting cell work, such as cell connection success rate, cell disconnection rate, cell handover success rate, etc.

[0054] Step 202, evaluate the quantization indicators of different dimensions according to the base station data through the quantization evaluation module to obtain the quality inspection score of the base station;

[0055] The base station data obtained by the data acquisition module can provide a data source for the quantization evaluation module to conduct quantization evaluation. At this time, the quantization evaluation module can evaluate the base station data from the big data lake to obtain the quality inspection score for the newly networked base station.

[0056] In the embodiment of the present invention, during the process of evaluating base station data, the quantization evaluation module can mainly evaluate quantization indicators of different dimensions according to the base station data. The quantization indicators of different dimensions can include indicators such as device online, working status, key parameters, service bearing, and key performance. In practical applications, a multi-dimensional quantization evaluation model can be constructed from the aspects of quantization indicators of different dimensions. Based on the use of the multi-dimensional quantization evaluation model, a comprehensive quantization evaluation based on quantization indicators of different dimensions can be realized, so as to objectively and accurately grasp the quality of newly added site base stations.

[0057] In practical applications, the evaluation of the quality inspection score of a base station is mainly manifested as the use of the constructed multi-dimensional quantization evaluation module. The obtained quality inspection score can include the total quality inspection score and the score situation for different quantization indicators. During the calculation process of the score situation for different quantization indicators, it can be specifically manifested as obtaining the single-item score of each quantization indicator and determining the first weight coefficient of each quantization indicator according to the base station data. At this time, the single-item score of each quantization indicator and the first weight coefficient of each quantization indicator can be used to obtain the score situation of each quantization indicator; then, during the calculation process of the total quality inspection score, the score situations of each quantization indicator can be summed up to obtain the total quality inspection score of the base station.

[0058] Specifically, in the constructed multi-dimensional quantization evaluation model, the quantization indicators of different dimensions can be set using a 100-point system. Its evaluation system is mainly shown in Table 1. The single-item score of each quantization indicator is set to 100. At this time, the score situations of each quantization indicator can be summed up according to the weight coefficient to obtain the total quality inspection score of the base station.

[0059] Serial Number Quantitative Index Weight Score Situation Single Item Score 1 Equipment Online k1 Score1 100 2 Working Status k2 Score2 100 3 Key Parameters k3 Score3 100 4 Business Load k4 Score4 100 5 Key Performance k5 Score5 100

[0060] Table 1

[0061] The formula for the total quality inspection score of a base station can be shown as formula (1):

[0062] Quality_Score=k1*Score1+k2*Score2+k3*Score3+k4*Score4+k5*Score5 Formula (1)

[0063] As shown in Table 1, k1 is the first weight coefficient for the device online index, and Score1 is the score of the device online index; k2 is the first weight coefficient for the working status index, and Score2 is the score of the working status index; k3 is the first weight coefficient for the parameter index, and Score3 is the score of the parameter index; k4 is the first weight coefficient for the service carrying index, and Score4 is the score of the service carrying index; k5 is the first weight coefficient for the performance index, and Score5 is the score of the performance index.

[0064] Among them, the constructed multi-dimensional evaluation model can endow a dynamic adaptive adjustment mechanism for the weight coefficient. In the process of evaluating the quantization indexes of different dimensions according to the base station data, the quality inspection score obtained mainly can be determined by means of the first weight coefficient of the quantization indexes of different dimensions, and this first weight coefficient can be adaptively adjusted based on the score loss situation of the quantization indexes of different dimensions, that is, the quality inspection score can be adaptively adjusted based on the score loss situation of the quantization indexes of different dimensions.

[0065] It should be noted that the first weight coefficient is used to adjust the proportion of different quantization indexes in the overall quality. In actual situations, the first weight coefficient is positively correlated with the score loss situation, that is, the first weight coefficient inclines towards the short-board items. For example, it is manifested that the first weight coefficient corresponding to the quantization index with a large score loss is larger, so as to present the specific construction problems of the base station when displaying the quality inspection results, expose the short boards of the constructed base stations, directly reveal the problems, reduce unnecessary calculation amounts, and strengthen the targeted control of the 5G base station network access process.

[0066] Since the first weight coefficient can be adaptively adjusted based on the score loss situation of the quantization indexes of different dimensions, then for the determination of the first weight system, it can be mainly determined based on the score loss situation of the quantization indexes. It can be mainly determined through the quality inspection results of the base stations accessing the network within a preset time period. Specifically, the quality inspection results of the base stations accessing the network within a preset time period can be obtained, and the quality inspection results of the base stations accessing the network in the most recent cycle are analyzed periodically, the score loss situations of each quantization index are counted, and then the first weight coefficient of each quantization index is determined according to the score loss situations of each quantization index. Among them, the preset time period can be within a certain cycle of a period. In the embodiment of the present invention, the quality inspection results of the base stations accessing the network within the preset time period can refer to the quality inspection results of the base stations accessing the network in the most recent cycle. For example, the base stations accessing the network in the most recent cycle include Base Station 1, Base Station 2, and Base Station 3. Then the quality inspection results obtained at this time are the quality inspection results of Base Station 1, Base Station 2, and Base Station 3 within this cycle.

[0067] In practical applications, the score loss of each quantization index is determined based on the average score loss of each quantization index. When determining the score loss of a quantization index, in fact, the average score loss of the quantization index over a certain period is determined. The average score loss can be expressed as the average score loss of each quantization index in the quality inspection results of the networked base stations in the most recent period. In the initial situation of the base station network access quality inspection, the first weight coefficients of each quantization index can be tentatively set to be the same, for example, 20%. After running for one period, analyze the quality inspection results of the networked base stations in the most recent period, and count the average score loss of each quantization index, which can be expressed as 100 - Score i (where i is used to represent the type of quantization index), then at this time, the average score loss ratio of each quantization index can be determined by using the average score loss of each quantization index, and the average score loss ratio of each quantization index can be used as the first weight coefficient of the corresponding quantization index to achieve the adaptive adjustment of the first weight coefficient.

[0068] In specific implementation, the single - item scores of each quantization index in the quality inspection results of the networked base stations in the most recent period can be obtained, and the average score loss of each quantization index can be calculated. Also, by using the average score loss of each quantization index in the most recent period, the total score loss of the quantization indexes for different dimensions in the most recent period can be obtained, and the average score loss ratio of each quantization index can be obtained by using the ratio of the average score loss of each quantization index to the total score loss.

[0069] In the embodiments of the present invention, when obtaining the score situation of each quantization index of a base station within the preset duration of network access according to the base station data, specifically, the device online index, working state index, parameter index, service - bearing index, and performance index of the base station within the preset duration can be respectively quantitatively evaluated according to the base station data, and the score situations for the device online index, working state index, parameter index, service - bearing index, and performance index can be respectively obtained to achieve this.

[0070] Specifically, the formula for the average score loss ratio can be as shown in formula (2):

[0071]

[0072] Among them, 100 - Scorei can be used to represent the average score loss of each quantization index, where i is used to represent the type of quantization index. For example, the device online index in Table 1 corresponds to 1, the working state index corresponds to 2, etc.; is the total sum of the average score losses, that is, at this time, the ratio of the average score loss of a certain quantization index to the total sum of the average score losses is used to determine the average score loss ratio of this quantization index.

[0073] In a preferred embodiment, a weight baseline Baseline can also be set for each quantitative indicator. If the weight coefficient of a quantitative indicator is lower than this Baseline, it can be corrected to the Baseline. In this case, the weight coefficients of the remaining quantitative indicators can be reduced accordingly in proportion, such as geometric reduction, to ensure that after the weight coefficients below the weight baseline are adjusted to the weight baseline, the total weight of each quantitative indicator is ensured to be 100%.

[0074] For example, assuming that the initial value of the weight coefficient of the quantitative indicator is 20%, after analyzing the quality inspection results of the base stations that have been connected to the network in the most recent period, it is found that the equipment is not fully online and the key parameter conflicts are prominent. At this time, the weights of various quantitative indicators can be optimized to 35%, 15%, 25%, 10%, and 15% after adaptive adjustment. At this time, the average loss of equipment online indicators and parameter performance is large, and the average loss of points as the weight coefficient accounts for a relatively large proportion, highlighting the shortcomings.

[0075] Step 203: Display the quality inspection score of the networked base station through the quality inspection result output module to obtain the quality inspection result of the base station.

[0076] The quality inspection result output module is responsible for displaying the quality inspection scores of the base stations obtained by the quantitative scoring module in order to present the quality inspection results of the base stations. When displaying the quality inspection results, it can mainly conduct quality inspection and evaluation from two perspectives: single item and overall, so as to comprehensively, objectively and accurately evaluate the work of the newly-connected 5G base stations.

[0077] The quality inspection scores of the base stations obtained include the total quality inspection scores and the scores for different quantitative indicators. At this time, the total quality inspection scores of the base stations and the scores for different quantitative indicators in the base stations can be displayed, and the quality inspection results of the base stations from an overall perspective and from a single perspective can be obtained respectively. Among them, the quality inspection results from an overall perspective can be mainly used to evaluate the overall quality audit results of the base stations when they are connected to the network, and the quality inspection results from a single perspective can be mainly used to present the specific construction problems of the base stations, mainly the shortcomings.

[0078] In the embodiment of the present invention, a multi-dimensional evaluation model for network access quality inspection is constructed to perform quantitative scoring based on the aforementioned quantitative indicators such as equipment online, working status, key parameters, service carrying, key performance, etc., to conduct quality inspection on whether the newly-connected 5G base stations are qualified for network access.

[0079] In actual applications, you can set a single inspection pass score for each quantitative indicator, as well as a total inspection pass score for the total quality inspection score. If both the single inspection and the total inspection are qualified, it is considered qualified, otherwise it is unqualified, and the reasons for the failure are presented to the user. For example, Figure 3As shown, when presenting the quality inspection results, relevant results and short-board items can be intuitively presented from a radar chart or the like. For example, the radar chart can divide different score lines, draw the scores of each quantitative index based on the pentagon ability release situation, and display the total quality inspection score in the pentagon drawn for the quantitative index. At the same time, a quantitative scoring table can also be provided to directly reveal the problems in the network access of the base station. In this regard, the embodiments of the present invention are not limited.

[0080] In the embodiments of the present invention, a base station network access quality inspection system is involved. The base station network access quality inspection system includes a data acquisition module, a quantitative evaluation module, and a quality inspection result output module. Mainly, the base station data of the base station can be acquired through the data acquisition module, and then the quantitative evaluation module evaluates the quantitative indexes of different dimensions based on the acquired base station data to obtain a quality inspection score that can be adaptively adjusted based on the score loss of the quantitative indexes of different dimensions, so as to display the quality inspection score of the network-accessed base station through the quality inspection result output module and obtain the quality inspection result of the base station. By automatically performing quantitative evaluation and quality inspection result output on the acquired base station data through the base station network access quality inspection system, without relying on manual network access quality inspection, a comprehensive evaluation of the network access quality inspection of the base station is carried out from the quantitative indexes of different dimensions, and based on the weight coefficients that can be adaptively adjusted, the specific construction problems of the base station are presented when presenting the quality inspection results, exposing the short boards of the built base stations, so as to strengthen the targeted control of the 5G base station network access process.

[0081] Refer to Figure 4 , which shows the step flowchart of another embodiment of the base station network access quality inspection method of the present invention, involving the base station network access quality inspection system as shown in Figure 1 shown, and the base station network access quality inspection system as shown in Figure 1 shown includes a data acquisition module, a quantitative evaluation module, and a quality inspection result output module. The embodiments of the present invention focus on describing the process in which the quantitative evaluation module respectively performs quantitative evaluation on the equipment online index, working status index, parameter index, service bearing index, and performance index of the base station within a preset time period according to the base station data, and respectively obtains the score situations for the equipment online index, working status index, parameter index, service bearing index, and performance index. Specifically, the following steps may be included:

[0082] Step 401: Obtain the score situation of the base station for the equipment online index according to the comparison situations of the cell information and equipment information of the base station with the site planning information respectively;

[0083] The base station data acquired by the data acquisition module can provide a data source for the quantitative evaluation module to perform quantitative evaluation. At this time, the quantitative evaluation module can evaluate the base station data from the big data lake to obtain the quality inspection score for the newly network-accessed base station.

[0084] In the embodiment of the present invention, during the process of evaluating the base station data, the quantization evaluation module can mainly evaluate the quantization indicators of different dimensions according to the base station data. The quantization indicators of different dimensions can include indicators such as device online, working status, key parameters, service bearing, and key performance. In practical applications, a multi-dimensional quantization evaluation model can be constructed from the quantization indicators of different dimensions. Based on the use of the multi-dimensional quantization evaluation model, a comprehensive quantization evaluation based on the quantization indicators of different dimensions can be realized, so as to objectively and accurately grasp the quality of newly added site stations.

[0085] When quantifying and evaluating the device online indicator according to the base station data, the obtained base station data can be the configuration data of the base station. The obtained configuration data includes cell information, device information, and site planning information. Among them, the site planning information is used to provide the cells planned by the base station and the number of devices allowed to access the network planned for each cell. Then, during the process of quantifying and evaluating the device online indicator, it can be mainly manifested as obtaining the score of the base station for the device online indicator according to the comparison between the cell information and device information of the base station and the site planning information respectively.

[0086] In practical applications, the cell information can be compared with the site planning information to obtain a first matching result, and the device information can be compared with the site planning information to obtain a second matching result. Then, the online score corresponding to the first matching result is obtained, and the online score corresponding to the second matching result is obtained. Then, the online score corresponding to the first matching result and the score corresponding to the second matching result are summed to obtain the score of the base station for the device online indicator.

[0087] Among them, the device online indicator is mainly used to evaluate whether all devices are online to check the situation where only some devices are online. For example, if the base station is planned with 3 cells, and each cell corresponds to 1 AAU device, it is necessary to check whether 3 cells and 3 AAUs are all online. Then, the first matching result obtained by comparing the cell information with the site planning information can be manifested as the online rate of the cell, and the second matching result obtained by comparing the device information with the site planning information can be manifested as the online rate of the device.

[0088] In a specific implementation, for the online score corresponding to the first matching result, the single score for the equipment online indicator and the first weight for the cell information can be obtained, and then the single score for the equipment online indicator and the first weight can be used based on the number of online cells to obtain the online score corresponding to the first matching result. For the online score corresponding to the second matching result, the single score for the equipment online indicator and the second weight for the equipment information can be obtained, and then the single score for the equipment online indicator and the second weight can be used based on the number of online devices to obtain the online score corresponding to the second matching result. As shown in Table 2, the configuration information can be collected through the equipment network management to obtain the cell information and AAU / RRU equipment information, and then compared with the site planning information. If there is a match, a score is given, and if there is no match, no score is given.

[0089] Serial Number Item Weight Score Single Item Score 1 Cell 1 1 / 2i K1_C1_Score 100 2 Cell 2 1 / 2i K1_C2_Score 100 …… …… …… …… I Cell i 1 / 2i K1_C(i)_Score 100 i+1 Equipment 1 1 / 2i K1_Q1_Score 100 i+2 Equipment 2 1 / 2i K1_Q2_Score 100 …… …… …… …… 2i Equipment i 1 / 2i K1_Q(i)_Score 100

[0090] Specifically, the formula for the score of the device online index can be shown as formula (3):

[0091]

[0092] Among them, the first weight Related to the number of online cells, k1_C x Refers to the score of whether the online cell matches the site planning information. Can be used to indicate the online score corresponding to the first matching result; the second weight Related to the number of online devices, k1_Q x Refers to the score of whether the online equipment matches the site planning information. It can be used to indicate the online score corresponding to the second matching result.

[0093] For example, suppose a new base station plans three cells, and each cell corresponds to one AAU device, that is, there are three devices in total. If the cell information collected by the device network management is three cells, and the device information collected is two AAUs, then the first weight for the cell information is 1 / 6, and the second weight for the device information is 1 / 6. The online score corresponding to the first matching result is 50 points, and the online score corresponding to the second matching result is 33.3 points, that is, the score for the device online indicator is 83.3 points.

[0094] Step 402, obtaining the score of the base station for the working status indicator according to the alarm type of the alarm information of the base station;

[0095] When quantitatively evaluating the working status indicators based on the base station data, the obtained base station data can be the fault alarm data of the base station, and the obtained fault alarm data includes the alarm information of each cell. Then, in the process of quantitatively evaluating the working status indicators, it is mainly manifested as determining the alarm types of the alarm information of each cell, determining the working score situation for each cell according to the alarm types, and then summing up the working score situations of each cell to obtain the score situation of the base station for the working status indicators.

[0096] In practical applications, the working score situation for each cell in the event of corresponding fault alarms can be determined according to the alarm types, and the third weight for the alarm information can be obtained; then, based on the number of cells, the working score situations of each cell in the event of corresponding fault alarms and the third weight are used to obtain the working score situation for each cell. Its scoring rules for the working status indicators can be as shown in Table 3:

[0097] Serial Number Item Weight Score Single Item Score 1 Cell 1 1 / i K2_C1_Score 100 2 Cell 2 1 / i K2_C2_Score 100 3 …… …… …… …… i Cell i 1 / i K2_C(i)_Score 100

[0098] Table 3

[0099] Among them, the working status indicators are mainly used to evaluate whether the equipment can provide services normally and audit the existence of cell outages and other fault alarms affecting performance. For example, 1 cell of the base station is out of service, and another cell has a CPRI optical power shortage alarm.

[0100] In specific implementation, if the alarm type is the cell outage alarm type, the working score situation for the cell is counted as zero; and / or, if the alarm type is other alarm types except the cell outage alarm type, the single score value for the working status indicator and the number of fault alarms for other alarm types are obtained, and the single score value of the working status indicator is deducted by a preset score according to the number of alarm, and the deducted score situation is used as the working score situation of the cell.

[0101] Specifically, the formula for the score situation of the working status indicator can be as shown in Formula (4) and Formula (5):

[0102]

[0103]

[0104] Among them, the third weight is related to the number of cells, and k2_C x _Score is used to represent the score situation of each alarm information after being deducted points based on its alarm type.

[0105] Exemplarily, assume that a newly networked base station plans 3 cells. Then, the third weight for the alarm information is 1 / 3. And when there are other active alarms affecting performance, each deducts 10 points, that is, x = 10. At this time, if among the alarm information collected through the device network management, 1 cell is out of service and 1 cell has a CPRI optical power shortage alarm, then the score for the working state is 63.3 points.

[0106] Step 403: Obtain the score for the parameter index according to the configuration parameters of the base station;

[0107] When quantitatively evaluating the parameter index based on the base station data, the obtained base station data can be the configuration data of the base station. The obtained configuration data includes the configuration parameters of the base station, such as the cell identification parameter PCI, the access channel parameter PRACH, the tracking area parameter TAC, and the neighbor cell parameter. Then, in the process of quantitatively evaluating the parameter index, it mainly shows obtaining the single score of the configuration parameters of the base station, determining the second weight coefficient of each configuration parameter according to the configuration parameters of the base station, then using the single score of the configuration parameter and the second weight coefficient of each configuration parameter to obtain the score of each configuration parameter, and then summing up the scores of each configuration parameter to obtain the score for the parameter index.

[0108] Specifically, the formula for the score of the parameter index can be shown as formula (6):

[0109] Score3 = p1 * PCI_Score + p2 * PRACH_Score + p3 * TAC_Score + p4 * NB_Score Formula (6)

[0110] Among them, p1, p2, p3, and p4 are the second weight coefficients of each parameter.

[0111] In order to more efficiently discover the short-board problems, the weights of the 4 parameters for auditing key parameters also adopt a dynamic adaptive adjustment mechanism, and the weights are inclined towards the short-board items. That is, the second weight coefficient can be used to adjust the proportion of different configuration parameters in the score of the overall parameter index. And in actual situations, the second weight coefficient is positively correlated with the score loss. For example, it is shown that the larger the score loss of the configuration parameter, the larger the corresponding second weight coefficient, so as to directly reveal the problem and reduce unnecessary calculation amounts.

[0112] For the determination of the second weight coefficient, it can be determined based on the quality inspection results of the networked base stations within a preset time period. Specifically, the quality inspection results of the networked base stations within the preset time period can be analyzed, the score losses of each configuration parameter can be counted, and then the second weight coefficient of each configuration parameter can be determined according to the score losses of each configuration parameter. Among them, the preset time period can be within a certain period of a cycle. In the embodiments of the present invention, the quality inspection results of the networked base stations within the preset time period can refer to the quality inspection results of the base stations networked in the most recent cycle. For example, if the base stations networked in the most recent cycle include Base Station 1, Base Station 2, and Base Station 3, then the obtained quality inspection results are the quality inspection results of Base Station 1, Base Station 2, and Base Station 3 within this cycle.

[0113] And the score losses of each configuration parameter are determined based on the average score losses of each configuration parameter. Then, when obtaining the score losses of each configuration parameter, according to the average score losses of each configuration parameter in the quality inspection results of the networked base stations in the most recent cycle, the average score loss ratio of each configuration parameter can be determined by using the average score losses of each configuration parameter, and the average score loss ratio of each configuration parameter can be used as the second weight coefficient of the corresponding configuration parameter.

[0114] In a specific implementation, the single score of each configuration parameter can be obtained, and according to the score situation of each configuration parameter in the quality inspection results of the networked base stations in the most recent cycle, then by using the single score of each configuration parameter and the score situation of each configuration parameter in the quality inspection results of the networked base stations in the most recent cycle, the average score loss of each configuration parameter in the most recent cycle can be calculated; finally, by using the average score loss of each configuration parameter in the most recent cycle, the total score loss for different configuration parameters in the most recent cycle can be obtained, and by using the ratio of the average score loss of each configuration parameter to the total score loss, the average score loss ratio of each configuration parameter can be obtained, and this average score loss ratio can be used as the second weight coefficient. That is, the average score losses of configuration parameters such as the physical cell identifier (PCI) of the 5G base stations networked in the most recent cycle, the physical random access channel (PRACH) parameter, the tracking area code (TAC) parameter, and the neighbor cell parameter can be calculated, and the average score loss ratio of each item of parameter can be used as the weight coefficient. Specifically, the formula for the average score loss ratio can be as shown in formula (7):

[0115]

[0116] Among them, 100 - p i _Score can be used to represent the average score loss of each quantization index, where i is used to represent the type of configuration parameter. For example, the physical cell identifier parameter corresponds to 1, the physical random access channel parameter corresponds to 2, etc.; Used to indicate the sum of the average loss scores of various configuration parameters; where x is used to indicate the type of each configuration parameter, for example, 1 corresponds to the cell identification parameter, 2 corresponds to the access channel parameter, etc. At this time, the average loss score of a certain configuration parameter and the proportion of the average loss score sum are used to determine the average loss score proportion of this configuration parameter.

[0117] In a preferred embodiment, a weight baseline Baseline may be set for each configuration parameter. If the weight coefficient of a configuration parameter is lower than the Baseline, it may be corrected to the Baseline. In this case, the weight coefficients of the remaining configuration parameters may be reduced accordingly in proportion, such as geometric reduction, to ensure that after the weight coefficients lower than the weight baseline are adjusted to the weight baseline, the total weight of each configuration parameter is ensured to be 100%.

[0118] Exemplarily, the initial values ​​of the parameter weights are all 25%. After analyzing the quality inspection results of the base stations connected to the network in the most recent period, it was found that the problem of missed configuration in neighboring cells was more prominent. After adaptive adjustment, the weights of each configuration parameter can be optimized to 20%, 20%, 20%, and 40%.

[0119] In an embodiment of the present invention, a key parameter audit algorithm for the impact domain can be proposed to accurately audit within a reasonable and necessary range, improve the accuracy of the audit, and reduce unnecessary calculations. Among them, the impact domain refers to the adjacent base stations within a certain range around the newly-connected base station, mainly the base stations that may be affected or affected by the newly-connected base station. When determining the score of each configuration parameter, the cell identification parameter PCI, access channel parameter PRACH, tracking area parameter TAC and neighboring cell parameters of the base station within a preset time period can be quantitatively evaluated mainly based on the configuration parameters, and the scores for the cell identification parameter, access channel parameter, tracking area parameter and neighboring cell parameter can be obtained respectively.

[0120] Specifically, (1) when determining the score of the cell identification parameter, a triangulated network can be constructed with the newly connected base station as the center, and the constructed triangulated network is used to determine the impact domain of the cell identification parameter. At this time, the cell identification reuse of adjacent base stations in the impact domain can be obtained, and the cell identification reuse is determined based on the first cell identification reuse layer number (i.e., PCI reuse layer number) and the second cell identification reuse layer number (i.e., PCIMOD30 reuse layer number).

[0121] When conducting PCI audits, it is necessary to determine the impact domain of PCI. Taking the base station as a point, a Delaunay triangulation network can be constructed. With the newly installed 5G base station as the center, the other vertices of its adjacent triangles are the first-layer adjacent base stations, and the other vertices of the adjacent triangles of the first-layer adjacent base stations are the second-layer adjacent base stations, and so on. In this way, the n-layer adjacent base stations of the newly installed 5G base station can be accurately obtained as the impact domain for key parameter audits. Usually, n can be 3 or 4.

[0122] When constructing the triangulation network, first, the base station data can be preprocessed. Mainly, for nearby sites in terms of site address, they can be merged within a certain range to reduce the complexity of the triangulation network. For example, sites within 50 meters of each other are merged into one site, which participates in the triangulation network generation as one point, and this point carries the information of all sites involved in the merger. Then, the triangulation network can be constructed. As Figure 5 shown, it is constructed by taking the site address as a point and following the Delaunay triangulation network rules. The growth method can be used, that is, taking the newly installed 5G base station as the initial point, finding the nearest existing site to it, the connection line between the two points is used as the initial edge, and the third point that conforms to the Delaunay rule is searched on one side of the initial edge to construct the first triangle. Then, taking the three sides of the first triangle as the initial edges respectively, the third point is searched outward in turn for expansion. Among them, in the triangulation network as Figure 5 shown, "★" can be used to represent the newly installed 5G base station, followed by the first-layer adjacent base stations, the second-layer adjacent base stations, and the third-layer adjacent base stations.

[0123] When quantitatively scoring according to the PCI reuse situation of adjacent base stations within the impact domain, the first cell identifier reuse layer (i.e., the PCI reuse layer) and the second cell identifier reuse layer (i.e., the PCIMOD30 reuse layer) can be mainly used to quantitatively evaluate the cell identifier parameters of the base station within a preset time period. Specifically, it can be as shown in Table 4:

[0124]

[0125] Table 4

[0126] Among them, as shown in Table 4, the value ranges of PCI_Reuse_Layer_C(x) and PCIMOD30_Reuse_Layer_C(x) are 1 to n + 1. If there is the same PCI reuse among the first-layer adjacent sites, then PCI_Reuse_Layer_C(i) = 1; if there is the same PCI reuse among the second-layer adjacent sites, then PCI_Reuse_Layer_C(i) = 2, and so on; if there is no PCI reuse among the n-layer adjacent sites, then PCI_Reuse_Layer_C(i) = n + 1.

[0127] In specific practice, the score corresponding to the number of multiplexing layers of the first cell identity can be obtained, as well as the score corresponding to the number of multiplexing layers of the second cell identity, and then the score corresponding to the number of multiplexing layers of the first cell identity and the score corresponding to the number of multiplexing layers of the second cell identity are summed to obtain the score of the base station for the cell identity parameters.

[0128] Specifically, for the score corresponding to the number of multiplexing layers of the first cell identity, the single score for the cell identity parameter, the preset score value range threshold, the reuse score for the first cell identity multiplexing number, and the fourth weight for the first cell identity multiplexing number can be obtained, and then based on the number of cells, the single score for the cell identity parameter, the preset score value range threshold, the reuse score for the first cell identity multiplexing number, and the fourth weight are used to obtain the score corresponding to the number of multiplexing layers of the first cell identity. For the score corresponding to the number of multiplexing layers of the second cell identity, the single score for the cell identity parameter, the preset score value range threshold, the reuse score for the second cell identity multiplexing number, and the fifth weight for the second cell identity multiplexing number can be obtained, and then based on the number of cells, the single score for the cell identity parameter, the preset score value range threshold, the reuse score for the second cell identity multiplexing number, and the fifth weight are used to obtain the score corresponding to the second cell identity multiplexing number.

[0129] The formula for the score of the cell identification parameter can be shown as formula (8):

[0130]

[0131] Among them, the fourth weight It is related to the number of cells of the newly connected base station. The reuse score of the first cell identification reuse layer is positively correlated with the first cell identification reuse layer, mainly increasing linearly. Represents; fifth weight It is related to the number of cells of the newly connected base station. The reuse score of the second cell identification multiplexing layer is positively correlated with the number of second cell identification multiplexing layers, mainly increasing linearly. express.

[0132] For example, if n=3, the first cell of a newly connected base station uses the same PCI in the first layer of adjacent base stations, then PCI_Reuse_Layer_C(1)=1; the second cell and the third cell do not use the same PCI in the three adjacent base stations, then PCI_Reuse_Layer_C(2)=4, PCI_Reuse_Layer_C(3)=4; the first cell has the same PCIMOD30 in the first layer of adjacent base stations, then PCIMOD30_Reuse_Layer_C(1)=1; the second cell and the third cell do not have the same PCIMOD30 in the three adjacent base stations, then PCIMOD30Reuse_Layer_C(2)=4, PCIMOD30_Reuse_Layer_C(3)=4. The PCI audit score is 66.7 points.

[0133] (2) When determining the score of the access channel parameters, a triangular network can be constructed with the base station connected to the network as the center. The constructed triangular network is used to determine the impact domain of the access channel parameters. At this time, the access channel reuse of adjacent base stations in the impact domain can be obtained; the access channel reuse is determined based on the number of access channel reuse layers, and then the number of access channel reuse layers is used to quantitatively evaluate the access channel parameters of the base station within a preset time length to obtain the score of the base station for the access channel parameters. In actual applications, quantitative scoring can be performed based on the PRACH reuse of adjacent base stations in the impact domain, as shown in Table 5:

[0134]

[0135] Table 5

[0136] In actual applications, the individual scores for the access channel parameters, the preset score range threshold, the multiplexing score for the number of access channel multiplexing layers, and the sixth weight for the number of access channel multiplexing layers can be obtained. Then, based on the number of cells, the individual scores for the access channel parameters, the preset score range threshold, the multiplexing score for the number of access channel multiplexing layers and the sixth weight are used to obtain the score for the access channel parameters.

[0137] Specifically, the formula for the score of the access channel parameters may be as shown in formula (9):

[0138]

[0139] Among them, the sixth weight It is related to the number of cells of the newly connected base station, and the reuse score of the access channel reuse layer is positively correlated with the number of access channel reuse layers, mainly increasing linearly. Indicates that, the value rule of PRACH_Reuse_Layer_C(x) shown in Table 5 is the same as that of PCI.

[0140] (3) When determining the score of the tracking area parameters, the tracking area parameters of the base station entering the network can be obtained, and the tracking area parameter set of the neighboring base stations in the affected area can be obtained.

[0141] In practical applications, the TAC influence domain can be determined based on the first layer of adjacent base stations in the constructed Delaunay triangulation network. That is, the TAC mainly checks whether there is a flower-splitting phenomenon, that is, the TAC of the newly connected 5G base station should be one of the TACs of the adjacent first layer of base stations. Figure 6 As shown, the first layer of adjacent base stations in the triangulated network can be divided into TAC1, TAC2, and TAC3, that is, the TAC set of adjacent base stations in one layer is NB_TAC = {TAC1, TAC2, ...}, and the TAC of the newly-connected 5G base station is NewSite_TAC, which should be a subset of NB_TAC.

[0142] Specifically, the formula for the score of the tracking area parameter can be shown as formula (10):

[0143]

[0144] That is, if the tracking area parameters of the base station entering the network do not belong to the tracking area parameter set of the adjacent base stations in the influence domain, the score for the tracking area parameters will be counted as zero; and / or, if the tracking area parameters of the base station entering the network belong to the tracking area parameter set of the adjacent base stations in the influence domain, the single score for the tracking area parameters will be determined as the score for the tracking area parameters.

[0145] (4) When determining the scores of neighboring cell parameters, the focus is on checking the first-layer neighboring base stations, which can also be expanded to the second-layer and above neighboring base stations as needed. The neighboring cells mainly check the missed configuration of adjacent cells, including 5G-5G and 5G-4G neighboring cells, and make quantitative scores based on the missed configuration of neighboring cells in the impact area. With the azimuth of the newly connected 5G base station cell as the center, the surrounding 360-degree range is divided into three zones: green, blue, and red according to the visual angle of "seeing and being seen". Among them, the adjacent cells of this station, all cells of the first-layer neighboring base stations in the green zone, the same-direction and opposite-direction cells of the first-layer neighboring base stations in the blue zone, and the same-direction cells of the first-layer neighboring base stations in the red zone need to be set as adjacent cells.

[0146] In practical applications, the azimuth information of the networked base station and the adjacent base stations can be obtained with the networked base station as the center, and then the necessary neighboring cell set can be determined based on the mutual differences between the azimuth information of each networked base station and the adjacent base station. Finally, the necessary neighboring cell set and the configured neighboring cell set are used to quantitatively evaluate the neighboring cell parameters of the base station within a preset time length.

[0147] Among them, the azimuth information includes the cell azimuth of the accessed base station, the cell azimuth of adjacent base stations within the influence area, the azimuth of the neighboring station relative to the main cell, and the azimuth of the main cell relative to the neighboring station.

[0148] Referring to Figure 7 , a schematic diagram of neighboring cell set division of the "visual angle" provided by an embodiment of the present invention is shown. Among them, the determination method for the green area can be |α - γ| ≤ 60° or |α - γ| ≥ 300°; the determination condition for the blue area can be 60° < |α - γ| ≤ 120° or 240° ≤ |α - γ| < 300°; the determination condition for co-directional cells can be |α - β| ≤ 60° or |α - β| ≥ 300°; the determination condition for opposite-direction cells: 60° < |α - β| < 300°, and (|β - θ| ≤ 60 or |β - θ| ≥ 300°); the determination condition for the red area can be 120° < |γ - α| < 240°; the determination condition for co-directional cells can be |β - α| ≤ 60° or |β - α| ≥ 300°.

[0149] Among them, α is the cell azimuth of the newly built 5G base station, β is the cell azimuth of the adjacent base station, γ is the azimuth of the neighboring station relative to the main cell, and θ is the azimuth of the main cell relative to the neighboring station, and the values are all 0 - 360°. As Figure 8 shown, γ and θ can be calculated through longitude and latitude coordinates, and specifically can be expressed as

[0150] Tanγ = (n - y) / (m - x)

[0151] γ = arctan[(n - y) / (m - x)], with the value range [-π / 2, π / 2]

[0152] Among them, through the positive and negative of m - x and n - y, the quadrant can be judged, and then the accurate angle of γ can be judged: [+, +] the first quadrant, the true angle [0, π / 2]; [-, +] the second quadrant, the true angle [π / 2, π]; [-,-] the third quadrant, the true angle [π, 3π / 2]; [+,-] the fourth quadrant, the true angle [3π / 2, 2π]. It should be noted that the 5G - 4G neighboring cell verification principle is the same as the aforementioned 5G - 5G, and only the surrounding base stations need to be replaced with 4G base stations, and the Delaunay triangulation is reconstructed to obtain adjacent base stations.

[0153] At this time, the neighboring cells that can be obtained are the necessary neighboring cell sets (Necessary_5to5_NB, Necessary_5to4_NB), and the neighboring cells obtained from the device network management are the configured neighboring cell sets (Necessary_5to5_NB, Necessary_5to4_NB). The proportion of the number of adjacent cells in the intersection of the necessary neighboring cell set and the configured neighboring cell set to the number of adjacent cells in the necessary neighboring cell set is the neighboring cell configuration effective factor. Specifically, as shown in Table 7:

[0154]

[0155] Table 7

[0156] When the necessary neighbor cell set and the configured neighbor cell set are used to quantitatively evaluate the neighbor cell parameters of the base station within a preset time period, the formula for the score of the neighbor cell parameters can be shown as formula (11):

[0157]

[0158] In practical applications, the individual scores for the neighboring cell parameters, the ratio of the number of neighboring cells in the intersection of the necessary neighboring cell set and the configured neighboring cell set to the number of neighboring cells in the necessary neighboring cell set, and the seventh weight can be obtained, and the ratio is used as the effective factor of the neighboring cell configuration for neighboring cell auditing, and the seventh weight; wherein the seventh weight is related to the number of cells. Then, the individual scores for the neighboring cell parameters, the ratio of the number of neighboring cells in the intersection of the necessary neighboring cell set and the configured neighboring cell set to the number of neighboring cells in the necessary neighboring cell set, and the seventh weight can be used to obtain the score for the neighboring cell parameters.

[0159] For example, for a newly connected base station, the necessary neighbor cell set of the first cell has 50 5G-5G neighbor cells and 30 5G-4G neighbor cells, but the actually configured neighbor cells are 45 and 28 respectively in the necessary neighbor cell set; the necessary neighbor cell set of the second cell has 60 5G-5G neighbor cells and 40 5G-4G neighbor cells, but the actually configured neighbor cells are 52 and 35 respectively in the necessary neighbor cell set; the necessary neighbor cell set of the third cell has 30 5G-5G neighbor cells and 18 5G-4G neighbor cells, but the actually configured neighbor cells are 30 and 18 respectively in the necessary neighbor cell set. Then the neighbor cell audit score is 92.9 points.

[0160] Step 404, obtaining a score of the base station for a service carrying indicator according to the cell traffic of the base station and the number of users connected to the cell;

[0161] When the service carrying indicator is quantitatively evaluated based on the base station data, the base station data obtained may be performance indicator data for the base station, and the performance indicator data obtained include the cell traffic of the base station and the number of cell connected users. Then, in the process of quantitatively evaluating the service carrying indicator, it can be mainly manifested as obtaining the adjacent base stations of the base station connected to the network, and obtaining the cell traffic and the number of cell connected users of cells of different levels in the adjacent base stations; determining the average value of the cell traffic for cells of different levels in the adjacent base stations, and the average value of the number of cell connected users for cells of different levels, as a benchmark for the service carrying indicator.

[0162] In practical applications, given that base stations in different regions such as urban areas, county seats, and rural areas are affected by their locations and objectively differ in terms of traffic, the number of connected users, etc., it is not appropriate to adopt a unified standard, but a minimum baseline (BaseLine) can be set. Among them, service bearing is also evaluated for the influence domain, comparable to surrounding stations, and the first-layer adjacent base stations in the Delaunay triangulation can be taken as the influence domain.

[0163] Then, considering the influence of the covered area, the traffic volumes of the three cells of the same base station are often not balanced. Therefore, three reference lines (ReferenceLine), namely high, medium, and low, are set for high-, medium-, and low-traffic cells respectively. Exemplarily, as Figure 9 shown, the average value of the cells with the highest traffic and the largest number of connected users in each base station among the first-layer adjacent base stations can be taken as the reference line for high-traffic cells (ReferenceLine_H); the average value of the cells with medium traffic and medium number of connected users in each base station among the first-layer adjacent base stations can be taken as the reference line for medium-traffic cells (ReferenceLine_M); the average value of the cells with the lowest traffic and the smallest number of connected users in each base station among the first-layer adjacent base stations can be taken as the reference line for low-traffic cells (ReferenceLine_L).

[0164] Then, the service bearing scores of each cell in the base stations that have accessed the network for cell traffic and the service bearing scores for the number of connected users in the cell can be obtained, and the service bearing scores of the base stations that have accessed the network for traffic and the service bearing scores for the number of connected users in the cell are summed to obtain the score situation of the base stations for the service bearing index within the preset time period. Specifically, as shown in Table 8:

[0165]

[0166] Table 8 Specifically, the score situation for the service bearing index can be as shown in Formula (12) and Formula (13):

[0167]

[0168]

[0169] Among them, the service bearing score for traffic is determined based on the cell traffic of each cell in the base stations that have accessed the network, the baseline and reference line for traffic, the single-item score for the service bearing index, and the eighth weight. The service bearing score for the number of connected users in the cell is determined based on the number of cell user connections of each cell in the base stations that have accessed the network, the baseline and reference line for the number of connected users in the cell, the single-item score for the service bearing index, and the eighth weight. The obtained eighth weight is related to the number of cells.

[0170] For example, for a newly connected base station, the first layer of adjacent base stations in the Delaunay triangulation network is taken as the influence domain, and the traffic and number of connected users of the high, medium and low traffic cells of the first layer of adjacent base stations are calculated. Assume that the benchmark line of the high traffic cell is 10GB / cell / hour of traffic and 87 connected users / cell / hour; the benchmark line of the medium traffic cell is 5GB / cell / hour of traffic and 45 connected users / cell / hour; the benchmark line of the low traffic cell is 2GB / cell / hour of traffic and 15 connected users / cell / hour; and the baseline is based on the minimum requirements, as long as there is traffic and users, the traffic can be 0.1GB / cell / hour and the number of connected users can be 1 / cell / hour, which can be set as the baseline.

[0171] Then, when the network manager counts the newly connected base stations, the traffic in the first cell is 12GB / hour, and the average number of connected users is 95 / hour, which is applicable to the high-traffic cell benchmark line; the traffic in the second cell is 3GB / hour, and the average number of connected users is 25 / hour, which is applicable to the medium-traffic cell benchmark line; the traffic in the third cell is 0GB / hour, and the average number of connected users is 0 / hour, which is applicable to the low-traffic cell benchmark line, the service bearer audit score is 52.3 points.

[0172] Step 405: Determine the score of the base station for the performance indicator according to the cell connection success rate, cell disconnection rate, cell handover success rate and cell channel quality excellence rate of the base station.

[0173] When quantitatively evaluating the performance indicators based on the base station data, the base station data obtained can be the session statistics of the base station. The obtained session statistics include the cell connection success rate, the cell drop rate, the cell switching success rate and the cell channel quality excellence rate. Then, in the process of quantitatively evaluating the performance indicators, it can be mainly manifested in obtaining the first-layer adjacent base stations of the base station that has entered the network, and obtaining the cell connection success rate, cell drop rate, cell switching success rate and cell channel quality excellence rate of each cell in the adjacent base stations, and determining the average value of the cell connection success rate, cell drop rate, cell switching success rate and cell channel quality excellence rate of each cell in the adjacent base stations as a benchmark for the performance indicators.

[0174] In practical applications, the key performance focuses on evaluating whether the performance indicators of newly networked base stations are normal. The evaluation method is the same as the aforementioned service bearing, also facing the influence domain, setting a baseline and a reference line for quantitative evaluation. The difference is that in the selection of specific indicators, the reference line can also be set without distinguishing between high and low traffic. Usually, key performance indicators such as connection success rate, disconnection rate, handover success rate, and excellent rate of 5G CQI are selected for quantitative evaluation. Set the lowest baseline (BaseLine), and the influence domain is the adjacent base stations in the first layer of the Delaunay triangulation network. Take the average value of the key performance indicators of the adjacent base stations in the first layer as the reference line (ReferenceLine).

[0175] Then, obtain the performance scores of each cell in the networked base stations for the cell connection success rate, cell disconnection rate, cell handover success rate, and excellent rate of cell channel quality respectively. Sum up the performance scores of the networked base stations for the cell connection success rate, cell disconnection rate, cell handover success rate, and excellent rate of cell channel quality to obtain the score situation of the base station for the performance indicators within the preset time period. Specifically, as shown in Table 9:

[0176]

[0177] Table 9 Specifically, the formula for the score situation of the performance indicators can be as shown in Formula (14) and Formula (15):

[0178]

[0179]

[0180] Among them, the performance score for the cell connection success rate is determined based on the cell connection success rate of each cell in the networked base stations, the baseline and reference line for the cell connection success rate, the single-item score for the performance indicator, and the ninth weight; the performance score for the cell disconnection rate is determined based on the cell disconnection rate of each cell in the networked base stations, the baseline and reference line for the cell disconnection rate, the single-item score for the performance indicator, and the ninth weight; the performance score for the cell handover success rate is determined based on the cell handover success rate of each cell in the networked base stations, the baseline and reference line for the cell handover success rate, the single-item score for the performance indicator, and the ninth weight; the performance score for the excellent rate of cell channel quality is determined based on the excellent rate of cell channel quality of each cell in the networked base stations, the baseline and reference line for the excellent rate of cell channel quality, the single-item score for the performance indicator, and the ninth weight, and the ninth weight is related to the number of cells.

[0181] Refer to Figure 10, which shows a schematic diagram of the application scenario of the base station network access quality inspection provided by the embodiment of the present invention. This application scenario can be a scenario for comprehensively evaluating and inspecting newly networked base stations. In this scenario, assume that the planned cells of the newly networked base station include Cell 1, Cell 2, and Cell 3. The network access quality inspection can be manifested as evaluating the situation when the newly networked base station conducts services for the planned cells. When conducting network access quality inspection on the newly networked base station, it involves a base station network access quality inspection system, which includes a data acquisition module, a quantization scoring module, and a quality evaluation module. That is, based on the aforementioned modules of the base station network access quality inspection system, a comprehensive quantitative evaluation of the newly networked base station can be carried out.

[0182] Specifically, the data acquisition module is responsible for collecting relevant data of the newly networked base station. It can mainly collect base station data through the device network management. The collected data can include relevant base station data such as configuration data, fault alarm data, and performance index data. In addition to collecting relevant data of the base station, it can also serve as a data providing module of the base station network access quality inspection system. Specifically, it can be manifested as parsing the collected relevant base station data and storing the parsed data in the big data lake. The data stored in the big data lake can be used as the data source for the quantization scoring module to conduct quantization scoring.

[0183] The quantization scoring module is responsible for constructing a multi-dimensional evaluation model to conduct a comprehensive quantitative evaluation based on quantization indicators of different dimensions. It mainly constructs a multi-dimensional evaluation model for quantization indicators of different dimensions. The quantization indicators of different dimensions can include indicators such as device online, working status, key parameters, service bearing, and key performance. Among them, the constructed multi-dimensional evaluation model can be given a weight coefficient dynamic adaptive adjustment mechanism, construct an auditing algorithm for the influence domain based on a triangular mesh, and a scenario-based adaptive auditing algorithm through coordination of the baseline and the benchmark line, so that during network access quality inspection, auditing can be carried out within the range of the newly networked 5G base station and the relevant base stations affected by it to evaluate the working conditions of the newly networked 5G base station. It should be noted that for the process of the quantization evaluation module separately conducting quantitative evaluations on the device online indicator, working status indicator, parameter indicator, service bearing indicator, and performance indicator of the base station within a preset time period according to the base station data, and obtaining the score situations for the device online indicator, working status indicator, parameter indicator, service bearing indicator, and performance indicator respectively, and the process of obtaining the total quality inspection score of the base station based on the score situations of each quantization indicator, the details can refer to the description of the method embodiment and will not be elaborated here.

[0184] The quantitative evaluation module can be equivalent to the quality inspection result output module, which is responsible for displaying the quality inspection scores of the base stations obtained by the quantitative scoring module to present the quality inspection results of the base stations. When presenting the quality inspection results, it can mainly conduct quality inspection evaluations from two perspectives: single item and overall, so as to comprehensively, objectively, and accurately evaluate the work of newly networked 5G base stations. In practical applications, the single-inspection passing score lines for each quantitative indicator and the overall inspection passing score line for the total quality inspection score can be set. Those that pass both the single inspection and the overall inspection are determined to be qualified in quality inspection, otherwise they are unqualified, and the reasons for non-conformance are presented to the user. Exemplarily, as Figure 3 shown, when presenting the quality inspection results, relevant results and short-board items can be intuitively presented from radar charts, etc. For example, the radar chart can divide different score lines, draw the score situations of each quantitative indicator based on the pentagon capability release situation, and display the total quality inspection score in the pentagon drawn for the quantitative indicator; at the same time, a quantitative scoring table as shown in Table 10 can also be provided to directly reveal the problems in the network access of the base station.

[0185]

[0186] Table 10

[0187] In the embodiments of the present invention, by innovatively proposing a multi-dimensional weight adaptive quantitative evaluation model, a quantitative scoring model is constructed from five aspects: device online, working status, key parameters, service bearing, and key performance. The sub-item weight coefficients are dynamically and adaptively adjusted according to the score loss situation, and the weights are inclined towards the short-board items, which can more efficiently discover short-board problems; by innovatively proposing a precise auditing algorithm for the influence domain, an influence domain is constructed based on the Delaunay triangulation network, and precise auditing is carried out within the range of the newly networked 5G base station and the related base stations affected by it and affecting it, which is more objective, scientific, and efficient; by innovatively proposing a neighboring cell auditing algorithm based on the "visual angle", the adjacency relationship of the newly networked base station is calculated based on the relative angle, and the 360 degrees is divided into three areas: green, blue, and red according to "seeing and being seen", and targeted measures are taken to achieve precise identification and auditing of neighboring cells; by innovatively proposing a baseline + reference line scenario adaptive auditing algorithm, it adapts to location differences, based on the baseline of expert experience, combined with the scenario adaptive reference line for the influence domain, to achieve key performance auditing of "one station, one threshold".

[0188] It should be noted that the base station access method proposed in the embodiments of the present invention is universal and can be efficiently deployed and applied based on network management data sources and IT technologies. It can be widely applied to the construction of mobile communication wireless networks, especially the current 5G construction, providing comprehensive quality inspection for newly accessed base stations, avoiding the situation of accessing the network with problems, providing a strong guarantee for network quality and customer perception, and having wide applicability to each communication operator. For subsequent evolved networks, as long as the cellular network structure continues to be used, the method proposed in this patent is also applicable; and based on the base station access method proposed in the embodiments of the present invention, it can not only efficiently manage and control the quality of 5G construction and opening, comprehensively, objectively, and accurately evaluate the working conditions of newly accessed 5G base stations, more efficiently discover short-board problems, guard the access gateway, ensure the quality of the wireless network and customer perception, but also be extended to the daily monitoring of 5G wireless networks, and master the working status and quality of 5G base stations at any time.

[0189] It should be noted that for the method embodiments, for the sake of simple description, they are all expressed as a series of action combinations. However, those skilled in the art should know that the embodiments of the present invention are not limited by the described action sequence, because according to the embodiments of the present invention, certain steps can be carried out in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions involved are not necessarily essential for the embodiments of the present invention.

[0190] Referring to Figure 11 , a structural block diagram of an embodiment of a base station access quality inspection system of the present invention is shown, which specifically may include the following modules:

[0191] A data acquisition module 1101, configured to acquire base station data of a base station;

[0192] A quantization evaluation module 1102, configured to evaluate quantization indicators in different dimensions according to the base station data to obtain a quality inspection score of the base station; wherein, the quality inspection score of the base station is determined based on a first weight coefficient of quantization indicators in different dimensions, and the first weight coefficient is adaptively adjusted based on the score loss situation of quantization indicators in different dimensions;

[0193] A quality inspection result output module 1103, configured to display the quality inspection score of the accessed base station to obtain the quality inspection result of the base station.

[0194] In an embodiment of the present invention, the quality inspection score of the base station includes a total quality inspection score and the score situation for different quantization indicators; the quantization evaluation module 1102 may include the following sub-modules:

[0195] The first weight coefficient determination sub-module is used to obtain the quality inspection results for the networked base stations within a preset time period, determine the score losses of each quantization index in the quality inspection results, and determine the first weight coefficient of each quantization index according to the score losses of each quantization index;

[0196] The score situation determination sub-module is used to obtain the single-item scores of each quantization index, and use the single-item scores of each quantization index and the first weight coefficient to obtain the score situation of each quantization index;

[0197] The total quality inspection score determination sub-module is used to sum up the score situations of each quantization index to obtain the total quality inspection score of the base station.

[0198] In an embodiment of the present invention, the score losses of each quantization index are determined based on the average score losses of each quantization index. The first weight coefficient determination sub-module may include the following units:

[0199] The average score loss generation unit is used to calculate the average score loss of each quantization index according to the quality inspection results for the networked base stations within a preset time period;

[0200] The first weight coefficient determination unit is used to determine the proportion of the average score loss of each quantization index by using the average score loss of each quantization index, and use the proportion of the average score loss of each quantization index as the first weight coefficient of the corresponding quantization index.

[0201] In an embodiment of the present invention, the average score loss generation unit may include the following sub-units:

[0202] The score situation acquisition sub-unit is used to obtain the single-item scores of each quantization index and the score situation of each quantization index according to the quality inspection results for the networked base stations within a preset time period;

[0203] The average score loss generation sub-unit is used to calculate the average score loss of each quantization index by using the single-item scores of each quantization index and the score situation of each quantization index in the quality inspection results for the networked base stations within a preset time period.

[0204] Among them, the score situation of each quantization index in the quality inspection results for the networked base stations within a preset time period can be mainly realized by respectively quantifying and evaluating the equipment online index, working status index, parameter index, service carrying index, and performance index of the base station within a preset time length according to the base station data, and respectively obtaining the score situations of the equipment online index, working status index, parameter index, service carrying index, and performance index. For the detailed calculation process of the score situation of each quantization index, reference can be made to the description in the method embodiment part, which will not be elaborated here.

[0205] In an embodiment of the present invention, the quality inspection score of the base station includes the total quality inspection score and the score for different quantization indicators; the quality inspection result output module 1103 may include the following sub-modules:

[0206] A quality inspection result display sub-module, configured to display the total quality inspection score of the base station and the scores for different quantization indicators in the base station, respectively obtaining the quality inspection result of the base station from the overall perspective and the quality inspection result from the single-item perspective; wherein, the quality inspection result from the overall perspective is used to evaluate the overall quality audit result of the base station when it is connected to the network, and the quality inspection result from the single-item perspective is used to present the specific construction problems of the base station.

[0207] In an embodiment of the present invention, it relates to a base station network access quality inspection system. The base station network access quality inspection system includes a data acquisition module, a quantization evaluation module, and a quality inspection result output module. It mainly obtains the base station data of the base station through the data acquisition module, and then the quantization evaluation module evaluates different-dimensional quantization indicators based on the obtained base station data to obtain a quality inspection score that can be adaptively adjusted based on the score loss of different-dimensional quantization indicators, so as to display the quality inspection score of the network-accessed base station through the quality inspection result output module and obtain the quality inspection result of the base station. By automatically performing quantization evaluation and quality inspection result output on the obtained base station data through the base station network access quality inspection system, without relying on manual network access quality inspection, it comprehensively evaluates the network access quality inspection of the base station from different-dimensional quantization indicators, and based on the weight coefficient that can be adaptively adjusted based on the score loss, it can present the specific construction problems of the base station when displaying the quality inspection result, expose the shortcomings of the constructed base station, so as to strengthen the targeted control of the 5G base station network access process.

[0208] For the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple. For the relevant parts, refer to the partial description of the method embodiment.

[0209] The embodiment of the present invention also provides an electronic device, including:

[0210] It includes a processor, a memory, and a computer program stored on the memory and capable of running on the processor. When the computer program is executed by the processor, it realizes each process of the above-mentioned base station network access quality inspection method embodiment and can achieve the same technical effect. To avoid repetition, it will not be elaborated here.

[0211] The embodiment of the present invention also provides a computer-readable storage medium. A computer program is stored on the computer-readable storage medium. When the computer program is executed by the processor, it realizes each process of the above-mentioned base station network access quality inspection method embodiment and can achieve the same technical effect. To avoid repetition, it will not be elaborated here.

[0212] Each embodiment in this specification is described in a progressive manner. Each embodiment focuses on the differences from other embodiments. For the same or similar parts among the embodiments, reference can be made to each other.

[0213] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a device, or a computer program product. Therefore, the embodiments of the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the embodiments of the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0214] The embodiments of the present invention are described with reference to the flowcharts and / or block diagrams of methods, terminal devices (systems), and computer program products according to the embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing terminal devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing terminal devices generate a device for implementing the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0215] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing terminal device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including an instruction device that implements the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0216] These computer program instructions can also be loaded onto a computer or other programmable data processing terminal device, such that a series of operation steps are executed on the computer or other programmable terminal device to generate a computer-implemented process. Thus, the instructions executed on the computer or other programmable terminal device provide steps for implementing the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0217] Although the preferred embodiments of the embodiments of the present invention have been described, those skilled in the art can make additional changes and modifications once they learn the basic creative concepts. Therefore, the appended claims are intended to be construed to include the preferred embodiments and all changes and modifications that fall within the scope of the embodiments of the present invention.

[0218] Finally, it should also be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or terminal device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent to such process, method, article or terminal device. Without further limitation, an element defined by the statement "comprising one..." does not exclude the presence of additional identical elements in the process, method, article or terminal device comprising the said element.

[0219] The above has introduced in detail a base station access quality inspection method, a base station access quality inspection system, a corresponding electronic device, and a corresponding computer storage medium provided by the present invention. Specific examples are used in this article to elaborate on the principles and implementation manners of the present invention. The description of the above embodiments is only used to help understand the method and its core idea of the present invention; at the same time, for those of ordinary skill in the art, according to the idea of the present invention, there will be changes in the specific implementation manners and application scopes. In summary, the content of this specification should not be construed as a limitation to the present invention.

Claims

1. A base station network access quality inspection method, characterized in that, A base station network access quality inspection system is provided, wherein the base station network access quality inspection system comprises a data acquisition module, a quantitative evaluation module and a quality inspection result output module, and the method comprises: Acquire base station data of the base station through the data acquisition module; The quantitative evaluation module evaluates the quantitative indicators of different dimensions according to the base station data to obtain a quality inspection score of the base station; wherein the quality inspection score of the base station is determined based on a first weight coefficient of the quantitative indicators of different dimensions; The quality inspection result output module displays the quality inspection score of the base station accessed, and obtains the quality inspection result of the base station; Wherein, the method further comprises: Obtain the individual scores of each quantitative indicator in the quality inspection results of the base stations entering the network within a period, and calculate the average loss score of each quantitative indicator; The total loss scores of the quantitative indicators for different dimensions in the most recent period are calculated according to the average loss scores of the quantitative indicators, and the average loss score ratio of each quantitative indicator is obtained according to the ratio of the average loss scores of each quantitative indicator to the total loss scores; The average loss score ratio of each quantitative indicator is used as the first weight coefficient of the corresponding quantitative indicator.

2. The method according to claim 1, wherein The quality inspection score of the base station includes a total quality inspection score and scores for different quantitative indicators; the quantitative indicators of different dimensions are evaluated according to the base station data to obtain the quality inspection score of the base station, including: Obtaining quality inspection results for network access base stations within a preset time period, and determining first weight coefficients of each quantitative indicator according to the loss of scores of each quantitative indicator in the quality inspection results; Obtaining individual scores of each quantitative indicator, and using the individual scores of each quantitative indicator and the first weight coefficient to obtain the scores of each quantitative indicator; The scores of the various quantitative indicators are summed up to obtain the total quality inspection score of the base station.

3. The method according to claim 2, wherein The loss of scores of each quantitative indicator is determined based on the average loss of scores of each quantitative indicator, and the first weight coefficient of each quantitative indicator is determined according to the loss of scores of each quantitative indicator, including: According to the average loss of each quantitative indicator in the quality inspection results of the network access base station within a preset time period; The average loss score of each quantitative indicator is used to determine the average loss score ratio of each quantitative indicator, and the average loss score ratio of each quantitative indicator is used as the first weight coefficient of the corresponding quantitative indicator.

4. The method according to claim 3, characterized in that The average loss of each quantitative indicator in the quality inspection result of the access base station within the preset time period includes: Obtain the individual scores of each quantitative indicator, and calculate the scores of each quantitative indicator in the quality inspection results of the base stations entering the network within a preset time period; The average loss score of each quantitative indicator is calculated by using the individual score of each quantitative indicator and the score of each quantitative indicator in the quality inspection results within a preset time period.

5. The method according to claim 4, characterized in that, The quantitative indicators of different dimensions include equipment online indicators; the base station data includes configuration data for the base station, and the configuration data includes cell information, equipment information and site planning information; The obtaining, according to the base station data, scores of various quantitative indicators of the base station within a preset time period of access to the network includes: Compare the cell information with the site planning information to obtain a first matching result, and compare the device information with the site planning information to obtain a second matching result; Obtain the online score situation corresponding to the first matching result, and obtain the online score situation corresponding to the second matching result; the online score situation corresponding to the first matching result is determined based on the number of cells going online, the single score value for the device online index, and the first weight; the online score situation corresponding to the second matching result is determined based on the number of devices going online, the single score value for the device online index, and the second weight; wherein, the first weight is related to the number of cells going online, and the second weight is related to the number of devices going online; Sum the online score situation corresponding to the first matching result and the score situation corresponding to the second matching result to obtain the score situation of the base station for the device online index.

6. The method according to claim 4, characterized in that, The quantization indexes of different dimensions include the working state index; the base station data includes the fault alarm data for the base station, and the fault alarm data includes the alarm information for each cell; The obtaining of the score situation of each quantization index of the base station within a preset duration after network access according to the base station data includes: Obtain the alarm types of the alarm information for each cell, and determine the working score situation for each cell according to the alarm types; the working score situation is determined based on the number of cells, the working score situation for each cell in the event of corresponding fault alarms, and the third weight; wherein, the third weight is related to the number of cells; Sum the working score situations for each cell to obtain the score situation of the base station for the working state index.

7. The method according to claim 4, characterized in that, The quantization indexes of different dimensions include the parameter index; the base station data includes the configuration data for the base station, and the configuration data includes the configuration parameters for the base station; The obtaining of the score situation of each quantization index of the base station within a preset duration after network access according to the base station data includes: According to the score loss situations of each configuration parameter in the quality inspection results for the base stations accessing the network within a preset time period, and determine the second weight coefficient for each configuration parameter according to the score loss situations of each configuration parameter; Obtain the single score value for the configuration parameters of the base station, and use the single score value for the configuration parameters and the second weight coefficient to obtain the score situation for each configuration parameter; Sum the score situations for each configuration parameter to obtain the score situation for the parameter index.

8. The method according to claim 7, wherein The score loss situation of each configuration parameter is determined based on the average score loss of each configuration parameter, and the determining of the second weight coefficient for each configuration parameter according to the score loss situations of each configuration parameter includes: According to the average score loss of each configuration parameter in the quality inspection results for the base stations accessing the network within a preset time period; Use the average score loss of each configuration parameter to determine the average score loss ratio of each configuration parameter, and use the average score loss ratio of each configuration parameter as the second weight coefficient for the corresponding configuration parameter.

9. The method according to claim 8, wherein The according to the average score loss of each configuration parameter in the quality inspection results for the base stations accessing the network within a preset time period includes: Obtain the single scores of each configuration parameter, and according to the scoring situations of each configuration parameter in the quality inspection results of the networked base stations within a preset time period; Use the single scores of each configuration parameter and the scoring situations of each configuration parameter in the quality inspection results of the networked base stations within a preset time period to calculate the average score loss of each configuration parameter.

10. The method according to claim 9, characterized in that, The configuration parameters for the base station include cell identification parameters; the method for obtaining the scoring situations of each configuration parameter of the base station within a preset duration after network access according to the configuration parameters includes: Construct a triangular network centered on the networked base station; the constructed triangular network is used to determine the influence domain for the cell identification parameters; Obtain the cell identification reuse situation of adjacent base stations within the influence domain; the cell identification reuse situation is determined based on the first cell identification reuse layer and the second cell identification reuse layer; Obtain the scoring situation corresponding to the first cell identification reuse layer and the scoring situation corresponding to the second cell identification reuse layer; the scoring situation corresponding to the first cell identification reuse layer is based on the number of cells, the single score of the cell identification parameter, the threshold of the preset score value range, the reuse score for the first cell identification reuse layer, and the fourth weight; the scoring situation corresponding to the second cell identification reuse layer is based on the number of cells, the single score of the cell identification parameter, the threshold of the preset score value range, the reuse score for the second cell identification reuse layer, and the fifth weight; wherein, the fourth weight is related to the number of cells of the base station, the fifth weight is related to the number of cells of the base station, the reuse score of the first cell identification reuse layer is positively correlated with the first cell identification reuse layer, and the reuse score of the second cell identification reuse layer is positively correlated with the second cell identification reuse layer; Sum up the scoring situation corresponding to the first cell identification reuse layer and the scoring situation corresponding to the second cell identification reuse layer to obtain the scoring situation of the base station for the cell identification parameter.

11. The method according to claim 9, wherein The configuration parameters for the base station include access channel parameters; the method for obtaining the scoring situations of each configuration parameter of the base station within a preset duration after network access according to the configuration parameters includes: Construct a triangular network centered on the networked base station; the constructed triangular network is used to determine the influence domain for the access channel parameters; Obtain the access channel reuse situation of adjacent base stations within the influence domain; the access channel reuse situation is determined based on the access channel reuse layer; Obtain the single score of the access channel parameter, the threshold of the preset score value range, the reuse score for the access channel reuse layer, and the sixth weight for the access channel reuse layer; the sixth weight is related to the number of cells of the base station; wherein, the reuse score of the access channel reuse layer is positively correlated with the access channel reuse layer; Based on the number of cells, use the single score of the cell identification parameter, the threshold of the preset score value range, the reuse score for the access channel reuse layer, and the sixth weight to obtain the scoring situation for the access channel parameter.

12. The method according to claim 9, wherein The configuration parameters for the base station include tracking area parameters; obtaining the score of each configuration parameter of the base station within a preset duration after network access according to the configuration parameters includes: Obtaining the tracking area parameters of the base station accessing the network, and obtaining the set of tracking area parameters of adjacent base stations within the influence domain; If the tracking area parameters of the base station accessing the network do not belong to the set of tracking area parameters of adjacent base stations within the influence domain, then record the score for the tracking area parameters as zero; And / or, if the tracking area parameters of the base station accessing the network belong to the set of tracking area parameters of adjacent base stations within the influence domain, then determine that the single-item score for the tracking area parameters is the score for the tracking area parameters.

13. The method according to claim 9, characterized in that, The configuration parameters for the base station include neighboring cell parameters; obtaining the score of each configuration parameter of the base station within a preset duration after network access according to the configuration parameters includes: Taking the base station accessing the network as the center, obtaining the azimuth information of the base station accessing the network and adjacent base stations; the azimuth information includes the cell azimuth of the base station accessing the network, the cell azimuth of adjacent base stations within the influence domain, the azimuth of neighboring stations relative to the main cell, and the azimuth of the main cell relative to neighboring stations; Respectively based on the mutual difference between the azimuth information of each base station accessing the network and adjacent base stations, determining the necessary neighboring cell set; Obtaining the proportion of the number of adjacent cells in the intersection of the single-item score for the neighboring cell parameters, the necessary neighboring cell set and the configured neighboring cell set to the number of adjacent cells in the necessary neighboring cell set, and the seventh weight; the seventh weight is related to the number of cells; Using the single-item score for the neighboring cell parameters, the proportion of the number of adjacent cells in the intersection of the necessary neighboring cell set and the configured neighboring cell set, and the seventh weight, to obtain the score for the neighboring cell parameters.

14. The method according to claim 4, characterized in that The quantization indicators in different dimensions include service bearing indicators; the base station data includes performance indicator data for the base station, and the performance indicator data includes the cell traffic and the number of cell-connected users of the base station; Obtaining the score of each quantization indicator of the base station within a preset duration after network access according to the base station data includes: Obtaining the adjacent base stations of the base station accessing the network, and obtaining the cell traffic and the number of cell-connected users of different-level cells among the adjacent base stations; Determining the average value of the cell traffic of different-level cells among the adjacent base stations as the benchmark line, and the average value of the number of cell-connected users of different-level cells as the benchmark line; Obtaining the service bearing scores for the cell traffic and the service bearing scores for the number of cell-connected users for each cell in the base station accessing the network; the service bearing score for the cell traffic is determined based on the cell traffic of each cell in the base station accessing the network, the baseline and benchmark line for traffic, the single-item score for the service bearing indicator, and the eighth weight, and the service bearing score for the number of cell-connected users is determined based on the number of cell-connected users of each cell in the base station accessing the network, the baseline and benchmark line for the number of cell-connected users, the single-item score for the service bearing indicator, and the eighth weight; wherein, the eighth weight is related to the number of cells; Sum the service bearer scores of the networked base station for traffic and the service bearer scores for the number of cell-connected users to obtain the score of the base station for the service bearer indicator.

15. The method according to claim 4, wherein The quantization indicators of different dimensions include performance indicators; the base station data includes session statistical data for the base station, and the session statistical data includes cell connection success rate, cell disconnection rate, cell handover success rate, and excellent cell channel quality rate. The obtaining of the score of each quantization indicator of the base station within a preset duration after network access based on the base station data includes: Obtain the first-layer adjacent base stations of the networked base station, and obtain the cell connection success rate, cell disconnection rate, cell handover success rate, and excellent cell channel quality rate of each cell in the adjacent base stations, and determine the average value of the cell connection success rate, cell disconnection rate, cell handover success rate, and excellent cell channel quality rate of each cell in the adjacent base stations as the benchmark line. Obtain the performance scores of each cell in the networked base station for the cell connection success rate, cell disconnection rate, cell handover success rate, and excellent cell channel quality rate respectively; the performance score for the cell connection success rate is determined based on the cell connection success rate of each cell in the networked base station, the baseline and benchmark line for the cell connection success rate, the single-item score for the performance indicator, and the ninth weight; the performance score for the cell disconnection rate is determined based on the cell disconnection rate of each cell in the networked base station, the baseline and benchmark line for the cell disconnection rate, the single-item score for the performance indicator, and the ninth weight; the performance score for the cell handover success rate is determined based on the cell handover success rate of each cell in the networked base station, the baseline and benchmark line for the cell handover success rate, the single-item score for the performance indicator, and the ninth weight; the performance score for the excellent cell channel quality rate is determined based on the excellent cell channel quality rate of each cell in the networked base station, the baseline and benchmark line for the excellent cell channel quality rate, the single-item score for the performance indicator, and the ninth weight; wherein, the ninth weight is related to the number of cells. Sum the performance scores of the networked base station for the cell connection success rate, cell disconnection rate, cell handover success rate, and excellent cell channel quality rate to obtain the score of the base station for the performance indicator.

16. The method according to claim 1, wherein The quality inspection score of the base station includes the total quality inspection score and the score for different quantization indicators. The presenting of the quality inspection score of the networked base station to obtain the quality inspection result of the base station includes: Present the total quality inspection score of the base station and the score for different quantization indicators in the base station to obtain the quality inspection result from the overall perspective and the quality inspection result from the single-item perspective of the base station respectively; wherein, the quality inspection result from the overall perspective is used to evaluate the overall quality audit result of the base station during network access, and the quality inspection result from the single-item perspective is used to present the specific construction problems of the base station.

17. A base station network access quality inspection system, characterized in that, The system includes: A data acquisition module for acquiring the base station data of the base station. A quantitative evaluation module, used to evaluate the quantitative indicators of different dimensions according to the base station data to obtain the quality inspection score of the base station; wherein the quality inspection score of the base station is determined based on the first weight coefficient of the quantitative indicators of different dimensions, and the first weight coefficient is adaptively adjusted based on the loss of the quantitative indicators of different dimensions; The quality inspection result output module is used to display the quality inspection scores of the base stations entering the network and obtain the quality inspection results of the base stations; Wherein, the quantitative evaluation module is also specifically used for: Obtain the individual scores of each quantitative indicator in the quality inspection results of the base stations entering the network within a period, and calculate the average loss score of each quantitative indicator; The total loss scores of the quantitative indicators for different dimensions in the most recent period are calculated according to the average loss scores of the quantitative indicators, and the average loss score ratio of each quantitative indicator is obtained according to the ratio of the average loss scores of each quantitative indicator to the total loss scores; The average loss score ratio of each quantitative index is used as the first weight coefficient of the corresponding quantitative index, and the first weight coefficient is adaptively adjusted.

18. An electronic device, characterized in that, include: A processor, a memory, and a computer program stored in the memory and capable of running on the processor, wherein when the computer program is executed by the processor, the steps of the base station network access quality inspection method as described in any one of claims 1 to 16 are implemented.

19. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, and when the computer program is executed by the processor, the steps of the base station network access quality inspection method as described in any one of claims 1 to 16 are implemented.

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