Method, device and system for evaluating network performance of a slice

Abstract

The application provides a method, device and system for evaluating network performance of a region, the method comprising: dividing a target region into a plurality of sub-regions; performing weighted average on network data corresponding to test points of each sub-region to obtain three average signal strengths and three average signal-to-noise ratios of each sub-region, wherein the network data comprises road testing data, measurement report data and user feedback data; performing weighted average on the three average signal strengths of each sub-region to obtain a comprehensive average signal strength of each sub-region, and performing weighted average on the three average signal-to-noise ratios of each sub-region to obtain a comprehensive average signal-to-noise ratio of each sub-region; and determining that the network performance of a target sub-region is poor when the comprehensive average signal strength of the network of a to-be-evaluated operator in the target sub-region is less than a predetermined signal strength or the comprehensive average signal-to-noise ratio is less than a predetermined signal-to-noise ratio, thereby solving the problem of inaccurate evaluation results of network performance in the prior art.

Description

Technical Field

[0001] This invention relates to the field of communication service technology, and more specifically, to a method, apparatus, computer-readable storage medium, computer program product, and system for evaluating the performance of a local area network. Background Technology

[0002] Mobile communication, as an indispensable infrastructure in modern society, is facing unprecedented challenges. With the rapid growth of mobile data traffic and the increasing demand from users for high-quality network connections, operators not only need to cope with fierce market competition but also need to continuously improve network performance to meet user expectations. However, current technologies have many shortcomings in evaluating the network performance of different operators, particularly in effectively comparing the service quality differences between different operators in various regions, making it difficult to accurately implement network optimization measures.

[0003] Current technologies for monitoring and analyzing mobile network performance, while offering some solutions, still have significant limitations. For example, existing technologies propose basic methods for testing and evaluating wireless mobile network quality, such as fixed-size grid partitioning and testing methods combining DT (Drive Test) and CQT (Call Quality Test). However, these methods have obvious limitations. Fixed-size grid partitioning cannot provide sufficient testing accuracy when facing areas with varying user densities and geographical characteristics, easily leading to insufficient test points in high-density areas and blind spots in low-density areas. Furthermore, the testing methods are limited, mainly relying on DT and CQT, which cannot comprehensively reflect the actual network performance, especially in terms of multi-source data fusion and comprehensive evaluation.

[0004] Similarly, existing technologies have proposed a method for evaluating cell sensing performance based on measurement report data, taking into account factors such as the number of user devices and the power of the reference signal reception. However, this method also ignores the need for comprehensive analysis of multi-source data and does not adopt dynamic adaptive grid division technology, resulting in poor performance in network performance monitoring accuracy, especially in areas with different population densities, making it difficult to provide detailed network performance evaluation and comparison.

[0005] Therefore, existing technologies generally fail to effectively integrate multiple data sources (such as drive test data (DT), measurement report data (MR), and user feedback data (UR), and lack comprehensive scoring calculation functions. This deficiency may lead to the neglect of certain key indicators or improper weight allocation during network performance evaluation, thus affecting the accuracy and reliability of the evaluation results. Furthermore, existing technical solutions typically lack the ability for dynamic adaptive grid division, meaning they cannot automatically adjust the size of monitoring units based on changes in user density and geographical features. In densely populated or geographically complex areas, traditional static, fixed-size grid division methods easily result in unbalanced data distribution—too few data points in high-density areas and too many in low-density areas, thus failing to accurately reflect the actual network conditions. In addition, existing technologies lack the ability for differentiated labeling and visualization. This deficiency not only limits the intuitive display of network performance differences but also hinders the development of effective user interaction mechanisms, thereby affecting the rapid identification and resolution of network problem areas. Summary of the Invention

[0006] The main objective of this application is to provide a method, apparatus, computer-readable storage medium, computer program product, and system for evaluating the performance of a regional network, so as to at least solve the problem of inaccurate network performance evaluation results in the prior art.

[0007] To achieve the above objectives, according to one aspect of this application, a method for evaluating the performance of a regional network is provided, comprising: dividing a target region into multiple sub-regions; and performing a weighted average of network data corresponding to test points in each sub-region to obtain, for each sub-region, drive test average signal-to-noise ratio (SNR), measurement report average signal strength, measurement report average SNR, user feedback average signal strength, and user feedback average SNR. The network data includes drive test data, measurement report data, and user feedback data. The drive test data includes the signal strength and SNR of the test points during drive testing; the measurement report data includes the signal strength and SNR of the test point uploaded by the terminal; and the user feedback data includes the user's feedback regarding the test point. The signal strength and signal-to-noise ratio (SNR) of each sub-area are calculated. A weighted average of the drive-test average signal strength, the measurement report average signal strength, and the user feedback average signal strength of each sub-area is obtained. A weighted average of the drive-test average SNR, the measurement report average SNR, and the user feedback average SNR of each sub-area is also calculated to obtain the overall average SNR of each sub-area. If the overall average signal strength of the network of the operator to be evaluated in the target sub-area is less than a predetermined signal strength, or if the overall average SNR of the network of the operator to be evaluated in the target sub-area is less than a predetermined SNR, the network performance of the target sub-area is determined to be poor. The target sub-area can be any one of the sub-areas.

[0008] Optionally, the target area is divided into multiple sub-areas, including: obtaining a map of the target area; dividing the target area into N sub-regions on the map, such that the boundaries of the sub-regions are located on roads in the map and the area of ​​the sub-regions is less than a predetermined area, N≥2; weighting the user density, data traffic density, and connection density of each sub-region to obtain the network usage density of each sub-region, where the user density is the ratio of the total number of users in the sub-region to the area of ​​the sub-region, the data traffic density is the ratio of the total data traffic in the sub-region to the area of ​​the sub-region within a predetermined time period, and the connection density is the ratio of the number of connections of users to the network in the sub-region to the area of ​​the sub-region within the predetermined time period; calculating the average network usage density of each sub-region to obtain the average network usage density of the target area; and calculating the adjusted area of ​​each sub-region using New Grid Size = Base Grid Size × (1 + (Network Density - Average Density) / (Average Density)), where New Grid Size is the adjusted area, Base Grid Size is the average network usage density of the target area, and Base Grid Size is the average network usage density of the target area. Grid Size is the area of ​​the sub-region before adjustment, Network Density is the network usage density of the sub-region, and Average Density is the average network usage density. The target area is re-divided according to the adjusted area of ​​each sub-region to obtain multiple sub-regions.

[0009] Optionally, dividing the target area into N sub-regions on the map includes: using a quadtree algorithm to divide the target area into 4 sub-regions, such that the boundaries of the sub-regions are located on the roads of the map; a division step, in which, if the area of ​​the target sub-region is greater than the predetermined area, the quadtree algorithm is used to divide the target sub-region into 4 new sub-regions, where the target sub-region is any one of the sub-regions; repeating the division step at least once until the area of ​​all the sub-regions is less than the predetermined area, resulting in N sub-regions.

[0010] Optionally, the network data corresponding to the test points of each sub-area is obtained and weighted averaged to obtain the drive test average signal strength, drive test average signal-to-noise ratio, measurement report average signal strength, measurement report average signal-to-noise ratio, user feedback average signal strength, and user feedback average signal-to-noise ratio of each sub-area, including: dividing the grid of the target sub-area into multiple sub-grids; according to Calculate the time difference weight of each test point in each sub-grid, Weigh_t jiThe time difference weight is the time difference of the i-th test point within the j-th sub-grid. ji Let ∈ be the time difference between the acquisition time of the network data of the i-th test point in the j-th sub-grid and the current time, where ∈ is a constant; according to the time difference weight of each test point in each sub-grid, the network data corresponding to the test point is weighted and averaged to obtain the drive test average signal strength, drive test average signal-to-noise ratio, measurement report average signal strength, measurement report average signal-to-noise ratio, user feedback average signal strength, and user feedback average signal-to-noise ratio of the target sub-area.

[0011] Optionally, a weighted average is performed on the average signal strength of the drive test data, the average signal strength of the measurement report data, and the average signal strength of the user feedback data in each of the sub-areas to obtain the comprehensive average signal strength of each sub-area. A weighted average is also performed on the average signal-to-noise ratio (SNR) of the drive test data, the average SNR of the measurement report data, and the average SNR of the user feedback data in each of the sub-areas to obtain the comprehensive average SNR of each sub-area. This includes setting a weight W1 for the drive test data, a weight W2 for the measurement report data, and a weight W3 for the user feedback data, such that W1 + W2 + W3 = 1; and using AVG(SS-RSRP) = W1·AVG. DT (SS-RSRP)+W2·AVG MR (SS-RSRP)+W3·AVG UR (SS-RSRP) is used to calculate the overall average signal strength of each sub-region, where AVG(SS-RSRP) is the overall average signal strength of the sub-region, and AVG... DT (SS-RSRP) is the average signal strength measured during drive testing in the sub-area, AVG MR (SS-RSRP) is the average signal strength of the measurement report for the sub-area, and AVGUR(SS-RSRP) is the average signal strength of the user feedback for the sub-area; AVG(SS-SINR) = W1·AVG is used. DT (SS-SINR)+W2·AVG MR (SS-SINR)+W3·AVG UR (SS-SINR) is used to calculate the overall average signal-to-noise ratio of each sub-region, where AVG(SS-SINR) is the overall average signal-to-noise ratio of the sub-region, and AVG... DT (SS-SINR) is the average signal strength measured during drive testing in the sub-area, AVG MR (SS-SINR) is the average signal-to-noise ratio of the measurement reports for the sub-region, AVG. UR(SS-SINR) is the average signal-to-noise ratio of the user feedback in the sub-region.

[0012] Optionally, if the overall average signal strength of the network of the operator to be evaluated in the target sub-area is less than a predetermined signal strength or the overall average signal-to-noise ratio of the network of the operator to be evaluated in the target sub-area is less than a predetermined signal-to-noise ratio, determining that the network performance of the target sub-area is poor includes: if the network of the operator to be evaluated is co-built and shared by multiple operators and a reference operator exists, determining the minimum value of the overall average signal strength of the networks of the multiple operators in the target sub-area as the overall average signal strength of the network of the operator to be evaluated in the target sub-area, and determining the minimum value of the overall average signal strength of the networks of the multiple operators in the target sub-area as the overall average signal strength of the network of the operator to be evaluated in the target sub-area. The minimum value of the combined average signal-to-noise ratio is determined as the combined average signal-to-noise ratio of the network of the operator to be evaluated in the target sub-area. The combined average signal strength of the network of the reference operator in the target sub-area is determined as the predetermined signal strength, and the combined average signal-to-noise ratio of the network of the reference operator in the target sub-area is determined as the predetermined signal-to-noise ratio. If the combined average signal strength of the network of the operator to be evaluated in the target sub-area is less than the predetermined signal strength, or if the combined average signal-to-noise ratio of the network of the operator to be evaluated in the target sub-area is less than the predetermined signal-to-noise ratio, the network performance of the target sub-area is determined to be poor.

[0013] Optionally, the method further includes: identifying the test points in the target area where the signal strength is greater than a signal strength threshold and the signal-to-noise ratio is greater than a signal-to-noise ratio threshold as network coverage available test points; calculating the ratio of the number of network coverage available test points to the total number of test points in the target area to obtain the network coverage availability rate; and determining the network coverage availability rate difference of the target area when the network coverage availability rate is less than a predetermined network coverage availability rate.

[0014] Optionally, the method further includes: setting weights for the integrated average signal strength and the integrated average signal-to-noise ratio; performing a weighted average of the integrated average signal strength and the integrated average signal-to-noise ratio of each sub-area according to the weights of the integrated average signal strength and the integrated average signal-to-noise ratio to obtain an integrated score for each sub-area; determining the reciprocal of the distance between the center of each sub-area and the center of the target area as the weight of each sub-area; performing a weighted average of the integrated scores of each sub-area according to the weights of each sub-area to obtain an area integrated score for the target area; when there are multiple operators in the target area, calculating the mean and standard deviation of the area integrated score based on the area integrated score of each operator in the target area; calculating the standardized deviation corresponding to the area integrated score of each operator based on the mean and standard deviation of the area integrated score; and determining the overall performance poor of operators whose standardized deviation is less than 0.

[0015] Optionally, the method further includes: acquiring a map of the target area; acquiring the overall average signal strength and the overall average signal-to-noise ratio of each of the sub-areas; marking the sub-areas in the map whose overall average signal strength is greater than the predetermined signal strength using a first color, marking the sub-areas in the map whose overall average signal strength is equal to the predetermined signal strength using a second color, marking the sub-areas in the map whose overall average signal strength is equal to the predetermined signal strength using a third color, to obtain a signal strength display map of the target area; marking the sub-areas in the map whose overall average signal-to-noise ratio is greater than the predetermined signal-to-noise ratio using a fourth color, marking the sub-areas in the map whose overall average signal-to-noise ratio is equal to the predetermined signal-to-noise ratio using a fifth color, marking the sub-areas in the map whose overall average signal-to-noise ratio is equal to the predetermined signal-to-noise ratio using a sixth color, to obtain a signal-to-noise ratio display map of the target area.

[0016] Optionally, the method further includes: obtaining a map of the target area; obtaining the standardized difference corresponding to the comprehensive score of each operator in the target area; marking operators with a standardized difference greater than 0 using positive altitude, marking operators with a standardized difference equal to 0 using zero altitude, and marking operators with a standardized difference less than 0 using negative altitude, to obtain a comprehensive network coverage performance display map of each operator in the target area.

[0017] According to another aspect of this application, a device for evaluating the performance of a regional network is provided, comprising: a partitioning unit for dividing a target region into multiple sub-regions; and a first calculation unit for weighted averaging of network data corresponding to test points in each of the sub-regions to obtain drive test average signal strength, drive test average signal-to-noise ratio, measurement report average signal strength, measurement report average signal-to-noise ratio, user feedback average signal strength, and user feedback average signal-to-noise ratio for each of the sub-regions, wherein the network data includes drive test data, measurement report data, and user feedback data, the drive test data includes the signal strength and signal-to-noise ratio of the test points during drive testing, the measurement report data includes the signal strength and signal-to-noise ratio of the test point uploaded by the terminal, and the user feedback data includes the test point reported by the user. The signal strength and signal-to-noise ratio (SNR) of the target sub-area are calculated as follows: A second calculation unit is used to perform a weighted average of the drive test average signal strength, the measurement report average signal strength, and the user feedback average signal strength of each sub-area to obtain a comprehensive average signal strength of each sub-area; and a weighted average of the drive test SNR, the measurement report SNR, and the user feedback SNR of each sub-area to obtain a comprehensive average SNR of each sub-area; a first determination unit is used to determine that the network performance of the target sub-area is poor when the comprehensive average signal strength of the network of the operator to be evaluated in the target sub-area is less than a predetermined signal strength or the comprehensive average SNR of the network of the operator to be evaluated in the target sub-area is less than a predetermined SNR, wherein the target sub-area is any one of the sub-areas.

[0018] According to another aspect of this application, a computer-readable storage medium is provided, the computer-readable storage medium including a stored program, wherein, when the program is executed, it controls the device on which the computer-readable storage medium is located to perform any of the methods described.

[0019] According to another aspect of this application, a computer program product is provided, comprising a computer program that, when executed by a processor, implements any of the methods described.

[0020] According to another aspect of this application, a system for evaluating the performance of a regional network is provided, comprising: one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs include methods for performing any one of the methods described.

[0021] Applying the technical solution of this application, the above-mentioned method for evaluating the network performance of a region involves dividing the target region into multiple sub-regions, acquiring network data corresponding to test points in each sub-region, including drive test data, measurement report data, and user feedback data acquired through three channels respectively, weighted averaging the signal strength of the drive test data at each test point to obtain the average drive test signal strength of the sub-region, weighted averaging the signal strength of the measurement report data at each test point to obtain the average measurement report signal strength of the sub-region, and weighted averaging the signal strength of the user feedback data at each test point to obtain the average user feedback signal strength of the sub-region. Similarly, the average drive test signal-to-noise ratio, average measurement report signal-to-noise ratio, and average user feedback signal-to-noise ratio of the sub-region are obtained. Finally, a weighted average of the average drive test signal strength, average measurement report signal strength, and average user feedback signal strength of each sub-region is calculated to obtain the overall signal strength of each sub-region. The method uses a weighted average signal strength to reflect the signal strength of the entire target area. It calculates the weighted average signal-to-noise ratio (SNR) of each sub-area based on drive test average SNR, measurement report average SNR, and user feedback average SNR to obtain the comprehensive average SNR of each sub-area, reflecting the signal quality of the entire target area. If the comprehensive average signal strength of the network of the operator being evaluated in the target sub-area is less than the predetermined signal strength, it indicates that the signal strength of the target sub-area is weak. If the comprehensive average SNR of the network of the operator being evaluated in the target sub-area is less than the predetermined SNR, it indicates that the signal quality of the target sub-area is poor. In other words, the coverage network performance of the operator being evaluated in the target sub-area is poor and needs improvement. This method comprehensively evaluates drive test data, measurement report data, and user feedback data, overcoming the shortcomings of inaccurate evaluation results caused by the large bias of data from a single channel, and solving the problem of inaccurate network performance evaluation results in existing technologies. Attached Figure Description

[0022] Figure 1 A hardware block diagram of a mobile terminal for performing a method for evaluating the performance of a regional network, according to an embodiment of this application, is shown.

[0023] Figure 2 A flowchart illustrating a method for evaluating the performance of a regional network according to an embodiment of this application is shown.

[0024] Figure 3 A flowchart illustrating another method for evaluating the performance of a regional network according to an embodiment of this application is shown.

[0025] Figure 4 A structural block diagram of a network performance evaluation apparatus provided according to an embodiment of this application is shown.

[0026] The above figures include the following reference numerals:

[0027] 102. Processor; 104. Memory; 106. Transmission device; 108. Input / output device. Detailed Implementation

[0028] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0029] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0030] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate for the embodiments of this application described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0031] For ease of description, the following explains some of the nouns or terms used in the embodiments of this application:

[0032] SS-RSRP: Synchronization Reference Signal Received Power, defined as the linear average of the power contribution (in watts) of the elements carrying SSS resources. SS-RSRP measurement time resources are limited to the SS / PBCH block configuration time (SMTC) window. Simply put, this parameter represents the signal strength of the wireless base station in the coverage area; the higher the value, the stronger the coverage signal.

[0033] SS-SINR: Signal-to-Noise Ratio and Interference Ratio, defined as the linear average of the power contribution (in watts) of the SSS resource element carrying the SSS resource element, divided by the linear average of the noise and interference power contributions (in watts) of the SSS resource element carrying the SSS resource element within the same frequency bandwidth. Simply put, this parameter represents the signal quality of a wireless base station within its coverage area; the higher the value, the better the coverage signal quality.

[0034] DT: Drive Test (DT) refers to the process of using a vehicle equipped with specialized testing instruments to drive on a predetermined road to collect and record network performance data along the way. This data includes, but is not limited to, key indicators such as SS-RSRP (Synchronization Reference Received Power) and SS-SINR (SS Signal-to-Noise Ratio and Interference Ratio) to evaluate the quality and performance of mobile communication networks and support network optimization, troubleshooting, and service quality improvement.

[0035] MR: Measurement Report. MR is one of the main bases for assessing the quality of the wireless environment. A Measurement Report (MR) is a data report sent by a mobile terminal (such as a smartphone) to a network base station in a mobile communication system to assess the quality of the wireless communication environment. MR includes a series of key indicators, such as Synchronization Reference Signal Received Power (SS-RSRP), SS Signal-to-Noise Ratio (SS-SINR), etc. These indicators reflect the signal strength and quality at the terminal's location. MR data is an important basis for network operators to optimize the network, troubleshoot problems, and improve service quality.

[0036] UR: User Report. Unlike MR, which is reported periodically or intentionally collected at certain times, UR is initiated by the user and can provide timely and efficient feedback on network issues. The reports are mainly SS-RSRP and SS-SINR.

[0037] As described in the background section, the network performance evaluation results in the prior art are inaccurate. To solve this technical problem, embodiments of this application provide a method, apparatus, computer-readable storage medium, computer program product, and system for evaluating the performance of a regional network.

[0038] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0039] The methods and embodiments provided in this application can be executed on a mobile terminal, computer terminal, or similar computing device. Taking running on a mobile terminal as an example, Figure 1 This is a hardware structure block diagram of a mobile terminal for a method of evaluating the performance of a regional network according to an embodiment of the present invention. Figure 1 As shown, a mobile terminal may include one or more ( Figure 1Only one is shown in the diagram. A processor 102 (which may include, but is not limited to, a microprocessor MCU or a programmable logic device FPGA, etc.) and a memory 104 for storing data are also shown. The mobile terminal may further include a transmission device 106 for communication functions and an input / output device 108. Those skilled in the art will understand that... Figure 1 The structure shown is for illustrative purposes only and does not limit the structure of the mobile terminal described above. For example, the mobile terminal may also include components that are more... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown.

[0040] The memory 104 can be used to store computer programs, such as application software programs and modules, like the computer program corresponding to the area network performance evaluation method in this embodiment of the invention. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, thereby implementing the above-described method. The memory 104 may include high-speed random access memory and non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include memory remotely located relative to the processor 102, and these remote memories can be connected to the mobile terminal via a network. Examples of the aforementioned networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof. The transmission device 106 is used to receive or send data via a network. Specific examples of the aforementioned networks may include wireless networks provided by the mobile terminal's communication provider. In one example, the transmission device 106 includes a network interface controller (NIC), which can be connected to other network devices via a base station to communicate with the Internet. In one example, the transmission device 106 may be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0041] This embodiment provides a method for evaluating the performance of a local area network running on a mobile terminal, computer terminal, or similar computing device. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0042] Figure 2 This is a flowchart of a method for evaluating the performance of a regional network according to an embodiment of this application. Figure 2 As shown, the method includes the following steps:

[0043] Step S201: Divide the target area into multiple sub-areas;

[0044] Step S202: The network data corresponding to the test points of each of the above-mentioned sub-areas are weighted and averaged to obtain the average signal strength, average signal-to-noise ratio, average signal strength, average signal-to-noise ratio of the measurement report, average signal strength, average signal strength and average signal-to-noise ratio of the user feedback for each of the above-mentioned sub-areas. The network data includes drive test data, measurement report data and user feedback data. The drive test data includes the signal strength and signal-to-noise ratio of the test points during the drive test. The measurement report data includes the signal strength and signal-to-noise ratio of the test point where the terminal is located, uploaded by the terminal. The user feedback data includes the signal strength and signal-to-noise ratio of the test point where the user is located, reported by the user.

[0045] Step S203: Perform a weighted average of the average signal strength of the road test, the average signal strength of the measurement report, and the average signal strength of the user feedback for each of the above-mentioned sub-areas to obtain the comprehensive average signal strength of each of the above-mentioned sub-areas; and perform a weighted average of the average signal-to-noise ratio of the road test, the average signal-to-noise ratio of the measurement report, and the average signal-to-noise ratio of the user feedback for each of the above-mentioned sub-areas to obtain the comprehensive average signal-to-noise ratio of each of the above-mentioned sub-areas.

[0046] Step S204: If the overall average signal strength of the network of the operator to be evaluated in the target sub-area is less than the predetermined signal strength or the overall average signal-to-noise ratio of the network of the operator to be evaluated in the target sub-area is less than the predetermined signal-to-noise ratio, the network performance of the target sub-area is determined to be poor, and the target sub-area is any one of the sub-areas.

[0047] In the aforementioned method for evaluating network performance in a specific area, the target area is divided into multiple sub-areas. Network data corresponding to test points in each sub-area is acquired, including drive test data, measurement report data, and user feedback data obtained from three channels. The signal strength of the drive test data from each test point is weighted and averaged to obtain the average drive test signal strength of the sub-area. The signal strength of the measurement report data from each test point is weighted and averaged to obtain the average measurement report signal strength of the sub-area. The signal strength of the user feedback data from each test point is weighted and averaged to obtain the average user feedback signal strength of the sub-area. Similarly, the average signal-to-noise ratio (SNR) of the drive test, the average SNR of the measurement report, and the average SNR of the user feedback are obtained for each sub-area. Finally, the average drive test signal strength, the average measurement report signal strength, and the average user feedback signal strength of each sub-area are weighted and averaged to obtain the comprehensive average signal strength of each sub-area. The signal strength is used to reflect the signal strength of the entire target area. A weighted average of the average signal-to-noise ratio (SNR) from drive tests, measurement reports, and user feedback is calculated for each sub-area to obtain the comprehensive average SNR of each sub-area, reflecting the signal quality of the entire target area. If the comprehensive average signal strength of the network of the operator being evaluated in the target sub-area is less than the predetermined signal strength, it indicates that the signal strength of the target sub-area is weak. If the comprehensive average SNR of the network of the operator being evaluated in the target sub-area is less than the predetermined SNR, it indicates that the signal quality of the target sub-area is poor. In other words, the coverage network performance of the operator being evaluated in the target sub-area is poor and needs improvement. This method comprehensively evaluates drive test data, measurement report data, and user feedback data, overcoming the deficiency of inaccurate evaluation results caused by the large bias of data from a single channel, and solving the problem of inaccurate network performance evaluation results in existing technologies.

[0048] It should be noted that drive test (DT) data collection involves collecting the SS-RSRP and SS-SINR of each operator within each grid cell, which are denoted as DT(SS-RSRP) and DT(SS-SINR). The drive test method can involve using a test terminal with a built-in SIM card of the corresponding operator and connected to a professional testing tool (such as Pilot Pioneer). Tests are performed at fixed time intervals, and the professional tool can then output the aforementioned network parameters for each operator. As an example, three mobile phones with built-in SIM cards from different operators are used as test terminals. Based on GPS positioning, the three phones are moved to multiple random locations within the grid cell to be tested, and data is collected every second. The data collected by each phone is then output using a professional tool. Measurement Report (MR) Data Acquisition: SS-RSRP and SS-SINR for each operator within each grid are collected through Measurement Reports (MR), namely MR(SS-RSRP) and MR(SS-SINR). The method involves user equipment (such as smartphones) periodically sending measurement reports containing key indicators such as SS-RSRP and SS-SINR to the network base station during normal use. These reports are collected and processed by the network backend system to form a dataset with time and geographic location information. As an example, user equipment moves within a designated area and spontaneously reports measurement reports at different locations. The backend system categorizes these reports into corresponding grids based on GPS positioning information. Each report contains information such as the user equipment's location at a specific time, SS-RSRP, and SS-SINR, and after processing by specialized tools, outputs various network parameters from each report. Data collection based on user feedback (User Report, UR): SS-RSRP and SS-SINR for each operator within each grid are collected through proactive user feedback, which are UR(SS-RSRP) and UR(SS-SINR). The method involves user devices (such as smartphones) proactively submitting reports containing key indicators such as SS-RSRP and SS-SINR through a dedicated application or channel when encountering network problems. These reports include the user's location at a specific time, timestamp, and a description of the problem encountered. As an example, users can mark locations they believe have poor network performance in interactive map tools and provide basic information (such as time, device type, and a description of the problem encountered). The backend system collects and processes these reports to form a dataset with time and geographic location information, used to assess network performance and identify problem areas.

[0049] In order to implement the area division taking into account user density and geographical characteristics, in an optional implementation, step S201 above includes:

[0050] Step S2011: Obtain a map of the target area mentioned above;

[0051] Step S2012: Divide the target area into N sub-regions on the map, such that the boundaries of the sub-regions are located on the roads of the map and the area of ​​the sub-regions is less than a predetermined area, N≥2;

[0052] Step S2013: Calculate the weighted average of the user density, data traffic density, and connection density of each of the above-mentioned sub-regions to obtain the network usage density of each of the above-mentioned sub-regions. The user density is the ratio of the total number of users in the above-mentioned sub-region to the area of ​​the above-mentioned sub-region. The data traffic density is the ratio of the total data traffic of the above-mentioned sub-region to the area of ​​the above-mentioned sub-region within a predetermined time period. The connection density is the ratio of the number of connections of the users in the above-mentioned sub-region to the network to the area of ​​the above-mentioned sub-region within the predetermined time period.

[0053] Step S2014: Calculate the average network usage density of each of the above sub-regions to obtain the average network usage density of the target area.

[0054] Step S2015: The adjusted area of ​​each of the above sub-regions is calculated using New Grid Size = Base Grid Size × (1 + (Network Density - Average Density) / (Average Density)), where New Grid Size is the adjusted area, Base Grid Size is the area of ​​the sub-region before adjustment, Network Density is the network usage density of the sub-region, and Average Density is the average network usage density.

[0055] Step S2016: The target area is re-divided according to the adjusted area of ​​each of the above sub-regions to obtain multiple of the above sub-regions.

[0056] In the above embodiments, the target area division can be based on a fixed-size static grid division using latitude and longitude, dividing several specified areas into grids. Specifically, geographical areas can be divided into grids according to latitude and longitude, for example, dividing geographical areas into 20m*20m rectangular grid networks, with one grid corresponding to one sub-area. Alternatively, dynamic grid division based on user density and geographical features can be used to divide the target area into N sub-regions, considering drawing grids along streets or traffic routes to better capture network performance differences in the city. Furthermore, the area of ​​each sub-region is smaller than a predetermined area to meet fine-grained requirements. NetworkDensity = w uUserDwnsity+w t TrafficDensity+w c • ConnectionDensity; where NetworkDensity is network usage density, UserDensity is user density, TrafficDensity is data traffic density, and ConnectionDensity is connection density. u w t and w c These are weights used for the number of users, data traffic, and connections, respectively. These weights can be set according to actual conditions. The grid size of each sub-region is adjusted according to network usage density. Simply put, high-density areas are divided into larger grids, and low-density areas are divided into smaller grids. This increases the proportion of grids with weak signals and poor user experience, ensuring that network performance data is more accurate and meaningful for analysis. The specific calculation formula is as follows: New Grid Size = Base GridSize × (1 + (Network Density - Average Density) / (Average Density)). The adjusted area of ​​each of the above sub-regions is calculated, where New Grid Size is the adjusted area, Base Grid Size is the area of ​​the above sub-region before adjustment, Network Density is the network usage density of the above sub-region, and Average Density is the average network usage density. By adopting a division method based on user density and geographical features, the size and shape of the grid can be dynamically adjusted according to network usage and regional geographical features, thereby better reflecting differences in network performance.

[0057] To meet the fine-grained requirements of area division, in one optional implementation, step S2012 includes:

[0058] Step S20121: The target area is divided into four sub-regions using a quadtree algorithm, such that the boundaries of the sub-regions are located on the roads in the map.

[0059] Step S20122, partitioning step: if the area of ​​the target sub-region is greater than the predetermined area, the target sub-region is divided into 4 new sub-regions using the quadtree algorithm. The target sub-region can be any one of the sub-regions.

[0060] Step S20123: Repeat the above division steps at least once until the area of ​​all the above sub-regions is smaller than the predetermined area, thus obtaining N of the above sub-regions.

[0061] In the above implementation, a quadtree algorithm is used to divide a specified area into smaller sub-areas, and these sub-areas are used as the starting points for grid division. The quadtree can be constructed recursively, starting from the root node, by recursively executing the following steps to construct the quadtree: A. Check if the current node needs to be divided; B. If it needs to be divided, divide the current node into four child nodes, each child node representing one of the four sub-regions of the current node's area, and calculate the boundary coordinates of the child nodes; C. Recursively perform the same division process on each child node until the required fineness is reached or the stopping condition is met. One child node corresponds to one sub-region. The fineness can be that the area of ​​all the above-mentioned sub-regions is less than the predetermined area. The stopping condition can be that the number of repetitions reaches the maximum number, thereby obtaining N sub-regions that meet the requirements.

[0062] To accurately assess network performance, in one optional implementation, step S202 includes:

[0063] Step S2021: Divide the target sub-region into multiple sub-grids;

[0064] Step S2022, according to Calculate the time difference weight of each test point in each of the above sub-grids, Weigh_t ji Timedifference is the time difference weight for the i-th test point within the j-th sub-grid. ji The time difference between the acquisition time of the network data of the i-th test point within the j-th sub-grid and the current time, where ∈ is a constant;

[0065] Step S2023: The network data corresponding to the test points are weighted and averaged according to the time difference weight of each test point in each sub-grid to obtain the average signal strength of the drive test, the average signal-to-noise ratio of the drive test, the average signal strength of the measurement report, the average signal-to-noise ratio of the measurement report, the average signal strength of the user feedback, and the average signal-to-noise ratio of the user feedback in the target sub-area.

[0066] In the above implementation, when there are three operators, firstly, for each data source (DT, MR, UR), the average value AVG is calculated, including: AVG DT (Operator 1 SS-RSRP), AVG DT (S2 operator SS-RSRP), AVG DT (S3 operator SS-RSRP), AVG DT (Operator 1 SS-SINR), AVG DT (Signal 2 SS-SINR), AVGDT (Signal 3 SS-SINR); AVG MR(Operator 1 SS-RSRP), AVG MR (S2 operator SS-RSRP), AVG MR (S3 operator SS-RSRP), AVG MR (Operator 1 SS-SINR), AVG MR (S2 operator SS-SINR), AVG MR (S3 operator SS-SINR); AVG UR (Operator 1 SS-RSRP), AVG UR (S2 operator SS-RSRP), AVG UR (S3 operator SS-RSRP), AVG UR (Operator 1 SS-SINR), AVG UR (S2 operator SS-SINR), AVG UR (Signal 3 SS-SINR). The average value can be calculated using either a standard average or a weighted average. For example, when using a weighted average, the AVG is calculated. DT (Operator 1 SS-RSRP) can be implemented in the following way: The current sub-area grid is further divided into several sub-grids (e.g., a 5m*5m rectangular grid). For drive test data falling within the same sub-grid, the collection time of different data is considered, giving higher weight to data collected closer to the current time. Taking AVG (Operator 1 SS-RSRP) as an example, its calculation formula is as follows: AVG (Operator 1 SS-RSRP) DT, MR, and UR data are all calculated using the same formula, among which SS-RSRP ji This represents the SS-RSRP value of operator 1 collected by the i-th test point within the j-th sub-grid, where n is the number of test points within the j-th sub-grid, m is the number of sub-grids within the current grid, and Weight_t ji This is the time difference weight of the i-th test point within the j-th sub-grid, which can be set according to the acquisition time. As an example: Among them, Weigh_t ji The time difference weight is the time difference weight of the i-th test point within the j-th sub-grid. ji Let ∈ be the time difference between the acquisition time of the network data of the i-th test point within the j-th sub-grid and the current time. ∈ is a constant; to avoid division by zero, it is usually a very small positive number, such as 1 × 10⁻⁶. -6 .

[0067] To comprehensively consider the impact of road test data, measurement report data, and user feedback data, in one optional implementation, step S203 includes:

[0068] Step S2031: Set the weights W1 of the road test data, W2 of the measurement report data, and W3 of the user feedback data, such that W1+W2+W3=1;

[0069] Step S2032, using AVG(SS-RSRP) = W1·AVG DT (SS-RSRP)+W2·AVG MR (SS-RSRP)+W3·AVG UR (SS-RSRP) is used to calculate the overall average signal strength of each of the above sub-regions, where AVG(SS-RSRP) is the overall average signal strength of the above sub-regions, and AVG DT (SS-RSRP) represents the average signal strength of the aforementioned sub-area during road testing. AVG MR (SS-RSRP) is the average signal strength of the above measurement reports for the aforementioned sub-area, AVG UR (SS-RSRP) represents the average signal strength of the user feedback in the aforementioned sub-area;

[0070] Step S2033: The above-mentioned comprehensive average signal-to-noise ratio of each sub-region is calculated using AVG(SS-SINR) = W1·AVGDT(SS-SINR) + W2·AVGMR(SS-SINR) + W3·AVGUR(SS-SINR), where AVG(SS-SINR) is the above-mentioned comprehensive average signal-to-noise ratio of the sub-region, and AVG... DT (SS-SINR) represents the average signal strength measured during the aforementioned road tests in the aforementioned sub-area. AVG MR (SS-SINR) is the average signal-to-noise ratio of the above measurement reports for the above sub-regions, AVG UR (SS-SINR) is the average signal-to-noise ratio of the user feedback in the above sub-regions.

[0071] In the above implementation, after calculating the average value of each individual performance indicator for each data source, a comprehensive average value can be further calculated. The specific calculation formula is as follows: AVG(SS-RSRP)=W1*AVG DT (SS-RSRP)+W2*AVG MR (SS-RSRP)+W3*AVG UR (SS-RSRP), AVG (SS-SINR) = W1*AVG DT (SS-SINR)+W2*AVG MRThe formula is (SS-SINR) + W3 * AVGUR(SS-SINR), where W1, W2, and W3 are used to assign weights to DT, MR, and UR data in SS-RSRP and SS-SINR, respectively. To reasonably balance the contribution of different data sources (DT, MR, UR) in calculating the overall score, the weights W1, W2, and W3 must be set based on factors such as the reliability, importance, timeliness, and coverage of the data sources. Specifically, DT data is considered the most reliable data source due to its collection in a controlled environment, and therefore is given a higher weight W1; MR data is also given a higher weight W2 due to its wide coverage and long-term stability; UR data, despite being slightly less reliable, still needs a certain weight W3 due to its real-time nature and the importance of direct user feedback. Based on this, the initial weights are set as W1 = 0.4, W2 = 0.4, and W3 = 0.2, which can be adjusted according to different scenarios such as dense urban villages, high-end office buildings, and open roads. This ensures that the comprehensive score fully considers both the reliability and breadth of DT and MR data, as well as the real-time nature of UR data, thereby providing a comprehensive and accurate network performance evaluation result.

[0072] To identify sub-segments with poor network performance, in one optional implementation, step S204 includes:

[0073] Step S2041: When the network of the operator to be evaluated is co-constructed and shared by multiple operators and a reference operator exists, the minimum value of the comprehensive average signal strength of the networks of the multiple operators in the target sub-area is determined as the comprehensive average signal strength of the network of the operator to be evaluated in the target sub-area, the minimum value of the comprehensive average signal-to-noise ratio of the networks of the multiple operators in the target sub-area is determined as the comprehensive average signal-to-noise ratio of the network of the operator to be evaluated in the target sub-area, the comprehensive average signal strength of the network of the reference operator in the target sub-area is determined as the predetermined signal strength, and the comprehensive average signal-to-noise ratio of the network of the reference operator in the target sub-area is determined as the predetermined signal-to-noise ratio.

[0074] Step S2042: If the overall average signal strength of the network of the operator to be evaluated in the target sub-area is less than the predetermined signal strength, or the overall average signal-to-noise ratio of the network of the operator to be evaluated in the target sub-area is less than the predetermined signal-to-noise ratio, then the network performance of the target sub-area is determined to be poor.

[0075] In the above implementation, since Operator 1 and Operator 2 are implementing 5G co-construction and sharing nationwide, the network performance quality received by their mobile terminals is basically the same. For ease of analysis, the operator with the worse average value between Operator 1 and Operator 2 is compared with Operator 3. That is, SS-RSRP difference = MIN(AVG(Operator 1's SS-RSRP), AVG(Operator 2's SS-RSRP)) - AVG(Operator 3's SS-RSRP), SS-SINR difference = MIN(AVG(Operator 1's SS-SINR), AVG(Operator 2's SS-SINR)) - AVG(Operator 3's SS-SINR). The areas with negative SS-RSRP or SS-SINR differences are the sub-areas where Operator 1 and Operator 2 need to focus on improving network quality.

[0076] To identify areas with poor network coverage availability, in one optional implementation, the method further includes:

[0077] Step S301: The test points in the target area with signal strength greater than the signal strength threshold and signal-to-noise ratio greater than the signal-to-noise ratio threshold are identified as network coverage available test points.

[0078] Step S302: Calculate the ratio of the number of available network coverage test points to the total number of test points in the target area to obtain the network coverage availability rate;

[0079] Step S303: If the network coverage availability rate is less than the predetermined network coverage availability rate, determine the network coverage availability rate difference of the target area.

[0080] In the above implementation, the network coverage availability rate C of the main operator and the target operator in each area is calculated. The calculation method of network coverage availability rate C is as follows: the test point that simultaneously meets the conditions of SS-RSRP>-105dBm and SS-SINR>-3dBm is used as the numerator, and all test points are used as the denominator. The numerator and denominator are divided to obtain the network coverage availability rate C. If the network coverage availability rate C is less than the predetermined network coverage availability rate, then the network coverage availability rate difference of the target area is considered. Of course, the network coverage availability rate difference between the main operator and the target operator can also be calculated: similarly, the worse one between operator 1 and operator 2 is selected and subtracted from operator 2, that is, network coverage availability rate difference = MIN(operator 1 C, operator 2 C) - operator 3 C. The area with a negative network coverage availability rate difference is the area where operator 1 and operator 2 need to focus on improving network coverage.

[0081] To analyze the comprehensive score of a region, in one optional implementation, the above method further includes:

[0082] Step S401: Set the weights of the above-mentioned comprehensive average signal strength and the above-mentioned comprehensive average signal-to-noise ratio;

[0083] Step S402: Based on the weights of the above-mentioned comprehensive average signal strength and the above-mentioned comprehensive average signal-to-noise ratio, the above-mentioned comprehensive average signal strength and the above-mentioned comprehensive average signal-to-noise ratio of each of the above-mentioned sub-regions are weighted and averaged to obtain the comprehensive score of each of the above-mentioned sub-regions.

[0084] Step S403: The reciprocal of the distance between the center of each of the above sub-regions and the center of the target region is determined as the weight of each of the above sub-regions;

[0085] Step S404: The comprehensive scores of the above-mentioned sub-regions are weighted and averaged according to the weight of each of the above-mentioned sub-regions to obtain the comprehensive score of the target region.

[0086] Step S405: When there are multiple operators in the target area, calculate the mean and standard deviation of the comprehensive score of the target area based on the comprehensive score of each operator in the target area.

[0087] Step S406: Calculate the standardized deviation of the comprehensive score of the above-mentioned area for each of the above-mentioned operators based on the mean and standard deviation of the comprehensive score of the above-mentioned area.

[0088] Step S407: Determine the overall performance difference of the above-mentioned operators whose standardized difference is less than 0.

[0089] In the above implementation, a composite score for each grid cell is calculated based on SS-RSRP and SS-SINR: CompositeScore. i =α1×AVG(SS-RSRP) i +α2×AVG(SS-SINR) i Among them, CompositeScore i It is the overall score of the i-th grid, AVG(SS-RSRP). i AVG(SS-SINR) i α1 and α2 are the mean SS-RSRP and SS-SINR values ​​of the i-th grid (see above for details), respectively. α1 and α2 are the weights of SS-RSRP and SS-SINR, which can be set according to actual conditions. The overall area score is calculated based on the overall score of each grid. Among them, Area ComositeScore is the overall score for the area, and Composite Score is the composite score for the area. i It is the overall score of the i-th grid cell within the area, where n is the number of grid cells in the area, and Weight_l iThis represents the geographic location weight of the i-th grid cell, which can be set based on geographic location and regional importance. As an example: Among them, Weight_l i Distance is the geographic location weight of the i-th grid cell. i It is the distance from the center of the i-th grid cell to the center of the area, which can be calculated using latitude and longitude coordinates. (Importance) i This represents the regional importance of the i-th grid, which can be user-defined and indicates the importance of a specific area to the overall region. The standardized regional comprehensive score difference between the main operator and the target operator is calculated. As an example, the average regional comprehensive score of all operators is calculated. Where N is the total number of operators, AreaComposite Score i Let be the comprehensive regional score of the i-th operator; calculate the standard deviation of the comprehensive regional scores of all operators: Calculate the standardized difference of the comprehensive regional score for the k-th operator: Among them, Standardized Difference k It is the standardized difference of the area composite score of the kth operator. k This is the comprehensive regional score of the kth operator. The mean is calculated as above, and SD is calculated as above. By calculating the difference in the comprehensive scores of the standardized areas, the comprehensive performance of the main operator and the target operator in a specified area can be compared intuitively. If the difference is positive, it means that the comprehensive performance of the main operator in that area is higher than that of the target operator; if it is negative, it means that the comprehensive performance of the main operator needs to be improved.

[0090] To provide a clear visual representation of network performance differences, in one optional implementation, the method further includes:

[0091] Step S501: Obtain a map of the target area mentioned above;

[0092] Step S502: Obtain the above-mentioned comprehensive average signal strength and the above-mentioned comprehensive average signal-to-noise ratio of each of the above-mentioned sub-regions;

[0093] Step S503: Use a first color to mark the sub-area in the map where the overall average signal strength is greater than the predetermined signal strength, use a second color to mark the sub-area in the map where the overall average signal strength is equal to the predetermined signal strength, and use a third color to mark the sub-area in the map where the overall average signal strength is equal to the predetermined signal strength, to obtain the signal strength display map of the target area.

[0094] Step S504: Use a fourth color to mark the sub-area in the map where the overall average signal-to-noise ratio is greater than the predetermined signal-to-noise ratio, use a fifth color to mark the sub-area in the map where the overall average signal-to-noise ratio is equal to the predetermined signal-to-noise ratio, and use a sixth color to mark the sub-area in the map where the overall average signal-to-noise ratio is equal to the predetermined signal-to-noise ratio, thereby obtaining the signal-to-noise ratio display map of the target area.

[0095] In the above implementation, based on the aforementioned individual performance differences, a unique marker is determined for each grid cell. Specifically, different colors are used to visualize the magnitude of the difference. For example, when the difference is positive, a green marker is used, indicating that operators 1 and 2 have better performance; when the difference is zero, a yellow marker is used, indicating that operators 1 and 2 have performance comparable to operator 3; when the difference is negative, an orange or red marker is used, indicating that operators 1 and 2 have poor performance. The color selection rules can be adjusted according to actual conditions, such as using a more refined grading method and a more optimized difference evaluation method. Based on the aforementioned unique markers and the initial map, a unique display map is generated. Specifically, color markers are added to the corresponding grid cells of the initial map, and the strength of individual network performance in each grid cell is presented through maps with different color markers.

[0096] To provide a clear visual representation of network performance differences, in one optional implementation, the method further includes:

[0097] Step S601: Obtain a map of the target area mentioned above;

[0098] Step S602: Obtain the standardized difference corresponding to the comprehensive score of the above-mentioned area for each of the above-mentioned operators;

[0099] Step S603: Use positive altitude to mark the operators whose standardized difference is greater than 0, use zero altitude to mark the operators whose standardized difference is equal to 0, and use negative altitude to mark the operators whose standardized difference is less than 0, to obtain a comprehensive network coverage performance display map of each operator in the target area.

[0100] In the above implementation, a comprehensive differentiation marker is determined for each area based on the aforementioned comprehensive performance difference. Specifically, different altitude markers are used to visualize the magnitude of the difference. For example, when the difference is positive, an altitude of 10m is used, indicating that operators 1 and 2 have better performance; when the difference is zero, a zero-point altitude is used, indicating that the performance of operators 1 and 2 is on par with operator 3; when the difference is negative, an altitude of -10m is used, indicating that the performance of operators 1 and 2 is poor. The altitude selection rules can be adjusted according to actual conditions, such as using a more refined classification method and a more optimized difference evaluation method. Based on the aforementioned comprehensive differentiation markers and the initial map, a comprehensive differentiation display map is generated. Specifically, altitude markers are added to the corresponding areas of the initial map, and the comprehensive network performance of each area is presented through maps with different altitude markers.

[0101] It's worth noting that the interactive map tool provides interactive functionality on both individual and comprehensive differentiated display maps. Users can hover over specific areas to obtain detailed information or click to display performance data for specific grids. Furthermore, users can mark locations on the interactive map that they perceive as having poor network performance and provide basic information (such as time, device type, and a description of the problem encountered). By applying color and elevation markers to the initial map, individual and comprehensive differentiated display maps are generated. This not only visually presents the network performance differences between various grids and areas but also provides rich user interaction features through the interactive map tool. Users can hover over specific areas to obtain detailed information, click to display performance data for specific grids, and mark locations on the map that they perceive as having poor network performance, providing basic information (such as time, device type, and a description of the problem encountered). The advantage of this design is that it not only enhances map readability and information delivery efficiency but also increases user engagement and feedback speed, thus providing network operators with an effective means to quickly identify problem areas and take targeted optimization measures.

[0102] To enable those skilled in the art to better understand the technical solution of this application, the implementation process of the method for evaluating the performance of regional networks in this application will be described in detail below with reference to specific embodiments.

[0103] This embodiment relates to a specific method for evaluating the performance of a regional network, such as... Figure 3 As shown, it includes the following steps:

[0104] Step 1: Fixed-size static raster division based on latitude and longitude, and dynamic raster division based on user density and geographic features;

[0105] Step 2: Obtain network data from the main operator and at least one target operator within grids in several designated areas through three methods: drive tests, measurement reports, and user feedback;

[0106] Step 3: Based on the above network data, determine the individual performance difference between the main operator and the target operator in each grid and the comprehensive performance difference in each area;

[0107] Step 4: Based on the above individual performance differences and the above comprehensive performance differences, determine the individual differentiation mark for each grid and the comprehensive differentiation mark for each region.

[0108] Step 5: Based on the above individual differentiation markers, the above comprehensive differentiation markers, and the initial map, generate an individual differentiation display map and a comprehensive differentiation display map.

[0109] As shown in Table 1, the network coverage availability rates of China Telecom, China Unicom, and China Mobile in different areas were calculated. In the Futian area, the coverage availability rates of China Telecom, China Unicom, and China Mobile were 98.80%, 98.86%, and 98.22%, respectively. Therefore, the difference in coverage availability rates among the three networks can be calculated as MIN(C China Telecom, C China Unicom) - C China Mobile = 98.80% - 98.22% = 0.58pp.

[0110] Table 1

[0111]

[0112] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0113] This application also provides a device for evaluating the performance of a regional network. It should be noted that this device can be used to execute the method for evaluating regional network performance provided in this application. This device is used to implement the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0114] The following describes the network performance evaluation apparatus provided in the embodiments of this application.

[0115] Figure 4 This is a structural block diagram of a network performance evaluation apparatus according to an embodiment of this application. Figure 4 As shown, the device includes:

[0116] Division unit 10 is used to divide the target area into multiple sub-areas;

[0117] The first calculation unit 20 is used to perform weighted averaging of the network data corresponding to the test points of each of the above-mentioned sub-areas to obtain the average signal strength, average signal-to-noise ratio, average signal strength, average signal-to-noise ratio of the measurement report, average signal strength, average signal strength and average signal-to-noise ratio of the user feedback for each of the above-mentioned sub-areas. The network data includes drive test data, measurement report data and user feedback data. The drive test data includes the signal strength and signal-to-noise ratio of the test points in the drive test. The measurement report data includes the signal strength and signal-to-noise ratio of the test point where the terminal is located, uploaded by the terminal. The user feedback data includes the signal strength and signal-to-noise ratio of the test point where the user is located, reported by the user.

[0118] The second calculation unit 30 is used to perform a weighted average of the average signal strength of the road test, the average signal strength of the measurement report, and the average signal strength of the user feedback in each of the above-mentioned sub-areas to obtain the comprehensive average signal strength of each of the above-mentioned sub-areas, and to perform a weighted average of the signal-to-noise ratio of the road test, the signal-to-noise ratio of the measurement report, and the signal-to-noise ratio of the user feedback in each of the above-mentioned sub-areas to obtain the comprehensive average signal-to-noise ratio of each of the above-mentioned sub-areas.

[0119] The first determining unit 40 is configured to determine that the network performance of the target sub-area is poor when the overall average signal strength of the network of the operator to be evaluated in the target sub-area is less than a predetermined signal strength or the overall average signal-to-noise ratio of the network of the operator to be evaluated in the target sub-area is less than a predetermined signal-to-noise ratio, wherein the target sub-area is any one of the sub-areas.

[0120] In the aforementioned network performance evaluation device, the target area is divided into multiple sub-areas. Network data corresponding to test points in each sub-area is acquired, including drive test data, measurement report data, and user feedback data obtained from three channels. The signal strength of the drive test data from each test point is weighted and averaged to obtain the average drive test signal strength of the sub-area. Similarly, the signal strength of the measurement report data from each test point is weighted and averaged to obtain the average measurement report signal strength of the sub-area. The signal strength of the user feedback data from each test point is weighted and averaged to obtain the average user feedback signal strength of the sub-area. Likewise, the average drive test signal-to-noise ratio, average measurement report signal-to-noise ratio, and average user feedback signal-to-noise ratio of the sub-area are obtained. Finally, the average drive test signal strength, average measurement report signal strength, and average user feedback signal strength of each sub-area are weighted and averaged to obtain the comprehensive average signal strength of each sub-area. The signal strength is used to reflect the signal strength of the entire target area. A weighted average of the average signal-to-noise ratio (SNR) from drive tests, measurement reports, and user feedback is calculated for each sub-area to obtain the comprehensive average SNR of each sub-area, reflecting the signal quality of the entire target area. If the comprehensive average signal strength of the network of the operator being evaluated in the target sub-area is less than the predetermined signal strength, it indicates that the signal strength of the target sub-area is weak. If the comprehensive average SNR of the network of the operator being evaluated in the target sub-area is less than the predetermined SNR, it indicates that the signal quality of the target sub-area is poor. In other words, the coverage network performance of the operator being evaluated in the target sub-area is poor and needs improvement. This method comprehensively evaluates drive test data, measurement report data, and user feedback data, overcoming the deficiency of inaccurate evaluation results caused by the large bias of data from a single channel, and solving the problem of inaccurate network performance evaluation results in existing technologies.

[0121] To implement area division considering user density and geographical characteristics, in one optional implementation, the aforementioned division unit includes:

[0122] The acquisition module is used to acquire a map of the aforementioned target area;

[0123] The first division module is used to divide the target area into N sub-regions on the map, such that the boundaries of the sub-regions are located on the roads of the map and the area of ​​the sub-regions is less than a predetermined area, N≥2;

[0124] The first calculation module is used to perform a weighted average of the user number density, data traffic density, and connection density of each of the above-mentioned sub-regions to obtain the network usage density of each of the above-mentioned sub-regions. The user number density is the ratio of the total number of users in the above-mentioned sub-region to the area of ​​the above-mentioned sub-region. The data traffic density is the ratio of the total data traffic of the above-mentioned sub-region to the area of ​​the above-mentioned sub-region within a predetermined time period. The connection density is the ratio of the number of connections of the users in the above-mentioned sub-region to the network to the area of ​​the above-mentioned sub-region within the predetermined time period.

[0125] The second calculation module is used to calculate the average network usage density of each of the above-mentioned sub-regions to obtain the average network usage density of the target area.

[0126] The third calculation module is used to calculate the adjusted area of ​​each of the above sub-regions using New Grid Size = Base Grid Size × (1 + (NetworkDensity - Average Density) / (Average Density)), where New Grid Size is the adjusted area, Base Grid Size is the area of ​​the above sub-region before adjustment, NetworkDensity is the network usage density of the above sub-region, and Average Density is the average network usage density.

[0127] The second division module is used to redivide the target area based on the adjusted area of ​​each of the above sub-regions, resulting in multiple sub-regions.

[0128] To meet the fine-grained requirements of area division, in one optional implementation, the first division module includes:

[0129] The first partitioning submodule is used to divide the target area into four sub-regions using a quadtree algorithm, such that the boundaries of the sub-regions are located on the roads in the map.

[0130] The second partitioning submodule is used to perform the partitioning steps. If the area of ​​the target subregion is greater than the predetermined area, the target subregion is divided into 4 new subregions using the quadtree algorithm. The target subregion can be any one of the subregions.

[0131] The repeating submodule is used to repeat the above division steps at least once until the area of ​​all the above sub-regions is smaller than the predetermined area, resulting in N of the above sub-regions.

[0132] To accurately evaluate network performance, in one optional implementation, the first computing unit includes:

[0133] The third partitioning module is used to divide the target sub-region's grid into multiple sub-grids;

[0134] The fourth calculation module is used to calculate based on Calculate the time difference weight of each test point in each of the above sub-grids, Weigh_t ji The time difference weight is the time difference weight of the i-th test point within the j-th sub-grid. jiThe time difference between the acquisition time of the network data of the i-th test point within the j-th sub-grid and the current time, where ∈ is a constant;

[0135] The fifth calculation module is used to perform a weighted average of the network data corresponding to the test points based on the time difference weight of each of the test points in each of the sub-grids, so as to obtain the average signal strength of the drive test, the average signal-to-noise ratio of the drive test, the average signal strength of the measurement report, the average signal-to-noise ratio of the measurement report, the average signal strength of the user feedback, and the average signal-to-noise ratio of the user feedback in the target sub-area.

[0136] To comprehensively consider the impact of road test data, measurement report data, and user feedback data, in one optional implementation, the second calculation unit includes:

[0137] The setting module is used to set the weights W1 of the aforementioned road test data, W2 of the aforementioned measurement report data, and W3 of the aforementioned user feedback data, such that W1+W2+W3=1;

[0138] The sixth calculation module is used to apply AVG(SS-RSRP) = W1·AVG DT (SS-RSRP)+W2·AVG MR (SS-RSRP)+W3·AVG UR (SS-RSRP) is used to calculate the overall average signal strength of each of the above sub-regions, where AVG(SS-RSRP) is the overall average signal strength of the above sub-regions, and AVG DT (SS-RSRP) represents the average signal strength of the aforementioned sub-area during road testing. AVG MR (SS-RSRP) is the average signal strength of the above measurement reports for the aforementioned sub-area, AVG UR (SS-RSRP) represents the average signal strength of the user feedback in the aforementioned sub-area;

[0139] The seventh calculation module is used to calculate the overall average signal-to-noise ratio of each of the above sub-regions using AVG(SS-SINR) = W1·AVGDT(SS-SINR) + W2·AVGMR(SS-SINR) + W3·AVGUR(SS-SINR), where AVG(SS-SINR) is the overall average signal-to-noise ratio of the above sub-regions, and AVG... DT (SS-SINR) represents the average signal strength measured during the aforementioned road tests in the aforementioned sub-area. AVG MR (SS-SINR) is the average signal-to-noise ratio of the above measurement reports for the above sub-regions, AVG UR (SS-SINR) is the average signal-to-noise ratio of the user feedback in the above sub-regions.

[0140] To identify sub-segments with poor network performance, in one optional implementation, the first determining unit includes:

[0141] The first determining module is configured to, when the network of the operator to be evaluated is co-constructed and shared by multiple operators and a reference operator exists, determine the minimum value of the comprehensive average signal strength of the networks of the multiple operators in the target sub-area as the comprehensive average signal strength of the network of the operator to be evaluated in the target sub-area, determine the minimum value of the comprehensive average signal-to-noise ratio of the networks of the multiple operators in the target sub-area as the comprehensive average signal-to-noise ratio of the network of the operator to be evaluated in the target sub-area, determine the comprehensive average signal strength of the network of the reference operator in the target sub-area as the predetermined signal strength, and determine the comprehensive average signal-to-noise ratio of the network of the reference operator in the target sub-area as the predetermined signal-to-noise ratio;

[0142] The second determining module is used to determine that the network performance of the target sub-area is poor when the overall average signal strength of the network of the operator to be evaluated in the target sub-area is less than the predetermined signal strength or the overall average signal-to-noise ratio of the network of the operator to be evaluated in the target sub-area is less than the predetermined signal-to-noise ratio.

[0143] To identify areas with poor network coverage availability, in one optional implementation, the above-mentioned apparatus further includes:

[0144] The second determining unit is used to determine the test points in the target area where the signal strength is greater than the signal strength threshold and the signal-to-noise ratio is greater than the signal-to-noise ratio threshold as network coverage available test points.

[0145] The third calculation unit is used to calculate the ratio of the number of available network coverage test points to the total number of test points in the target area, and obtain the network coverage availability rate.

[0146] The third determining unit is used to determine the network coverage availability difference of the target area when the network coverage availability is less than the predetermined network coverage availability.

[0147] In order to analyze the comprehensive score of the area, in one optional embodiment, the above-mentioned device further includes:

[0148] The setting unit is used to set the weights of the above-mentioned comprehensive average signal strength and the above-mentioned comprehensive average signal-to-noise ratio;

[0149] The fourth calculation unit is used to perform a weighted average of the comprehensive average signal strength and the comprehensive average signal-to-noise ratio of each of the above-mentioned sub-regions according to the weights of the comprehensive average signal strength and the comprehensive average signal-to-noise ratio, so as to obtain a comprehensive score for each of the above-mentioned sub-regions.

[0150] The fourth determining unit is used to determine the weight of each of the above-mentioned sub-regions as the reciprocal of the distance between the center of each of the above-mentioned sub-regions and the center of the above-mentioned target region;

[0151] The fifth calculation unit is used to perform a weighted average of the comprehensive scores of the aforementioned sub-regions according to the weights of each of the aforementioned sub-regions, so as to obtain the comprehensive score of the target region.

[0152] The sixth calculation unit is used to calculate the mean and standard deviation of the comprehensive score of the target area based on the comprehensive score of each of the operators in the target area when there are multiple operators in the target area.

[0153] The seventh calculation unit is used to calculate the standardized difference of the comprehensive score of the above-mentioned area for each of the above-mentioned operators based on the mean and standard deviation of the comprehensive score of the above-mentioned area.

[0154] The fifth determining unit is used to determine the overall performance difference of the aforementioned operators whose standardized difference is less than 0.

[0155] To provide a clear visual representation of network performance differences, in one optional implementation, the above-mentioned apparatus further includes:

[0156] The first acquisition unit is used to acquire a map of the aforementioned target area;

[0157] The second acquisition unit is used to acquire the above-mentioned comprehensive average signal strength and the above-mentioned comprehensive average signal-to-noise ratio of each of the above-mentioned sub-regions;

[0158] The first marking unit is used to mark the sub-area in the map where the overall average signal strength is greater than the predetermined signal strength using a first color, mark the sub-area in the map where the overall average signal strength is equal to the predetermined signal strength using a second color, and mark the sub-area in the map where the overall average signal strength is equal to the predetermined signal strength using a third color, thereby obtaining a signal strength display map of the target area.

[0159] The second marking unit is used to mark the sub-area in the map where the overall average signal-to-noise ratio is greater than the predetermined signal-to-noise ratio using the fourth color, to mark the sub-area in the map where the overall average signal-to-noise ratio is equal to the predetermined signal-to-noise ratio using the fifth color, and to mark the sub-area in the map where the overall average signal-to-noise ratio is equal to the predetermined signal-to-noise ratio using the sixth color, so as to obtain the signal-to-noise ratio display map of the target area.

[0160] To provide a clear visual representation of network performance differences, in one optional implementation, the above-mentioned apparatus further includes:

[0161] The third acquisition unit is used to acquire a map of the aforementioned target area;

[0162] The fourth acquisition unit is used to acquire the standardized difference corresponding to the comprehensive score of the above-mentioned area for each of the above-mentioned operators.

[0163] The third marking unit is used to mark the operators whose standardized difference is greater than 0 using positive altitude, mark the operators whose standardized difference is equal to 0 using zero altitude, and mark the operators whose standardized difference is less than 0 using negative altitude, so as to obtain a comprehensive network coverage performance display map of each operator in the target area.

[0164] The aforementioned network performance evaluation device includes a processor and a memory. The partitioning unit, first computing unit, second computing unit, and first determining unit are all stored as program units in the memory. The processor executes these program units stored in the memory to achieve the corresponding functions. All of the above modules are located in the same processor; alternatively, the modules may be located in different processors in any combination.

[0165] The processor contains a kernel, which retrieves the corresponding program units from memory. One or more kernels can be configured, and adjusting kernel parameters can address the problem of inaccurate network performance evaluation results in existing technologies.

[0166] The memory may include non-permanent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0167] This invention provides a computer-readable storage medium including a stored program, wherein, when the program is executed, it controls the device where the computer-readable storage medium is located to perform the method for evaluating the performance of the regional network.

[0168] Specifically, the methods for evaluating the performance of a regional network include:

[0169] Step S201: Divide the target area into multiple sub-areas; Step S202: Perform a weighted average of the network data corresponding to the test points in each sub-area to obtain the average signal strength, average signal-to-noise ratio (SNR), average signal strength in the measurement report, average SNR in the measurement report, average signal strength in user feedback, and average SNR in user feedback for each sub-area. The network data includes drive test data, measurement report data, and user feedback data. The drive test data includes the signal strength and SNR of the test points during the drive test. The measurement report data includes the signal strength and SNR of the test point uploaded by the terminal. The user feedback data includes the signal strength and SNR of the test point reported by the user. Step S203: [The text abruptly ends here, likely due to an incomplete sentence or missing information.] The average signal strength of the drive test, the average signal strength of the measurement report, and the average signal strength of the user feedback in the aforementioned sub-area are weighted and averaged to obtain the comprehensive average signal strength of each of the aforementioned sub-areas. The average signal-to-noise ratio of the drive test, the average signal-to-noise ratio of the measurement report, and the average signal-to-noise ratio of the user feedback in each of the aforementioned sub-areas are weighted and averaged to obtain the comprehensive average signal-to-noise ratio of each of the aforementioned sub-areas. In step S204, if the comprehensive average signal strength of the network of the operator to be evaluated in the target sub-area is less than a predetermined signal strength or the comprehensive average signal-to-noise ratio of the network of the operator to be evaluated in the target sub-area is less than a predetermined signal-to-noise ratio, the network performance of the target sub-area is determined to be poor. The target sub-area can be any one of the aforementioned sub-areas.

[0170] This invention provides a processor for running a program, wherein the program executes the method for evaluating the performance of the regional network.

[0171] Specifically, the methods for evaluating the performance of a regional network include:

[0172] Step S201: Divide the target area into multiple sub-areas; Step S202: Perform a weighted average of the network data corresponding to the test points in each sub-area to obtain the average signal strength, average signal-to-noise ratio (SNR), average signal strength in the measurement report, average SNR in the measurement report, average signal strength in user feedback, and average SNR in user feedback for each sub-area. The network data includes drive test data, measurement report data, and user feedback data. The drive test data includes the signal strength and SNR of the test points during the drive test. The measurement report data includes the signal strength and SNR of the test point uploaded by the terminal. The user feedback data includes the signal strength and SNR of the test point reported by the user. Step S203: [The text abruptly ends here, likely due to an incomplete sentence or missing information.] The average signal strength of the drive test, the average signal strength of the measurement report, and the average signal strength of the user feedback in the aforementioned sub-area are weighted and averaged to obtain the comprehensive average signal strength of each of the aforementioned sub-areas. The average signal-to-noise ratio of the drive test, the average signal-to-noise ratio of the measurement report, and the average signal-to-noise ratio of the user feedback in each of the aforementioned sub-areas are weighted and averaged to obtain the comprehensive average signal-to-noise ratio of each of the aforementioned sub-areas. In step S204, if the comprehensive average signal strength of the network of the operator to be evaluated in the target sub-area is less than a predetermined signal strength or the comprehensive average signal-to-noise ratio of the network of the operator to be evaluated in the target sub-area is less than a predetermined signal-to-noise ratio, the network performance of the target sub-area is determined to be poor. The target sub-area can be any one of the aforementioned sub-areas.

[0173] This invention provides a system for evaluating the performance of a regional network, including a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, it performs at least the following steps:

[0174] Step S201: Divide the target area into multiple sub-areas; Step S202: Perform a weighted average of the network data corresponding to the test points in each sub-area to obtain the average signal strength, average signal-to-noise ratio (SNR), average signal strength in the measurement report, average SNR in the measurement report, average signal strength in user feedback, and average SNR in user feedback for each sub-area. The network data includes drive test data, measurement report data, and user feedback data. The drive test data includes the signal strength and SNR of the test points during the drive test. The measurement report data includes the signal strength and SNR of the test point uploaded by the terminal. The user feedback data includes the signal strength and SNR of the test point reported by the user. Step S203: [The text abruptly ends here, likely due to an incomplete sentence or missing information.] The average signal strength of the drive test, the average signal strength of the measurement report, and the average signal strength of the user feedback in the aforementioned sub-area are weighted and averaged to obtain the comprehensive average signal strength of each of the aforementioned sub-areas. The average signal-to-noise ratio of the drive test, the average signal-to-noise ratio of the measurement report, and the average signal-to-noise ratio of the user feedback in each of the aforementioned sub-areas are weighted and averaged to obtain the comprehensive average signal-to-noise ratio of each of the aforementioned sub-areas. In step S204, if the comprehensive average signal strength of the network of the operator to be evaluated in the target sub-area is less than a predetermined signal strength or the comprehensive average signal-to-noise ratio of the network of the operator to be evaluated in the target sub-area is less than a predetermined signal-to-noise ratio, the network performance of the target sub-area is determined to be poor. The target sub-area can be any one of the aforementioned sub-areas.

[0175] This application also provides a computer program product, which, when executed on a data processing device, is suitable for executing an initialization program having at least the following method steps:

[0176] Step S201: Divide the target area into multiple sub-areas; Step S202: Perform a weighted average of the network data corresponding to the test points in each sub-area to obtain the average signal strength, average signal-to-noise ratio (SNR), average signal strength in the measurement report, average SNR in the measurement report, average signal strength in user feedback, and average SNR in user feedback for each sub-area. The network data includes drive test data, measurement report data, and user feedback data. The drive test data includes the signal strength and SNR of the test points during the drive test. The measurement report data includes the signal strength and SNR of the test point uploaded by the terminal. The user feedback data includes the signal strength and SNR of the test point reported by the user. Step S203: [The text abruptly ends here, likely due to an incomplete sentence or missing information.] The average signal strength of the drive test, the average signal strength of the measurement report, and the average signal strength of the user feedback in the aforementioned sub-area are weighted and averaged to obtain the comprehensive average signal strength of each of the aforementioned sub-areas. The average signal-to-noise ratio of the drive test, the average signal-to-noise ratio of the measurement report, and the average signal-to-noise ratio of the user feedback in each of the aforementioned sub-areas are weighted and averaged to obtain the comprehensive average signal-to-noise ratio of each of the aforementioned sub-areas. In step S204, if the comprehensive average signal strength of the network of the operator to be evaluated in the target sub-area is less than a predetermined signal strength or the comprehensive average signal-to-noise ratio of the network of the operator to be evaluated in the target sub-area is less than a predetermined signal-to-noise ratio, the network performance of the target sub-area is determined to be poor. The target sub-area can be any one of the aforementioned sub-areas.

[0177] It is obvious to those skilled in the art that the modules or steps of the present invention described above can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a network of multiple computing devices. They can be implemented using computer-executable program code, and thus can be stored in a storage device for execution by a computing device. In some cases, the steps shown or described can be performed in a different order than those described herein, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, the present invention is not limited to any particular combination of hardware and software.

[0178] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0179] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0180] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0181] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0182] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0183] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0184] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0185] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0186] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A method for evaluating the performance of a regional network, characterized in that, include: Divide the target area into multiple sub-areas; The network data corresponding to the test points in each sub-area are weighted and averaged to obtain the average signal strength, average signal-to-noise ratio (SNR), average signal strength in the measurement report, average SNR in the measurement report, average signal strength in user feedback, and average SNR in user feedback for each sub-area. The network data includes drive test data, measurement report data, and user feedback data. The drive test data includes the signal strength and SNR of the test points during the drive test. The measurement report data includes the signal strength and SNR of the test point uploaded by the terminal. The user feedback data includes the signal strength and SNR of the test point reported by the user. The average signal strength of the drive test, the average signal strength of the measurement report, and the average signal strength of the user feedback in each sub-area are weighted and averaged to obtain the comprehensive average signal strength of each sub-area. The average signal-to-noise ratio of the drive test, the average signal-to-noise ratio of the measurement report, and the average signal-to-noise ratio of the user feedback in each sub-area are weighted and averaged to obtain the comprehensive average signal-to-noise ratio of each sub-area. If the overall average signal strength of the network of the operator to be evaluated in the target sub-area is less than the predetermined signal strength, or the overall average signal-to-noise ratio of the network of the operator to be evaluated in the target sub-area is less than the predetermined signal-to-noise ratio, the network performance of the target sub-area is determined to be poor, and the target sub-area is any one of the sub-areas. The network data corresponding to the test points of each sub-area is obtained and weighted averaged to obtain the drive test average signal strength, drive test average signal-to-noise ratio, measurement report average signal strength, measurement report average signal-to-noise ratio, user feedback average signal strength, and user feedback average signal-to-noise ratio for each sub-area. This includes: dividing the grid of the target sub-area into multiple sub-grids; according to... Calculate the time difference weight of each test point in each sub-grid, Weigh_t ji The time difference weight is the time difference of the i-th test point within the j-th sub-grid. ji The time difference between the acquisition time of the network data of the i-th test point in the j-th sub-grid and the current time is ϵ, which is a constant. The network data corresponding to the test point is weighted and averaged according to the time difference weight of each test point in each sub-grid to obtain the average signal strength of the drive test, the average signal-to-noise ratio of the drive test, the average signal strength of the measurement report, the average signal-to-noise ratio of the measurement report, the average signal strength of the user feedback, and the average signal-to-noise ratio of the user feedback in the target sub-area.

2. The method according to claim 1, characterized in that, The target area is divided into multiple sub-areas, including: Obtain a map of the target area; The target area is divided into N sub-regions on the map, such that the boundaries of the sub-regions are located on the roads of the map and the area of ​​the sub-regions is less than a predetermined area, N≥2; The network usage density of each sub-region is obtained by weighted averaging of the user number density, data traffic density, and connection density. The user number density is the ratio of the total number of users in the sub-region to the area of ​​the sub-region. The data traffic density is the ratio of the total data traffic in the sub-region to the area of ​​the sub-region within a predetermined time period. The connection density is the ratio of the number of connections made by users to the network in the sub-region to the area of ​​the sub-region within the predetermined time period. Calculate the average network usage density of each of the sub-regions to obtain the average network usage density of the target region; use The adjusted area of ​​each sub-region is calculated, where New Grid Size is the adjusted area, Base Grid Size is the area of ​​the sub-region before adjustment, Network Density is the network usage density of the sub-region, and AverageDensity is the average network usage density. The target area is redivided based on the adjusted area of ​​each sub-region to obtain multiple sub-regions.

3. The method according to claim 2, characterized in that, The target area is divided into N sub-regions on the map, including: The target area is divided into four sub-regions using a quadtree algorithm, such that the boundaries of the sub-regions lie on the roads in the map. The partitioning step involves dividing the target sub-region into four new sub-regions using the quadtree algorithm when the area of ​​the target sub-region is greater than the predetermined area. The target sub-region can be any one of the sub-regions. Repeat the division step at least once until the area of ​​all the sub-regions is smaller than the predetermined area, to obtain N sub-regions.

4. The method according to claim 1, characterized in that, The average signal strength of the drive test, the average signal strength of the measurement report, and the average signal strength of the user feedback are weighted and averaged in each of the sub-areas to obtain the comprehensive average signal strength of each sub-area. The average signal-to-noise ratio (SNR) of the drive test, the average SNR of the measurement report, and the average SNR of the user feedback are weighted and averaged in each of the sub-areas to obtain the comprehensive average SNR of each sub-area, including: Set the weights W1 of the road test data, W2 of the measurement report data, and W3 of the user feedback data such that W1+W2+W3=1; Using AVG(SS-RSRP) = W1·AVG DT (SS-RSRP)+W2·AVG MR (SS-RSRP) + W3·AVG UR (SS-RSRP) is used to calculate the overall average signal strength of each sub-region, where AVG(SS-RSRP) is the overall average signal strength of the sub-region, and AVG... DT (SS-RSRP) represents the average signal strength of the drive test in the sub-area, AVG. MR (SS-RSRP) is the average signal strength of the measurement report for the sub-area, AVG. UR (SS-RSRP) is the average signal strength of the user feedback in the sub-area; Using AVG(SS-SINR) = W1·AVG DT (SS-SINR)+W2·AVG MR (SS-SINR)+W3·AVG UR The overall average signal-to-noise ratio (SNR) of each sub-region is calculated using (SS-SINR), where AVG(SS-SINR) is the overall average SNR of the sub-region, and AVG... DT (SS-SINR) is the average signal strength of the drive test in the sub-area, AVG MR (SS-SINR) is the average signal-to-noise ratio of the measurement reports for the sub-region, AVG. UR (SS-SINR) is the average signal-to-noise ratio of the user feedback in the sub-segment.

5. The method according to claim 1, characterized in that, If the overall average signal strength of the network of the operator to be evaluated in the target sub-area is less than a predetermined signal strength, or the overall average signal-to-noise ratio of the network of the operator to be evaluated in the target sub-area is less than a predetermined signal-to-noise ratio, the network performance of the target sub-area is determined to be poor, including: When the network of the operator to be evaluated is co-built and shared by multiple operators and a reference operator exists, the minimum value of the comprehensive average signal strength of the networks of the multiple operators in the target sub-area is determined as the comprehensive average signal strength of the network of the operator to be evaluated in the target sub-area, the minimum value of the comprehensive average signal-to-noise ratio of the networks of the multiple operators in the target sub-area is determined as the comprehensive average signal-to-noise ratio of the network of the operator to be evaluated in the target sub-area, the comprehensive average signal strength of the network of the reference operator in the target sub-area is determined as the predetermined signal strength, and the comprehensive average signal-to-noise ratio of the network of the reference operator in the target sub-area is determined as the predetermined signal-to-noise ratio. If the overall average signal strength of the network of the operator to be evaluated in the target sub-area is less than the predetermined signal strength, or the overall average signal-to-noise ratio of the network of the operator to be evaluated in the target sub-area is less than the predetermined signal-to-noise ratio, the network performance of the target sub-area is determined to be poor.

6. The method according to any one of claims 1 to 5, characterized in that, The method further includes: The test points in the target area with signal strength greater than the signal strength threshold and signal-to-noise ratio greater than the signal-to-noise ratio threshold are identified as network coverage available test points; The network coverage availability rate is obtained by calculating the ratio of the number of available test points to the total number of test points in the target area. If the network coverage availability rate is less than the predetermined network coverage availability rate, the network coverage availability rate difference of the target area is determined.

7. The method according to any one of claims 1 to 5, characterized in that, The method further includes: Set the weights for the integrated average signal strength and the integrated average signal-to-noise ratio; The comprehensive average signal strength and comprehensive average signal-to-noise ratio of each sub-region are weighted and averaged to obtain a comprehensive score for each sub-region. The reciprocal of the distance between the center of each sub-region and the center of the target region is determined as the weight of each sub-region; The comprehensive score of the target area is obtained by weighting the comprehensive scores of each sub-area according to the weight of each sub-area. When there are multiple operators in the target area, the mean and standard deviation of the comprehensive score of the target area are calculated based on the comprehensive score of each operator in the target area. The standardized deviation of the comprehensive score for each of the aforementioned operators in the aforementioned area is calculated based on the mean and standard deviation of the comprehensive score for the aforementioned area. The overall performance of the operator is determined when the standardized difference is less than 0.

8. The method according to any one of claims 1 to 5, characterized in that, The method further includes: Obtain a map of the target area; Obtain the overall average signal strength and the overall average signal-to-noise ratio of each of the sub-regions; The first color is used to mark the sub-area in the map where the overall average signal strength is greater than the predetermined signal strength; the second color is used to mark the sub-area in the map where the overall average signal strength is equal to the predetermined signal strength; and the third color is used to mark the sub-area in the map where the overall average signal strength is less than the predetermined signal strength, thus obtaining a signal strength display map of the target area. The sub-regions in the map whose overall average signal-to-noise ratio is greater than the predetermined signal-to-noise ratio are marked with a fourth color, the sub-regions in the map whose overall average signal-to-noise ratio is equal to the predetermined signal-to-noise ratio are marked with a fifth color, and the sub-regions in the map whose overall average signal-to-noise ratio is less than the predetermined signal-to-noise ratio are marked with a sixth color, thereby obtaining the signal-to-noise ratio display map of the target region.

9. The method according to claim 7, characterized in that, The method further includes: Obtain a map of the target area; Obtain the standardized difference corresponding to the comprehensive score of each region for each operator; By using positive altitude to mark operators whose standardized difference is greater than 0, using zero altitude to mark operators whose standardized difference is equal to 0, and using negative altitude to mark operators whose standardized difference is less than 0, a comprehensive network coverage performance display map of each operator in the target area is obtained.

10. A device for evaluating the performance of a regional network, characterized in that, include: A partitioning unit is used to divide a target area into multiple sub-areas. The first calculation unit is used to perform a weighted average of the network data corresponding to the test points of each sub-area to obtain the average signal strength, average signal-to-noise ratio (SNR), average signal strength, average SNR of the measurement report, average signal strength, and average SNR of user feedback for each sub-area. The network data includes drive test data, measurement report data, and user feedback data. The drive test data includes the signal strength and SNR of the test points during drive testing. The measurement report data includes the signal strength and SNR of the test point uploaded by the terminal. The user feedback data includes the signal strength and SNR of the test point reported by the user. The second calculation unit is used to perform a weighted average of the average signal strength of the drive test, the average signal strength of the measurement report, and the average signal strength of the user feedback in each sub-area to obtain the comprehensive average signal strength of each sub-area, and to perform a weighted average of the signal-to-noise ratio of the drive test, the signal-to-noise ratio of the measurement report, and the signal-to-noise ratio of the user feedback in each sub-area to obtain the comprehensive average signal-to-noise ratio of each sub-area. The first determining unit is configured to determine that the network performance of the target sub-segment is poor when the overall average signal strength of the network of the operator to be evaluated in the target sub-segment is less than a predetermined signal strength or the overall average signal-to-noise ratio of the network of the operator to be evaluated in the target sub-segment is less than a predetermined signal-to-noise ratio, wherein the target sub-segment is any one of the sub-segments. The first calculation unit includes: a third partitioning module, used to divide the raster of the target sub-region into multiple sub-rasteres; and a fourth calculation module, used to... Calculate the time difference weight of each test point in each sub-grid, Weigh_t ji Timedifference is the time difference weight of the i-th test point within the j-th sub-grid. ji The time difference between the acquisition time of the network data of the i-th test point within the j-th sub-grid and the current time, where ϵ is a constant; the fifth calculation module is used to perform a weighted average of the network data corresponding to the test point according to the time difference weight of each test point in each sub-grid, to obtain the average signal strength of the drive test, the average signal-to-noise ratio of the drive test, the average signal strength of the measurement report, the average signal-to-noise ratio of the measurement report, the average signal strength of the user feedback, and the average signal-to-noise ratio of the user feedback in the target sub-area.

11. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored program, wherein, when the program is executed, it controls the device on which the computer-readable storage medium is located to perform the method according to any one of claims 1 to 9.

12. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the method described in any one of claims 1 to 9.

13. A system for evaluating the performance of a regional network, characterized in that, include: One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs comprising methods for performing any one of claims 1 to 9.

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