Wireless network measurement point extraction method and device, electronic equipment and storage medium

Abstract

The application relates to a wireless network measurement point extraction method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring feature data of each measurement point in a measurement area; dividing each measurement point in the measurement area into measurement sub-areas corresponding to different environment types; determining the feature score of the measurement point in the measurement sub-area according to the feature data, wherein the measurement point in the measurement sub-area does not include a boundary point; and extracting a target measurement point from each measurement point in the measurement area, wherein the target measurement point comprises a first preset number of measurement points with a high feature score and a boundary point of the measurement sub-area. In this way, the feature score of the measurement point in each measurement sub-area can be determined, and the measurement point with a high feature score and the boundary point of the measurement sub-area can be extracted, that is, the measurement point with obvious features can be extracted for subsequent analysis, thereby improving the accuracy of the measurement point extraction and improving the wireless network performance analysis accuracy of the measurement area.

Description

Technical Field

[0001] This application relates to the field of wireless network optimization, and in particular to a method, apparatus, electronic device, and storage medium for extracting wireless network measurement points. Background Technology

[0002] When conducting wireless network measurements in a certain measurement area, the amount of data increases rapidly as the number of measurement points increases. The massive amount of data poses great difficulties for data storage, retrieval, analysis, and transmission. Therefore, it is usually necessary to sample the wireless network measurement points to reduce the amount of data.

[0003] Currently, traditional methods for extracting wireless network measurement points involve either sampling according to a time period and using a uniform thinning method, or extracting the measurement point with the largest distance from the reference projection based on the curve fluctuation characteristics of the measurement trajectory. However, regardless of the method used, neither fully considers the characteristic data of the wireless network performance of the measurement points and fails to extract the measurement points with the most analytical value. Therefore, traditional methods for extracting wireless network measurement points have low accuracy, resulting in low precision in the analysis of the wireless network performance of the measurement area. Summary of the Invention

[0004] This application provides a method, apparatus, electronic device, and storage medium for extracting wireless network measurement points, in order to solve the problem that traditional wireless network measurement point extraction methods have low extraction accuracy, resulting in low accuracy of wireless network performance analysis in the measurement area.

[0005] In a first aspect, this application provides a method for extracting measurement points in a wireless network, the method comprising:

[0006] Acquire feature data of each measurement point within the measurement area, wherein the feature data is data used to characterize the wireless network performance of the measurement points;

[0007] Each measurement point within the measurement area is divided into measurement sub-areas corresponding to different environment types. The environment type is used to characterize the requirements of user equipment within the measurement sub-area for wireless network performance.

[0008] Based on the feature data, a feature score is determined for the measurement points within the measurement sub-region. The feature score is determined based on a preset score corresponding to the parameter value when the parameter value is greater than or equal to a preset threshold. The parameter value refers to the rate of change and / or absolute value difference between the feature data of the current measurement point and the feature data of the previous measurement point. The measurement points within the measurement sub-region do not include boundary points.

[0009] Target measurement points are extracted from each measurement point within the measurement area. The target measurement points include a first preset number of measurement points ranked first by the feature scores and the boundary points of the measurement sub-region.

[0010] Optionally, dividing the measurement points within the measurement area into measurement sub-regions corresponding to different environment types includes:

[0011] Obtain the station distribution information within the measurement area;

[0012] Based on the site distribution information, construct the Thiessen polygon corresponding to each site;

[0013] The Thiessen polygon is divided according to the station spacing between adjacent stations in the measurement sub-region to form measurement sub-regions corresponding to different environment types. The station spacing between adjacent stations in each measurement sub-region belongs to the same distance range, and the station spacing for different environment types corresponds to different distance ranges. The measurement sub-region includes at least one Thiessen polygon.

[0014] Based on the positional distribution of each measurement point within the measurement area in the Thiessen polygon, the measurement sub-region corresponding to each measurement point is determined.

[0015] Optionally, determining the feature scores of measurement points within the measurement sub-region based on the feature data includes:

[0016] Based on the feature data, calculate the parameter values ​​corresponding to each measurement point within the measurement sub-region;

[0017] The parameter value is compared with a preset threshold corresponding to the measurement sub-region, wherein different preset thresholds correspond to different measurement sub-regions;

[0018] If the parameter value is greater than or equal to a preset threshold corresponding to the measurement sub-region, the preset score corresponding to the parameter value is determined as the feature score.

[0019] Optionally, the preset threshold corresponding to the measurement sub-region includes a rate of change threshold and / or an absolute value difference threshold set corresponding to the parameter value;

[0020] The step of comparing the parameter value with the preset threshold corresponding to the measurement sub-region includes:

[0021] When the parameter value is a rate of change, the parameter value is compared with the rate of change threshold; and / or

[0022] When the parameter value is an absolute value difference, the parameter value is compared with the absolute value difference threshold.

[0023] Optionally, the parameter values ​​are calculated based on at least one of the following feature data: signal quality, service quality, critical events, driving speed, slope of the measurement trajectory curve, and jitter of the measurement trajectory curve; the change rate threshold and absolute value difference threshold corresponding to different parameter values ​​within the same measurement sub-region are all different;

[0024] When the parameter value is greater than or equal to a preset threshold corresponding to the measurement sub-region, the preset score corresponding to the parameter value is determined as the feature score, including:

[0025] If the target parameter value is greater than or equal to the change rate threshold and / or absolute value difference threshold corresponding to the target parameter value, a preset score corresponding to the target parameter value is obtained, wherein the target parameter value is a parameter value calculated based on any one or more feature data;

[0026] Calculate the sum of the preset scores, and determine the sum as the feature score.

[0027] Optionally, the feature score can be determined using the following formula:

[0028]

[0029] Where, x i Let represent the parameter value corresponding to the i-th feature data, R represent the rate of change threshold, and s grad (x i R) represents when x i When x is greater than or equal to R, the feature scores corresponding to the parameter values ​​of the i-th feature data are summed. i When the value is less than R, no feature score is assigned to the parameter value corresponding to the i-th type of feature data, i∈(1,m); x j Let R' represent the parameter value corresponding to the j-th feature data, and let s' represent the absolute value difference threshold. abs (x j R′) represents when x j When x is greater than or equal to R′, the feature scores corresponding to the parameter values ​​of the j-th feature data are summed. j When the value is less than R′, no score is given for the feature score corresponding to the parameter value of the j-th feature data, j∈(m+1,n), where m represents the number of types of feature data whose parameter value is the rate of change, and n represents the total number of types of all feature data.

[0030] Optionally, extracting the target measurement point from each measurement point within the measurement area includes:

[0031] The feature scores are sorted in descending order to obtain the sorting results;

[0032] From the sorting results, a first preset number of measurement points with the highest scores are determined. The first preset number is equal to the number of measurement points to be extracted minus the number of boundary points of all the measurement sub-regions. The number of measurement points to be extracted is determined based on the total number of measurement points in the measurement region and a preset extraction ratio.

[0033] The first preset number of measurement points and the boundary points of the measurement sub-region are extracted as the target measurement points.

[0034] Secondly, this application also provides a device for extracting wireless network measurement points, the device comprising:

[0035] The acquisition module is used to acquire feature data of each measurement point within the measurement area, wherein the feature data is data used to characterize the wireless network performance of the measurement points;

[0036] The partitioning module is used to partition each measurement point within the measurement area into measurement sub-areas corresponding to different environment types. The environment type is used to characterize the requirements of user equipment within the measurement sub-area for wireless network performance.

[0037] The determination module is used to determine the feature score of the measurement points in the measurement sub-region based on the feature data. The feature score is determined based on the preset score corresponding to the parameter value when the parameter value is greater than or equal to the preset threshold. The parameter value refers to the rate of change and / or absolute value difference between the feature data of the current measurement point and the feature data of the previous measurement point. The measurement points in the measurement sub-region do not include boundary points.

[0038] The extraction module is used to extract target measurement points from each measurement point within the measurement area. The target measurement points include a first preset number of measurement points ranked first by the feature scores and the boundary points of the measurement sub-region.

[0039] Thirdly, this application also provides an electronic device, including a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus;

[0040] Memory, used to store computer programs;

[0041] When a processor executes a program stored in a memory, it implements the steps of the wireless network measurement point extraction method according to any embodiment of the first aspect.

[0042] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the wireless network measurement point extraction method as described in any embodiment of the first aspect.

[0043] The technical solutions provided in this application have the following advantages compared with the prior art:

[0044] The method provided in this application embodiment acquires feature data of each measurement point within a measurement area, wherein the feature data is data used to characterize the wireless network performance of the measurement points; divides each measurement point within the measurement area into measurement sub-regions corresponding to different environment types, wherein the environment type is used to characterize the wireless network performance requirements of user equipment within the measurement sub-region; determines feature scores of the measurement points within the measurement sub-regions based on the feature data, wherein the feature scores are determined based on a preset score corresponding to the parameter value when the parameter value is greater than or equal to a preset threshold, wherein the parameter value refers to the rate of change and / or absolute value difference between the feature data of the current measurement point and the feature data of the previous measurement point, and the measurement points within the measurement sub-regions do not include boundary points; extracts target measurement points from each measurement point within the measurement area, wherein the target measurement points include a first preset number of measurement points ranked first by the feature scores and the boundary points of the measurement sub-regions. In this way, the feature scores of measurement points in each measurement sub-region can be determined, and measurement points with high feature scores and boundary points of measurement sub-regions can be extracted. That is, measurement points with more obvious features are extracted for subsequent analysis, thereby improving the accuracy of measurement point extraction and improving the accuracy of wireless network performance analysis of the measurement area. At the same time, since the extracted target measurement points also include the boundary points of the measurement sub-regions, the original measurement trajectory can be better preserved, avoiding the situation where the trajectory after compressing measurement points cannot objectively reflect the original measurement trajectory. Attached Figure Description

[0045] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0046] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, those skilled in the art can obtain other drawings based on these drawings without creative effort.

[0047] Figure 1 A flowchart illustrating a method for extracting wireless network measurement points provided in an embodiment of this application;

[0048] Figure 2 A schematic diagram of a Thiessen polygon provided for an embodiment of this application;

[0049] Figure 3 A flowchart illustrating another method for extracting wireless network measurement points provided in this application embodiment;

[0050] Figure 4 This is a schematic diagram of the structure of a wireless network measurement point extraction device provided in an embodiment of this application;

[0051] Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0052] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0053] See Figure 1 , Figure 1 This is a flowchart illustrating a method for extracting measurement points in a wireless network, as provided in an embodiment of this application. Figure 1 As shown, the method for extracting measurement points in this wireless network may include the following steps:

[0054] Step 101: Obtain the feature data of each measurement point within the measurement area, wherein the feature data is data used to characterize the wireless network performance of the measurement points.

[0055] Specifically, the aforementioned measurement area can refer to a real geographical area, such as a city or province, or a simulated measurement area. Measurement points within this area can be measurement points on the measurement trajectory of drive-testing equipment within a specific geographical area, or measurement points within a simulated measurement area. The aforementioned characteristic data may include, but is not limited to: signal quality, service quality, critical events, driving speed, the slope of the measurement trajectory curve, and the jitter of the measurement trajectory curve. Signal quality can represent network signal strength and coverage quality (e.g., overlapping coverage, weak coverage); service quality can represent service rate, latency, etc.; critical events can represent the number of event failures / successes; driving speed can represent the speed of the drive-testing equipment; the slope of the measurement trajectory curve can represent the slope change of the measurement trajectory; and the jitter of the measurement trajectory curve is used to represent the jitter of the measurement trajectory.

[0056] The characteristic data can be obtained through drive-test equipment or from base stations; this application does not impose any specific limitations. The method for extracting the aforementioned wireless network measurement points can be executed by drive-test equipment or by electronic devices (such as servers) connected to base stations within the measurement area; this application does not impose any specific limitations.

[0057] Step 102: Divide each measurement point in the measurement area into measurement sub-areas corresponding to different environment types. The environment type is used to characterize the requirements of user equipment in the measurement sub-area for wireless network performance.

[0058] Specifically, the aforementioned environment types characterize the performance requirements of user equipment within the measurement sub-area for the wireless network. Different environment types correspond to different performance requirements. This environment type can be determined by the distance between base stations. Environment types can include densely populated urban areas, general urban areas, suburbs, and rural areas. The distance between base stations increases sequentially from densely populated urban areas to general urban areas, suburbs, and rural areas, with the smallest distance between base stations in densely populated urban areas and the largest distance between base stations in rural areas. Since different environment types have different performance requirements for the wireless network—for example, densely populated urban areas have larger fluctuations in user access and signal strength, allowing for more measurement points to be retained—and rural areas have smaller fluctuations in user access and signal strength, allowing for fewer measurement points to be retained, the measurement area can be divided into multiple measurement sub-areas according to environment type. Then, the positional relationship between the measurement sub-areas and the measurement points can be determined, thus identifying the measurement sub-area corresponding to each measurement point. This facilitates subsequent analysis of network characteristics within the measurement sub-areas under different environment types, identifying and extracting sampling points with obvious network characteristics, thereby improving the accuracy of wireless network performance analysis.

[0059] Step 103: Based on the feature data, determine the feature score of the measurement points in the measurement sub-region. The feature score is determined based on the preset score corresponding to the parameter value when the parameter value is greater than or equal to the preset threshold. The parameter value refers to the rate of change and / or absolute value difference between the feature data of the current measurement point and the feature data of the previous measurement point. The measurement points in the measurement sub-region do not include boundary points.

[0060] Specifically, the above parameter values ​​are used to characterize the changes in the feature data of the measurement point. The parameter values ​​can be the rate of change and / or the absolute difference between the feature data of the current measurement point and the feature data of the previous measurement point within the same measurement sub-region.

[0061] In this step, a preset threshold and preset score corresponding to the parameter value can be pre-set for each measurement sub-region. The parameter value can then be compared with the preset threshold. When the parameter value is greater than or equal to the preset threshold, the preset score corresponding to that parameter value can be used as the feature score for the current measurement point; when the parameter value is less than the preset threshold, no score is assigned to the current measurement point. Using the same method, the feature scores for each measurement point within each measurement sub-region can be obtained. A higher feature score indicates a more prominent feature at the measurement point, making it more likely to be extracted; a lower feature score indicates a less prominent feature at the measurement point, making it more likely to be discarded.

[0062] It should be noted that the preset score for each parameter value can be a single value or a range of values. When the preset score is a single value (e.g., 5 points), if the parameter value is greater than or equal to the preset threshold, the current measurement point is scored 5 points. When the preset score is a range of values ​​(e.g., 5-10 points), if the parameter value is greater than or equal to the preset threshold, an appropriate score can be selected from that range based on how much the parameter value exceeds the preset threshold. For example, if the parameter value just exceeds the preset threshold, the current measurement point is scored 5 points; if the parameter value is twice the preset threshold, the current measurement point is scored 10 points, and so on. Furthermore, when determining the feature scores of measurement points in a measurement sub-region, the feature scores of each measurement point in the measurement sub-region can be determined, or only the feature scores of internal measurement points other than the boundary points of the measurement sub-region can be determined. This helps improve processing efficiency. Here, the boundary points of the measurement sub-region refer to the critical points between two different measurement sub-regions. Since the measurement sub-regions are divided based on environment type, the features of the critical points between two different measurement sub-regions are usually quite obvious, and these boundary points need to be extracted by default.

[0063] Step 104: Extract target measurement points from each measurement point within the measurement area. Target measurement points include a first preset number of measurement points ranked first by feature score and boundary points of the measurement sub-region.

[0064] In this step, a first preset number of measurement points ranked high in feature scores and the boundary points of the measurement sub-regions can be extracted as target measurement points, and then the performance of the wireless network in the measurement area can be analyzed based on these target measurement points.

[0065] In this embodiment, the feature scores of measurement points in each measurement sub-region can be determined, and measurement points with high feature scores and boundary points of measurement sub-regions can be extracted. That is, measurement points with more obvious features are extracted for subsequent analysis, thereby improving the accuracy of measurement point extraction and improving the accuracy of wireless network performance analysis of the measurement area. At the same time, since the extracted target measurement points also include the boundary points of the measurement sub-regions, the original measurement trajectory can be better preserved, avoiding the fact that the trajectory after compressing the measurement points cannot objectively reflect the original measurement trajectory.

[0066] Furthermore, the above steps, including dividing the measurement points within the measurement area into measurement sub-regions corresponding to different environmental types, include:

[0067] Obtain information on the distribution of stations within the measurement area;

[0068] Based on the site distribution information, construct the Thiessen polygon corresponding to each site;

[0069] The Thiessen polygon is divided according to the station spacing between adjacent stations in the measurement sub-region to form measurement sub-regions corresponding to different environment types. The station spacing between adjacent stations in each measurement sub-region belongs to the same distance range, and the station spacing for different environment types corresponds to different distance ranges. The measurement sub-region includes at least one Thiessen polygon.

[0070] Based on the positional distribution of each measurement point within the measurement area in the Thiessen polygon, the measurement sub-region corresponding to each measurement point is determined.

[0071] In one embodiment, the measurement area can be divided into multiple different measurement sub-regions according to the environment type. Specifically, based on the obtained site (i.e., base station) distribution information within the measurement area, a Thiessen polygon (also known as a von Lono diagram) can be constructed for each site. A schematic diagram of the Thiessen polygon is shown below. Figure 2 As shown in the diagram, each Thiessen polygon contains one station. Measurement points within a Thiessen polygon are closest to their corresponding station, and measurement points on the edges of a Thiessen polygon are equidistant from the stations on either side. After constructing the Thiessen polygon for each station, Thiessen polygons with station spacing within the same range can be clustered and merged based on the station spacing between adjacent stations within the measurement sub-region. This divides the region into measurement sub-regions corresponding to different environment types. Furthermore, based on the location distribution of each measurement point within the measurement sub-region within the Thiessen polygon, the measurement sub-region corresponding to each measurement point is determined. This facilitates subsequent feature scoring of the measurement points within different measurement sub-regions according to different wireless network performance requirements.

[0072] Furthermore, the above steps, based on the feature data, determine the feature scores of the measurement points within the measurement sub-region, including:

[0073] Based on the feature data, calculate the parameter values ​​corresponding to each measurement point within the measurement sub-region;

[0074] The parameter value is compared with the preset threshold corresponding to the measurement sub-region, where different measurement sub-regions correspond to different preset thresholds;

[0075] If the parameter value is greater than or equal to the preset threshold corresponding to the measurement sub-region, the preset score corresponding to the parameter value will be determined as the feature score.

[0076] In one embodiment, different preset thresholds correspond to different measurement sub-regions, meaning that the feature scoring requirements differ for different measurement sub-regions. For example, after dividing the measurement area into multiple measurement sub-regions corresponding to dense urban areas, general urban areas, suburbs, and rural areas according to environmental type, different preset thresholds can be set for each of these sub-regions. For instance, the preset threshold for measurement sub-regions corresponding to dense urban areas is higher, while the preset threshold for measurement sub-regions corresponding to rural areas is lower. In this way, feature scores for measurement points within different measurement sub-regions can be determined based on the preset thresholds of different measurement sub-regions, thereby extracting measurement points with wireless network characteristics within each measurement sub-region.

[0077] In this embodiment, when different environmental types are involved in the measurement sub-regions during wireless network measurement, since each measurement sub-region has different requirements for wireless network performance, the measurement area can be divided into multiple measurement sub-regions based on the environmental type. For example, the number of users accessing the network in densely populated urban areas varies greatly, and the signal fluctuations are significant, requiring more measurement points to be retained in densely populated urban areas. Conversely, the number of users accessing the network in rural areas varies less, and the signal fluctuations are smaller, requiring fewer measurement points to be retained in rural areas. Then, based on the positional relationship between each measurement point and each measurement sub-region, the measurement sub-region corresponding to each measurement point is determined. This facilitates the subsequent extraction of sampling points with obvious network characteristics within the measurement sub-regions under different environmental types, thereby improving the accuracy of wireless network performance analysis.

[0078] Furthermore, the preset thresholds corresponding to the measurement sub-regions include the rate of change threshold and / or absolute value difference threshold set corresponding to the parameter values;

[0079] The above steps, comparing the parameter values ​​with the preset thresholds corresponding to the measurement sub-regions, include:

[0080] When the parameter value is a rate of change, compare the parameter value with a rate of change threshold; and / or

[0081] When the parameter value is the absolute difference, the parameter value is compared with the absolute difference threshold.

[0082] In one embodiment, preset thresholds can be set according to the type of the parameter value. Specifically, when the parameter value is a rate of change, the parameter value can be compared with a rate of change threshold; when the parameter value is an absolute value difference, the parameter value can be compared with an absolute value difference threshold; when the parameter value is both a rate of change and an absolute value difference, the parameter value can be compared with both the rate of change threshold and the absolute value difference threshold, respectively. This increases the flexibility of the parameter values, allowing for comparison with corresponding preset thresholds regardless of whether the parameter value is a rate of change or an absolute value difference.

[0083] Furthermore, the parameter values ​​are calculated based on at least one of the following characteristic data: signal quality, service quality, critical events, driving speed, slope of the measurement trajectory curve, and jitter of the measurement trajectory curve; the change rate threshold and absolute value difference threshold corresponding to different parameter values ​​within the same measurement sub-region are all different.

[0084] The above steps, where the parameter value is greater than or equal to a preset threshold corresponding to the measurement sub-region, determine the preset score corresponding to the parameter value as the feature score, including:

[0085] If the target parameter value is greater than or equal to the change rate threshold and / or absolute value difference threshold corresponding to the target parameter value, a preset score corresponding to the target parameter value is obtained, wherein the target parameter value is a parameter value calculated based on any one or more feature data.

[0086] Calculate the sum of the preset scores and determine the sum as the feature score.

[0087] In one embodiment, the aforementioned feature data may include, but is not limited to: signal quality, service quality, critical events, driving speed, slope of the measurement trajectory curve, and jitter of the measurement trajectory curve. Signal quality can be used to represent network signal strength and coverage quality (e.g., overlapping coverage, weak coverage); service quality can be used to represent service rate, latency, etc.; critical events can be used to represent the number of event failures / successes; driving speed can be used to represent the speed of the road test equipment; the slope of the measurement trajectory curve can be used to represent the slope change of the measurement trajectory; and the jitter of the measurement trajectory curve is used to represent the jitter of the measurement trajectory. The aforementioned parameter values ​​can be calculated based on one or more of the aforementioned feature data. For each parameter value corresponding to each type of feature data, there exists a corresponding rate of change threshold and / or absolute value difference threshold. Thus, each measurement point within the measurement sub-region can determine the feature score of the parameter value corresponding to each type of feature data based on the magnitude of the corresponding parameter value and the corresponding rate of change threshold and / or absolute value difference threshold. Then, by accumulating the preset scores corresponding to the parameter values ​​of all types of feature data, the final feature score of each measurement point can be obtained.

[0088] Furthermore, the feature score is determined using the following formula:

[0089]

[0090] Where, x i Let represent the parameter value corresponding to the i-th feature data, R represent the rate of change threshold, and s grad (x i R) represents when x i When x is greater than or equal to R, the feature scores corresponding to the parameter values ​​of the i-th feature data are summed. i When the value is less than R, no feature score is assigned to the parameter value corresponding to the i-th type of feature data, i∈(1,m); x j Let R' represent the parameter value corresponding to the j-th feature data, and let s' represent the absolute value difference threshold. abs (x j R′) represents when x j When x is greater than or equal to R′, the feature scores corresponding to the parameter values ​​of the j-th feature data are summed. j When the value is less than R′, no score is given for the feature score corresponding to the parameter value of the j-th feature data, j∈(m+1,n), where m represents the number of types of feature data whose parameter value is the rate of change, and n represents the total number of types of all feature data.

[0091] In one optional embodiment, the feature scores can be determined using two methods based on the feature data. One method is based on the rate of change of feature data between preceding and following measurement points, and a comparison with a rate of change threshold. This part of the feature data can include signal quality, service quality, etc., because the change trend of this part of the feature data is generally relatively smooth in adjacent grid measurement points, and the rate of change index clearly reflects the network jitter status. We divide this part of the feature data into i, i∈(1,m). The other method is based on the absolute value difference of feature data between preceding and following measurement points (or the current measurement point), and a comparison with an absolute value difference threshold. This part of the feature data can include key events, driving speed, slope of the measurement trajectory curve, jitter of the measurement trajectory curve, etc., because as long as this part of the feature data exceeds the corresponding preset threshold, it can better reflect the network state change. We divide this part of the feature data into j, j∈(m+1,n). Regardless of which method is used, the goal is to measure the comprehensive changes of multiple factors at the measurement points within the measurement area, extract measurement points that reflect the large fluctuations in the network state as much as possible, and thus evaluate potential network problems through these measurement points.

[0092] Specifically, for the feature data in the first part, due to its corresponding parameter value x i Let x be the rate of change. i The calculation formula can be:

[0093]

[0094] in, This represents the i-th feature data of the current measurement point. This represents the i-th feature data of the previous measurement point.

[0095] For the feature data in the second part, due to its corresponding parameter value x j The absolute value difference, x j The calculation formula can be:

[0096]

[0097] in, This represents the j-th feature data of the current measurement point. This represents the j-th feature data of the previous measurement point.

[0098] Therefore, the following formula can be used to calculate the sum of the preset scores corresponding to the parameter values ​​of all feature data at the current measurement point, which can then be used as the feature score for the current measurement point:

[0099]

[0100] Where, x iLet represent the parameter value corresponding to the i-th feature data, R represent the rate of change threshold, and s grad (x i R) represents when x i When x is greater than or equal to R, the feature scores corresponding to the parameter values ​​of the i-th feature data are summed. i When the value is less than R, no feature score is assigned to the parameter value corresponding to the i-th type of feature data, i∈(1,m); x j Let R' represent the parameter value corresponding to the j-th feature data, and let s' represent the absolute value difference threshold. abs (x j R′) represents when x j When x is greater than or equal to R′, the feature scores corresponding to the parameter values ​​of the j-th feature data are summed. j When the value is less than R′, no score is given for the feature score corresponding to the parameter value of the j-th feature data, j∈(m+1,n), where m represents the number of types of feature data whose parameter value is the rate of change, and n represents the total number of types of all feature data.

[0101] Assume that for any test track point within the measurement area containing k measurement segments, ... The composition is defined as follows: k represents the measurement segment corresponding to the k-th measurement sub-region, and N represents the total number of measurement points on that measurement segment. Adjacent measurement points on each measurement segment are grouped one by one, such as... and Designated as the first group, and Divide into the second group, and so on, for a total of n-1 groups, of which and These are the boundary points at both ends of the measurement segment, which need to be preserved when extracting measurement points. Meanwhile, for measurement points within the measurement segment... The feature score for each measurement point within the k measurement segments can be determined according to the above formula. Specifically, for comparison x i And the size of R, if x i If R is greater than or equal to R, then the corresponding preset integral s is obtained. i For example, when evaluating the signal quality at point P2, the rate of change of the signal quality at point P2 relative to the signal quality at point P1 is calculated. If this rate of change is greater than or equal to a threshold, then point P2 obtains the corresponding preset integral s. i Otherwise, no points will be awarded. When evaluating the slope of the measured trajectory at point P2, the absolute difference between the slope of point P2 and the slope of point P1 is calculated. If this absolute difference is greater than or equal to the absolute difference threshold, then point P2 will obtain the corresponding preset integral s in the slope consideration. jOtherwise, no points will be awarded. For example, when evaluating critical events at point P2, if point P2 experiences x number of handover failures... j If the score is greater than the switching threshold R′, point P2 receives the corresponding score in the critical event consideration; otherwise, it receives no score. Similarly, the feature score for each measurement point within each measurement segment can be determined according to the above formula.

[0102] Further, step 104 above, extracting the target measurement point from each measurement point within the measurement area, includes:

[0103] The feature scores are sorted from highest to lowest to obtain the sorting results;

[0104] The first preset number of measurement points with the highest scores are determined from the ranking results. The first preset number is equal to the number of measurement points to be extracted minus the number of boundary points of all measurement sub-regions. The number of measurement points to be extracted is determined based on the total number of measurement points in the measurement region and the preset extraction ratio.

[0105] The first preset number of measurement points and the boundary points of the measurement sub-regions are extracted as target measurement points.

[0106] In one embodiment, the feature scores of measurement points in each measurement sub-region can be sorted from high to low. A higher score indicates that the measurement point significantly reflects the network state characteristics, and its importance is greater. For measurement points with lower scores, it indicates that, under the premise of reflecting network state characteristics through different feature data, the effect is not fully and comprehensively reflecting the current network state. Then, according to a certain thinning rate (i.e., a preset extraction ratio), inliers with higher scores and boundary points of the measurement sub-region are selected for extraction. For example, assuming there are 1000 original measurement points in the measurement region, a thinning rate of 10%, and 10 boundary points in the measurement sub-region, then 90 measurement points with higher feature scores can be selected from the measurement points in the measurement sub-region. These 90 measurement points and 10 boundary points are then extracted as the feature points that ultimately reflect the current network state of the measurement region, thereby increasing the amount of data for subsequent processing and analysis, and improving the efficiency of processing and analysis.

[0107] In an optional embodiment, the method for extracting measurement points in the wireless network may include the following steps, such as... Figure 3 As shown:

[0108] Step 301: Divide each measurement point within the measurement area into measurement sub-regions corresponding to different environment types;

[0109] Step 302: Determine the feature data of the measurement points in each measurement sub-region that participate in the evaluation of network state features;

[0110] Step 303: Adapt the corresponding preset threshold and preset score to each feature data participating in the network state feature evaluation;

[0111] Step 304: Group any two adjacent measurement points within each measurement sub-region into pairs;

[0112] Step 305: Calculate the rate of change and / or absolute value difference for each group of feature data, and compare the rate of change and / or absolute value difference with a preset threshold.

[0113] Step 306: Calculate the sum of the preset scores corresponding to each feature data of the measurement points in each measurement sub-region in turn to obtain the feature score;

[0114] Step 307: Sort the feature scores in descending order;

[0115] Step 308: Extract the first preset number of measurement points and boundary points of the measurement sub-regions that rank first in the feature score ranking by a preset thinning rate, and use them as the output of the current network environment.

[0116] In this embodiment, by setting a thinning rate, the feature data of measurement points can be comprehensively used for scoring and evaluation. The selection of measurement points is then determined based on the scoring results. This compresses the measurement trajectory curve while preserving wireless network characteristics to the greatest extent possible, thereby improving network analysis efficiency. Compared to existing technologies, traditional network feature evaluation is based on a single indicator or uses joint analysis of multiple indicators. However, this approach requires acquiring indicator data for all measurement points, which consumes significant processing and transmission resources for scenarios requiring rapid processing, such as small-screen applications. The wireless network measurement point extraction method in this application, through feature extraction and evaluation, effectively distinguishes the importance of measurement points, thereby guiding the extraction of more important measurement points.

[0117] The wireless network measurement point extraction method in this application can be applied to post-testing processing and analysis scenarios for wireless networks. By evaluating the importance of the characteristics of wireless network measurement points, targeted optimization of regional issues can be performed. For example, by extracting highly important measurement points, the network status of the current test or optimization area can be characterized. This method is of great significance for both measurement point extraction and status assessment.

[0118] In addition, embodiments of this application also provide a device for extracting wireless network measurement points. See [link to related document]. Figure 4 , Figure 4 This is a schematic diagram of a wireless network measurement point extraction device provided in an embodiment of this application. Figure 4 As shown, the wireless network measurement point extraction device 400 includes:

[0119] The acquisition module 401 is used to acquire feature data of each measurement point within the measurement area, wherein the feature data is data used to characterize the wireless network performance of the measurement points;

[0120] The partitioning module 402 is used to partition each measurement point within the measurement area into measurement sub-areas corresponding to different environment types. The environment type is used to characterize the requirements of user equipment within the measurement sub-area for wireless network performance.

[0121] The determination module 403 is used to determine the feature score of the measurement points in the measurement sub-region based on the feature data. The feature score is determined based on the preset score corresponding to the parameter value when the parameter value is greater than or equal to the preset threshold. The parameter value refers to the rate of change and / or absolute value difference between the feature data of the current measurement point and the feature data of the previous measurement point. The measurement points in the measurement sub-region do not include boundary points.

[0122] The extraction module 404 is used to extract target measurement points from each measurement point within the measurement area. The target measurement points include a first preset number of measurement points ranked first by feature score and the boundary points of the measurement sub-region.

[0123] Furthermore, the partitioning module 402 includes:

[0124] The acquisition submodule is used to acquire station distribution information within the measurement area;

[0125] The construction submodule is used to construct the Thiessen polygon corresponding to each site based on the site distribution information;

[0126] The sub-division module is used to divide the Thiessen polygon according to the station spacing between adjacent stations in the measurement sub-region, forming measurement sub-regions corresponding to different environment types. The station spacing between adjacent stations in each measurement sub-region belongs to the same distance range, and the station spacing for different environment types corresponds to different distance ranges. The measurement sub-region includes at least one Thiessen polygon.

[0127] The determination submodule is used to determine the measurement sub-region corresponding to each measurement point based on the positional distribution of each measurement point in the Thiessen polygon within the measurement area.

[0128] Furthermore, the sub-modules are identified as including:

[0129] The calculation unit is used to calculate the parameter values ​​corresponding to each measurement point within the measurement sub-region based on the feature data.

[0130] The comparison unit is used to compare the parameter value with the preset threshold corresponding to the measurement sub-region, wherein the preset threshold is different for different measurement sub-regions;

[0131] The determining unit is used to determine the preset score corresponding to the parameter value as the feature score when the parameter value is greater than or equal to the preset threshold corresponding to the measurement sub-region.

[0132] Furthermore, the preset thresholds corresponding to the measurement sub-regions include the rate of change threshold and / or absolute value difference threshold set corresponding to the parameter values;

[0133] The comparator unit is specifically used for:

[0134] When the parameter value is a rate of change, compare the parameter value with a rate of change threshold; and / or

[0135] When the parameter value is the absolute difference, the parameter value is compared with the absolute difference threshold.

[0136] Furthermore, the parameter values ​​are calculated based on at least one of the following characteristic data: signal quality, service quality, critical events, driving speed, slope of the measurement trajectory curve, and jitter of the measurement trajectory curve; the change rate threshold and absolute value difference threshold corresponding to different parameter values ​​within the same measurement sub-region are all different.

[0137] The specific unit is used for:

[0138] If the target parameter value is greater than or equal to the change rate threshold and / or absolute value difference threshold corresponding to the target parameter value, a preset score corresponding to the target parameter value is obtained, wherein the target parameter value is a parameter value calculated based on any one or more feature data.

[0139] Calculate the sum of the preset scores and determine the sum as the feature score.

[0140] Furthermore, the feature score is determined using the following formula:

[0141]

[0142] Where, x i Let represent the parameter value corresponding to the i-th feature data, R represent the rate of change threshold, and s grad (x i R) represents when x i When x is greater than or equal to R, the feature scores corresponding to the parameter values ​​of the i-th feature data are summed. i When the value is less than R, no feature score is assigned to the parameter value corresponding to the i-th type of feature data, i∈(1,m); x j Let R' represent the parameter value corresponding to the j-th feature data, and let s' represent the absolute value difference threshold. abs (x j R′) represents when x jWhen x is greater than or equal to R′, the feature scores corresponding to the parameter values ​​of the j-th feature data are summed. j When the value is less than R′, no score is given for the feature score corresponding to the parameter value of the j-th feature data, j∈(m+1,n), where m represents the number of types of feature data whose parameter value is the rate of change, and n represents the total number of types of all feature data.

[0143] Furthermore, the extraction module 404 includes:

[0144] The sorting submodule is used to sort the feature scores from highest to lowest to obtain the sorting results;

[0145] The sub-unit is determined to identify the first preset number of measurement points with the highest scores from the sorting results. The first preset number is equal to the number of measurement points to be extracted minus the number of boundary points of all measurement sub-regions. The number of measurement points to be extracted is determined based on the total number of measurement points in the measurement region and the preset extraction ratio.

[0146] The extraction sub-unit is used to extract the first preset number of measurement points and the boundary points of the measurement sub-region as target measurement points.

[0147] It should be noted that the wireless network measurement point extraction device 400 can implement the steps of the wireless network measurement point extraction method provided in any of the aforementioned method embodiments, and can achieve the same technical effect, which will not be described in detail here.

[0148] like Figure 5 As shown in the illustration, this application also provides an electronic device, including a processor 511, a communication interface 512, a memory 513, and a communication bus 514, wherein the processor 511, the communication interface 512, and the memory 513 communicate with each other via the communication bus 514.

[0149] Memory 513 is used to store computer programs;

[0150] In one embodiment of this application, when the processor 511 executes the program stored in the memory 513, it implements the wireless network measurement point extraction method provided in any of the foregoing method embodiments, including:

[0151] Acquire feature data for each measurement point within the measurement area, where the feature data is used to characterize the wireless network performance of the measurement points;

[0152] Each measurement point within the measurement area is divided into measurement sub-areas corresponding to different environment types. The environment type is used to characterize the requirements of user equipment within the measurement sub-area for wireless network performance.

[0153] Based on the feature data, the feature scores of the measurement points in the measurement sub-region are determined. The feature scores are determined based on the preset scores corresponding to the parameter values ​​when the parameter values ​​are greater than or equal to the preset threshold. The parameter values ​​refer to the rate of change and / or absolute value difference between the feature data of the current measurement point and the feature data of the previous measurement point. The measurement points in the measurement sub-region do not include boundary points.

[0154] Target measurement points are extracted from each measurement point within the measurement area. The target measurement points include a first preset number of measurement points ranked first by feature score and the boundary points of the measurement sub-region.

[0155] In addition, embodiments of this application also provide a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the wireless network measurement point extraction method provided in any of the foregoing method embodiments.

[0156] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, 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 a process, method, article, or apparatus. Without further limitations, 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 said element.

[0157] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features claimed herein.

Claims

1. A method for extracting measurement points in a wireless network, characterized in that, The method includes: Acquire feature data of each measurement point within the measurement area, wherein the feature data is data used to characterize the wireless network performance of the measurement points; Based on the positional relationship between each measurement point within the measurement area and the measurement sub-areas corresponding to different environment types, each measurement point is assigned to a measurement sub-area. The environment type is used to characterize the requirements of user equipment within the measurement sub-area for wireless network performance. The environment type is determined based on the distance between base stations. Based on the feature data, a feature score is determined for the measurement points within the measurement sub-region. The feature score is determined based on a preset score corresponding to the parameter value when the parameter value is greater than or equal to a preset threshold. The parameter value refers to the rate of change and / or absolute value difference between the feature data of the current measurement point and the feature data of the previous measurement point. The measurement points within the measurement sub-region do not include boundary points. Target measurement points are extracted from each measurement point within the measurement area. The target measurement points include a first preset number of measurement points ranked first by the feature scores and the boundary points of the measurement sub-region.

2. The method according to claim 1, characterized in that, The step of dividing each measurement point into its respective measurement sub-region based on the positional relationship between each measurement point within the measurement area and the measurement sub-regions corresponding to different environmental types includes: Obtain the station distribution information within the measurement area; Based on the site distribution information, construct the Thiessen polygon corresponding to each site; The Thiessen polygon is divided according to the station spacing between adjacent stations in the measurement sub-region to form measurement sub-regions corresponding to different environment types. The station spacing between adjacent stations in each measurement sub-region belongs to the same distance range, and the station spacing for different environment types corresponds to different distance ranges. The measurement sub-region includes at least one Thiessen polygon. Based on the positional distribution of each measurement point within the measurement area in the Thiessen polygon, the measurement sub-region corresponding to each measurement point is determined.

3. The method according to claim 2, characterized in that, The step of determining the feature score of the measurement points within the measurement sub-region based on the feature data includes: Based on the feature data, calculate the parameter values ​​corresponding to each measurement point within the measurement sub-region; The parameter value is compared with a preset threshold corresponding to the measurement sub-region, wherein different preset thresholds correspond to different measurement sub-regions; If the parameter value is greater than or equal to a preset threshold corresponding to the measurement sub-region, the preset score corresponding to the parameter value is determined as the feature score.

4. The method according to claim 3, characterized in that, The preset thresholds corresponding to the measurement sub-regions include a rate of change threshold and / or an absolute value difference threshold set corresponding to the parameter values; The step of comparing the parameter value with the preset threshold corresponding to the measurement sub-region includes: When the parameter value is a rate of change, the parameter value is compared with the rate of change threshold; and / or When the parameter value is an absolute value difference, the parameter value is compared with the absolute value difference threshold.

5. The method according to claim 4, characterized in that, The parameter values ​​are calculated based on at least one of the following feature data: signal quality, service quality, critical events, driving speed, slope of the measurement trajectory curve, and jitter of the measurement trajectory curve; the change rate threshold and absolute value difference threshold corresponding to different parameter values ​​within the same measurement sub-region are all different; When the parameter value is greater than or equal to a preset threshold corresponding to the measurement sub-region, the preset score corresponding to the parameter value is determined as the feature score, including: If the target parameter value is greater than or equal to the change rate threshold and / or absolute value difference threshold corresponding to the target parameter value, a preset score corresponding to the target parameter value is obtained, wherein the target parameter value is a parameter value calculated based on any one or more feature data; Calculate the sum of the preset scores, and determine the sum as the feature score.

6. The method according to claim 5, characterized in that, The feature score is determined using the following formula: in, Indicates the first The parameter values ​​corresponding to the feature data, This represents the rate of change threshold. Indicates when Greater than or equal to At that time, for the first The feature scores corresponding to the parameter values ​​of the feature data are summed and calculated. Less than At that time, for the first The feature scores corresponding to the parameter values ​​of this type of feature data are not assigned. ; Indicates the first The parameter values ​​corresponding to the feature data, This represents the absolute value difference threshold. Indicates when Greater than or equal to At that time, for the first The feature scores corresponding to the parameter values ​​of the feature data are summed and calculated. Less than At that time, for the first The feature scores corresponding to the parameter values ​​of this type of feature data are not assigned. , This indicates the number of types of feature data whose parameter value is a rate of change. This indicates the number of all feature data categories.

7. The method according to claim 1, characterized in that, Extracting the target measurement point from each measurement point within the measurement area includes: The feature scores are sorted in descending order to obtain the sorting results; From the sorting results, a first preset number of measurement points with the highest scores are determined. The first preset number is equal to the number of measurement points to be extracted minus the number of boundary points of all the measurement sub-regions. The number of measurement points to be extracted is determined based on the total number of measurement points in the measurement region and a preset extraction ratio. The first preset number of measurement points and the boundary points of the measurement sub-region are extracted as the target measurement points.

8. A device for extracting measurement points in a wireless network, characterized in that, The device includes: The acquisition module is used to acquire feature data of each measurement point within the measurement area, wherein the feature data is data used to characterize the wireless network performance of the measurement points; The partitioning module is used to partition each measurement point into each measurement sub-region according to the positional relationship between each measurement point in the measurement area and the measurement sub-regions corresponding to different environment types. The environment type is used to characterize the requirements of user equipment in the measurement sub-region for wireless network performance. The environment type is determined according to the distance between base stations. The determination module is used to determine the feature score of the measurement points in the measurement sub-region based on the feature data. The feature score is determined based on the preset score corresponding to the parameter value when the parameter value is greater than or equal to the preset threshold. The parameter value refers to the rate of change and / or absolute value difference between the feature data of the current measurement point and the feature data of the previous measurement point. The measurement points in the measurement sub-region do not include boundary points. The extraction module is used to extract target measurement points from each measurement point within the measurement area. The target measurement points include a first preset number of measurement points ranked first by the feature scores and the boundary points of the measurement sub-region.

9. An electronic device, characterized in that, It includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; Memory, used to store computer programs; When a processor executes a program stored in a memory, it implements the steps of the wireless network measurement point extraction method according to any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the wireless network measurement point extraction method as described in any one of claims 1-7.

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