CN117014928A - Method and device for extracting wireless network measurement points, electronic equipment and storage medium - Google Patents

Abstract

This application relates to a method, device, electronic equipment and storage medium for extracting wireless network measurement points. The method includes: obtaining characteristic data of each measurement point in the measurement area; dividing each measurement point in the measurement area into different environmental types. Corresponding measurement sub-area; determine the feature scores of the measurement points in the measurement sub-area based on the feature data. The measurement points in the measurement sub-area do not include boundary points; extract the target measurement points from each measurement point in the measurement area, and target measurement The points include a first preset number of measurement points ranked high in feature scores and boundary points of the measurement sub-area. In this way, the feature scores of the measurement points in each measurement sub-area can be determined, and the measurement points with higher feature scores and the boundary points of the measurement sub-area can be extracted, that is, the measurement points with more obvious characteristics can be extracted for subsequent analysis, thereby improving This improves the accuracy of measurement point extraction and improves the accuracy of wireless network performance analysis in the measurement area.

Description

Extraction method, device, electronic equipment and storage medium for wireless network measurement points

Technical field

The present application relates to the field of wireless network optimization, and in particular, to a method, device, electronic equipment and storage medium for extracting wireless network measurement points.

Background technique

When conducting wireless network measurements in a certain measurement area, as the number of measurement points increases, the amount of data also increases rapidly. The huge amount of data causes great difficulties in data storage, query, analysis and transmission. Therefore, it is usually necessary to Sampling wireless network measurement points to reduce the amount of data.

At present, the traditional way to extract wireless network measurement points is to sample according to time periods and use a uniform thinning method to extract measurement points; or based on the curve fluctuation characteristics of the measurement trajectory, extract the measurement points with the largest projection distance from the reference. However, no matter which of the above methods is used for extraction, the characteristic data of the wireless network performance of the measurement points is not fully considered to extract the most valuable measurement points. Therefore, the traditional extraction method of wireless network measurement points has a lower extraction accuracy. Low, resulting in low accuracy of wireless network performance analysis in the measurement area.

Contents of the invention

This application provides a method, device, electronic equipment and storage medium for extracting wireless network measurement points to solve the problem that the traditional extraction method of wireless network measurement points has low extraction accuracy, resulting in low accuracy of wireless network performance analysis in the measurement area. The problem.

In a first aspect, this application provides a method for extracting wireless network measurement points. The method includes:

Obtain characteristic data of each measurement point within the measurement area, where the characteristic data is data used to characterize the wireless network performance of the measurement point;

Divide each measurement point in the measurement area into measurement sub-areas corresponding to different environment types, where the environment types are used to characterize the wireless network performance requirements of the user equipment in the measurement sub-area;

According to the characteristic data, the characteristic score of the measurement point in the measurement sub-area is determined, and the characteristic 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. It is obtained that the parameter value refers to the change rate and/or absolute value difference between the characteristic data of the current measurement point and the characteristic data of the previous measurement point, and the measurement points in the measurement sub-region do not include boundary points;

A target measurement point is extracted from each measurement point in the measurement area, where the target measurement point includes a first preset number of measurement points ranked higher in the feature score and boundary points of the measurement sub-region.

Optionally, dividing each measurement point in the measurement area into measurement sub-areas corresponding to different environment types includes:

Obtain site distribution information within the measurement area;

Based on the site distribution information, construct a Thiessen polygon corresponding to each site;

The Thiessen polygon is divided according to the distance between adjacent stations in the measurement sub-area to form measurement sub-areas corresponding to different environment types, wherein the adjacent stations in each measurement sub-area The distance between stations belongs to the same distance range, the distance between stations of different environmental types corresponds to different distance ranges, and the measurement sub-area includes at least one of the Thiessen polygons;

According to the position distribution of each measurement point in the measurement area in the Thiessen polygon, the measurement sub-region corresponding to each measurement point is determined.

Optionally, determining the characteristic score of the measurement point within the measurement sub-area based on the characteristic data includes:

According to the characteristic data, calculate the parameter values corresponding to each measurement point in the measurement sub-area;

Compare the parameter value with a preset threshold corresponding to the measurement sub-region, wherein the preset thresholds corresponding to different measurement sub-regions are different;

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 is determined as the feature score.

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

Comparing the parameter value with a preset threshold corresponding to the measurement sub-area includes:

In the case where the parameter value is a rate of change, comparing the parameter value with the rate of change threshold; and/or

In the case where the parameter value is an absolute value difference, the parameter value is compared with the absolute value difference threshold.

Optionally, the parameter value is calculated based on at least one characteristic data among signal quality, service quality, key events, driving speed, curve slope of the measurement trajectory, and curve jitter of the measurement trajectory; within the same measurement sub-area The change rate thresholds and absolute value difference thresholds corresponding to different parameter values are different;

Determining 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-area includes:

When the target parameter value is greater than or equal to the change rate threshold and/or the absolute 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 Parameter values calculated based on any one or more characteristic data;

Calculate the summation result of the preset score, and determine the summation result as the feature score.

Optionally, the following formula is used to determine the feature score:

Among them, ^xi represents the parameter value corresponding to the i-th feature data, R represents the change rate threshold, and s _grad ( ^xi , R) represents the parameter value for the i-th feature data when x ⁱ is greater than or equal to R. The corresponding feature scores are summed and calculated. When x ⁱ is less than R, the feature score corresponding to the parameter value of the i-th feature data is not scored, i∈(1,m); x ^j represents the j-th feature data corresponding to Parameter value, R′ represents the absolute value difference threshold, s _abs (x ^j ,R′) indicates that when x ^j is greater than or equal to R′, the feature scores corresponding to the parameter values of the jth feature data are summed Calculate, when x ^j is less than R′, the feature score corresponding to the parameter value of the jth feature data will not be scored, j∈(m+1,n), m represents the number of feature data types whose parameter value is the change rate, n represents the number of types of all feature data.

Optionally, extracting target measurement points from each measurement point within the measurement area includes:

Sort the feature scores from high to low to obtain the sorting results;

Determine a first preset number of measurement points with top 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 the measurement sub-areas. The number of measurement points to be extracted is determined based on the total number of measurement points in the measurement area and the preset extraction ratio;

The determined first preset number of measurement points and the boundary points of the measurement sub-area are extracted as the target measurement points.

In a second aspect, this application also provides a device for extracting wireless network measurement points. The device includes:

An acquisition module, configured to acquire characteristic data of each measurement point within the measurement area, where the characteristic data is data used to characterize the wireless network performance of the measurement point;

A division module, configured to divide each measurement point in the measurement area into measurement sub-areas corresponding to different environment types, where the environment types are used to characterize the wireless network performance requirements of user equipment in the measurement sub-area;

Determining module, configured to determine the characteristic score of the measurement point in the measurement sub-area according to the characteristic data. The characteristic score is based on the parameter value corresponding to the parameter value when the parameter value is greater than or equal to the preset threshold. The preset score of including boundary points;

An extraction module, configured to extract target measurement points from each measurement point in the measurement area, where the target measurement points include a first preset number of measurement points ranked first in the feature score and the measurement sub-area. Boundary point.

In a third aspect, 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 complete communication with each other through the communication bus;

Memory, used to store computer programs;

The processor is configured to implement the steps of the wireless network measurement point extraction method described in any embodiment of the first aspect when executing a program stored in the memory.

In a fourth aspect, the present application also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the wireless network measurement point as described in any embodiment of the first aspect is implemented. The steps of the extraction method.

Compared with the existing technology, the above technical solutions provided by the embodiments of the present application have the following advantages:

The method provided by the embodiment of the present application obtains the characteristic data of each measurement point in the measurement area, where the characteristic data is data used to characterize the wireless network performance of the measurement point; The points are divided into measurement sub-areas corresponding to different environment types. The environment types are used to characterize the wireless network performance requirements of user equipment in the measurement sub-area; according to the characteristic data, determine the measurement points of the measurement sub-areas. Feature score, which 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 difference between the feature data of the current measurement point and the previous one. The change rate and/or absolute value difference between the characteristic data of the measurement points, the measurement points in the measurement sub-area do not include boundary points; the target measurement point is extracted from each measurement point in the measurement area, the The target measurement points include a first preset number of measurement points with the highest feature score ranking and boundary points of the measurement sub-area. In this way, the feature scores of the measurement points in each measurement sub-area can be determined, and the measurement points with higher feature scores and the boundary points of the measurement sub-areas can be extracted, that is, the measurement points with more obvious characteristics can be extracted for subsequent analysis, thus improving the accuracy of measurement point extraction and improving the accuracy of wireless network performance analysis in the measurement area; at the same time, because the extracted target measurement points also include the demarcation points of the measurement sub-areas, the original measurements can be better retained trajectory to avoid that the trajectory after compressing the measurement points cannot objectively reflect the original measurement trajectory.

Description of the drawings

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

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those of ordinary skill in the art, It is said that other drawings can be obtained based on these drawings without exerting creative labor.

Figure 1 is a schematic flow chart of a method for extracting wireless network measurement points provided by an embodiment of the present application;

Figure 2 is a schematic diagram of a Thiessen polygon provided by an embodiment of the present application;

Figure 3 is a schematic flow chart of another method for extracting wireless network measurement points provided by an embodiment of the present application;

Figure 4 is a schematic structural diagram of a device for extracting wireless network measurement points provided by an embodiment of the present application;

FIG. 5 is a schematic structural diagram of an electronic device provided by an embodiment of the present application.

Detailed ways

In order to make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments These are part of the embodiments of this application, but not all of them. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of this application.

Referring to Figure 1, Figure 1 is a schematic flow chart of a method for extracting wireless network measurement points provided by an embodiment of the present application. As shown in Figure 1, the wireless network measurement point extraction method may include the following steps:

Step 101: Obtain characteristic data of each measurement point in the measurement area, where the characteristic data is data used to characterize the wireless network performance of the measurement point.

Specifically, the above-mentioned measurement area may refer to a real geographical area, such as a geographical area of a certain city, a certain province, etc., or it may be a simulated measurement area. The measurement points in the above-mentioned measurement area can be measurement points on the measurement trajectory of equipment such as drive test equipment in a certain geographical area; they can also be measurement points in a simulated measurement area. The above characteristic data can include but is not limited to: signal quality, service quality, key events, driving speed, curve slope of the measurement trajectory, curve jitter of the measurement trajectory, etc., where the signal quality can be used to represent network signal strength and coverage quality (such as Overlapping coverage, weak coverage), etc.; service quality can be used to represent service rate, delay, etc.; key events can be used to represent the number of event failures/successes, etc.; driving speed can be used to represent the traveling speed of road test equipment; the curve of the measurement trajectory The slope can be used to represent the slope change of the measurement trajectory; the curve jitter of the measurement trajectory is used to represent the jitter of the measurement trajectory.

The method of obtaining characteristic data can be obtained through drive test equipment or from a base station, which is not specifically limited in this application. The above method for extracting wireless network measurement points can be performed by a drive test setting or by an electronic device (such as a server) connected to a base station in the measurement area, which is not specifically limited in this application.

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 wireless network performance requirements of the user equipment in the measurement sub-area.

Specifically, the above environment type is used to characterize the performance requirements of the user equipment in the measurement sub-area for the wireless network. Different environment types have different performance requirements. The type of environment can be determined by the distance between base stations. The environment type can include dense urban areas, general urban areas, suburbs, rural areas, etc. Among them, the distance between stations corresponding to dense urban areas, general urban areas, suburbs, and rural areas increases in order. Among them, the distance between stations corresponding to dense urban areas is the smallest, and the distance between stations corresponding to rural areas is the largest. Since different environmental types have different performance requirements for wireless networks, for example, in dense urban areas, user access volume changes greatly and signal fluctuations are large, so more measurement points can be reserved in dense urban areas; while in rural areas, user access volume changes less, The signal fluctuation is small, and fewer measurement points can be reserved in rural areas. Therefore, the measurement area can be divided into multiple measurement sub-areas according to environmental types, and then the positional relationship between the measurement sub-areas and the measurement points can be determined, and the corresponding measurement points of each measurement point can be determined. Measure sub-areas. In this way, it is convenient to conduct subsequent analysis of network characteristics in measurement sub-areas under different environmental types, and determine and extract sampling points with obvious network characteristics, thereby improving the accuracy of wireless network performance analysis.

Step 103: Determine the characteristic score of the measurement point in the measurement sub-area based on the characteristic data. The characteristic 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 is Refers to the change rate and/or absolute value difference between the characteristic data of the current measurement point and the characteristic data of the previous measurement point. The measurement points within the measurement sub-area do not include boundary points.

Specifically, the above parameter value is used to characterize the change of the characteristic data of the measurement point. The parameter value can be the change rate and/or the change rate between the characteristic data of the current measurement point and the characteristic data of the previous measurement point in the same measurement sub-area. or absolute value difference.

In this step, a preset threshold and a preset score corresponding to the parameter value can be preset for each measurement sub-area. In this way, the parameter value can be compared with the preset threshold. When the parameter value is greater than or When it is equal to the preset threshold corresponding to the parameter value, the preset score corresponding to the parameter value can be used as the feature score of the current measurement point; when the parameter value is less than the preset threshold corresponding to the parameter value, the current measurement point will not be scored. . Using the same method as above, the feature scores of each measurement point in each measurement sub-area can be obtained. Among them, the higher the feature score is, the more obvious the features of the measurement point are and the more they need to be extracted; the lower the feature score is, the less obvious the features of the measurement point are and the more they can be discarded.

It should be noted that the preset score corresponding to each parameter value can be a numerical value or a numerical range. When the preset score is a numerical value (such as 5 points), when the parameter value is greater than or equal to the preset threshold, the current measurement point is scored as 5 points; when the preset score is a numerical range, such as 5-10 points, then when the parameter value is greater than or equal to the preset threshold, an appropriate score can be selected from the value range according to the degree to which the parameter value exceeds the preset threshold. If the parameter value just exceeds the preset threshold, then the The current measurement point is scored as 5 points. If the parameter value is twice the preset threshold, the current measurement point is scored as 10 points, etc. Moreover, when determining the feature scores of the measurement points of the measurement sub-region, the feature scores of each measurement point on the measurement sub-region may be determined, or only the feature scores of internal measurement points other than the boundary points of the measurement sub-region may be determined, This will help improve processing efficiency. The boundary point of the measurement sub-area here refers to the critical point between two different measurement sub-areas. Since the measurement sub-area is divided based on the environment type, it is at the critical point between two different measurement sub-areas. The features are usually obvious, and these boundary points need to be extracted by default.

Step 104: Extract target measurement points from each measurement point in the measurement area. The target measurement points include a first preset number of measurement points ranked first in feature scores and boundary points of the measurement sub-region.

In this step, the first preset number of measurement points and the boundary points of the measurement sub-area with the highest feature scores can be extracted as target measurement points, and then the performance of the wireless network in the measurement area can be evaluated based on these target measurement points. Perform analysis.

In this embodiment, the characteristic scores of the measurement points in each measurement sub-region can be determined, and the measurement points with higher characteristic scores and the boundary points of the measurement sub-regions can be extracted, that is, the measurement points with more obvious characteristics can be extracted for further processing. Subsequent analysis improves the accuracy of measurement point extraction and improves the accuracy of wireless network performance analysis in the measurement area. At the same time, because the extracted target measurement points also include the demarcation points of the measurement sub-areas, the original data can be better retained. Measure the trajectory to avoid that the trajectory after compressing the measurement points cannot objectively reflect the original measurement trajectory.

Further, the above steps divide each measurement point in the measurement area into measurement sub-areas corresponding to different environment types, including:

Obtain site distribution information within the measurement area;

Based on the site distribution information, the Thiessen polygon corresponding to each site is constructed;

According to the station spacing between adjacent stations in the measurement sub-area, the Thiessen polygon is divided to form measurement sub-areas corresponding to different environmental types. Among them, the station spacing between adjacent stations in each measurement sub-area belongs to In the same distance range, the station spacing of different environment types corresponds to different distance ranges, and the measurement sub-area includes at least one Thiessen polygon;

According to the position distribution of each measurement point in the Thiessen polygon within the measurement area, the measurement sub-region corresponding to each measurement point is determined.

In an embodiment, the measurement area can be divided into multiple different measurement sub-areas according to environment types. Specifically, the Thiessen polygon (also known as Voronoi diagram in English) corresponding to each site can be constructed based on the obtained distribution information of sites (i.e., base stations) in the measurement area. The schematic diagram of the Thiessen polygon as shown in picture 2. Among them, there is a station in each Thiessen polygon. The distance between the measurement point within the Thiessen polygon and the corresponding station is the shortest, and the distance between the measurement point located on the edge of the Thiessen polygon and the stations on both sides is equal. After constructing the Thiessen polygons corresponding to each site, the Thiessen polygons with the same distance range can be clustered and merged according to the spacing between adjacent sites in the measured sub-area, thereby dividing them into different The measurement sub-area corresponding to the environment type, and then according to the position distribution of each measurement point in the measurement area in the Thiessen polygon, the measurement sub-area corresponding to each measurement point is determined, so as to facilitate subsequent measurement of different measurement sub-areas according to different wireless Network performance requires that the characteristic scores of the measurement points within it be scored separately.

Further, the above steps determine the characteristic scores of the measurement points within the measurement sub-area based on the characteristic data, including:

According to the characteristic data, calculate the parameter values corresponding to each measurement point in the measurement sub-area;

Compare the parameter value with a preset threshold corresponding to the measurement sub-region, where the preset thresholds corresponding to different measurement sub-regions are different;

When 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 is determined as the feature score.

In one embodiment, the preset thresholds corresponding to different measurement sub-regions are different, that is to say, the scoring requirements for feature scores corresponding to different measurement sub-regions are different. For example, after dividing the measurement area into multiple measurement sub-areas corresponding to dense urban areas, general urban areas, suburbs, and rural areas according to environmental types, different presets can be set for the measurement sub-areas corresponding to dense urban areas, general urban areas, suburbs, and rural areas. Set thresholds. For example, the preset thresholds for measurement sub-areas corresponding to dense urban areas are higher, and the preset thresholds for measurement sub-areas corresponding to rural areas are lower. In this way, different measurement sub-areas can be measured based on the preset thresholds of different measurement sub-areas. The characteristic scores of the measurement points within each measurement sub-area are determined, thereby extracting the measurement points with wireless network characteristics in each measurement sub-area.

In this embodiment, when the wireless network measurement process involves measurement sub-areas of different environmental types, since each measurement sub-area has different requirements for wireless network performance, the wireless network performance can be measured based on different environmental types. According to the requirements, the measurement area is divided into multiple measurement sub-areas according to the environment type. For example, if the access volume of users in dense urban areas changes greatly and the signal fluctuations are large, more measurement points need to be reserved in dense urban areas. However, in rural areas, the access volume of users changes less and the signal fluctuations are smaller, so fewer measurement points need to be reserved in rural areas. Measuring point. 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 subsequent extraction of sampling points with obvious network characteristics in measurement sub-areas under different environmental types, thereby improving the accuracy of wireless network performance analysis.

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

The above steps compare the parameter value with the preset threshold corresponding to the measurement sub-area, including:

where the parameter value is a rate of change, compare the parameter value to a rate of change threshold; and/or

In the case where the parameter value is an absolute difference, the parameter value is compared to an absolute difference threshold.

In an embodiment, the preset thresholds of corresponding types can be set respectively according to the types corresponding to the parameter values. Specifically, when the parameter value is a change rate, the parameter value can be compared with the change rate threshold; when the parameter value is an absolute value difference, the parameter value can be compared with the absolute value difference threshold; when the parameter value For rate of change and absolute difference, the parameter value can be compared with the rate of change threshold and the absolute difference threshold respectively. In this way, the flexibility of parameter values can be increased. Regardless of whether the parameter value is a change rate or an absolute value difference, there can be a corresponding preset threshold for comparison.

Further, the parameter value is calculated based on at least one characteristic data among signal quality, service quality, key events, driving speed, curve slope of the measurement trajectory, and curve jitter of the measurement trajectory; different parameter values in the same measurement sub-area correspond to The change rate threshold and absolute value difference threshold of are different;

In the above steps, when the parameter value is greater than or equal to the preset threshold corresponding to the measurement sub-area, the preset score corresponding to the parameter value is determined as the feature score, including:

When the target parameter value is greater than or equal to the change rate threshold and/or the absolute 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 based on any one or more Parameter values calculated from characteristic data;

Calculate the summation result of the preset scores and determine the summation result as the feature score.

In an embodiment, the above characteristic data may include but is not limited to: signal quality, service quality, key events, driving speed, curve slope of the measurement trajectory, curve jitter of the measurement trajectory, etc., where the signal quality may be used to represent the network signal Strength and coverage quality (such as overlapping coverage, weak coverage), etc.; service quality can be used to represent service rate, delay, etc.; key events can be used to represent the number of event failures/successes, etc.; driving speed can be used to represent the speed of road test equipment Travel speed; the curve slope of the measurement trajectory can be used to represent the slope change of the measurement trajectory; the curve jitter of the measurement trajectory is used to represent the jitter of the measurement trajectory. The above parameter values may be calculated based on one or more characteristic data among the above characteristic data, and for the parameter value corresponding to each characteristic data, there is a corresponding change rate threshold and/or absolute value difference threshold, In this way, each measurement point in the measurement sub-area can determine the feature score of the parameter value corresponding to each feature data based on the parameter value corresponding to each feature data and the corresponding change rate threshold and/or the size of the absolute value difference threshold. , and then by accumulating the preset scores corresponding to the parameter values corresponding to all types of feature data, the final feature score of each measurement point can be obtained.

Further, the following formula is used to determine the feature score:

Among them, ^xi represents the parameter value corresponding to the i-th feature data, R represents the change rate threshold, and s _grad ( ^xi , R) represents the parameter value for the i-th feature data when x ⁱ is greater than or equal to R. The corresponding feature scores are summed and calculated. When x ⁱ is less than R, the feature score corresponding to the parameter value of the i-th feature data is not scored, i∈(1,m); x ^j represents the j-th feature data corresponding to Parameter value, R′ represents the absolute value difference threshold, s _abs (x ^j ,R′) indicates that when x ^j is greater than or equal to R′, the feature scores corresponding to the parameter values of the jth feature data are summed Calculate, when x ^j is less than R′, the feature score corresponding to the parameter value of the jth feature data will not be scored, j∈(m+1,n), m represents the number of feature data types whose parameter value is the change rate, n represents the number of types of all feature data.

In an optional embodiment, the above characteristic data can be used to determine the characteristic score in two ways. One way is to determine based on the change rate of the characteristic data of the previous and later measurement points and the change rate threshold. The characteristic data of this part It can include signal quality, service quality, etc., because this part of the characteristic data generally has a smooth changing trend at adjacent grid measurement points, and the network jitter status is clearly reflected based on the change rate indicator. We divide this part of the characteristic data into i, i∈(1,m); Another way is to determine based on the absolute value difference of the characteristic data of the previous and later measurement points (or the current measurement point) and the absolute value difference threshold. This part of the characteristic data can include key events. , driving speed, curve slope of the measurement trajectory, curve jitter of the measurement trajectory, etc. Because this part of the characteristic data can better reflect the network status changes as long as it exceeds the corresponding preset threshold, we divide this part of the characteristic data into j,j∈(m+1,n). No matter based on any of the above methods, by measuring the comprehensive changes of multiple factors at the measurement points in the measurement area, we try to extract measurement points that reflect large fluctuations in network status, so as to evaluate potential network problems through the above measurement points.

Specifically, for the first part of the characteristic data, since its corresponding parameter value ^xi is the change rate, the calculation formula of ^xi can be:

in, Represents the i-th characteristic data of the current measurement point,/> Represents the i-th characteristic data of the previous measurement point.

For the second part of the feature data, since its corresponding parameter value x ^j is an absolute value difference, the calculation formula of x ^j can be:

in, Represents the jth characteristic data of the current measurement point,/> Represents the jth characteristic data of the previous measurement point.

From this, the following formula can be used to calculate the sum of the preset scores corresponding to the parameter values of all feature data of the current measurement point, as the feature score of the current measurement point:

Among them, ^xi represents the parameter value corresponding to the i-th feature data, R represents the change rate threshold, and s _grad ( ^xi , R) represents the parameter value for the i-th feature data when x ⁱ is greater than or equal to R. The corresponding feature scores are summed and calculated. When x ⁱ is less than R, the feature score corresponding to the parameter value of the i-th feature data is not scored, i∈(1,m); x ^j represents the j-th feature data corresponding to Parameter value, R′ represents the absolute value difference threshold, s _abs (x ^j ,R′) indicates that when x ^j is greater than or equal to R′, the feature scores corresponding to the parameter values of the jth feature data are summed Calculate, when x ^j is less than R′, the feature score corresponding to the parameter value of the jth feature data will not be scored, j∈(m+1,n), m represents the number of feature data types whose parameter value is the change rate, n represents the number of types of all feature data.

Assume that for any test line trajectory point containing k measurement segments in the measurement area, Composition, where k represents the measurement segment corresponding to the kth measurement sub-area, and N represents the total number of measurement points on the measurement segment. Group the two adjacent measurement points one by one on the measurement segment, such as //> and/> Classified as Group 1,/> and/> Divided into the second group, and so on, a total of n-1 groups, among which/> and/> They are the boundary points at both ends of the measurement segment, which need to be retained when extracting measurement points. At the same time, for the measurement points within the measurement segment/> The feature score to each measurement point within k measurement segments can be determined according to the above formula. Specifically, for comparing the magnitudes of ^xi and R, if ^xi is greater than or equal to R, the corresponding preset integral s ⁱ is obtained. For example, when evaluating the signal quality of point _P2 , calculate the change rate of the signal quality of point _P2 relative to the signal quality of point _P1 . If the change rate is greater than or equal to the change rate threshold, then point _P2 will obtain the corresponding preset points s ⁱ , otherwise no score. When evaluating the curve slope of the measurement trajectory of point _P2 , calculate the absolute value difference between the slope of the curve of point _P2 and the slope of the curve of point _P1 . If the absolute value difference is greater than or equal to the absolute value difference threshold, then P Point ₂ obtains the corresponding preset integral s ^j based on the slope of the curve, otherwise no points are scored. For another example, when evaluating the key event of point _P2 , if the number of handover failures x ^j at point _P2 is greater than the handover threshold R′, then point _P2 will obtain the corresponding score in the consideration of key events, otherwise no score will be obtained. In the same way, the characteristic score to each measurement point in each measurement segment can be determined according to the above formula.

Further, the above step 104, extracting target measurement points from each measurement point in the measurement area, includes:

Sort the feature scores from high to low to get the sorting results;

Determine a first preset number of measurement points with top 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 in all measurement sub-areas. The number of measurement points to be extracted is based on the measurement The total number of measurement points in the area and the preset extraction ratio are determined;

The determined first preset number of measurement points and the boundary points of the measurement sub-area are extracted as target measurement points.

In one embodiment, the characteristic scores of the measurement points in each measurement sub-area can be sorted from high to low. The higher the score, the more significant the characteristic of the measurement point reflecting the network status, and the greater its importance. For measurement points with low scores, it means that on the premise of reflecting network status characteristics through different characteristic data, the effect does not fully reflect the current network status. Then according to a certain thinning rate (that is, the preset extraction ratio), the inner points with larger scores and the boundary points of the measurement sub-area are selected for extraction. For example, assuming that there are 1,000 original measurement points in the measurement area, the thinning rate is 10%, and there are 10 boundary points in the measurement sub-area, then 90 measurement points with larger feature scores in the measurement sub-area can be selected. Measurement points, these 90 measurement points and 10 boundary points are extracted as the characteristic points of the measurement area that ultimately reflect the current network status, thereby increasing the amount of data for subsequent processing and analysis and improving the efficiency of processing and analysis.

In an optional embodiment, the method for extracting wireless network measurement points may include the following steps, as shown in Figure 3:

Step 301: Divide each measurement point in the measurement area into measurement sub-areas corresponding to different environment types;

Step 302: Determine the characteristic data of the measurement points in each measurement sub-area that participate in the network status characteristic evaluation;

Step 303: Adapt the corresponding preset threshold and preset score for each feature data participating in the network status feature evaluation;

Step 304: Group any two adjacent measurement points in each measurement sub-area into two groups;

Step 305: Calculate the change rate and/or absolute value difference of each set of feature data, and compare the change rate and/or absolute value difference with the preset threshold;

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

Step 307: Sort the feature scores from high to low;

Step 308: Extract the first preset number of measurement points and the boundary points of the measurement sub-area with the highest feature score ranking through the preset thinning rate, and use them as the output of the current network environment.

In this embodiment, the thinning rate can be set, the characteristic data of the measurement points can be comprehensively used for scoring and evaluation, and then the selection of measurement points can be determined based on the scoring results, so as to preserve the characteristics of the wireless network to the greatest extent possible, thereby improving the measurement trajectory curve. Perform compression to improve network analysis efficiency. Compared with existing technologies, traditional network feature evaluation is based on a single indicator or uses joint analysis of multi-dimensional indicators. However, this method needs to obtain the indicator data of all measurement points. For some scenarios that require fast processing, such as small screen scenarios, it requires more processing and transmission resources; and the extraction method of wireless network measurement points in this application , the importance of measurement points can be effectively distinguished through feature extraction and evaluation, thereby guiding the extraction of measurement points with higher importance.

The extraction method of wireless network measurement points in this application can be applied to the processing and analysis scenario after wireless network testing. By evaluating the characteristic importance of wireless network measurement points, subsequent optimization processing of regional problems can be carried out in a targeted manner, for example, Characterize the network status of the current test or optimization area by extracting measurement points of high importance. It is of great significance for the extraction of measurement points and status assessment.

In addition, embodiments of the present application also provide a device for extracting wireless network measurement points. Referring to Figure 4, Figure 4 is a schematic structural diagram of a device for extracting wireless network measurement points provided by an embodiment of the present application. As shown in Figure 4, the wireless network measurement point extraction device 400 includes:

The acquisition module 401 is used to acquire the characteristic data of each measurement point in the measurement area, where the characteristic data is data used to characterize the wireless network performance of the measurement point;

The division module 402 is used to 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 wireless network performance requirements of the user equipment in the measurement sub-area;

The determination module 403 is used to determine the characteristic score of the measurement point in the measurement sub-area based on the characteristic data. The characteristic 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. Parameter value refers to the change rate and/or absolute value difference between the characteristic data of the current measurement point and the characteristic data of the previous measurement point. The measurement points within the measurement sub-area do not include boundary points;

The extraction module 404 is configured to extract target measurement points from each measurement point in the measurement area, where the target measurement points include a first preset number of measurement points ranked first in feature scores and boundary points of the measurement sub-region.

Further, the partitioning module 402 includes:

Obtain submodule, used to obtain site distribution information within the measurement area;

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

The division sub-module is used to divide the Thiessen polygon according to the station distance between adjacent stations in the measurement sub-area to form measurement sub-areas corresponding to different environment types. Among them, the adjacent stations in each measurement sub-area The distance between stations belongs to the same distance range. The distance between stations of different environmental types corresponds to different distance ranges. The measurement sub-area includes at least one Thiessen polygon;

The determination sub-module is used to determine the measurement sub-area corresponding to each measurement point according to the position distribution of each measurement point in the Thiessen polygon in the measurement area.

Further, the determination sub-module includes:

The calculation unit is used to calculate the parameter values corresponding to each measurement point in the measurement sub-area based on the characteristic data;

A comparison unit used to compare the parameter value with a preset threshold corresponding to the measurement sub-region, where the preset thresholds corresponding to different measurement sub-regions are different;

A determination unit configured to determine a preset score corresponding to the parameter value as a feature score when the parameter value is greater than or equal to a preset threshold corresponding to the measurement sub-region.

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

The comparison unit is specifically used for:

where the parameter value is a rate of change, compare the parameter value to a rate of change threshold; and/or

In the case where the parameter value is an absolute difference, the parameter value is compared to an absolute difference threshold.

Further, the parameter value is calculated based on at least one characteristic data among signal quality, service quality, key events, driving speed, curve slope of the measurement trajectory, and curve jitter of the measurement trajectory; different parameter values in the same measurement sub-area correspond to The change rate threshold and absolute value difference threshold of are different;

Determine the unit specifically for:

When the target parameter value is greater than or equal to the change rate threshold and/or the absolute 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 based on any one or more Parameter values calculated from characteristic data;

Calculate the summation result of the preset scores and determine the summation result as the feature score.

Further, the following formula is used to determine the feature score:

Among them, ^xi represents the parameter value corresponding to the i-th feature data, R represents the change rate threshold, and s _grad ( ^xi , R) represents the parameter value for the i-th feature data when x ⁱ is greater than or equal to R. The corresponding feature scores are summed and calculated. When x ⁱ is less than R, the feature score corresponding to the parameter value of the i-th feature data is not scored, i∈(1,m); x ^j represents the j-th feature data corresponding to Parameter value, R′ represents the absolute value difference threshold, s _abs (x ^j ,R′) indicates that when x ^j is greater than or equal to R′, the feature scores corresponding to the parameter values of the jth feature data are summed Calculate, when x ^j is less than R′, the feature score corresponding to the parameter value of the jth feature data will not be scored, j∈(m+1,n), m represents the number of feature data types whose parameter value is the change rate, n represents the number of types of all feature data.

Further, the extraction module 404 includes:

The sorting submodule is used to sort the feature scores from high to low to obtain the sorting results;

Determination subunit, used to determine a first preset number of measurement points with top 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-areas to be extracted. The number of measurement points is determined based on the total number of measurement points in the measurement area and the preset extraction ratio;

The extraction subunit is configured to extract the determined first preset number of measurement points and boundary points of the measurement sub-region as target measurement points.

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 foregoing method embodiments, and can achieve the same technical effect, which will not be described again here. .

As shown in Figure 5, the embodiment of the present application also provides an electronic device, including a processor 511, a communication interface 512, a memory 513, and a communication bus 514. The processor 511, the communication interface 512, and the memory 513 communicate through the communication bus 514. complete mutual communication,

Memory 513, used to store computer programs;

In one embodiment of the present application, the processor 511 is used to implement the wireless network measurement point extraction method provided in any of the foregoing method embodiments when executing the program stored on the memory 513, including:

Obtain characteristic data of each measurement point within the measurement area, where the characteristic data is data used to characterize the wireless network performance of the measurement point;

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 wireless network performance requirements of user equipment in the measurement sub-area;

According to the characteristic data, the characteristic score of the measurement point in the measurement sub-area is determined. The characteristic 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 current measurement The change rate and/or absolute value difference between the characteristic data of a point and the characteristic data of the previous measurement point, the measurement points within the measurement sub-area do not include boundary points;

A target measurement point is extracted from each measurement point in the measurement area, and the target measurement point includes a first preset number of measurement points ranked first in feature scores and boundary points of the measurement sub-region.

In addition, embodiments of the present application also provide a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the extraction of wireless network measurement points as provided in any of the foregoing method embodiments is implemented. Method steps.

It should be noted that in this article, relational terms such as “first” and “second” are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply these There is no such actual relationship or sequence between entities or operations. Furthermore, the terms "comprises," "comprises," or any other variations thereof are intended to cover a non-exclusive inclusion such that a process, method, article, or apparatus that includes a list of elements includes not only those elements, but also those not expressly listed other elements, or elements inherent to the process, method, article or equipment. Without further limitation, an element defined by the statement "comprises a..." does not exclude the presence of additional identical elements in a process, method, article, or apparatus that includes the stated element.

The above descriptions are only specific embodiments of the present invention, enabling those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be practiced in other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended 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 (10)

1. A method for extracting wireless network measurement points, characterized in that the method includes: Obtain characteristic data of each measurement point within the measurement area, where the characteristic data is data used to characterize the wireless network performance of the measurement point; Divide each measurement point in the measurement area into measurement sub-areas corresponding to different environment types, where the environment types are used to characterize the wireless network performance requirements of the user equipment in the measurement sub-area; According to the characteristic data, the characteristic score of the measurement point in the measurement sub-area is determined, and the characteristic 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. It is obtained that the parameter value refers to the change rate and/or absolute value difference between the characteristic data of the current measurement point and the characteristic data of the previous measurement point, and the measurement points in the measurement sub-region do not include boundary points; A target measurement point is extracted from each measurement point in the measurement area, where the target measurement point includes a first preset number of measurement points ranked higher in the feature score and boundary points of the measurement sub-region. 2. The method according to claim 1, characterized in that dividing each measurement point in the measurement area into measurement sub-areas corresponding to different environmental types includes: Obtain site distribution information within the measurement area; Based on the site distribution information, construct a Thiessen polygon corresponding to each site; The Thiessen polygon is divided according to the distance between adjacent stations in the measurement sub-area to form measurement sub-areas corresponding to different environment types, wherein the adjacent stations in each measurement sub-area The distance between stations belongs to the same distance range, the distance between stations of different environmental types corresponds to different distance ranges, and the measurement sub-area includes at least one of the Thiessen polygons; According to the position distribution of each measurement point in 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, wherein determining the characteristic score of the measurement point in the measurement sub-area based on the characteristic data includes: According to the characteristic data, calculate the parameter values corresponding to each measurement point in the measurement sub-area; Compare the parameter value with a preset threshold corresponding to the measurement sub-region, wherein the preset thresholds corresponding to different measurement sub-regions are different; 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 is determined as the feature score. 4. The method according to claim 3, wherein the preset threshold corresponding to the measurement sub-region includes a change rate threshold and/or an absolute value difference threshold set corresponding to the parameter value; Comparing the parameter value with a preset threshold corresponding to the measurement sub-area includes: In the case where the parameter value is a rate of change, comparing the parameter value with the rate of change threshold; and/or In the case where 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 value is based on at least one characteristic of signal quality, service quality, key events, driving speed, curve slope of the measurement trajectory, and curve jitter of the measurement trajectory. The data is calculated; the change rate thresholds and absolute value difference thresholds corresponding to different parameter values in the same measurement sub-area are different; Determining 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-area includes: When the target parameter value is greater than or equal to the change rate threshold and/or the absolute 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 Parameter values calculated based on any one or more characteristic data; Calculate the summation result of the preset score, and determine the summation result as the feature score. 6. The method according to claim 5, characterized in that the following formula is used to determine the feature score: Among them, ^xi represents the parameter value corresponding to the i-th feature data, R represents the change rate threshold, and s _grad ( ^xi , R) represents the parameter value for the i-th feature data when x ⁱ is greater than or equal to R. The corresponding feature scores are summed and calculated. When x ⁱ is less than R, the feature score corresponding to the parameter value of the i-th feature data is not scored, i∈(1, m); x ^j represents the j-th feature data corresponding to Parameter value, R′ represents the absolute value difference threshold, s _abs (x ^j , R′) indicates that when x ^j is greater than or equal to R′, the feature scores corresponding to the parameter values of the jth feature data are summed Calculate, when x ^j is less than R′, the feature score corresponding to the parameter value of the jth feature data will not be scored, j∈(m+1, n), m represents the number of types of feature data whose parameter value is the change rate, n represents the number of types of all feature data. 7. The method of claim 1, wherein extracting target measurement points from each measurement point in the measurement area includes: Sort the feature scores from high to low to obtain the sorting results; Determine a first preset number of measurement points with top 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 the measurement sub-areas. The number of measurement points to be extracted is determined based on the total number of measurement points in the measurement area and the preset extraction ratio; The determined first preset number of measurement points and the boundary points of the measurement sub-area are extracted as the target measurement points. 8. A device for extracting wireless network measurement points, characterized in that the device includes: An acquisition module, configured to acquire characteristic data of each measurement point within the measurement area, where the characteristic data is data used to characterize the wireless network performance of the measurement point; A division module, configured to divide each measurement point in the measurement area into measurement sub-areas corresponding to different environment types, where the environment types are used to characterize the wireless network performance requirements of user equipment in the measurement sub-area; Determining module, configured to determine the characteristic score of the measurement point in the measurement sub-area according to the characteristic data. The characteristic score is based on the parameter value corresponding to the parameter value when the parameter value is greater than or equal to the preset threshold. The preset score of including boundary points; An extraction module, configured to extract target measurement points from each measurement point in the measurement area, where the target measurement points include a first preset number of measurement points ranked first in the feature score and the measurement sub-area. Boundary point. 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 complete communication with each other through the communication bus; Memory, used to store computer programs; The processor is configured to implement the steps of the wireless network measurement point extraction method described in any one of claims 1 to 7 when executing a program stored in the memory. 10. A computer-readable storage medium with a computer program stored thereon, characterized in that when the computer program is executed by a processor, the method for extracting wireless network measurement points according to any one of claims 1-7 is implemented. A step of.