WO2023207070A1 - 无线网络测量点的提取方法、装置、电子设备及存储介质 - Google Patents

无线网络测量点的提取方法、装置、电子设备及存储介质 Download PDF

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WO2023207070A1
WO2023207070A1 PCT/CN2022/135030 CN2022135030W WO2023207070A1 WO 2023207070 A1 WO2023207070 A1 WO 2023207070A1 CN 2022135030 W CN2022135030 W CN 2022135030W WO 2023207070 A1 WO2023207070 A1 WO 2023207070A1
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measurement
area
parameter value
points
sub
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French (fr)
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张海
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中兴通讯股份有限公司
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/02—Arrangements for optimising operational condition
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/08—Testing, supervising or monitoring using real traffic
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

Definitions

  • the present disclosure relates to the field of wireless network optimization, and in particular, to a method, device, electronic device and storage medium for extracting wireless network measurement points.
  • the present disclosure provides a method, device, electronic device and storage medium for extracting wireless network measurement points.
  • the present disclosure provides a method for extracting wireless network measurement points.
  • the method includes: obtaining characteristic data of each measurement point in the measurement area, where the characteristic data is a wireless network used to characterize the measurement point. Performance data; divide each measurement point in the measurement area into measurement sub-areas corresponding to different environment types, and the environment types are used to characterize the requirements of user equipment in the measurement sub-area for wireless network performance; according to the characteristics data to determine the characteristic score of the measurement point within the measurement sub-area, 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 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.
  • the measurement points in the measurement sub-area do not include boundary points; and from the measurement A target measurement point is extracted from each measurement point in the area, and the target measurement point includes a first preset number of measurement points ranked first in the feature score and the boundary points of the measurement sub-region.
  • the present disclosure 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, wherein the characteristic data is for user use. Data representing 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, and the environment type is used to characterize the measurement sub-area.
  • the determination module is configured to determine the characteristic score of the measurement point in the measurement sub-area based on the characteristic data, the characteristic score is when the parameter value is greater than or equal to the preset threshold In this case, it is determined based on the preset score corresponding to the parameter value, which 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 in the measurement sub-area do not include boundary points; and an extraction module is configured to extract target measurement points from each measurement point in the measurement area, the target measurement points including the feature scores ranked first a first preset number of measurement points and boundary points of the measurement sub-area.
  • the present disclosure 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; the memory is configured to store A computer program; a processor, configured to implement the steps of the wireless network measurement point extraction method described in any embodiment of the first aspect when executing the program stored in the memory.
  • the present disclosure also provides a computer-readable storage medium on which a computer program is stored.
  • 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.
  • Figure 1 is a schematic flow chart of a method for extracting wireless network measurement points provided by an embodiment of the present disclosure
  • FIG. 2 is a schematic diagram of a Thiessen polygon provided by an embodiment of the present disclosure
  • Figure 3 is a schematic flow chart of yet another method for extracting wireless network measurement points provided by an embodiment of the present disclosure
  • Figure 4 is a schematic structural diagram of a device for extracting wireless network measurement points provided by an embodiment of the present disclosure.
  • FIG. 5 is a schematic structural diagram of an electronic device provided by an embodiment of the present disclosure.
  • 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.
  • 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.
  • Figure 1 is a schematic flow chart of a method for extracting wireless network measurement points provided by an embodiment of the present disclosure.
  • the wireless network measurement point extraction method may include the following steps 101 to 104.
  • 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.
  • 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.
  • 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 disclosure.
  • 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 disclosure.
  • 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.
  • the above environment types are used to characterize the performance requirements of user equipment in the measurement sub-area for wireless networks. 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.
  • 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 identify and extract sampling points with obvious network characteristics, thus 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.
  • 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 absolute value between the characteristic data of the current measurement point and the characteristic data of the previous measurement point in the same measurement sub-area. difference.
  • a preset threshold and a preset score corresponding to the parameter value can be preset for each measurement sub-area.
  • the parameter value can be compared with the preset threshold.
  • 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. .
  • 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.
  • the preset score corresponding to each parameter value can be a numerical value or a numerical range.
  • the preset score is a numerical value (such as 5 points)
  • the current measurement point is scored as 5 points
  • the preset score is a numerical range, such as 5-10 points
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • each measurement point in the measurement area is divided into measurement sub-areas corresponding to different environment types, including: obtaining site distribution information in the measurement area; based on the site distribution information, constructing a Thiessen polygon corresponding to each site ; According to the distance between adjacent stations in the measurement sub-area, the Thiessen polygon is divided to form measurement sub-areas corresponding to different environmental types, where the distance between adjacent stations in each measurement sub-area is Belonging to the same distance range, different environmental types correspond to different distance ranges.
  • the measurement sub-area includes at least one Thiessen polygon; and each measurement point is determined according to the position distribution of each measurement point in the measurement area in the Thiessen polygon. The corresponding measurement sub-area.
  • the measurement area can be divided into multiple different measurement sub-areas according to environment types.
  • 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 is shown in Figure 2 shown. 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.
  • the Thiessen polygons corresponding to each site 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.
  • the characteristic scores of the measurement points in the measurement sub-area are determined, including:
  • the characteristic data calculate the parameter values corresponding to each measurement point in the measurement sub-region respectively; compare the parameter values with the preset thresholds corresponding to the measurement sub-regions, where the preset thresholds corresponding to different measurement sub-regions are different; and 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.
  • 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.
  • the wireless network performance can be measured based on different environmental types.
  • 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.
  • 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 the preset threshold corresponding to the measurement sub-area includes: comparing the parameter value with the change rate threshold when the parameter value is the change rate; and/or comparing the parameter value with the absolute value difference when the parameter value is the change rate. In the case of a value, the parameter value is compared to the absolute value difference threshold.
  • preset thresholds for corresponding types can be set respectively according to the types corresponding to the parameter values.
  • the parameter value When the parameter value is the change rate, the parameter value can be compared with the change rate threshold; when the parameter value is the absolute value difference, the parameter value can be compared with the absolute value difference threshold; when the parameter value is the change rate and absolute value difference, the parameter value can be compared with the change rate threshold and the absolute value 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.
  • 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 the absolute value difference threshold of are both different.
  • 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 target parameter value, the corresponding change In the case of rate threshold and/or absolute difference threshold, obtain the preset score corresponding to the target parameter value, where the target parameter value is a parameter value calculated based on any one or more feature data; and calculate the preset The summation result of the scores, and the summation result is determined as the feature score.
  • the above characteristic data may include but is not limited to: signal quality, service quality, key events, driving speed, curve slope of the measured trajectory, curve jitter of the measured trajectory, etc.
  • 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.
  • x i represents the parameter value corresponding to the i-th feature data
  • R represents the change rate threshold
  • s grad ( xi , R) represents the parameter value corresponding to the i-th feature data when x i is greater than or equal to R.
  • the 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 parameter value corresponding to the j-th feature data.
  • 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 and calculated.
  • x When j is less than R′, the feature score corresponding to the parameter value of the jth type of 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, and n represents all features. The number of types of data.
  • the above feature data can be used to determine the feature score in two ways.
  • One way is to determine based on the change rate of the feature data of the previous and later measurement points and the change rate threshold comparison.
  • This part of the feature data can be Including 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 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.
  • x i represents the parameter value corresponding to the i-th feature data
  • R represents the change rate threshold
  • s grad ( xi , R) represents the parameter value corresponding to the i-th feature data when x i is greater than or equal to R.
  • the 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 parameter value corresponding to the j-th feature data.
  • 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 and calculated.
  • x When j is less than R′, the feature score corresponding to the parameter value of the jth type of 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, and n represents all features. The number of types of data.
  • 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 .
  • P Point 2 obtains the corresponding preset points s j based on the slope of the curve, otherwise no points are scored.
  • the characteristic score to each measurement point in each measurement segment can be determined according to the above formula.
  • extracting target measurement points from each measurement point in the measurement area includes: sorting the feature scores in order from high to low to obtain the sorting results; determining the top scoring points from the sorting results.
  • a first preset number of measurement points 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 total number of measurement points in the measurement area and the preset number.
  • the extraction ratio is determined; and the determined first preset number of measurement points and boundary points of the measurement sub-area are extracted as target measurement points.
  • 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 characteristics 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.
  • 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.
  • the wireless network measurement point extraction method may include the following steps 301 to 308, 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 a preset threshold.
  • Step 306 Calculate the sum of preset scores corresponding to each feature data of the measurement points in each measurement sub-area in sequence to obtain a 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.
  • 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.
  • traditional network feature evaluation is based on a single indicator or uses joint analysis of multi-dimensional indicators.
  • 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 disclosure , 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 disclosure can be applied in the post-test processing and analysis scenario of wireless network.
  • 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.
  • FIG. 4 is a schematic structural diagram of a device for extracting wireless network measurement points provided by an embodiment of the present disclosure.
  • the wireless network measurement point extraction device 400 includes: an acquisition module 401, configured to obtain characteristic data of each measurement point in the measurement area, where the characteristic data is the wireless network used to characterize the measurement point. Performance data; the division module 402 is configured 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 requirements of the user equipment in the measurement sub-area for wireless network performance; determine Module 403 is configured to determine the characteristic score of the measurement point within 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 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-area do not include boundary points; and the extraction module 404 is configured In order to extract the 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.
  • the division module 402 includes: an acquisition sub-module, configured to obtain site distribution information within the measurement area; a construction sub-module, configured to construct a Thiessen polygon corresponding to each site based on the site distribution information; a division sub-module, It is configured to divide the Thiessen polygon according to the distance between adjacent stations in the measurement sub-area to form measurement sub-areas corresponding to different environment types, where the distance between adjacent stations in each measurement sub-area is The station spacing belongs to the same distance range, and the station spacing of different environmental types corresponds to different distance ranges.
  • the measurement sub-area includes at least one Thiessen polygon; and the determination sub-module is configured to determine the location of the Thiessen polygon according to each measurement point in the measurement area. Position distribution in , determine the measurement sub-area corresponding to each measurement point.
  • the determination sub-module includes: a calculation unit configured to respectively calculate parameter values corresponding to each measurement point in the measurement sub-area based on the characteristic data; a comparison unit configured to compare the parameter value with a preset value corresponding to the measurement sub-area. The thresholds are compared, wherein the preset thresholds corresponding to different measurement sub-areas are different; and the determination unit is configured to, when the parameter value is greater than or equal to the preset threshold value corresponding to the measurement sub-area, set the preset threshold value corresponding to the parameter value. Let the score be determined as the feature score.
  • 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 configured to: compare the parameter value with the change rate threshold when the parameter value is a change rate; and/or compare the parameter value with the absolute value difference threshold when the parameter value is an absolute value difference. Compare.
  • 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 the absolute value difference threshold of are both different.
  • the determination unit is configured to: obtain a preset score corresponding to the target parameter value when the target parameter value is greater than or equal to the change rate threshold and/or the absolute value difference threshold corresponding to the target parameter value, wherein the target parameter value is Parameter values calculated based on any one or more feature data; and calculating the summation result of the preset score, and determining the summation result as the feature score.
  • x i represents the parameter value corresponding to the i-th feature data
  • R represents the change rate threshold
  • s grad ( xi , R) represents the parameter value corresponding to the i-th feature data when x i is greater than or equal to R.
  • the 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 parameter value corresponding to the j-th feature data.
  • 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 and calculated.
  • x When j is less than R′, the feature score corresponding to the parameter value of the jth feature data will not be scored
  • m represents the number of types of feature data whose parameter value is the change rate
  • n represents all features. The number of types of data.
  • the extraction module 404 includes: a sorting sub-module, which is configured to sort the feature scores in order from high to low to obtain a sorting result; and a determination sub-unit, which is configured to determine the top-scoring feature from the sorting results.
  • a preset number of measurement points 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 total number of measurement points in the measurement area and the preset extraction The proportion is determined; and the extraction subunit is configured to extract the determined first preset number of measurement points and boundary points of the measurement sub-area as target measurement points.
  • 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. .
  • the embodiment of the present disclosure 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.
  • the memory 513 is configured to store the computer program.
  • the processor 511 is configured 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.
  • the method includes: obtaining the measurement points within the measurement area. Characteristic data of each measurement point, where the characteristic data is data used to characterize the wireless network performance of the measurement point; each measurement point in the measurement area is divided into measurement sub-areas corresponding to different environment types, and the environment type is used to characterize the measurement The requirements for wireless network performance of user equipment in the sub-area; based on the characteristic data, determine the characteristic score of the measurement points in the measurement sub-area.
  • 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 is determined.
  • 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.
  • the measurement points within the measurement sub-area do not include boundary points; and extracting 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.
  • embodiments of the present disclosure also provide a computer-readable storage medium on which a computer program is stored.
  • 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.
  • the above technical solutions provided by the embodiments of the present disclosure have the following advantages compared with related technologies:
  • the method provided by the embodiments of the present disclosure obtains characteristic data of each measurement point in the measurement area, where the characteristic data is used to characterize the measurement points.
  • Wireless network performance data ; 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; based on the characteristic data, determine The characteristic score of the measurement point in the measurement sub-area.
  • 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 characteristic data of the current measurement point and The change rate and/or absolute value difference between the characteristic data of the previous measurement point.
  • the measurement points in the measurement sub-area do not include boundary points; the target measurement points are extracted from each measurement point in the measurement area.
  • the target measurement points include The first preset number of measurement points and the boundary points of the measurement sub-area with the highest feature score ranking.
  • 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.

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Abstract

本公开涉及一种无线网络测量点的提取方法、装置、电子设备及存储介质，该方法包括：获取测量区域内的各测量点的特征数据；将测量区域内的各测量点划分至不同环境类型对应的测量子区域；根据特征数据，确定测量子区域内的测量点的特征评分，测量子区域内的测量点不包括边界点；以及从测量区域内的各测量点中提取目标测量点，目标测量点包括特征评分排序靠前的第一预设数量的测量点和测量子区域的边界点。

Description

无线网络测量点的提取方法、装置、电子设备及存储介质
相关申请的交叉引用
本公开要求享有2022年04月29日提交的名称为“无线网络测量点的提取方法、装置、电子设备及存储介质”的中国专利申请CN202210474439.2的优先权，其全部内容通过引用并入本公开中。
技术领域
本公开涉及无线网络优化领域，尤其涉及一种无线网络测量点的提取方法、装置、电子设备及存储介质。
背景技术
在对某一测量区域进行无线网络测量时，随着测量点的增多，数据量也在快速增长，庞大的数据量为数据的存储、查询、分析及传送造成极大的困难，因此，通常需要对无线网络测量点进行抽样，以减小数据量。
发明内容
本公开提供了一种无线网络测量点的提取方法、装置、电子设备及存储介质。
第一方面，本公开提供了一种无线网络测量点的提取方法，所述方法包括：获取测量区域内的各测量点的特征数据，其中，所述特征数据为用于表征测量点的无线网络性能的数据；将所述测量区域内的各测量点划分至不同环境类型对应的测量子区域，所述环境类型用于表征测量子区域内的用户设备对无线网络性能的要求；根据所述特征数据，确定所述测量子区域内的测量点的特征评分，所述特征评分是在参数值大于或等于预设阈值的情况下，基于与所述参数值对应的预设评分确定得到，所述参数值是指当前测量点的特征数据与前一测量点的特征数据之间的变化率和/或绝对值差值，所述测量子区域内的测量点不包括边界点；以及从所述测量区域内的各测量点中提取目标测量点，所述目标测量点包括所述特征评分排序靠前的第一预设数量的测量点和所述测量子区域的边界点。
第二方面，本公开还提供了一种无线网络测量点的提取装置，所述装置包括：获取模块，被配置为获取测量区域内的各测量点的特征数据，其中，所述特征数据为用于表征测量点的无线网络性能的数据；划分模块，被配置为将所述测量区域内的各测量点划分至不同环境类型对应的测量子区域，所述环境类型用于表征测量子区域内的用户设备对无线网 络性能的要求；确定模块，被配置为根据所述特征数据，确定所述测量子区域内的测量点的特征评分，所述特征评分是在参数值大于或等于预设阈值的情况下，基于与所述参数值对应的预设评分确定得到，所述参数值是指当前测量点的特征数据与前一测量点的特征数据之间的变化率和/或绝对值差值，所述测量子区域内的测量点不包括边界点；以及提取模块，被配置为从所述测量区域内的各测量点中提取目标测量点，所述目标测量点包括所述特征评分排序靠前的第一预设数量的测量点和所述测量子区域的边界点。
第三方面，本公开还提供了一种电子设备，包括处理器、通信接口、存储器和通信总线，其中，处理器，通信接口，存储器通过通信总线完成相互间的通信；存储器，被配置为存放计算机程序；处理器，被配置为执行存储器上所存放的程序时，实现第一方面任一项实施例所述的无线网络测量点的提取方法的步骤。
第四方面，本公开还提供了一种计算机可读存储介质，其上存储有计算机程序，所述计算机程序被处理器执行时实现如第一方面任一项实施例所述的无线网络测量点的提取方法的步骤。
附图说明
此处的附图被并入说明书中并构成本说明书的一部分，示出了符合本公开的实施例，并与说明书一起用于解释本公开的原理。
为了更清楚地说明本公开实施例或相关技术中的技术方案，下面将对实施例或相关技术描述中所需要使用的附图作简单地介绍，显而易见地，对于本领域普通技术人员而言，在不付出创造性劳动性的前提下，还可以根据这些附图获得其他的附图。
图1为本公开实施例提供的一种无线网络测量点的提取方法的流程示意图；
图2为本公开实施例提供的一种泰森多边形的示意图；
图3为本公开实施例提供的又一种无线网络测量点的提取方法的流程示意图；
图4为本公开实施例提供的一种无线网络测量点的提取装置的结构示意图；以及
图5为本公开实施例提供的一种电子设备的结构示意图。
具体实施方式
为使本公开实施例的目的、技术方案和优点更加清楚，下面将结合本公开实施例中的附图，对本公开实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例是本公开的一部分实施例，而不是全部的实施例。基于本公开中的实施例，本领域普通技术人员在没有做出创造性劳动的前提下所获得的所有其他实施例，都属于本公开保护的范围。
目前，传统的无线网络测量点的提取方式是按照时间周期抽样，采用一种均匀抽稀的方式提取测量点；或者是根据测量轨迹的曲线波动特征，提取与基准投影距离最大的测量点。但不管采用上述哪种方式进行提取，均未充分考虑测量点的无线网络性能的特征数据，将最有分析价值的测量点提取出来，因此，传统的无线网络测量点的提取方式提取准确率较低，导致测量区域的无线网络性能分析精度较低。
参见图1，图1为本公开实施例提供的一种无线网络测量点的提取方法的流程示意图。如图1所示，该无线网络测量点的提取方法可以包括如下步骤101至步骤104。
步骤101、获取测量区域内的各测量点的特征数据，其中，特征数据为用于表征测量点的无线网络性能的数据。
上述测量区域可以是指真实的地理区域，比如某个地市、某个省份的地理区域等等，也可以是仿真出来的测量区域。上述测量区域内的测量点可以是路测设备等设备在某一地理区域内的测量轨迹上的测量点；也可以是仿真出来的测量区域内的测量点。上述特征数据可以包括但不限于：信号质量、业务质量、关键事件、行驶速度、测量轨迹的曲线斜率、测量轨迹的曲线抖动等，其中，信号质量可以用于表示网络信号强度和覆盖质量(如重叠覆盖、弱覆盖)等；业务质量可以用于表示业务速率、时延等；关键事件可以用于表示事件失败/成功次数等；行驶速度可以用于表示路测设备行进速度；测量轨迹的曲线斜率可以用于表示测量轨迹的斜率变化情况；测量轨迹的曲线抖动用于表示测量轨迹的抖动情况。
获取特征数据的方式可以通过路测设备来获取，也可以从基站中获取，本公开不做具体限定。上述无线网络测量点的提取方法可以由路测设置来执行，也可以由与测量区域内的基站连接的电子设备(如服务器等)来执行，本公开不做具体限定。
步骤102、将测量区域内的各测量点划分至不同环境类型对应的测量子区域，环境类型用于表征测量子区域内的用户设备对无线网络性能的要求。
上述环境类型用于表征测量子区域内的用户设备对无线网络的性能要求，不同的环境类型对应的性能要求不同。该环境类型可以通过基站之间的站间距来确定的。该环境类型可以包括密集城区、一般城区、郊区、农村等类型。其中，密集城区、一般城区、郊区、农村对应的站间距依次增大，其中，密集城区对应的站间距最小，农村对应的站间距最大。由于不同环境类型对无线网络的性能要求不同，例如，密集城区用户接入量变化较大，信号起伏较大，可以在密集城区保留较多的测量点；而农村用户接入量变化较小，信号起伏较小，可以在农村保留较少的测量点，因而可以将测量区域按照环境类型划分成多个测量子区域，再确定测量子区域与测量点的位置关系，确定各测量点所对应的测量子区域。这样，方便后续针对不同环境类型下的测量子区域内的网络特征进行分析，确定并提取具有 网络特征明显的采样点，从而提升无线网络性能分析的精度。
步骤103、根据特征数据，确定测量子区域内的测量点的特征评分，特征评分是在参数值大于或等于预设阈值的情况下，基于与参数值对应的预设评分确定得到，参数值是指当前测量点的特征数据与前一测量点的特征数据之间的变化率和/或绝对值差值，测量子区域内的测量点不包括边界点。
上述参数值用于表征测量点的特征数据的变化情况，该参数值可以是同一测量子区域内的当前测量点的特征数据与前一测量点的特征数据之间的变化率和/或绝对值差值。
在该步骤中，可以针对每个测量子区域，预先设置与该参数值对应的预设阈值和预设评分，这样，就可以将该参数值与预设阈值进行比较，当该参数值大于或等于参数值对应的预设阈值时，可以将该参数值对应的预设评分，作为当前测量点的特征评分；当该参数值小于参数值对应的预设阈值时，则不对当前测量点进行打分。采用上述相同的方式，可以得到每个测量子区域内的各测量点的特征评分。其中，特征评分越高，表示该测量点的特征越明显，越需要进行提取；特征评分越低，表示该测量点特征越不明显，越可以进行舍弃。
需要说明的是，每个参数值对应的预设评分可以是一个数值，也可以是一个数值范围。当预设评分为一个数值(比如5分)时，当参数值大于或等于预设阈值时，则给该当前测量点打分为5分；当预设评分为一个数值范围时，比如5-10分，那么当参数值大于或等于预设阈值时，可以根据参数值超出预设阈值的程度，从该数值范围中选择一个合适的评分进行打分，如参数值刚好超过预设阈值，则给该当前测量点打分为5分，如参数值是预设阈值的两倍，则给该当前测量点打分为10分等。并且，在确定测量子区域的测量点的特征评分时，可以确定测量子区域上的每个测量点的特征评分，也可以仅确定除测量子区域的边界点以外的内部测量点的特征评分，这样有利于提高处理效率。此处的测量子区域的边界点是指处于两个不同的测量子区域之间的临界点，由于测量子区域是基于环境类型划分的，因而处于两个不同的测量子区域之间的临界点的特征通常较为明显，默认需要提取这些边界点。
步骤104、从测量区域内的各测量点中提取目标测量点，目标测量点包括特征评分排序靠前的第一预设数量的测量点和测量子区域的边界点。
在该步骤中，可以将特征评分排序靠前的第一预设数量的测量点和测量子区域的边界点，作为目标测量点进行提取，进而针对这些目标测量点对测量区域的无线网络的性能进行分析。
在本实施例中，可以确定出各测量子区域内的测量点的特征评分，并对特征评分较高的测量点和测量子区域的边界点进行提取，即将特征较为明显的测量点提取出来进行后续 分析，从而提高了测量点提取的准确率，使得测量区域的无线网络性能分析精度提高；同时，由于提取的目标测量点中还包含有测量子区域的分界点，因而可以较好地保留原始测量轨迹，避免压缩测量点后的轨迹无法客观反映原始测量轨迹。
进一步地，上述步骤中将测量区域内的各测量点划分至不同环境类型对应的测量子区域，包括：获取测量区域内的站点分布信息；基于站点分布信息，构建每个站点对应的泰森多边形；根据测量子区域内的相邻站点之间的站间距，对泰森多边形进行划分，形成不同环境类型对应的测量子区域，其中，每个测量子区域内的相邻站点之间的站间距属于同一距离范围，不同的环境类型的站间距对应不同的距离范围，测量子区域包括至少一个泰森多边形；以及根据测量区域内的各测量点在泰森多边形中的位置分布，确定各测量点所对应的测量子区域。
在一些实施例中，可以将测量区域按照环境类型划分为不同的多个测量子区域。可以根据获取到的测量区域内的站点(即基站)分布信息，构建每个站点对应的泰森多边形(又称为冯洛诺伊图，英文为Voronoi diagram)，泰森多边形的示意图如图2所示。其中，每个泰森多边形内均有一个站点，泰森多边形内的测量点到相应站点的距离最近，且位于泰森多边形边上的测量点到其两边的站点的距离相等。在构建完每个站点对应的泰森多边形后，可以根据测量子区域内的相邻站点之间的站间距，将站间距属于同于距离范围的泰森多边形进行聚类合并，从而划分成不同环境类型对应的测量子区域，进而根据测量区域内的各测量点在泰森多边形中的位置分布，确定各测量点所对应的测量子区域，从而方便后续针对不同测量子区域，按照不同的无线网络性能要求分别对其内的测量点的特征评分进行打分。
进一步地，上述步骤中根据特征数据，确定测量子区域内的测量点的特征评分，包括：
根据特征数据，分别计算测量子区域内的各测量点对应的参数值；将参数值和测量子区域对应的预设阈值进行比较，其中，不同的测量子区域对应的预设阈值不同；以及在参数值大于或等于测量子区域对应的预设阈值的情况下，将与参数值对应的预设评分确定为特征评分。
在一些实施例中，不同的测量子区域对应的预设阈值不同，也就是说，不同的测量子区域对应的特征评分的评分要求不同。例如，在将测量区域按照环境类型划分成密集城区、一般城区、郊区、农村对应的多个测量子区域后，可以对密集城区、一般城区、郊区、农村对应的测量子区域分别设置不同的预设阈值，如密集城区对应的测量子区域的预设阈值较高，农村对应的测量子区域的预设阈值较低，这样，就可以基于不同测量子区域的预设阈值，对不同测量子区域内的测量点的特征评分进行确定，从而提取出每个测量子区域内 具有无线网络特征的测量点。
在本实施例中，在无线网络测量过程中涉及到不同环境类型的测量子区域时，由于每个测量子区域对无线网路性能要求有所不同，因而可以基于不同环境类型对无线网络性能的要求，将测量区域按照环境类型划分成多个测量子区域。例如，密集城区用户接入量变化较大，信号起伏较大，需要在密集城区保留较多的测量点，而农村用户接入量变化较小，信号起伏较小，需要在农村保留较少的测量点。再根据各测量点和各测量子区域的位置关系，确定各测量点所对应的测量子区域。这样方便后续针对不同环境类型下的测量子区域内的网络特征明显的采样点进行提取，以此来提升无线网络性能分析精度。
进一步地，测量子区域对应的预设阈值包括与参数值对应设置的变化率阈值和/或绝对值差值阈值。
上述步骤中将参数值和测量子区域对应的预设阈值进行比较，包括：在参数值为变化率的情况下，将参数值与变化率阈值进行比较；和/或在参数值为绝对值差值的情况下，将参数值与绝对值差值阈值进行比较。
在一些实施例中，可以根据参数值对应的类型，分别设置对应类型的预设阈值。当参数值为变化率时，可以将该参数值与变化率阈值进行比较；当参数值为绝对值差值时，可以将该参数值与绝对值差值阈值进行比较；当参数值为变化率和绝对值差值时，可以将该参数值分别与变化率阈值和绝对值差值阈值进行比较。这样，可以增加参数值的灵活性，不管参数值是变化率，还是绝对值差值，均可以有对应的预设阈值进行比较。
进一步地，参数值是基于信号质量、业务质量、关键事件、行驶速度、测量轨迹的曲线斜率、测量轨迹的曲线抖动中的至少一种特征数据计算得到；同一测量子区域内的不同参数值对应的变化率阈值和绝对值差值阈值均不相同。
上述步骤中在参数值大于或等于测量子区域对应的预设阈值的情况下，将与参数值对应的预设评分确定为特征评分，包括：在目标参数值大于或等于目标参数值对应的变化率阈值和/或绝对值差值阈值的情况下，获取与目标参数值对应的预设评分，其中，目标参数值为基于任意一种或多种特征数据计算得到的参数值；以及计算预设评分的求和结果，并将求和结果确定为特征评分。
在一些实施例中，上述特征数据可以包括但不限于：信号质量、业务质量、关键事件、行驶速度、测量轨迹的曲线斜率、测量轨迹的曲线抖动等，其中，信号质量可以用于表示网络信号强度和覆盖质量(如重叠覆盖、弱覆盖)等；业务质量可以用于表示业务速率、时延等；关键事件可以用于表示事件失败/成功次数等；行驶速度可以用于表示路测设备的行进速度；测量轨迹的曲线斜率可以用于表示测量轨迹的斜率变化情况；测量轨迹的曲线 抖动用于表示测量轨迹的抖动情况。上述参数值可以是基于上述特征数据中的一种或者多种特征数据计算得到，并且对于每种特征数据对应的参数值，均存在与之对应的变化率阈值和/或绝对值差值阈值，这样，测量子区域内的每个测量点可以根据每种特征数据对应的参数值和对应的变化率阈值和/或绝对值差值阈值的大小，确定每种特征数据对应的参数值的特征评分，进而将所有种类的特征数据对应的参数值对应的预设评分进行累加，就可以得到每个测量点最终的特征评分。
进一步地，采用如下公式确定特征评分：
Figure PCTCN2022135030-appb-000001
其中，x i表示第i种特征数据对应的参数值，R表示变化率阈值，s grad(x i,R)表示当x i大于或等于R时，对第i种特征数据的参数值对应的特征评分进行求和计算，当x i小于R时，对第i种特征数据的参数值对应的特征评分不打分，i∈(1,m)；x j表示第j种特征数据对应的参数值，R′表示绝对值差值阈值，s abs(x j,R′)表示当x j大于或等于R′时，对第j种特征数据的参数值对应的特征评分进行求和计算，当x j小于R′时，对第j种特征数据的参数值对应的特征评分不打分，j∈(m+1,n)，m表示参数值为变化率的特征数据的种类数量，n表示所有特征数据的种类数量。
在可选实施例中，可以将上述特征数据按照两种方式来确定特征评分，一种方式是根据前后测量点的特征数据的变化率，以及变化率阀值比较确定，该部分的特征数据可以包括信号质量、业务质量等，因为该部分的特征数据一般在相邻栅格测量点的变化趋势较为平滑，基于变化率指标反映网络抖动状态较为清晰，我们将该部分特征数据划分至i，i∈(1,m)；另一种方式是基于前后测量点的特征数据的绝对值差值(或者当前测量点)，以及绝对值差值阈值比较确定，该部分的特征数据可以包括关键事件、行驶速度、测量轨迹的曲线斜率、测量轨迹的曲线抖动等，因为该部分的特征数据只要超过对应的预设阀值，即能够较好的体现网络状态变化，我们将该部分特征数据划分至j，j∈(m+1,n)。无论基于上述任意一种方式，均是通过衡量测量区域内测量点的多因素的综合变化情况，尽量提取反映网络状态起伏变化较大的测量点，从而通过上述测量点评估网络潜在问题。
对于第一部分的特征数据，由于其对应的参数值x i为变化率，该x i的计算公式可以为：
Figure PCTCN2022135030-appb-000002
其中，
Figure PCTCN2022135030-appb-000003
表示当前测量点的第i种特征数据，
Figure PCTCN2022135030-appb-000004
表示前一测量点的第i种特征数据。
对于第二部分的特征数据，由于其对应的参数值x j为绝对值差值，该x j的计算公式可以为：
Figure PCTCN2022135030-appb-000005
其中，
Figure PCTCN2022135030-appb-000006
表示当前测量点的第j种特征数据，
Figure PCTCN2022135030-appb-000007
表示前一测量点的第j种特征数据。
由此可以采用如下公式，计算出当前测量点的所有特征数据的参数值对应的预设评分之和，作为当前测量点的特征评分：
Figure PCTCN2022135030-appb-000008
其中，x i表示第i种特征数据对应的参数值，R表示变化率阈值，s grad(x i,R)表示当x i大于或等于R时，对第i种特征数据的参数值对应的特征评分进行求和计算，当x i小于R时，对第i种特征数据的参数值对应的特征评分不打分，i∈(1,m)；x j表示第j种特征数据对应的参数值，R′表示绝对值差值阈值，s abs(x j,R′)表示当x j大于或等于R′时，对第j种特征数据的参数值对应的特征评分进行求和计算，当x j小于R′时，对第j种特征数据的参数值对应的特征评分不打分，j∈(m+1,n)，m表示参数值为变化率的特征数据的种类数量，n表示所有特征数据的种类数量。
假设对于测量区域内的任意一条包含k个测量分段的测试线路轨迹点由
Figure PCTCN2022135030-appb-000009
组成，其中，k表示位于第k个测量子区域对应的测量分段，N表示该测量分段上的测量点的总量。将测量分段上相邻的两个测量点一一进行分组，如将
Figure PCTCN2022135030-appb-000010
和
Figure PCTCN2022135030-appb-000011
划为第一组，
Figure PCTCN2022135030-appb-000012
和
Figure PCTCN2022135030-appb-000013
划为第二组，依次类推，共计n-1组，其中
Figure PCTCN2022135030-appb-000014
和
Figure PCTCN2022135030-appb-000015
分别是测量分段两端的边界点，在提取测量点时需予以保留。同时，对于测量分段内的测量点
Figure PCTCN2022135030-appb-000016
可以根据上述公式确定的到k个测量分段内的每个测量点的特征评分。对于比较x i和R的大小，若x i大于或等于R,则取得相应地预设积分s i。例如，在评价P 2点的信号质量时，计算P 2点信号质量相对与P 1点的信号质量的变化率，若该变化率大于或等于变化率阈值，则P 2点获得对应预设积分s i，否则不得分。在评价P 2点的测量轨迹的曲线斜率时，计算P 2点曲线斜率相对与P 1点的曲线斜率的绝对值差值，若该绝对值差值大于或等于绝对值差值阈值，则P 2点在曲线斜率考量上获取对应的预设积分s j，否则不得分。又例如在评价P 2点的关键事件时，若P 2点发生切换失败次数x j大于切换门限R′，则P 2点在关键事件考量上获取对应的分值，否则不得分。同理，可以根据上述公式确定的到每个测量分段内的每个测量点的特征评分。
进一步地，上述步骤104中从测量区域内的各测量点中提取目标测量点，包括：将特征评分按照从高到低的顺序进行排序，得到排序结果；从排序结果中确定出评分靠前的第一预设数量的测量点，第一预设数量等于待提取测量点数量减去所有测量子区域的边界点的数量，待提取测量点数量是根据测量区域内的测量点总量和预设提取比例确定得到；以及将确定出的第一预设数量的测量点和测量子区域的边界点作为目标测量点进行提取。
在一些实施例中，可以对各测量子区域的测量点的特征评分按照分值从高到低进行排序，分值越该表征该测量点反映网络状态特征越显著，其重要性越大，而对于分值较低的测量点，说明在通过不同特征数据反映网络状态特征的前提下，其效果并未完全充分体现当前网络状态。然后按照一定抽稀率(即预设提取比例)选择分值较大的内点，以及测量子区域的边界点进行提取。例如，假设测量区域内的原始测量点有1000个，抽稀率为10％，测量子区域的边界点有10个，那么可以选取出测量子区域内的测量点中特征评分较大的90个测量点，将这90个测量点和10个边界点作为该测量区域最终反映当前网络状态的特征点进行提取，从而提升后续处理分析的数据量，提高处理分析的效率。
在可选实施例中，该无线网络测量点的提取方法可以包括如下步骤301至步骤308，如图3所示。
步骤301、将测量区域内的各测量点划分至不同环境类型对应的测量子区域。
步骤302、确定每个测量子区域内的测量点参与网络状态特征评价的特征数据。
步骤303、为每个参与网络状态特征评价的特征数据适配对应的预设阈值和预设评分；
步骤304、将每个测量子区域内的任意相邻的两个测量点两两分组。
步骤305、计算每组特征数据的变化率和/或绝对值差值，并将变化率和/或绝对值差值与预设阈值比较。
步骤306、依次计算每个测量子区域内的测量点的各个特征数据对应的预设评分之和，得到特征评分。
步骤307、将特征评分按照从高到低的顺序排序。
步骤308、通过预设的抽稀率，提取特征评分排序靠前的第一预设数量的测量点和测量子区域的边界点，并将其作为当前网络环境的输出。
在本实施例中，可以通过设置抽稀率，综合利用测量点的特征数据进行打分评价，再根据打分结果确定测量点的取舍，在最大可能保留无线网络特征的情况下，从而对测量轨迹曲线进行压缩，提升网络分析效率。与相关技术相比，传统的网络特征评估基于单一指标，或者采用多维指标的联合分析。但这种方式需要获取所有测量点的指标数据，对于一些需要快速处理场景，如小屏场景等应用时，需要耗费较多的处理和传输资源；而本公开 中的无线网络测量点的提取方法，可以通过特征提取和评价，有效区分测量点的重要性，从而指导重要性较高的测量点的提取。
本公开中的无线网络测量点的提取方法可以应用于无线网络测试后的处理分析场景中，通过评估无线网络测量点的特征重要性，从而有针对性的对区域问题进行后续优化处理，例如，通过提取重要性高的测量点，表征当前测试或优化区域的网络状态。对于测量点的提取、状态评估，都具有较大的意义。
除此之外，本公开实施例还提供一种无线网络测量点的提取装置。参见图4，图4为本公开实施例提供的一种无线网络测量点的提取装置的结构示意图。如图4所示，该无线网络测量点的提取装置400，包括：获取模块401，被配置为获取测量区域内的各测量点的特征数据，其中，特征数据为用于表征测量点的无线网络性能的数据；划分模块402，被配置为将测量区域内的各测量点划分至不同环境类型对应的测量子区域，环境类型用于表征测量子区域内的用户设备对无线网络性能的要求；确定模块403，被配置为根据特征数据，确定测量子区域内的测量点的特征评分，特征评分是在参数值大于或等于预设阈值的情况下，基于与参数值对应的预设评分确定得到，参数值是指当前测量点的特征数据与前一测量点的特征数据之间的变化率和/或绝对值差值，测量子区域内的测量点不包括边界点；以及提取模块404，被配置为从测量区域内的各测量点中提取目标测量点，目标测量点包括特征评分排序靠前的第一预设数量的测量点和测量子区域的边界点。
进一步地，划分模块402包括：获取子模块，被配置为获取测量区域内的站点分布信息；构建子模块，被配置为基于站点分布信息，构建每个站点对应的泰森多边形；划分子模块，被配置为根据测量子区域内的相邻站点之间的站间距，对泰森多边形进行划分，形成不同环境类型对应的测量子区域，其中，每个测量子区域内的相邻站点之间的站间距属于同一距离范围，不同的环境类型的站间距对应不同的距离范围，测量子区域包括至少一个泰森多边形；以及确定子模块，被配置为根据测量区域内的各测量点在泰森多边形中的位置分布，确定各测量点所对应的测量子区域。
进一步地，确定子模块包括：计算单元，被配置为根据特征数据，分别计算测量子区域内的各测量点对应的参数值；比较单元，被配置为将参数值和测量子区域对应的预设阈值进行比较，其中，不同的测量子区域对应的预设阈值不同；以及确定单元，被配置为在参数值大于或等于测量子区域对应的预设阈值的情况下，将与参数值对应的预设评分确定为特征评分。
进一步地，测量子区域对应的预设阈值包括与参数值对应设置的变化率阈值和/或绝对值差值阈值。
比较单元被配置为：在参数值为变化率的情况下，将参数值与变化率阈值进行比较；和/或在参数值为绝对值差值的情况下，将参数值与绝对值差值阈值进行比较。
进一步地，参数值是基于信号质量、业务质量、关键事件、行驶速度、测量轨迹的曲线斜率、测量轨迹的曲线抖动中的至少一种特征数据计算得到；同一测量子区域内的不同参数值对应的变化率阈值和绝对值差值阈值均不相同。
确定单元被配置为：在目标参数值大于或等于目标参数值对应的变化率阈值和/或绝对值差值阈值的情况下，获取与目标参数值对应的预设评分，其中，目标参数值为基于任意一种或多种特征数据计算得到的参数值；以及计算预设评分的求和结果，并将求和结果确定为特征评分。
进一步地，采用如下公式确定特征评分：
Figure PCTCN2022135030-appb-000017
其中，x i表示第i种特征数据对应的参数值，R表示变化率阈值，s grad(x i,R)表示当x i大于或等于R时，对第i种特征数据的参数值对应的特征评分进行求和计算，当x i小于R时，对第i种特征数据的参数值对应的特征评分不打分，i∈(1,m)；x j表示第j种特征数据对应的参数值，R′表示绝对值差值阈值，s abs(x j,R′)表示当x j大于或等于R′时，对第j种特征数据的参数值对应的特征评分进行求和计算，当x j小于R′时，对第j种特征数据的参数值对应的特征评分不打分，j∈(m+1,n)，m表示参数值为变化率的特征数据的种类数量，n表示所有特征数据的种类数量。
进一步地，提取模块404包括：排序子模块，被配置为将特征评分按照从高到低的顺序进行排序，得到排序结果；确定子单元，被配置为从排序结果中确定出评分靠前的第一预设数量的测量点，第一预设数量等于待提取测量点数量减去所有测量子区域的边界点的数量，待提取测量点数量是根据测量区域内的测量点总量和预设提取比例确定得到；以及提取子单元，被配置为将确定出的第一预设数量的测量点和测量子区域的边界点作为目标测量点进行提取。
需要说明的是，该无线网络测量点的提取装置400可以实现如前述任意一个方法实施例提供的无线网络测量点的提取方法的步骤，且能达到相同的技术效果，在此不再一一赘述。
如图5所示，本公开实施例还提供了一种电子设备，包括处理器511、通信接口512、存储器513和通信总线514，其中，处理器511、通信接口512、存储器513通过通信总线514完成相互间的通信，存储器513被配置为存放计算机程序。
在本公开的一些实施例中，处理器511被配置为执行存储器513上所存放的程序时，实现前述任意一个方法实施例提供的无线网络测量点的提取方法，该方法包括：获取测量区域内的各测量点的特征数据，其中，特征数据为用于表征测量点的无线网络性能的数据；将测量区域内的各测量点划分至不同环境类型对应的测量子区域，环境类型用于表征测量子区域内的用户设备对无线网络性能的要求；根据特征数据，确定测量子区域内的测量点的特征评分，特征评分是在参数值大于或等于预设阈值的情况下，基于与参数值对应的预设评分确定得到，参数值是指当前测量点的特征数据与前一测量点的特征数据之间的变化率和/或绝对值差值，测量子区域内的测量点不包括边界点；以及从测量区域内的各测量点中提取目标测量点，目标测量点包括特征评分排序靠前的第一预设数量的测量点和测量子区域的边界点。
除此之外，本公开实施例还提供了一种计算机可读存储介质，其上存储有计算机程序，计算机程序被处理器执行时实现如前述任意一个方法实施例提供的无线网络测量点的提取方法的步骤。
本公开实施例提供的上述技术方案与相关技术相比具有如下优点：本公开实施例提供的该方法，通过获取测量区域内的各测量点的特征数据，其中，特征数据为用于表征测量点的无线网络性能的数据；将测量区域内的各测量点划分至不同环境类型对应的测量子区域，环境类型用于表征测量子区域内的用户设备对无线网络性能的要求；根据特征数据，确定测量子区域内的测量点的特征评分，特征评分是在参数值大于或等于预设阈值的情况下，基于与参数值对应的预设评分确定得到，参数值是指当前测量点的特征数据与前一测量点的特征数据之间的变化率和/或绝对值差值，测量子区域内的测量点不包括边界点；从测量区域内的各测量点中提取目标测量点，目标测量点包括特征评分排序靠前的第一预设数量的测量点和测量子区域的边界点。通过这种方式，可以确定出各测量子区域内的测量点的特征评分，并对特征评分较高的测量点和测量子区域的边界点进行提取，即将特征较为明显的测量点提取出来进行后续分析，从而提高了测量点提取的准确率，使得测量区域的无线网络性能分析精度提高；同时，由于提取的目标测量点中还包含有测量子区域的分界点，因而可以较好地保留原始测量轨迹，避免压缩测量点后的轨迹无法客观反映原始测量轨迹。
需要说明的是，在本文中，诸如“第一”和“第二”等之类的关系术语仅仅用来将一个实体或者操作与另一个实体或操作区分开来，而不一定要求或者暗示这些实体或操作之间存在任何这种实际的关系或者顺序。而且，术语“包括”、“包含”或者其任何其他变体意在涵盖非排他性的包含，从而使得包括一系列要素的过程、方法、物品或者设备不仅 包括那些要素，而且还包括没有明确列出的其他要素，或者是还包括为这种过程、方法、物品或者设备所固有的要素。在没有更多限制的情况下，由语句“包括一个……”限定的要素，并不排除在包括所述要素的过程、方法、物品或者设备中还存在另外的相同要素。
以上所述仅是本公开的具体实施方式，使本领域技术人员能够理解或实现本公开。对这些实施例的多种修改对本领域的技术人员来说将是显而易见的，本文中所定义的一般原理可以在不脱离本公开的精神或范围的情况下，在其它实施例中实现。因此，本公开将不会被限制于本文所示的这些实施例，而是要符合与本文所公开的原理和新颖特点相一致的最宽的范围。

Claims (10)

  1. 一种无线网络测量点的提取方法，包括：
    获取测量区域内的各测量点的特征数据，其中，所述特征数据为用于表征测量点的无线网络性能的数据；
    将所述测量区域内的各测量点划分至不同环境类型对应的测量子区域，所述环境类型用于表征测量子区域内的用户设备对无线网络性能的要求；
    根据所述特征数据，确定所述测量子区域内的测量点的特征评分，所述特征评分是在参数值大于或等于预设阈值的情况下，基于与所述参数值对应的预设评分确定得到，所述参数值是指当前测量点的特征数据与前一测量点的特征数据之间的变化率和/或绝对值差值，所述测量子区域内的测量点不包括边界点；以及
    从所述测量区域内的各测量点中提取目标测量点，所述目标测量点包括所述特征评分排序靠前的第一预设数量的测量点和所述测量子区域的边界点。
  2. 根据权利要求1所述的方法，其中，所述将所述测量区域内的各测量点划分至不同环境类型对应的测量子区域，包括：
    获取所述测量区域内的站点分布信息；
    基于所述站点分布信息，构建每个站点对应的泰森多边形；
    根据所述测量子区域内的相邻站点之间的站间距，对所述泰森多边形进行划分，形成不同环境类型对应的测量子区域，其中，每个所述测量子区域内的相邻站点之间的站间距属于同一距离范围，不同的所述环境类型的站间距对应不同的距离范围，所述测量子区域包括至少一个所述泰森多边形；以及
    根据所述测量区域内的各测量点在所述泰森多边形中的位置分布，确定各测量点所对应的测量子区域。
  3. 根据权利要求1或2所述的方法，其中，所述根据所述特征数据，确定所述测量子区域内的测量点的特征评分，包括：
    根据所述特征数据，分别计算所述测量子区域内的各测量点对应的参数值；
    将所述参数值和所述测量子区域对应的预设阈值进行比较，其中，不同的所述测量子区域对应的预设阈值不同；以及
    在所述参数值大于或等于所述测量子区域对应的预设阈值的情况下，将与所述参数值对应的预设评分确定为所述特征评分。
  4. 根据权利要求3所述的方法，其中，所述测量子区域对应的预设阈值包括与所述参数 值对应设置的变化率阈值和/或绝对值差值阈值；
    所述将所述参数值和所述测量子区域对应的预设阈值进行比较，包括：
    在所述参数值为变化率的情况下，将所述参数值与所述变化率阈值进行比较；和/或
    在所述参数值为绝对值差值的情况下，将所述参数值与所述绝对值差值阈值进行比较。
  5. 根据权利要求4所述的方法，其中，所述参数值是基于信号质量、业务质量、关键事件、行驶速度、测量轨迹的曲线斜率、测量轨迹的曲线抖动中的至少一种特征数据计算得到；同一所述测量子区域内的不同参数值对应的变化率阈值和绝对值差值阈值均不相同；
    所述在所述参数值大于或等于所述测量子区域对应的预设阈值的情况下，将与所述参数值对应的预设评分确定为所述特征评分，包括：
    在目标参数值大于或等于所述目标参数值对应的变化率阈值和/或绝对值差值阈值的情况下，获取与所述目标参数值对应的预设评分，其中，所述目标参数值为基于任意一种或多种特征数据计算得到的参数值；以及
    计算所述预设评分的求和结果，并将所述求和结果确定为所述特征评分。
  6. 根据权利要求5所述的方法，其中，采用如下公式确定所述特征评分：
    Figure PCTCN2022135030-appb-100001
    其中，x i表示第i种特征数据对应的参数值，R表示所述变化率阈值，s grad(x i,R)表示当x i大于或等于R的情况下，对第i种特征数据的参数值对应的特征评分进行求和计算，当x i小于R的情况下，对第i种特征数据的参数值对应的特征评分不打分，i∈(1,m)；x j表示第j种特征数据对应的参数值，R′表示所述绝对值差值阈值，s abs(x j,R′)表示当x j大于或等于R′的情况下，对第j种特征数据的参数值对应的特征评分进行求和计算，当x j小于R′的情况下，对第j种特征数据的参数值对应的特征评分不打分，j∈(m+1,n)，m表示参数值为变化率的特征数据的种类数量，n表示所有特征数据的种类数量。
  7. 根据权利要求1-6中任一项所述的方法，其中，所述从所述测量区域内的各测量点中提取目标测量点，包括：
    将所述特征评分按照从高到低的顺序进行排序，得到排序结果；
    从所述排序结果中确定出评分靠前的第一预设数量的测量点，所述第一预设数量等于待提取测量点数量减去所有所述测量子区域的边界点的数量，所述待提取测量点数量是根据所述测量区域内的测量点总量和预设提取比例确定得到；以及
    将确定出的所述第一预设数量的测量点和所述测量子区域的边界点作为所述目标测量 点进行提取。
  8. 一种无线网络测量点的提取装置，包括：
    获取模块，被配置为获取测量区域内的各测量点的特征数据，其中，所述特征数据为用于表征测量点的无线网络性能的数据；
    划分模块，被配置为将所述测量区域内的各测量点划分至不同环境类型对应的测量子区域，所述环境类型用于表征测量子区域内的用户设备对无线网络性能的要求；
    确定模块，被配置为根据所述特征数据，确定所述测量子区域内的测量点的特征评分，所述特征评分是在参数值大于或等于预设阈值的情况下，基于与所述参数值对应的预设评分确定得到，所述参数值是指当前测量点的特征数据与前一测量点的特征数据之间的变化率和/或绝对值差值，所述测量子区域内的测量点不包括边界点；以及
    提取模块，被配置为从所述测量区域内的各测量点中提取目标测量点，所述目标测量点包括所述特征评分排序靠前的第一预设数量的测量点和所述测量子区域的边界点。
  9. 一种电子设备，包括处理器、通信接口、存储器和通信总线，其中，处理器、通信接口、存储器通过通信总线完成相互间的通信；
    存储器，被配置为存储计算机程序；以及
    处理器，被配置为执行存储器上所存储的程序时，实现权利要求1-7中任一项所述的无线网络测量点的提取方法。
  10. 一种计算机可读存储介质，其上存储有计算机程序，其中，所述计算机程序被处理器执行时实现如权利要求1-7中任一项所述的无线网络测量点的提取方法。
PCT/CN2022/135030 2022-04-29 2022-11-29 无线网络测量点的提取方法、装置、电子设备及存储介质 WO2023207070A1 (zh)

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