WO2022183729A1 - 用户感知评估方法、装置、终端及存储介质 - Google Patents

用户感知评估方法、装置、终端及存储介质 Download PDF

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WO2022183729A1
WO2022183729A1 PCT/CN2021/122333 CN2021122333W WO2022183729A1 WO 2022183729 A1 WO2022183729 A1 WO 2022183729A1 CN 2021122333 W CN2021122333 W CN 2021122333W WO 2022183729 A1 WO2022183729 A1 WO 2022183729A1
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user perception
user
perception
cell
frequency
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PCT/CN2021/122333
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English (en)
French (fr)
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杨伟伟
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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
    • H04W24/08—Testing, supervising or monitoring using real traffic
    • 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
    • H04W24/06—Testing, supervising or monitoring using simulated traffic

Definitions

  • the embodiments of the present application relate to, but are not limited to, the field of mobile Internet technologies, and in particular, relate to a user perception evaluation method, a cell perception evaluation method, an apparatus, a terminal, and a storage medium.
  • the embodiments of the present application provide a user perception evaluation method, a user perception evaluation device, a cell perception evaluation method, a cell perception evaluation device, a terminal, and a storage medium, which extract relevant perception parameters through user data of a mobile network and generate a data for perception evaluation.
  • User perception frequency and then comprehensively analyze the perception situation of users or regions based on the statistical method of probability distribution to generate user perception scores or regional perception scores, timely discover network problems of specific users or specific regions, and provide clear solutions for network optimization.
  • the operation and maintenance cost of the network is reduced, and the user experience of the network user is improved.
  • the embodiments of the present application provide a user perception evaluation method.
  • the method obtains the quality parameter of the mobile data service of a single user under the unit time granularity, and performs user evaluation on the quality of the mobile data service according to the quality parameter.
  • Perception level classification count the number of single-user mobile data services under at least one of the user perception levels to obtain a single user perception frequency corresponding to the user perception level; according to the user perception frequency and the user perception frequency corresponding to the user perception frequency
  • the number of users to obtain the user perception probability distribution of group users corresponding to the user perception level, wherein the group users include the single user; according to the single user perception frequency and the user perception probability distribution of the group users, Evaluate the user perception score for a single user.
  • an embodiment of the present application provides a user perception evaluation device, which is configured to execute the user perception evaluation method described in the first aspect.
  • the embodiments of the present application also provide a cell perception evaluation method.
  • the method obtains a quality parameter of a mobile data service of a single user at a unit time granularity, and evaluates the mobile data service according to the quality parameter.
  • the quality of user perception level is classified; the number of single-user mobile data services under at least one of the user perception levels is counted, and the single-user perception frequency corresponding to the user perception level is obtained; according to the single-user perception frequency and the preset cell
  • the perception threshold is to obtain the user perception ratio of the cell corresponding to the user perception level, wherein the single user belongs to the cell; according to the cell user perception ratio and the cell corresponding to the cell user perception ratio
  • the area user perception probability distribution corresponding to the user perception level is obtained, wherein the area includes at least one cell; according to the cell user perception ratio and the area user perception probability distribution, the regional user perception score is evaluated.
  • an embodiment of the present application provides a cell awareness evaluation apparatus, which is configured to execute the cell awareness evaluation method described in the third aspect.
  • embodiments of the present application further provide a terminal, at least including: a memory, a processor, and a computer program stored in the memory and running on the processor, the processor implements the above when executing the program
  • a terminal at least including: a memory, a processor, and a computer program stored in the memory and running on the processor, the processor implements the above when executing the program
  • embodiments of the present application further provide a computer-readable storage medium storing computer-executable instructions, the computer-executable instructions being used to execute the user perception evaluation method of the first aspect above, or the above The cell awareness evaluation method of the third aspect.
  • FIG. 1 is a schematic diagram of a mobile network architecture provided by an embodiment of the present application.
  • FIG. 2 is a schematic flowchart of a user perception evaluation method provided by an embodiment of the present application
  • FIG. 3 is a schematic flowchart of a user perception evaluation method provided by another embodiment of the present application.
  • FIG. 4 is a schematic diagram of classification of mobile data services provided by an embodiment of the present application.
  • 5 is a user perception probability density function curve provided by another embodiment of the present application.
  • FIG. 6 is a schematic flowchart of a cell perception evaluation method provided by an embodiment of the present application.
  • FIG. 7 is a schematic diagram of regional user perception probability distribution according to another embodiment of the present application.
  • the user's perception of the mobile network can reflect the network quality of the mobile network.
  • the fixed empirical threshold based on the quality index cannot accurately evaluate the increasingly rich types of network services and network performance, and it is difficult to effectively guide the network optimization and network performance. To achieve the purpose of further improving user perception or regional perception.
  • the embodiments of the present application provide a user perception evaluation method, a user perception evaluation device, a cell perception evaluation method, a cell perception evaluation device, a terminal, and a storage medium, which extract relevant perception parameters from user data of a mobile network and generate User perception frequency of perceptual evaluation, and then comprehensively analyze the perceptual situation of users or regions based on the statistical method of probability distribution to generate user perception scores or regional perception scores, timely discover network problems of specific users or specific regions, and provide clear information for network optimization.
  • the object is solved, the operation and maintenance cost of the network is reduced, and the user experience of the network user is improved.
  • the user perception evaluation method provided by the embodiments of the present application can be applied to different network architectures, that is, different methods are used to obtain network data of the user plane and the control plane. Therefore, in the following embodiments of the present application, the 4G network LTE is adopted
  • the network architecture is given as an example.
  • FIG. 1 is a schematic diagram of a mobile network architecture provided by an embodiment of the present application.
  • Serving Gateway SGW is mainly responsible for user plane data processing, routing and forwarding of data packets and other functions, and supports different access technologies in the Third Generation Partnership Project (3GPP). switch.
  • 3GPP Third Generation Partnership Project
  • UE User Equipment
  • EPS Evolved Packet System
  • SGW Serving Gateway
  • MME Mobility Management Entity
  • S1-U and S1-MME are the two main interfaces of the EPC network, S1-U is the interface between the evolved Node B (Evolved Node B, eNodeB) and the SGW, and S1-MME is the interface between the eNodeB and the MME.
  • eNodeB evolved Node B
  • S1-MME is the interface between the eNodeB and the MME.
  • the user terminal accesses from the base station (eNodeB) to the SGW gateway.
  • the probe Probe is connected between the base station (eNodeB) and the SGW gateway, and is used to collect the LTE data service S1-U port User plane signaling data.
  • the user terminal accesses from the base station (eNodeB) to the MME gateway.
  • the probe Probe is connected between the base station (eNodeB) and the MME gateway, and is used to collect the LTE data service S1-MME port Control plane signaling data.
  • the probes between the base station (eNodeB) and the SGW gateway and between the base station (eNodeB) and the MME gateway can be set at the same time, and the user plane signaling data and the control plane signaling data can be obtained at the same time.
  • FIG. 2 is a schematic flowchart of a user perception evaluation provided by an embodiment of the present application. As shown in FIG. 2 , the user perception evaluation provided by this embodiment at least includes:
  • Step S100 Acquire the quality parameter of the mobile data service of a single user under the unit time granularity, and classify the quality of the mobile data service according to the quality parameter by user perception level.
  • user plane signaling data and control plane signaling data can be acquired through probes disposed at different interface positions.
  • XDR External Data Representation
  • S1 - U interface unit time granularity T1 respectively screen out the web page, video, game, OTT voice, and OTT video bills, and from the S1-MME
  • the key performance parameters that can measure the network quality are screened out from the XDR detailed list of the interface unit time granularity T1 as the quality parameters of the mobile data service.
  • the quality level of the mobile data service is determined by comparing the quality parameter of the mobile data service with a preset threshold.
  • Step S200 Count the number of single-user mobile data services under at least one user perception level, and obtain a single-user perception frequency corresponding to the user perception level.
  • statistics are performed on the number of mobile data services with a poor quality level to obtain the number of services perceived by a single user as being poor in unit time granularity. It is worth noting that the number of services with poor quality is calculated separately for each service type, and the comprehensive single-user perception frequency is obtained according to the number of poor mobile data services of each service type.
  • Step S300 Obtain a user perception probability distribution of group users corresponding to the user perception level according to the user perception frequency and the number of users corresponding to the user perception frequency, wherein the group user includes a single user.
  • steps S100 and S200 are respectively performed for a plurality of single users to obtain the user perception probability distribution of the user group. It is worth noting that the user's perception is evaluated from a statistical point of view. In theory, a small number of people's perception should be very good or very poor, and most people are in the middle state, that is, the perception is good or the actual distribution pattern is accurate. There is no restriction, that is, only its probability distribution is concerned.
  • Step S400 Evaluate the user perception score of a single user according to the perception frequency of a single user and the user perception probability distribution of group users.
  • the perception frequency of the user whose user perception score is to be evaluated is mapped to a user perception probability distribution, and its user perception score is evaluated according to its position in the user perception frequency interval.
  • the user perception evaluation method of this embodiment it is possible to timely discover network problems of specific users by accurately and reasonably calculating their user perception scores, provide a clear solution object for network optimization, reduce network operation and maintenance costs, and improve network user experience. user experience.
  • FIG. 3 is a schematic flowchart of a user perception evaluation method provided by another embodiment of the present application.
  • FIG. 3 is a schematic flowchart of a user perception evaluation provided by another embodiment of the present application.
  • the user perception evaluation provided by this embodiment at least includes:
  • Step S101 Acquire the quality parameters of the mobile data service of a single user in the unit time granularity.
  • the user plane signaling data and the control plane signaling data can be acquired through probes disposed at different interface positions.
  • XDR External Data Representation
  • the S1 - U interface unit time granularity T1 respectively screen out the web page, video, game, OTT voice, and OTT video bills, and from the S1-MME
  • the key performance parameters that can measure the network quality are screened out from the XDR detailed list of the interface unit time granularity T1 as the quality parameters of the mobile data service.
  • any available quality parameter of the mobile data service can be used as the original data of the user perception evaluation method of the present application.
  • Step S102 Preset a user perception threshold corresponding to the mobile data service.
  • mobile data services include web page, video, game, OTT voice, and OTT video services, taking a certain type of service as an example, assuming that there are P KQI indicators, KQI It is a service quality parameter proposed mainly for different services, which is close to the user's experience, wherein the ith KQI is recorded as KQI i , and the threshold of poor perception corresponding to the set index KQI i is thui .
  • the mobile data service also includes key performance parameters for measuring network quality, assuming that there are Q KPI indicators, KPI is a key performance parameter for measuring network quality, wherein the ith KPI is recorded as KPI i , and the index KPI is set
  • the perception threshold of i is thci .
  • Step S103 According to the user perception threshold, classify the quality of the mobile data service by user perception level.
  • aggregation is performed through the user dimension and the cell dimension. If the value is less than the sensing threshold thui, the number of perceived poor services is recorded once, and after aggregation, the total number of such services Count ij and the number of poor perceived services Count poor ij are obtained for each user IMSI j under cell CI i .
  • IMSI International Mobile Subscriber Identification Number
  • aggregation is performed through the user dimension and the cell dimension. If the value of any KPI i in the CDR is less than the perceived poor threshold thci , it is recorded once The times of poor perception are aggregated to obtain the total times Count ij-access and the times Count poor ij-access of each user IMSI j in the cell CI i of accessing the Internet and the times of being unable to access the Internet.
  • Step S200 Count the number of single-user mobile data services under at least one user perception level, and obtain a single-user perception frequency corresponding to the user perception level.
  • the detailed list of webpage, video, game, OTT voice, and OTT video is selected, and after statistics, the total number of services and the number of poor perceptions of each category of user IMSI j under cell CI i are obtained as follows:
  • the total number of OTT video services is Count ij-ottvideo and the number of OTT video services with poor perception is Count poor ij-ottvideo .
  • the total number of times Count ij-access and the number of times of inability to access the Internet Count poor ij-access of each user IMSI j in cell CI i are obtained after statistics.
  • the comprehensive user perception frequency is obtained by synthesizing the user perception frequency of the user plane and the control plane.
  • the calculation methods of the total user perceived frequency and the comprehensive user perceived poor frequency are as follows:
  • Count ij Count ij-web +Count ij-video +Count ij-game +Count ij-ottvoice +Count ij-ottvideo +Count ij-access ;
  • Count poor ij Count poor ij-web +Count poor ij-video +Count poor ij-game +Count poor ij-ottvoice +Count poor ij-ottvideo +Count poor ij-access .
  • the comprehensive user perception poor times Count poor ij of each user IMSI j in the cell CI i calculated in step S200 are aggregated according to the user IMSI j dimension to obtain the comprehensive user perception of each user IMSI j Count poor j .
  • the embodiments involved in the above steps provide a method for calculating the user perception frequency.
  • the user perception probability distribution of the group users corresponding to the user perception level will be obtained according to the user perception frequency and the number of users corresponding to the user perception frequency. specific examples.
  • Fig. 5 shows a user perception probability density function curve similar to normal distribution, wherein the horizontal axis represents the number of times of poor comprehensive perception of a single user under the time granularity T 1 , the vertical axis represents the proportion of the number of users under the corresponding number of times, From a statistical point of view, the vertical axis represents the probability of a one-dimensional real random variable X (that is, the number of times the user comprehensively perceives poorness at the time granularity T1 ). Subsequent embodiments are described based on FIG. 5 as an example.
  • Step S301 Presetting the number of intervals of the user perception frequency corresponding to the user perception level and the probability distribution corresponding to the intervals.
  • the preset user perceived score interval is 0 to 5 points, and the comprehensive user perceived poor frequency is divided into five intervals, and the probability that the user falls in each interval is set as
  • Step S302 Obtain the quantile corresponding to each interval according to the number of intervals and the probability distribution, and obtain the user perception frequency interval according to the quantile.
  • the data ⁇ IMSI j , Count poor j > calculated in step S200 is sorted in ascending order according to the times of comprehensive user perception poor to obtain a sequence ⁇ X n ⁇ , and the calculated quantiles are p 1 , p 1 + p 2 , p 1 +p 2 +p 3 , p 1 +p 2 +p 3 +p 4 , the comprehensive perception difference times x 1 ,x 2 ,x 3 ,x 4 , corresponding to the comprehensive perception difference times interval [1 ,x 1 ),[x 1 ,x 2 ),[x 2 ,x 3 ),[x 3 ,x 4 ),[x 4 ,+ ⁇ ), users whose comprehensive user perception frequency is 0 are not considered here.
  • sequence ⁇ X n ⁇ is divided into 5 ascending sequences ⁇ X 1,a ⁇ , ⁇ X 2,b ⁇ , ⁇ X 3,c ⁇ , ⁇ X 4,d ⁇ , ⁇ X 5, e ⁇ , where 1 in the subscript of ⁇ X 1, a ⁇ represents the sequence number, a in the subscript represents the length of the sequence, and so on.
  • the user perception score interval corresponding to each sequence is set as (S1, S2], (S2, S3], (S3, S4], (S4, S5], (S5, 0], where: 5 ⁇ S1 ⁇ S2 ⁇ S3 ⁇ S4 ⁇ S5 ⁇ 0.
  • the adjacent values among the values 1, x 1 , x 2 , x 3 , and x 4 are caused.
  • the number of users in one or some intervals is 0. Therefore, after setting the interval boundary points S1, S2, S3, S4, and S5, it needs to be properly adjusted and optimized to avoid the same poor comprehensive perception value.
  • the user perception score of , jumps, and the specific implementation method is as follows:
  • Step S400 Map a single user's perceived frequency to a user's perceived frequency interval, and evaluate the user's perceived score of a single user according to the user's perceived score interval corresponding to the user's perceived frequency interval.
  • the specific calculation method of the user score in each comprehensive user perception poor interval is as follows:
  • the obtained user perception score QoE usr can objectively reflect the user's perception of network quality, and serve as the basis for operators to optimize the network and improve user satisfaction.
  • perception dimensions such as better user perception can also be obtained.
  • Statistical modeling and analysis based on a dimension with better user perception can also obtain typical distribution forms such as normal distribution or chi-square distribution, and the user perception evaluation method provided in this embodiment of the present application can still be used for perception score evaluation.
  • the user perception evaluation method of this embodiment based on the statistical method of probability distribution, ensures the stability of the number of samples in each comprehensive user perception poor interval, and solves the problem that targeted optimization is difficult due to too many users in the poor quality interval.
  • the network optimization personnel of the operator can timely discover the network quality problems of the users of the whole network, and optimize the network for the users with poor quality and the areas where the users are located, which reduces the maintenance cost of the mobile network.
  • the user experience of network users is improved.
  • video services have 5 KQI indicators, namely video playback success rate, video playback latency, video freeze frequency, video freeze duration percentage, and video download rate.
  • the comprehensive user perception index of the user IMSI in the cell ECI is obtained:
  • the calculated quantiles are p 1 , p 1 +p 2 , p 1 +p 2 +p 3 , p 1 +p 2 +p 3 +p 4
  • the frequency interval of the comprehensive user perception difference is [1, 24), [24, 31), [31, 80), [80, 161), [161, + ⁇ ).
  • the user with an IMSI of 414054027618804 has a frequency of 131 in the hourly granularity, which is located in the interval of poor comprehensive user perception [x 3 , x 4 ), and the calculated frequency of poor comprehensive user perception is 131 in the ascending sequence ⁇ X 4 ,d ⁇
  • the sorting k is calculated to be 252, and the length d of the sequence ⁇ X 4,d ⁇ is 1921, that is, the number of users whose comprehensive perception difference times are in the interval [x 3 ,x 4 ) is 1921, then the user's perception score for Indicates that the user's user perception is poor.
  • the operator found that the user's poor perception was due to weak coverage in the cell where his work place was located. At the same time, combined with regional perception analysis, it was found that the cell's perception score was also low. After closed-loop optimization, the operator can significantly improve the user perception of the cell by adjusting the azimuth of the cell.
  • the embodiments of the present application provide a user perception evaluation apparatus, which is configured to execute the user perception evaluation method provided by the embodiments of the first aspect.
  • an embodiment of the present application provides a cell perception evaluation method, which analyzes the perception situation of users in a cell to obtain the overall perception situation of the cell, that is, according to the proportion of users with poor perception in each cell and the number of cells Probability distributions are analyzed to generate perception scores for each region.
  • the cell perception evaluation method includes the following steps:
  • Step S100 Acquire the quality parameter of the mobile data service of a single user under the unit time granularity, and classify the quality of the mobile data service according to the quality parameter by user perception level.
  • user plane signaling data and control plane signaling data can be acquired through probes disposed at different interface positions.
  • XDR External Data Representation
  • S1 - U interface unit time granularity T1 respectively screen out the web page, video, game, OTT voice, and OTT video bills, and from the S1-MME
  • the key performance parameters that can measure the network quality are screened out from the XDR detailed list of the interface unit time granularity T1 as the quality parameters of the mobile data service.
  • the quality level of the mobile data service is determined by comparing the quality parameter of the mobile data service with a preset threshold.
  • Step S200 Count the number of single-user mobile data services under at least one user perception level, and obtain a single-user perception frequency corresponding to the user perception level.
  • statistics are performed on the number of mobile data services with a poor quality level to obtain the number of services perceived by a single user as being poor in unit time granularity. It is worth noting that the number of services with poor quality is calculated separately for each service type, and the comprehensive single-user perception frequency is obtained according to the number of poor mobile data services of each service type.
  • the first two steps of the cell perception evaluation method provided in this embodiment are the same as the user perception evaluation method in the above embodiment, both of which are to obtain the user perception frequency of a single user under a specific user perception level.
  • the nomenclature and symbols involved in the user perception evaluation method are used, and the expressions and meanings thereof are similar to or the same as those in the above embodiment.
  • the calculated comprehensive user perception frequency Count ij and the comprehensive user perception poor frequency Count poor ij of each user IMSI j in the cell CI i are analyzed, and the user perception in the cell is set to be poor.
  • the frequency threshold is th 1 , or, the threshold for the proportion of times that the user experiences poor user perception in the cell is set to be th 2 .
  • Step S500 According to the single user sensing frequency and the preset cell sensing threshold, obtain the cell user sensing ratio corresponding to the user sensing level, wherein the single user belongs to the cell.
  • user IMSI j is a poor user perception user in cell CI i .
  • user IMSI j in cell CI i has a ratio of Count poor ij /Count ij *100 ⁇ th 2 , then user IMSI j is a user with poor user perception in cell CI i .
  • Ratio i is obtained by dividing the number of users with poor perception in cell CI i by the total number of users.
  • Step S600 Obtain an area user perception probability distribution corresponding to the user perception level according to the cell user perception ratio and the number of cells corresponding to the cell user perception ratio, wherein the area includes at least one cell.
  • Figure 7 shows a region-aware probability density function curve similar to a normal distribution, in which the horizontal axis represents the proportion of users with poor perception of users in a single cell under the time granularity T 1 , and the vertical axis represents the proportion of cells under the corresponding value.
  • the horizontal axis represents the proportion of users with poor perception of users in a single cell under the time granularity T 1
  • the vertical axis represents the proportion of cells under the corresponding value.
  • the preset user perceived score interval is 0 to 5 points, and the comprehensive user perceived poor frequency is divided into five intervals, and the probability that the user falls in each interval is set as
  • the calculated data ⁇ CI i , z i > are sorted in ascending order according to the proportion of cells with poor user perception to obtain the sequence ⁇ Z n ⁇ , and the quantiles are calculated as p 1 , p 1 +p 2 , p 1 +p 2 + When p 3 , p 1 +p 2 +p 3 +p 4 , the proportion of users with poor perception is z 1 , z 2 , z 3 , z 4 , corresponding to the interval of proportion of users with poor perception [0,z 1 ),[ z 1 ,z 2 ),[z 2 ,z 3 ),[z 3 ,z 4 ),[z 4 ,+ ⁇ ).
  • the sequence ⁇ Z n ⁇ is divided into 5 ascending sequences ⁇ Z 1,a ⁇ , ⁇ Z 2,b ⁇ , ⁇ Z 3,c ⁇ , ⁇ Z 4,d ⁇ , ⁇ Z 5,e ⁇ according to the interval range, where ⁇ Z 1,a ⁇
  • the 1 in the subscript represents the sequence number
  • the a in the subscript represents the sequence length, and so on.
  • Set the user perception score interval corresponding to each sequence as [S1, S2), [S2, S3), [S3, S4), [S4, S5), [S5, 0), where: 5 ⁇ S1 ⁇ S2 ⁇ S3 ⁇ S4 ⁇ S5 ⁇ 0.
  • Step S700 Evaluate the perception score of cell users according to the percentage of cell user perception and the regional user perception probability distribution.
  • the specific calculation method of the user score in each comprehensive user perception poor interval is as follows:
  • Ratio i is located in the interval of poor comprehensive user perception [z 4 ,+ ⁇ ):
  • the obtained user perception score QoE ci can objectively reflect the influence of the network quality of the cell on the user perception, and the operator takes the cell with the lower score in the regional user perception as the optimization object.
  • perception dimensions such as better user perception can also be obtained.
  • Statistical modeling and analysis based on a dimension with better user perception can also obtain typical distribution forms such as normal distribution or chi-square distribution, and the user perception evaluation method provided in this embodiment of the present application can still be used for perception score evaluation.
  • the ratio of the comprehensive user perception poor times of the user IMSI j in the cell CI i is Count poor ij /Count ij ⁇ th2, then the user IMSI j is in the cell CI i .
  • the ratio of the comprehensive user perception poor times of the user IMSI j in the cell CI i is Count poor ij /Count ij ⁇ th2, then the user IMSI j is in the cell CI i .
  • the calculated quantiles are p 1 ,p 1 +p 2 ,p 1 +p 2 +p 3 ,p 1 +p 2 +p 3 +p 4
  • the proportion of users with poor user perception at the granularity of the day is 4.2, which is located in the range of users with poor regional perception [z 1 , z 2 ), and it is calculated that the proportion of users with poor regional perception is 4.2 in ascending order
  • the sorting k of the sequence ⁇ Z 2,b ⁇ is calculated to be 312, and the length b of the sequence ⁇ Z 2,b ⁇ is 537, that is, the number of cells in the interval [z 1 ,z 2 ) with the proportion of users with poor area perception is 537 , then the cell perception score of the cell is (312-1)/537*(4.5-4) ⁇ 4.21, indicating that the user perception of this cell is better.
  • the user perception situation in the cell dimension is analyzed, that is, the comprehensive perception frequency participating in the perception evaluation under the fixed time granularity of the cell and user dimensions is used.
  • Set different levels of user perception judging criteria and then count the probability distribution of the proportion of user perception users and the number of cells in each cell.
  • the probability value of each interval is set separately, and the quantile value of each scoring interval is calculated by the probability value, and then the perception score of each cell is further calculated, so as to evaluate the perception of the cell and ensure that each comprehensive
  • the stability of the number of samples in the interval with poor user perception solves the problem that it is difficult to perform targeted optimization due to the excessive number of users in some specific intervals.
  • the embodiments of the present application provide a cell awareness evaluation apparatus, which is configured to execute the cell awareness evaluation method provided by the embodiments of the third aspect.
  • embodiments of the present application provide a terminal, including a memory, a processor, and a computer program stored in the memory and running on the processor, the processor implementing the program in the first aspect when the processor executes the program.
  • a terminal including a memory, a processor, and a computer program stored in the memory and running on the processor, the processor implementing the program in the first aspect when the processor executes the program.
  • embodiments of the present application further provide a computer-readable storage medium storing computer-executable instructions, where the computer-executable instructions are used to execute the user perception evaluation method described in the first aspect; or, to execute The cell perception evaluation method described in the third aspect.
  • Such software may be distributed on computer-readable media, which may include computer storage media (or non-transitory media) and communication media (or transitory media).
  • computer storage media includes both volatile and nonvolatile implemented in any method or technology for storage of information, such as computer readable instructions, data structures, program modules or other data flexible, removable and non-removable media.
  • Computer storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disk (DVD) or other optical disk storage, magnetic cartridges, magnetic tape, magnetic disk storage or other magnetic storage devices, or may Any other medium used to store desired information and which can be accessed by a computer.
  • communication media typically embodies computer readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and can include any information delivery media, as is well known to those of ordinary skill in the art .
  • the mobile terminal equipment can be a mobile phone, a tablet computer, a notebook computer, a handheld computer, a vehicle terminal equipment, a wearable device, a super mobile personal computer, a netbook, a personal digital assistant, a CPE, a UFI (wireless hotspot device), etc.; Specific restrictions.

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Abstract

一种用户感知评估方法、用户感知评估装置、小区感知评估方法、小区感知评估装置、终端及存储介质，通过移动网络的用户数据提取相关感知参数并产生用于感知评价的用户感知频次，然后基于概率分布的统计方法对用户或区域的感知情况进行综合分析产生用户感知分数或区域感知分数。

Description

用户感知评估方法、装置、终端及存储介质
相关申请的交叉引用
本申请基于申请号为202110243747.X、申请日为2021年03月05日的中国专利申请提出，并要求该中国专利申请的优先权，该中国专利申请的全部内容在此引入本申请作为参考。
技术领域
本申请实施例涉及但不限于移动互联网技术领域，尤其涉及一种用户感知评估方法、小区感知评估方法、装置、终端及存储介质。
背景技术
随着现代通信技术的高速发展，用户对网络质量的要求越来越高，以希望获得更高的业务体验，而用户对移动互联网的感知情况直接决定用户对移动运营商的满意程度，关系到移动运营商的用户粘性和长远发展。以往无线网络运营商对用户感知和区域感知的评估手段主要是基于质量指标的固定经验阈值，采用简单的评估方法，无法准确客观地衡量用户或区域的感知情况，导致难以有效地指导网络优化以及提升用户体验。
发明内容
以下是对本文详细描述的主题的概述。本概述并非是为了限制权利要求的保护范围。
本申请实施例提供了一种用户感知评估方法、用户感知评估装置、小区感知评估方法、小区感知评估装置、终端及存储介质，通过移动网络的用户数据提取相关感知参数并产生用于感知评价的用户感知频次，然后基于概率分布的统计方法对用户或区域的感知情况进行综合分析产生用户感知分数或区域感知分数，及时发现具体用户或具体区域的网络问题，为网络优化提供明确的解决对象，降低了网络的运行维护成本，提高了网络用户的用户体验。
第一方面，本申请的实施例提供了用户感知评估方法，本方法通过获取单位时间粒度下单一用户的移动数据业务的质量参数，并根据所述质量参数对所述移动数据业务的质量进行用户感知等级分类；统计至少一个所述用户感知等级下的单一用户移动数据业务数量，获得对应于所述用户感知等级的单一用户感知频次；根据所述用户感知频次与对应于所述用户感知频次的用户数量，获得对应于所述用户感知等级的群体用户的用户感知概率分布，其中，所述群体用户包括所述单一用户；根据所述单一用户感知频次与所述群体用户的用户感知概率分布，评估单一用户的用户感知分数。
第二方面，本申请的实施例提供了一种用户感知评估装置，被设置为执行第一方面所述的用户感知评估方法。
第三方面，本申请的实施例还提供了一种小区感知评估方法，本方法通过获取单位时间粒度下单一用户的移动数据业务的质量参数，并根据所述质量参数对所述移动数据业务的质量进行用户感知等级分类；统计至少一个所述用户感知等级下的单一用户移动数据业务数量，获得对应于所述用户感知等级的单一用户感知频次；根据所述单一用户感知频次与预设的小 区感知阈值，获得对应于所述用户感知等级的小区用户感知占比，其中，所述单一用户归属于所述小区；根据所述小区用户感知占比与对应于所述小区用户感知占比的小区数量，获得对应于所述用户感知等级的区域用户感知概率分布，其中，所述区域包括至少一个小区；根据所述小区用户感知占比与所述区域用户感知概率分布，评估区域用户感知分数。
第四方面，本申请的实施例提供了一种小区感知评估装置，被设置为执行第三方面所述的小区感知评估方法。
第五方面，本申请的实施例还提供了一种终端，至少包括：存储器、处理器及存储在存储器上并可在处理器上运行的计算机程序，所述处理器执行所述程序时实现如上所述第一方面的用户感知评估方法，或如上所述第三方面的小区感知评估方法。
第六方面，本申请的实施例还提供了一种计算机可读存储介质，存储有计算机可执行指令，所述计算机可执行指令用于执行如上所述第一方面的用户感知评估方法，或如上所述第三方面的小区感知评估方法。
本申请的其它特征和优点将在随后的说明书中阐述，并且，部分地从说明书中变得显而易见，或者通过实施本申请而了解。本申请的目的和其他优点可通过在说明书、权利要求书以及附图中所特别指出的结构来实现和获得。
附图说明
图1为本申请实施例提供的移动网络架构示意图；
图2为本申请一实施例提供的用户感知评估方法的流程示意图；
图3为本申请另一实施例提供的用户感知评估方法的流程示意图；
图4为本申请一实施例提供的移动数据业务分类示意图；
图5为本申请另一实施例提供的用户感知概率密度函数曲线；
图6为本申请一实施例提供的小区感知评估方法的流程示意图；
图7为本申请另一实施例提供的区域用户感知概率分布示意图。
具体实施方式
为了使本申请的目的、技术方案及优点更加清楚明白，以下结合附图及实施例，对本申请进行进一步详细说明。应当理解，此处所描述的具体实施例仅用以解释本申请，并不用于限定本申请。
需要说明的是，虽然在装置示意图中进行了功能模块划分，在流程图中示出了逻辑顺序，但是在某些情况下，可以以不同于装置中的模块划分，或流程图中的顺序执行所示出或描述的步骤。说明书和权利要求书及上述附图中的术语“第一”、“第二”等是用于区别类似的对象，而不必用于描述特定的顺序或先后次序。
本申请实施例的描述中，除非另有明确的限定，设置、安装、连接等词语应做广义理解，所属技术领域技术人员可以结合技术方案的具体内容合理确定上述词语在本申请实施例中的具体含义。
用户对移动网络的感知情况能够反映移动网络的网络质量，采用基于质量指标的固定经验阈值不能够很好的对日渐丰富的网络业务种类以及网络性能进行精确的评估，难以有效地指导网络优化并达到进一步提升用户感知或区域感知的目的。
基于此，本申请实施例提供了一种用户感知评估方法、用户感知评估装置、小区感知评估方法、小区感知评估装置、终端及存储介质，通过移动网络的用户数据提取相关感知参数 并产生用于感知评价的用户感知频次，然后基于概率分布的统计方法对用户或区域的感知情况进行综合分析产生用户感知分数或区域感知分数，及时发现具体用户或具体区域的网络问题，为网络优化提供明确的解决对象，降低了网络的运行维护成本，提高了网络用户的用户体验。
下面结合附图，对本申请实施例作进一步阐述。
本申请实施例提供的用户感知评估方法可应用在在不同网络架构中，即采用不同的方法获取用户面与控制面的网络数据，因此，在本申请后文的实施例中，采用4G网络LTE网络架构进行举例说明。
图1为本申请实施例提供的移动网络架构示意图。如图所示，服务网关(Serving Gateway，SGW)主要负责用户面数据处理，负责数据包的路由和转发等功能，支持第三代合作伙伴计划(Third Generation Partnership Project，3GPP)中不同接入技术的切换。发生切换时作为用户面的锚点，对每一个与演进的分组系统(Evolved Packet System，EPS)相关的用户设备(User Equipment，UE)，在任何一个时间点上，都有一个服务网关(Serving Gateway，SGW)为之服务。移动管理实体(Mobility Management Entity，MME)是核心网中负责处理信令的网元，是一个信令实体，主要负责移动性管理、承载管理、用户的鉴权认证、SGW和PGW的选择等功能。S1-U和S1-MME是EPC网络的两个主要接口，S1-U是演进型Node B(Evolved Node B，eNodeB)和SGW之间的接口，S1-MME是eNodeB和MME之间的接口。
在本实施例中，用户终端从基站(eNodeB)接入，到SGW网关，在上述过程中，探针Probe接于基站(eNodeB)与SGW网关之间，用于采集LTE数据业务S1-U口用户面信令数据。在一些实施例中，用户终端从基站(eNodeB)接入，到MME网关，在上述过程中，探针Probe接于基站(eNodeB)与MME网关之间，用于采集LTE数据业务S1-MME口控制面信令数据。在另一些实施例中，可同时设置基站(eNodeB)与SGW网关之间与基站(eNodeB)与MME网关之间的探针，同时获得用户面信令数据与控制面信令数据。
第一方面，本申请实施例提供了一种用户感知评估方法。图2为本申请实施例提供的用户感知评估的流程示意图。如图2所示，本实施例提供的用户感知评估至少包括：
步骤S100：获取单位时间粒度下单一用户的移动数据业务的质量参数，并根据质量参数对所述移动数据业务的质量进行用户感知等级分类。
在一些实施例中，通过设置在不同接口位置的探针能够获取用户面信令数据以及控制面信令数据。从S1-U接口单位时间粒度T 1的外部数据表示(External Data Representation，XDR)详单中分别筛选出网页类、视频类、游戏类、OTT语音类、OTT视频类的话单，从S1-MME接口单位时间粒度T 1的XDR详单中筛选出能够衡量网络质量关键性能参数，作为移动数据业务的质量参数。
在一些实施例中，通过将上述移动数据业务的质量参数与预设的阈值作比较，判断移动数据业务的质量等级，如较好、较差等。
步骤S200：统计至少一个用户感知等级下的单一用户移动数据业务数量，获得对应于用户感知等级的单一用户感知频次。
在一些的实施例中，对质量等级为差的移动数据业务数量进行统计，获得单一用户在单位时间粒度下用户感知较差的业务数量。值得注意的是，每种业务种类都分别计算质量较差的业务数量，并依据每种业务种类的较差的移动数据业务数量获得综合单一用户感知频次。
步骤S300：根据用户感知频次与对应于用户感知频次的用户数量，获得对应于用户感知等级的群体用户的用户感知概率分布，其中，群体用户包括单一用户。
在一些实施例中，对多个由单一用户分别执行步骤S100与S200，获得用户群体的用户感知概率分布。值得注意的是，用户的感知从统计学角度评估，理论上应当是是小部分人的感知为很好或是很差，大部分人处于中间状态，即感知良好或是对实际的准确分布形态不做限制，即只关注其概率分布。
步骤S400：根据单一用户感知频次与群体用户的用户感知概率分布，评估单一用户的用 户感知分数。
在一些实施例中，将待评估用户感知分数的用户的感知频次映射至用户感知概率分布，根据其在用户感知频次区间的位置评估其用户感知分数。
根据本实施例的用户感知评估方法，能够通过精确合理计算其用户感知分数，及时发现具体用户的网络问题，为网络优化提供明确的解决对象，降低了网络的运行维护成本，提高了网络用户的用户体验。
图3为本申请另一实施例提供的用户感知评估方法的流程示意图。图3为本申请另一实施例提供的用户感知评估的流程示意图。如图3所示，本实施例提供的用户感知评估至少包括：
步骤S101：获取单位时间粒度下单一用户的移动数据业务的质量参数。
在本实施例中，通过设置在不同接口位置的探针能够获取用户面信令数据以及控制面信令数据。从S1-U接口单位时间粒度T 1的外部数据表示(External Data Representation，XDR)详单中分别筛选出网页类、视频类、游戏类、OTT语音类、OTT视频类的话单，从S1-MME接口单位时间粒度T 1的XDR详单中筛选出能够衡量网络质量关键性能参数，作为移动数据业务的质量参数。本领域技术人员可以知晓，任何能够获得的移动数据业务的质量参数均可作为本申请用户感知评估方法的原始数据。
步骤S102：预设对应于移动数据业务的用户感知阈值。
在本实施例中，参考图4，移动数据业务包括了网页类、视频类、游戏类、OTT语音类、OTT视频类的业务以其中某一类业务为例，假设有P个KQI指标，KQI是主要针对不同业务提出的贴近用户感受的业务质量参数，其中第i个KQI记为KQI i，设定指标KQI i对应的感知较差的感知阈值为thu i。
在本实施例中，移动数据业务还包括衡量网络质量的关键性能参数，假设有Q个KPI指标，KPI是衡量网络质量的关键性能参数，其中第i个KPI记为KPI i，设定指标KPI i的感知阈值为thc i。
步骤S103：根据用户感知阈值，对移动数据业务的质量进行用户感知等级分类。
在本实施例中，以固定时间粒度T 1，基于国际移动用户识别码(International Mobile Subscriber Identification Number，IMSI)，通过用户维度和小区维度进行聚集，如果该条话单中的任意一个KQI i的值小于感知阈值thu i，则记一次感知较差业务次数，聚集后得到每个用户IMSI j在小区CI i下该类业务的总次数Count ij和感知较差次数Count poor ij。
在本实施例中，以固定时间粒度T 1，同样基于IMSI，通过用户维度和小区维度进行聚集，如果该条话单中的任意一个KPI i的值小于感知较差阈值thc i，则记一次感知较差次数，聚集后得到每个用户IMSI j在小区CI i的接入上网总次数Count ij-access和无法上网次数Count poor ij-access。
步骤S200：统计至少一个用户感知等级下的单一用户移动数据业务数量，获得对应于用户感知等级的单一用户感知频次。
在本实施例中，选取网页类、视频类、游戏类、OTT语音类及OTT视频类的详单，统计后得到用户IMSI j在小区CI i下各大类的业务总次数和感知较差次数如下：
网页类业务总次数Count ij-web和网页类感知较差次数Count poor ij-web，
视频类业务总次数Count ij-video和视频类感知较差次数Count poor ij-video，
游戏类业务总次数Count ij-game和游戏类感知较差次数Count poor ij-game，
OTT语音类业务总次数Count ij-ottvoice和OTT语音类感知较差次数Count poor ij-ottvoice，
OTT视频类业务总次数Count ij-ottvideo和OTT视频类感知较差次数Count poor ij-ottvideo。
在本实施例中，统计后得到每个用户IMSI j在小区CI i的接入上网总次数Count ij-access和无法上网次数Count poor ij-access。
综合上述用户面及控制面用户感知频次，获得综合的用户感知频次。
在一些实施例中，综合用户总感知频次与综合用户感知较差频次的计算方法如下：
Count ij＝Count ij-web+Count ij-video+Count ij-game+Count ij-ottvoice+Count ij-ottvideo+Count ij-access；
Count poor ij＝Count poor ij-web+Count poor ij-video+Count poor ij-game+Count poor ij-ottvoice+Count poor  ij-ottvideo+Count poor ij-access。
在时间粒度T 1下，步骤S200计算所得的每个用户IMSI j在小区CI i的综合用户感知较差次数Count poor ij，按照用户IMSI j维度进行聚集，得到每个用户IMSI j的综合用户感知较差次数Count poor j。
上述步骤涉及的实施例提供了计算用户感知频次的方法，在后续步骤中，将提供根据用户感知频次与对应于用户感知频次的用户数量，获得对应于用户感知等级的群体用户的用户感知概率分布的具体实施例。
图5示出了一种类似于正态分布的用户感知概率密度函数曲线，其中，横轴代表时间粒度T 1下单一用户的综合感知较差次数，纵轴表示对应次数下用户数占比，从统计学角度看，纵轴表示一维实随机变量X(即用户在该时间粒度T1下综合感知较差次数)的概率，后续实施例依据图5为例进行说明。
步骤S301：预设对应于用户感知等级的用户感知频次的区间数量与区间对应的概率分布。
在本实施例中，预设用户感知分数区间为0到5分，同时将综合用户感知较差频次划分为五个区间，设定用户落在每个区间的概率为
Figure PCTCN2021122333-appb-000001
步骤S302：根据区间数量与概率分布获得每个区间对应的分位数，并根据分位数获得用户感知频次区间。
在本实施例中，对于步骤S200计算所得的数据<IMSI j,Count poor j>按照综合用户感知较差次数进行升序排序得到序列{X n}，分别计算分位数为p 1,p 1+p 2,p 1+p 2+p 3,p 1+p 2+p 3+p 4时的综合感知差次x 1,x 2,x 3,x 4，对应于综合感知差次数区间[1,x 1),[x 1,x 2),[x 2,x 3),[x 3,x 4),[x 4,+∞)，此处不考虑综合用户感知频次为0的用户。进一步地，将序列{X n}按照区间范围划分为5个升序序列{X 1,a},{X 2,b},{X 3,c},{X 4,d},{X 5,e}，其中{X 1,a}下标中1代表序列编号，下标中a代表序列长度，其他以此类推。设定每个序列对应的用户感知评分区间为(S1,S2],(S2,S3],(S3,S4],(S4,S5],(S5,0]，其中：5≥S1≥S2≥S3≥S4≥S5≥0。
在一些实施例中，考虑到存在某个综合用户感知较差数值对应的用户数过多的可能性，导致1,x 1,x 2,x 3,x 4这几个值中相邻的值存在相等的情况，即某个或某些区间的用户数为0，因此，对区间分界点S1,S2,S3,S4,S5设定后需要适当调整及优化，以避免相同综合感知较差数值的用户感知分数出现跳变，具体实施方法如下：
如果序列{X 1,a}的长度a>0，则S1,S2按照S2<S1设定即可；如果序列{X 1,a}的长度a＝0，则S2设定时需要调整为S2＝S1；
如果序列{X 2,b}的长度b>0，则S3按照S3<S2设定即可；如果序列{X 2,b}的长度b＝0，则S3设定时需要调整为S3＝S2；
如果序列{X 3,c}的长度c>0，则S4按照S4<S3设定即可；如果序列{X 3,c}的长度c＝0，则S4设定时需要调整为S4＝S3；
如果序列{X 4,d}的长度d>0，则S5按照S5<S4设定即可；如果序列{X 4,d}的长度d＝0，则S5设定时需要调整为S5＝S4；
如果序列{X 5,e}的长度e>0，则S5>0。
步骤S400：映射单一用户感知频次至用户感知频次区间，并根据用户感知频次区间对应的用户感知分数区间，评估单一用户的用户感知分数。
在本实施例中，各综合用户感知较差区间对用户得分进行具体计算的方法如下：
如果用户IMSI j的综合用户感知较差次数Count poor j位于综合用户感知较差区间[1,x 1)：
计算获得Count poor j在升序序列{X 1,a}的排序计算为k(k∈[1,N],N≤a)，则该用户的感知分数为
Figure PCTCN2021122333-appb-000002
如果用户IMSI j的综合用户感知较差次数Count poor j位于综合用户感知较差区间[x 1,x 2)：
计算获得Count poor j在升序序列{X 2,b}的排序计算为k(k∈[1,N],N≤b)，则该用户的感知分数为
Figure PCTCN2021122333-appb-000003
如果用户IMSI j的综合用户感知较差次数Count poor j位于综合用户感知较差区间[x 2,x 3)：
计算获得Count poor j在升序序列{X 3,c}的排序计算为k(k∈[1,N],N≤c)，该用户的感知得分为
Figure PCTCN2021122333-appb-000004
如果用户IMSI j的综合用户感知较差次数Count poor j位于综合用户感知较差区间[x3,x4)：
Count poor j在升序序列{X 4,d}的排序计算为k(k∈[1,N],N≤d)，该用户的感知得分为
Figure PCTCN2021122333-appb-000005
如果用户IMSI j的综合用户感知较差次数Count poor j位于综合用户感知较差区间[x 4,+∞)：
Count poor j在升序序列{X 5,e}的排序计算为k(k∈[1,N],N≤e)，该用户的感知得分为
Figure PCTCN2021122333-appb-000006
获得的用户感知得分QoE usr能够客观地反映用户对于网络质量的感知情况，并作为运营商进行网络优化和提升用户满意度的依据。
本领域技术人员可知，使用不同的阈值能够筛选出不同等级的用户感知等级，即也可以获得用户感知较好等感知维度。基于用户感知较好的维度进行统计学建模与分析，同样能够得到类似于正态分布或卡方分布等典型分布形式，仍可以采用本申请实施例提供的用户感知评估方法进行感知分数评估。
本实施例的用户感知评估方法，基于概率分布的统计方法，保证各综合用户感知较差区间样本数量的稳定，解决了质量较差区间内的用户数过多导致难以进行针对性优化的问题。通过综合分析产生的用户感知分数，让运营商的网优人员能够及时发现全网用户的网络质量问题，针对质量较差的用户及用户所在的区域，进行网络优化，降低了移动网络的维护成本提高了网络用户的用户体验。
在另一实施例中，提供了一种具体的用户感知评估方法的应用场景。
在本实施例中，视频类业务有5个KQI指标，即视频播放成功率、视频播放等待时延、视频卡顿频次、视频卡顿时长占比和视频下载速率，某小时粒度下用户IMSI＝414054027618804在小区ECI＝441405030765857的总视频话单数为320，其中任意一个KQI差于阈值的话单数为45。则用户IMSI在小区ECI下的视频类业务总次数和视频类感知较差次数为：
视频类业务总次数Count ij-video＝320和视频类感知较差次数Count poor ij-video＝45。
以此类推，计算得到其他几类业务的总次数和感知较差次数为：
网页类业务总次数Count ij-web＝238和网页类感知较差次数Count poor ij-web＝20，
游戏类业务总次数Count ij-game＝180和游戏类感知较差次数Count poor ij-game＝10，
OTT语音类业务总次数Count ij-ottvoice＝98和OTT语音类感知较差次数Count poor ij-ottvoice＝11，
OTT视频类业务总次数Count ij-ottvideo＝102和OTT视频类感知较差次数Count poor ij-ottvideo＝17；
接入上网总次数Count ij-access＝167和无法上网次数Count poor ij-access＝16。
根据上述数据得到用户IMSI在小区ECI的综合用户感知指标：
综合用户感知频次：
Count ij＝Count ij-web+Count ij-video+Count ij-game+Count ij-ottvoice+Count ij-ottvideo+Count ij-access＝238+320+180+98+102+167＝1105
综合用户感知较差频次：
Count poor ij＝Count poor ij-web+Count poor ij-video+Count poor ij-game+Count poor ij-ottvoice+Count poor  ij-ottvideo+Count poor ij-access＝20+45+10+11+17+16＝119
将综合用户感知较差频次划分为五个区间，设定用户落在每个区间的概率p i为p 1＝7.5％,p 2＝12.5％,p 3＝60％,p 4＝12.5％,p 5＝7.5％，评分区间分界值S1＝5，S2＝4.5，S3＝4，S4＝3，S5＝2，即对应的用户感知评分区间为(5,4.5],(4.5,4],(4,3],(3,2],(2,0]。
根据综合用户感知较差频次和用户数的概率分布，计算得到分位数为p 1,p 1+p 2,p 1+p 2+p 3,p 1+p 2+p 3+p 4时的综合感知差次x 1＝24,x 2＝31,x 3＝80,x 4＝161，综合用户感知较差频次区间[1,24),[24,31),[31,80),[80,161),[161,+∞)。
IMSI为414054027618804的用户在该小时粒度的综合用户感知较差频次为131，位于综合用户感知较差区间[x 3,x 4)，并且计算得到综合用户感知较差频次131在升序序列{X 4,d}的排序k经过计算为252，序列{X 4,d}的长度d为1921，即综合感知差次数在区间[x 3,x 4)的用户数为1921，则该用户的感知分数为
Figure PCTCN2021122333-appb-000007
表明该用户的用户感知较差。
依据上述得分，运营商经过进一步定界定位分析，发现该用户感知较差是因为其工作地点所在小区存在弱覆盖现象，同时结合区域感知分析发现该小区的感知得分也同样较低。经过闭环优化，运营商通过调整该小区的方位角，使该小区的用户感知情况明显提升。
第二方面，本申请的实施例提供了一种用户感知评估装置，被设置为执行第一方面的实施例提供的用户感知评估方法。
第三方面，本申请实施例提供了一种小区感知评估方法，对小区下的用户感知情况进行分析，获得小区的总体感知情况，即根据各小区下用户感知较差用户占比和小区数的概率分布进行分析产生各区域感知得分。如图6所示，本实施例提供的小区感知评估方法包括以下步骤：
步骤S100：获取单位时间粒度下单一用户的移动数据业务的质量参数，并根据质量参数对所述移动数据业务的质量进行用户感知等级分类。
在一些实施例中，通过设置在不同接口位置的探针能够获取用户面信令数据以及控制面信令数据。从S1-U接口单位时间粒度T 1的外部数据表示(External Data Representation，XDR)详单中分别筛选出网页类、视频类、游戏类、OTT语音类、OTT视频类的话单，从S1-MME接口单位时间粒度T 1的XDR详单中筛选出能够衡量网络质量关键性能参数，作为移动数据业务的质量参数。
在一些实施例中，通过将上述移动数据业务的质量参数与预设的阈值作比较，判断移动数据业务的质量等级，如较优、较差等。
步骤S200：统计至少一个用户感知等级下的单一用户移动数据业务数量，获得对应于用户感知等级的单一用户感知频次。
在一些的实施例中，对质量等级为差的移动数据业务数量进行统计，获得单一用户在单位时间粒度下用户感知较差的业务数量。值得注意的是，每种业务种类都分别计算质量较差的业务数量，并依据每种业务种类的较差的移动数据业务数量获得综合单一用户感知频次。
本实施例提供的小区感知评估方法的前两个步骤与上文实施例中的用户感知评估方法相同，均是计算获得特定用户感知等级下对单一用户的用户感知频次。
因此，在本实施例中，沿用用户感知评估方法中涉及的命名及符号，其表达含义与上文实施例相似或相同。在时间粒度T 1下，计算所得的每个用户IMSI j在小区CI i的综合用户感知频次Count ij和综合用户感知较差次数Count poor ij进行分析，设定用户在小区下的用户感知较差频次阈值为th 1，或者，设定用户在小区下的用户感知较差次数占比阈值为th 2。
步骤S500：根据单一用户感知频次与预设的小区感知阈值，获得对应于用户感知等级的小区用户感知占比，其中，单一用户归属于小区。
在一些实施例中，如果用户IMSI j在小区CI i的综合用户感知较差次数Count poor ij<th 1，则用户IMSI j在小区CI i下为用户感知较差用户。
在一些实施例中，如果用户IMSI j在小区CI i的综合用户感知较差次数占比Count poor  ij/Count ij*100<th 2，则用户IMSI j在小区CI i下为用户感知较差用户。
上述两个实施例仅提供了两种用户感知较差用户的评判方法，但是本领域技术人员知晓，任何一种能够筛选判断用户感知较差用户的方法均可以应用在本申请实施例提供的小区感知评估方法中。
在本实施例中，获得用户感知较差用户数量后，计算小区CI i的感知较差用户占比Ratio i，Ratio i为小区CI i下的用户感知较差用户数除以总用户数得到。
步骤S600：根据小区用户感知占比与对应于小区用户感知占比的小区数量，获得对应于用户感知等级的区域用户感知概率分布，其中，区域包括至少一个小区。
图7示出了一种类似于正态分布的区域感知概率密度函数曲线，其中，横轴代表时间粒度T 1下单小区的用户感知较差用户占比，纵轴表示对应数值下小区的占比，后续实施例依据图7为例进行说明。
在本实施例中，预设用户感知分数区间为0到5分，同时将综合用户感知较差频次划分为五个区间，设定用户落在每个区间的概率为
Figure PCTCN2021122333-appb-000008
计算所得的数据<CI i,z i>按照用户感知较差小区占比进行升序排序得到序列{Z n}，分别计算分位数为p 1,p 1+p 2,p 1+p 2+p 3,p 1+p 2+p 3+p 4时的感知较差用户占比z 1,z 2,z 3,z 4，对应于感知差用户占比区间[0,z 1),[z 1,z 2),[z 2,z 3),[z 3,z 4),[z 4,+∞)。
序列{Z n}按照区间范围划分为5个升序序列{Z 1,a},{Z 2,b},{Z 3,c},{Z 4,d},{Z 5,e}，其中{Z 1,a}下标中1代表序列编号，下标中a代表序列长度，其他以此类推。设定每个序列对应的用户感知评分区间为[S1,S2),[S2,S3),[S3,S4),[S4,S5),[S5,0)，其中：5≥S1≥S2≥S3≥S4≥S5≥0。
在一些实施例中，考虑到存在某个综合用户感知较差数值对应的用户数过多的可能性，导致1,z 1,z 2,z 3,z 4，这几个值中相邻的值存在相等的情况，即某个或某些区间的小区数为0，因此，对区间分界点S1,S2,S3,S4,S5设定后需要适当调整及优化，以避免相同用户感知较差用户占比数值的小区用户感知得分出现跳变，具体实施方法如下：
如果序列{Z 1,a}的长度a>0，则S1,S2按照S2<S1设定即可；如果序列{Z 1,a}的长度a＝0，则S2设定时需要调整为S2＝S1；
如果序列{Z 2,b}的长度b>0，则S3按照S3<S2设定即可；如果序列{Z 2,b}的长度b＝0，则S3设定时需要调整为S3＝S2；
如果序列{Z 3,c}的长度c>0，则S4按照S4<S3设定即可；如果序列{Z 3,c}的长度c＝0，则S4设定时需要调整为S4＝S3；
如果序列{Z 4,d}的长度d>0，则S5按照S5<S4设定即可；如果序列{Z 4,d}的长度d＝0，则S5设定时需要调整为S5＝S4；
如果序列{Z 5,e}的长度e>0，则S5>0。
步骤S700：根据小区用户感知占比与区域用户感知概率分布，评估小区用户感知分数。
在本实施例中，各综合用户感知较差区间对用户得分进行具体计算的方法如下：
如果小区CI i的综合用户感知较差占比Ratio i位于综合用户感知较差区间[0,z 1)：
计算获得Ratio i在升序序列{Z 1,a}的排序计算为k(k∈[1,N],N≤a)，则该小区的感知分数为
Figure PCTCN2021122333-appb-000009
如果小区CI i的综合用户感知较差占比Ratio i位于综合用户感知较差区间[z 1,z 2)：
计算获得Ratio i在升序序列{Z 2,b}的排序计算为k(k∈[1,N],N≤b)，则该小区的感知分数为
Figure PCTCN2021122333-appb-000010
如果小区CI i的综合用户感知较差占比Ratio i位于综合用户感知较差区间[z 2,z 3)：
计算获得Ratio i在升序序列{Z 3,c}的排序计算为k(k∈[1,N],N≤c)，则该小区的感知分数为
Figure PCTCN2021122333-appb-000011
如果小区CI i的综合用户感知较差占比Ratio i位于综合用户感知较差区间[z 3,z 4)：
计算获得Ratio i在升序序列{Z 4,d}的排序计算为k(k∈[1,N],N≤d)，则该小区的感知分数为
Figure PCTCN2021122333-appb-000012
如果小区CI i的综合用户感知较差占比Ratio i位于综合用户感知较差区间[z 4,+∞)：
计算获得Ratio i在升序序列{Z 5,e}的排序计算为k(k∈[1,N],N≤e)，则该小区的感知分数为
Figure PCTCN2021122333-appb-000013
获得的用户感知得分QoE ci能够客观地反映小区的网络质量对用户感知的影响，运营商将区域用户感知中分数较低的小区作为优化对象。
本领域技术人员可知，使用不同的阈值能够筛选出不同等级的用户感知等级，即也可以获得用户感知较好等感知维度。基于用户感知较好的维度进行统计学建模与分析，同样能够得到类似于正态分布或卡方分布等典型分布形式，仍可以采用本申请实施例提供的用户感知评估方法进行感知分数评估。
在另一实施例中，提供了一种具体的小区感知评估方法的应用场景。
在本实施例中，以天粒度作为时间粒度为例，定义用户IMSI j在小区CI i的综合用户感知较差次数占比Count poor ij/Count ij<th2，则用户IMSI j在小区CI i下为用户感知较差用户。
将全网小区的用户感知较差用户占比划分为五个区间，设定用户落在每个区间的概率pi为p 1＝6.5％,p 2＝10.5％,p 3＝54.0％,p 4＝18.5％,p 5＝10.5％，评分区间分界值S1＝5，S2＝4.5，S3＝4，S4＝3，S5＝2，即对应的小区感知评分区间为(5,4.5],(4.5,4],(4,3],(3,2],(2,0]。
根据小区感知较差用户占比和小区数的概率分布，计算得到分位数为p 1,p 1+p 2,p 1+p 2+p 3,p 1+p 2+p 3+p 4时的区域感知较差用户占比z 1＝2.8,z 2＝5.3,z 3＝10.7,z 4＝25.6，以及综合区域感知较差频次区间[0,2.8),[2.8,5.3),[5.3,10.7),[10.7,25.6),[25.6,100]。
ECI为441405030765857的小区在该天粒度的用户感知较差用户占比为4.2，位于区域感知较差用户占比区间[z 1,z 2)，并且计算得到区域感知较差用户占比4.2在升序序列{Z 2,b}的排序k经过计算为312，序列{Z 2,b}的长度b为537，即区域感知较差用户占比在区间[z 1,z 2)的小区数为537，则该小区的小区感知得分为
Figure PCTCN2021122333-appb-000014
(312-1)/537*(4.5-4)≈4.21，表明该小区的用户感知较好。
本实施例的小区感知评估方法，基于前述用户感知评估算法计算过程的中间数据，对小区维度下的用户感知情况进行分析，即根据小区和用户维度固定时间粒度下参与感知评估的综合感知频次，设定不同等级的用户感知评判标准，然后统计各小区下用户感知用户占比和小区数的概率分布。基于该概率分布，分别设定每个区间的概率值，通过概率值再计算各评分区间的分位点值，然后再进一步计算各小区的感知分数，从而评估该小区的感知情况，保证各综合用户感知较差区间样本数量的稳定，解决了某些特定区间内的用户数过多导致难以进行针对性优化的问题。
第四方面，本申请的实施例提供了一种小区感知评估装置，被设置为执行第三方面的实施例提供的小区感知评估方法。
第五方面，本申请的实施例提供了一种终端，包括存储器、处理器及存储在存储器上并可在处理器上运行的计算机程序，所述处理器执行所述程序时实现第一方面所述的用户感知评估方法；或，第三方面所述的小区感知评估方法。
第六方面，本申请的实施例还提供了一种计算机可读存储介质，存储有计算机可执行指令，所述计算机可执行指令用于执行第一方面所述的用户感知评估方法；或，执行第三方面 所述的小区感知评估方法。
本申请实施例通过分析探针采集的移动网络数据业务用户面信令和控制面信令数据，提取相关感知参数并产生用于感知评价的用户感知频次，然后基于概率分布的统计方法对用户或区域的感知情况进行综合分析产生用户感知分数或区域感知分数，从而发现具体用户或具体区域的感知问题，并针对性地进行优化，不仅可以提升网络问题分析效率，降低运行维护成本，还能提升网络用户的用户体验。
以上所描述的装置实施例仅仅是示意性的，其中作为分离部件说明的单元可以是或者也可以不是物理上分开的，即可以位于一个地方，或者也可以分布到多个网络单元上。可以根据实际的需要选择其中的部分或者全部模块来实现本实施例方案的目的。
本领域普通技术人员可以理解，上文中所公开方法中的全部或某些步骤、系统、装置中的功能模块/单元可以被实施为软件、固件、硬件及其适当的组合。在硬件实施方式中，在以上描述中提及的功能模块/单元之间的划分不一定对应于物理组件的划分；例如，一个物理组件可以具有多个功能，或者一个功能或步骤可以由若干物理组件合作执行。某些物理组件或所有物理组件可以被实施为由处理器，如中央处理器、数字信号处理器或微处理器执行的软件，或者被实施为硬件，或者被实施为集成电路，如专用集成电路。这样的软件可以分布在计算机可读介质上，计算机可读介质可以包括计算机存储介质(或非暂时性介质)和通信介质(或暂时性介质)。如本领域普通技术人员公知的，术语计算机存储介质包括在用于存储信息(诸如计算机可读指令、数据结构、程序模块或其他数据)的任何方法或技术中实施的易失性和非易失性、可移除和不可移除介质。计算机存储介质包括但不限于RAM、ROM、EEPROM、闪存或其他存储器技术、CD-ROM、数字多功能盘(DVD)或其他光盘存储、磁盒、磁带、磁盘存储或其他磁存储装置、或者可以用于存储期望的信息并且可以被计算机访问的任何其他的介质。此外，本领域普通技术人员公知的是，通信介质通常包含计算机可读指令、数据结构、程序模块或者诸如载波或其他传输机制之类的调制数据信号中的其他数据，并且可包括任何信息递送介质。移动终端设备可以为手机、平板电脑、笔记本电脑、掌上电脑、车载终端设备、可穿戴设备、超级移动个人计算机、上网本、个人数字助理、CPE、UFI(无线热点设备)等；本申请实施方案不作具体限定。
以上是对本申请的若干实施进行了具体说明，但本申请并不局限于上述实施方式，熟悉本领域的技术人员在不违背本申请精神的前提下还可作出种种的等同变形或替换，这些等同的变形或替换均包含在本申请权利要求所限定的范围内。

Claims (16)

  1. 一种用户感知评估方法，包括：
    获取单位时间粒度下单一用户的移动数据业务的质量参数，并根据所述质量参数对所述移动数据业务的质量进行用户感知等级分类；
    统计至少一个所述用户感知等级下的单一用户移动数据业务数量，获得对应于所述用户感知等级的单一用户感知频次；
    根据所述用户感知频次与对应于所述用户感知频次的用户数量，获得对应于所述用户感知等级的群体用户的用户感知概率分布，其中，所述群体用户包括所述单一用户；
    根据所述单一用户感知频次与所述群体用户的用户感知概率分布，评估单一用户的用户感知分数。
  2. 根据权利要求1所述的方法，其中，所述根据所述单一用户感知频次与所述群体用户的用户感知概率分布，评估单一用户的用户感知分数包括：
    预设对应于所述用户感知等级的所述用户感知频次的区间数量与区间对应的概率分布；
    根据所述区间数量与所述概率分布获得每个区间对应的分位数，并根据所述分位数获得用户感知频次区间；
    映射所述单一用户感知频次至所述用户感知频次区间，并根据所述用户感知频次区间对应的用户感知分数区间，评估单一用户的用户感知分数。
  3. 根据权利要求2所述的方法，其中，所述根据所述用户感知频次区间对应的用户感知分数区间，评估单一用户的用户感知分数包括：
    根据所述单一用户感知频次在所述用户感知频次区间的位置与所述用户感知分数区间，评估单一用户的用户感知分数。
  4. 根据权利要求1所述的方法，其中，所述根据所述质量参数对所述移动数据业务的质量进行用户感知等级分类包括：
    预设对应于所述移动数据业务的用户感知阈值；
    根据所述用户感知阈值，对所述移动数据业务的质量进行用户感知等级分类。
  5. 根据权利要求1至4任一项所述的方法，其中，所述移动数据业务的质量参数至少用于描述以下之一：
    用户面的业务质量、或控制面的网络性能。
  6. 一种用户感知评估装置，被设置为执行权利要求1至5任一项所述的用户感知评估方法。
  7. 一种小区感知评估方法，包括：
    获取单位时间粒度下单一用户的移动数据业务的质量参数，并根据所述质量参数对所述移动数据业务的质量进行用户感知等级分类；
    统计至少一个所述用户感知等级下的单一用户移动数据业务数量，获得对应于所述用户感知等级的单一用户感知频次；
    根据所述单一用户感知频次与预设的小区感知阈值，获得对应于所述用户感知等级的小区用户感知占比，其中，所述单一用户归属于所述小区；
    根据所述小区用户感知占比与对应于所述小区用户感知占比的小区数量，获得对应于所 述用户感知等级的区域用户感知概率分布，其中，所述区域包括至少一个小区；
    根据所述小区用户感知占比与所述区域用户感知概率分布，评估小区用户感知分数。
  8. 根据权利要求7所述的方法，其中，所述根据所述小区用户感知占比与所述区域用户感知概率分布，评估小区用户感知分数包括：
    预设对应于所述用户感知等级的所述小区用户感知占比的区间数量与区间对应的概率分布；
    根据所述区间数量与所述概率分布获得每个区间对应的分位数，并根据所述分位数获得区域用户感知占比区间；
    映射所述小区用户感知占比至所述区域用户感知占比区间，并根据所述小区用户感知占比对应的区域用户感知分数区间，评估小区用户感知分数。
  9. 根据权利要求8所述的方法，其中，所述根据所述小区用户感知占比对应的区域用户感知分数区间，评估小区用户感知分数包括：
    根据所述小区用户感知占比在所述区域用户感知占比区间的位置与所述区域用户感知分数区间，评估小区用户感知分数。
  10. 根据权利要求7所述的方法，其中，所述根据所述质量参数对所述移动数据业务的质量进行用户感知等级分类包括：
    预设对应于所述移动数据业务的用户感知阈值；
    根据所述用户感知阈值，对所述移动数据业务的质量进行用户感知等级分类。
  11. 根据权利要求7所述的方法，其中，所述小区感知阈值为小区用户感知频次阈值，所述根据所述单一用户感知频次与预设的小区感知阈值，获得对应于所述用户感知等级的小区用户感知占比包括：
    根据所述单一用户感知频次与所述小区用户感知频次阈值，获得对应于所述用户感知等级的小区用户数量；
    根据所述对应于所述用户感知等级的小区用户数量与小区总用户数量，获得对应于所述用户感知等级的小区用户感知占比。
  12. 根据权利要求7所述的方法，其中，所述小区感知阈值为小区用户感知占比阈值，所述根据所述单一用户感知频次与预设的小区感知阈值，获得对应于所述用户感知等级的小区用户感知占比包括：
    根据对应于所述用户感知等级的所述单一用户感知频次与单一用户的感知总频次，获得对应于所述用户感知等级的单一用户感知频次占比；
    根据所述单一用户感知频次占比与所述小区用户感知占比阈值，获得对应于所述用户感知等级的小区用户数量；
    根据所述对应于所述用户感知等级的小区用户数量与小区总用户数量，获得对应于所述用户感知等级的小区用户感知占比。
  13. 根据权利要求7至12任一项所述的方法，其中，所述移动数据业务的质量参数至少用于描述以下之一：
    用户面的业务质量、或控制面的网络性能。
  14. 一种小区感知评估装置，被设置为执行如权利要求7至13任一项所述的小区感知评估方法。
  15. 一种终端，包括：
    存储器、处理器及存储在存储器上并可在处理器上运行的计算机程序，所述处理器执行所述程序时实现如权利要求1至5中任一项所述的用户感知评估方法；或，如权利要求7至13中任一项所述的小区感知评估方法。
  16. 一种计算机可读存储介质，存储有计算机可执行指令，所述计算机可执行指令用于执行如权利要求1至5中任一项所述的用户感知评估方法；或，如权利要求7至13中任一项所述的小区感知评估方法。
PCT/CN2021/122333 2021-03-05 2021-09-30 用户感知评估方法、装置、终端及存储介质 WO2022183729A1 (zh)

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