WO2020103733A1 - 用于实现质差根因分析的方法及网络设备 - Google Patents

用于实现质差根因分析的方法及网络设备

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Publication number
WO2020103733A1
WO2020103733A1 PCT/CN2019/117933 CN2019117933W WO2020103733A1 WO 2020103733 A1 WO2020103733 A1 WO 2020103733A1 CN 2019117933 W CN2019117933 W CN 2019117933W WO 2020103733 A1 WO2020103733 A1 WO 2020103733A1
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WO
WIPO (PCT)
Prior art keywords
quality difference
quality
records
root cause
window
Prior art date
Application number
PCT/CN2019/117933
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English (en)
French (fr)
Inventor
张�浩
薛莉
谢于明
Original Assignee
华为技术有限公司
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Publication date
Application filed by 华为技术有限公司 filed Critical 华为技术有限公司
Publication of WO2020103733A1 publication Critical patent/WO2020103733A1/zh

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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
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/50—Network service management, e.g. ensuring proper service fulfilment according to agreements
    • H04L41/5003—Managing SLA; Interaction between SLA and QoS
    • H04L41/5009—Determining service level performance parameters or violations of service level contracts, e.g. violations of agreed response time or mean time between failures [MTBF]
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L43/00—Arrangements for monitoring or testing data switching networks
    • H04L43/06—Generation of reports
    • H04L43/067—Generation of reports using time frame reporting

Definitions

  • the present application relates to the field of communication technology, and in particular, to a method and network device for implementing root cause analysis of poor quality.
  • APs poor quality of access points
  • QoE quality of experience
  • a static analysis time window is set for the poor quality access point, such as 1 day, 12 hours, etc., to identify the poor quality time period in the static analysis time window.
  • the quality difference time period is a time period when the above QoE does not satisfy the preset QoE condition.
  • the proportion of the quality difference duration of each technical indicator is counted, that is, the ratio of the time when the technical indicator does not satisfy the preset condition and the total duration of all quality difference time periods. If the proportion of the qualitative difference duration of a technical indicator is greater than the preset duration threshold, then the technical indicator does not meet the preset condition as a root cause of qualitative difference.
  • the above preset The duration threshold is set to a smaller value.
  • a smaller value is set for the above-mentioned preset duration threshold, it is easy to cause a false alarm, that is, to identify the non-qualitative root cause as the poor quality root cause.
  • the present application provides a method and network device for implementing root cause analysis of poor quality, which can dynamically determine the duration and position of the window for root cause analysis of poor quality, and improve the accuracy of root cause analysis of poor quality.
  • a method for implementing root cause analysis of quality difference including: calculating each quality difference record among N quality difference records according to a time corresponding to N quality difference records of a specified user within a specified time period The time interval between the adjacent bad quality records. Then, according to the time interval between each of the N poor quality records and the adjacent poor quality records, the N poor quality records are divided into K windows to be filtered. Among them, the time interval between any two adjacent windows to be filtered in the K windows to be filtered is greater than or equal to the time interval threshold, and any two adjacent windows in the window to be filtered that contain at least two quality difference records The time interval between poor quality records is less than the time interval threshold, 1 ⁇ K ⁇ N. After that, some or all of the K windows to be filtered are used as root cause analysis windows.
  • the time interval between the first quality difference record and the adjacent quality difference records in the above N quality difference records is: the time corresponding to the second quality difference record in the N quality difference records is the same as the first quality difference record
  • the difference records the corresponding time difference.
  • the time interval between the Nth poor quality record and the adjacent poor quality record in the above N poor quality records is: the time corresponding to the Nth poor quality record and the N-1 bad quality record in the N poor quality records
  • the time interval between the nth quality difference record and the adjacent quality difference records in the above N quality difference records is: The time corresponding to the n + 1 quality difference record in the N quality difference records is the same as the nth quality difference record.
  • the difference between the time corresponding to the difference record and the time difference between the time corresponding to the nth quality difference record and the time corresponding to the n-1 quality difference record in the N quality difference records where, 1 ⁇ n ⁇ N.
  • the method for root cause analysis of quality difference provided by this application can calculate the time between each quality difference record and the adjacent quality difference record according to the time corresponding to the N quality difference records of the specified user within the specified time period.
  • Time interval, and according to the time interval, the above N bad quality records have strong time correlation, that is, the bad quality records with the same probability of the same quality root cause are divided into the same window to be filtered, and the time correlation Poor, that is, the quality difference records with the lower probability of the same quality difference root cause are divided into different windows to be filtered, which can avoid the quality difference root caused by the window length and position of the set static analysis time window cannot be dynamically determined Due to omissions and / or false alarms, the accuracy of root cause analysis of poor quality is improved.
  • the number of quality difference records included in the root cause analysis window is greater than the threshold value of quality difference records.
  • the proportion of the number of poor quality records in the above root cause analysis window is greater than the threshold of the proportion of poor quality records.
  • the ratio of the number of quality difference records in the root cause analysis window is: the ratio of the number of quality difference records contained in the root cause analysis window to N.
  • the window duration of the foregoing root cause analysis window is greater than the window duration threshold.
  • the window duration ratio of the root cause analysis window is greater than the window duration ratio threshold.
  • the ratio of the window duration of the root cause analysis window is: the ratio of the window duration of the root cause analysis window and the window duration of the K windows to be filtered.
  • the above-mentioned N quality difference records may be quality records whose QoE does not satisfy the preset QoE conditions.
  • a network device in a second aspect, includes: a calculation module, a division module, and a determination module.
  • the calculation module is configured to calculate the time interval between each quality difference record and the adjacent quality difference records of the N quality difference records according to the time corresponding to the N quality difference records of the specified user within the specified time period.
  • the dividing module is used to divide the N quality difference records into K windows to be filtered according to the time interval between each quality difference record and the adjacent quality difference records in the N quality difference records.
  • the time interval between any two adjacent windows to be filtered in the above K windows to be filtered is greater than or equal to the time interval threshold, and any two adjacent windows to be filtered that contain at least two quality difference records
  • the time interval between the poor quality records is less than the time interval threshold, 1 ⁇ K ⁇ N.
  • the determining module is used to select some or all of the K windows to be filtered as the root cause analysis window.
  • the time interval between the first quality difference record and the adjacent quality difference records in the above N quality difference records is: the time corresponding to the second quality difference record in the N quality difference records is the same as the first The bar quality difference records the corresponding time difference.
  • the time interval between the Nth poor quality record and the adjacent poor quality record in the above N poor quality records is: the time corresponding to the Nth poor quality record and the N-1 bad quality record in the N poor quality records.
  • the difference records the corresponding time difference.
  • the time interval between the nth quality difference record and the adjacent quality difference records in the above N quality difference records is: The time corresponding to the n + 1 quality difference record in the N quality difference records is the same as the nth quality difference record.
  • the difference between the time corresponding to the difference record and the time difference between the time corresponding to the nth quality difference record and the time corresponding to the n-1 quality difference record in the N quality difference records is: where, 1 ⁇ n ⁇ N.
  • the number of quality difference records contained in the root cause analysis window is greater than the threshold value of quality difference records.
  • the proportion of the number of poor quality records in the above root cause analysis window is greater than the threshold of the proportion of poor quality records.
  • the ratio of the number of quality difference records in the root cause analysis window is: the ratio of the number of quality difference records contained in the root cause analysis window to N.
  • the window duration of the foregoing root cause analysis window is greater than the window duration threshold.
  • the window duration ratio of the root cause analysis window is greater than the window duration ratio threshold.
  • the ratio of the window duration of the root cause analysis window is: the ratio of the window duration of the root cause analysis window and the window duration of the K windows to be filtered.
  • the above-mentioned N quality difference records may be quality records whose QoE does not satisfy the preset QoE conditions.
  • a network device in a third aspect, includes: a processor, which is coupled to the memory.
  • the memory is used to store a computer program; the processor is used to execute the computer program stored in the memory, so that the network device executes the first aspect or any possible implementation manner described in the first aspect The method of root cause analysis of poor quality.
  • a communication system includes one or more terminals and one or more network devices.
  • a computer-readable storage medium that stores a program or instruction, and when the program or instruction is executed on a computer, the computer is caused to execute the first aspect or any possible implementation manner of the first aspect.
  • the method used to realize the root cause analysis of quality difference is not limited to.
  • a computer program product which includes computer program code.
  • the computer program code runs on a computer, the computer is caused to perform the functions described in the first aspect or any possible implementation manner of the first aspect. To realize the method of root cause analysis of poor quality.
  • FIG. 1 is a schematic structural diagram of a wireless communication system provided by an embodiment of this application.
  • FIG. 2A is a schematic diagram of a scene of a root cause distribution form of poor quality
  • FIG. 2B is a schematic diagram of a scene of another type of root cause distribution of poor quality
  • FIG. 2C is a schematic diagram of another scenario of the root cause distribution of poor quality
  • FIG. 3 is a schematic flowchart of a method for implementing root cause analysis of quality difference provided by an embodiment of the present application
  • 4A is a schematic diagram 1 of a method for determining a time interval threshold provided by an embodiment of the present application
  • 4B is a second schematic diagram of a method for determining a time interval threshold provided by an embodiment of this application.
  • FIG. 5 shows a schematic structural diagram 1 of a network device provided by an embodiment of the present application.
  • FIG. 6 shows a second structural diagram of a network device provided by an embodiment of the present application.
  • Wi-Fi wireless fidelity
  • LTE long-term evolution
  • 5G 5th generation
  • NR new radio
  • the subscript such as W 1 may be typo mistaken as a non-subscript form such as W 1.
  • the meaning to be expressed is the same.
  • the network architecture and business scenarios described in the embodiments of the present application are intended to more clearly explain the technical solutions of the embodiments of the present application, and do not constitute a limitation on the technical solutions provided by the embodiments of the present application. With the evolution of the architecture and the emergence of new business scenarios, the technical solutions provided in the embodiments of the present application are also applicable to similar technical problems.
  • the embodiments of the present application take the Wi-Fi system as an example for description. It should be noted that the technical solutions provided by the embodiments of the present application can also be applied to other wireless communication systems, such as an LTE system, an evolved LTE system, etc., and the corresponding names can also be replaced by names of corresponding functions in other wireless communication systems .
  • the communication system includes one or more terminals, and one or more network devices, such as network device 1 and network device 2.
  • the above terminal can be connected to an access network device such as a home wireless router through a wireless air interface, such as the network device 1 in FIG. 1 in order to receive network services.
  • the above-mentioned network device 1 is mainly used to provide an access point for the above-mentioned terminal to access an external network, and can collect various technical indicators for indicating the quality of service, such as delay, packet loss rate, signal strength, noise floor strength, number of users, etc.
  • the network device 2 is connected to the network device 1 and is used to collect the collected data or processing results of each home wireless router, and perform further analysis and processing, or complete the network optimization and upgrade according to the result of the poor quality root cause analysis reported by the network device 1.
  • the above network device 1 may be an access network device with wireless transceiver function or a chip provided in the access network device.
  • the access network equipment includes but is not limited to: access points (access points (AP) in the Wi-Fi system, such as home wireless routers, wireless relay nodes, wireless backhaul nodes, transmission points (transmission and reception points, TRP) Or transmission point (TP), evolved Node B (evolved Node B, eNB), radio network controller (RNC), node B (Node B, NB), base station controller (BSC) 1.
  • access points access points (access points (AP) in the Wi-Fi system, such as home wireless routers, wireless relay nodes, wireless backhaul nodes, transmission points (transmission and reception points, TRP) Or transmission point (TP), evolved Node B (evolved Node B, eNB), radio network controller (RNC), node B (Node B, NB), base station controller (BSC) 1.
  • access points access points (access points (AP) in the Wi-Fi system
  • Base transceiver station BTS
  • home base station for example, home evolved NodeB, or home Node B, HNB
  • baseband unit BBU
  • 5G such as NR, gNB in the system , Or, transmission point (TRP or TP), one or a group (including multiple antenna panels) of an antenna panel of a base station in a 5G system, or a network node constituting a gNB or a transmission point, such as a baseband unit (BBU) ), Or, distributed unit (DU), etc.
  • BBU baseband unit
  • DU distributed unit
  • the above network device 2 may be another network device connected to the access network device, such as the network device 1 in FIG. 1, or a chip provided in the network device.
  • the network equipment includes but is not limited to: cloud equipment, network optimization network planning equipment, network operation management equipment, etc., which may be servers, mainframe computers, etc.
  • the above terminal may be a user equipment with wireless transceiver function or a chip provided in the user equipment.
  • the above terminal may also be called a station (STA), user equipment (UE), access terminal, subscriber unit, user station, mobile station, mobile station, remote station, remote terminal, mobile device, user terminal, Wireless communication equipment, user agent or user device.
  • STA station
  • UE user equipment
  • access terminal subscriber unit, user station, mobile station, mobile station, remote station, remote terminal, mobile device, user terminal, Wireless communication equipment, user agent or user device.
  • the above terminals include but are not limited to: mobile phones, tablets, computers with wireless transceiver functions, virtual reality (VR) terminal devices, augmented reality (AR) terminal devices, industrial control (industrial control) wireless terminal, self-driving (self-driving) wireless terminal, remote medical (remote medical) wireless terminal, smart grid (smart grid) wireless terminal, transportation safety (transportation safety) Wireless terminals, wireless terminals in smart cities (smart cities), wireless terminals in smart homes (smart homes), etc.
  • VR virtual reality
  • AR augmented reality
  • industrial control industrial control
  • self-driving self-driving
  • remote medical remote medical
  • smart grid smart grid
  • transportation safety transportation safety
  • the embodiments of the present application can be applied to both time division duplex (time division duplexing, TDD) scenarios and frequency division duplex (frequency division duplexing, FDD) scenarios.
  • FIG. 1 is only a simplified schematic diagram for ease of understanding and examples.
  • the communication system may further include other network devices or other terminals, which are not shown in FIG. 1.
  • a static analysis time window is usually set, and then the cause of the quality difference in each quality difference time period in the static analysis time window is identified, such as "signal difference”, "large interference” and “strong competition”. If the ratio of the duration of a quality difference in all quality time periods to the sum of the durations of all quality time periods is greater than or equal to the preset time threshold, the quality reason is considered as the static analysis time window A root cause of poor quality during the period.
  • the poor quality time period refers to a time period when the quality of user experience (quality of experience, QoE) does not satisfy the preset QoE conditions.
  • the duration of each qualitative root cause may be shorter, so a duration threshold with a smaller value needs to be set, otherwise it is easy to cause false negative root cause .
  • FIG. 2A shows a schematic diagram of a scene of a root cause distribution form of poor quality.
  • the static analysis time window for example, there are three consecutive periods of poor quality within one day.
  • the main causes of quality difference in the above three periods of quality difference are: "signal difference”, "large interference” and “strong competition”, the durations are 40 minutes, 30 minutes, and 40 minutes, respectively, and the main causes of quality difference in the three periods
  • the first quality difference period was mainly due to "signal difference", a total of 28 minutes
  • the second quality difference period was mainly due to "interference", a total of 21 minutes
  • the third quality difference period Mainly because of "strong competition", a total of 28 minutes.
  • the main cause of quality difference is regarded as the root cause of quality difference.
  • the three main causes of poor quality in the figure will not be reported as the root cause of poor quality. Therefore, in the scenario in which multiple poor quality root causes are alternately distributed as shown in FIG. 2A, a time threshold with a small value, such as 20%, needs to be set. Otherwise, it is easy to cause false negative root causes.
  • the above duration threshold is set too small, it may also cause false alarms, that is, to report the non-quality poor root cause as the quality poor root cause.
  • FIG. 2B shows a schematic diagram of another scene of the root cause distribution of poor quality.
  • the main causes of the poor quality are "large interference”, “large interference” and “strong competition”, each accounting for 70% of the time, and the immediate lengths are 28 minutes, 21 minutes and 28 minutes respectively.
  • signal difference exists in all three quality difference periods, and each accounts for 21% of the time, that is, there is 8.4 minutes in the first quality difference period and 6.3 minutes in the second quality difference period. There is 8.4 minutes in the third period of poor quality.
  • the duration threshold is set to 20%, that is, in the scenario shown in FIG. 2B, the quality difference time of an indicator exceeding 20 minutes will be reported as the root cause of quality difference, thereby mistakenly referring to the "signal difference" as the quality root As a result of the report, a false alarm appeared.
  • the existing quality analysis root cause analysis method of the static analysis time window needs to set up an analysis window with a long duration to obtain a sufficient number and types of quality difference event samples, which makes it difficult to set one that can be applied to all types
  • the threshold of the duration of the root cause of poor quality which leads to the underreporting or false alarm of the root cause of poor quality, that is, the accuracy of the root cause analysis method of setting the static analysis time window is poor.
  • the main cause of the poor quality period is the root cause of the poor quality
  • the duration The main cause of poor quality in a short period of poor quality may be due to accidental factors such as noise and interference, and does not need to be reported. It is only necessary to report the main cause of poor quality during a long period of poor quality as the root cause of poor quality That's it.
  • the judgment is simply based on whether the duration of the quality difference period is greater than the preset quality difference threshold, and the quality difference period and duration with a longer duration cannot be considered
  • FIG. 2C shows a schematic diagram of a scene of another type of root cause distribution of poor quality.
  • a static analysis time window for example, there are 3 quality difference time periods within 1 day, and the durations are 80 minutes, 10 minutes, and 10 minutes, respectively.
  • the duration threshold is set to 20%, that is, in this scenario, the quality difference of an indicator exceeds 20 minutes and it will be reported as the root cause of the quality difference.
  • the duration of the "signal difference” exceeds the duration threshold, so it is reported as the root cause of poor quality.
  • the "signal difference” within the above two 10-minute quality-difference time periods is most likely due to noise (dispersion and short duration) and should not be considered.
  • the static analysis time window simply counts whether the duration of the "signal difference" in the specified time period exceeds the duration threshold, and does not consider the influence of accidental factors such as noise, so there is no way to identify such false alarms.
  • the quality-difference time periods in the above-mentioned specified time periods are all time periods with short durations, they still need to be considered.
  • the quality difference time is likely to be caused by other factors besides the above-mentioned noise and other accidental factors, such as the interference caused by the downlink signal periodically sent by the adjacent access point to the local access point.
  • the quality difference root cause analysis method of the existing static analysis time window cannot identify the above two quality difference distribution forms, that is, the influence of the quality difference event caused by accidental factors cannot be eliminated, but simply count and judge a certain type of quality difference. Whether the duration of the time period is greater than a preset duration threshold to determine the root cause of poor quality, resulting in poor accuracy of the root cause analysis method.
  • FIG. 3 shows a schematic flowchart of a method for implementing root cause analysis of quality difference provided by an embodiment of the present application, which may be applied to the communication system shown in FIG. 1.
  • the method may include S301-S303:
  • S301 Calculate the time interval between each quality difference record in the N quality difference records and the adjacent quality difference record according to the time corresponding to the N quality difference records of the specified user within the specified time period.
  • Step 1 Obtain L quality records according to preset conditions such as specified time period and specified user.
  • each quality record in the above L quality records may include a corresponding time and quality score.
  • quality score may be QoE or quality of service (QoS), which is not limited in this application.
  • the above-mentioned designated user may be a user account of a designated family, such as a Wi-Fi access point identifier opened by an operator for the family user, and the above designated time period may be a continuous time period such as 0.5 days, 1 day, 1 week .
  • the quality record may be collected for the home user according to a preset sampling period, such as 10 minutes, within the above specified time period.
  • the quality record can be stored in the form of ⁇ sampling time, user identification, quality score ⁇ , such as a table.
  • Table 1 shows an example of a table of the above quality records.
  • the first column in Table 1 is the sampling time, that is, the time corresponding to the quality record, the interval between two adjacent quality records is a sampling period, and the second column is the user identification (AAAA).
  • the third column is the quality score, such as QoE score.
  • a quality score of 1 indicates that the service quality is good, that is, the quality score meets the preset quality condition
  • a quality score of -1 indicates that the service quality is poor, that is, the quality score does not meet the preset quality condition
  • a quality score of 0 indicates that the service quality is average.
  • AAAA -1 1 AAAA -1 2
  • AAAA 1 3 AAAA -1 4
  • AAAA 0 5
  • AAAA 1 6 AAAA 0
  • AAAA -1 8 AAAA 1 9
  • AAAA 1 11 AAAA -1 12 AAAA -1 13 AAAA -1
  • Step 2 Determine the quality records in which the quality scores in the above L quality records do not satisfy the preset quality condition are quality difference records, and count the number of quality difference records.
  • N the number of statistically poor quality records must be less than or equal to the number of quality records. Therefore, assuming that the number of poor quality records is N, then N ⁇ L.
  • the quality records with a quality score of -1 in Table 1 are selected to generate a quality difference record table as shown in Table 2.
  • AAAA -1 1 AAAA -1 3
  • AAAA -1 7 AAAA -1 11
  • AAAA -1 12 AAAA -1 13
  • S301 can be executed to calculate the time between each quality difference record in the N quality difference records and the adjacent quality difference records according to the time corresponding to the N quality difference records of the specified user within the specified time period time interval.
  • S301 may include step 3 and step 4:
  • Step 3 calculate the time interval between each quality difference record and the adjacent quality difference record.
  • the unit of the time interval may be the above sampling period or the actual length of time, which is not limited in this application.
  • the time interval between the first poor quality record and the adjacent poor quality record in the above N poor quality records is: the time corresponding to the second poor quality record in the N poor quality records is the same as the first The quality difference records the corresponding time difference.
  • the time interval between the Nth poor quality record and the adjacent poor quality record in the above N poor quality records is: the time corresponding to the Nth poor quality record and the N-1 bad quality record in the N poor quality records
  • the time interval between the nth quality difference record and the adjacent quality difference records in the above N quality difference records is:
  • the time corresponding to the n + 1 quality difference record in the N quality difference records is the same as the nth quality difference record.
  • Step 4 according to the calculated time interval between each quality difference record and the adjacent quality difference record, a table as shown in Table 3 is generated.
  • the last column in Table 3 is the value of the time interval calculated in step 3.
  • the sampling time corresponding to the first poor quality record is 0, and the sampling time corresponding to the second poor quality record is 1, then the difference between the first poor quality record and the adjacent poor quality record
  • the time interval is the difference between 1 and 0, which is 1 sampling period.
  • the sampling time corresponding to the seventh quality difference record is 13, and the sampling time corresponding to the sixth quality difference record is 12, then the time interval between the seventh quality difference record and the adjacent quality difference record is 13 and 12.
  • the difference of 1 is 1 sampling period.
  • the sampling time corresponding to the first poor quality record is 0, the sampling time corresponding to the second poor quality record is 1, and the sampling time corresponding to the third poor quality record is 3, then the second poor quality record is adjacent to it
  • the time interval between the quality difference records is the maximum value of 3-1 and 1-0, which is 2 sampling cycles.
  • N quality difference records may be quality records where QoE does not meet the preset QoE conditions, or may be quality records where the QoS does not meet the preset QoS conditions, which is not limited in the embodiments of the present application.
  • S302 Divide the N quality difference records into K windows to be filtered according to the time interval between each quality difference record and the adjacent quality difference records in the N quality difference records.
  • S302 divides the N quality difference records into K windows to be filtered according to the time interval between each quality difference record and the adjacent quality difference records in the N quality difference records, which can be specifically implemented as steps 5 and Step 6:
  • Step 5 Determine the time interval threshold between each of the N poor quality records and the adjacent poor quality records.
  • the following method may be used to determine the time interval threshold.
  • the above-mentioned time interval threshold can be directly configured according to an empirical value. For example, as shown in Table 3, the above time interval threshold can be directly configured as the maximum time interval, that is, 4 sampling periods.
  • the above time interval threshold may also be determined according to the preset number of poor quality records.
  • the fifth quality difference record The time interval between the adjacent and poor quality records is the time interval threshold.
  • the time interval threshold there are 3 quality difference records at time interval 1, 1 quality difference record at time interval 2, 1 quality difference record at time interval 3, and time interval 4
  • the time interval 4 between the fifth qualitative record and the adjacent qualitative records is the time interval threshold.
  • the abscissa d is a time interval
  • the unit is a sampling period
  • the ordinate n is the number of quality difference records sorted from small to large.
  • the above-mentioned time interval threshold may also be determined according to a preset ratio.
  • the preset ratio is 0.8
  • the time interval between each quality difference record and the adjacent quality difference records in the above N quality difference records is counted from small to large, and the number of quality difference records is calculated and calculated
  • the time interval between the last quality difference record and the adjacent quality difference records in the statistical quality difference records is the time interval threshold.
  • the above time interval threshold is the time interval 4 between the sixth bad quality record and the adjacent bad quality record.
  • the abscissa d in FIG. 4B is a time interval, and the unit is a sampling period, and the ordinate p is the proportion of the number of recorded quality difference records from small to large.
  • Step 6 according to the time interval between each quality difference record and the adjacent quality difference records in the N quality difference records, the above time interval threshold and the window aggregation rule, divide the N quality difference records into K to be filtered window.
  • the window aggregation rule may be: the time interval between any two adjacent window to be filtered out of K window to be filtered is greater than or equal to the time interval threshold, and any one of the window to be filtered contains at least two quality difference records The time interval between any two adjacent quality-difference records in is less than the time interval threshold, 1 ⁇ K ⁇ N.
  • the calculation method of the time interval between two adjacent windows to be filtered is: the time corresponding to the first quality difference record in the latter window to be filtered, which is the same as the previous window to be filtered The last difference in quality records in the corresponding time difference.
  • the above seven poor quality records may be divided into three windows to be filtered: [0,3], [7], [11,13].
  • [0, 3] includes 3 quality difference records
  • [7] includes 1 quality difference record 7
  • [11, 13] includes 3 quality difference records 11, 12, 13.
  • the window to be filtered among the K windows to be filtered that meets the window filtering rule can be determined as the root cause analysis window, that is, the quality difference record caused by accidental factors such as instantaneous noise and instantaneous interference (usually not the root cause of poor quality) Excluded from the root cause analysis process to further improve the accuracy of poor quality root cause analysis.
  • the window filtering rule can be one of the following:
  • the number of quality difference records included in the root cause analysis window is greater than or equal to a threshold value of quality difference records.
  • the root cause analysis windows are the windows to be filtered [0,3] and [11,13], and the window to be filtered [7] is eliminated.
  • the threshold of the number of poor quality records may also be set, and then the threshold of the number of poor quality records is dynamically calculated according to the actually determined number of poor quality records. Therefore, optionally, the ratio of the number of poor quality records in the root cause analysis window is greater than or equal to the threshold of the proportion of low quality records.
  • the ratio of the number of quality difference records in the root cause analysis window is: the ratio of the number of quality difference records contained in the root cause analysis window to N.
  • the threshold of the number of poor quality records can be calculated according to the threshold of the number of poor quality records and the determined number of poor quality records, and then the root cause analysis window can be determined according to the threshold of the number of poor quality records.
  • the root cause analysis window can be determined according to the threshold of the number of poor quality records.
  • the window to be filtered among the K windows to be filtered may be greater than or equal to a preset window duration threshold as the root cause analysis window. Therefore, the window duration of the foregoing root cause analysis window is greater than or equal to the window duration threshold.
  • the calculation method of the window duration of the window to be filtered is: the time corresponding to the last quality difference record in the window to be filtered, and the time corresponding to the first quality difference record in the window to be filtered The difference in time, plus a sampling period.
  • the window duration examples of the window to be filtered described below are window durations calculated by using the method described in this paragraph.
  • the window duration threshold is 3 sampling periods
  • the window durations of the windows [0, 3] and [11, 13] to be filtered are 4 sampling periods and 3 sampling periods, respectively.
  • the windows to be filtered [0,3] and [11,13] are taken as the root cause analysis window, and the window duration of the window to be filtered [7] is 1, not used as the root cause analysis window.
  • the time corresponding to the last quality difference record in a window to be filtered and the time corresponding to the first quality difference record in the window to be filtered may also be used as the waiting time.
  • the window duration of the filtering window will not be repeated here.
  • the window duration ratio threshold may also be set, and then the actual window duration threshold is calculated according to the actually determined total duration of the window to be filtered. Therefore, optionally, the window duration ratio of the root cause analysis window is greater than the window duration ratio threshold.
  • the ratio of the window duration of the root cause analysis window is: the ratio of the window duration of the root cause analysis window to the sum of the window durations of the K windows to be filtered.
  • the window duration ratio threshold is 0.3
  • the window duration ratio corresponding to the window to be filtered [0,3] 4/8 50%
  • the window duration ratio of the window to be filtered [0,3] and [11,13] are both greater than the window duration ratio threshold 0.3, which can be used as the root cause analysis window, and the window duration ratio of the window to be filtered [7] is smaller than The window duration accounted for a threshold of 0.3, which is not used as a window for root cause analysis.
  • the window duration threshold may be calculated according to the window duration ratio threshold and the total window duration of the K windows to be filtered, and then the root cause analysis window may be determined according to the window duration threshold.
  • the window duration threshold may be calculated according to the window duration ratio threshold and the total window duration of the K windows to be filtered, and then the root cause analysis window may be determined according to the window duration threshold.
  • the content related to the above window duration threshold please refer to the content related to the above window duration threshold, which will not be repeated here.
  • quality difference record number threshold and quality difference record number threshold can be used in combination with the above-mentioned window duration threshold and window duration proportion threshold.
  • the number of quality difference records contained in the window to be filtered [0,3] and [11,13] is 3, which is greater than or equal to the threshold of the number of quality difference records 3, and the window to be filtered [0,3] and [11,13 ]
  • the method for implementing root cause analysis of quality difference provided by the embodiments of the present application may be executed by the network device 1 or the network device 2, which is not limited in this application.
  • the method for implementing the root cause analysis of quality difference provided by the embodiments of the present application may be executed by the network device 1 and report the result of the quality difference analysis to the network device 2 so that the network device 2 according to the reported quality root Optimize and upgrade the existing communication system due to the analysis results.
  • the method for implementing root cause analysis of quality difference may also be executed by the network device 2 after the network device 1 uploads the above quality difference record or quality record to the network device 2, and Optimize and upgrade the existing communication system based on the reported results of poor quality root cause analysis.
  • quality root cause analysis may be separately performed for each root cause analysis window.
  • a root cause analysis window can be divided into multiple quality-difference time periods, and the quality-difference causes of each quality-difference time period can be identified, such as "signal difference", "strong competition” and "large interference”. If the ratio of the duration of a quality difference in all quality time periods to the sum of the durations of all quality time periods is greater than or equal to the preset duration threshold, the quality cause is regarded as the root cause analysis window A root cause of poor quality during the period.
  • the method for root cause analysis of quality difference provided by this application can calculate the time between each quality difference record and the adjacent quality difference record according to the time corresponding to the N quality difference records of the specified user within the specified time period.
  • Time interval, and according to the time interval, the above N bad quality records have strong time correlation, that is, the bad quality records with the same probability of the same quality root cause are divided into the same window to be filtered, and the time correlation Poor, that is, the quality difference records with the lower probability of the same quality difference root cause are divided into different to-be-filtered windows, which can avoid the quality difference root caused by the window length and window position of the set static analysis time window cannot be dynamically adjusted Due to omissions and / or false alarms, the accuracy of root cause analysis of poor quality is improved.
  • the method for implementing root cause analysis of poor quality provided by the embodiments of the present application is described in detail above in conjunction with FIGS. 3, 4A and 4B.
  • the following describes a network device provided by an embodiment of the present application that can perform the above-described method for implementing root cause analysis of quality difference with reference to FIGS. 5 and 6.
  • FIG. 5 is a schematic structural diagram of a network device provided by an embodiment of the present application. As shown in FIG. 5, the network device 500 includes a calculation module 501, a division module 502, and a determination module 503.
  • the calculation module 501 is used to calculate the time interval between each quality difference record and the adjacent quality difference records of the N quality difference records according to the time corresponding to the N quality difference records of the specified user within the specified time period .
  • the dividing module 502 is configured to divide the N quality difference records into K windows to be filtered according to the time interval between each quality difference record and the adjacent quality difference records in the N quality difference records.
  • the time interval between any two adjacent windows to be filtered in the above K windows to be filtered is greater than or equal to the time interval threshold, and any two adjacent windows to be filtered that contain at least two quality difference records
  • the time interval between the poor quality records is less than the time interval threshold, 1 ⁇ K ⁇ N.
  • the determining module 503 is configured to use some or all of the K windows to be filtered as the root cause analysis window.
  • the time interval between the first quality difference record and the adjacent quality difference records in the above N quality difference records is: the time corresponding to the second quality difference record in the N quality difference records is the same as the first The bar quality difference records the corresponding time difference.
  • the time interval between the Nth poor quality record and the adjacent poor quality record in the above N poor quality records is: the time corresponding to the Nth poor quality record and the N-1 bad quality record in the N poor quality records
  • the time interval between the nth quality difference record and the adjacent quality difference records in the above N quality difference records is:
  • the time corresponding to the n + 1 quality difference record in the N quality difference records is the same as the nth quality difference record.
  • the number of quality difference records contained in the root cause analysis window is greater than the threshold value of quality difference records.
  • the proportion of the number of poor quality records in the above root cause analysis window is greater than the threshold of the proportion of poor quality records.
  • the ratio of the number of quality difference records in the root cause analysis window is: the ratio of the number of quality difference records contained in the root cause analysis window to N.
  • the window duration of the foregoing root cause analysis window is greater than the window duration threshold.
  • the window duration ratio of the root cause analysis window is greater than the window duration ratio threshold.
  • the ratio of the window duration of the root cause analysis window is: the ratio of the window duration of the root cause analysis window and the window duration of the K windows to be filtered.
  • the N poor quality records may be quality records whose QoE does not satisfy the preset QoE conditions.
  • the network device 500 may further include a storage module (not shown in FIG. 5) for storing instructions and data of the network device 500.
  • the network device 600 includes: a processor 601 coupled to the memory 602.
  • the memory 602 is used to store computer programs.
  • the processor 601 is configured to execute a computer program stored in the memory 602, so that the network device 600 executes the method for implementing root cause analysis of quality difference described in the foregoing method embodiment.
  • the coupling of the processor 601 and the memory 602 means that there is a signal connection between the processor 601 and the memory 602.
  • the memory 602 may be a memory inside the processor 601, such as a memory, or may be another memory provided inside the network device 500 and in signal connection with the processor 601, or may be located outside the network device 600, and
  • the network device 600 has a signal-connected memory, which is not limited in this application.
  • the processor 601 and the memory 602 are coupled, and the processor 601 and the memory 602 may be connected through a bus 603.
  • the network device 600 may further include a transceiver 604.
  • the transceiver 604 may be a transceiver circuit or a communication interface, and is used to communicate with other devices, such as the terminal in FIG. 1, or communicate with other network devices.
  • the transceiver 604 can also be an input / output interface for receiving input instructions or outputting processing results.
  • the network device 500 and the network device 600 may be the network device 1 shown in FIG. 1, may also be the network device 2 shown in FIG. 1, or may be provided on the network device 1 or the network device 2.
  • the chip in this application is not limited in this application.
  • An embodiment of the present application provides a communication system.
  • the communication system includes one or more terminals and one or more network devices.
  • An embodiment of the present application provides a computer-readable storage medium that stores a program or instruction, and when the program or instruction is executed on a computer, the computer is allowed to execute the method for implementing root cause analysis of quality difference described in the method embodiment .
  • Embodiments of the present application provide a computer program product, including computer program code.
  • the computer program code runs on a computer, the computer is caused to execute the method for implementing root cause analysis of quality difference described in the foregoing method embodiment.
  • the processor in the embodiments of the present application may be a central processing unit (central processing unit, CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), and dedicated integration Circuit (application specific integrated circuit, ASIC), ready-made programmable gate array (field programmable gate array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.
  • the general-purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
  • the memory in the embodiments of the present application may be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory.
  • the non-volatile memory can be read-only memory (read-only memory, ROM), programmable read-only memory (programmable ROM, PROM), erasable programmable read-only memory (erasable PROM, EPROM), electronically Erasable programmable read-only memory (electrically EPROM, EEPROM) or flash memory.
  • the volatile memory may be a random access memory (random access memory, RAM), which is used as an external cache.
  • random access memory random access memory
  • static random access memory static random access memory
  • DRAM dynamic random access memory
  • DRAM synchronous dynamic random access Access memory
  • SDRAM double data rate synchronous dynamic random access memory
  • double data Srate double data Srate
  • DDR SDRAM enhanced synchronous dynamic random access memory
  • ESDRAM synchronous connection dynamic random access memory Take memory (synchlink DRAM, SLDRAM) and direct memory bus random access memory (direct rambus RAM, DR RAM).
  • the above embodiments can be implemented in whole or in part by software, hardware (such as a circuit), firmware, or any other combination.
  • the above-described embodiments may be implemented in whole or in part in the form of computer program products.
  • the computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions according to the embodiments of the present application are generated in whole or in part.
  • the computer may be a general-purpose computer, a dedicated computer, a computer network, or other programmable devices.
  • the computer instructions may be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be from a website site, computer, server or data center Transmit to another website, computer, server or data center by wired (such as infrared, wireless, microwave, etc.).
  • the computer-readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more collections of available media.
  • the usable medium may be a magnetic medium (for example, a floppy disk, a hard disk, a magnetic tape), an optical medium (for example, a DVD), or a semiconductor medium.
  • the semiconductor medium may be a solid state drive.
  • At least one refers to one or more, and “multiple” refers to two or more.
  • At least one of the following or a similar expression refers to any combination of these items, including any combination of a single item or a plurality of items.
  • at least one item (a) in a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, c can be a single or multiple .
  • the disclosed system, device, and method may be implemented in other ways.
  • the device embodiments described above are only schematic.
  • the division of the units is only a division of logical functions.
  • there may be other divisions for example, multiple units or components may be combined or Can be integrated into another system, or some features can be ignored, or not implemented.
  • the displayed or discussed mutual coupling or direct coupling or communication connection may be indirect coupling or communication connection through some interfaces, devices or units, and may be in electrical, mechanical or other forms.
  • the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or may be distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
  • each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units may be integrated into one unit.
  • the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium.
  • the technical solution of the present application essentially or part of the contribution to the existing technology or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including Several instructions are used to enable a computer device (which may be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in the embodiments of the present application.
  • the aforementioned storage media include: U disk, mobile hard disk, read-only memory (ROM), random access memory (random access memory, RAM), magnetic disk or optical disk and other media that can store program codes .

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Abstract

本申请提供一种用于实现质差根因分析的方法及网络设备，能够提高通信系统的质差根因分析的准确性。该方法包括：根据指定时间段内指定用户的N条质差记录对应的时间，计算N条质差记录中每条质差记录和与其相邻的质差记录之间的时间间隔。然后，根据N条质差记录中每条质差记录和与其相邻的质差记录之间的时间间隔，将N条质差记录划分为K个待过滤窗口。其中，K个待过滤窗口中任意两个相邻的待过滤窗口之间的时间间隔大于或等于时间间隔阈值，且任意一个包含至少两条质差记录的待过滤窗口中任意两条相邻的质差记录之间的时间间隔小于时间间隔阈值，1≤K≤N。之后，将K个待过滤窗口中的部分或全部待过滤窗口作为根因分析窗口。

Description

用于实现质差根因分析的方法及网络设备 技术领域
本申请涉及通信技术领域，尤其涉及一种用于实现质差根因分析的方法及网络设备。
背景技术
为了提升用户体验，需要分析Wi-Fi系统中用户体验质量(quality of experience,QoE)评分较低的接入点(access point，AP)(下文简称质差接入点)的质差根因。具体地，为该质差接入点设置一个静态分析时间窗口，如1天、12个小时等，识别该静态分析时间窗口中的质差时间段。其中，所述质差时间段为上述QoE不满足预设QoE条件的时间段。然后，统计每项技术指标的质差时长占比，即该项技术指标不满足预设条件的时长与所有质差时间段的总时长的比值。若某项技术指标的质差时长占比大于预设时长阈值，则将该项技术指标不满足预设条件视为一个质差根因。
然而，鉴于导致QoE较差的质差根因的种类较多，且存在噪声、干扰等不确定因素的影响，如何设置静态分析时间窗口的大小和位置成为一大难题。例如，当多种质差根因多次交替发生，且每种质差根因发生的时间较短时，为避免漏报，即无法识别出部分或全部质差根因，需要为上述预设时长阈值设置一个较小值。但是，当为上述预设时长阈值设置一个较小值时，又容易导致虚警，即将非质差根因识别为质差根因。
发明内容
本申请提供一种用于实现质差根因分析的方法及网络设备，能够动态地确定质差根因分析窗口的时长和位置，提高质差根因分析的准确性。
第一方面，提供一种用于实现质差根因分析的方法，包括：根据指定时间段内指定用户的N条质差记录对应的时间，计算N条质差记录中每条质差记录和与其相邻的质差记录之间的时间间隔。然后，根据N条质差记录中每条质差记录和与其相邻的质差记录之间的时间间隔，将N条质差记录划分为K个待过滤窗口。其中，K个待过滤窗口中任意两个相邻的待过滤窗口之间的时间间隔大于或等于时间间隔阈值，且任意一个包含至少两条质差记录的待过滤窗口中任意两条相邻的质差记录之间的时间间隔小于时间间隔阈值，1≤K≤N。之后，将K个待过滤窗口中的部分或全部待过滤窗口作为根因分析窗口。
其中，上述N条质差记录中第1条质差记录和与其相邻的质差记录之间的时间间隔为：N条质差记录中第2条质差记录对应的时间与第1条质差记录对应的时间的差值。上述N条质差记录中第N条质差记录和与其相邻的质差记录之间的时间间隔为：第N条质差记录对应的时间与N条质差记录中第N-1条质差记录对应的时间的差值。上述N条质差记录中第n条质差记录和与其相邻的质差记录之间的时间间隔为：N条质差记录中第n+1条质差记录对应的时间与第n条质差记录对应的时间的差值，和第n条质差记录对应的时间与N条质差记录中第n-1条质差记录对应的时间的差值中的最大值；其中，1＜n＜N。
本申请提供的用于实现质差根因分析的方法，能够根据指定时间段内指定用户的N条质差记录对应的时间，计算每条质差记录和与其相邻的质差记录之间的时间间隔，并根据时间间隔将上述N条质差记录中时间相关性较强，即发生相同质差根因的概率较大的质差记录划分在同一个待过滤窗口中，而将时间相关性较差，即发生相同质差根因的概率较小的质差记录划分在不同的待过滤窗口中，可以避免设置的静态分析时间窗口的窗口时长和窗口位置无 法动态确定所导致的质差根因漏报和/或虚警问题，从而提高质差根因分析的准确性。
在一种可能的设计方法中，上述根因分析窗口包含的质差记录数大于质差记录数阈值。
可选地，上述根因分析窗口的质差记录数占比大于质差记录数占比阈值。其中，上述根因分析窗口的质差记录数占比为：根因分析窗口包含的质差记录数与N的比值。
在另一种可能的设计方法中，上述根因分析窗口的窗口时长大于窗口时长阈值。
可选地，上述根因分析窗口的窗口时长占比大于窗口时长占比阈值。其中，上述根因分析窗口的窗口时长占比为：根因分析窗口的窗口时长与K个待过滤窗口的窗口时长之和的比值。
可选地，上述N条质差记录可以为QoE不满足预设QoE条件的质量记录。
第二方面，提供一种网络设备。该网络设备包括：计算模块、划分模块和确定模块。其中，计算模块，用于根据指定时间段内指定用户的N条质差记录对应的时间，计算N条质差记录中每条质差记录和与其相邻的质差记录之间的时间间隔。划分模块，用于根据N条质差记录中每条质差记录和与其相邻的质差记录之间的时间间隔，将N条质差记录划分为K个待过滤窗口。其中，上述K个待过滤窗口中任意两个相邻的待过滤窗口之间的时间间隔大于或等于时间间隔阈值，且任意一个包含至少两条质差记录的待过滤窗口中任意两条相邻的质差记录之间的时间间隔小于时间间隔阈值，1≤K≤N。确定模块，用于将K个待过滤窗口中的部分或全部待过滤窗口作为根因分析窗口。
示例性地，上述N条质差记录中第1条质差记录和与其相邻的质差记录之间的时间间隔为：N条质差记录中第2条质差记录对应的时间与第1条质差记录对应的时间的差值。上述N条质差记录中第N条质差记录和与其相邻的质差记录之间的时间间隔为：第N条质差记录对应的时间与N条质差记录中第N-1条质差记录对应的时间的差值。上述N条质差记录中第n条质差记录和与其相邻的质差记录之间的时间间隔为：N条质差记录中第n+1条质差记录对应的时间与第n条质差记录对应的时间的差值，和第n条质差记录对应的时间与N条质差记录中第n-1条质差记录对应的时间的差值中的最大值；其中，1＜n＜N。
在一种可能的设计中，上述根因分析窗口包含的质差记录数大于质差记录数阈值。
可选地，上述根因分析窗口的质差记录数占比大于质差记录数占比阈值。其中，根因分析窗口的质差记录数占比为：根因分析窗口包含的质差记录数与N的比值。
在另一种可能的设计中，上述根因分析窗口的窗口时长大于窗口时长阈值。
可选地，上述根因分析窗口的窗口时长占比大于窗口时长占比阈值。其中，根因分析窗口的窗口时长占比为：根因分析窗口的窗口时长与K个待过滤窗口的窗口时长之和的比值。
可选地，上述N条质差记录可以为QoE不满足预设QoE条件的质量记录。
第三方面，还提供一种网络设备。该网络设备包括：处理器，该处理器与存储器耦合。其中，存储器，用于存储计算机程序；处理器，用于执行存储器中存储的计算机程序，使得该网络设备执行上述第一方面或第一方面中任一种可能的实现方式所述的用于实现质差根因分析的方法。
第四方面，提供一种通信系统，该系统包括一台或多台终端，以及一台或多台上述网络设备。
第五方面，提供一种计算机可读存储介质，存储有程序或指令，当该程序或指令在计算机上执行时，使得该计算机执行上述第一方面或第一方面中任一种可能实现方式所述的用于 实现质差根因分析的方法。
第六方面，提供一种计算机程序产品，包括计算机程序代码，当上述计算机程序代码在计算机上运行时，使得该计算机执行上述第一方面或第一方面中任一种可能实现方式所述的用于实现质差根因分析的方法。
附图说明
图1为本申请实施例提供的无线通信系统的架构示意图；
图2A为一种质差根因分布形态的场景示意图；
图2B为又一种质差根因分布形态的场景示意图；
图2C为另一种质差根因分布形态的场景示意图；
图3为本申请实施例提供的用于实现质差根因分析的方法的流程示意图；
图4A为本申请实施例提供的确定时间间隔阈值的方法示意图一；
图4B为本申请实施例提供的确定时间间隔阈值的方法示意图二；
图5示出了本申请实施例提供的网络设备的结构示意图一；
图6示出了本申请实施例提供的网络设备的结构示意图二。
具体实施方式
下面将结合附图，对本申请中的技术方案进行描述。
本申请实施例的技术方案可以应用于各种无线通信系统，如无线保真(wireless fidelity，Wi-Fi)系统、长期演进(long term evolution，LTE)系统，第五代(5th generation，5G)系统，如新无线(new radio，NR)系统，及未来的通信系统，如6G系统等。
本申请将围绕可包括多个设备、组件、模块等的系统来呈现各个方面、实施例或特征。应当理解和明白的是，各个系统可以包括另外的设备、组件、模块等，并且/或者可以并不包括结合附图讨论的所有设备、组件、模块等。此外，还可以使用这些方案的组合。
另外，在本申请实施例中，“示例”、“例如”用于表示作例子、例证或说明。本申请中被描述为“示例”、“例如”的任何实施例或设计方案不应被解释为比其它实施例或设计方案更优选或更具优势。确切而言，使用示例的一词旨在以具体方式呈现概念。
本申请实施例中，“的(of)”，“相应的(corresponding，relevant)”和“对应的(corresponding)”有时可以混用，应当指出的是，在不强调其区别时，其所要表达的含义是一致的。
本申请实施例中，有时候下标如W 1可能会笔误为非下标的形式如W1，在不强调其区别时，其所要表达的含义是一致的。
本申请实施例描述的网络架构以及业务场景是为了更加清楚的说明本申请实施例的技术方案，并不构成对于本申请实施例提供的技术方案的限定，本领域普通技术人员可知，随着网络架构的演变和新业务场景的出现，本申请实施例提供的技术方案对于类似的技术问题，同样适用。
本申请实施例以Wi-Fi系统为例进行说明。应当指出的是，本申请实施例提供的技术方案还可以应用于其他无线通信系统，如LTE系统、演进的LTE系统等，相应的名称也可以用其他无线通信系统中的对应功能的名称进行替代。
为便于理解本申请实施例，首先以图1中示出的通信系统为例详细说明适用于本申请实施例的无线通信系统。如图1所示，该通信系统包括一个或多个终端，以及一个或多个网络设备，如网络设备1和网络设备2。其中，上述终端可以通过无线空口连接到家用无线路由器等接入网设备，如图1中的网络设备1，以便接收网络服务。上述网络设备1主要用于为 上述终端访问外部网络提供接入点，且可以采集用于指示服务质量的各项技术指标，如时延、丢包率、信号强度、底噪强度、用户数等。上述网络设备2与网络设备1连接，用于收集各个家用无线路由器的采集数据或处理结果，并进行进一步分析处理，或者根据网络设备1上报的质差根因分析结果完成网络优化升级。
其中，上述网络设备1可以为具有无线收发功能的接入网设备或设置于该接入网设备中的芯片。该接入网设备包括但不限于：Wi-Fi系统中的接入点(access point，AP)，如家用无线路由器、无线中继节点、无线回传节点、传输点(transmission and reception point，TRP或者transmission point，TP)，演进型节点B(evolved Node B，eNB)、无线网络控制器(radio network controller，RNC)、节点B(Node B，NB)、基站控制器(base station controller，BSC)、基站收发台(base transceiver station，BTS)、家庭基站(例如，home evolved NodeB，或home Node B，HNB)、基带单元(baseband unit，BBU)，还可以为5G，如NR，系统中的gNB，或，传输点(TRP或TP)，5G系统中的基站的一个或一组(包括多个天线面板)天线面板，或者，还可以为构成gNB或传输点的网络节点，如基带单元(BBU)，或，分布式单元(distributed unit，DU)等。
上述网络设备2可以是与接入网设备，如图1中的网络设备1连接的其他网络设备或设置于该网络设备中的芯片。该网络设备包括但不限于：云端设备、网优网规设备、网络运营管理设备等，可以为服务器、大型计算机等。
上述终端可以为具有无线收发功能的用户设备或设置于该用户设备中的芯片。上述终端也可以称为站点(station，STA)、用户设备(user equipment，UE)、接入终端、用户单元、用户站、移动站、移动台、远方站、远程终端、移动设备、用户终端、无线通信设备、用户代理或用户装置。上述终端包括但不限于：手机(mobile phone)、平板电脑(Pad)、带无线收发功能的电脑、虚拟现实(virtual reality，VR)终端设备、增强现实(augmented reality，AR)终端设备、工业控制(industrial control)中的无线终端、无人驾驶(self driving)中的无线终端、远程医疗(remote medical)中的无线终端、智能电网(smart grid)中的无线终端、运输安全(transportation safety)中的无线终端、智慧城市(smart city)中的无线终端、智慧家庭(smart home)中的无线终端等。
本申请实施例既可以应用于时分双工(time division duplexing，TDD)的场景，也可以适用于频分双工(frequency division duplexing，FDD)的场景。
应理解，图1仅为便于理解而示例的简化示意图，该通信系统中还可以包括其他网络设备或者还可以包括其他终端，图1中未予以画出。
下面以图1所示的无线通信系统为例，详细介绍现有的质差根因分析方法。
具体地，通常采用设置一个静态分析时间窗口，然后识别该静态分析时间窗口中每个质差时间段的质差原因，如“信号差”、“干扰大”和“竞争强”等。倘若所有质差时间段中某一质差原因的持续时长与所有质差时间段的持续时长总和的比值大于或等于预设时长阈值，则将该某一质差原因视为该静态分析时间窗口期间的一个质差根因。其中，质差时间段是指用户体验质量(quality of experience，QoE)不满足预设QoE条件的时间段。
实际应用中，为了尽可能采集到足够数量的质差记录(样本量足够大)，通常需要设置一个持续时长较长的静态分析时间窗口。其原因在于：倘若静态分析时间窗口持续时长设置较小，很难保证该静态分析时间窗口内会出现足够数量和种类的质差事件样本，甚至可能没有发生质差事件。
一方面，在多个质差根因交替出现的场景下，每个质差根因的持续时长可能都较短，因 此需要设置一个数值较小的时长阈值，否则容易导致质差根因漏报。
图2A示出了一种质差根因分布形态的场景示意图。如图2A所示，静态分析时间窗口，如1天内存在三个连续的质差时间段。假定上述三段质差时间段的质差主因分别为：“信号差”、“干扰大”和“竞争强”，持续时长分别为40分钟,30分钟,40分钟，且三段中质差主因各占70％时间，即第一个质差时间段主因为“信号差”，共28分钟，第二个质差时间段主因为“干扰大”，共21分钟，第三个质差时间段主因为“竞争强”，共28分钟。倘若某一质差主因持续时长超过所有质差时间段时长总和的50％，方才将该质差主因视为质差根因，则在图2A所示的场景中，某一质差主因的持续时长超过50％*(40+30+40)＝50分钟才会被作为质差根因报出。显然图中三段质差主因均不会作为质差根因报出。因此，在图2A所示的多个质差根因交替分布的场景中，需要设置一个数值较小的时长阈值，如20％，否则很容易导致质差根因漏报。
但是，倘若上述时长阈值设置过小，也有可能导致虚警，即将非质差根因作为质差根因报出。
图2B示出了另一种质差根因分布形态的场景示意图。如图2B所示，假设静态分析时间窗口，如1天内存在三个连续的质差时间段，三段质差时间段时长仍分别为40分钟,30分钟,40分钟，三个质差时间段中质差主因分别为“干扰大”，“干扰大”，“竞争强”，各占70％时间，即时长分别为28分钟，21分钟，28分钟。此外，“信号差”在三个质差时间段中均存在，且各占21％时间，即在第一个质差时间段内存在8.4分钟，第二个质差时间段内存在6.3分钟，第三个质差时间段内存在8.4分钟。鉴于“信号差”在上述三个质差时间段中持续时间较短，很可能并不是每个质差时间段的质差主因，如可能是噪声引起的瞬时信号变差。倘若设置时长阈值为20％，即在图2B所示的场景下，某一指标的质差时间超过20分钟就会被作为质差根因报出，从而误将“信号差”作为质差根因报出，即出现了虚警。
可见，现有静态分析时间窗口的质差根因分析方法需要设置一个持续时长较长的分析窗口方能获得足够数量和种类的质差事件样本，这就导致很难设置一个可适用于所有种类的质差根因的持续时长阈值，从而导致质差根因的漏报或虚警，即设置静态分析时间窗口的质差根因分析方法的准确性较差。
另一方面，在部分质差时段持续时间较长，而另一些质差时段持续时间较短的场景下，持续时间较长的质差时段的质差主因才是质差根因，而持续时间较短的质差时段的质差主因可能是由于噪声、干扰等偶然因素导致的，并不需要报出来，只需要将持续时间较长的质差时段的质差主因作为质差根因报出即可。然而，在采用静态分析时间窗口的质差根因分析方法中，只是简单地根据质差时段持续时长是否大于预设质差时长阈值进行判断，无法考虑持续时间较长的质差时段和持续时间较短的质差时段这两种质差分布形态，从而导致质差根因分析结果的准确性较差。
图2C示出了又一种质差根因分布形态的场景示意图。如图2C所示，假设静态分析时间窗口，如1天内有3个质差时间段，持续时长分别为80分钟,10分钟,10分钟。其中，“信号差”时间段有四段，每段时间为6分钟(共24分钟)，其中持续时长为10分钟的质差时间段内各包含一段，其余2段存在于持续时长为80分钟的质差时间段内。假定时长阈值设置为20％，即在该场景下某一指标的质差时长超过20分钟就会被作为质差根因报出。在该场景下，“信号差”持续时长超过时长阈值，因此作为质差根因报出。然而，位于上述2个10分钟质差时间段内的“信号差”很可能是由于噪声(分散且持续时间较短)，不应该考虑。但是，静态分析时间窗口只是简单的统计上述指定时间段内“信号差”时长是否超过时长阈值，并没 有考虑噪声等偶然因素的影响，因此没有办法识别出这种误报。
需要说明的是，如果上述指定时间段内的质差时间段均为持续时间较短的时间段，还是需要都考虑的。在这种场景下，质差时间很可能是由除上述噪声等偶然性因素之外的其他因素导致的，如相邻接入点周期性地发送的下行信号对本接入点造成的干扰。
可见，现有静态分析时间窗口的质差根因分析方法无法识别上述两种质差分布形态，即不能剔除偶然因素导致的质差事件的影响，只是简单地统计并判断某种类型的质差时间段的持续时长是否大于预设时长阈值来确定质差根因，从而导致质差根因分析方法的准确性较差。
针对上述问题，本申请实施例提供一种用于实现质差根因分析的方法。下面结合附图详细说明。
图3示出了本申请实施例提供的一种用于实现质差根因分析的方法的流程示意图，可以应用于如图1所示的通信系统。如图3所示，该方法可以包括S301-S303：
S301，根据指定时间段内指定用户的N条质差记录对应的时间，计算N条质差记录中每条质差记录和与其相邻的质差记录之间的时间间隔。
需要说明的是，在执行上述S301之前，还需要获取指定时间段内指定用户的N条质差记录对应的时间。具体地，可以包括如下步骤1和步骤2：
步骤1，根据指定时间段、指定用户等预设条件，获取L条质量记录。
其中，上述L条质量记录中的每条质量记录均可以包括对应的时间和质量评分。
需要说明的是，上述质量评分可以为QoE，也可以为服务质量(quality of service，QoS)，本申请对此不做限定。
示例性地，上述指定用户可以是指定家庭的用户账户，如运营商为该家庭用户开通的Wi-Fi接入点标识，上述指定时间段可以为0.5天、1天、1周等连续时间段。具体地，可以在上述指定时间段内，对该家庭用户按照预设的采样周期，如10分钟采集质量记录。其中，质量记录可以采用{采样时间，用户标识，质量评分}的形式保存质量记录，如可以为表格。
表1示出了上述质量记录的一个表格示例。如表1所示，表1中第1列为采样时间，也就是质量记录对应的时间，相邻两条质量记录之间的时间间隔为一个采样周期，第2列为用户标识(AAAA)，第3列为质量评分，如QoE评分。其中，质量评分为1表示服务质量好，即质量评分满足预设质量条件，质量评分为-1表示服务质量差，即质量评分不满足预设质量条件，质量评分为0表示服务质量一般。
表1
采样时间 用户标识 质量评分
0 AAAA -1
1 AAAA -1
2 AAAA 1
3 AAAA -1
4 AAAA 0
5 AAAA 1
6 AAAA 0
7 AAAA -1
8 AAAA 1
9 AAAA 0
10 AAAA 1
11 AAAA -1
12 AAAA -1
13 AAAA -1
步骤2，将上述L条质量记录中质量评分不满足预设质量条件的质量记录确定为质差记录，并统计质差记录的数量。
可以理解，统计得到的质差记录的数量肯定要小于或等于质量记录的数量。因此，假定质差记录的数量为N，则N≤L。
示例性地，结合表1，将表1中质量评分为-1的质量记录挑选出来，生成如表2所示的质差记录表。
表2
采样时间 用户标识 质量评分
0 AAAA -1
1 AAAA -1
3 AAAA -1
7 AAAA -1
11 AAAA -1
12 AAAA -1
13 AAAA -1
然后，即可根据表2，执行S301根据指定时间段内指定用户的N条质差记录对应的时间，计算N条质差记录中每条质差记录和与其相邻的质差记录之间的时间间隔。具体地，S301可以包括步骤3和步骤4：
步骤3，根据表2，计算每条质差记录和与其相邻的质差记录之间的时间间隔。
其中，时间间隔的单位可以为上述采样周期，也可以为实际的时间长度，本申请对此不做限定。
具体地，可以采用如下方法计算：
上述N条质差记录中第1条质差记录质差记录和与其相邻的质差记录之间的时间间隔为：N条质差记录中第2条质差记录对应的时间与第1条质差记录对应的时间的差值。
上述N条质差记录中第N条质差记录和与其相邻的质差记录之间的时间间隔为：第N条质差记录对应的时间与N条质差记录中第N-1条质差记录对应的时间的差值。
上述N条质差记录中第n条质差记录和与其相邻的质差记录之间的时间间隔为：N条质差记录中第n+1条质差记录对应的时间与第n条质差记录对应的时间的差值，和第n条质差记录对应的时间与N条质差记录中第n-1条质差记录对应的时间的差值中的最大值；其中，1＜n＜N。
步骤4，根据计算的每条质差记录和与其相邻的质差记录之间的时间间隔，生成如表3所示的表格。
其中，表3中的最后一列为步骤3中计算得到的时间间隔的数值。
表3
采样时间 用户标识 质量评分 时间间隔
0 AAAA -1 1
1 AAAA -1 2
3 AAAA -1 4
7 AAAA -1 4
11 AAAA -1 4
12 AAAA -1 1
13 AAAA -1 1
如表3所示，第1条质差记录对应的采样时间为0，第2条质差记录对应的采样时间为1，则第1条质差记录和与其相邻的质差记录之间的时间间隔为1与0的差值1，即为1个采样周期。
第7条质差记录对应的采样时间为13，第6条质差记录对应的采样时间为12，则第7条质差记录和与其相邻的质差记录之间的时间间隔为13与12的差值1，即为1个采样周期。
第1条质差记录对应的采样时间为0，第2条质差记录对应的采样时间为1，第3条质差记录对应的采样时间为3，则第2条质差记录和与其相邻的质差记录之间的时间间隔为3-1与1-0中的最大值2，即为2个采样周期。
需要说明的是，上述N条质差记录可以为QoE不满足预设QoE条件的质量记录，也可以为QoS不满足预设QoS条件的质量记录，本申请实施例对此不做限定。
S302，根据N条质差记录中每条质差记录和与其相邻的质差记录之间的时间间隔，将N条质差记录划分为K个待过滤窗口。
其中，S302根据N条质差记录中每条质差记录和与其相邻的质差记录之间的时间间隔，将N条质差记录划分为K个待过滤窗口，可以具体实现为步骤5和步骤6：
步骤5，确定上述N条质差记录中每条质差记录和与其相邻的质差记录之间的时间间隔阈值。
具体地，可以采用如下方法确定时间间隔阈值。
在一种可能的设计方法中，上述时间间隔阈值可以根据经验值直接配置。例如，如表3所示，上述时间间隔阈值可以直接配置为最大时间间隔，即为4个采样周期。
在另一种可能的设计方法中，上述时间间隔阈值也可以根据质差记录的预设条数确定。
示例性地，假定预设条数为5，且将N条质差记录中每条质差记录和与其相邻的质差记录之间的时间间隔从小到大排序，则第5条质差记录和与其相邻的质差记录之间的时间间隔即为时间间隔阈值。结合表3，如图4A所示，时间间隔为1的质差记录有3条，时间间隔为2的质差记录有1条，时间间隔为3的质差记录有0条，时间间隔为4的质差记录有3条，可知第5条质差记录和与其相邻的质差记录之间的时间间隔4即为时间间隔阈值。其中，图4A中横坐标d为时间间隔，单位为一个采样周期，纵坐标n为按照从小到大排序后的质差记录条数。
在又一种可能的设计方法中，上述时间间隔阈值也可以根据预设比例确定。
示例性地，假定预设比例为0.8，且将上述N条质差记录中每条质差记录和与其相邻的质差记录之间的时间间隔从小到大统计质差记录条数，并计算已统计的质差记录条数与N比值。当上述比值首次大于或等于预设比例时，已统计的质差记录中的最后一条质差记录和与其相邻的质差记录之间的时间间隔，即为时间间隔阈值。结合表3，如图4B所示，时间间隔为1的质差记录有3条，已统计质差记录条数占比为3/7＝42.8％；时间间隔为2的质差记录有1条，已统计质差记录条数占比为4/7＝57.1％；时间间隔为3的质差记录有0条，已统计质差记录条数占比仍然为57.1％；时间间隔为4的质差记录有3条，当统计至第6条质差记 录时，已统计质差记录条数占比为6/7＝85.7％，即已统计质差记录条数占比首次大于预设比例，可知上述时间间隔阈值为第6条质差记录和与其相邻的质差记录之间的时间间隔4。其中，图4B中横坐标d为时间间隔，单位为一个采样周期，纵坐标p为按照从小到大已统计的质差记录条数占比。
步骤6，根据N条质差记录中每条质差记录和与其相邻的质差记录之间的时间间隔、上述时间间隔阈值和窗口聚合规则，将N条质差记录划分为K个待过滤窗口。
其中，窗口聚合规则可以为：K个待过滤窗口中任意两个相邻的待过滤窗口之间的时间间隔大于或等于时间间隔阈值，且任意一个包含有至少两条质差记录的待过滤窗口中任意两条相邻的质差记录之间的时间间隔小于时间间隔阈值，1≤K≤N。
具体地，以采样周期为例，两个相邻的待过滤窗口之间的时间间隔的计算方法为：后一个待过滤窗口中的第一条质差记录对应的时间，与前一个待过滤窗口中的最后一条质差记录对应的时间的差值。
示例性地，参见表3，可以将上述7条质差记录划分为3个待过滤窗口：[0,3]，[7]，[11,13]。其中，[0,3]包括3条质差记录0、1、3，[7]包括1条质差记录7，[11,13]包括3条质差记录11、12、13。待过滤窗口[0,3]和[7]之间的时间间隔为7-3＝4个采样周期，待过滤窗口[7]和[11,13]之间的时间间隔为11-7＝4个采样周期。
S303，将K个待过滤窗口中的部分或全部待过滤窗口作为根因分析窗口。
具体地,可以将K个待过滤窗口中满足窗口过滤规则的待过滤窗口确定为根因分析窗口，也就是将由瞬时噪声、瞬时干扰等偶然因素(通常不是质差根因)导致的质差记录排除在根因分析过程之外，以进一步提高质差根因分析的准确性。
其中，窗口过滤规则可以为如下之一：
在一种可能的设计方法中，上述根因分析窗口包含的质差记录数大于或等于质差记录数阈值。
示例性地，参见表3，假定质差记录数阈值为3，则根因分析窗口为待过滤窗口[0,3]和[11,13]，而待过滤窗口[7]被剔除。
可以理解，为了进一步提高窗口过滤规则的可适用性，也可以设置质差记录数占比阈值，然后根据实际确定的质差记录数动态计算质差记录数阈值。因此，可选地，上述根因分析窗口的质差记录数占比大于或等于质差记录数占比阈值。其中，根因分析窗口的质差记录数占比为：根因分析窗口包含的质差记录数与N的比值。
示例性地，参见表3，假定质差记录数占比阈值为0.3，K个待过滤窗口中质差记录数为7，则待过滤窗口[0,3]对应的质差记录数占比为3/(3+1+3)＝42.9％，待过滤窗口[7]对应的质差记录数占比为1/(3+1+3)＝14.3％，待过滤窗口[11,13]对应的质差记录数占比为3/(3+1+3)＝42.8％。由此，待过滤窗口[0,3]和[11,13]的质差记录数占比大于质差记录数占比阈值0.3，可以作为根因分析窗口，而待过滤窗口[7]的质差记录数占比为1/7＝14.3％，小于质差记录数占比阈值0.3，不作为根因分析窗口。
可以理解，也可以先根据质差记录数占比阈值和确定的质差记录数计算出质差记录数阈值，然后再根据质差记录数阈值确定根因分析窗口。具体可以参见上述质差记录数阈值相关内容，此处不再赘述。
在另一种可能的设计方法中，也可以将上述K个待过滤窗口中窗口时长大于或等于预设窗口时长阈值的待过滤窗口，作为根因分析窗口。因此，上述根因分析窗口的窗口时长大于或等于窗口时长阈值。
示例性地，以采样周期为例，上述待过滤窗口的窗口时长的计算方法为：上述待过滤窗口中最后一条质差记录对应的时间，与上述待过滤窗口中第一条质差记录对应的时间的差值，再加上一个采样周期。下文所述待过滤窗口的窗口时长示例，均为采用本段所述方法计算得到的窗口时长。
示例性地，参见表3，假定窗口时长阈值为3个采样周期，则待过滤窗口[0,3]和[11,13]的窗口时长分别为4个采样周期和3个采样周期，则可以将待过滤窗口[0,3]和[11,13]作为根因分析窗口，而待过滤窗口[7]的窗口时长为1，不作为根因分析窗口。
应理解，实际应用中，为简化计算，也可以将一个待过滤窗口中最后一条质差记录对应的时间，与该待过滤窗口中第一条质差记录对应的时间的差值，作为该待过滤窗口的窗口时长，此处不再赘述。
可以理解，为了进一步提高窗口过滤规则的可适用性，也可以设置窗口时长占比阈值，然后根据实际确定的待过滤窗口总时长计算实际的窗口时长阈值。因此，可选地，上述根因分析窗口的窗口时长占比大于窗口时长占比阈值。
其中，上述根因分析窗口的窗口时长占比为：上述根因分析窗口的窗口时长与K个待过滤窗口的窗口时长之和的比值。
示例性地，参见表3，假定窗口时长占比阈值为0.3，K个待过滤窗口的窗口总时长为4+1+3＝8，则待过滤窗口[0,3]对应的窗口时长占比为4/8＝50％，待过滤窗口[7]对应的窗口时长占比为1/8＝12.5％，待过滤窗口[11,13]对应的窗口时长占比为3/8＝37.5％。其中，待过滤窗口[0,3]和[11,13]的窗口时长占比均大于窗口时长占比阈值0.3，可作为根因分析窗口，而待过滤窗口[7]的窗口时长占比小于窗口时长占比阈值0.3，不作为根因分析窗口。
可以理解，也可以先根据窗口时长占比阈值和上述K个待过滤窗口的窗口总时长计算出窗口时长阈值，然后再根据窗口时长阈值确定根因分析窗口。具体可以参见上述窗口时长阈值相关内容，此处不再赘述。
需要说明的是，上述质差记录数阈值和质差记录数阈值，可以与上述窗口时长阈值和窗口时长占比阈值结合使用。
示例性地，假定质差记录数阈值为3，窗口时长占比阈值为0.3，则参见表3，质差记录总数为7，待过滤窗口总时长为4+1+3＝8个采样周期。
其中，待过滤窗口[0,3]和[11,13]包含的质差记录数均为3，大于或等于质差记录数阈值3，且待过滤窗口[0,3]和[11,13]窗口时长占比分别为4/8＝50％和3/8＝37.5％，均大于窗口时长占比阈值0.3。因此，待过滤窗口[0,3]和[11,13]作为根因分析窗口。
但是，待过滤窗口[7]仅包含1条质差记录数，小于质差记录数阈值3，且待过滤窗口[7]的窗口时长占比为1/(4+1+3)＝12.5％，小于窗口时长占比阈值0.3。因此，待过滤窗口[7]不可作为根因分析窗口，需要剔除。
需要说明的是，本申请实施例提供的用于实现质差根因分析的方法，可以由网络设备1执行，也可以由网络设备2执行，本申请对此不做限定。
示例性地，本申请实施例提供的用于实现质差根因分析的方法，可以由网络设备1执行，并将质差分析结果上报给网络设备2，以便网络设备2根据上报的质差根因分析结果对现有通信系统优化升级。
示例性地，本申请实施例提供的用于实现质差根因分析的方法，也可以在网络设备1将上述质差记录或质量记录上传给网络设备2之后，再由网络设备2执行，并根据上报的质差 根因分析结果对现有通信系统优化升级。
需要说明的是，在执行上述S301-S303之后，还可以针对每个根因分析窗口分别进行质差根因分析。具体地，可以将一个根因分析窗口划分为多个质差时间段，并识别每个质差时间段的质差原因，如“信号差”、“竞争强”和“干扰大”等。倘若所有质差时间段中某一质差原因的持续时长与所有质差时间段的持续时长总和的比值大于或等于预设时长阈值，则将该某一质差原因视为该根因分析窗口期间的一个质差根因。
本申请提供的用于实现质差根因分析的方法，能够根据指定时间段内指定用户的N条质差记录对应的时间，计算每条质差记录和与其相邻的质差记录之间的时间间隔，并根据时间间隔将上述N条质差记录中时间相关性较强，即发生相同质差根因的概率较大的质差记录划分在同一个待过滤窗口中，而将时间相关性较差，即发生相同质差根因的概率较小的质差记录划分在不同的待过滤窗口中，可以避免设置的静态分析时间窗口的窗口时长和窗口位置无法动态调整所导致的质差根因漏报和/或虚警问题，从而提高质差根因分析的准确性。
以上结合图3、图4A和图4B详细说明了本申请实施例提供的用于实现质差根因分析的方法。以下结合图5和图6说明本申请实施例提供的可执行上述用于实现质差根因分析的方法的网络设备。
图5是本申请实施例提供的一种网络设备的结构示意图。如图5所示，网络设备500包括：计算模块501、划分模块502和确定模块503。
其中，计算模块501，用于根据指定时间段内指定用户的N条质差记录对应的时间，计算N条质差记录中每条质差记录和与其相邻的质差记录之间的时间间隔。
划分模块502，用于根据N条质差记录中每条质差记录和与其相邻的质差记录之间的时间间隔，将N条质差记录划分为K个待过滤窗口。
其中，上述K个待过滤窗口中任意两个相邻的待过滤窗口之间的时间间隔大于或等于时间间隔阈值，且任意一个包含至少两条质差记录的待过滤窗口中任意两条相邻的质差记录之间的时间间隔小于时间间隔阈值，1≤K≤N。
确定模块503，用于将K个待过滤窗口中的部分或全部待过滤窗口作为根因分析窗口。
示例性地，上述N条质差记录中第1条质差记录和与其相邻的质差记录之间的时间间隔为：N条质差记录中第2条质差记录对应的时间与第1条质差记录对应的时间的差值。
上述N条质差记录中第N条质差记录和与其相邻的质差记录之间的时间间隔为：第N条质差记录对应的时间与N条质差记录中第N-1条质差记录对应的时间的差值。
上述N条质差记录中第n条质差记录和与其相邻的质差记录之间的时间间隔为：N条质差记录中第n+1条质差记录对应的时间与第n条质差记录对应的时间的差值，和第n条质差记录对应的时间与N条质差记录中第n-1条质差记录对应的时间的差值中的最大值；其中，1＜n＜N。
在一种可能的设计中，上述根因分析窗口包含的质差记录数大于质差记录数阈值。
可选地，上述根因分析窗口的质差记录数占比大于质差记录数占比阈值。其中，根因分析窗口的质差记录数占比为：根因分析窗口包含的质差记录数与N的比值。
在另一种可能的设计中，上述根因分析窗口的窗口时长大于窗口时长阈值。
可选地，上述根因分析窗口的窗口时长占比大于窗口时长占比阈值。其中，根因分析窗口的窗口时长占比为：根因分析窗口的窗口时长与K个待过滤窗口的窗口时长之和的比值。
可选地，N条质差记录可以为QoE不满足预设QoE条件的质量记录。
此外，网络设备500还可以包括存储模块(图5中未示出)，用于存储网络设备500的指令和数据。
图6是本申请实施例提供的另一种网络设备的结构示意图。如图6所示，网络设备600包括：处理器601，处理器601与存储器602耦合。
其中，存储器602，用于存储计算机程序。
处理器601，用于执行存储器602中存储的计算机程序，使得网络设备600执行上述方法实施例所述的用于实现质差根因分析的方法。
其中，处理器601与存储器602耦合，是指处理器601与存储器602之间存在信号连接。应当理解，存储器602可以是处理器601内部的存储器，如内存，也可以是设置于网络设备500内部，且与处理器601存在信号连接的其他存储器，还可以是位于网络设备600外部，且与网络设备600存在信号连接的存储器，本申请对此不做限定。
可选地，如图6所示，处理器601与存储器602耦合，可以是处理器601与存储器602之间通过总线603连接。
可选地，网络设备600还可以包括收发器604。其中，收发器604可以为收发电路或通信接口，用于与其他设备，如图1中的终端通信，或者与其他网络设备通信。
可选地，收发器604还可以为输入/输出接口，用于接收输入指令，或者输出处理结果。
需要说明的是，上述网络设备500和网络设备600可以为如图1所示的网络设备1，也可以为图1所示的网络设备2，还可以是设置于上述网络设备1或网络设备2中的芯片，本申请对此不做限定。
本申请实施例提供一种通信系统，该通信系统包括一台或多台终端，以及一台或多台上述网络设备。
本申请实施例提供一种计算机可读存储介质，存储有程序或指令，当该程序或指令在计算机上执行时，使得该计算机执行方法实施例所述的用于实现质差根因分析的方法。
本申请实施例提供一种计算机程序产品，包括计算机程序代码，当上述计算机程序代码在计算机上运行时，使得该计算机执行上述方法实施例所述的用于实现质差根因分析的方法。
应理解，在本申请实施例中的处理器可以是中央处理单元(central processing unit，CPU)，该处理器还可以是其他通用处理器、数字信号处理器(digital signal processor，DSP)、专用集成电路(application specific integrated circuit，ASIC)、现成可编程门阵列(field programmable gate array，FPGA)或者其他可编程逻辑器件、分立门或者晶体管逻辑器件、分立硬件组件等。通用处理器可以是微处理器或者该处理器也可以是任何常规的处理器等。
还应理解，本申请实施例中的存储器可以是易失性存储器或非易失性存储器，或可包括易失性和非易失性存储器两者。其中，非易失性存储器可以是只读存储器(read-only memory，ROM)、可编程只读存储器(programmable ROM，PROM)、可擦除可编程只读存储器(erasable PROM，EPROM)、电可擦除可编程只读存储器(electrically EPROM，EEPROM)或闪存。易失性存储器可以是随机存取存储器(random access memory，RAM)，其用作外部高速缓存。通过示例性但不是限制性说明，许多形式的随机存取存储器(random access memory，RAM)可用，例如静态随机存取存储器(static RAM，SRAM)、动态随机存取存储器(DRAM)、同步动态随机存取存储器(synchronous DRAM，SDRAM)、双倍数据速率同步动态随机存取存储器(double data rate SDRAM，DDR SDRAM)、增强型同步动态随机存取存储器(enhanced SDRAM，ESDRAM)、同步连接动态随机存取存储器(synchlink DRAM，SLDRAM)和直接内存总线随机存 取存储器(direct rambus RAM，DR RAM)。
上述实施例，可以全部或部分地通过软件、硬件(如电路)、固件或其他任意组合来实现。当使用软件实现时，上述实施例可以全部或部分地以计算机程序产品的形式实现。所述计算机程序产品包括一个或多个计算机指令或计算机程序。在计算机上加载或执行所述计算机指令或计算机程序时，全部或部分地产生按照本申请实施例所述的流程或功能。所述计算机可以为通用计算机、专用计算机、计算机网络、或者其他可编程装置。所述计算机指令可以存储在计算机可读存储介质中，或者从一个计算机可读存储介质向另一个计算机可读存储介质传输，例如，所述计算机指令可以从一个网站站点、计算机、服务器或数据中心通过有线(例如红外、无线、微波等)方式向另一个网站站点、计算机、服务器或数据中心进行传输。所述计算机可读存储介质可以是计算机能够存取的任何可用介质或者是包含一个或多个可用介质集合的服务器、数据中心等数据存储设备。所述可用介质可以是磁性介质(例如，软盘、硬盘、磁带)、光介质(例如，DVD)、或者半导体介质。半导体介质可以是固态硬盘。
应理解，本文中术语“和/或”，仅仅是一种描述关联对象的关联关系，表示可以存在三种关系，例如，A和/或B，可以表示：单独存在A，同时存在A和B，单独存在B这三种情况，其中A,B可以是单数或者复数。另外，本文中字符“/”，一般表示前后关联对象是一种“或”的关系，但也可能表示的是一种“和/或”的关系，具体可参考前后文进行理解。
本申请中，“至少一个”是指一个或者多个，“多个”是指两个或两个以上。“以下至少一项(个)”或其类似表达，是指的这些项中的任意组合，包括单项(个)或复数项(个)的任意组合。例如，a,b,或c中的至少一项(个)，可以表示：a,b,c,a-b,a-c,b-c,或a-b-c，其中a,b,c可以是单个，也可以是多个。
应理解，在本申请的各种实施例中，上述各过程的序号的大小并不意味着执行顺序的先后，各过程的执行顺序应以其功能和内在逻辑确定，而不应对本申请实施例的实施过程构成任何限定。
本领域普通技术人员可以意识到，结合本文中所公开的实施例描述的各示例的单元及算法步骤，能够以电子硬件、或者计算机软件和电子硬件的结合来实现。这些功能究竟以硬件还是软件方式来执行，取决于技术方案的特定应用和设计约束条件。专业技术人员可以对每个特定的应用来使用不同方法来实现所描述的功能，但是这种实现不应认为超出本申请的范围。
所属领域的技术人员可以清楚地了解到，为描述的方便和简洁，上述描述的系统、装置和单元的具体工作过程，可以参考前述方法实施例中的对应过程，在此不再赘述。
在本申请所提供的几个实施例中，应该理解到，所揭露的系统、装置和方法，可以通过其它的方式实现。例如，以上所描述的装置实施例仅仅是示意性的，例如，所述单元的划分，仅仅为一种逻辑功能划分，实际实现时可以有另外的划分方式，例如多个单元或组件可以结合或者可以集成到另一个系统，或一些特征可以忽略，或不执行。另一点，所显示或讨论的相互之间的耦合或直接耦合或通信连接可以是通过一些接口，装置或单元的间接耦合或通信连接，可以是电性，机械或其它的形式。
所述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，作为单元显示的部件可以是或者也可以不是物理单元，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单元来实现本实施例方案的目的。
另外，在本申请各个实施例中的各功能单元可以集成在一个处理单元中，也可以是各个单元单独物理存在，也可以两个或两个以上单元集成在一个单元中。
所述功能如果以软件功能单元的形式实现并作为独立的产品销售或使用时，可以存储在一个计算机可读取存储介质中。基于这样的理解，本申请的技术方案本质上或者说对现有技术做出贡献的部分或者该技术方案的部分可以以软件产品的形式体现出来，该计算机软件产品存储在一个存储介质中，包括若干指令用以使得一台计算机设备(可以是个人计算机，服务器，或者网络设备等)执行本申请各个实施例所述方法的全部或部分步骤。而前述的存储介质包括：U盘、移动硬盘、只读存储器(read-only memory，ROM)、随机存取存储器(random access memory，RAM)、磁碟或者光盘等各种可以存储程序代码的介质。
以上所述，仅为本申请的具体实施方式，但本申请的保护范围并不局限于此，任何熟悉本技术领域的技术人员在本申请揭露的技术范围内，可轻易想到变化或替换，都应涵盖在本申请的保护范围之内。因此，本申请的保护范围应以所述权利要求的保护范围为准。

Claims (17)

  1. 一种用于实现质差根因分析的方法，其特征在于，包括：
    根据指定时间段内指定用户的N条质差记录对应的时间，计算所述N条质差记录中每条质差记录和与其相邻的质差记录之间的时间间隔；
    根据所述N条质差记录中每条质差记录和与其相邻的质差记录之间的时间间隔，将所述N条质差记录划分为K个待过滤窗口；其中，所述K个待过滤窗口中任意两个相邻的待过滤窗口之间的时间间隔大于或等于时间间隔阈值，且任意一个包含至少两条质差记录的待过滤窗口中任意两条相邻的质差记录之间的时间间隔小于所述时间间隔阈值，1≤K≤N；
    将所述K个待过滤窗口中的部分或全部待过滤窗口作为根因分析窗口。
  2. 根据权利要求1所述的用于实现质差根因分析的方法，其特征在于，
    所述N条质差记录中第1条质差记录和与其相邻的质差记录之间的时间间隔为：所述N条质差记录中第2条质差记录对应的时间与所述第1条质差记录对应的时间的差值；
    所述N条质差记录中第N条质差记录和与其相邻的质差记录之间的时间间隔为：所述第N条质差记录对应的时间与所述N条质差记录中第N-1条质差记录对应的时间的差值；
    所述N条质差记录中第n条质差记录和与其相邻的质差记录之间的时间间隔为：所述N条质差记录中第n+1条质差记录对应的时间与所述第n条质差记录对应的时间的差值，和所述第n条质差记录对应的时间与所述N条质差记录中第n-1条质差记录对应的时间的差值中的最大值；其中，1＜n＜N。
  3. 根据权利要求1或2所述的用于实现质差根因分析的方法，其特征在于，所述根因分析窗口包含的质差记录数大于质差记录数阈值。
  4. 根据权利要求1或2所述的用于实现质差根因分析的方法，其特征在于，所述根因分析窗口的质差记录数占比大于质差记录数占比阈值；其中，所述根因分析窗口的质差记录数占比为：所述根因分析窗口包含的质差记录数与N的比值。
  5. 根据权利要求1或2所述的用于实现质差根因分析的方法，其特征在于，所述根因分析窗口的窗口时长大于窗口时长阈值。
  6. 根据权利要求1或2所述的用于实现质差根因分析的方法，其特征在于，所述根因分析窗口的窗口时长占比大于窗口时长占比阈值；其中，所述根因分析窗口的窗口时长占比为：所述根因分析窗口的窗口时长与所述K个待过滤窗口的窗口时长之和的比值。
  7. 根据权利要求1-6中任一项所述的质差根因分析方法，其特征在于，所述N条质差记录为用户体验质量QoE不满足预设QoE条件的质量记录。
  8. 一种网络设备，其特征在于，包括：计算模块、划分模块和确定模块；其中，
    所述计算模块，用于根据指定时间段内指定用户的N条质差记录对应的时间，计算所述N条质差记录中每条质差记录和与其相邻的质差记录之间的时间间隔；
    所述划分模块，用于根据所述N条质差记录中每条质差记录和与其相邻的质差记录之间的时间间隔，将所述N条质差记录划分为K个待过滤窗口；所述K个待过滤窗口中任意两个相邻的待过滤窗口之间的时间间隔大于或等于时间间隔阈值，且任意一个包含至少两条质差记录的待过滤窗口中任意两条相邻的质差记录之间的时间间隔小于所述时间间隔阈值，1≤K≤N；
    所述确定模块，用于将所述K个待过滤窗口中的部分或全部待过滤窗口作为根因分析窗口。
  9. 根据权利要求8所述的网络设备，其特征在于，
    所述N条质差记录中第1条质差记录和与其相邻的质差记录之间的时间间隔为：所述N条质差记录中第2条质差记录对应的时间与所述第1条质差记录对应的时间的差值；
    所述N条质差记录中第N条质差记录和与其相邻的质差记录之间的时间间隔为：所述第N条质差记录对应的时间与所述N条质差记录中第N-1条质差记录对应的时间的差值；
    所述N条质差记录中第n条质差记录和与其相邻的质差记录之间的时间间隔为：所述N条质差记录中第n+1条质差记录对应的时间与所述第n条质差记录对应的时间的差值，和所述第n条质差记录对应的时间与所述N条质差记录中第n-1条质差记录对应的时间的差值中的最大值；其中，1＜n＜N。
  10. 根据权利要求8或9所述的网络设备，其特征在于，所述根因分析窗口包含的质差记录数大于质差记录数阈值。
  11. 根据权利要求8或9所述的网络设备，其特征在于，所述根因分析窗口的质差记录数占比大于质差记录数占比阈值；其中，所述根因分析窗口的质差记录数占比为：所述根因分析窗口包含的质差记录数与N的比值。
  12. 根据权利要求8或9所述的网络设备，其特征在于，所述根因分析窗口的窗口时长大于窗口时长阈值。
  13. 根据权利要求8或9所述的网络设备，其特征在于，所述根因分析窗口的窗口时长占比大于窗口时长占比阈值；其中，所述根因分析窗口的窗口时长占比为：所述根因分析窗口的窗口时长与所述K个待过滤窗口的窗口时长之和的比值。
  14. 根据权利要求8-13中任一项所述的网络设备，其特征在于，所述N条质差记录为用户体验质量QoE不满足预设QoE条件的质量记录。
  15. 一种网络设备，其特征在于，包括：处理器，所述处理器与存储器耦合；
    所述存储器，用于存储计算机程序；
    所述处理器，用于执行所述存储器中存储的计算机程序，使得所述网络设备执行如权利要求1-7中任一项所述的用于实现质差根因分析的方法。
  16. 一种可读存储介质，其特征在于，存储有程序或指令，当所述程序或指令在计算机上运行时，使得所述计算机执行如权利要求1-7中任一项所述的用于实现质差根因分析的方法。
  17. 一种计算机程序产品，其特征在于，包括计算机程序代码，当所述计算机程序代码在计算机上运行时，使得所述计算机执行如权利要求1-7中任一项所述的用于实现质差根因分析的方法。
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