An FTTR network data quality optimization method, device, equipment and medium

Abstract

The present application discloses an FTTR network data quality optimization method, device, equipment and medium, including: collecting various network performance data of a target terminal device; for any network performance data, obtaining multiple missing intervals of the network performance data and calculating the correlation degree between the network performance data and other network performance data; for any missing interval, determining multiple target intervals of the missing interval from the network performance data, calculating the influence factor between the missing interval and each target interval based on the correlation degree, and taking the target interval corresponding to the minimum value of the influence factor as the reference interval of the missing interval; using a preset data interpolation algorithm to interpolate the network performance data, and correcting the interpolated result based on the reference interval of each missing interval to generate corrected network performance data. The present application solves the problem that the traditional single-variable interpolation method has a large deviation in the interpolation result due to ignoring the index correlation.

Description

Technical Field

[0001] This application relates to the technical field of data quality optimization, and particularly to a method, device, equipment and medium for optimizing the data quality of an FTTR network. Background Art

[0002] FTTR (Fiber to the Room) is a fiber optic communication technology for home or enterprise scenarios, aiming to directly transmit high-speed Internet signals to terminal devices in each room for use by devices in the home or office environment. In practical applications, the network performance data of terminal devices may be missing due to reasons such as propagation delay or network interruption. At this time, data interpolation is required to fill in the missing items.

[0003] Traditional interpolation methods usually only rely on the time series characteristics of the index to be interpolated itself to fill in the missing items. For example, when filling in the missing value of signal strength, only the historical data of signal strength is used for calculation. However, there are dynamic correlations between different types of network performance data. For example, when the signal strength decreases, the bandwidth utilization rate often increases synchronously. The traditional interpolation method ignores the interaction between different network performance data, resulting in a large deviation between the interpolation result and the real data, affecting the overall data quality optimization effect. Summary of the Invention

[0004] The main purpose of this application is to provide a method, device, equipment and medium for optimizing the data quality of an FTTR network, aiming to solve the technical problem that the traditional single-variable interpolation method has a large deviation in the interpolation result due to ignoring the index correlation.

[0005] To achieve the above purpose, this application provides a method for optimizing the data quality of an FTTR network, including: collecting various network performance data of a target terminal device, where the target terminal device is a terminal device under the FTTR architecture; for any network performance data of the target terminal device, obtaining multiple missing intervals of the network performance data and calculating the correlation degree between the network performance data and other network performance data; for any missing interval, determining multiple target intervals of the missing interval from the network performance data, calculating the influence factor between the missing interval and each target interval based on the network performance data, other network performance data, and the correlation degree between the network performance data and other network performance data, and using the target interval corresponding to the minimum value of the influence factor as the reference interval of the missing interval; using a preset data interpolation algorithm to interpolate the network performance data, and correcting the interpolated result based on the reference interval of each missing interval to generate corrected network performance data.

[0006] Optionally, calculating an influence factor between the missing interval and each target interval based on the network performance data, other network performance data, and the correlation degree between the network performance data and other network performance data includes: calculating the actual numerical difference degree between the missing interval and each target interval based on the network performance data; calculating the associated index difference degree between the missing interval and each target interval based on other network performance data and the correlation degree between the network performance data and other network performance data; and determining the influence factor between the missing interval and each target interval based on the actual numerical difference degree and the associated index difference degree.

[0007] Optionally, calculating the associated index difference degree between the missing interval and each target interval based on other network performance data and the correlation degree between the network performance data and other network performance data includes: for any target interval, taking the time interval corresponding to the missing interval as the missing time period, taking the time interval corresponding to the target interval as the target time period, and calculating the numerical difference of other network performance data between the missing time period and the target time period; and determining the associated index difference degree between the missing interval and the target interval based on each numerical difference and the correlation degree between the network performance data and other network performance data.

[0008] Optionally, calculating the associated index difference degree between the missing interval and each target interval based on other network performance data and the correlation degree between the network performance data and other network performance data includes: calculating the associated index difference degree between the missing interval and each target interval using the following formula (1):

[0009]

[0010] In the formula, represents the associated index difference degree between the missing interval and the th target interval, represents the number of network performance data, represents the correlation degree between the th network performance data other than the network performance data and the network performance data, represents the th value corresponding to the missing time period in the th network performance data, represents the th value corresponding to the target time period in the th network performance data, represents in the The number of data shared by the missing time period and the target time period among the network performance data.

[0011] Optionally, before correcting the interpolated result based on the reference intervals of the missing intervals to generate the corrected network performance data, the method further includes: for any missing interval, obtaining the time interval between the reference interval of the missing interval and the missing interval; the correcting the interpolated result based on the reference intervals of the missing intervals to generate the corrected network performance data includes: for any missing interval, correcting each interpolated data point within the missing interval based on the reference interval of the missing interval and the time interval to obtain a corrected missing interval; generating the corrected network performance data based on the corrected missing intervals.

[0012] Optionally, the correcting each interpolated data point within the missing interval based on the reference interval of the missing interval and the time interval includes: correcting each interpolated data point within the missing interval using the following formula (2):

[0013]

[0014] In the formula, represents the corrected value of the th data point in the missing interval, represents the interpolation result of the th data point in the missing interval, represents the value of the data point corresponding to the th data point in the reference interval of the missing interval, represents the time interval between the reference interval of the missing interval and the missing interval.

[0015] Optionally, the number of data included in each target interval of the missing interval is the same as that of the missing interval.

[0016] In addition, to achieve the above object, the present application further provides an FTTR network data quality optimization device, including: a data acquisition module for acquiring various network performance data of a target terminal device, where the target terminal device is a terminal device under the FTTR architecture; a correlation degree construction module for, for any one of the network performance data of the target terminal device, obtaining multiple missing intervals of the network performance data and calculating the correlation degree between the network performance data and each other network performance data; a reference interval obtaining module for, for any one of the missing intervals, determining multiple target intervals of the missing interval from the network performance data, calculating the influence factor between the missing interval and each target interval based on the network performance data, each other network performance data, and the correlation degree between the network performance data and each other network performance data, and taking the target interval corresponding to the minimum influence factor as the reference interval of the missing interval; and an interpolation correction module for interpolating the network performance data using a preset data interpolation algorithm and correcting the interpolated result based on the reference intervals of each missing interval to generate corrected network performance data.

[0017] The present application further provides an FTTR network data quality optimization device, including: at least one processor; and a memory communicatively connected to the at least one processor; wherein, the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the method in any one of the above possible implementation manners.

[0018] The present application further provides a computer-readable storage medium, including: storing a computer program, and when the computer program is executed by a processor, the method in any one of the above possible implementation manners is implemented.

[0019] A method, device, device, and medium for optimizing the data quality of an FTTR network proposed by the present application first obtains multiple missing intervals of any one of the network performance data of a target terminal device and calculates the correlation degree between the network performance data and each other network performance data; secondly, for any one of the missing intervals, determines multiple target intervals of the missing interval from the network performance data, calculates the influence factor between the missing interval and each target interval, and takes the target interval corresponding to the minimum influence factor as the reference interval of the missing interval; finally, interpolates the network performance data using a preset data interpolation algorithm and corrects the interpolated result based on the reference intervals of each missing interval to generate corrected network performance data. The present application solves the technical problem that the traditional single-variable interpolation method has a large deviation in the interpolation result due to ignoring the index correlation, and can effectively restore the true state when the data is missing, and improve the credibility and accuracy of the complemented data. Description of the Drawings

[0020] Figure 1 This is a flowchart of the FTTR network data quality optimization method provided in the first embodiment of this application;

[0021] Figure 2 This is a flowchart of the FTTR network data quality optimization method provided in the second embodiment of this application;

[0022] Figure 3 This is a structural block diagram of the FTTR network data quality optimization device provided in an embodiment of this application;

[0023] Figure 4 This is a schematic structural diagram of the FTTR network data quality optimization device provided in an embodiment of this application.

[0024] The realization of the purpose of this application, functional characteristics and advantages will be further described in conjunction with the embodiments with reference to the accompanying drawings. Detailed implementation manners

[0025] It should be understood that the specific embodiments described herein are only used to explain this application and are not used to limit this application.

[0026] Traditional interpolation methods usually only rely on the time series characteristics of the interpolation index itself to fill in the missing items. For example, when filling in the missing value of the signal strength, only the historical data of the signal strength is used for calculation. However, there are dynamic correlations between different types of network performance data. For example, when the signal strength drops, the bandwidth utilization rate often rises synchronously. The traditional interpolation method ignores the interaction between different network performance data, resulting in a large deviation between the interpolation result and the real data, which affects the overall data quality optimization effect.

[0027] To solve the above problems, this application provides an FTTR network data quality optimization method, device, equipment and medium. The solutions of this application will be introduced in detail below.

[0028] Figure 1 This is a flowchart of the FTTR network data quality optimization method provided in the first embodiment of this application. The FTTR network data quality optimization method can be applied to the FTTR network architecture. Among them, the FTTR architecture can include a management platform, a main gateway, a slave gateway, an optical fiber splitter, and terminal devices. The operator's optical fiber accesses the main gateway, and the main gateway distributes optical signals to each slave gateway through the optical fiber splitter. Each slave gateway connects to each terminal device in each room by wired or wireless means to provide network services for it. The FTTR network data quality optimization method can be executed by a data quality optimization device communicatively connected to each terminal device. The data quality optimization device can be, for example, an FTTR network data quality optimization device. Refer to Figure 1 , this FTTR network data quality optimization method can include the following steps:

[0029] S11, Collect various network performance data of the target terminal device, where the target terminal device is a terminal device under the FTTR architecture.

[0030] Among them, the network performance data is used to characterize the network connection status and network operation status of each terminal device. The various network performance data can be optical power data, optical fiber attenuation data, signal interference data, etc.

[0031] In the specific implementation process, obtain the network performance data of the target terminal device through the management platform or the management interface of each terminal device.

[0032] It should be noted that in this embodiment, the main gateway or the management platform can send a synchronous sampling instruction to the terminal device through a network protocol to ensure that the timestamps of the collected network performance data are aligned.

[0033] S12, For any kind of network performance data of the target terminal device, obtain multiple missing intervals of the network performance data and calculate the correlation degree between the network performance data and other network performance data.

[0034] Among them, the missing interval represents an interval composed of consecutive data missing items in the network performance data caused by propagation delay or network interruption. The correlation degree can reflect the dynamic association degree between two network performance data.

[0035] In the specific implementation process, taking any kind of network performance data of the target terminal device as an example, first obtain multiple missing intervals of the network performance data. It can be understood that in this embodiment, a single data missing item is also recorded as a missing interval.

[0036] Furthermore, use the Pearson correlation coefficient to calculate the correlation degree between the network performance data and other network performance data.

[0037] It should be noted that in other embodiments, the partial correlation coefficient or mutual information can also be used to calculate the correlation degree between network performance data. This embodiment does not make specific limitations on the algorithm used to calculate the correlation degree. Since the correlation between two network performance data may be negative, that is, the obtained correlation coefficient value is negative, in this embodiment, the absolute value of the result obtained by calculating the correlation coefficient is first taken, and then the obtained absolute value is used as the correlation degree between the two network performance data.

[0038] Further, it should be noted that when the change range of a certain network performance data at both ends of a missing interval is small, but other network performance data associated with it fluctuates significantly during the corresponding period, if only the data at the adjacent time points of the missing interval is used for interpolation, the dynamic association characteristics cannot be captured, resulting in the interpolation result deviating from the true physical state. For example, within the missing interval of a certain optical power data, due to the sudden change of the environmental temperature, abnormal attenuation of the optical fiber occurs. At this time, the optical power values at both ends of the missing interval only show a slight change of ±0.1 dB, but the bit error rate increases sharply from 1E-9 to 1E-6 during the same period. Directly using the optical power values at both ends of the missing interval for data interpolation will miss the actual non-linear attenuation change.

[0039] Based on this, this embodiment uses the following method to improve the problems existing in the traditional interpolation algorithm (that is, using the data at the adjacent time points of the missing interval for interpolation): First, extract a target interval with the same length as the missing interval from the network performance data, and then use the interval that best meets the following two conditions in the target interval as the reference interval for the missing interval: 1. The difference between the data at both ends of this interval and the data at both ends of the missing data is small; 2. The change trend of other network performance data in the corresponding time period of this interval is similar to the change trend of other network performance data in the corresponding time period of the missing interval. Finally, use the reference interval to correct the data interpolated by the traditional interpolation algorithm for the missing interval, which can effectively restore the true state when the data is missing and improve the credibility and accuracy of the complemented data.

[0040] S13. For any missing interval of any network performance data, determine multiple target intervals of this missing interval from this network performance data, calculate the influence factor between this missing interval and each target interval based on this network performance data, other network performance data, and the correlation degree between this network performance data and other network performance data, and use the target interval corresponding to the minimum influence factor as the reference interval for this missing interval;

[0041] S14. Use a preset data interpolation algorithm to interpolate this network performance data, and correct the interpolated result based on the reference interval of each missing interval to generate the corrected network performance data.

[0042] Among them, the influence factor characterizes the correction credibility of each target interval when using the target interval to correct the result of interpolating the missing interval.

[0043] In the specific implementation process, determine multiple target intervals of each missing interval from the network performance data. The number of data included in each target interval is the same as that of the missing interval, and there is no missing data in each target interval.

[0044] It should be noted that in this embodiment, the target intervals of each missing interval do not include the missing interval, and there may be overlapping sampling times between the target intervals. For example, if a certain network performance data uses seconds as the sampling time unit, a missing interval in this network performance data is from the 7th second to the 10th second and there is no missing data within the 1st second to the 6th second of this network performance data, then starting from the first sampling point of this network performance data, the target intervals of this missing interval are successively: the 1st second to the 4th second, the 2nd second to the 5th second, the 3rd second to the 6th second, and so on.

[0045] It can be understood that taking any missing interval as an example, the target interval of this missing interval and this missing interval are both included in the same network performance data.

[0046] Furthermore, based on this network performance data, other network performance data, and the correlation degree between this network performance data and other network performance data, the influence factors between this missing interval and each target interval are calculated.

[0047] In one embodiment, in step S13, calculating the influence factors between this missing interval and each target interval based on this network performance data, other network performance data, and the correlation degree between this network performance data and other network performance data may specifically include:

[0048] S131. Calculate the actual numerical difference degree between this missing interval and each target interval based on this network performance data;

[0049] S132. Calculate the difference degree of the correlation index between this missing interval and each target interval based on other network performance data and the correlation degree between this network performance data and other network performance data;

[0050] S133. Determine the influence factors between this missing interval and each target interval based on this actual numerical difference degree and this difference degree of the correlation index.

[0051] Among them, the actual numerical difference degree characterizes the numerical difference between the observed values (actual numerical values) at both ends of the missing interval and the target interval, and the difference degree of the correlation index characterizes the difference in the change trends of other network performance data within the corresponding time period of the missing interval and the change trends of other network performance data within the corresponding time period of the missing data.

[0052] In the specific implementation process, first calculate the absolute value of the difference between the observed value at one end of the missing interval and the observed value at one end of each target interval, and then calculate the absolute value of the difference between the observed value at the other end of the missing interval and the observed value at the other end of each target interval. Take the sum of the two absolute values of the differences as the actual numerical difference degree between the missing interval and each target interval. For example: In a certain network performance data, there is a missing interval from the 7th second to the 10th second, and there is no missing data within the 1st second to the 6th second of this network performance data. Taking the target interval from the 2nd second to the 5th second of this missing interval as an example, first calculate the absolute value of the difference between the observed value at one end of the missing interval (i.e., the data corresponding to the 6th second) and the observed value at one end of the target interval (i.e., the data corresponding to the 1st second), and then calculate the absolute value of the difference between the observed value at the other end of the missing interval (i.e., the data corresponding to the 11th second) and the observed value at the other end of the target interval (i.e., the data corresponding to the 6th second). Add the two absolute values of the differences to obtain the actual numerical difference degree between the missing interval and the target interval.

[0053] It should be noted that if there is no data at one end of the target interval, for example, if the target interval is from the 1st second to the 4th second, then take the first data of the target interval as the observed value at one end of the target interval. In addition, if there is no data at one end of the missing interval, for example, if the missing interval starts from the 1st second, then only calculate the absolute value of the difference between the observed value at the other end of the missing interval and the observed value at the other end of the target interval, and take this absolute value of the difference as the actual numerical difference degree between the missing interval and the target interval.

[0054] Furthermore, taking any target interval as an example, take the time interval corresponding to this target interval as the target time period, and take the time interval corresponding to the missing interval as the missing time period, and calculate the numerical difference between each other network performance data in the missing time period and this target interval in the target time period.

[0055] It can be understood that calculating the numerical difference between each other network performance data in the missing time period and this target interval in the target time period is to obtain the difference between the change trend of other network performance data in the time period corresponding to this target interval and the change trend of other network performance data in the time period corresponding to the missing interval. For example, taking any other network performance data as an example, if the numerical difference between this network performance data in the missing time period and the target time period is small, it means that the change trends of this network performance data in the missing time period and the target time period are similar.

[0056] Furthermore, based on the numerical difference between each other network performance data in the missing time period and the target time period and the correlation degree between the network performance data where the target time period is located and each other network performance data, determine the difference degree of the correlation index between the missing interval and this target interval.

[0057] Specifically, for the Taking a target interval as an example, the following formula (1) can be used to calculate the degree of difference in the correlation index between the missing interval and the th target interval :

[0058]

[0059] In the formula, represents the degree of difference in the correlation index between the missing interval and the th target interval, represents the number of network performance data, represents the degree of correlation between the th network performance data and this network performance data except this network performance data, represents the th value corresponding to the missing time period in the th network performance data, represents the th value corresponding to the target time period in the th network performance data, represents the number of data shared by the missing time period and the target time period in the th network performance data.

[0060] It should be noted that after obtaining the degree of correlation between this network performance data and other network performance data in this embodiment, all degrees of correlation are linearly normalized, that is, the values of the degrees of correlation fall within the range of 0 to 1. In addition, if the th network performance data does not exist (i.e., belongs to a missing value) at the th value during the missing time period or the th network performance data does not exist (i.e., belongs to a missing value) at the th value during the target time period , then is not calculated, that is, the obtained by summation does not include the difference between non-existent (belonging to missing value) data.

[0061] It can be understood that represents the number of types of other network performance data except the network performance data where this missing interval is located. represents the numerical difference (i.e., the sum of the corresponding bit numerical differences) between the th network performance data between the missing time period and the target time period. If is small, it indicates that the numerical difference (i.e., the sum of the corresponding bit numerical differences) is small, and the change trends of this network performance data between the missing time period and the target time period are similar. Indicates the degree of correlation. Taking the degree of correlation as a weight, more attention can be paid to the network performance data with a greater degree of correlation with the network performance data. Furthermore, the finally obtained The smaller it is, the more similar the change trends of other network performance data in the missing time period and the target time period are.

[0062] Furthermore, the weighted sum of the actual numerical difference degree and the correlation index difference degree between the missing interval and each target interval is calculated to obtain the influence factor between the missing interval and each target interval. And the target interval corresponding to the minimum value of the influence factor is used as the reference interval for the missing interval. Among them, the weights of the actual numerical difference degree and the correlation index difference degree are both decimals between 0 and 1 (excluding 0 and excluding 1), and the sum of the two weights is 1.

[0063] It should be noted that in order to avoid the influence of different dimensions (that is, there are differences in the numerical ranges of the actual numerical difference degree and the correlation index difference degree), in this embodiment, both the numerical difference degree and the correlation index difference degree can be linearly normalized first, that is, the numerical difference degree is linearly adjusted to the range of 0 to 1, and the correlation index difference degree is also linearly adjusted to the range of 0 to 1.

[0064] It can be understood that the smaller the actual numerical difference degree between the missing interval and a certain target interval is, the smaller the difference between the data at both ends of the target interval and the data at both ends of the missing data is; the smaller the correlation index difference degree between the missing interval and a certain target interval is, the more similar the change trends of other network performance data in the time period corresponding to the target interval and the other network performance data in the time period corresponding to the missing interval are; multiplying the actual numerical difference degree and the correlation index difference degree to construct an influence factor, the smaller the obtained influence factor is, the more similar the numerical fluctuations between the target interval and the missing interval are and the more similar the degree of influence by other network performance data is. Furthermore, the target interval with the smallest influence factor can be selected as the reference interval for the missing interval to correct the difference of the reference interval.

[0065] Thus, based on the dynamic correlation between each network performance data, this embodiment obtains the reference interval for each missing interval, and then the data interpolated by using the traditional interpolation algorithm can be corrected by using this reference interval.

[0066] In the specific implementation process, a preset data interpolation algorithm is used to interpolate the network performance data. Among them, the preset data interpolation algorithm can be a linear interpolation algorithm. In other embodiments, the preset data interpolation algorithm can also be other interpolation algorithms, such as a spline interpolation algorithm or a polynomial interpolation algorithm. This embodiment does not make a specific limitation on the preset data interpolation algorithm.

[0067] Further, based on the reference intervals of each missing interval, the interpolated result is corrected to generate the corrected network performance data.

[0068] Specifically, taking the th data point in any missing interval as an example, a preset adjustment parameter is used, and the following formula (3) can be used to correct this data point:

[0069]

[0070] In the formula, represents the corrected value of the th data point in this missing interval, represents the interpolation result of the th data point in the missing interval, represents the value of the data point corresponding to the th data point in the reference interval of this missing interval, represents the adjustment parameter. Among them, is a decimal between 0 and 1. Exemplarily, can be 0.7.

[0071] It can be understood that if the missing interval is from the 7th second to the 10th second, the reference interval is from the 20th second to the 23rd second, the data point corresponding to the 8th second data in the missing interval is the data point at the 21st second in the reference interval, and the data point corresponding to the 9th second data in the missing interval is the data point at the 22nd second in the reference interval.

[0072] It should be noted that if the data point corresponding to the th data point in the reference interval of this missing interval does not exist (i.e., is a missing value), then this data point is not applicable for correction, that is, at this time, the corrected value of the th data point in this missing interval

[0073] A method for optimizing the data quality of an FTTR network proposed in an embodiment of the present application. First, for any network performance data of any terminal device, multiple missing intervals of the network performance data are obtained, and the correlation degree between the network performance data and other network performance data is calculated. Secondly, for any missing interval, multiple target intervals of the missing interval are determined from the network performance data, the influence factor between the missing interval and each target interval is calculated, and the target interval corresponding to the minimum value of the influence factor is used as the reference interval for the missing interval. Finally, a preset data interpolation algorithm is used to interpolate the network performance data, and the interpolated result is corrected based on the reference intervals of each missing interval to generate corrected network performance data. The embodiment of the present application solves the technical problem that the traditional single-variable interpolation method has a large deviation in the interpolation result due to ignoring the index correlation, and can effectively restore the real state when the data is missing, and improve the credibility and accuracy of the complemented data.

[0074] Based on the above embodiment, Figure 2 The flowchart of the FTTR network data quality optimization method provided in the second embodiment of the present application is Figure 2 Based on Figure 1 The preferred embodiment of the corresponding FTTR network data quality optimization method. The FTTR network data quality optimization method can be executed by a data quality optimization device communicatively connected to each terminal device. The data quality optimization device can be, for example, an FTTR network data quality optimization device. Referring to Figure 2 , the method for optimizing the data quality of an FTTR network may include the following steps:

[0075] S21. Collect various network performance data of the target terminal device, where the target terminal device is a terminal device under the FTTR architecture;

[0076] S22. For any network performance data of the target terminal device, obtain multiple missing intervals of the network performance data, and calculate the correlation degree between the network performance data and other network performance data;

[0077] S23. For any missing interval of any network performance data, determine multiple target intervals of the missing interval from the network performance data, calculate the influence factor between the missing interval and each target interval based on the network performance data, other network performance data, and the correlation degree between the network performance data and other network performance data, and use the target interval corresponding to the minimum value of the influence factor as the reference interval for the missing interval;

[0078] S24. Use a preset data interpolation algorithm to interpolate the network performance data;

[0079] S25. For any missing interval, obtain the time interval between the reference interval of the missing interval and the missing interval;

[0080] S26. Based on the reference interval of the missing interval and the time interval, correct each interpolated data point within the missing interval to obtain a corrected missing interval;

[0081] S27. Generate corrected network performance data based on each corrected missing interval.

[0082] It should be noted that as the sampling time increases, the surrounding environment of each network performance data is constantly changing and the network performance data is continuously affected by environmental parameters (such as temperature, mechanical stress, etc.). Therefore, when the sampling time interval between the reference interval and the missing interval is large, the cumulative difference in environmental parameters will cause the direct correction to fail. For example: If the optical power data collected 12 hours ago is directly used to interpolate and correct a missing interval in a certain early morning period, the difference in the core expansion coefficient caused by the temperature difference between day and night will cause a slight deviation between the corrected optical power data and the true value.

[0083] Based on this, in this embodiment, on the basis of the above embodiment, first obtain the time interval between the reference interval and the missing interval, and then correct the interpolated data of the missing interval according to the time interval and the reference interval.

[0084] In the specific implementation process, first obtain the time interval between the reference interval of each missing interval and each missing interval, and perform a linear normalization operation on all time intervals, that is, map the time interval to the range of 0 to 1, where the time interval is the absolute value of the difference between the first sampling time in the missing time and the first sampling time of the reference interval.

[0085] Furthermore, for any missing interval, based on the reference interval of the missing interval and the time interval between the missing interval and the reference interval of the missing interval, correct each interpolated data point within the missing interval to obtain a corrected missing interval, and generate corrected network performance data based on each corrected missing interval.

[0086] Specifically, taking the th data point in any missing interval as an example, the following formula (2) can be used to correct this data point:

[0087]

[0088] In the formula, represents the corrected value of the th data point in the missing interval, represents the interpolation result of the th data point in the missing interval, represents the value of the data point corresponding to the th data point within the reference interval of the missing interval, represents the time interval between the missing interval and the reference interval.

[0089] Based on the above embodiments, this exemplary embodiment considers the sampling time intervals of the reference interval and the missing interval, and then corrects each interpolated data point within the missing interval in combination with the time interval, which can improve the accuracy of the correction result of network performance data while suppressing the cumulative error caused by environmental parameter effects.

[0090] Based on the above embodiments, Figure 3 is a structural block diagram of an FTTR network data quality optimization device according to an embodiment of the present application. As Figure 3 shown, the FTTR network data quality optimization device 300 may include: a data acquisition module 310, a correlation degree construction module 320, a reference interval acquisition module 330, and an interpolation correction module 340. Among them,

[0091] The data acquisition module 310 is configured to acquire various network performance data of a target terminal device, and the target terminal device is a terminal device under the FTTR architecture;

[0092] The correlation degree construction module 320 is configured to, for any network performance data of the target terminal device, obtain multiple missing intervals of the network performance data and calculate the correlation degree between the network performance data and each other network performance data;

[0093] The reference interval acquisition module 330 is configured to, for any missing interval of any network performance data, determine multiple target intervals of the missing interval from the network performance data, calculate the influence factor between the missing interval and each target interval based on the network performance data, each other network performance data, and the correlation degree between the network performance data and each other network performance data, and use the target interval corresponding to the minimum influence factor as the reference interval of the missing interval;

[0094] The interpolation correction module 340 is configured to interpolate the network performance data using a preset data interpolation algorithm and correct the interpolated result based on the reference intervals of each missing interval to generate corrected network performance data.

[0095] In an exemplary embodiment, the reference interval obtaining module 330 may further be configured to calculate the actual numerical difference degree between the missing interval and each target interval based on the network performance data; calculate the associated index difference degree between the missing interval and each target interval based on each of the other network performance data and the correlation degree between the network performance data and each of the other network performance data; and determine the influence factor between the missing interval and each target interval based on the actual numerical difference degree and the associated index difference degree.

[0096] In an exemplary embodiment, for any target interval, the reference interval obtaining module 330 may further be configured to use the time interval corresponding to the missing interval as the missing time period, use the time interval corresponding to the target interval as the target time period, and calculate the numerical difference between each of the other network performance data in the missing time period and the target time period; and determine the associated index difference degree between the missing interval and the target interval based on each numerical difference.

[0097] In an exemplary embodiment, the reference interval obtaining module 330 may also use the following formula (1) to calculate the associated index difference degree between the missing interval and each target interval:

[0098]

[0099] In the formula, represents the associated index difference degree between the missing interval and the th target interval, represents the number of network performance data, represents the correlation degree between the th network performance data other than the network performance data and the network performance data, represents the th value corresponding to the missing time period in the th network performance data, represents the th value corresponding to the target time period in the th network performance data, represents the number of common data between the missing time period and the target time period in the th network performance data.

[0100] In an exemplary embodiment, the interpolation correction module 340 may further be configured to obtain the time interval between the reference interval of the missing interval and the missing interval for any missing interval.

[0101] In an exemplary embodiment, the interpolation correction module 340 may also use the following formula (2) to correct each interpolated data point in the missing interval:

[0102]

[0103] In the formula, represents the corrected value of the th data point in the missing interval, represents the interpolation result of the th data point in the missing interval, represents the value of the data point corresponding to the th data point in the reference interval of the missing interval, represents the time interval between the reference interval of the missing interval and the missing interval.

[0104] In an exemplary embodiment, the number of data included in each target interval of the missing interval in the correlation degree construction module 320 is the same as that of the missing interval.

[0105] Those skilled in the art should understand that the division of each module in the embodiment is only a logical function division. In actual application, it can be fully or partially integrated into one or more actual carriers, and these modules can all be implemented in the form of software called by a processing unit, or all in the form of hardware, or in the form of a combination of software and hardware. It should be noted that each module in an FTTR network data quality optimization device in this embodiment corresponds one by one to each step in an FTTR network data quality optimization method in the foregoing embodiment. Therefore, the specific implementation manner of this embodiment can refer to the implementation manner of the foregoing FTTR network data quality optimization method, which will not be elaborated here.

[0106] Based on the above embodiment, Figure 4 is a schematic structural diagram of an FTTR network data quality optimization device according to an embodiment of the present application. As Figure 4As shown, the electronic device may include: a processor 410, a communications interface 420, a memory 430, and a communication bus 440. Among them, the processor 410, the communications interface 420, and the memory 430 complete communication with each other through the communication bus 440. The processor 410 may call logic instructions in the memory 430 to execute an FTTR network data quality optimization method, which includes: collecting various network performance data of a target terminal device, where the target terminal device is a terminal device under the FTTR architecture; for any one of the network performance data of the target terminal device, obtaining multiple missing intervals of the network performance data and calculating the correlation degree between the network performance data and each other network performance data; for any one of the missing intervals, determining multiple target intervals of the missing interval from the network performance data, calculating the influence factor between the missing interval and each target interval based on the network performance data, each other network performance data, and the correlation degree between the network performance data and each other network performance data, and taking the target interval corresponding to the minimum influence factor as the reference interval of the missing interval; using a preset data interpolation algorithm to interpolate the network performance data, and correcting the interpolated result based on the reference intervals of each missing interval to generate corrected network performance data.

[0107] In addition, when the logic instructions in the above-mentioned memory 430 are implemented in the form of software function units and sold or used as an independent product, they can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs that can store program codes.

[0108] On the basis of the above embodiments, on the other hand, the present invention further provides a computer program product, which includes a computer program. The computer program can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute an FTTR network data quality optimization method provided by each of the above methods. The method includes: collecting various network performance data of a target terminal device, where the target terminal device is a terminal device under the FTTR architecture; for any network performance data of the target terminal device, obtaining multiple missing intervals of the network performance data and calculating the correlation degree between the network performance data and each of the other network performance data; for any missing interval, determining multiple target intervals of the missing interval from the network performance data, calculating the influence factor between the missing interval and each target interval based on the network performance data, each of the other network performance data, and the correlation degree between the network performance data and each of the other network performance data, and using the target interval corresponding to the minimum value of the influence factor as the reference interval of the missing interval; using a preset data interpolation algorithm to interpolate the network performance data, and correcting the interpolated result based on the reference intervals of each missing interval to generate corrected network performance data.

[0109] On the basis of the above embodiments, on another aspect, the present invention further provides a non-transitory computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements an FTTR network data quality optimization method provided by each of the above methods. The method includes: collecting various network performance data of a target terminal device, where the target terminal device is a terminal device under the FTTR architecture; for any network performance data of the target terminal device, obtaining multiple missing intervals of the network performance data and calculating the correlation degree between the network performance data and each of the other network performance data; for any missing interval, determining multiple target intervals of the missing interval from the network performance data, calculating the influence factor between the missing interval and each target interval based on the network performance data, each of the other network performance data, and the correlation degree between the network performance data and each of the other network performance data, and using the target interval corresponding to the minimum value of the influence factor as the reference interval of the missing interval; using a preset data interpolation algorithm to interpolate the network performance data, and correcting the interpolated result based on the reference intervals of each missing interval to generate corrected network performance data.

[0110] The above are only the preferred embodiments of the present application, and do not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made by using the content of the specification and drawings of the present application, or directly or indirectly applied in other related technical fields, shall be included in the patent protection scope of the present application by the same token.

Claims

1. A method for optimizing FTTR network data quality, characterized in that: include: Collecting various network performance data of a target terminal device, where the target terminal device is a terminal device under an FTTR architecture; For any type of network performance data of the target terminal device, obtain multiple missing intervals of the network performance data and calculate the correlation between the network performance data and other network performance data; For any missing interval, determine multiple target intervals of the missing interval from the network performance data, calculate the impact factor between the missing interval and each target interval based on the network performance data, other network performance data, and the degree of correlation between the network performance data and other network performance data, and use the target interval corresponding to the minimum impact factor as the reference interval of the missing interval; Interpolate the network performance data using a preset data interpolation algorithm, and correct the interpolated result based on the reference interval of each missing interval to generate corrected network performance data; The calculating of the impact factor between the missing interval and each target interval based on the network performance data, other network performance data, and the correlation between the network performance data and other network performance data includes: Calculate the actual numerical difference between the missing interval and each target interval based on the network performance data; Calculate the difference degree of the correlation index between the missing interval and each target interval based on other network performance data and the correlation degree between the network performance data and other network performance data; Determine the impact factor between the missing interval and each target interval based on the difference between the actual value and the difference between the associated index; The calculating the difference degree of the correlation index between the missing interval and each target interval based on other network performance data and the correlation degree between the network performance data and other network performance data includes: For any target interval, the time interval corresponding to the missing interval is taken as the missing time period, and the time interval corresponding to the target interval is taken as the target time period, and the numerical difference between the missing time period and the target time period of other network performance data is calculated; Determine the degree of difference in the correlation index between the missing interval and the target interval based on the degree of correlation between each numerical difference and the network performance data and other network performance data; The calculating the difference degree of the correlation index between the missing interval and each target interval based on other network performance data and the correlation degree between the network performance data and other network performance data includes: The following formula (1) is used to calculate the difference degree of the correlation index between the missing interval and each target interval: In the formula, Indicates that the missing interval is The degree of difference in the correlation indicators between the target intervals, Indicates the number of network performance data. In addition to the network performance data, the degree of correlation between the network performance data and the network performance data, Indicates that the missing time period is The network performance data corresponds to numerical values, Indicates that the target time period is The network performance data corresponds to numerical values, Indicated in The number of network performance data shared by the missing time period and the target time period.

2. The method according to claim 1, characterized in that Before correcting the interpolated result based on the reference interval of each missing interval to generate the corrected network performance data, the method further includes: For any missing interval, obtaining the time interval between a reference interval of the missing interval and the missing interval; The step of correcting the interpolated result based on the reference interval of each missing interval to generate corrected network performance data comprises: For any missing interval, correct each interpolated data point in the missing interval based on the reference interval of the missing interval and the time interval to obtain a corrected missing interval; Corrected network performance data is generated based on each corrected missing interval.

3. The method according to claim 2, characterized in that The step of correcting each interpolated data point in the missing interval based on the reference interval of the missing interval and the time interval comprises: The following formula (2) is used to correct each data point after interpolation in the missing interval: In the formula, Indicates the missing interval The corrected value of the data point, Indicates the missing interval The interpolation result of data points is Indicates that the reference interval of the missing interval is The value of the data point corresponding to the data point, The time interval between the reference interval representing the missing interval and the missing interval.

4. The method according to claim 1, characterized in that Each target interval of the missing interval has the same number of data as that of the missing interval.

5. A FTTR network data quality optimization device, characterized in that: include: A data collection module, used to collect various network performance data of a target terminal device, wherein the target terminal device is a terminal device under the FTTR architecture; A correlation degree building module, for obtaining, for any network performance data of a target terminal device, a plurality of missing intervals of the network performance data and calculating the correlation degree between the network performance data and other network performance data; A reference interval acquisition module, for determining, for any missing interval, a plurality of target intervals of the missing interval from the network performance data, calculating an impact factor between the missing interval and each target interval based on the network performance data, other network performance data, and the degree of correlation between the network performance data and other network performance data, and taking the target interval corresponding to the minimum impact factor as a reference interval for the missing interval; An interpolation correction module, used to interpolate the network performance data using a preset data interpolation algorithm, and correct the interpolated result based on the reference interval of each missing interval to generate corrected network performance data; The reference interval acquisition module is further used to calculate the actual numerical difference between the missing interval and each target interval based on the network performance data; Calculate the difference degree of the correlation index between the missing interval and each target interval based on other network performance data and the correlation degree between the network performance data and other network performance data; Determine the impact factor between the missing interval and each target interval based on the difference between the actual value and the difference between the associated index; The reference interval acquisition module is further used to calculate the difference in values ​​of other network performance data between the missing time period and the target time period, with respect to any target time period, by taking the time period corresponding to the missing time period as the missing time period and the time period corresponding to the target time period; Determine the degree of difference in the correlation index between the missing interval and the target interval based on the degree of correlation between each numerical difference and the network performance data and other network performance data; The reference interval acquisition module is also used to calculate the difference degree of the association index between the missing interval and each target interval using the following formula (1): In the formula, Indicates that the missing interval is The degree of difference in the correlation indicators between the target intervals, Indicates the number of network performance data. In addition to the network performance data, the degree of correlation between the network performance data and the network performance data, Indicates that the missing time period is The network performance data corresponds to numerical values, Indicates that the target time period is The network performance data corresponds to numerical values, Indicated in The number of network performance data shared by the missing time period and the target time period.

6. A FTTR network data quality optimization device, characterized in that: include: at least one processor; And, a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the method as described in any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that: A computer program is stored, and when the computer program is executed by a processor, the method according to any one of claims 1 to 4 is implemented.

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