Processing method and device for fusion of account data and storage medium

Abstract

The application provides a processing method and device of fusion billing data and a storage medium, and relates to the field of operator billing processing. The method comprises the following steps: determining a target set from M pieces of fusion billing data to be processed, the target set comprising two-dimensional data, i.e. broadband billing data and network migration billing data; grouping the target set according to the same set field and the number of associated element fields to obtain N first subsets, each first subset comprising at least one piece of broadband billing data and at least one piece of network migration billing data; respectively splitting the broadband billing data and the network migration billing data in the N first subsets to obtain N third subsets and N fourth subsets; selecting at least one target broadband billing data from each third subset and at least one group of target network migration billing data from each fourth subset; and calculating the fusion billing data of each user based on the at least one target broadband billing data and the at least one group of target network migration billing data.

Description

Technical Field

[0001] This application relates to the field of operator billing processing, and in particular to a processing method, apparatus and storage medium for merging billing data. Background Technology

[0002] With the continuous development of the telecommunications industry, billing and settlement for digital home networks and broadband converged services are facing unprecedented challenges. In response to the rapid development of converged networks, operators are vigorously promoting and applying broadband and mobile network convergence strategies. Faced with complex data sources numbering in the tens or even hundreds of millions, rapid, accurate, and timely billing and settlement for customers is particularly important and poses a significant challenge to the business support system.

[0003] Currently, the processing methods for operator bundled billing data generally involve iterative calculations or manual calculations using Excel, which accumulate different bundled bills from the operator. This method is inefficient and prone to errors.

[0004] Thus, the existing solution has the following problems: the calculation efficiency of integrating billing data is low and the calculation is prone to errors. Summary of the Invention

[0005] This application provides a method, apparatus, and storage medium for processing fused billing data, which can solve the problems of low computational efficiency and easy error in fused billing data.

[0006] To achieve the above objectives, this application adopts the following technical solution:

[0007] Firstly, this application provides a method for processing fused billing data, the method comprising:

[0008] From the M unprocessed converged billing data entries, a target set is determined. The M converged billing data entries represent the converged billing data of all users within a preset time period from the target operator. The target set includes two dimensions of data from the converged billing data: broadband billing data and mobile billing data, where M is a positive integer. Based on the number of common set fields and related element fields, the target set is grouped into N first subsets. Each first subset includes at least one broadband billing data entry and at least one mobile billing data entry, where N is a positive integer less than or equal to M. The broadband billing data and mobile billing data within the N first subsets are then further split to obtain N third subsets and N fourth subsets. Each third subset includes broadband billing data from a first subset, and each fourth subset includes mobile billing data from a first subset. At least one target broadband billing data is selected from each third subset, and at least one set of target mobile billing data is selected from each fourth subset. Each target broadband billing data is the first broadband billing data in each column of broadband billing data in each third subset, and each set of target mobile billing data is each column of mobile billing data in each fourth subset. Based on at least one target broadband billing data and at least one set of target mobile billing data, the converged billing data for each user in the target operator within a preset time period is calculated.

[0009] Based on the above technical solution, the method for processing converged billing data provided in this application embodiment can determine the target set from the M converged billing data to be processed by the operator, and then group the target set by the number of identical set fields and related element fields to divide the large set (i.e., the target set) into multiple subsets. Then, the subsets are split to obtain subsets of broadband billing data and subsets of mobile billing data. At least one target broadband billing data and at least one set of target mobile billing data are selected from the subsets of broadband billing data and the subsets of mobile billing data, respectively. The converged billing data for each user is calculated using at least one target broadband billing data and at least one set of target mobile billing data. This achieves dimensionality reduction processing of converged billing data. By reducing dimensionality, the calculation of complex data is transformed into the calculation of two one-dimensional arrays. Finally, the result can be obtained by a simple two-dimensional summation. This greatly reduces the processing time and computational complexity of converged billing data, and improves the processing efficiency and accuracy of converged billing data.

[0010] In the first possible implementation of the first aspect, before determining the target set from the M unprocessed converged billing data, the method further includes: obtaining M converged billing data from the target operator's system, each converged billing data including four dimensions: broadband account, broadband billing data, mobile account, and mobile billing data; determining the target set from the M unprocessed converged billing data includes: storing the broadband billing data and mobile billing data from the M converged billing data in a two-dimensional list format to obtain the target set.

[0011] In the second possible implementation of the first aspect, the target set is grouped according to the number of identical set fields and associated element fields to obtain N first subsets, including:

[0012] Merged billing data with the same number of associated element fields corresponding to the same set field in the target set are divided into a first subset, resulting in N first subsets.

[0013] In the third possible implementation of the first aspect, before splitting the broadband billing data and mobile billing data in the N first subsets to obtain the N third subsets and the N fourth subsets, the method further includes: transposing the N first subsets to obtain the transposed N first subsets; wherein each transposed first subset includes at least one row of data, each row of data includes at least one broadband billing data and at least one mobile billing data, and each row of data in each first subset includes the same broadband billing data and different mobile billing data corresponding to the same broadband billing data.

[0014] In the fourth possible implementation of the first aspect, the above-mentioned calculation of the converged billing data of each user in the target operator within a preset time period based on at least one target broadband billing data and at least one set of target mobile network billing data includes: calculating the sum of each set of target mobile network billing data to obtain at least one first data; calculating the sum of each target broadband billing data and its corresponding first data to obtain at least one second data, wherein each second data is the converged billing data of a user in the target operator within a preset time period.

[0015] Secondly, this application provides a processing apparatus for converged billing data, comprising: a determining module, a grouping module, a splitting module, a selecting module, and a calculating module. The determining module is used to determine a target set from M pieces of converged billing data to be processed. The M pieces of converged billing data are the converged billing data of all users within a preset time period in the target operator. The target set includes two dimensions of data from the converged billing data: broadband billing data and mobile billing data, where M is a positive integer. The grouping module is used to group the target set according to the number of common set fields and the number of associated element fields, obtaining N first subsets. Each first subset includes at least one broadband billing data and at least one mobile billing data, where N is a positive integer less than or equal to M. The splitting module is used to split the broadband billing data and mobile billing data in the N first subsets respectively, obtaining N third subsets and N... A fourth subset, each third subset including broadband billing data from a first subset, and each fourth subset including mobile billing data from a first subset; a selection module, used to select at least one target broadband billing data from each third subset and at least one set of target mobile billing data from each fourth subset, wherein each target broadband billing data is the first broadband billing data in each column of broadband billing data in each third subset, and each set of target mobile billing data is each column of broadband billing data in each fourth subset; a calculation module, used to calculate the converged billing data for each user in the target operator within a preset time period based on at least one target broadband billing data and at least one set of target mobile billing data.

[0016] In a first possible implementation of the second aspect, the apparatus further includes an acquisition module. The acquisition module is used to acquire M pieces of converged billing data from the target operator's system before the determining module determines the target set from the M pieces of converged billing data to be processed. Each piece of converged billing data includes four dimensions: broadband account, broadband billing data, mobile account, and mobile billing data. Specifically, the determining module is used to store the broadband billing data and mobile billing data from the M pieces of converged billing data in a two-dimensional list format to obtain the target set.

[0017] In the second possible implementation of the second aspect, the aforementioned grouping module is specifically used to divide the fused billing data with the same number of associated element fields corresponding to the same set field in the target set into a first subset, so as to obtain N first subsets.

[0018] In a third possible implementation of the second aspect, the above-mentioned apparatus further includes a transposition module. The transposition module is used to transpose the N first subsets before the splitting module splits the broadband billing data and mobile billing data in the N first subsets to obtain N third subsets and N fourth subsets, respectively, to obtain N transposed first subsets; wherein each transposed first subset includes at least one row of data, each row of data includes at least one broadband billing data and at least one mobile billing data, and each row of data in each first subset includes the same broadband billing data and different mobile billing data corresponding to the same broadband billing data.

[0019] In the fourth possible implementation of the second aspect, the above calculation module is specifically used to: calculate the sum of each group of target mobile network billing data to obtain at least one first data; and calculate the sum of each target broadband billing data and its corresponding first data to obtain at least one second data, wherein each second data is the converged billing data of a user in the target operator within a preset time period.

[0020] Thirdly, this application provides a processing apparatus for merging billing data, the apparatus comprising: a processor and a communication interface; the communication interface and the processor are coupled, the processor being used to run computer programs or instructions to implement the processing method for merging billing data as described in the first aspect and any possible implementation thereof.

[0021] Fourthly, this application provides a computer-readable storage medium storing instructions that, when executed on a terminal, cause the terminal to perform a processing method for merging billing data as described in the first aspect and any possible implementation thereof.

[0022] Fifthly, embodiments of this application provide a computer program product containing instructions that, when run on a processing apparatus for merging billing data, cause the processing apparatus for merging billing data to perform the merging billing data processing method as described in the first aspect and any possible implementation thereof.

[0023] In a sixth aspect, embodiments of this application provide a chip including a processor and a communication interface, the communication interface being coupled to the processor, the processor being used to run computer programs or instructions to implement the processing method for merging billing data as described in the first aspect and any possible implementation thereof.

[0024] Specifically, the chip provided in this application embodiment also includes a memory for storing computer programs or instructions. Attached Figure Description

[0025] Figure 1One of the flowcharts for a method of processing fused billing data provided in this application embodiment;

[0026] Figure 2 A second flowchart illustrating a method for processing fused billing data provided in this application embodiment;

[0027] Figure 3 A flowchart of a method for processing fused billing data provided in this application embodiment;

[0028] Figure 4 A flowchart of a method for processing fused billing data provided in this application embodiment;

[0029] Figure 5 The fifth flowchart of a method for processing fused billing data provided in this application embodiment;

[0030] Figure 6 A schematic diagram of a processing device for integrating billing data provided in this application embodiment;

[0031] Figure 7 A schematic diagram of another processing apparatus for integrating billing data provided in this application embodiment;

[0032] Figure 8 This is a schematic diagram of the structure of a chip provided in an embodiment of this application. Detailed Implementation

[0033] The processing method and apparatus for merging billing data provided in the embodiments of this application will be described in detail below with reference to the accompanying drawings.

[0034] In this article, the term "and / or" is merely a description of the relationship between related objects, indicating that there can be three relationships. For example, A and / or B can represent three situations: A exists alone, A and B exist simultaneously, and B exists alone.

[0035] The terms "first" and "second," etc., used in the specification and drawings of this application are used to distinguish different objects or to distinguish different treatments of the same object, rather than to describe a specific order of objects.

[0036] Furthermore, the terms "comprising" and "having," and any variations thereof, used in the description of this application are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the steps or units listed, but may optionally include other steps or units not listed, or may optionally include other steps or units inherent to such process, method, product, or apparatus.

[0037] It should be noted that in the embodiments of this application, the words "exemplary" or "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design scheme described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design schemes. Specifically, the use of the words "exemplary" or "for example" is intended to present the relevant concepts in a specific manner.

[0038] In the description of this application, unless otherwise stated, "a plurality of" means two or more.

[0039] With the continuous development of the telecommunications industry, billing and settlement for digital home networks and broadband converged services are facing unprecedented challenges. In response to the rapid development of converged networks, operators are vigorously promoting and applying broadband and mobile network convergence strategies. Faced with complex data sources numbering in the tens or even hundreds of millions, timely and accurate monthly billing and settlement for customers is particularly important and poses a significant challenge to the business support system. Therefore, how to quickly and accurately process operators' converged billing data and calculate the converged billing data for each user to support timely monthly billing and settlement for customers has become an urgent problem for operators to solve.

[0040] Currently, the processing of existing operator converged billing data generally adopts iterative calculation or manual calculation in Excel, which involves accumulating different converged bills from different operators. This method is inefficient and prone to errors.

[0041] To address the issues of low computational efficiency and susceptibility to errors in the computation of operator converged billing data in existing technologies, this application provides a method for processing converged billing data. This method identifies a target set from M pieces of converged billing data to be processed by the operator. Then, the target set is grouped based on the number of identical set fields and related element fields, effectively dividing the large set (i.e., the target set) into multiple subsets. These subsets are then further split to obtain subsets of broadband billing data and mobile billing data. At least one set of target broadband billing data and at least one set of target mobile billing data are selected from each subset. Using these subsets, the converged billing data for each user is calculated. This method achieves dimensionality reduction of the converged billing data, transforming the computation of complex data into calculations on two one-dimensional arrays. A simple two-dimensional summation yields the result, significantly reducing the computation time and complexity of converged billing data processing and improving its efficiency and accuracy.

[0042] The method for processing bundled billing data in this application is applied to scenarios where operators settle bundled billing with users.

[0043] like Figure 1 The diagram shows a flowchart of a method for processing fused billing data provided in an embodiment of this application. The method includes the following steps S101 to S105:

[0044] S101. Determine the target set from the M merged billing data to be processed.

[0045] In this embodiment of the application, the aforementioned M bundled billing data are the bundled billing data of all users within a preset time period in the target operator.

[0046] It should be noted that an account includes at least one sub-account, and a single set of integrated billing data refers to the tariff data of an account and the tariff data of a sub-account under that account.

[0047] For example, suppose account A includes three sub-accounts, namely account a1, account a2 and account a3; then the tariff data of account A and the tariff data of account a1 will be combined into one set of integrated billing data, the tariff data of account A and the tariff data of account a2 will be combined into one set of integrated billing data, and the tariff data of account A and the tariff data of account a3 will be combined into one set of integrated billing data.

[0048] It should be noted that each of the above users can be understood as an account. The above account can refer to a broadband account, and the account's tariff data refers to broadband billing data; the sub-account refers to a mobile network account, and the sub-account's tariff data refers to mobile network billing data.

[0049] For example, the preset duration can be the period during which the target operator settles the user's billing data each time, such as a preset duration of 1 month or 1 week.

[0050] In this embodiment of the application, the target set includes two dimensions of data in the fused billing data: broadband billing data and mobile billing data, where M is a positive integer.

[0051] In this embodiment of the application, the target set can be determined from the M bundled billing data to be processed based on the operator's processing capability for bundled billing data.

[0052] Optionally, in one implementation of this application embodiment, combined with Figure 1 ,like Figure 2 As shown, before step S101 above, the method for processing fused billing data provided in this application embodiment further includes step S101A below, and step S101 above can be specifically implemented by step S101B below.

[0053] Step S101A: Obtain M sets of converged billing data from the target operator's system.

[0054] In this embodiment of the application, each of the above M bundled billing data includes four dimensions: broadband account, broadband billing data, mobile account, and mobile billing data.

[0055] As exemplarily shown in Table 1, M sets of fused billing data provided in this application embodiment are illustrated.

[0056] Table 1

[0057]

[0058]

[0059] In Table 1 above, each row of data represents one bundled billing record. For example, broadband account 001, broadband billing data a1, mobile account 011, mobile billing data b1, and time 202209 can be considered as one bundled billing record; broadband account 001, broadband billing data a1, mobile account 012, mobile billing data b2, and time 202209 can be considered as one bundled billing record; and broadband account 002, broadband billing data a2, mobile account 021, mobile billing data b4, and time 202209 can be considered as one bundled billing record. It can be understood that Table 1 includes 21 bundled billing records, which will not be listed one by one here.

[0060] Optionally, each piece of converged billing data may include data in the time dimension, in addition to the four dimensions of broadband account, broadband billing data, mobile account, and mobile billing data.

[0061] Step S101B: Use a two-dimensional list to store broadband billing data and mobile billing data from the M merged billing data to obtain the target set.

[0062] In this embodiment, multiple sets of converged billing data are obtained from the target operator's system, and the broadband billing data and mobile billing data are stored in a two-dimensional list. Using a two-dimensional list can avoid time-consuming and laborious iterative loop calculations, greatly reducing the calculation time and complexity, and improving the processing efficiency of converged billing data.

[0063] As exemplary, Table 2 illustrates the target set provided in embodiments of this application.

[0064] Table 2

[0065]

[0066]

[0067] In Table 2 above, the broadband billing data and mobile billing data from the 21 integrated billing data in Table 1 are stored in a 2D list to obtain the target set, namely Table 2.

[0068] S102. Based on the number of identical set fields and the number of associated element fields, group the target set to obtain N first subsets.

[0069] In this embodiment of the application, each of the first subsets includes at least one broadband billing data and at least one mobile billing data, where N is a positive integer less than or equal to M.

[0070] Optionally, in the embodiments of this application, combined with Figure 1 ,like Figure 3 As shown, the above step S102 can be specifically implemented through the following step S102A.

[0071] Step S102A: Merge the billing data with the same number of associated element fields corresponding to the same set field in the target set into a first subset, so as to obtain N first subsets.

[0072] Optionally, in this embodiment of the application, the fused billing data stored in the two-dimensional list is grouped according to the number of the same set field and the number of associated element fields. A set field with 1 associated element field is grouped into 1 group, a set field with 2 associated element fields is grouped into 1 group, a set field with 3 associated element fields is grouped into 1 group, a set field with 4 associated element fields is grouped into 1 group, a set field with 5 associated element fields is grouped into 1 group, and so on.

[0073] In this embodiment, the fused billing data is split into different arrays according to the project. By grouping, the data dimensionality is reduced, which greatly reduces the computation time and computational complexity and improves the processing efficiency of the fused billing data.

[0074] For example, as shown in Tables 3 to 7, five first subsets provided in the embodiments of this application are illustrated.

[0075]

[0076]

[0077] In Table 3 above, the number of associated element fields corresponding to the same set field is 1, that is, the same broadband billing data corresponds to 1 mobile billing data, such as broadband billing data a4 corresponding to mobile billing data b11.

[0078] In Table 4 above, the number of associated element fields corresponding to the same set field is 2, that is, the same broadband billing data corresponds to 2 mobile billing data, such as broadband billing data a2 corresponding to mobile billing data b4 and mobile billing data b5.

[0079] In Table 5 above, the number of associated element fields corresponding to the same set field is 3. That is, the same broadband billing data corresponds to 3 mobile billing data. For example, broadband billing data a1 corresponds to mobile billing data b1, mobile billing data b2 and mobile billing data b3.

[0080] In Table 6 above, the number of associated element fields corresponding to the same set field is 4. That is, the same broadband billing data corresponds to 4 mobile billing data. For example, broadband billing data a7 corresponds to mobile billing data b16, mobile billing data b17, mobile billing data b18 and mobile billing data b19.

[0081] In Table 7 above, the number of associated element fields corresponding to the same set field is 5. That is, the same broadband billing data corresponds to 5 mobile billing data. For example, broadband billing data a3 corresponds to mobile billing data b6, mobile billing data b7, mobile billing data b8, mobile billing data b9 and mobile billing data b10.

[0082] S103. Split the broadband billing data and mobile billing data in the N first subsets respectively to obtain N third subsets and N fourth subsets.

[0083] Optionally, in this embodiment of the application, each of the above N third subsets includes broadband billing data from a first subset, and each of the above N fourth subsets includes mobile billing data from a first subset.

[0084] In this embodiment of the application, by splitting the first subset into a subset containing only broadband billing data and a subset containing only mobile billing data, the complex calculation is transformed into a calculation on the two subsets, thereby improving the processing efficiency of the fused billing data.

[0085] Optionally, in the embodiments of this application, combined with Figure 1 ,like Figure 4 As shown, prior to step S103 above, the method for processing fused billing data provided in this application embodiment further includes the following step S103A.

[0086] Step S103A: Transpose each of the N first subsets to obtain the transposed N first subsets.

[0087] In this embodiment of the application, each first subset after transposition includes at least one row of data, each row of data includes at least one broadband billing data and at least one mobile network billing data, and each row of data in each first subset includes the same broadband billing data and different mobile network billing data corresponding to the same broadband billing data.

[0088] Optionally, in this embodiment of the application, data dimensionality reduction is achieved by transposing the first subset of the division, which greatly reduces the computation time and computational complexity, thereby improving the processing efficiency of the fused billing data.

[0089] For example, as shown in Tables 8 to 12, five transposed first subsets provided in the embodiments of this application are illustrated.

[0090] Table 8

[0091] a4 b11 a6 b15 ··· ···

[0092] Table 9

[0093]

[0094] Table 10

[0095]

[0096]

[0097] Table 11

[0098]

[0099] Table 12

[0100]

[0101] In Table 8 above, the data in Table 3 is transposed so that each row includes the same broadband billing data and the different mobile network billing data corresponding to the same broadband billing data. For example, there is one identical broadband billing data a4 in Table 3. After transposing, Table 8 will have only one row.

[0102] In Table 9 above, the data in Table 4 is transposed so that each row includes the same broadband billing data and the different mobile network billing data corresponding to the same broadband billing data. For example, there are two identical broadband billing data a2 in Table 4. After transposing, Table 9 will have two rows.

[0103] In Table 10 above, the data in Table 5 is transposed so that each row of data includes the same broadband billing data and the different mobile network billing data corresponding to the same broadband billing data. For example, there are 3 identical broadband billing data a1 in Table 5. After transposing, Table 10 will have 3 rows.

[0104] In Table 11 above, the data in Table 6 is transposed so that each row of data includes the same broadband billing data and the different mobile network billing data corresponding to the same broadband billing data. For example, there are 4 identical broadband billing data a7 in Table 6. After transposing, Table 11 will have 4 rows.

[0105] In Table 12 above, the data in Table 7 is transposed so that each row includes the same broadband billing data and the different mobile network billing data corresponding to the same broadband billing data. For example, there are 5 identical broadband billing data a3 in Table 7. After transposing, Table 12 will have 5 rows.

[0106] As exemplarily, Tables 13 to 17 show five third subsets provided in the embodiments of this application, and Tables 18 to 22 show five fourth subsets provided in the embodiments of this application.

[0107]

[0108]

[0109] In Table 13 above, the broadband billing data and mobile billing data in Table 8 are split, and the broadband billing data in Table 8 is put into a third subset, namely Table 13.

[0110] In Table 14 above, the broadband billing data and mobile billing data in Table 9 are separated, and the broadband billing data in Table 9 is put into a third subset, namely Table 14.

[0111] In Table 15 above, the broadband billing data and mobile billing data in Table 10 are split, and the broadband billing data in Table 10 is put into a third subset, namely Table 15.

[0112] In Table 16 above, the broadband billing data and mobile billing data in Table 11 are split, and the broadband billing data in Table 11 is put into a third subset, namely Table 16.

[0113] In Table 17 above, the broadband billing data and mobile billing data in Table 12 are split, and the broadband billing data in Table 12 is put into a third subset, namely Table 17.

[0114] In Table 18 above, the broadband billing data and mobile billing data in Table 8 are split, and the mobile billing data in Table 8 is put into a fourth subset, namely Table 18.

[0115] In Table 19 above, the broadband billing data and mobile billing data in Table 9 are separated, and the mobile billing data in Table 9 is placed into a fourth subset, namely Table 19.

[0116] In Table 20 above, the broadband billing data and mobile billing data in Table 10 are split, and the mobile billing data in Table 10 is put into a fourth subset, namely Table 20.

[0117] In Table 21 above, the broadband billing data and mobile billing data in Table 11 are separated, and the mobile billing data in Table 11 is put into a fourth subset, namely Table 21.

[0118] In Table 22 above, the broadband billing data and mobile billing data in Table 12 are split, and the mobile billing data in Table 12 is put into a fourth subset, namely Table 22.

[0119] S104. Select at least one target broadband billing data from each third subset, and select at least one set of target mobile billing data from each fourth subset.

[0120] In this embodiment of the application, each of the above-mentioned target broadband billing data is the first broadband billing data in each column of broadband billing data in each third subset, and each of the above-mentioned target mobile network billing data is each column of mobile network billing data in each fourth subset.

[0121] In this embodiment, by selecting the target broadband billing data in the third subset and the target mobile billing data in the fourth subset, the complex calculation is transformed into a calculation of two one-dimensional arrays, and the result can be obtained by a simple two-dimensional summation, thereby improving the processing efficiency of the fused billing data.

[0122] For example, as shown in Tables 23 to 27, target broadband billing data is shown in each third subset provided in the embodiments of this application; and as shown in Tables 28 to 32, target mobile billing data is shown in each fourth subset provided in the embodiments of this application.

[0123]

[0124] In Table 23 above, the broadband billing data in Table 23 is the first broadband billing data in each column of broadband billing data in Table 13. For example, broadband billing data a4 in Table 23 is the first broadband billing data a4 in the first column of Table 13.

[0125] In Table 24 above, the broadband billing data in Table 24 is the first broadband billing data in each column of broadband billing data in Table 14. For example, broadband billing data a2 in Table 24 is the first broadband billing data a2 in the first column of Table 14.

[0126] In Table 25 above, the broadband billing data in Table 25 is the first broadband billing data in each column of broadband billing data in Table 15. For example, broadband billing data a1 in Table 25 is the first broadband billing data a1 in the first column of Table 15.

[0127] In Table 26 above, the broadband billing data in Table 26 is the first broadband billing data in each column of broadband billing data in Table 16. For example, broadband billing data a7 in Table 26 is the first broadband billing data a7 in the first column of Table 16.

[0128] In Table 27 above, the broadband billing data in Table 27 is the first broadband billing data in each column of broadband billing data in Table 17. For example, broadband billing data a3 in Table 27 is the first broadband billing data a3 in the first column of Table 17.

[0129] In Table 28 above, the relocation billing data in Table 28 is the relocation billing data for each column in Table 18. For example, the relocation billing data b11 in Table 28 is the relocation billing data a3 selected from the first column of Table 18.

[0130] In Table 29 above, the relocation billing data in Table 29 is the relocation billing data in each column of Table 19. For example, the relocation billing data (b4+b5) in Table 29 is the relocation billing data b4 and relocation billing data b5 selected from the first column of Table 19.

[0131] In Table 30 above, the relocation billing data in Table 30 is the relocation billing data in each column of Table 20. For example, the relocation billing data (b1+b2+b3) in Table 30 is the relocation billing data b1, b2 and b3 selected from the first column of Table 20.

[0132] In Table 31 above, the relocation billing data in Table 31 is the relocation billing data of each column in Table 21. For example, the relocation billing data (b16+b17+b18+19) in Table 31 is the relocation billing data b16, b17, b18 and b19 selected from the first column of Table 21.

[0133] In Table 32 above, the relocation billing data in Table 32 is the relocation billing data for each column in Table 22. For example, the relocation billing data (b6+b7+b8+b9+b10) in Table 32 is the relocation billing data b6, b7, b8, b9 and b10 selected from the first column of Table 22.

[0134] S105. Based on at least one target broadband billing data and at least one set of target mobile billing data, calculate the converged billing data for each user in the target operator within a preset time period.

[0135] In this embodiment, each target broadband billing data corresponds to a set of target mobile network billing data, and each target broadband billing data corresponds to a broadband account, i.e., one user among all the aforementioned users. Based on a target broadband billing data and its corresponding set of target mobile network billing data, the converged billing data for a user corresponding to that target broadband billing data can be calculated.

[0136] Optionally, in the embodiments of this application, combined with Figure 1 ,like Figure 5 As shown, step S105 can be implemented through steps S105A and S105B as described below.

[0137] Step S105A: Calculate the sum of the target network migration data for each group to obtain at least one first data.

[0138] It is understandable that the sum of at least one set of target mobile network billing data is at least one first data, and the sum of one set of target mobile network billing data is a first data.

[0139] For example, referring to Table 29 above, the mobile network billing data b4 and mobile network billing data b5 in Table 29 are added together to obtain a first data (b4+b5); referring to Table 30 above, the mobile network billing data b1, mobile network billing data b2 and mobile network billing data b3 in Table 30 are added together to obtain a first data (b1+b2+b3), and so on.

[0140] Step S105B: Calculate the sum of each target broadband billing data and its corresponding first data to obtain at least one second data.

[0141] In this embodiment of the application, each of the at least one second data is the bundled billing data of a user within a preset time period in the target operator.

[0142] In this embodiment, the sum of the billing data for each group of target mobile networks is first calculated, and then the sum of the billing data for each group of target mobile networks is summed with the corresponding target broadband billing data to finally obtain the converged billing data for each user.

[0143] As exemplarily shown in Table 33, the converged billing data for each user within a preset duration in the target operator provided in the embodiments of this application are illustrated.

[0144] Table 33

[0145]

[0146] In Table 33 above, the first unified billing result is the sum of the first broadband billing data in Table 23 and the corresponding first mobile billing data in Table 28, which is used as the unified billing result for broadband account 004 corresponding to broadband billing data a4; the second unified billing result is the sum of the first broadband billing data in Table 24 and the corresponding first mobile billing data in Table 29, which is used as the unified billing result for broadband account 002 corresponding to broadband billing data a2; the third unified billing result is the sum of the first broadband billing data in Table 25 and the corresponding first mobile billing data in Table 30. The sum of the broadband billing data is calculated by adding the broadband billing data a1 together, and this sum is used as the bundled billing result for broadband account 001 corresponding to broadband billing data a1. The fourth bundled billing result is the sum of the first broadband billing data in Table 26 and the first mobile billing data in Table 31 corresponding to it, and this sum is used as the bundled billing result for broadband account 007 corresponding to broadband billing data a7. The fifth bundled billing result is the sum of the first broadband billing data in Table 27 and the first mobile billing data in Table 32 corresponding to it, and this sum is used as the bundled billing result for broadband account 003 corresponding to broadband billing data a3.

[0147] This application provides a method for processing converged billing data. It can determine a target set from M pieces of converged billing data to be processed by the operator, and then group the target set by the number of identical set fields and related element fields to divide the large set (i.e., the target set) into multiple subsets. These subsets are then further split to obtain subsets of broadband billing data and mobile billing data. At least one set of target broadband billing data and at least one set of target mobile billing data are selected from these subsets, respectively. The converged billing data for each user is then calculated using at least one set of target broadband billing data and at least one set of target mobile billing data. This method achieves dimensionality reduction of the converged billing data, transforming the calculation of complex data into calculations on two one-dimensional arrays. Finally, a simple two-dimensional summation yields the result, significantly reducing the processing time and computational complexity of the converged billing data, and improving the processing efficiency and accuracy.

[0148] This application embodiment can divide the processing device for merging billing data into functional modules or functional units according to the above method example. For example, each function can be divided into a separate functional module or functional unit, or two or more functions can be integrated into one processing module. The integrated module can be implemented in hardware or in software functional modules or functional units. The module or unit division in this application embodiment is illustrative and only represents one logical functional division; other division methods may be used in actual implementation.

[0149] like Figure 6 The diagram shown is a structural schematic of a processing device for merging billing data provided in an embodiment of this application. The device includes: a determination module 41, a grouping module 42, a splitting module 43, a selection module 44, and a calculation module 45.

[0150] The determination module 41 is used to determine the target set from the M unprocessed converged billing data. The M converged billing data are the converged billing data of all users in the target operator within a preset time period. The target set includes two dimensions of data in the converged billing data: broadband billing data and mobile billing data, where M is a positive integer. The grouping module 42 is used to group the target set according to the number of common set fields and related element fields, obtaining N first subsets. Each first subset includes at least one broadband billing data and at least one mobile billing data, where N is a positive integer less than or equal to M. The splitting module 43 is used to split the broadband billing data and mobile billing data in the N first subsets respectively, obtaining N third subsets and... N fourth subsets, each third subset includes broadband billing data from one first subset, and each fourth subset includes mobile billing data from one first subset; selection module 44 is used to select at least one target broadband billing data from each third subset and at least one set of target mobile billing data from each fourth subset, where each target broadband billing data is the first broadband billing data in each column of broadband billing data in each third subset, and each set of target mobile billing data is each column of mobile billing data in each fourth subset; calculation module 45 is used to calculate the converged billing data for each user in the target operator within a preset time period based on at least one target broadband billing data and at least one set of target mobile billing data.

[0151] In one possible implementation, the above apparatus further includes an acquisition module. The acquisition module is used to acquire M pieces of converged billing data from the target operator's system before the determining module 41 determines the target set from the M pieces of converged billing data to be processed. Each piece of converged billing data includes four dimensions: broadband account, broadband billing data, mobile account, and mobile billing data. Specifically, the determining module 41 is used to store the broadband billing data and mobile billing data from the M pieces of converged billing data in a two-dimensional list format to obtain the target set.

[0152] In one possible implementation, the grouping module 42 is specifically used to divide the fused billing data with the same number of associated element fields corresponding to the same set field in the target set into a first subset, so as to obtain N first subsets.

[0153] In one possible implementation, the above apparatus further includes a transposition module. The transposition module is used to transpose the N first subsets before the splitting module 43 splits the broadband billing data and mobile billing data in the N first subsets to obtain N third subsets and N fourth subsets, respectively, to obtain N transposed first subsets; wherein each transposed first subset includes at least one row of data, each row of data includes at least one broadband billing data and at least one mobile billing data, and each row of data in each first subset includes the same broadband billing data and different mobile billing data corresponding to the same broadband billing data.

[0154] In one possible implementation, the above-mentioned calculation module 45 is specifically used to: calculate the sum of each group of target mobile network billing data to obtain at least one first data; calculate the sum of each target broadband billing data and its corresponding first data to obtain at least one second data, wherein each second data is the converged billing data of a user in the target operator within a preset time period.

[0155] When implemented in hardware, the determining module 41, grouping module 42, splitting module 43, selecting module 44, calculating module 45, and transposing module in this embodiment can be integrated onto the processor. Specific implementation methods are as follows: Figure 7 As shown.

[0156] Figure 7 A schematic diagram of another possible structure of the processing apparatus for fused billing data involved in the above embodiments is shown. This processing apparatus for fused billing data includes a processor 302 and a communication interface 303. The processor 302 is used to control and manage the operation of the processing apparatus for fused billing data, for example, executing the steps performed by the determination module 41, grouping module 42, splitting module 43, selection module 44, calculation module 45, and transposition module, and / or to execute other processes of the technology described herein. The communication interface 303 is used to support communication between the processing apparatus for fused billing data and other network entities, for example, executing the steps performed by the acquisition unit. The processing apparatus for fused billing data may also include a memory 301 and a bus 304. The memory 301 is used to store the program code and data of the processing apparatus for fused billing data.

[0157] The memory 301 may be a memory in a processing device for merging billing data, and the memory may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as read-only memory, flash memory, hard disk or solid-state drive; the memory may also include a combination of the above types of memory.

[0158] The processor 302 described above can implement or execute various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. The processor can be a central processing unit, a general-purpose processor, a digital signal processor, an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. The processor can also be a combination that implements computational functions, such as including one or more microprocessor combinations, a combination of a DSP and a microprocessor, etc.

[0159] Bus 304 can be an Extended Industry Standard Architecture (EISA) bus, etc. Bus 304 can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 7 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0160] Figure 8 This is a schematic diagram of the structure of chip 170 provided in an embodiment of this application. Chip 170 includes one or more (including two) processors 1710 and communication interfaces 1730.

[0161] Optionally, the chip 170 also includes a memory 1740, which may include read-only memory and random access memory, and provides operation instructions and data to the processor 1710. A portion of the memory 1740 may also include non-volatile random access memory (NVRAM).

[0162] In some implementations, memory 1740 stores elements such as execution modules or data structures, or subsets thereof, or extended sets thereof.

[0163] In this embodiment of the application, the corresponding operation is executed by calling the operation instructions stored in the memory 1740 (the operation instructions can be stored in the operating system).

[0164] The processor 1710 described above can implement or execute various exemplary logic blocks, units, and circuits described in conjunction with the disclosure of this application. The processor can be a central processing unit, a general-purpose processor, a digital signal processor, an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute various exemplary logic blocks, units, and circuits described in conjunction with the disclosure of this application. The processor can also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0165] The memory 1740 may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as read-only memory, flash memory, hard disk or solid-state drive; the memory may also include combinations of the above types of memory.

[0166] The Bus 1720 can be an Extended Industry Standard Architecture (EISA) bus, etc. The Bus 1720 can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 8 The symbol is represented by only one line, but this does not mean that there is only one bus or one type of bus.

[0167] Through the above description of the embodiments, those skilled in the art will clearly understand that, for the sake of convenience and brevity, only the division of the above functional modules is used as an example. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. The specific working process of the system, device, and unit described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0168] This application provides a computer program product containing instructions that, when run on a computer, cause the computer to execute the data processing method for merging billing data described in the above method embodiments.

[0169] This application also provides a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform the fusion billing data processing method in the method flow shown in the above method embodiments.

[0170] The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: electrical connections having one or more wires; portable computer disks; hard disks; random access memory (RAM); read-only memory (ROM); erasable programmable read-only memory (EPROM); registers; hard disks; optical fibers; portable compact disc read-only memory (CD-ROM); optical storage devices; magnetic storage devices; or any suitable combination thereof; or any other form of computer-readable storage medium known in the art. An exemplary storage medium is coupled to a processor, enabling the processor to read information from and write information to the storage medium. Of course, the storage medium may also be a component of the processor. The processor and the storage medium may reside in an application-specific integrated circuit (ASIC). In the embodiments of this application, the computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0171] Embodiments of the present invention provide a computer program product containing instructions that, when executed on a computer, cause the computer to perform the processing method for merging billing data as described in the above embodiments.

[0172] Since the processing device for merging billing data, the computer-readable storage medium, and the computer program product in the embodiments of the present invention can be applied to the above-described method, the technical effects that can be obtained can also be referred to the above-described method embodiments. The embodiments of the present invention will not be described again here.

[0173] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0174] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0175] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0176] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for processing integrated billing data, characterized in that, The method includes: Determine the target set from the M unprocessed bundled billing data, wherein the M bundled billing data are the bundled billing data of all users in the target operator within a preset time period, and the target set includes two dimensions of data in the bundled billing data: broadband billing data and mobile billing data, where M is a positive integer; The fused billing data with the same number of associated element fields corresponding to the same set field in the target set are divided into a first subset to obtain N first subsets; each first subset includes at least one broadband billing data and at least one mobile network billing data, where N is a positive integer less than or equal to M; The N first subsets are transposed to obtain the N transposed first subsets; wherein each transposed first subset includes at least one row of data, each row of data includes at least one broadband billing data and at least one mobile billing data, and each row of data in each first subset includes the same broadband billing data and different mobile billing data corresponding to the same broadband billing data; The broadband billing data and mobile billing data in the transposed N first subsets are split into N third subsets and N fourth subsets respectively. Each third subset includes one broadband billing data in the first subset, and each fourth subset includes one mobile billing data in the first subset. At least one target broadband billing data is selected from each third subset, and at least one set of target mobile billing data is selected from each fourth subset. Each target broadband billing data is the first broadband billing data in each column of broadband billing data in each third subset, and each set of target mobile billing data is each column of mobile billing data in each fourth subset. Based on the at least one target broadband billing data and the at least one set of target mobile billing data, the converged billing data for each user within the preset time period of the target operator is calculated.

2. The method according to claim 1, characterized in that, Before determining the target set from the M merged billing data to be processed, the method further includes: The M bundled billing data entries are obtained from the target operator's system. Each bundled billing data entry includes four dimensions: broadband account, broadband billing data, mobile account, and mobile billing data. Determining the target set from the M merged billing data to be processed includes: The broadband billing data and mobile billing data from the M fused billing data are stored in a two-dimensional list format to obtain the target set.

3. The method according to claim 1, characterized in that, The step of calculating the converged billing data for each user within the preset duration in the target operator based on the at least one target broadband billing data and the at least one set of target mobile billing data includes: Calculate the sum of the outgoing data for each group of target mobile networks to obtain at least one first data point; The sum of each target broadband billing data and its corresponding first data is calculated to obtain at least one second data. Each second data is the converged billing data of a user in the target operator within the preset time period.

4. A processing device for integrating billing data, characterized in that, The device includes: a determination module, a grouping module, a transpose module, a splitting module, a selection module, and a calculation module; The determining module is used to determine a target set from the M bundled billing data to be processed. The M bundled billing data are the bundled billing data of all users in the target operator within a preset time period. The target set includes two dimensions of data in the bundled billing data: broadband billing data and mobile billing data, where M is a positive integer. The grouping module is used to divide the fused billing data with the same number of associated element fields corresponding to the same set field in the target set into a first subset, so as to obtain N first subsets; each first subset includes at least one broadband billing data and at least one mobile network billing data, where N is a positive integer less than or equal to M; The transpose module is used to transpose the N first subsets before the splitting module splits the broadband billing data and mobile billing data in the N first subsets to obtain N third subsets and N fourth subsets, to obtain the transposed N first subsets; wherein each transposed first subset includes at least one row of data, each row of data includes at least one broadband billing data and at least one mobile billing data, and each row of data in each first subset includes the same broadband billing data and different mobile billing data corresponding to the same broadband billing data; The splitting module is used to split the broadband billing data and mobile billing data in the transposed N first subsets respectively to obtain N third subsets and N fourth subsets. Each third subset includes broadband billing data from one first subset, and each fourth subset includes mobile billing data from one first subset. The selection module is used to select at least one target broadband billing data from each third subset and at least one set of target mobile billing data from each fourth subset. Each target broadband billing data is the first broadband billing data in each column of broadband billing data in each third subset, and each set of target mobile billing data is each column of mobile billing data in each fourth subset. The calculation module is used to calculate the converged billing data for each user in the target operator within the preset time period based on the at least one target broadband billing data and the at least one set of target mobile billing data.

5. The apparatus according to claim 4, characterized in that, The device further includes: an acquisition module; The acquisition module is used to acquire the M bundled billing data from the system of the target operator before the determining module determines the target set from the M bundled billing data to be processed. Each bundled billing data includes four dimensions: broadband account, broadband billing data, mobile account, and mobile billing data. The determining module is specifically used to store the broadband billing data and mobile billing data in the M fused billing data in a two-dimensional list format to obtain the target set.

6. The apparatus according to claim 4, characterized in that, The computing module is specifically used for: Calculate the sum of the outgoing data for each group of target mobile networks to obtain at least one first data point; The sum of each target broadband billing data and its corresponding first data is calculated to obtain at least one second data. Each second data is the converged billing data of a user in the target operator within the preset time period.

7. A processing apparatus for integrating billing data, characterized in that, include: A processor and a communication interface; the communication interface is coupled to the processor, the processor being used to run computer programs or instructions to implement the processing method for merging billing data as described in any one of claims 1-3.

8. A computer-readable storage medium storing instructions, characterized in that, When the computer executes the instruction, the computer performs the processing method for merging billing data as described in any one of claims 1-3.

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