Data processing method, device, apparatus, system, and storage medium

Abstract

This application provides a data processing method, device, apparatus, system, and storage medium. The method includes: determining a first key performance indicator (KPI) statistical item for data processing; splitting the first KPI statistical item according to a set splitting rule to obtain one or more intermediate counters, each intermediate counter consisting of one or more original counters from the first KPI statistical item; and processing the values ​​of each original counter in the first KPI statistical item through the intermediate counters. This application can improve data processing efficiency and save computing resources and database storage resources.

Description

Technical Field

[0001] This application relates to the field of communication technology, and in particular to a data processing method, device, apparatus, system and storage medium. Background Technology

[0002] In 5G network performance statistics, the raw performance data of base stations needs to be summarized according to different summarization methods and summarization periods such as hours, days, weeks, and months, so as to view the key performance indicators (KPIs) of the network in different time periods.

[0003] However, the aforementioned business scenarios require substantial database computing and storage space. The rapid increase in the number of 5G network base stations and the continuous rise in network KPIs pose significant challenges to existing database computing and storage. Traditional KPI calculation methods not only consume considerable storage resources but also exhibit slow data retrieval speeds. Summary of the Invention

[0004] This application provides a data processing method, device, apparatus, system, and storage medium to address the shortcomings of traditional KPI calculation methods in the prior art, which consume a lot of storage resources and have a slow data retrieval speed, thereby saving database storage and computing resources.

[0005] In a first aspect, embodiments of this application provide a data processing method, including:

[0006] Identify the primary key performance indicator (KPI) statistics for data processing;

[0007] The first KPI statistical item is split according to the set splitting rules to obtain one or more intermediate counters. Each intermediate counter is composed of one or more original counters in the first KPI statistical item.

[0008] The intermediate counter processes the values ​​of each original counter in the first KPI statistical item.

[0009] Optionally, according to a data processing method of one embodiment of this application, the step of splitting the first KPI statistical item according to a set splitting rule includes:

[0010] If the first KPI statistic includes a division operation, then the first KPI statistic is split according to the division operation, and the numerator and denominator of the division operation each correspond to an intermediate counter.

[0011] Optionally, the data processing method according to one embodiment of this application further includes:

[0012] If the intermediate counters split according to the division operation include a set intermediate counter, which is used to represent an intermediate counter that includes at least two data aggregation methods, then the set intermediate counter is split again according to each data aggregation method until each intermediate counter obtained after the second split corresponds to a data aggregation method.

[0013] Optionally, according to a data processing method of one embodiment of this application, the step of splitting the first KPI statistical item according to a set splitting rule includes:

[0014] If the first KPI statistical item includes at least two data aggregation methods, then the first KPI statistical item is split according to each data aggregation method, and each intermediate counter corresponds to one data aggregation method.

[0015] Optionally, according to a data processing method of one embodiment of this application, the at least two data aggregation methods include:

[0016] Calculate the average; and / or

[0017] Summation operation; sum / or

[0018] Maximum value operation; AND / OR

[0019] Minimum value calculation.

[0020] Optionally, the data processing method according to one embodiment of this application further includes:

[0021] If the intermediate counter is not found in the information storage, a number is added to the intermediate counter, and the intermediate counter with the added number is saved to the information storage.

[0022] Optionally, according to a data processing method of one embodiment of this application, the step of processing the values ​​of each original counter in the first KPI statistical item through the intermediate counter includes:

[0023] Based on the first KPI statistical item and the intermediate counter, a second KPI statistical item composed of the intermediate counter is generated;

[0024] Obtain the values ​​of each original counter in the first KPI statistics item;

[0025] Based on the values ​​of each original counter in the first KPI statistical item, determine the values ​​of each intermediate counter in the second KPI statistical item;

[0026] The values ​​of each intermediate counter in the second KPI statistics item are saved to the data storage device, and the smallest storage unit of the data storage device is the intermediate counter.

[0027] Optionally, according to a data processing method of one embodiment of this application, the values ​​of each intermediate counter in the second KPI statistic include the values ​​of each intermediate counter determined according to a set time dimension and / or a set spatial dimension.

[0028] Secondly, embodiments of this application also provide a data processing device, including: a memory, a transceiver, and a processor.

[0029] A memory for storing computer programs; a transceiver for sending and receiving data under the control of the processor; and a processor for reading the computer programs from the memory and performing the following operations:

[0030] Identify the primary key performance indicator (KPI) statistics for data processing;

[0031] The first KPI statistical item is split according to the set splitting rules to obtain at least two intermediate counters, wherein the intermediate counters are composed of one or more original counters in the first KPI statistical item;

[0032] The intermediate counter processes the values ​​of each original counter in the first KPI statistical item.

[0033] Thirdly, embodiments of this application also provide a data processing apparatus, including:

[0034] The determination unit is used to determine the first key performance indicator (KPI) statistical item for data processing.

[0035] The splitting unit is used to split the first KPI statistical item according to a set splitting rule to obtain at least two intermediate counters, wherein the intermediate counters are composed of one or more original counters in the first KPI statistical item;

[0036] The processing unit is used to process the values ​​of each original counter in the first KPI statistical item through the intermediate counter.

[0037] Fourthly, embodiments of this application also provide a data processing system, the data processing system including the data processing device, information storage, and data storage described in the data processing method of the first aspect above; wherein the information storage is used to store an intermediate counter, and the data storage is used to store the value of the intermediate counter.

[0038] Fifthly, embodiments of this application also provide a processor-readable storage medium storing a computer program for causing the processor to perform the steps of the data processing method described in the first aspect above.

[0039] The data processing method, device, apparatus, system, and storage medium provided in this application embodiment can determine a first KPI statistical item for data processing, split the first KPI statistical item according to a set splitting rule, and obtain one or more intermediate counters. Each intermediate counter is composed of one or more original counters in the first KPI statistical item. The values ​​of each original counter in the first KPI statistical item are processed by the intermediate counters, thereby effectively saving the storage and computing resources of the database. Attached Figure Description

[0040] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0041] Figure 1 This is one of the flowcharts illustrating the data processing method provided in the embodiments of this application;

[0042] Figure 2 This is a second schematic flowchart of the data processing method provided in the embodiments of this application;

[0043] Figure 3 This is the third flowchart illustrating the data processing method provided in the embodiments of this application;

[0044] Figure 4 This is the fourth flowchart illustrating the data processing method provided in the embodiments of this application;

[0045] Figure 5 This is a schematic diagram of the structure of the data processing apparatus provided in the embodiments of this application;

[0046] Figure 6 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0047] In the embodiments of this application, the term "and / or" describes the relationship between associated objects, indicating that three relationships can exist. For example, A and / or B can represent three cases: A alone, A and B simultaneously, and B alone. The character " / " generally indicates that the preceding and following associated objects have an "or" relationship.

[0048] In the embodiments of this application, the term "multiple" refers to two or more, and other quantifiers are similar.

[0049] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0050] The following is an explanation of the relevant technical terms and their meanings used in the embodiments of this application:

[0051] Raw counter: The smallest unit of counting performance data reported by a network device for its own operation, such as the number of contexts requested to be released by the next generation node B (gNB), in units of: units.

[0052] Statistical items: These are calculated from several raw counters to facilitate manual understanding of the overall network performance, equivalent to KPIs. For example: Wireless disconnection rate, unit: %.

[0053] Summary method: This refers to the calculation method used when performing operations on statistical items according to time or spatial dimensions. Common methods include Sum (summation) and Avg (average).

[0054] In 5G network performance statistics, KPI values ​​are calculated using formulas based on the raw counters of relevant basic services reported by base stations. For example, Table 1 below shows the relationship between KPIs and raw counters, and Table 2 below shows the raw counters.

[0055] Table 1

[0056]

[0057] Table 2

[0058]

[0059] Among them, "wireless drop rate (cell level)" is an indicator needed for network management and network optimization. The "gNB request to release context number" in the formula is the smallest data recording unit reported by the base station, which is called the raw counter.

[0060] In addition to calculating the KPI value, it is also necessary to calculate the KPI value for different statistical periods, such as hours, days, weeks, and months. Relevant operations and maintenance personnel will then review this data for network management and performance tuning. This requires calculating the KPI level by level, from hours to days, weeks, and months, based on the aggregation method of the original counters. For example, the hourly data for "wireless drop rate (cell level)" is equal to the sum (or difference) of the data from its four 15-minute cycles of the original counter, followed by multiplication (or division). Similarly, the daily data for the original counter is equal to the sum (or difference) of its 24-hour data, followed by multiplication (or division), and so on.

[0061] For example, calculating and acquiring base station-related indicator data for the entire network over a day (week, month, etc.) can result in hundreds of millions of raw counter data entries in a prefecture-level city network, consuming huge storage and computing resources. This not only requires expensive storage space but also results in slow data retrieval speeds.

[0062] Therefore, embodiments of this application provide a data processing method, device, apparatus, system, and storage medium for splitting KPI statistical items into several intermediate counters. By processing the values ​​of each original counter in the KPI statistical items through the intermediate counters, the computational efficiency can be effectively improved and the computational and storage resources of the database can be saved.

[0063] The method and apparatus are based on the same concept of the application. Since the methods and apparatus solve problems in similar ways, the implementation of the apparatus and methods can refer to each other, and the repeated parts will not be described again.

[0064] The technical solutions provided in this application can be applied to various systems, especially 5G systems. For example, applicable systems include Global System for Mobile Communication (GSM), Code Division Multiple Access (CDMA), Wideband Code Division Multiple Access (WCDMA) General Packet Radio Service (GPRS), Long Term Evolution (LTE), LTE Frequency Division Duplex (FDD), LTE Time Division Duplex (TDD), Long Term Evolution Advanced (LTE-A), Universal Mobile Telecommunication System (UMTS), Worldwide Interoperability for Microwave Access (WiMAX), and 5G New Radio (NR). All of these systems include terminal equipment and network equipment. The systems may also include a core network component, such as Evolved Packet System (EPS) and 5G system (5GS).

[0065] The terminal devices involved in the embodiments of this application can be devices that provide voice and / or data connectivity to users, handheld devices with wireless connectivity, or other processing devices connected to a wireless modem. The names of the terminal devices may differ in different systems; for example, in a 5G system, a terminal device can be called User Equipment (UE). Wireless terminal devices can communicate with one or more core networks (CNs) via a Radio Access Network (RAN). Wireless terminal devices can be mobile terminal devices, such as mobile phones (or "cellular" phones) and computers with mobile terminal devices, for example, portable, pocket-sized, handheld, computer-embedded, or vehicle-mounted mobile devices that exchange voice and / or data with the RAN. Examples include Personal Communication Service (PCS) phones, cordless phones, Session Initiated Protocol (SIP) phones, Wireless Local Loop (WLL) stations, and Personal Digital Assistants (PDAs). Wireless terminal equipment can also be referred to as a system, subscriber unit, subscriber station, mobile station, mobile station, remote station, access point, remote terminal, access terminal, user terminal, user agent, or user device, but is not limited to these terms in the embodiments of this application.

[0066] The data processing device involved in this application embodiment can be a base station, which may include multiple cells providing services to terminals. Depending on the specific application, the base station may also be called an access point, or a device in the access network that communicates with the wireless terminal device through one or more sectors on the air interface, or other names. The network device can be used to exchange received air frames with Internet Protocol (IP) packets, acting as a router between the wireless terminal device and the rest of the access network, where the rest of the access network may include an Internet Protocol (IP) communication network. The network device can also coordinate the attribute management of the air interface. For example, the network equipment involved in the embodiments of this application can be a base transceiver station (BTS) in a Global System for Mobile communications (GSM) or Code Division Multiple Access (CDMA), a NodeB in a Wide-band Code Division Multiple Access (WCDMA) system, an evolved Node B (eNB or e-NodeB) in a long term evolution (LTE) system, a 5G base station (gNB) in a next generation system, a Home evolved Node B (HeNB), a relay node, a femto, a pico, etc., and is not limited in the embodiments of this application. In some network structures, the network equipment may include centralized unit (CU) nodes and distributed unit (DU) nodes, and the centralized unit and distributed unit may be geographically separated.

[0067] Data processing equipment and terminal equipment can each use one or more antennas for multiple-input multiple-output (MIMO) transmission. MIMO transmission can be single-user MIMO (SU-MIMO) or multiple-user MIMO (MU-MIMO). Depending on the configuration and number of antenna combinations, MIMO transmission can be 2D-MIMO, 3D-MIMO, FD-MIMO, or massive-MIMO, and can also be diversity transmission, precoding transmission, or beamforming transmission, etc.

[0068] Figure 1 This is one of the flowcharts illustrating a data processing method provided in an embodiment of this application. This data processing method can be used in network devices, such as base stations, core network devices, and signaling collection adapters (SCAs). The data processing method may include the following steps:

[0069] Step 101: Determine the first KPI statistic to be used for data processing;

[0070] Specifically, before data processing, the first KPI statistic is determined. This first KPI statistic can be obtained by calculation from several raw counters and is used to characterize the overall performance of the network.

[0071] Step 102: Split the first KPI statistical item according to the set splitting rules to obtain one or more intermediate counters. Each intermediate counter consists of one or more original counters from the first KPI statistical item.

[0072] Specifically, after determining the first KPI statistical item, it is split according to the set splitting rules, resulting in one or more intermediate counters (Mcounter) to replace the functions of several original counters. Each intermediate counter is a formula composed of some of the original counters from the first KPI statistical item.

[0073] For example, the first KPI statistical item is shown in the following formula (1):

[0074] Wireless disconnection rate (cell level) = (number of gNB requests to release context - number of normal gNB requests to release context) / (number of successful initial context establishments + number of legacy contexts + number of successful handovers + number of successful RRC connection reconstructions (non-source cell)) * 100% ... Formula (1)

[0075] The specific splitting rules are as follows:

[0076] (1) According to the division: M1 = number of gNB requests to release context - number of normal gNB requests to release context, M2 = number of successful initial context establishment + number of legacy contexts + number of successful handover + number of successful RRC connection reconstruction (non-source cell).

[0077] (2) Since the summation methods of the intermediate counters M1 and M2 are both summation, that is, the summation methods are the same, there is no need to split them further.

[0078] (3) The statistical items after splitting are shown in the following formula (2):

[0079] Wireless disconnection rate (cell level) = M1 / M2*100………………..............Formula (2)

[0080] In formula (2), M1 and M2 are the two intermediate counters obtained by splitting the first KPI statistical item in formula (1).

[0081] Step 103: Process the values ​​of each original counter in the first KPI statistical item using intermediate counters.

[0082] Specifically, after splitting the data into several intermediate counters, these intermediate counters can replace the functions of several original counters, performing related summarization, calculation, and storage of the values ​​of each original counter in the first KPI statistical item.

[0083] As can be seen from the above embodiments, by determining the KPI statistical items used for data processing, splitting the first KPI statistical item according to the set splitting rules, one or more intermediate counters are obtained. Each intermediate counter consists of one or more original counters in the first KPI statistical item. The values ​​of each original counter in the first KPI statistical item are processed by the intermediate counters, thereby effectively saving the storage and computing resources of the database.

[0084] Optionally, the first KPI statistic can be split according to the set splitting rules, including:

[0085] If the first KPI statistic includes division, then the first KPI statistic is split according to the division operation, and the numerator and denominator of the division operation each correspond to an intermediate counter.

[0086] Specifically, if the first KPI statistic includes division, to ensure the accuracy of summation, averaging, and other aggregation methods when calculating statistics over time, the numerator and denominator must be calculated first, followed by the division operation, when calculating the first KPI statistic. Therefore, the first KPI statistic needs to be broken down into several counters based on division. Specifically, when breaking down the first KPI statistic according to division, the numerator and denominator of the division operation each correspond to an intermediate counter.

[0087] As can be seen from the above embodiments, by splitting the first KPI statistical item, which includes division operations, according to the division operation, the accuracy of the statistical items of other summarization methods calculated according to the time dimension can be guaranteed.

[0088] Optionally, the data processing method may also include the following steps:

[0089] If the intermediate counters split by division include a set intermediate counter, which is used to represent an intermediate counter that includes at least two data summarization methods, then the set intermediate counter is split again according to each data summarization method until each intermediate counter obtained after the split corresponds to a data summarization method.

[0090] Specifically, since the intermediate counter's calculation engine only supports one aggregation method, it needs to be split according to the aggregation method. The intermediate counter (Mcounter) calculation engine calculates the value of the intermediate counter (Mcounter) based on the intermediate counter (Mcounter) formula and the value of its original counter, according to business needs.

[0091] After splitting the first KPI statistical item, which includes division operations, into intermediate counters, if the split intermediate counters include a set intermediate counter, which is used to represent an intermediate counter that includes at least two data aggregation methods, then the set intermediate counter is split again according to each data aggregation method until each intermediate counter obtained after the split corresponds to a data aggregation method.

[0092] As can be seen from the above embodiments, by further splitting the first KPI statistical item according to the aggregation method, the calculation speed can be improved and the computing resources can be saved.

[0093] Optionally, the first KPI statistic can be split according to the set splitting rules, including:

[0094] If the first KPI statistical item includes at least two data aggregation methods, then the first KPI statistical item is split according to each data aggregation method, and each intermediate counter corresponds to one data aggregation method.

[0095] Specifically, each intermediate counter corresponds to a data aggregation method. Therefore, if the first KPI statistic includes at least two data aggregation methods, the first KPI statistic needs to be split according to each aggregation method.

[0096] As can be seen from the above embodiments, by splitting the first KPI statistical item according to each data aggregation method, the calculation speed can be improved and the computing resources can be saved.

[0097] Optional, at least two data aggregation methods may be included:

[0098] Calculate the average (avg); and / or

[0099] The summation operation (i.e., sum); and / or

[0100] Maximum value operation (i.e., max); and / or

[0101] The minimum value operation (i.e., min).

[0102] Specifically, if the first KPI statistical item has at least two data aggregation methods, then the first KPI statistical item is split according to each data aggregation method. The at least two aggregation methods include averaging; summing; finding the maximum value; and / or finding the minimum value.

[0103] As can be seen from the above embodiments, by further limiting the aggregation method, the first KPI statistical item can be better split.

[0104] Optionally, the data processing method may also include the following steps:

[0105] If the intermediate counter is not found in the information storage, add a number to the intermediate counter and save the intermediate counter with the added number to the information storage.

[0106] Specifically, the information storage device can be used to store intermediate counters (i.e., Mcounters) and their numbers, formulas, etc. If an intermediate counter is not found in the information storage device, a number is added to the intermediate counter, and the intermediate counter with the added number is saved to the information storage device.

[0107] As can be seen from the above embodiments, by introducing an information storage device to facilitate the querying of intermediate counters, the efficiency of data processing can be effectively improved.

[0108] Optionally, the values ​​of each raw counter in the first KPI statistical item are processed by intermediate counters, including:

[0109] Based on the first KPI statistical item and the intermediate counter, generate the second KPI statistical item consisting of the intermediate counter;

[0110] Obtain the values ​​of each raw counter in the first KPI statistics item;

[0111] Based on the values ​​of each raw counter in the first KPI statistical item, determine the values ​​of each intermediate counter in the second KPI statistical item.

[0112] The values ​​of each intermediate counter in the second KPI statistics item are saved to the data storage device, and the smallest storage unit of the data storage device is the intermediate counter.

[0113] Specifically, based on the first KPI statistical item and the intermediate counters obtained after splitting, a second KPI statistical item composed of intermediate counters is generated. The values ​​of each original counter in the first KPI statistical item are calculated, and then the values ​​of each intermediate counter in the second KPI statistical item are determined based on these values. Finally, the values ​​of each intermediate counter in the second KPI statistical item are saved to the data storage. The smallest storage unit in the data storage is the intermediate counter.

[0114] For example: the first KPI statistical item is shown in the following formula (3):

[0115] Wireless drop rate (cell level) = (number of gNB requests to release context - number of normal gNB requests to release context) / (number of successful initial context establishments + number of legacy contexts + number of successful handovers + number of successful RRC connection reconstructions (non-source cell)) * 100% ... Formula (3)

[0116] The specific splitting rules are as follows:

[0117] (1) Decomposed by division: M1 = number of gNB requests to release context - number of normal gNB requests to release context, M2 = number of successful initial context establishments + number of legacy contexts + number of successful handovers + number of successful RRC connection reconstructions (non-source cell)

[0118] (2) Since the summation methods of the above intermediate counters are all summation, that is, the summation methods are the same, there is no need to split them further.

[0119] (3) The statistical items obtained after splitting are shown in the following formula (4):

[0120] Wireless disconnection rate (cell level) = M1 / M2*100………………..............Formula (4)

[0121] In formula (3), M1 and M2 are the two intermediate counters obtained by splitting the first KPI statistical item in formula (3), and formula (4) is the second KPI statistical item composed of the two intermediate counters.

[0122] As can be seen from the above embodiments, by using intermediate counters to store the data in the statistical items, the calculation speed of data processing is improved, and computing and storage resources are better saved.

[0123] Optionally, the values ​​of each intermediate counter in the second KPI statistics item include the values ​​of each intermediate counter determined according to a set time dimension and / or a set spatial dimension.

[0124] Specifically, the values ​​of each intermediate counter in the second KPI statistics item can be calculated according to the time dimension (15 minutes, 30 minutes, hour, day, week, month) and / or the spatial dimension (object, network element, city, region, entire network) to determine the value of each intermediate counter.

[0125] As can be seen from the above embodiments, by determining the values ​​of each intermediate counter according to a set time dimension and / or a set spatial dimension, better statistics and calculations can be performed, improving calculation speed and saving computing and storage resources.

[0126] The following is through Figures 2 to 4 These three examples illustrate the implementation process of the above data processing method.

[0127] Figure 2 This is a second flowchart illustrating the data processing method provided in this application embodiment. This data processing method can be used in network devices, such as base stations, core network devices, and traffic aggregation adapters. The specific implementation process of splitting the data using division is as follows:

[0128] (1) Create a statistical item (i.e., the first KPI statistical item).

[0129] (2) Determine whether the statistical item (i.e. the first KPI statistical item) contains division; if it does, proceed to step (3); if it does not, the process ends.

[0130] (3) The statistical item containing division (i.e. the first KPI statistical item) is split into several intermediate counters (i.e. Mcounter) according to the division.

[0131] (4) Determine whether the summarization methods of the intermediate counters (i.e., Mcounter) are the same; if they are different, proceed to step (5); if they are the same, the process ends.

[0132] (5) Split according to the summary method, create an intermediate counter (i.e., Mcounter), and the process ends.

[0133] Figure 3 This is the third flowchart illustrating the data processing method provided in this application embodiment. This data processing method can be used in network devices, such as base stations, core network equipment, and traffic aggregation adapters. The specific implementation process of splitting the data using division and aggregation methods is as follows:

[0134] (1) Initialize the encoder, i.e. the encoder of the intermediate counter (i.e. Mcounter).

[0135] (2) The original statistical item (the first KPI statistical item) is split into several intermediate counters by division.

[0136] (3) Determine whether the intermediate counter already exists, that is, whether the intermediate counter (i.e., Mcounter) information storage includes the intermediate counter; if it exists, then replace the original statistical item (i.e., the first KPI statistical item) with the existing intermediate counter (i.e., Mcounter) and the process ends; if it does not exist, then execute step (4).

[0137] (4) Obtain the number of the intermediate counter, that is, obtain the latest number of the intermediate counter from the information storage of the intermediate counter (i.e., Mcounter).

[0138] (5) Generate an intermediate counter (i.e., Mcounter) and replace the original statistical item (i.e., the first KPI statistical item).

[0139] (6) Determine whether the summarization methods of the intermediate counters (i.e., Mcounter) are the same; if they are the same, proceed to step (7); if they are not the same, use the summarization method classifier to summarize and then proceed to step (7).

[0140] (7) Update the information storage of the intermediate counter (i.e., Mcounter).

[0141] (8) The calculation engine of the intermediate counter (i.e., Mcounter) is added, and the process ends.

[0142] Figure 4 The fourth flowchart of the data processing method provided in this application mainly illustrates the processing flow of the above-mentioned aggregation method classifier, which further splits the intermediate counter, which includes at least two aggregation methods, according to the aggregation method (i.e., Figure 3 The implementation process of the summary classifier is as follows:

[0143] (1) For setting intermediate counters, split them into several sub-item statistical items according to the summarization method. Each sub-item statistical item corresponds to an intermediate counter, that is, an intermediate counter that only includes one summarization method.

[0144] (2) Delete the number of the originally set intermediate counter from the information storage.

[0145] (3) Determine whether the intermediate counter corresponding to the sub-item statistics item already exists, that is, whether the information storage includes the intermediate counter corresponding to the sub-item statistics item; if it exists, use the existing intermediate counter to replace the set intermediate counter and the process ends; if it does not exist, execute step (4).

[0146] (4) Obtain the number of the intermediate counter corresponding to the sub-item statistics item, that is, obtain the latest number of the intermediate counter corresponding to the sub-item statistics item from the information storage.

[0147] (5) Generate the intermediate counter corresponding to the statistical item of the sub-item, and replace the set intermediate counter. The process ends.

[0148] Figure 5 This is a schematic diagram of a data processing device provided in an embodiment of this application. This data processing device can be used in network equipment, such as base stations, core network equipment, and traffic aggregation adapters. This data processing device can be used to perform... Figures 1 to 4 The data processing method shown; such as Figure 5 As shown, the data processing apparatus may include:

[0149] The determination unit is used to determine the first key performance indicator (KPI) statistical item for data processing.

[0150] The splitting unit is used to split the first KPI statistical item according to the set splitting rules to obtain one or more intermediate counters. Each intermediate counter consists of one or more original counters in the first KPI statistical item.

[0151] The processing unit is used to process the values ​​of each raw counter in the first KPI statistical item through an intermediate counter.

[0152] Furthermore, based on the aforementioned device, the splitting unit also includes:

[0153] The first splitting subunit is used to split the first KPI statistical item according to the division operation if the first KPI statistical item includes a division operation. The numerator and denominator of the division operation correspond to an intermediate counter.

[0154] Furthermore, based on the aforementioned device, it also includes:

[0155] The second splitting subunit is used to further split the intermediate counter according to each data aggregation method if the intermediate counters split by division include a set intermediate counter, which is used to represent an intermediate counter that includes at least two data aggregation methods, until each intermediate counter obtained after the second split corresponds to a data aggregation method.

[0156] Furthermore, based on the aforementioned device, the splitting unit also includes:

[0157] The third splitting subunit is used to split the first KPI statistical item according to each data aggregation method if the first KPI statistical item includes at least two data aggregation methods. Each intermediate counter corresponds to one data aggregation method.

[0158] Furthermore, based on the aforementioned device, at least two data aggregation methods include:

[0159] Calculate the average; and / or

[0160] Summation operation; sum / or

[0161] Maximum value operation; AND / OR

[0162] Minimum value calculation.

[0163] Furthermore, based on the aforementioned device, it also includes:

[0164] The addition unit is used to add a number to the intermediate counter if the intermediate counter is not found in the information storage, and to save the intermediate counter with the added number to the information storage.

[0165] Furthermore, based on the aforementioned device, the processing unit also includes:

[0166] A generation sub-unit is used to generate a second KPI statistical item consisting of intermediate counters based on the first KPI statistical item and the intermediate counters;

[0167] Get sub-unit, used to obtain the values ​​of each raw counter in the first KPI statistics item;

[0168] The sub-unit is determined based on the values ​​of the original counters in the first KPI statistical item to determine the values ​​of the intermediate counters in the second KPI statistical item.

[0169] The storage subunit is used to save the values ​​of each intermediate counter in the second KPI statistics item to the data storage. The smallest storage unit of the data storage is the intermediate counter.

[0170] Furthermore, based on the aforementioned device, the values ​​of each intermediate counter in the second KPI statistical item include the values ​​of each intermediate counter determined according to a set time dimension and / or a set spatial dimension.

[0171] It should be noted that the division of units in the embodiments of this application is illustrative and only represents one logical functional division. In actual implementation, other division methods may be used. Furthermore, 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. The integrated units described above can be implemented in hardware or as software functional units.

[0172] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a processor-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0173] It should be noted that the apparatus provided in this embodiment of the invention can implement all the method steps implemented in the above method embodiment and can achieve the same technical effect. Therefore, the parts and beneficial effects that are the same as those in the method embodiment will not be described in detail here.

[0174] Figure 6 This is a schematic diagram of the structure of a data processing device provided in an embodiment of this application; the data processing device can be used to perform... Figures 1 to 4 The data processing method shown. For example... Figure 6 As shown, transceiver 600 is used to receive and send data under the control of processor 610.

[0175] Among them, Figure 6In this context, the bus architecture can include any number of interconnected buses and bridges, specifically linking various circuits together, represented by one or more processors (processor 610) and memory (memory 620). The bus architecture can also link together various other circuits such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and therefore will not be described further herein. The bus interface provides an interface. The transceiver 600 can be multiple elements, including transmitters and receivers, providing units for communicating with various other devices over transmission media, including wireless channels, wired channels, optical fibers, etc. The processor 610 is responsible for managing the bus architecture and general processing, and the memory 620 can store data used by the processor 610 during operation.

[0176] The processor 610 can be a central processing unit (CPU), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or a complex programmable logic device (CPLD). The processor can also adopt a multi-core architecture.

[0177] On the other hand, embodiments of this application also provide a processor-readable storage medium storing a computer program for causing a processor to execute the methods provided in the above embodiments, including:

[0178] Identify the primary key performance indicator (KPI) statistics for data processing;

[0179] The first KPI statistical item is split according to the set splitting rules to obtain one or more intermediate counters. Each intermediate counter consists of one or more original counters from the first KPI statistical item.

[0180] The values ​​of each original counter in the first KPI statistical item are processed by an intermediate counter.

[0181] Processor-readable storage media can be any available medium or data storage device that the processor can access, including but not limited to magnetic storage (e.g., floppy disks, hard disks, magnetic tapes, magneto-optical disks (MOs), etc.), optical storage (e.g., CDs, DVDs, BDs, HVDs, etc.), and semiconductor storage (e.g., ROMs, EPROMs, EEPROMs, non-volatile memory (NAND flash), solid-state drives (SSDs)).

[0182] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage and optical storage) containing computer-usable program code.

[0183] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer-executable instructions. These computer-executable instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0184] These processor-executable instructions may also be stored in a processor-readable memory that can direct a computer or other programmable data processing device to operate in a particular manner, such that the instructions stored in the processor-readable memory produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0185] These processors can execute instructions that can also be loaded onto a computer or other programmable data processing device, causing a series of operational steps to be performed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable device for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0186] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A data processing method, characterized in that, include: Determine the first KPI statistic to be used for data processing; The first KPI statistical item is split according to the set splitting rules to obtain one or more intermediate counters. Each intermediate counter is composed of one or more original counters in the first KPI statistical item. The intermediate counter processes the values ​​of each original counter in the first KPI statistical item. The step of processing the values ​​of each original counter in the first KPI statistical item using the intermediate counter includes: Based on the first KPI statistical item and the intermediate counter, a second KPI statistical item composed of the intermediate counter is generated; Obtain the values ​​of each original counter in the first KPI statistics item; Based on the values ​​of each original counter in the first KPI statistical item, determine the values ​​of each intermediate counter in the second KPI statistical item; The values ​​of each intermediate counter in the second KPI statistics item are saved to the data storage device, and the smallest storage unit of the data storage device is the intermediate counter.

2. The data processing method according to claim 1, characterized in that, The step of splitting the first KPI statistical item according to the set splitting rules includes: If the first KPI statistic includes a division operation, then the first KPI statistic is split according to the division operation, and the numerator and denominator of the division operation each correspond to an intermediate counter.

3. The data processing method according to claim 2, characterized in that, Also includes: If the intermediate counters split according to the division operation include a set intermediate counter, which is used to represent an intermediate counter that includes at least two data aggregation methods, then the set intermediate counter is split again according to each data aggregation method until each intermediate counter obtained after the second split corresponds to a data aggregation method.

4. The data processing method according to claim 1, characterized in that, The step of splitting the first KPI statistical item according to the set splitting rules includes: If the first KPI statistical item includes at least two data aggregation methods, then the first KPI statistical item is split according to each data aggregation method, and each intermediate counter corresponds to one data aggregation method.

5. The data processing method according to claim 3 or 4, characterized in that, The at least two data aggregation methods include: Calculate the average; and / or Summation operation; sum / or Maximum value operation; AND / OR Minimum value calculation.

6. The data processing method according to any one of claims 1 to 4, characterized in that, Also includes: If the intermediate counter is not found in the information storage, a number is added to the intermediate counter, and the intermediate counter with the added number is saved to the information storage.

7. The data processing method according to claim 1, characterized in that, The values ​​of each intermediate counter in the second KPI statistics item include the values ​​of each intermediate counter determined according to the set time dimension and / or set spatial dimension.

8. A data processing device, characterized in that, Includes memory, transceiver, and processor: A memory for storing computer programs; a transceiver for sending and receiving data under the control of the processor; and a processor for reading the computer programs from the memory and performing the following operations: Determine the first KPI statistic to be used for data processing; The first KPI statistical item is split according to the set splitting rules to obtain at least two intermediate counters, wherein the intermediate counters are composed of one or more original counters in the first KPI statistical item; The intermediate counter processes the values ​​of each original counter in the first KPI statistical item. The step of processing the values ​​of each original counter in the first KPI statistical item using the intermediate counter includes: Based on the first KPI statistical item and the intermediate counter, a second KPI statistical item composed of the intermediate counter is generated; Obtain the values ​​of each original counter in the first KPI statistics item; Based on the values ​​of each original counter in the first KPI statistical item, determine the values ​​of each intermediate counter in the second KPI statistical item; The values ​​of each intermediate counter in the second KPI statistics item are saved to the data storage device, and the smallest storage unit of the data storage device is the intermediate counter.

9. The data processing device according to claim 8, characterized in that, The step of splitting the first KPI statistical item according to the set splitting rules includes: If the first KPI statistic includes a division operation, then the first KPI statistic is split according to the division operation, and the numerator and denominator of the division operation each correspond to an intermediate counter.

10. The data processing device according to claim 9, characterized in that, Also includes: If the intermediate counters split according to the division operation include a set intermediate counter, which is used to represent an intermediate counter that includes at least two data aggregation methods, then the set intermediate counter is split again according to each data aggregation method until each intermediate counter obtained after the second split corresponds to a data aggregation method.

11. The data processing device according to claim 8, characterized in that, The step of splitting the first KPI statistical item according to the set splitting rules includes: If the first KPI statistical item includes at least two data aggregation methods, then the first KPI statistical item is split according to each data aggregation method, and each intermediate counter corresponds to one data aggregation method.

12. The data processing apparatus according to claim 10 or 11, characterized in that, The at least two data aggregation methods include: Calculate the average; and / or Summation operation; sum / or Maximum value operation; AND / OR Minimum value calculation.

13. The data processing apparatus according to any one of claims 8 to 11, characterized in that, Also includes: If the intermediate counter is not found in the information storage, a number is added to the intermediate counter, and the intermediate counter with the added number is saved to the information storage.

14. The data processing device according to claim 8, characterized in that, The values ​​of each intermediate counter in the second KPI statistics item include the values ​​of each intermediate counter determined according to the set time dimension and / or set spatial dimension.

15. A data processing apparatus, characterized in that, include: The determination unit is used to determine the first KPI statistic for data processing; The splitting unit is used to split the first KPI statistical item according to a set splitting rule to obtain at least two intermediate counters, wherein the intermediate counters are composed of one or more original counters in the first KPI statistical item; The processing unit is used to process the values ​​of each original counter in the first KPI statistical item through the intermediate counter; The processing unit is configured to process the values ​​of each original counter in the first KPI statistical item using the intermediate counter, including: Based on the first KPI statistical item and the intermediate counter, a second KPI statistical item composed of the intermediate counter is generated; Obtain the values ​​of each original counter in the first KPI statistics item; Based on the values ​​of each original counter in the first KPI statistical item, determine the values ​​of each intermediate counter in the second KPI statistical item; The values ​​of each intermediate counter in the second KPI statistics item are saved to the data storage device, and the smallest storage unit of the data storage device is the intermediate counter.

16. A data processing system, characterized in that, The data processing system includes the data processing device, information storage, and data storage as described in any one of claims 8 to 14; wherein the information storage is used to store an intermediate counter, and the data storage is used to store the value of the intermediate counter.

17. A processor-readable storage medium, characterized in that, The processor-readable storage medium stores a computer program that causes the processor to perform the method according to any one of claims 1 to 7.

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