CN115550132A - Data acquisition method, system and producer network element - Google Patents
Data acquisition method, system and producer network element Download PDFInfo
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Abstract
The disclosure relates to a data acquisition method, a data acquisition system and a producer network element, and relates to the technical field of communication. The data acquisition method comprises the following steps: generating an initial data queue corresponding to the minimum granularity according to the minimum granularity acquisition data sent by the consumer network element; according to the proportional relation between the other granularities sent by the consumer network element and the minimum granularity, carrying out aggregation processing on the data in the initial data queue to generate other data queues corresponding to the other granularities, wherein the other granularities are greater than or equal to the minimum granularity; and sending the data in the other data queues to the consumer network element.
Description
Technical Field
The present disclosure relates to the field of communications technologies, and in particular, to a data acquisition method, a data acquisition system, a producer network element, and a non-volatile computer-readable storage medium.
Background
An NWDAF (Network Data analysis Function) entity is introduced into the 5G system to perform Network Data analysis.
In the related art, an NWDAF entity or a DCCF (Data Collection Coordination Function) entity is a Consumer NF (Network Function) entity, and may collect Data from a Producer NF entity of a 5GC (core Network) to generate an analysis result. The analysis results are used to assist the NF entity that addresses the analysis requirements in making the relevant strategy selection.
Disclosure of Invention
The inventors of the present disclosure found that the following problems exist in the above-described related art: due to functional needs, the Producer NF entity may frequently generate event reports and frequently send event notifications to the Consumer NF entity, resulting in increased transmission overhead.
In view of this, the present disclosure provides a data acquisition technical solution, which can reduce transmission overhead.
According to some embodiments of the present disclosure, there is provided a method of acquiring data, including: generating an initial data queue corresponding to the minimum granularity according to the minimum granularity acquisition data sent by the consumer network element; according to the proportional relation between the other granularities sent by the consumer network element and the minimum granularity, carrying out aggregation processing on the data in the initial data queue to generate other data queues corresponding to the other granularities, wherein the other granularities are greater than or equal to the minimum granularity; and sending the data in the other data queues to the consumer network element.
In some embodiments, -said further granularity comprises a plurality of levels of granularity, each level of granularity being smaller than the granularity of the next level thereof, said further data queue comprising a data queue of each level corresponding to the granularity of each level.
In some embodiments, the generating the other data queue corresponding to the other granularity comprises: and according to the proportional relation between the granularity of the first level and the minimum granularity, carrying out aggregation processing on the data in the initial data queue in a quantity corresponding to the proportional relation to generate a data queue of the first level.
In some embodiments, the generating the other data queue corresponding to the other granularity comprises: according to the proportional relation between the granularity of the current level and the granularity of the previous level, carrying out aggregation processing on data in the data queue of the previous level, wherein the data is in a quantity corresponding to the proportional relation, and generating the data queue of the current level, wherein the current level is other levels except the first level; and repeating the steps until a data queue of each hierarchy is generated.
In some embodiments, said sending data in said other data queue to said consumer network element comprises: and sending the data in the other data queues to the consumer network element under the condition that the data quantity in the other data queues meets the triggering data quantity in the reporting triggering conditions of the other data queues sent by the consumer network element.
In some embodiments, the collecting data according to the minimum granularity sent by the consumer network element, and generating an initial data queue corresponding to the minimum granularity includes: determining a data acquisition range according to the reporting trigger condition of each other data queue and the proportional relation of the granularity corresponding to each other data queue; and acquiring historical data according to the data acquisition range to generate the initial data queue.
In some embodiments, the determining the data acquisition range according to the proportional relationship between the reporting trigger condition of each other data queue and the granularity corresponding to each other data queue includes: and determining the data acquisition range according to the weighted sum of the triggering data quantity in the reporting triggering conditions of the other data queues, wherein the weight of each triggering data quantity is the proportional relation of the corresponding other data queues.
In some embodiments, the type of aggregation process is issued by the consumer network element, the aggregation process comprising one of a summation process, an averaging process, a maximum process, a minimum process, a variance process, a sample extraction process.
According to further embodiments of the present disclosure, there is provided a producer network element, including: the generating unit is used for generating an initial data queue corresponding to the minimum granularity according to the minimum granularity acquisition data sent by the consumer network element, and performing aggregation processing on the data in the initial data queue according to the proportional relation between other granularities sent by the consumer network element and the minimum granularity to generate other data queues corresponding to the other granularities, wherein the other granularities are greater than or equal to the minimum granularity; and the sending unit is used for sending the data in the other data queues to the consumer network element.
According to still further embodiments of the present disclosure, there is provided a data acquisition system including: a producer network element for executing the data acquisition method in any of the above embodiments; and the consumer network element is used for sending the minimum granularity, the proportion relation between other granularities and the minimum granularity to the producer network element.
According to still further embodiments of the present disclosure, there is provided a producer network element, including: a memory; and a processor coupled to the memory, the processor configured to perform the method of data acquisition of any of the above embodiments based on instructions stored in the memory device.
According to still further embodiments of the present disclosure, there is provided a non-transitory computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the method of data acquisition in any of the above embodiments.
In the above embodiment, the collected data is aggregated according to the proportional relationship provided by the consumer network element, so that the transmission frequency is reduced, and the transmission overhead is reduced.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the disclosure and together with the description, serve to explain the principles of the disclosure.
The present disclosure may be more clearly understood from the following detailed description, taken with reference to the accompanying drawings, in which:
fig. 1 illustrates a flow diagram of some embodiments of a method of acquisition of data of the present disclosure;
FIG. 2 illustrates a flow diagram of some embodiments of step 110 of FIG. 1;
FIG. 3 illustrates a flow diagram of some embodiments of step 120 of FIG. 1;
figure 4 shows a block diagram of some embodiments of a producer network element of the present disclosure;
figure 5 shows a block diagram of further embodiments of a producer network element of the present disclosure;
figure 6 shows a block diagram of yet further embodiments of a producer network element of the present disclosure;
fig. 7 illustrates a block diagram of some embodiments of an acquisition system for data of the present disclosure.
Detailed Description
Various exemplary embodiments of the present disclosure will now be described in detail with reference to the accompanying drawings. It should be noted that: the relative arrangement of the components and steps, the numerical expressions, and numerical values set forth in these embodiments do not limit the scope of the present disclosure unless specifically stated otherwise.
Meanwhile, it should be understood that the sizes of the respective portions shown in the drawings are not drawn in an actual proportional relationship for the convenience of description.
The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the disclosure, its application, or uses.
Techniques, methods, and apparatus known to those of ordinary skill in the relevant art may not be discussed in detail but are intended to be part of the specification where appropriate.
In all examples shown and discussed herein, any particular value should be construed as merely illustrative, and not limiting. Thus, other examples of the exemplary embodiments may have different values.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, further discussion thereof is not required in subsequent figures.
As previously mentioned, in some scenarios, aggregating the event reports described above has been demonstrated to have no impact on the Consumer NF entity. Therefore, the amount of data transmitted can be reduced by the aggregation process.
However, for data sequences generated in the time dimension, the required time precision (granularity) of the data is different according to the time distance of the data from a given time point.
For example, if it is considered that each data represents data or an event generated at a certain time point T in a time data series, a time data series of length T can be obtained at a certain time point T0. This sequence of event data represents the collection of data generated at T time points forward of time T0.
Then, for these data, the data generated at the time point closer to the time interval of t0 has higher time correlation with t0, needs to maintain a lower granularity, and is not suitable for the polymerization treatment; on the contrary, data generated at a time point which is farther away from the time t0 has smaller time correlation with the time t0, and in some application scenarios, the data are allowed to have a certain error, and the aggregation operation can be performed.
Therefore, when polymerization is performed for the same set of data, different polymerization particle sizes may be used depending on the data generation time. Also, different aggregation granularity should only be applied to a particular portion of the set of data, avoiding unnecessary aggregation operations.
For the same group of data, if single aggregation is performed according to different granularities according to different needs, and different aggregated data are independent from each other, resource waste in two aspects may be caused:
1. part of data is aggregated unnecessarily or according to wrong granularity, so that the waste of operation resources is caused;
2. the same data is repeatedly aggregated by aggregation operations of different granularities, and then is transmitted for multiple times as output of different aggregation operations, which causes waste of transmission resources.
In some embodiments, the data for network data analysis is hierarchically aggregated. Different parts of the data are aggregated step by step through a hierarchical aggregation processing method, so that the transmission frequency is reduced, and the transmitted data volume can also be reduced.
For example, the aggregation processing of data needs to be completed according to the requirement of the Consumer NF network element under the request of the Consumer NF network element. Accordingly, the present disclosure encompasses corresponding service request, data provision procedures.
For example, the technical solution of the present disclosure can be realized by the following embodiments.
Fig. 1 illustrates a flow diagram of some embodiments of a method of acquisition of data of the present disclosure.
As shown in fig. 1, in step 110, an initial data queue corresponding to the minimum granularity is generated according to the minimum granularity acquisition data sent by the consumer network element.
In some embodiments, the minimum granularity may be defined in terms of a temporal attribute of the data acquisition. For example, the minimum time granularity is determined as the minimum granularity of the acquisition data according to the time interval of the acquisition data.
In some embodiments, a minimum granularity may also be defined in terms of the occurrence of an event. For example, the minimum granularity of the acquired data is determined based on the occurrence of an event that satisfies the trigger condition.
In some embodiments, the Consumer NF entity makes a data request to the Producer NF entity. For example, the Consumer NF entity needs to provide the Producer NF entity with a granularity D of data of aggregation level 0 0 The minimum granularity, the aggregation level relation identification (the proportional relation of the granularities corresponding to other data queues), the aggregation method indication, the report triggering condition and the like.
In some embodiments, the data request includes a granularity D of data for aggregation level 0 0 . For example, the Producer NF entity needs to assign this granularity D 0 As the minimum granularity to produce the data that needs to be provided. For example, D 0 One data can be generated for 1s, i.e. every 1 s.
In some embodiments, the data request includes an aggregate hierarchical relationship identification. For example, if K levels are involved, K =1,2, \8230, the aggregate level relationship flag is denoted as { r } 1 ,…r K }。r k Granularity D of data for the k-th aggregation level k Aggregation granularity D of data with k-1 aggregation level k-1 The proportional relationship between them.
For example, K =4, the aggregation level relation identifies { r } 1 ,r 2 ,r 3 ,r 4 Are {1,10,6,30, then represent:
D 1 is through 1D 0 Polymerizing to obtain the product;
D 2 is through 10D 1 Polymerizing to obtain the product;
D 3 is through 6D 2 Polymerizing to obtain the product;
D 4 is passed through 30D 3 And polymerizing to obtain the product.
In some embodiments, the data request includes an aggregation method indication. For example, the aggregation method may be a method specified by the Consumer NF entity, including summation, averaging, maximum, minimum, variance, and the like.
In some embodiments, the data request includes a reporting trigger condition. For example, the reporting trigger specifies the number of data in the data queue corresponding to the K aggregation levels to reach a certain number { RP } 1 ,RP 2 …RP k And reporting the data.
In some embodiments, the Producer network element maintains a D 0 Hierarchically corresponding data queue s 0 And K aggregation queues s 1 …s K . For example, all queues add data in a first-in-first-out manner.
In some embodiments, the Producer NF entity aggregates the level 0 data according to a granularity D 0 To s 0 To which data is added. For example, the collected data may be data generated by the Producer NF entity or may be historical data already owned by the Producer NF entity. Step 110 may be implemented, for example, by the embodiment of fig. 2.
Fig. 2 illustrates a flow diagram of some embodiments of step 110 of fig. 1.
As shown in fig. 2, in step 1110, a data acquisition range is determined according to a proportional relationship between a reporting trigger condition of each other data queue and a granularity corresponding to each other data queue.
In some embodiments, the data collection range is determined according to a weighted sum of the number of trigger data in the reporting trigger condition of each of the other data queues. The weight of each trigger data quantity is the proportional relation of the corresponding other data queues.
In step 1120, historical data is collected according to the data collection range, and an initial data queue is generated. For example, the data acquisition range is the data acquisition duration, and the historical data can be acquired in time sequence.
For example, producer NF determines the length L of the required historical data according to the reporting trigger condition and the aggregation level relation identifier:
in some embodiments, the minimum granularity is defined based on the time attribute, the time length of the minimum time granularity is Δ t, and the data acquisition duration L corresponding to the required historical data t Comprises the following steps:
in some embodiments, the granularity D of the data at aggregation level 0 may be started from the current time 0 Taking out the latest L data from the historical data; the L data are arranged according to the sequence of original generation, collection or recording, are sequentially used as generated data and are sent to s 0 And (4) queues.
After the initial data queue is generated, data collection may continue through other steps in FIG. 1.
In step 120, according to the proportional relationship between the other granularities sent by the consumer network element and the minimum granularity, the data in the initial data queue is aggregated, and another data queue corresponding to the other granularities is generated. The other particle size is greater than or equal to the minimum particle size. For example, the proportional relationship is a proportional relationship of the number of acquired data.
For example, the type of aggregation process is initiated by the consumer network element, and the aggregation process includes one of a summation process, an averaging process, a maximum process, a minimum process, a variance process, and a sample extraction process.
In some embodiments, the other granularity includes a granularity of multiple levels, each level having a granularity that is smaller than the granularity of its next level. The other data queues include per-level data queues corresponding to per-level granularity.
In some embodiments, step 120 may be implemented by the embodiment in fig. 3.
Fig. 3 illustrates a flow diagram of some embodiments of step 120 of fig. 1.
As shown in fig. 3, in step 1210, according to the proportional relationship between the granularity of the first hierarchy and the minimum granularity, the data in the initial data queue corresponding to the proportional relationship is aggregated to generate a data queue of the first hierarchy.
In step 1220, according to the proportional relationship between the granularity of the current level and the granularity of the previous level, the data in the data queue of the previous level corresponding to the proportional relationship is aggregated to generate the data queue of the current level. The current level is a level other than the first level.
In some embodiments, for n =0 \ 8230k, if r is present n+1 Then when s is n The number of data in the queue reaches r n+1 At +1, from s n Fetch r from queue n+1 Data(s) n The queue does not hold the data) to perform an aggregation operation; storing the aggregated data into s n+1 And (4) queues.
In some embodiments, r n+1 And determining according to the proportional relation of each layer. For example, D 2 Corresponding hierarchy to D 1 The scale relation of the corresponding hierarchy is 10, then at s 1 When the data in the queue reaches 10, performing aggregation processing on the 10 data; at s 1 Delete these 10 data in the queue; taking the polymerization result as s 2 Adding data in the queue; and in the same way, generating data in the data queue of each hierarchy.
After each tier of data queues is generated, data collection may continue through the remaining steps in FIG. 1.
In step 130, the data in the other data queue is sent to the consumer network element.
In some embodiments, when the amount of data in the other data queues meets the trigger data amount in the report trigger condition of the other data queues sent by the consumer network element, the data in the other data queues is sent to the consumer network element.
In some embodiments, if subscription is used, then s is used 1 …s K When the length of the queue meets the report triggering condition, the Producer NF entity sends s 1 …s K And the data in the queue is provided to the Consumer NF entity.
For example, K =4, and the trigger condition is set to { RP } 1 ,RP 2 ,RP 3 ,RP 4 } = {0, 1}, when s is 1 …s 4 When the data volume of the queue reaches 0,0 and 1, the Producer NF entity follows s 1 …s 4 The queue takes out 0,1 data(s) in turn 1 …s 4 The queue does not hold the data) as provided data to the Consumer NF entity.
In some embodiments, aspects of the present disclosure include the following 3 parts.
In section 1, the Consumer NF entity makes a data request to the Producer NF entity. For example, the Consumer NF entity needs to provide the Producer NF entity with a granularity D of data of aggregation level 0 0 The minimum granularity, aggregation level relation identification (the proportion relation of the granularity corresponding to other data queues), aggregation method indication, reporting trigger conditions and the like.
In some embodiments, the data request includes a granularity D of data for aggregation level 0 0 . For example, the Producer NF entity needs to assign this granularity D 0 As the minimum granularity to generate the data that needs to be provided. For example, D 0 One data can be generated for 1s, i.e. every 1 s.
In some embodiments, the data request includes an aggregate hierarchical relationship identification. For example, if K levels are involved, K =1,2, \ 8230;, the aggregate level relationship label is denoted as { r } 1 ,…r K }。r k Granularity D of data for the k-th aggregation level k Aggregation granularity D of data with k-1 aggregation level k-1 The proportional relationship between them.
For example, K =4, aggregation level relation identification { r } 1 ,r 2 ,r 3 ,r 4 Is {1,10,6,30}, then represents:
D 1 is through 1D 0 Polymerizing to obtain the product;
D 2 is through 10D 1 Polymerizing to obtain the product;
D 3 is through 6D 2 Polymerizing to obtain the product;
D 4 is passed through 30D 3 And polymerizing to obtain the product.
In some embodiments, the data request includes an aggregation method indication. For example, the aggregation method may be a method specified by the Consumer NF entity, including summation, averaging, maximum, minimum, variance, and the like.
In some embodiments, the data request includes a reporting trigger condition. For example, the reporting trigger condition specifies the number of data in the data queue corresponding to K aggregation levels to reach a certain number { RP } 1 ,RP 2 …RP k And reporting the data.
And 2, the Producer NF entity carries out data aggregation according to the data request.
In some embodiments, the Producer network element maintains a D 0 Hierarchically corresponding data queue s 0 And K aggregation queues s 1 …s K . For example, all queues add data in a first-in-first-out manner.
In some embodiments, the Producer NF entity aggregates the hierarchy of data according to granularity D of the 0 th aggregation level 0 To s 0 To which data is added. For example, the collected data may be data generated by the Producer NF entity or may be historical data already owned by the Producer NF entity.
In some embodiments, for n =0 \ 8230k, if r is present n+1 Then when s is n The number of data in the queue reaches r n+1 +1From s n Fetch r from queue n+1 Data(s) n The queue does not hold the data) to perform an aggregation operation; storing the aggregated data into s n+1 And (4) queues.
Part 2.1, the Producer NF entity picks data from the historical data.
For example, producer NF determines the length L of the required historical data according to the reporting trigger condition and the aggregation level relation identifier:
in some embodiments, the granularity D of the data at aggregation level 0 may be started from the current time 0 Taking out the latest L data from the historical data; the L data are arranged according to the sequence of original generation, collection or recording, are sequentially used as generated data and are sent to s 0 And (4) queues.
Part 3, the Producer NF entity provides data to the Consumer NF entity.
In some embodiments, if a subscription approach is used, then s is the number of times 1 …s K When the length of the queue meets the report triggering condition, the Producer NF entity sends s 1 …s K And the data in the queue is provided to the Consumer NF entity.
For example, K =4, and the trigger condition is set to { RP } 1 ,RP 2 ,RP 3 ,RP 4 = {0, 1}, then when s 1 …s 4 When the data volume of the queue reaches 0,0 and 1, the Producer NF entity follows s 1 …s 4 The queue takes out 0,1 data(s) in turn 1 …s 4 The queue does not hold the data) as provided data to the Consumer NF entity.
In the above embodiment, a hierarchical structure is adopted, each level of calculation is based on the aggregation result of the previous level, and different aggregation cycles have a hierarchical relationship.
By adopting a hierarchical structure, when aggregation data of a plurality of aggregation periods (hierarchies) are provided at the same time, the method provided by the patent can configure aggregation granularity of data generated in different time ranges in a data set provided for a Consumer NF entity according to time correlation, and balance the relationship between data quantity and data precision by utilizing the aggregation proportional relationship between different hierarchies.
A method is provided that includes requesting, generating, providing hierarchical aggregated data for a process. The aggregation granularity of the data may be adjusted according to the length of the time interval from the time of data provision. Therefore, the purpose of reducing the data transmission quantity is achieved while the time precision of effective data is guaranteed.
The technical scheme of the disclosure can be applied to:
reducing the transmission quantity of data collection in the process of collecting data from the NF entity by the NWDAF entity (through the DCCF entity);
and 2, the NWDAF entity reduces the transmission amount of Data collection in the process of extracting historical Data from an ADRF (Analytic Data Repository Function) entity or a DRF (Data Repository Function) entity through the DCCF entity.
In some embodiments, the DCCF entity acts as a Producer NF entity, and in providing data to the NWDAF entity:
1. allowing the NWDAF entity to serve as a Consumer NF entity to provide a request for hierarchical data aggregation to the DCCF entity;
DCCF entity at granularity D in retrieval from NF entity 0 Data, carrying out layered aggregation on the data in the process of carrying out data aggregation according to the Producer NF entity;
and 3, the DCCF entity provides the collected data (after aggregation) to the NWDAF entity according to the data triggering condition reported by the Producer NF entity.
In some embodiments, the DCCF entity acts as a Producer NF entity, and in providing data to the NWDAF entity:
1. allowing the NWDAF entity to serve as a Consumer NF entity to send a request of hierarchical data aggregation to the DCCF entity;
and 2. The DCCF entity judges the historical data of the data and stores the historical data in the DRF entity.
The DCCF entity selects data from the historical data according to the Producer NF entity to form D 0 The method of (3), retrieve history data from DRF entity, and according to the course of data aggregation of Producer NF entity, carry on the hierarchical aggregation to the data;
and 4, the DCCF entity provides the collected data (after aggregation) to the NWDAF entity according to the data triggering condition reported by the Producer NF entity.
In some embodiments, the NF entity acts as a Producer NF entity, and in providing data to the NWDAF entity or the DCCF entity:
1. allowing the NWDAF entity or the DCCF entity to serve as a Consumer NF entity and making a request for hierarchical data aggregation to the NF entity;
NF entity Generation granularity of D 0 According to the process of data aggregation of the Producer NF entity, carrying out layered aggregation on the data;
and 3, the NF entity provides the collected data (after aggregation) to the NWDAF entity or the DCCF entity according to the data triggering condition reported by the Producer NF entity.
Figure 4 illustrates a block diagram of some embodiments of a producer network element of the present disclosure.
As shown in fig. 4, the producer network element 4 includes a generating unit 41 and a sending unit 42.
The generating unit 41 generates an initial data queue corresponding to the minimum granularity according to the minimum granularity acquisition data sent by the consumer network element; and according to the proportional relation between the other granularities sent by the consumer network element and the minimum granularity, carrying out aggregation processing on the data in the initial data queue to generate other data queues corresponding to the other granularities. The other particle size is greater than or equal to the minimum particle size.
The sending unit 42 sends the data in the other data queues to the consumer network element.
In some embodiments, the other granularity comprises a plurality of levels of granularity, each level of granularity being less than the granularity of the next level thereof, the other data queues comprising a data queue of each level corresponding to the granularity of each level.
In some embodiments, the generating unit 41 performs aggregation processing on data in the initial data queue, the quantity of which corresponds to the proportional relation, according to the proportional relation between the granularity of the first hierarchy and the minimum granularity, and generates the data queue of the first hierarchy.
In some embodiments, the generating unit 41 performs aggregation processing on the data in the data queue of the previous hierarchy corresponding to the proportional relationship according to the proportional relationship between the granularity of the current hierarchy and the granularity of the previous hierarchy, so as to generate the data queue of the current hierarchy. The current level is a level other than the first level. The generation unit 41 repeats the above steps until a data queue of each hierarchy is generated.
In some embodiments, the sending unit 42 sends the data in the other data queues to the consumer network element when the amount of the data in the other data queues meets the trigger data amount in the reporting trigger condition of the other data queues sent by the consumer network element.
In some embodiments, the generating unit 41 determines the data acquisition range according to the proportional relationship between the reporting trigger condition of each other data queue and the corresponding granularity of each other data queue; and acquiring historical data according to the data acquisition range and the time sequence to generate an initial data queue.
In some embodiments, the generating unit 41 determines the data acquisition range according to a weighted sum of the trigger data quantities in the reporting trigger conditions of the other data queues, where the weight of each trigger data quantity is a proportional relationship of the corresponding other data queues.
In some embodiments, the type of aggregation process is initiated by the consumer network element, the aggregation process comprising one of a summation process, an averaging process, a maximum process, a minimum process, a variance process, and a sample extraction process.
Figure 5 shows a block diagram of further embodiments of a producer network element of the present disclosure.
As shown in fig. 5, the producer network element 5 of this embodiment includes: a memory 51 and a processor 52 coupled to the memory 51, the processor 52 being configured to execute the data acquisition method in any one of the embodiments of the present disclosure based on instructions stored in the memory 51.
The memory 51 may include, for example, a system memory, a fixed nonvolatile storage medium, and the like. The system memory stores, for example, an operating system, an application program, a Boot Loader (Boot Loader), a database, and other programs.
Figure 6 shows a block diagram of further embodiments of a producer network element of the present disclosure.
As shown in fig. 6, the producer network element 6 of this embodiment includes: a memory 610 and a processor 620 coupled to the memory 610, wherein the processor 620 is configured to execute the data acquisition method of any of the above embodiments based on instructions stored in the memory 610.
The memory 610 may include, for example, system memory, fixed non-volatile storage media, and the like. The system memory stores, for example, an operating system, an application program, a Boot Loader (Boot Loader), and other programs.
The producer network element 6 may also include an input-output interface 630, a network interface 640, a storage interface 650, etc. These interfaces 630, 640, 650 and the connections between the memory 610 and the processor 620 may be, for example, via a bus 660. The input/output interface 630 provides a connection interface for input/output devices such as a display, a mouse, a keyboard, a touch screen, a microphone, and a sound box. The network interface 640 provides a connection interface for various networking devices. The storage interface 650 provides a connection interface for external storage devices such as an SD card and a usb disk.
Fig. 7 illustrates a block diagram of some embodiments of an acquisition system of data of the present disclosure.
As shown in fig. 7, the data acquisition system 7 includes: a producer network element 71 for executing the data collection method in any of the above embodiments; and the consumer network element 72 is used for sending the minimum granularity, the proportion relation between other granularities and the minimum granularity to the producer network element.
As will be appreciated by one skilled in the art, embodiments of the present disclosure may be provided as a method, system, or computer program product. Accordingly, the present disclosure may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present disclosure may take the form of a computer program product embodied on one or more computer-usable non-transitory storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
So far, the acquisition method of data, the acquisition system of data, the producer network element and the non-volatile computer-readable storage medium according to the present disclosure have been described in detail. Some details well known in the art have not been described in order to avoid obscuring the concepts of the present disclosure. Those skilled in the art can now fully appreciate how to implement the teachings disclosed herein, in view of the foregoing description.
The method and system of the present disclosure may be implemented in a number of ways. For example, the methods and systems of the present disclosure may be implemented in software, hardware, firmware, or any combination of software, hardware, and firmware. The above-described order for the steps of the method is for illustration only, and the steps of the method of the present disclosure are not limited to the order specifically described above unless specifically stated otherwise. Further, in some embodiments, the present disclosure may also be embodied as programs recorded in a recording medium, the programs including machine-readable instructions for implementing the methods according to the present disclosure. Thus, the present disclosure also covers a recording medium storing a program for executing the method according to the present disclosure.
Although some specific embodiments of the present disclosure have been described in detail by way of example, it should be understood by those skilled in the art that the foregoing examples are for purposes of illustration only and are not intended to limit the scope of the present disclosure. It will be appreciated by those skilled in the art that modifications can be made to the above embodiments without departing from the scope and spirit of the present disclosure. The scope of the present disclosure is defined by the appended claims.
Claims (12)
1. A method of data acquisition comprising:
generating an initial data queue corresponding to the minimum granularity according to the minimum granularity acquisition data sent by the consumer network element;
according to the proportional relation between the other granularities sent by the consumer network element and the minimum granularity, carrying out aggregation processing on the data in the initial data queue to generate other data queues corresponding to the other granularities, wherein the other granularities are larger than or equal to the minimum granularity;
and sending the data in the other data queues to the consumer network element.
2. The acquisition method according to claim 1, wherein the other granularities include granularities of a plurality of levels, each level having a granularity smaller than that of a level next thereto, and the other data queues include a data queue of each level corresponding to the granularity of each level.
3. The acquisition method of claim 2, wherein the generating of the other data queue corresponding to the other granularity comprises:
and according to the proportional relation between the granularity of the first level and the minimum granularity, carrying out aggregation processing on the data in the initial data queue in a quantity corresponding to the proportional relation to generate a data queue of the first level.
4. The acquisition method of claim 3, wherein the generating of the other data queue corresponding to the other granularity comprises:
according to the proportional relation between the granularity of the current level and the granularity of the previous level, carrying out aggregation processing on data in the data queue of the previous level, wherein the data is in a quantity corresponding to the proportional relation, and generating the data queue of the current level, wherein the current level is other levels except the first level;
and repeating the steps until a data queue of each hierarchy is generated.
5. The acquisition method according to claim 1, wherein said sending data in said other data queues to said consumer network element comprises:
and sending the data in the other data queues to the consumer network element under the condition that the data quantity in the other data queues meets the triggering data quantity in the reporting triggering condition of the other data queues sent by the consumer network element.
6. The acquisition method as claimed in claim 5, wherein said acquiring data according to a minimum granularity transmitted from a consumer network element, and generating an initial data queue corresponding to the minimum granularity comprises:
determining a data acquisition range according to the reporting trigger condition of each other data queue and the proportional relation of the corresponding granularity of each other data queue;
and acquiring historical data according to the data acquisition range to generate the initial data queue.
7. The acquisition method according to claim 6, wherein the determining the data acquisition range according to the proportional relationship between the reporting trigger condition of each other data queue and the corresponding granularity of each other data queue comprises:
and determining the data acquisition range according to the weighted sum of the triggering data quantity in the reporting triggering conditions of the other data queues, wherein the weight of each triggering data quantity is the proportional relation of the corresponding other data queues.
8. The acquisition method according to any one of claims 1 to 7, wherein the type of aggregation process is issued by the consumer network element, the aggregation process comprising one of a summation process, an averaging process, a maximum process, a minimum process, a variance process, a sample extraction process.
9. A producer network element, comprising:
the generating unit is used for generating an initial data queue corresponding to the minimum granularity according to the minimum granularity acquisition data sent by the consumer network element, and performing aggregation processing on the data in the initial data queue according to the proportional relation between other granularities sent by the consumer network element and the minimum granularity to generate other data queues corresponding to the other granularities, wherein the other granularities are greater than or equal to the minimum granularity;
and the sending unit is used for sending the data in the other data queues to the consumer network element.
10. A system for acquiring data, comprising:
a producer network element for performing the method of collecting data of any one of claims 1-8;
and the consumer network element is used for sending the minimum granularity, the proportion relation between other granularities and the minimum granularity to the producer network element.
11. A producer network element, comprising:
a memory; and
a processor coupled to the memory, the processor configured to perform the method of acquiring data of any of claims 1-8 based on instructions stored in the memory.
12. A non-transitory computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the method of acquiring data of any of claims 1-8.
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| CN116567674B (en) * | 2023-07-07 | 2023-10-03 | 中国电信股份有限公司 | Data processing method, device, network element and readable storage medium |
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