Method, system and producer network element for data collection

Abstract

The present disclosure relates to a data collection method, system and producer network element, and relates to the technical field of communication. The data collection method comprises: generating an initial data queue corresponding to a minimum granularity according to minimum granularity data sent by a consumer network element; performing aggregation processing on data in the initial data queue according to a proportional relationship between other granularities and the minimum granularity sent by the consumer network element, to generate other data queues corresponding to the other granularities, the other granularities being greater than or equal to the minimum granularity; and sending data in the other data queues to the consumer network element.

Description

Technical Field

[0001] This disclosure relates to the field of communication technology, 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 Technology

[0002] The 5G system introduces the NWDAF (Network Data Analytics Function) entity to perform network data analysis.

[0003] In related technologies, NWDAF entities or DCCF (Data Collection Coordination Function) entities, acting as Consumer NF (Network Function) entities, can collect data from Producer NF entities in the 5GC (core network) and generate analysis results. These analysis results are then used to assist the NF entity that requests analysis in selecting relevant strategies. Summary of the Invention

[0004] The inventors of this disclosure have discovered the following problem in the above-mentioned related technologies: due to functional requirements, the ProducerNF entity may frequently generate event reports and frequently send event notifications to the ConsumerNF entity, resulting in increased transmission overhead.

[0005] In view of this, this disclosure proposes a data acquisition technology solution that can reduce transmission overhead.

[0006] According to some embodiments of this disclosure, a data acquisition method is provided, comprising: generating an initial data queue corresponding to the minimum granularity based on the minimum granularity data collected from a consumer network element; aggregating the data in the initial data queue according to the ratio of other granularities sent by the consumer network element to 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 sending the data in the other data queues to the consumer network element.

[0007] In some embodiments, the other granularity comprises multiple levels of granularity, each level having a smaller granularity than the next level below it, and the other data queues comprise data queues for each level corresponding to the granularity of each level.

[0008] In some embodiments, generating other data queues corresponding to the other granularities includes: aggregating data in the initial data queue that corresponds to the ratio between the granularity of the first level and the smallest granularity to generate a data queue of the first level.

[0009] In some embodiments, generating other data queues corresponding to the other granularities includes: according to the ratio between the granularity of the current level and the granularity of the level above, aggregating the data in the data queue of the level above that corresponds to the ratio, generating the data queue of the current level, wherein the current level is a level other than the first level; repeating the above steps until data queues for each level are generated.

[0010] In some embodiments, sending the data in the other data queues to the consumer network element includes: sending the data in the other data queues to the consumer network element when the number of data in the other data queues meets the number of trigger data in the reporting trigger conditions of the other data queues sent by the consumer network element.

[0011] In some embodiments, generating an initial data queue corresponding to the minimum granularity based on the minimum granularity data collected from the consumer network element includes: determining the data collection range based on the reporting trigger conditions of each other data queue and the proportional relationship between the granularity corresponding to each other data queue; collecting historical data based on the data collection range, and generating the initial data queue.

[0012] In some embodiments, determining the data collection range based on the reporting trigger conditions of each other data queue and the proportional relationship of the granularity corresponding to each other data queue includes: determining the data collection range based on the weighted sum of the number of trigger data in the reporting trigger conditions of each other data queue, wherein the weight of each trigger data number is the proportional relationship of the corresponding other data queue.

[0013] In some embodiments, the type of aggregation processing is sent by the consumer network element, and the aggregation processing includes one of summation processing, average processing, maximum value processing, minimum value processing, variance processing, and sample extraction processing.

[0014] According to some other embodiments of this disclosure, a producer network element is provided, comprising: a generation unit, configured to generate an initial data queue corresponding to the minimum granularity based on the minimum granularity data collected from a consumer network element, and to aggregate data in the initial data queue according to the ratio of other granularities sent by the consumer network element to the minimum granularity, thereby generating other data queues corresponding to the other granularities, wherein the other granularities are greater than or equal to the minimum granularity; and a sending unit, configured to send data in the other data queues to the consumer network element.

[0015] According to further embodiments of this disclosure, a data acquisition system is provided, comprising: a producer network element, configured to execute the data acquisition method in any of the above embodiments; and a consumer network element, configured to send to the producer network element a minimum granularity and a proportional relationship between other granularities and the minimum granularity.

[0016] According to further embodiments of this disclosure, a producer network element is provided, including: a memory; and a processor coupled to the memory, the processor being configured to execute the data acquisition method of any of the above embodiments based on instructions stored in the memory device.

[0017] According to further embodiments of the present disclosure, a non-volatile computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a processor, implements the data acquisition method of any of the above embodiments.

[0018] In the above embodiments, the collected data is aggregated based on the proportional relationship provided by the consumer network element, which reduces the transmission frequency and thus reduces transmission overhead. Attached Figure Description

[0019] The accompanying drawings, which form part of this specification, illustrate embodiments of this disclosure and, together with the specification, serve to explain the principles of this disclosure.

[0020] This disclosure will become clearer with reference to the accompanying drawings and the following detailed description, wherein:

[0021] Figure 1 Flowcharts illustrating some embodiments of the data acquisition method of this disclosure;

[0022] Figure 2 Show Figure 1 Flowcharts of some embodiments of step 110;

[0023] Figure 3 Show Figure 1 Flowcharts of some embodiments of step 120;

[0024] Figure 4Block diagrams showing some embodiments of the producer network element of this disclosure;

[0025] Figure 5 Block diagrams showing other embodiments of the producer network element of this disclosure;

[0026] Figure 6 Block diagrams illustrating further embodiments of the producer network element of this disclosure;

[0027] Figure 7 Block diagrams illustrating some embodiments of the data acquisition system of this disclosure are shown. Detailed Implementation

[0028] Various exemplary embodiments of the present disclosure will now be described in detail with reference to the accompanying drawings. It should be noted that, unless otherwise specifically stated, the relative arrangement, numerical expressions, and values ​​of the components and steps set forth in these embodiments do not limit the scope of the present disclosure.

[0029] At the same time, it should be understood that, for ease of description, the dimensions of the various parts shown in the accompanying drawings are not drawn according to actual scale.

[0030] The following description of at least one exemplary embodiment is merely illustrative and is in no way intended to limit this disclosure or its application or use.

[0031] Techniques, methods, and equipment known to those skilled in the art may not be discussed in detail, but where appropriate, such techniques, methods, and equipment should be considered part of the specification.

[0032] In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not as limitations. Therefore, other examples of exemplary embodiments may have different values.

[0033] It should be noted that similar labels and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be discussed further in subsequent figures.

[0034] As mentioned earlier, in some scenarios, it has been demonstrated that aggregating the aforementioned event reports does not affect the Consumer NF entity. Therefore, aggregation can reduce the amount of data transmitted.

[0035] However, for data sequences generated in the time dimension, the required data time precision (granularity) varies depending on the time distance between the data and a given point in time.

[0036] For example, if we consider that in a time data sequence, each data point represents data or an event generated at a certain time point t, then at a certain time point t0, we can obtain a time data sequence of length T. This event data sequence represents the set of data generated at T time points preceding time t0.

[0037] Therefore, for this data, the closer the time interval to time t0, the higher the temporal correlation with time t0, and the lower the granularity should be, making it unsuitable for aggregation. Conversely, the further the time interval to time t0, the lower the temporal correlation with time t0. In some application scenarios, this data can tolerate a certain degree of error and can be aggregated.

[0038] Therefore, when aggregating the same set of data, different aggregation granularities can be used depending on when the data was generated. Furthermore, different aggregation granularities should only be applied to specific parts of the data set to avoid unnecessary aggregation operations.

[0039] If the same set of data is aggregated at different granularities according to different needs, and the different aggregated data are independent of each other, it may lead to waste of resources in two aspects:

[0040] 1. Some data was aggregated "unnecessarily" or "at the wrong granularity," resulting in a waste of computing resources;

[0041] 2. The same data is repeatedly aggregated by aggregation operations of different granularities and then transmitted multiple times as the output of different aggregation operations, resulting in a waste of transmission resources.

[0042] In some embodiments, hierarchical aggregation processing is performed on the data used for network data analysis. By using hierarchical aggregation processing methods, different parts of the data are aggregated level by level, reducing the frequency of transmission and also reducing the amount of data transmitted.

[0043] For example, data aggregation processing needs to be completed at the request of the Consumer NF network element and according to the requirements of the Consumer NF network element. Therefore, this disclosure includes the corresponding service request and data provision process.

[0044] For example, the technical solution of this disclosure can be implemented through the following embodiments.

[0045] Figure 1 Flowcharts illustrating some embodiments of the data acquisition methods of this disclosure are shown.

[0046] like Figure 1As shown, in step 110, an initial data queue corresponding to the smallest granularity is generated based on the smallest granularity data collected from the consumer network element.

[0047] In some embodiments, the minimum granularity can be defined based on the time attribute of data acquisition. For example, the minimum time granularity can be determined as the minimum granularity of data acquisition based on the time interval between data acquisitions.

[0048] In some embodiments, the minimum granularity can also be defined based on the occurrence of an event. For example, the minimum granularity of the collected data can be determined based on the occurrence of an event that meets a triggering condition.

[0049] 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 the granularity D0 (minimum granularity) of the data at the 0th aggregation level, the aggregation hierarchy relationship identifier (the proportional relationship of the granularity corresponding to each other data queue), the aggregation method indication, the reporting trigger condition, etc.

[0050] In some embodiments, the data request includes the granularity D0 of the data at the 0th aggregation level. For example, the ProducerNF entity needs to use this granularity D0 as the smallest granularity for generating the data that needs to be provided. For example, D0 can be 1s, meaning that one piece of data is generated every 1 second.

[0051] In some embodiments, the data request includes an aggregation hierarchy identifier. For example, if there are K levels, K = 1, 2, ..., the aggregation hierarchy identifier is represented as {r1, ... r2}. K}. r k The granularity D of the data at the k-th aggregation level k The aggregation granularity D of the data at the (k-1)th aggregation level k-1 The proportional relationship between them.

[0052] For example, if K=4, and the aggregation hierarchy identifier {r1,r2,r3,r4} is {1,10,6,30}, then it represents:

[0053] D1 is obtained by aggregating one D0;

[0054] D2 is obtained by aggregating 10 D1s;

[0055] D3 is obtained by aggregating 6 D2s;

[0056] D4 is obtained by aggregating 30 D3s.

[0057] In some embodiments, the data request includes an aggregation method indication. For example, the aggregation method may be a method specified by the ConsumerNF entity, including summation, average, maximum, minimum, variance, etc.

[0058] 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 queues corresponding to K aggregation levels, reaching a certain number {RP1, RP2…RP}. k Data is reported at that time.

[0059] In some embodiments, the Producer network element maintains a data queue s0 corresponding to the D0 level, and K aggregation queues s1…s K For example, all queues use a first-in, first-out (FIFO) approach to add data.

[0060] In some embodiments, the Producer NF entity adds data to s0 based on the granularity D0 of the data at the 0th aggregation level. For example, the collected data can be data generated by the Producer NF entity or historical data already possessed by the Producer NF entity. For instance, it can be achieved through... Figure 2 The implementation of step 110 in the embodiment.

[0061] Figure 2 Show Figure 1 A flowchart of some embodiments of step 110.

[0062] like Figure 2 As shown, in step 1110, the data collection range is determined based on the reporting trigger conditions of each other data queue and the proportional relationship of the granularity corresponding to each other data queue.

[0063] In some embodiments, the data collection range is determined based on the weighted sum of the number of trigger data items in the reporting trigger conditions of each other data queue. The weight of each trigger data item is a proportional relationship with the corresponding other data queue.

[0064] In step 1120, historical data is collected according to the data collection range to generate an initial data queue. For example, if the data collection range is the data collection duration, historical data can be collected in chronological order.

[0065] For example, Producer NF determines the required length L of historical data based on the reporting trigger conditions and aggregation hierarchy relationship identifiers:

[0066]

[0067] In some embodiments, the minimum granularity is defined based on the time attribute, and the time length of the minimum time granularity is Δt, then the data collection duration L corresponding to the required historical data is... t for:

[0068]

[0069] In some embodiments, starting from the current moment, the latest L data points can be extracted from historical data according to the granularity D0 of the data at the 0th aggregation level; these L data points are arranged in the order in which they were originally generated, collected, or recorded, and are then sent to the s0 queue as generated data.

[0070] After the initial data queue is generated, it can be accessed through... Figure 1 The remaining steps continue to collect data.

[0071] In step 120, based on the ratio between other granularities and the smallest granularity sent by the consumer network element, the data in the initial data queue is aggregated to generate other data queues corresponding to other granularities. Other granularities are greater than or equal to the smallest granularity. For example, the ratio is the proportion of the number of collected data items.

[0072] For example, the type of aggregation processing is sent by the consumer network element, and aggregation processing includes one of the following: summation processing, average processing, maximum processing, minimum processing, variance processing, and sample extraction processing.

[0073] In some embodiments, other granularities comprise multiple levels of granularity, each level having a smaller granularity than the next level below it. Other data queues comprise data queues for each level corresponding to the granularity of each level.

[0074] In some embodiments, it can be achieved through Figure 3 The embodiment in the example implements step 120.

[0075] Figure 3 Show Figure 1 A flowchart of some embodiments of step 120.

[0076] like Figure 3 As shown, in step 1210, according to the ratio between the granularity of the first level and the smallest granularity, the data in the initial data queue corresponding to the ratio is aggregated to generate the data queue of the first level.

[0077] In step 1220, based on the ratio between the granularity of the current level and the granularity of the level above it, the data in the data queue of the level above it is aggregated to generate the data queue of the current level. The current level is any level other than the first level.

[0078] Repeat step 1220 until the data queue for each level is generated.

[0079] In some embodiments, for n = 0…K, if there exists r n+1 Then when s n The number of data in the queue reaches r n+1 When +1, from s n Take r from the queue n+1 Data (s) n The queue does not retain this data. Aggregation operations are performed on the aggregated data; the aggregated data is then stored in s. n+1 queue.

[0080] In some embodiments, r n+1 The ratio of each level is determined based on the proportions. For example, if the ratio of level D2 to level D1 is 10, then when the number of data in queue s1 reaches 10, these 10 data points are aggregated; these 10 data points are deleted from queue s1; the aggregation result is added to queue s2; and so on, to generate the data in the data queues of each level.

[0081] After generating the data queues at each level, it can be done through... Figure 1 The remaining steps continue to collect data.

[0082] In step 130, data from other data queues is sent to the consumer network element.

[0083] In some embodiments, if the amount of data in other data queues meets the trigger data quantity in the reporting trigger condition of other data queues sent by the consumer network element, the data in other data queues is sent to the consumer network element.

[0084] In some embodiments, if a subscription method is used, then when s1…s K When the queue length meets the reporting trigger condition, the Producer NF entity will send s1…s K The data in the queue is provided to the Consumer NF entity.

[0085] For example, if K=4 and the trigger condition is set as {RP1,RP2,RP3,RP4}={0,0,0,1}, then when the number of data in queues s1…s4 reaches 0, 0, 0, 1, the Producer NF entity will sequentially retrieve 0, 0, 0, 1 data from queues s1…s4 (the queues s1…s4 do not retain this data) and send it to the Consumer NF entity as provided data.

[0086] In some embodiments, the technical solution disclosed herein includes the following three parts.

[0087] Part 1 involves the Consumer NF entity making a data request to the Producer NF entity. For example, the Consumer NF entity needs to provide the Producer NF entity with the granularity D0 (minimum granularity) of the data at the 0th aggregation level, the aggregation level relationship identifier (the proportional relationship of the granularity corresponding to each other data queue), the aggregation method indication, and the reporting trigger conditions, etc.

[0088] In some embodiments, the data request includes the granularity D0 of the data at the 0th aggregation level. For example, the ProducerNF entity needs to use this granularity D0 as the smallest granularity for generating the data that needs to be provided. For example, D0 can be 1s, meaning that one piece of data is generated every 1 second.

[0089] In some embodiments, the data request includes an aggregation hierarchy identifier. For example, if there are K levels, K = 1, 2, ..., the aggregation hierarchy identifier is represented as {r1, ... r2}. K}. r k The granularity D of the data at the k-th aggregation level k The aggregation granularity D of the data at the (k-1)th aggregation level k-1 The proportional relationship between them.

[0090] For example, if K=4, and the aggregation hierarchy identifier {r1,r2,r3,r4} is {1,10,6,30}, then it represents:

[0091] D1 is obtained by aggregating one D0;

[0092] D2 is obtained by aggregating 10 D1s;

[0093] D3 is obtained by aggregating 6 D2s;

[0094] D4 is obtained by aggregating 30 D3s.

[0095] In some embodiments, the data request includes an aggregation method indication. For example, the aggregation method may be a method specified by the ConsumerNF entity, including summation, average, maximum, minimum, variance, etc.

[0096] 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 queues corresponding to K aggregation levels, reaching a certain number {RP1, RP2…RP}. k Data is reported at that time.

[0097] Part 2, the Producer NF entity performs data aggregation according to the data request.

[0098] In some embodiments, the Producer network element maintains a data queue s0 corresponding to the D0 level, and K aggregation queues s1…s K For example, all queues use a first-in, first-out (FIFO) approach to add data.

[0099] In some embodiments, the Producer NF entity adds data to s0 based on the granularity D0 of the data at the 0th aggregation level. For example, the collected data can be data generated by the Producer NF entity or historical data already possessed by the Producer NF entity.

[0100] In some embodiments, for n = 0…K, if there exists r n+1 Then when s n The number of data in the queue reaches r n+1 When +1, from s n Take r from the queue n+1 Data (s) n The queue does not retain this data. Aggregation operations are performed on the aggregated data; the aggregated data is then stored in s. n+1 queue.

[0101] In Part 2.1, the Producer NF entity selects data from historical data.

[0102] For example, Producer NF determines the required length L of historical data based on the reporting trigger conditions and aggregation hierarchy relationship identifiers:

[0103]

[0104] In some embodiments, starting from the current moment, the latest L data points can be extracted from historical data according to the granularity D0 of the data at the 0th aggregation level; these L data points can be arranged in the order in which they were originally generated, collected, or recorded, and then sent to the s0 queue as generated data.

[0105] Part 3: The Producer NF entity provides data to the Consumer NF entity.

[0106] In some embodiments, if a subscription method is used, then when s1…s K When the queue length meets the reporting trigger condition, the Producer NF entity will send s1…s K The data in the queue is provided to the Consumer NF entity.

[0107] For example, if K=4 and the trigger condition is set as {RP1,RP2,RP3,RP4}={0,0,0,1}, then when the number of data in queues s1…s4 reaches 0, 0, 0, 1, the Producer NF entity will sequentially retrieve 0, 0, 0, 1 data from queues s1…s4 (the queues s1…s4 do not retain this data) and send it to the Consumer NF entity as provided data.

[0108] In the above embodiments, a hierarchical structure is adopted, and each level of calculation is based on the aggregation result of the previous level. There is a hierarchical relationship between different aggregation cycles.

[0109] By adopting a hierarchical structure, when providing aggregated data for multiple aggregation cycles (levels) simultaneously, the method proposed in this patent can configure the aggregation granularity of data generated within different time ranges based on time correlation in the data set provided to the Consumer NF entity, and balance the relationship between data volume and data accuracy by utilizing the aggregation ratio relationship between different levels.

[0110] This paper provides a hierarchical method for aggregating data, encompassing the processes of requesting, generating, and providing data. The granularity of data aggregation can be adjusted according to the time interval between the data provision time and the actual data delivery time. This achieves the goal of ensuring the time accuracy of valid data while reducing the amount of data transmitted.

[0111] The technical solution disclosed herein can be applied to:

[0112] 1. During the process of the NWDAF entity (through the DCCF entity) collecting data from the NF entity, the amount of data collected is reduced;

[0113] 2. The NWDAF entity reduces the amount of data collection and transmission during the process of retrieving historical data from the ADRF (Analytic Data Repository Function) or DRF (Data Repository Function) entity through the DCCF entity.

[0114] In some embodiments, the DCCF entity, acting as a Producer NF entity, provides data to the NWDAF entity during the following process:

[0115] 1. Allow NWDAF entities to act as Consumer NF entities and submit hierarchical data aggregation requests to DCCF entities;

[0116] 2. When the DCCF entity retrieves data with a granularity of D0 from the NF entity and performs data aggregation according to the Producer NF entity, it performs hierarchical aggregation on the data.

[0117] 3. The DCCF entity provides the collected data (after aggregation) to the NWDAF entity based on the data reporting trigger conditions of the Producer NF entity.

[0118] In some embodiments, the DCCF entity, acting as a Producer NF entity, provides data to the NWDAF entity during the following process:

[0119] 1. Allow NWDAF entities to act as Consumer NF entities and submit hierarchical data aggregation requests to DCCF entities;

[0120] 2. The DCCF entity determines the historical data of this data and stores it in the DRF entity.

[0121] 3. The DCCF entity retrieves historical data from the DRF entity based on how the Producer NF entity selects data from historical data to form D0, and performs hierarchical aggregation of the data according to the data aggregation process of the Producer NF entity.

[0122] 4. The DCCF entity provides the collected data (after aggregation) to the NWDAF entity based on the data reporting trigger conditions of the Producer NF entity.

[0123] In some embodiments, during the process of an NF entity acting as a Producer NF entity providing data to an NWDAF entity or a DCCF entity:

[0124] 1. Allow NWDAF entities or DCCF entities to act as Consumer NF entities and submit requests for hierarchical data aggregation to NF entities;

[0125] 2. NF entity data with granularity D0 is generated, and the data is aggregated hierarchically according to the Producer NF entity data aggregation process;

[0126] 3. The NF entity provides the collected data (after aggregation) to the NWDAF entity or DCCF entity based on the data reporting trigger conditions of the Producer NF entity.

[0127] Figure 4 Block diagrams illustrating some embodiments of the producer network element of this disclosure are shown.

[0128] like Figure 4 As shown, the producer network element 4 includes a generation unit 41 and a sending unit 42.

[0129] The generation unit 41 generates an initial data queue corresponding to the minimum granularity of the collected data sent by the consumer network element; and aggregates the data in the initial data queue according to the ratio of other granularities sent by the consumer network element to the minimum granularity to generate other data queues corresponding to other granularities. Other granularities are greater than or equal to the minimum granularity.

[0130] The sending unit 42 sends data from other data queues to the consumer network element.

[0131] In some embodiments, - other granularities include multiple levels of granularity, each level having a smaller granularity than the next level below it, and other data queues include data queues for each level corresponding to the granularity of each level.

[0132] In some embodiments, the generation unit 41 aggregates the data in the initial data queue that corresponds to the ratio between the granularity of the first level and the smallest granularity to generate the data queue of the first level.

[0133] In some embodiments, the generation unit 41 aggregates data in the data queue of the previous level according to the ratio between the granularity of the current level and the granularity of the previous level, generating a data queue for the current level. The current level is any level other than the first level. The generation unit 41 repeats the above steps until a data queue for each level is generated.

[0134] In some embodiments, if the number of data in other data queues meets the trigger data quantity in the reporting trigger condition of the other data queues sent by the consumer network element, the sending unit 42 sends the data in the other data queues to the consumer network element.

[0135] In some embodiments, the generation unit 41 determines the data collection range based on the reporting trigger conditions of each other data queue and the proportional relationship of the granularity corresponding to each other data queue; based on the data collection range, it collects historical data in chronological order to generate an initial data queue.

[0136] In some embodiments, the generation unit 41 determines the data collection range based on the weighted sum of the number of trigger data in the reporting trigger conditions of each other data queue, wherein the weight of each trigger data quantity is the proportional relationship of the corresponding other data queues.

[0137] In some embodiments, the type of aggregation processing is sent by the consumer network element, and the aggregation processing includes one of the following: summation processing, average processing, maximum processing, minimum processing, variance processing, and sample extraction processing.

[0138] Figure 5Block diagrams illustrating other embodiments of the producer network element of this disclosure are shown.

[0139] like Figure 5 As shown, the producer network element 5 in this embodiment includes a memory 51 and a processor 52 coupled to the memory 51. The processor 52 is configured to execute a data acquisition method in any embodiment of this disclosure based on instructions stored in the memory 51.

[0140] The memory 51 may include, for example, system memory, fixed non-volatile storage media, etc. The system memory stores, for example, the operating system, application programs, boot loader, database, and other programs.

[0141] Figure 6 Block diagrams illustrating further embodiments of the producer network element of this disclosure are shown.

[0142] like Figure 6 As shown, the producer network element 6 in this embodiment includes a memory 610 and a processor 620 coupled to the memory 610. The processor 620 is configured to execute the data acquisition method in any of the foregoing embodiments based on instructions stored in the memory 610.

[0143] The memory 610 may include, for example, system memory, fixed non-volatile storage media, etc. The system memory may store, for example, the operating system, application programs, boot loader, and other programs.

[0144] The producer network element 6 may also include input / output interfaces 630, network interfaces 640, and storage interfaces 650. These interfaces 630, 640, and 650, as well as the memory 610 and processor 620, can be connected via, for example, a bus 660. Specifically, the input / output interface 630 provides a connection interface for input / output devices such as monitors, mice, keyboards, touchscreens, microphones, and speakers. The network interface 640 provides a connection interface for various networked devices. The storage interface 650 provides a connection interface for external storage devices such as SD cards and USB flash drives.

[0145] Figure 7 Block diagrams illustrating some embodiments of the data acquisition system of this disclosure are shown.

[0146] like Figure 7 As shown, the data acquisition system 7 includes: a producer network element 71, used to execute the data acquisition method in any of the above embodiments; and a consumer network element 72, used to send the minimum granularity and the ratio of other granularities to the minimum granularity to the producer network element.

[0147] Those skilled in the art will understand that embodiments of this disclosure can be provided as methods, systems, or computer program products. Therefore, this disclosure can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this disclosure can 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, etc.) containing computer-usable program code.

[0148] The data acquisition method, data acquisition system, producer network element, and non-volatile computer-readable storage medium according to this disclosure have been described in detail above. To avoid obscuring the concept of this disclosure, some details known in the art have not been described. Those skilled in the art will fully understand how to implement the technical solutions disclosed herein based on the above description.

[0149] The methods and systems of this disclosure may be implemented in many ways. For example, they may be implemented by software, hardware, firmware, or any combination of software, hardware, and firmware. The above-described order of steps for the methods is for illustrative purposes only, and the steps of the methods of this disclosure are not limited to the specific order described above unless otherwise specifically stated. Furthermore, in some embodiments, this disclosure may also be implemented as a program recorded on a recording medium, the program including machine-readable instructions for implementing the methods according to this disclosure. Thus, this disclosure also covers recording media storing programs for performing the methods according to this disclosure.

[0150] While specific embodiments of this disclosure have been described in detail by way of example, those skilled in the art should understand that the examples are for illustrative purposes only and not intended to limit the scope of this disclosure. Those skilled in the art should understand that modifications can be made to the above embodiments without departing from the scope and spirit of this disclosure. The scope of this disclosure is defined by the appended claims.

Claims

1. A data acquisition method, comprising: Based on the smallest granularity of the collected data sent by the consumer network element, an initial data queue corresponding to the smallest granularity is generated; Based on the ratio between other granularities sent by the consumer network element and the minimum granularity, the data in the initial data queue is aggregated to generate other data queues corresponding to the other granularities. The other granularities are greater than or equal to the minimum granularity, and the other granularities include multiple levels of granularity, with each level of granularity being smaller than the next level of granularity. The data in the other data queues is sent to the consumer network element. These other data queues contain data queues corresponding to the granularity of each level, and each level of data queue corresponds to a different time range. The generation of other data queues corresponding to the other granularities includes: Based on the ratio between the first-level granularity and the smallest granularity, the data in the initial data queue corresponding to this ratio is aggregated to generate the first-level data queue. Sending data from the other data queues to the consumer network element includes: If the amount of data in the other data queues meets the trigger data quantity in the reporting trigger conditions of the other data queues sent by the consumer network element, the data in the other data queues will be sent to the consumer network element. The step of generating an initial data queue corresponding to the smallest granularity based on the smallest granularity of the collected data sent by the consumer network element includes: The data collection scope is determined based on the reporting trigger conditions of each other data queue and the proportional relationship of the granularity of each other data queue. Based on the data collection range, historical data is collected to generate the initial data queue.

2. The data acquisition method according to claim 1, wherein, The generation of other data queues corresponding to the other granularities includes: Based on the ratio between the granularity of the current level and the granularity of the level above it, the data in the data queue of the level above it that corresponds to the ratio is aggregated to generate the data queue of the current level. The current level is any level other than the first level. Repeat the above steps until data queues for each level are generated.

3. The data acquisition method according to claim 1, wherein, The determination of the data collection range based on the reporting trigger conditions of each other data queue and the proportional relationship of the granularity corresponding to each other data queue includes: The data collection range is determined by the weighted sum of the number of trigger data in the reporting trigger conditions of each of the other data queues, and the weight of each trigger data quantity is the proportional relationship of the corresponding other data queues.

4. The data acquisition method according to any one of claims 1-3, wherein, The type of aggregation processing is sent by the consumer network element, and the aggregation processing includes one of the following: summation processing, average processing, maximum value processing, minimum value processing, variance processing, and sample extraction processing.

5. A producer network element, comprising: The generation unit is used to generate an initial data queue corresponding to the minimum granularity of the collected data sent by the consumer network element, and to aggregate the data in the initial data queue according to the ratio of other granularities sent by the consumer network element to the minimum granularity, thereby generating other data queues corresponding to other granularities, wherein the other granularities are greater than or equal to the minimum granularity, and the other granularities include multiple levels of granularity, with each level of granularity being smaller than the next level of granularity. The sending unit is used to send data from the other data queues to the consumer network element. The other data queues include data queues at each level, corresponding to a specific granularity. Each level of data queue corresponds to a different time range. Specifically, the generation unit aggregates data in the initial data queue that corresponds to the ratio between the granularity of the first level and the smallest granularity, thereby generating the first-level data queue. If the number of data in the other data queues meets the trigger data quantity in the reporting trigger condition of the other data queues sent by the consumer network element, the sending unit sends the data in the other data queues to the consumer network element. The generation unit determines the data collection range based on the reporting trigger conditions of each other data queue and the proportional relationship of the granularity corresponding to each other data queue; based on the data collection range, it collects historical data and generates the initial data queue.

6. A data acquisition system, comprising: A producer network element is used to perform the data acquisition method according to any one of claims 1-4; The consumer network element is used to send the minimum granularity and the ratio of other granularities to the minimum granularity to the producer network element.

7. A producer network element, comprising: Memory; and A processor coupled to the memory, the processor being configured to execute the data acquisition method of any one of claims 1-4 based on instructions stored in the memory.

8. A non-volatile computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the data acquisition method according to any one of claims 1-4.

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