CN112492637B - Method and device for predicting cell traffic - Google Patents
Method and device for predicting cell traffic Download PDFInfo
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- CN112492637B CN112492637B CN202011586996.0A CN202011586996A CN112492637B CN 112492637 B CN112492637 B CN 112492637B CN 202011586996 A CN202011586996 A CN 202011586996A CN 112492637 B CN112492637 B CN 112492637B
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
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- H04W24/08—Testing, supervising or monitoring using real traffic
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D30/00—Reducing energy consumption in communication networks
- Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks
Abstract
The application provides a method and a device for predicting cell traffic. The method comprises the following steps: acquiring historical data in a first period, wherein the historical data is used for indicating the cell traffic in the first period; determining the type of the cell from a plurality of predefined types according to the historical data; and determining a target prediction model from a plurality of prediction models corresponding to the plurality of types according to the type of the cell, wherein the target prediction model is used for predicting the traffic of the cell in a second period, and the second period is the next period of the first period. The method comprises the steps of analyzing historical data of the cell traffic, determining the type of the cell, and determining a target prediction model according to the type of the cell, so that the selection of the model is more reasonable, and the accuracy of the cell traffic prediction can be improved.
Description
Technical Field
The present application relates to the field of communications, and more particularly, to a method and apparatus for cell traffic prediction.
Background
Along with the iterative updating of the communication technology, the number of the base stations is continuously increased, the energy consumption of a single base station is greatly increased compared with that of the previous generation base station, and the realization of the high efficiency and the energy conservation of the base station has important significance for the healthy and sustainable development of the industry.
Because the cell traffic is a data index reflecting the situation of the user traffic around the base station and the busyness of the base station, it is desirable to provide a method which can reasonably select a prediction model, further accurately predict the cell traffic and help operators to formulate a more accurate and effective energy-saving strategy.
Disclosure of Invention
The embodiment of the application provides a method and a device for predicting cell traffic, so as to reasonably select a prediction model and accurately predict the cell traffic.
In a first aspect, the present application provides a method for cell traffic prediction, the method comprising: acquiring historical data in a first period, wherein the historical data is used for indicating the cell traffic in the first period;
determining the type of the cell from a plurality of predefined types according to the historical data;
and determining a target prediction model from a plurality of prediction models corresponding to the multiple types according to the type of the cell, wherein the target prediction model is used for predicting the traffic of the cell in a second period, and the second period is the next period of the first period.
Based on the scheme, the type of the cell is determined by analyzing the historical data of the cell traffic, and different prediction models are determined according to the type of the cell, so that reasonable prediction models can be selected for the cells of different types, the advantages of the different models in different aspects are exerted, and the accuracy of the cell traffic prediction is improved.
Optionally, the plurality of types includes: data incomplete cell, traffic stable cell, traffic tidal obvious cell and traffic tidal unobvious cell.
Optionally, the plurality of prediction models includes: a time series model corresponding to the data incomplete cell and the traffic tidal insignificant cell, an exponential smoothing algorithm model corresponding to the traffic stable cell, and a cubic exponential smoothing model corresponding to the traffic tidal significant cell.
Optionally, the history data includes: bandwidth utilization or radio resource control (radio resource control, RRC) signaling quantity.
Optionally, the acquiring the historical data in the first period includes:
and acquiring historical data in the first period based on a preset sampling time interval.
Optionally, the preset sampling time interval is one hour.
Optionally, the first period includes M consecutive days, M being greater than or equal to 7 and being an integer.
Optionally, the determining, according to the historical data, a type to which the cell belongs from a predefined plurality of types includes:
under the condition that the data volume of the historical data in the first period is smaller than a first preset threshold, determining that the cell is a data incomplete cell; or (b)
Under the condition that the mean square error of the historical data in the first period is smaller than a second preset threshold, determining the cell as a business volume stable cell; or (b)
Determining that the cell is a traffic tidal unobvious cell when the difference between the average values of traffic in any two adjacent phases is smaller than a third preset threshold in a plurality of phases included in each day in the first period; or (b)
And determining the cell as a traffic tidal obvious cell when the difference between the average values of the traffic in at least two adjacent phases is greater than or equal to the third preset threshold in a plurality of phases included in each day in the first period.
Optionally, in a case where the first period includes M consecutive days and M is greater than or equal to 7, the first period includes a first subperiod including a weekday and a second subperiod including a weekend.
In a second aspect, there is provided an apparatus for cell traffic prediction comprising means or units for implementing the method for cell traffic prediction as described in any of the first and second aspects. It will be understood that each module or unit may implement a corresponding function by executing a computer program.
In a third aspect, there is provided an apparatus for cell traffic prediction comprising a processor configured to perform the method for cell traffic prediction as set forth in any one of the first and second aspects.
The apparatus may also include a memory to store instructions and data. The memory is coupled to the processor, and the processor, when executing instructions stored in the memory, may implement the method described in the first aspect. The apparatus may also include a communication interface for the apparatus to communicate with other devices, which may be, for example, a transceiver, circuit, bus, module, or other type of communication interface.
In a fourth aspect, there is provided a computer readable storage medium comprising instructions which, when run on a computer, cause the computer to implement the method as described in any one of the first and second aspects.
In a fifth aspect, there is provided a computer program product comprising: a computer program (which may also be referred to as code, or instructions) which, when executed, causes a computer to perform the method of the first aspect and any of the first aspects.
It should be understood that, the second aspect to the fifth aspect of the present application correspond to the technical solutions of the first aspect of the present application, and the beneficial effects obtained by each aspect and the corresponding possible embodiments are similar, and are not repeated.
Drawings
Fig. 1 is a schematic diagram of a communication system suitable for use in the communication method of the embodiments of the present application;
fig. 2 is a schematic flow chart of a method for cell traffic prediction provided in an embodiment of the present application;
fig. 3 is a schematic flowchart of determining a type of a cell according to an embodiment of the present application;
fig. 4 is a schematic diagram of determining a target prediction model according to a cell belonging type according to an embodiment of the present application;
fig. 5 is a schematic block diagram of an apparatus for cell traffic prediction provided in an embodiment of the present application;
fig. 6 is another schematic block diagram of an apparatus for cell traffic prediction provided by an embodiment of the present application.
Detailed Description
The technical solutions in the present application will be described below with reference to the accompanying drawings.
The technical solution of the embodiment of the application can be applied to various communication systems, for example: long term evolution (long term evolution, LTE) system, LTE frequency division duplex (frequency division duplex, FDD) system, LTE time division duplex (time division duplex, TDD), universal mobile telecommunications system (universal mobile telecommunication system, UMTS), worldwide interoperability for microwave access (worldwide interoperability for microwave access, wiMAX) telecommunications system, future fifth generation (5th generation,5G) system or New Radio (NR), vehicle-to-other devices (vehicle-to-X V2X), where V2X may include vehicle-to-internet (vehicle to network, V2N), vehicle-to-vehicle (V2V), vehicle-to-infrastructure (vehicle to infrastructure, V2I), vehicle-to-pedestrian (vehicle to pedestrian, V2P), etc., workshop communication long term evolution technology (long term evolution-vehicle, LTE-V), vehicle networking, machine-type communications (machine type communication, MTC), internet of things (Internet of Things, ioT), inter-machine communication long term evolution technology (long term evolution-vehicle, LTE-M), machine-to-machine (machine to machine, M2M), etc. The access network device in the embodiment of the present application may be any device having a wireless transceiver function. Access network devices include, but are not limited to: an evolved Node B (eNB), a radio network controller (radio network controller, RNC), a Node B (NB), a base station controller (base station controller, BSC), a base transceiver station (base transceiver station, BTS), a home base station (home evolved Node B, or home Node B, HNB, for example), a Base Band Unit (BBU), an Access Point (AP) in a wireless fidelity (wireless fidelity, WIFI) system, a wireless relay Node, a wireless backhaul Node, a transmission point (transmission point, TP), or a transmission reception point (transmission and reception point, TRP), etc., may also be a gNB or a transmission point (TRP, TP) in a 5G (such as NR) system, or one or a group (including multiple antenna panels) of antenna panels of a base station in a 5G system, or may also be a network Node constituting a gNB or a transmission point, such as a baseband unit (BBU), or a Distributed Unit (DU), etc.
A terminal device can also be called a User Equipment (UE), an access terminal, a subscriber unit, a subscriber station, a mobile station, a remote terminal, a mobile device, a user terminal, a wireless communication device, a user agent, or a user equipment. The terminal device in the embodiments of the present application may be a mobile phone (mobile phone), a tablet (pad), a computer with a wireless transceiving function, a Virtual Reality (VR) terminal device, an augmented reality (augmented reality, AR) terminal device, a wireless terminal in an industrial control (industrial control), a wireless terminal in an unmanned driving (self driving), a wireless terminal in a remote medical (remote media), a wireless terminal in a smart grid (smart grid), a wireless terminal in a transportation security (transportation safety), a wireless terminal in a smart city (smart city), a wireless terminal in a smart home (smart home), a cellular phone, a cordless phone, a session initiation protocol (session initiation protocol, SIP) phone, a wireless local loop (wireless local loop, WLL) station, a personal digital assistant (personal digital assistant, PDA), a handheld device with a wireless communication function, a computing device or other processing device connected to a wireless modem, a vehicle-mounted device, a wearable device, a terminal in a 5G network, a land-based terminal in an evolved-state network (PLMN), a public terminal in a public network (public network), or a public communication device in a future network (PLMN).
Furthermore, the terminal device may also be a terminal device in an internet of things (Internet of things, ioT) system. IoT is an important component of future information technology development, and its main technical feature is to connect an item with a network through a communication technology, so as to implement man-machine interconnection and an intelligent network for object interconnection.
The specific form of the terminal device is not limited in this application.
It should be noted that a cell (cell) is described by a higher layer from the viewpoint of resource management or mobility management or service units. The coverage area of each access network device may be divided into one or more cells, and each cell may correspond to one or more frequency points, or each cell may be considered as an area formed by the coverage area of one or more frequency points. In the embodiment of the application, different cells may correspond to the same or different access network devices.
To facilitate understanding of the embodiments of the present application, a communication system suitable for the embodiments of the present application will be described in detail, taking the communication system shown in fig. 1 as an example. Fig. 1 shows a schematic diagram of a communication system suitable for use in the communication method of the embodiments of the present application. As shown in fig. 1, the communication system 100 may include at least one network device (e.g., network device 102) and at least one terminal device (e.g., terminal device 104), where the network device 102 may communicate with the terminal device 104. Optionally, the communication system 100 may further include more network devices and/or more terminal devices, which is not limited in this application.
The method for cell traffic prediction provided in the embodiment of the present application will be described in detail with reference to fig. 2. It should be understood that the method may be performed by an apparatus for cell traffic prediction. The apparatus may be, for example, a stand-alone device or may be a module deployed in an access network device, which is not limited in this application.
Fig. 2 is a schematic flow chart of a method for cell traffic prediction provided in an embodiment of the present application. As shown in fig. 2, the method 200 may include steps 210 through 230.
In step 210, historical data for a first period of time is obtained, the historical data being indicative of cell traffic for the first period of time.
The historical data may include, for example, bandwidth utilization and/or number of radio resource control (radio resource control, RRC) signaling. The bandwidth utilization may include, for example, an uplink physical resource block (physical resource block, PRB) utilization and/or a downlink PRB utilization; the number of RRC signaling may include, for example, the number of uplink RRC and/or the number of downlink RRC signaling.
It should be appreciated that the historical data may also include, but is not limited to, call strength, traffic volume, drive test statistics, device network management statistics, northbound interface statistics, and the like. The embodiments of the present application are not limited in this regard.
Optionally, the first period of time includes M consecutive days, wherein M may be a predefined value. For example, M.gtoreq.7 and is an integer. M may take other values as well, which is not limited in this application.
The first period of time may also include a succession of hours, or a succession of one or more weeks, or a succession of one or more months, etc. The present application is not limited in this regard.
Alternatively, the above-mentioned acquiring the history data in the first period may be based on a preset sampling time interval. The preset sampling time interval may be, for example, one hour, or may be other time intervals such as half an hour, which is not limited in the embodiment of the present application.
In step 220, the type to which the cell belongs is determined among a predefined plurality of types based on the history data within the first period.
The predefined types may include, among others, data incomplete cells, traffic stable cells, traffic tidal evident cells, and traffic tidal unobvious cells.
In fact, the types to which the cells belong are not limited to the above list, and the above-described types are merely illustrated for convenience in correspondence with a plurality of prediction models. The types to which the cell belongs may include: a data integrity cell, a data incompleteness cell, a traffic stabilization cell, and a traffic instability cell. Further, traffic-unstable cells can be classified into traffic-tidal evident cells and traffic-tidal non-evident cells.
For ease of understanding, a specific flow of determining the type of cell is described below in connection with fig. 3.
Fig. 3 shows a flow chart of determining the type of the cell from the history data in the first period, among the predefined types. The flow may include steps 301 through 303.
In step 301, the integrity of the historical data is analyzed.
Specifically, under the condition that the data volume of the historical data in the first period is smaller than a first preset threshold, determining the cell as a data incomplete cell; and determining that the cell is a complete cell under the condition that the data volume of the historical data in the first period is larger than or equal to a first preset threshold.
It should be understood that the above first preset threshold may be adjusted according to the data transmission situation and the scene, where setting the first preset threshold should consider increasing the applicability of the corresponding prediction model as much as possible.
In case the cell type is a data stable cell, step 302 may be continued to analyze the stability of the cell traffic.
Specifically, under the condition that the mean square error of the cell traffic in the first period is smaller than a second preset threshold, determining the cell as a traffic stable cell; and under the condition that the mean square error of the cell traffic in the first time period is greater than or equal to a second preset threshold, determining the cell as the traffic unstable cell.
Assuming that the first period includes M consecutive days, and the sampling interval is one hour, the calculation formula of the mean square error S of the traffic of the cell can be expressed as follows:
where i represents the i-th hour of the 24×m hours, i may take an integer value between 1 and 24×m; s is the mean square error of the traffic of the cell in the first period; d (D) i Traffic for the i-th hour of the cell.
It is to be understood that the above-listed formulas for calculating the mean square error of cell traffic are merely examples, and that equivalent transformations may be performed on formulas by those skilled in the art based on the same concepts.
For example, if the first period includes M consecutive days and the sampling time interval is one day, the calculation formula of the mean square error S of the cell traffic may be expressed as follows:
where j represents the j-th day of the M days, j may take an integer value between 1 and M.
It should be appreciated that the second predetermined threshold described above may vary with data conditions and model conditions. The present application is not limited in this regard.
It should also be appreciated that analyzing the stability of the cell traffic by the mean square error of the cell traffic over the first period is only one possible implementation. For example, stability of cell traffic may also be analyzed by calculating variance, maximum difference, percentage, etc. of cell traffic over a first period of time, including but not limited to.
In case the cell type is a traffic unstable cell, step 303 may be continued to analyze the tidal nature of the cell traffic.
Specifically, each day in the first period may be further divided into a plurality of phases, and traffic in the same phase in M days in the first period may be counted, and when the difference between the average value of traffic in any two adjacent phases in the first period is smaller than a third preset threshold, the cell is determined to be a traffic tidal unobvious cell; or determining the cell as a traffic tidal obvious cell when the difference of the average value of the traffic of at least two adjacent stages of the cell in the first period is greater than or equal to the third preset threshold.
Alternatively, in the case where the above-described first period includes M consecutive days and M is greater than or equal to 7, the first period includes a first sub-period and a second sub-period. Wherein the first sub-period comprises X (X is greater than or equal to 1 and is an integer) days, the second sub-period comprises Y (Y is greater than or equal to 1 and is an integer) days, and X+Y=M.
For example, if the first period includes 7 consecutive days of the week, the first subperiod may include, for example, 5 consecutive weekdays of the week, and the second subperiod may include, for example, a weekend of 2 days.
It should be appreciated that the first time period may be divided into a plurality of sub-periods for different scenarios, and the tidal nature of the cell may be analyzed on a per sub-period basis. For ease of understanding only, a general case, namely, a case of dividing weekdays and weekends, is exemplarily shown herein, and should not constitute any limitation to the embodiments of the present application.
Further, different phases may be divided for each day in the first and second sub-periods, respectively. For example, in the two sub-periods of weekdays and weekends exemplified above, for example, the day of the weekday is divided into n (n > 1 and is an integer) phases, and the day of the weekend is divided into m (m > 1 and is an integer) phases.
The division of multiple stages in each day can be divided according to idle time and busy time of a place in a cell, for example, the busy time of an office work day is 9 early to 6 late, and the idle time is 6 late to 9 early; the busy hour of the bar concentrated block is 6 late to 6 early the next day, etc. It should be understood that several possible staged cases are given here by way of example only for ease of understanding and should not constitute any limitation to the present application.
As an example, X workdays (i.e., one example of the first sub-period described above) are divided into 4 phases, i.e., n=4, and the specific division of the 4 phases is as follows:
the first stage: 00:00-05:59;
and a second stage: 06:00-11:59;
and a third stage: 12:00-17:59;
fourth stage: 18:00-23:59.
Thereafter, the average value V of the cell traffic for each of the n phases can be calculated k Wherein k is more than or equal to 1 and n is more than or equal to n.
For the four phases listed above, the average of the cell traffic for each phase can be obtained separately as follows:
V 1 ＝∑ 0≤i＜6 D i /X；
V 2 ＝∑ 6≤i＜12 D i /X；
V 3 ＝∑ 12≤i＜18 D i /X；
V 4 ＝∑ 18≤i＜24 D i /X。
in the case of calculating the average value of the cell traffic of each stage, two adjacent V's can be continued k Taking the difference and taking the absolute value to obtain B l Wherein l is more than or equal to 1 and n-1.
For example, B 1 ＝V 2 —V 1 ，B 2 ＝V 3 —V 2 ，B 3 ＝V 4 —V 3 。
At B l Under the condition that the tidal power of the cell is larger than a third preset threshold, determining that the tidal power of the cell is obvious in business volume on a working day, wherein the cell is a cell with obvious tidal power of the business volume; at B l And if the tidal power of the cell is smaller than or equal to a third preset threshold, determining that the tidal power of the cell is not obvious on the working day, wherein the cell is a cell with the tidal power of the service being not obvious.
Tidal analysis of cell traffic over the weekend (i.e., one example of the second sub-period) may also be implemented with reference to the above example, and is not repeated here for brevity.
The analysis of the first sub-period and the second sub-period may or may not be identical based on the analysis of the tidal nature of the cell traffic for the first sub-period and the second sub-period. In case that the two are different, the cells may be classified into different types according to the analysis results of the different sub-periods.
It should be understood that the above analysis of tidal nature of traffic is only one possible implementation and should not be construed as limiting the application in any way. The tidal nature of the cell traffic can also be analysed, for example, based on the difference in the sum of the traffic of the two adjacent phases, the difference in the mean square error of the traffic of the two adjacent phases, etc.
On the other hand, the data may also be cleaned in order to improve the prediction accuracy.
Optionally, before step 220, the method further comprises: the historical data in the first period is cleaned.
The cleaning of the historical data over the first period of time may include, but is not limited to, culling duplicate values and outliers. Through data cleansing, structured data can be obtained that can be used for analysis. Therefore, the analysis of the cell type is based on the effective data, so that the interference caused by repeated values, abnormal values and the like is eliminated, and the prediction accuracy is improved.
In step 230, a target prediction model is determined from a plurality of prediction models corresponding to the above-described plurality of types according to the type to which the cell belongs, the target prediction model being used to predict traffic of the cell in a second period, the second period being a period next to the first period.
Wherein, the prediction model may include, but is not limited to: a time series model corresponding to the incomplete data cell and the traffic tidal unobvious cell, an exponential smoothing algorithm model corresponding to the traffic stable cell, and a cubic exponential smoothing model corresponding to the traffic tidal unobvious cell.
The determination of the target prediction model according to the type to which the cell belongs will be described in detail with reference to fig. 4.
Fig. 4 is a schematic diagram of determining a target prediction model according to the type of a cell. The specific step of determining the type of the cell may be referred to the detailed description in step 220, and will not be described herein.
As shown in fig. 4, for the incomplete data cell, a time sequence algorithm model may be selected to perform traffic prediction; for a traffic stable cell, an exponential smoothing algorithm model can be selected as a target prediction model; aiming at the traffic tidal obvious cell, a cubic exponential smoothing algorithm model can be selected as a target prediction model; for data-complete, traffic tidal unobvious cells, a time series algorithm model may be selected as the target prediction model.
By way of example, the time series algorithm model may be, but is not limited to, a propset model (it should be understood that the propset model is a model dedicated to large-scale time series analysis by Facebook (Facebook) corporation), the three-order exponential smoothing prediction model may be, for example, a holter-Winters model, etc., and the above list of various prediction models is merely an example, and should not be construed as limiting the present application. The present application includes, but is not limited to, this. The selected target prediction model may be used to predict traffic for a second period of the cell. Here, the second period may be a period next to the first period described above, and the number of days included in the second period may be the same as or different from the number of days included in the first period.
It should be understood that according to the predicted traffic volume in the second period, the method can help the operator to make a more accurate and effective energy-saving strategy, improve the energy-saving effect, and also help the operator to adjust the performance of the cell or the base station in time, thereby ensuring the user experience.
It should also be appreciated that traffic in this second period may also be used for prediction of subsequent periods, which is not limited by the embodiments of the present application.
Based on the scheme, the historical data of the cell traffic is analyzed, the type of the cell is determined, and a proper prediction model is selected according to the type of the cell, so that reasonable prediction models can be selected for different types of cells, the advantages of different models in different aspects are exerted, and the accuracy of the cell traffic prediction is improved.
Alternatively, for a plurality of cells, the foregoing steps 210 to 230 may be performed to analyze traffic history data of the plurality of cells, determine the types to which each cell belongs, and determine different prediction models according to each of the types to which each cell belongs to predict the traffic of the cell. The embodiments of the present application are not limited herein.
It should be appreciated that many of the embodiments of the present application relate to comparison to thresholds. For example, a comparison of the data volume with a first preset threshold, a comparison of the mean square error of the cell traffic volume with a second preset threshold, a comparison of the difference of the mean values of the traffic volumes with a third preset threshold, etc. The result of the comparison can generally be split into two branches, one designed with one branch being greater than or equal to and the other branch being less than; alternatively, one branch may be greater than and the other branch may be less than or equal to. Both designs may be applicable hereinafter, without any particular explanation, when reference is made to a comparison with a preset threshold. Examples of one of these designs are given herein for ease of understanding and description only, and should not be construed as limiting the present application in any way.
Fig. 5 is a schematic block diagram of an apparatus for cell traffic prediction provided in an embodiment of the present application. As shown in fig. 5, the apparatus 500 may include: an acquisition module 510 and a determination module 520.
The acquisition module 510 may be configured to acquire historical data for a first period of time, the historical data being indicative of cell traffic for the first period of time.
The determining module 520 may be configured to determine, according to the history data, a type to which the cell belongs from a predefined plurality of types; and the method can be used for determining a target prediction model from a plurality of prediction models corresponding to the multiple types according to the type of the cell, wherein the target prediction model is used for predicting the traffic of the cell in a second period, and the second period is the next period of the first period.
Optionally, the plurality of types includes: data incomplete cell, traffic stable cell, traffic tidal obvious cell and traffic tidal unobvious cell.
Optionally, the plurality of prediction models includes: a time series model corresponding to the data incomplete cell and the traffic tidal insignificant cell, an exponential smoothing algorithm model corresponding to the traffic stable cell, and a cubic exponential smoothing model corresponding to the traffic tidal significant cell.
Optionally, the history data includes: bandwidth utilization or radio resource control, RRC, signaling quantity.
Optionally, the obtaining module 510 may be further configured to obtain the historical data in the first period based on a preset sampling time interval.
Wherein the first period of time may comprise, for example, M consecutive days, M.gtoreq.7 and is an integer.
Optionally, the determining, according to the historical data, a type to which the cell belongs from a predefined plurality of types includes:
under the condition that the data volume of the historical data in the first period is smaller than a first preset threshold, determining that the cell is a data incomplete cell; or (b)
Under the condition that the mean square error of the historical data in the first period is smaller than a second preset threshold, determining the cell as a business volume stable cell; or (b)
Determining that the cell is a traffic tidal unobvious cell when the difference between the average values of traffic in any two adjacent phases is smaller than a third preset threshold in a plurality of phases included in each day in the first period; or (b)
And determining the cell as a traffic tidal obvious cell when the difference between the average values of the traffic in at least two adjacent phases is greater than or equal to the third preset threshold in a plurality of phases included in each day in the first period.
It should be understood that the division of the modules in the embodiments of the present application is illustrative, and is merely a logic function division, and other division manners may be implemented in practice. In addition, each functional module in the embodiments of the present application may be integrated in one processor, or may exist alone physically, or two or more modules may be integrated in one module. The integrated modules may be implemented in hardware or in software functional modules.
Fig. 6 is another schematic block diagram of an apparatus for cell traffic prediction provided by an embodiment of the present application. The device can be used for realizing the functions of the acquisition module and the determination module in the method. Wherein the device may be a system-on-chip. In the embodiment of the application, the chip system may be formed by a chip, and may also include a chip and other discrete devices.
As shown in fig. 6, the apparatus 600 may include at least one processor 610. Illustratively, the processor 610 is operable to determine, from the history data, a type to which the cell belongs among a predefined plurality of types; and determining a target prediction model from a plurality of prediction models corresponding to the multiple types according to the type of the cell, wherein the target prediction model is used for predicting the traffic of the cell in a second period, and the second period is the next period of the first period. Reference is made specifically to the detailed description in the method examples, and details are not described here.
The apparatus 600 may also include at least one memory 620 for storing program instructions and/or data. The memory 620 is coupled to the processor 610. The coupling in the embodiments of the present application is an indirect coupling or communication connection between devices, units, or modules, which may be in electrical, mechanical, or other forms for information interaction between the devices, units, or modules. The processor 610 may operate in conjunction with the memory 620. The processor 610 may execute program instructions stored in the memory 620. At least one of the at least one memory may be included in the processor.
The apparatus 600 may also include a communication interface 630 for communicating with other devices over a transmission medium so that an apparatus for use in the apparatus 600 may communicate with other devices. The communication interface 630 may be, for example, a transceiver, an interface, a bus, a circuit, or a device capable of implementing a transceiver function. Processor 610 may utilize communication interface 630 to transceive data and/or information and to implement the method for cell traffic prediction described in the corresponding embodiment of fig. 2.
The specific connection medium between the processor 610, the memory 620, and the communication interface 630 is not limited in the embodiments of the present application. The present embodiment is illustrated in fig. 6 as being coupled between processor 610, memory 620, and communication interface 630 via bus 640. Bus 640 is shown in bold in fig. 6, and the manner in which other components are connected is illustrated schematically and not by way of limitation. The buses may be classified as address buses, data buses, control buses, etc. For ease of illustration, only one thick line is shown in fig. 6, but not only one bus or one type of bus.
The present application also provides a computer program product comprising: a computer program (which may also be referred to as code, or instructions) which, when executed, causes an electronic device to perform the method of the embodiment shown in fig. 2.
The present application also provides a computer-readable storage medium storing a computer program (which may also be referred to as code, or instructions). The computer program, when executed, causes the electronic device to perform the method in the embodiment shown in fig. 2.
It should be appreciated that the processor in the embodiments of the present application may be an integrated circuit chip with signal processing capabilities. In implementation, the steps of the above method embodiments may be implemented by integrated logic circuits of hardware in a processor or instructions in software form. The processor may be a general purpose processor, a digital signal processor (digital signal processor, DSP), an application specific integrated circuit (application specific integrated circuit, ASIC), a field programmable gate array (field programmable gate array, FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware components. The disclosed methods, steps, and logic blocks in the embodiments of the present application may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of a method disclosed in connection with the embodiments of the present application may be embodied directly in hardware, in a decoded processor, or in a combination of hardware and software modules in a decoded processor. The software modules may be located in a random access memory, flash memory, read only memory, programmable read only memory, or electrically erasable programmable memory, registers, etc. as well known in the art. The storage medium is located in a memory, and the processor reads the information in the memory and, in combination with its hardware, performs the steps of the above method.
It should also be appreciated that the memory in embodiments of the present application may be either volatile memory or nonvolatile memory, or may include both volatile and nonvolatile memory. The nonvolatile memory may be a read-only memory (ROM), a Programmable ROM (PROM), an Erasable PROM (EPROM), an electrically Erasable EPROM (EEPROM), or a flash memory. The volatile memory may be random access memory (random access memory, RAM) which acts as an external cache. By way of example, and not limitation, many forms of RAM are available, such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous DRAM (SLDRAM), and direct memory bus RAM (DR RAM). It should be noted that the memory of the systems and methods described herein is intended to comprise, without being limited to, these and any other suitable types of memory.
The terms "unit," "module," and the like as used in this specification may be used to refer to a computer-related entity, either hardware, firmware, a combination of hardware and software, or software in execution.
Those of ordinary skill in the art will appreciate that the various illustrative logical blocks (illustrative logical block) and steps (steps) described in connection with the embodiments disclosed herein can be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application. In the several embodiments provided in this application, it should be understood that the disclosed apparatus, device, and method may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in each embodiment of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.
In the above-described embodiments, the functions of the respective functional units may be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions (programs). When the computer program instructions (program) are loaded and executed on a computer, the processes or functions described in accordance with the embodiments of the present application are fully or partially produced. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may be stored in or transmitted from one computer-readable storage medium to another, for example, by wired (e.g., coaxial cable, optical fiber, digital Subscriber Line (DSL)), or wireless (e.g., infrared, wireless, microwave, etc.). The computer readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that contains an integration of one or more available media. The usable medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a magnetic tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a Solid State Disk (SSD)), or the like.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a read-only memory (ROM), a random access memory (random access memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The foregoing is merely specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily think about changes or substitutions within the technical scope of the present application, and the changes and substitutions are intended to be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (8)
1. A method for cell traffic prediction, comprising:
acquiring historical data in a first period, wherein the historical data is used for indicating the cell traffic in the first period;
determining the type of the cell from a plurality of predefined types according to the historical data;
determining a target prediction model from a plurality of prediction models corresponding to the plurality of types according to the type of the cell, wherein the target prediction model is used for predicting the traffic of the cell in a second period, and the second period is the next period of the first period;
the plurality of predictive models includes: a time series model corresponding to the data incomplete cell and the traffic tidal insignificant cell, an exponential smoothing algorithm model corresponding to the traffic stable cell, and a cubic exponential smoothing model corresponding to the traffic tidal significant cell;
the determining, according to the history data, a type to which the cell belongs from a predefined plurality of types includes:
under the condition that the data volume of the historical data in the first period is smaller than a first preset threshold, determining that the cell is a data incomplete cell; or (b)
Under the condition that the mean square error of the historical data in the first period is smaller than a second preset threshold, determining the cell as a business volume stable cell; or (b)
Determining that the cell is a traffic tidal unobvious cell when the difference between the average values of traffic in any two adjacent phases is smaller than a third preset threshold in a plurality of phases included in each day in the first period; or (b)
And determining the cell as a traffic tidal obvious cell when the difference between the average values of the traffic in at least two adjacent phases is greater than or equal to the third preset threshold in a plurality of phases included in each day in the first period.
2. The method of claim 1, wherein the plurality of types comprises: data incomplete cell, traffic stable cell, traffic tidal obvious cell and traffic tidal unobvious cell.
3. The method of claim 1 or 2, the historical data comprising: bandwidth utilization or radio resource control, RRC, signaling quantity.
4. The method of claim 1, wherein the acquiring historical data for the first period of time comprises:
and acquiring historical data in the first period based on a preset sampling time interval.
5. The method of claim 4, wherein the first period of time comprises M consecutive days, M being greater than or equal to 7 and being an integer.
6. An apparatus for cell traffic prediction, comprising means for implementing the method of any of claims 1 to 5.
7. An apparatus for cell traffic prediction, comprising:
a memory for storing program instructions;
a processor for invoking and executing program instructions in said memory, the method of any of claims 1-5.
8. A computer-readable storage medium, characterized in that the storage medium stores a computer program which, when executed by a processor, implements the method according to any one of claims 1 to 5.
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