CN111130932A - Method and device for predicting flow trend based on historical flow and storage medium - Google Patents
Method and device for predicting flow trend based on historical flow and storage medium Download PDFInfo
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- CN111130932A CN111130932A CN201911312472.XA CN201911312472A CN111130932A CN 111130932 A CN111130932 A CN 111130932A CN 201911312472 A CN201911312472 A CN 201911312472A CN 111130932 A CN111130932 A CN 111130932A
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
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- H04L43/00—Arrangements for monitoring or testing data switching networks
- H04L43/08—Monitoring or testing based on specific metrics, e.g. QoS, energy consumption or environmental parameters
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- H—ELECTRICITY
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- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
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- H04L41/147—Network analysis or design for predicting network behaviour
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
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Abstract
The invention provides a method, a device and a storage medium for predicting flow trend based on historical flow, comprising the following steps: reading historical traffic data information, wherein the historical traffic data comprises actual traffic data of a plurality of historical unit time periods, and the actual traffic data of the plurality of historical unit time periods are associated; determining effective historical traffic data information; determining predicted flow data for a predicted unit time period, the predicted flow data for the predicted unit time period being associated with actual flow data for the plurality of historical unit time periods. And eliminating invalid historical flow data, eliminating the influence of the invalid historical abnormal flow data on a calculation result, determining a prediction time point, predicting the flow data, and improving the accuracy of flow prediction.
Description
The technical field is as follows:
the present invention relates to the field of communications, and in particular, to a method, an apparatus, and a storage medium for predicting a traffic trend based on historical traffic.
Background art:
with the development of network communication technology, the types of network traffic data and services are more and more, and people have an increasing demand for network traffic. Network traffic prediction helps to analyze network security conditions, scientifically manage networks, and prevent improper network behavior, and the prior art generally predicts in the following two ways:
reading historical flow data of the same minute point t in the previous n days, then calculating an arithmetic mean value of the flow of the t minute point in the previous n days, and taking the value as a simple predicted value of the flow of the t minute point in the n +1 th day;
reading historical flow data of the same minute point t in the previous N days, assigning the flow weight of the t minute point in the previous day to be N, assigning the flow weight of the t minute point in the previous two days to be N-1, and calculating the weighted average value of the flow of the t minute point in the previous N days by analogy, wherein the value is used as a simple predicted value of the flow of the t minute point in the N +1 th day.
When the two mentioned technical schemes are used for flow prediction calculation, historical abnormal flow data are not considered to be processed, and the abnormal flow data participate in the flow prediction calculation to cause that a calculation result is greatly deviated from normal flow, and finally the flow prediction is inaccurate;
therefore, there is a need in the art for a method for predicting a flow trend based on historical flow, which eliminates abnormal flow data when performing flow prediction calculation, does not participate in flow prediction calculation, and improves the accuracy of flow prediction.
The invention content is as follows:
the present invention has been made to solve at least one of the problems occurring in the prior art.
Specifically, one aspect of the present invention provides a method for predicting a flow trend based on historical flow, including:
reading historical traffic data information, wherein the historical traffic data comprises actual traffic data of a plurality of historical unit time periods, and the actual traffic data of the plurality of historical unit time periods are associated;
determining effective historical traffic data information;
determining predicted flow data for a predicted unit time period, the predicted flow data for the predicted unit time period being associated with actual flow data for the plurality of historical unit time periods.
By adopting the scheme, the method for predicting the flow trend based on the historical flow is based on the historical flow data in a certain range, and part of invalid historical flow data is removed, so that the influence of the invalid historical flow data on the calculation result is eliminated, and then the flow data is predicted, so that the accuracy of flow prediction is improved, and the accuracy of flow trend prediction is improved.
Further, the reading of the historical flow rate data includes reading actual flow rate data of a plurality of historical unit time periods of a preset time range, and the actual flow rate data of the plurality of historical unit time periods are continuous or discontinuous time periods.
By adopting the scheme, the historical flow data is flexibly read, and the historical flow data is read according to the requirement, so that the time wasted by multi-reading of the historical flow data is reduced, and the inaccuracy of the final prediction result caused by the loss of part of the historical flow data is avoided.
Further, the determining valid historical traffic data information includes:
determining invalid historical traffic data information;
deleting invalid historical traffic data information from the historical traffic data information;
and obtaining effective historical flow data information.
Preferably, the determining invalid historical traffic data information comprises:
first screening: determining that the data of which the actual flow rate data is 0 in the historical unit time periods in the plurality of historical unit time periods is invalid historical flow rate data information;
and (3) second screening: and determining whether the actual flow data of the historical unit time period is invalid historical flow data information again by taking the threshold parameter as a determination condition.
Further, the first screening includes:
judging whether the actual flow data of the historical unit time period is 0 or not;
if yes, the actual flow data of the historical unit time period belongs to invalid historical flow data information;
if not, judging whether the data belongs to invalid historical flow data information again according to the second screening method.
Further, the second screening comprises:
determining an arithmetic mean of actual flow data for the plurality of historical unit time periods;
receiving a flow threshold parameter value;
judging whether the actual flow data/average value of the historical unit time period is larger than or equal to a threshold parameter value;
if yes, the actual flow data of the historical unit time period belongs to invalid historical flow data information;
and if not, the actual flow data of the historical unit time period does not belong to invalid historical flow data information.
By adopting the scheme, the flow values which do not meet the conditions are accurately removed, so that the flow values do not participate in flow prediction calculation, the influence of historical abnormal flow data on the final calculation result is eliminated, and the problem that the result error is increased due to the influence of the abnormal flow values on the final calculation result is avoided.
Further, the determining predicted flow data for a predicted unit time period includes:
determining the predicted flow data of the predicted unit time period according to the effective historical flow data information
Further, the determining predicted flow data for the predicted unit time period is according to the formula:
vn＝β*vn-1+(1-β)*fn；
v. the0When 0, we get: v. ofn＝(1-β)(fn+βfn-1+β2fn-2+…+βn-1f1)。
F isnActual flow rate value, v, representing the nth unit time periodn、vn-1Respectively represent the n-thThe predicted flow volume data of the unit time period and the n-1 th unit time period, β represents a weight coefficient.
By adopting the scheme, the formula vn＝(1-β)(fn+βfn-1+β2fn-2+…+βn-1f1) The weighting coefficient is reduced in an exponential equal ratio mode, the weighting influence of the flow data in the unit time period closer to the prediction unit time period is larger, the weighting influence of the flow data in the unit time period away from the prediction unit time period on the flow data is reduced, short-term flow fluctuation can be smoothed, a certain smoothing effect is achieved, and the data are processed more accurately.
Preferably, the method for predicting the flow trend based on the historical flow further comprises: determining a predicted flow trend according to the predicted flow data:
determining predicted flow data for a plurality of predicted unit time periods according to the method for determining predicted flow data for the predicted unit time periods;
and determining the predicted flow trend according to the predicted flow data of the predicted unit time periods.
By adopting the scheme, the flow trend is predicted by determining the final flow predicted values of a plurality of time points, and the accuracy of flow trend prediction is improved.
In a second aspect of the present invention, the present invention further provides an apparatus for predicting a flow trend based on historical flow, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method when executing the program.
In a third aspect of the invention, a computer-readable storage medium is provided, on which a computer program is stored which, when being executed by a processor, carries out the above-mentioned method.
In conclusion, the invention has the following beneficial effects:
1. according to the historical flow trend prediction method, the invalid historical flow data can be removed by setting the threshold parameter, so that the influence of the invalid historical flow data on a prediction result is avoided.
2. From the formula vn＝(1-β)(fn+βfn-1+β2fn-2+…+βn-1f1) Therefore, by setting the weight coefficient, the weight value decreases exponentially along with the time point of the data, so that the influence of the data at the time point far away from the current time point on the flow prediction at the current time point is smaller, short-term flow fluctuation can be smoothed, and a certain smoothing effect is achieved.
3. And completing the prediction of the flow trend according to the final flow predicted values of the multiple time points, and improving the accuracy of the flow trend prediction.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a flow chart of a preferred embodiment of a method for predicting flow trends based on historical flow in accordance with the present invention;
FIG. 2 is a flow diagram of a preferred embodiment of a method for determining valid historical traffic data information;
FIG. 3 is a flow chart of a preferred embodiment of the second screen.
The specific implementation mode is as follows:
the exemplary embodiments will be described herein in detail, and the embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the invention, as detailed in the appended claims.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in this specification and the appended claims, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and/or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.
The present invention will be described in detail below by way of examples.
In a preferred embodiment of the present invention,
as shown in fig. 1, the present invention provides a method for predicting a flow trend based on historical flow, which includes:
reading historical traffic data information, wherein the historical traffic data comprises actual traffic data of a plurality of historical unit time periods, and the actual traffic data of the plurality of historical unit time periods are associated;
determining effective historical traffic data information;
determining predicted flow data for a predicted unit time period, the predicted flow data for the predicted unit time period being associated with actual flow data for the plurality of historical unit time periods.
By adopting the scheme, the method for predicting the flow trend based on the historical flow is based on the historical flow data in a certain range, and part of invalid historical flow data is removed, so that the influence of the invalid historical flow data on the calculation result is eliminated, and then the flow data is predicted, so that the accuracy of flow prediction is improved, and the accuracy of flow trend prediction is improved.
As an alternative embodiment, the reading the historical flow data includes reading actual flow data of a plurality of historical unit time periods in a preset time range, and the actual flow data of the plurality of historical unit time periods are continuous or discontinuous time periods.
As an alternative embodiment, the preset time range may be from the first 0 to 10 days, from the first 10 to 30 days, from the first 50 to 100 days, etc.
As an optional implementation manner, the historical flow rate data in the preset time range may be all flow rate data in the preset time range, or may be flow rate data in a partial time period, and the unit time period may be seconds, minutes, hours, and the like.
By adopting the scheme, the historical flow data in the preset time range is flexibly read, and the historical flow data is read according to the requirement, so that the time wasted by multi-reading of the historical flow data is reduced, and the inaccuracy of the final prediction result caused by the loss of part of the historical flow data is avoided.
In a specific implementation, the reading of the historical flow data may be as follows in table 1:
| time | bps |
| 2018/11/27 0:01 | 18453880 |
| 2018/11/28 0:01 | 0 |
| 2018/11/29 0:01 | 216852896 |
| 2018/11/30 0:01 | 325263114 |
| 2018/12/1 0:01 | 360253485 |
| 2018/12/2 0:01 | 255128058 |
| 2018/12/3 0:01 | 320198522 |
| 2018/12/4 0:01 | 132624221 |
| 2018/12/5 0:01 | 267977229 |
| 2018/11/27 0:00 | 9519792 |
| 2018/11/28 0:00 | 0 |
| 2018/11/29 0:00 | 237301160 |
| 2018/11/30 0:00 | 313207754 |
| 2018/12/1 0:00 | 350231341 |
| 2018/12/2 0:00 | 295797069 |
| 2018/12/3 0:00 | 308625781 |
| 2018/12/4 0:00 | 120660685 |
| 2018/12/5 0:00 | 240285824 |
as shown in fig. 2, in a specific implementation process, the determining effective historical traffic data information includes:
determining invalid historical traffic data information;
deleting invalid historical traffic data information from the historical traffic data information;
and obtaining effective historical flow data information.
As an optional implementation, the determining invalid historical traffic data information includes:
first screening: determining that the data of which the actual flow rate data is 0 in the historical unit time periods in the plurality of historical unit time periods is invalid historical flow rate data information;
and (3) second screening: and determining whether the actual flow data of the historical unit time period is invalid historical flow data information again by taking the threshold parameter as a determination condition.
As an optional implementation, the first screening includes:
judging whether the actual flow data of the historical unit time period is 0 or not;
if yes, the actual flow data of the historical unit time period belongs to invalid historical flow data information;
if not, judging whether the data belongs to invalid historical flow data information again according to the second screening method.
In the specific implementation process, in the group of data, the two pieces of data in the following table belong to invalid historical flow data information,
as in table 2 below:
| time | bps |
| 2018/11/28 0:01 | 0 |
| 2018/11/28 0:00 | 0 |
as an alternative embodiment, as shown in fig. 3, the second screening includes:
determining an arithmetic mean of actual flow data for the plurality of historical unit time periods;
receiving a flow threshold parameter value;
judging whether the actual flow data/average value of the historical unit time period is larger than or equal to a threshold parameter value;
if yes, the actual flow data of the historical unit time period belongs to invalid historical flow data information;
and if not, the actual flow data of the historical unit time period does not belong to invalid historical flow data information.
In the specific implementation process, according to the historical flow data, the flow average value of 0:00 is set as favg(0) 0:01 flow average of favg(1) According to the formula favg＝(fn+fn-1+…+f1) The arithmetic mean of the flow values at the same time point can be obtained:
favg(0)＝234453675.75；
favg(1)＝210750156.111111。
in a specific implementation process, setting the threshold parameter as m, and setting m to 5;
as an alternative embodiment, the flow threshold parameter may be 6, 7, or 8, which is set according to actual conditions.
In the specific implementation process, it can be known that the data of the group does not have the invalid historical flow data information by judging whether the flow value/average value is larger than or equal to the threshold parameter value. By adopting the scheme, the flow values which do not meet the conditions are accurately removed, so that the flow values do not participate in flow prediction calculation, the influence of historical abnormal flow data on the final calculation result is eliminated, and the problem that the result error is increased due to the influence of the abnormal flow values on the final calculation result is avoided.
As an optional implementation, the determining predicted flow data for a predicted unit time period includes:
determining the predicted flow data of the predicted unit time period according to the effective historical flow data information
In a specific implementation process, the determining the predicted flow data of the predicted unit time period is performed according to a formula:
vn＝β*vn-1+(1-β)*fn；
v. the0When 0, we get: v. ofn＝(1-β)(fn+βfn-1+β2fn-2+…+βn-1f1)。
F isnActual flow value, v, representing the nth time periodn、vn-1The predicted flow volume data respectively representing the nth unit time period and the (n-1) th unit time period, and β representing the weight coefficient.
In a specific implementation, let β be 0.9, and the predicted flow data for each time segment is calculated and obtained as shown in table 3 below:
| time | predicting flow data |
| 2018/11/27 0:01 | 1845388 |
| 2018/11/29 0:01 | 23346138.8 |
| 2018/11/30 0:01 | 53537836.32 |
| 2018/12/1 0:01 | 84209401.19 |
| 2018/12/2 0:01 | 101301266.9 |
| 2018/12/3 0:01 | 123190992.4 |
| 2018/12/4 0:01 | 124134315.2 |
| 2018/12/5 0:01 | 138518606.6 |
| 2018/11/27 0:00 | 8567812.8 |
| 2018/11/29 0:00 | 31441147.52 |
| 2018/11/30 0:00 | 59617808.17 |
| 2018/12/1 0:00 | 88679161.45 |
| 2018/12/2 0:00 | 109390952.2 |
| 2018/12/3 0:00 | 129314435.1 |
| 2018/12/4 0:00 | 128449060.1 |
| 2018/12/5 0:00 | 139632736.5 |
By adopting the scheme, the formula vn＝(1-β)(fn+βfn-1+β2fn-2+…+βn-1f1) It can be seen that the weighting coefficient of the flow f at the time point t is reduced in an exponential equal ratio mode, the weighting influence of the flow data closer to the current time is larger, the weighting influence of the flow data away from the current time on the flow data is reduced, short-term flow fluctuation can be smoothed, a certain smoothing effect is achieved, and the data are processed more accurately.
As an alternative embodiment, 138518606.6 corresponding to 2018/12/50: 01 in table 3 is used as the final predicted flow value of 2018/12/60: 01, and 139632736.5 corresponding to 2018/12/50: 00 is used as the final predicted flow value of 2018/12/60: 00.
As an alternative embodiment, 138518606.6 corresponding to 2018/12/50: 01 and 139632736.5 corresponding to 2018/12/50: 00 in table 3 are calculated again, and the final predicted flow value of 2018/12/60: 01 is determined, according to the formula:
vn＝β*vn-1+(1-β)*fn；
bringing the 138518606.6 corresponding to the 2018/12/50: 01 into fnBringing the 139632736.5 corresponding to the 2018/12/50: 00 into vn-1β is 0.9, and v is calculatedn139521323.5 is used as the final predicted flow value of 2018/12/60: 01.
As an optional implementation, the method for predicting a flow trend based on historical flow further includes: determining a predicted flow trend according to the predicted flow data:
determining predicted flow data for a plurality of predicted unit time periods according to the method for determining predicted flow data for the predicted unit time periods;
and determining the predicted flow trend according to the predicted flow data of the predicted unit time periods.
In a specific implementation process, according to the calculation method of the final flow predicted value of 2018/12/60: 01, a plurality of final flow predicted values near 2018/12/60: 01 are calculated, and the predicted flow trend is determined.
By adopting the scheme, the flow trend is predicted by determining the final flow predicted values in a plurality of time periods, and the accuracy of flow trend prediction is improved.
Based on the same inventive concept, the invention provides a device for predicting flow trend from historical flow, which comprises:
a processor;
storage means for storing one or more programs;
the one or more programs are executed by the one or more processors, causing the one or more processors to implement the methods described above.
Based on the same inventive concept, the present invention provides a storage medium including one or more programs, which can be executed by a processor to perform the above-described method.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may 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 implementation. 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 invention.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed 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 can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.
It should be understood that the technical problems can be solved by combining and combining the features of the embodiments from the claims.
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 invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. A method for predicting flow trends based on historical flow, comprising:
reading historical traffic data information, wherein the historical traffic data comprises actual traffic data of a plurality of historical unit time periods, and the actual traffic data of the plurality of historical unit time periods are associated;
determining effective historical traffic data information;
determining predicted flow data for a predicted unit time period, the predicted flow data for the predicted unit time period being associated with actual flow data for the plurality of historical unit time periods.
2. The method for predicting flow trends based on historical flow according to claim 1, wherein the determining valid historical flow data information includes:
determining invalid historical traffic data information;
deleting invalid historical traffic data information from the historical traffic data information;
and obtaining effective historical flow data information.
3. The method for predicting flow trends based on historical flow according to claim 2, wherein the determining invalid historical flow data information comprises:
first screening: determining that the data of which the actual flow rate data is 0 in the historical unit time periods in the plurality of historical unit time periods is invalid historical flow rate data information;
and (3) second screening: and determining whether the actual flow data of the historical unit time period is invalid historical flow data information again by taking the threshold parameter as a determination condition.
4. The method for predicting flow trends based on historical flow according to claim 3, wherein the first filtering comprises:
judging whether the actual flow data of the historical unit time period is 0 or not;
if yes, the actual flow data of the historical unit time period belongs to invalid historical flow data information;
if not, judging whether the data belongs to invalid historical flow data information again according to the second screening method.
5. The method for predicting flow trend based on historical flow according to claim 3 or 4, wherein the second screening comprises:
determining an arithmetic mean of actual flow data for the plurality of historical unit time periods;
receiving a flow threshold parameter value;
judging whether the actual flow data/average value of the historical unit time period is larger than or equal to a threshold parameter value;
if yes, the actual flow data of the historical unit time period belongs to invalid historical flow data information;
and if not, the actual flow data of the historical unit time period does not belong to invalid historical flow data information.
6. The method of predicting flow trends based on historical flow according to claim 5, wherein the determining predicted flow data for a predicted time unit period comprises:
and determining the predicted flow data of the predicted unit time period according to the effective historical flow data information.
7. The method for predicting flow trend based on historical flow according to claim 6, wherein the determining the predicted flow data for the predicted unit time period is according to the formula:
vn＝β*vn-1+(1-β)*fn；
v. the0When 0, we get: v. ofn＝(1-β)(fn+βfn-1+β2fn-2+…+βn-1f1)。
8. The method for predicting flow trends based on historical flow according to claim 7, further comprising: determining a predicted flow trend according to the predicted flow data:
determining predicted flow data for a plurality of predicted unit time periods according to the method for determining predicted flow data for the predicted unit time periods;
and determining the predicted flow trend according to the predicted flow data of the predicted unit time periods.
9. An apparatus for predicting flow trends based on historical flow, comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the method of any of the preceding claims 1-8 when executing the program.
10. A computer-readable storage medium, characterized in that a computer program is stored thereon, which program, when being executed by a processor, carries out the method of any one of the preceding claims 1-8.
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| CN111970206A (en) * | 2020-08-21 | 2020-11-20 | 北京浪潮数据技术有限公司 | FC network flow control method, device and related components |
| CN112543152A (en) * | 2020-12-08 | 2021-03-23 | 贝壳技术有限公司 | Method and device for adaptively adjusting service timeout time |
| CN112653589A (en) * | 2020-07-13 | 2021-04-13 | 福建奇点时空数字科技有限公司 | Network data flow abnormity detection method based on host data flow characteristic extraction |
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| CN106875027A (en) * | 2016-06-06 | 2017-06-20 | 阿里巴巴集团控股有限公司 | The Forecasting Methodology and device of resource request value, the Forecasting Methodology of trading volume |
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| CN112543152A (en) * | 2020-12-08 | 2021-03-23 | 贝壳技术有限公司 | Method and device for adaptively adjusting service timeout time |
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