CN111865635A - Method and device for determining out-of-limit time of ring network capacity - Google Patents
Method and device for determining out-of-limit time of ring network capacity Download PDFInfo
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- CN111865635A CN111865635A CN201910355934.XA CN201910355934A CN111865635A CN 111865635 A CN111865635 A CN 111865635A CN 201910355934 A CN201910355934 A CN 201910355934A CN 111865635 A CN111865635 A CN 111865635A
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/14—Network analysis or design
- H04L41/145—Network analysis or design involving simulating, designing, planning or modelling of a network
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L12/00—Data switching networks
- H04L12/28—Data switching networks characterised by path configuration, e.g. LAN [Local Area Networks] or WAN [Wide Area Networks]
- H04L12/42—Loop networks
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/14—Network analysis or design
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- 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
- H04L43/0876—Network utilisation, e.g. volume of load or congestion level
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- 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
- H04L43/0876—Network utilisation, e.g. volume of load or congestion level
- H04L43/0882—Utilisation of link capacity
Abstract
The embodiment of the invention discloses a method and a device for determining the out-of-limit time of looped network capacity, wherein the method comprises the steps of carrying out cluster training on the peak value change coefficient of looped network flow in unit time in a preset historical time period based on two classifications to obtain a flow mutation screening model, wherein the two classifications comprise a normal value and a mutation value; correcting a network looped network flow peak value per unit time in a historical investigation period based on a flow mutation screening model; determining a unit time increase coefficient of the network ring network flow peak value based on the corrected unit time network ring network flow peak value in the historical investigation period; and determining a time point when the network ring network flow peak value reaches a preset capacity threshold value based on the current network ring network flow peak value in unit time and the unit time increase coefficient of the network ring network flow peak value. The embodiment of the invention solves the problem that the capacity expansion time cannot be accurately obtained in the prior art.
Description
Technical Field
The embodiment of the invention relates to the technical field of communication, in particular to a method and a device for determining out-of-limit time of ring network capacity.
Background
At present, the prediction of the capacity of the transmission PTN looped network is generally determined based on manual check, so the prediction result is inaccurate and time-consuming, meanwhile, the existing partial network management prediction scheme cannot accurately inform a specific capacity expansion time point, and only can inform the current occupation condition of the looped network, so that the specific date that the capacity of the looped network is full of 100% cannot be predicted, the capacity expansion of the transmission PTN network is lagged, the capacity expansion is generally arranged only when the load of the looped network is very high, and the capacity expansion cannot be prepared in advance, thereby bringing adverse effects to the development of the 4G mobile internet service.
Disclosure of Invention
The embodiment of the invention provides a method and a device for determining out-of-limit time of capacity of a ring network, and aims to solve the problem that the capacity-expanding time cannot be accurately determined in the prior art.
In order to solve the technical problem, the invention is realized as follows:
in a first aspect, a method for determining a ring network capacity out-of-limit time is provided, including:
performing cluster training on the network looped network flow peak value change coefficient in unit time in a preset historical time period based on two classifications to obtain a flow mutation screening model, wherein the two classifications comprise a normal value and a mutation value;
based on the flow mutation screening model, modifying a mutation value in a network looped network flow peak value per unit time in a historical investigation period;
Determining a unit time increase coefficient of the network ring network flow peak value based on the corrected unit time network ring network flow peak value in the historical investigation period;
and determining a time point when the network ring network flow peak value reaches a preset capacity threshold value based on the current network ring network flow peak value in unit time and the unit time increase coefficient of the network ring network flow peak value.
In a second aspect, there is provided an apparatus for determining a ring capacity out-of-limit time, including:
the model obtaining unit is used for carrying out clustering training on the network looped network flow peak value change coefficient in unit time in a preset historical time period based on two classifications to obtain a flow mutation screening model, wherein the two classifications comprise a normal value and a mutation value;
the flow peak value correcting unit is used for correcting the sudden change value in the flow peak value of the network ring network per unit time in the historical investigation period based on the flow sudden change screening model;
the growth coefficient determining unit is used for determining the growth coefficient of the network ring network flow peak value in unit time based on the corrected network ring network flow peak value in the historical investigation period;
and the time determining unit is used for determining a time point when the network ring network flow peak value reaches a preset capacity threshold value based on the network ring network flow peak value of the current unit time and the unit time increase coefficient of the network ring network flow peak value.
In a third aspect, there is also provided a terminal device comprising a processor, a memory and a computer program stored on the memory and executable on the processor, the computer program, when executed by the processor, implementing the steps of the method according to the first aspect.
In a fourth aspect, there is also provided a computer readable storage medium having stored thereon a computer program which, when executed by a processor, carries out the steps of the method according to the first aspect.
In the embodiment of the invention, the method for determining the ring network capacity out-of-limit time corrects the flow peak value of the ring network at a single time in a historical investigation period through a flow mutation screening model obtained by performing cluster training on the flow peak value change coefficient of the ring network at the single time in a preset historical time period according to two categories, determines the unit time increase coefficient of the flow peak value of the ring network according to the flow peak value of the ring network at the single time in the corrected historical investigation period, and determines the time point when the flow peak value of the ring network reaches the preset capacity threshold value according to the flow peak value of the ring network at the current unit time and the unit time increase coefficient of the flow peak value of the ring network. Therefore, the unit time increase coefficient of the looped network flow peak value determined by the looped network flow peak value per unit time in the corrected historical investigation period can be obtained according to the flow mutation screening model, so that the time point when the looped network flow peak value reaches the preset capacity threshold value can be accurately determined, the problem that the capacity expansion time cannot be accurately obtained in the prior art can be solved, and the problems that time consumption is caused and accuracy is low due to the fact that the looped network capacity is predicted in a manual checking mode in the prior art can be solved.
Drawings
FIG. 1 is a schematic flow chart diagram of a method for determining a ring network capacity violation time according to one embodiment of the invention;
FIG. 2 is a schematic flow chart diagram of a method for determining the ring network capacity violation time according to another embodiment of the present invention;
FIG. 3 is a schematic flow chart diagram of a method for determining the ring network capacity violation time according to another embodiment of the present invention;
FIG. 4 is a schematic flow chart diagram of a method for determining the ring network capacity violation time according to another embodiment of the present invention;
FIG. 5 is a schematic flow chart diagram of a method for determining a ring network capacity violation time according to an embodiment of the invention;
FIG. 6 is a schematic diagram of a method for determining a ring network capacity violation time according to one embodiment of the present invention;
fig. 7 is a schematic block diagram of a device for determining the ring network capacity out-of-limit time according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The technical solutions provided by the embodiments of the present invention are described in detail below with reference to the accompanying drawings.
Fig. 1 is a schematic flow chart of a method for determining an out-of-limit time of a ring network capacity according to an embodiment of the present invention, so as to solve a problem in the prior art that an expansion time cannot be accurately obtained. The method can comprise the following steps:
and 102, performing cluster training on the network looped network flow peak value change coefficient in unit time in a preset historical time period based on two classifications to obtain a flow mutation screening model, wherein the two classifications comprise a normal value and a mutation value. The peak value change coefficient is used to characterize the peak value change amplitude, for example, the peak value change coefficient is determined based on the flow peak value change conditions of two days before and after the current day.
Specifically, PTN network looped network flow data of the last two years can be collected, daily peak value change conditions of the last two years are calculated according to the network looped network flow data, then clustering training is carried out through an artificial intelligence K-means algorithm, and a flow mutation screening model of the current looped network is trained. When the PTN ring network flow rate is collected for two years, the unit time may correspond to each hour, even each minute, and the like, and the operation may be specifically performed according to specific operation requirements, which is not limited to the manner described in the embodiment of the present invention.
And 104, correcting the mutation value in the network ring network flow peak value per unit time in the historical investigation period based on the flow mutation screening model.
As shown in fig. 2, the operation of correcting the mutation value in the network ring network traffic peak per unit time in the historical investigation period may include:
and 202, determining a sudden change value in the network looped network flow peak value per unit time in the historical investigation period based on the flow sudden change threshold value output by the flow sudden change screening model.
And 204, deleting the mutation value to correct the network ring network flow peak value per unit time in the historical investigation period.
After the flow mutation screening model is obtained, a mutation value in the network looped network flow peak value in unit time in the historical investigation period can be determined and deleted according to the output flow mutation threshold value, so that the network looped network flow peak value in unit time in the historical investigation period can be corrected, that is, data which cannot be referred to in the network looped network flow peak value in unit time in the historical investigation period can be removed through the flow mutation screening model, a relatively smooth flow peak value growth curve can be obtained, and therefore the unit time growth coefficient of the network looped network flow peak value can be determined in the subsequent steps.
And 106, determining the unit time increase coefficient of the network ring network flow peak value based on the corrected unit time network ring network flow peak value in the historical investigation period. Wherein, unit time may correspond to each day, then the daily average peak flow growth coefficient formula is:
wherein t represents the current day, t-1 represents the previous day, NtIndicates the peak flow rate on day t, Nt-1The peak flow rate on the t-1 th day, Δ N represents the average daily growth factor (i.e., the growth factor per unit time).
the value range of t may represent a historical investigation period, for example, the current day is advanced by 20 days, and the maximum is not more than 30 days. The number of days for acquiring the flow peak of each PTN looped network on the OMC network management system can be the number of days defined by the historical investigation period.
It can be understood that the flow peak value of the network looped network collected in the historical investigation period is led into the artificial intelligence module, the flow mutation screening model is started to perform data smoothing so as to remove the mutation value in the collected looped network flow peak value, the smoothed data is summed, and the ratio of the summed result and the time number (such as 20) of the investigation period after the mutation value is removed is the unit time increase coefficient delta N of the PTN looped network in the historical investigation period. The smoothed data may be evaluated by weighted average or mean square error to obtain a unit time increase coefficient Δ N in the historical investigation period, and the specific implementation process is not set forth herein.
Therefore, the unit time increase coefficient delta N of all PTN looped networks to be inspected in the historical inspection period can be obtained according to the formula (1), so that the unit time bandwidth historical increase rate of each looped network in each area can be calculated, and the unit time increase rate obtained according to the unit time historical increase rate in the subsequent steps and the final bandwidth utilization rate which can be achieved by the looped networks can be obtained.
And 108, determining a time point when the network ring network flow peak value reaches a preset capacity threshold value based on the network ring network flow peak value of the current unit time and the unit time increase coefficient of the network ring network flow peak value.
As shown in fig. 3, determining a time point at which the network ring network traffic peak value reaches the preset capacity threshold value based on the network ring network traffic peak value of the current unit time and the unit time growth coefficient of the network ring network traffic peak value includes:
and 302, determining the network ring network flow peak value in unit time based on the network ring network flow peak value in the current unit time, the unit time increase coefficient of the network ring network flow peak value and the holiday flow peak value compensation coefficient.
The formula for calculating the network ring network flow peak value in unit time is as follows:
At1＝Nt1-1+ΔN+ΔM (2)
wherein, t 1Indicating a certain day in the historical investigation period, At1Indicating the t-th time in the history investigation period1Peak daily flow, Nt1-1Indicating the t-th time in the history investigation period1The flow peak value of 1 day, Δ N is a unit time increase coefficient in a historical investigation period, and Δ M is a holiday compensation coefficient.
Specifically, the Δ N of the PTN ring network calculated by the formula (1) is substituted into the formula (2), and first, the flow peak value a on the first day in the future is calculated1. Then, the calculated A is calculated1Substituting equation (2) again to replace N with a valuet1-1Term, calculate A2. And the rest can be analogized until all A in the historical investigation period are calculated to form an array N0＝{A1，A2，A3，......At1}. In the calculation of At1In the process, the system judges whether the future investigation date covers the holiday, and if the holiday is covered, the flow increase coefficient corresponding to the same holiday of the PTN looped network history is extracted for compensation.
Therefore, the flow peak value of a certain PTN looped network in a future investigation period (namely the investigation period after the current moment) is calculated by substituting the result obtained by the formula (1) into the formula (2) and is displayed in the system, then the specific days that each looped network exceeds a certain utilization rate are counted, and the information that the operation and maintenance personnel screen out the high-frequency occupied looped network is provided, so that the important attention and analysis can be performed.
And 304, determining a time point when the network ring network flow peak value reaches a preset capacity threshold value based on the network ring network flow peak value in unit time.
The calculation formula of the time point when the ring network flow peak value reaches the preset capacity threshold value in the future investigation period is shown as a formula (3):
T＝max{N0}≥W×70％ (3)
wherein, T represents an overrun day (an expansion completion day, that is, a time point when a preset capacity threshold is reached), w represents a static capacity, the static capacity of the PTN ring network to be investigated can be obtained through an interface of the ring network in the network management system, and Wx 70% can be represented as an early warning value of the capacity upper limit.
By obtaining N0A in (A)1，A2… …, the maximum value obtained by the system is substituted into equation (3) to determine max { N }0Whether the formula condition is satisfied is judged to be W multiplied by 70%.
And if the flow peak value which enables the formula (3) to be established is obtained, taking out the ring network, and determining the time corresponding to the flow peak value as the time point reaching the preset capacity threshold value. Of course, if the traffic peak that makes equation (3) true is not obtained, it indicates that the PTN ring network does not exceed the capacity expansion threshold (preset capacity threshold) in the future investigation period. After all the ring networks are subjected to the logic, the PTN ring network needing capacity expansion is formed, the capacity expansion date and the overrun countdown calculated from the current date are presented to operation and maintenance personnel, the capacity expansion date and the capacity expansion countdown can be conveniently and visually observed, and therefore the operation and maintenance personnel can conveniently and timely make a scientific capacity expansion strategy.
In the embodiment of the invention, the method for determining the ring network capacity out-of-limit time corrects the flow peak value of the ring network at a single time in a historical investigation period through a flow mutation screening model obtained by performing cluster training on the flow peak value change coefficient of the ring network at the single time in a preset historical time period according to two categories, determines the unit time increase coefficient of the flow peak value of the ring network according to the flow peak value of the ring network at the single time in the corrected historical investigation period, and determines the time point when the flow peak value of the ring network reaches the preset capacity threshold value according to the flow peak value of the ring network at the current unit time and the unit time increase coefficient of the flow peak value of the ring network. Therefore, the unit time increase coefficient of the looped network flow peak value determined by the looped network flow peak value per unit time in the corrected historical investigation period can be obtained according to the flow mutation screening model, so that the time point when the looped network flow peak value reaches the preset capacity threshold value can be accurately determined, the problem that the capacity expansion time cannot be accurately obtained in the prior art can be solved, and the problems that time consumption is caused and accuracy is low due to the fact that the looped network capacity is predicted in a manual checking mode in the prior art can be solved.
In the above further embodiment, as shown in fig. 4, the method for determining the ring network capacity out-of-limit time further includes:
and 406, correcting the time point when the network ring network flow peak value reaches the preset capacity threshold value based on the current network ring network flow peak value in unit time and the unit time increase coefficient of the corrected network ring network flow peak value.
Wherein the operation of adjusting the flow mutation screening model may comprise:
if the time point when the current unit time network ring network flow peak value reaches the preset capacity threshold value is earlier than the time point when the network ring network flow peak value reaches the preset capacity threshold value, increasing the flow sudden change threshold value output by the flow sudden change screening model;
And if the time point when the current unit time network ring network flow peak value reaches the preset capacity threshold value is later than the time point when the network ring network flow peak value reaches the preset capacity threshold value, reducing the flow sudden change threshold value output by the flow sudden change screening model.
It can be understood that, after the system determines the capacity expansion date of a certain PTN ring network (i.e. the time point when the flow peak value of the network ring network reaches the preset capacity threshold), since the system collects the occupation status of the ring network in unit time and judges the occupation status, when it is determined that the flow peak value of the network ring network reaches 100%, the time point when the flow peak value of the network ring network in unit time reaches the preset capacity threshold is compared with the capacity expansion date determined by the system, and if the compared difference value exceeds the preset capacity expansion time deviation threshold, at this time, the artificial intelligent learning module can be triggered to adjust the flow mutation eliminating model so as to change the flow mutation threshold value output by the flow mutation eliminating model, thereby re-determining and deleting the mutation value in the flow peak value of the network ring network in unit time in the historical investigation period according to the re-output flow mutation threshold value, and correcting the unit time increase coefficient of the looped network flow peak value, so that the time point when the corrected looped network flow peak value reaches the preset capacity threshold value is closer to the purpose of actual capacity expansion date.
When the sudden change value in the network ring network flow peak value per unit time in the historical investigation period is re-determined according to the flow sudden change threshold value output after the flow sudden change screening model is adjusted, the previously determined sudden change value is probably not the sudden change value any more when the newly determined sudden change value is re-determined after the flow sudden change screening model is adjusted, and therefore the previously determined sudden change value needs to be re-determined.
In any of the above embodiments, the method for determining the ring network capacity out-of-limit time further includes: and displaying the time point when the looped network flow peak value reaches the preset capacity threshold value to the user. That is, after the method described in any of the above embodiments is performed on all ring networks, the PTN ring network that needs capacity expansion is formed, the capacity expansion date and the overrun countdown calculated from the current day are presented to the operation and maintenance staff, and the capacity expansion date and the capacity expansion countdown can be visually observed, so that the operation and maintenance staff can make a scientific capacity expansion strategy in time.
With reference to fig. 5 and fig. 6, the implementation process of the method for determining the ring network capacity out-of-limit time according to the embodiment of the present invention may be:
firstly, PTN looped network flow data of the last two years are collected, a daily flow peak value change coefficient is calculated, then clustering training is carried out through a K-means algorithm in an artificial intelligence module, and a flow mutation screening model is trained.
Secondly, in a historical investigation period, collecting a daily flow peak value of the PTN looped network, and then removing a sudden change value in the daily flow peak value of the looped network in the historical investigation period through a K flow sudden change screening model.
Thirdly, after the mutation value is screened out, the daily growth coefficient of the looped network flow peak value in the historical investigation period is calculated.
Fourthly, calculating the daily looped network flow peak value according to the daily increase coefficient of the looped network flow peak value in the historical investigation period and the holiday compensation coefficient.
Fifthly, according to 70% of the ring network occupancy rate investigation threshold w (red and yellow cards can be set to be listed for early warning, 45-70% of the ring network occupancy rate investigation threshold w is yellow cards, and more than 70% of the ring network occupancy rate investigation threshold w is red cards) which is manually set, when the calculated ring network flow peak value of the target day reaches 70% of the ring network occupancy rate investigation threshold w, the target day is the capacity expansion date.
And sixthly, when the difference value between the expansion date obtained by comparison calculation and the expansion date determined by the actual looped network proportion exceeds a preset expansion time deviation threshold value, correcting the flow mutation screening model through the self-learning adjusting module to change the output flow mutation threshold value, and returning to the first step to continue execution to correct the unit time increase coefficient of the looped network flow peak value, so that the time point when the corrected looped network flow peak value reaches the preset capacity threshold value is closer to the actual expansion date.
As shown in fig. 6, the data processed by the artificial intelligence function module is sent to the database for storage, and is called by algorithms 1, 2, and 3, and after calling, the ring network to be expanded is calculated and stored in the storage table of the super tolerance ring network detail, and the data integration and calling presentation are performed by the front-end web interface.
Therefore, the method provided by the embodiment of the invention carries out cluster training on the collected historical data by adopting a K-means algorithm, establishes the flow mutation screening model, calculates the full load date of the PTN looped network after screening out the data of the flow mutation value, namely the non-reference condition, by the flow mutation screening model, and displays the full load date by the front-end BS framework, so that a PTN network manager can prepare in advance and plan in advance.
And the K-means algorithm has artificial intelligence learning capability, after a full load date is displayed, the actual flow occupation condition of the PTN looped network acquired by the system is compared and calculated, when the current day is full, the full load date of the PTN looped network and the current full load date are compared, when a difference threshold value is exceeded, the machine self-learning is started to perform interval dynamic adjustment, a flow mutation screening model is changed, the flow mutation threshold value output by the model is changed, the daily growth coefficient is automatically changed, the full load date calculated for the PTN looped network is closer to the actual condition through continuous dynamic adjustment, the problem that the capacity expansion time cannot be accurately obtained in the prior art is solved, and the problem that time consumption and accuracy are low due to the fact that the looped network capacity is predicted in a manual checking mode in the prior art can be solved.
An embodiment of the present invention further provides a device for determining a ring network capacity out-of-limit time, as shown in fig. 7, the device may include: the model obtaining unit 702 is configured to perform cluster training on a network ring network traffic peak value change coefficient in unit time within a predetermined historical time period based on two classifications to obtain a traffic sudden change screening model, where the two classifications include a normal value and a sudden change value; the flow peak value correcting unit 704 is used for correcting the flow peak value of the network ring network per unit time in the historical investigation period based on the flow mutation screening model; a growth coefficient determining unit 706, configured to determine a unit time growth coefficient of a network ring network traffic peak value based on the corrected network ring network traffic peak value per unit time in the history investigation period; the time determining unit 708 is configured to determine, based on the current unit time network ring network traffic peak value and the unit time increase coefficient of the network ring network traffic peak value, a time point at which the network ring network traffic peak value reaches the preset capacity threshold value.
The device for determining the ring network capacity out-of-limit time in the embodiment of the invention corrects the flow peak value of the ring network at a unit time in a historical investigation period through a flow sudden change screening model obtained by performing cluster training on the flow peak value change coefficient of the ring network at the unit time in a preset historical time period based on two classifications by a flow peak value correcting unit 704 according to a model obtaining unit 702, determines the unit time increase coefficient of the flow peak value of the ring network at the unit time in the historical investigation period through an increase coefficient determining unit 706 according to the flow peak value of the ring network at the unit time in the corrected historical investigation period, and determines the time point when the flow peak value of the ring network reaches the preset capacity threshold value by a time determining unit 708 according to the flow peak value of the ring network at the current unit time and the unit. Therefore, the unit time increase coefficient of the looped network flow peak value determined by the looped network flow peak value per unit time in the corrected historical investigation period can be obtained according to the flow mutation screening model, so that the time point when the looped network flow peak value reaches the preset capacity threshold value can be accurately determined, the problem that the capacity expansion time cannot be accurately obtained in the prior art can be solved, and the problems that time consumption is caused and accuracy is low due to the fact that the looped network capacity is predicted in a manual checking mode in the prior art can be solved.
In the above embodiment, the apparatus for determining the ring network capacity out-of-limit time further includes: a mutation value determination unit 710, configured to determine a mutation value in a network ring network traffic peak value per unit time in a historical investigation period based on a traffic mutation threshold output by the traffic mutation screening model; the traffic peak value correcting unit 704 is configured to delete the mutation value to correct the network ring network traffic peak value per unit time in the historical investigation period.
After the flow mutation screening model is obtained, a mutation value in the network looped network flow peak value in unit time in the historical investigation period can be determined and deleted according to the output flow mutation threshold value, so that the network looped network flow peak value in unit time in the historical investigation period can be corrected, that is, data which cannot be referred to in the network looped network flow peak value in unit time in the historical investigation period can be removed through the flow mutation screening model, a relatively smooth flow peak value growth curve can be obtained, and therefore the unit time growth coefficient of the network looped network flow peak value can be determined in the subsequent steps.
In the above further embodiment, further comprising: an adjusting unit 712, configured to adjust the sudden flow rate screening model when a target difference value exceeds a preset capacity expansion time deviation threshold value, where the target difference value is a difference value between a time point when a network ring network flow peak value reaches a preset capacity threshold value and a time point when the network ring network flow peak value of a current unit time reaches the preset capacity threshold value; the flow peak value correcting unit 704 is configured to revise the flow peak value of the single time network ring network in the historical investigation period based on the adjusted flow mutation screening model, so as to revise the unit time increase coefficient of the flow peak value of the network ring network based on the revised flow peak value of the single time network ring network in the historical investigation period; and a time correction unit 714, configured to correct a time point at which the network ring network traffic peak value reaches the preset capacity threshold, based on the network ring network traffic peak value in the current unit time and the unit time growth coefficient of the corrected network ring network traffic peak value.
Wherein the adjusting unit 712 is further configured to: if the time point when the current unit time network ring network flow peak value reaches the preset capacity threshold value is earlier than the time point when the network ring network flow peak value reaches the preset capacity threshold value, increasing the flow sudden change threshold value output by the flow sudden change screening model; and if the time point when the current unit time network ring network flow peak value reaches the preset capacity threshold value is later than the time point when the network ring network flow peak value reaches the preset capacity threshold value, reducing the flow sudden change threshold value output by the flow sudden change screening model.
It can be understood that, after the system determines the capacity expansion date of a certain PTN ring network (i.e. the time point when the flow peak value of the network ring network reaches the preset capacity threshold), since the system collects the occupation status of the ring network in unit time and judges the occupation status, when it is determined that 100% of the flow peak value of the network ring network reaches the preset capacity threshold, the time point when the current flow peak value of the ring network in unit time reaches the preset capacity threshold is compared with the capacity expansion date determined by the system, and if the compared difference value exceeds the preset capacity expansion time deviation threshold, at this time, the artificial intelligent learning module can be triggered to adjust the flow mutation screening model to change the flow mutation threshold value output by the flow mutation screening model, so as to re-determine and delete the mutation value in the flow peak value of the network in unit time in the historical investigation period according to the re-output flow mutation threshold value, and correcting the unit time increase coefficient of the network ring network flow peak value, so that the time point when the corrected network ring network flow peak value reaches the preset capacity threshold value is closer to the actual capacity expansion date.
In any of the above embodiments, the determining apparatus may further include a traffic peak determining unit 716, configured to determine a network ring network traffic peak per unit time based on the network ring network traffic peak per current unit time, a unit time growth coefficient of the network ring network traffic peak, and a holiday traffic peak compensation coefficient; the time determination unit 708 is configured to determine a time point when the network ring network traffic peak value reaches the preset capacity threshold value based on the network ring network traffic peak value in unit time. A display unit 718 may also be included for displaying a time point when the peak value of the network ring network traffic reaches a preset capacity threshold value to a user.
That is to say, the network ring network flow peak value in unit time is calculated according to the network ring network flow peak value in current unit time and the unit time increase coefficient of the network ring network flow peak value, the expansion date is determined according to the corresponding time when the calculated network ring network flow peak value reaches the preset capacity threshold value, after all ring networks are subjected to the logic, the PTN ring network needing expansion is formed, the expansion date and the overrun countdown calculated from the current date are displayed to operation and maintenance personnel, the expansion date and the expansion countdown are conveniently and visually observed, and therefore the operation and maintenance personnel can conveniently and timely make a scientific expansion strategy.
An embodiment of the present invention further provides a terminal device, which may include a processor, a memory, and a computer program stored in the memory and capable of running on the processor, where the computer program, when executed by the processor, implements each process of the method embodiments shown in fig. 1 to 5, and can achieve the same technical effect, and details are not repeated here to avoid repetition.
An embodiment of the present invention further provides a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the computer program implements each process of the method shown in fig. 1 to 5, and can achieve the same technical effect, and in order to avoid repetition, details are not repeated here. The computer-readable storage medium may be a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The above description is only an example of the present invention, and is not intended to limit the present invention. Various modifications and alterations to this invention will become apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of the claims of the present invention.
Claims (10)
1. A method for determining out-of-limit time of ring network capacity comprises the following steps:
performing cluster training on the network looped network flow peak value change coefficient in unit time in a preset historical time period based on two classifications to obtain a flow mutation screening model, wherein the two classifications comprise a normal value and a mutation value;
based on the flow mutation screening model, modifying a mutation value in a network looped network flow peak value per unit time in a historical investigation period;
determining a unit time increase coefficient of the network ring network flow peak value based on the corrected unit time network ring network flow peak value in the historical investigation period;
and determining a time point when the network ring network flow peak value reaches a preset capacity threshold value based on the current network ring network flow peak value in unit time and the unit time increase coefficient of the network ring network flow peak value.
2. The method of claim 1, wherein modifying the abrupt change value of the time unit network ring network traffic peak in the historical investigation period comprises:
determining a sudden change value in a network looped network flow peak value per unit time in a historical investigation period based on a flow sudden change threshold value output by the flow sudden change screening model;
and deleting the mutation value so as to correct the network ring network flow peak value per unit time in the historical investigation period.
3. The method of claim 1, further comprising:
when the target difference value exceeds a preset capacity expansion time deviation threshold value, adjusting the flow mutation screening model, wherein the target difference value is the difference value between the time point when the network ring network flow peak value reaches the preset capacity threshold value and the time point when the current unit time network ring network flow peak value reaches the preset capacity threshold value;
based on the adjusted flow mutation screening model, revising the flow peak value of the single time network looped network in the historical investigation period, so as to revise the unit time increase coefficient of the flow peak value of the network looped network based on the revised flow peak value of the single time network looped network in the historical investigation period;
and correcting the time point when the network ring network flow peak value reaches the preset capacity threshold value based on the current network ring network flow peak value in unit time and the unit time increase coefficient of the corrected network ring network flow peak value.
4. The method of claim 3, wherein adjusting the flow mutation screening model comprises:
if the time point when the current unit time network ring network flow peak value reaches the preset capacity threshold value is earlier than the time point when the current unit time network ring network flow peak value reaches the preset capacity threshold value, increasing the flow sudden change threshold value output by the flow sudden change screening model;
And if the time point when the current unit time network ring network flow peak value reaches the preset capacity threshold value is later than the time point when the network ring network flow peak value reaches the preset capacity threshold value, reducing the flow sudden change threshold value output by the flow sudden change screening model.
5. The method of claim 1, wherein determining a time point at which the network ring network traffic peak value reaches the preset capacity threshold based on the network ring network traffic peak value at the current unit time and a unit time growth coefficient of the ring network traffic peak value comprises:
determining a network looped network flow peak value in unit time based on the current network looped network flow peak value in unit time, a unit time increase coefficient of the looped network flow peak value and a holiday flow peak value compensation coefficient;
and determining a time point when the network ring network flow peak value reaches a preset capacity threshold value based on the network ring network flow peak value in unit time.
6. The method of claim 1, further comprising:
and displaying the time point when the network ring network flow peak value reaches the preset capacity threshold value to the user.
7. A device for determining a capacity violation time of a ring network, comprising:
the model obtaining unit is used for carrying out clustering training on the network looped network flow peak value change coefficient in unit time in a preset historical time period based on two classifications to obtain a flow mutation screening model, wherein the two classifications comprise a normal value and a mutation value;
The flow peak value correcting unit is used for correcting the sudden change value in the flow peak value of the network ring network per unit time in the historical investigation period based on the flow sudden change screening model;
the growth coefficient determining unit is used for determining the growth coefficient of the network ring network flow peak value in unit time based on the corrected network ring network flow peak value in the historical investigation period;
and the time determining unit is used for determining a time point when the network ring network flow peak value reaches a preset capacity threshold value based on the network ring network flow peak value of the current unit time and the unit time increase coefficient of the network ring network flow peak value.
8. The apparatus of claim 7, further comprising:
the sudden change value determining unit is used for determining a sudden change value in a network looped network flow peak value per unit time in a historical investigation period based on a flow sudden change threshold value output by the flow sudden change screening model;
and the flow peak value correction unit is used for deleting the mutation value so as to correct the flow peak value of the network looped network in the single time in the historical investigation period.
9. The apparatus of claim 7, further comprising:
the adjusting unit is used for adjusting the flow mutation screening model when a target difference value exceeds a preset capacity expansion time deviation threshold value, wherein the target difference value is a difference value between a time point when a network ring network flow peak value reaches a preset capacity threshold value and a time point when the network ring network flow peak value of the current unit time reaches the preset capacity threshold value;
The flow peak value correction unit is used for correcting the flow peak value of the network looped network per unit time in the historical investigation period again based on the adjusted flow mutation screening model so as to correct the unit time increase coefficient of the flow peak value of the network looped network based on the flow peak value of the network looped network per unit time in the historical investigation period after being corrected again;
and the time correction unit is used for correcting the time point when the flow peak value of the network ring network reaches the preset capacity threshold value based on the current flow peak value of the network ring network in unit time and the unit time growth coefficient of the corrected flow peak value of the network ring network.
10. A computer-readable storage medium, characterized in that a computer program is stored on the computer-readable storage medium, which computer program, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 6.
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