CN111163006B - Multipath preferred online game acceleration method based on waveform judgment - Google Patents

Multipath preferred online game acceleration method based on waveform judgment Download PDF

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CN111163006B
CN111163006B CN201911360416.3A CN201911360416A CN111163006B CN 111163006 B CN111163006 B CN 111163006B CN 201911360416 A CN201911360416 A CN 201911360416A CN 111163006 B CN111163006 B CN 111163006B
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path
waveform
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distribution information
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CN111163006A (en
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覃艳君
张兴
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Sichuan Subao Network Technology Co ltd
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L45/00—Routing or path finding of packets in data switching networks
    • H04L45/12—Shortest path evaluation
    • H04L45/121—Shortest path evaluation by minimising delays
    • A—HUMAN NECESSITIES
    • A63—SPORTS; GAMES; AMUSEMENTS
    • A63F—CARD, BOARD, OR ROULETTE GAMES; INDOOR GAMES USING SMALL MOVING PLAYING BODIES; VIDEO GAMES; GAMES NOT OTHERWISE PROVIDED FOR
    • A63F13/00—Video games, i.e. games using an electronically generated display having two or more dimensions
    • A63F13/30—Interconnection arrangements between game servers and game devices; Interconnection arrangements between game devices; Interconnection arrangements between game servers
    • A63F13/33—Interconnection arrangements between game servers and game devices; Interconnection arrangements between game devices; Interconnection arrangements between game servers using wide area network [WAN] connections
    • A63F13/332—Interconnection arrangements between game servers and game devices; Interconnection arrangements between game devices; Interconnection arrangements between game servers using wide area network [WAN] connections using wireless networks, e.g. cellular phone networks
    • A—HUMAN NECESSITIES
    • A63—SPORTS; GAMES; AMUSEMENTS
    • A63F—CARD, BOARD, OR ROULETTE GAMES; INDOOR GAMES USING SMALL MOVING PLAYING BODIES; VIDEO GAMES; GAMES NOT OTHERWISE PROVIDED FOR
    • A63F13/00—Video games, i.e. games using an electronically generated display having two or more dimensions
    • A63F13/30—Interconnection arrangements between game servers and game devices; Interconnection arrangements between game devices; Interconnection arrangements between game servers
    • A63F13/35—Details of game servers
    • A63F13/352—Details of game servers involving special game server arrangements, e.g. regional servers connected to a national server or a plurality of servers managing partitions of the game world
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L45/00—Routing or path finding of packets in data switching networks
    • H04L45/12—Shortest path evaluation
    • H04L45/123—Evaluation of link metrics
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W40/00—Communication routing or communication path finding
    • H04W40/02—Communication route or path selection, e.g. power-based or shortest path routing
    • H04W40/12—Communication route or path selection, e.g. power-based or shortest path routing based on transmission quality or channel quality
    • A—HUMAN NECESSITIES
    • A63—SPORTS; GAMES; AMUSEMENTS
    • A63F—CARD, BOARD, OR ROULETTE GAMES; INDOOR GAMES USING SMALL MOVING PLAYING BODIES; VIDEO GAMES; GAMES NOT OTHERWISE PROVIDED FOR
    • A63F2300/00—Features of games using an electronically generated display having two or more dimensions, e.g. on a television screen, showing representations related to the game
    • A63F2300/40—Features of games using an electronically generated display having two or more dimensions, e.g. on a television screen, showing representations related to the game characterised by details of platform network
    • A63F2300/406—Transmission via wireless network, e.g. pager or GSM
    • A—HUMAN NECESSITIES
    • A63—SPORTS; GAMES; AMUSEMENTS
    • A63F—CARD, BOARD, OR ROULETTE GAMES; INDOOR GAMES USING SMALL MOVING PLAYING BODIES; VIDEO GAMES; GAMES NOT OTHERWISE PROVIDED FOR
    • A63F2300/00—Features of games using an electronically generated display having two or more dimensions, e.g. on a television screen, showing representations related to the game
    • A63F2300/50—Features of games using an electronically generated display having two or more dimensions, e.g. on a television screen, showing representations related to the game characterized by details of game servers
    • A63F2300/51—Server architecture
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L45/00—Routing or path finding of packets in data switching networks
    • H04L45/24—Multipath

Abstract

The invention provides a multi-path optimal online game acceleration method based on waveform judgment, which comprises the following steps: establishing link connection between a multipath generated by a client and an acceleration node server; the method comprises the steps of receiving and transmitting speed measurement data of multiple paths establishing link connection, and recording delay distribution information of each path in the multiple paths; performing waveform qualification on delay distribution information of each path by adopting a preset multi-dimensional mathematical model; performing quality comprehensive scoring on the network of each path based on the waveform qualitative result and a preset scoring index; and selecting an optimal path in the multiple paths according to the quality comprehensive scoring result, and switching the current online tour acceleration path of the client into the optimal path. A plurality of links are sent to the acceleration node server through the mobile phone end at the same time, judgment and comparison are carried out according to the waveforms of the links, and the optimal link is selected as the link for data acceleration, so that the game acceleration effect is improved.

Description

Multipath preferred online game acceleration method based on waveform judgment
Technical Field
The invention relates to the technical field of mobile phone online games, in particular to a multipath optimal online game acceleration method based on waveform judgment.
Background
In the present day that the mobile internet industry is prosperous, the mobile phone online game has become a favorite leisure and entertainment mode for most people. Under the background, the rapid-tour hand-tour accelerator provides mobile terminal game acceleration service for the hand-tour players, and provides high-quality online-tour acceleration service for the hand-tour players by finding out the fastest network from the mobile phone to the game.
Due to the uncertain factors of network route pointing, in the game acceleration process, the problem that the game acceleration link becomes inefficient because of unreasonable route distribution, and the expected acceleration effect cannot be achieved during game acceleration exists.
Disclosure of Invention
The invention provides a multi-path optimal online game acceleration method based on waveform judgment, which is used for simultaneously sending a plurality of links to an acceleration node server through a mobile phone terminal, carrying out judgment and comparison according to the waveforms of the plurality of links, and selecting an optimal link as a data acceleration link so as to improve the game acceleration effect.
The embodiment of the invention provides a multi-channel optimal online game acceleration method based on waveform judgment, which comprises the following steps:
establishing link connection between a multipath generated by a client and an acceleration node server;
the method comprises the steps of receiving and transmitting speed measurement data of multiple paths establishing link connection, and recording delay distribution information of each path in the multiple paths;
performing waveform qualification on the delay distribution information of each path by adopting a preset multi-dimensional mathematical model;
performing quality comprehensive scoring on the network of each path based on the waveform qualitative result and a preset scoring index;
and selecting an optimal path in the multiple paths according to a quality comprehensive scoring result, and switching the current online tour acceleration path of the client to the optimal path.
In a possible implementation manner, after switching the current online tour acceleration path of the client to the optimal path, the method further includes:
starting a current network quality monitoring mechanism, and evaluating the current network quality of the optimal path according to preset timing time;
judging whether link connection and subsequent operation between the multipath generated by the client and the acceleration node server need to be executed again or not according to the evaluation processing result;
when the evaluation value of the evaluation processing result is greater than or equal to the preset network quality value, the re-execution is not needed;
otherwise, it needs to be executed again.
In a possible implementation manner, the waveform characterization of the delay distribution information of each path by using a preset multi-dimensional mathematical model is performed on the basis of a statistical method based on a delay mean and a delay standard deviation.
In a possible implementation manner, the waveform qualifying the delay distribution information of each path by using a preset multidimensional mathematical model includes:
judging whether undetermined delay distribution information corresponds to a large probability waveform according to a formula (1);
M>A1+2*σ1 (1)；
wherein M represents a delay peak value corresponding to the delay distribution information to be qualified; a1 represents a first delay mean value corresponding to the delay distribution information to be qualified; sigma1Representing a first delay standard deviation corresponding to the delay distribution information to be qualified;
if the formula (1) does not hold, judging that the undetermined delay distribution information corresponds to the approximate probability waveform; if the formula (1) is established, judging whether the delay distribution information to be qualified corresponds to a local deteriorated waveform according to the formula (2);
f(t)＞A1+3*σ1 (2)；
wherein f (t) represents the delay value of the input delay time axis sequence related to the undetermined delay profile information;
if the formula (2) does not hold, judging that the undetermined delay distribution information corresponds to the local deterioration waveform; if the formula (2) is satisfied, filtering high-amplitude pulse points existing in the delay value f (t), and recalculating a second delay mean value and a second delay standard deviation corresponding to the delay value after the high-amplitude pulse points are filtered;
determining whether the second delay standard deviation substantially decreases based on the first delay standard deviation;
if yes, judging whether the second delay standard deviation is smaller than a preset threshold value or not;
otherwise, judging that the information of the undetermined delay distribution corresponds to a local deterioration waveform;
when the second delay standard deviation is smaller than a preset threshold, judging whether the delay distribution information to be qualified corresponds to a low-frequency high-amplitude pulse waveform or not according to a formula (3);
M>A2+2*σ2 (3)；
wherein, a2 represents a second delay mean value corresponding to the delay distribution information to be qualified; sigma2Representing a second delay standard deviation corresponding to the delay distribution information to be qualified;
if the formula (3) does not hold, determining that the pending delay distribution information corresponds to a low-frequency high-amplitude pulse waveform; if the formula (3) is established, the undetermined delay distribution information corresponds to a high-amplitude deteriorated waveform;
and when the second delay standard deviation is not less than a preset threshold value, re-filtering high-amplitude pulse points existing in the delay value and subsequent operations.
In one possible way of realisation,
after the waveform qualification is performed on the delay distribution information of each path by adopting a preset multi-dimensional mathematical model, the method further comprises the following steps:
when the undetermined delay distribution information is judged to correspond to the high-amplitude deteriorated waveform, removing a path corresponding to the high-amplitude deteriorated waveform;
when the undetermined delay distribution information is judged to correspond to a high-probability waveform, acquiring a first delay mean value and a first delay standard deviation, and taking the first delay mean value and the first delay standard deviation as the input of a quality comprehensive score of a path corresponding to the high-probability waveform;
and when the information of the delay distribution to be determined corresponds to the low-frequency high-amplitude pulse waveform and the local deterioration waveform, calibrating the input delay value f (t), obtaining a third mean value and a third standard deviation according to the calibration processing result, and taking the third delay mean value and the third delay standard deviation as the input of the quality comprehensive score of the path corresponding to the low-frequency high-amplitude pulse waveform and the local deterioration waveform.
In one possible way of realisation,
the delay peak value, the first delay mean value and the first delay standard deviation are calculated according to the input delay value f (t).
In one possible way of realisation,
when it is determined that the information of the pending delay profile corresponds to the low-frequency high-amplitude pulse waveform, the process of performing calibration processing on the input delay value f (t) includes:
acquiring all delay points in the low-frequency high-amplitude pulse waveform, and judging whether the mean value corresponding to the delay values of the delay points is larger than a preset mean value or not;
if yes, filtering the delay points, and recalculating a new mean value and a new standard deviation according to the delay values of the residual delay points of the delay points of which the filtered mean value is greater than the preset mean value;
otherwise, filtering the delay points corresponding to the delay peak values in the low-frequency high-amplitude pulse waveform, and recalculating a new mean value and a new standard deviation according to the delay values of the residual delay points of the delay points corresponding to the filtered delay peak values.
In one possible way of realisation,
when the network of each path is subjected to quality comprehensive evaluation, performing quality comprehensive evaluation q by adopting the following formula;
q＝wavg×Avg0+(1-wavg)×σ0；
wherein, wavgRepresents a delayed evaluation weight value with a value range of [0, 1%]；σ0Represents the standard deviation of the delay; avg0 denotes the delayed mean.
In a possible implementation manner, the multi-path preferred online tour acceleration method based on waveform evaluation as claimed in claim 1, wherein:
the specific operation of waveform qualification is carried out on the delay distribution information of each path by adopting a preset multi-dimensional mathematical model;
step A1, filtering the speed measurement data of each path according to a pre-established data preprocessing model, extracting basic parameter characteristics of the speed measurement data of each path according to a formula (1), and acquiring a basic delay parameter set;
Figure BDA0002337031720000051
where n is the number of paths, expIs an exponential function with a natural constant e as a base, ln is a logarithmic function with the natural constant e as a base, i is a path serial number, t0Starting time t of sending speed measurement data for each path of the clientiThe response time of the node server to the ith path speed measurement data is shown, x is the number of speed measurement data packets, a0The client sends out the initial number a of speed measurement data packets in each pathxReceiving the number of test packets of each path for the node server, y being the network load transient, b0Network load transient value when the client sends speed measurement data packet in each path, byFor the network load transient values at which the node server receives the test packets for each path,
Figure BDA0002337031720000052
the average response time of the node server for sending speed measurement data to each path,
Figure BDA0002337031720000053
sending the packet loss rate of the speed measurement data for each path by the client,
Figure BDA0002337031720000054
for each path network load transient mean, E (t)i,ax,by) To obtain a base set of delay parameters;
step A2, substituting the basic delay parameter set obtained in step A1 into a formula (2) for calculation to obtain delay distribution information of each path;
Figure BDA0002337031720000055
wherein pi is a circumferential ratio, exp is an exponential function with a natural constant e as a base,
Figure BDA0002337031720000057
for each path network delay value weight, ciThe mean value of the network delay of the ith path is sigma which is the network delay of each path according to the speed measurement dataThe load transient value and the packet loss rate are obtained,
Figure BDA0002337031720000056
for each path of the network delay profile information,
Figure BDA0002337031720000061
delaying the canonical difference distribution information for each path network, F (c)iσ) obtaining delay distribution information of each path;
step A3, substituting the delay distribution information of each path obtained in the step A2 into the waveform analysis model, automatically matching and identifying a large probability waveform, executing quality comprehensive scoring and distributing an optimal link;
Figure BDA0002337031720000062
wherein M is a delay peak value corresponding to the delay distribution information to be qualified, cj+2 σ is the delay distribution information corresponding to the large probability waveform of each path,
Figure BDA0002337031720000063
and (3) obtaining a rough probability waveform delay peak value for each path through derivation iterative convergence, and when the value P (M) is 1, indicating that the path delay distribution mean value is matched with the rough probability waveform, executing quality comprehensive scoring and distributing an optimal link.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and drawings.
The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
fig. 1 is a flowchart of a multipath preferred online game acceleration method based on waveform evaluation according to an embodiment of the present invention;
FIG. 2 is a flow chart of qualitative descriptions of waveforms in an embodiment of the present invention.
Detailed Description
The preferred embodiments of the present invention will be described in conjunction with the accompanying drawings, and it will be understood that they are described herein for the purpose of illustration and explanation and not limitation.
The embodiment of the invention provides a multi-channel optimal online game acceleration method based on waveform judgment, which comprises the following steps of:
step 1: establishing link connection between a multipath generated by a client and an acceleration node server;
step 2: the method comprises the steps of receiving and transmitting speed measurement data of multiple paths establishing link connection, and recording delay distribution information of each path in the multiple paths;
and step 3: performing waveform qualification on the delay distribution information of each path by adopting a preset multi-dimensional mathematical model;
and 4, step 4: performing quality comprehensive scoring on the network of each path based on the waveform qualitative result and a preset scoring index;
and 5: and selecting an optimal path in the multiple paths according to a quality comprehensive scoring result, and switching the current online tour acceleration path of the client to the optimal path.
The waveform qualification of the delay distribution information of each path is performed on the basis of a delay mean value and delay standard deviation statistical method by adopting a preset multi-dimensional mathematical model.
The client can be a smart phone;
the speed measurement data may refer to a speed of data transmission between the client and the acceleration node server, such as a sending speed and a receiving speed;
the delay profile information includes: path information, such as: a first path, a second path, a third path, etc.; delay point information of the speed measurement data, delay value information corresponding to the delay point, and a delay oscillogram constructed according to the delay point and the delay time (delay value);
the delay point information is, for example, a delay time of each data packet in the velocity measurement data.
The waveform qualitative result comprises the following steps: any one or more of a low-frequency high-amplitude pulse waveform, a local deterioration waveform, a high-amplitude deterioration waveform and a high-frequency waveform, and each waveform has a corresponding standard deviation and mean value.
The scoring indexes include, for example: average delay, standard deviation of delay, packet drop rate and other indexes of each path.
The relationship between the current online tour acceleration path of the client and the optimal path is a multi-path established on the basis that the client uses the current online tour acceleration path.
The quality comprehensive scoring result can be displayed in the form of score or grade.
And selecting an optimal path in the multiple paths according to the quality comprehensive scoring result, and assuming that the default quality comprehensive scoring result is arranged from high to low, and the path corresponding to the highest score in the default quality comprehensive scoring result is the optimal path.
The beneficial effects of the above technical scheme are: the method is used for simultaneously sending a plurality of links to the acceleration node server through the mobile phone end, judging and comparing according to the waveforms of the plurality of links, and selecting the optimal link as a data acceleration link, so that the game acceleration effect is improved.
The embodiment of the invention provides a multi-path optimal online game acceleration method based on waveform evaluation, which comprises the following steps of after switching the current online game acceleration path of a client into the optimal path:
starting a current network quality monitoring mechanism, and evaluating the current network quality of the optimal path according to preset timing time;
judging whether link connection and subsequent operation between the multipath generated by the client and the acceleration node server need to be executed again or not according to the evaluation processing result;
when the evaluation value of the evaluation processing result is greater than or equal to the preset network quality value, the re-execution is not needed;
otherwise, it needs to be executed again.
The current network quality monitoring mechanism is mainly used for determining the network quality of the switched optimal path so as to determine the acceleration of the online tour;
according to the evaluation processing result, the fact that new optimal paths need to be searched is determined, and the effect of accelerating the online game can be effectively guaranteed.
The above-mentioned steps 1 to 5 are executed to establish the link connection between the multipath generated by the client and the acceleration node server and the subsequent operations.
The beneficial effects of the above technical scheme are: by carrying out network monitoring, the network quality can be conveniently and timely known, and by carrying out evaluation processing, the optimal link can be conveniently found and selected as a data acceleration path, so that the effect of accelerating the game can be always improved, and the experience effect of a user can be improved.
The embodiment of the invention provides a multi-path optimal online game acceleration method based on waveform evaluation, wherein the waveform qualitative process of delay distribution information of each path by adopting a preset multi-dimensional mathematical model comprises the following steps:
step 31: judging whether undetermined delay distribution information corresponds to a large probability waveform according to a formula (1);
M>A1+2*σ1 (1)；
wherein M represents a delay peak value corresponding to the delay distribution information to be qualified; a1 represents a first delay mean value corresponding to the delay distribution information to be qualified; sigma1Representing a first delay standard deviation corresponding to the delay distribution information to be qualified;
step 32: if the formula (1) does not hold, judging that the undetermined delay distribution information corresponds to the approximate probability waveform; if the formula (1) is established, judging whether the delay distribution information to be qualified corresponds to a local deteriorated waveform according to the formula (2);
f(t)＞A1+3*σ1 (2)；
wherein f (t) represents the delay value of the input delay time axis sequence related to the undetermined delay profile information;
step 33: if the formula (2) does not hold, judging that the undetermined delay distribution information corresponds to the local deterioration waveform; if the formula (2) is satisfied, filtering high-amplitude pulse points existing in the delay value f (t), and recalculating a second delay mean value and a second delay standard deviation corresponding to the delay value after the high-amplitude pulse points are filtered;
step 34: determining whether the second delay standard deviation substantially decreases based on the first delay standard deviation;
if yes, judging whether the second delay standard deviation is smaller than a preset threshold value or not;
otherwise, judging that the information of the undetermined delay distribution corresponds to a local deterioration waveform;
step 35: when the second delay standard deviation is smaller than a preset threshold, judging whether the delay distribution information to be qualified corresponds to a low-frequency high-amplitude pulse waveform or not according to a formula (3);
M>A2+2*σ2 (3)；
wherein, a2 represents a second delay mean value corresponding to the delay distribution information to be qualified; sigma2Representing a second delay standard deviation corresponding to the delay distribution information to be qualified;
if the formula (3) does not hold, determining that the pending delay distribution information corresponds to a low-frequency high-amplitude pulse waveform; if the formula (3) is established, the undetermined delay distribution information corresponds to a high-amplitude deteriorated waveform;
step 36: and when the second delay standard deviation is not less than a preset threshold value, re-filtering high-amplitude pulse points existing in the delay value and subsequent operations.
The information of the delay distribution to be determined refers to the information of the delay distribution which needs to be subjected to waveform qualification;
the waveforms are qualitatively classified into the following four types:
one is a low-frequency high-amplitude pulse waveform: most points fall within 2 σ from the mean, and few points are outside 2 σ (far from the mean);
secondly, local deterioration waveform: in a few continuous time intervals, the amplitude exceeds the delay mean value by more than 2 sigma;
third is the high amplitude deteriorated waveform: the amplitude exceeds more than 2 sigma in a plurality of continuous intervals;
four are the large probability waveforms: the waveform of the jitter condition can be accurately expressed by using the mean value and the standard deviation;
the above f (t) represents the delay value of the input delay time axis sequence related to the undetermined delay distribution information, that is, the delay value is the time when the client receives the data packet in the data transmission process between the client and the acceleration node server, for example, the time is marked in the delay time axis sequence, and each time point of receiving the data packet corresponds to a delay point, and each delay point has a corresponding delay value.
The significant drop means whether the second delay standard deviation is significantly reduced based on the first delay standard deviation, and the significant drop is indicated when the difference between the second delay standard deviation and the first delay standard deviation is smaller than a preset difference;
the preset threshold is reliability data obtained according to scientific data;
the above-mentioned operations of re-filtering the high amplitude pulse points present in the delay values and subsequent operations refer to the operations of repeating steps 33-36.
And as shown in fig. 2, it is a flowchart of the specific embodiment of the above steps 31-36, wherein the mean Avg of the "calculated mean Avg, standard deviation sd, peak M" in fig. 2 is the first mean delay a1, and the standard deviation sd is the first standard deviation σ1；
“M>Avg in Avg +2 × sd "means the first mean delay value a1, sd means the first standard deviation of delay σ1；
A in "f (t) > A +3 sd" means the first mean retardation value A1, and sd means the first standard deviation of retardation σ1；
The high-amplitude pulse points are filtered, and the mean value in the mean value and the standard deviation are calculated and referred to as a second delay mean value and a second standard deviation;
sd in "sd significantly decreased and sd below the threshold" refers to the second standard deviation.
And according to fig. 2, the inputs of the waveform qualitative evaluation are: delay values f (t), mean Avg, standard deviation sigma of delay time axis sequence; the output is a qualitative description of the waveform, which is one of the four waveforms described above.
The beneficial effects of the above technical scheme are: the waveform corresponding to the qualitative delay distribution information is convenient to be determined, and the qualitative description of the waveform by the output is effectively realized.
The embodiment of the invention provides a multi-path optimal online game acceleration method based on waveform evaluation, which adopts a preset multi-dimensional mathematical model to perform waveform qualification on delay distribution information of each path, and further comprises the following steps:
when the undetermined delay distribution information is judged to correspond to the high-amplitude deteriorated waveform, removing a path corresponding to the high-amplitude deteriorated waveform;
when the undetermined delay distribution information is judged to correspond to a high-probability waveform, acquiring a first delay mean value and a first delay standard deviation, and taking the first delay mean value and the first delay standard deviation as the input of a quality comprehensive score of a path corresponding to the high-probability waveform;
and when the information of the delay distribution to be determined corresponds to the low-frequency high-amplitude pulse waveform and the local deterioration waveform, calibrating the input delay value f (t), obtaining a third mean value and a third standard deviation according to the calibration processing result, and taking the third delay mean value and the third delay standard deviation as the input of the quality comprehensive score of the path corresponding to the low-frequency high-amplitude pulse waveform and the local deterioration waveform.
Wherein, the delay peak value, the first delay mean value and the first delay standard deviation are calculated according to the input delay value f (t).
The beneficial effects of the above technical scheme are: the paths are eliminated, the workload of obtaining the optimal paths can be effectively reduced, the efficiency is improved, calibration processing is carried out, and the accuracy of obtaining the comprehensive quality scores is convenient to improve.
The embodiment of the invention provides a multi-path optimal online game acceleration method based on waveform judgment, wherein a delay peak value, a first delay mean value and a first delay standard deviation are obtained by calculation according to an input delay value f (t);
when it is determined that the information of the pending delay profile corresponds to the low-frequency high-amplitude pulse waveform, the process of performing calibration processing on the input delay value f (t) includes:
step 01: acquiring all delay points in the low-frequency high-amplitude pulse waveform, and judging whether the mean value corresponding to the delay values of the delay points is larger than a preset mean value or not;
step 02: if yes, filtering the delay points, and recalculating a new mean value and a new standard deviation according to the delay values of the residual delay points of the delay points of which the filtered mean value is greater than the preset mean value;
and 03, otherwise, filtering the delay points corresponding to the delay peak values in the low-frequency high-amplitude pulse waveform, and recalculating a new mean value and a new standard deviation according to the delay values of the residual delay points of the delay points corresponding to the filtered delay peak values.
The preset average value is generally 3 σ.
In one embodiment, when it is determined that the delay profile information to be qualified corresponds to the local deteriorated waveform, the calibration process for the input delay value f (t) includes:
taking the mean and standard deviation of delay points in a local deterioration window (more than 2 sigma of the mean);
the delay in the local deterioration window is processed in the step 01-03 to obtain a new average value AvgdAnd standard deviation σd；
Recalculating mean Avg for delays outside of local degradation windownStandard deviation σn
Weighting and integrating the outputs of b and c, and setting the width of local deterioration window (deterioration point number) as WdThe total window width (total number of points) is W, and the final mean and standard deviation of the path are obtained:
Figure BDA0002337031720000131
Figure BDA0002337031720000132
the calibration processing for the input delay value f (t) when the delay profile information to be qualified is determined to correspond to the local deteriorated waveform is realized by the above steps.
The beneficial effects of the above technical scheme are: through the calibration of the delay standard deviation corresponding to the low-frequency high-amplitude pulse waveform, the reliability of the input used as the quality comprehensive score is improved conveniently.
The embodiment of the invention provides a multi-path optimal selection online game acceleration method based on waveform judgment, wherein when the network of each path is subjected to comprehensive quality evaluation, the following formula is adopted to perform comprehensive quality evaluation q;
q＝wavg×Avg0+(1-wavg)×σ0；
wherein, wavgRepresents a delayed evaluation weight value with a value range of [0, 1%]；σ0Represents the standard deviation of the delay; avg0 denotes the delayed mean.
The Avg0 may be the corresponding new standard deviation and mean value obtained after the calibration process, or may be the standard deviation and mean value obtained before the calibration process.
The beneficial effects of the above technical scheme are: an effective data base is provided for evaluating the optimal path.
The embodiment of the invention provides a multi-path optimal selection online game acceleration method based on waveform judgment, which adopts a preset multi-dimensional mathematical model and further comprises the specific operation of waveform qualification on delay distribution information of each path;
step A1, filtering the speed measurement data of each path according to a pre-established data preprocessing model, extracting basic parameter characteristics of the speed measurement data of each path according to a formula (1), and acquiring a basic delay parameter set;
Figure BDA0002337031720000133
where n is the number of paths, exp is an exponential function based on a natural constant e, ln is a logarithmic function based on a natural constant e, i is the path sequence number, t is the number of paths0Starting time t of sending speed measurement data for each path of the clientiThe response time of the node server to the ith path speed measurement data is shown, x is the number of speed measurement data packets, a0The client sends out the initial number a of speed measurement data packets in each pathxReceiving the number of test packets of each path for the node server, y being the network load transient, b0Network load transient value when the client sends speed measurement data packet in each path, byFor the network load transient values at which the node server receives the test packets for each path,
Figure BDA0002337031720000141
the average response time of the node server for sending speed measurement data to each path,
Figure BDA0002337031720000142
sending the packet loss rate of the speed measurement data for each path by the client,
Figure BDA0002337031720000143
for each path network load transient mean, E (t)i,ax,by) To obtain a base set of delay parameters;
step A2, substituting the basic delay parameter set obtained in step A1 into a formula (2) for calculation to obtain delay distribution information of each path;
Figure BDA0002337031720000144
wherein pi is a circumferential ratio, exp is an exponential function with a natural constant e as a base,
Figure BDA0002337031720000148
for each path network delay value weight, ciIs the network delay mean value of the ith path, sigma is the delay standard deviation obtained by each path according to the speed measurement data network load transient value and the packet loss rate,
Figure BDA0002337031720000145
for each path of the network delay profile information,
Figure BDA0002337031720000146
delaying the canonical difference distribution information for each path network, F (c)iσ) obtaining delay distribution information of each path;
wherein the content of the first and second substances,
Figure BDA0002337031720000149
for each path network delay value weight,
Figure BDA0002337031720000147
representing the network delay value weight of each path corresponding to the network load transient value change in unit time under the fixed packet loss rate of each path;
step A3, substituting the delay distribution information of each path obtained in the step A2 into the waveform analysis model, automatically matching and identifying a large probability waveform, executing quality comprehensive scoring and distributing an optimal link;
Figure BDA0002337031720000151
wherein M is a delay peak value corresponding to the delay distribution information to be qualified, cj+2 σ is the delay distribution information corresponding to the large probability waveform of each path,
Figure BDA0002337031720000152
the maximum probability waveform delay peak value obtained by the iterative convergence of derivation for each path, when the value of P (M) is 1, it indicates that the path delay distribution mean value matches the maximum probability waveformAnd performing the operation of quality comprehensive grading and distributing the optimal link.
The beneficial effects of the above technical scheme are: according to the technical scheme, basic parameter data information of each path is identified and analyzed at the initial stage of speed measurement data transmission, when a multi-path optimal online tour acceleration method based on waveform judgment is adopted, the delay distribution state of each path is calculated through the existing parameter information, the path corresponding to a high-probability waveform is rapidly screened, the parameter value corresponding to the high-probability waveform is transmitted to a quality comprehensive score, and the operation of distributing an optimal link is executed; according to the technical scheme, the system load is prevented from being wasted by repeatedly matching various waveforms, the requirement of a client for optimizing a path can be effectively responded, the online game acceleration efficiency is improved, and the economy of the multi-path optimal online game acceleration method based on waveform judgment is improved.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.

Claims (7)

1. A multi-path optimal online game acceleration method based on waveform judgment is characterized by comprising the following steps:
establishing link connection between a multipath generated by a client and an acceleration node server;
the method comprises the steps of receiving and transmitting speed measurement data of multiple paths establishing link connection, and recording delay distribution information of each path in the multiple paths;
performing waveform qualification on the delay distribution information of each path by adopting a preset multi-dimensional mathematical model;
performing quality comprehensive scoring on the network of each path based on the waveform qualitative result and a preset scoring index;
selecting an optimal path in the multiple paths according to a quality comprehensive scoring result, and switching a current online tour acceleration path of the client to the optimal path;
wherein the delay profile information includes: path information, delay point information of speed measurement data, delay value information corresponding to the delay point, and a delay oscillogram constructed according to the delay point and the delay time;
the waveform qualification of the delay distribution information of each path by adopting a preset multi-dimensional mathematical model is carried out on the basis of a delay mean value and delay standard deviation statistical method;
the waveform qualitative process of the delay distribution information of each path by adopting a preset multidimensional mathematical model comprises the following steps:
judging whether undetermined delay distribution information corresponds to a large probability waveform according to a formula (1);
M>A1+2*σ1 (1)；
wherein M represents a delay peak value corresponding to the delay distribution information to be qualified; a1 represents a first delay mean value corresponding to the delay distribution information to be qualified; sigma1Representing a first delay standard deviation corresponding to the delay distribution information to be qualified;
if the formula (1) does not hold, judging that the undetermined delay distribution information corresponds to the approximate probability waveform; if the formula (1) is established, judging whether the delay distribution information to be qualified corresponds to a local deteriorated waveform according to the formula (2);
f(t)＞A1+3*σ1 (2)；
wherein f (t) represents the delay value of the input delay time axis sequence related to the undetermined delay profile information;
if the formula (2) does not hold, judging that the undetermined delay distribution information corresponds to the local deterioration waveform; if the formula (2) is satisfied, filtering high-amplitude pulse points existing in the delay value f (t), and recalculating a second delay mean value and a second delay standard deviation corresponding to the delay value after the high-amplitude pulse points are filtered;
determining whether the second delay standard deviation substantially decreases based on the first delay standard deviation;
if yes, judging whether the second delay standard deviation is smaller than a preset threshold value or not;
otherwise, judging that the information of the undetermined delay distribution corresponds to a local deterioration waveform;
when the second delay standard deviation is smaller than a preset threshold, judging whether the delay distribution information to be qualified corresponds to a low-frequency high-amplitude pulse waveform or not according to a formula (3);
M>A2+2*σ2 (3)；
wherein, a2 represents a second delay mean value corresponding to the delay distribution information to be qualified; sigma2Representing a second delay standard deviation corresponding to the delay distribution information to be qualified;
if the formula (3) does not hold, determining that the pending delay distribution information corresponds to a low-frequency high-amplitude pulse waveform; if the formula (3) is established, the undetermined delay distribution information corresponds to a high-amplitude deteriorated waveform;
when the second delay standard deviation is not smaller than a preset threshold value, re-filtering high-amplitude pulse points existing in the delay value and subsequent operation;
wherein, f (t) represents the delay value of the input delay time axis sequence related to the undetermined delay distribution information, that is, the delay value is the time when the client receives the data packet in the data transmission process between the client and the acceleration node server, and the time is marked in the delay time axis sequence, and each time point of receiving the data packet corresponds to a delay point, and each delay point has a corresponding delay value.
2. The multi-path preferred online game acceleration method according to claim 1, wherein after switching the current online game acceleration path of the client to the optimal path, the method further comprises:
starting a current network quality monitoring mechanism, and evaluating the current network quality of the optimal path according to preset timing time;
judging whether link connection and subsequent operation between the multipath generated by the client and the acceleration node server need to be executed again or not according to the evaluation processing result;
when the evaluation value of the evaluation processing result is greater than or equal to the preset network quality value, the re-execution is not needed;
otherwise, it needs to be executed again.
3. The method for accelerating a multipath preferred network according to claim 1, wherein after performing waveform qualification on the delay profile information of each path by using a preset multidimensional mathematical model, the method further comprises:
when the undetermined delay distribution information is judged to correspond to the high-amplitude deteriorated waveform, removing a path corresponding to the high-amplitude deteriorated waveform;
when the undetermined delay distribution information is judged to correspond to a high-probability waveform, acquiring a first delay mean value and a first delay standard deviation, and taking the first delay mean value and the first delay standard deviation as the input of a quality comprehensive score of a path corresponding to the high-probability waveform;
and when the information of the delay distribution to be determined corresponds to the low-frequency high-amplitude pulse waveform and the local deterioration waveform, calibrating the input delay value f (t), obtaining a third delay mean value and a third standard deviation according to the calibration processing result, and taking the third delay mean value and the third delay standard deviation as the input of the quality comprehensive score of the path corresponding to the low-frequency high-amplitude pulse waveform and the local deterioration waveform.
4. The multi-way preferred network acceleration method of claim 1,
the delay peak value, the first delay mean value and the first delay standard deviation are calculated according to the input delay value f (t).
5. The method for accelerating a multipath preferred network according to claim 3, wherein when it is determined that the delay profile information to be qualified corresponds to a low-frequency high-amplitude pulse waveform, the step of calibrating the input delay value f (t) comprises:
acquiring all delay points in the low-frequency high-amplitude pulse waveform, and judging whether the mean value corresponding to the delay values of the delay points is larger than a preset mean value or not;
if yes, filtering the delay points, and recalculating a new mean value and a new standard deviation according to the delay values of the residual delay points of the delay points of which the filtered mean value is greater than the preset mean value;
otherwise, filtering the delay points corresponding to the delay peak values in the low-frequency high-amplitude pulse waveform, and recalculating a new mean value and a new standard deviation according to the delay values of the residual delay points of the delay points corresponding to the filtered delay peak values.
6. The multi-path preferred network acceleration method of claim 1, wherein, in the quality comprehensive scoring for the network of each path, the quality comprehensive scoring q is performed by using the following formula;
q＝wavg×Avg0+(1-wavg)×σ0；
wherein, wavgRepresents a delayed evaluation weight value with a value range of [0, 1%]；σ0Represents the standard deviation of the delay; avg0 denotes the delayed mean.
7. The method for accelerating the multi-channel optimized webgame based on waveform evaluation as claimed in claim 1,
the specific operation of waveform qualification is carried out on the delay distribution information of each path by adopting a preset multi-dimensional mathematical model;
step A1, according to a pre-established data preprocessing model, filtering the speed measurement data of each path, extracting basic parameter characteristics of the speed measurement data of each path according to a formula (4), and acquiring a basic delay parameter set;
Figure FDA0003181143160000041
where n is the number of paths, exp is an exponential function based on a natural constant e, ln is a logarithmic function based on a natural constant e, i is the path sequence number, t is the number of paths0Starting time t of sending speed measurement data for each path of the clientiIs the nodeThe response time of the server to the ith path speed measurement data, x is the number of speed measurement data packets, a0The client sends out the initial number a of speed measurement data packets in each pathxReceiving the number of test packets of each path for the node server, y being the network load transient, b0Network load transient value when the client sends speed measurement data packet in each path, byFor the network load transient values at which the node server receives the test packets for each path,
Figure FDA0003181143160000051
the average response time of the node server for sending speed measurement data to each path,
Figure FDA0003181143160000052
sending the packet loss rate of the speed measurement data for each path by the client,
Figure FDA0003181143160000053
for each path network load transient mean, E (t)i,ax,by) To obtain a base set of delay parameters;
step A2, substituting the basic delay parameter set obtained in step A1 into formula (5) for calculation to obtain delay distribution information of each path;
Figure FDA0003181143160000054
wherein pi is a circumferential ratio, exp is an exponential function with a natural constant e as a base,
Figure FDA0003181143160000055
for each path network delay value weight, ciIs the network delay mean value of the ith path, sigma is the delay standard deviation obtained by each path according to the speed measurement data network load transient value and the packet loss rate,
Figure FDA0003181143160000056
for each path of the network delay profile information,
Figure FDA0003181143160000057
delaying the canonical difference distribution information for each path network, F (c)iσ) obtaining delay distribution information of each path;
step A3, substituting the delay distribution information of each path obtained in the step A2 into the waveform analysis model, automatically matching and identifying a large probability waveform, executing quality comprehensive scoring and distributing an optimal link;
Figure FDA0003181143160000058
wherein M is a delay peak value corresponding to the delay distribution information to be qualified, cj+2 σ is the delay distribution information corresponding to the large probability waveform of each path,
Figure FDA0003181143160000059
and (3) obtaining a rough probability waveform delay peak value for each path through derivation iterative convergence, and when the value P (M) is 1, indicating that the path delay distribution mean value is matched with the rough probability waveform, executing quality comprehensive scoring and distributing an optimal link.
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