CN104809059A - Application online index detection method and device - Google Patents
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Abstract
The invention provides an application online index detection method and device. According to the embodiment, the application online index detection method comprises the following steps: obtaining a first time sequence of an application online index of an application after a moment to be detected, and a second time sequence before the moment to be detected; utilizing the first time sequence to construct a first Hankel matrix; further obtaining a first feature vector of the first Hankel matrix according to the first Hankel matrix; constructing a second Hankel matrix by using the second time sequence, and further obtaining a plurality of second feature vectors of the second Hankel matrix according to the second Hankel matrix and the first feature vector; and determining whether the application online index has sudden change points at the moment to be detected or not according to the plurality of second feature vectors. According to the application online index detection method, a detection process can be carried out without manpower, the operation is simple and the accuracy is high, so that the detection efficiency and reliability of the application online index are improved.
Description
[technical field]
The present invention relates to detection technique, particularly relate to one and apply reach the standard grade finger object detection method and device.
[background technology]
Along with the development of the communication technology, terminal is integrated with increasing function, thus makes to contain more and more corresponding application (Application, APP) in the systemic-function list of terminal.In internet, all can there is hundreds of application every day and reach the standard grade in content supplier.It is to provide new application to user that some application are reached the standard grade, and other application is reached the standard grade, and is to improve old application.After application is reached the standard grade, there is mistake, provide the slip-stick artist of application generally can simulate the process of reaching the standard grade in simulated environment, and test the reliability of reaching the standard grade.But operating system, complicated interactive environment and thousands of server farms various in true upper thread environment are that simulated environment is difficult to simulation.Some in simulated environment not produced problem but there will be in true environment.Application if there is mistake, can affect the experience of user, and then affect the income of ICP after reaching the standard grade and occurring.General, when application reach the standard grade make a mistake time, the reach the standard grade time series of index of the application such as the performance index of associated server and performance index there will be drastic change.Therefore, fast and stable detect application reach the standard grade the application such as the performance index of relevant server and performance index reach the standard grade index time series in existing drastic change point, significant.
Current, after application is reached the standard grade each time, the slip-stick artist that this application is relevant generally adopts artificial observation method, observes the time series applying index of reaching the standard grade with the performance index of this relevant server of reaching the standard grade and performance index etc., with detect apply index of reaching the standard grade time series in whether there is drastic change point.This method, detection time is long, and easily makes mistakes, thus result in the efficiency of detection and the reduction of reliability of applying index of reaching the standard grade.
[summary of the invention]
Many aspects of the present invention provide a kind of and apply reach the standard grade finger object detection method and device, in order to improve efficiency and the reliability of the detection of applying index of reaching the standard grade.
An aspect of of the present present invention, provides a kind of and applies finger object detection method of reaching the standard grade, comprising:
The application obtaining application is reached the standard grade the very first time sequence of index after the moment to be detected and the second time series before the moment to be detected;
According to described very first time sequence, obtain the first transformation matrix, according to described first transformation matrix, obtain at least one eigenwert of described first transformation matrix, an eigenwert minimum at least one eigenwert according to described first transformation matrix, obtains first eigenvector;
According to described second time series, obtain the second transformation matrix, according to described second transformation matrix and described first eigenvector, obtain triple diagonal matrix, according to described triple diagonal matrix, obtain at least one eigenwert of described triple diagonal matrix, according to N number of eigenwert minimum at least one eigenwert of described triple diagonal matrix, obtain N number of second feature vector, N be more than or equal to 1 integer;
According to described N number of second feature vector, determine whether described application index of reaching the standard grade exists drastic change point in the described moment to be detected.
Aspect as above and arbitrary possible implementation, provide a kind of implementation further, described according to described very first time sequence, obtains the first transformation matrix, comprising:
According to described very first time sequence, obtain a Hankel matrix, according to a described Hankel matrix and a described Hankel transpose of a matrix matrix, obtain described first transformation matrix.
Aspect as above and arbitrary possible implementation, provide a kind of implementation further, described according to described second time series, obtains the second transformation matrix, comprising:
According to described second time series, obtain the 2nd Hankel matrix, according to described 2nd Hankel matrix and described 2nd Hankel transpose of a matrix matrix, obtain described second transformation matrix.
Aspect as above and arbitrary possible implementation, provide a kind of implementation further, and described application index of reaching the standard grade comprises at least one item in following data:
The performance index of the server that described application uses; And
The performance index of described application.
Aspect as above and arbitrary possible implementation, provide a kind of implementation further, described according to described triple diagonal matrix, obtains at least one eigenwert of described triple diagonal matrix, comprising:
According to described triple diagonal matrix, adopt QL alternative manner, obtain at least one eigenwert of described triple diagonal matrix.
Aspect as above and arbitrary possible implementation, provide a kind of implementation further, described according to described N number of second feature vector, determines whether described application index of reaching the standard grade exists drastic change point in the described moment to be detected, comprising:
According to described N number of second feature vector, calculate quadratic sum;
If the numerical value of specifying numerical value to deduct described quadratic sum is more than or equal to detection threshold, determine that described application index of reaching the standard grade exists drastic change point in the described moment to be detected.
Aspect as above and arbitrary possible implementation, provide a kind of implementation further, described according to described N number of second feature vector, determines whether described application index of reaching the standard grade exists drastic change point in the described moment to be detected, also comprises:
If the numerical value that described appointment numerical value deducts described quadratic sum is less than described detection threshold, determine that described application index of reaching the standard grade does not exist drastic change point in the described moment to be detected.
Another aspect of the present invention, the pick-up unit providing a kind of application to reach the standard grade index, comprising:
Acquiring unit, the application for obtaining application is reached the standard grade the very first time sequence of index after the moment to be detected and the second time series before the moment to be detected;
Fisrt feature unit, for according to described very first time sequence, obtain the first transformation matrix, according to described first transformation matrix, obtain at least one eigenwert of described first transformation matrix, an eigenwert minimum at least one eigenwert according to described first transformation matrix, obtains first eigenvector;
Second feature unit, for according to described second time series, obtain the second transformation matrix, according to described second transformation matrix and described first eigenvector, obtain triple diagonal matrix, according to described triple diagonal matrix, obtain at least one eigenwert of described triple diagonal matrix, according to N number of eigenwert minimum at least one eigenwert of described triple diagonal matrix, obtain N number of second feature vector, N be more than or equal to 1 integer;
Determining unit, for according to described N number of second feature vector, determines whether described application index of reaching the standard grade exists drastic change point in the described moment to be detected.
Aspect as above and arbitrary possible implementation, provide a kind of implementation further, described fisrt feature unit, specifically for
According to described very first time sequence, obtain a Hankel matrix, according to a described Hankel matrix and a described Hankel transpose of a matrix matrix, obtain described first transformation matrix.
Aspect as above and arbitrary possible implementation, provide a kind of implementation further, described second feature unit, specifically for
According to described second time series, obtain the 2nd Hankel matrix, according to described 2nd Hankel matrix and described 2nd Hankel transpose of a matrix matrix, obtain described second transformation matrix.
Aspect as above and arbitrary possible implementation, provide a kind of implementation further, and described application index of reaching the standard grade comprises at least one item in following data:
The performance index of the server that described application uses; And
The performance index of described application.
Aspect as above and arbitrary possible implementation, provide a kind of implementation further, described second feature unit, specifically for
According to described triple diagonal matrix, adopt QL alternative manner, obtain at least one eigenwert of described triple diagonal matrix.
Aspect as above and arbitrary possible implementation, provide a kind of implementation further, described second feature unit, specifically for
According to described N number of second feature vector, calculate quadratic sum; And
If the numerical value of specifying numerical value to deduct described quadratic sum is more than or equal to detection threshold, determine that described application index of reaching the standard grade exists drastic change point in the described moment to be detected.
Aspect as above and arbitrary possible implementation, provide a kind of implementation, described determining unit further, also for
If the numerical value that described appointment numerical value deducts described quadratic sum is less than described detection threshold, determine that described application index of reaching the standard grade does not exist drastic change point in the described moment to be detected.
As shown from the above technical solution, the embodiment of the present invention to be reached the standard grade the very first time sequence of index after the moment to be detected and the second time series before the moment to be detected by the application obtaining application, and then utilize described very first time sequence, build a Hankel matrix, further according to a Hankel matrix, obtain the first eigenvector of a Hankel matrix, and utilize described second time series, build the 2nd Hankel matrix, further according to the 2nd Hankel matrix and first eigenvector, obtain several second feature vectors of the 2nd Hankel matrix, make it possible to according to several obtained second feature vectors, determine whether described application index of reaching the standard grade exists drastic change point in the described moment to be detected, without the need to manually participating in testing process, simple to operate, and accuracy is high, thus improve efficiency and the reliability of the detection of applying index of reaching the standard grade.
[accompanying drawing explanation]
In order to be illustrated more clearly in the technical scheme in the embodiment of the present invention, be briefly described to the accompanying drawing used required in embodiment or description of the prior art below, apparently, accompanying drawing in the following describes is some embodiments of the present invention, for those of ordinary skill in the art, under the prerequisite not paying creative work, other accompanying drawing can also be obtained according to these accompanying drawings.
Fig. 1 reaches the standard grade for application that one embodiment of the invention provides and refers to the schematic flow sheet of object detection method;
Fig. 2 to reach the standard grade the structural representation of pick-up unit of index for application that another embodiment of the present invention provides.
[embodiment]
For making the object of the embodiment of the present invention, technical scheme and advantage clearly, below in conjunction with the accompanying drawing in the embodiment of the present invention, technical scheme in the embodiment of the present invention is clearly and completely described, obviously, described embodiment is the present invention's part embodiment, instead of whole embodiments.Based on the embodiment in the present invention, those of ordinary skill in the art, not making other embodiments whole obtained under creative work prerequisite, belong to the scope of protection of the invention.
It should be noted that, terminal involved in the embodiment of the present invention can include but not limited to mobile phone, personal digital assistant (Personal Digital Assistant, PDA), radio hand-held equipment, panel computer (Tablet Computer), PC (Personal Computer, PC), MP3 player, MP4 player, wearable device (such as, intelligent glasses, intelligent watch, Intelligent bracelet etc.) etc.
In addition, term "and/or" herein, being only a kind of incidence relation describing affiliated partner, can there are three kinds of relations in expression, and such as, A and/or B, can represent: individualism A, exists A and B simultaneously, these three kinds of situations of individualism B.In addition, character "/" herein, general expression forward-backward correlation is to the relation liking a kind of "or".
Fig. 1 reaches the standard grade for application that one embodiment of the invention provides and refers to the schematic flow sheet of object detection method, as shown in Figure 1.
101, the application obtaining application is reached the standard grade the very first time sequence of index after the moment to be detected and the second time series before the moment to be detected.
So-called application can be browser application, or can also be other application except browser application, and such as, Baidu search application, Baidu's map application etc., as long as can realize representing of the page, the present embodiment is not particularly limited this.
The so-called page, sometimes WWW (World Wide Web can be also called, Web) page, can be based on HTML (Hypertext Markup Language) (HyperText Markup Language, HTML) webpage (Web Page) write, i.e. html page, or can also be the webpage write based on HTML and Java language, the i.e. java server page (Java Server Page, JSP), or the webpage can also write for other programming languages, the present embodiment is not particularly limited this.
Particularly, the page can comprise by one or more page-tag such as, HTML (Hypertext Markup Language) (HyperText Markup Language, HTML) label, JSP label etc., the display block of definition, is called page elements, such as, text, picture, hyperlink, button, edit box, combobox etc., the present embodiment is not particularly limited this.
Wherein, the application of described application is reached the standard grade at least one item that index can include but not limited in following data:
The performance index of the server that described application uses; And
The performance index of described application.
The performance index of the server that so-called application uses, refer to the hardware index of server, such as, and CPU (central processing unit) (Central Processing Unit, CPU) utilization rate, memory usage etc.
The performance index of so-called application, refer to the software index of application, such as, and clicking rate, click mortality, click time delay, average response time etc.
Be understandable that, apply the numerical value of index in the moment to be detected of reaching the standard grade, can be included in very first time sequence, or can also be included in the second time series, the present embodiment is not particularly limited this.
102, according to described very first time sequence, obtain the first transformation matrix, according to described first transformation matrix, obtain at least one eigenwert of described first transformation matrix, an eigenwert minimum at least one eigenwert according to described first transformation matrix, obtains first eigenvector.
Particularly, specifically according to described very first time sequence, a Hankel matrix can be obtained, according to a described Hankel matrix and a described Hankel transpose of a matrix matrix, obtains described first transformation matrix.
In a concrete implementation procedure, specifically according to described first transformation matrix, singular value decomposition method can be adopted, obtains at least one eigenwert of described first transformation matrix.
103, according to described second time series, obtain the second transformation matrix, according to described second transformation matrix and described first eigenvector, obtain triple diagonal matrix, according to described triple diagonal matrix, obtain at least one eigenwert of described triple diagonal matrix, according to N number of eigenwert minimum at least one eigenwert of described triple diagonal matrix, obtain N number of second feature vector, N be more than or equal to 1 integer.
Particularly, specifically according to described second time series, the 2nd Hankel matrix can be obtained, according to described 2nd Hankel matrix and described 2nd Hankel transpose of a matrix matrix, obtains described second transformation matrix.
Such as, suppose that the moment to be detected is t, the length of time series (i.e. very first time sequence and the second time series) to be w, w be more than or equal to 2 integer; So, very first time sequence can be then x (t), x (t+1), x (t+2) ..., x (t+w-1); Second time series can be then x (t-w) ..., x (t-3), x (t-2), x (t-1).
One Hankel matrix can be following form, is designated as G (t):
G (t)=[r (t), r (t+1), r (t+2) ..., r (t+w-1)]; Wherein,
r(t)＝x(t)
T,x(t+1)
T,x(t+2)
T,…,x(t+w-1)
T；
2nd Hankel matrix can be following form, is designated as H (t):
H (t)=[s (t-w) ..., s (t-3), s (t-2), s (t-1)]; Wherein,
s(t)＝x(t-w+1)
T,…,x(t-3)
T,x(t-2)
T,x(t-1)
T,x(t)
T。
104, according to described N number of second feature vector, determine whether described application index of reaching the standard grade exists drastic change point in the described moment to be detected.
It should be noted that, the executive agent of 101 ~ 104 partly or entirely can for being positioned at the application of local terminal, or can also for being arranged in plug-in unit or SDK (Software Development Kit) (the Software Development Kit of the application of local terminal, the functional unit such as SDK), or can also for being arranged in the processing engine of the server of network side, or can also for being positioned at the distributed system of network side, the present embodiment is not particularly limited this, and the present embodiment is not particularly limited this.
Be understandable that, described application can be mounted in the local program (nativeApp) in terminal, or can also be a web page program (webApp) of browser in terminal, and the present embodiment is not particularly limited this.
Like this, to be reached the standard grade the very first time sequence of index after the moment to be detected and the second time series before the moment to be detected by the application obtaining application, and then utilize described very first time sequence, build a Hankel matrix, further according to a Hankel matrix, obtain the first eigenvector of a Hankel matrix, and utilize described second time series, build the 2nd Hankel matrix, further according to the 2nd Hankel matrix and first eigenvector, obtain several second feature vectors of the 2nd Hankel matrix, make it possible to according to several obtained second feature vectors, determine whether described application index of reaching the standard grade exists drastic change point in the described moment to be detected, without the need to manually participating in testing process, simple to operate, and accuracy is high, thus improve efficiency and the reliability of the detection of applying index of reaching the standard grade.
Alternatively, in one of the present embodiment possible implementation, in 103, specifically according to described second transformation matrix and described first eigenvector, Lanczos algorithm can be adopted, obtain triple diagonal matrix.
Particularly, specifically can by the arbitrary integer between a 0 ~ w, w is seasonal effect in time series length, and described second transformation matrix obtained and described first eigenvector, substitutes into Lanczos algorithm, obtains triple diagonal matrix.
In a concrete implementation procedure, specifically according to described triple diagonal matrix, singular value decomposition method can be adopted, obtains at least one eigenwert of described triple diagonal matrix.This method, computation complexity is larger.
In the implementation procedure that another is concrete, specifically according to described triple diagonal matrix, QL alternative manner can be adopted, obtains at least one eigenwert of described triple diagonal matrix.This method, computation complexity is less, effectively can improve the efficiency that eigenwert obtains.
Alternatively, in one of the present embodiment possible implementation, in 104, specifically according to described N number of second feature vector, quadratic sum can be calculated.And then, then can according to quadratic sum and the detection threshold pre-set, determine whether described application index of reaching the standard grade exists drastic change point in the described moment to be detected.
In a concrete implementation procedure, if the numerical value of specifying numerical value (such as, 1) to deduct described quadratic sum is more than or equal to detection threshold, then can determine that described application index of reaching the standard grade exists drastic change point in the described moment to be detected.
In the implementation procedure that another is concrete, if the numerical value that described appointment numerical value deducts described quadratic sum is less than described detection threshold, then can determine that described application index of reaching the standard grade does not exist drastic change point in the described moment to be detected.
In the present embodiment, to be reached the standard grade the very first time sequence of index after the moment to be detected and the second time series before the moment to be detected by the application obtaining application, and then utilize described very first time sequence, build a Hankel matrix, further according to a Hankel matrix, obtain the first eigenvector of a Hankel matrix, and utilize described second time series, build the 2nd Hankel matrix, further according to the 2nd Hankel matrix and first eigenvector, obtain several second feature vectors of the 2nd Hankel matrix, make it possible to according to several obtained second feature vectors, determine whether described application index of reaching the standard grade exists drastic change point in the described moment to be detected, without the need to manually participating in testing process, simple to operate, and accuracy is high, thus improve efficiency and the reliability of the detection of applying index of reaching the standard grade.
In addition, to be reached the standard grade finger object detection method by application provided by the present invention, can fast and stable detect that application is reached the standard grade the impact caused, the application avoiding mistake is reached the standard grade the experience causing Internet user poor, thus decreases the loss of ICP.Meanwhile, adopt application provided by the present invention to reach the standard grade finger object detection method, can detect whether application there occurs drastic change point after reaching the standard grade automatically, thus decrease and artificially detect drastic change point and the human and material resources loss that produces in the past.
It should be noted that, for aforesaid each embodiment of the method, in order to simple description, therefore it is all expressed as a series of combination of actions, but those skilled in the art should know, the present invention is not by the restriction of described sequence of movement, because according to the present invention, some step can adopt other orders or carry out simultaneously.Secondly, those skilled in the art also should know, the embodiment described in instructions all belongs to preferred embodiment, and involved action and module might not be that the present invention is necessary.
In the above-described embodiments, the description of each embodiment is all emphasized particularly on different fields, in certain embodiment, there is no the part described in detail, can see the associated description of other embodiments.
Fig. 2 to reach the standard grade the structural representation of pick-up unit of index for application that another embodiment of the present invention provides, as shown in Figure 2.The reach the standard grade pick-up unit of index of the application of the present embodiment can comprise acquiring unit 21, fisrt feature unit 22, second feature unit 23 and determining unit 24.Wherein, acquiring unit 21, the application for obtaining application is reached the standard grade the very first time sequence of index after the moment to be detected and the second time series before the moment to be detected; Fisrt feature unit 22, for according to described very first time sequence, obtain the first transformation matrix, according to described first transformation matrix, obtain at least one eigenwert of described first transformation matrix, an eigenwert minimum at least one eigenwert according to described first transformation matrix, obtains first eigenvector; Second feature unit 23, for according to described second time series, obtain the second transformation matrix, according to described second transformation matrix and described first eigenvector, obtain triple diagonal matrix, according to described triple diagonal matrix, obtain at least one eigenwert of described triple diagonal matrix, according to N number of eigenwert minimum at least one eigenwert of described triple diagonal matrix, obtain N number of second feature vector, N be more than or equal to 1 integer; Determining unit 24, for according to described N number of second feature vector, determines whether described application index of reaching the standard grade exists drastic change point in the described moment to be detected.
Particularly, described fisrt feature unit, specifically may be used for according to described very first time sequence, obtains a Hankel matrix, according to a described Hankel matrix and a described Hankel transpose of a matrix matrix, obtains described first transformation matrix.
Particularly, described second feature unit, specifically may be used for according to described second time series, obtains the 2nd Hankel matrix, according to described 2nd Hankel matrix and described 2nd Hankel transpose of a matrix matrix, obtains described second transformation matrix.
It should be noted that, the application that the present embodiment provides reach the standard grade index pick-up unit partly or entirely can for being positioned at the application of local terminal, or can also for being arranged in plug-in unit or SDK (Software Development Kit) (the Software Development Kit of the application of local terminal, the functional unit such as SDK), or can also for being arranged in the processing engine of the server of network side, or can also for being positioned at the distributed system of network side, the present embodiment is not particularly limited this, and the present embodiment is not particularly limited this.
Be understandable that, described application can be mounted in the local program (nativeApp) in terminal, or can also be a web page program (webApp) of browser in terminal, and the present embodiment is not particularly limited this.
Wherein, the application of described application is reached the standard grade at least one item that index can include but not limited in following data:
The performance index of the server that described application uses; And
The performance index of described application.
The performance index of the server that so-called application uses, refer to the hardware index of server, such as, and CPU (central processing unit) (Central Processing Unit, CPU) utilization rate, memory usage etc.
The performance index of so-called application, refer to the software index of application, such as, and clicking rate, click mortality, click time delay, average response time etc.
Be understandable that, apply the numerical value of index in the moment to be detected of reaching the standard grade, can be included in very first time sequence, or can also be included in the second time series, the present embodiment is not particularly limited this.
Alternatively, in one of the present embodiment possible implementation, described second feature unit 23, specifically may be used for according to described triple diagonal matrix, adopts QL alternative manner, obtains at least one eigenwert of described triple diagonal matrix.
Alternatively, in one of the present embodiment possible implementation, described second feature unit 23, specifically may be used for, according to described N number of second feature vector, calculating quadratic sum; And if the numerical value of specifying numerical value to deduct described quadratic sum be more than or equal to detection threshold, determine that described application index of reaching the standard grade exists drastic change point in the described moment to be detected.
Further, described determining unit 24, the numerical value that described appointment numerical value deducts described quadratic sum if can also be further used for is less than described detection threshold, determines that described application index of reaching the standard grade does not exist drastic change point in the described moment to be detected.
It should be noted that, method in the embodiment that Fig. 1 is corresponding, the application that can be provided by the present embodiment reach the standard grade index pick-up unit realize.Detailed description see the related content in embodiment corresponding to Fig. 1, can repeat no more herein.
In the present embodiment, the application being obtained application by acquiring unit is reached the standard grade the very first time sequence of index after the moment to be detected and the second time series before the moment to be detected, and then utilize described very first time sequence by fisrt feature unit, build a Hankel matrix, further according to a Hankel matrix, obtain the first eigenvector of a Hankel matrix, and utilize described second time series by second feature unit, build the 2nd Hankel matrix, further according to the 2nd Hankel matrix and first eigenvector, obtain several second feature vectors of the 2nd Hankel matrix, make determining unit can according to several second feature vectors obtained, determine whether described application index of reaching the standard grade exists drastic change point in the described moment to be detected, without the need to manually participating in testing process, simple to operate, and accuracy is high, thus improve efficiency and the reliability of the detection of applying index of reaching the standard grade.
Those skilled in the art can be well understood to, and for convenience and simplicity of description, the system of foregoing description, the specific works process of device and unit, with reference to the corresponding process in preceding method embodiment, can not repeat them here.
In several embodiment provided by the present invention, should be understood that, disclosed system, apparatus and method, can realize by another way.Such as, device embodiment described above is only schematic, such as, the division of described unit, be only a kind of logic function to divide, actual can have other dividing mode when realizing, such as multiple unit or assembly can in conjunction with or another system can be integrated into, or some features can be ignored, or do not perform.Another point, shown or discussed coupling each other or direct-coupling or communication connection can be by some interfaces, and the indirect coupling of device or unit or communication connection can be electrical, machinery or other form.
The described unit illustrated as separating component or can may not be and physically separates, and the parts as unit display can be or may not be physical location, namely can be positioned at a place, or also can be distributed in multiple network element.Some or all of unit wherein can be selected according to the actual needs to realize the object of the present embodiment scheme.
In addition, each functional unit in each embodiment of the present invention can be integrated in a processing unit, also can be that the independent physics of unit exists, also can two or more unit in a unit integrated.Above-mentioned integrated unit both can adopt the form of hardware to realize, and the form that hardware also can be adopted to add SFU software functional unit realizes.
The above-mentioned integrated unit realized with the form of SFU software functional unit, can be stored in a computer read/write memory medium.Above-mentioned SFU software functional unit is stored in a storage medium, comprising some instructions in order to make a computer equipment (can be personal computer, server, or the network equipment etc.) or processor (processor) perform the part steps of method described in each embodiment of the present invention.And aforesaid storage medium comprises: USB flash disk, portable hard drive, ROM (read-only memory) (Read-Only Memory, ROM), random access memory (Random Access Memory, RAM), magnetic disc or CD etc. various can be program code stored medium.
Last it is noted that above embodiment is only in order to illustrate technical scheme of the present invention, be not intended to limit; Although with reference to previous embodiment to invention has been detailed description, those of ordinary skill in the art is to be understood that: it still can be modified to the technical scheme described in foregoing embodiments, or carries out equivalent replacement to wherein portion of techniques feature; And these amendments or replacement, do not make the essence of appropriate technical solution depart from the spirit and scope of various embodiments of the present invention technical scheme.
Claims (14)
1. application is reached the standard grade a finger object detection method, it is characterized in that, comprising:
The application obtaining application is reached the standard grade the very first time sequence of index after the moment to be detected and the second time series before the moment to be detected;
According to described very first time sequence, obtain the first transformation matrix, according to described first transformation matrix, obtain at least one eigenwert of described first transformation matrix, an eigenwert minimum at least one eigenwert according to described first transformation matrix, obtains first eigenvector;
According to described second time series, obtain the second transformation matrix, according to described second transformation matrix and described first eigenvector, obtain triple diagonal matrix, according to described triple diagonal matrix, obtain at least one eigenwert of described triple diagonal matrix, according to N number of eigenwert minimum at least one eigenwert of described triple diagonal matrix, obtain N number of second feature vector, N be more than or equal to 1 integer;
According to described N number of second feature vector, determine whether described application index of reaching the standard grade exists drastic change point in the described moment to be detected.
2. method according to claim 1, is characterized in that, described according to described very first time sequence, obtains the first transformation matrix, comprising:
According to described very first time sequence, obtain a Hankel matrix, according to a described Hankel matrix and a described Hankel transpose of a matrix matrix, obtain described first transformation matrix.
3. method according to claim 1, is characterized in that, described according to described second time series, obtains the second transformation matrix, comprising:
According to described second time series, obtain the 2nd Hankel matrix, according to described 2nd Hankel matrix and described 2nd Hankel transpose of a matrix matrix, obtain described second transformation matrix.
4. method according to claim 1, is characterized in that, described application index of reaching the standard grade comprises at least one item in following data:
The performance index of the server that described application uses; And
The performance index of described application.
5. method according to claim 1, is characterized in that, described according to described triple diagonal matrix, obtains at least one eigenwert of described triple diagonal matrix, comprising:
According to described triple diagonal matrix, adopt QL alternative manner, obtain at least one eigenwert of described triple diagonal matrix.
6. the method according to the arbitrary claim of Claims 1 to 5, is characterized in that, described according to described N number of second feature vector, determines whether described application index of reaching the standard grade exists drastic change point in the described moment to be detected, comprising:
According to described N number of second feature vector, calculate quadratic sum;
If the numerical value of specifying numerical value to deduct described quadratic sum is more than or equal to detection threshold, determine that described application index of reaching the standard grade exists drastic change point in the described moment to be detected.
7. method according to claim 6, is characterized in that, described according to described N number of second feature vector, determines whether described application index of reaching the standard grade exists drastic change point in the described moment to be detected, also comprises:
If the numerical value that described appointment numerical value deducts described quadratic sum is less than described detection threshold, determine that described application index of reaching the standard grade does not exist drastic change point in the described moment to be detected.
8. application is reached the standard grade a pick-up unit for index, it is characterized in that, comprising:
Acquiring unit, the application for obtaining application is reached the standard grade the very first time sequence of index after the moment to be detected and the second time series before the moment to be detected;
Fisrt feature unit, for according to described very first time sequence, obtain the first transformation matrix, according to described first transformation matrix, obtain at least one eigenwert of described first transformation matrix, an eigenwert minimum at least one eigenwert according to described first transformation matrix, obtains first eigenvector;
Second feature unit, for according to described second time series, obtain the second transformation matrix, according to described second transformation matrix and described first eigenvector, obtain triple diagonal matrix, according to described triple diagonal matrix, obtain at least one eigenwert of described triple diagonal matrix, according to N number of eigenwert minimum at least one eigenwert of described triple diagonal matrix, obtain N number of second feature vector, N be more than or equal to 1 integer;
Determining unit, for according to described N number of second feature vector, determines whether described application index of reaching the standard grade exists drastic change point in the described moment to be detected.
9. device according to claim 8, is characterized in that, described fisrt feature unit, specifically for
According to described very first time sequence, obtain a Hankel matrix, according to a described Hankel matrix and a described Hankel transpose of a matrix matrix, obtain described first transformation matrix.
10. device according to claim 8, is characterized in that, described second feature unit, specifically for
According to described second time series, obtain the 2nd Hankel matrix, according to described 2nd Hankel matrix and described 2nd Hankel transpose of a matrix matrix, obtain described second transformation matrix.
11. devices according to claim 8, is characterized in that, described application index of reaching the standard grade comprises at least one item in following data:
The performance index of the server that described application uses; And
The performance index of described application.
12. devices according to claim 8, is characterized in that, described second feature unit, specifically for
According to described triple diagonal matrix, adopt QL alternative manner, obtain at least one eigenwert of described triple diagonal matrix.
Device described in 13. according to Claim 8 ~ 12 arbitrary claims, is characterized in that, described second feature unit, specifically for
According to described N number of second feature vector, calculate quadratic sum; And
If the numerical value of specifying numerical value to deduct described quadratic sum is more than or equal to detection threshold, determine that described application index of reaching the standard grade exists drastic change point in the described moment to be detected.
14. devices according to claim 13, is characterized in that, described determining unit, also for
If the numerical value that described appointment numerical value deducts described quadratic sum is less than described detection threshold, determine that described application index of reaching the standard grade does not exist drastic change point in the described moment to be detected.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107871190A (en) * | 2016-09-23 | 2018-04-03 | 阿里巴巴集团控股有限公司 | A kind of operational indicator monitoring method and device |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20040181710A1 (en) * | 2001-04-12 | 2004-09-16 | Horst Sauer | Method for determining and recording system information and function in distributed parallel component based software systems |
| CN101561797A (en) * | 2008-04-14 | 2009-10-21 | 国际商业机器公司 | Method and device for singular value and feature value composition of matrix on processing system |
| CN101741650A (en) * | 2010-01-04 | 2010-06-16 | 北京航空航天大学 | QoS prediction-based service combination method and QoS prediction-based service combination device |
| CN102123061A (en) * | 2011-03-28 | 2011-07-13 | 杭州电子科技大学 | Method for determining performance of Web server |
| CN103237082A (en) * | 2013-05-06 | 2013-08-07 | 百度在线网络技术（北京）有限公司 | Cloud testing method, system, and cloud server |
| US8516449B2 (en) * | 2009-07-14 | 2013-08-20 | International Business Machines Corporation | Detecting and localizing security vulnerabilities in client-server application |
| CN104298589A (en) * | 2013-07-16 | 2015-01-21 | 阿里巴巴集团控股有限公司 | Performance test method and performance test equipment |
| CN104331287A (en) * | 2014-10-31 | 2015-02-04 | 北京思特奇信息技术股份有限公司 | Method and system for releasing applications |
-
2015
- 2015-04-01 CN CN201510153157.2A patent/CN104809059B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20040181710A1 (en) * | 2001-04-12 | 2004-09-16 | Horst Sauer | Method for determining and recording system information and function in distributed parallel component based software systems |
| CN101561797A (en) * | 2008-04-14 | 2009-10-21 | 国际商业机器公司 | Method and device for singular value and feature value composition of matrix on processing system |
| US8516449B2 (en) * | 2009-07-14 | 2013-08-20 | International Business Machines Corporation | Detecting and localizing security vulnerabilities in client-server application |
| CN101741650A (en) * | 2010-01-04 | 2010-06-16 | 北京航空航天大学 | QoS prediction-based service combination method and QoS prediction-based service combination device |
| CN102123061A (en) * | 2011-03-28 | 2011-07-13 | 杭州电子科技大学 | Method for determining performance of Web server |
| CN103237082A (en) * | 2013-05-06 | 2013-08-07 | 百度在线网络技术（北京）有限公司 | Cloud testing method, system, and cloud server |
| CN104298589A (en) * | 2013-07-16 | 2015-01-21 | 阿里巴巴集团控股有限公司 | Performance test method and performance test equipment |
| CN104331287A (en) * | 2014-10-31 | 2015-02-04 | 北京思特奇信息技术股份有限公司 | Method and system for releasing applications |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107871190A (en) * | 2016-09-23 | 2018-04-03 | 阿里巴巴集团控股有限公司 | A kind of operational indicator monitoring method and device |
| CN107871190B (en) * | 2016-09-23 | 2021-12-14 | 阿里巴巴集团控股有限公司 | Service index monitoring method and device |
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|---|---|
| CN104809059B (en) | 2018-02-02 |
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