CN111368169A - Method, device, equipment and storage medium for detecting brushing amount behavior - Google Patents
Method, device, equipment and storage medium for detecting brushing amount behavior Download PDFInfo
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- 230000001680 brushing effect Effects 0.000 title claims abstract description 47
- 238000000034 method Methods 0.000 title claims abstract description 36
- 238000004458 analytical method Methods 0.000 claims abstract description 56
- 230000006399 behavior Effects 0.000 claims description 111
- 238000001228 spectrum Methods 0.000 claims description 28
- 238000013480 data collection Methods 0.000 claims description 10
- 101100204393 Arabidopsis thaliana SUMO2 gene Proteins 0.000 claims description 7
- 101100311460 Schizosaccharomyces pombe (strain 972 / ATCC 24843) sum2 gene Proteins 0.000 claims description 7
- 230000009466 transformation Effects 0.000 claims description 7
- 101150112492 SUM-1 gene Proteins 0.000 claims description 6
- 101150096255 SUMO1 gene Proteins 0.000 claims description 6
- 238000006243 chemical reaction Methods 0.000 claims description 5
- 238000004806 packaging method and process Methods 0.000 claims description 2
- 238000010586 diagram Methods 0.000 description 8
- 230000000694 effects Effects 0.000 description 6
- 230000008569 process Effects 0.000 description 6
- 230000006870 function Effects 0.000 description 3
- 238000005070 sampling Methods 0.000 description 3
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- 230000001419 dependent effect Effects 0.000 description 2
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- 230000005540 biological transmission Effects 0.000 description 1
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- 238000007405 data analysis Methods 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
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Abstract
The invention discloses a method for detecting a traffic brushing behavior, which comprises the steps of acquiring APP page access data of continuous time, performing behavior analysis on the page access data to obtain a behavior analysis result, and judging whether a traffic brushing behavior exists according to a preset rule.
Description
Technical Field
The invention relates to the field of operator service monitoring, in particular to a method, a device, equipment and a storage medium for detecting a brushing amount behavior.
Background
In the process of popularizing APP by a channel, behaviors of user quantity and user access quantity can be forged by the user quantity brushing, so that the flow of APP of a channel can be improved, an operator of the channel needs to detect the cheating behavior of the user quantity brushing, whether the user quantity brushing is normal or not is judged at present through characteristics of hardware information (such as IMEI, MAC, positioning and the like) of a mobile phone, and the mobile phone with the user quantity brushing is limited, so that the user quantity brushing behavior can be determined if a plurality of users concentrate on equipment with certain IMEI information, or behavior characteristics of the users are generated according to behavior data of the users, the users are classified according to the behavior characteristics, and the behavior characteristics of classified group users are determined to be the user quantity brushing behavior when the behavior characteristics meet preset judgment strategies.
However, the reliability of the refresh rate behavior judgment through the hardware information is not high, because the IMIE and the MAC information can be forged in many ways at present, the refresh rate behavior is judged through the simple hardware environment detection, the effect is not good, and secondly, through the user grouping and the grouping behavior judgment strategy, the using range is not wide, the complexity is high, the grouping and the judgment strategies of different APP users are different, when the number of the users is small, the grouping judgment cannot be supported well, the judgment strategy depends on the user characteristics of the judgment strategy, and also depends on the user data analysis of large data, and the required cost is also high.
Therefore, it is necessary to develop a brushing method which is independent of hardware environment and APP property and has a wide application range.
Disclosure of Invention
The present invention is directed to solving, at least to some extent, one of the technical problems in the related art. Therefore, the invention aims to provide a brushing method, a brushing device, brushing equipment and a brushing storage medium which are independent of hardware environment and APP property and have wide application range.
The technical scheme adopted by the invention is as follows:
in a first aspect, the present invention provides a method of detecting a brush stroke behavior, comprising the steps of:
acquiring APP page access data of continuous time;
performing behavior analysis on the page access data;
and judging whether the brushing amount behavior exists according to a preset rule by obtaining the behavior analysis result.
Further, the acquiring APP page access data of continuous time specifically includes: and regularly packaging the page access data and storing the page access data into a database.
Further, the behavior analysis specifically includes the steps of:
discretizing the page access data and carrying out spectrum conversion;
calculating an energy spectrum of the frequency spectrum to obtain an energy spectrogram;
and calculating the SUM of energy values at the left end and the right end of the boundary, namely SUM SUM1 of the right energy value and SUM SUM2 of the left energy value by taking 0.5Hz as the boundary, comparing the magnitudes of the energy values, and taking the magnitude comparison result as a behavior analysis result.
Further, the preset rule is as follows: when SUM1 is greater than or equal to SUM2, the brush amount behavior is judged to be present, otherwise, the brush amount behavior is judged to be absent.
Further, the behavior analysis also comprises the steps of identifying high-frequency components after carrying out frequency spectrum conversion, directly judging the behavior without the momentum if the high-frequency components do not exist in the frequency spectrum, and otherwise, carrying out the next analysis.
Further, the page access data includes: the dwell time of the page and the number of pages currently present.
In a second aspect, the present invention also provides an apparatus for detecting a brushing behavior, comprising:
the acquisition module is used for acquiring APP page access data of continuous time;
the behavior analysis module is used for performing behavior analysis on the page access data;
and the brushing amount result analysis module is used for obtaining the result of the behavior analysis and judging whether brushing amount behaviors exist or not according to a preset rule.
Further, the behavior analysis system further comprises a database, the acquisition module is specifically an APP behavior acquisition agent module, the behavior analysis module comprises an analysis submodule and a data collection submodule, the APP behavior acquisition agent module acquires data and sends the data to the behavior data collection submodule, the behavior data collection submodule sends the received data to the database for storage, and the analysis submodule executes the method according to any one of claims 1 to 6 to perform the brushing behavior analysis by reading continuous time data in the database.
In a third aspect, the present invention provides a control apparatus that detects a brush amount behavior, comprising:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of the first aspects.
In a fourth aspect, the present invention provides a computer-readable storage medium having stored thereon computer-executable instructions for causing a computer to perform the method of any of the first aspects.
The invention has the beneficial effects that:
the method and the device have the advantages that the APP page access data of continuous time are collected, the behavior analysis is carried out on the page access data, the behavior analysis result is obtained, and whether the traffic brushing behavior exists is judged according to the preset rule, so that the problems that in the prior art, the reliability and the effect of the traffic brushing behavior are not high through judgment of hardware information, the judgment strategy is high in complexity and high in cost through a user grouping and grouping behavior judgment strategy are solved, the fact that the traffic brushing behavior is judged through the behavior analysis on the data through collection of the page access data is not dependent on hardware environment judgment and APP properties is achieved, the judgment strategy is efficient, and the use range is wide.
Drawings
FIG. 1 is a flow chart of an implementation of a method for detecting a brush amount behavior according to an embodiment of the present invention;
FIGS. 2 a-2 b are schematic diagrams of a discrete spectral transformation process according to an embodiment of the present invention;
FIGS. 3 a-3 b are schematic diagrams of a first set of data discretization and spectral transformation processes according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of an energy spectrum of an embodiment of the present invention;
FIGS. 5 a-5 b are schematic diagrams illustrating the behavior analysis effect of the second set of data according to an embodiment of the present invention;
FIG. 6 is a block diagram of a device for detecting a brushing behavior according to an embodiment of the present invention;
Detailed Description
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following description will be made with reference to the accompanying drawings. It is obvious that the drawings in the following description are only some examples of the invention, and that for a person skilled in the art, other drawings and embodiments can be derived from them without inventive effort.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention.
The embodiment of the invention provides a method for detecting a brushing amount behavior.
Fig. 1 is a flowchart illustrating an implementation of a method for detecting a brushing amount behavior according to an embodiment of the present invention, as shown in fig. 1, the method may include the following steps:
s1: acquiring data, specifically acquiring page access data of target APP in continuous time;
s2: behavior analysis, namely performing behavior analysis on the page access data;
s3: judging whether the brushing amount behavior exists or not, and judging whether the brushing amount behavior exists in the target APP or not according to a preset rule after the result of behavior analysis is obtained.
In step S1, page access data are packaged at regular time and stored in a database, specifically, entry time points and exit time points of an App interface are collected by a behavior collection agent method to obtain the stay time of a user on different pages and the data of the number of currently existing pages, and then the data are transmitted to a data collection database at regular time through network transmission.
For example, when entering page a, the collection time is T1, the current interface number is P1, when leaving page a, the collection time is T2, at this time, page a is not closed, and the current APP interface number is P2, when entering page B, the collection time is T3, the interface number is P3, the time of leaving page B is T4, the current page number is P4, and the collection is cycled. Data are represented as [ (T1, T2), P1], [ (T2, T3), P2], [ (T3, T4), P3], [ (T4, T5), P4], wherein such a data [ (T1, T2), P1] represents that during the time T1 to T2, the number of pages is P1, and a string of such temporally continuous data can be obtained by continuous acquisition.
The data is packaged at regular time and transmitted into a database through a network, so that the data in a continuous time range is recorded in the database.
The behavior analysis of step S2 specifically includes:
1) discretizing page access data and performing spectrum conversion;
the method in the embodiment is as follows: reading data from a database at time from Ti to Tj, sampling continuous data of the time into discrete data of (Ti, Pi) according to a sampling frequency Fs (which can be set to be 100ms-500ms), wherein Pi refers to an average of a maximum value and a minimum value, obtaining discrete data of N points after sampling, performing Fast Fourier Transform (FFT) on the obtained discrete data of N points to obtain FFT results of the N points, namely performing a spectrum conversion process to obtain a discrete spectrum, and the formula is as follows:
Y＝fft(P，N) (1)
where P is the number of pages and N is the number of samples, and after FTT, the resulting Y is the discrete spectrum.
As shown in fig. 2 a-2 b, the process of discrete spectral transformation is illustrated schematically, fig. 2a is a TIME/P function diagram, and fig. 2b is a frequency domain diagram after transformation.
As shown in fig. 3a to fig. 3b, an exemplary process of discrete and spectrum transformation of the first group of data in this embodiment is shown, for example, the data obtained from the database is sampled at 50Hz frequency in 0-40 seconds to obtain discrete data, (0.0, -0.5), (0.2, -0.5), (0.4,0.5), (0.6,0.5), (0.8, -0.5), (1.0, -0.5), (1.2, -0.5), (1.4,0.5), (1.6,0.5), (1.8, -0.5), (2.0, -0.5), …, (38.0, -0.5), (38.2, -0.5), (38.4, -0.5), (38.6,0.5), (38.8,0.5), (39.0, -0.5), (39.2, -0.5), (39.4,0.5), (39.6,0.5), (39.8, -0.84) after the spectrum transformation of the first group of data is shown in fig. 3a, and the frequency domain is shown in fig. 3b, namely, the function graph of 'TIME/P' is converted into a frequency domain graph of 'frequency/amplitude'.
2) High frequency components are identified.
And (3) identifying a high-frequency component in a frequency domain through a filter, wherein the high-frequency component in the frequency domain indicates that the faster the variation of P in the time domain is, the continuous frequent interface switching within a period of time when P is changed fast is possible to be a brush amount behavior, and then continuing to judge, if the high-frequency component exists in the frequency spectrum in fig. 3b, the situations of leakage and the like do not exist, so that the next analysis can be carried out, and if the high-frequency component does not exist in the frequency spectrum, the brushless behavior is directly judged.
3) Calculating an energy spectrum of the frequency spectrum to obtain an energy spectrogram;
the energy spectrum aims at a signal with limited energy, also called energy spectrum density, and refers to the distribution of signal energy at each frequency point by using the concept of density, i.e. the energy spectrum shows the energy of each frequency component, and the formula for calculating the energy spectrum is as follows:
Pyy＝Y.*conj(Y)/N (2)
where conj (Y) is the conjugate of complex number Y, which is the discrete spectrum data.
The first N/2+1 points of Pyy are plotted on the horizontal axis with frequency as the energy spectrum, which indicates the energy level of each frequency component, and it is possible to determine whether the brushing behavior is present or not by determining the energy level at high and low frequencies.
Calculating the SUM of energy values at the left end and the right end of the boundary by taking 0.5Hz as the boundary, calculating the SUM of energy values smaller than 0.5Hz, namely the SUM of energy values at the right side 1, calculating the SUM of energy values larger than 0.5Hz, namely the SUM of energy values at the left side SUM2, comparing the magnitude of the two values, and taking the magnitude comparison result as a behavior analysis result.
The preset rule in step S3 is:
when SUM1 is greater than or equal to SUM2, it indicates that the high frequency operation is more than the low frequency operation, and the high frequency operation is completed within 0.5Hz, it can be determined as the brushing behavior, otherwise it is determined as none.
As shown in FIG. 4, which is an energy spectrum of the data in FIG. 3a, it can be seen that, with 0.5Hz as a boundary, SUM1 is greater than or equal to SUM2, and most of the energy is on the right side of 0.5Hz, and the operation in this period is judged to belong to the brushing behavior.
As shown in fig. 5a to 5b, the behavior analysis effect of the second set of data is shown schematically, wherein the data is sampled at 50Hz and 0-80 seconds, and the data is as follows: (0.0, -1), (0.2, -1), (0.4,0), (0.6,0), (0.8,0), (1.0,0), (1.2,0), (1.4,0), (1.6,1), (1.8,1), (2.0,1), (2.2,1), (2.4,1), (2.6,1), (2.8,0), (3.0,0), (3.2, -1), (3.4, -1), (3.6, -1), (3.8, -1), (4.0, -1), (….6,1), (78.8,0), (79.0,0), (79.2, -1), (79.4, -1), (79.6, -1), (79.8, -1) and (…) 3578
Wherein, fig. 5a is a data effect graph, fig. 5b is an energy spectrogram, it can be seen that 0.5Hz is taken as a boundary, most of the energy is on the left side of 0.5Hz, and the behavior in this period does not belong to the brushing volume behavior according to the judgment of the preset rule.
A second embodiment of the present invention provides a device for detecting a brush amount behavior, as shown in fig. 6, which is a block diagram of a structure of the device for detecting a brush amount behavior according to the second embodiment of the present invention, and includes: the acquisition module is used for acquiring APP page access data of continuous time; the behavior analysis module is used for performing behavior analysis on the page access data; and the brushing amount result analysis module is used for obtaining the result of the behavior analysis and judging whether brushing amount behaviors exist or not according to a preset rule.
The behavior analysis module comprises an analysis submodule and a data collection submodule, the APP behavior collection agent module collects data and sends the data to the behavior data collection submodule, the behavior data collection submodule sends the received data to the database for storage, the analysis submodule carries out the method according to any one of the embodiments through reading continuous time data in the database to carry out the brushing behavior analysis, and then sends an analysis result to the brushing result analysis module to carry out result analysis.
In addition, the present invention also provides a control apparatus for detecting a brush amount behavior, including:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein,
the memory stores instructions executable by the at least one processor, and the instructions are executable by the at least one processor to enable the at least one processor to perform the method according to the first embodiment.
In addition, the present invention also provides a computer-readable storage medium, which stores computer-executable instructions for causing a computer to perform the method according to the first embodiment.
The method and the device have the advantages that the APP page access data of continuous time are collected, the behavior analysis is carried out on the page access data, the behavior analysis result is obtained, and whether the traffic brushing behavior exists is judged according to the preset rule, so that the problems that in the prior art, the reliability and the effect of the traffic brushing behavior are not high through judgment of hardware information, the judgment strategy is high in complexity and high in cost through a user grouping and grouping behavior judgment strategy are solved, the fact that the traffic brushing behavior is judged through the behavior analysis on the data through collection of the page access data is not dependent on hardware environment judgment and APP properties is achieved, the judgment strategy is efficient, and the use range is wide.
In the several embodiments provided in the present invention, it should be understood that the described apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one type of division of logical functions, and there may be other divisions when actually implemented, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted or not executed.
The above embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same, although the present invention is described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; such modifications and substitutions do not depart from the spirit and scope of the present invention, and they should be construed as being included in the following claims and description.
Claims (10)
1. A method of detecting a stroke behavior, comprising the steps of:
acquiring APP page access data of continuous time;
performing behavior analysis on the page access data;
and judging whether the brushing amount behavior exists according to a preset rule by obtaining the behavior analysis result.
2. The method for detecting a swiping volume behavior according to claim 1, wherein the collecting continuous-time APP page access data specifically comprises: and regularly packaging the page access data and storing the page access data into a database.
3. The method for detecting a brushing behavior according to claim 1, wherein the behavior analysis specifically comprises the steps of:
discretizing the page access data and carrying out spectrum conversion;
calculating an energy spectrum of the frequency spectrum to obtain an energy spectrogram;
and calculating the SUM of energy values at the left end and the right end of the boundary, namely SUM SUM1 of the right energy value and SUM SUM2 of the left energy value by taking 0.5Hz as the boundary, comparing the magnitudes of the energy values, and taking the magnitude comparison result as a behavior analysis result.
4. The method for detecting a brushing behavior according to claim 3, wherein the preset rule is: when SUM1 is greater than or equal to SUM2, the brush amount behavior is judged to be present, otherwise, the brush amount behavior is judged to be absent.
5. The method as claimed in claim 3, wherein the behavior analysis further comprises performing spectrum transformation, and then identifying high frequency components, if no high frequency components exist in the spectrum, directly determining that the behavior is a non-momentum behavior, otherwise, performing the next analysis.
6. The method of claim 1, wherein the page access data comprises: the dwell time of the page and the number of pages currently present.
7. An apparatus for detecting a brushstroke behavior, comprising:
the acquisition module is used for acquiring APP page access data of continuous time;
the behavior analysis module is used for performing behavior analysis on the page access data;
and the brushing amount result analysis module is used for obtaining the result of the behavior analysis and judging whether brushing amount behaviors exist or not according to a preset rule.
8. The apparatus for detecting a brushing behavior according to claim 7, further comprising a database, wherein the collection module is specifically an APP behavior collection agent module, the behavior analysis module comprises an analysis sub-module and a data collection sub-module, the APP behavior collection agent module collects data and sends the data to the behavior data collection sub-module, the behavior data collection sub-module sends the received data to the database for storage, and the analysis sub-module performs a brushing behavior analysis by reading continuous time data in the database according to any one of claims 1 to 6.
9. A control apparatus that detects a brushamount behavior, comprising:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1 to 6.
10. A computer-readable storage medium having stored thereon computer-executable instructions for causing a computer to perform the method of any one of claims 1 to 6.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112801650A (en) * | 2021-03-31 | 2021-05-14 | 南泽(广东)科技股份有限公司 | Machine service platform for consumption data processing |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6167490A (en) * | 1996-09-20 | 2000-12-26 | University Of Washington | Using global memory information to manage memory in a computer network |
| JP2006185049A (en) * | 2004-12-27 | 2006-07-13 | Kan:Kk | Access record notification device |
| CN106294508A (en) * | 2015-06-10 | 2017-01-04 | 深圳市腾讯计算机系统有限公司 | A kind of brush amount tool detection method and device |
| CN106650433A (en) * | 2016-12-15 | 2017-05-10 | 咪咕数字传媒有限公司 | Abnormal behavior detection method and system |
| CN107566897A (en) * | 2017-07-19 | 2018-01-09 | 北京奇艺世纪科技有限公司 | A kind of discrimination method, device and the electronic equipment of video brush amount |
| CN107578263A (en) * | 2017-07-21 | 2018-01-12 | 北京奇艺世纪科技有限公司 | A kind of detection method, device and the electronic equipment of advertisement abnormal access |
| WO2018077072A1 (en) * | 2016-10-24 | 2018-05-03 | 阿里巴巴集团控股有限公司 | Method and apparatus for detecting page redirection circulation |
| CN108647240A (en) * | 2018-04-04 | 2018-10-12 | 沈文策 | A kind of method, apparatus, electronic equipment and the storage medium of statistics visit capacity |
-
2018
- 2018-12-25 CN CN201811595071.5A patent/CN111368169B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6167490A (en) * | 1996-09-20 | 2000-12-26 | University Of Washington | Using global memory information to manage memory in a computer network |
| JP2006185049A (en) * | 2004-12-27 | 2006-07-13 | Kan:Kk | Access record notification device |
| CN106294508A (en) * | 2015-06-10 | 2017-01-04 | 深圳市腾讯计算机系统有限公司 | A kind of brush amount tool detection method and device |
| WO2018077072A1 (en) * | 2016-10-24 | 2018-05-03 | 阿里巴巴集团控股有限公司 | Method and apparatus for detecting page redirection circulation |
| CN106650433A (en) * | 2016-12-15 | 2017-05-10 | 咪咕数字传媒有限公司 | Abnormal behavior detection method and system |
| CN107566897A (en) * | 2017-07-19 | 2018-01-09 | 北京奇艺世纪科技有限公司 | A kind of discrimination method, device and the electronic equipment of video brush amount |
| CN107578263A (en) * | 2017-07-21 | 2018-01-12 | 北京奇艺世纪科技有限公司 | A kind of detection method, device and the electronic equipment of advertisement abnormal access |
| CN108647240A (en) * | 2018-04-04 | 2018-10-12 | 沈文策 | A kind of method, apparatus, electronic equipment and the storage medium of statistics visit capacity |
Non-Patent Citations (2)
| Title |
|---|
| 樊颖军;: "傅里叶变换下的直方图均衡化图像特征识别算法的研究" * |
| 樊颖军;: "傅里叶变换下的直方图均衡化图像特征识别算法的研究", 计算机与数字工程, no. 09, pages 168 - 172 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112801650A (en) * | 2021-03-31 | 2021-05-14 | 南泽(广东)科技股份有限公司 | Machine service platform for consumption data processing |
| CN112801650B (en) * | 2021-03-31 | 2021-07-02 | 南泽(广东)科技股份有限公司 | A machine service platform for consumer data processing |
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