CN111368169B - 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 57
- 238000000034 method Methods 0.000 title claims abstract description 34
- 230000006399 behavior Effects 0.000 claims description 101
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- 238000005070 sampling Methods 0.000 claims description 9
- 238000006243 chemical reaction Methods 0.000 claims description 8
- 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
- 101150112492 SUM-1 gene Proteins 0.000 claims description 6
- 101150096255 SUMO1 gene Proteins 0.000 claims description 6
- 238000013480 data collection Methods 0.000 claims description 6
- 230000000694 effects Effects 0.000 abstract description 7
- 238000010586 diagram Methods 0.000 description 14
- 230000003595 spectral effect Effects 0.000 description 5
- 230000009466 transformation Effects 0.000 description 5
- 230000008569 process Effects 0.000 description 4
- 230000009471 action Effects 0.000 description 2
- 230000008859 change Effects 0.000 description 2
- 238000000844 transformation Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000001419 dependent effect Effects 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
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- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 238000012856 packing Methods 0.000 description 1
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Abstract
The invention discloses a method for detecting a brushing amount behavior, which is characterized in that continuous time APP page access data are collected, behavior analysis is carried out on the page access data to obtain a behavior analysis result, whether brushing amount behavior exists or not is judged according to preset rules, the problems that in the prior art, the brushing amount behavior is not high in reliability and poor in effect through hardware information judgment, the complexity and the cost are high through a user grouping and grouping behavior judgment strategy are solved, the judgment of whether brushing amount behavior exists or not is realized through collecting page access data, the judgment strategy is efficient, and the application range is wide.
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 behavior.
Background
In the process of spreading APP in channels, many companies forge the behavior of user quantity and user access quantity through the brushing quantity to improve the flow of the APP, so that operators of the channels need to detect the cheating behavior of brushing quantity, whether the cheating behavior is normal or not is judged through the characteristics of hardware information (such as IMEI, MAC, positioning and the like) of mobile phones, and the brushing quantity of mobile phones is limited.
However, the reliability of judging the brushing amount behavior through the hardware information is not high, because the IMIE and the MAC information can be forged in a plurality of ways at present, the effect is not good through the simple hardware environment detection, and secondly, the user grouping and grouping behavior judging strategy is not wide in application range and high in complexity, grouping and judging strategies of different APP users are different, when the number of users is not large, grouping judgment cannot be supported by the good grouping, the judging strategy is very dependent on the user characteristics of the judging strategy, and the required cost is high.
Therefore, it is necessary to develop a brush method that does not depend on the hardware environment and the APP property and has a wide application range.
Disclosure of Invention
The present invention aims to solve at least one of the technical problems in the related art to some extent. Therefore, the invention aims to provide a brushing amount method, device, equipment and storage medium which are independent of hardware environment and APP properties and wide in application range.
The technical scheme adopted by the invention is as follows:
in a first aspect, the present invention provides a method of detecting brushing behavior, comprising the steps of:
collecting APP page access data in continuous time;
performing behavior analysis on the page access data;
and obtaining a result of the behavior analysis, and judging whether the brushing behavior exists according to a preset rule.
Further, the collecting APP page access data in continuous time specifically includes: the page access data is packed at regular intervals and saved in a database.
Further, the behavior analysis specifically includes the steps of:
discretizing the page access data and performing spectrum conversion;
calculating the energy spectrum of the frequency spectrum to obtain an energy spectrum;
and calculating the SUM of energy values at the left end and the right end of the boundary, namely SUM1 and SUM2 of the right energy value and the left energy value, by taking 0.5Hz as the boundary, comparing the sizes, and taking the size comparison result as a behavior analysis result.
Further, the preset rule is: when SUM1 is more than or equal to SUM2, the action of brushing is judged, otherwise, the action of brushing is judged to be absent.
Further, the behavior analysis further comprises the steps of identifying high-frequency components after frequency spectrum conversion, directly judging as brushless quantity behavior if the high-frequency components do not exist in the frequency spectrum, and otherwise, performing 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 brushing behavior, including:
the acquisition module is used for acquiring APP page access data in 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 according to a preset rule.
Further, the system further comprises a database, the acquisition module is specifically an APP behavior acquisition proxy module, the behavior analysis module comprises an analysis submodule and a data acquisition submodule, the APP behavior acquisition proxy module acquires data and sends the data to the behavior data acquisition submodule, the behavior data acquisition submodule sends the received data to the database for storage, and the analysis submodule performs the brush amount behavior analysis by reading continuous time data in the database and executing the method according to any one of claims 1 to 6.
In a third aspect, the present invention provides a control device for detecting brushing behavior, comprising:
at least one processor; the method comprises the steps of,
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 storing computer-executable instructions for causing a computer to perform the method of any one of the first aspects.
The beneficial effects of the invention are as follows:
according to the invention, through collecting continuous-time APP page access data and performing behavior analysis on the page access data, a behavior analysis result is obtained, and whether the brushing amount behavior exists or not is judged according to the preset rule, so that the problems of low reliability, poor effect, high complexity and high cost in judging the brushing amount behavior through hardware information, and the problems of high complexity and wide application range in judging the brushing amount behavior through user grouping and grouping behavior in the prior art are overcome, the judgment of whether the brushing amount behavior exists or not is realized without depending on hardware environment judgment and APP properties by collecting page access data, and the behavior analysis is performed on the data.
Drawings
FIG. 1 is a flow chart of an implementation of a method of detecting a brush volume behavior in accordance with one embodiment of the present invention;
FIGS. 2 a-2 b are schematic diagrams of discrete and spectral transformations in accordance with one embodiment of the present invention;
FIGS. 3 a-3 b are schematic diagrams of a first set of discrete and spectral transformations of data according to one embodiment of the present invention;
FIG. 4 is a schematic representation 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 a second set of data according to an embodiment of the present invention;
FIG. 6 is a block diagram of a device for detecting brush volume 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 explain the specific embodiments of the present invention with reference to the accompanying drawings. It is evident that the drawings in the following description are only examples of the invention, from which other drawings and other embodiments can be obtained by a person skilled in the art 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 herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention.
The first embodiment of the invention provides a method for detecting brushing behavior, wherein the existing brushing mode is to quickly enter an interface, quickly exit the interface after operation, and the residence time of the interface is not too long.
Fig. 1 is a flowchart of an implementation of a method for detecting a brushing behavior according to an embodiment of the present invention, where, as shown in fig. 1, the method may include the following steps:
s1: collecting data, in particular page access data of a target APP in continuous time;
s2: behavior analysis, namely performing behavior analysis on page access data;
s3: judging whether the brushing amount behaviors exist or not, and judging whether the brushing amount behaviors exist in the target APP according to a preset rule after a behavior analysis result is obtained.
The method comprises the steps of (1) packing page access data at fixed time in step (S1) and storing the data in a database, specifically, collecting an entering time point and an exiting time point of an App interface through a behavior collection agent method, obtaining the residence time of a user on different pages and the data of the number of the currently existing pages, and then transmitting the data into a data collection database through a network at fixed time.
For example, when entering page a, the acquisition time is T1, the current interface number is P1, when leaving page a, the acquisition time is T2, at this time page a is not closed, and the current APP interface number is P2, when entering page B, the acquisition time is T3, the interface number is P3, the time leaving page B is T4, and the current page number is P4, thus the acquisition is cyclically performed. The data are expressed as [ (T1, T2), P1], [ (T2, T3), P2], [ (T3, T4), P3], [ (T4, T5), P4], where such a data that [ (T1, T2), P1] represents the number of pages P1 during the time period T1 to T2, a series of such data that are continuous in time can be obtained by continuous acquisition.
The data is packed at regular time and transferred into a database via a network, so that data within a continuous time range is recorded in the database.
The behavior analysis in step S2 specifically includes:
1) Discretizing the page access data and performing spectrum conversion;
the practice in this embodiment is: reading data from a database at time Ti to Tj, sampling continuous data of the time into discrete data (Ti, pi) according to sampling frequency Fs (the range can be set to be 100ms-500 ms), wherein Pi refers to average of maximum value and minimum value, obtaining discrete data of N points after sampling, performing Fast Fourier Transform (FFT) on the obtained discrete N data to obtain FFT results of N points, namely performing a spectrum conversion process, and obtaining a discrete spectrum, wherein the formula is as follows:
Y＝fft(P，N) (1)
where P is the number of pages, N is the number of samples, and Y is the discrete spectrum after FTT.
Fig. 2a to 2b are schematic diagrams of discrete and spectral transformation processes, fig. 2a is a TIME/P function diagram, and fig. 2b is a transformed frequency domain diagram.
As shown in fig. 3 a-3 b, which are schematic diagrams of a process of the first set of discrete and spectral transformation of data in this embodiment, for example, data are obtained from a database, and the discrete data are obtained by sampling at 50Hz frequency and in the interval of 0-40 seconds, (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), (39.4,0.5), (39.6,0.5), (39.8, -0.5) … fig. 3a is a frequency domain diagram after the discrete and spectral transformation operation, and fig. 3b is a frequency domain diagram, i.e. the frequency domain diagram of "TIME/P" is obtained.
2) High frequency components are identified.
The high-frequency component in the frequency domain is identified through the filter, and as the high-frequency component in the frequency domain indicates that the change amount of P in the time domain is faster, the continuous frequent interface switching in a period of time with the fast change of P is possible to be the brushing amount behavior, the next step is continued to judge, if the high-frequency component exists in fig. 3b, the conditions such as leakage and the like are not existed, the next step analysis can be performed, and if the high-frequency component does not exist in the frequency spectrum, the brushless amount behavior is directly judged.
3) Calculating the energy spectrum of the frequency spectrum to obtain an energy spectrum;
the energy spectrum is used for signals with limited energy, which are also called energy spectrum density, and the distribution condition of the signal energy at each frequency point is represented by the concept of density, namely the energy spectrum represents 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, Y is the discrete spectrum data.
Taking the frequency as the horizontal axis, the first N/2+1 points of Pyy are taken to obtain an energy spectrum which shows the energy magnitude of each frequency component, and whether the brushing behavior is judged by judging the energy conditions at high frequency and low frequency.
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 SUM1 of energy values at the right end, calculating the SUM of energy values larger than 0.5Hz, namely the SUM SUM2 of energy values at the left end, comparing the magnitudes 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 larger than or equal to SUM2, the high-frequency operation is more than the low-frequency operation, and the high-frequency operation is completed within 0.5Hz, the brushing amount behavior can be judged, and otherwise, the brushing amount behavior is judged to be absent.
As shown in FIG. 4, which is an energy spectrum of the data in FIG. 3a, it can be seen that SUM 1. Gtoreq.SUM2 is demarcated by 0.5Hz, 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, a schematic diagram of the behavior analysis effect of the second set of data, wherein the second set of data is sampled at a frequency of 50Hz and in a range of 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), …, (78.6,1), (78.8,0), (79.0,0), (79.2, -1), (79.4, -1), (79.6, -1), (79.8, -1) …
Fig. 5a is a data effect diagram, and fig. 5b is an energy spectrum, and it can be seen that the energy spectrum is delimited by 0.5Hz, most of the energy is on the left side of 0.5Hz, and the behavior in the period of time is judged according to a preset rule not to belong to the brushing behavior.
A second embodiment of the present invention provides a device for detecting a brushing amount behavior, as shown in FIG. 6, which is a block diagram of a device for detecting a brushing amount behavior according to an embodiment of the present invention, including: the acquisition module is used for acquiring APP page access data in 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 according to a preset rule.
The system comprises a database, a collection module, an APP behavior collection agent module, an analysis module and a brushing result analysis module, wherein the collection module is specifically an APP behavior collection agent module, the APP behavior collection agent module collects data and sends the data to the behavior data collection module, the behavior data collection module sends the received data to the database for storage, and the analysis module performs brushing behavior analysis by reading continuous time data in the database, performs the method according to any one of the embodiments, and then sends the analysis result to the brushing result analysis module for result analysis.
In addition, the invention also provides a control device for detecting brushing behaviors, which comprises:
at least one processor; the method comprises the steps of,
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 as described in embodiment one.
In addition, the invention also provides a computer readable storage medium, wherein the computer readable storage medium stores computer executable instructions for causing a computer to execute the method according to the first embodiment.
According to the invention, through collecting continuous-time APP page access data and performing behavior analysis on the page access data, a behavior analysis result is obtained, and whether the brushing amount behavior exists or not is judged according to the preset rule, so that the problems of low reliability, poor effect, high complexity and high cost in judging the brushing amount behavior through hardware information, and the problems of high complexity and wide application range in judging the brushing amount behavior through user grouping and grouping behavior in the prior art are overcome, the judgment of whether the brushing amount behavior exists or not is realized without depending on hardware environment judgment and APP properties by collecting page access data, and the behavior analysis is performed on the data.
In the several embodiments provided by the present invention, it should be understood that the described apparatus and methods may be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the elements is merely a logical functional division, and there may be additional divisions when actually implemented, e.g., multiple elements or components may be combined or integrated into another system, or some features may or may not be omitted.
The above embodiments are only for illustrating the technical solution of the present invention, not for limiting the same, and although the present invention has been described in detail with reference to the above embodiments, it should be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the invention, and are intended to be included within the scope of the appended claims and description.
Claims (8)
1. A method of detecting a brushing behavior, comprising the steps of:
collecting APP page access data in continuous time, wherein the page access data comprises page stay time and the number of currently existing pages;
discretizing and performing spectrum conversion on the page access data, wherein the step of discretizing and performing spectrum conversion on the page access data comprises the following steps: reading data from the database at time Ti to Tj, sampling continuous data between time Ti to Tj according to sampling frequency to obtainDiscrete data, wherein the sampling frequency can be set to be 100ms-500ms, and each discrete data is subjected to fast Fourier transform to obtain a Fourier transform result of the discrete data; obtaining a discrete frequency spectrum according to the Fourier transform results of all the discrete data, wherein a fast Fourier transform formula is as follows: />Where P is the number of pages, N is the number of samples, and Y is the discrete spectrum;
identifying high-frequency components in the discrete frequency spectrum based on a filter, and directly judging as brushless quantity behavior if the high-frequency components do not exist in the discrete frequency spectrum;
if the discrete frequency spectrum has high-frequency components, calculating an energy spectrum of the discrete frequency spectrum to obtain an energy spectrum, wherein the energy spectrum is used for signals with limited energy, which are also called energy spectrum density, and the concept of density is used for representing the distribution condition of signal energy at each frequency point, namely the energy spectrum represents the energy of each frequency component;
wherein the calculating the energy spectrum of the discrete spectrum, the obtaining the energy spectrum comprises:
calculating the energy spectrum, wherein the formula for calculating the energy spectrum is as followsWherein Pyy is the energy spectrum,is the conjugate of complex number Y, Y being discrete spectrum data;
taking the frequency as a horizontal axis, and taking the first N/2+1 points of Pyy to obtain the energy spectrogram;
calculating the SUM of energy values at the left end and the right end of the demarcation, namely SUM1 of the SUM of energy values at the right end and SUM2 of the SUM of energy values at the left end, comparing the magnitudes of the energy spectrograms by taking 0.5Hz as the demarcation, and taking the magnitude comparison result as a behavior analysis result;
judging whether the brushing amount behavior exists according to a preset rule and the behavior analysis result.
2. The method for detecting brushing behavior according to claim 1, wherein the collecting APP page access data for a continuous time is specifically: the page access data is packed at regular intervals and saved in a database.
3. The method for detecting brushing behavior according to claim 1, wherein the preset rule is: when (when)If so, the brush amount behavior is judged, otherwise, the brush amount behavior is judged to be none.
4. The method of claim 1, wherein the page access data comprises: the dwell time of the page and the number of pages currently present.
5. An apparatus for detecting brushing behavior, comprising:
the acquisition module is used for acquiring APP page access data in continuous time, wherein the page access data comprises page stay time and the number of currently existing pages;
the behavior analysis module is used for discretizing and performing spectrum conversion on the page access data, and the step of discretizing and performing spectrum conversion on the page access data comprises the following steps: reading from the database at times Ti to TjContinuous data between time Ti and Tj is sampled according to sampling frequency to obtainDiscrete data, wherein the sampling frequency can be set to be 100ms-500ms, and each discrete data is subjected to fast Fourier transform to obtain a Fourier transform result of the discrete data; obtaining a discrete frequency spectrum according to the Fourier transform results of all the discrete data, wherein a fast Fourier transform formula is as follows: />Where P is the number of pages, N is the number of samples, and Y is the discrete spectrum;
identifying high-frequency components in the discrete frequency spectrum based on a filter, and directly judging as brushless quantity behavior if the high-frequency components do not exist in the discrete frequency spectrum;
if the discrete frequency spectrum has high-frequency components, calculating an energy spectrum of the discrete frequency spectrum to obtain an energy spectrum, wherein the energy spectrum is used for signals with limited energy, which are also called energy spectrum density, and the concept of density is used for representing the distribution condition of signal energy at each frequency point, namely the energy spectrum represents the energy of each frequency component;
wherein the calculating the energy spectrum of the discrete spectrum, the obtaining the energy spectrum comprises:
calculating the energy spectrum, wherein the formula for calculating the energy spectrum is as followsN, wherein Pyy is the energy spectrum,is the conjugate of complex number Y, Y being discrete spectrum data;
taking the frequency as a horizontal axis, and taking the first N/2+1 points of Pyy to obtain the energy spectrogram;
calculating the SUM of energy values at the left end and the right end of the demarcation, namely SUM1 of the SUM of energy values at the right end and SUM2 of the SUM of energy values at the left end, comparing the magnitudes of the energy spectrograms by taking 0.5Hz as the demarcation, and taking the magnitude comparison result as a behavior analysis result;
and the brushing amount result analysis module is used for judging whether brushing amount behaviors exist according to preset rules and the behavior analysis result.
6. The device for detecting a brushing behavior according to claim 5, further comprising a database, wherein the collection module is specifically an APP behavior collection agent module, 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 to be stored, and the analysis submodule performs brushing behavior analysis by reading continuous time data in the database and executing the method according to any one of claims 1 to 4.
7. A control device that detects a brushing behavior, characterized by comprising:
at least one processor; the method comprises the steps of,
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 4.
8. A computer-readable storage medium storing computer-executable instructions for causing a computer to perform the method of any one of claims 1 to 4.
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