CN111047433A - Method and device for analyzing reasons of user number abnormality, server and storage medium - Google Patents
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Abstract
The embodiment of the invention provides a method and a device for analyzing a reason of user number abnormity, a server and a storage medium. The method for analyzing the reason for the abnormal user number comprises the following steps: acquiring data to be analyzed, wherein the data to be analyzed comprises the number of users completing a service, at least one timestamp and a statistical result of at least one process corresponding to the timestamp; performing first judgment on the data to be analyzed based on a first preset algorithm to obtain a first abnormal result of abnormal user number, wherein the first abnormal result comprises a first timestamp and at least one first process corresponding to the first timestamp; and carrying out second judgment on the first abnormal result to obtain a second abnormal result of the abnormal user number. The effect of improving the efficiency of analyzing the abnormal reasons is achieved.
Description
Technical Field
The embodiment of the invention relates to the technical field of computers, in particular to a method and a device for analyzing reasons of user number abnormity, a server and a storage medium.
Background
In a certain period of time, the related department can analyze whether the number of users completing the service is abnormal, for example, whether the number of users entering the loan products is abnormal. If the number of users is abnormal, the user needs to be analyzed as an abnormal cause of the user number abnormality, such as the position of the flow where the abnormality occurs. Currently, in the industry, real-time line charts are mostly used for monitoring the number of users who submit loan products in unit time, and analysts observe line charts of the users and manually judge whether the users are abnormal or not based on business experience.
However, the subjectivity is too strong by means of manual identification, and the definition of the anomaly by different data analysts is inconsistent. In addition, a link is needed for checking after problems occur, the period is long, and the reaction speed is slow.
Disclosure of Invention
The embodiment of the invention provides a method and a device for analyzing a reason of abnormal user number, a server and a storage medium, so as to achieve the effect of improving the efficiency of analyzing the reason of abnormal user number.
In a first aspect, an embodiment of the present invention provides a method for analyzing a reason for a user number abnormality, where the method includes:
acquiring data to be analyzed, wherein the data to be analyzed comprises the number of users completing a service, at least one timestamp and a statistical result of at least one process corresponding to the timestamp;
performing first judgment on the data to be analyzed based on a first preset algorithm to obtain a first abnormal result of abnormal user number, wherein the first abnormal result comprises a first timestamp and at least one first process corresponding to the first timestamp;
and carrying out second judgment on the first abnormal result to obtain a second abnormal result of the abnormal user number.
Optionally, the second determining the first abnormal result to obtain a second abnormal result with an abnormal user number includes:
calculating a first difference value of a first statistical result of the first flow of the first timestamp and a second statistical result of the first flow of a second timestamp, wherein the second timestamp is adjacent to the first timestamp;
judging the first difference value based on a second preset algorithm;
and taking a first timestamp of which the first difference is judged to be abnormal by the second preset algorithm and a first flow corresponding to the first timestamp as a second abnormal result.
Optionally, the second determining the first abnormal result to obtain a second abnormal result with an abnormal user number includes:
calculating a statistical average value of third statistical results of the first process of a plurality of third timestamps, wherein the plurality of third timestamps are adjacent in sequence;
calculating a second difference value between a first statistical result of the first flow of the first timestamp and the statistical average value;
judging the second difference value based on a second preset algorithm;
and taking the first timestamp of which the second difference value is judged to be abnormal by the second preset algorithm and the first flow corresponding to the first timestamp as the second abnormal result.
Optionally, the second determining the first abnormal result to obtain a second abnormal result with an abnormal user number includes:
obtaining a fourth statistical result of the first process of at least one fourth timestamp, wherein the fourth timestamp is before the first timestamp;
judging whether a first statistical result of a first process of the first timestamp is greater than a fourth statistical result;
and taking a first time stamp of which the first statistical result is greater than the fourth statistical result and a first flow corresponding to the first time stamp as the second abnormal result.
Optionally, after the second determining is performed on the first abnormal result to obtain a second abnormal result with an abnormal user number, the method includes:
and displaying the second abnormal result to a user in a visual form.
Optionally, the first determination of the data to be analyzed based on the first preset algorithm is performed to obtain a first abnormal result of the abnormal user number, and the method includes:
and performing first judgment on the statistical result of each flow based on the first preset algorithm to obtain a first abnormal result of abnormal user number.
Optionally, before the acquiring the data to be analyzed, the method includes:
and storing the data to be analyzed in a preset format.
In a second aspect, an embodiment of the present invention provides an apparatus for analyzing a reason for a user number abnormality, including:
the system comprises an acquisition module, a processing module and a processing module, wherein the acquisition module is used for acquiring data to be analyzed, and the data to be analyzed comprises the number of users completing a service, at least one time stamp and a statistical result of at least one process corresponding to the time stamp;
the first judging module is used for carrying out first judgment on the data to be analyzed based on a first preset algorithm to obtain a first abnormal result of abnormal user number, wherein the first abnormal result comprises a first time stamp and at least one first flow corresponding to the first time stamp;
and the second judgment module is used for performing second judgment on the first abnormal result to obtain a second abnormal result of the abnormal user number.
In a third aspect, an embodiment of the present invention provides a server, including:
one or more processors;
a storage device for storing one or more programs,
when the one or more programs are executed by the one or more processors, the one or more processors implement the method for analyzing the reason for the user number abnormality according to any embodiment of the present invention.
In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements a method for analyzing a cause of a user number abnormality according to any embodiment of the present invention.
The embodiment of the invention obtains the data to be analyzed, wherein the data to be analyzed comprises the number of users completing the service, at least one timestamp and a statistical result of at least one process corresponding to the timestamp; performing first judgment on the data to be analyzed based on a first preset algorithm to obtain a first abnormal result of abnormal user number, wherein the first abnormal result comprises a first timestamp and at least one first process corresponding to the first timestamp; and performing second judgment on the first abnormal result to obtain a second abnormal result of the abnormal user number, so that the problems of long period and low reaction speed caused by a manual identification mode are solved, and the effect of improving the efficiency of analyzing the abnormal reason is realized.
Drawings
Fig. 1 is a schematic flow chart of a method for analyzing a reason for an abnormal user number according to an embodiment of the present invention;
fig. 2 is a schematic flow chart of a method for analyzing a reason for a user number abnormality according to a second embodiment of the present invention;
fig. 3 is a schematic structural diagram of an analysis apparatus for a reason of a user number abnormality according to a third embodiment of the present invention;
fig. 4 is a schematic structural diagram of a server according to a fourth embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be further noted that, for the convenience of description, only some of the structures related to the present invention are shown in the drawings, not all of the structures.
Before discussing exemplary embodiments in more detail, it should be noted that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although a flowchart may describe the steps as a sequential process, many of the steps can be performed in parallel, concurrently or simultaneously. In addition, the order of the steps may be rearranged. A process may be terminated when its operations are completed, but may have additional steps not included in the figure. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc.
Furthermore, the terms "first," "second," and the like may be used herein to describe various orientations, actions, steps, elements, or the like, but the orientations, actions, steps, or elements are not limited by these terms. These terms are only used to distinguish one direction, action, step or element from another direction, action, step or element. For example, the first timestamp may be referred to as a second timestamp, and similarly, the second timestamp may be referred to as the first timestamp, without departing from the scope of the present application. The first timestamp and the second timestamp are both timestamps, but they are not the same timestamp. The terms "first", "second", etc. are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of the present invention, "a plurality" means at least two, e.g., two, three, etc., unless specifically limited otherwise.
Example one
Fig. 1 is a schematic flow chart of a method for analyzing a user number abnormal cause according to an embodiment of the present invention, which is applicable to a scenario of analyzing an abnormal cause of a user number abnormal for completing a service.
As shown in fig. 1, a method for analyzing a reason for an abnormal user number according to an embodiment of the present invention includes:
s110, obtaining data to be analyzed, wherein the data to be analyzed comprises the number of users completing the service, at least one time stamp and a statistical result of at least one process corresponding to the time stamp.
The data to be analyzed refers to data to be analyzed. The number of users completing a service refers to the number of users completing a whole service flow. In this embodiment, the number of users completing a service refers to the number of users completing incoming documents. The step of entering the piece means that the user completes the whole loan application process and submits the loan application. The time stamp refers to a tag for embodying the time of the data to be analyzed. Specifically, each day may be used as a timestamp; or one time stamp can be taken every half day; it is also possible to have a timestamp of one hour, without limitation. The flow refers to each step of completing the whole service. Taking loan as an example, the user needs to go through the following steps: the user opens APP (application); the user enters a loan page; a user starts to enter a piece; the user completes the filling process. When the user finishes the last flow, the work entering is finished; at least one process in this embodiment may include one or more of the above-described steps. The statistical result is a result of statistics for each flow. Specifically, the statistical result may be a specific value, or may be a conversion rate, which is not limited herein and may be set as required. Conversion rate refers to the percentage of users remaining upstream to downstream of a business process. Preferably, the statistical result may be that the first process is numerical and the statistical results of the remaining downstream processes are conversion. For example, when the flow is that the user opens the APP, the statistical result may be the total number of people who open the APP; when the flow is that the user enters the loan page, the statistical result can be the proportion of the user entering the loan page. A schematic of the data to be analyzed can be found in table 1 below:
TABLE 1
Specifically, the number of users completing the service is the total number of people the user opens the APP: (user entering loan page ratio (%) and the user starting part entering ratio (%)) and the user completing filling process (%).
S120, performing first judgment on the data to be analyzed based on a first preset algorithm to obtain a first abnormal result of abnormal user number, wherein the first abnormal result comprises a first time stamp and at least one first process corresponding to the first time stamp.
The first preset algorithm is an algorithm for performing first judgment on data to be analyzed. In this embodiment, the first predetermined algorithm may be a time series data algorithm, such as an S-ESD (Seasonal ESD) algorithm or an S-H-ESD (Seasonal Hybrid ESD) algorithm, which is not limited herein. Preferably, the first preset algorithm in the present embodiment is an S-H-ESD algorithm. In this embodiment, the first determination refers to an initial determination of the data to be analyzed using a first preset algorithm. Specifically, after the data to be analyzed is input into the first preset algorithm, a first abnormal result is output. The first abnormal result refers to a timestamp corresponding to the user number abnormality in the data to be analyzed and a result of the first flow corresponding to the timestamp. The first timestamp is a time corresponding to the user number abnormality. The first flow corresponding to the first timestamp is a flow with an exception. Illustratively, the first preset algorithm determines that the number of users is abnormal in 2011, 11/11, and the first flow of the abnormality is that the users enter a loan page.
In an optional implementation manner, the performing a first determination on the data to be analyzed based on a first preset algorithm to obtain a first abnormal result of the abnormal user number may include:
and performing first judgment on the statistical result of each flow based on the first preset algorithm to obtain a first abnormal result of abnormal user number.
In the present embodiment, the statistical result of each flow in the data to be analyzed is subjected to the first determination, so as to obtain the first abnormal result.
S130, performing second judgment on the first abnormal result to obtain a second abnormal result of the abnormal user number.
The second determination is a determination of the first abnormal result. In the present embodiment, the second determination may be regarded as a re-determination of the data to be analyzed. The second abnormal result is an abnormal result obtained by the second determination. In the present embodiment, the second abnormal result refers to the final determination result. Specifically, the first determination is a rough determination, and there may be a case of erroneous determination. By performing the second determination on the first abnormal result, the second abnormal result obtained by the determination is more accurate.
In an optional implementation manner, the second determining the first abnormal result to obtain a second abnormal result with an abnormal user number may include:
calculating a first difference value of a first statistical result of the first flow of the first timestamp and a second statistical result of the first flow of a second timestamp, wherein the second timestamp is adjacent to the first timestamp;
judging the first difference value based on a second preset algorithm;
and taking a first timestamp of which the first difference is judged to be abnormal by the second preset algorithm and a first flow corresponding to the first timestamp as a second abnormal result.
The first statistical result refers to a statistical result of the first flow corresponding to the first timestamp. The second statistical result refers to the statistical result of the first flow corresponding to the second timestamp. Illustratively, if the first flow corresponding to the first timestamp is the user entering a loan page, then the first flow corresponding to the second timestamp is also the user entering the loan page. The second timestamp is adjacent to the first timestamp, meaning that the second timestamp is a time before or after the first timestamp. Illustratively, the first timestamp is 1/2/2019, and the second timestamp is 1/2019 or 1/3/2019. Preferably, the second timestamp precedes the first timestamp. The second preset algorithm is an algorithm for determining the first difference. In this embodiment, the second preset algorithm may be an S-ESD algorithm or an S-H-ESD algorithm, which is not limited herein. Preferably, the second preset algorithm is an S-H-ESD algorithm. Specifically, a first timestamp of which x (t) -x (t-1) is determined to be abnormal by the S-H-ESD and a first process corresponding to the first timestamp are taken as a second abnormal result. Where x (t) is a first statistical result of the first flow at a first time stamp, and x (t-1) is a second statistical result at a second time stamp prior to the first time stamp. Illustratively, there are A, B, C, D four processes, and the first abnormal result after the first determination is that the B, C and D processes of 1 month and 1 day in 2019 are abnormal, a first difference is calculated for each of B, C and D processes, and a second determination is performed by a second preset algorithm, so as to output a second abnormal result of the D process of 1 month and 1 day in 2019. I.e. the second exception result includes the specific time of the exception and the corresponding flow.
In another optional implementation, the second determining the first abnormal result to obtain a second abnormal result with an abnormal user number may include:
calculating a statistical average value of third statistical results of the first process of a plurality of third timestamps, wherein the plurality of third timestamps are adjacent in sequence;
calculating a second difference value between a first statistical result of the first flow of the first timestamp and the statistical average value;
judging the second difference value based on a second preset algorithm;
and taking the first timestamp of which the second difference value is judged to be abnormal by the second preset algorithm and the first flow corresponding to the first timestamp as the second abnormal result.
And the third statistical result refers to the statistical result of the first flow corresponding to the third timestamp. Specifically, the plurality of third time stamps are adjacent in sequence. Optionally, one of the plurality of third timestamps is adjacent to the first timestamp. The statistical average is an average of the third statistical results of the first flow corresponding to each third timestamp. Illustratively, the first timestamp is t, and the plurality of third timestamps may be t-1, t-2, t-3, and t-4. The second difference is x (t) -avg { x (t-1), x (t-2), x (t-3), x (t-4) }. Wherein, x (t) is the first statistical result, and avg { x (t-1), x (t-2), x (t-3), x (t-4) } is the statistical average value.
In another optional implementation, the second determining the first abnormal result to obtain a second abnormal result with an abnormal user number may include:
obtaining a fourth statistical result of the first process of at least one fourth timestamp, wherein the fourth timestamp is before the first timestamp;
judging whether a first statistical result of a first process of the first timestamp is greater than a fourth statistical result;
and taking a first time stamp of which the first statistical result is greater than the fourth statistical result and a first flow corresponding to the first time stamp as the second abnormal result.
Wherein the fourth timestamp refers to a timestamp that precedes the first timestamp. Optionally, one of the at least one fourth timestamp is adjacent to the first timestamp. Specifically, if there is only one fourth timestamp, the fourth statistical result is the statistical result of the first process corresponding to the fourth timestamp; and if the number of the fourth timestamps is multiple, the fourth statistical result is the maximum value of the statistical results corresponding to the multiple fourth timestamps. Illustratively, the fourth plurality of timestamps is t-1, t-2, t-3, and t-4, and the fourth statistic is max { x (t-1), x (t-2), x (t-3), x (t-4) }.
In another alternative embodiment, the second abnormal result may be determined when one or more conditions of the first difference being determined as abnormal by the second preset algorithm, the second difference being determined as abnormal by the second preset algorithm, and the first statistical result being greater than the fourth statistical result are simultaneously satisfied.
According to the technical scheme of the embodiment of the invention, data to be analyzed is obtained, wherein the data to be analyzed comprises the number of users completing a service, at least one timestamp and a statistical result of at least one process corresponding to the timestamp; performing first judgment on the data to be analyzed based on a first preset algorithm to obtain a first abnormal result of abnormal user number, wherein the first abnormal result comprises a first timestamp and at least one first process corresponding to the first timestamp; and performing second judgment on the first abnormal result to obtain a second abnormal result with abnormal user number, outputting a second abnormal result after inputting the data to be analyzed without manually identifying the abnormal flow, and achieving the technical effect of improving the efficiency of analyzing the abnormal reason. In addition, the technical scheme of the embodiment is simple and easy to use, and the analysis of the abnormal reasons can be realized without setting a threshold value and a quantile. And the abnormal result is directly analyzed, so that the corresponding abnormal process can be positioned, and the strategy can be pertinently adjusted.
Example two
Fig. 2 is a schematic flow chart of a method for analyzing a reason for a user number abnormality according to a second embodiment of the present invention. The embodiment is further detailed in the technical scheme, and is suitable for a scene of analyzing the abnormal reason of the abnormal user number completing the service. The method can be executed by an analysis device for the reason of the abnormal user number, and the device can be realized in a software and/or hardware mode and can be integrated on a server.
As shown in fig. 2, the method for analyzing the reason for the abnormality of the user number according to the second embodiment of the present invention includes:
s210, storing the data to be analyzed in a preset format.
The preset format is a preset format. Specifically, the preset format may be a format of a preset relationship table, and is not limited herein. And storing the data to be analyzed in a preset format so as to more conveniently extract the time stamp and the corresponding flow.
S220, obtaining data to be analyzed, wherein the data to be analyzed comprises the number of users completing the service, at least one time stamp and a statistical result of at least one process corresponding to the time stamp.
The data to be analyzed refers to data to be analyzed. The number of users completing a service refers to the number of users completing a whole service flow. In this embodiment, the number of users completing a service refers to the number of users completing incoming documents. The step of entering the piece means that the user completes the whole loan application process and submits the loan application. The time stamp refers to a tag for embodying the time of the data to be analyzed. Specifically, each day may be used as a timestamp; or one time stamp can be taken every half day; it is also possible to have a timestamp of one hour, without limitation. The flow refers to each step of completing the whole service. Taking a loan as an example, the user needs to go through the following steps to complete the work in: the user opens APP (application); the user enters a loan page; a user starts to enter a piece; the user completes the filling process. When the user finishes the last flow, the work entering is finished; at least one process in this embodiment may include one or more of the above-described steps. The statistical result is a result of statistics for each flow. Specifically, the statistical result may be a specific value, or may be a conversion rate, which is not limited herein and may be set as required. Conversion rate refers to the percentage of users remaining upstream to downstream of a business process. Preferably, the statistical result may be that the first process is numerical and the statistical results of the remaining downstream processes are conversion. For example, when the flow is that the user opens the APP, the statistical result may be the total number of people who open the APP; when the flow is that the user enters the loan page, the statistical result can be the proportion of the user entering the loan page.
S230, performing first judgment on the data to be analyzed based on a first preset algorithm to obtain a first abnormal result of abnormal user number, wherein the first abnormal result comprises a first time stamp and at least one first process corresponding to the first time stamp.
The first preset algorithm is an algorithm for performing first judgment on data to be analyzed. In this embodiment, the first predetermined algorithm may be a time series data algorithm, such as an S-ESD (Seasonal ESD) algorithm or an S-H-ESD (Seasonal Hybrid ESD) algorithm, which is not limited herein. Preferably, the first preset algorithm in the present embodiment is an S-H-ESD algorithm. In this embodiment, the first determination refers to an initial determination of the data to be analyzed using a first preset algorithm. Specifically, after the data to be analyzed is input into the first preset algorithm, a first abnormal result is output. The first abnormal result refers to a timestamp corresponding to the user number abnormality in the data to be analyzed and a result of the first flow corresponding to the timestamp. The first timestamp is a time corresponding to the user number abnormality. The first flow corresponding to the first timestamp is a flow with an exception. Illustratively, the first preset algorithm determines that the number of users in 11/2011 is abnormal, and the abnormal flow is that the users enter a loan page.
S240, second judgment is carried out on the first abnormal result, and a second abnormal result of abnormal user number is obtained.
The second determination is a determination of the first abnormal result. In the present embodiment, the second determination may be regarded as a re-determination of the data to be analyzed. The second abnormal result is an abnormal result obtained by the second determination. In the present embodiment, the second abnormal result refers to the final determination result. Specifically, the first determination is a rough determination, and there may be a case of erroneous determination. By performing the second determination on the first abnormal result, the second abnormal result obtained by the determination is more accurate.
And S250, displaying the second abnormal result to a user in a visual mode.
In this embodiment, the visualization may alternatively be in the form of a line graph that includes the second anomaly result. Specifically, the timestamp corresponding to the user number abnormality and the abnormal flow corresponding to the user number abnormality can be visually determined through the line graph.
According to the technical scheme of the embodiment of the invention, data to be analyzed is obtained, wherein the data to be analyzed comprises the number of users completing a service, at least one timestamp and a statistical result of at least one process corresponding to the timestamp; performing first judgment on the data to be analyzed based on a first preset algorithm to obtain a first abnormal result of abnormal user number, wherein the first abnormal result comprises a first timestamp and at least one first process corresponding to the first timestamp; and performing second judgment on the first abnormal result to obtain a second abnormal result with abnormal user number, outputting a second abnormal result after inputting the data to be analyzed without manually identifying the abnormal flow, and achieving the technical effect of improving the efficiency of analyzing the abnormal reason.
EXAMPLE III
Fig. 3 is a schematic structural diagram of an apparatus for analyzing a user number abnormal cause according to a third embodiment of the present invention, where this embodiment is applicable to a scenario of analyzing an abnormal cause of a user number abnormal that completes a service, and the apparatus may be implemented in a software and/or hardware manner and may be integrated on a server.
As shown in fig. 3, the apparatus for analyzing the reason for the user number abnormality according to this embodiment may include an obtaining module 310, a first determining module 320, and a second determining module 330, where:
an obtaining module 310, configured to obtain data to be analyzed, where the data to be analyzed includes a number of users who complete a service, at least one timestamp, and a statistical result of at least one process corresponding to the timestamp;
a first determining module 320, configured to perform a first determination on the data to be analyzed based on a first preset algorithm to obtain a first abnormal result of the abnormal user number, where the first abnormal result includes a first timestamp and at least one first process corresponding to the first timestamp;
the second determining module 330 is configured to perform a second determination on the first abnormal result to obtain a second abnormal result indicating that the number of users is abnormal.
Optionally, the second determining module 330 includes:
a first difference calculation unit, configured to calculate a first difference between a first statistical result of the first flow of the first timestamp and a second statistical result of the first flow of a second timestamp, where the second timestamp is adjacent to the first timestamp;
a first difference determination unit configured to determine the first difference based on a second preset algorithm;
a second abnormal result determining unit, configured to use a first timestamp of the first difference determined to be abnormal by the second preset algorithm and a first procedure corresponding to the first timestamp as the second abnormal result.
Optionally, the second determining module 330 further includes:
the statistical average calculating unit is used for calculating the statistical average of the third statistical results of the first process of a plurality of third timestamps, and the plurality of third timestamps are adjacent in sequence;
a second difference calculation unit, configured to calculate a second difference between a first statistical result of the first flow of the first timestamp and the statistical average;
a second difference determination unit configured to determine the second difference based on a second preset algorithm;
the second abnormal result determining unit is further configured to use a first timestamp of the second difference determined to be abnormal by the second preset algorithm and a first procedure corresponding to the first timestamp as the second abnormal result.
Optionally, the second determining module 330 further includes:
a fourth statistical result obtaining unit, configured to obtain a fourth statistical result of the first flow of at least one fourth timestamp, where the fourth timestamp is before the first timestamp;
the judging unit is used for judging whether a first statistical result of the first flow of the first timestamp is greater than a fourth statistical result or not;
the second abnormal result determination unit is further configured to use a first timestamp of the first statistical result being greater than the fourth statistical result and a first flow corresponding to the first timestamp as the second abnormal result.
Optionally, the apparatus further comprises:
and the display module is used for displaying the second abnormal result to a user in a visual mode.
Optionally, the first determining module 320 is specifically configured to perform a first determination on the statistical result of each process based on the first preset algorithm, so as to obtain a first abnormal result of the abnormal user number.
Optionally, the apparatus further comprises:
and the storage module is used for storing the data to be analyzed in a preset format.
The analysis device for the reason of the abnormal user number, provided by the embodiment of the invention, can execute the analysis method for the reason of the abnormal user number, provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method. Reference may be made to the description of any method embodiment of the invention not specifically described in this embodiment.
Example four
Fig. 4 is a schematic structural diagram of a server according to a fourth embodiment of the present invention. FIG. 4 illustrates a block diagram of an exemplary server 612 suitable for use in implementing embodiments of the present invention. The server 612 shown in fig. 4 is only an example, and should not bring any limitation to the function and the scope of the use of the embodiments of the present invention.
As shown in fig. 4, the server 612 is in the form of a general-purpose server. The components of server 612 may include, but are not limited to: one or more processors 616, a memory device 628, and a bus 618 that couples the various system components including the memory device 628 and the processors 616.
The server 612 typically includes a variety of computer system readable media. Such media can be any available media that is accessible by server 612 and includes both volatile and nonvolatile media, removable and non-removable media.
A program/utility 640 having a set (at least one) of program modules 642 may be stored, for example, in storage 628, such program modules 642 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which examples or some combination thereof may comprise an implementation of a network environment. The program modules 642 generally perform the functions and/or methods of the described embodiments of the present invention.
The server 612 may also communicate with one or more external devices 614 (e.g., keyboard, pointing terminal, display 624, etc.), with one or more terminals that enable a user to interact with the server 612, and/or with any terminals (e.g., network card, modem, etc.) that enable the server 612 to communicate with one or more other computing terminals. Such communication may occur via input/output (I/O) interfaces 622. Further, server 612 may communicate with one or more networks (e.g., a Local Area Network (LAN), Wide Area Network (WAN), and/or a public Network such as the internet) via Network adapter 620. As shown in FIG. 4, the network adapter 620 communicates with the other modules of the server 612 via the bus 618. It should be appreciated that although not shown, other hardware and/or software modules may be used in conjunction with the server 612, including but not limited to: microcode, end drives, Redundant processors, external disk drive Arrays, RAID (Redundant Arrays of Independent Disks) systems, tape drives, and data backup storage systems, among others.
The processor 616 executes various functional applications and data processing by running programs stored in the storage device 628, for example, implementing a method for analyzing the reason for the user number abnormality according to any embodiment of the present invention, where the method includes:
acquiring data to be analyzed, wherein the data to be analyzed comprises the number of users completing a service, at least one timestamp and a statistical result of at least one process corresponding to the timestamp;
performing first judgment on the data to be analyzed based on a first preset algorithm to obtain a first abnormal result of abnormal user number, wherein the first abnormal result comprises a first timestamp and at least one first process corresponding to the first timestamp;
and carrying out second judgment on the first abnormal result to obtain a second abnormal result of the abnormal user number.
According to the technical scheme of the embodiment of the invention, data to be analyzed is obtained, wherein the data to be analyzed comprises the number of users completing a service, at least one timestamp and a statistical result of at least one process corresponding to the timestamp; performing first judgment on the data to be analyzed based on a first preset algorithm to obtain a first abnormal result of abnormal user number, wherein the first abnormal result comprises a first timestamp and at least one first process corresponding to the first timestamp; and performing second judgment on the first abnormal result to obtain a second abnormal result with abnormal user number, outputting a second abnormal result after inputting the data to be analyzed without manually identifying the abnormal flow, and achieving the technical effect of improving the efficiency of analyzing the abnormal reason.
EXAMPLE five
An embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements a method for analyzing a user number abnormality cause, where the method includes:
acquiring data to be analyzed, wherein the data to be analyzed comprises the number of users completing a service, at least one timestamp and a statistical result of at least one process corresponding to the timestamp;
performing first judgment on the data to be analyzed based on a first preset algorithm to obtain a first abnormal result of abnormal user number, wherein the first abnormal result comprises a first timestamp and at least one first process corresponding to the first timestamp;
and carrying out second judgment on the first abnormal result to obtain a second abnormal result of the abnormal user number.
The computer-readable storage media of embodiments of the invention may take any combination of one or more computer-readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a storage medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or terminal. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
According to the technical scheme of the embodiment of the invention, data to be analyzed is obtained, wherein the data to be analyzed comprises the number of users completing a service, at least one timestamp and a statistical result of at least one process corresponding to the timestamp; performing first judgment on the data to be analyzed based on a first preset algorithm to obtain a first abnormal result of abnormal user number, wherein the first abnormal result comprises a first timestamp and at least one first process corresponding to the first timestamp; and performing second judgment on the first abnormal result to obtain a second abnormal result with abnormal user number, outputting a second abnormal result after inputting the data to be analyzed without manually identifying the abnormal flow, and achieving the technical effect of improving the efficiency of analyzing the abnormal reason.
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.
Claims (10)
1. A method for analyzing reasons of user number abnormity is characterized by comprising the following steps:
acquiring data to be analyzed, wherein the data to be analyzed comprises the number of users completing a service, at least one timestamp and a statistical result of at least one process corresponding to the timestamp;
performing first judgment on the data to be analyzed based on a first preset algorithm to obtain a first abnormal result of abnormal user number, wherein the first abnormal result comprises a first timestamp and at least one first process corresponding to the first timestamp;
and carrying out second judgment on the first abnormal result to obtain a second abnormal result of the abnormal user number.
2. The method for analyzing the reason for the abnormal user number according to claim 1, wherein the second determination of the first abnormal result to obtain a second abnormal result of the abnormal user number includes:
calculating a first difference value of a first statistical result of the first flow of the first timestamp and a second statistical result of the first flow of a second timestamp, wherein the second timestamp is adjacent to the first timestamp;
judging the first difference value based on a second preset algorithm;
and taking a first timestamp of which the first difference is judged to be abnormal by the second preset algorithm and a first flow corresponding to the first timestamp as a second abnormal result.
3. The method for analyzing the reason for the abnormal user number according to claim 1, wherein the second determination of the first abnormal result to obtain a second abnormal result of the abnormal user number includes:
calculating a statistical average value of third statistical results of the first process of a plurality of third timestamps, wherein the plurality of third timestamps are adjacent in sequence;
calculating a second difference value between a first statistical result of the first flow of the first timestamp and the statistical average value;
judging the second difference value based on a second preset algorithm;
and taking the first timestamp of which the second difference value is judged to be abnormal by the second preset algorithm and the first flow corresponding to the first timestamp as the second abnormal result.
4. The method for analyzing the reason for the abnormal user number according to claim 1, wherein the second determination of the first abnormal result to obtain a second abnormal result of the abnormal user number includes:
obtaining a fourth statistical result of the first process of at least one fourth timestamp, wherein the fourth timestamp is before the first timestamp;
judging whether a first statistical result of a first process of the first timestamp is greater than a fourth statistical result;
and taking a first time stamp of which the first statistical result is greater than the fourth statistical result and a first flow corresponding to the first time stamp as the second abnormal result.
5. The method for analyzing the reason for the abnormal user number according to claim 1, wherein after the second determination of the first abnormal result to obtain a second abnormal result of the abnormal user number, the method comprises:
and displaying the second abnormal result to a user in a visual form.
6. The method for analyzing the reason for the abnormal user number according to claim 1, wherein the first determining the data to be analyzed based on a first preset algorithm to obtain a first abnormal result of the abnormal user number includes:
and performing first judgment on the statistical result of each flow based on the first preset algorithm to obtain a first abnormal result of abnormal user number.
7. The method for analyzing the reason for the abnormality of the user number according to claim 1, before the acquiring the data to be analyzed, comprising:
and storing the data to be analyzed in a preset format.
8. An apparatus for analyzing a cause of a user number abnormality, comprising:
the system comprises an acquisition module, a processing module and a processing module, wherein the acquisition module is used for acquiring data to be analyzed, and the data to be analyzed comprises the number of users completing a service, at least one time stamp and a statistical result of at least one process corresponding to the time stamp;
the first judging module is used for carrying out first judgment on the data to be analyzed based on a first preset algorithm to obtain a first abnormal result of abnormal user number, wherein the first abnormal result comprises a first time stamp and at least one first flow corresponding to the first time stamp;
and the second judgment module is used for performing second judgment on the first abnormal result to obtain a second abnormal result of the abnormal user number.
9. A server, comprising:
one or more processors;
storage means for storing one or more programs;
when the one or more programs are executed by the one or more processors, the one or more processors implement the method for analyzing the cause of the user number abnormality according to any one of claims 1 to 7.
10. A computer-readable storage medium on which a computer program is stored, the program, when being executed by a processor, implementing a method for analyzing a cause of a user number abnormality according to any one of claims 1 to 7.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114064757A (en) * | 2021-11-16 | 2022-02-18 | 中国平安人寿保险股份有限公司 | Application program optimization method, device, equipment and medium |
| CN114693328A (en) * | 2020-12-31 | 2022-07-01 | 奥动新能源汽车科技有限公司 | Method, system, equipment and storage medium for processing battery swapping behavior data |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108334530A (en) * | 2017-08-24 | 2018-07-27 | 平安普惠企业管理有限公司 | User behavior information analysis method, equipment and storage medium |
| CN108763017A (en) * | 2018-05-18 | 2018-11-06 | 平安普惠企业管理有限公司 | Application of software data processing method, server-side and the storage medium of financial business |
| CN109522190A (en) * | 2018-10-12 | 2019-03-26 | 中国平安人寿保险股份有限公司 | Abnormal user Activity recognition method and device, electronic equipment, storage medium |
-
2019
- 2019-12-16 CN CN201911293668.9A patent/CN111047433B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108334530A (en) * | 2017-08-24 | 2018-07-27 | 平安普惠企业管理有限公司 | User behavior information analysis method, equipment and storage medium |
| CN108763017A (en) * | 2018-05-18 | 2018-11-06 | 平安普惠企业管理有限公司 | Application of software data processing method, server-side and the storage medium of financial business |
| CN109522190A (en) * | 2018-10-12 | 2019-03-26 | 中国平安人寿保险股份有限公司 | Abnormal user Activity recognition method and device, electronic equipment, storage medium |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114693328A (en) * | 2020-12-31 | 2022-07-01 | 奥动新能源汽车科技有限公司 | Method, system, equipment and storage medium for processing battery swapping behavior data |
| CN114064757A (en) * | 2021-11-16 | 2022-02-18 | 中国平安人寿保险股份有限公司 | Application program optimization method, device, equipment and medium |
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