CN109189805B - Behavior processing method and apparatus, electronic device and computer-readable storage medium - Google Patents
Behavior processing method and apparatus, electronic device and computer-readable storage medium Download PDFInfo
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Abstract
The embodiment of the disclosure discloses a behavior processing method, a behavior processing device, an electronic device and a computer readable storage medium, wherein the behavior processing method comprises the following steps: acquiring user behavior evaluation data, wherein the user behavior evaluation data is structured data consisting of user behavior evaluation subdata; when the user behavior meets a preset condition, calculating a user behavior evaluation score according to the user behavior evaluation data; and executing preset grading processing on the user according to the user behavior evaluation score. The embodiment of the disclosure can improve the real-time performance and accuracy of a user management mechanism, save labor cost, simplify the use process, and enhance the use experience of a user.
Description
Technical Field
The present disclosure relates to the field of information processing technologies, and in particular, to a behavior processing method and apparatus, an electronic device, and a computer-readable storage medium.
Background
With the development of internet technology and intelligent terminals, more and more activities are carried out by depending on the internet, while the activity subject not only relates to users, but also relates to merchants and third parties, and how to effectively standardize multi-party behaviors in the activities participated in by multiple parties is a problem to be solved. Currently, a corresponding management workflow is formulated according to various service states of an activity subject, and then management of the activity subject, such as online, offline, penalty, etc., is implemented on a workflow node through manual operation. However, this approach has the following drawbacks: 1. the timeliness is poor, the management of the activity subject needs manual participation, and emergency situations cannot be handled in time; 2. the management processing rules are disordered, the existing activity main body processing rules are numerous, and the rules are mutually included or crossed, so that the applicable rules are not clearly identified; 3. the labor cost is high, and the current processing scheme needs a lot of manual operations, so that the labor cost is high; 4. the cross-terminal interaction is complex, and because the existing activity main body processing rules are numerous and change rapidly, the rules need to be synchronized when the activity main body application terminal cooperates with other application terminals, so that the cross-terminal cooperation is complex.
Disclosure of Invention
The embodiment of the disclosure provides a behavior processing method and device, electronic equipment and a computer-readable storage medium.
In a first aspect, an embodiment of the present disclosure provides a behavior processing method.
Specifically, the behavior processing method includes:
acquiring user behavior evaluation data, wherein the user behavior evaluation data is structured data consisting of user behavior evaluation subdata;
when the user behavior meets a preset condition, calculating a user behavior evaluation score according to the user behavior evaluation data;
and executing preset grading processing on the user according to the user behavior evaluation score.
With reference to the first aspect, in a first implementation manner of the first aspect, the obtaining user behavior evaluation data includes:
acquiring initial user behavior evaluation data;
filtering the initial user behavior evaluation data;
and carrying out structuring operation on the filtered user behavior evaluation data according to a preset structure rule.
With reference to the first aspect and the first implementation manner of the first aspect, in a second implementation manner of the first aspect, when the user behavior satisfies a preset condition, the calculating a user behavior evaluation score according to the user behavior evaluation data includes:
when the user behavior meets a preset condition, obtaining a user behavior evaluation rule;
calculating a user behavior evaluation sub-score corresponding to the user behavior evaluation sub-data based on the user behavior evaluation rule;
and performing weighted calculation on the user behavior evaluation sub-scores to obtain user behavior evaluation scores corresponding to the user behavior evaluation data.
With reference to the first aspect, the first implementation manner of the first aspect, and the second implementation manner of the first aspect, in a third implementation manner of the first aspect, the performing, according to the user behavior evaluation score, a preset ranking process on a user includes:
judging whether the user belongs to a user with abnormal behavior;
when the user belongs to the user with abnormal behavior, performing abnormal processing on the user;
and when the user does not belong to the abnormal behavior user, executing preset grading processing on the user according to the user behavior evaluation score.
With reference to the first aspect, the first implementation manner of the first aspect, the second implementation manner of the first aspect, and the third implementation manner of the first aspect, in a fourth implementation manner of the first aspect, in an embodiment of the present invention, when the user belongs to a user with abnormal behavior, performing abnormal processing on the user includes:
when the user meets a first abnormal behavior condition, checking the user behavior evaluation score of the user;
and when the user meets a second abnormal behavior condition, recalculating the user evaluation score of the user behavior.
With reference to the first aspect, the first implementation manner of the first aspect, the second implementation manner of the first aspect, the third implementation manner of the first aspect, and the fourth implementation manner of the first aspect, in a fifth implementation manner of the first aspect, in an embodiment of the present invention, when the user does not belong to a user with an abnormal behavior, performing preset ranking processing on the user according to the user behavior evaluation score includes:
when the user does not belong to the abnormal behavior user, acquiring a grading processing rule, wherein the grading processing rule comprises one or more user behavior evaluation score ranges and a processing rule corresponding to the user behavior evaluation score ranges;
determining a user behavior evaluation score range to which the user behavior evaluation score belongs;
and processing the user according to a processing rule corresponding to the user behavior evaluation score range to which the user behavior evaluation score belongs.
In a second aspect, an embodiment of the present disclosure provides a behavior processing apparatus.
Specifically, the behavior processing device includes:
the system comprises an acquisition module, a storage module and a processing module, wherein the acquisition module is configured to acquire user behavior evaluation data, and the user behavior evaluation data is structured data composed of user behavior evaluation subdata;
the calculation module is configured to calculate a user behavior evaluation score according to the user behavior evaluation data when the user behavior meets a preset condition;
and the processing module is configured to execute preset grading processing on the user according to the user behavior evaluation score.
With reference to the second aspect, in a first implementation manner of the second aspect, the obtaining module includes:
a first obtaining submodule configured to obtain initial user behavior evaluation data;
a filtering submodule configured to filter the initial user behavior evaluation data;
and the operation submodule is configured to perform structuring operation on the filtered user behavior evaluation data according to a preset structure rule.
With reference to the second aspect and the first implementation manner of the second aspect, in a second implementation manner of the second aspect, the computing module includes:
the second obtaining submodule is configured to obtain a user behavior evaluation rule when the user behavior meets a preset condition;
the first calculation submodule is configured to calculate a user behavior evaluation sub-score corresponding to the user behavior evaluation sub-data based on the user behavior evaluation rule;
and the second calculation submodule is configured to perform weighted calculation on the user behavior evaluation sub-scores to obtain user behavior evaluation scores corresponding to the user behavior evaluation data.
With reference to the second aspect, the first implementation manner of the second aspect, and the second implementation manner of the second aspect, in a third implementation manner of the second aspect, the embodiment of the present invention includes:
the judging submodule is configured to judge whether the user belongs to an abnormal behavior user;
a first processing submodule configured to perform exception processing on the user when the user belongs to an exception behavior user;
and the second processing submodule is configured to execute preset grading processing on the user according to the user behavior evaluation score when the user does not belong to the abnormal behavior user.
With reference to the second aspect, the first implementation manner of the second aspect, the second implementation manner of the second aspect, and the third implementation manner of the second aspect, in a fourth implementation manner of the second aspect, the embodiment of the present invention includes:
the checking sub-module is configured to check the user behavior evaluation score of the user when the user meets a first abnormal behavior condition;
a third computing sub-module configured to recalculate the user rating score for the user behavior when the user satisfies a second abnormal behavior condition.
With reference to the second aspect, the first implementation manner of the second aspect, the second implementation manner of the second aspect, the third implementation manner of the second aspect, and the fourth implementation manner of the second aspect, in a fifth implementation manner of the second aspect, the second processing sub-module includes:
a third obtaining sub-module, configured to obtain a hierarchical processing rule when the user does not belong to an abnormal behavior user, where the hierarchical processing rule includes one or more user behavior evaluation score ranges and a processing rule corresponding to the user behavior evaluation score range;
a determination submodule configured to determine a user behavior evaluation score range to which the user behavior evaluation score belongs;
and the third processing submodule is configured to process the user according to a processing rule corresponding to the user behavior evaluation score range to which the user behavior evaluation score belongs.
In a third aspect, an embodiment of the present disclosure provides an electronic device, including a memory and a processor, where the memory is used to store one or more computer instructions that support a behavior processing apparatus to execute the behavior processing method in the first aspect, and the processor is configured to execute the computer instructions stored in the memory. The behavior processing apparatus may further include a communication interface for the behavior processing apparatus to communicate with other devices or a communication network.
In a fourth aspect, an embodiment of the present disclosure provides a computer-readable storage medium for storing computer instructions for a behavior processing apparatus, where the computer instructions include computer instructions for executing the behavior processing method in the first aspect as a behavior processing apparatus.
The technical scheme provided by the embodiment of the disclosure can have the following beneficial effects:
according to the technical scheme, the health condition of the user is determined by evaluating the user, and real-time, automatic and unified user management is realized by carrying out grading processing on health evaluation results. The technical scheme can improve the real-time performance and accuracy of a user management mechanism, save labor cost, simplify the use flow and enhance the use experience of a user.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
Drawings
Other features, objects, and advantages of the present disclosure will become more apparent from the following detailed description of non-limiting embodiments when taken in conjunction with the accompanying drawings. In the drawings:
FIG. 1 illustrates a flow diagram of a behavior processing method according to an embodiment of the present disclosure;
fig. 2 shows a flow chart of step S101 of the behavior processing method according to the embodiment shown in fig. 1;
FIG. 3 shows a flow chart of step S102 of the behavior processing method according to the embodiment shown in FIG. 1;
fig. 4 shows a flow chart of step S103 of the behavior processing method according to the embodiment shown in fig. 1;
FIG. 5 shows a flow chart of step S402 of the behavior processing method according to the embodiment shown in FIG. 4;
fig. 6 shows a flowchart of step S403 of the behavior processing method according to the embodiment shown in fig. 4;
FIG. 7 is a diagram illustrating an application scenario for performing predetermined hierarchical processing on a user according to an embodiment of the present disclosure;
fig. 8 shows a block diagram of a behavior processing device according to an embodiment of the present disclosure;
fig. 9 is a block diagram showing the configuration of an acquisition module 801 of the behavior processing apparatus according to the embodiment shown in fig. 8;
fig. 10 is a block diagram showing a configuration of a calculation module 802 of the behavior processing apparatus according to the embodiment shown in fig. 8;
fig. 11 is a block diagram showing the configuration of a processing module 803 of the behavior processing apparatus according to the embodiment shown in fig. 8;
fig. 12 is a block diagram showing the structure of a first processing submodule 1102 of the behavior processing device according to the embodiment shown in fig. 11;
fig. 13 is a block diagram showing the structure of a second processing submodule 1103 of the behavior processing apparatus according to the embodiment shown in fig. 11;
FIG. 14 shows a block diagram of an electronic device according to an embodiment of the present disclosure;
fig. 15 is a schematic structural diagram of a computer system suitable for implementing a behavior processing method according to an embodiment of the present disclosure.
Detailed Description
Hereinafter, exemplary embodiments of the present disclosure will be described in detail with reference to the accompanying drawings so that those skilled in the art can easily implement them. Also, for the sake of clarity, parts not relevant to the description of the exemplary embodiments are omitted in the drawings.
In the present disclosure, it is to be understood that terms such as "including" or "having," etc., are intended to indicate the presence of the disclosed features, numbers, steps, behaviors, components, parts, or combinations thereof, and are not intended to preclude the possibility that one or more other features, numbers, steps, behaviors, components, parts, or combinations thereof may be present or added.
It should be further noted that the embodiments and features of the embodiments in the present disclosure may be combined with each other without conflict. The present disclosure will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
The technical scheme provided by the embodiment of the disclosure determines the health condition of the user by evaluating the user, and realizes real-time, automatic and uniform user management by grading processing according to the health evaluation result. The technical scheme can improve the real-time performance and accuracy of a user management mechanism, save labor cost, simplify the use flow and enhance the use experience of a user.
Fig. 1 illustrates a flow diagram of a behavior processing method according to an embodiment of the present disclosure. As shown in fig. 1, the behavior processing method includes the following steps S101 to S103:
in step S101, user behavior evaluation data is obtained, where the user behavior evaluation data is structured data composed of user behavior evaluation sub-data;
in step S102, when the user behavior meets a preset condition, calculating a user behavior evaluation score according to the user behavior evaluation data;
in step S103, a preset ranking process is performed on the user according to the user behavior evaluation score.
As mentioned above, with the development of internet technology and intelligent terminals, more and more activities are performed by relying on the internet, and how to effectively standardize multi-party behaviors in activities in which multi-party subjects participate is a problem to be solved. The current processing scheme has the defects of poor timeliness, disordered management and processing rules, high labor cost, complex cross-terminal interaction and the like.
In view of the above drawbacks, the embodiment proposes a behavior processing method, which determines the health condition of the user behavior by evaluating the user behavior and performs a grading process based on the health evaluation result to realize real-time, automatic and uniform user behavior management. The technical scheme can improve the real-time performance and accuracy of a user behavior management mechanism, save labor cost, simplify the use flow and enhance the use experience of the user.
The user is a broad concept, which may refer to either a merchant, a user making a transaction with the merchant, or a third party other than the merchant and the user.
The user behavior evaluation data comprises one or more of the following data, or the user behavior evaluation subdata is one or more of the following data: user registration data, user-related transaction data, user application data, user operation data, and the like, wherein the user registration data may be, for example, user registration information such as a user registration ID, a user name, a user nickname, a user name, a user gender, a user age, a user birth date, a user mailbox, a user contact address, and the like; the user-related transaction data may be, for example, user transaction time, user transaction identification, user transaction content, user transaction amount, user transaction remarks, and the like; the user application data may be, for example, download time, version information, etc. of the user application client; the user operation data may be, for example, user operation time, user operation identifier, user operation content, user operation remark, user operation log, and the like.
The user behavior evaluation score is used for representing the behavior health degree of the user or the behavior well degree of the user, the higher the user behavior evaluation score is, the higher the user health degree is, the better the behavior well degree is, the lighter the corresponding processing is, otherwise, the lower the user behavior evaluation score is, the lower the user health degree is, the worse the behavior well degree is, the heavier the corresponding processing is.
In an optional implementation manner of this embodiment, as shown in fig. 2, the step S101, that is, the step of acquiring the user behavior evaluation data, includes steps S201 to S203:
in step S201, initial user behavior evaluation data is acquired;
in step S202, filtering the initial user behavior evaluation data;
in step S203, the filtered user behavior evaluation data is structured according to a preset structure rule.
In order to manage user behavior data more conveniently, a uniform user behavior processing mechanism is established and executed effectively, and the acquired user behavior evaluation data needs to be processed in a unified manner. Specifically, in this embodiment, initial user behavior evaluation data is first obtained, and then the initial user behavior evaluation data is filtered and cleaned; and finally, carrying out structuring operation on the user behavior evaluation data obtained after filtering and cleaning according to a preset structure rule to obtain the user behavior evaluation data.
Wherein, the filtering and cleaning of the initial user behavior evaluation data comprises one or more of the following operations: deleting data that is totally or partially missing, deleting data that is incomplete in content, deleting data that is erroneous in content, deleting data that is deviating from content that may be due to an abnormal situation, and so forth. By filtering and cleaning the initial user behavior evaluation data, most noise data in the initial user behavior evaluation data can be filtered out, so that the correctness of data participating in user behavior evaluation and subsequent user behavior management is ensured.
The preset structure rule refers to a rule for structuring data so that the data can present certain structure characteristics. The specific rule content can be defined or selected by those skilled in the art according to the needs of the actual application, and the present disclosure does not specifically limit the same.
In an optional implementation manner of this embodiment, the structure rule is a rule that processes the filtered user behavior evaluation data to generate a structure array with a user uniquely identifiable identifier as an index, where the structure array includes one or more user behavior evaluation sub-data related to the user behavior, which may also be referred to as feature data of the user, such as a shop _ id (d1, d2, d3, …, dn), where the shop _ id represents a unique identifiable identifier of a certain user, d1 and d2 … … are the feature data of the user, that is, the user behavior evaluation sub-data, and n is the number of the user behavior evaluation sub-data.
In an optional implementation manner of this embodiment, as shown in fig. 3, the step S102, that is, when the user behavior satisfies the preset condition, the step of calculating the user behavior evaluation score according to the user behavior evaluation data includes steps S301 to S303:
in step S301, when the user behavior meets a preset condition, a user behavior evaluation rule is obtained;
in step S302, calculating a user behavior evaluation sub-score corresponding to the user behavior evaluation sub-data based on the user behavior evaluation rule;
in step S303, the user behavior evaluation sub-scores are weighted to obtain user behavior evaluation scores corresponding to the user behavior evaluation data.
Considering that some behavior specifications are important or sensitive, for example, the seller is prevented from performing the operation of the seller or the buyer, and the result is serious if the user violates the operation. In order to maintain the behavior specifications not to be damaged or to timely remedy the behavior specifications at the first time after the behavior specifications are damaged, when the fact that the user violates the important behavior specifications is detected, the behavior of the user is not evaluated according to the flow, but the user is directly treated or punished; when the user is not detected to violate the behavior specifications, that is, when the user behavior meets the preset condition, the behavior of the user can be continuously evaluated according to the above flow. Therefore, the preset condition may be, for example, that the user does not perform a preset action or that the sensitivity of the user action is lower than a preset sensitivity threshold, and so on, where the preset action is an action that violates the above-mentioned more important or sensitive action specification.
In order to fully consider the characteristics of all behavior data of a user when calculating the user behavior evaluation score according to the user behavior evaluation data, in the embodiment, a user behavior evaluation rule is used to evaluate each subdata in the user behavior evaluation data, and then the obtained behavior evaluation result is weighted, so that the user behavior evaluation score corresponding to the user behavior evaluation data is relatively, comprehensively and comprehensively obtained.
The user behavior evaluation rules correspond to the user behavior evaluation subdata, for example, one evaluation rule corresponds to user registration data, one evaluation rule corresponds to user-related transaction data, one evaluation rule corresponds to user application data, and one evaluation rule also corresponds to user operation data, and the corresponding data is evaluated by using the corresponding evaluation rule, so that a corresponding evaluation score can be obtained. In practical application, the evaluation rules corresponding to different data may be the same or different, and may be specifically set according to the needs of practical application as long as the behavior goodness of the user can be reflected.
When the user behavior evaluation sub-scores are weighted and calculated, corresponding weights are set for different evaluation rules, different user behavior evaluation sub-scores or different user behavior evaluation sub-scores, and then the user behavior evaluation scores corresponding to the user behavior evaluation data can be obtained through weighting or weighted average calculation. When setting each weight, the principle that the more important the user behavior evaluation subdata is, the higher the corresponding weight is can be followed, and of course, other assignment principles can also be adopted, and the specific value of each weight can be set according to the needs of practical application, and the disclosure is not particularly limited.
For example, for the user behavior evaluation data shop _ id (d1, d2, d3, …, dn), after the evaluation rule corresponding to each user behavior evaluation subdata is evaluated, a corresponding user behavior evaluation subdue value is obtained: shop _ id (r1_ score, r2_ score, r3_ score, …, rk _ score), where r1 and r2 … … represent evaluation rules, r1_ score and r2_ score … … represent corresponding user behavior evaluation sub-scores, k is the number of user behavior evaluation sub-scores, and k is not greater than n, when k is equal to n, all user behavior evaluation sub-data in the user behavior evaluation data are taken for evaluation, and when k is less than n, part of the user behavior evaluation sub-data in the user behavior evaluation data are taken for evaluation; if r1_ score is set to correspond to a weight m1, r2_ score to correspond to a weight m2, …, and so on, then the user behavior evaluation score may be expressed as score m1 r1_ score + m2 r2_ score + m3 r3_ score + … + mk rk _ score, or score (m1 r1_ score + m2 r2 r score + m3 r3_ score + … + mk rk _ score)/(1 + m2+ m3+ … + mk).
In an optional implementation manner of this embodiment, as shown in fig. 4, the step S103 of performing a preset rating process on the user according to the user behavior evaluation score includes steps S401 to S403:
in step S401, it is determined whether the user belongs to a user with abnormal behavior;
in step S402, when the user belongs to a user with abnormal behavior, performing exception processing on the user;
in step S403, when the user does not belong to the abnormal behavior user, performing preset classification processing on the user according to the user behavior evaluation score.
Considering that there may be noise data that is not filtered when obtaining the user behavior evaluation data, and the noise data may affect the accuracy of the subsequent evaluation score calculation, for example, if the evaluation score calculated by a certain user at a certain time is lower than a constant value, it may be an accident in the calculation process, and if the user is strictly processed according to the score at this time, it may have a great, unnecessary and possibly wrong negative impact on the service of the user. Therefore, in order to ensure the accuracy of the calculation result, a step of performing abnormality judgment is also required to correct the calculation deviation. That is, in this embodiment, it is first determined whether the user belongs to a user with abnormal behavior, and when the user belongs to a user with abnormal behavior, the user is directly subjected to abnormal processing, and when the user does not belong to a user with abnormal behavior, the user is subjected to preset classification processing according to the user behavior evaluation score.
In an optional implementation manner of this embodiment, as shown in fig. 5, the step S402, that is, when the user belongs to a user with abnormal behavior, the step of performing exception handling on the user includes steps S501 to S502:
in step S501, when the user satisfies a first abnormal behavior condition, checking a user behavior evaluation score of the user;
in step S502, when the user satisfies a second abnormal behavior condition, the user evaluation score of the user behavior is recalculated.
The first abnormal behavior condition may be, for example: the difference value between the behavior evaluation score of the user and the mean value of the behavior evaluation scores of the user in a first preset historical time period is larger than or equal to a first preset threshold value. Or further, a certain user is divided into a certain preset user range, namely a white list, according to whether the behavior evaluation score or the mean value of the behavior evaluation score in a second preset historical time period of the certain user is continuously higher than the first preset threshold value or whether the behavior evaluation score or the mean value of the behavior evaluation score in a third preset historical time period of the certain user is higher than the first preset threshold value for a time greater than the second preset threshold value, and if the behavior evaluation score of the user in the white list is lower than the third preset threshold value, namely, the behavior evaluation score of the user deviates from a normal value, which indicates that errors may occur in the calculation of the behavior evaluation score of the user, the behavior evaluation score of the user needs to be checked and determined.
The second abnormal behavior condition may be, for example: the absolute value of the difference value between the preset proportion and the proportion between the number of the processed users corresponding to each processing measure and all the numbers of the users needing to be processed is greater than or equal to a first preset threshold value. For a stable service circle, the ratio between the number of processed users corresponding to each processing measure and the number of all users to be processed is usually constant, i.e. a preset ratio is maintained, or the ratio fluctuates only in a small range, but if the fluctuation of the ratio is large, this indicates to some extent that the behavior evaluation score of the corresponding user is calculated to have a large deviation, the user evaluation score of the user behavior needs to be recalculated, and then the judgment is performed again, wherein the preset ratio can be determined by calculating the average value of the ratios between the number of processed users and the number of all users to be processed in a preset historical time period.
In an optional implementation manner of this embodiment, as shown in fig. 6, the step S403, that is, when the user does not belong to a user with abnormal behavior, the step of performing a preset rating process on the user according to the user behavior evaluation score includes steps S601 to S603:
in step S601, when the user does not belong to an abnormal behavior user, obtaining a hierarchical processing rule, where the hierarchical processing rule includes one or more user behavior evaluation score ranges and a processing rule corresponding to the user behavior evaluation score ranges;
in step S602, determining a user behavior evaluation score range to which the user behavior evaluation score belongs;
in step S603, the user is processed according to a processing rule corresponding to the user behavior evaluation score range to which the user behavior evaluation score belongs.
When a certain user is determined not to belong to the abnormal behavior user, calculation can be performed according to a normal flow, namely, preset grading processing is performed on the user according to the user behavior evaluation score. Specifically, in this embodiment, a hierarchical processing rule is first obtained, where the hierarchical processing rule includes one or more preset user behavior evaluation score ranges and a processing rule corresponding to the user behavior evaluation score ranges; then determining which user behavior evaluation score range the behavior evaluation score of a certain user belongs to; and finally, carrying out corresponding processing on the user according to a processing rule corresponding to the user behavior evaluation score range to which the user behavior evaluation score belongs.
For example, if the user behavior evaluation score is divided into 5 ranges according to the height: [0, s1), [ s1, s2), [ s2, s3), [ s3, s4 ], and [ s4,100], where the 5 score ranges respectively correspond to 5 processing rules (f1, f2, f3, f4, f5), and a range with a smaller score value indicates that the corresponding user behavior is less good, so the corresponding processing rule is more strict.
Wherein the processing rule may be to prohibit online, restrict online, force offline, phase offline, prohibit open transactions, restrict open transactions, release transaction protocols, restrict transaction protocols, warn, severe warn, penalize, etc.
Fig. 7 is a schematic view of an application scenario for executing preset hierarchical processing on a user according to an embodiment of the present disclosure, and behavior evaluation scores and corresponding selected processing measures of multiple users, such as user 1, user 2, user 3, and user 4, can be displayed on a foreground through background operation.
The following are embodiments of the disclosed apparatus that may be used to perform embodiments of the disclosed methods.
Fig. 8 shows a block diagram of a behavior processing apparatus according to an embodiment of the present disclosure, which may be implemented as part or all of an electronic device by software, hardware, or a combination of both. As shown in fig. 8, the behavior processing device includes:
an obtaining module 801 configured to obtain user behavior evaluation data, where the user behavior evaluation data is structured data composed of user behavior evaluation sub-data;
a calculating module 802 configured to calculate a user behavior evaluation score according to the user behavior evaluation data when the user behavior satisfies a preset condition;
a processing module 803 configured to perform a preset ranking process on the user according to the user behavior evaluation score.
As mentioned above, with the development of internet technology and intelligent terminals, more and more activities are performed by relying on the internet, and how to effectively standardize multi-party behaviors in activities in which multi-party subjects participate is a problem to be solved. The current processing scheme has the defects of poor timeliness, disordered management and processing rules, high labor cost, complex cross-terminal interaction and the like.
In view of the above drawbacks, the present invention provides a behavior processing device, which determines the health condition of user behavior by evaluating the user behavior and performs a grading process based on the health evaluation result to realize real-time, automatic and uniform user behavior management. The technical scheme can improve the real-time performance and accuracy of a user behavior management mechanism, save labor cost, simplify the use flow and enhance the use experience of the user.
The user is a broad concept, which may refer to either a merchant, a user making a transaction with the merchant, or a third party other than the merchant and the user.
The user behavior evaluation data comprises one or more of the following data, or the user behavior evaluation subdata is one or more of the following data: user registration data, user-related transaction data, user application data, user operation data, and the like, wherein the user registration data may be, for example, user registration information such as a user registration ID, a user name, a user nickname, a user name, a user gender, a user age, a user birth date, a user mailbox, a user contact address, and the like; the user-related transaction data may be, for example, user transaction time, user transaction identification, user transaction content, user transaction amount, user transaction remarks, and the like; the user application data may be, for example, download time, version information, etc. of the user application client; the user operation data may be, for example, user operation time, user operation identifier, user operation content, user operation remark, user operation log, and the like.
The user behavior evaluation score is used for representing the behavior health degree of the user or the behavior well degree of the user, the higher the user behavior evaluation score is, the higher the user health degree is, the better the behavior well degree is, the lighter the corresponding processing is, otherwise, the lower the user behavior evaluation score is, the lower the user health degree is, the worse the behavior well degree is, the heavier the corresponding processing is.
In an optional implementation manner of this embodiment, as shown in fig. 9, the obtaining module 801 includes:
a first obtaining sub-module 901 configured to obtain initial user behavior evaluation data;
a filtering submodule 902 configured to filter the initial user behavior evaluation data;
and the operation sub-module 903 is configured to perform structuring operation on the filtered user behavior evaluation data according to a preset structure rule.
In order to manage user behavior data more conveniently, a uniform user behavior processing mechanism is established and executed effectively, and the acquired user behavior evaluation data needs to be processed in a unified manner. Specifically, in this embodiment, the first obtaining sub-module 901 obtains initial user behavior evaluation data, and the filtering sub-module 902 filters and cleans the initial user behavior evaluation data; the operation sub-module 903 performs structuring operation on the filtered and cleaned user behavior evaluation data according to a preset structure rule to obtain user behavior evaluation data.
Wherein, the filtering and cleaning of the initial user behavior evaluation data comprises one or more of the following operations: deleting data that is totally or partially missing, deleting data that is incomplete in content, deleting data that is erroneous in content, deleting data that is deviating from content that may be due to an abnormal situation, and so forth. By filtering and cleaning the initial user behavior evaluation data, most noise data in the initial user behavior evaluation data can be filtered out, so that the correctness of data participating in user behavior evaluation and subsequent user behavior management is ensured.
The preset structure rule refers to a rule for structuring data so that the data can present certain structure characteristics. The specific rule content can be defined or selected by those skilled in the art according to the needs of the actual application, and the present disclosure does not specifically limit the same.
In an optional implementation manner of this embodiment, the structure rule is a rule that processes the filtered user behavior evaluation data to generate a structure array with a user uniquely identifiable identifier as an index, where the structure array includes one or more user behavior evaluation sub-data related to the user behavior, which may also be referred to as feature data of the user, such as a shop _ id (d1, d2, d3, …, dn), where the shop _ id represents a unique identifiable identifier of a certain user, d1 and d2 … … are the feature data of the user, that is, the user behavior evaluation sub-data, and n is the number of the user behavior evaluation sub-data.
In an optional implementation manner of this embodiment, as shown in fig. 10, the calculating module 802 includes:
a second obtaining sub-module 1001 configured to obtain a user behavior evaluation rule when the user behavior satisfies a preset condition;
a first calculating sub-module 1002, configured to calculate, based on the user behavior evaluation rule, a user behavior evaluation sub-score corresponding to the user behavior evaluation sub-data;
the second calculating submodule 1003 is configured to perform weighted calculation on the user behavior evaluation sub-score to obtain a user behavior evaluation score corresponding to the user behavior evaluation data.
Considering that some behavior specifications are important or sensitive, for example, the seller is prevented from performing the operation of the seller or the buyer, and the result is serious if the user violates the operation. In order to maintain the behavior specifications not to be damaged or to timely remedy the behavior specifications at the first time after the behavior specifications are damaged, when the fact that the user violates the important behavior specifications is detected, the behavior of the user is not evaluated according to the flow, but the user is directly treated or punished; when the user is not detected to violate the behavior specifications, that is, when the user behavior meets the preset condition, the behavior of the user can be continuously evaluated according to the above flow. Therefore, the preset condition may be, for example, that the user does not perform a preset action or that the sensitivity of the user action is lower than a preset sensitivity threshold, and so on, where the preset action is an action that violates the above-mentioned more important or sensitive action specification.
In order to fully consider the characteristics of all behavior data of the user when calculating the user behavior evaluation score according to the user behavior evaluation data, in this embodiment, the first calculation sub-module 1002 evaluates each subdata in the user behavior evaluation data by using a user behavior evaluation rule, and the second calculation sub-module 1003 performs weighting processing on the obtained behavior evaluation result, so as to obtain a relatively comprehensive and comprehensive user behavior evaluation score corresponding to the user behavior evaluation data.
The user behavior evaluation rules correspond to the user behavior evaluation subdata, for example, one evaluation rule corresponds to user registration data, one evaluation rule corresponds to user-related transaction data, one evaluation rule corresponds to user application data, and one evaluation rule also corresponds to user operation data, and the corresponding data is evaluated by using the corresponding evaluation rule, so that a corresponding evaluation score can be obtained. In practical application, the evaluation rules corresponding to different data may be the same or different, and may be specifically set according to the needs of practical application as long as the behavior goodness of the user can be reflected.
When the user behavior evaluation sub-scores are weighted and calculated, corresponding weights are set for different evaluation rules, different user behavior evaluation sub-scores or different user behavior evaluation sub-scores, and then the user behavior evaluation scores corresponding to the user behavior evaluation data can be obtained through weighting or weighted average calculation. When setting each weight, the principle that the more important the user behavior evaluation subdata is, the higher the corresponding weight is can be followed, and of course, other assignment principles can also be adopted, and the specific value of each weight can be set according to the needs of practical application, and the disclosure is not particularly limited.
For example, for the user behavior evaluation data shop _ id (d1, d2, d3, …, dn), after the evaluation rule corresponding to each user behavior evaluation subdata is evaluated, a corresponding user behavior evaluation subdue value is obtained: shop _ id (r1_ score, r2_ score, r3_ score, …, rk _ score), where r1 and r2 … … represent evaluation rules, r1_ score and r2_ score … … represent corresponding user behavior evaluation sub-scores, k is the number of user behavior evaluation sub-scores, and k is not greater than n, when k is equal to n, all user behavior evaluation sub-data in the user behavior evaluation data are taken for evaluation, and when k is less than n, part of the user behavior evaluation sub-data in the user behavior evaluation data are taken for evaluation; if r1_ score is set to correspond to a weight m1, r2_ score to correspond to a weight m2, …, and so on, then the user behavior evaluation score may be expressed as score m1 r1_ score + m2 r2_ score + m3 r3_ score + … + mk rk _ score, or score (m1 r1_ score + m2 r2 r score + m3 r3_ score + … + mk rk _ score)/(1 + m2+ m3+ … + mk).
In an optional implementation manner of this embodiment, as shown in fig. 11, the processing module 803 includes:
a determining submodule 1101 configured to determine whether the user belongs to an abnormally-behaving user;
a first processing submodule 1102 configured to perform exception processing on the user when the user belongs to an exception behavior user;
and the second processing sub-module 1103 is configured to, when the user does not belong to the abnormal behavior user, perform preset grading processing on the user according to the user behavior evaluation score.
Considering that there may be noise data that is not filtered when obtaining the user behavior evaluation data, and the noise data may affect the accuracy of the subsequent evaluation score calculation, for example, if the evaluation score calculated by a certain user at a certain time is lower than a constant value, it may be an accident in the calculation process, and if the user is strictly processed according to the score at this time, it may have a great, unnecessary and possibly wrong negative impact on the service of the user. Therefore, in order to ensure the accuracy of the calculation result, a step of performing abnormality judgment is also required to correct the calculation deviation. That is, in this embodiment, the determining sub-module 1101 determines whether the user belongs to a user with abnormal behavior, when the user belongs to the user with abnormal behavior, the first processing sub-module 1102 directly performs abnormal processing on the user, and when the user does not belong to the user with abnormal behavior, the second processing sub-module 1103 performs preset hierarchical processing on the user according to the user behavior evaluation score.
In an optional implementation manner of this embodiment, as shown in fig. 12, the first processing submodule 1102 includes:
the checking sub-module 1201 is configured to check the user behavior evaluation score of the user when the user meets a first abnormal behavior condition;
a third computing sub-module 1202 configured to recalculate the user rating score for the user behavior when the user satisfies a second abnormal behavior condition.
The first abnormal behavior condition may be, for example: the difference value between the behavior evaluation score of the user and the mean value of the behavior evaluation scores of the user in a first preset historical time period is larger than or equal to a first preset threshold value. Or further, a certain user is divided into a certain preset user range, namely a white list, according to whether the behavior evaluation score or the mean value of the behavior evaluation score in a second preset historical time period of the certain user is continuously higher than the first preset threshold value or whether the behavior evaluation score or the mean value of the behavior evaluation score in a third preset historical time period of the certain user is higher than the first preset threshold value for a time greater than the second preset threshold value, and if the behavior evaluation score of the user in the white list is lower than the third preset threshold value, namely, the behavior evaluation score of the user deviates from a normal value, which indicates that errors may occur in the calculation of the behavior evaluation score of the user, the behavior evaluation score of the user needs to be checked and determined.
The second abnormal behavior condition may be, for example: the absolute value of the difference value between the preset proportion and the proportion between the number of the processed users corresponding to each processing measure and all the numbers of the users needing to be processed is greater than or equal to a first preset threshold value. For a stable service circle, the ratio between the number of processed users corresponding to each processing measure and the number of all users to be processed is usually constant, i.e. a preset ratio is maintained, or the ratio fluctuates only in a small range, but if the fluctuation of the ratio is large, this indicates to some extent that the behavior evaluation score of the corresponding user is calculated to have a large deviation, the user evaluation score of the user behavior needs to be recalculated, and then the judgment is performed again, wherein the preset ratio can be determined by calculating the average value of the ratios between the number of processed users and the number of all users to be processed in a preset historical time period.
In an optional implementation manner of this embodiment, as shown in fig. 13, the second processing sub-module 1103 includes:
a third obtaining sub-module 1301, configured to obtain a hierarchical processing rule when the user does not belong to an abnormal behavior user, where the hierarchical processing rule includes one or more user behavior evaluation score ranges and a processing rule corresponding to the user behavior evaluation score range;
a determining sub-module 1302 configured to determine a user behavior evaluation score range to which the user behavior evaluation score belongs;
and the third processing sub-module 1303 is configured to process the user according to a processing rule corresponding to the user behavior evaluation score range to which the user behavior evaluation score belongs.
When a certain user is determined not to belong to the abnormal behavior user, calculation can be performed according to a normal flow, namely, preset grading processing is performed on the user according to the user behavior evaluation score. Specifically, in this embodiment, the third obtaining sub-module 1301 obtains a hierarchical processing rule, where the hierarchical processing rule includes one or more preset user behavior evaluation score ranges and a processing rule corresponding to the user behavior evaluation score ranges; the determining sub-module 1302 determines to which user behavior evaluation score range the behavior evaluation score of a certain user belongs; the third processing sub-module 1303 performs corresponding processing on the user according to the processing rule corresponding to the user behavior evaluation score range to which the user behavior evaluation score belongs.
For example, if the user behavior evaluation score is divided into 5 ranges according to the height: [0, s1), [ s1, s2), [ s2, s3), [ s3, s4 ], and [ s4,100], where the 5 score ranges respectively correspond to 5 processing rules (f1, f2, f3, f4, f5), and a range with a smaller score value indicates that the corresponding user behavior is less good, so the corresponding processing rule is more strict.
Wherein the processing rule may be to prohibit online, restrict online, force offline, phase offline, prohibit open transactions, restrict open transactions, release transaction protocols, restrict transaction protocols, warn, severe warn, penalize, etc.
The present disclosure also discloses an electronic device, fig. 14 shows a block diagram of an electronic device according to an embodiment of the present disclosure, and as shown in fig. 14, the electronic device 1400 includes a memory 1401 and a processor 1402; wherein the content of the first and second substances,
the memory 1401 is used to store one or more computer instructions, which are executed by the processor 1402 to implement any of the method steps described above.
Fig. 15 is a schematic block diagram of a computer system suitable for implementing a behavior processing method according to an embodiment of the present disclosure.
As shown in fig. 15, the computer system 1500 includes a Central Processing Unit (CPU)1501 which can execute various processes in the above-described embodiments in accordance with a program stored in a Read Only Memory (ROM)1502 or a program loaded from a storage section 1508 into a Random Access Memory (RAM) 1503. In the RAM1503, various programs and data necessary for the operation of the system 1500 are also stored. The CPU1501, the ROM1502, and the RAM1503 are connected to each other by a bus 1504. An input/output (I/O) interface 1505 is also connected to bus 1504.
The following components are connected to the I/O interface 1505: an input portion 1506 including a keyboard, a mouse, and the like; an output portion 1507 including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage portion 1508 including a hard disk and the like; and a communication section 1509 including a network interface card such as a LAN card, a modem, or the like. The communication section 1509 performs communication processing via a network such as the internet. A drive 1510 is also connected to the I/O interface 1505 as needed. A removable medium 1511 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 1510 as necessary, so that a computer program read out therefrom is mounted into the storage section 1508 as necessary.
In particular, the above described methods may be implemented as computer software programs, according to embodiments of the present disclosure. For example, embodiments of the present disclosure include a computer program product comprising a computer program tangibly embodied on a medium readable thereby, the computer program comprising program code for performing the behavior processing method described above. In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 1509, and/or installed from the removable medium 1511.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowcharts or block diagrams may represent a module, a program segment, or a portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units or modules described in the embodiments of the present disclosure may be implemented by software or hardware. The units or modules described may also be provided in a processor, and the names of the units or modules do not in some cases constitute a limitation of the units or modules themselves.
As another aspect, the present disclosure also provides a computer-readable storage medium, which may be the computer-readable storage medium included in the apparatus in the above-described embodiment; or it may be a separate computer readable storage medium not incorporated into the device. The computer readable storage medium stores one or more programs for use by one or more processors in performing the methods described in the present disclosure.
The foregoing description is only exemplary of the preferred embodiments of the disclosure and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the invention in the present disclosure is not limited to the specific combination of the above-mentioned features, but also encompasses other embodiments in which any combination of the above-mentioned features or their equivalents is possible without departing from the inventive concept. For example, the above features and (but not limited to) the features disclosed in this disclosure having similar functions are replaced with each other to form the technical solution.
Claims (12)
1. A behavior processing method, comprising:
acquiring user behavior evaluation data, wherein the user behavior evaluation data is structured data consisting of user behavior evaluation subdata;
when the user behavior meets a preset condition, calculating a user behavior evaluation score according to the user behavior evaluation data;
executing preset grading processing on the user according to the user behavior evaluation score, wherein the preset grading processing comprises the following steps: judging whether the user belongs to a user with abnormal behavior; when the user belongs to the user with abnormal behavior, executing abnormal processing on the user, wherein the executing abnormal processing comprises correcting the calculation deviation of the user behavior evaluation score; and when the user does not belong to the abnormal behavior user, executing preset grading processing on the user according to the user behavior evaluation score.
2. The method of claim 1, wherein the obtaining user behavior evaluation data comprises:
acquiring initial user behavior evaluation data;
filtering the initial user behavior evaluation data;
and carrying out structuring operation on the filtered user behavior evaluation data according to a preset structure rule.
3. The method according to claim 1 or 2, wherein when the user behavior satisfies a preset condition, the calculating a user behavior evaluation score according to the user behavior evaluation data includes:
when the user behavior meets a preset condition, obtaining a user behavior evaluation rule;
calculating a user behavior evaluation sub-score corresponding to the user behavior evaluation sub-data based on the user behavior evaluation rule;
and performing weighted calculation on the user behavior evaluation sub-scores to obtain user behavior evaluation scores corresponding to the user behavior evaluation data.
4. The method according to claim 1, wherein when the user belongs to a user with abnormal behavior, performing exception handling on the user comprises:
when the user meets a first abnormal behavior condition, checking the user behavior evaluation score of the user;
and when the user meets a second abnormal behavior condition, recalculating the user evaluation score of the user behavior.
5. The method according to claim 1 or 4, wherein when the user does not belong to a user with abnormal behavior, performing a preset grading process on the user according to the user behavior evaluation score comprises:
when the user does not belong to the abnormal behavior user, acquiring a grading processing rule, wherein the grading processing rule comprises one or more user behavior evaluation score ranges and a processing rule corresponding to the user behavior evaluation score ranges;
determining a user behavior evaluation score range to which the user behavior evaluation score belongs;
and processing the user according to a processing rule corresponding to the user behavior evaluation score range to which the user behavior evaluation score belongs.
6. A behavior processing apparatus characterized by comprising:
the system comprises an acquisition module, a storage module and a processing module, wherein the acquisition module is configured to acquire user behavior evaluation data, and the user behavior evaluation data is structured data composed of user behavior evaluation subdata;
the calculation module is configured to calculate a user behavior evaluation score according to the user behavior evaluation data when the user behavior meets a preset condition;
the processing module is configured to execute preset grading processing on the user according to the user behavior evaluation score, and comprises: the judging submodule is configured to judge whether the user belongs to an abnormal behavior user; a first processing submodule configured to perform exception processing on the user when the user belongs to an exception behavior user, the performing exception processing including correcting a calculated deviation of the user behavior evaluation score; and the second processing submodule is configured to execute preset grading processing on the user according to the user behavior evaluation score when the user does not belong to the abnormal behavior user.
7. The apparatus of claim 6, wherein the obtaining module comprises:
a first obtaining submodule configured to obtain initial user behavior evaluation data;
a filtering submodule configured to filter the initial user behavior evaluation data;
and the operation submodule is configured to perform structuring operation on the filtered user behavior evaluation data according to a preset structure rule.
8. The apparatus of claim 6 or 7, wherein the computing module comprises:
the second obtaining submodule is configured to obtain a user behavior evaluation rule when the user behavior meets a preset condition;
the first calculation submodule is configured to calculate a user behavior evaluation sub-score corresponding to the user behavior evaluation sub-data based on the user behavior evaluation rule;
and the second calculation submodule is configured to perform weighted calculation on the user behavior evaluation sub-scores to obtain user behavior evaluation scores corresponding to the user behavior evaluation data.
9. The apparatus of claim 6, wherein the first processing sub-module comprises:
the checking sub-module is configured to check the user behavior evaluation score of the user when the user meets a first abnormal behavior condition;
a third computing sub-module configured to recalculate the user rating score for the user behavior when the user satisfies a second abnormal behavior condition.
10. The apparatus of claim 6 or 9, wherein the second processing sub-module comprises:
a third obtaining sub-module, configured to obtain a hierarchical processing rule when the user does not belong to an abnormal behavior user, where the hierarchical processing rule includes one or more user behavior evaluation score ranges and a processing rule corresponding to the user behavior evaluation score range;
a determination submodule configured to determine a user behavior evaluation score range to which the user behavior evaluation score belongs;
and the third processing submodule is configured to process the user according to a processing rule corresponding to the user behavior evaluation score range to which the user behavior evaluation score belongs.
11. An electronic device comprising a memory and a processor; wherein the content of the first and second substances,
the memory is configured to store one or more computer instructions, wherein the one or more computer instructions are executed by the processor to implement the method steps of any of claims 1-5.
12. A computer-readable storage medium having stored thereon computer instructions, characterized in that the computer instructions, when executed by a processor, carry out the method steps of any of claims 1-5.
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| CN102945279A (en) * | 2012-11-14 | 2013-02-27 | 清华大学 | Evaluating method and device of influence effect of microblog users |
| CN103577991A (en) * | 2012-08-03 | 2014-02-12 | 阿里巴巴集团控股有限公司 | User identification method and device |
| CN103631898A (en) * | 2013-11-19 | 2014-03-12 | 西安电子科技大学 | Multimedia social network reputation value calculating method based on strong and weak contact feedback |
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| CN103577991A (en) * | 2012-08-03 | 2014-02-12 | 阿里巴巴集团控股有限公司 | User identification method and device |
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