CN110609929A - Data processing method and device, storage medium and electronic equipment - Google Patents
Data processing method and device, storage medium and electronic equipment Download PDFInfo
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Abstract
The present disclosure relates to a data processing method and apparatus, a storage medium, and an electronic device, the data processing method including: according to the characteristic information of the target user, determining the hit result of the target user on each effective rule in the preset effective rule group; determining the score value of each effective rule corresponding to the target user according to the hit result of the target user on each effective rule and a pre-established rule score model, wherein the rule score model comprises the score values of each effective rule in the effective rule group under the condition that the effective rule is hit and not hit respectively; and determining the final score value of the target user according to the score values of the target user corresponding to the effective rules in the effective rule group. Through the technical scheme, the probability of user misevaluation can be reduced, and the accuracy of the evaluation result is improved.
Description
Technical Field
The present disclosure relates to the field of information technology, and in particular, to a data processing method and apparatus, a storage medium, and an electronic device.
Background
Under many service scenes such as strategy approval, anti-fraud and the like, user information needs to be processed, and a user is evaluated based on the user information. The evaluation result not only influences the convenience degree of the business handling of the user, but also influences the business authorization limit of the business handling enterprise to the user.
In the data processing method in the prior art, a series of rules are generally preset, whether a user hits the rules is judged according to the characteristic information of the user, and if the user does not hit any rule, the user is determined to fail to evaluate. The information processing mode of 'single point rejection' has the problems of high false evaluation probability and inaccurate evaluation result.
Disclosure of Invention
The present disclosure is directed to a data processing method and apparatus, a storage medium, and an electronic device, so as to solve the problems in the prior art.
In order to achieve the above object, according to a first aspect of embodiments of the present disclosure, there is provided a data processing method including:
according to the characteristic information of the target user, determining the hit result of the target user on each effective rule in the preset effective rule group;
determining the score value of each effective rule corresponding to the target user according to the hit result of the target user on each effective rule and a pre-established rule score model, wherein the rule score model comprises the score values of each effective rule in the effective rule group under the condition that the effective rule is hit and not hit respectively;
and determining the final score value of the target user according to the score values of the target user corresponding to the effective rules in the effective rule group.
Optionally, a method for setting the valid rule group is further included, and the method for setting the valid rule group includes:
according to the value of each index of each rule in a preset rule base, sequencing all the rules in the rule base according to different indexes;
aiming at each rule in the rule base, determining the contribution degree of the rule according to the sequencing result of the rule under each index;
and selecting a plurality of rules from the rule base as effective rules according to the contribution degree of each rule in the rule base, and combining to obtain the effective rule group.
Optionally, the values of the indexes of each rule are determined according to a historical data set corresponding to the rule, where the historical data set includes feature information of different users and evaluation results obtained by evaluating the users using the rule.
Optionally, the determining a final score value of the target user according to the score values of the target user corresponding to the effective rules in the effective rule group includes:
and taking the sum of the scoring values of the target user corresponding to each effective rule as the final scoring value of the target user.
According to a second aspect of the embodiments of the present disclosure, there is provided a data processing apparatus including:
the first determining module is used for determining the hit result of the target user on each effective rule in the preset effective rule group according to the characteristic information of the target user;
the second determination module is used for determining the score value of each effective rule corresponding to the target user according to the hit result of the target user on each effective rule and a pre-established rule score model, wherein the rule score model comprises the score values of each effective rule in the effective rule group under the two conditions of hit and miss respectively;
and the scoring module is used for determining the final scoring value of the target user according to the scoring values of all effective rules in the effective rule group corresponding to the target user.
Optionally, the apparatus further includes a setting module, where the setting module is configured to set the valid rule group, and the setting module includes:
the sorting submodule is used for sorting all the rules in the rule base according to different indexes and the values of all the indexes of each rule in a preset rule base;
the determining submodule is used for determining the contribution degree of each rule in the rule base according to the sequencing result of the rule under each index;
and the selection submodule is used for selecting a plurality of rules from the rule base as effective rules according to the contribution degree of each rule in the rule base, and combining the effective rules to obtain the effective rule group.
Optionally, the values of the indexes of each rule are determined according to a historical data set corresponding to the rule, where the historical data set includes feature information of different users and evaluation results obtained by evaluating the users using the rule.
Optionally, the scoring module comprises:
and the scoring submodule is used for taking the sum of the scoring values of the target user corresponding to each effective rule as the final scoring value of the target user.
According to a third aspect of embodiments of the present disclosure, there is provided a computer readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of the method of the first aspect.
According to a fourth aspect of the embodiments of the present disclosure, there is provided an electronic apparatus including: a memory having a computer program stored thereon; a processor for executing the computer program in the memory to implement the steps of the method of the first aspect.
Through the technical scheme, the following technical effects can be at least achieved: the method comprises the steps of grading the user by adopting an effective rule group comprising a plurality of effective rules, obtaining the score value of the user corresponding to each effective rule no matter whether each effective rule is hit by the user, and determining the final score value of the user according to the score value of each effective rule corresponding to the user.
Additional features and advantages of the disclosure will be set forth in the detailed description which follows.
Drawings
The accompanying drawings, which are included to provide a further understanding of the disclosure and are incorporated in and constitute a part of this specification, illustrate embodiments of the disclosure and together with the description serve to explain the disclosure without limiting the disclosure. In the drawings:
FIG. 1 is a flow chart illustrating a method of data processing according to an exemplary embodiment of the present disclosure;
FIG. 2 is a block diagram illustrating a data processing apparatus according to an exemplary embodiment of the present disclosure;
FIG. 3 is a block diagram illustrating a data processing apparatus according to another exemplary embodiment of the present disclosure;
fig. 4 is a block diagram illustrating an electronic device according to an exemplary embodiment of the present disclosure.
Detailed Description
The following detailed description of specific embodiments of the present disclosure is provided in connection with the accompanying drawings. It should be understood that the detailed description and specific examples, while indicating the present disclosure, are given by way of illustration and explanation only, not limitation.
It is noted that the terms "first," "second," and the like in the description and claims of the present disclosure and in the above-described drawings are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order.
The present disclosure provides a data processing method. As shown in fig. 1, fig. 1 is a flowchart illustrating a data processing method according to an exemplary embodiment of the present disclosure, which may include the steps of:
s101, determining a hit result of the target user on each effective rule in a preset effective rule group based on the characteristic information of the target user.
The characteristic information of the target user may be information representing the identity of the target user, and may include, but is not limited to, at least one of the following information: academic calendar, income, occupation, etc.
The set of validity rules includes a plurality of validity rules, such as: the subject and the income of the user reach the set threshold, and the like, which is not limited by the embodiment of the disclosure.
For each valid rule, the hit of the target user to the valid rule includes either hitting the valid rule or missing the valid rule.
In an optional implementation manner, feature information of the target user may be obtained, and for each valid rule, whether the target user hits the valid rule is determined according to the feature information of the target user, so as to obtain a hit result of the target user on the valid rule.
S102, inputting the hit result of the target user to each effective rule into a pre-established rule scoring model, and determining the scoring value of each effective rule corresponding to the target user.
The rule scoring model comprises scoring values of all effective rules in the effective rule group under the two conditions of hit and miss respectively.
S103, determining the final score value of the target user according to the score values of all effective rules in the effective rule group corresponding to the target user.
In an alternative implementation manner, the sum of the score values of the target user corresponding to each valid rule may be used as the final score value of the target user.
According to the data processing method, the effective rule group comprising the effective rules is adopted to score the user, the score value of the effective rule corresponding to the user is obtained whether each effective rule is hit by the user or not, and the final score value of the user is determined according to the score value of each effective rule corresponding to the user.
It should be noted that, in an actual service scenario, the data processing method may be combined with other scoring methods to evaluate the user. Therefore, the user is evaluated through the scoring values obtained by different methods, and the accuracy of the evaluation result can be further improved.
In another exemplary embodiment of the present disclosure, the data processing method may further include a method of setting a valid rule group. Specifically, the method for setting the valid rule group includes: firstly, according to the value of each index of each rule in a preset rule base, sequencing all the rules in the rule base according to different indexes; then, aiming at each rule in the rule base, determining the contribution degree of the rule according to the sequencing result of the rule under each index; and finally, selecting a plurality of rules from the rule base as effective rules according to the contribution degree of each rule in the rule base, and combining to obtain an effective rule group.
The indexes corresponding to each rule may include probability odds, false positive rate, and the like, which is not limited in the embodiment of the disclosure. It should be noted that the values of the indicators of each rule are determined according to the historical data set corresponding to the rule, where the historical data set includes the feature information of different users and the evaluation results obtained by evaluating the users using the rule.
For example, the probability of a rule refers to the ratio between the probability of a sample that is overdue and the probability of a sample that is not overdue for the rule; the misjudgment rate of the rule refers to the ratio of the number of users who perform false early warning through the rule to the total number of users who perform early warning.
In specific implementation, all the rules in the rule base can be sorted according to different indexes such as probability, misjudgment rate and the like, so as to obtain the arrangement sequence of all the rules under different indexes. Further, different weights can be set for different indexes, and for each rule, the contribution degree of the rule is determined according to the sorting result of the rule under each index and the weight of each index, wherein the larger the contribution degree of the rule is, the larger the role of the rule on the evaluation user is; conversely, the less the rule will play a role in evaluating the user. Further, a plurality of rules with contribution degrees larger than a set value can be selected from the rule base to serve as effective rules, or the rules in the rule base can be sorted according to the contribution degrees, and a plurality of rules are selected according to the obtained sorting result to serve as effective rules to obtain an effective rule group.
Therefore, all the rules in the rule base are sorted according to different indexes, the contribution degree of each rule is obtained by integrating the sorting results of each rule under different indexes, and an effective rule is selected according to the contribution degree, so that the accuracy of the user scoring result can be further improved, and the misjudgment probability is reduced. It should be noted that the scoring model according to the above embodiments of the present disclosure may be established based on the historical scoring data of each rule by using a method existing in the art.
By establishing the rule scoring model, each effective rule in the effective rule group participates in user scoring no matter whether the effective rule is hit or not, the condition that the scoring value is low due to the fact that the user does not hit any rule can be avoided, and the accuracy of the scoring result is improved. In addition, when the effective rules in the effective rule group are updated, the updated effective rules can participate in user scoring in a mode of updating the model, and then defense holes cannot be generated due to the fact that the effective rules are updated.
Based on the same inventive concept, the present disclosure also provides a data processing apparatus. As shown in fig. 2, fig. 2 is a block diagram illustrating a data processing apparatus according to an exemplary embodiment, the apparatus 200 including:
a first determining module 201, configured to determine, according to the feature information of the target user, a hit result of the target user on each valid rule in the predetermined valid rule group;
a second determining module 202, configured to determine, according to a hit result of the target user on each valid rule and a pre-established rule scoring model, a scoring value of each valid rule corresponding to the target user, where the rule scoring model includes scoring values of each valid rule in the valid rule group in the case of being hit and not being hit, respectively;
and the scoring module 203 is used for determining the final score value of the target user according to the score values of the target user corresponding to the effective rules in the effective rule group.
Optionally, as shown in fig. 3, the apparatus further includes a setting module 204, where the setting module 204 is configured to set the valid rule group, and the setting module 204 includes:
the sorting submodule 241 is configured to sort all the rules in the rule base according to different indexes respectively according to values of indexes of each rule in a predetermined rule base;
the determining submodule 242 is configured to determine, for each rule in the rule base, a contribution degree of the rule according to a sorting result of the rule under each index;
and a selecting submodule 243, configured to select multiple rules from the rule base as effective rules according to the contribution degrees of the rules in the rule base, and combine the rules to obtain the effective rule group.
Optionally, the values of the indexes of each rule are determined according to a historical data set corresponding to the rule, where the historical data set includes feature information of different users and evaluation results obtained by evaluating the users using the rule.
Optionally, as shown in fig. 3, the scoring module 203 includes:
the scoring submodule 231 is configured to use a sum of the scoring values of the target user corresponding to each effective rule as a final scoring value of the target user.
With regard to the apparatus in the above-described embodiment, the specific manner in which each module performs the operation has been described in detail in the embodiment related to the method, and will not be elaborated here.
It is obvious to those skilled in the art that, for convenience and simplicity of description, the foregoing division of the functional modules is merely used as an example, and in practical applications, the above function distribution may be performed by different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to perform all or part of the above described functions.
Based on the same inventive concept, the present disclosure also provides a computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, implements the steps of the data processing method according to any of the above embodiments.
Based on the same inventive concept, the present disclosure also provides an electronic device, comprising: a memory having a computer program stored thereon; a processor for executing the computer program in the memory to implement the steps of the data processing method according to any of the above embodiments.
Fig. 4 is a block diagram illustrating an electronic device 400 according to an example embodiment. As shown in fig. 4, the electronic device 400 may include: a processor 401 and a memory 402. The electronic device 400 may also include one or more of a multimedia component 403, an input/output (I/O) interface 404, and a communications component 405.
The processor 401 is configured to control the overall operation of the electronic device 400, so as to complete all or part of the steps in the data processing method. The memory 402 is used to store various types of data to support operation at the electronic device 400, such as instructions for any application or method operating on the electronic device 400 and application-related data, such as contact data, transmitted and received messages, pictures, audio, video, and so forth. The Memory 402 may be implemented by any type of volatile or non-volatile Memory device or combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic Memory, flash Memory, magnetic disk or optical disk. The multimedia components 403 may include a screen and an audio component. Wherein the screen may be, for example, a touch screen and the audio component is used for outputting and/or inputting audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signal may further be stored in the memory 402 or transmitted through the communication component 405. The audio assembly also includes at least one speaker for outputting audio signals. The I/O interface 404 provides an interface between the processor 401 and other interface modules, such as a keyboard, mouse, buttons, etc. These buttons may be virtual buttons or physical buttons. The communication component 405 is used for wired or wireless communication between the electronic device 400 and other devices. Wireless communication, such as Wi-Fi, bluetooth, Near Field Communication (NFC), 2G, 3G, or 4G, or a combination of one or more of them, so that the corresponding communication component 405 may include: Wi-Fi module, bluetooth module, NFC module.
In an exemplary embodiment, the electronic Device 400 may be implemented by one or more Application Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), Programmable Logic Devices (PLDs), Field Programmable Gate Arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components for performing the above-mentioned data Processing method.
In another exemplary embodiment, there is also provided a computer readable storage medium comprising program instructions which, when executed by a processor, implement the steps of the data processing method described above. For example, the computer readable storage medium may be the memory 402 comprising program instructions executable by the processor 401 of the electronic device 400 to perform the data processing method described above.
The preferred embodiments of the present disclosure are described in detail with reference to the accompanying drawings, however, the present disclosure is not limited to the specific details of the above embodiments, and various simple modifications may be made to the technical solution of the present disclosure within the technical idea of the present disclosure, and these simple modifications all belong to the protection scope of the present disclosure.
It should be noted that the various features described in the above embodiments may be combined in any suitable manner without departing from the scope of the invention. In order to avoid unnecessary repetition, various possible combinations will not be separately described in this disclosure.
In addition, any combination of various embodiments of the present disclosure may be made, and the same should be considered as the disclosure of the present disclosure, as long as it does not depart from the spirit of the present disclosure.
Claims (10)
1. A data processing method, comprising:
according to the characteristic information of the target user, determining the hit result of the target user on each effective rule in the preset effective rule group;
determining the score value of each effective rule corresponding to the target user according to the hit result of the target user on each effective rule and a pre-established rule score model, wherein the rule score model comprises the score values of each effective rule in the effective rule group under the condition that the effective rule is hit and not hit respectively;
and determining the final score value of the target user according to the score values of the target user corresponding to the effective rules in the effective rule group.
2. The method of claim 1, further comprising setting the active rule set by a method comprising:
according to the value of each index of each rule in a preset rule base, sequencing all the rules in the rule base according to different indexes;
aiming at each rule in the rule base, determining the contribution degree of the rule according to the sequencing result of the rule under each index;
and selecting a plurality of rules from the rule base as effective rules according to the contribution degree of each rule in the rule base, and combining to obtain the effective rule group.
3. The method of claim 2, wherein the value of each index of each rule is determined according to a historical data set corresponding to the rule, and the historical data set comprises feature information of different users and evaluation results obtained by evaluating each user by using the rule.
4. The method according to any one of claims 1-3, wherein determining the final score value of the target user according to the score values of the target user corresponding to the respective valid rules in the valid rule group comprises:
and taking the sum of the scoring values of the target user corresponding to each effective rule as the final scoring value of the target user.
5. A data processing apparatus, comprising:
the first determining module is used for determining the hit result of the target user on each effective rule in the preset effective rule group according to the characteristic information of the target user;
the second determination module is used for determining the score value of each effective rule corresponding to the target user according to the hit result of the target user on each effective rule and a pre-established rule score model, wherein the rule score model comprises the score values of each effective rule in the effective rule group under the two conditions of hit and miss respectively;
and the scoring module is used for determining the final scoring value of the target user according to the scoring values of all effective rules in the effective rule group corresponding to the target user.
6. The apparatus of claim 5, further comprising a setting module configured to set the valid rule set, the setting module comprising:
the sorting submodule is used for sorting all the rules in the rule base according to different indexes and the values of all the indexes of each rule in a preset rule base;
the determining submodule is used for determining the contribution degree of each rule in the rule base according to the sequencing result of the rule under each index;
and the selection submodule is used for selecting a plurality of rules from the rule base as effective rules according to the contribution degree of each rule in the rule base, and combining the effective rules to obtain the effective rule group.
7. The apparatus of claim 6, wherein the value of each index of each rule is determined according to a historical data set corresponding to the rule, and the historical data set comprises feature information of different users and evaluation results obtained by evaluating each user by using the rule.
8. The apparatus of any one of claims 5-7, wherein the scoring module comprises:
and the scoring submodule is used for taking the sum of the scoring values of the target user corresponding to each effective rule as the final scoring value of the target user.
9. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 4.
10. An electronic device, comprising:
a memory having a computer program stored thereon;
a processor for executing the computer program in the memory to carry out the steps of the method of any one of claims 1 to 4.
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| CN201910829521.0A CN110609929B (en) | 2019-09-03 | 2019-09-03 | Data processing method and device, storage medium and electronic equipment |
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| CN111369344A (en) * | 2020-03-06 | 2020-07-03 | 中国建设银行股份有限公司 | Method and device for dynamically generating early warning rule |
| CN112182592A (en) * | 2020-12-01 | 2021-01-05 | 蚂蚁智信(杭州)信息技术有限公司 | Method and device for determining and processing safety of rule set |
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