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CN-121981831-A - Risk level assessment method, risk level assessment device, electronic equipment, medium and product

CN121981831ACN 121981831 ACN121981831 ACN 121981831ACN-121981831-A

Abstract

The specification provides a risk level assessment method, a risk level assessment device, electronic equipment, a risk level assessment medium and a risk level assessment product, and relates to the technical field of computers. The method comprises the steps of obtaining a scene set corresponding to a data set according to the data set after the data set in a database is obtained, and further carrying out risk classification on the scene set according to the number of various types of scenes in the scene set to obtain target risk levels corresponding to the various types of scenes. By the method, the evaluation efficiency of the scene risk level can be improved.

Inventors

  • GUO LIN

Assignees

  • 重庆蚂蚁消费金融有限公司

Dates

Publication Date
20260505
Application Date
20260327

Claims (11)

  1. 1. A method of evaluating a risk level, the method comprising: acquiring a data set in a database; Based on the data set, obtaining a scene set corresponding to the data set; Obtaining target risk levels corresponding to various types of scenes based on the number of the various types of scenes in the scene set, wherein the risk levels refer to severity of influence on the whole system when abnormal conditions occur in the various types of scenes; The evaluation method further comprises: Acquiring a preset field extraction rule; the step of obtaining a scene set corresponding to the data set based on the data set comprises the following steps: And obtaining the scene set corresponding to the data set based on the field extraction rule and the data set.
  2. 2. The evaluation method according to claim 1, wherein the obtaining, based on the number of each type of scene in the scene set, a target risk level corresponding to each type of scene includes: Determining the number duty ratio corresponding to each type of scene based on the number of each type of scene and the total number of data pieces in the data set; And obtaining the target risk levels corresponding to the various types of scenes based on the quantity duty ratio and a target mapping relation, wherein the target risk levels are positively correlated with the quantity duty ratio, and the target mapping relation is used for indicating the mapping relation between the quantity duty ratio range and the risk levels.
  3. 3. The evaluation method according to claim 1, wherein the obtaining the scene set corresponding to the data set based on the field extraction rule and the data set includes: performing field extraction on the data set based on the field extraction rule to obtain a field value set corresponding to the data set; and obtaining the scene set corresponding to the data set based on the field value set.
  4. 4. The evaluation method according to claim 3, wherein when the data set includes at least one set of target type data, the performing field extraction on the data set based on the field extraction rule to obtain a set of field values corresponding to the data set includes: splitting the target type data aiming at each group of the target type data to obtain at least two pieces of data corresponding to the target type data; Obtaining at least two data sets based on at least two pieces of data corresponding to each set of the target type data and/or non-target type data in the data set; Performing field extraction on the at least two data sets based on the field extraction rule to obtain the field value set corresponding to the data set; the target type data comprises at least two pieces of data which belong to the same data table and have consistent data structures.
  5. 5. The evaluation method according to claim 3, wherein the field extraction rule includes a preset field name, and the performing field extraction on the data set based on the field extraction rule to obtain a field value set corresponding to the data set includes: Aiming at each group of data in the data set, obtaining candidate field values corresponding to the preset field names in each group of data based on the preset field names and each group of data; If all the candidate field values corresponding to the preset field names exist in each group of data, determining that the candidate field values are included in the field value set corresponding to each group of data; and obtaining the field value set corresponding to the data set based on the field values corresponding to the groups of data.
  6. 6. The method for evaluating according to claim 5, wherein the field extraction rule further includes a preset value manner, and the obtaining candidate field values corresponding to the preset field names in the respective sets of data based on the preset field names and the respective sets of data includes: determining an extractor corresponding to the preset value mode based on the preset value mode; and extracting corresponding field values in each group of data based on the preset value mode by the extractor to obtain candidate field values corresponding to the preset field names in each group of data.
  7. 7. The method of evaluating according to claim 6, wherein the extractor comprises at least one of a normal field extractor, a set field extractor, a digital field extractor, a table extractor, an equivalent scene extractor, and a transparent field extractor.
  8. 8. An evaluation device of risk level, characterized in that the evaluation device comprises: The data monitoring module is used for acquiring a data set in the database; the scene dividing module is used for obtaining a scene set corresponding to the data set based on the data set; The risk assessment module is used for obtaining target risk levels corresponding to various types of scenes based on the number of the various types of scenes in the scene set, wherein the risk levels refer to severity degrees of influence on the whole system when abnormal conditions occur in the various types of scenes; the data monitoring module is also used for acquiring a preset field extraction rule; The scene dividing module is specifically configured to obtain the scene set corresponding to the data set based on the field extraction rule and the data set.
  9. 9. An electronic device, the electronic device comprising: A memory for storing executable program code; a processor for calling and running the executable program code from the memory, causing the electronic device to perform the assessment method according to any one of claims 1 to 7.
  10. 10. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program which, when executed, implements the evaluation method according to any one of claims 1 to 7.
  11. 11. A computer program product, characterized in that it stores at least one instruction which, when executed by a processor, implements the evaluation method according to any one of claims 1 to 7.

Description

Risk level assessment method, risk level assessment device, electronic equipment, medium and product Technical Field The present disclosure relates to the field of computer technology, and more particularly, to a risk level assessment method, apparatus, electronic device, medium, and product in the field of computer technology. Background In the testing process of the consumption finance scene, in order to ensure the comprehensiveness of the test, the whole coverage of part of the risk level scene is required. Before that, firstly, the scenes are classified into grades, and the scenes needing full coverage are determined. The existing scene risk level classification mainly depends on subjective experience of testers or developers, or is evaluated in an offline mode, so that the risk level evaluation efficiency is low. Therefore, how to improve the evaluation efficiency of the scene risk level becomes a technical problem to be solved. Disclosure of Invention The specification provides a risk level assessment method, a risk level assessment device, electronic equipment, media and products. In a first aspect, embodiments of the present disclosure provide a method for evaluating a risk level, including: acquiring a data set in a database; acquiring a scene set corresponding to the data set based on the data set; And obtaining target risk levels corresponding to the various types of scenes based on the number of the various types of scenes in the scene set. With reference to the first aspect, in some possible implementations, based on the number of each type of scene in the scene set, obtaining a target risk level corresponding to each type of scene includes: Determining the number proportion corresponding to each type of scene based on the number of each type of scene and the total number of data pieces in the data set; And obtaining target risk levels corresponding to various scenes based on the quantity duty ratio and the target mapping relation, wherein the target risk levels and the quantity duty ratio are positively correlated, and the target mapping relation is used for indicating the mapping relation between the quantity duty ratio range and the risk levels. With reference to the first aspect, in some possible implementations, the evaluation method further includes: Acquiring a preset field extraction rule; Based on the data set, obtaining a scene set corresponding to the data set, including: And obtaining a scene set corresponding to the data set based on the field extraction rule and the data set. With reference to the first aspect, in some possible implementations, based on the field extraction rule and the data set, obtaining a scene set corresponding to the data set includes: Performing field extraction on the data set based on the field extraction rule to obtain a field value set corresponding to the data set; And obtaining a scene set corresponding to the data set based on the field value set. With reference to the first aspect, in some possible implementations, when the data set includes at least one set of target type data, field extraction is performed on the data set based on a field extraction rule to obtain a field value set corresponding to the data set, including: Splitting the target type data aiming at each group of target type data to obtain at least two pieces of data corresponding to the target type data; obtaining at least two data groups based on at least two pieces of data corresponding to each group of target type data and/or non-target type data in a data set; performing field extraction on at least two data sets based on a field extraction rule to obtain a field value set corresponding to the data set; The target type data comprises at least two pieces of data which belong to the same data table and have consistent data structures. With reference to the first aspect, in some possible implementations, the field extraction rule includes a preset field name, and field extraction is performed on the data set based on the field extraction rule to obtain a field value set corresponding to the data set, including: Aiming at each group of data in the data set, obtaining candidate field values corresponding to the preset field names in each group of data based on the preset field names and each group of data; If all the candidate field values corresponding to the preset field names exist in each group of data, determining that the field values corresponding to each group of data comprise the candidate field values; And obtaining a field value set corresponding to the data set based on the field values corresponding to the data sets. With reference to the first aspect, in some possible implementations, the field extraction rule further includes a preset value manner, and based on the preset field name and each set of data, obtaining a candidate field value corresponding to the preset field name in each set of data includes: Determining an extractor corresponding to the preset value mode b