CN114511200A

CN114511200A - Job data generation method and device, computer equipment and storage medium

Info

Publication number: CN114511200A
Application number: CN202210033726.XA
Authority: CN
Inventors: 明卫
Original assignee: Ping An Puhui Enterprise Management Co Ltd
Current assignee: Ping An Puhui Enterprise Management Co Ltd
Priority date: 2022-01-12
Filing date: 2022-01-12
Publication date: 2022-05-17

Abstract

The application relates to the technical field of artificial intelligence, and provides a method and a device for generating operation data, computer equipment and a storage medium, wherein the method comprises the following steps: when receiving a job data generation request triggered by a user, extracting a job index identifier from the job data generation request; acquiring a first data table corresponding to the operation index identification from a preset database; calling a calculation rule of the operation index corresponding to the operation index identification to calculate the data of the first data table to generate operation result data; and inputting the operation result data into a second data table to obtain a target data table corresponding to the operation index identification. The method and the device can quickly and accurately generate the target data table containing the operation result data of the operation index, ensure the accuracy of the obtained operation result data of the operation index, and improve the processing efficiency of generating the target data table. The method and the device can also be applied to the field of block chains, and the data such as the target data table can be stored on the block chains.

Description

Job data generation method and device, computer equipment and storage medium

Technical Field

The application relates to the technical field of artificial intelligence, in particular to a method and a device for generating operation data, computer equipment and a storage medium.

Background

In each loan institution company on the market, more and more clients transact loans, but the overdue payment of the clients is more and more frequent. Therefore, much attention has been paid to the specific execution of the post-loan acceptance job. Therefore, how to realize real-time data capable of quickly knowing relevant indexes of post-credit collection operation becomes a problem which needs to be solved at present. However, in the conventional method for generating the index data related to the job, a professional business analyst usually performs statistical analysis on the data in the recorded statement of the overdue case after credit so as to generate the result data of the corresponding job index. The manual data generation method needs to occupy a large amount of human resources and time, has large workload, low automation degree, low efficiency of generating result data and low accuracy of the generated result data.

Disclosure of Invention

The application mainly aims to provide a method and a device for generating operation data, a computer device and a storage medium, and aims to solve the technical problems that the existing method for generating operation-related index data needs to occupy a large amount of human resources and time, is high in workload, low in automation degree, low in efficiency of generating result data and low in accuracy of the generated result data.

The application provides a method for generating job data, which comprises the following steps:

judging whether a job data generation request triggered by a user is received; the operation data generation request carries an operation index identifier;

if the job data generation request is received, extracting the job index identification from the job data generation request;

acquiring a first data table corresponding to the operation index identification from a preset database;

calling a calculation rule of a work index corresponding to the work index identification to perform calculation processing on the data of the first data table, and generating work result data corresponding to the work index;

and inputting the operation result data into a preset second data table to obtain a target data table corresponding to the operation index.

Optionally, the job data generation request further carries user information, and before the step of obtaining the first data table corresponding to the job index identifier from the preset database, the method includes:

acquiring all identity verification results corresponding to the user information in the preset time period;

screening out an appointed authentication result from all the authentication results, and counting the number of the appointed authentication results to obtain the authentication failure times; wherein, the specified identity authentication result is the result that the identity authentication fails;

determining an authentication level of the user based on the authentication failure times;

acquiring a target verification rule corresponding to the authentication level, performing authentication on the user based on the target verification rule, and judging whether the authentication passes;

if the identity authentication is passed, generating an acquisition instruction for acquiring a first data table corresponding to the operation index identifier from a preset database;

and if the identity authentication is not passed, limiting the response to the operation data generation request.

Optionally, the step of obtaining a target verification rule corresponding to the authentication level, performing authentication on the user based on the target verification rule, and determining whether the authentication passes or not includes:

if the authentication grade of the user is the low-risk authentication grade, acquiring an authentication problem corresponding to the user information, and displaying the authentication problem and preset reminding information to remind the user to feed back the authentication problem through the reminding information;

acquiring feedback answer handwriting input by the user at a preset position;

determining whether the content corresponding to the feedback answer handwriting is consistent with the content of the standard answer of the verification question based on an OCR technology;

if the answer handwriting is consistent with the verification question, extracting first handwriting characteristic data of the feedback answer handwriting, and extracting second handwriting characteristic data of preset answer handwriting corresponding to the verification question;

judging whether the first handwriting characteristic data is matched with the second handwriting characteristic data;

and if the identity authentication is matched with the user, judging that the identity authentication passes, otherwise, judging that the identity authentication fails.

if the identity authentication level of the user is the high-risk authentication level, acquiring a fingerprint image of the user, and acquiring a pre-stored target fingerprint image corresponding to the user information;

calculating a first similarity between the fingerprint image and the target fingerprint image, and judging whether the first similarity is greater than a preset first similarity threshold value;

if the similarity is larger than the first similarity threshold, acquiring a palm print image of the user, and acquiring a pre-stored target palm print image corresponding to the user information;

calculating a second similarity between the palm print image and the target palm print image, and judging whether the second similarity is greater than a preset second similarity threshold value;

if the similarity is larger than the second similarity threshold, acquiring a first preset weight corresponding to the first similarity and acquiring a second preset weight corresponding to the second similarity;

carrying out weighted summation operation on the first similarity and the second similarity based on the first preset weight and the second preset weight to obtain a corresponding target comprehensive score;

judging whether the target comprehensive score is larger than a preset score threshold value or not;

and if the value is larger than the score threshold value, judging that the identity authentication is passed, otherwise, judging that the identity authentication is not passed.

Optionally, after the step of inputting the job result data into a preset second data table to obtain a target data table corresponding to the job index, the method includes:

acquiring the target data table;

calling a preset user authority data table, and inquiring an authority score corresponding to the user information from the user authority data table;

judging whether the permission score is larger than a preset permission score threshold value or not;

if the authority score is larger than the authority score threshold value, displaying the target data table;

if the authority score is not greater than the authority score threshold, performing data desensitization treatment on the target data table to obtain a desensitized target data table;

and displaying the target data table after desensitization.

Optionally, the step of performing data desensitization processing on the target data table to obtain a desensitized target data table includes:

acquiring a preset sensitive index list; the sensitive index list is internally stored with a plurality of sensitive indexes and level identifications respectively corresponding to each sensitive index;

matching each operation index contained in the target data table with all sensitive indexes contained in a sensitive index list, and screening out an index to be desensitized from all operation indexes contained in the target data table according to a matching result;

acquiring target level identifications corresponding to the indexes to be desensitized respectively from the sensitive index list;

generating desensitization rules respectively corresponding to the indexes to be desensitized based on the target level identification;

and performing one-to-one desensitization treatment on the index result data of each index to be desensitized based on each desensitization rule to obtain the desensitized target data table.

Optionally, the level identifier includes a high level identifier, a medium level identifier, and a low level identifier, and the step of generating desensitization rules corresponding to the to-be-desensitized indicators based on the target level identifier includes:

acquiring an appointed level identification corresponding to an appointed index to be desensitized; the specified index to be desensitized is any one of all indexes to be desensitized;

if the designated level identification is the high-level identification, generating a first desensitization rule corresponding to the designated index to be desensitized; the first desensitization rule is used for performing data desensitization processing on index result data corresponding to the specified to-be-desensitized index by adopting a preset encryption mode;

if the designated level identification is the medium level identification, generating a second desensitization rule corresponding to the designated index to be desensitized; the second desensitization rule is used for performing data desensitization processing on index result data corresponding to the specified to-be-desensitized index by adopting an alternative mode;

the designated level identification is the low-level identification, and a third desensitization rule corresponding to the designated index to be desensitized is generated; and performing data desensitization treatment on the index result data corresponding to the specified index to be desensitized by adopting a fuzzy mode according to the third desensitization rule.

The present application also provides an apparatus for generating job data, including:

the first judgment module is used for judging whether a job data generation request triggered by a user is received or not; the operation data generation request carries an operation index identifier;

the extracting module is used for extracting the operation index identification from the operation data generation request if the operation data generation request is received;

the first acquisition module is used for acquiring a first data table corresponding to the operation index identifier from a preset database;

the first generation module is used for calling a calculation rule of the operation index corresponding to the operation index identification to calculate and process the data of the first data table and generate operation result data corresponding to the operation index;

and the second generation module is used for inputting the operation result data into a preset second data table to obtain a target data table corresponding to the operation index.

The present application further provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

The present application also provides a computer-readable storage medium having stored thereon a computer program which, when being executed by a processor, carries out the steps of the above-mentioned method.

The method and the device for generating the job data, the computer equipment and the storage medium have the following beneficial effects:

the method, the device, the computer equipment and the storage medium for generating the job data are different from the existing mode of generating the job result data of the job index by manual statistical processing, after receiving a job data generation request triggered by a user, extracting an operation index identifier from the operation data generation request, acquiring a first data table corresponding to the operation index identifier from a preset database, calling a calculation rule of an operation index corresponding to the operation index identifier to perform calculation processing on data of the first data table to generate operation result data corresponding to the operation index, and finally inputting the operation result data into a preset second data table to obtain a target data table corresponding to the operation index, therefore, the generation of the operation result data of the operation index can be rapidly completed in an automatic mode. By the method and the device, the target data sheet containing the operation result data of the operation index can be automatically, quickly and accurately generated according to the first data sheet corresponding to the operation index, so that a large amount of human resources do not need to be occupied, the automatic generation of the target data sheet is realized, the workload is greatly reduced, the accuracy of the obtained operation result data of the operation index is effectively ensured, and the processing efficiency of generating the target data sheet is improved.

Drawings

Fig. 1 is a flowchart illustrating a method of generating job data according to an embodiment of the present application;

fig. 2 is a schematic structural diagram of a job data generation apparatus according to an embodiment of the present application;

fig. 3 is a schematic structural diagram of a computer device according to an embodiment of the present application.

The implementation, functional features and advantages of the objectives of the present application will be further explained with reference to the accompanying drawings.

Detailed Description

It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application. The embodiment of the application can acquire and process related data based on an artificial intelligence technology. Among them, Artificial Intelligence (AI) is a theory, method, technique and application system that simulates, extends and expands human Intelligence using a digital computer or a machine controlled by a digital computer, senses the environment, acquires knowledge and uses the knowledge to obtain the best result.

The artificial intelligence infrastructure generally includes technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technologies, operation/interaction systems, mechatronics, and the like. The artificial intelligence software technology mainly comprises a computer vision technology, a robot technology, a biological recognition technology, a voice processing technology, a natural language processing technology, machine learning/deep learning and the like.

Referring to fig. 1, a method for generating job data according to an embodiment of the present application includes:

s10: judging whether a job data generation request triggered by a user is received; the operation data generation request carries an operation index identifier;

s20: if the job data generation request is received, extracting the job index identification from the job data generation request;

s30: acquiring a first data table corresponding to the operation index identification from a preset database;

s40: calling a calculation rule of a work index corresponding to the work index identification to perform calculation processing on the data of the first data table, and generating work result data corresponding to the work index;

s50: and inputting the operation result data into a preset second data table to obtain a target data table corresponding to the operation index.

As described in the above steps S10 to S50, the execution subject of the embodiment of the present method is a generation apparatus of job data. In practical applications, the device for generating the operation data may be implemented by a virtual device, such as a software code, or may be implemented by a physical device in which a relevant execution code is written or integrated, and may perform human-computer interaction with a user through a keyboard, a mouse, a remote controller, a touch panel, or a voice control device. The device for generating the job data in the embodiment can automatically, quickly and accurately generate the target data table containing the job result data of the job index according to the first data table corresponding to the job index, thereby realizing the automatic generation of the target data table, effectively ensuring the accuracy of the obtained job result data of the job index, and improving the processing efficiency of generating the target data table. Specifically, firstly, judging whether a job data generation request triggered by a user is received; and the operation data generation request carries an operation index identifier. The job data generation request is a request triggered by a user to generate job result data of a corresponding job index. The operation index may refer to an index of the post-loan operation, and may include, for example, a total account amount, an effective coverage rate month-to-month ratio, a principal availability rate month-to-month ratio, a per-average user duration month-to-month ratio, a per-average user number of times per month-to-month ratio, and the like. In addition, the operation index identification may refer to name information of the operation index. In addition, the job data generation request may also carry user information, which may refer to name information or id information of the user. And if the job data generation request is received, extracting the job index identification from the job data generation request. The job data generation request may be parsed to extract user information from the job index identifier.

And then acquiring a first data table corresponding to the operation index identification from a preset database. The preset database can also be called a first database, the first database can be specifically an oracle database, and the oracle database is a source database stored with a first data table corresponding to the operation index identifier. The first data table corresponding to the operation index identification can be extracted from the first database by using spark codes written according to actual data extraction requirements in advance. And the timing task can be configured by the azkaban task scheduling tool to execute the extraction operation, and the timing time is not limited, for example, the timing can be timed to be 1 point 30 minutes in the morning every day. Specifically, when the operation index indicates an index of an after-credit operation, the first data table is specifically an overdue case call detail table (overlap _ acc _ talk _ detail). In addition, after the first data table is obtained from the preset database, the first data table may be sent to a preset second database. The second database may specifically be a hive database. The step of sending the first data table to the preset second database means that the first data table is synchronized into the hive database, and after the synchronization of the first data table is completed in the hive database, a corresponding data synchronization completion identifier can be generated.

And subsequently calling a calculation rule of the operation index corresponding to the operation index identification to perform calculation processing on the data of the first data table, and generating operation result data corresponding to the operation index. The calculation rule may be a calculation logic rule for writing generated job result data for generating a job index in advance according to an actual service requirement. Specifically, when the data synchronization completion flag is detected, the calculation rule may be used to perform calculation processing on the data in the first data table in the second database, specifically, a pre-writing is performed in the second database to generate a hivesql statement corresponding to the calculation rule, so that the hivesql statement is executed to perform calculation processing on the data in the first data table in the second database according to the calculation rule, and further, the job result data corresponding to the job index is generated. The operation index may refer to an index of the post-loan operation, and may include, for example, a total account amount, an effective coverage rate month-to-month ratio, a principal availability rate month-to-month ratio, a per-average user duration month-to-month ratio, a per-average user number of times per month-to-month ratio, and the like. The calculation rule of the operation index can comprise: (1) total account amount: the case distribution time in the corresponding architecture personnel queue is the account total number of the current month; (2) effective coverage rate: the account proportion (numerator/denominator) covered by the effective call (denominator: the case distribution time in the corresponding architecture personnel queue is the account total number of the current month; numerator: the number of the effective call accounts in the corresponding denominator case range; effective call definition: connection and call duration is more than or equal to 5 seconds); (3) effective coverage rate-to-month-to-ring ratio: (effective coverage of current month-effective coverage of last month)/effective coverage of last month; (4) effective coverage rate is comparable to month: (effective coverage of current month-effective coverage of same month in previous year)/effective coverage of same month in previous year; (5) the per se availability ratio: account occupation ratio (numerator/denominator) which can be effectively connected by an account (denominator: the total account number of cases in a staff queue under a corresponding architecture is the total account number of the current month; numerator: the number of accounts which can effectively communicate under the case range of the corresponding denominator and is given to the account owner; effective communication definition: connection and communication time length is more than or equal to 5 seconds); (6) the I availability ratio month-to-ring ratio: (the self-association rate of the current month-the self-association rate of the last month)/the self-association rate of the last month; (7) the I liabilities are comparable to the month: (the principal availability ratio of the current month-the principal availability ratio of the same month in the previous year)/the principal availability ratio of the same month in the previous year; (8) the average time of the households is as follows: the time length (numerator/denominator) of all the users for making calls (denominator: the total account number of the current month corresponding to the case distribution time in the staff queue under the architecture; numerator: the total effective call time length under the case range corresponding to the denominator; effective call definition: connection and call time length more than or equal to 5 seconds); (9) average household time-month-ring ratio: (average household duration of current month-average household duration of last month)/average household duration of last month; (10) average family time and month are compared: (average household duration of current month-average household duration of the same month in the previous year)/average household duration of the same month in the previous year; (11) average number of times of households: the number of times (numerator/denominator) that the user dials the call (denominator: the total number of accounts in the current month corresponding to the case allocation time in the staff queue under the framework; numerator: the total number of effective calls corresponding to the case range of the denominator; effective call definition: connection and call duration is more than or equal to 5 seconds); (12) average number of households per month-to-ring ratio: (average number of households in current month-average number of households in last month)/average number of households in last month; (13) the average times of the households are the same as the months: (average number of households in the current month-average number of households in the same month in the previous year)/average number of households in the same month in the previous year. In addition, when the calculation generates the job result data, a corresponding result data calculation completion identifier is generated subsequently.

And finally, inputting the operation result data into a preset second data table to obtain a target data table corresponding to the operation index. The second data table is a data table stored in a third database, and the third database may be an hbase database. After the result data calculation completion identifier is detected, the operation result data can be input into a second data table of a preset third database, and then a target data table corresponding to the operation index is obtained. In addition, the method can read out the job result data in the target data table of the hbase database by using the scale code writing interface, and access the backend interface through vue on the preset job bulletin board page to display the result data of each job index dimension in the target data table on the page, and can provide query and download functions. Further, after the target data table is generated, the target data table can be displayed. The manner of displaying the target data table is not limited. For example, a preset operation bulletin board can be used to display the result data of each operation index contained in the target data table. For the case collection service scene, the operation related index details and the change trend of the month ring ratio/month year-on-year ratio of the operation related indexes can be visually displayed through the billboard page, so that the management layer can visually see the accurate collection operation data and change trend at any time, know the progress trend of the working efficiency of the staff, and can be used as a support to timely adjust and formulate various high-pertinence and high-efficiency working strategies and urge the staff to further improve the working efficiency. Finally, the overdue cases can follow up in time, the case recovery accelerating efficiency is improved, and the loss is reduced.

In this embodiment, different from the existing manner of generating the operation result data of the operation index through manual statistical processing, in this embodiment, after receiving an operation data generation request triggered by a user, an operation index identifier is extracted from the operation data generation request, a first data table corresponding to the operation index identifier is obtained from a preset database, then a calculation rule of the operation index corresponding to the operation index identifier is invoked to perform calculation processing on data of the first data table, so as to generate operation result data corresponding to the operation index, and finally, the operation result data is input into a preset second data table, so as to obtain a target data table corresponding to the operation index, thereby achieving rapid completion of generation of the operation result data of the operation index in an automated manner. According to the embodiment, the target data table containing the operation result data of the operation index can be automatically, quickly and accurately generated according to the first data table corresponding to the operation index, so that a large amount of human resources do not need to be occupied, the automatic generation of the target data table is realized, the workload is greatly reduced, the accuracy of the obtained operation result data of the operation index is effectively ensured, and the processing efficiency of generating the target data table is improved. In addition, when the target data table is obtained, visual display of relevant index details of operation can be achieved through display of the target data table subsequently, so that the leader layer can take real and accurate data as a support in real time when a decision is made through analysis, convenience is provided for subsequent business processing, and the success rate and the processing efficiency of the business processing are effectively improved.

Further, in an embodiment of the present application, the job data generation request further carries user information, and before the step S30, the method includes:

s300: acquiring all identity verification results corresponding to the user information in the preset time period;

s301: screening out an appointed authentication result from all the authentication results, and counting the number of the appointed authentication results to obtain the authentication failure times; wherein, the specified identity authentication result is the result that the identity authentication fails;

s302: determining an authentication level of the user based on the authentication failure times;

s303: acquiring a target verification rule corresponding to the authentication level, performing authentication on the user based on the target verification rule, and judging whether the authentication passes;

s304: if the identity authentication is passed, generating an acquisition instruction for acquiring a first data table corresponding to the operation index identifier from a preset database;

s305: and if the identity authentication is not passed, limiting the response to the operation data generation request.

As described in the foregoing steps S300 to S305, the job data generation request further carries user information, and before the step of obtaining the first data table corresponding to the job index identifier from the preset database, an authentication process for the user may be further included. Specifically, all the authentication results corresponding to the user information within the preset time period are obtained first. The value of the preset time period is not specifically limited, and may be set according to actual requirements, for example, the value may be one month before the current time. And then, screening out specified identity verification results from all the identity verification results, and counting the number of the specified identity verification results to obtain the number of identity verification failures. Wherein, the specified authentication result is the result that the authentication fails. In addition, the authentication result refers to an authentication result generated after each authentication performed by the user corresponding to the user information within the preset time period. And then determining the authentication level of the user based on the authentication failure times. Wherein the authentication levels include a low risk authentication level and a high risk authentication level, and the determining of the authentication level of the user may include: and judging whether the authentication failure times are smaller than a preset failure time threshold value or not. And if the number of times of failure is less than the threshold value of the number of times of failure, determining that the authentication level of the user is a low-risk authentication level. And if the number of the failures is not less than the threshold value of the number of the failures, determining that the authentication level of the user is a high-risk authentication level. The value of the failure number threshold is not particularly limited, and may be set according to actual requirements, for example, may be set to 3. And subsequently acquiring a target verification rule corresponding to the authentication level, authenticating the user based on the target verification rule, and judging whether the authentication passes. Wherein, if the authentication level of user corresponds to low risk authentication level, then can judge that the user belongs to the user of lower safe risk of use to can carry out authentication to the user through using comparatively simple convenient mode, for example can adopt the mode that question asking and handwriting are compared, can judge that the user passes authentication if both pass through after question asking and handwriting comparison, thereby can effectively reduce the time that user's verification spent, improve user's use and experience. If the authentication level of the user corresponds to the high-risk authentication level, it can be determined that the user belongs to a user with a higher use safety risk, and a mode which is more strict than the low-risk authentication level is correspondingly used, for example, a comprehensive comparison mode of fingerprint comparison and palm print comparison can be used for carrying out authentication processing on the user, and only if the comprehensive comparison of fingerprint comparison and palm print comparison is verified, it can be determined that the user passes the authentication, so that the accuracy and the normalization of the authentication on the user can be effectively improved. And if the identity authentication is passed, generating an acquisition instruction for acquiring a first data table corresponding to the operation index identifier from a preset database. And if the identity authentication is not passed, limiting the response to the job data generation request. According to the embodiment, the authentication level of the user can be quickly determined based on the authentication failure times corresponding to the user information in the preset time period, and then different authentication strategies can be correspondingly adopted according to different authentication levels of the user, so that the condition that the user is authenticated by only using one authentication mode in the prior art is avoided, the intelligence and the accuracy of the user authentication can be improved, and the use experience of the user is effectively improved. In addition, only when the user passes the identity authentication, the processing of acquiring the first data table corresponding to the operation index identifier from the preset database is executed subsequently, otherwise, the response to the operation data generation request is limited, so that adverse consequences caused by the response to the operation data generation request sent by an illegal user, such as the condition of important data leakage, can be avoided, the processing normalization of the operation data generation request is effectively improved, and the generation safety of the operation result data corresponding to the operation index is ensured.

Further, in an embodiment of the present application, the authentication levels include a low risk authentication level and a high risk authentication level, and the step S303 includes:

s3030: if the authentication grade of the user is the low-risk authentication grade, acquiring an authentication problem corresponding to the user information, and displaying the authentication problem and preset reminding information to remind the user to feed back the authentication problem through the reminding information;

s3031: acquiring feedback answer handwriting input by the user at a preset position;

s3032: determining whether the content corresponding to the feedback answer handwriting is consistent with the content of the standard answer of the verification question based on an OCR technology;

s3033: if the answer handwriting is consistent with the verification question, extracting first handwriting characteristic data of the feedback answer handwriting, and extracting second handwriting characteristic data of preset answer handwriting corresponding to the verification question;

s3034: judging whether the first handwriting characteristic data is matched with the second handwriting characteristic data;

s3035: and if the identity authentication is matched with the user, judging that the identity authentication passes, otherwise, judging that the identity authentication fails.

As described in the foregoing steps S3030 to S3035, the authentication levels include a low risk authentication level and a high risk authentication level, and the step of obtaining the target authentication rule corresponding to the authentication level, performing authentication on the user based on the target authentication rule, and determining whether the authentication passes or not may specifically include: if the authentication grade of the user is the low-risk authentication grade, firstly, acquiring an authentication problem corresponding to the user information, and displaying the authentication problem and preset reminding information to remind the user to feed back the authentication problem through the reminding information. The verification problem corresponding to the user information can be acquired from a preset problem database. The problem database is a pre-created database which stores verification problems corresponding to the user information of each legal user one by one, and the stored verification problems are used for asking questions of the users to realize the legal identity verification processing of the users. In addition, the content of the reminding information is not particularly limited, and may be set according to actual requirements, for example, "please answer the displayed question by handwriting, thank you". And then collecting feedback answer handwriting input by the user at a preset position. The preset position can be a position corresponding to the handwriting input device, and the user can write a feedback answer corresponding to the verification question on the handwriting input device of the device, for example, a touch screen of the device, so that the device can acquire a feedback answer script corresponding to the feedback answer. And then determining whether the content corresponding to the feedback answer script is consistent with the content of the standard answer of the verification question based on an OCR (Optical Character Recognition) technology. The content corresponding to the handwriting of the feedback answer and the content of the standard answer of the verification question can be determined whether to be consistent or not by utilizing a handwriting OCR recognition technology, that is, the content corresponding to the handwriting of the feedback answer is recognized firstly according to the handwriting OCR recognition technology, and then whether the recognized content is consistent with the content of the standard answer of the verification question or not is determined. And if the answer handwriting is consistent with the verification question, extracting first handwriting characteristic data of the feedback answer handwriting, and extracting second handwriting characteristic data of preset answer handwriting corresponding to the verification question. And finally, judging whether the first handwriting characteristic data is matched with the second handwriting characteristic data. And if the identity authentication is matched with the user, judging that the identity authentication passes, otherwise, judging that the identity authentication fails. And judging whether the first handwriting characteristic data of the feedback answer handwriting is consistent with the second handwriting characteristic data of the preset answer handwriting corresponding to the verification question, namely whether the first handwriting characteristic data is matched with the second handwriting characteristic data. In addition, if any one of the condition that the content corresponding to the feedback answer handwriting is not consistent with the content of the standard answer of the verification question or the condition that the first handwriting characteristic data is not matched with the second handwriting characteristic data exists, the identity verification of the user is judged to be failed. And judging that the identity of the user passes the authentication only if the content corresponding to the feedback answer handwriting is consistent with the content of the standard answer of the authentication question and the first handwriting characteristic data is matched with the second handwriting characteristic data. In this embodiment, after determining that the authentication level of the user is the low-risk authentication level, accurate authentication processing for the user can be intelligently implemented by using a simple authentication method of comparing a question and handwriting, so that time spent for authentication can be greatly reduced on the basis of ensuring reliability of authentication, authentication efficiency of authentication is improved, and user experience is improved. In addition, the received operation data generation request is correspondingly processed based on the obtained identity verification result, adverse effects caused by responding to the operation data generation request input by an illegal user can be effectively avoided, the leakage of operation result data is avoided, and the processing normalization and the safety in the operation data generation request processing process are ensured.

s3040: if the identity authentication level of the user is the high-risk authentication level, acquiring a fingerprint image of the user, and acquiring a pre-stored target fingerprint image corresponding to the user information;

s3041: calculating a first similarity between the fingerprint image and the target fingerprint image, and judging whether the first similarity is greater than a preset first similarity threshold value;

s3042: if the similarity is larger than the first similarity threshold, acquiring a palm print image of the user, and acquiring a pre-stored target palm print image corresponding to the user information;

s3043: calculating a second similarity between the palm print image and the target palm print image, and judging whether the second similarity is greater than a preset second similarity threshold value;

s3044: if the similarity is larger than the second similarity threshold, acquiring a first preset weight corresponding to the first similarity and acquiring a second preset weight corresponding to the second similarity;

s3045: carrying out weighted summation operation on the first similarity and the second similarity based on the first preset weight and the second preset weight to obtain a corresponding target comprehensive score;

s3046: judging whether the target comprehensive score is larger than a preset score threshold value or not;

s3047: if the value is larger than the score threshold value, the identity authentication is judged to be passed, otherwise, the identity authentication is judged not to be passed.

As described in the above steps S3040 to S3047, the steps of obtaining the target verification rule corresponding to the authentication level, performing authentication on the user based on the target verification rule, and determining whether the authentication passes or not may specifically include: if the user identity authentication level is the high risk authentication level, firstly, acquiring a fingerprint image of the user, and acquiring a pre-stored target fingerprint image corresponding to the user information. The fingerprint image and the palm print image of the user can be collected through a camera arranged in the device. In addition, an image database corresponding to the legal users is created in advance, and the image database stores the mapping relationship between the user information of the legal users and the fingerprint images and the mapping relationship between the user information of the legal users and the palm print images. And then calculating a first similarity between the fingerprint image and the target fingerprint image, and judging whether the first similarity is greater than a preset first similarity threshold value. Wherein a first similarity between the fingerprint image and the target fingerprint image and a second similarity between the palm print image and the target palm print image can be calculated by adopting the existing similarity algorithm. In addition, the values of the first similarity threshold and the second similarity threshold are not particularly limited, and may be set according to actual requirements. And if the similarity is larger than the first similarity threshold, acquiring the palm print image of the user, and acquiring a pre-stored target palm print image corresponding to the user information. And then calculating a second similarity between the palm print image and the target palm print image, and judging whether the second similarity is greater than a preset second similarity threshold value. And if the similarity is greater than the second similarity threshold, acquiring a first preset weight corresponding to the first similarity and acquiring a second preset weight corresponding to the second similarity. The values of the first preset weight and the second preset weight are not particularly limited, and can be set according to actual requirements. Preferably, the sum of the first preset weight and the second preset weight is equal to 1. And subsequently, carrying out weighted summation operation on the first similarity and the second similarity based on the first preset weight and the second preset weight to obtain a corresponding target comprehensive score. Specifically, the first preset weight may be multiplied by the first similarity to obtain a first score, the second preset weight may be multiplied by the second similarity to obtain a second score, and a sum of the first score and the second score is calculated to obtain the target integrated score. And finally, judging whether the target comprehensive score is larger than a preset score threshold value. The value of the score threshold is not particularly limited, and can be set according to actual requirements. And if the value is larger than the score threshold value, judging that the identity authentication is passed, otherwise, judging that the identity authentication is not passed. In this embodiment, after the authentication level of the user is determined to be the high-risk authentication level, multiple authentication processing for the user can be intelligently implemented by using a combination of more accurate fingerprint comparison and palm print comparison, and it is determined that the user passes authentication only after the user passes multi-dimensional authentication, so that accuracy and reliability of authentication are further improved. And only when the user passes the identity authentication, the received job data generation request triggered by the user is responded subsequently, so that adverse consequences caused by responding to the job data generation request input by an illegal user are avoided, the leakage of job result data is avoided, and the processing normalization and the safety in the process of processing the job data generation request are effectively ensured.

Further, in an embodiment of the present application, after the step S50, the method includes:

s500: acquiring the target data table;

s501: calling a preset user authority data table, and inquiring an authority score corresponding to the user information from the user authority data table;

s502: judging whether the permission score is larger than a preset permission score threshold value or not;

s503: if the authority score is larger than the authority score threshold value, displaying the target data table;

s504: if the authority score is not greater than the authority score threshold, performing data desensitization treatment on the target data table to obtain a desensitized target data table;

s505: and displaying the target data table after desensitization.

As described in steps S500 to S505, after the step of inputting the job result data into the preset second data table to obtain the target data table corresponding to the job index is completed, an intelligent display process for the target data table may be further included. Specifically, the target data table is first acquired. And then calling a preset user authority data table, and inquiring an authority score corresponding to the user information from the user authority data table. The user authority data table is pre-generated and stores user information of legal users and authority scores corresponding to the user information of each legal user. And subsequently judging whether the permission score is larger than a preset permission score threshold value. And if the authority score is larger than the authority score threshold value, displaying the target data table. And if the authority score is not larger than the authority score threshold, performing data desensitization treatment on the target data table to obtain a desensitized target data table. And displaying the target data table after desensitization. The permission score threshold is a score value used for judging whether the current user has the integral data in the checking target data table. If the authority score of the user is larger than the authority score threshold value, the user is indicated to have the authority of viewing the complete data in the target data table, and if the authority score of the user is not larger than the authority score threshold value, the user is indicated to not have the authority of viewing the complete data in the target data table, at the moment, data desensitization processing can be intelligently carried out on the target data table, and then the data desensitization processing is carried out on the target data table, and then the target data table is displayed to the user. In addition, the value of the permission score threshold is not limited, and can be set according to actual requirements. In this embodiment, before the process of displaying the target data table in the third database, the authority score of the user is obtained first, and the data display mode corresponding to the user is determined according to the authority score, the target data table is directly and completely displayed to the user only if the authority score of the user is greater than the authority score threshold, and if the authority score of the user is less than the authority score threshold, the target data table is intelligently desensitized and then displayed to the user, so that the important index result data is prevented from being leaked to the user who does not have the authority to view the important index result data, and the viewing accuracy and the viewing intelligence of the index data result are effectively improved.

Further, in an embodiment of the application, the step S504 includes:

s5040: acquiring a preset sensitive index list; the sensitive index list is internally stored with a plurality of sensitive indexes and level identifications respectively corresponding to each sensitive index;

s5041: matching each operation index contained in the target data table with all sensitive indexes contained in a sensitive index list, and screening out an index to be desensitized from all operation indexes contained in the target data table according to a matching result;

s5042: acquiring target level identifications corresponding to the indexes to be desensitized respectively from the sensitive index list;

s5043: generating desensitization rules respectively corresponding to the indexes to be desensitized based on the target level identification;

s5044: and performing one-to-one desensitization treatment on the index result data of each index to be desensitized based on each desensitization rule to obtain the desensitized target data table.

As described in the foregoing steps S5040 to S5044, the step of performing data desensitization processing on the target data table to obtain a desensitized target data table may specifically include: firstly, a preset sensitive index list is obtained. The sensitive index list stores a plurality of sensitive indexes and level identifiers corresponding to the sensitive indexes, wherein the level identifiers may include a high level identifier, a medium level identifier and a low level identifier. In addition, the sensitive index list is generated according to sensitive indexes input by related users, the sensitive indexes are indexes corresponding to information with higher sensitivity, and when the target data table is subsequently displayed to a user without looking up the complete data content of the target data table, data desensitization processing needs to be performed on index result data, corresponding to the sensitive indexes, of the target data table. And then matching each operation index contained in the target data table with all sensitive indexes contained in a sensitive index list, and screening out an index to be desensitized from all the operation indexes contained in the target data table according to a matching result. Wherein, the screening process of the index to be desensitized can comprise the following steps: matching the designated operation index with all sensitive indexes contained in a sensitive index list, and judging whether a target index which is the same as the designated operation index exists in the sensitive indexes; the specified operation index is any one of all the operation indexes; if the target index exists in the sensitive indexes, marking the specified operation index as an index to be desensitized, and extracting the index to be desensitized. In addition, the matching processing between the designated operation index and all the sensitive indexes can be performed based on a preset parallel comparison instruction, and the parallel comparison instruction can be a single instruction stream multiple data (SIMD) instruction. The data matching processing is simultaneously carried out on the designated operation index and each sensitive index in all the sensitive indexes by utilizing the parallel computing capability of the parallel comparison instruction, so that the comparison processing speed between each designated operation index and each sensitive index is effectively improved, and the acquisition speed of the index to be desensitized is improved. And then acquiring target level identifications respectively corresponding to the indexes to be desensitized from the sensitive index list. And then generating desensitization rules respectively corresponding to the indexes to be desensitized based on the target level identification. In the present application, for the specific implementation process of generating the desensitization rule corresponding to each of the to-be-desensitized indicators based on the target level identifier, further details will be described in subsequent specific embodiments, which are not set forth herein too much. Finally, performing one-to-one desensitization treatment on the index result data of each index to be desensitized based on each desensitization rule to obtain the desensitized target data table. In this embodiment, the to-be-desensitized indexes with higher sensitivity are screened from all the operation indexes contained in the target data table based on a sensitive index list, the target level identifications respectively corresponding to the to-be-desensitized indexes are obtained from the sensitive index list, desensitization rules respectively corresponding to the to-be-desensitized indexes are generated based on the target level identifications, and then desensitization processing corresponding to the index result data of each to-be-desensitized index is performed one-to-one by using each desensitization rule to obtain the desensitized target data table, so that when the authority score of a user is judged to be smaller than the authority score threshold, the data desensitization processing is performed on the target data table first and then the desensitization processing is performed on the user, so as to prevent the important index result data from being leaked to the user who does not have the authority to look up the important index result data, the checking accuracy and the checking intelligence of the index data result are effectively improved. In addition, because only the index result data corresponding to the index to be desensitized is desensitized, and all the operation result data are not desensitized, unnecessary consumption generated in data processing is effectively reduced, and the intelligence of data desensitization is improved.

Further, the generating process of the sensitive index list may include: firstly, a plurality of sensitive indexes which are input in advance and importance description information which corresponds to each sensitive index are received. The sensitive index can be set according to actual requirements, for example, the information with higher sensitivity is the effective coverage rate month-to-ring ratio, and the corresponding sensitive index can be the effective coverage rate month-to-ring ratio index. In addition, the importance description information is used for indicating the importance degree of the sensitive indexes, and the user can input corresponding importance description information for each sensitive index according to actual experience, wherein the importance description information can comprise high importance, medium importance and low importance, for example. And then generating level identifications corresponding to the sensitive indexes one by one based on the importance description information. The level identification comprises a high level identification, a middle level identification and a low level identification. The level identification is generated by the importance description information and is used for identifying the sensitivity level of the sensitive index, generating a corresponding high-level identification for the sensitive index with high importance, generating a corresponding middle-level identification for the sensitive index with high importance, and generating a corresponding low-level identification for the sensitive index with low importance. And then establishing a mapping relation between the sensitive indexes and the level identification and storing the mapping relation in a preset data list to obtain the sensitive index list. And the list of sensitive indicators may be stored within a blockchain. The block chain is used for storing and managing the sensitive index list, so that the safety and the non-tamper property of the sensitive index list can be effectively ensured. The sensitive index list is obtained by establishing the corresponding relation between the sensitive index and the level identification and storing the corresponding relation in a preset data list, so that the sensitive index list is beneficial to quickly screening out the index to be desensitized from the operation index contained in the target data list according to the sensitive index list.

Further, in an embodiment of the present application, the level identifier includes a high level identifier, a medium level identifier, and a low level identifier, and the step S5043 includes:

s50430: acquiring an appointed level identification corresponding to an appointed index to be desensitized; the specified index to be desensitized is any one of all indexes to be desensitized;

s50431: if the designated level identification is the high-level identification, generating a first desensitization rule corresponding to the designated index to be desensitized; the first desensitization rule is used for performing data desensitization processing on index result data corresponding to the specified to-be-desensitized index by adopting a preset encryption mode;

s50432: if the designated level identification is the medium level identification, generating a second desensitization rule corresponding to the designated index to be desensitized; the second desensitization rule is used for performing data desensitization processing on index result data corresponding to the specified to-be-desensitized index by adopting an alternative mode;

s50433: the designated level identification is the low-level identification, and a third desensitization rule corresponding to the designated index to be desensitized is generated; and performing data desensitization treatment on the index result data corresponding to the specified index to be desensitized by adopting a fuzzy mode according to the third desensitization rule.

As described in the above steps S50430 to S50433, the level identifier includes a high level identifier, a medium level identifier, and a low level identifier, and the step of generating desensitization rules corresponding to the to-be-desensitized indexes based on the target level identifier may specifically include: and acquiring a designated level identifier corresponding to the designated index to be desensitized. The specified index to be desensitized is any one of all indexes to be desensitized. And if the specified level identification is the high-level identification, generating a first desensitization rule corresponding to the specified index to be desensitized. And the first desensitization rule is used for performing data desensitization processing on index result data corresponding to the specified to-be-desensitized index by adopting a preset encryption mode. In addition, a higher level of sensitivity index for level identification may correspond to a more secure desensitization. In addition, the preset encryption manner is not particularly limited, and may include a hash encryption algorithm, an asymmetric encryption algorithm, and the like. And if the specified level identification is the medium level identification, generating a second desensitization rule corresponding to the specified index to be desensitized. And performing data desensitization treatment on the index result data corresponding to the specified to-be-desensitized index by adopting an alternative mode according to the second desensitization rule. In addition, the replacement mode can comprise preset character replacement, random character replacement and the like. And the specified level identification is the low-level identification, and a third desensitization rule corresponding to the specified index to be desensitized is generated. And performing data desensitization treatment on the index result data corresponding to the specified index to be desensitized by adopting a fuzzy mode according to the third desensitization rule. Additionally, the obfuscation manner may include a manner of processing sensitive data into an obscured state that is not readily viewable. In this embodiment, for the sensitive indexes in the screened target data table, the desensitization rules that are adapted to each other are allocated to different sensitive indexes according to the level identifiers corresponding to the sensitive indexes, so that intelligence and rationality of desensitization rule configuration are realized, data desensitization processing on index result data corresponding to each index to be desensitized can be performed respectively based on the generated desensitization rules to obtain a desensitized target data table, and data normalization and accuracy of the generated desensitized target data table are ensured.

The method for generating the operation data in the embodiment of the present application may also be applied to the field of a block chain, for example, data such as the target data table is stored on the block chain. By storing and managing the target data table using a block chain, the security and the non-tamper property of the target data table can be effectively ensured.

The block chain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, a consensus mechanism and an encryption algorithm. A block chain (Blockchain), which is essentially a decentralized database, is a series of data blocks associated by using a cryptographic method, and each data block contains information of a batch of network transactions, so as to verify the validity (anti-counterfeiting) of the information and generate a next block. The blockchain may include a blockchain underlying platform, a platform product service layer, an application service layer, and the like.

The block chain underlying platform can comprise processing modules such as user management, basic service, intelligent contract and operation monitoring. The user management module is responsible for identity information management of all blockchain participants, and comprises public and private key generation maintenance (account management), key management, user real identity and blockchain address corresponding relation maintenance (authority management) and the like, and under the authorization condition, the user management module supervises and audits the transaction condition of certain real identities and provides rule configuration (wind control audit) of risk control; the basic service module is deployed on all block chain node equipment and used for verifying the validity of the service request, recording the service request to storage after consensus on the valid request is completed, for a new service request, the basic service firstly performs interface adaptation analysis and authentication processing (interface adaptation), then encrypts service information (consensus management) through a consensus algorithm, transmits the service information to a shared account (network communication) completely and consistently after encryption, and performs recording and storage; the intelligent contract module is responsible for registering and issuing contracts, triggering the contracts and executing the contracts, developers can define contract logics through a certain programming language, issue the contract logics to a block chain (contract registration), call keys or other event triggering and executing according to the logics of contract clauses, complete the contract logics and simultaneously provide the function of upgrading and canceling the contracts; the operation monitoring module is mainly responsible for deployment, configuration modification, contract setting, cloud adaptation in the product release process and visual output of real-time states in product operation, such as: alarm, monitoring network conditions, monitoring node equipment health status, and the like.

Referring to fig. 2, an embodiment of the present application further provides an apparatus for generating job data, including:

the system comprises a first judgment module 1, a first display module and a second judgment module, wherein the first judgment module is used for judging whether a job data generation request triggered by a user is received or not; the operation data generation request carries an operation index identifier;

the extraction module 2 is used for extracting the operation index identifier from the operation data generation request if the operation data generation request is received;

the first obtaining module 3 is used for obtaining a first data table corresponding to the operation index identifier from a preset database;

the first generation module 4 is configured to invoke a calculation rule of a job index corresponding to the job index identifier to perform calculation processing on data in the first data table, and generate job result data corresponding to the job index;

and the second generating module 5 is configured to input the job result data into a preset second data table to obtain a target data table corresponding to the job index.

In this embodiment, the operations that the modules or units are respectively used to execute correspond to the steps of the method for generating job data in the foregoing embodiment one to one, and are not described herein again.

Further, in an embodiment of the present application, the job data generation request further carries user information, and the apparatus for generating job data includes:

the second acquisition module is used for acquiring all identity verification results corresponding to the user information in the preset time period;

the statistical module is used for screening out specified identity verification results from all the identity verification results and counting the number of the specified identity verification results to obtain the number of identity verification failure times; wherein, the specified identity authentication result is the result that the identity authentication fails;

a determining module, configured to determine an authentication level of the user based on the authentication failure times;

the second judgment module is used for acquiring a target verification rule corresponding to the authentication level, authenticating the identity of the user based on the target verification rule and judging whether the authentication passes or not;

the third generation module is used for generating an acquisition instruction for acquiring the first data table corresponding to the operation index identifier from a preset database if the identity authentication is passed;

and the processing module is used for limiting the response to the operation data generation request if the identity authentication is not passed.

Further, in an embodiment of the application, the authentication levels include a low-risk authentication level and a high-risk authentication level, and the second determining module includes:

the first obtaining unit is used for obtaining a verification problem corresponding to the user information if the identity verification level of the user is the low-risk verification level, and displaying the verification problem and preset reminding information so as to remind the user to feed back the verification problem through the reminding information;

the first acquisition unit is used for acquiring feedback answer handwriting input by the user at a preset position;

a determining unit, configured to determine whether content corresponding to the feedback answer script is consistent with content of a standard answer of the verification question based on an OCR technology;

the extraction unit is used for extracting first handwriting characteristic data of the feedback answer handwriting and extracting second handwriting characteristic data of preset answer handwriting corresponding to the verification question if the first handwriting characteristic data is consistent with the second handwriting characteristic data;

the first judging unit is used for judging whether the first handwriting characteristic data is matched with the second handwriting characteristic data;

and the first judging unit is used for judging that the identity authentication passes if the matching is performed, or else, judging that the identity authentication fails.

the second acquisition unit is used for acquiring the fingerprint image of the user and acquiring a pre-stored target fingerprint image corresponding to the user information if the identity authentication level of the user is the high-risk authentication level;

the first calculating unit is used for calculating a first similarity between the fingerprint image and the target fingerprint image and judging whether the first similarity is larger than a preset first similarity threshold value or not;

a third collecting unit, configured to collect the palm print image of the user and obtain a pre-stored target palm print image corresponding to the user information if the similarity is greater than the first similarity threshold;

the second calculation unit is used for calculating a second similarity between the palm print image and the target palm print image and judging whether the second similarity is larger than a preset second similarity threshold value or not;

a second obtaining unit, configured to obtain a first preset weight corresponding to the first similarity and obtain a second preset weight corresponding to the second similarity if the first similarity is greater than the second similarity threshold;

the third calculating unit is used for performing weighted summation operation on the first similarity and the second similarity based on the first preset weight and the second preset weight to obtain a corresponding target comprehensive score;

the second judgment unit is used for judging whether the target comprehensive score is larger than a preset score threshold value;

and the second judging unit is used for judging that the identity authentication is passed if the value is larger than the score threshold value, and otherwise, judging that the identity authentication is not passed.

Further, in an embodiment of the present application, the apparatus for generating job data includes:

the third acquisition module is used for acquiring the target data table;

the query module is used for calling a preset user authority data table and querying the authority score corresponding to the user information from the user authority data table;

the third judging module is used for judging whether the authority score is larger than a preset authority score threshold value;

the first display module is used for displaying the target data table if the authority score is larger than the authority score threshold;

the desensitization module is used for performing data desensitization treatment on the target data table to obtain a desensitized target data table if the authority score threshold is not larger than the authority score threshold;

and the second display module is used for displaying the desensitized target data table.

Further, in an embodiment of the present application, the desensitization module includes:

the third acquisition unit is used for acquiring a preset sensitive index list; the sensitive index list is internally stored with a plurality of sensitive indexes and level identifications respectively corresponding to each sensitive index;

the matching unit is used for respectively matching each operation index contained in the target data table with all sensitive indexes contained in a sensitive index list and screening out an index to be desensitized from all the operation indexes contained in the target data table according to a matching result;

a fourth obtaining unit, configured to obtain, from the sensitive index list, target level identifiers corresponding to the to-be-desensitized indexes respectively;

a generating unit, configured to generate desensitization rules corresponding to the to-be-desensitized indicators, respectively, based on the target level identifier;

and the desensitization unit is used for performing one-to-one desensitization treatment on the index result data of each index to be desensitized based on each desensitization rule to obtain the desensitized target data table.

Further, in an embodiment of the present application, the level identifier includes a high level identifier, a medium level identifier, and a low level identifier, and the generating unit includes:

the acquisition subunit is used for acquiring the designated level identification corresponding to the designated index to be desensitized; the specified index to be desensitized is any one of all indexes to be desensitized;

the first generation subunit is configured to generate a first desensitization rule corresponding to the specified to-be-desensitized index if the specified level identifier is the high-level identifier; the first desensitization rule is used for performing data desensitization processing on index result data corresponding to the specified to-be-desensitized index by adopting a preset encryption mode;

a second generating subunit, configured to generate a second desensitization rule corresponding to the specified index to be desensitized if the specified level identifier is the medium level identifier; the second desensitization rule is used for performing data desensitization processing on index result data corresponding to the specified to-be-desensitized index by adopting an alternative mode;

a third generating subunit, configured to generate a third desensitization rule corresponding to the specified to-be-desensitized index, where the specified level identifier is the low-level identifier; and performing data desensitization treatment on the index result data corresponding to the specified index to be desensitized by adopting a fuzzy mode according to the third desensitization rule.

Referring to fig. 3, a computer device, which may be a server and whose internal structure may be as shown in fig. 3, is also provided in the embodiment of the present application. The computer device comprises a processor, a memory, a network interface, a display screen, an input device and a database which are connected through a system bus. Wherein the processor of the computer device is designed to provide computing and control capabilities. The memory of the computer device comprises a storage medium and an internal memory. The storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operating system and computer programs in the storage medium to run. The database of the computer device is used for storing operation index identification, user information, authentication failure times, authentication grades, a first data table, calculation rules, operation result data and a target data table. The network interface of the computer device is used for communicating with an external terminal through a network connection. The display screen of the computer equipment is an indispensable image-text output equipment in the computer, and is used for converting digital signals into optical signals so that characters and figures are displayed on the screen of the display screen. The input device of the computer equipment is the main device for information exchange between the computer and the user or other equipment, and is used for transmitting data, instructions, some mark information and the like to the computer. The computer program is executed by a processor to implement a method of generating job data.

The processor executes the method for generating the job data, and comprises the following steps:

Those skilled in the art will appreciate that the structure shown in fig. 3 is only a block diagram of a part of the structure related to the present application, and does not constitute a limitation to the apparatus and the computer device to which the present application is applied.

An embodiment of the present application further provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements a method for generating job data, and specifically includes:

It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and can include the processes of the embodiments of the methods described above when the computer program is executed. Any reference to memory, storage, database, or other medium provided herein and used in the examples may include non-volatile and/or volatile memory. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), double-rate SDRAM (SSRSDRAM), Enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

The above description is only a preferred embodiment of the present application, and not intended to limit the scope of the present application, and all modifications of equivalent structures and equivalent processes, which are made by the contents of the specification and the drawings of the present application, or which are directly or indirectly applied to other related technical fields, are also included in the scope of the present application.

Claims

1. A method for generating job data, comprising:

2. The method according to claim 1, wherein the job data generation request further carries user information, and the step of obtaining the first data table corresponding to the job index identifier from a preset database includes:

3. The method according to claim 2, wherein the authentication levels include a low-risk authentication level and a high-risk authentication level, and the step of acquiring a target authentication rule corresponding to the authentication levels, authenticating the user based on the target authentication rule, and determining whether the authentication is passed includes:

acquiring feedback answer handwriting input by the user at a preset position;

4. The method according to claim 2, wherein the authentication levels include a low-risk authentication level and a high-risk authentication level, and the step of acquiring a target authentication rule corresponding to the authentication levels, authenticating the user based on the target authentication rule, and determining whether the authentication is passed includes:

5. The method according to claim 1, wherein the step of inputting the job result data into a preset second data table to obtain a target data table corresponding to the job index is followed by the step of:

acquiring the target data table;

and displaying the target data table after desensitization.

6. The method according to claim 5, wherein the step of performing data desensitization processing on the target data table to obtain a desensitized target data table includes:

7. The method according to claim 6, wherein the level identifiers include a high level identifier, a medium level identifier, and a low level identifier, and the step of generating desensitization rules corresponding to the respective indices to be desensitized based on the target level identifiers includes:

8. An apparatus for generating job data, comprising:

9. A computer device comprising a memory and a processor, the memory having stored therein a computer program, characterized in that the processor, when executing the computer program, implements the steps of the method according to any one of claims 1 to 7.

10. 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 of any one of claims 1 to 7.