CN113435990B

CN113435990B - Certificate generation method and device based on rule engine and computer equipment

Info

Publication number: CN113435990B
Application number: CN202110722061.9A
Authority: CN
Inventors: 刘琪
Original assignee: Ping An Technology Shenzhen Co Ltd
Current assignee: Ping An Technology Shenzhen Co Ltd
Priority date: 2021-06-28
Filing date: 2021-06-28
Publication date: 2022-09-30
Anticipated expiration: 2041-06-28
Also published as: CN113435990A

Abstract

The application relates to the technical field of data processing, and provides a certificate generation method and device based on a rule engine, computer equipment and a storage medium, wherein the method comprises the following steps: judging whether a target bank flow to be processed input by a user is received or not; if so, performing identity authentication processing on the user based on the standard face image and the standard question data, and judging whether the authentication is passed; if so, analyzing the user account information from the target bank flow; calling a rule engine to inquire a target certificate identification rule corresponding to the user account information from a rule base; respectively identifying the target bank flow based on each target certificate identification rule to obtain a corresponding identification result; and determining the target financial certificate type of the target bank flow based on all the identification results. The method and the device can automatically and accurately generate the target financial voucher type of the target bank flow. The method and the device can also be applied to the field of block chains, and the data such as the target financial certificate type can be stored on the block chains.

Description

Certificate generation method and device based on rule engine and computer equipment

Technical Field

The application relates to the technical field of data processing, in particular to a certificate generation method and device based on a rule engine and computer equipment.

Background

The bank flow refers to deposit and withdrawal transaction records of bank current accounts (including current passbooks and bank cards). The method is divided into personal running water and public running water according to the account properties. Companies generate a lot of bank pipelining on public accounts every day. The financial affairs of the bank flow of the company need to be certified. The financial certificates belonging to different types of bank flows are different, for example, the interest flow belongs to the interest certificate, the commission charge flow belongs to the commission charge certificate, and the like.

Traditional bank pipelining financial certification is manually identified, and financial staff need to review the pipelining information of the bank to identify the type of the certificate to which the bank pipelining belongs. Some accounts have detailed flow and can be up to thousands of accounts one day, and financial staff judges the accounts one by one and needs to consume a large amount of energy, so that the workload is large, and misjudgment easily occurs. Moreover, different financial staff may have differences in judgment criteria, which may result in two running water belonging to the same type, and different running water vouchers identified by different financial staff may be different, thus bringing uncertain trouble and risk to subsequent financial voucher checks. Therefore, the existing mode for identifying the financial voucher to which the bank assembly line belongs has the problems of high identification cost, low efficiency and low accuracy.

Disclosure of Invention

The application mainly aims to provide a certificate generation method, a device, computer equipment and a storage medium based on a rule engine, and aims to solve the technical problems of high identification cost, low efficiency and low accuracy in the existing mode for identifying financial certificates to which bank running water belongs.

The application provides a certificate generation method based on a rule engine, which comprises the following steps:

judging whether a target bank flow to be processed input by a user is received;

if the target bank flow is received, performing identity authentication processing on the user based on a preset standard face image and standard question data, and judging whether the authentication is passed;

if the verification is passed, analyzing user account information from the target bank flow;

calling a preset rule engine to inquire out a target certificate identification rule corresponding to the user account information from a pre-stored rule base;

respectively identifying the target bank flow based on each target certificate identification rule to obtain identification results corresponding to each target certificate identification rule;

and determining the target financial voucher type of the target bank flow based on all the identification results.

Optionally, the step of performing identity authentication processing on the user based on a preset standard face image and standard question data, and determining whether the authentication is passed includes:

acquiring a face image of the user;

comparing the face image of the user with the standard face image;

when the comparison is consistent, pre-stored standard question data are obtained, the standard question data and answer reminding information are displayed on a current interface, and the user is reminded to perform answer feedback on the standard question data based on the answer reminding information;

receiving feedback answer data input by the user;

after verifying that the feedback answer data is the same as the preset standard answer data, calculating the response time length of the user, and judging whether the response time length is greater than a preset normal response time length threshold value;

if the response time length is larger than the normal response time length threshold, generating a corresponding risk coefficient based on the response time length and the normal response time length threshold, and judging whether the risk coefficient is larger than a preset risk coefficient threshold;

if the risk factor is not larger than the risk factor threshold, judging that the user passes the verification;

and if the risk factor is larger than the risk factor threshold, judging that the user is not verified.

Optionally, before the step of invoking the preset rule engine to query the target credential identification rule corresponding to the user account information from the pre-stored rule base, the method includes:

judging whether a rule configuration request input by an administrator user is received; wherein, the rule configuration request carries administrator user information;

if the rule configuration request is received, performing authority verification processing on the administrator user based on the administrator user information, and judging whether the authority verification is passed or not;

if the authority passes the verification, displaying a preset rule configuration page; the rule configuration page comprises a plurality of rule attributes and a data selection list corresponding to each rule attribute, wherein the rule attributes comprise account information, financial voucher types, positions and keywords;

receiving selection data which is input by the administrator user on the rule configuration page and corresponds to each rule attribute respectively;

generating corresponding designated account information based on designated selection data corresponding to the account information;

based on other selection data except the appointed selection data in all the selection data, calling the rule engine to generate a certificate identification rule corresponding to the appointed account information;

and storing the certificate identification rule in a preset initial rule base to obtain the rule base.

Optionally, the step of performing, based on the administrator user information, an authority verification process on the administrator user and determining whether the authority verification is passed includes:

calling a preset classification tree model;

determining a role class corresponding to the administrator user information through the classification tree model, and determining a target authority level corresponding to the class of the administrator user information based on a preset corresponding relation between the class and the authority level;

acquiring a configuration authority level of the service operation corresponding to the configuration rule based on a preset service operation authority table;

judging whether the target permission level is greater than the configuration permission level;

if the target permission level is greater than the configuration permission level, judging that permission verification is passed;

and if the target permission level is not greater than the configuration permission level, judging that permission verification is not passed.

Optionally, the step of performing identification processing on the target bank flow based on each target credential identification rule to obtain an identification result corresponding to each target credential identification rule includes:

acquiring a designated position and a designated keyword contained in a first designated certificate identification rule; wherein the first specified credential identification rule is any one of all the target credential identification rules;

extracting character data corresponding to the designated position from the target bank flow;

judging whether the character data contains the specified keywords or not;

if the character data contains the specified keywords, generating a recognition result of successful recognition corresponding to the first specified certificate recognition rule;

and if the character data does not contain the specified keyword, generating an identification result of identification failure corresponding to the identification rule of the first specified certificate.

Optionally, the step of determining the target financial voucher type of the target bank flow based on all the identification results includes:

acquiring all recognition results;

screening out the identification results with contents of successful identification from all the identification results;

acquiring a second specified certificate identification rule corresponding to an identification result of successful identification from all the target certificate identification rules;

acquiring a specified financial certificate type contained in the second specified certificate identification rule;

taking the specified financial credential type as the target financial credential type.

Optionally, after the step of determining the target financial credential type of the target banking flow based on all the recognition results, the method includes:

generating corresponding financial certificate marking information based on the target financial certificate type;

marking the target bank running water by using the financial certificate marking information to obtain the marked target bank running water;

acquiring terminal information corresponding to the user;

and sending the marked target bank flow to a user terminal corresponding to the terminal information.

The present application further provides a credential generating device based on a rules engine, comprising:

the first judgment module is used for judging whether the target bank flow to be processed input by the user is received or not;

the first verification module is used for performing identity verification processing on the user based on a preset standard face image and standard question and question data if the target bank flow is received, and judging whether the user passes the verification;

the analysis module is used for analyzing the user account information from the target bank flow if the verification is passed;

the query module is used for calling a preset rule engine to query a target certificate identification rule corresponding to the user account information from a pre-stored rule base;

the identification module is used for respectively identifying the target bank assembly line based on each target certificate identification rule to obtain an identification result corresponding to each target certificate identification rule;

and the determining module is used for determining the type of the target financial voucher of the target bank flow based on all the identification results.

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 certificate generation method and device based on the rule engine, the computer equipment and the storage medium have the following advantages that:

according to the rule engine-based voucher generation method, the rule engine-based voucher generation device, the rule engine-based voucher generation computer equipment and the storage medium, after a target bank flow input by a user is received and the user passes identity verification, corresponding user account information is analyzed from the target bank flow, a preset rule engine is called to inquire out a target voucher identification rule corresponding to the user account information, then the target bank flow is identified and processed based on each target voucher identification rule, and the target financial voucher type of the target bank flow is determined rapidly and accurately based on each obtained identification result. Different from the existing mode of manually identifying the financial voucher types of the running water of the bank, the method and the device can automatically and accurately generate the target financial voucher types of the running water of the target bank, save manpower and material resources, effectively improve the generation efficiency of the target financial voucher types, reduce the generation cost of the target financial voucher types and reduce the misidentification rate of the target financial voucher types.

Drawings

FIG. 1 is a flowchart illustrating a method for generating a credential based on a rules engine according to an embodiment of the present application;

FIG. 2 is a schematic structural diagram of a credential generating device based on a rules engine 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.

It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.

Referring to fig. 1, a credential generation method based on a rule engine according to an embodiment of the present application includes:

s1: judging whether a target bank flow to be processed input by a user is received or not;

s2: if the target bank flow is received, performing identity authentication processing on the user based on a preset standard face image and standard question data, and judging whether the authentication is passed;

s3: if the verification is passed, analyzing user account information from the target bank flow;

s4: calling a preset rule engine to inquire out a target certificate identification rule corresponding to the user account information from a pre-stored rule base;

s5: respectively identifying the target bank assembly line based on each target voucher identification rule to obtain identification results corresponding to each target voucher identification rule;

s6: and determining the target financial certificate type of the target bank flow based on all the identification results.

As described in steps S1-S6, the subject of the present method embodiment is a credential generator based on a rules engine. In practical applications, the credential generating device based on the rule engine may be implemented by a virtual device, such as a software code, or may be implemented by an entity device written with or integrated with a relevant execution code, 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 certificate generation device based on the rule engine in the embodiment can automatically and accurately generate the target financial certificate type of the target bank running water, so that manpower and material resources are saved, the generation efficiency of the target financial certificate type is effectively improved, and the generation cost and the misrecognition rate of the target financial certificate type are reduced. Specifically, it is first determined whether a target bank flow to be processed input by a user is received. The target bank flow can comprise information such as transaction date, transaction amount, transfer-in/out identification, abstract, transaction purpose, user account information, opposite side account number, opposite side account name and the like, wherein the account information comprises the name of the principal account and the number of the principal account. And if the target bank flow is received, performing identity authentication processing on the user based on a preset standard face image and standard question data, and judging whether the user passes the authentication. The face comparison result corresponding to the face image of the user can be generated based on the standard face image, the analysis result of the feedback answer data submitted by the user based on the standard question data is generated, and the identity verification result of the user is determined by combining the face comparison result and the analysis result. And if the verification is passed, analyzing the user account information from the target bank flow. The user account information at least comprises one of a principal account name and a principal account number.

And then calling a preset rule engine to inquire out a target certificate identification rule corresponding to the user account information from a pre-stored rule base. The rule engine can be called through the related calling code only when being used, and the rule engine can be in a dormant state when not being used, so that the running consumption of the device can be reduced. In addition, the number of the target credential identification rules is plural. In addition, the pre-created rule base stores credential identification rules corresponding to different user account information. The certificate identification rule is a rule strategy corresponding to different rule attributes, and is specifically used for identifying the financial certificate type corresponding to the bank flow.

And then respectively identifying the target bank assembly line based on each target certificate identification rule to obtain identification results respectively corresponding to each target certificate identification rule. Wherein the content of the identification result comprises identification success and identification failure. Specifically, the text data corresponding to the designated position can be extracted from the target bank flow according to the designated position contained in each target certificate identification rule, and then the identification result between each target certificate identification rule and the target bank flow can be rapidly and intelligently obtained by judging whether the obtained text data contains the designated keyword, if the text data contains the designated keyword, the identification result with successful identification is generated, and if the text data does not contain the designated keyword, the identification result with failed identification is generated.

And finally, determining the type of the target financial voucher of the target bank running water based on all the identification results. Specifically, the content may be screened from all the identification results as an identification result which is successfully identified, and then a second specified certificate identification rule corresponding to the identification result which is successfully identified is obtained from all the target certificate identification rules, so that the specified financial certificate type included in the second specified certificate identification rule is used as the target financial certificate type.

In this embodiment, after a target bank flow input by a user is received and the user passes identity authentication, corresponding user account information is analyzed from the target bank flow, a preset rule engine is called to query a target credential identification rule corresponding to the user account information, the target bank flow is identified based on each target credential identification rule, and a target financial credential type of the target bank flow is determined quickly and accurately based on each obtained identification result. Different from the existing mode of manually identifying the financial voucher types of the running water of the bank, the embodiment can automatically and accurately generate the target financial voucher types of the running water of the target bank, saves manpower and material resources, effectively improves the generation efficiency of the target financial voucher types, reduces the generation cost of the target financial voucher types and reduces the misidentification rate of the target financial voucher types.

Further, in an embodiment of the application, the step S2 includes:

s200: acquiring a face image of the user;

s201: comparing the face image of the user with the standard face image;

s202: when the comparison is consistent, pre-stored standard question-asking data is obtained, the standard question-asking data and answer reminding information are displayed on a current interface, and the user is reminded to perform answer feedback on the standard question-asking data based on the answer reminding information;

s203: receiving feedback answer data input by the user;

s204: after verifying that the feedback answer data are the same as the preset standard answer data, calculating the response time length of the user, and judging whether the response time length is greater than a preset normal response time length threshold value or not;

s205: if the response time length is larger than the normal response time length threshold, generating a corresponding risk coefficient based on the response time length and the normal response time length threshold, and judging whether the risk coefficient is larger than a preset risk coefficient threshold;

s206: if the risk factor is not larger than the risk factor threshold, judging that the user passes the verification;

s207: and if the risk factor is larger than the risk factor threshold, judging that the user is not verified.

As described in the foregoing steps S200 to S207, the step of performing authentication processing on the user based on the preset standard face image and the standard question data, and determining whether the authentication is passed may specifically include: firstly, a face image of the user is obtained. And then comparing the facial image of the user with the standard facial image. The system comprises a preset face database, a plurality of standard face images and a plurality of image processing units, wherein the preset face database stores the standard face images of authorized users. In addition, the similarity between the face image of the user and all the standard face images can be determined by adopting a face recognition technology, for example, the face recognition technology can adopt any one of a local feature analysis method, a feature face method and a perceptual hash algorithm. And extracting the target similarity with the maximum value from all the similarities obtained by calculation, judging whether the target similarity is greater than a preset similarity threshold, if so, generating a face comparison result with consistent comparison, and otherwise, generating a face comparison result with inconsistent comparison. And when the comparison is consistent, acquiring pre-stored standard question-asking data, displaying the standard question-asking data and answer reminding information on a current interface, and reminding the user to perform answer feedback on the standard question-asking data based on the answer reminding information. The standard question data is question data corresponding to a legal user identity. And then receiving feedback answer data input by the user. After the feedback answer data is verified to be the same as the preset standard answer data, calculating the response time length of the user, and judging whether the response time length is greater than a preset normal response time length threshold value. Wherein the standard answer data is answer data corresponding to the standard quiz question data. The response time length is the time length used from the time when the user checks the content of the standard question data and answers the question. In addition, the normal reaction duration threshold may be set based on empirical data. For example, a plurality of feedback data of the standard question data in the case of normal answer can be collected, and the response time length of the plurality of feedback data is counted to determine the threshold value of the normal response time length. If the current user is not a legal user corresponding to the standard question data, the response time of the current user in answering the question will be long and the situation of wrong answer is easy to occur. Further, the normal reaction duration threshold value can be determined according to age information of the user. The numerical correspondence between the age and the normal reaction duration threshold value can be preset based on a rule that the larger the age is, the larger the numerical value of the normal reaction duration threshold value is, and an age-normal reaction duration threshold value mapping table is correspondingly created, wherein various ages and normal reaction duration threshold values respectively corresponding to the various ages are stored in the age-normal reaction duration threshold value mapping table. In a specific implementation process, the obtained face image of the user can be analyzed to obtain the age information of the user, and then the age information of the user is subjected to query processing on an age-normal reaction duration threshold mapping table, so that a normal reaction duration threshold corresponding to the age information of the user can be queried. Further, the step of calculating the response time of the user may include: acquiring first time for displaying the standard question data on the current page; acquiring a second time for the user to input the feedback answer data; calculating a difference between the second time and the first time; the difference is determined as the response time period. If the response time length is larger than the normal response time length threshold, generating a corresponding risk coefficient based on the response time length and the normal response time length threshold, and judging whether the risk coefficient is larger than a preset risk coefficient threshold. And if the risk factor is not larger than the risk factor threshold, judging that the user passes the verification. And if the user is larger than the risk coefficient threshold value, judging that the user is not verified. Wherein, the process of calculating the risk coefficient may comprise: the risk factor may be the square of the difference between the response reaction time duration and the normal reaction time threshold. In addition, the risk coefficient threshold is a relaxation index for determining whether the identity of the target object has a suspicious risk. When the risk coefficient is larger than the risk coefficient threshold value, the response reaction time length is basically the same as the normal reaction time length, and the user is known not to answer the question correctly immediately but to feed back the answer after long thought, so that the current user can be judged to have certain risk suspicion, and the identity verification result of the current user is judged to be not verified. In the embodiment, the user is authenticated from multiple dimensions, that is, on the basis of verifying the face image of the user, the user is further authenticated with respect to the standard question data, that is, the user is accurately authenticated by comparing the response time length of the user when answering the standard question data with the normal response time length threshold value and comparing the risk index generated based on the response time length and the normal response time length threshold value with the risk coefficient threshold value, so that the reliability of the user authentication can be further improved.

Further, in an embodiment of the present application, before the step S4, the method includes:

s400: judging whether a rule configuration request input by an administrator user is received; wherein, the rule configuration request carries administrator user information;

s401: if the rule configuration request is received, performing authority verification processing on the administrator user based on the administrator user information, and judging whether the authority verification is passed;

s402: if the authority passes the verification, displaying a preset rule configuration page; the rule configuration page comprises a plurality of rule attributes and a data selection list corresponding to each rule attribute, wherein the rule attributes comprise account information, financial voucher types, positions and keywords;

s403: receiving selection data which is input by the administrator user on the rule configuration page and corresponds to each rule attribute respectively;

s404: generating corresponding designated account information based on designated selection data corresponding to the account information;

s405: based on other selection data except the appointed selection data in all the selection data, calling the rule engine to generate a certificate identification rule corresponding to the appointed account information;

s406: and storing the certificate identification rule in a preset initial rule base to obtain the rule base.

As described in the foregoing steps S400 to S406, before the step of invoking the preset rule engine to query the target credential identification rule corresponding to the user account information from the pre-stored rule base is performed, a process of creating a rule base may be further included. Specifically, it is first determined whether a rule configuration request input by an administrator user is received. And the rule configuration request carries administrator user information. The administrator user information may include various types of information, such as job level, business team, development tasks, and the like. And if the rule configuration request is received, performing authority verification processing on the administrator user based on the administrator user information, and judging whether the authority verification is passed. The target permission level corresponding to the administrator user can be determined based on a preset classification tree model, then the configuration permission level of the business operation corresponding to the configuration rule is obtained based on a preset business operation permission table, and the target permission level and the configuration permission level are compared to complete permission verification processing of the administrator user. And if the authority passes the verification, displaying a preset rule configuration page. The rule configuration page comprises a plurality of rule attributes and a data selection list corresponding to each rule attribute, wherein the rule attributes comprise account information, financial voucher types, positions and keywords. In addition, the financial voucher types can include bank interest, commission expenditure, housing public accumulation fund and the like, and if a new financial voucher type is proposed in the financial affairs due to the updating and development of the business, enumeration can be set in a configuration page in advance. And then receiving selection data which is input by the administrator user on the rule configuration page and respectively corresponds to each rule attribute. And then generating corresponding appointed account information based on appointed selection data corresponding to the account information. And calling the rule engine to generate a credential identification rule corresponding to the designated account information based on other selection data except the designated selection data in all the selection data. The financial voucher identification rules of different accounts for the same financial voucher type can be different, such as bank interest-bearing vouchers, some bank accounts are identified by the summary field being 'interest', some accounts are identified by the summary field being 'interest', and some accounts can be identified by other fields such as some keywords for transaction purposes. Therefore, the rule engine can set corresponding certificate identification rules for each account according to actual requirements. For example, account a may set the credential identification rule as a bank running home bank interest credential whose digest field contains the keyword "interest", and account B may set the credential identification rule as a bank running home bank interest credential whose digest field contains the keyword "interest". And finally, storing the certificate identification rule in a preset initial rule base to obtain the rule base. In the embodiment, after the administrator user passes the authority verification, the corresponding certificate identification rule is generated intelligently based on the selection data input by the administrator user on the rule configuration page, and the certificate identification rule is stored in the preset initial rule base to obtain the rule base, so that the target certificate identification rule corresponding to the target account information can be accurately and quickly inquired based on the rule base in the following process.

Further, in an embodiment of the application, the step S401 includes:

s4010: calling a preset classification tree model;

s4011: determining a role class corresponding to the administrator user information through the classification tree model, and determining a target authority level corresponding to the class of the administrator user information based on a preset corresponding relation between the class and the authority level;

s4012: acquiring a configuration authority level of the service operation corresponding to the configuration rule based on a preset service operation authority table;

s4013: judging whether the target permission level is greater than the configuration permission level;

s4014: if the target permission level is greater than the configuration permission level, judging that permission verification is passed;

s4015: and if the target permission level is not greater than the configuration permission level, judging that the permission verification is not passed.

As described in steps S4010 to S4015, the performing authorization verification processing on the administrator user based on the administrator user information, and determining whether the authorization verification is passed may specifically include: first, a preset classification tree model is called. And then determining the role category corresponding to the administrator user information through the classification tree model, and determining a target authority level corresponding to the category of the administrator user information based on the corresponding relation between the preset category and the authority level. Each node except the leaf node in the classification tree model corresponds to one classification rule, and each classification rule classifies one type of data in the administrator user information. Therefore, the administrator user information can be classified layer by layer through the classification tree model, and finally, the administrator user information is distributed to one leaf node. And then, according to the corresponding relation between the preset leaf nodes and the authority levels, determining the target authority levels corresponding to the administrator user information. For example, assume that the administrator user information includes: "job level: 3, a business team: a, developing tasks: and 6 ', assuming that the root nodes of the classification tree model are classified through ' position level ', the second-level nodes are classified through ' business team ', and the third-level nodes are classified through ' research and development task ', the administrator user information can be distributed to one leaf node through three-layer classification, and then the target authority level corresponding to the administrator user information can be determined according to the preset corresponding relation between the leaf node and the authority level. And then acquiring the configuration authority level of the service operation corresponding to the configuration rule based on a preset service operation authority table. A service operation authority table is created in advance, and authority levels corresponding to each service operation one to one are recorded in the service operation authority table. And finally, judging whether the target authority level is greater than the configuration authority level. And if the target permission level is greater than the configuration permission level, judging that permission verification is passed. And if the target permission level is not greater than the configuration permission level, judging that permission verification is not passed. In the embodiment, the classification tree model is used for rapidly acquiring the target permission level corresponding to the information of the administrator user, and then the target permission level of the administrator user is compared with the configuration permission level corresponding to the business operation of the configuration rule in numerical value to obtain a corresponding comparison result, so that whether the administrator user has the authority of the configuration rule can be accurately and rapidly judged according to the comparison result. Only when the administrator user is judged to have the configuration rule authority, namely the administrator passes the authority verification, the subsequent configuration rule processing flow is executed based on the rule configuration request, and adverse effects caused by responding to the rule configuration request input by an illegal user are avoided. In addition, the configuration rule function is only opened for the user with the configuration rule authority, so that the normalization and the safety of the configuration rule are realized.

Further, in an embodiment of the present application, the step S5 includes:

s500: acquiring a designated position and a designated keyword contained in a first designated certificate identification rule; the first appointed certificate identification rule is any one rule in all the target certificate identification rules;

s501: extracting character data corresponding to the designated position from the target bank flow;

s502: judging whether the character data contains the specified keyword or not;

s503: if the character data contains the specified keywords, generating a recognition result of successful recognition corresponding to the first specified certificate recognition rule;

s504: and if the character data does not contain the specified keyword, generating an identification result of identification failure corresponding to the identification rule of the first specified certificate.

As described in the foregoing steps S500 to S504, the step of performing identification processing on the target bank flow based on each target credential identification rule to obtain an identification result corresponding to each target credential identification rule may specifically include: first, a specified position and a specified keyword contained in a first specified certificate identification rule are obtained. Wherein the first specified credential identification rule is any one of all the target credential identification rules. And then extracting the character data corresponding to the specified position from the target bank flow. And then judging whether the character data contains the specified keyword or not. And if the character data contains the specified keywords, generating a recognition result of successful recognition corresponding to the first specified certificate recognition rule. And if the character data does not contain the specified keyword, generating an identification result of identification failure corresponding to the identification rule of the first specified certificate. For example, if the first designated credential identification rule is a bank running affiliation bank settlement credential whose digest field contains a keyword "interest", and the text data at the digest field in the target bank running contains the keyword "interest", an identification result corresponding to the first designated credential identification rule is generated. In the embodiment, the text data corresponding to the designated position is extracted from the target bank running water according to the designated position contained in each target certificate identification rule, and then the identification result between each target certificate identification rule and the target bank running water can be rapidly and intelligently obtained by judging whether the obtained text data contains the designated keyword, so that the target financial certificate type of the target bank running water can be intelligently and accurately determined based on all the obtained identification results subsequently, and the accuracy and the generation efficiency of the obtained target financial certificate type are effectively ensured.

Further, in an embodiment of the present application, the step S6 includes:

s600: acquiring all recognition results;

s601: screening out the identification results with contents of successful identification from all the identification results;

s602: acquiring a second specified certificate identification rule corresponding to an identification result of successful identification from all the target certificate identification rules;

s603: acquiring a specified financial certificate type contained in the second specified certificate identification rule;

s604: taking the specified financial credential type as the target financial credential type.

As described in the foregoing steps S600 to S604, the step of determining the target financial voucher type of the target bank flow based on all the identification results may specifically include: first all recognition results are obtained. And then screening out the contents from all the identification results as the identification results with successful identification. And then acquiring a second specified certificate identification rule corresponding to the identification result which is successfully identified from all the target certificate identification rules. Subsequently obtaining a specified financial credential type included in the second specified credential identification rule. And finally, taking the specified financial certificate type as the target financial certificate type. After the target voucher identification rules corresponding to the target account information are inquired from a pre-stored rule base based on a rule engine, the method and the device can intelligently use each target voucher identification rule to respectively identify and process the target bank flow, further screen out identification results with contents being successfully identified from the identification results corresponding to each rule, and accordingly use the specified financial voucher type with the corresponding relation to the successfully identified identification results as the target financial voucher type, so that the target financial voucher type of the target bank flow is intelligently and accurately determined, and the accuracy and the generation efficiency of the obtained target financial voucher type are guaranteed.

Further, in an embodiment of the present application, after the step S6, the method includes:

s610: generating corresponding financial certificate marking information based on the target financial certificate type;

s611: marking the target bank running water by using the financial certificate marking information to obtain the marked target bank running water;

s612: acquiring terminal information corresponding to the user;

s613: and sending the marked target bank flow to a user terminal corresponding to the terminal information.

As described in the foregoing steps S610 to S613, after the step of determining the target financial voucher type of the target bank flow based on all the identification results is completed, a process of generating financial voucher marking information corresponding to the target financial voucher type and feeding the financial voucher marking information back to the user may be further included. Specifically, first, based on the target financial voucher type, corresponding financial voucher marking information is generated. The financial certificate marking information refers to information obtained by highlighting the target financial certificate type, and the highlighting process can include deepening, highlighting and the like. And then, the financial voucher marking information is used for marking the target bank running water to obtain the marked target bank running water. The marking processing refers to the step of placing the financial voucher marking information at an appointed position in the target bank flow, and the appointed position can be set according to actual requirements. And then acquiring terminal information corresponding to the user. And finally, the marked target bank is sent to the user terminal corresponding to the terminal information in a running mode. In this embodiment, corresponding financial document labeling information is further generated when the target financial document type of the target bank flow is obtained, the target bank flow is automatically labeled based on the financial document labeling information, and the labeled target bank flow is sent to the user terminal corresponding to the user, so that the user can clearly and clearly check the target financial document type of the target bank flow based on the labeled target bank flow, and the user experience is improved.

The credential generation method based on the rule engine in the embodiment of the present application may also be applied to the field of blockchains, for example, data such as the target financial credential type is stored on a blockchain. By using the block chain to store and manage the target financial certificate type, the security and the non-tamper property of the target financial certificate type 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 string of data blocks associated by using a cryptographic method, and each data block contains information of a batch of network transactions, which is used for verifying the validity (anti-counterfeiting) of the information and generating 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 a credential generating apparatus based on a rule engine, including:

the first judgment module 1 is used for judging whether a target bank flow to be processed input by a user is received or not;

the first verification module 2 is used for performing identity verification processing on the user based on a preset standard face image and standard question and question data if the target bank flow is received, and judging whether the user passes the verification;

the analysis module 3 is used for analyzing the user account information from the target bank flow if the verification is passed;

the query module 4 is configured to invoke a preset rule engine to query a target credential identification rule corresponding to the user account information from a pre-stored rule base;

the identification module 5 is configured to perform identification processing on the target bank flow based on each target credential identification rule, so as to obtain an identification result corresponding to each target credential identification rule;

and the determining module 6 is used for determining the target financial voucher type of the target bank flow based on all the identification results.

In this embodiment, the operations respectively executed by the modules or units correspond to the steps of the rule engine-based credential generation method in the foregoing embodiment one to one, and are not described herein again.

Further, in an embodiment of the present application, the first verification module 2 includes:

the first acquisition unit is used for acquiring a face image of the user;

the comparison unit is used for comparing the face image of the user with the standard face image;

the second obtaining unit is used for obtaining pre-stored standard question-asking data when the comparison is consistent, displaying the standard question-asking data and answer reminding information on a current interface, and reminding the user to perform answer feedback on the standard question-asking data based on the answer reminding information;

the receiving unit is used for receiving feedback answer data input by the user;

the calculating unit is used for calculating the response time length of the user after verifying that the feedback answer data is the same as the preset standard answer data, and judging whether the response time length is greater than a preset normal response time length threshold value or not;

a first judging unit, configured to generate a corresponding risk coefficient based on the reply response duration and the normal response duration threshold if the response duration is greater than the normal response duration threshold, and judge whether the risk coefficient is greater than a preset risk coefficient threshold;

the first judgment unit is used for judging that the user passes the verification if the risk coefficient is not larger than the risk coefficient threshold;

and the second judgment unit is used for judging that the user is not authenticated if the risk coefficient is larger than the risk coefficient threshold.

Further, in an embodiment of the present application, the credential generating device based on the rule engine includes:

the second judgment module is used for judging whether a rule configuration request input by an administrator user is received or not; wherein, the rule configuration request carries administrator user information;

the second verification module is used for performing authority verification processing on the administrator user based on the administrator user information and judging whether the authority verification is passed or not if the rule configuration request is received;

the display module is used for displaying a preset rule configuration page if the authority passes the verification; the rule configuration page comprises a plurality of rule attributes and a data selection list corresponding to each rule attribute, wherein the rule attributes comprise account information, financial voucher types, positions and keywords;

the receiving module is used for receiving selection data which are input by the administrator user on the rule configuration page and respectively correspond to the rule attributes;

the first generation module is used for generating corresponding appointed account information based on appointed selection data corresponding to the account information;

the second generation module is used for calling the rule engine to generate a certificate identification rule corresponding to the specified account information based on other selection data except the specified selection data in all the selection data;

and the storage module is used for storing the certificate identification rule in a preset initial rule base to obtain the rule base.

Further, in an embodiment of the application, the second verification module includes:

the calling unit is used for calling a preset classification tree model;

the first determining unit is used for determining the role class corresponding to the administrator user information through the classification tree model and determining a target authority class corresponding to the class of the administrator user information based on the corresponding relation between the preset class and the authority class;

a third obtaining unit, configured to obtain, based on a preset service operation permission table, a configuration permission level of a service operation corresponding to the configuration rule;

the second judging unit is used for judging whether the target permission level is greater than the configuration permission level;

the third judging unit is used for judging that the authority verification is passed if the target authority level is greater than the configuration authority level;

and the fourth judging unit is used for judging that the authority verification is not passed if the target authority level is not greater than the configuration authority level.

In this embodiment, the operations that the modules or units are respectively configured to execute correspond to the steps of the rule engine-based credential generation method in the foregoing embodiment one to one, and are not described herein again.

Further, in an embodiment of the present application, the identification module 5 includes:

a fourth obtaining unit, configured to obtain a specified position and a specified keyword included in the first specified credential identification rule; the first appointed certificate identification rule is any one rule in all the target certificate identification rules;

the extraction unit is used for extracting the character data corresponding to the specified position from the target bank flow;

a third judging unit, configured to judge whether the text data includes the specified keyword;

a first generating unit, configured to generate a successful recognition result corresponding to the first designated credential recognition rule if the text data includes the designated keyword;

and the second generation unit is used for generating an identification result of identification failure corresponding to the identification rule of the first specified certificate if the specified keyword is not contained in the character data.

Further, in an embodiment of the present application, the determining module 6 includes:

a fifth acquiring unit, configured to acquire all the recognition results;

the screening unit is used for screening the contents from all the identification results as the identification results which are successfully identified;

a sixth obtaining unit, configured to obtain, from all the target credential identification rules, a second specified credential identification rule corresponding to an identification result that is successfully identified;

a seventh obtaining unit, configured to obtain a specified financial credential type included in the second specified credential identification rule;

a second determining unit to take the specified financial credential type as the target financial credential type.

the third generation module is used for generating corresponding financial certificate marking information based on the target financial certificate type;

the marking module is used for marking the target bank running water by using the financial voucher marking information to obtain the marked target bank running water;

the acquisition module is used for acquiring terminal information corresponding to the user;

and the sending module is used for sending the marked target bank to the user terminal corresponding to the terminal information in a running mode.

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 target bank flow, standard face images, standard question data, a rule engine, target certificate identification rules, identification results and target financial certificate types. 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 when executed by a processor implements a rules engine based credential generation method.

The processor executes the steps of the credential generation method based on the rule engine:

judging whether a target bank flow to be processed input by a user is received or not;

and determining the target financial certificate type of the target bank flow based on all the identification results.

It will be understood by those skilled in the art 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 credential generation method based on a rule engine, and specifically includes:

To sum up, in the rule engine-based credential generation method, the rule engine-based credential generation device, the computer device, and the storage medium provided in the embodiments of the present application, after a target bank flow input by a user is received and the user passes identity verification, corresponding user account information is first analyzed from the target bank flow, a preset rule engine is then invoked to query a target credential identification rule corresponding to the user account information, the target bank flow is identified based on each target credential identification rule, and a target financial credential type of the target bank flow is quickly and accurately determined based on each obtained identification result. Different from the existing mode of manually identifying the financial voucher types of the running water of the bank, the embodiment of the application can automatically and accurately generate the target financial voucher types of the running water of the target bank, saves manpower and material resources, effectively improves the generation efficiency of the target financial voucher types, reduces the generation cost of the target financial voucher types and reduces the error identification rate of the target financial voucher types.

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).

It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, apparatus, article, or method. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, apparatus, article, or method that includes the element.

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 credential generation method based on a rule engine is characterized by comprising the following steps:

judging whether a target bank flow to be processed input by a user is received;

determining the type of the target financial voucher of the target bank flow based on all the identification results;

the step of respectively identifying the target bank assembly line based on each target certificate identification rule to obtain an identification result corresponding to each target certificate identification rule comprises the following steps:

acquiring a designated position and a designated keyword contained in a first designated certificate identification rule; the first appointed certificate identification rule is any one rule in all the target certificate identification rules;

judging whether the character data contains the specified keywords or not;

2. The method for generating a credential based on a rules engine as claimed in claim 1, wherein the step of performing an authentication process on the user based on the preset standard face image and the standard question data and determining whether the authentication is passed comprises:

acquiring a face image of the user;

comparing the face image of the user with the standard face image;

when the comparison is consistent, pre-stored standard question-asking data is obtained, the standard question-asking data and answer reminding information are displayed on a current interface, and the user is reminded to perform answer feedback on the standard question-asking data based on the answer reminding information;

receiving feedback answer data input by the user;

after verifying that the feedback answer data are the same as the preset standard answer data, calculating the response time length of the user, and judging whether the response time length is greater than a preset normal response time length threshold value or not;

and if the risk coefficient is larger than the risk coefficient threshold value, judging that the user is not verified.

3. The method for generating a credential based on a rule engine as claimed in claim 1, wherein before the step of invoking the preset rule engine to query the target credential identification rule corresponding to the user account information from a pre-stored rule base, the method comprises:

if the rule configuration request is received, performing authority verification processing on the administrator user based on the administrator user information, and judging whether the authority verification is passed;

4. The method for generating voucher based on rules engine as claimed in claim 3, wherein the step of performing the authority verification process for the administrator user based on the administrator user information and determining whether the authority verification is passed comprises:

calling a preset classification tree model;

acquiring a configuration authority level of the business operation corresponding to the configuration rule based on a preset business operation authority table;

and if the target permission level is not greater than the configuration permission level, judging that the permission verification is not passed.

5. The rules engine based credential generation method of claim 1 wherein the step of determining the target financial credential type of the target banking flow based on all recognition results comprises:

acquiring all recognition results;

6. The rules engine based credential generation method of claim 1, wherein said step of determining a target financial credential type for the target bank flow based on all recognition results is followed by:

acquiring terminal information corresponding to the user;

7. A rules engine based credential generation apparatus comprising:

the determining module is used for determining the type of the target financial voucher of the target bank flow based on all the identification results;

the identification module comprises:

8. 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 of claims 1 to 6.

9. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 6.