WO2020087981A1

WO2020087981A1 - Method and apparatus for generating risk control audit model, device and readable storage medium

Info

Publication number: WO2020087981A1
Application number: PCT/CN2019/095838
Authority: WO
Inventors: 罗成洋
Original assignee: 平安医疗健康管理股份有限公司
Priority date: 2018-10-29
Filing date: 2019-07-12
Publication date: 2020-05-07
Also published as: CN109615534A

Abstract

A method and an apparatus for generating a risk control audit model, a device, and a readable storage medium. The method comprises steps of: generating a JS object notation (JSON) file according to target data and a corresponding data storage template, and importing the JSON file into a preset audit engine to generate a risk control audit model (S30). According to the present method, a risk control audit model is generated by means of big data analysis; in the process of generating the specific content of a risk control audit rule, developers do not need to programmatically input to a database the content of the rules provided by business personnel; and a JSON file corresponding to the risk control audit rule can be generated directly according to the obtained target data, so that the efficiency of generating the risk control audit model corresponding to the risk control audit rule is improved.

Description

Risk control audit model generation method, device, equipment and readable storage medium The

This application requires the priority of the Chinese patent application submitted to the China Patent Office on October 29, 2018, with the application number 201811272598.4 and the invention titled "Risk control audit model generation method, device, equipment and readable storage medium", all of which are The content is incorporated into the application by reference.

Technical field

The present application relates to the field of data processing technology, and in particular, to a method, device, device, and readable storage medium for generating a risk control audit model.

Background technique

Today, medical expenses have risen sharply, and the income of medical insurance funds is less than the expenditure. The risk control of medical insurance is extremely important and urgent. Through information technology, risk monitoring and analysis of medical data related to medical insurance claims are conducted to effectively avoid the loss of insurance funds due to fraud and abuse. The purpose of protecting the medical insurance fund. In order to effectively avoid the risk of medical insurance being abused, risk control review rules are required to monitor and review medical data. At present, the specific content of the risk control audit rules needs to be programmed and entered into the database by the developers according to the rule details provided by the business personnel. However, this method leads to the low efficiency of generating risk control audit rules corresponding to the risk control audit model.

Summary of the invention

The main purpose of the present application is to provide a method, device, equipment and readable storage medium for generating a risk control audit model, aiming to solve the technical problem of low efficiency of the risk control audit model corresponding to the existing risk control audit rules.

In order to achieve the above objective, the present application provides a method for generating a risk control audit model. The method for generating a risk control audit model includes the steps of:

Obtain preset risk control audit rules, and configure the data association model according to the risk control audit rules;

Generating a data storage template in a preset format according to the data association model, and obtaining target data corresponding to the risk control audit rules through the data storage template;

Generate JS according to the target data and the corresponding data storage template Object notation JSON file, and import the JSON file into a preset audit engine to generate a risk control audit model.

Preferably, the step of acquiring a preset risk control audit rule and configuring a data association model according to the risk control audit rule includes:

Obtain preset risk control audit rules and determine the audit type corresponding to the risk control audit rules;

Configure audit rules corresponding to the risk control audit rules according to the audit type;

Determine the data type corresponding to the review details, and configure the data association model according to the limiting conditions and logical relationships corresponding to the various data types.

Preferably, the step of determining the data type corresponding to the review details, and configuring the data association model according to the limiting conditions and logical relationships corresponding to the various data types includes:

Determine the data type corresponding to the review rules, and generate a tree-like data structure according to the limiting conditions and logical relationships corresponding to each data type in the review rules corresponding to the same review type;

Correlate each data type in each of the tree-shaped data structures according to the logical relationship and limiting conditions of the corresponding data types of various audit types to generate a mesh data structure to obtain the data association model.

Preferably, the step of generating a data storage template in a preset format according to the data association model includes:

Generating a characteristic field of the data storage template according to each data type in the data association model;

Using the characteristic field as a header of a table corresponding to the data association model;

At least one link channel is set in the table to associate each table through the link channel to generate a data storage template in a preset format.

Preferably, the step of acquiring the target data corresponding to the risk control review rule through the data storage template includes:

When the entry instruction of the target data is detected, the corresponding data storage template is displayed according to the entry instruction for the entry personnel to enter the corresponding target data in the data storage template.

Preferably, the step of generating a JSON file according to the target data and the corresponding data storage template, and importing the JSON file into a preset audit engine to generate a risk control audit model, further includes:

When an update instruction to update the target data corresponding to the risk control review rule is detected, a JSON file corresponding to the risk control review rule is obtained;

Update the target data in the JSON file according to the update instruction.

When an audit request for auditing the data to be audited is detected, the data to be audited is compared with the target data corresponding to the JSON file in the auditing engine to audit the data to be audited.

In addition, in order to achieve the above object, the present application also provides a risk control audit model generation device, the risk control audit model generation device includes:

The acquisition module is used to obtain preset risk control audit rules;

A configuration module, configured to configure a data association model according to the risk control review rules;

A generating module, configured to generate a data storage template in a preset format according to the data association model;

The obtaining module is further used to obtain the target data corresponding to the risk control audit rule through the data storage template;

The generating module is further configured to generate a JS object notation JSON file according to the target data and the corresponding data storage template;

The import module is used to import the JSON file into a preset audit engine to generate a risk control audit model.

In addition, in order to achieve the above object, the present application also provides a risk control audit model generation device, which includes a memory, a processor, and a A risk control audit model generation program that implements the steps of the risk control audit model generation method described above when executed by the processor.

In addition, in order to achieve the above object, the present application also provides a computer-readable storage medium on which a risk control audit model generation program is stored, which is implemented when the processor is executed by the processor The steps of the risk control audit model generation method as described above.

This application configures the data association model according to the acquired risk control audit rules, generates a data storage template corresponding to the data association model, and obtains the target data corresponding to the risk control audit rules through the data storage template, based on the target data and the corresponding data The storage template generates a JSON file, and imports the JSON file into a preset audit engine to generate a risk control audit model, which realizes that in the process of generating specific content of the risk control audit rules, developers do not need to program according to the rule details provided by business personnel After input into the database, the JSON file corresponding to the risk control audit rules can be directly generated according to the obtained target data, which improves the efficiency of generating the risk control audit model corresponding to the risk control audit rules.

BRIEF DESCRIPTION

FIG. 1 is a schematic flowchart of a first embodiment of a method for generating a risk control audit model of this application;

2 is a schematic flowchart of a second embodiment of a method for generating a risk control audit model of this application;

3 is a schematic flowchart of a third embodiment of a method for generating a risk control audit model of the present application;

4 is a functional schematic block diagram of a preferred embodiment of a risk control audit model generation device of this application;

5 is a schematic structural diagram of a hardware operating environment involved in a solution of an embodiment of the present application.

The implementation, functional characteristics and advantages of the present application will be further described in conjunction with the embodiments and with reference to the drawings.

detailed description

It should be understood that the specific embodiments described herein are only used to explain the present application, and are not used to limit the present application.

This application provides a method for generating a risk control audit model. Referring to FIG. 1, FIG. 1 is a schematic flowchart of a first embodiment of a method for generating a risk control audit model in this application.

The embodiment of the present application provides an embodiment of a method for generating a risk control audit model. It should be noted that although the logic sequence is shown in the flowchart, in some cases, the sequence may be performed differently from the sequence shown here. Out or describe the steps.

The risk control audit model generation method is applied to a server or a terminal, and the terminal may include, for example, a mobile phone, a tablet computer, a notebook computer, a palmtop computer, and a personal digital assistant (Personal Digital Assistant, PDA) and other mobile terminals, and fixed terminals such as digital TV, desktop computers and so on. In each embodiment of the risk control audit model generation method, for convenience of description, the execution subject is omitted to explain each embodiment. Risk control audit model generation methods include:

Step S10: Acquire preset risk control audit rules, and configure a data association model according to the risk control audit rules.

After detecting the generation command of the generated data risk control audit model, the preset risk control audit rules are obtained, and the data association model is configured according to the risk control audit rules. Among them, the generation instruction can be triggered by the corresponding staff as needed, and the preset risk control audit rules are used to check whether various data meet the preset conditions. For example, when the risk control review rules are used to review data related to medical insurance claims, the risk control review rules should include rules for reviewing the logical relationship and limiting conditions between various medical data. In this embodiment, the logical relationship specifically includes a positive correlation relationship, a negative correlation relationship, and a conditional relationship. The positive correlation relationship is a relationship that must or can exist simultaneously between data, and the negative correlation relationship is a relationship that cannot exist simultaneously between data. A conditional relationship is a relationship between data when a piece of data satisfies a certain condition, there is corresponding another data, or there is no corresponding another data relationship. The limiting condition may be the condition that the data to be audited needs to be satisfied, such as a numerical range, a preset type, etc.

Further, in order to improve the accuracy of the configured data association model, step S10 includes:

Step a: Acquire preset risk control audit rules and determine the audit type corresponding to the risk control audit rules.

Different audit types are set in the risk control audit rules, and the audit types corresponding to the risk control audit rules can be determined according to the audit requirements and audit content of the risk control audit rules. For example, when reviewing medical data, different risk control audit rules are set according to the different conditions of the disease, and the audit types corresponding to the risk control audit rules are divided according to the audit requirements and audit content in the risk control audit rules. For example, the audit types of risk control audit rules corresponding to medical data include but are not limited to medication audit types, inspection item audit types, and expense audit types.

After the preset risk control audit rules are obtained, the audit type corresponding to the risk control audit rules is determined according to the audit requirements and / or audit content corresponding to the risk control audit rules. If the audit content of the risk control audit rule is to check whether the cost of the drug used by the user is correct, then determine that the audit type corresponding to the risk control audit rule is the expense audit type; If the application medicine is correct, it is determined that the audit type corresponding to the risk control audit rule is the medicine audit type.

Step b: Configure audit rules corresponding to the risk control audit rules according to the audit type.

After determining the audit type corresponding to the risk control audit rule, configure the audit rules corresponding to the risk control audit rule according to the audit type. It should be noted that each audit type needs to be configured with corresponding audit rules, and the same audit type corresponds to multiple audit rules. Specifically, the audit rules can be set by the corresponding staff according to the specific situation, and the audit rules corresponding to different types of audits are different.

Step c: Determine the data type corresponding to the audit details, and configure the data association model according to the limiting conditions and logical relationships corresponding to the various data types.

After determining the audit rules, determine the data types corresponding to the audit rules, and determine the corresponding qualifications and logical conditions for each data type, and associate the various data types according to the qualifications and logical relationships corresponding to the various data types to configure Data association model. It should be noted that the data association model is preset to establish an association relationship between data types. In the data association model, there are different association relationships between various data types. For example, in the review rules corresponding to the medication review type, it is necessary to determine whether the insured's medication contains duplicate drugs, that is, drugs that cannot occur at the same time. The duplicate drugs may be drugs with the same curative effect or drugs with mutually exclusive curative effects Wait. Because the drugs used in different conditions will be different, the data types corresponding to the audit rules include but are not limited to the insured's condition, drugs with the same effect under the same condition, and drugs with mutually exclusive effects under the same condition. Further, the dosage of drugs can also be reviewed in the review rules corresponding to the drug review type. For example, the dosage of a certain drug should be within the preset range. At this time, the data types corresponding to the review rules include but are not limited to the patient ’s condition, Drugs used in the same condition and the dosage range of the drugs used. For example, the drugs with the same curative effect in the same condition in the data type are related to the dosage range of the corresponding drugs. If drug A and drug B are drugs with the same effect, when configuring the data correlation model, the dosage range of drug A and the dosage range of drug B can be associated with drugs A and B of the same efficacy. If there are three possible dosage ranges of drug A, b, and c, when drug A is combined with drug B, drug A is in the dosage range of drug A, drug B should be in the dosage range of d, when drug A is in dosage b When within the range, drug B should be within the range of e dosage. (The dosage between different drugs will affect)

Further, in order to improve the efficiency of generating the data association model and ensure the integrity of the generated data association model, step c includes:

Step c1: Determine the data type corresponding to the audit details, and generate a tree-like data structure according to the qualification conditions and logical relationships corresponding to each data type in the audit details corresponding to the same audit type.

After the audit rules are determined, the audit type corresponding to each audit rule is determined, and the data type corresponding to the audit rules of the same audit type is obtained and recorded as the first data type. It can be understood that multiple data types are included in the first data type. After acquiring the first data type, the first data types are correlated according to the limiting conditions and logical relationships corresponding to the various first data types to generate a tree-like data structure.

Step c2: Correlate each data type in each of the tree-shaped data structures according to the logical relationship and limiting conditions of the corresponding data types of various audit types to generate a mesh data structure to obtain the data association model.

After generating the tree-like data structure, obtain the data type corresponding to each audit type and record it as the second data type, and then perform the various data types in each tree-like data structure according to the logical relationship and the limiting conditions corresponding to the second data type Correlation, generate a mesh data structure to get a data correlation model. It can be understood that the mesh data result is the resulting data association model. The tree data structure is for the same audit type, which is to associate various data types in the same audit type; the mesh data structure is for different audit types, which are various data corresponding to different audit types Types are related. In this embodiment, by first constructing a tree-like data structure in the same audit type, and then constructing a mesh-like data structure according to the tree-like data structure in different audit types, the data association model is generated in an orderly manner, and the efficiency of generating the data association model is improved And the generated data association model includes the association relationship between all data types, ensuring the integrity of the generated data association model.

In step S20, a data storage template in a preset format is generated according to the data association model, and target data corresponding to the risk control audit rule is obtained through the data storage template.

When the data association model is obtained, a data storage template in a preset format is generated according to the data association model, and the target data corresponding to the risk control audit rules is obtained through the data storage template. It should be noted that the target data is the specific data in the audit rules corresponding to the risk control audit rules. For example, the drug name of the duplicate drug in the review rules, the upper limit and lower limit of the corresponding range of the dosage of a certain drug, etc. The preset formats include but are not limited to EXCEL format and TXT format.

Further, the step of generating a data storage template in a preset format according to the data association model includes:

Step d: Generate a characteristic field of the data storage template according to each data type in the data association model.

After the data association model is obtained, a feature field of a data storage template in a preset format is generated according to each data type in the data association model, and the data storage template displayed by the feature field is stored. In this embodiment, the data storage template may be an EXCEL table, and in other embodiments, the data storage template may be a TXT file or the like. If a certain data type is a drug with the same curative effect under the same condition, the corresponding characteristic field is a drug with the same curative effect.

Step e, using the feature field as a table header of the table corresponding to the data association model.

After determining the characteristic field, use the characteristic field as the header of the table corresponding to the data association model. It can be understood that the characteristic fields corresponding to different data types can be respectively named as the corresponding EXCEL table, and the EXCEL table can be used to obtain the target data entered by the corresponding entry person. In the data association model, a data type can correspond to an EXCEL table.

Step f: Set at least one link channel in the table to associate each table through the link channel to generate a data storage template in a preset format.

At least one link channel is set in each table to associate each table through the link channel to generate a data storage template in a preset format. Specifically, one or more link channels can be set for each item in the table. The link channel can be determined according to the association relationship of the data types in the data association model. Specifically, when there is an association relationship between the two data types in the data association model, the link channel needs to be set in the table corresponding to the two data types . Different link channels are inserted into the EXCEL table with different icons, text, etc. as logos. Through the link channel, the person entering the target data can quickly find other forms associated with the type of data filled in and fill in the corresponding relevant data.

Further, the step of obtaining the target data corresponding to the risk control audit rule through the data storage template includes:

In step g, when an entry instruction of target data is detected, the data storage template is displayed according to the entry instruction, so that an entry person can enter the corresponding target data in the data storage template.

When the entry instruction of the target data is detected, the data storage template is displayed according to the entry instruction for the entry personnel to enter the corresponding target data in the data storage template. Among them, the entry instruction is triggered by the entry personnel according to specific needs.

Further, the corresponding target data can be automatically entered in the data storage template, specifically, the audit record corresponding to the audited data within a preset time period can be obtained, and the first data that has been audited by the risk control audit rules in the audited data, and Analyze the second data in the audited data that has not passed the audit of the risk control audit rules to extract the corresponding target data and fill it in the data storage template. If a certain two medicines pass the review of the risk control corresponding risk control review rules, the two medicines can be determined to be mutually exclusive, and the target data of the data storage template corresponding to the mutually exclusive medicine should be determined. The preset duration can be set according to specific needs, and in this embodiment, there is no specific limit to the length of time corresponding to the preset duration.

Step S30, generating JS according to the target data and the corresponding data storage template Object notation JSON file, and import the JSON file into a preset audit engine to generate a risk control audit model.

When the target data is obtained, fill the target data into the location corresponding to the data storage template, obtain the data storage template after filling the target data, and convert the data storage template after filling the target data into JSON (JavaScript Object Notation, JS Object notation) file, and import the JSON file into the preset audit engine to generate a risk control audit model. It can be understood that the data storage template may be a data table including multiple EXCEL tables. Specifically, VBA (Visual Basic for Applications) or eclipse to convert the data storage template filled with the target data into a JSON file.

In this embodiment, by configuring the data association model according to the acquired risk control audit rules, a data storage template corresponding to the data association model is generated, and the target data corresponding to the risk control audit rules are obtained through the data storage template. According to the target data and corresponding The data storage template generates a JSON file, and imports the JSON file into a preset audit engine to generate a risk control audit model, which realizes that in the process of generating specific content of the risk control audit rules, developers do not need to proceed according to the rules details provided by business personnel The program is input into the database, and the JSON file corresponding to the risk control audit rules can be generated directly according to the obtained target data, which improves the efficiency of generating the risk control audit rules corresponding to the risk control audit model.

Further, a second embodiment of the method for generating a risk control audit model of the present application is proposed.

The difference between the second embodiment of the risk control audit model generation method and the first embodiment of the risk control audit model generation method is that, referring to FIG. 2, the risk control audit model generation method further includes:

Step S40, when an update instruction to update the target data corresponding to the risk control review rule is detected, a JSON file corresponding to the risk control review rule is obtained.

When an update instruction to update the target data corresponding to the risk control review rule is detected, a JSON file corresponding to the risk control review rule is obtained. Among them, the update instruction includes but is not limited to the delete instruction to delete the existing target data in the risk control audit model, the add instruction to add the target data in the risk control audit model, and the modify instruction to modify the target data in the risk control audit model. The update instruction can be triggered by the user according to specific needs.

Step S50: Update the target data in the JSON file according to the update instruction.

When the JSON file is obtained, the target data in the JSON file is updated according to the update instruction, that is, the target data in the JSON file is deleted, modified, or added according to the update instruction.

In this embodiment, when the data corresponding to the risk control review rules needs to be modified, the target data in the JSON file can be directly modified to modify the data corresponding to the risk control review rules, thereby improving the efficiency of updating the risk control review rules.

Further, a third embodiment of the method for generating a risk control audit model of the present application is proposed.

The difference between the third embodiment of the risk control audit model generation method and the first or second embodiment of the risk control audit model generation method is that, referring to FIG. 3, the risk control audit model generation method further includes:

Step S60, when an audit request for auditing the data to be audited is detected, the data to be audited is compared with the target data corresponding to the JSON file in the audit engine to audit the data to be audited.

When an audit request for auditing the data to be audited is detected, the process corresponding to the audit engine is called according to the audit request to run the risk control audit model in the audit engine, and the data to be audited corresponds to the risk control audit model in a JSON file To compare the target data to review the data to be reviewed. Among them, when the risk control review model is a medical risk control review model, the corresponding data to be reviewed is medical data; when the risk control review model is a loan risk control review model, the corresponding data to be reviewed is loan data.

In this embodiment, the generated JSON file is used to audit the data to be audited, which improves the security of the data to be audited.

It should be noted that, those of ordinary skill in the art may understand that all or part of the steps to implement the above embodiments may be completed by hardware, or may be completed by a program instructing related hardware. The program may be stored in a computer-readable In the storage medium, the above-mentioned storage medium may be a non-volatile storage medium, such as a read-only memory, a magnetic disk, or an optical disk.

In addition, referring to FIG. 4, the present application also provides a risk control audit model generation device, which includes:

The obtaining module 10 is used to obtain preset risk control audit rules;

A configuration module 20, configured to configure a data association model according to the risk control audit rules;

The generating module 30 is configured to generate a data storage template in a preset format according to the data association model;

The obtaining module 10 is further configured to obtain the target data corresponding to the risk control audit rule through the data storage template;

The generating module 30 is further configured to generate a JS object notation JSON file according to the target data and the corresponding data storage template;

The import module 40 is used to import the JSON file into a preset audit engine to generate a risk control audit model.

Further, the configuration module 20 includes:

A determination unit for determining the audit type corresponding to the risk control audit rule;

A configuration unit, configured to configure audit rules corresponding to the risk control audit rules according to the audit type;

The determining unit is also used to determine the data type corresponding to the review details;

The configuration unit is further configured to configure the data association model according to the limiting conditions and logical relationships corresponding to the various data types.

Further, the configuration unit is also used to generate a tree-like data structure according to the qualification conditions and logical relationships corresponding to each data type in the audit details corresponding to the same audit type; according to the logical relationship of the data types corresponding to each audit type And limiting conditions, associate each data type in each of the tree-like data structures to generate a mesh data structure to obtain the data association model.

Further, the generating module 30 is also used to:

A generating unit, configured to generate a characteristic field of the data storage template according to each data type in the data association model;

A definition unit, configured to use the characteristic field as a header of a table corresponding to the data association model;

The setting unit is configured to set at least one link channel in the table to associate each table through the link channel to generate a data storage template in a preset format.

Further, the acquisition module 10 is also used to display the corresponding data storage template according to the input instruction when the input instruction of the target data is detected, for the entry personnel to enter the corresponding target data in the data storage template .

Further, the obtaining module 10 is further configured to obtain a JSON file corresponding to the risk control review rule after detecting an update instruction to update the target data corresponding to the risk control review rule;

The risk control audit model generation device also includes:

The update module is used to update the target data in the JSON file according to the update instruction.

Further, the risk control audit model generating device further includes:

The comparison module is used for comparing the data to be audited with the target data corresponding to the JSON file in the audit engine after detecting the audit request to audit the data to be audited, so as to audit the data to be audited.

It should be noted that the embodiments of the risk control audit model generation device are basically the same as the embodiments of the risk control audit model generation method described above, and details are not repeated here.

In addition, this application also provides a risk control audit model generation device. As shown in FIG. 5, FIG. 5 is a schematic structural diagram of a hardware operating environment involved in a solution of an embodiment of the present application.

It should be noted that FIG. 5 is a schematic diagram of the hardware operating environment of the risk control audit model generation device. The risk control audit model generation device in the embodiment of the present application may be a terminal device such as a PC or a portable computer.

As shown in FIG. 5, the risk control audit model generation device may include: a processor 1001, such as a CPU, a memory 1005, a user interface 1003, a network interface 1004, and a communication bus 1002. Among them, the communication bus 1002 is used to implement connection communication between these components. The user interface 1003 may include a display (Display), an input unit such as a keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface and a wireless interface. The network interface 1004 may optionally include a standard wired interface and a wireless interface (such as a WI-FI interface). The memory 1005 may be a high-speed RAM memory or a stable memory (non-volatile memory), such as disk storage. The memory 1005 may optionally be a storage device independent of the foregoing processor 1001.

Optionally, the risk control audit model generation device may also include a camera, RF (Radio Frequency (radio frequency) circuits, sensors, audio circuits, WiFi modules, etc.

Those skilled in the art may understand that the structure of the risk control audit model generation device shown in FIG. 5 does not constitute a limitation on the risk control audit model generation device, and may include more or fewer components than shown, or a combination of certain Components, or different component arrangements.

As shown in FIG. 5, the memory 1005 as a computer storage medium may include an operating system, a network communication module, a user interface module, and a risk control audit model generation program. Among them, the operating system is a program that manages and controls the hardware and software resources of the risk control audit model generation device, and supports the operation of the risk control audit model generation program and other software or programs.

In the risk control audit model generation device shown in FIG. 5, the user interface 1003 can be used to receive the generation data risk control audit model generation instruction, and the target data entry instruction, etc .; the network interface 1004 is mainly used to connect the background server and the background server Perform data communication; the processor 1001 can be used to call the risk control audit model generation program stored in the memory 1005 and execute the steps of the risk control audit model generation method as described above.

The specific implementation manner of the risk control audit model generation device of the present application is basically the same as the above embodiments of the risk control audit model generation method, which will not be repeated here.

In addition, the embodiments of the present application also provide a computer-readable storage medium on which is stored a risk control audit model generation program, which is implemented as described above when executed by a processor Steps of the risk control audit model generation method.

The specific implementation of the computer-readable storage medium of the present application is basically the same as the embodiments of the above-mentioned risk control audit model generation method, and details are not described herein again.

It should be noted that in this article, the terms "include", "include" or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device that includes a series of elements includes not only those elements, It also includes other elements that are not explicitly listed, or include elements inherent to such processes, methods, objects, or devices. Without more restrictions, the element defined by the sentence "include one ..." does not exclude that there are other identical elements in the process, method, article or device that includes the element.

The sequence numbers of the above embodiments of the present application are for description only, and do not represent the advantages and disadvantages of the embodiments.

Through the description of the above embodiments, those skilled in the art can clearly understand that the methods in the above embodiments can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware, but in many cases the former is better Implementation. Based on this understanding, the technical solution of the present application can essentially be reflected in the form of a software product that contributes to the existing technology. The computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, The CD-ROM includes several instructions to enable a terminal device (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to perform the methods described in the embodiments of the present application.

The above are only the preferred embodiments of the present application, and do not limit the scope of the patent of the present application. Any equivalent structure or equivalent process transformation made by the description and drawings of this application, or directly or indirectly used in other related technical fields The same reason is included in the patent protection scope of this application.

Claims

A method for generating a risk control audit model, characterized in that the method for generating a risk control audit model includes the following steps:

Obtain preset risk control audit rules, and configure the data association model according to the risk control audit rules;

Generating a data storage template in a preset format according to the data association model, and obtaining target data corresponding to the risk control audit rules through the data storage template;

Generate JS according to the target data and the corresponding data storage template Object notation JSON file, and import the JSON file into a preset audit engine to generate a risk control audit model.
The method for generating a risk control audit model according to claim 1, wherein the step of acquiring a preset risk control audit rule and configuring a data association model according to the risk control audit rule includes:

Obtain preset risk control audit rules and determine the audit type corresponding to the risk control audit rules;

Configure audit rules corresponding to the risk control audit rules according to the audit type;

Determine the data type corresponding to the review details, and configure the data association model according to the limiting conditions and logical relationships corresponding to the various data types.
The method for generating a risk control audit model according to claim 2, wherein the step of determining the data type corresponding to the audit details and configuring the data association model according to the limiting conditions and logical relationships corresponding to the various data types include:

Determine the data type corresponding to the review rules, and generate a tree-like data structure according to the limiting conditions and logical relationships corresponding to each data type in the review rules corresponding to the same review type;

Correlate each data type in each of the tree-shaped data structures according to the logical relationship and limiting conditions of the corresponding data types of various audit types to generate a mesh data structure to obtain the data association model.
The method for generating a risk control audit model according to claim 1, wherein the step of generating a data storage template in a preset format according to the data association model includes:

Generating a characteristic field of the data storage template according to each data type in the data association model;

Using the characteristic field as a header of a table corresponding to the data association model;

At least one link channel is set in the table to associate each table through the link channel to generate a data storage template in a preset format.
The method for generating a risk control audit model according to claim 1, wherein the step of acquiring the target data corresponding to the risk control audit rule through the data storage template includes:

When the entry instruction of the target data is detected, the corresponding data storage template is displayed according to the entry instruction for the entry personnel to enter the corresponding target data in the data storage template.
The method for generating a risk control audit model according to claim 1, wherein the JSON file is generated according to the target data and the corresponding data storage template, and the JSON file is imported into a preset audit engine After the steps to generate a risk control audit model, it also includes:

When an update instruction to update the target data corresponding to the risk control review rule is detected, a JSON file corresponding to the risk control review rule is obtained;

Update the target data in the JSON file according to the update instruction.
The method for generating a risk control audit model according to claim 1, wherein the JSON file is generated according to the target data and the corresponding data storage template, and the JSON file is imported into a preset audit engine After the steps to generate a risk control audit model, it also includes:

When an audit request for auditing the data to be audited is detected, the data to be audited is compared with the target data corresponding to the JSON file in the auditing engine to audit the data to be audited.
The method for generating a risk control audit model according to claim 2, wherein the JSON file is generated according to the target data and the corresponding data storage template, and the JSON file is imported into a preset audit engine After the steps to generate a risk control audit model, it also includes:

When an audit request for auditing the data to be audited is detected, the data to be audited is compared with the target data corresponding to the JSON file in the auditing engine to audit the data to be audited.
The method for generating a risk control audit model according to claim 3, wherein the JSON file is generated according to the target data and the corresponding data storage template, and the JSON file is imported into a preset audit engine After the steps to generate a risk control audit model, it also includes:

When an audit request for auditing the data to be audited is detected, the data to be audited is compared with the target data corresponding to the JSON file in the auditing engine to audit the data to be audited.
The method for generating a risk control audit model according to claim 4, wherein the JSON file is generated according to the target data and the corresponding data storage template, and the JSON file is imported into a preset audit engine After the steps to generate a risk control audit model, it also includes:

When an audit request for auditing the data to be audited is detected, the data to be audited is compared with the target data corresponding to the JSON file in the auditing engine to audit the data to be audited.
The method for generating a risk control audit model according to claim 5, wherein the JSON file is generated according to the target data and the corresponding data storage template, and the JSON file is imported into a preset audit engine After the steps to generate a risk control audit model, it also includes:

When an audit request for auditing the data to be audited is detected, the data to be audited is compared with the target data corresponding to the JSON file in the auditing engine to audit the data to be audited.
A risk control audit model generation device, characterized in that the risk control audit model generation device includes:

The acquisition module is used to obtain preset risk control audit rules;

A configuration module, configured to configure a data association model according to the risk control review rules;

A generating module, configured to generate a data storage template in a preset format according to the data association model;

The obtaining module is further used to obtain the target data corresponding to the risk control audit rule through the data storage template;

The generating module is further configured to generate a JS object notation JSON file according to the target data and the corresponding data storage template;

The import module is used to import the JSON file into a preset audit engine to generate a risk control audit model.
The risk control audit model generation device according to claim 12, wherein the configuration module comprises:

A determination unit for determining the audit type corresponding to the risk control audit rule;

A configuration unit, configured to configure audit rules corresponding to the risk control audit rules according to the audit type;

The determining unit is also used to determine the data type corresponding to the review details;

The configuration unit is further configured to configure the data association model according to the limiting conditions and logical relationships corresponding to the various data types.
The risk control audit model generation device according to claim 13, wherein the configuration unit is further configured to generate a spanning tree according to the limiting conditions and logical relationships corresponding to each data type in the audit rules corresponding to the same audit type Data structure; according to the logical relationship and qualifications of the corresponding data types of various audit types, associate each data type in each of the tree data structures to generate a mesh data structure to obtain the data association model.
The risk control audit model generation device according to claim 12, wherein the generation module is further configured to:

A generating unit, configured to generate a characteristic field of the data storage template according to each data type in the data association model;

A definition unit, configured to use the characteristic field as a header of a table corresponding to the data association model;

The setting unit is configured to set at least one link channel in the table to associate each table through the link channel to generate a data storage template in a preset format.
The risk control audit model generating device according to claim 12, wherein the acquisition module is further used to display the corresponding data storage template according to the entry instruction when the entry instruction of the target data is detected, for The entry personnel enter the corresponding target data in the data storage template.
The risk control audit model generation device according to claim 12, wherein the acquisition module is further configured to acquire the risk control after detecting an update instruction to update the target data corresponding to the risk control audit rule JSON file corresponding to the audit rules;

The risk control audit model generation device also includes:

The update module is used to update the target data in the JSON file according to the update instruction.
The risk control audit model generation device according to claim 12, wherein the risk control audit model generation device further comprises:

The comparison module is used for comparing the data to be audited with the target data corresponding to the JSON file in the audit engine after detecting the audit request to audit the data to be audited, so as to audit the data to be audited.
A risk control audit model generation device, characterized in that the risk control audit model generation device includes a memory, a processor, and a risk control audit model generation program stored on the memory and executable on the processor, When the risk control audit model generation program is executed by the processor, the following steps are implemented:

Obtain preset risk control audit rules, and configure the data association model according to the risk control audit rules;

Generating a data storage template in a preset format according to the data association model, and obtaining target data corresponding to the risk control audit rules through the data storage template;

Generate JS according to the target data and the corresponding data storage template Object notation JSON file, and import the JSON file into a preset audit engine to generate a risk control audit model.
A computer readable storage medium, characterized in that a risk control audit model generation program is stored on the computer readable storage medium, and when the risk control audit model generation program is executed by a processor, the following steps are realized:

Obtain preset risk control audit rules, and configure the data association model according to the risk control audit rules;

Generating a data storage template in a preset format according to the data association model, and obtaining target data corresponding to the risk control audit rules through the data storage template;

Generate JS according to the target data and the corresponding data storage template Object notation JSON file, and import the JSON file into a preset audit engine to generate a risk control audit model.