CN112580983B - A universal intelligent risk resolution method and equipment - Google Patents

Abstract

The application relates to a general intelligent risk dissolving method and equipment, in the application, risk data are identified based on a pre-butted third-party big data management platform in a discovery stage, the risk data are cleaned and converted, business data comprising risk data portrait labels and risk scores are generated, the business data are imported from the third-party big data management platform, the multiple third-party big data management platforms can be butted in the stage to carry out risk data merging collision, and the generation of data islands is reduced. Then analyzing and classifying the business data in the local platform according to the portrait labels and the risk scores; according to the category of the service data, the service data is sent to a third party service disposal system which is in butt joint in advance and is subjected to customized configuration based on a corresponding service policy, and the third party service disposal system can identify the service data due to the customized configuration of the third party service disposal system, so that the service data is more effectively processed through a professional third party service disposal system, and finally service data processing feedback sent by the third party service disposal system is received. In the method, a business closed loop for finding, analyzing and feeding back four links is completed, and different third-party functional departments are cooperated to perform cooperative treatment, so that the risk treatment is more efficient.

Description

A universal intelligent risk resolution method and equipment

Technical field

This application relates to the technical field of risk resolution platforms, and in particular to a general intelligent risk resolution method and equipment.

Background technique

Currently, there are various business scenarios for risk warning in the field of e-government, including risk warning for key persons, risk warning for places, public opinion risk warning, etc. In the past, risk warning platforms were developed in a chimney style, and each risk warning platform was designed individually. , data is isolated, and risk warning and processing can only be carried out within this platform. When cross-systems and cross-platforms, due to different business standards, specifications, and technical architectures for risk resolution or management, they are all local single-line docking, or repeated docking in a star structure.

Contents of the invention

In order to overcome the problems existing in related technologies at least to a certain extent, this application provides a general intelligent risk resolution method and equipment.

The plan for this application is as follows:

According to the first aspect of the embodiment of the present application, a general intelligent risk resolution method is provided, including:

Identify risk data based on a pre-connected third-party big data governance platform, clean and transform the risk data, generate business data including risk data portrait labels and risk scores, and import all data from the third-party big data governance platform Describe business data;

Analyze and classify the business data according to the portrait labels and risk scores;

According to the category of the business data and based on the corresponding business policy, the business data is sent to a third-party business processing system that is pre-connected and configured in a customized manner;

Receive business data processing feedback sent by the third-party business processing system.

Preferably, in an implementable manner of this application, it also includes:

Connect with the third-party business processing system and perform customized configuration on the third-party business processing system so that the third-party business processing system can identify the business data;

Classify the third-party business processing system according to the business categories that the third-party business processing system can handle; associate the category of the third-party business processing system with the category of the business data to generate the business policy .

Preferably, in an implementable manner of this application, the customized configuration of the third-party business processing system specifically includes:

Through form configuration, the third-party business processing system is configured with field-level customization.

Preferably, in an implementable manner of this application, the business data is analyzed and classified according to the portrait labels and risk scores, specifically including:

Classify the business data with conditional labels based on the portrait labels, and the classification at least includes: people, places and public opinions;

Classify the risk level of the business data according to the risk score, and the classification includes at least: low risk, medium risk and high risk.

Preferably, in an implementable manner of this application, the customized configuration of the third-party business processing system further includes:

The third-party business processing system generates an intelligent early warning module based on the customized configuration information and the risk level classification of the business data; based on the condition label classification of the business data, it filters specific feature labels according to the super engine to obtain complete Label data to generate a special early warning module.

Preferably, in an implementable manner of this application, it also includes:

The business data is reviewed through the third-party business processing system, and the business data that passes the review is added to the risk list.

Preferably, in an implementable manner of this application, receiving the business data processing feedback sent by the third-party business processing system specifically includes:

Receive business data processing result feedback and processing process feedback sent by the third-party business processing system.

Preferably, in an implementable manner of the present application, it also includes: updating the risk score of the business data in the risk list based on the processing result feedback, and based on the updated business data Risk score, classify the updated business data, and send the updated business data to the third party that has been pre-connected and customized based on the category of the updated business data and based on the corresponding business policy. Third-party business disposal system.

Preferably, in an implementable manner of this application, the customized configuration of the third-party business processing system further includes:

Standardize the event handling status of the third-party business handling system.

According to the second aspect of the embodiment of the present application, a general intelligent risk resolution device is provided, including:

processor and memory;

The processor and memory are connected through a communication bus:

Wherein, the processor is used to call and execute the program stored in the memory;

The memory is used to store programs, and the programs are at least used to execute a general intelligent risk resolution method as described in any one of the above.

The technical solution provided by this application can include the following beneficial effects: This application provides a universal intelligent risk resolution method. The general risk warning process can be divided into four standard links of discovery, analysis, solution and feedback. In this application, The discovery phase identifies risk data based on a pre-connected third-party big data governance platform, cleans and transforms the risk data, generates business data including risk data portrait labels and risk scores, and imports business data from the third-party big data governance platform. This phase It can connect to multiple third-party big data governance platforms to merge and collide risk data to reduce the occurrence of data islands. Then the business data is analyzed and classified in the local platform according to the portrait tags and risk scores; according to the category of the business data and based on the corresponding business strategy, the business data is sent to a third-party business processing system that is pre-connected and customized. Due to the customized configuration of the third-party business processing system, the third-party business processing system can identify the business data, process the business data more effectively through the professional third-party business processing system, and finally receive the data sent by the third-party business processing system business data processing feedback. In this application, a closed business loop of four links of discovery, analysis, resolution and feedback was completed, and various third-party functional departments were collaborated for collaborative disposal, making the risk management more efficient.

It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and do not limit the present application.

Description of the drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the application and together with the description, serve to explain the principles of the application.

Figure 1 is a schematic flow chart of a general intelligent risk resolution method provided by an embodiment of the present application;

Figure 2 is a schematic structural diagram of a general intelligent risk mitigation device provided by an embodiment of the present application.

Reference numbers: processor-21; memory-22.

Detailed ways

Exemplary embodiments will be described in detail herein, examples of which are illustrated in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings refer to the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with this application. Rather, they are merely examples of apparatus and methods consistent with aspects of the application as detailed in the appended claims.

A general intelligent risk resolution method, refer to Figure 1, includes:

S11: Identify risk data based on the pre-connected third-party big data governance platform, clean and transform the risk data, generate business data including risk data portrait labels and risk scores, and import business data from the third-party big data governance platform;

In the existing technology, the risk warning process of all platforms can be divided into four standard links: discovery, analysis, resolution, and feedback.

This application supports the docking and import of third-party big data governance platforms and data analysis platforms. In the data discovery process, risk data can be screened at the first level by various big data governance platforms and data analysis platforms (such as Alibaba Cloud, Tencent Cloud, etc., including the self-developed Hadoop platform). The screening actions include data cleaning and transformation. Data labeling, etc., perform risk scoring on risk data, generate business data including risk data portrait labels and risk scores, and finally adapt through a unified interface or import it offline.

In this step, third-party big data management platforms such as Alibaba Cloud and Tencent Cloud are used to collide and transform data, solving the problem of data islands. Moreover, due to the collision and transformation of data, a large number of invalid or repeated risks can be filtered out during the discovery process, which can effectively improve the work efficiency of grassroots staff.

In this step, it is technically possible to technically support the coexistence of multiple third-party big data governance platforms. During general implementation, each region only needs to connect to one third-party big data governance platform.

S12: Analyze and classify business data based on portrait labels and risk scores;

In this step, the business data is analyzed and classified based on the portrait labels and risk scores. Specifically, the business data is classified with conditional labels based on the portrait labels. The classification at least includes: people, places and public opinions; the business data is risk-based based on the risk scores. Level classification, including at least: low risk, medium risk and high risk.

S13: According to the category of business data and based on the corresponding business policy, the business data is sent to the third-party business processing system that is pre-connected and customized;

Before the method in this embodiment is implemented, it is necessary to complete the docking with the third-party big data governance platform and the third-party business disposal system in advance. When docking with the third-party business disposal system, the third-party business disposal system needs to be customized. Configuration so that the third-party business processing system can identify business data;

Classify the third-party business disposal system according to the business categories that the third-party business disposal system can handle; associate the categories of the third-party business disposal system with the categories of business data to generate business strategies.

Customized configuration of the third-party business processing system, including:

Through form configuration, the third-party business processing system can be customized at the field level.

Customized configuration is mainly field control of business data. Each third-party business processing system has different field requirements for business data, so field-level customized configuration can be achieved through form configuration.

Customizing the configuration of the third-party business processing system also includes: standardizing the event processing status of the third-party business processing system. Specifically, the processing flow and process status of each business system are different. In this embodiment, the process status of this problem is standardized and three statuses of newly added, processing, and completed are set. The handling opinions of specific operations are set as A piece of text information is pieced together by various third-party business processing system technologies based on their own data field conditions. For example, the incident handling of grassroots governance has various states such as new addition, acceptance, reporting, rollback, submission, and settlement. According to the technical interface of risk resolution, the acceptance, reporting, rollback, reporting, and all intermediate states They all belong to the status of being processed. The approval opinions are uniformly formatted as a certain person or a certain department performed a certain operation at a certain time. The operation content is a text description, which corresponds to the approval or processing opinions.

In this step, customized configuration information is generated and sent to each third-party business processing system, so that the third-party business processing system can generate an intelligent early warning module based on the customized configuration information and the risk level classification of the business data; based on the conditions of the business data Tag classification, filter specific feature tags according to the super engine to obtain complete tag data, and generate a special early warning module.

In this embodiment, in the analysis process, the risk data can be freely combined according to different high, medium and low risks to form different high, medium and low risk lists.

In this step, the business data is reviewed through the third-party business processing system, and the business data that passes the review is added to the risk list.

The review can be machine review or manual review, or a combination of machine review and manual review. The review process is to avoid direct distribution of dirty data.

According to the risk level of the risk data in the business data, the approved business data will be added to the corresponding high, medium and low risk list formed in the analysis process.

After business data is added to the risk list, the corresponding business strategy is automatically triggered. Third-party business processing systems can include Xueliang platform, emergency command platform, grassroots governance platform, etc. Specific business strategies may include: For example, the business strategy for handling the business data of high-risk drug addicts is sent to the Xueliang platform and the emergency command platform for processing, while the business strategy for handling the business data of low-risk subsistence insured persons is sent to grassroots governance. Conduct regular visits and inspections.

The business strategy is a rule engine composed of the triplet of "customized label + risk level + docking with configurable third-party business processing system". The customized tag is a customized tag in this embodiment, so that each third-party business processing system only obtains tag data within the customized authority.

In this step, the resolution link is realized through the third-party business disposal system.

S14: Receive business data processing feedback sent by the third-party business processing system.

In this step, a standard interface for data feedback is provided. After each third-party business processing system processes the business data, some information will be fed back to the platform, such as the reduction of the risk level of key personnel, the update of certain fields, etc. Related The interface for data feedback is unified and registered and permissions are assigned by this platform.

Specifically, it receives business data processing result feedback and processing process feedback sent by the third-party business processing system.

And based on the processing result feedback, the risk score of the business data in the risk list is updated, and the updated business data is classified according to the risk score of the updated business data. According to the category of the updated business data, based on the corresponding Business strategy sends updated business data to a third-party business processing system that is pre-connected and customized.

The universal intelligent risk resolution method in this embodiment has sorted out a universal risk resolution business process standard and can collaborate with various third-party functional departments for collaborative disposal. In addition, the technical interfaces have been standardized, and system docking has changed from the past star-shaped structure to a central structure. It can be docked once without repeated docking. Risk data belongs to the construction model of data middle platform, which effectively avoids the phenomenon of data islands.

A universal intelligent risk mitigation device, refer to Figure 2, including:

Processor 21 and memory 22;

The processor 21 and the memory 22 are connected through a communication bus:

Among them, the processor 21 is used to call and execute the program stored in the memory 22;

The memory 22 is used to store programs, and the programs are at least used to execute one of the general intelligent risk resolution methods in the above embodiments.

A general intelligent risk resolution platform, including: algorithm model module, relationship map module, holographic archive module, rule engine module, tag center module, metadata management module, form configuration module, user management module, data management module and docking management module;

Used to analyze business data and connect to third-party big data management platforms and third-party business processing systems respectively.

The third-party big data governance platform mainly includes: offline computing module, data exchange module, task management module, data code table module, space management module, etc.

The third-party business disposal system mainly includes: risk data resolution module and data feedback module;

The risk data resolution section is divided into risk warning section, risk management and control section and risk situation section;

The risk warning module includes: intelligent warning module, super search module and risk library module;

The risk management and control module includes: risk research and judgment module, task issuance module, inspection feedback module, correlation analysis module, responsibility implementation module, result evaluation module, special investigation module, disposal tracking module and supervision and assessment module;

The risk situation section includes: macro analysis module, trend research and judgment module and situation control module;

The data feedback module includes: task receiving module, task feedback module, task completion module, data feedback module, interface management module and data statistics module.

It can be understood that the same or similar parts in the above-mentioned embodiments can be referred to each other, and the content that is not described in detail in some embodiments can be referred to the same or similar content in other embodiments.

It should be noted that in the description of this application, the terms "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance. Furthermore, in the description of the present application, unless otherwise stated, the meaning of "plurality" means at least two.

Any process or method descriptions in flowcharts or otherwise described herein may be understood to represent modules, segments, or portions of code that include one or more executable instructions for implementing the specified logical functions or steps of the process. , and the scope of the preferred embodiments of the present application includes additional implementations in which functions may be performed out of the order shown or discussed, including in a substantially simultaneous manner or in the reverse order, depending on the functionality involved, which shall It should be understood by those skilled in the technical field to which the embodiments of this application belong.

It should be understood that various parts of the present application can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if it is implemented in hardware, as in another embodiment, it can be implemented by any one or a combination of the following technologies known in the art: a logic gate circuit with a logic gate circuit for implementing a logic function on a data signal. Discrete logic circuits, application specific integrated circuits with suitable combinational logic gates, programmable gate arrays (PGA), field programmable gate arrays (FPGA), etc.

Those of ordinary skill in the art can understand that all or part of the steps involved in implementing the methods of the above embodiments can be completed by instructing relevant hardware through a program. The program can be stored in a computer-readable storage medium. The program can be stored in a computer-readable storage medium. When executed, one of the steps of the method embodiment or a combination thereof is included.

In addition, each functional unit in various embodiments of the present application can be integrated into a processing module, or each unit can exist physically alone, or two or more units can be integrated into one module. The above integrated modules can be implemented in the form of hardware or software function modules. If the integrated module is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

The storage media mentioned above can be read-only memory, magnetic disks or optical disks, etc.

In the description of this specification, reference to the terms "one embodiment," "some embodiments," "an example," "specific examples," or "some examples" or the like means that specific features are described in connection with the embodiment or example. , structures, materials or features are included in at least one embodiment or example of the present application. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

Although the embodiments of the present application have been shown and described above, it can be understood that the above-mentioned embodiments are illustrative and cannot be understood as limitations of the present application. Those of ordinary skill in the art can make modifications to the above-mentioned embodiments within the scope of the present application. The embodiments are subject to changes, modifications, substitutions and variations.

Claims (9)

1. A universal intelligent risk resolution method, characterized by: Identify risk data based on a pre-connected third-party big data governance platform, clean and transform the risk data, generate business data including risk data portrait labels and risk scores, and import all data from the third-party big data governance platform Describe business data; Analyze and classify the business data according to the portrait labels and risk scores; According to the category of the business data and based on the corresponding business policy, the business data is sent to a third-party business processing system that is pre-connected and configured in a customized manner; Receive business data processing feedback sent by the third-party business processing system; Methods also include: Connect with the third-party business processing system and perform customized configuration on the third-party business processing system so that the third-party business processing system can identify the business data; Classify the third-party business processing system according to the business categories that the third-party business processing system can handle; associate the category of the third-party business processing system with the category of the business data to generate the business policy . 2. The method according to claim 1, characterized in that the customized configuration of the third-party business processing system specifically includes: Through form configuration, the third-party business processing system is configured with field-level customization. 3. The method according to claim 2, characterized in that analyzing and classifying the business data according to the portrait labels and risk scores specifically includes: Classify the business data with conditional labels based on the portrait labels, and the classification at least includes: people, places and public opinions; Classify the risk level of the business data according to the risk score, and the classification includes at least: low risk, medium risk and high risk. 4. The method according to claim 3, characterized in that the customized configuration of the third-party business processing system further includes: The third-party business processing system generates an intelligent early warning module based on the customized configuration information and the risk level classification of the business data; based on the condition label classification of the business data, it filters specific feature labels according to the super engine to obtain complete Label data to generate a special early warning module. 5. The method of claim 4, further comprising: The business data is reviewed through the third-party business processing system, and the business data that passes the review is added to the risk list. 6. The method according to claim 5, wherein the receiving the business data processing feedback sent by the third-party business processing system specifically includes: Receive business data processing result feedback and processing process feedback sent by the third-party business processing system. 7. The method according to claim 6, further comprising: updating the risk score of the business data in the risk list based on the processing result feedback, and based on the updated business data risk score, classify the updated business data, and send the updated business data to the pre-connected and customized configuration based on the category of the updated business data and based on the corresponding business strategy. Third-party business disposal system. 8. The method according to claim 2, wherein the customized configuration of the third-party business processing system further includes: Standardize the event handling status of the third-party business handling system. 9. A universal intelligent risk resolution device, characterized by: processor and memory; The processor and memory are connected through a communication bus: Wherein, the processor is used to call and execute the program stored in the memory; The memory is used to store a program, and the program is at least used to execute a general intelligent risk resolution method described in any one of claims 1-8.