CN112580983B - Universal intelligent risk dissolving method and device - Google Patents
Universal intelligent risk dissolving method and device Download PDFInfo
- Publication number
- CN112580983B CN112580983B CN202011524629.8A CN202011524629A CN112580983B CN 112580983 B CN112580983 B CN 112580983B CN 202011524629 A CN202011524629 A CN 202011524629A CN 112580983 B CN112580983 B CN 112580983B
- Authority
- CN
- China
- Prior art keywords
- data
- risk
- party
- service
- business
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 45
- 238000012545 processing Methods 0.000 claims abstract description 31
- 238000013523 data management Methods 0.000 claims abstract description 20
- 210000001503 joint Anatomy 0.000 claims abstract description 11
- 238000003032 molecular docking Methods 0.000 claims description 7
- 238000004140 cleaning Methods 0.000 claims description 4
- 238000012216 screening Methods 0.000 claims description 4
- 238000004891 communication Methods 0.000 claims description 3
- 238000007726 management method Methods 0.000 description 9
- 230000008569 process Effects 0.000 description 8
- 238000004458 analytical method Methods 0.000 description 4
- 238000006243 chemical reaction Methods 0.000 description 3
- 230000006870 function Effects 0.000 description 3
- 238000003491 array Methods 0.000 description 2
- 238000007405 data analysis Methods 0.000 description 2
- 238000007689 inspection Methods 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 230000006978 adaptation Effects 0.000 description 1
- 238000012550 audit Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 238000010219 correlation analysis Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 238000011835 investigation Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000026676 system process Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0635—Risk analysis of enterprise or organisation activities
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/26—Government or public services
Landscapes
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Engineering & Computer Science (AREA)
- Strategic Management (AREA)
- Tourism & Hospitality (AREA)
- Economics (AREA)
- General Business, Economics & Management (AREA)
- General Physics & Mathematics (AREA)
- Marketing (AREA)
- Entrepreneurship & Innovation (AREA)
- Educational Administration (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Development Economics (AREA)
- General Health & Medical Sciences (AREA)
- Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Game Theory and Decision Science (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
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
Technical Field
The application relates to the technical field of risk resolution platforms, in particular to a general intelligent risk resolution method and device.
Background
At present, various business scenes related to risk early warning exist in the field of electronic government affairs, including important person risk early warning, site risk early warning, public opinion risk early warning and the like, the past risk early warning platforms are all chimney-type development, each risk early warning platform is independently designed and data island is realized, and the risk early warning and treatment can only be carried out in the platform. And in the process of cross-system and cross-platform, because the risk resolution or the managed service standards are different, the specifications are different, and the technical architecture is different, and the local single-wire butt joint or the repeated butt joint of a star-shaped structure is adopted.
Disclosure of Invention
In order to overcome the problems existing in the related art at least to a certain extent, the application provides a general intelligent risk resolving method and device.
The scheme of the application is as follows:
according to a first aspect of embodiments of the present application, there is provided a general intelligent risk solution method, including:
identifying risk data based on a pre-docked third-party big data management platform, cleaning and converting the risk data, generating business data comprising the risk data portrait tag and risk scores, and importing the business data from the third-party big data management platform;
analyzing and classifying the business data according to the portrait tags and the risk scores;
according to the category of the service data, based on a corresponding service strategy, the service data is sent to a third party service handling system which is in pre-butt joint and in customized configuration;
and receiving service data processing feedback sent by the third-party service processing system.
Preferably, in one implementable manner of the present application, the method further includes:
docking a third party service handling system, and performing customized configuration on the third party service handling system to enable the third party service handling system to identify the service data;
classifying the third party business handling systems according to business categories that the third party business handling systems can handle; and associating the category of the third-party service handling system with the category of the service data to generate the service policy.
Preferably, in an implementation manner of the present application, the customizing configuration of the third party service handling system specifically includes:
and performing field-level customized configuration on the third-party service handling system through form configuration.
Preferably, in one implementation manner of the present application, the analyzing and classifying the business data according to the portrait tag and the risk score specifically includes:
and classifying the condition labels of the service data according to the portrait labels, wherein the classification at least comprises: personage, locale and public opinion;
and classifying the risk grade of the business data according to the risk score, wherein the classification at least comprises: low risk, medium risk and high risk.
Preferably, in an implementation manner of the present application, the customizing configuration of the third party service handling system specifically further includes:
the third party service processing system generates an intelligent early warning module according to the customized configuration information and based on the risk level classification of the service data; based on the condition label classification of the service data, screening specific characteristic labels according to a super engine to obtain complete label data, and generating a special early warning module.
Preferably, in one implementable manner of the present application, the method further includes:
and auditing the service data through the third-party service handling system, and adding the service data which passes the auditing into a risk list.
Preferably, in an implementation manner of the present application, the receiving service data processing feedback sent by the third party service handling system specifically includes:
and receiving service data processing result feedback and processing procedure feedback sent by the third-party service processing system.
Preferably, in one implementable manner of the present application, the method further includes: and updating the risk scores of the business data in the risk list according to the processing result feedback, classifying the updated business data according to the updated risk scores of the business data, and transmitting the updated business data to a third party business disposal system which is in butt joint in advance and in customized configuration according to the type of the updated business data and based on a corresponding business strategy.
Preferably, in an implementation manner of the present application, the customizing configuration of the third party service handling system specifically further includes:
and normalizing the event handling state of the third party service handling system.
According to a second aspect of embodiments of the present application, there is provided a general intelligent risk resolution device, including:
a processor and a memory;
the processor is connected with the memory through a communication bus:
the processor is used for calling and executing the program stored in the memory;
the memory is used for storing a program at least for executing a general intelligent risk solving method as set forth in any one of the above
The technical scheme that this application provided can include following beneficial effect: the application provides a general intelligent risk dissolving method, a general risk early warning process can be divided into four standard links of finding, analyzing and feeding back, in the application, risk data are identified based on a pre-butted third-party big data management platform in a finding 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, and the stage can be used for butting a plurality of third-party big data management platforms to carry out risk data merging and collision, so that 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.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the application.
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.
FIG. 1 is a flow chart of a general intelligent risk solution method according to an embodiment of the present application;
fig. 2 is a schematic structural diagram of a general intelligent risk solving device according to an embodiment of the present application.
Reference numerals: a processor-21; and a memory 22.
Detailed Description
Reference will now be made in detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The implementations described in the following exemplary examples are not representative of all implementations consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with some aspects of the present application as detailed in the accompanying claims.
A general intelligent risk resolution method, referring to fig. 1, includes:
s11: identifying risk data based on a pre-docked third-party big data management platform, cleaning and converting the risk data, generating business data comprising risk data portrait labels and risk scores, and importing the business data from the third-party big data management platform;
in the prior art, the risk early warning process of all platforms can be divided into four standard links of discovery, analysis, solution, feedback and the like.
In the application, a big data management platform of a third party is supported, and the data analysis platform is connected and imported. In the data discovery link, risk data can be screened by each big data management platform and a data analysis platform (such as an ali cloud, a Tencent cloud and the like, including a self-grinding hadoop platform), wherein screening actions comprise data cleaning conversion, data tagging and the like, risk scoring is carried out on the risk data, business data comprising risk data portrait tags and risk scoring is generated, and finally, the business data is imported through unified interface adaptation or offline.
In the step, the collision conversion of data is carried out by means of third-party big data management platforms such as the Arian cloud, the Tengxun cloud and the like, so that the problem of data island is solved. In addition, due to collision conversion of data, a large number of invalid or repeated risks can be filtered in the discovery link, and the working efficiency of basic staff can be effectively improved.
In the step, the technical support is that a plurality of third party big data management platforms are simultaneously accessed, and in general implementation, each area is only in butt joint with one third party big data management platform.
S12: analyzing and classifying the business data according to the portrait labels and the risk scores;
in this step, the business data is analyzed and classified according to the portrait tag and the risk score, specifically, the business data is classified according to the portrait tag, and the classification at least includes: personage, locale and public opinion; and classifying the risk grade of the business data according to the risk score, wherein the classification at least comprises the following steps: low risk, medium risk and high risk.
S13: according to the category of the service data, based on the corresponding service strategy, the service data is sent to a third party service disposal system which is in pre-butt joint and in customized configuration;
before implementing the method in this embodiment, the docking with the third party big data management platform and the third party service handling system needs to be completed in advance, where when the third party service handling system is docked, customized configuration needs to be performed on the third party service handling system, so that the third party service handling system can identify service data;
classifying the third party business handling systems according to business categories that the third party business handling systems can handle; and associating the category of the third party service handling system with the category of the service data to generate a service policy.
The customized configuration of the third party service handling system specifically comprises the following steps:
and performing field-level customized configuration on the third-party service handling system through form configuration.
The customized configuration is mainly field control of service data, and the field requirements of each third-party service handling system on the service data are different, so that the customized configuration of the field level can be realized through form configuration.
The customized configuration of the third party service handling system further comprises: the event handling status of the third party traffic handling system is normalized. Specifically, the processing flow and flow state of each service system are different, in this embodiment, the standardization is performed for the problem flow state, and a new addition is set, in the process, three states are handled, and the handling opinion of specific operations is set to be a piece of text information, and the processing opinion is formed by splicing the technologies of each third-party service processing system according to the own data field condition. For example, the event handling of the basic level treatment has the states of new addition, acceptance, reporting, rollback, report, office, and the like, and is docked according to the technical interfaces of risk resolution, and all the intermediate states belong to the states in the process, the approval comments are uniformly formatted into someone or a department, and certain operation is performed in a certain time, and the operation content is a text description, and the corresponding states are approval or approval comments.
In the step, customized configuration information is generated and sent to each third party service disposal system, so that the third party service disposal system generates an intelligent early warning module according to the customized configuration information and based on risk class classification of service data; based on the condition label classification of the service data, screening specific characteristic labels according to the super engine to obtain complete label data, and generating a special early warning module.
In this embodiment, in the analysis link, risk data may be freely combined according to different high, medium and low risks, so as to form different high, medium and low risk lists.
In the step, the business data is audited through a third party business handling system, and the business data which passes the audit is added into a risk list.
The auditing can be machine auditing or manual auditing, and can also be the combination of machine auditing and manual auditing. The auditing process is to avoid direct issue of dirty data.
And adding the business data passing the auditing into a corresponding high-low risk list formed in the analysis link according to the risk grade of the risk data in the business data.
And after the business data is added into the risk list, automatically triggering a corresponding business strategy. The third party business handling system may include a snow lighting platform, an emergency command platform, a primary management platform, and the like. The service policy may specifically include: for example, the treatment business strategy of business data of high-risk drug-taking crowd is to send to the snow-lighting platform and the emergency command platform for processing, and the treatment business strategy of business data of low-risk low-security personnel is to send to the base layer for regular visit and inspection.
The business policy is a rules engine composed of triplets of "custom tags + risk levels + interfacing configurable third party business handling systems". The customized tag is the customized tag in this embodiment, so that each third party service handling system only obtains tag data in the customization authority.
In this step, the solution link is implemented by the third party service handling system.
S14: and receiving service data processing feedback sent by the third-party service processing system.
In this step, a standard interface for data feedback is provided, after each third party service processing system processes the service data, part of information is fed back to the platform, for example, the risk level of key personnel is reduced, some fields are updated, and the relevant data feedback interfaces are uniformly registered and assigned by the platform.
Specifically, service data processing result feedback and processing procedure feedback sent by the third-party service processing system are received.
And updating the risk scores of the business data in the risk list according to the feedback of the processing result, classifying the updated business data according to the risk scores of the updated business data, and transmitting the updated business data to a third party business disposal system which is in butt joint in advance and in customized configuration based on the corresponding business strategy according to the classification of the updated business data.
The universal intelligent risk resolution method in the embodiment has the advantages that a universal risk resolution business flow standard is combined, and collaborative treatment can be carried out by collaboration of different third-party functional departments. And the technical interface is standardized, the system docking is changed into a central structure from a past star-shaped structure, and the docking is performed once without repeated docking. The risk data belongs to the construction mode of the data center, and the data island phenomenon is effectively avoided.
A generic intelligent risk resolution device, referring to fig. 2, comprising:
a processor 21 and a memory 22;
the processor 21 is connected to the memory 22 via a communication bus:
wherein the processor 21 is used for calling and executing the program stored in the memory 22;
the memory 22 is configured to store a program for performing at least one of the general intelligent risk solving methods in the above embodiments.
A universal intelligent risk solution platform, comprising: the system comprises an algorithm model module, a relation map module, a holographic archive module, a rule engine module, a tag center module, a metadata management module, a form configuration module, a user management module, a data management module and a docking management module;
and the system is used for analyzing the service data and respectively butting a third party big data management platform and a third party service treatment system.
The third party big data governance platform mainly includes: the system comprises an offline computing module, a data exchange module, a task management module, a data code table module, a space management module and the like.
The third party service handling system mainly comprises: a risk data resolving plate and a data feedback plate;
the risk data resolving plate is divided into a risk early warning plate, a risk management and control plate and a risk situation plate;
the risk early warning plate comprises: the system comprises an intelligent early warning module, a super search module and a risk library module;
the risk management and control plate includes: the system comprises a risk research and judgment module, a task issuing module, an inspection feedback module, a correlation analysis module, a responsibility implementation module, a result evaluation module, a special investigation module, a disposal tracking module and a supervision and assessment module;
the risk situation plate comprises: the system comprises a macroscopic analysis module, a trend research and judgment module and a situation control module;
wherein the data feedback plate includes: the system comprises a task receiving module, a task feedback module, a task handling module, a data feedback module, an interface management module and a data statistics module.
It is to be understood that the same or similar parts in the above embodiments may be referred to each other, and that in some embodiments, the same or similar parts in other embodiments may be referred to.
It should be noted that in the description of the present application, the terms "first," "second," and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. Furthermore, in the description of the present application, unless otherwise indicated, the meaning of "plurality" means at least two.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and further implementations are included within the scope of the preferred embodiment of the present application in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the embodiments of the present application.
It is to be understood that portions of the present application may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the 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 implemented in hardware, as in another embodiment, may be implemented using any one or combination of the following techniques, as is well known in the art: discrete logic circuits having logic gates for implementing logic functions on data signals, application specific integrated circuits having suitable combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like.
Those of ordinary skill in the art will appreciate that all or a portion of the steps carried out in the method of the above-described embodiments may be implemented by a program to instruct related hardware, where the program may be stored in a computer readable storage medium, and where the program, when executed, includes one or a combination of the steps of the method embodiments.
In addition, each functional unit in each embodiment of the present application may be integrated in one processing module, or each unit may exist alone physically, or two or more units may be integrated in one module. The integrated modules may be implemented in hardware or in software functional modules. The integrated modules may also be stored in a computer readable storage medium if implemented in the form of software functional modules and sold or used as a stand-alone product.
The above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, or the like.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is 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 embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
Although embodiments of the present application have been shown and described above, it will be understood that the above embodiments are illustrative and not to be construed as limiting the application, and that variations, modifications, alternatives, and variations may be made to the above embodiments by one of ordinary skill in the art within the scope of the application.
Claims (9)
1. A universal intelligent risk solution method, comprising:
identifying risk data based on a pre-docked third-party big data management platform, cleaning and converting the risk data, generating business data comprising the risk data portrait tag and risk scores, and importing the business data from the third-party big data management platform;
analyzing and classifying the business data according to the portrait tags and the risk scores;
according to the category of the service data, based on a corresponding service strategy, the service data is sent to a third party service handling system which is in pre-butt joint and in customized configuration;
receiving service data processing feedback sent by the third-party service processing system;
the method further comprises the steps of:
docking a third party service handling system, and performing customized configuration on the third party service handling system to enable the third party service handling system to identify the service data;
classifying the third party business handling systems according to business categories that the third party business handling systems can handle; and associating the category of the third-party service handling system with the category of the service data to generate the service policy.
2. The method according to claim 1, wherein the customizing the third party business handling system comprises:
and performing field-level customized configuration on the third-party service handling system through form configuration.
3. The method according to claim 2, wherein said analyzing and classifying said business data according to said portrayal labels and risk scores, in particular comprises:
and classifying the condition labels of the service data according to the portrait labels, wherein the classification at least comprises: personage, locale and public opinion;
and classifying the risk grade of the business data according to the risk score, wherein the classification at least comprises: low risk, medium risk and high risk.
4. The method of claim 3, wherein the customizing the third party business processing system further comprises:
the third party service processing system generates an intelligent early warning module according to the customized configuration information and based on the risk level classification of the service data; based on the condition label classification of the service data, screening specific characteristic labels according to a super engine to obtain complete label data, and generating a special early warning module.
5. The method as recited in claim 4, further comprising:
and auditing the service data through the third-party service handling system, and adding the service data which passes the auditing into a risk list.
6. The method of claim 5, wherein receiving the service data processing feedback sent by the third party service handling system specifically comprises:
and receiving service data processing result feedback and processing procedure feedback sent by the third-party service processing system.
7. The method as recited in claim 6, further comprising: and updating the risk scores of the business data in the risk list according to the processing result feedback, classifying the updated business data according to the updated risk scores of the business data, and transmitting the updated business data to a third party business disposal system which is in butt joint in advance and in customized configuration according to the type of the updated business data and based on a corresponding business strategy.
8. The method according to claim 2, wherein said customizing the third party traffic handling system further comprises:
and normalizing the event handling state of the third party service handling system.
9. A universal intelligent risk resolution device, comprising:
a processor and a memory;
the processor is connected with the memory through a communication bus:
the processor is used for calling and executing the program stored in the memory;
the memory is configured to store a program at least for executing a general intelligent risk solving method according to any one of claims 1 to 8.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011524629.8A CN112580983B (en) | 2020-12-22 | 2020-12-22 | Universal intelligent risk dissolving method and device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011524629.8A CN112580983B (en) | 2020-12-22 | 2020-12-22 | Universal intelligent risk dissolving method and device |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112580983A CN112580983A (en) | 2021-03-30 |
| CN112580983B true CN112580983B (en) | 2024-03-19 |
Family
ID=75136619
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011524629.8A Active CN112580983B (en) | 2020-12-22 | 2020-12-22 | Universal intelligent risk dissolving method and device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112580983B (en) |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101714273A (en) * | 2009-05-26 | 2010-05-26 | 北京银丰新融科技开发有限公司 | Rule engine-based method and system for monitoring exceptional service of bank |
| EP2801942A1 (en) * | 2013-05-09 | 2014-11-12 | Rockwell Automation Technologies, Inc. | Risk assessment for industrial systems using big data |
| CN107491885A (en) * | 2017-08-25 | 2017-12-19 | 上海找钢网信息科技股份有限公司 | A kind of air control platform and risk control management method for steel trade financial business |
| CN110991883A (en) * | 2019-12-03 | 2020-04-10 | 中国民用航空总局第二研究所 | Operation control system and method based on flight risk preposition |
| CN111709603A (en) * | 2020-05-15 | 2020-09-25 | 北京健康之家科技有限公司 | Service request processing method, device and system based on wind control |
-
2020
- 2020-12-22 CN CN202011524629.8A patent/CN112580983B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101714273A (en) * | 2009-05-26 | 2010-05-26 | 北京银丰新融科技开发有限公司 | Rule engine-based method and system for monitoring exceptional service of bank |
| EP2801942A1 (en) * | 2013-05-09 | 2014-11-12 | Rockwell Automation Technologies, Inc. | Risk assessment for industrial systems using big data |
| CN107491885A (en) * | 2017-08-25 | 2017-12-19 | 上海找钢网信息科技股份有限公司 | A kind of air control platform and risk control management method for steel trade financial business |
| CN110991883A (en) * | 2019-12-03 | 2020-04-10 | 中国民用航空总局第二研究所 | Operation control system and method based on flight risk preposition |
| CN111709603A (en) * | 2020-05-15 | 2020-09-25 | 北京健康之家科技有限公司 | Service request processing method, device and system based on wind control |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112580983A (en) | 2021-03-30 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CA3098838A1 (en) | Systems and methods for enriching modeling tools and infrastructure with semantics | |
| CN108197131A (en) | A kind of construction method and device of electric power asset portrait | |
| CN107886238A (en) | A kind of business process management system and method based on mass data analysis | |
| US11704186B2 (en) | Analysis of deep-level cause of fault of storage management | |
| CN111179139B (en) | Internet + supervision data processing method and device | |
| CN113706101B (en) | Intelligent system architecture and method for power grid project management | |
| Mantha et al. | Cybersecurity in construction: Where do we stand and how do we get better prepared | |
| JP6153331B2 (en) | Project management system based on associative memory | |
| CN113434575A (en) | Data attribution processing method and device based on data warehouse and storage medium | |
| CN112580983B (en) | Universal intelligent risk dissolving method and device | |
| CN112948509B (en) | Dangerous chemical management method and device and management database construction method | |
| CN114119315A (en) | Enterprise-level intelligent security elimination management system and management method thereof | |
| Habayeb et al. | The firefox temporal defect dataset | |
| US11838171B2 (en) | Proactive network application problem log analyzer | |
| CN117540965A (en) | Metadata-based data asset inventory method and system | |
| CN115545455A (en) | Engineering construction process information data integrated management method and system | |
| CN116821106A (en) | Enterprise data digital management system and method | |
| Kouhestani et al. | Towards level 3 BIM process maps with IFC & XES process mining | |
| CN114186979B (en) | Distributed collaborative material development method and system based on natural language processing | |
| KR20240102324A (en) | Method and appratus for analyzing root cause of network based on artificial intelligence | |
| Mohamad et al. | Dashboard for analyzing SCADA data log: a case study of urban railway in Malaysia | |
| TWM639234U (en) | Hierarchical detailed-list management system of information communication system | |
| CN116320014A (en) | Method, device, equipment and medium for scheduling messages during new and old system parallelism | |
| Oyoo | Collaboration-Based Automatic Data Validation Framework for Enterprise Asset Management | |
| CN116029503A (en) | Rail transit emergency event processing method and device and electronic equipment |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |