CN111240648B - Intelligent management system and method for variables - Google Patents
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Abstract
The invention relates to the technical field of wind control data model construction, in particular to an intelligent management system and method for variables. The method comprises a data source management step, a basic variable management step, a derivative variable management step, a model assembly management step and a real-time API release management step. The invention can construct model variables and custom-assemble offline or real-time data models according to data sources, and solves the problems of low data development efficiency and high development cost.
Description
Technical Field
The invention relates to the technical field of wind control data model construction, in particular to an intelligent management system and method for variables.
Background
Wind control means risk control, and means that a risk manager takes various measures and methods to eliminate or reduce various possibilities of occurrence of a risk event, or a risk controller reduces losses caused when a risk event occurs. The wind control business personnel of the bank have very important significance in wind control.
When a wind control business person of a bank processes a wind control task of the bank, if necessary business requirement data needs to be extracted from a business system of the bank on line, for example, the business requirement data is copied through a U disk or a memory card, however, the wind control management of the extracted business requirement data is very strict, even if the wind control business person needs to go through a complicated process procedure, so that the time for the wind control business person to obtain the business requirement data is long, and the processing business of the wind control business person is influenced. And compared with the process procedures required to be experienced offline, the process is less and faster when necessary business demand data is extracted online. However, the dimensionality of data in the business system database of the bank is high, and the required information is difficult to extract without professional ability.
The current wind control service personnel do not have the capability of analyzing semi-structured data in complex languages completely, and also do not have the capability of developing data variables in a programming mode or by using an SQL language, and can only submit the requirements to product developers and technicians; related developers or technicians develop the data variables one by one according to requirements, and the developed model data can reduce the dimensions of the data variables, so that wind control business personnel can conveniently look up and use the data variables, and the requirements of the wind control business personnel can be met. And after the verification of the wind control service personnel, if an unsatisfactory place exists, the feedback information is sent to the related developers or technicians to modify the feedback information in a targeted manner. The whole process is offline processing, and the cyclic processing can be performed only in one link after another until the wind control business personnel are satisfied. The data development efficiency is seriously influenced by the mode, development work of related developers or technicians is repeated and large in workload, so that the data is large and complex, and the data is unsafe due to lack of effective management. And the related developers or technicians and the wind control business personnel continuously conduct opinion exchange, so that the communication cost is high, and the whole development cost is high.
Disclosure of Invention
One of the main purposes of the present invention is to provide an intelligent management system for variables, which enables wind control service personnel to participate in the development of data variables, constructs a data model convenient for the invocation of the data variables, and solves the problems of low development efficiency and high development cost of the data variables.
In order to achieve the above purpose, the invention provides an intelligent management system of variables, which comprises the following modules:
the data source management module: the system is used for configuring a database, selecting a data source and outputting complete data source information; the data sources comprise real-time data sources and non-real-time data sources;
a basic variable management module: the system is used for loading complete data source information and analyzing basic variables by using a unified analysis tool;
a derived variable management module: the system comprises a base variable generation module, a mathematical algorithm generation module, a data processing module and a data processing module, wherein the mathematical algorithm generation module is used for generating a derivative variable by introducing the base variable and selecting the mathematical algorithm, carrying out iterative calculation on the base variable according to the mathematical algorithm and outputting the derivative variable and the mathematical algorithm, and the mathematical algorithm is a self-defined algorithm function;
a model assembly management module: and the method is used for obtaining the created basic variables, the created derivative variables and the mathematical algorithm output custom model logic table, and assembling the basic variables and the derivative variables according to the custom model logic table to generate the data model.
The working principle and the advantages of the invention are as follows:
1. the data source management module is arranged, so that data exchange among multiple parties and collection and export of related data can be facilitated.
2. The basic variable management module and the derivative variable management module are arranged, wind control service personnel or a wind control service department to which the wind control service personnel belongs only need to define a corresponding algorithm, a system establishes a corresponding function for the defined algorithm, and the wind control service personnel only need to consider the calculation of the data variable from a service perspective, so that the workload of a developer for programming and processing the data variable is reduced, the processing efficiency of the data variable is correspondingly improved, and the purpose that the wind control service personnel with related knowledge skills can participate in developing the variable or the data model instead of being used as a bystander for guiding and improving opinions is achieved. When the wind control service personnel participate in the development of the data variables, related developers or technicians and the wind control service personnel can generate the basic variables and the derivative variables to exchange opinions in time, and the communication cost is conveniently controlled, so that the development cost is controlled.
Further, the data model comprises an off-line model and a real-time model, the assembling of the data model in the model assembling management module is realized by the following modules,
an offline model management module: the system is used for scheduling and assembling basic variables and derived variables through a user-defined model logic table to generate an offline model;
the real-time API release management module: the system is used for docking the real-time data source and the derived variables, analyzing and verifying whether the real-time data source, the derived variables and the custom model logic table are correct or not, if so, generating a real-time model by combining the real-time data source, the derived variables and the custom model logic table, and distributing a real-time API.
The setting of the off-line model and the real-time model is convenient for users to specifically select according to actual conditions.
Further, the method also comprises the following steps of,
the editing data source module: and the attributes are used for editing the data source in detail, and comprise a data source name, a real-time interface, a database, a table, field information, a json field, an associated primary key, a service type and model field prefix information.
And the property of the data source is convenient to edit so as to be convenient for calling the data in the later period.
Further, the method also comprises the following steps of,
unified analytic tool module: the method is used for loading the edited complete data source information, selecting the fields to be analyzed, and editing the basic attributes of the basic variables by using the same analysis tool, wherein the basic attributes comprise variable names, field types, aliases, desensitization methods, service classifications, associated main keys, jsonnpath, aggregation functions and filtering condition information.
And the basic attribute of the basic variable is convenient to edit so as to be convenient for later calling of the data.
Further, the method also comprises the following steps of,
a data template editing module: the method is used for editing the json data template in the json analysis process, recording the json data template in a standardized mode, completely expressing the path and the type of the data, and storing the version of the json data template.
The json data template is input in a standardized mode, so that the data can be more conveniently called.
Further, the model assembly management module further comprises the following sub-modules:
assembling a submission recording module: the method is used for viewing the submission records of the user assembly data model, and the submission records comprise a custom algorithm function, a data model, a json data template version and data model assembly scheduling information.
The assembly of the data model is convenient to manage.
Further, the model assembly management module further comprises the following sub-modules:
a user data security module: the real-time model or the off-line model is stored in the user authority database.
Through the setting of the authority, the authorized user can check the real-time model or the off-line model in the database, so that the data security is guaranteed.
Further, the real-time data source includes real-time semi-structured data and the non-real-time data source includes structured data and unstructured data.
Through the classification of the data sources, the data can be conveniently called according to the requirements.
Further, the derived variable management module comprises the following sub-modules:
visualization submodule: the method is used for carrying out visual chart display on the iterative calculation process and results.
The iterative calculation process and the iterative calculation result can be clearly and visually observed, and wind control business personnel and developers can conveniently check and analyze the iterative calculation process and the iterative calculation result.
The second objective of the present invention is to provide an intelligent management method for variables, which is used in the above system, and comprises the following steps:
a data source management step: configuring a database, selecting a data source and outputting complete data source information; the data sources comprise real-time data sources and non-real-time data sources;
basic variable management: loading complete data source information, and analyzing basic variables by using a uniform analysis tool;
and (3) derivative variable management: introducing a basic variable and selecting a mathematical algorithm, performing iterative calculation on the basic variable according to the mathematical algorithm to generate a derivative variable, and outputting the derivative variable and the mathematical algorithm, wherein the mathematical algorithm is a self-defined algorithm function;
model assembly management: acquiring a created basic variable, a created derivative variable and a created mathematical algorithm output custom model logic table, and assembling the basic variable and the derivative variable according to the custom model logic table to generate a data model; the data model comprises an off-line model and a real-time model;
an offline model management step: scheduling and assembling the basic variables and the derivative variables through a custom model logic table to generate an offline model;
a real-time API release management step: and butting the real-time data source and the derived variables, analyzing and verifying whether the real-time data source, the derived variables and the custom model logic table are correct, if so, generating a real-time model by combining the real-time data source, the derived variables and the custom model logic table, and publishing a real-time API.
The working principle and the advantages of the invention are as follows:
1. the data source management step is arranged, so that data exchange of multiple parties and collection and export of related data can be facilitated.
2. The method comprises the steps of basic variable management and derivative variable management, wherein wind control service personnel or wind control service departments to which the wind control service personnel belong only need to define corresponding algorithms, a system establishes corresponding functions for the defined algorithms, and the wind control service personnel only need to consider the calculation of data variables from the service perspective, so that the workload of developers for programming and processing the data variables is reduced, and the processing efficiency of the data variables is correspondingly improved, so that the wind control service personnel with related knowledge skills can participate in the purpose of developing the variables or data models instead of serving as bystanders for guidance and suggestion improvement. When the wind control service personnel participate in the development of the data variables, related developers or technicians and the wind control service personnel can generate the basic variables and the derivative variables to exchange opinions in time, and the communication cost is conveniently controlled, so that the development cost is controlled.
Drawings
FIG. 1 is a logical block diagram of an intelligent management system for variables in accordance with an embodiment of the present invention;
fig. 2 is a flowchart of an intelligent management method for variables according to an embodiment of the present invention.
Detailed Description
The following is further detailed by way of specific embodiments:
examples
An intelligent management system for variables, substantially as shown in fig. 1, mainly comprising the following modules: the system comprises a data source management module, a basic variable management module, a derivative variable management module and a model assembly management module.
The data source management module: the system is used for configuring a database, selecting a data source and outputting complete data source information; the data sources include real-time data sources and non-real-time data sources. The real-time data source includes real-time semi-structured data and the non-real-time data source includes offline batch structured data and unstructured data. In the scheme, the user refers to a wind control service staff, and the user can select corresponding data sources according to requirements, wherein the data sources comprise HIVE, impala and MySQL and corresponding real-time data server addresses, projects, interfaces and versions.
And editing the information of the data source through the data source editing module.
The data source editing module: the attribute for editing the data source in detail comprises the data source name, a real-time interface, a database, version information, a table, field information, a json field, a service type and model field prefix information:
the data source name is used for defining a unique data source name and identifying and managing a data source;
the real-time interface is used for acquiring the data returned by the real-time interface for analyzing the data;
the version information is used for marking the version of data analysis;
the table editing is used for offline data analysis;
the field information is used for marking an associated main key, is used for connecting other variables by using a model assembling function, and is similar to a join function of a relational database;
the json field is used for judging whether a variable obtains path analysis from the json of the semi-structured data;
the service types are used for storing data sources in a classified manner, so that the functions of searching and managing are facilitated;
the model field prefix information is used for marking the same name variable, generating model data after assembly and distinguishing each field name when establishing a newly created table.
The edit data source module also comprises a delete data source submodule and a self-defined data source submodule.
Deleting the data source submodule: for user logic to delete the source information of the useless data.
A custom data source submodule: the method is used for filling in a data source through SQL statements by a user, mainly aiming at the user with development capability, filtering useless field information and establishing an associated relation base table to be stored as the data source in a view mode.
A basic variable management module: and the method is used for loading complete data source information and analyzing the basic variables by using a uniform analysis tool.
When the unified analysis tool is used, the unified analysis tool module is called to analyze and edit the complete data source information.
Unified analytic tool module: the method is used for loading edited complete data source information, selecting fields to be analyzed, and editing basic attributes of basic variables by using the same analysis tool, wherein the basic attributes comprise variable names, field types, aliases, desensitization methods, service classifications, associated primary keys, jsonnpath, aggregation functions and filtering condition information.
An example of the functionality of the unified parsing tool module is as follows: and selecting a data source, if the data field is a structured field, only inputting a variable name, an alias and a desensitization method, selecting a service classification, selecting a data association main key, and inputting a tag to finish editing the basic variable. If the data field is json data, besides the basic information, jsonnpath is also input, and a specific example is presented as $. If data needs to be filtered, a filtering condition $ data [? (@. Id = 100) ]. Score. Max ().
A data template editing module: the method is used for editing the json data template in the json analysis process, inputting the json data template in a standardized mode, completely expressing the path and the type of data and storing the version of the json data template.
The basic variable management module further comprises a shopping cart sub-module,
a shopping cart sub-module: the method is used for selecting and adding the edited basic variables by the user, and the basic variables are conveniently submitted to the model assembly management module for assembly.
A derived variable management module: the method is used for introducing basic variables, selecting a mathematical algorithm, performing iterative operation on the basic variables according to the mathematical algorithm to generate derivative variables, and outputting the derivative variables and the mathematical algorithm, wherein the mathematical algorithm is a self-defined algorithm function.
The derived variable management module also comprises a custom algorithm function editing submodule and a derived variable editing submodule.
The user-defined algorithm function editing submodule: the method is used for a user with programming capability to enter the mathematical algorithm script, and the mathematical algorithm script is specifically an interpretation execution script. The interpretation and execution script comprises the functions of inputting the names, inputting the parameters and types, outputting the parameters and types, and marking the functions as single-row functions, aggregation functions and conversion function types.
Edit derived variables submodule: the method is used for a user to edit and store the derived variables, and comprises the specific steps of selecting the name of a derived function, inputting a basic variable or derived variables, and describing the names and types of the parameter x and the parameter y completely by using f (x, y) = x (basic variable) + y (derived variable).
A shopping cart sub-module: the method is also used for selecting and adding the edited derivative variables by the user, so that the derivative variables are conveniently submitted to the model assembly management module, and the derivative variables are selected by the user in a self-defined manner so as to be conveniently assembled into a new data model.
A visualization module: the method is used for carrying out visual chart display on the iterative calculation process and results.
A model assembly management module: the model assembling method is used for obtaining basic variables or derivative variables which are selected by a user to be equipped according to the shopping cart submodule to carry out model assembling and generating a data model through logic assembling. The data model includes an offline model and a real-time model.
The model assembly management module specifically comprises an assembly editing sub-module, an SQL preview sub-module, a deletion assembly sub-module, an assembly submission record sub-module and a user data safety sub-module.
Assembling and editing the submodule: the method is used for enabling a user to select basic variables or derivative variables needing to be equipped according to a shopping cart submodule, outputting a custom model logic table according to the basic variables, the derivative variables and a mathematical algorithm, and assembling the basic variables and the derivative variables according to the custom model logic table to generate a data model;
SQL preview sub-module: the method is used for presenting the assembled data model to a user in an SQL mode, can be used for problem troubleshooting and data immediate preview, and can use SQL sentences to query HIVE or impala through jdbc to verify whether the offline model data are correct.
Deleting the assembly submodule: for the user to delete useless data models.
An assembly submission record submodule: the method is used for viewing the submission records of the user assembly data model, and the submission records comprise a custom algorithm function, a data model, a json data template version and data model assembly scheduling information.
The user data safety sub-module: the real-time model or the off-line model is stored in the database.
And assembling the offline model and the real-time model in the assembly editing submodule is respectively executed through the offline model management module and the real-time API release management module.
An offline model management module: the system is used for scheduling and assembling basic variables and derived variables through a user-defined model logic table to generate an offline model;
the real-time API release management module: the system is used for docking the real-time data source, the custom model logic table and the derived variables, analyzing and verifying whether the real-time data source, the derived variables and the custom model logic table are correct or not, if so, generating a real-time model by combining the real-time data source, the derived variables and the custom model logic table, and publishing a real-time API (application program interface).
And the real-time API release management module acquires real-time return data from a real-time interface stored in a data source, verifies the current variable or logic table model, and if the variable analysis data is found to be incorrect, the user can iteratively edit the variable information again. If the verification is correct, the application issuing of the API interface can be carried out, and the interface is labeled to request the interface address, the input parameters and the output sample.
An intelligent management method for variables, as shown in fig. 2, is applied to the system, and includes the following steps:
a data source management step: configuring a database, selecting a data source and outputting complete data source information; the data sources include real-time data sources and non-real-time data sources. The real-time data source includes real-time semi-structured data and the non-real-time data source includes structured data and unstructured data. Editing the name, real-time interface, database, table, field information, json field, associated key, service type and model field prefix information of the data source;
basic variable management: loading edited data source information, selecting a field to be analyzed, filling an edited basic variable name, a field type, an alias, a desensitization method, a service classification and an associated main key by using the same analysis tool, and selecting jsonnpath, a clustering function and filtering condition information from a template if the json field is the json field;
and (3) derivative variable management: introducing a basic variable and selecting a mathematical algorithm, performing iterative operation on the basic variable according to the mathematical algorithm to generate a derivative variable, and outputting the derivative variable and the mathematical algorithm, wherein the mathematical algorithm is a self-defined algorithm function;
model assembly management: and acquiring the created basic variables, derivative variables and mathematical algorithm output custom model logic tables, and assembling the basic variables and the derivative variables according to the custom model logic tables to generate the data model. The data model includes an offline model and a real-time model.
An offline model management step: scheduling and assembling the basic variables and the derivative variables through a custom model logic table to generate an offline model;
a real-time API release management step: the system is used for docking the real-time data source, the custom model logic table and the derived variables, analyzing and verifying whether the real-time data source, the derived variables and the custom model logic table are correct, if so, generating a real-time model by combining the real-time data source, the derived variables and the custom model logic table, and distributing a real-time API.
The foregoing are embodiments of the present invention and are not intended to limit the scope of the invention to the particular forms set forth in the specification, which are set forth in the claims below, but rather are to be construed as the full breadth and scope of the claims, as defined by the appended claims, as defined in the appended claims, in order to provide a thorough understanding of the present invention. It should be noted that, for those skilled in the art, without departing from the structure of the present invention, several changes and modifications can be made, which should also be regarded as the protection scope of the present invention, and these will not affect the effect of the implementation of the present invention and the practicability of the patent. The scope of the claims of the present application shall be determined by the contents of the claims, and the description of the embodiments and the like in the specification shall be used to explain the contents of the claims.
Claims (6)
1. An intelligent management system for variables, characterized by: comprises the following modules which are used for realizing the functions of the system,
the data source management module: the system is used for configuring a database, selecting a data source and outputting complete data source information; the data sources comprise real-time data sources and non-real-time data sources;
a basic variable management module: the system is used for loading complete data source information and analyzing basic variables by using a unified analysis tool;
a derived variable management module: the system comprises a base variable generation module, a mathematical algorithm generation module, a data processing module and a data processing module, wherein the mathematical algorithm generation module is used for generating a derivative variable by introducing the base variable and selecting the mathematical algorithm, carrying out iterative calculation on the base variable according to the mathematical algorithm and outputting the derivative variable and the mathematical algorithm, and the mathematical algorithm is a self-defined algorithm function;
a model assembly management module: the system comprises a logic table, a data model and a data model, wherein the logic table is used for acquiring created basic variables, derived variables and mathematical algorithm output custom models and assembling the basic variables and the derived variables according to the custom model logic table to generate the data model;
the data model comprises an offline model and a real-time model, and the assembly of the data model in the model assembly management module is realized by the following offline model management module and a real-time API release management module;
an offline model management module: the system comprises a model logic table, a basic variable and a derivative variable, wherein the model logic table is used for scheduling and assembling the basic variable and the derivative variable through the model logic table to generate an offline model;
the real-time API release management module: the system comprises a real-time data source, a derivative variable and a custom model logic table, wherein the real-time data source is used for receiving a real-time data source and the derivative variable, analyzing and verifying whether the real-time data source, the derivative variable and the custom model logic table are correct or not, if so, generating a real-time model by combining the real-time data source, the derivative variable and the custom model logic table, and issuing a real-time API;
the editing data source module: the attribute editing method comprises the steps that attributes of a data source are edited, and the attributes comprise a data source name, a real-time interface, a database, a table, field information, a json field, an associated main key, a service type and model field prefix information;
unified analytic tool module: the method comprises the steps that the method is used for loading edited complete data source information, selecting fields to be analyzed, editing basic attributes of basic variables by using the same analysis tool, wherein the basic attributes comprise variable names, field types, aliases, desensitization methods, service classification, associated main keys, jsonnpath, aggregation functions and filtering condition information;
a data template editing module: the method is used for editing the json data template in the json analysis process, recording the json data template in a standardized mode, completely expressing the path and the type of the data, and storing the version of the json data template.
2. The intelligent management system for variables according to claim 1, characterized in that: the model assembly management module further comprises the following sub-modules:
assembling a submission recording module: the method is used for viewing the submission records of the user assembly data model, and the submission records comprise a custom algorithm function, a data model, a json data template version and data model assembly scheduling information.
3. The intelligent management system for variables according to claim 2, characterized in that: the model assembly management module further comprises the following sub-modules:
a user data security sub-module: the real-time model or the off-line model is stored in the database.
4. The intelligent management system for variables according to claim 1, characterized in that: the real-time data source includes real-time semi-structured data and the non-real-time data source includes structured data and unstructured data.
5. The intelligent management system for variables according to claim 1, characterized in that: the derived variable management module comprises the following sub-modules:
visualization submodule: the method is used for carrying out visual chart display on the iterative calculation process and results.
6. An intelligent management method for variables, characterized by: the method comprises the following steps:
a data source management step: configuring a database, selecting a data source and outputting complete data source information; the data sources comprise real-time data sources and non-real-time data sources;
basic variable management: loading complete data source information, and analyzing basic variables by using a uniform analysis tool;
and (3) derivative variable management: introducing a basic variable and selecting a mathematical algorithm, performing iterative operation on the basic variable according to the mathematical algorithm to generate a derivative variable, and outputting the derivative variable and the mathematical algorithm, wherein the mathematical algorithm is a self-defined algorithm function;
model assembly management: acquiring a created basic variable, a created derivative variable and a created mathematical algorithm output custom model logic table, and assembling the basic variable and the derivative variable according to the custom model logic table to generate a data model; the data model comprises an off-line model and a real-time model; the assembly of the data model in the model assembly management step is realized by the following offline model management step and real-time API release management step;
an off-line model management step: scheduling and assembling the basic variables and the derivative variables through a custom model logic table to generate an offline model;
a real-time API release management step: the real-time data source and the derived variables are connected in an abutting mode, whether the real-time data source, the derived variables and the custom model logic table are correct or not is analyzed and verified, if yes, a real-time model is generated by combining the real-time data source, the derived variables and the custom model logic table, and a real-time API is distributed;
editing a data source: editing attributes of a data source, wherein the attributes comprise a data source name, a real-time interface, a database, a table, field information, a json field, an associated main key, a service type and model field prefix information;
unifying analysis tools: loading edited complete data source information, selecting a field to be analyzed, and editing basic attributes of a basic variable by using the same analysis tool, wherein the basic attributes comprise variable names, field types, aliases, desensitization methods, service classification, associated main keys, jsonnpath, aggregation functions and filtering condition information;
editing a data template: and editing the json data template in the json analysis process, inputting the json data template in a standardized mode, completely expressing the path and the type of the data, and storing the version of the json data template.
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| CN111798309B (en) * | 2020-07-22 | 2021-08-17 | 睿智合创(北京)科技有限公司 | Wind-controlled variable processing system, method and readable medium |
| CN112685439B (en) * | 2020-12-29 | 2023-09-22 | 上海豹云网络信息服务有限公司 | Count making method, system, device and storage medium for wind control system |
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107025509A (en) * | 2016-02-01 | 2017-08-08 | 腾讯科技（深圳）有限公司 | Decision system and method based on business model |
| CN109656914A (en) * | 2018-11-07 | 2019-04-19 | 上海前隆信息科技有限公司 | On-line off-line mixed air control modeling training and production dissemination method and system |
| CN109767227A (en) * | 2018-12-28 | 2019-05-17 | 深圳市讯联智付网络有限公司 | The system and method for payment risk intelligent decision and control is realized by RDS |
| CN109799976A (en) * | 2019-01-11 | 2019-05-24 | 上海凯岸信息科技有限公司 | Real-time air control variable calculation method based on distributive type computing engines |
| CN110222318A (en) * | 2019-06-05 | 2019-09-10 | 卡斯柯信号(成都)有限公司 | Data generating tool development approach based on XML |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20030210329A1 (en) * | 2001-11-08 | 2003-11-13 | Aagaard Kenneth Joseph | Video system and methods for operating a video system |
| US8606550B2 (en) * | 2009-02-11 | 2013-12-10 | Johnathan C. Mun | Autoeconometrics modeling method |
-
2020
- 2020-01-20 CN CN202010062798.8A patent/CN111240648B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107025509A (en) * | 2016-02-01 | 2017-08-08 | 腾讯科技（深圳）有限公司 | Decision system and method based on business model |
| CN109656914A (en) * | 2018-11-07 | 2019-04-19 | 上海前隆信息科技有限公司 | On-line off-line mixed air control modeling training and production dissemination method and system |
| CN109767227A (en) * | 2018-12-28 | 2019-05-17 | 深圳市讯联智付网络有限公司 | The system and method for payment risk intelligent decision and control is realized by RDS |
| CN109799976A (en) * | 2019-01-11 | 2019-05-24 | 上海凯岸信息科技有限公司 | Real-time air control variable calculation method based on distributive type computing engines |
| CN110222318A (en) * | 2019-06-05 | 2019-09-10 | 卡斯柯信号(成都)有限公司 | Data generating tool development approach based on XML |
Non-Patent Citations (2)
| Title |
|---|
| Research on Risk Assessment in Engineering Project Based on Route Analysis and Hierarchical Variable Weight Fuzzy Evaluation;Yuansheng Huang 等;《2008 International Conference on Risk Management & Engineering Management》;20081117;全文 * |
| 不确定环境下风险变量的止损序比较;余清 等;《黄冈师范学院学报》;20110411;第30卷(第06期);6-10,16 * |
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