CN108428103B - Decision engine and decision method - Google Patents
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Abstract
The invention discloses a decision engine, comprising: the system comprises a rule base, a variable base, a strategy engine and an input/output interface. Rules are stored in the rule base, and each rule corresponds to the logic operation of the variable. Variables for rule operations are stored in the variable library. The strategy engine is a rule settlement engine, each strategy corresponds to one application scene, and for each strategy, the strategy engine uses the rules in the rule base and the variables in the variable base to carry out operation. The input-output interface performs input and/or output of data. The input and output interface receives user data and application scene data, the strategy engine generates a strategy according to the application scene data, and rules settlement is carried out on the user according to the strategy and the user data. The invention also discloses a decision method.
Description
Technical Field
The present invention relates to internet technology, and more particularly, to a data-based decision engine and decision method.
Background
Internet finance has been rapidly developed along with the development of internet technology. Unlike the traditional financial industry, most of the internet finance is done online, not on-site. Traditional risk control in the financial industry relies more on offline auditing and background surveys. The offline auditing and background investigation can take a long time, so the period of the traditional financial industry wind control business is long. In addition, the content of background survey in the traditional financial industry is relatively fixed, and the same set of background survey items is almost used for all users.
Internet finance is quite different from the driving finance industry, for example, the internet finance emphasizes timeliness, a user can hope to reply as soon as possible, preferably in real time, when applying online, and the traditional finance industry is obviously not suitable for long offline auditing and investigation. In addition, as online activities increase and big data technology develops, more information about users can be obtained by using big data, and the online activities of users can provide more comprehensive, reliable and dynamic user information than a single background survey. In addition, the dimensions and weights of various elements for evaluating the user risk are also continuously changed, and the dimensions and weights for evaluating the user risk also need to be flexibly changed and adjusted according to actual conditions.
Disclosure of Invention
The invention aims to provide a decision engine and a decision method, which can flexibly adjust rule operation.
According to an embodiment of the present invention, a decision engine is provided, including: the system comprises a rule base, a variable base, a strategy engine and an input/output interface. Rules are stored in the rule base, and each rule corresponds to the logic operation of the variable. Variables for rule operations are stored in the variable library. The strategy engine is a rule settlement engine, each strategy corresponds to one application scene, and for each strategy, the strategy engine uses the rules in the rule base and the variables in the variable base to carry out operation. The input-output interface performs input and/or output of data. The input and output interface receives user data and application scene data, the strategy engine generates a strategy according to the application scene data, and rules settlement is carried out on the user according to the strategy and the user data.
In one embodiment, the variable library comprises: a function library and a parameter library. The function library stores functions and provides function operation functions, and the functions are used for rule operation. The parameter library stores parameters and provides a parameter operation function, and the parameters are used for rule operation.
In one embodiment, the function operation functions provided by the function library include: a function adding function and a function editing function. The parameter operation functions provided by the parameter library comprise: the system comprises a parameter editing function, a draft function, a parameter importing and exporting function, a parameter and rule associating function and a parameter and strategy associating function.
In one embodiment, the decision engine further includes a policy repository, where the policy repository stores policies generated according to existing application scenario data, each policy corresponds to an application scenario, and the policy engine performs rule settlement for each policy. And when the application scene data received by the input and output interface is matched with the existing application scene data, directly calling the stored strategy from the strategy library.
In one embodiment, the policy operations functions provided by the policy repository include: the system comprises a strategy editing function, a strategy stopping and starting function, a strategy unloading and deploying function, a strategy refreshing function, a strategy shallow copy function, a strategy deep copy function and a strategy testing function.
In one embodiment, there is an association and mapping between the function library, the parameter library, the rule library, and the policy library, wherein operations on one of the libraries are hinted at the remaining libraries.
In one embodiment, the rule base also provides rule operation functions, including: the system comprises a rule editing function, a rule labeling function, a rule copying function, a rule stopping and starting function, a rule pushing strategy function, a rule checking and testing function and a rule and strategy correlation function.
In one embodiment, the decision engine further comprises a workflow engine, wherein the workflow engine has a conditional logic judgment function and assists the strategy engine in operation.
According to an embodiment of the present invention, a decision method is provided, including:
receiving user data and application scene data;
generating strategies according to the application scene data by the strategy engine, wherein each strategy corresponds to one application scene, and for each strategy, the strategy engine is a rule settlement engine and performs operation by using rules in a rule base and variables in a variable base;
and carrying out rule settlement and output for the user under the strategy according to the user data.
In one embodiment, the decision method further comprises:
generating strategies according to the existing application scene data and storing the strategies into a strategy library, wherein each strategy corresponds to one application scene, and a strategy engine performs rule settlement for each strategy;
and when the application scene data received by the input and output interface is matched with the existing application scene data, directly calling the stored strategy from the strategy library.
In one embodiment, the policy repository also provides policy operation functions including: the system comprises a strategy editing function, a strategy stopping and starting function, a strategy unloading and deploying function, a strategy refreshing function, a strategy shallow copy function, a strategy deep copy function and a strategy testing function.
In one embodiment, the rule base stores rules and rule operation functions, each rule corresponding to a logical operation of a variable, the rule operation functions including: the system comprises a rule editing function, a rule labeling function, a rule copying function, a rule stopping and starting function, a rule pushing strategy function, a rule checking and testing function and a rule and strategy correlation function.
In one embodiment, the variable library comprises: a function library and a parameter library. The function library stores functions and provides function operation functions, the functions are used for rule operation, and the function operation functions provided by the function library comprise: a function adding function and a function editing function. The parameter library stores parameters and provides parameter operation functions, the parameters are used for rule operation, and the parameter operation functions provided by the parameter library comprise: the system comprises a parameter editing function, a draft function, a parameter importing and exporting function, a parameter and rule associating function and a parameter and strategy associating function.
In one embodiment, the decision method further comprises:
establishing association and mapping among a function library, a parameter library, a rule library and a strategy library;
when an operation is performed on one of the libraries, a hint is made in each of the remaining libraries.
In one embodiment, the decision method further comprises: and using a workflow engine to assist the strategy engine to carry out operation, wherein the workflow engine has a conditional logic judgment function.
The decision engine and the decision method can configure and adjust the rules of the strategy so as to quickly respond to continuously changing uncertain factors, and the functions of rule multiplexing, rule online editing, rich function base, rule flow, hot deployment, authority control and management and the like are realized by utilizing the rule settlement engine, so that the problems of inflexible modification, slow deployment, low safety and the like of the traditional strategy engine are greatly solved.
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The above and other features, properties and advantages of the present invention will become more apparent from the following description of the embodiments with reference to the accompanying drawings in which like reference numerals denote like features throughout the several views, wherein:
FIG. 1 discloses a block diagram of a decision engine according to an embodiment of the invention.
FIG. 2 is a diagram illustrating the association of libraries in a decision engine according to an embodiment of the present invention.
FIG. 3 discloses an architecture block diagram of a policy in a decision engine according to an embodiment of the invention.
Detailed Description
The invention provides a decision engine, comprising: the system comprises a rule base, a variable base, a strategy engine and an input/output interface. Rules are stored in the rule base, and each rule corresponds to the logic operation of the variable. Variables for rule operations are stored in the variable library. The strategy engine is a rule settlement engine, each strategy corresponds to one application scene, and for each strategy, the strategy engine uses the rules in the rule base and the variables in the variable base to carry out operation. The input-output interface performs input and/or output of data. The input and output interface receives the user data and the application scene data, the strategy engine generates a strategy according to the application scene data, and the rule settlement is carried out on the user under the strategy according to the user data.
FIG. 1 discloses a block diagram of a decision engine according to an embodiment of the invention. In the embodiment shown in FIG. 1, rules are stored in rules repository 106 and provide rule operation functionality. Each rule corresponds to a logical operation of a variable. Referring to FIG. 3, a schematic diagram of the logical operation of a rule on a variable is disclosed in FIG. 3. The rule is simply to judge various conditions on variables and then draw conclusions. In the embodiment shown in fig. 3, the variables may include functions, input parameters, output parameters, and temporary parameters. Conditional decisions can be implemented by logical operations, such as the introduction of operators and operators. The conclusion can be reached by the condition judgment of the variable, and the conclusion is the output of the rule. Returning to FIG. 1, in the illustrated embodiment, the rule operation functions provided by the rule base 106 include: the system comprises a rule editing function, a rule labeling function, a rule copying function, a rule stopping and starting function, a rule pushing strategy function, a rule checking and testing function and a rule and strategy correlation function.
In the embodiment shown in FIG. 1, the variable library includes a function library 102 and a parameter library 104. The function library 102 stores functions and provides function operation functions, which are operated on by rules. In the illustrated embodiment, the function operating functions provided by the function library 102 include: a function adding function and a function editing function. The parameter repository 104 stores parameters and provides parameter manipulation functionality, with the parameters being manipulated by the rules. In the illustrated embodiment, the parameter operating functions provided by the parameter library 104 include: the system comprises a parameter editing function, a draft function, a parameter importing and exporting function, a parameter and rule associating function and a parameter and strategy associating function. In connection with FIG. 3, the function and the parameter are variables called by the rule, for example, in the embodiment shown in FIG. 3, the function called by the rule may come from the function library 102, and the input parameter, the output parameter and the temporary parameter called by the rule may come from the parameter library 104.
In the embodiment shown in FIG. 1, policy engine 110 is a rules settlement engine, implemented using a Drools rules engine. The policy engine 110 is used in match with the policies, specifically, each policy corresponds to one application scenario, and for each policy, the policy engine 110 performs operations using the rules in the rule base and the variables in the variable base. Referring to FIG. 3, FIG. 3 discloses an architecture diagram of a policy in a decision engine according to an embodiment of the invention. The policy may invoke one or several rules according to the requirements of the reference scenario, where each rule is to make a conditional judgment on its respective variable and then to reach a conclusion. Each rule calls functions and parameters needed to be used in a function library and a parameter library respectively, judgment is carried out according to the conditions of each rule, and then a conclusion is obtained. The policy engine 110(Drools rules engine) performs rule settlement on each rule and conclusion to obtain the final output of the policy. Because the policy engine 110(Drools rule engine) independently uses the rules in the rule base and the variables in the variable base to perform operations for each policy, the deployment of the policy is very flexible, and the policy can be flexibly changed or adjusted according to different application scenarios. Moreover, when needed, in the same strategy, different rules can be settled by calling different rules and/or variables for different users.
With continued reference to FIG. 1, in the embodiment shown in FIG. 1, the decision engine also includes a policy repository 108. The policy repository 108 stores policies generated according to existing application scenario data, each policy corresponds to an application scenario, and the policy engine performs rule settlement for each policy and provides a policy operation function. The policy operation functions provided by the policy repository include: the system comprises a strategy editing function, a strategy stopping and starting function, a strategy unloading and deploying function, a strategy refreshing function, a strategy shallow copy function, a strategy deep copy function and a strategy testing function. The policy repository 108 is operable to expedite real-time deployment of policies. For a policy that is frequently used and has better versatility, the policy may be saved in the policy repository 108 after the policy engine completes the rule settlement. In the subsequent use process, if an application scene matched with the existing strategy is encountered, the existing strategy can be directly called from the strategy library 108 without recalculation, and the deployment time can be shortened. The operation function provided by the strategy base can modify and adjust the strategy stored in the strategy base.
The input/output interface 112 performs input and/or output of data. The input/output interface 112 receives the user data and the application scenario data, and the policy engine 110 generates a policy according to the application scenario data, and performs rule settlement for the user under the policy according to the user data. In one embodiment, the saved policy is invoked directly from the policy repository 108 when the application context data received by the input output interface 112 matches existing application context data. In some applications, different rules and/or variables may be used for different users under the same policy.
The decision engine of the present invention: the relationships established and mapped and associated between function library 102, parameter library 104, rule library 106, and policy library 108. FIG. 2 is a diagram illustrating the association of libraries in a decision engine according to an embodiment of the present invention. The strategy is specific to a specific application scene and is closer to an application level. The policy calls and settles the rules to settle the rules for the user under the policy. And the rules further call functions and parameters to realize the logic judgment of specific variables. Rules, functions, and parameters are closer to the implementation level. From the implementation to the application perspective, functions and parameters are at the bottom level, rules are in the middle, and policies are at the top level. The bottom layer is closer to the implementation level and the top layer is closer to the application level. After the associations and mappings are established among function library 102, parameter library 104, rule library 106, and policy library 108, operations on one of the libraries are prompted in the remaining libraries. For example, if a function in the underlying function library is modified, a rule in the rule library associated with the function (calling the function) is prompted, and a policy in the policy library associated with the rule (calling the rule) is also prompted. As another example, if a rule is adjusted, a policy associated with (invoking) the rule in the policy repository may be prompted. Generally speaking, the modification of the bottom layer will prompt the associated content of the upper layer, because the modification of the bottom layer will affect the associated content of the upper layer. Modifications to the upper layer do not necessarily affect the lower layer, and therefore no hints are required for the upper layer modifications to the associated lower layer.
In one embodiment, the decision engine further comprises a workflow engine, such as jBPM. A workflow engine such as jBPM has a conditional logic judgment function. Because the Drools rule engine does not have the condition logic (if-then), in order to realize the complete and comprehensive logic judgment of the rule, a workflow engine (jBPM) with a condition logic judgment function is introduced to assist the strategy engine (Drools) to operate.
A specific application scenario is presented below to aid in understanding the decision engine of the present invention. In the field of internet finance, the most common application scenario is to apply for credit, for example, user a is applying for credit. The input/output interface receives application scenario data and user data, the application scenario data is credit application credit, and the user data is identity information of the user a. The policy engine (Drools rules engine) would then generate a policy from the application credit, which corresponds to the application scenario in which the credit is applied, such as may be named the application credit policy. The credit application credit policy is selectively invoked in the following rules: the system comprises a user identity identification rule, a basic information acquisition rule, a background investigation rule, a behavior investigation rule, a peer risk verification rule and the like. And the user identity identification rule, the basic information acquisition rule, the background investigation rule, the behavior investigation rule, the peer risk verification rule and other rules can call the functions or parameters required by the user respectively, and the user can calculate by utilizing respective logic judgment operation. It should be noted that the credit extension policy may choose to invoke different combinations of rules for different users. For example, for the user a, the user identity recognition rule and the basic information acquisition rule are called first to obtain the user identity and the basic information. For example, if the user identity information shows that the user a is a young person and the work history and credit records are few, the credit application policy may select to invoke behavior investigation rules, peer risk verification rules, and the like at this time, and perform rule calculation according to the information of the user a, such as network behavior, social range, credit records of other internet financial enterprises, and the like. And after the operation is finished, storing the credit application credit strategy into a strategy library. User B then also applies for credit. The input and output interface receives application scene data and user data, the application scene data is also credit application credit, and the user data is identity information of a user B. Because the credit application credit extension strategy is stored in the strategy library and is matched with the current application scene data, the credit application credit extension strategy is directly called from the strategy library to realize the rapid deployment of the strategy. The user identity information shows that the user B is a middle-aged person and has rich work records and credit records, the credit application strategy selects to call background investigation rules, peer risk verification rules and the like at the moment, and rule calculation is performed through the information of the background investigation of the user B, the credit records of other financial enterprises (including Internet financial enterprises) and the like. Therefore, different users can apply different rules to judge in the same strategy based on different user data.
From the perspective of implementation, the decision engine executes the following decision method, or the present invention further proposes a decision method, where the decision method includes:
and S1, receiving the user data and the application scene data.
And S2, generating strategies according to the application scene data by the strategy engine, wherein each strategy corresponds to one application scene, and for each strategy, the strategy engine is a rule settlement engine and performs operation by using the rules in the rule base and the variables in the variable base.
Rules and rule operation functions are stored in the rule base, each rule corresponds to the logic operation of the variable, and the rule operation functions comprise: the system comprises a rule editing function, a rule labeling function, a rule copying function, a rule stopping and starting function, a rule pushing strategy function, a rule checking and testing function and a rule and strategy correlation function. The variable library comprises: a function library and a parameter library. The function library stores functions and provides function operation functions, the functions are used for rule operation, and the function operation functions provided by the function library comprise: a function adding function and a function editing function. The parameter library stores parameters and provides parameter operation functions, the parameters are used for rule operation, and the parameter operation functions provided by the parameter library comprise: the system comprises a parameter editing function, a draft function, a parameter importing and exporting function, a parameter and rule associating function and a parameter and strategy associating function.
And S3, carrying out rule settlement for the user under the strategy according to the user data and outputting the result.
In one embodiment, the decision method further comprises:
s4, generating strategies according to the existing application scene data and storing the strategies in a strategy library, wherein each strategy corresponds to one application scene, and the strategy engine performs rule settlement for each strategy.
And S5, when the application scene data received by the input and output interface is matched with the existing application scene data, directly calling the stored strategy from the strategy library.
The policy repository also provides policy operation functions, including: the system comprises a strategy editing function, a strategy stopping and starting function, a strategy unloading and deploying function, a strategy refreshing function, a strategy shallow copy function, a strategy deep copy function and a strategy testing function.
To facilitate adjusting and editing variables, rules, and policies, in one embodiment, the decision method further comprises:
and S6, establishing association and mapping among the function library, the parameter library, the rule library and the strategy library. When an operation is performed on one of the libraries, a hint is made in each of the remaining libraries.
In order to solve the problem that the Drools rule engine has no conditional logic (if-then), the decision method uses a workflow engine (jBPM) with a conditional logic judgment function to assist the strategy engine (Drools) to operate.
The specific implementation details of the decision method are the same as those of the decision engine described above, and the description is not repeated here.
The decision engine and the decision method can configure and adjust the rules of the strategy so as to quickly respond to continuously changing uncertain factors, and the functions of rule multiplexing, rule online editing, rich function base, rule flow, hot deployment, authority control and management and the like are realized by utilizing the rule settlement engine, so that the problems of inflexible modification, slow deployment, low safety and the like of the traditional strategy engine are greatly solved.
The embodiments described above are provided to enable persons skilled in the art to make or use the invention and that modifications or variations can be made to the embodiments described above by persons skilled in the art without departing from the inventive concept of the present invention, so that the scope of protection of the present invention is not limited by the embodiments described above but should be accorded the widest scope consistent with the innovative features set forth in the claims.
Claims (15)
1. A decision engine, comprising:
the rule base stores rules, and each rule corresponds to the logic operation of the variable;
the variable library stores variables for rule operation;
the strategy engine is a rule settlement engine, each strategy corresponds to one application scene, and for each strategy, the strategy engine uses the rules in the rule base and the variables in the variable base to carry out operation;
an input/output interface that performs input and/or output of data;
the input and output interface receives user data and application scene data, the strategy engine generates a strategy according to the application scene data, and rules settlement is carried out on the user according to the strategy and the user data.
2. The decision engine of claim 1, wherein the variable library comprises:
the function library is used for storing functions and providing function operation functions, and the functions are used for rule operation;
and the parameter library is used for storing parameters and providing a parameter operation function, and the parameters are used for rule operation.
3. The decision engine of claim 2,
the function operation functions provided by the function library comprise: a function adding function and a function editing function;
the parameter operation functions provided by the parameter library comprise: the system comprises a parameter editing function, a draft function, a parameter importing and exporting function, a parameter and rule associating function and a parameter and strategy associating function.
4. The decision engine of claim 2, further comprising:
the strategy library is used for storing strategies and providing a strategy operation function, the strategies are generated according to the existing application scene data, each strategy corresponds to one application scene, and the strategy engine is used for carrying out rule settlement on each strategy;
and when the application scene data received by the input and output interface is matched with the existing application scene data, directly calling the stored strategy from the strategy library.
5. The decision engine of claim 4,
the policy operation functions provided by the policy repository include: the system comprises a strategy editing function, a strategy stopping and starting function, a strategy unloading and deploying function, a strategy refreshing function, a strategy shallow copy function, a strategy deep copy function and a strategy testing function.
6. A decision engine as recited in claim 4, wherein the function library, the parameter library, the rule library, and the policy library have associations and mappings therebetween, wherein operations on one of the libraries are hinted at the remaining libraries.
7. The decision engine of claim 1 wherein the rule base further provides rule operation functionality comprising: the system comprises a rule editing function, a rule labeling function, a rule copying function, a rule stopping and starting function, a rule pushing strategy function, a rule checking and testing function and a rule and strategy correlation function.
8. The decision engine according to claim 1 further comprising a workflow engine having conditional logic decision functionality, the workflow engine assisting the policy engine in performing operations.
9. A method of decision making, comprising:
receiving user data and application scene data;
generating strategies according to the application scene data by the strategy engine, wherein each strategy corresponds to one application scene, and for each strategy, the strategy engine is a rule settlement engine and performs operation by using rules in a rule base and variables in a variable base;
and carrying out rule settlement and output for the user under the strategy according to the user data.
10. The decision method of claim 9, further comprising:
generating strategies according to the existing application scene data and storing the strategies into a strategy library, wherein each strategy corresponds to one application scene, and a strategy engine performs rule settlement for each strategy;
and when the application scene data received by the input and output interface is matched with the existing application scene data, directly calling the stored strategy from the strategy library.
11. The decision method of claim 10,
the policy repository also provides policy operation functions, including: the system comprises a strategy editing function, a strategy stopping and starting function, a strategy unloading and deploying function, a strategy refreshing function, a strategy shallow copy function, a strategy deep copy function and a strategy testing function.
12. The decision method of claim 11,
rules and rule operation functions are stored in the rule base, each rule corresponds to the logic operation of a variable, and the rule operation functions comprise: the system comprises a rule editing function, a rule labeling function, a rule copying function, a rule stopping and starting function, a rule pushing strategy function, a rule checking and testing function and a rule and strategy correlation function.
13. The decision method of claim 12, wherein the variable library comprises:
the function library stores functions and provides function operation functions, the functions are used for rule operation, and the function operation functions provided by the function library comprise: a function adding function and a function editing function;
the parameter library is used for storing parameters and providing parameter operation functions, the parameters are used for rule operation, and the parameter operation functions provided by the parameter library comprise: the system comprises a parameter editing function, a draft function, a parameter importing and exporting function, a parameter and rule associating function and a parameter and strategy associating function.
14. The decision method of claim 13, further comprising:
establishing association and mapping among the function library, the parameter library, the rule library and the strategy library;
when an operation is performed on one of the libraries, a hint is made in each of the remaining libraries.
15. The decision method of claim 9, further comprising: and using a workflow engine to assist the strategy engine to carry out operation, wherein the workflow engine has a conditional logic judgment function.
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Families Citing this family (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109493213A (en) * | 2018-11-09 | 2019-03-19 | 杭州创金聚乾网络科技有限公司 | A kind of lending and borrowing business decision-making technique and system based on business rule base |
| CN110502483B (en) * | 2019-07-09 | 2022-06-17 | 招联消费金融有限公司 | Data processing method, data processing device, computer equipment and storage medium |
| CN110609830A (en) * | 2019-08-15 | 2019-12-24 | 上海新颜人工智能科技有限公司 | Decision engine system based on auditing strategy |
| CN110765327A (en) * | 2019-09-05 | 2020-02-07 | 深圳壹账通智能科技有限公司 | Data analysis method, data analysis device, computer device, and storage medium |
| CN110865804A (en) * | 2019-09-30 | 2020-03-06 | 天阳宏业科技股份有限公司 | Rule engine optimization method, device, system and storage medium |
| CN111311392A (en) * | 2020-03-17 | 2020-06-19 | 深圳市随手金服信息科技有限公司 | Wind control decision method, device, server and storage medium |
| CN113362038A (en) * | 2021-06-30 | 2021-09-07 | 武汉众邦银行股份有限公司 | Examination and approval authorization method and device based on parameterized rule set configuration |
| CN113409138A (en) * | 2021-07-19 | 2021-09-17 | 浙江孚临科技有限公司 | Risk control decision engine device |
| CN113628036A (en) * | 2021-08-16 | 2021-11-09 | 武汉众邦银行股份有限公司 | Big data risk detection model-based method and device |
| CN113778424A (en) * | 2021-09-27 | 2021-12-10 | 常州市公共资源交易中心 | Review configuration method, device and storage medium |
| CN116048523B (en) * | 2023-01-10 | 2023-10-20 | 睿智合创(北京)科技有限公司 | Method for configuring policy parameters by decision engine |
Family Cites Families (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20100223211A1 (en) * | 2000-10-11 | 2010-09-02 | Johnson Gregory A | Decision service method and system |
| US7324976B2 (en) * | 2004-07-19 | 2008-01-29 | Amazon Technologies, Inc. | Automatic authorization of programmatic transactions |
| CN103701799A (en) * | 2013-12-24 | 2014-04-02 | 金蝶软件（中国）有限公司 | Parameter acquiring method, server and terminal |
| CN106874007A (en) * | 2017-02-23 | 2017-06-20 | 李庆亮 | A kind of aid decision logic rules creation method, aid decision-making method and system |
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