CN111709647A - Data source dynamic management method based on policy engine control - Google Patents
Data source dynamic management method based on policy engine control Download PDFInfo
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Abstract
The invention discloses a data source dynamic management method based on policy engine control, which comprises the following steps: s1, collecting information; s2 comprehensive decision: the decision engine carries out comprehensive decision through one or more combination modes of a decision tree, a policy table, a rule set, a scoring model and the like based on the collected 1-5 point data information, selects an optimal solution to give an optimal query suggestion, and synchronously starts a matched alternative service strategy in real time to carry out comprehensive decision if a certain data source calls abnormally; s3 conveying an instruction; the S4 instruction executes. The invention is based on the flexible configuration of service use as the starting point, abandons the traditional IT system flexible configuration as the starting point, fuses the dynamic adjustment of the data source and the dynamic adjustment of the service strategy, and applies artificial intelligence to the dynamic management of the data source through the intelligent strategy engine, thereby reducing the development and maintenance cost and improving the calling efficiency.
Description
Technical Field
The invention relates to the technical field of data management, in particular to a data source dynamic management method based on policy engine control.
Background
With the wide application of big data in the field of intelligent risks, the dynamic management of a data source is crucial to meet the requirements of big data wind control real-time approval business. At present, there are two main ways for dynamically managing data sources: the first is a configuration table management mode, which controls the timeout, the reset times, the calling sequence and the like of the data source through the configuration table, and the second is a configuration file management mode, which controls the timeout, the reset times, the calling sequence and the like of the data source through the configuration table. Thirdly, the management tool/system mode controls the timeout, the readjustment times, the calling sequence, the concurrent calling and the like of the data source by researching and developing or purchasing a special management tool/system
The first and second methods have the disadvantage that the simultaneous coexistence of the sequential call and the concurrent call cannot be realized, and the flexible configuration is difficult to realize even the coexistence is realized. Each parameter adjustment appears to enable flexible configuration of the interface, whereas otherwise, each parameter adjustment or interface call exception must be adjusted synchronously with the service system to ensure that the service is not affected.
And the third mode combines a parameter configuration and hard coding mode to realize dynamic management of the data source, and is more flexible than the first mode and the second mode. The defects are that the system is not intelligent enough, the development or purchase cost is high, and the maintenance or secondary development cost is high. Meanwhile, a large amount of development needs to be carried out synchronously on a matched service system. Once a certain interface is abnormally called, the supporting service system needs to be synchronously adjusted to ensure that the service is not influenced. The existing data source dynamic management mode is flexibly configured, but is high in cost, mechanical and incapable of being intelligentized.
Disclosure of Invention
Based on the technical problems in the background art, the invention provides a data source dynamic management method based on policy engine control.
The invention provides a data source dynamic management method based on policy engine control, which comprises the following steps:
and S1, information acquisition:
the collection of data by the policy engine includes, but is not limited to, the following:
(1) the condition of the data source which is called currently, the condition of successful and failed calling, the calling of return data and the like;
(2) control information such as timeout control, cost control, concurrency control and the like, for example: the total overtime duration of the whole process, the overtime duration of a single interface, the total expense upper limit of the whole process, the expense of the current node, the maximum concurrent query number, how to control the concurrent cost and the like;
(3) alternative data source information, such as whether a certain data source calls abnormally, whether an alternative data source needs to be started, whether multiple or multiple levels of alternative data sources exist, which alternative business strategies need to be started synchronously when the alternative data source is started, and the like;
(4) the last instruction is issued to the query system for executing the condition, returning data information and the like;
(5) other useful data;
s2 comprehensive decision:
the decision engine carries out comprehensive decision through one or more combination modes of a decision tree, a policy table, a rule set, a scoring model and the like based on the collected 1-5 point data information, selects an optimal solution to give an optimal query suggestion, and synchronously starts a matched alternative service strategy in real time to carry out comprehensive decision if a certain data source calls abnormally;
s3 conveys an instruction:
the strategy engine converts the optimal query suggestion into a query command executable by the query system and conveys the query command to the query system, wherein the query command comprises:
(1) inquiring the interface name, and outputting a plurality of interfaces when the plurality of interfaces are inquired;
(2) inquiring the abnormal readjustment times, the overtime duration and the like of the interface;
(3) whether alternative data sources are started or not when the interface is abnormal is inquired, a plurality of alternative data sources are started, and the like;
(4) other instructions;
the S4 instruction performs:
s41 receives the instruction:
the query system provides an external data source interface to receive an execution instruction sent by the decision engine;
s42 executes the query:
the query system receives an execution instruction sent by the decision engine and executes the execution instruction one by one according to the requirement of the execution instruction;
the result of S43 returns:
and after the query system finishes executing the instruction, returning an execution result to the decision engine according to the interface standard, and waiting for receiving the next execution instruction.
According to the data source dynamic management method based on the policy engine control, the data source dynamic adjustment and the service policy dynamic adjustment are fused based on the flexible configuration of service use as the starting point, the traditional IT system flexible configuration as the starting point is abandoned, and artificial intelligence is applied to the data source dynamic management through the intelligent policy engine, so that the development and maintenance cost is reduced, and the calling efficiency is improved.
Drawings
Fig. 1 is a schematic flowchart of a data source dynamic management method based on policy engine control according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments.
Referring to fig. 1, a method for dynamically managing a data source based on policy engine control includes the following steps:
and S1, information acquisition:
the collection of data by the policy engine includes, but is not limited to, the following:
(1) the condition of the data source which is called currently, the condition of successful and failed calling, the calling of return data and the like;
(2) control information such as timeout control, cost control, concurrency control and the like, for example: the total overtime duration of the whole process, the overtime duration of a single interface, the total expense upper limit of the whole process, the expense of the current node, the maximum concurrent query number, how to control the concurrent cost and the like;
(3) alternative data source information, such as whether a certain data source calls abnormally, whether an alternative data source needs to be started, whether multiple or multiple levels of alternative data sources exist, which alternative business strategies need to be started synchronously when the alternative data source is started, and the like;
(4) the last instruction is issued to the query system for executing the condition, returning data information and the like;
(5) other useful data;
s2 comprehensive decision:
the decision engine carries out comprehensive decision through one or more combination modes of a decision tree, a policy table, a rule set, a scoring model and the like based on the collected 1-5 point data information, selects an optimal solution to give an optimal query suggestion, and synchronously starts a matched alternative service strategy in real time to carry out comprehensive decision if a certain data source calls abnormally;
s3 conveys an instruction:
the strategy engine converts the optimal query suggestion into a query command executable by the query system and conveys the query command to the query system, wherein the query command comprises:
(1) inquiring the interface name, and outputting a plurality of interfaces when the plurality of interfaces are inquired;
(2) inquiring the abnormal readjustment times, the overtime duration and the like of the interface;
(3) whether alternative data sources are started or not when the interface is abnormal is inquired, a plurality of alternative data sources are started, and the like;
(4) other instructions;
the S4 instruction performs:
s41 receives the instruction:
the query system provides an external data source interface to receive an execution instruction sent by the decision engine;
s42 executes the query:
the query system receives an execution instruction sent by the decision engine and executes the execution instruction one by one according to the requirement of the execution instruction;
the result of S43 returns:
and after the query system finishes executing the instruction, returning an execution result to the decision engine according to the interface standard, and waiting for receiving the next execution instruction.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art should be considered to be within the technical scope of the present invention, and the technical solutions and the inventive concepts thereof according to the present invention should be equivalent or changed within the scope of the present invention.
Claims (1)
1. A data source dynamic management method based on policy engine control is characterized by comprising the following steps:
and S1, information acquisition:
the collection of data by the policy engine includes, but is not limited to, the following:
(1) the condition of the data source which is called currently, the condition of successful and failed calling, the calling of return data and the like;
(2) control information such as timeout control, cost control, concurrency control and the like, for example: the total overtime duration of the whole process, the overtime duration of a single interface, the total expense upper limit of the whole process, the expense of the current node, the maximum concurrent query number, how to control the concurrent cost and the like;
(3) alternative data source information, such as whether a certain data source calls abnormally, whether an alternative data source needs to be started, whether multiple or multiple levels of alternative data sources exist, which alternative business strategies need to be started synchronously when the alternative data source is started, and the like;
(4) the last instruction is issued to the query system for executing the condition, returning data information and the like;
(5) other useful data;
s2 comprehensive decision:
the decision engine carries out comprehensive decision through one or more combination modes of a decision tree, a policy table, a rule set, a scoring model and the like based on the collected 1-5 point data information, selects an optimal solution to give an optimal query suggestion, and synchronously starts a matched alternative service strategy in real time to carry out comprehensive decision if a certain data source calls abnormally;
s3 conveys an instruction:
the strategy engine converts the optimal query suggestion into a query command executable by the query system and conveys the query command to the query system, wherein the query command comprises:
(1) inquiring the interface name, and outputting a plurality of interfaces when the plurality of interfaces are inquired;
(2) inquiring the abnormal readjustment times, the overtime duration and the like of the interface;
(3) whether alternative data sources are started or not when the interface is abnormal is inquired, a plurality of alternative data sources are started, and the like;
(4) other instructions;
the S4 instruction performs:
s41 receives the instruction:
the query system provides an external data source interface to receive an execution instruction sent by the decision engine;
s42 executes the query:
the query system receives an execution instruction sent by the decision engine and executes the execution instruction one by one according to the requirement of the execution instruction;
the result of S43 returns:
and after the query system finishes executing the instruction, returning an execution result to the decision engine according to the interface standard, and waiting for receiving the next execution instruction.
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Citations (7)
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| CN104756111A (en) * | 2012-09-28 | 2015-07-01 | 甲骨文国际公司 | Tactical query to continuous query conversion |
| CN107609130A (en) * | 2017-09-18 | 2018-01-19 | 链家网（北京）科技有限公司 | A kind of method and server for selecting data query engine |
| CN109086031A (en) * | 2018-06-28 | 2018-12-25 | 阿里巴巴集团控股有限公司 | A kind of operational decision making method and apparatus of rule-based engine |
| CN110737430A (en) * | 2019-09-12 | 2020-01-31 | 中信百信银行股份有限公司 | Decision-making method and device |
| CN110929879A (en) * | 2019-11-06 | 2020-03-27 | 南方电网财务有限公司 | Business decision logic updating method based on decision engine and model platform |
| CN111125199A (en) * | 2019-12-30 | 2020-05-08 | 中国农业银行股份有限公司 | Database access method and device and electronic equipment |
-
2020
- 2020-06-18 CN CN202010560764.1A patent/CN111709647A/en active Pending
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102130965A (en) * | 2011-04-13 | 2011-07-20 | 北京邮电大学 | Method and system for dynamically combining services based on rule engine |
| CN104756111A (en) * | 2012-09-28 | 2015-07-01 | 甲骨文国际公司 | Tactical query to continuous query conversion |
| CN107609130A (en) * | 2017-09-18 | 2018-01-19 | 链家网（北京）科技有限公司 | A kind of method and server for selecting data query engine |
| CN109086031A (en) * | 2018-06-28 | 2018-12-25 | 阿里巴巴集团控股有限公司 | A kind of operational decision making method and apparatus of rule-based engine |
| CN110737430A (en) * | 2019-09-12 | 2020-01-31 | 中信百信银行股份有限公司 | Decision-making method and device |
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| CN111125199A (en) * | 2019-12-30 | 2020-05-08 | 中国农业银行股份有限公司 | Database access method and device and electronic equipment |
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