A data source dynamic management method based on policy engine control

Abstract

The present invention discloses a method for dynamic management of data sources based on policy engine control, including the following steps: S1 information collection; S2 comprehensive decision-making: the decision engine makes a comprehensive decision based on the above collected 1-5 point data information through one or more combinations of decision trees, policy tables, rule sets, scoring models, etc., selects the best solution to give the best query suggestion, and if a data source call is abnormal, the decision engine will synchronize and enable the matching alternative business strategy in real time to make a comprehensive decision; S3 convey instructions; S4 execute instructions. The present invention is based on the flexible configuration of business use as the starting point, abandons the traditional flexible configuration of IT systems as the starting point, integrates the dynamic adjustment of data sources and the dynamic adjustment of business strategies, and applies artificial intelligence to the dynamic management of data sources through an intelligent policy engine, which not only reduces the development and maintenance costs, but also improves the calling efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of data management, and in particular to a data source dynamic management method based on policy engine control. Background Art

[0002] With the widespread application of big data in the field of intelligent risk, dynamic management of data sources is crucial to meet the needs of real-time approval of big data risk control. There are currently two main ways to dynamically manage data sources: the first is the configuration table management method, which controls data source timeout, reset times, call sequence, etc. through the configuration table; the second is the configuration file management method, which controls data source timeout, reset times, call sequence, etc. through the configuration table. The third is the management tool / system method, which controls data source timeout, reset times, call sequence, concurrent calls, etc. through the development or purchase of special management tools / systems.

[0003] The disadvantage of the first and second methods is that it is impossible to achieve the coexistence of sequential calls and concurrent calls. Even if they can coexist, it is difficult to achieve flexible configuration. Each parameter adjustment seems to achieve flexible configuration of the interface, but in fact, each parameter adjustment or interface call exception must be synchronized with the business system to ensure that the business is not affected.

[0004] The third method combines parameter configuration and hard coding to achieve dynamic management of data sources, which is more flexible than the first and second methods. The disadvantages are that it is not smart enough, has high development or procurement costs, and high maintenance or secondary development costs. At the same time, a large amount of development of supporting business systems is required. Once an interface call is abnormal, the supporting business system needs to be adjusted synchronously to ensure that the business is not affected. The existing dynamic management method of data sources has achieved flexible configuration, but it is costly, mechanized, and cannot be intelligent. Summary of the invention

[0005] Based on the technical problems existing in the background technology, the present invention proposes a data source dynamic management method based on policy engine control.

[0006] The present invention proposes a data source dynamic management method based on policy engine control, comprising the following steps:

[0007] S1 Information Collection:

[0008] The data collected by the policy engine includes but is not limited to the following:

[0009] (1) The data source that has been called, the success or failure of the call, the call return data, etc.;

[0010] (2) Control information such as timeout control, cost control, and concurrency control, such as the total timeout duration of the entire process, the timeout duration of a single interface, the total cost limit of the entire process, the cost incurred by the current node, the maximum number of concurrent queries, how to control the concurrency cost, etc.;

[0011] (3) Alternative data source information, such as when a data source call is abnormal, whether an alternative data source needs to be enabled, whether there are multiple or multi-level alternative data sources, and which alternative business policies need to be enabled simultaneously when an alternative data source is enabled;

[0012] (4) The last instruction sent to the query system to check the execution status, returned data information, etc.;

[0013] (5) other useful data;

[0014] S2 comprehensive decision:

[0015] Based on the data information collected from points 1 to 5 above, the decision engine makes a comprehensive decision through one or more combinations of decision trees, strategy tables, rule sets, scoring models, etc., selects the best solution and gives the best query suggestion. If a data source call is abnormal, the decision engine will synchronously enable the supporting alternative business strategy in real time to make a comprehensive decision.

[0016] S3 conveys the command:

[0017] The policy engine converts the optimal query suggestion into a query command that can be executed by the query system and transmits it to the query system. The query command includes:

[0018] (1) Query the interface name and output multiple interfaces when querying multiple interfaces concurrently;

[0019] (2) Query the number of abnormal interface readjustments, timeout duration, etc.;

[0020] (3) Check whether to enable alternative data sources when an interface is abnormal, and how many alternative data sources to enable;

[0021] (4) other instructions;

[0022] S4 instruction execution:

[0023] S41 receives the command:

[0024] The query system provides an external data source interface to receive execution instructions issued by the decision engine;

[0025] S42 executes the query:

[0026] After receiving the execution instructions from the decision engine, the query system executes them one by one according to the execution instructions;

[0027] S43 result returned:

[0028] After executing the instruction, the query system returns the execution result to the decision engine according to the interface standard and waits to receive the next execution instruction.

[0029] In the present invention, a dynamic data source management method based on policy engine control is described. This method is based on the flexible configuration of business use as the starting point, abandons the traditional flexible configuration of IT system as the starting point, integrates the dynamic adjustment of data source and the dynamic adjustment of business strategy, and applies artificial intelligence to the dynamic management of data source through the intelligent policy engine, which not only reduces the development and maintenance costs, but also improves the calling efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 A flow chart of a method for dynamic data source management based on policy engine control proposed by the present invention. DETAILED DESCRIPTION

[0031] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0032] Reference Figure 1 , a data source dynamic management method based on policy engine control, comprising the following steps:

[0033] S1 Information Collection:

[0034] The data collected by the policy engine includes but is not limited to the following:

[0035] (1) The data source that has been called, the success or failure of the call, the call return data, etc.;

[0036] (2) Control information such as timeout control, cost control, and concurrency control, such as the total timeout duration of the entire process, the timeout duration of a single interface, the total cost limit of the entire process, the cost incurred by the current node, the maximum number of concurrent queries, how to control the concurrency cost, etc.;

[0037] (3) Alternative data source information, such as when a data source call is abnormal, whether an alternative data source needs to be enabled, whether there are multiple or multi-level alternative data sources, and which alternative business policies need to be enabled simultaneously when an alternative data source is enabled;

[0038] (4) The last instruction sent to the query system to check the execution status, returned data information, etc.;

[0039] (5) other useful data;

[0040] S2 comprehensive decision:

[0041] Based on the data information collected from points 1 to 5 above, the decision engine makes a comprehensive decision through one or more combinations of decision trees, strategy tables, rule sets, scoring models, etc., selects the best solution and gives the best query suggestion. If a data source call is abnormal, the decision engine will synchronously enable the supporting alternative business strategy in real time to make a comprehensive decision.

[0042] S3 conveys the command:

[0043] The policy engine converts the optimal query suggestion into a query command that can be executed by the query system and transmits it to the query system. The query command includes:

[0044] (1) Query the interface name and output multiple interfaces when querying multiple interfaces concurrently;

[0045] (2) Query the number of abnormal interface readjustments, timeout duration, etc.;

[0046] (3) Check whether to enable alternative data sources when an interface is abnormal, and how many alternative data sources to enable;

[0047] (4) other instructions;

[0048] S4 instruction execution:

[0049] S41 receives the command:

[0050] The query system provides an external data source interface to receive execution instructions issued by the decision engine;

[0051] S42 executes the query:

[0052] After receiving the execution instructions from the decision engine, the query system executes them one by one according to the execution instructions;

[0053] S43 result returned:

[0054] After executing the instruction, the query system returns the execution result to the decision engine according to the interface standard and waits to receive the next execution instruction.

[0055] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A data source dynamic management method based on policy engine control, characterized in that: The steps include: S1 Information Collection: The policy engine collects data including the following: (1) The data source that has been called, the success or failure of the call, and the call return data; (2) Timeout control, cost control, and / or concurrency control control information: total timeout duration of the entire process, timeout duration of a single interface, total cost cap of the entire process, costs incurred by the current node, maximum number of concurrent queries, and how to control concurrency costs; (3) Alternative data source information, including: if a data source call is abnormal, whether an alternative data source needs to be enabled, whether there are multiple or multi-level alternative data sources, and which alternative business policies need to be enabled simultaneously when enabling an alternative data source; (4) The last instruction was sent to the query system to check the execution status and return data information; S2 comprehensive decision: Based on the data information collected from points 1 to 4 above, the decision engine makes a comprehensive decision through one or more combinations of decision trees, strategy tables, rule sets, and scoring models, selects the best solution and gives the best query suggestion. If a data source call is abnormal, the decision engine will synchronously enable the supporting alternative business strategy in real time to make a comprehensive decision. S3 conveys the command: The policy engine converts the optimal query suggestion into a query command that can be executed by the query system and transmits it to the query system. The query command includes: (1) Query the interface name. When querying multiple interfaces concurrently, multiple interfaces are output; (2) Query the number of abnormal interface readjustments and timeout duration; (3) Check whether to enable alternative data sources when an interface is abnormal, and enable several alternative data sources; S4 instruction execution: S41 receives the command: The query system provides an external data source interface to receive execution instructions issued by the decision engine; S42 executes the query: After receiving the execution instructions from the decision engine, the query system executes them one by one according to the execution instructions; S43 result returned: After executing the instruction, the query system returns the execution result to the decision engine according to the interface standard and waits to receive the next execution instruction.

Images