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Legal status (The authorized standing is an assumption and is not a legal conclusion. Current Assignee (The listed assignees may be inaccurate. Priority date (The precedence date is an assumption and is not a authorized conclusion. The invention discloses a wind management knowledge tracking technique based mostly on a block chain, and relates to the technical field of knowledge tracking. The strategy comprises the next steps: building a block chain; earlier than loan implementation begins, the info source node uploads wind management knowledge to a server of a block chain, a credit evaluation node calculates a credit rating of a borrower, and then chain hyperlink is performed on the credit rating, and the mortgage node judges whether or not mortgage is released or not and chain link is carried out on a judgment consequence; in the mortgage implementation process, the loan node uploads the repayment report of a borrower to a server of the block chain; after the loan is carried out, inquiring whether overdue repayment and/or interrupted repayment exists or not, feeding again an inquiry result to the credit evaluation mannequin, and itagpro smart tracker sending the inquiry consequence to a server of the block chain for chaining; the credit score analysis model accommodates elements which might be affected by the query outcomes.

The invention can monitor the wind control information tracking of the entire loan process, so that the danger management is extra good and the loan reservation judgment is extra accurate. The invention relates to the technical area of data tracking, specifically to a block chain-based wind management information tracking methodology, terminal tools and a storage medium. In the standard wind management technology, expertise management is performed in a guide mode by the wind management workforce of every mechanism. However, with the steady development of web expertise, the entire society is vastly accelerated, and the normal wind management mode can't help the enterprise growth of the mechanism; the large information can be used for intelligently processing multidimensional and enormous quantity of knowledge, and batch and standardized execution processes can higher meet the event requirements of the wind management enterprise in the knowledge improvement period; increasingly intense business competitors is also the vital purpose for immediately's big data to control such fires.

Big knowledge wind management, particularly huge knowledge danger management, refers to the risk control and danger prompt of a borrower by utilizing a method of building a mannequin by big knowledge. Different from the original synthetic expertise sort wind control on the borrowing enterprise or the borrower, the massive data wind management for carrying out data modeling by buying numerous indexes of numerous borrowers or the borrowing enterprise is more scientific and efficient. However, the invention patent solely displays the data of the enterprise before the mortgage is carried out, however doesn't relate to the information monitoring after the implementation, and iTagPro Smart Tracker a suggestions mechanism after the mortgage is lacked, in order that the management on the chance just isn't perfect, and the scenario that the judgment is just not accurate enough usually happens. Therefore, the best way to develop a wind management knowledge monitoring technique capable of realizing the whole mortgage course of is certainly one of the problems to be solved urgently.

In order to unravel not less than one technical downside talked about within the background art, an object of the current invention is to offer a block chain-based mostly wind management data tracking methodology, a terminal system, and a storage medium, which might monitor wind control information monitoring of a mortgage full process, so that risk control is more complete and mortgage willpower is more accurate. Further, if a plurality of knowledge source nodes present the identical merchandise of wind management knowledge, setting confidence degrees aiming on the merchandise of wind management knowledge for the plurality of knowledge supply nodes respectively; when the credit score evaluation model calculates the credit score rating, choosing the credit score evaluation node with the highest confidence coefficient to offer corresponding item wind control data; and when the overdue fee and/or the interrupted payment exist in a certain information chain, performing confidence punishment on all knowledge source nodes collaborating in providing the wind management knowledge in the information chain, and/or when the overdue payment and the interrupted cost do not exist in the certain knowledge chain, performing confidence reward on all knowledge source nodes participating in providing the wind control data in the data chain.

Further, the arrogance penalty method is as follows: when overdue repayment exists in a certain information chain however interrupted repayment does not exist, decreasing the boldness levels of all data supply nodes participating in offering the wind management data in the info chain by a primary step length, and/or decreasing the boldness degrees of all data supply nodes collaborating in providing the wind management information in the info chain by a second step size when interrupted repayment exists within the certain data chain; the first step size is smaller than the second step dimension. Further, the arrogance reward method is as follows: and when the situations of overdue repayment and interrupted repayment don't exist in a certain knowledge chain, rising the boldness levels of all knowledge source nodes taking part in providing the wind control information in the info chain by a 3rd step length. Further, in the strategy of deciding on the credit evaluation node providing the wind control knowledge, one of the credit analysis nodes is chosen if the credit score analysis node with the highest confidence coefficient is supplied.