CN115660819B - Data source docking platform for credit decision - Google Patents
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Abstract
The invention provides a data source docking platform for credit decision, comprising: the data receiving end is used for receiving a request message sent by the user terminal based on the data source docking platform, splitting the request message and transmitting the split request message to the third-party data source system; the data butt joint end is used for analyzing the request message based on a third-party data source system, inquiring a credit decision data set based on an analysis result and feeding the credit decision data set back to the data source butt joint platform; the data feedback end is used for desensitizing the credit decision data set, splicing the desensitized credit decision data, and feeding back the splicing result to the user terminal. The request message of the user terminal is analyzed through the data source docking platform, so that the user terminal and the third party data source system are effectively interconnected and intercommunicated, the user terminal can conveniently and rapidly dock credit decision data, and the security of the credit decision data is ensured while the docking efficiency is ensured.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a data source docking platform for credit decision.
Background
At present, in the present day of the explosive development of the financial credit industry, credit data are fully analyzed and high-value data sources are deeply mined, so that customer risks can be comprehensively insight, risk decision is assisted, and the wind control capability is improved;
however, at present, when the credit data is acquired, the credit data is mostly called from a third party data source system, because a large amount of credit data exists in the third party data source system, the type and standard of the call are required to be definitely called, once the call type or standard deviates, the call effect of the credit data is greatly reduced, meanwhile, because the third party data source exists, the butt joint efficiency between the user terminal and the third party data source is slow, the credit data is easy to leak, and the safety coefficient of the credit data call is reduced;
accordingly, the present invention provides a data source docking platform for credit decisions.
Disclosure of Invention
The invention provides a data source docking platform for credit decision, which is used for analyzing a request message of a user terminal through the data source docking platform, realizing effective interconnection and interworking between the user terminal and a third party data source system, facilitating the rapid docking of credit decision data of the user terminal, and ensuring the security of the credit decision data while ensuring the docking efficiency.
The invention provides a data source docking platform for credit decision, comprising:
the data receiving end is used for receiving a request message sent by the user terminal based on the data source docking platform, splitting the request message and transmitting the split request message to the third-party data source system;
the data butt joint end is used for analyzing the request message based on a third-party data source system, inquiring a credit decision data set based on an analysis result and feeding the credit decision data set back to the data source butt joint platform;
the data feedback end is used for desensitizing the credit decision data set, splicing the desensitized credit decision data, and feeding back the splicing result to the user terminal.
Preferably, a data source docking platform for credit decision, the data receiving end includes:
the data receiving unit is used for acquiring credit decision business of the user, determining a target docking data type based on the credit decision business and generating an initial request message based on the target docking data type;
the message setting unit is used for adding a terminal identifier to the packet header of the initial request message and obtaining a final request message based on an addition result;
the message transmission unit is used for determining a communication link between the user terminal and the data source docking platform, sending a data docking request to the data source docking platform based on the communication link, and transmitting a request message to the data source docking platform based on the user terminal after the data source docking platform receives the docking request.
Preferably, a data source docking platform for credit decision, the data receiving end includes:
the message splitting unit is used for acquiring a received request message, analyzing the request message and determining a data type set to be docked contained in the request message;
the message splitting unit is used for extracting data characteristics of various data types to be docked in the data type set to be docked, and splitting the request message based on the data characteristics to obtain N sub-data blocks;
the transmission unit is used for acquiring configuration parameters of all preset interfaces in the third-party data source system and respectively carrying out format conversion on N sub-data blocks based on the configuration parameters, wherein the N sub-data blocks respectively correspond to the preset interfaces one by one;
the transmission unit is further configured to correspondingly transmit the N sub data blocks to a preset interface based on the format conversion result.
Preferably, a data source docking platform for credit decision, the message splitting unit comprises:
the message analysis subunit is used for acquiring an analysis result of the request message and extracting the identity information of the user terminal based on the analysis result;
The permission verification subunit is used for calling a preset network white list based on the data source docking platform and matching the identity information of the user terminal with each preset identity information in the preset network white list;
the verification subunit is further configured to determine that the user terminal has the right to dock with the third party data source system when the preset identity information matches with the identity information of the user terminal, and if not, determine that the user terminal does not have the right to dock with the third party data source system, and reject the request message of the user terminal.
Preferably, a data source docking platform for credit decision, the message analysis subunit comprises:
the identity information acquisition subunit is used for acquiring the identity information of the user terminal and extracting the identity characteristics of the identity information when the user terminal is judged to have the right to be in butt joint with the third-party data source system based on the identity information;
the private line construction subunit is used for determining the target requirement of the user terminal on the data butt joint quality based on the identity characteristics, and constructing a first network private line and a second network private line of the user terminal and the third-party data source system when the target requirement is higher than a preset threshold;
The special line marking subunit is configured to add a first network channel and a second network channel to a first network special line and a second network special line, and mark the first network special line and the second network special line based on an addition result, where the first network special line is configured to send a request message to a third party data source system, and the second network special line is configured to feed back credit decision data to a user terminal.
Preferably, a data source docking platform for credit decision, the data docking end comprising:
the message analysis unit is used for acquiring the received split request message, analyzing the request message and determining a credit decision data query request of the user terminal;
the query unit is used for extracting a target data index in a request message based on a credit decision data query request, and searching preset data in a third-party data source system through each data interface based on the target data index to obtain a credit decision data set;
and the data feedback unit is used for determining the data docking sequence of each data interface and feeding back the credit decision data set to the data source docking platform based on the data docking sequence.
Preferably, a data source docking platform for credit decisions, the querying element comprising:
The monitoring subunit is used for acquiring preset monitoring items, formulating a monitoring strategy based on the preset monitoring items, and carrying out real-time monitoring on the retrieval process of preset data in the third-party data source system by each data interface based on the monitoring strategy;
the monitoring result analysis subunit is used for transmitting the real-time monitoring result to the monitoring center, analyzing the real-time monitoring result based on a preset monitoring index, and judging whether the retrieval process meets a preset alarm condition based on the analysis result;
and the alarm subunit is used for determining the alarm type and calling a preset alarm rule to alarm based on the alarm type when the preset alarm condition is met.
Preferably, a data source docking platform for credit decision, the data feedback end comprises:
the data acquisition unit is used for acquiring a credit decision data set, carrying out similar data aggregation on the credit decision data set, converting each type of credit decision data into a target text based on a similar aggregation result, and carrying out inspection on each type of credit decision data based on a preset sensitive character inspection rule to determine a desensitization data set;
a data type determining unit for determining bytes of each desensitization data of the desensitization data set, extracting data characteristics of the desensitization data, and determining a target data type of the desensitization data based on the data characteristics;
The data desensitization unit is used for matching target desensitization rules from a desensitization rule base based on the target data type, determining a desensitization character string corresponding to the bytes of the desensitization data through the target desensitization rules based on the asynchronous thread, and replacing the bytes of the desensitization data through the desensitization character string based on the preset desensitization logic to finish the desensitization.
Preferably, a data source docking platform for credit decisions, the data desensitization unit comprising:
the data acquisition subunit is used for extracting the first character and the tail character of the desensitized credit decision data and determining the associated field between different credit decision data based on the first character and the tail character;
the data splicing subunit is used for pre-fusing the credit decision data based on the associated fields, determining compatible probability values between adjacent credit decision data based on the pre-fusion, judging that the splicing logic is accurate when the compatible probability values are larger than a preset threshold value, and generating a temporary data splicing file;
and the data screening subunit is used for carrying out forward and reverse characteristic self-adaptive differential matching on adjacent credit decision data in the temporary data splicing file to obtain an overlapped data set, screening the overlapped data in the overlapped data set and finishing the splicing of the desensitized credit decision data.
Preferably, a data source docking platform for credit decisions, the data screening subunit comprising:
the data feedback subunit is used for determining the data quantity of the spliced credit decision data and determining a return node of a transmission link between the data source docking platform and the user terminal based on the data quantity;
the data return subunit is used for sending a data uploading request to the return node based on the data source docking platform, uploading the spliced credit decision data to the return node after receiving the request, and feeding back the spliced credit decision data to the user terminal based on the return node;
and the data recording subunit is used for updating and recording the currently-butted credit decision data in the local log after the feedback is finished, and finishing the butting of the credit decision data.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims thereof as well as the appended drawings.
The technical scheme of the invention is further described in detail through the drawings and the embodiments.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention. In the drawings:
FIG. 1 is a block diagram of a data source docking platform for credit decisions in accordance with an embodiment of the present invention;
FIG. 2 is a block diagram of a data receiving end in a data source docking platform for credit decision in accordance with an embodiment of the present invention;
fig. 3 is a block diagram of a data docking end in a data source docking platform for credit decision in an embodiment of the present invention.
Detailed Description
The preferred embodiments of the present invention will be described below with reference to the accompanying drawings, it being understood that the preferred embodiments described herein are for illustration and explanation of the present invention only, and are not intended to limit the present invention.
Example 1:
the present embodiment provides a data source docking platform for credit decisions, as shown in fig. 1, comprising:
the data receiving end is used for receiving a request message sent by the user terminal based on the data source docking platform, splitting the request message and transmitting the split request message to the third-party data source system;
the data butt joint end is used for analyzing the request message based on a third-party data source system, inquiring a credit decision data set based on an analysis result and feeding the credit decision data set back to the data source butt joint platform;
The data feedback end is used for desensitizing the credit decision data set, splicing the desensitized credit decision data, and feeding back the splicing result to the user terminal.
In this embodiment, the data source docking platform is a platform for connecting the user terminal and the third party data source system, so as to implement interconnection and interworking between the user terminal and the third party data source system, thereby improving the utilization efficiency of data.
In this embodiment, the request message refers to information such as a data type that the user terminal needs to call and authentication sent to the management terminal.
In this embodiment, splitting the request message and transmitting the split request message to the third party data source system refers to splitting the message according to the request and transmitting the split request message to each data source interface of the third party data source system, so as to facilitate querying of the corresponding data source.
In this embodiment, the credit decision dataset refers to data obtained from different data source interfaces in the third party data source system.
In this embodiment, desensitizing the credit decision data set refers to transforming sensitive data in the credit decision data, thereby guaranteeing the security reliability of the credit decision data.
In this embodiment, stitching the desensitized credit decision data refers to combining the queried credit decision data according to a preset credit decision data call sequence, so as to facilitate feedback of complete credit decision data to the user terminal.
The beneficial effects of the technical scheme are as follows: the request message of the user terminal is analyzed through the data source docking platform, so that the user terminal and the third party data source system are effectively interconnected and intercommunicated, the user terminal can conveniently and rapidly dock credit decision data, and the security of the credit decision data is ensured while the docking efficiency is ensured.
Example 2:
on the basis of embodiment 1, this embodiment provides a data source docking platform for credit decision, as shown in fig. 2, the data receiving end includes:
the data receiving unit is used for acquiring credit decision business of the user, determining a target docking data type based on the credit decision business and generating an initial request message based on the target docking data type;
the message setting unit is used for adding a terminal identifier to the packet header of the initial request message and obtaining a final request message based on an addition result;
the message transmission unit is used for determining a communication link between the user terminal and the data source docking platform, sending a data docking request to the data source docking platform based on the communication link, and transmitting a request message to the data source docking platform based on the user terminal after the data source docking platform receives the docking request.
In this embodiment, the credit decision service refers to the type of credit decision that the user needs to make, thereby facilitating the determination of the type of data interfacing with the third party data source system.
In this embodiment, the target docking data type refers to a category of credit decision data corresponding to a credit decision service, in order to generate a corresponding request message, so as to facilitate docking with a third party data source system.
In this embodiment, the initial request message refers to a request message generated according to a data type to be docked, and no terminal identifier is added.
In this embodiment, the terminal identifier is a tag label for marking different terminal types or addresses, and the type or address of the terminal can be quickly and accurately determined through the identifier.
The beneficial effects of the technical scheme are as follows: the credit decision service of the user is analyzed, so that the type of the butted data is accurately judged, then, the terminal identification is added to the generated initial request message, the accuracy and the effectiveness of the request message are guaranteed, and finally, the butted request is sent to the data source butting platform, the request message is transmitted to the data source butting platform, and the safety and the accuracy corresponding to the credit decision data are guaranteed.
Example 3:
on the basis of embodiment 1, this embodiment provides a data source docking platform for credit decision, where the data receiving end includes:
the message splitting unit is used for acquiring a received request message, analyzing the request message and determining a data type set to be docked contained in the request message;
the message splitting unit is used for extracting data characteristics of various data types to be docked in the data type set to be docked, and splitting the request message based on the data characteristics to obtain N sub-data blocks;
the transmission unit is used for acquiring configuration parameters of all preset interfaces in the third-party data source system and respectively carrying out format conversion on N sub-data blocks based on the configuration parameters, wherein the N sub-data blocks respectively correspond to the preset interfaces one by one;
the transmission unit is further configured to correspondingly transmit the N sub data blocks to a preset interface based on the format conversion result.
In this embodiment, the set of data types to be docked refers to the type of credit decision data that needs to be invoked from a third party data source system.
In this embodiment, the data features refer to data feature values of each type of data to be docked, association relationships between data, and the like.
In this embodiment, the sub data block refers to splitting request data of different data types to be docked in the request message to obtain independent request parameters of various types.
In this embodiment, the preset interface is set in advance in the third party data source system, and is used for searching the corresponding credit decision data according to the request parameters in the request message.
In this embodiment, the configuration parameters refer to the format of receiving data by the preset interface, the speed of receiving or uploading data, and the like.
The beneficial effects of the technical scheme are as follows: the request message is analyzed, the type of data to be docked contained in the request message is determined, so that the request message is split, the sub data blocks obtained after the splitting are subjected to format conversion according to the configuration parameters of all preset interfaces in the third-room data source system, the request message is accurately and effectively transmitted to the third-party data source system, and meanwhile, the efficiency of data source docking is conveniently improved.
Example 4:
on the basis of embodiment 3, this embodiment provides a data source docking platform for credit decision, where the message splitting unit includes:
the message analysis subunit is used for acquiring an analysis result of the request message and extracting the identity information of the user terminal based on the analysis result;
The permission verification subunit is used for calling a preset network white list based on the data source docking platform and matching the identity information of the user terminal with each preset identity information in the preset network white list;
the verification subunit is further configured to determine that the user terminal has the right to dock with the third party data source system when the preset identity information matches with the identity information of the user terminal, and if not, determine that the user terminal does not have the right to dock with the third party data source system, and reject the request message of the user terminal.
In this embodiment, the identity information refers to the user name of the user terminal and the IP address of the user terminal.
In this embodiment, the preset network whitelist is set in advance for storing user information with a docking third party data source platform.
In this embodiment, the preset identity information is stored in a preset network whitelist, and is used to characterize the identity condition of the user terminal that can effectively interface with the third party data source system.
The beneficial effects of the technical scheme are as follows: the identity information of the user terminal is accurately and effectively confirmed from the analysis result of the request message, so that the accurate and reliable judgment of the docking authority of the user terminal is conveniently carried out according to the obtained identity information, the safety corresponding to credit decision data in a third-party data source system is ensured, and the docking effect is ensured.
Example 5:
on the basis of embodiment 4, this embodiment provides a data source docking platform for credit decision, and the packet analysis subunit includes:
the identity information acquisition subunit is used for acquiring the identity information of the user terminal and extracting the identity characteristics of the identity information when the user terminal is judged to have the right to be in butt joint with the third-party data source system based on the identity information;
the private line construction subunit is used for determining the target requirement of the user terminal on the data butt joint quality based on the identity characteristics, and constructing a first network private line and a second network private line of the user terminal and the third-party data source system when the target requirement is higher than a preset threshold;
the special line marking subunit is configured to add a first network channel and a second network channel to a first network special line and a second network special line, and mark the first network special line and the second network special line based on an addition result, where the first network special line is configured to send a request message to a third party data source system, and the second network special line is configured to feed back credit decision data to a user terminal.
In this embodiment, the identity may be a specific user name of the user terminal, so as to determine whether the user terminal is a special user, where the special user is a user having special requirements on the docking efficiency or the network quality.
In this embodiment, the target requirement refers to a specific requirement parameter of the speed or accuracy of the data docking of the user terminal.
In this embodiment, the preset threshold is set in advance, so as to measure whether the requirement of the user terminal on the data docking instruction exceeds the set range.
In this embodiment, the first network private line refers to a physical private line that connects the user terminal and the third party data source system, where the first network private line can only be used as a transmitting end by the user terminal.
In this embodiment, the second network private line refers to a physical private line that connects the third party data source system and the user terminal, where the second network private line can only be used by the third party data source system as a transmitting end.
In this embodiment, the first network channel and the second network channel are transmission media acting inside the first network dedicated line and the second network dedicated line, respectively, in order to ensure that data can be effectively transmitted.
In this embodiment, marking the first network dedicated line and the second network dedicated line refers to marking the first network dedicated line and the second network dedicated line with different marking symbols, so as to facilitate distinguishing specific roles of different network dedicated lines.
The beneficial effects of the technical scheme are as follows: the identity information of the user terminal is deeply analyzed, so that the requirement of the user terminal on the data docking quality is accurately judged, and when the requirement exceeds a preset threshold value, a network dedicated line of the user terminal and a third-party data source system is constructed, so that the efficiency and the safety of the data source docking of the user terminal and the third-party data source system are ensured.
Example 6:
on the basis of embodiment 1, this embodiment provides a data source docking platform for credit decision, as shown in fig. 3, where the data docking end includes:
the message analysis unit is used for acquiring the received split request message, analyzing the request message and determining a credit decision data query request of the user terminal;
the query unit is used for extracting a target data index in a request message based on a credit decision data query request, and searching preset data in a third-party data source system through each data interface based on the target data index to obtain a credit decision data set;
and the data feedback unit is used for determining the data docking sequence of each data interface and feeding back the credit decision data set to the data source docking platform based on the data docking sequence.
In this embodiment, the credit decision data query request is obtained by parsing a request message, and is used to characterize the data type of the user terminal that needs to be docked.
In this embodiment, the target data index refers to a search condition or a search requirement of data to be docked, which is included in the request message.
In this embodiment, the preset data is stored in advance in the third party data source.
In this embodiment, the data docking order is set in advance, and is used to normalize the calling order of the data in different interfaces.
In this embodiment, searching preset data in the third party data source system through each data interface based on the target data index includes:
acquiring a credit decision data query request, analyzing the credit decision data query request, and extracting data features in the credit decision data query request;
performing first retrieval on preset data in a third-party data source system through each data interface based on the data characteristics according to the target data indexes, determining the similarity between the preset data and the data labels, and summarizing the preset data with the similarity larger than a preset similarity threshold value to obtain an alternative data set;
extracting public field information and private field information of each preset data in the alternative data set, and carrying out second search on the alternative data set according to the public field information and the private field information based on the data characteristics to determine the Hamming distance between each preset data and the data characteristics in the alternative data set;
and judging the preset data with the hamming distance smaller than the preset distance threshold value as a credit decision data set corresponding to the credit decision data query request.
The data features refer to indexes which are corresponding to the credit decision data query requests and need to be subjected to data retrieval, and specifically can be the type of data, the value condition of the data and the like.
The first search refers to coarse search of preset data in the third party data source system, so as to reduce the range of data analysis, thereby facilitating improvement of search efficiency.
The preset similarity threshold is set in advance, and is used for measuring whether the similarity between preset data and data features in the third-party data source system can meet the requirement of retrieval.
The candidate data set refers to preset data with the similarity greater than a preset similarity threshold after the first retrieval.
The public field information and the private field information refer to composition components of each preset data in the alternative data set, so that the purpose is to further analyze the preset data and guarantee the retrieval accuracy.
The second search refers to determining a hamming distance between the preset data and the data feature, and the smaller the distance is, the more similar the preset data and the data feature are.
The preset distance threshold is set in advance.
In this embodiment, feeding back credit decision data sets to a data source docking platform based on a data docking order includes:
Obtaining the retrieval speed of each data interface for retrieving preset data in the third-party data source system, calculating the time length value for obtaining the credit decision data set from the third-party data source system based on the retrieval speed, and calculating the docking efficiency of the data source docking platform and the third-party data source system for the credit decision data set based on the time length value, wherein the specific steps comprise:
calculating a time length value for acquiring a credit decision dataset from a third party data source system according to the formula:
wherein T represents a time length value of acquiring a credit decision dataset from a third party data source system; max { · } represents taking the maximum function; i represents the number of the current data interfaces, and the value range is [1, n ]]The method comprises the steps of carrying out a first treatment on the surface of the n represents the total number of data interfaces; m is M i Representing the credit decision data quantity contained in a search library corresponding to the ith data interface in the third party data source system; v (V) i Representing a speed value of the ith data interface in the third party data source system for retrieving credit decision data contained in a corresponding retrieval library; t represents a corresponding buffer time length value of the data source docking platform made by the third party data source system;
calculating the docking efficiency of the data source docking platform and the third party data source system for the credit decision data set according to the following formula:
Wherein η represents a docking efficiency between the data source docking platform and the third party data source system for the credit decision data set; mu represents an error factor, and the value range is (0.01, 0.05); t represents a time length value of acquiring a credit decision data set from a third party data source system; k (k) i Representing the data volume of credit decision data obtained after the i-th data interface is searched, and the value is smaller than M i ；w i Representing the data source docking speed of the ith data interface and the data source docking platform; s represents an expected docking time length value;
comparing the calculated docking efficiency with a preset efficiency threshold;
if the docking efficiency is greater than or equal to a preset efficiency threshold, judging that the docking efficiency of the credit decision data set is qualified;
otherwise, judging that the docking efficiency of the credit decision data set is unqualified, and optimizing the docking measures between the data source docking platform and the third-party data source system until the docking efficiency is greater than or equal to a preset efficiency threshold.
The preset efficiency threshold is set in advance and is used for measuring whether the docking efficiency of the credit decision data set meets the expected requirement.
The expected docking time length value is preset in advance and can be adjusted.
The beneficial effects of the technical scheme are as follows: the method has the advantages that the split request message is analyzed, so that the credit decision data query request is effectively acquired, the corresponding credit decision data set is conveniently and accurately queried from the third-party data source system, meanwhile, the docking sequence of each data interface is determined, a data source docking platform for accurately and effectively feeding back the queried credit decision data set is realized, the accuracy of data docking is improved, and the docking effect is guaranteed.
Example 7:
on the basis of embodiment 6, this embodiment provides a data source docking platform for credit decision, the query unit includes:
the monitoring subunit is used for acquiring preset monitoring items, formulating a monitoring strategy based on the preset monitoring items, and carrying out real-time monitoring on the retrieval process of preset data in the third-party data source system by each data interface based on the monitoring strategy;
the monitoring result analysis subunit is used for transmitting the real-time monitoring result to the monitoring center, analyzing the real-time monitoring result based on a preset monitoring index, and judging whether the retrieval process meets a preset alarm condition based on the analysis result;
and the alarm subunit is used for determining the alarm type and calling a preset alarm rule to alarm based on the alarm type when the preset alarm condition is met.
In this embodiment, the preset monitoring item refers to a time type required to monitor the retrieval process, and specifically may be a network running condition, a feedback speed of a query request to a user terminal, a query accuracy of a third-party data source system to credit decision data, and the like.
In this embodiment, the monitoring policy refers to a method of monitoring a preset monitoring item.
In this embodiment, the preset monitoring index is obtained in advance, and is a reference basis for analyzing the real-time monitoring result, and specifically may be a safe value range of each preset monitoring item.
In this embodiment, determining whether the search process meets the preset alarm condition based on the analysis result refers to determining that the preset alarm condition is met when the value parameter of the monitoring result of the preset monitoring items in the real-time monitoring result is greater than the safety threshold range, where the preset alarm condition is set in advance and is used to characterize the safety operation interval of each preset monitoring item.
In this embodiment, the alarm types are in one-to-one correspondence with the preset monitoring item types, and different preset monitoring items are corresponding to different alarm modes.
In this embodiment, the preset alarm rule is set in advance, and specifically may be that alarm information is sent to only the terminal of the related person and physical alarm is performed synchronously, where the physical alarm includes sound and light alarm.
The beneficial effects of the technical scheme are as follows: the third-party data source system is used for monitoring the retrieval process of the preset data in real time, and determining the abnormal type in time when the abnormality exists in the butt joint process, and alarming the abnormal type through the preset alarming rule, so that the safety of the butt joint of the data sources is ensured, and the reliability of the butt joint of the data sources is improved.
Example 8:
on the basis of embodiment 1, this embodiment provides a data source docking platform for credit decision, and the data feedback end includes:
the data acquisition unit is used for acquiring a credit decision data set, carrying out similar data aggregation on the credit decision data set, converting each type of credit decision data into a target text based on a similar aggregation result, and carrying out inspection on each type of credit decision data based on a preset sensitive character inspection rule to determine a desensitization data set;
a data type determining unit for determining bytes of each desensitization data of the desensitization data set, extracting data characteristics of the desensitization data, and determining a target data type of the desensitization data based on the data characteristics;
the data desensitization unit is used for matching target desensitization rules from a desensitization rule base based on the target data type, determining a desensitization character string corresponding to the bytes of the desensitization data through the target desensitization rules based on the asynchronous thread, and replacing the bytes of the desensitization data through the desensitization character string based on the preset desensitization logic to finish the desensitization.
In this embodiment, the same class of data aggregation refers to categorizing a credit decision data set and aggregating the same class of data into the same set.
In this embodiment, the target text refers to converting each type of credit decision data into a corresponding text form, so as to facilitate determining the value, constituent components and morphological characteristics of the data.
In this embodiment, a preset rule for checking sensitive characters is set in advance, and is used for checking target text of each type of credit decision data, and judging whether there is a sensitive vocabulary therein, where the sensitive vocabulary may be a name, an identification card number, or the like.
In this embodiment, the desensitization data set refers to sensitive data contained in the credit decision data set, i.e., data that needs to be desensitized.
In this embodiment, a byte refers to the smallest constituent unit of desensitized data, i.e., each character that makes up the desensitized data.
In this embodiment, the data features refer to the association relationship between the data of the desensitized data and the specific value condition.
In this embodiment, the target data type refers to the data type to which the desensitized data corresponds.
In this embodiment, the target desensitization rule refers to a manner or method suitable for desensitizing the current desensitization data.
In this embodiment, asynchronous threads refer to synchronous desensitization processing of desensitized data contained in different classes, so as to facilitate improving the desensitization efficiency.
In this embodiment, the desensitization character string refers to a character capable of replacing bytes in the desensitization data, and is identical to the content of the bytes of the desensitization data, and is different in form.
In this embodiment, the preset desensitization logic is set in advance, specifically, a mode or a method for replacing bytes in desensitization data, so as to ensure the desensitization effect.
The beneficial effects of the technical scheme are as follows: the credit decision data are subjected to similar aggregation, so that each type of credit decision data are conveniently converted into corresponding target texts, desensitization data are conveniently determined, meanwhile, the efficiency and the accuracy of determining desensitization rules are improved, and secondly, after the desensitization rules are determined, the desensitization data are subjected to desensitization processing, so that the desensitization effect of the desensitization data is guaranteed, and the security of butt joint of the credit decision data is improved.
Example 9:
on the basis of embodiment 8, this embodiment provides a data source docking platform for credit decisions, the data desensitization unit comprising:
the data acquisition subunit is used for extracting the first character and the tail character of the desensitized credit decision data and determining the associated field between different credit decision data based on the first character and the tail character;
The data splicing subunit is used for pre-fusing the credit decision data based on the associated fields, determining compatible probability values between adjacent credit decision data based on the pre-fusion, judging that the splicing logic is accurate when the compatible probability values are larger than a preset threshold value, and generating a temporary data splicing file;
and the data screening subunit is used for carrying out forward and reverse characteristic self-adaptive differential matching on adjacent credit decision data in the temporary data splicing file to obtain an overlapped data set, screening the overlapped data in the overlapped data set and finishing the splicing of the desensitized credit decision data.
In this embodiment, the first and last characters refer to the specific content of the front-end data and the back-end data of the credit decision data after desensitization.
In this embodiment, the association field refers to data in which there is an association relationship between different credit decision data.
In this embodiment, pre-fusion refers to coarse stitching of different credit decision data according to an association.
In this embodiment, the compatibility probability value characterizes the degree of fusion between adjacent credit decision data after rough stitching.
In this embodiment, the preset threshold is set in advance, and is used to measure whether the splice of the credit decision data is qualified, so that adjustment can be performed.
In this embodiment, the temporary data splicing file refers to a splicing result obtained after the credit decision data is spliced.
In this embodiment, performing forward and reverse feature adaptive differential matching on adjacent credit decision data refers to performing refinement processing on the splice results of the adjacent credit decision data, so as to facilitate determination of coincidence data between the adjacent credit decision data.
In this embodiment, filtering overlapping data in overlapping data sets refers to removing one copy of overlapping data sets, leaving only one copy of the same data.
The beneficial effects of the technical scheme are as follows: by analyzing the first character and the last character of the desensitized credit decision data, the correlation fields among the credit decision data are conveniently determined, and the credit decision data are fused and spliced according to the correlation fields, so that the splicing accuracy and the splicing effect of the credit decision data are ensured, accurate and reliable credit decision data are conveniently provided for a user terminal, and the data source docking effect is ensured.
Example 10:
on the basis of embodiment 9, this embodiment provides a data source docking platform for credit decision, the data screening subunit comprising:
The data feedback subunit is used for determining the data quantity of the spliced credit decision data and determining a return node of a transmission link between the data source docking platform and the user terminal based on the data quantity;
the data return subunit is used for sending a data uploading request to the return node based on the data source docking platform, uploading the spliced credit decision data to the return node after receiving the request, and feeding back the spliced credit decision data to the user terminal based on the return node;
and the data recording subunit is used for updating and recording the currently-butted credit decision data in the local log after the feedback is finished, and finishing the butting of the credit decision data.
In this embodiment, the backhaul node refers to a node for transmitting credit decision data.
In this embodiment, the local log is used to record the situation that the user terminal performs data source interfacing with the third party data source system.
The beneficial effects of the technical scheme are as follows: and finally, after the feedback is finished, the current data source docking result is recorded and stored in a local log, so that the accurate and effective recording of the data source docking condition is ensured, and the accuracy and the safety of the data source docking are ensured.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Claims (9)
1. A data source docking platform for credit decisions, comprising:
the data receiving end is used for receiving a request message sent by the user terminal based on the data source docking platform, splitting the request message and transmitting the split request message to the third-party data source system;
the data butt joint end is used for analyzing the request message based on a third-party data source system, inquiring a credit decision data set based on an analysis result and feeding the credit decision data set back to the data source butt joint platform;
the data feedback end is used for desensitizing the credit decision data set, splicing the desensitized credit decision data, and feeding back the splicing result to the user terminal;
wherein, the data butt joint end includes:
the message analysis unit is used for acquiring the received split request message, analyzing the request message and determining a credit decision data query request of the user terminal;
The query unit is used for extracting a target data index in a request message based on a credit decision data query request, and searching preset data in a third-party data source system through each data interface based on the target data index to obtain a credit decision data set;
the data feedback unit is used for determining the data docking sequence of each data interface and feeding back a credit decision data set to the data source docking platform based on the data docking sequence;
wherein, searching preset data in a third party data source system through each data interface based on the target data index comprises the following steps:
acquiring a credit decision data query request, analyzing the credit decision data query request, and extracting data features in the credit decision data query request;
performing first retrieval on preset data in a third-party data source system through each data interface based on the data characteristics according to the target data indexes, determining the similarity between the preset data and the data labels, and summarizing the preset data with the similarity larger than a preset similarity threshold value to obtain an alternative data set;
extracting public field information and private field information of each preset data in the alternative data set, and carrying out second search on the alternative data set according to the public field information and the private field information based on the data characteristics to determine the Hamming distance between each preset data and the data characteristics in the alternative data set;
And judging the preset data with the hamming distance smaller than the preset distance threshold value as a credit decision data set corresponding to the credit decision data query request.
2. The data source docking platform for credit decisions of claim 1, wherein the data receiving end comprises:
the data receiving unit is used for acquiring credit decision business of the user, determining a target docking data type based on the credit decision business and generating an initial request message based on the target docking data type;
the message setting unit is used for adding a terminal identifier to the packet header of the initial request message and obtaining a final request message based on an addition result;
the message transmission unit is used for determining a communication link between the user terminal and the data source docking platform, sending a data docking request to the data source docking platform based on the communication link, and transmitting a request message to the data source docking platform based on the user terminal after the data source docking platform receives the docking request.
3. The data source docking platform for credit decisions of claim 1, wherein the data receiving end comprises:
the message splitting unit is used for acquiring a received request message, analyzing the request message and determining a data type set to be docked contained in the request message;
The message splitting unit is used for extracting data characteristics of various data types to be docked in the data type set to be docked, and splitting the request message based on the data characteristics to obtain N sub-data blocks;
the transmission unit is used for acquiring configuration parameters of all preset interfaces in the third-party data source system and respectively carrying out format conversion on N sub-data blocks based on the configuration parameters, wherein the N sub-data blocks respectively correspond to the preset interfaces one by one;
the transmission unit is further configured to correspondingly transmit the N sub data blocks to a preset interface based on the format conversion result.
4. A data source docking platform for credit decisions according to claim 3, characterized in that the message splitting unit comprises:
the message analysis subunit is used for acquiring an analysis result of the request message and extracting the identity information of the user terminal based on the analysis result;
the permission verification subunit is used for calling a preset network white list based on the data source docking platform and matching the identity information of the user terminal with each preset identity information in the preset network white list;
the verification subunit is further configured to determine that the user terminal has the right to dock with the third party data source system when the preset identity information matches with the identity information of the user terminal, and if not, determine that the user terminal does not have the right to dock with the third party data source system, and reject the request message of the user terminal.
5. The data source docking platform for credit decisions of claim 4, wherein the message analysis subunit comprises:
the identity information acquisition subunit is used for acquiring the identity information of the user terminal and extracting the identity characteristics of the identity information when the user terminal is judged to have the right to be in butt joint with the third-party data source system based on the identity information;
the private line construction subunit is used for determining the target requirement of the user terminal on the data butt joint quality based on the identity characteristics, and constructing a first network private line and a second network private line of the user terminal and the third-party data source system when the target requirement is higher than a preset threshold;
the special line marking subunit is configured to add a first network channel and a second network channel to a first network special line and a second network special line, and mark the first network special line and the second network special line based on an addition result, where the first network special line is configured to send a request message to a third party data source system, and the second network special line is configured to feed back credit decision data to a user terminal.
6. A data source docking platform for credit decisions according to claim 1, characterized in that the querying element comprises:
The monitoring subunit is used for acquiring preset monitoring items, formulating a monitoring strategy based on the preset monitoring items, and carrying out real-time monitoring on the retrieval process of preset data in the third-party data source system by each data interface based on the monitoring strategy;
the monitoring result analysis subunit is used for transmitting the real-time monitoring result to the monitoring center, analyzing the real-time monitoring result based on a preset monitoring index, and judging whether the retrieval process meets a preset alarm condition based on the analysis result;
and the alarm subunit is used for determining the alarm type and calling a preset alarm rule to alarm based on the alarm type when the preset alarm condition is met.
7. The data source docking platform for credit decisions of claim 1, wherein the data feedback end comprises:
the data acquisition unit is used for acquiring a credit decision data set, carrying out similar data aggregation on the credit decision data set, converting each type of credit decision data into a target text based on a similar aggregation result, and carrying out inspection on each type of credit decision data based on a preset sensitive character inspection rule to determine a desensitization data set;
a data type determining unit for determining bytes of each desensitization data of the desensitization data set, extracting data characteristics of the desensitization data, and determining a target data type of the desensitization data based on the data characteristics;
The data desensitization unit is used for matching target desensitization rules from a desensitization rule base based on the target data type, determining a desensitization character string corresponding to the bytes of the desensitization data through the target desensitization rules based on the asynchronous thread, and replacing the bytes of the desensitization data through the desensitization character string based on the preset desensitization logic to finish the desensitization.
8. The data source docking platform for credit decisions of claim 7, wherein the data desensitization unit comprises:
the data acquisition subunit is used for extracting the first character and the tail character of the desensitized credit decision data and determining the associated field between different credit decision data based on the first character and the tail character;
the data splicing subunit is used for pre-fusing the credit decision data based on the associated fields, determining compatible probability values between adjacent credit decision data based on the pre-fusion, judging that the splicing logic is accurate when the compatible probability values are larger than a preset threshold value, and generating a temporary data splicing file;
and the data screening subunit is used for carrying out forward and reverse characteristic self-adaptive differential matching on adjacent credit decision data in the temporary data splicing file to obtain an overlapped data set, screening the overlapped data in the overlapped data set and finishing the splicing of the desensitized credit decision data.
9. The data source docking platform for credit decisions of claim 8, wherein the data screening subunit comprises:
the data feedback subunit is used for determining the data quantity of the spliced credit decision data and determining a return node of a transmission link between the data source docking platform and the user terminal based on the data quantity;
the data return subunit is used for sending a data uploading request to the return node based on the data source docking platform, uploading the spliced credit decision data to the return node after receiving the request, and feeding back the spliced credit decision data to the user terminal based on the return node;
and the data recording subunit is used for updating and recording the currently-butted credit decision data in the local log after the feedback is finished, and finishing the butting of the credit decision data.
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