CA3179620A1

CA3179620A1 - Method and system for querying abnormal financial data on basis of knowledge map

Info

Publication number: CA3179620A1
Application number: CA3179620A
Authority: CA
Inventors: Cen Lu
Original assignee: 10353744 Canada Ltd
Current assignee: 10353744 Canada Ltd
Priority date: 2018-12-25
Filing date: 2019-09-18
Publication date: 2020-07-02
Also published as: CA3230500A1; WO2020134213A1; CN109491995A

Abstract

Provided are a method and system for querying abnormal financial data on the basis of a knowledge map; a knowledge map means is used to accurately and quickly identify abnormal financial data therein. The method comprises: designing the structural composition of a map database according to query requirements concerning abnormal financial data, said structural composition comprising an expression of nodes and the relationships between the nodes; collecting data from a plurality of sample sources, and, after cleaning the data, obtaining a plurality of sample data conforming to the structural composition of the map database; importing the sample data into the map database to output a knowledge map, then finding abnormal financial data from the knowledge map.

Description

METHOD AND SYSTEM FOR QUERYING ABNORMAL FINANCIAL DATA ON
BASIS OF KNOWLEDGE MAP
BACKGROUND OF THE INVENTION
Technical Field [0001] The present invention relates to the field of financial anti-fraud technology, and more particularly to a method of and a system for enquiring abnormal financial data based on a knowledge map.
Description of Related Art

[0002] With the development of online finance, loan intermediary has gradually come into being, the middlemen fabricate user materials for populations on the blacklist or the whitelist of credit investigation who would usually find it very hard to pass loan approval, and help them cunningly evade risk control of platforms. Since such customers are mostly not in the capability to normally pay back the loan, loaning to them may engender bad loans for financial platforms and cause capital losses to the financial platforms. Accordingly, to eliminate such fraudulent behaviors, it is of great importance as how to identify the fraudulent behaviors.

[0003] Telephone follow-up or twice confirmation of identification is mainly employed in the state of the art to identify fraudulent behaviors, but it is found in practical application that although the above modes can achieve certain identifying effects with respect to simply answered fraudulent behaviors, it is very difficult for the mode of telephone follow-up or twice confirmation of identification to accurately identify elaborately fabricated fraudulent behaviors, as such behaviors involve a complicated relational network. Therefore, a new challenge has been raised for the identification of fraudulent behaviors.
SUMMARY OF THE INVENTION

Date Regue/Date Received 2022-06-27

[0004] An objective of the present invention it is to provide a method of and a system for enquiring abnormal financial data based on a knowledge map, whereby abnormal financial data is accurately and quickly identified by means of a knowledge map.

[0005] In order to achieve the above objective, according to one aspect, the present invention provides a method of enquiring abnormal financial data based on a knowledge map, the method comprises:

[0006] designing structural constitution of a map database according to query requirement of abnormal financial data, wherein the structural constitution includes expressions of internode relations and nodes;

[0007] collecting plural pieces of sample source data, and subjecting the same to data cleaning to obtain plural pieces of sample data that conform to the structural constitution of the map database; and

[0008] importing the sample data to the map database to output a knowledge map, and thereafter searching out the abnormal financial data from the knowledge map.

[0009] Preferably, the steps of designing structural constitution of a map database according to query requirement of abnormal financial data include:

[0010] the query requirement of abnormal financial data including to search out illegitimate middleman information from registration information of plural lenders, wherein the registration information of the lenders includes lender information, contact information, transferor information and/or addressee information, and the information includes name data, telephone data, and identification code data; and

[0011] correspondingly setting plural node types based on plural data types, and designing the map database in accordance with a principle of one node corresponding to one piece of data.

[0012] Preferably, the steps of collecting plural pieces of sample source data, and subjecting Date Regue/Date Received 2022-06-27 the same to data cleaning to obtain plural pieces of sample data that conform to the structural constitution of the map database include:

[0013] obtaining plural pieces of lender registration information from a database, and extracting lender information, contact information, transferor information, and/or addressee information from each piece of lender registration information to serve as sample source data;

[0014] preliminarily screening the sample source data, and eliminating any sample source data that contains no name data, telephone data, or identification code data;

[0015] duplicate-checking the remaining sample source data, and deleting repetitive sample source data; and

[0016] subjecting the duplicate-checked sample source data to legitimacy verification, removing any sample source data whose telephone data and/or identification code data are/is invalid, and finally retaining valid sample data.

[0017] Optionally, a method of identifying the telephone data and/or the identification code data as invalid is:

[0018] comparing whether the telephone data and/or the identification code data are/is consistent with a standard telephone number and/or a standard identification code in length to judge whether the telephone data and/or the identification code data are/is invalid.

[0019] Preferably, a method of identifying abnormal financial data from the knowledge map includes:

[0020] employing a Cypher language to preset plural abnormal financial data enquiring statements, including an abnormal name enquiring statement, an abnormal telephone enquiring statement, or an abnormal identification code enquiring statement;

[0021] setting up the abnormal name enquiring statement, the abnormal telephone enquiring statement, or the abnormal identification code enquiring statement on an enquiring interface in a modular form, so that a user correspondingly selects enquiring statement Date Regue/Date Received 2022-06-27 input according to the query requirement of the abnormal financial data;

[0022] scatteredly spreading out the plural pieces of sample data in the form of nodes, and associatively forming relational nodes into a knowledge map through indicator lines;
and

[0023] screening out relational nodes from the knowledge map according to an input enquiring statement, and then searching out illegitimate middleman information from the screened relational nodes.

[0024] Optionally, the step of screening out relational nodes from the knowledge map according to an input enquiring statement, and then searching out illegitimate middleman information from the screened relational nodes includes:

[0025] setting an abnormal node identifying threshold, outputting any node consistent in type with the enquiring statement in the relational nodes, when a correlation degree of the relational nodes is greater than the threshold, and obtaining a query result of the illegitimate middleman information.

[0026] Exemplarily, the correlation degree is defined and obtained according to the number of the indicator lines connected with the nodes.

[0027] In comparison with prior-art technology, the method of enquiring abnormal financial data based on a knowledge map provided by the present invention achieves the following advantageous effects.

[0028] In the method of enquiring abnormal financial data based on a knowledge map provided by the present invention, it is firstly needed to design the structural constitution of the map database according to the user's query requirement of abnormal financial data, when the query requirement of abnormal financial data is to enquire illegitimate middleman information from lenders, considering that the illegitimate middleman information obtainable by a platform not only includes names, but also includes such Date Regue/Date Received 2022-06-27 valid identification information as their telephones and identification codes, etc., so three types of nodes can be adopted in designing the structural constitution of the map database, with one node representing one piece of information data; relational nodes employ the mode of association by indicator lines to correspondingly design the structural constitution of the map database, plural pieces of sample source data are thereafter collected from the platfoim, a CSV file identifiable by the map database is formed after data cleaning, the CSV file is finally imported to the map database to construct a knowledge map of sample data, nodes whose correlation degree is higher than a threshold are screened out of the knowledge map, and corresponding information data in the nodes are extracted and output as abnormal financial data, e.g., such valid identification data as names, telephones or identification codes of illegitimate middlemen.

[0029] Seen as such, the present invention employs the mode of inputting great quantities of sample data into the map database to form a knowledge map to identify abnormal financial data, and utilizes the characteristic of the knowledge map that is good at processing complicated network relations to express plural pieces of sample data with a structured network, so as to quickly and accurately identify abnormal financial data therefrom.

[0030] According to the other aspect, the present invention provides a system for enquiring abnormal financial data based on a knowledge map, the system is applied to the method of enquiring abnormal financial data based on a knowledge map as recited in the foregoing technical solutions, and comprises:

[0031] a map designing unit, for designing structural constitution of a map database according to query requirement of abnormal financial data, wherein the structural constitution includes expressions of internode relations and nodes;

[0032] a sample collecting unit, for collecting plural pieces of sample source data, and subjecting the same to data cleaning to obtain plural pieces of sample data that conform Date Regue/Date Received 2022-06-27 to the structural constitution of the map database; and

[0033] an identifying and outputting unit, for importing the sample data to the map database to output a knowledge map, and thereafter searching out the abnormal financial data from the knowledge map.

[0034] Preferably, the sample collecting unit includes:

[0035] an information collecting module, for obtaining plural pieces of lender registration information from a database, and extracting lender information, contact information, transferor information, and/or addressee information from each piece of lender registration information to serve as sample source data;

[0036] a screening module, for preliminarily screening the sample source data, and eliminating any sample source data that contains no name data, telephone data, or identification code data;

[0037] a duplicate-checking module, for duplicate-checking the remaining sample source data, and deleting repetitive sample source data; and

[0038] a verifying module, for subjecting the duplicate-checked sample source data to legitimacy verification, removing any sample source data whose telephone data and/or identification code data are/is invalid, and finally retaining valid sample data.

[0039] Preferably, the identifying and outputting unit includes:

[0040] a pre-storing module, for employing a Cypher language to preset plural abnormal financial data enquiring statements, including an abnormal name enquiring statement, an abnormal telephone enquiring statement, or an abnormal identification code enquiring statement;

[0041] a setting module, for setting up the abnormal name enquiring statement, the abnormal telephone enquiring statement, or the abnormal identification code enquiring statement on an enquiring interface in a modular form, so that a user correspondingly selects enquiring statement input according to the query requirement of the abnormal financial data;

Date Regue/Date Received 2022-06-27

[0042] a processing module, for scatteredly spreading out the plural pieces of sample data in the form of nodes, and associatively forming relational nodes into a knowledge map through indicator lines; and

[0043] a query outputting module, for screening out relational nodes from the knowledge map according to an input enquiring statement, then identifying abnormal financial data from the screened relational nodes and outputting the same in the form of a query result.

[0044] In comparison with prior-art technology, the advantageous effects achieved by the system for enquiring abnormal financial data based on a knowledge map provided by the present invention are identical with the advantageous effects achievable by the method of enquiring abnormal financial data based on a knowledge map provided by the foregoing technical solution, so these are not redundantly described in this context.
BRIEF DESCRIPTION OF THE DRAWINGS

[0045] The drawings described here are meant to provide further understanding of the present invention, and constitute a part of the present invention. The exemplary embodiments of the present invention and the descriptions thereof are meant to explain the present invention, rather than to restrict the present invention. In the drawings:

[0046] Fig. 1 is a flowchart schematically illustrating the method of enquiring abnormal financial data based on a knowledge map in Embodiment 1 of the present invention;
and

[0047] Fig. 2 is a block diagram illustrating the structure of the system for enquiring abnormal financial data based on a knowledge map in Embodiment 2 of the present invention.

[0048] Reference numerals:

[0049] 1 ¨ map designing unit 2 ¨ sample collecting unit

[0050] 3 ¨ identifying and outputting unit 21 ¨ information collecting module Date Regue/Date Received 2022-06-27

[0051] 22 ¨ screening module 23 ¨ duplicate-checking module

[0052] 24 ¨ verifying module 31 ¨ pre-storing module

[0053] 32 ¨ setting module 33 ¨ processing module

[0054] 34¨ query outputting module DETAILED DESCRIPTION OF THE INVENTION

[0055] To make the objectives, features and advantages of the present invention more lucid and clear, the technical solutions in the embodiments of the present invention are clearly and comprehensively described below with reference to the accompanying drawings in the embodiments of the present invention. Apparently, the embodiments as described are merely partial, rather than the entire, embodiments of the present invention. All other embodiments obtainable by persons ordinarily skilled in the art on the basis of the embodiments in the present invention without spending creative effort shall all fall within the protection scope of the present invention.

[0056] Embodiment 1

[0057] Fig. 1 is a flowchart schematically illustrating the method of enquiring abnormal financial data based on a knowledge map in Embodiment 1 of the present invention.
Please refer to Fig. 1, this embodiment provides a method of enquiring abnormal financial data based on a knowledge map, the method comprises:

[0058] designing structural constitution of a map database according to query requirement of abnormal financial data, wherein the structural constitution includes expressions of internode relations and nodes; collecting plural pieces of sample source data, and subjecting the same to data cleaning to obtain plural pieces of sample data that conform to the structural constitution of the map database; and importing the sample data to the map database to output a knowledge map, and thereafter searching out the abnormal financial data from the knowledge map.

Date Regue/Date Received 2022-06-27

[0059] In the method of enquiring abnormal financial data based on a knowledge map provided by this embodiment, it is firstly needed to design the structural constitution of the map database according to the user's query requirement of abnormal financial data, when the query requirement of abnormal financial data is to enquire illegitimate middleman information from lenders, considering that the illegitimate middleman information obtainable by a platform not only includes names, but also includes such valid identification information as their telephones and identification codes, etc., so three types of nodes can be adopted in designing the structural constitution of the map database, with one node representing one piece of information data; relational nodes employ the mode of association by indicator lines to correspondingly design the structural constitution of the map database, plural pieces of sample source data are thereafter collected from the platfolin, a CSV file identifiable by the map database is formed after data cleaning, the CSV file is finally imported to the map database to construct a knowledge map of sample data, nodes whose correlation degree is higher than a threshold are screened out of the knowledge map, and corresponding information data in the nodes are extracted and output as abnormal financial data, e.g., such valid identification data as names, telephones or identification codes of illegitimate middlemen.

[0060] Seen as such, this embodiment employs the mode of inputting great quantities of sample data in the map database to form a knowledge map to identify abnormal financial data, and utilizes the characteristic of the knowledge map that is good at processing complicated network relations to express plural pieces of sample data with a structured network, so as to quickly and accurately identify abnormal financial data therefrom.

[0061] Specifically, the method of designing structural constitution of a map database according to query requirement of abnormal financial data in the foregoing embodiment includes:

[0062] the query requirement of abnormal financial data including to search out illegitimate Date Regue/Date Received 2022-06-27 middleman information from registration information of plural lenders, wherein the registration information of the lenders includes lender information, contact information, transferor information and/or addressee information, wherein the information includes name data, telephone data, and identification code data; and correspondingly setting plural node types based on plural data types, and designing the map database in accordance with a principle of one node corresponding to one piece of data.

[0063] During specific implementation, in order to facilitate comprehension, installment loan shopping is now taken for example for explanation, in the process of searching for any illegitimate middleman of an installment loan shopping activity, the lenders, the addressees of purchased commodities, and the relevant transferors must be taken as ingress to sort out suspectable clues and to find out illegitimate middlemen therefrom;
since the information of the relevant personnel obtainable by the platform includes name data, telephone data, and identification code data, so when the structural constitution of the map database is designed, three types of nodes can be correspondingly set in the map database to correspond to the above three types of data, and nodes with higher correlation degree are screened out therefrom to find out suspectable illegitimate middlemen after having subjected the plural pieces of installment loan shopping data to knowledge map analysis.

[0064] Specifically, the method of collecting plural pieces of sample source data, and subjecting the same to data cleaning to obtain plural pieces of sample data that conform to the structural constitution of the map database in the foregoing embodiment includes:

[0065] obtaining plural pieces of lender registration information from a database, and extracting lender information, contact information, transferor information, and/or addressee information from each piece of lender registration information to serve as sample source data; preliminarily screening the sample source data, and eliminating any sample source data that contains no name data, telephone data, or identification code data; duplicate-checking the remaining sample source data, and deleting repetitive Date Regue/Date Received 2022-06-27 sample source data; and subjecting the duplicate-checked sample source data to legitimacy verification, removing any sample source data whose telephone data and/or identification code data are/is invalid, and finally retaining valid sample data.

[0066] During specific implementation, after the plural pieces of sample source data have been obtained, any sample source data that does not conform to the structural constitution of the map database is eliminated; if the same and single lender has plural loan records, the platform records plural pieces of lender registration information of the same and single lender, and there might be repetitive lender registration information, accordingly, the sample source data will be duplicate-removed when the sample source data is obtained, and the duplicate-checked sample source data is thereafter subjected to legitimacy verification to remove any sample source data whose telephone data and/or identification code data are/is invalid, and to finally retain valid sample data. The method of identifying the telephone data and/or the identification code data as invalid is: comparing whether the telephone data and/or the identification code data are/is consistent with a standard telephone number and/or a standard identification code in length to judge whether the telephone data and/or the identification code data are/is invalid, for instance, any telephone number not being of 11 bits and any identification code not being of 18 bits in the sample source data are judged as invalid.

[0067] Preferably, the method of identifying abnormal financial data from the knowledge map in the foregoing embodiment includes:

[0068] employing a Cypher language to preset plural abnormal financial data enquiring statements, including an abnormal name enquiring statement, an abnormal telephone enquiring statement, or an abnormal identification code enquiring statement;
setting up the abnormal name enquiring statement, the abnormal telephone enquiring statement, or the abnormal identification code enquiring statement on an enquiring interface in a modular form, so that a user correspondingly selects enquiring statement input according to the query requirement of the abnormal financial data; scatteredly spreading Date Regue/Date Received 2022-06-27 out the plural pieces of sample data in the form of nodes, and associatively forming relational nodes into a knowledge map through indicator lines; and screening out relational nodes from the knowledge map according to an input enquiring statement, and then searching out illegitimate middleman information from the screened relational nodes.

[0069] During specific implementation, when the map database is used for enquiry and retrieval, it is required in each enquiry to employ a Cypher statement to compile an enquiring command identifiable by the map database, only then can the map database correspondingly output a query result. This is obviously not easily manipulable to business personnel without the computer professional background, so there are lot of inconveniences in use. In order to solve such problem, this embodiment employs an enquiring module with preset Cypher statements well compiled on the enquiring interface of the platform, such as an illegitimate middleman name enquiring module or an illegitimate middleman telephone enquiring module, so that, while enquiring the name of an illegitimate middleman, the business personnel can directly drag the illegitimate middleman name enquiring module to the enquiring box of the platform for searching, the program screens out relational nodes from the knowledge map after having received an enquiring instruction, the relational nodes here include name data, telephone data, and identification code data of illegitimate middlemen, and name data of the illegitimate middleman is finally searched out from the relational nodes and a result is output.

[0070] Understandably, the method of scatteredly spreading out the plural pieces of sample source data in the form of nodes, and associatively forming relational nodes into a knowledge map through indicator lines is as follows:

[0071] Since each piece of sample data includes three types of data, namely names data, telephones data, and identification code data, three nodes can be correspondingly constructed with reference to each piece of sample data during the process of Date Regue/Date Received 2022-06-27 constructing the knowledge map, so that each node represents one piece of data, at the same time, the three nodes in the same piece of sample data are associatively expressed in the mode of indicator lines; after the nodes to which the plural pieces of sample data correspond have been constructed to completion, nodes of the same data are duplicate-removed, the indicator lines originally connected with the removed nodes are thereafter re-connected to the nodes that remain after the duplicate-removal, and the knowledge map is finally formed.

[0072] As can be known from the above implementation process, this embodiment achieves the following advantages.

[0073] 1. Enquiring complexity of the map database can be simplified ¨
languages and grammars dedicated to the map database formerly were mastered only by professional data analyzers and engineers, but now they can also be utilized by business personnel without knowledge of computer programming languages to perform enquiring operations.

[0074] 2. The cost of communication between the business personnel and the developing personnel can be reduced ¨ it was formerly required for the business personnel to undergo a cooperative procedure through multiple departments such as writing requirement description ¨ scheduling dates by R&D department ¨ realizing the requirement by R&D department, but it is now only required for the R&D
department to import data in the map database, while subsequent use can be carried out by the business personnel on their own initiative.

[0075] 3. Enquiring efficiency is enhanced ¨ formerly, the analysis result derived by the data analyzing personnel could only be interacted by means of Cypher statements on the map database, and the map data could be used by the business depai _____ anent only after it was restored to a datasheet structure, but now the map database is set up on the Date Regue/Date Received 2022-06-27 platfoim, whereby the business personnel can directly obtain the query result, and the whole process is convenient and speedy.

[0076] Moreover, the method of screening out relational nodes from the knowledge map according to an input enquiring statement, and then searching out illegitimate middleman information from the screened relational nodes includes:

[0077] setting an abnormal node identifying threshold, outputting any node consistent in type with the enquiring statement in the relational nodes, when a correlation degree of the relational nodes is greater than the threshold, and obtaining a query result of the illegitimate middleman information. The correlation degree is defined and obtained according to the number of the indicator lines connected with the nodes.

[0078] Embodiment 2

[0079] Please refer to Fig. 1 and Fig. 2, this embodiment provides a system for enquiring abnormal financial data based on a knowledge map, the system comprises:

[0080] a map designing unit 1, for designing structural constitution of a map database according to query requirement of abnormal financial data, wherein the structural constitution includes expressions of intemode relations and nodes;

[0081] a sample collecting unit 2, for collecting plural pieces of sample source data, and subjecting the same to data cleaning to obtain plural pieces of sample data that conform to the structural constitution of the map database; and

[0082] an identifying and outputting unit 3, for importing the sample data to the map database to output a knowledge map, and thereafter searching out the abnormal financial data from the knowledge map.

[0083] Preferably, the sample collecting unit 2 includes:

[0084] an information collecting module 21, for obtaining plural pieces of lender registration information from a database, and extracting lender information, contact information, Date Regue/Date Received 2022-06-27 transferor information, and/or addressee information from each piece of lender registration information to serve as sample source data;

[0085] a screening module 22, for preliminarily screening the sample source data, and eliminating any sample source data that contains no name data, telephone data, or identification code data;

[0086] a duplicate-checking module 23, for duplicate-checking the remaining sample source data, and deleting repetitive sample source data; and

[0087] a verifying module 24, for subjecting the duplicate-checked sample source data to legitimacy verification, removing any sample source data whose telephone data and/or identification code data are/is invalid, and finally retaining valid sample data.

[0088] Preferably, the identifying and outputting unit 3 includes:

[0089] a pre-storing module 31, for employing a Cypher language to preset plural abnormal financial data enquiring statements, including an abnormal name enquiring statement, an abnormal telephone enquiring statement, or an abnormal identification code enquiring statement;

[0090] a setting module 32, for setting up the abnormal name enquiring statement, the abnormal telephone enquiring statement, or the abnormal identification code enquiring statement on an enquiring interface in a modular form, so that a user correspondingly selects enquiring statement input according to the query requirement of the abnormal financial data;

[0091] a processing module 33, for scatteredly spreading out the plural pieces of sample data in the form of nodes, and associatively forming relational nodes into a knowledge map through indicator lines; and

[0092] a query outputting module 34, for screening out relational nodes from the knowledge map according to an input enquiring statement, then identifying abnormal financial data from the screened relational nodes and outputting the same in the form of a query result.

[0093] In comparison with prior-art technology, the advantageous effects achieved by the Date Regue/Date Received 2022-06-27 system for enquiring abnormal financial data based on a knowledge map provided by this embodiment of the present invention are identical with the advantageous effects achievable by the method of enquiring abnormal financial data based on a knowledge map provided by the foregoing Embodiment 1, so these are not redundantly described in this context.

[0094] As understandable to persons ordinarily skilled in the art, the entire or partial steps realizing the method of the present invention can be completed via a program to instruct relevant hardware. The program can be stored in a computer-readable storage medium, and various steps of the method in the foregoing embodiment are involved when it is executed, and the storage medium can be an ROM/RAM, a magnetic disk, an optical disk, or a memory card, etc.

[0095] What the above describes is merely directed to specific modes of execution of the present invention, but the protection scope of the present invention is not restricted thereby. Any change or replacement easily conceivable to persons skilled in the art within the technical range disclosed by the present invention shall be covered by the protection scope of the present invention. Accordingly, the protection scope of the present invention shall be based on the protection scope as claimed in the Claims.

Date Regue/Date Received 2022-06-27

Claims

Claims:

1. A device comprising:
a map designing unit, configured to design structural constitution of a map database of query requirement of abnormal financial data, wherein the structural constitution includes expressions of internode relations and nodes;
a sample collecting unit, configured to:
collect plural pieces of sample source data;
subject plural pieces of the sample source data to data cleaning to obtain plural pieces of clean sample data which conforms to the structural constitution of the map database; and an identifying and outputting unit, configured to:
import the clean sample data to the map database to output a knowledge map;
and search out the abnormal financial data from the knowledge map.

2. The device of claim 1, wherein the sample collecting unit further comprises:
an information collecting module, configured to:
obtain plural pieces of lender registration information from a database;
extract lender information, contact information, transferor information, and addressee information from each piece of the lender registration information to serve as the sample source data;
a screening module, configured to:
preliminarily screen the sample source data;

DateRegue/Date Received 2022-06-27 eliminate any sample source data containing no name data, no telephone data, or no identification code data;
a duplicate-checking module, configured to:
duplicate-check remaining sample source data;
delete repetitive sample source data;
a verifying module, configured to:
subject duplicate-checked sample source data to legitimacy verification;
remove any sample source data whose telephone data and identification code data are invalid; and retaining valid sample data.

3. The device of claim 1, wherein the identifying and outputting unit further comprises:
a pre-storing module, configured to employ a Cypher language to preset plural abnormal financial data enquiring statements, including an abnormal name enquiring statement, an abnormal telephone enquiring statement, and an abnormal identification code enquiring statement;
a setting module, configured to set up the abnormal name enquiring statement, the abnormal telephone enquiring statement, and the abnormal identification code enquiring statement on an enquiring interface in a modular form, wherein a user correspondingly selects enquiring statement input of the query requirement of the abnormal financial data;
a processing module, configured to:
randomly spread out plural pieces of sample data in form of nodes;
associatively form relational nodes into the knowledge map through indicator lines;

DateRegue/Date Received 2022-06-27 a query outputting module, configured to:
screen out relational nodes from the knowledge map of an input enquiring statement;
identify the abnormal financial data from screened relational nodes; and output the abnormal financial data as a query result.

4. The device of claim 3, wherein screening out the relational nodes from the knowledge map of the input enquiring statement, and identifying the abnormal financial data ftom the screened relational nodes comprises:
setting an abnormal node identifying threshold;
outputting any node consistent in type with the input enquiring statement in the relational nodes, when a correlation degree of the relational nodes is greater than the threshold; and obtaining a query result of the abnormal financial data.

5. The device of any one of claims 3 to 4, wherein the correlation degree is defined and obtained of number of the indicator lines connected with the nodes.

6. The device of any one of claims 1 to 5, wherein plural pieces of the sample source data are collected from a platform.

7. The device of any one of claims 1 to 6, wherein a CM/ file identifiable by the map database is formed after data cleaning.

8. The device of any one of claims 1 to 7, wherein the CSV file is imported to the map database to construct the knowledge map of sample data.

9. The device of any one of claims 1 to 8, wherein any sample source data which does not conform to the structural constitution of the map database is eliminated.

DateRegue/Date Received 2022-06-27

10. The device of any one of claims 1 to 9, wherein personnel directly drags illegitimate middleman name enquiring module to enquiring box of the platform for searching.

11. The device of any one of claims 1 to 10, wherein each piece of the sample data includes three types of data, including the names data, the telephones data, and the identification code data, three nodes are constructed with reference to each piece of the sample data during constructing the knowledge map, wherein each node represents one piece of data.

12. The device of any one of claims 1 to 11, wherein the three nodes in same piece of the sample data are associatively expressed in mode of the indicator lines.

13. The device of any one of claims 1 to 12, wherein nodes of same data are duplicate, are removed.

14. The device of any one of claims 1 to 13, wherein the indicator lines originally connected with removed nodes are re-connected to the nodes which remain after the duplicate are removed, and the knowledge map is formed.

15. A system comprising:
a map designing unit, configured to design structural constitution of a map database of query requirement of abnormal financial data, wherein the structural constitution includes expressions of intemode relations and nodes;
a sample collecting unit, configured to:
collect plural pieces of sample source data;
subject plural pieces of the sample source data to data cleaning to obtain plural pieces of clean sample data which conforms to the structural constitution of the map database; and an identifying and outputting unit, configured to:
DateRegue/Date Received 2022-06-27 import the clean sample data to the map database to output a knowledge map;
and search out the abnormal financial data from the knowledge map.

16. The system of claim 15, wherein the sample collecting unit further comprises:
an information collecting module, configured to:
obtain plural pieces of lender registration information from a database;
extract lender information, contact information, transferor information, and addressee information from each piece of the lender registTation information to serve as the sample source data;
a screening module, configured to:
preliminarily screen the sample source data;
eliminate any sample source data containing no name data, no telephone data, or no identification code data;
a duplicate-checking module, configured to:
duplicate-check remaining sample source data;
delete repetitive sample source data;
a verifying module, configured to:
subject duplicate-checked sample source data to legitimacy verification;
remove any sample source data whose telephone data and identification code data are invalid; and retaining valid sample data.

DateRegue/Date Received 2022-06-27

17. The system of claim 15, wherein the identifying and outputting unit further comprises:
a pre-storing module, configured to employ a Cypher language to preset plural abnormal financial data enquiring statements, including an abnormal name enquiring statement, an abnormal telephone enquiring statement, and an abnormal identification code enquiring statement a setting module, configured to set up the abnormal name enquiring statement, the abnormal telephone enquiring statement, and the abnormal identification code enquiring statement on an enquiring interface in a modular form, wherein a user correspondingly selects enquiring statement input of the query requirement of the abnormal financial data;
a processing module, configured to:
randomly spread out plural pieces of sample data in folin of nodes;
associatively form relational nodes into the knowledge map through indicator lines;
a query outputting module, configured to:
screen out relational nodes from the knowledge map of an input enquiring statement;
identify the abnormal financial data from screened relational nodes; and output the abnormal financial data as a query result.

18. The system of claim 17, wherein screening out the relational nodes from the knowledge map of the input enquiring statement, and identifying the abnormal financial data from the screened relational nodes comprises:
setting an abnormal node identifying threshold;

DateRegue/Date Received 2022-06-27 outputting any node consistent in type with the input enquiring statement in the relational nodes, when a correlation degree of the relational nodes is greater than the threshold; and obtaining a query result of the abnormal financial data.

19. The system of any one of claims 17 to 18, wherein the correlation degree is defined and obtained of number of the indicator lines connected with the nodes.

20. The system of any one of claims 15 to 19, wherein plural pieces of the sample source data are collected from a platform.

21. The system of any one of claims 15 to 20, wherein a CSV file identifiable by the map database is formed after data cleaning.

22. The system of any one of claims 15 to 21, wherein the CSV file is imported to the map database to construct the knowledge map of sample data.

23. The system of any one of claims 15 to 22, wherein any sample source data which does not conform to the structural constitution of the map database is eliminated.

24. The system of any one of claims 15 to 23, wherein personnel directly drags illegitimate middleman name enquiring module to enquiring box of the platform for searching.

25. The system of any one of claims 15 to 24, wherein each piece of the sample data includes three types of data, including the names data, the telephones data, and the identification code data, three nodes are constructed with reference to each piece of the sample data during constructing the knowledge map, wherein each node represents one piece of data.

26. The system of any one of claims 15 to 25, wherein the three nodes in same piece of the sample data are associatively expressed in mode of the indicator lines.

27. The system of any one of claims 15 to 26, wherein nodes of same data are duplicate, are removed.

DateRegue/Date Received 2022-06-27

28. The system of any one of claims 15 to 27, wherein the indicator lines originally connected with removed nodes are re-connected to the nodes which remain after the duplicate are removed, and the knowledge map is formed.

29. A method comprising:
designing structural constitution of a map database of query requirement of abnormal financial data, wherein the structural constitution includes expressions of intemode relations and nodes;
collecting plural pieces of sample source data;
subjecting plural pieces of the sample source data to data cleaning to obtain plural pieces of clean sample data which conforms to the structural constitution of the map database;
importing the clean sample data to the map database to output a knowledge map;
and searching out the abnormal financial data from the knowledge map.

30. The method of claim 29, wherein designing the structural constitution of the map database of the query requirement of the abnormal financial data comprises:
the query requirement of the abnormal financial data includes to search out illegitimate middleman information from registration information of plural lenders, wherein the registration information of the lenders includes lender information, contact information, transferor information and addressee information, wherein the registration information includes name data, telephone data, and identification code data;
setting plural node types based on plural data types; and designing the map database in accordance with a principle of one node corresponding to one piece of data.

DateRegue/Date Received 2022-06-27

31. The method of claim 30, wherein collecting plural pieces of the sample source data, and subjecting plural pieces of the sample source data to the data cleaning to obtain plural pieces of the clean sample data which confolms to the structural constitution of the map database comprises:
obtaining plural pieces of lender registration information from a database;
extracting the lender information, the contact information, the transferor information, and the addressee information from each piece of the lender registration information to serve as the sample source data;
preliminarily screening the sample source data;
eliminating any sample source data containing no name data, no telephone data, or no identification code data;
duplicate-checking remaining sample source data;
deleting repetitive sample source data; and subjecting duplicate-checked sample source data to legitimacy verification;
removing any sample source data whose telephone data and identification code data are invalid; and retaining valid sample data.

32. The method of claim 31, wherein identifying the telephone data and the identification code data as invalid comprises:
comparing the telephone data and the identification code data are consistent with a standard telephone number and a standard identification code in length to judge the telephone data and the identification code data are invalid.
DateRegue/Date Received 2022-06-27

33. The method of claim 30, wherein identifying the abnormal financial data from the knowledge map comprises:
employing a Cypher language to preset plural abnormal financial data enquiring statements, including an abnoimal name enquiring statement, an abnormal telephone enquiring statement, and an abnoimal identification code enquiring statement;
setting up the abnormal name enquiring statement, the abnormal telephone enquiring statement, and the abnormal identification code enquiring statement on an enquiring interface in a modular form, wherein a user correspondingly selects enquiring statement input of the query requirement of the abnormal financial data;
randomly spreading out plural pieces of sample data in form of nodes;
associatively forming relational nodes into the knowledge map through indicator lines;
screening out the relational nodes from the knowledge map of an input enquiring statement; and searching out the illegitimate middleman infoimation from screened relational nodes.

34. The method of claim 33, wherein screening out the relational nodes from the knowledge map of the input enquiring statement, and searching out the illegitimate middleman information from the screened relational nodes comprises:
setting an abnoimal node identifying threshold;
outputting any node consistent in type with the input enquiring statement in the relational nodes, when a correlation degree of the relational nodes is greater than the threshold; and obtaining a query result of the illegitimate middleman information.

35. The method of any one of claims 33 to 34, wherein the correlation degree is defined and obtained of number of the indicator lines connected with the nodes.

DateRegue/Date Received 2022-06-27

36. The method of any one of claims 29 to 35, wherein plural pieces of the sample source data are collected from a platform.

37. The method of any one of claims 29 to 36, wherein a CSV file identifiable by the map database is formed after data cleaning.

38. The method of any one of claims 29 to 37, wherein the CSV file is imported to the map database to construct the knowledge map of sample data.

39. The method of any one of claims 29 to 38, wherein any sample source data which does not conform to the structural constitution of the map database is eliminated.

40. The method of any one of claims 29 to 39, wherein personnel directly drags illegitimate middleman name enquiring module to enquiring box of the platform for searching.

41. The method of any one of claims 29 to 40, wherein each piece of the sample data includes three types of data, including the names data, the telephones data, and the identification code data, three nodes are constructed with reference to each piece of the sample data during constructing the knowledge map, wherein each node represents one piece of data.

42. The method of any one of claims 29 to 41, wherein the three nodes in same piece of the sample data are associatively expressed in mode of the indicator lines.

43. The method of any one of claims 29 to 42, wherein nodes of same data are duplicate, are removed.

44. The method of any one of claims 29 to 43, wherein the indicator lines originally connected with removed nodes are re-connected to the nodes which remain after the duplicate are removed, and the knowledge map is formed.

45. An equipment comprising:

DateRegue/Date Received 2022-06-27 designing structural constitution of a map database of query requirement of abnormal financial data, wherein the structural constitution includes expressions of intemode relations and nodes;
collecting plural pieces of sample source data;
subjecting plural pieces of the sample source data to data cleaning to obtain plural pieces of clean sample data which conforms to the structural constitution of the map database;
importing the clean sample data to the map database to output a knowledge map;
and searching out the abnormal financial data from the knowledge map.

46. The equipment of claim 45, wherein designing the structural constitution of the map database of the query requirement of the abnormal financial data comprises:
the query requirement of the abnormal financial data includes to search out illegitimate middleman information from registration information of plural lenders, wherein the registration information of the lenders includes lender information, contact information, transferor information and addressee information, wherein the registration information includes name data, telephone data, and identification code data;
setting plural node types based on plural data types; and designing the map database in accordance with a principle of one node corresponding to one piece of data.

47. The equipment of claim 46, wherein collecting plural pieces of the sample source data, and subjecting plural pieces of the sample source data to the data cleaning to obtain plural pieces of the clean sample data which conforms to the structural constitution of the map database comprises:
obtaining plural pieces of lender registration information from a database;

DateRegue/Date Received 2022-06-27 extracting the lender information, the contact information, the transferor information, and the addressee information from each piece of the lender registration information to serve as the sample source data;
preliminarily screening the sample source data;
eliminating any sample source data containing no name data, no telephone data, or no identification code data;
duplicate-checking remaining sample source data;
deleting repetitive sample source data; and subjecting duplicate-checked sample source data to legitimacy verification;
removing any sample source data whose telephone data and identification code data are invalid; and retaining valid sample data.

48. The equipment of claim 47, wherein identifying the telephone data and the identification code data as invalid comprises:
comparing the telephone data and the identification code data are consistent with a standard telephone number and a standard identification code in length to judge the telephone data and the identification code data are invalid.

49. The equipment of claim 46, wherein identifying the abnormal financial data from the knowledge map comprises:
employing a Cypher language to preset plural abnormal financial data enquiring statements, including an abnormal name enquiring statement, an abnormal telephone enquiring statement, and an abnormal identification code enquiring statement;

DateRegue/Date Received 2022-06-27 setting up the abnormal name enquiring statement, the abnormal telephone enquiring statement, and the abnormal identification code enquiring statement on an enquiring interface in a modular form, wherein a user correspondingly selects enquiring statement input of the query requirement of the abnormal financial data;
randomly spreading out plural pieces of sample data in form of nodes;
associatively forming relational nodes into the knowledge map through indicator lines;
screening out the relational nodes from the knowledge map of an input enquiring statement; and searching out the illegitimate middleman information from screened relational nodes.

50. The equipment of claim 49, wherein screening out the relational nodes from the knowledge map of the input enquiring statement, and searching out the illegitimate middleman information from the screened relational nodes comprises:
setting an abnoimal node identifying threshold;
outputting any node consistent in type with the input enquiring statement in the relational nodes, when a correlation degree of the relational nodes is greater than the threshold; and obtaining a query result of the illegitimate middleman information.

51. The equipment of any one of claims 49 to 50, wherein the correlation degree is defined and obtained of number of the indicator lines connected with the nodes.

52. The equipment of any one of claims 45 to 51, wherein plural pieces of the sample source data are collected from a platform.

53. The equipment of any one of claims 45 to 52, wherein a CSV file identifiable by the map database is formed after data cleaning.
DateRegue/Date Received 2022-06-27

54. The equipment of any one of claims 45 to 53, wherein the CSV file is imported to the map database to construct the knowledge map of sample data.

55. The equipment of any one of claims 45 to 54, wherein any sample source data which does not conform to the structural constitution of the map database is eliminated.

56. The equipment of any one of claims 45 to 55, wherein personnel directly drags illegitimate middleman name enquiring module to enquiring box of the platform for searching.

57. The equipment of any one of claims 45 to 56, wherein each piece of the sample data includes three types of data, including the names data, the telephones data, and the identification code data, three nodes are constructed with reference to each piece of the sample data during constructing the knowledge map, wherein each node represents one piece of data.

58. The equipment of any one of claims 45 to 57, wherein the three nodes in same piece of the sample data are associatively expressed in mode of the indicator lines.

59. The equipment of any one of claims 45 to 58, wherein nodes of same data are duplicate, are removed.

60. The equipment of any one of claims 45 to 59, wherein the indicator lines originally connected with removed nodes are re-connected to the nodes which remain after the duplicate are removed, and the knowledge map is formed.

61. A computer readable physical memory having stored thereon a computer program executed by a computer configured to:
design structural constitution of a map database of query requirement of abnormal financial data, wherein the structural constitution includes expressions of intemode relations and nodes;
collect plural pieces of sample source data;

DateRegue/Date Received 2022-06-27 subject plural pieces of the sample source data to data cleaning to obtain plural pieces of clean sample data which conforms to the structural constitution of the map database;
import the clean sample data to the map database to output a knowledge map;
and search out the abnormal financial data from the knowledge map.

62. The memory of claim 61, wherein designing the structural constitution of the map database of the query requirement of the abnormal financial data comprises:
the query requirement of the abnormal financial data includes to search out illegitimate middleman information from registration information of plural lenders, wherein the registration information of the lenders includes lender information, contact information, transferor information and addressee information, wherein the registration information includes name data, telephone data, and identification code data;
setting plural node types based on plural data types; and designing the map database in accordance with a principle of one node corresponding to one piece of data.

63. The memory of claim 62, wherein collecting plural pieces of the sample source data, and subjecting plural pieces of the sample source data to the data cleaning to obtain plural pieces of the clean sample data which conforms to the structural constitution of the map database comprises:
obtaining plural pieces of lender registration information from a database;
extracting the lender information, the contact information, the transferor information, and the addressee information from each piece of the lender registration information to serve as the sample source data;
preliminarily screening the sample source data;

DateRegue/Date Received 2022-06-27 eliminating any sample source data containing no name data, no telephone data, or no identification code data;
duplicate-checking remaining sample source data;
deleting repetitive sample source data; and subjecting duplicate-checked sample source data to legitimacy verification;
removing any sample source data whose telephone data and identification code data are invalid; and retaining valid sample data.

64. The memory of claim 63, wherein identifying the telephone data and the identification code data as invalid comprises:
comparing the telephone data and the identification code data are consistent with a standard telephone number and a standard identification code in length to judge the telephone data and the identification code data are invalid.

65. The memory of claim 64, wherein identifying the abnormal financial data from the knowledge map comprises:
employing a Cypher language to preset plural abnormal financial data enquiring statements, including an abnormal name enquiring statement, an abnormal telephone enquiring statement, and an abnormal identification code enquiring statement;
setting up the abnormal name enquiring statement, the abnormal telephone enquiring statement, and the abnormal identification code enquiring statement on an enquiring interface in a modular form, wherein a user correspondingly selects enquiring statement input of the query requirement of the abnormal financial data;
randomly spreading out plural pieces of sample data in form of nodes;

DateRegue/Date Received 2022-06-27 associatively forming relational nodes into the knowledge map through indicator lines;
screening out the relational nodes from the knowledge map of an input enquiring statement; and searching out the illegitimate middleman information from screened relational nodes.

66. The memory of claim 65, wherein screening out the relational nodes from the knowledge map of the input enquiring statement, and searching out the illegitimate middleman information from the screened relational nodes comprises:
setting an abnormal node identifying threshold;
outputting any node consistent in type with the input enquiring statement in the relational nodes, when a correlation degree of the relational nodes is greater than the threshold; and obtaining a query result of the illegitimate middleman information.

67. The memory of any one of claims 65 to 66, wherein the correlation degree is defined and obtained of number of the indicator lines connected with the nodes.

68. The memory of any one of claims 61 to 67, wherein plural pieces of the sample source data are collected from a platform.

69. The memory of any one of claims 61 to 68, wherein a CSV file identifiable by the map database is formed after data cleaning.

70. The memory of any one of claims 61 to 69, wherein the CSV file is imported to the map database to construct the knowledge map of sample data.

71. The memory of any one of claims 61 to 70, wherein any sample source data which does not conform to the structural constitution of the map database is eliminated.

72. The memory of any one of claims 61 to 71, wherein personnel directly drags illegitimate middleman name enquiring module to enquiring box of the platform for searching.

DateRegue/Date Received 2022-06-27

73. The memory of any one of claims 61 to 72, wherein each piece of the sample data includes three types of data, including the names data, the telephones data, and the identification code data, three nodes are constructed with reference to each piece of the sample data during constructing the knowledge map, wherein each node represents one piece of data.

74. The memory of any one of claims 61 to 73, wherein the three nodes in same piece of the sample data are associatively expressed in mode of the indicator lines.

75. The memory of any one of claims 61 to 74, wherein nodes of same data are duplicate, are removed.

76. The memory of any one of claims 61 to 75, wherein the indicator lines originally connected with removed nodes are re-connected to the nodes which remain after the duplicate are removed, and the knowledge map is formed.

77. A device comprising:
a map designing unit, configured to design structural constitution of a map database of query requirement of abnormal financial data, wherein the structural constitution includes expressions of intemode relations and nodes;
an identifying and outputting unit, configured to:
import clean sample data to the map database to output a knowledge map; and search out the abnormal financial data from the knowledge map.

78. The device of claim 77, further comprises:
a sample collecting unit, configured to:
collect plural pieces of sample source data; and DateRegue/Date Received 2022-06-27 subject plural pieces of the sample source data to data cleaning to obtain plural pieces of the clean sample data which conforms to the structural constitution of the map database.

79. The device of claim 78, wherein the sample collecting unit further comprises:
an information collecting module, configured to:
obtain plural pieces of lender registration information from a database;
extract lender information, contact information, transferor information, and addressee information from each piece of the lender registration information to serve as the sample source data;
a screening module, configured to:
preliminuily screen the sample source data;
eliminate any sample source data containing no name data, no telephone data, or no identification code data;
a duplicate-checking module, configured to:
duplicate-check remaining sample source data;
delete repetitive sample source data;
a verifying module, configured to:
subject duplicate-checked sample source data to legitimacy verification;
remove any sample source data whose telephone data and identification code data are invalid; and retaining valid sample data.

80. The device of claim 78, wherein the identifying and outputting unit further comprises:

DateRegue/Date Received 2022-06-27 a pre-storing module, configured to employ a Cypher language to preset plural abnormal financial data enquiring statements, including an abnormal name enquiring statement, an abnormal telephone enquiring statement, and an abnormal identification code enquiring statement;
a setting module, configured to set up the abnormal name enquiring statement, the abnormal telephone enquiring statement, and the abnormal identification code enquiring statement on an enquiring interface in a modular form, wherein a user correspondingly selects enquiring statement input of the query requirement of the abnormal financial data;
a processing module, configured to:
randomly spread out plural pieces of sample data in form of nodes;
associatively form relational nodes into the knowledge map through indicator lines;
a query outputting module, configured to:
screen out relational nodes from the knowledge map of an input enquiring statement;
identify the abnormal financial data from screened relational nodes; and output the abnormal financial data as a query result.

81. The device of claim 80, wherein screening out the relational nodes from the knowledge map of the input enquiring statement, and identifying the abnormal financial data from the screened relational nodes comprises:
setting an abnormal node identifying threshold;
outputting any node consistent in type with the input enquiring statement in the relational nodes, when a correlation degree of the relational nodes is greater than the threshold; and DateRegue/Date Received 2022-06-27 obtaining a query result of the abnormal financial data.

82. The device of any one of claims 80 to 81, wherein the correlation degree is defined and obtained of number of the indicator lines connected with the nodes.

83. The device of any one of claims 77 to 82, wherein plural pieces of the sample source data are collected from a platform.

84. The device of any one of claims 77 to 83, wherein a CSV file identifiable by the map database is formed after data cleaning.

85. The device of any one of claims 77 to 84, wherein the CSV file is imported to the map database to construct the knowledge map of sample data.

86. The device of any one of claims 77 to 85, wherein any sample source data which does not conform to the structural constitution of the map database is eliminated.

87. The device of any one of claims 77 to 86, wherein personnel directly drags illegitimate middleman name enquiring module to enquiring box of the platform for searching.

88. The device of any one of claims 77 to 87, wherein each piece of the sample data includes three types of data, including the names data, the telephones data, and the identification code data, three nodes are constructed with reference to each piece of the sample data during constructing the knowledge map, wherein each node represents one piece of data.

89. The device of any one of claims 77 to 88, wherein the three nodes in same piece of the sample data are associatively expressed in mode of the indicator lines.

90. The device of any one of claims 77 to 89, wherein nodes of same data are duplicate, are removed.

91. The device of any one of claims 77 to 90, wherein the indicator lines originally connected with removed nodes are re-connected to the nodes which remain after the duplicate are removed, and the knowledge map is formed.

DateRegue/Date Received 2022-06-27

92. A system comprising:
a map designing unit, configured to design structural constitution of a map database of query requirement of abnormal financial data, wherein the structural constitution includes expressions of internode relations and nodes;
an identifying and outputting unit, configured to:
import clean sample data to the map database to output a knowledge map; and search out the abnormal financial data from the knowledge map.

93. The system of claim 92, further comprises:
a sample collecting unit, configured to:
collect plural pieces of sample source data; and subject plural pieces of the sample source data to data cleaning to obtain plural pieces of the clean sample data which conforms to the structural constitution of the map database.

94. The system of claim 93, wherein the sample collecting unit further comprises:
an information collecting module, configured to:
obtain plural pieces of lender registration information from a database;
extract lender information, contact information, transferor information, and addressee information from each piece of the lender registration information to serve as the sample source data;
a screening module, configured to:
preliminarily screen the sample source data;

DateRegue/Date Received 2022-06-27 eliminate any sample source data containing no name data, no telephone data, or no identification code data;
a duplicate-checking module, configured to:
duplicate-check remaining sample source data;
delete repetitive sample source data;
a verifying module, configured to:
subject duplicate-checked sample source data to legitimacy verification;
remove any sample source data whose telephone data and identification code data are invalid; and retaining valid sample data.

95. The system of claim 93, wherein the identifying and outputting unit further comprises:
a pre-storing module, configured to employ a Cypher language to preset plural abnormal financial data enquiring statements, including an abnormal name enquiring statement, an abnormal telephone enquiring statement, and an abnormal identification code enquiring statement;
a setting module, configured to set up the abnormal name enquiring statement, the abnormal telephone enquiring statement, and the abnormal identification code enquiring statement on an enquiring interface in a modular form, wherein a user correspondingly selects enquiring statement input of the query requirement of the abnormal financial data;
a processing module, configured to:
randomly spread out plural pieces of sample data in form of nodes;
associatively form relational nodes into the knowledge map through indicator lines;
DateRegue/Date Received 2022-06-27 a query outputting module, configured to:
screen out relational nodes from the knowledge map of an input enquiring statement;
identify the abnormal financial data from screened relational nodes; and output the abnormal financial data as a query result.

96. The system of claim 95, wherein screening out the relational nodes from the knowledge map of the input enquiring statement, and identifying the abnormal financial data ftom the screened relational nodes comprises:
setting an abnormal node identifying threshold;
outputting any node consistent in type with the input enquiring statement in the relational nodes, when a correlation degree of the relational nodes is greater than the threshold; and obtaining a query result of the abnormal financial data.

97. The system of any one of claims 95 to 96, wherein the correlation degree is defined and obtained of number of the indicator lines connected with the nodes.

98. The system of any one of claims 92 to 97, wherein plural pieces of the sample source data are collected from a platform.

99. The system of any one of claims 92 to 98, wherein a CSV file identifiable by the map database is formed after data cleaning.

100. The system of any one of claims 92 to 99, wherein the CSV file is imported to the map database to construct the knowledge map of sample data.

101. The system of any one of claims 92 to 100, wherein any sample source data which does not conform to the structural constitution of the map database is eliminated.

DateRegue/Date Received 2022-06-27

102. The system of any one of claims 92 to 101, wherein personnel directly drags illegitimate middleman name enquiring module to enquiring box of the platform for searching.

103. The system of any one of claims 92 to 102, wherein each piece of the sample data includes three types of data, including the names data, the telephones data, and the identification code data, three nodes are constructed with reference to each piece of the sample data during constructing the knowledge map, wherein each node represents one piece of data.

104. The system of any one of claims 92 to 103, wherein the three nodes in same piece of the sample data are associatively expressed in mode of the indicator lines.

105. The system of any one of claims 92 to 104, wherein nodes of same data are duplicate, are removed.

106. The system of any one of claims 92 to 105, wherein the indicator lines originally connected with removed nodes are re-connected to the nodes which remain after the duplicate are removed, and the knowledge map is formed.

107.A method comprising:
designing structural constitution of a map database of query requirement of abnormal financial data, wherein the structural constitution includes expressions of internode relations and nodes;
importing clean sample data to the map database to output a knowledge map; and searching out the abnormal financial data from the knowledge map.

108. The method of claim 107, further comprises:
collecting plural pieces of sample source data; and subjecting plural pieces of the sample source data to data cleaning to obtain plural pieces of the clean sample data which conforms to the structural constitution of the map database.

DateRegue/Date Received 2022-06-27

109. The method of claim 108, wherein designing the structural constitution of the map database of the query requirement of the abnormal financial data comprises:
the query requirement of the abnormal financial data includes to search out illegitimate middleman information from registration information of plural lenders, wherein the registration information of the lenders includes lender information, contact information, transferor information and addressee information, wherein the registration information includes name data, telephone data, and identification code data;
setting plural node types based on plural data types; and designing the map database in accordance with a principle of one node corresponding to one piece of data.

110. The method of claim 109, wherein collecting plural pieces of the sample source data, and subjecting plural pieces of the sample source data to the data cleaning to obtain plural pieces of the clean sample data which conforms to the structural constitution of the map database comprises:
obtaining plural pieces of lender registration information from a database;
extracting the lender information, the contact information, the transferor information, and the addressee information from each piece of the lender registration information to serve as the sample source data;
preliminarily screening the sample source data;
eliminating any sample source data containing no name data, no telephone data, or no identification code data;
duplicate-checking remaining sample source data;

DateRegue/Date Received 2022-06-27 deleting repetitive sample source data; and subjecting duplicate-checked sample source data to legitimacy verification;
removing any sample source data whose telephone data and identification code data are invalid; and retaining valid sample data.

111. The method of claim 110, wherein identifying the telephone data and the identification code data as invalid comprises:
comparing the telephone data and the identification code data are consistent with a standard telephone number and a standard identification code in length to judge the telephone data and the identification code data are invalid.

112. The method of claim 109, wherein identifying the abnormal financial data from the knowledge map comprises:
employing a Cypher language to preset plural abnormal financial data enquiring statements, including an abnormal name enquiring statement, an abnormal telephone enquiring statement, and an abnormal identification code enquiring statement;
setting up the abnormal name enquiring statement, the abnormal telephone enquiring statement, and the abnormal identification code enquiring statement on an enquiring interface in a modular form, wherein a user correspondingly selects enquiring statement input of the query requirement of the abnormal financial data;
randomly spreading out plural pieces of sample data in form of nodes;
associatively forming relational nodes into the knowledge map through indicator lines;
screening out the relational nodes from the knowledge map of an input enquiring statement; and DateRegue/Date Received 2022-06-27 searching out the illegitimate middleman information from screened relational nodes.

113. The method of claim 112, wherein screening out the relational nodes from the knowledge map of the input enquiring statement, and searching out the illegitimate middleman information from the screened relational nodes comprises:
setting an abnormal node identifying threshold;
outputting any node consistent in type with the input enquiring statement in the relational nodes, when a correlation degree of the relational nodes is greater than the threshold; and obtaining a query result of the illegitimate middleman information.

114. The method of any one of claims 112 to 113, wherein the correlation degree is defined and obtained of number of the indicator lines connected with the nodes.

115. The method of any one of claims 107 to 114, wherein plural pieces of the sample source data are collected from a platform.

116. The method of any one of claims 107 to 115, wherein a CSV file identifiable by the map database is formed after data cleaning.

117. The method of any one of claims 107 to 116, wherein the CSV file is imported to the map database to construct the knowledge map of sample data.

118. The method of any one of claims 107 to 117, wherein any sample source data which does not conform to the structural constitution of the map database is eliminated.

119. The method of any one of claims 107 to 118, wherein personnel directly drags illegitimate middleman name enquiring module to enquiring box of the platform for searching.

120. The method of any one of claims 107 to 119, wherein each piece of the sample data includes three types of data, including the names data, the telephones data, and the identification code data, three nodes are constructed with reference to each piece of the sample data during constructing the knowledge map, wherein each node represents one piece of data.
DateRegue/Date Received 2022-06-27

121. The method of any one of claims 107 to 120, wherein the three nodes in same piece of the sample data are associatively expressed in mode of the indicator lines.

122. The method of any one of claims 107 to 121, wherein nodes of same data are duplicate, are removed.

123. The method of any one of claims 107 to 122, wherein the indicator lines originally connected with removed nodes are re-connected to the nodes which remain after the duplicate are removed, and the knowledge map is formed.

124. An equipment comprising:
designing structural constitution of a map database of query requirement of abnormal financial data, wherein the structural constitution includes expressions of internode relations and nodes;
importing clean sample data to the map database to output a knowledge map; and searching out the abnormal financial data from the knowledge map.

125. The equipment of claim 124, further comprises:
collecting plural pieces of sample source data; and subjecting plural pieces of the sample source data to data cleaning to obtain plural pieces of the clean sample data which conforms to the structural constitution of the map database.

126. The equipment of claim 125, wherein designing the structural constitution of the map database of the query requirement of the abnormal financial data comprises:

DateRegue/Date Received 2022-06-27 the query requirement of the abnormal financial data includes to search out illegitimate middleman information from registration information of plural lenders, wherein the registration information of the lenders includes lender information, contact information, transferor information and addressee information, wherein the registration information includes name data, telephone data, and identification code data;
setting plural node types based on plural data types; and designing the map database in accordance with a principle of one node corresponding to one piece of data.

127. The equipment of claim 126, wherein collecting plural pieces of the sample source data, and subjecting plural pieces of the sample source data to the data cleaning to obtain plural pieces of the clean sample data which conforms to the structural constitution of the map database comprises:
obtaining plural pieces of lender registration information from a database;
extracting the lender information, the contact information, the transferor information, and the addressee information from each piece of the lender registration information to serve as the sample source data;
preliminarily screening the sample source data;
eliminating any sample source data containing no name data, no telephone data, or no identification code data;
duplicate-checking remaining sample source data;
deleting repetitive sample source data; and subjecting duplicate-checked sample source data to legitimacy verification;
removing any sample source data whose telephone data and identification code data are invalid; and DateRegue/Date Received 2022-06-27 retaining valid sample data.

128. The equipment of claim 127, wherein identifying the telephone data and the identification code data as invalid comprises:
comparing the telephone data and the identification code data are consistent with a standard telephone number and a standard identification code in length to judge the telephone data and the identification code data are invalid.

129. The equipment of claim 126, wherein identifying the abnormal financial data from the knowledge map comprises:
employing a Cypher language to preset plural abnormal financial data enquiring statements, including an abnormal name enquiring statement, an abnormal telephone enquiring statement, and an abnormal identification code enquiring statement;
setting up the abnormal name enquiring statement, the abnormal telephone enquiring statement, and the abnormal identification code enquiring statement on an enquiring interface in a modular form, wherein a user correspondingly selects enquiring statement input of the query requirement of the abnormal financial data;
randomly spreading out plural pieces of sample data in form of nodes;
associatively forming relational nodes into the knowledge map through indicator lines;
screening out the relational nodes from the knowledge map of an input enquiring statement; and searching out the illegitimate middleman information from screened relational nodes.

130. The equipment of claim 129, wherein screening out the relational nodes from the knowledge map of the input enquiring statement, and searching out the illegitimate middleman information from the screened relational nodes comprises:
setting an abnormal node identifying threshold;

DateRegue/Date Received 2022-06-27 outputting any node consistent in type with the input enquiring statement in the relational nodes, when a correlation degree of the relational nodes is greater than the threshold; and obtaining a query result of the illegitimate middleman information.

131. The equipment of any one of claims 129 to 130, wherein the correlation degree is defined and obtained of number of the indicator lines connected with the nodes.

132. The equipment of any one of claims 124 to 131, wherein plural pieces of the sample source data are collected from a platform.

133. The equipment of any one of claims 124 to 132, wherein a CSV file identifiable by the map database is formed after data cleaning.

134. The equipment of any one of claims 124 to 133, wherein the CSV file is imported to the map database to construct the knowledge map of sample data.

135. The equipment of any one of claims 124 to 134, wherein any sample source data which does not conform to the structural constitution of the map database is eliminated.

136. The equipment of any one of claims 124 to 135, wherein personnel directly drags illegitimate middleman name enquiring module to enquiring box of the platform for searching.

137. The equipment of any one of claims 124 to 136, wherein each piece of the sample data includes three types of data, including the names data, the telephones data, and the identification code data, three nodes are constructed with reference to each piece of the sample data during constructing the knowledge map, wherein each node represents one piece of data.

138. The equipment of any one of claims 124 to 137, wherein the three nodes in same piece of the sample data are associatively expressed in mode of the indicator lines.

139. The equipment of any one of claims 124 to 138, wherein nodes of same data are duplicate, are removed.

DateRegue/Date Received 2022-06-27

140. The equipment of any one of claims 124 to 139, wherein the indicator lines originally connected with removed nodes are re-connected to the nodes which remain after the duplicate are removed, and the knowledge map is formed.

141.A computer readable physical memory having stored thereon a computer program executed by a computer configured to:
design structural constitution of a map database of query requirement of abnormal financial data, wherein the structural constitution includes expressions of internode relations and nodes;
import clean sample data to the map database to output a knowledge map; and search out the abnormal financial data from the knowledge map.

142. The memory of claim 141, further comprises:
collect plural pieces of sample source data; and subject plural pieces of the sample source data to data cleaning to obtain plural pieces of the clean sample data which conforms to the structural constitution of the map database.

143. The memory of claim 142, wherein designing the structural constitution of the map database of the query requirement of the abnormal financial data comprises:
the query requirement of the abnormal financial data includes to search out illegitimate middleman information from registration information of plural lenders, wherein the registration information of the lenders includes lender information, contact information, transferor information and addressee information, wherein the registration information includes name data, telephone data, and identification code data;
setting plural node types based on plural data types; and designing the map database in accordance with a principle of one node corresponding to one piece of data.
DateRegue/Date Received 2022-06-27

144. The memory of claim 143, wherein collecting plural pieces of the sample source data, and subjecting plural pieces of the sample source data to the data cleaning to obtain plural pieces of the clean sample data which confolms to the structural constitution of the map database comprises:
obtaining plural pieces of lender registration information from a database;
extracting the lender information, the contact information, the transferor information, and the addressee information from each piece of the lender registration information to serve as the sample source data;
preliminarily screening the sample source data;
eliminating any sample source data containing no name data, no telephone data, or no identification code data;
duplicate-checking remaining sample source data;
deleting repetitive sample source data; and subjecting duplicate-checked sample source data to legitimacy verification;
removing any sample source data whose telephone data and identification code data are invalid; and retaining valid sample data.

145. The memory of claim 144, wherein identifying the telephone data and the identification code data as invalid comprises:
comparing the telephone data and the identification code data are consistent with a standard telephone number and a standard identification code in length to judge the telephone data and the identification code data are invalid.

DateRegue/Date Received 2022-06-27

146. The memory of claim 143, wherein identifying the abnormal financial data from the knowledge map comprises:
employing a Cypher language to preset plural abnormal financial data enquiring statements, including an abnormal name enquiring statement, an abnormal telephone enquiring statement, and an abnormal identification code enquiring statement;
setting up the abnormal name enquiring statement, the abnormal telephone enquiring statement, and the abnormal identification code enquiring statement on an enquiring interface in a modular form, wherein a user correspondingly selects enquiring statement input of the query requirement of the abnormal financial data;
randomly spreading out plural pieces of sample data in form of nodes;
associatively forming relational nodes into the knowledge map through indicator lines;
screening out the relational nodes from the knowledge map of an input enquiring statement; and searching out the illegitimate middleman information from screened relational nodes.

147. The memory of claim 146, wherein screening out the relational nodes from the knowledge map of the input enquiring statement, and searching out the illegitimate middleman information from the screened relational nodes comprises:
setting an abnormal node identifying threshold;
outputting any node consistent in type with the input enquiring statement in the relational nodes, when a correlation degree of the relational nodes is greater than the threshold; and obtaining a query result of the illegitimate middleman information.

148. The memory of any one of claims 146 to 147, wherein the correlation degree is defined and obtained of number of the indicator lines connected with the nodes.

149. The memory of any one of claims 141 to 148, wherein plural pieces of the sample source data are collected from a platform.

150. The memory of any one of claims 141 to 149, wherein a CSV file identifiable by the map database is formed after data cleaning.

151. The memory of any one of claims 141 to 150, wherein the CSV file is imported to the map database to construct the knowledge map of sample data.

152. The memory of any one of claims 141 to 151, wherein any sample source data which does not conform to the structural constitution of the map database is eliminated.

153. The memory of any one of claims 141 to 152, wherein personnel directly drags illegitimate middleman name enquiring module to enquiring box of the platform for searching.

154. The memory of any one of claims 141 to 153, wherein each piece of the sample data includes three types of data, including the names data, the telephones data, and the identification code data, three nodes are constructed with reference to each piece of the sample data during constructing the knowledge map, wherein each node represents one piece of data.

155. The memory of any one of claims 141 to 154, wherein the three nodes in same piece of the sample data are associatively expressed in mode of the indicator lines.

156. The memory of any one of claims 141 to 155, wherein nodes of same data are duplicate, are removed.

157. The memory of any one of claims 141 to 156, wherein the indicator lines originally connected with removed nodes are re-connected to the nodes which remain after the duplicate are removed, and the knowledge map is formed.

158.A device comprising:

DateRegue/Date Received 2022-06-27 a map designing unit, configured to design structural constitution of a map database of query requirement of abnormal financial data, wherein the structural constitution includes expressions of intemode relations and nodes;
a sample collecting unit, configured to:
collect plural pieces of sample source data;
subject plural pieces of the sample source data to data cleaning to obtain plural pieces of clean sample data which conforms to the structural constitution of the map database; and an identifying and outputting unit, configured to:
import the clean sample data to the map database to output a knowledge map;
and search out the abnormal financial data from the knowledge map;
the identifying and outputting unit, further comprises:
a setting module, configured to set up an abnormal name enquiring statement, an abnormal telephone enquiring statement, and an abnormal identification code enquiring statement on an enquiring interface in a modular form, wherein a user correspondingly selects enquiring statement input of the query requirement of the abnormal financial data;
a processing module, configured to:
randomly spread out plural pieces of sample data in form of nodes; and associatively form relational nodes into the knowledge map through indicator lines.

159. The device of claim 158, wherein the sample collecting unit further comprises:

DateRegue/Date Received 2022-06-27 an information collecting module, configured to:
obtain plural pieces of lender registration information from a database;
extract lender information, contact information, transferor information, and addressee information from each piece of the lender registration information to serve as the sample source data;
a screening module, configured to:
preliminarily screen the sample source data;
eliminate any sample source data containing no name data, no telephone data, or no identification code data;
a duplicate-checking module, configured to:
duplicate-check remaining sample source data;
delete repetitive sample source data;
a verifying module, configured to:
subject duplicate-checked sample source data to legitimacy verification;
remove any sample source data whose telephone data and identification code data are invalid; and retaining valid sample data.

160. The device of claim 158, wherein the identifying and outputting unit further comprises:
a pre-storing module, configured to employ a Cypher language to preset plural abnormal financial data enquiring statements, including the abnormal name enquiring statement, an abnormal telephone enquiring statement, and an abnormal identification code enquiring statement;
DateRegue/Date Received 2022-06-27 a query outputting module, configured to:
screen out relational nodes from the knowledge map of an input enquiring statement;
identify the abnormal financial data from screened relational nodes; and output the abnormal financial data as a query result.

161. The device of claim 160, wherein screening out the relational nodes from the knowledge map of the input enquiring statement, and identifying the abnormal financial data from the screened relational nodes comprises:
setting an abnormal node identifying threshold;
outputting any node consistent in type with the input enquiring statement in the relational nodes, when a correlation degree of the relational nodes is greater than the threshold; and obtaining a query result of the abnormal financial data.

162. The device of any one of claims 160 to 161, wherein the correlation degree is defined and obtained of number of the indicator lines connected with the nodes.

163. The device of any one of claims 158 to 162, wherein plural pieces of the sample source data are collected from a platform.

164. The device of any one of claims 158 to 163, wherein a CSV file identifiable by the map database is formed after data cleaning.

165. The device of any one of claims 158 to 164, wherein the CSV file is imported to the map database to construct the knowledge map of sample data.

166. The device of any one of claims 158 to 165, wherein any sample source data which does not conform to the structural constitution of the map database is eliminated.

DateRegue/Date Received 2022-06-27

167. The device of any one of claims 158 to 166, wherein personnel directly drags illegitimate middleman name enquiring module to enquiring box of the platform for searching.

168. The device of any one of claims 158 to 167, wherein each piece of the sample data includes three types of data, including the names data, the telephones data, and the identification code data, three nodes are constructed with reference to each piece of the sample data during constructing the knowledge map, wherein each node represents one piece of data.

169. The device of any one of claims 158 to 168, wherein the three nodes in same piece of the sample data are associatively expressed in mode of the indicator lines.

170. The device of any one of claims 158 to 169, wherein nodes of same data are duplicate, are removed.

171. The device of any one of claims 158 to 170, wherein the indicator lines originally connected with removed nodes are re-connected to the nodes which remain after the duplicate are removed, and the knowledge map is formed.

172.A system comprising:
a map designing unit, configured to design structural constitution of a map database of query requirement of abnormal financial data, wherein the structural constitution includes expressions of internode relations and nodes;
a sample collecting unit, configured to:
collect plural pieces of sample source data;
subject plural pieces of the sample source data to data cleaning to obtain plural pieces of clean sample data which conforms to the structural constitution of the map database; and an identifying and outputting unit, configured to:

DateRegue/Date Received 2022-06-27 import the clean sample data to the map database to output a knowledge map;
and search out the abnormal financial data from the knowledge map.
the identifying and outputting unit, further comprises:
a setting module, configured to set up an abnormal name enquiring statement, an abnormal telephone enquiring statement, and an abnormal identification code enquiring statement on an enquiring interface in a modular form, wherein a user correspondingly selects enquiring statement input of the query requirement of the abnormal financial data;
a processing module, configured to:
randomly spread out plural pieces of sample data in form of nodes; and associatively form relational nodes into the knowledge map through indicator lines.

173. The system of claim 172, wherein the sample collecting unit further comprises:
an information collecting module, configured to:
obtain plural pieces of lender registration information from a database;
extract lender information, contact information, transferor information, and addressee information from each piece of the lender registration information to serve as the sample source data;
a screening module, configured to:
preliminarily screen the sample source data;
eliminate any sample source data containing no name data, no telephone data, or no identification code data;

DateRegue/Date Received 2022-06-27 a duplicate-checking module, configured to:
duplicate-check remaining sample source data;
delete repetitive sample source data;
a verifying module, configured to:
subject duplicate-checked sample source data to legitimacy verification;
remove any sample source data whose telephone data and identification code data are invalid; and retaining valid sample data.

174. The system of claim 172, wherein the identifying and outputting unit further comprises:
a pre-storing module, configured to employ a Cypher language to preset plural abnormal financial data enquiring statements, including the abnormal name enquiring statement, the abnormal telephone enquiring statement, and the abnormal identification code enquiring statement;
a query outputting module, configured to:
screen out relational nodes from the knowledge map of an input enquiring statement;
identify the abnoimal financial data from screened relational nodes; and output the abnormal financial data as a query result.

175. The system of claim 174, wherein screening out the relational nodes from the knowledge map of the input enquiring statement, and identifying the abnormal financial data from the screened relational nodes comprises:
setting an abnoimal node identifying threshold;

DateRegue/Date Received 2022-06-27 outputting any node consistent in type with the input enquiring statement in the relational nodes, when a correlation degree of the relational nodes is greater than the threshold; and obtaining a query result of the abnormal financial data.

176. The system of any one of claims 174 to 175, wherein the correlation degree is defined and obtained of number of the indicator lines connected with the nodes.

177. The system of any one of claims 172 to 176, wherein plural pieces of the sample source data are collected from a platform.

178. The system of any one of claims 172 to 177, wherein a CSV file identifiable by the map database is formed after data cleaning.

179. The system of any one of claims 172 to 178, wherein the CSV file is imported to the map database to construct the knowledge map of sample data.

180. The system of any one of claims 172 to 179, wherein any sample source data which does not conform to the structural constitution of the map database is eliminated.

181. The system of any one of claims 172 to 180, wherein personnel directly drags illegitimate middleman name enquiring module to enquiring box of the platform for searching.

182. The system of any one of claims 172 to 181, wherein each piece of the sample data includes three types of data, including the names data, the telephones data, and the identification code data, three nodes are constructed with reference to each piece of the sample data during constructing the knowledge map, wherein each node represents one piece of data.

183. The system of any one of claims 172 to 182, wherein the three nodes in same piece of the sample data are associatively expressed in mode of the indicator lines.

184. The system of any one of claims 172 to 183, wherein nodes of same data are duplicate, are removed.
DateRegue/Date Received 2022-06-27

185. The system of any one of claims 172 to 184, wherein the indicator lines originally connected with removed nodes are re-connected to the nodes which remain after the duplicate are removed, and the knowledge map is formed.

186.A method comprising:
designing structural constitution of a map database of query requirement of abnormal financial data, wherein the structural constitution includes expressions of intemode relations and nodes;
collecting plural pieces of sample source data;
subjecting plural pieces of the sample source data to data clearring to obtain plural pieces of clean sample data which conforms to the structural constitution of the map database;
importing the clean sample data to the map database to output a knowledge map;

searching out the abnormal financial data from the knowledge map;
setting up an abnormal name enquiring statement, an abnormal telephone enquiring statement, and an abnormal identification code enquiring statement on an enquiring interface in a modulax form, wherein a user correspondingly selects enquiring statement input of the query requirement of the abnormal financial data;
randomly spreading out plural pieces of sample data in form of nodes; and associatively forming relational nodes into the knowledge map through indicator lines.

187. The method of claim 186, wherein designing the structural constitution of the map database of the query requirement of the abnormal financial data comprises:

DateRegue/Date Received 2022-06-27 the query requirement of the abnormal financial data includes to search out illegitimate middleman information from registration information of plural lenders, wherein the registration information of the lenders includes lender information, contact information, transferor information and addressee information, wherein the registration information includes name data, telephone data, and identification code data;
setting plural node types based on plural data types; and designing the map database in accordance with a principle of one node corresponding to one piece of data.

188. The method of claim 187, wherein collecting plural pieces of the sample source data, and subjecting plural pieces of the sample source data to the data cleaning to obtain plural pieces of the clean sample data which conforms to the structural constitution of the map database comprises:
obtaining plural pieces of lender registration information from a database;
extracting the lender information, the contact information, the transferor information, and the addressee information from each piece of the lender registration information to serve as the sample source data;
preliminarily screening the sample source data;
eliminating any sample source data containing no name data, no telephone data, or no identification code data;
duplicate-checking remaining sample source data;
deleting repetitive sample source data; and subjecting duplicate-checked sample source data to legitimacy verification;
removing any sample source data whose telephone data and identification code data are invalid; and DateRegue/Date Received 2022-06-27 retaining valid sample data.

189. The method of claim 188, wherein identifying the telephone data and the identification code data as invalid comprises:
comparing the telephone data and the identification code data are consistent with a standard telephone number and a standard identification code in length to judge the telephone data and the identification code data are invalid.

190. The method of claim 187, wherein identifying the abnormal financial data from the knowledge map comprises:
employing a Cypher language to preset plural abnormal financial data enquiring statements, including the abnormal name enquiring statement, the abnormal telephone enquiring statement, and the abnormal identification code enquiring statement;
screening out the relational nodes from the knowledge map of an input enquiring statement; and searching out the illegitimate middleman information from screened relational nodes.

191. The method of claim 190, wherein screening out the relational nodes from the knowledge map of the input enquiring statement, and searching out the illegitimate middleman information from the screened relational nodes comprises:
setting an abnormal node identifying threshold;
outputting any node consistent in type with the input enquiring statement in the relational nodes, when a correlation degree of the relational nodes is greater than the threshold; and obtaining a query result of the illegitimate middleman information.

192. The method of any one of claims 190 to 191, wherein the correlation degree is defined and obtained of number of the indicator lines connected with the nodes.

DateRegue/Date Received 2022-06-27

193. The method of any one of claims 186 to 192, wherein plural pieces of the sample source data are collected from a platform.

194. The method of any one of claims 186 to 193, wherein a CSV file identifiable by the map database is formed after data cleaning.

195. The method of any one of claims 186 to 194, wherein the CSV file is imported to the map database to construct the knowledge map of sample data.

196. The method of any one of claims 186 to 195, wherein any sample source data which does not conform to the structural constitution of the map database is eliminated.

197. The method of any one of claims 186 to 196, wherein personnel directly drags illegitimate middleman name enquiring module to enquiring box of the platform for searching.

198. The method of any one of claims 186 to 197, wherein each piece of the sample data includes three types of data, including the names data, the telephones data, and the identification code data, three nodes are constructed with reference to each piece of the sample data during constructing the knowledge map, wherein each node represents one piece of data.

199. The method of any one of claims 186 to 198, wherein the three nodes in same piece of the sample data are associatively expressed in mode of the indicator lines.

200. The method of any one of claims 186 to 199, wherein nodes of same data are duplicate, are removed.

201. The method of any one of claims 186 to 200, wherein the indicator lines originally connected with removed nodes are re-connected to the nodes which remain after the duplicate are removed, and the knowledge map is formed.

202. An equipment comprising:

DateRegue/Date Received 2022-06-27 designing structural constitution of a map database of query requirement of abnormal financial data, wherein the structural constitution includes expressions of intemode relations and nodes;
collecting plural pieces of sample source data;
subjecting plural pieces of the sample source data to data cleaning to obtain plural pieces of clean sample data which conforms to the structural constitution of the map database;
importing the clean sample data to the map database to output a knowledge map;

searching out the abnormal financial data from the knowledge map;
setting up an abnormal name enquiring statement, an abnormal telephone enquiring statement, and an abnormal identification code enquiring statement on an enquiring interface in a modular form, wherein a user correspondingly selects enquiring statement input of the query requirement of the abnormal financial data;
randomly spreading out plural pieces of sample data in form of nodes; and associatively forming relational nodes into the knowledge map through indicator lines.

203. The equipment of claim 202, wherein designing the structural constitution of the map database of the query requirement of the abnormal financial data comprises:
the query requirement of the abnormal financial data includes to search out illegitimate middleman information from registration information of plural lenders, wherein the registration information of the lenders includes lender information, contact information, transferor information and addressee information, wherein the registration information includes name data, telephone data, and identification code data;
setting plural node types based on plural data types; and designing the map database in accordance with a principle of one node corresponding to one piece of data.
DateRegue/Date Received 2022-06-27

204. The equipment of claim 203, wherein collecting plural pieces of the sample source data, and subjecting plural pieces of the sample source data to the data cleaning to obtain plural pieces of the clean sample data which confolms to the structural constitution of the map database comprises:
obtaining plural pieces of lender registration information from a database;
extracting the lender information, the contact information, the transferor information, and the addressee information from each piece of the lender registration information to serve as the sample source data;
preliminarily screening the sample source data;
eliminating any sample source data containing no name data, no telephone data, or no identification code data;
duplicate-checking remaining sample source data;
deleting repetitive sample source data; and subjecting duplicate-checked sample source data to legitimacy verification;
removing any sample source data whose telephone data and identification code data are invalid; and retaining valid sample data.

205. The equipment of claim 204, wherein identifying the telephone data and the identification code data as invalid comprises:
comparing the telephone data and the identification code data are consistent with a standard telephone number and a standard identification code in length to judge the telephone data and the identification code data are invalid.

DateRegue/Date Received 2022-06-27

206. The equipment of claim 203, wherein identifying the abnormal financial data from the knowledge map comprises:
employing a Cypher language to preset plural abnormal financial data enquiring statements, including the abnormal name enquiring statement, the abnormal telephone enquiring statement, and the abnormal identification code enquiring statement;
screening out the relational nodes from the knowledge map of an input enquiring statement and searching out the illegitimate middleman information from screened relational nodes.

207. The equipment of claim 206, wherein screening out the relational nodes from the knowledge map of the input enquiring statement, and searching out the illegitimate middleman information from the screened relational nodes comprises:
setting an abnormal node identifying threshold;
outputting any node consistent in type with the input enquiring statement in the relational nodes, when a correlation degree of the relational nodes is greater than the threshold; and obtaining a query result of the illegitimate middleman information.

208. The equipment of any one of claims 206 to 207, wherein the correlation degree is defined and obtained of number of the indicator lines connected with the nodes.

209. The equipment of any one of claims 202 to 208, wherein plural pieces of the sample source data are collected from a platform.

210. The equipment of any one of claims 202 to 209, wherein a CSV file identifiable by the map database is formed after data cleaning.

211. The equipment of any one of claims 202 to 210, wherein the CSV file is imported to the map database to construct the knowledge map of sample data.

DateRegue/Date Received 2022-06-27

212. The equipment of any one of claims 202 to 211, wherein any sample source data which does not conform to the structural constitution of the map database is eliminated.

213. The equipment of any one of claims 202 to 212, wherein personnel directly drags illegitimate middleman name enquiring module to enquiring box of the platform for searching.

214. The equipment of any one of claims 202 to 213, wherein each piece of the sample data includes three types of data, including the names data, the telephones data, and the identification code data, three nodes are constructed with reference to each piece of the sample data during constructing the knowledge map, wherein each node represents one piece of data.

215. The equipment of any one of claims 202 to 214, wherein the three nodes in same piece of the sample data are associatively expressed in mode of the indicator lines.

216. The equipment of any one of claims 202 to 215, wherein nodes of same data are duplicate, are removed.

217. The equipment of any one of claims 202 to 216, wherein the indicator lines originally connected with removed nodes are re-connected to the nodes which remain after the duplicate are removed, and the knowledge map is formed.

218.A computer readable physical memory having stored thereon a computer program executed by a computer configured to:
designing structural constitution of a map database of query requirement of abnormal financial data, wherein the structural constitution includes expressions of intemode relations and nodes;
collecting plural pieces of sample source data;
subjecting plural pieces of the sample source data to data cleaning to obtain plural pieces of clean sample data which conforms to the structural constitution of the map database;

DateRegue/Date Received 2022-06-27 importing the clean sample data to the map database to output a knowledge map;

searching out the abnormal financial data from the knowledge map;
setting up an abnormal name enquiring statement, an abnormal telephone enquiring statement, and an abnormal identification code enquiring statement on an enquiring interface in a modular form, wherein a user correspondingly selects enquiring statement input of the query requirement of the abnormal financial data;
randomly spreading out plural pieces of sample data in form of nodes; and associatively forming relational nodes into the knowledge map through indicator lines.

219. The memory of claim 218, wherein designing the structural constitution of the map database of the query requirement of the abnormal financial data comprises:
the query requirement of the abnormal financial data includes to search out illegitimate middleman information from registration information of plural lenders, wherein the registration information of the lenders includes lender information, contact information, transferor information and addressee information, wherein the registration information includes name data, telephone data, and identification code data;
setting plural node types based on plural data types; and designing the map database in accordance with a principle of one node corresponding to one piece of data.

220. The memory of claim 219, wherein collecting plural pieces of the sample source data, and subjecting plural pieces of the sample source data to the data cleaning to obtain plural pieces of the clean sample data which conforms to the structural constitution of the map database comprises:
obtaining plural pieces of lender registration information from a database;

DateRegue/Date Received 2022-06-27 extracting the lender information, the contact information, the transferor information, and the addressee information from each piece of the lender registration information to serve as the sample source data;
preliminarily screening the sample source data;
eliminating any sample source data containing no name data, no telephone data, or no identification code data;
duplicate-checking remaining sample source data;
deleting repetitive sample source data; and subjecting duplicate-checked sample source data to legitimacy verification;
removing any sample source data whose telephone data and identification code data are invalid; and retaining valid sample data.

221. The memory of claim 220, wherein identifying the telephone data and the identification code data as invalid comprises:
comparing the telephone data and the identification code data are consistent with a standard telephone number and a standard identification code in length to judge the telephone data and the identification code data are invalid.

222. The memory of claim 221, wherein identifying the abnormal financial data from the knowledge map comprises:
employing a Cypher language to preset plural abnormal financial data enquiring statements, including the abnormal name enquiring statement, the abnormal telephone enquiring statement, and the abnormal identification code enquiring statement DateRegue/Date Received 2022-06-27 screening out the relational nodes from the knowledge map of an input enquiring statement; and searching out the illegitimate middleman information from screened relational nodes.

223. The memory of claim 222, wherein screening out the relational nodes from the knowledge map of the input enquiring statement, and searching out the illegitimate middleman information from the screened relational nodes comprises:
setting an abnormal node identifying threshold;
outputting any node consistent in type with the input enquiring statement in the relational nodes, when a correlation degree of the relational nodes is greater than the threshold; and obtaining a query result of the illegitimate middleman information.

224. The memory of any one of claims 222 to 223, wherein the correlation degree is defined and obtained of number of the indicator lines connected with the nodes.

225. The memory of any one of claims 218 to 224, wherein plural pieces of the sample source data are collected from a platform.

226. The memory of any one of claims 218 to 225, wherein a CSV file identifiable by the map database is formed after data cleaning.

227. The memory of any one of claims 218 to 226, wherein the CSV file is imported to the map database to construct the knowledge map of sample data.

228. The memory of any one of claims 218 to 227, wherein any sample source data which does not conform to the structural constitution of the map database is eliminated.

229. The memory of any one of claims 218 to 228, wherein personnel directly drags illegitimate middleman name enquiring module to enquiring box of the platform for searching.

DateRegue/Date Received 2022-06-27

230. The memory of any one of claims 218 to 229, wherein each piece of the sample data includes three types of data, including the names data, the telephones data, and the identification code data, three nodes are constructed with reference to each piece of the sample data during constructing the knowledge map, wherein each node represents one piece of data.

231. The memory of any one of claims 218 to 230, wherein the three nodes in same piece of the sample data are associatively expressed in mode of the indicator lines.

232. The memory of any one of claims 218 to 231, wherein nodes of same data are duplicate, are removed.

233. The memory of any one of claims 218 to 232, wherein the indicator lines originally connected with removed nodes are re-connected to the nodes which remain after the duplicate are removed, and the knowledge map is formed.

DateRegue/Date Received 2022-06-27