CN114357970A - Credit investigation report analysis method, system, terminal equipment and storage medium - Google Patents
Credit investigation report analysis method, system, terminal equipment and storage medium Download PDFInfo
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Abstract
The invention provides a credit investigation report analysis method, a system, terminal equipment and a storage medium, which are used for constructing credit investigation report characteristics and a credit investigation report characteristic group, reporting a credit investigation report identifier in a database, taking out a corresponding credit investigation report and calculating the credit investigation report characteristics according to the taken-out credit investigation report; and filtering, marking and calculating all credit investigation reports in a folder in which the credit investigation reports are located, creating a model data set, and storing the model data set to a database. The method and the device realize that the same characteristic can acquire the user information from credit investigation reports of different formats, support the filtering, conversion and function calculation of the user information according to specific requirements, and develop and calculate the characteristic on line according to the service, so that the analysis of the credit investigation reports is more flexible, automatic and intelligent, and the analysis efficiency of the credit investigation reports is accelerated. The problem of the difficulty in analyzing credit investigation report files of different styles in the prior art is solved.
Description
Technical Field
The invention belongs to the technical field of financial services, and relates to a credit investigation report analysis method, a credit investigation report analysis system, terminal equipment and a storage medium.
Background
In the financial loan business, credit investigation reports play a significant role, all loan businesses need to use the credit investigation reports for risk assessment, and develop their own business according to the assessment reports, so each financial institution can establish its own credit investigation system to acquire, analyze and store credit investigation data, which results in the diversity of credit investigation reports. For example, there are various document structures such as object Markup Language JavaScript object notation, JSON, Extensible Markup Language (XML), and HTML in the personal information report. This creates great difficulty in parsing the report for this important module. At present, most report analysis work only aims at the credit investigation report with a fixed format, how to realize the analysis of credit investigation report files with various styles, and the problem of acquiring user information is to be solved.
Disclosure of Invention
The invention aims to solve the problems in the prior art and provides a credit investigation report analysis method, a credit investigation report analysis system, terminal equipment and a storage medium.
In order to achieve the purpose, the invention adopts the following technical scheme to realize the purpose:
a credit investigation report analysis method comprises the following steps:
s1: receiving related information for constructing credit investigation report characteristics, and storing the constructed credit investigation report characteristics into a database;
s2: grouping the credit investigation report characteristics, creating a credit investigation report characteristic group, and storing the created credit investigation report characteristic group into a database;
s3: according to the credit investigation report mark in the credit investigation report database, taking out the corresponding credit investigation report, and according to the taken-out credit investigation report, calculating the characteristics of the credit investigation report;
s4: receiving a folder where the credit investigation report is located and a credit investigation report characteristic group, filtering and marking all credit investigation reports in the folder, calculating all credit investigation report characteristics, creating a model data set, and storing the model data set to a database.
The invention is further improved in that:
the S1 includes the steps of:
receiving an input credit investigation report characteristic English name and Chinese name, wherein the Chinese name is used as a basic information identifier, and the English name is used as a unique credit investigation report characteristic identifier;
and packaging the received information into an object, serializing the object into a JSON character string, and persisting the JSON character string into a database.
The S3 includes the steps of:
and generating a class file for deserializing the credit report, converting the credit report into a CreditReport object, converting the CreditReport object into an ObjectTree structure object, extracting data from the ObjectTree, mapping and filtering the extracted data, and calculating a final result.
In S3, the method for generating the deserialized class file of the credit investigation report includes:
the method comprises the steps of transmitting a folder or a file name where a credit investigation report is located, deserializing the folder or the file name, converting an object generated by deserializing into a structNode structure, merging Trees obtained by all credit investigation reports into a monocular Tree, carrying out alias mapping on the merged monocular Tree according to a hierarchical structure, reading a configuration file, and obtaining a Package corresponding to a generated class file.
The S4 includes the steps of:
receiving the document where the credit investigation report is located, dividing all received credit investigation report characteristics into two parts, namely credit investigation report service characteristics and credit investigation report mark characteristics, sequentially calculating all the credit investigation report characteristics, splitting the characteristics into service characteristic groups and mark characteristics again after the calculation is completed, then packaging the calculation results in a JSON format, marking the calculated credit investigation report, serializing the final packaged results into JSON character strings, and uploading the JSON character strings to a database.
The calculation result of the credit investigation report service characteristic corresponds to the characteristic value of the credit investigation report in the service and is used as a main body of a data set; and the calculation result of the credit investigation report marking characteristic is used as the marking result of the credit investigation report.
A credit investigation report analysis system comprises a credit investigation report characteristic module, a credit investigation report characteristic group module, a credit investigation report characteristic calculation module and a model data set module;
the credit investigation report characteristic module is used for receiving relevant information for constructing the credit investigation report characteristic, constructing the credit investigation report characteristic and storing the constructed credit investigation report characteristic into a database;
the credit investigation report characteristic group module is used for grouping the credit investigation report characteristics, creating a credit investigation report characteristic group and storing the created credit investigation report characteristic group into a database;
the credit investigation report characteristic calculation module is used for taking out a corresponding credit investigation report according to the credit investigation report identifier in the credit investigation report database and calculating the credit investigation report characteristic according to the taken-out credit investigation report;
and the model data set module is used for receiving the folder where the credit investigation report is located and the credit investigation report characteristic group, filtering and marking all credit investigation reports in the folder, calculating all credit investigation report characteristics, creating a model data set and storing the model data set in a database.
A terminal device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the steps of the method according to any one of claims 1-6 when executing the computer program.
A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 6.
Compared with the prior art, the invention has the following beneficial effects:
the invention provides a credit investigation report analysis method, which analyzes and calculates the credit investigation report characteristics by constructing a credit investigation report characteristic and a credit investigation report characteristic group, realizes that the same characteristic can acquire user information from credit investigation reports of different formats, supports the filtration, conversion and function calculation of the user information according to specific requirements, can develop and calculate the characteristics on line according to services, ensures that the analysis of the credit investigation report is more flexible, automatic and intelligent, and accelerates the analysis efficiency of the credit investigation report.
Furthermore, the invention can dynamically generate the class file for analyzing the credit investigation report file, thereby realizing the dynamic analysis of credit investigation reports with different formats. And then through alias mapping, the problem of inconsistent label names of credit investigation reports of different organizations is solved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention, and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts;
fig. 1 is a flow chart of the credit investigation report feature construction according to the embodiment of the present invention;
fig. 2 is a diagram illustrating the calculation of credit reporting features according to an embodiment of the present invention;
FIG. 3 is a flowchart of class file construction according to an embodiment of the present invention;
FIG. 4 is a data set creation flow of an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. The components of embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures.
In the description of the embodiments of the present invention, it should be noted that if the terms "upper", "lower", "horizontal", "inner", etc. are used for indicating the orientation or positional relationship based on the orientation or positional relationship shown in the drawings or the orientation or positional relationship which is usually arranged when the product of the present invention is used, the description is merely for convenience and simplicity, and the indication or suggestion that the referred device or element must have a specific orientation, be constructed and operated in a specific orientation, and thus, cannot be understood as limiting the present invention. Furthermore, the terms "first," "second," and the like are used merely to distinguish one description from another, and are not to be construed as indicating or implying relative importance.
Furthermore, the term "horizontal", if present, does not mean that the component is required to be absolutely horizontal, but may be slightly inclined. For example, "horizontal" merely means that the direction is more horizontal than "vertical" and does not mean that the structure must be perfectly horizontal, but may be slightly inclined.
In the description of the embodiments of the present invention, it should be further noted that unless otherwise explicitly stated or limited, the terms "disposed," "mounted," "connected," and "connected" should be interpreted broadly, and may be, for example, fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood by those skilled in the art according to specific situations.
The invention is described in further detail below with reference to the accompanying drawings:
referring to fig. 1, an embodiment of the present invention discloses a credit investigation report parsing method, including the following steps:
s1: receiving related information for constructing the credit investigation report characteristics, storing the constructed credit investigation report characteristics into a database, and providing query and modification functions;
the credit investigation report features are divided into three parts, namely data acquisition, data processing and data calculation. After receiving the relevant configuration information, packaging the configuration information into an object, serializing the object into a JSON character string, and persisting the JSON character string into a database;
referring to fig. 1, the method specifically includes the following steps:
step S101, receiving an input credit investigation report characteristic English name, wherein the Chinese name is used as a basic information identifier, and the characteristic English name is used as a unique credit investigation report characteristic identifier in the invention.
Step S102, packaging the path of the needed data. The invention takes path as path identification. The value of path is a character string formed by splicing node names of all nodes passing through the character string from the root node to the target node, and is used as a separator between all the node names.
And step S103, packaging the pretreatment operation before filtration. Creating a function required by preprocessing, and adding preprocessing operation to the columns required to be preprocessed. The preprocessing function of the invention is realized by function call.
Step S104, packaging and filtering operation. The invention refers to the filtering operation in the database, and can adopt keywords such as more than, more than or equal to, less than or equal to, in, not in and the like, and filter the whole two-dimensional table according to rows according to a column of data;
step S105, encapsulate the calculation operation. Functions required for the computing operation are created to compute the final result of the feature. The calculation function of the invention is realized by adopting a function service calling method. The function inputs are a plurality of lists, and the function outputs are a character string as the final calculation result.
And step S106, performing serialization operation on all packaged objects to obtain a JSON character string, and returning, namely the characteristics obtained by creation.
S2: according to the service logic, the credit investigation report characteristics are grouped, a credit investigation report characteristic group is created, and the created credit investigation report characteristic group is stored in a database to provide inquiry and modification functions;
s3: according to the credit investigation report identification in the credit investigation report database, taking out the corresponding credit investigation report, and according to the credit investigation report and the credit investigation report characteristics, realizing the calculation of the credit investigation report characteristics;
the method comprises the steps of receiving credit investigation report identification or credit investigation report file and credit investigation report characteristic identification or credit investigation report characteristic JSON character string in a database, completing calculation of credit investigation report characteristics, and returning a calculation result.
And receiving the credit investigation report identifier or the credit investigation report and the credit investigation report feature group identifier or the credit investigation report feature group JSON character string in the database, finishing the calculation of one feature group, and returning a result in the form of the JSON character string.
And deserializing the credit investigation report to generate a Java object.
And converting the Java object into an ObjectTree structure with a fixed structure. There are four attributes in the ObjectTree structure, node name, ObjectChildren, ListChildren, and data.
And carrying out mapping operation on the data while carrying out ObjectTree structure conversion, and mapping the data identified by some numbers into Chinese data of corresponding services.
Referring to fig. 2, the method specifically includes the following steps:
step S301, according to the credit investigation report file, generating a class file for deserializing the credit investigation report. Different strategies are adopted for different types of credit investigation reports, and the types supporting the credit investigation reports in the invention are three types, namely XML, JSON and Excel hierarchical structure models.
Step S302, the credit report is deserialized and converted into a CreditReport object. Different strategies will be employed for different types of credit reporting. The credit report supported by the invention has two formats, which are respectively: XML, JSON.
Step S303, convert the CreditReport object into an ObjectTree structure object. There are four attributes in the ObjectTree structure, node name, ObjectChildren, ListChildren, and data. The present invention converts the CreditReport object into an ObjectTree object using a recursive algorithm. In the transformation process, all child nodes are divided into two classes, namely objects and arrays. HashMap is used as a data structure, the name of an object is used as the key of the map, and the Tree after the object is converted is used as the value of the map. And data mapping is carried out while conversion is carried out, and some digital identification data are mapped into corresponding Chinese meanings, so that understanding and operation are facilitated.
Step S304, a data fetch operation. The invention adopts a recursive algorithm to fetch the data from the ObjectTree according to the path encapsulated in the fetch logic. The process of taking numbers in the invention is divided into three conditions: first, a path is interrupted. When data is searched through the path, if a corresponding node cannot be found in the path searching process, the path is interrupted, which means that the path data does not exist, and a specific nonexistence identifier is directly returned. Secondly, if the path is not interrupted and the last node is a leaf node, directly returning the data of the leaf node as a search result. Thirdly, if the path is not interrupted but the last node is not a leaf node, the last node is wholly serialized to be converted into a JSON character string, and the JSON character string is used as a final result. And finally, packaging the obtained data into a two-dimensional table, and taking the identifier of each path as a column name.
Step S305, a preprocessing operation. And preprocessing the column data according to the filtering preprocessing logic encapsulated in the credit report characteristics. The invention adopts a scheme of function calling to realize preprocessing operation. The preprocessing operation contains function names and column names, the function computing service is called, and the function names and parameters are transmitted to carry out preprocessing operation. The function calculation service of the invention provides the functions of Java and python function calculation. Firstly, column data is obtained in a two-dimensional table according to column names, the data is converted, commas are used as separators, and a List is converted into a character string. And then, preprocessing operation is carried out at the function calculation server, a character string with commas as separators is also returned, and the character string is split into a List as new data. Old data in the table is deleted and new data is added.
Step S306, filtering operation. And filtering the two-dimensional table according to the filtering logic encapsulated in the credit report characteristics. The invention refers to the filtering operation in the Mysql database, and realizes the filtering of logics such as greater than, greater than or equal to, less than or equal to, in, not in and the like. Firstly, according to the column name, the column to be filtered is obtained, column data is traversed, whether the data meets the condition or not is judged according to the packaged filtering logic, and if the data does not meet the condition, the row of data is deleted in the two-dimensional table.
Step S307, a calculation operation. And calculating a final result according to the calculation logic encapsulated in the credit investigation report characteristics. The invention adopts the mode of calling functions to complete the calculation operation. The method is characterized in that function names and column names are packaged, function calculation services are called, and function names and parameters are transmitted to carry out calculation operation. Firstly, all the line data are taken from the two-dimensional table, data conversion is carried out on each line data, and commas are used as separators and converted into a character string. And then converting all column data into a JSON character string serving as a parameter by taking the column name as key. And at the function calculation server, positioning a function inlet according to the function name, calculating the transmitted parameters, and returning a character string as a calculation result.
Step S308, the calculation result is returned. And returning the result in the form of a JSON character string. The credit report characteristic name is used as a key, and the calculation result is value.
Referring to fig. 3, the creating process of the S301 class file in this embodiment:
step S301-1, a file folder or a file name where the credit investigation report is located is transmitted. And if the incoming file is a folder, deserializing all credit investigation reports in the folder by adopting a file deserializing tool. If the file is the file, the file is deserialized. The invention supports two file types, JSON and XML.
And S301-2, converting the object generated by the deserialization in the first step into a structNode structure. The structNode of the present invention contains four attributes, which are a node name, a node alias, a node type, and a child node. The data type of the child node is List, and the general type of the List is structNode. The conversion logic of the present invention is as follows: if the child of the current Object is the Object type, directly reserving the structNode converted by the element Object as a child node, and setting the node type as Object; if the child of the current object is an array type, the structNode converted by each element in the array is taken as a child node, and the node type is modified into a List.
Step S301-3, tree merging. And merging the Trees obtained by all credit reports into a monocular Tree. The meaning of the monocular tree in the invention is as follows: any one node does not have two children of the same name. Firstly, each structNode in the List is merged, and all the structNodes are converted into a monocular tree. The processing logic of the invention is as follows: if a child node of a node has a different name than all other child nodes, the child node is directly retained and the type of the node is modified to Object. If two or more child nodes in the node have the same name, merging all the child nodes with the same name to obtain a node, modifying the type of the node to be List, and merging to obtain a monocular tree. All merged monocular trees are then traversed from the second tree and merged with the first tree. The merging logic of the invention is as follows, the role positioning of the two trees is divided into a root tree and a resource tree, and the node resources in the resource tree are adopted to supplement the nodes of the root tree. If the resource tree contains a node which is not in the tree, the node is directly added into the root tree. If there are nodes with the same name, the combination is carried out recursively. And modifying the node type of the resource tree according to the node type of the resource tree while merging, wherein the modification rule is List > Object > String.
Step S301-4, alias mapping. And carrying out alias mapping on the merged monocular tree according to a hierarchical structure. The invention maps the alias of the node according to the hierarchical access path of the node in the tree. When writing class files, the attribute with class name as class file and the node name as the name of anti-sequence file are used to form one-to-one mapping relation
And step S301-5, writing the class file. And reading the configuration file, and acquiring the Package corresponding to the generated class file. The method adopts a depth-first traversal algorithm and uses a Velocity template technology to generate a corresponding class file for each node. The class name is the hierarchical access path of the node, and the attributes of the class are all the child nodes of the node. The class file generated in the invention needs to be used together with the logbook plug-in.
S4: and receiving the folder where the credit investigation report is located and the credit investigation report characteristic group, filtering and marking all credit investigation reports in the folder, creating a model data set, and storing the model data set in a database.
The incoming credit report features are divided into two parts, namely credit report service features and credit report marking features. After all credit investigation report characteristics are calculated, the calculation result of the service characteristics corresponds to the characteristic value of the credit investigation report in the service and is used as the main body of the data set. And processing the calculation result of the marking characteristic according to a corresponding strategy to obtain a result as a marking result of the credit investigation report.
Referring to fig. 4, step S401, a document where the credit report is located is transmitted. All credit reports in this document library are flagged.
And step S402, transmitting the credit report service characteristic group and the credit report mark characteristic.
In step S403, all features are calculated. And putting the rules and the marked features of the service feature group into a Set for duplication removal, so as to prevent repeated calculation. And then calling feature calculation service, transmitting a credit report and a feature set, and finishing the calculation of the whole feature set.
Step S404, packaging the single data set in JSON format. And after all feature set calculation is completed, further re-splitting the features into service feature groups and marked features. And then packaging the calculation result in a JSON format. The receiving and sending format is { "data":, "result": }. Where the data encapsulates the values of all sets of traffic characteristics. result is the calculated value of the signature.
And step S405, circularly marking all credit investigation reports in the document, and completely encapsulating. And inquiring all credit investigation reports in the document, and circulating 503 and 504 to finish the calculation of all credit investigation reports. And packaging the final result into a JSON character string in an array format.
And step S406, serializing the final encapsulated result into a JSON character string, uploading the JSON character string to a database, and providing a downloading function.
The embodiment of the invention also discloses a credit investigation report analysis system which comprises a credit investigation report characteristic module, a credit investigation report characteristic group module, a credit investigation report characteristic calculation module and a model data set module
The credit investigation report characteristic module is used for receiving relevant information for constructing the credit investigation report characteristic, constructing the credit investigation report characteristic and storing the constructed credit investigation report characteristic into a database;
the credit investigation report characteristic group module is used for grouping the credit investigation report characteristics, creating a credit investigation report characteristic group and storing the created credit investigation report characteristic group into a database;
the credit investigation report characteristic calculation module is used for taking out a corresponding credit investigation report according to the credit investigation report identifier in the credit investigation report database and calculating the credit investigation report characteristic according to the taken-out credit investigation report;
and the model data set module is used for receiving the folder where the credit investigation report is located and the credit investigation report characteristic group, filtering and marking all credit investigation reports in the folder, calculating all credit investigation report characteristics, creating a model data set and storing the model data set in a database.
According to the embodiment of the invention, the class file for analyzing the credit investigation report file can be dynamically generated through the class construction module, so that dynamic analysis of credit investigation reports in different formats is realized. And then through alias mapping, the problem of inconsistent label names of credit investigation reports of different organizations is solved. The feature construction is carried out from the data acquisition part, the data processing part and the data calculation part respectively, so that the user information can be acquired from credit investigation reports of different formats by the same feature, and the filtering, conversion and function calculation of the user information according to specific requirements are supported. Meanwhile, the characteristics can be developed and calculated on line according to the service, so that the analysis of the credit investigation report is more flexible, automatic and intelligent, and the analysis efficiency of the credit investigation report is accelerated.
An embodiment of the present invention provides a schematic diagram of a terminal device. The terminal device of this embodiment includes: a processor, a memory, and a computer program stored in the memory and executable on the processor. The processor realizes the steps of the above-mentioned method embodiments when executing the computer program. Alternatively, the processor implements the functions of the modules/units in the above device embodiments when executing the computer program.
The computer program may be partitioned into one or more modules/units that are stored in the memory and executed by the processor to implement the invention.
The terminal device can be a desktop computer, a notebook, a palm computer, a cloud server and other computing devices. The terminal device may include, but is not limited to, a processor, a memory.
The processor may be a Central Processing Unit (CPU), other general purpose processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, etc.
The memory may be used for storing the computer programs and/or modules, and the processor may implement various functions of the terminal device by executing or executing the computer programs and/or modules stored in the memory and calling data stored in the memory.
The terminal device integrated modules/units, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. Based on such understanding, all or part of the flow of the method according to the embodiments of the present invention may also be implemented by a computer program, which may be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps of the method embodiments may be implemented. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer memory, Read-only memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, etc. It should be noted that the computer readable medium may contain content that is subject to appropriate increase or decrease as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable media does not include electrical carrier signals and telecommunications signals as is required by legislation and patent practice.
The above is only a preferred embodiment of the present invention, and is not intended to limit the present invention, and various modifications and changes will occur to those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (9)
1. A credit investigation report analysis method is characterized by comprising the following steps:
s1: receiving related information for constructing credit investigation report characteristics, and storing the constructed credit investigation report characteristics into a database;
s2: grouping the credit investigation report characteristics, creating a credit investigation report characteristic group, and storing the created credit investigation report characteristic group into a database;
s3: according to the credit investigation report mark in the credit investigation report database, taking out the corresponding credit investigation report, and according to the taken-out credit investigation report, calculating the characteristics of the credit investigation report;
s4: receiving a folder where the credit investigation report is located and a credit investigation report characteristic group, filtering and marking all credit investigation reports in the folder, calculating all credit investigation report characteristics, creating a model data set, and storing the model data set to a database.
2. The method for resolving an investigation report according to claim 1, wherein the S1 comprises the following steps:
receiving an input credit investigation report characteristic English name and Chinese name, wherein the Chinese name is used as a basic information identifier, and the English name is used as a unique credit investigation report characteristic identifier;
and packaging the received information into an object, serializing the object into a JSON character string, and persisting the JSON character string into a database.
3. The method for resolving an investigation report according to claim 1, wherein the S3 comprises the following steps:
and generating a class file for deserializing the credit report, converting the credit report into a CreditReport object, converting the CreditReport object into an ObjectTree structure object, extracting data from the ObjectTree, mapping and filtering the extracted data, and calculating a final result.
4. The method for analyzing an investigation report according to claim 3, wherein in the step S3, the method for generating the desequenced class file of the investigation report comprises:
the method comprises the steps of transmitting a folder or a file name where a credit investigation report is located, deserializing the folder or the file name, converting an object generated by deserializing into a structNode structure, merging Trees obtained by all credit investigation reports into a monocular Tree, carrying out alias mapping on the merged monocular Tree according to a hierarchical structure, reading a configuration file, and obtaining a Package corresponding to a generated class file.
5. The method for resolving the credit report of claim 4, wherein the S4 comprises the following steps:
receiving the document where the credit investigation report is located, dividing all received credit investigation report characteristics into two parts, namely credit investigation report service characteristics and credit investigation report mark characteristics, sequentially calculating all the credit investigation report characteristics, splitting the characteristics into service characteristic groups and mark characteristics again after the calculation is completed, then packaging the calculation results in a JSON format, marking the calculated credit investigation report, serializing the final packaged results into JSON character strings, and uploading the JSON character strings to a database.
6. The credit investigation report parsing method of claim 5, wherein the calculation result of the credit investigation report service feature corresponds to the feature value of the credit investigation report in the service, and is used as the main body of the data set; and the calculation result of the credit investigation report marking characteristic is used as the marking result of the credit investigation report.
7. The credit investigation system of claim 1, comprising a credit investigation feature module, a credit investigation feature group module, a credit investigation feature calculation module and a model data set module;
the credit investigation report characteristic module is used for receiving relevant information for constructing the credit investigation report characteristic, constructing the credit investigation report characteristic and storing the constructed credit investigation report characteristic into a database;
the credit investigation report characteristic group module is used for grouping the credit investigation report characteristics, creating a credit investigation report characteristic group and storing the created credit investigation report characteristic group into a database;
the credit investigation report characteristic calculation module is used for taking out a corresponding credit investigation report according to the credit investigation report identifier in the credit investigation report database and calculating the credit investigation report characteristic according to the taken-out credit investigation report;
and the model data set module is used for receiving the folder where the credit investigation report is located and the credit investigation report characteristic group, filtering and marking all credit investigation reports in the folder, calculating all credit investigation report characteristics, creating a model data set and storing the model data set in a database.
8. A terminal device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the steps of the method according to any of claims 1-6 when executing the computer program.
9. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 6.
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