CN114219443A - Document data processing method, device and equipment - Google Patents
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Abstract
The application provides a document data processing method, a device and equipment, which relate to the data processing technology, and the method comprises the following steps: acquiring a plurality of documents to be processed; the multiple documents to be processed comprise first documents data of a first node and second documents data of a second node, and the second node is an upper node of the first node; acquiring a plurality of rule expressions in a preset rule base by using a preset rule engine; the rule base comprises a plurality of rule expressions, and the rule engine is a program for executing the rule expressions; and based on the corresponding relation between the document data to be processed and the regular expression, performing data processing on each document data to be processed through the regular expression corresponding to each document data to be processed to obtain auditing result information. According to the method, the rule expressions in the rule base are automatically called through the rule engine, data processing is carried out on the document data to be processed through the rule expressions, an automatic document examination process is formed, and the document examination efficiency and accuracy are greatly improved.
Description
Technical Field
The present application relates to data processing technologies, and in particular, to a method, an apparatus, and a device for processing document data.
Background
Currently, with the development of trade, the operation of examining the letter of credit is very important.
In the prior art, in the document examination operation process of the credit card, information in a paper document is usually manually entered into a system, then manually checked one by one according to an examination rule to obtain an examination result, and finally all examination results are summarized to generate an examination conclusion.
However, in the prior art, the time required for manual review is long, and omission or errors are easy to occur, so that the accuracy and efficiency of the list review are low.
Disclosure of Invention
The application provides a document data processing method, device and equipment, which are used for solving the technical problems of low accuracy and low efficiency of document examination business.
In a first aspect, the present application provides a document data processing method, including:
acquiring a plurality of documents to be processed; the document data to be processed comprises first document data of a first node and second document data of a second node, and the second node is an upper node of the first node;
acquiring a plurality of rule expressions in a preset rule base by using a preset rule engine; the rule base comprises a plurality of rule expressions, and the rule engine is a program for executing the rule expressions;
and based on the corresponding relation between the document data to be processed and the regular expression, performing data processing on each document data to be processed through the regular expression corresponding to each document data to be processed to obtain auditing result information.
Further, acquiring a plurality of to-be-processed document data includes:
determining data information of first bill data of a first node through a named entity identification method and a regular matching method, wherein the data information comprises a bill name and a bill value;
determining an upper node corresponding to the name in the data information of the first document data as a second node in a preset database based on the corresponding relation between the preset document name and the upper node; wherein the second node has data information of second document data;
and replacing the bill value of the second bill data of the second node with the bill value of the first bill data of the first node to obtain the updated second bill data of the second node.
Further, the document value comprises an audit element and an element value corresponding to the audit element.
Further, the method further comprises:
acquiring a plurality of document names;
determining an upper node corresponding to each bill name, generating a corresponding relation between the bill name and the upper node according to each bill name and the upper node corresponding to each bill name, and storing the corresponding relation in a database.
Further, the method further comprises:
acquiring a plurality of rule texts; the rule text comprises a document name, a document value and an operator name;
determining an upper node corresponding to each bill name according to the corresponding relation between the bill name and the upper node;
and generating a regular expression corresponding to the upper node according to the bill name, the auditing element and the operator name.
Further, generating a regular expression corresponding to the upper node according to the document name, the audit element, and the operator name, including:
training an initial pre-training language T5 model by a machine translation method based on the document name, the auditing elements and the operator name to obtain a conversion model for generating a regular expression;
and converting to obtain a regular expression corresponding to the bill name, the auditing element and the operator name through the conversion model.
Further, after the preset rule engine is used to obtain a plurality of rule expressions in the preset rule base, the method further includes:
converting the regular expression into a regular expression of a preset language; and the rule expression of the preset language is used for running through the rule engine.
Further, the method further comprises:
generating an analyzer corresponding to the rule expression through a preset language based on the standard grammar information and the operator name, and generating a rule engine for executing the analyzer; wherein the operator name is used for characterizing computation logic and parsing logic, and the standard grammar information is used for characterizing grammar information about the operator name.
In a second aspect, the present application provides a document data processing apparatus comprising:
the data acquisition unit is used for acquiring a plurality of documents to be processed; the document data to be processed comprises first document data of a first node and second document data of a second node, and the second node is an upper node of the first node;
the rule obtaining unit is used for obtaining a plurality of rule expressions in a preset rule base by using a preset rule engine; the rule base comprises a plurality of rule expressions, and the rule engine is a program for executing the rule expressions;
and the processing unit is used for carrying out data processing on each document data to be processed through the regular expression corresponding to each document data to be processed based on the corresponding relation between the document data to be processed and the regular expression to obtain auditing result information.
Further, the acquiring data unit includes:
the first bill data determining module is used for determining data information of first bill data of a first node through a named entity identification method and a regular matching method, wherein the data information comprises a bill name and a bill value;
a second node determining module, configured to determine, in a preset database, an upper node corresponding to a name in the data information of the first document data as a second node based on a correspondence between the preset document name and the upper node; wherein the second node has data information of second document data;
and the replacing module is used for replacing the bill value of the second bill data of the second node with the bill value of the first bill data of the first node to obtain the updated second bill data of the second node.
Further, the document value comprises an audit element and an element value corresponding to the audit element.
Further, the apparatus further comprises:
the acquiring document name unit is used for acquiring a plurality of document names;
and the storage relation unit is used for determining the upper node corresponding to each bill name, generating the corresponding relation between the bill name and the upper node according to each bill name and the upper node corresponding to each bill name, and storing the corresponding relation in a database.
Further, the apparatus further comprises:
the rule text acquiring unit is used for acquiring a plurality of rule texts; the rule text comprises a document name, a document value and an operator name;
the upper node determining unit is used for determining an upper node corresponding to each bill name according to the corresponding relation between the bill name and the upper node;
and the rule generating unit is used for generating a rule expression corresponding to the upper node according to the bill name, the audit element and the operator name.
Further, the rule generating unit includes:
the training module is used for training an initial pre-training language T5 model through a machine translation method based on the document name, the audit element and the operator name to obtain a conversion model for generating a regular expression;
and the generating module is used for converting the document name, the auditing element and the operator name to obtain a regular expression corresponding to the document name, the auditing element and the operator name through the conversion model.
Further, the apparatus further comprises:
the conversion unit is used for converting a plurality of regular expressions into regular expressions of a preset language after the regular expressions are obtained by a preset rule engine in a preset rule base; and the rule expression of the preset language is used for running through the rule engine.
Further, the apparatus further comprises:
the generating rule engine unit is used for generating a parser corresponding to the rule expression through a preset language based on the standard grammar information and the operator name and generating a rule engine for executing the parser; wherein the operator name is used for characterizing computation logic and parsing logic, and the standard grammar information is used for characterizing grammar information about the operator name.
In a third aspect, the present application provides an electronic device, comprising a memory and a processor, wherein the memory stores a computer program operable on the processor, and the processor implements the method of the first aspect when executing the computer program.
In a fourth aspect, the present application provides a computer-readable storage medium having stored thereon computer-executable instructions for implementing the method of the first aspect when executed by a processor.
In a fifth aspect, the present application provides a computer program product comprising a computer program which, when executed by a processor, implements the method of the first aspect.
The document data processing method, the document data processing device and the document data processing equipment obtain a plurality of document data to be processed; the document data to be processed comprises first document data of a first node and second document data of a second node, and the second node is an upper node of the first node; acquiring a plurality of rule expressions in a preset rule base by using a preset rule engine; the rule base comprises a plurality of rule expressions, and the rule engine is a program for executing the rule expressions; and based on the corresponding relation between the document data to be processed and the regular expression, performing data processing on each document data to be processed through the regular expression corresponding to each document data to be processed to obtain auditing result information. In the scheme, the preset rule base comprises a plurality of rule expressions, and the rule engine is a program for executing the rule expressions, so that the preset rule base can be used for acquiring the rule expressions by using the preset rule engine, then the rule expression corresponding to each document data to be processed is determined firstly based on the corresponding relation between the document data to be processed and the rule expressions, and then the determined rule expressions are used for processing the data of each document data to be processed to obtain the auditing result information. Therefore, the rule expression in the rule base is automatically called through the rule engine, and then the document data to be processed is subjected to data processing through the rule expression, so that an automatic document examination process is formed, the time required by document examination is reduced, the efficiency and accuracy of document examination are greatly improved, and the technical problems of low accuracy and low efficiency of document examination service are solved.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and together with the description, serve to explain the principles of the disclosure.
FIG. 1 is a schematic flow chart illustrating a document data processing method according to an embodiment of the present application;
FIG. 2 is a schematic flow chart diagram illustrating another document data processing method according to an embodiment of the present application;
FIG. 3 is a schematic structural diagram of a document data processing apparatus according to an embodiment of the present application;
FIG. 4 is a schematic structural diagram of another document data processing apparatus according to an embodiment of the present application;
fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present disclosure;
fig. 6 is a block diagram of an electronic device according to an embodiment of the present application.
With the foregoing drawings in mind, certain embodiments of the disclosure have been shown and described in more detail below. These drawings and written description are not intended to limit the scope of the disclosed concepts in any way, but rather to illustrate the concepts of the disclosure to those skilled in the art by reference to specific embodiments.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the exemplary embodiments below are not intended to represent all implementations consistent with the present disclosure.
In one example, as commerce progresses, the operation of a document of authenticity for a letter of credit is of paramount importance. In the prior art, in the document examination operation process of the credit card, information in a paper document is usually manually entered into a system, then manually checked one by one according to an examination rule to obtain an examination result, and finally all examination results are summarized to generate an examination conclusion. However, in the prior art, the time required for manual review is long, and omission or errors are easy to occur, so that the accuracy and efficiency of the list review are low.
The application provides a document data processing method, a document data processing device and document data processing equipment, and aims to solve the technical problems in the prior art.
The following describes the technical solutions of the present application and how to solve the above technical problems with specific embodiments. The following several specific embodiments may be combined with each other, and details of the same or similar concepts or processes may not be repeated in some embodiments. Embodiments of the present application will be described below with reference to the accompanying drawings.
Fig. 1 is a schematic flowchart of a document data processing method according to an embodiment of the present application, and as shown in fig. 1, the method includes:
101. acquiring a plurality of documents to be processed; the multiple to-be-processed bill data comprise first bill data of a first node and second bill data of a second node, and the second node is an upper node of the first node.
For example, the executing body of the embodiment may be an electronic device, or a terminal device, or a document data processing apparatus or device, or other apparatuses or devices that can execute the embodiment, which is not limited in this respect. In this embodiment, an execution main body is described as an electronic device.
First, a plurality of documents to be processed need to be acquired. The document data to be processed can be obtained from the memory; or acquiring the document data to be processed from the webpage, or receiving the document data to be processed transmitted by other equipment. The multiple documents to be processed comprise first documents data of a first node and second documents data of a second node, and the second node is an upper node of the first node, namely the second node is a father node of the first node.
102. Acquiring a plurality of rule expressions in a preset rule base by using a preset rule engine; the rule base comprises a plurality of rule expressions, and the rule engine is a program for executing the rule expressions.
Illustratively, the preset rule base comprises a plurality of regular expressions, each regular expression is a preset expression for processing the document data to be processed, and the rule engine is a preset program for executing the regular expressions, so that the plurality of regular expressions can be obtained by using the preset rule engine in the preset rule base.
103. And based on the corresponding relation between the document data to be processed and the regular expression, performing data processing on each document data to be processed through the regular expression corresponding to each document data to be processed to obtain auditing result information.
Exemplarily, for each document data to be processed, first determining whether a corresponding regular expression exists, if so, performing data processing on each document data to be processed through the corresponding regular expression corresponding to each document data to be processed to obtain audit result information, where the audit result information includes: the serial number of the related regular expression, the auditing element and the calculation result related to each operator name in the regular expression; and if the corresponding rule expression does not exist, skipping the rule expression.
For example, the audit result information includes a data stream number, information about whether the audit process is successful or not, audit result details, a rule number, a rule result, each audit point, an operator name, an operator result, and audit elements related to an operator. The data stream number represents the number of the document data to be processed, and the information about whether the auditing process is successful or not comprises the following steps: error information, correct information, etc., the details of the audit result is a list, and each element in the list records the condition of each audit rule. Therefore, by means of key information such as data stream numbers, data numbers and the like, review and tracing of each audit service can be conveniently carried out, and development of audit and management work is facilitated.
In the embodiment of the application, a plurality of documents to be processed are obtained; the multiple to-be-processed bill data comprise first bill data of a first node and second bill data of a second node, and the second node is an upper node of the first node. Acquiring a plurality of rule expressions in a preset rule base by using a preset rule engine; the rule base comprises a plurality of rule expressions, and the rule engine is a program for executing the rule expressions. And based on the corresponding relation between the document data to be processed and the regular expression, performing data processing on each document data to be processed through the regular expression corresponding to each document data to be processed to obtain auditing result information. In the scheme, the preset rule base comprises a plurality of rule expressions, and the rule engine is a program for executing the rule expressions, so that the preset rule base can be used for acquiring the rule expressions by using the preset rule engine, then the rule expression corresponding to each document data to be processed is determined firstly based on the corresponding relation between the document data to be processed and the rule expressions, and then the determined rule expressions are used for processing the data of each document data to be processed to obtain the auditing result information. Therefore, the rule expression in the rule base is automatically called through the rule engine, and then the document data to be processed is subjected to data processing through the rule expression, so that an automatic document examination process is formed, the time required by document examination is reduced, the efficiency and accuracy of document examination are greatly improved, and the technical problems of low accuracy and low efficiency of document examination service are solved.
Fig. 2 is a schematic flow chart of another document data processing method according to an embodiment of the present application, and as shown in fig. 2, the method includes:
201. a plurality of document names are obtained.
Illustratively, in a document examination service, document data D1, D2, … and Dn involved are sequentially scanned and recorded into a preprocessing module, at the moment, each document data is converted into a picture format P1, P2, … and Pn, then documents P1, P2, … and Pn are input into an Intelligent Character Recognition (ICR) module, the ICR module firstly converts characters in an image into a text format by using a Character Recognition (OCR) technology, and then performs semantic reasoning and semantic analysis by combining context statement information and a semantic network knowledge base of each Character through a deep learning algorithm to achieve the purposes of correcting errors and improving the Recognition accuracy, at the moment, each document data is converted into text formats T1, T2, … and Tn to obtain a plurality of document names.
202. And determining an upper node corresponding to each document name, generating a corresponding relation between the document name and the upper node according to each document name and the upper node corresponding to each document name, and storing the corresponding relation in a database.
Exemplarily, in the international settlement field, because of various types of documents, a set of top and bottom attribution relations can be combed and stored in a graph database, and various documents in the same large class are applicable to the same set of rules, so that the workload for establishing a rule base can be reduced. Therefore, the electronic device can determine the upper node corresponding to each document name, generate the corresponding relationship between the document name and the upper node according to each document name and the upper node corresponding to each document name, and store the corresponding relationship in a database, wherein the database comprises a non-relational database such as a database. And document names and auditing elements related in all business scenes, English terms corresponding to the document names and English terms corresponding to the auditing elements are also required to be stored in a relational database.
203. Acquiring a plurality of rule texts; the rule text comprises a document name, a document value and an operator name.
For example, in order to generate a regular expression, the electronic device needs to acquire a plurality of regular texts, wherein the regular texts comprise a document name, a document value and an operator name.
204. And determining the upper node corresponding to each bill name according to the corresponding relation between the bill name and the upper node.
Exemplarily, the electronic device can determine the upper node corresponding to each document name according to the corresponding relationship between the document name and the upper node, which is equivalent to determining the parent node corresponding to each document name, so that the next step of generating the regular expression for the upper node is facilitated.
205. And training the initial pre-training language T5 model by a machine translation method based on the document name, the auditing element and the operator name to obtain a conversion model for generating a regular expression.
Illustratively, the mode of generating the rule expression comprises three modes of manual operation, semi-automatic operation and full-automatic operation. When carrying out rule configuration fully automatically, can type rule expression in batches, for raise the efficiency, can use automatic conversion toolbox, automatic conversion process includes: based on the receipt name, the auditing factors and the operator name, inputting the rule text and the rule expression corresponding to the rule text into an initial pre-training language T5 model, training the initial pre-training language T5 model of Google through a machine translation method, completing transfer learning, and obtaining a conversion model for generating the rule expression, wherein the conversion model is a model capable of adapting to a rule conversion task and can be applied to various scenes.
Illustratively, when rule configuration is carried out manually, the document examiner can carry out the operations of adding, deleting, modifying and checking on the database. For example, for the rule "payer (payee) of Contract (Contract) should be consistent with consumer enterprise (entreprise) of entry form (BillofEntry)":
a) regular expressions can be written directly if the person is familiar with domain terms and operators.
b) If the person is uncertain about terms or operators in certain fields, the required document names, element names and operator names can be selected by using a database toolbox through a drop-down box query mode, and the rule expressions are formed through splicing.
c) The person can also delete and modify the regular expression.
206. And converting to obtain a regular expression corresponding to the document name, the auditing element and the operator name through the conversion model.
Exemplarily, in a scene of processing document data, a rule text including a document name, an audit element and an operator name is input into the conversion model, and a rule expression corresponding to the document name, the audit element and the operator name can be obtained through conversion. And finally, inputting the obtained rule expression into a rule checking tool generated by a preset rule engine, if the grammar is correct, storing the grammar into a rule base, and otherwise, revising the rule according to the error information. In other scenarios, for example, in a scenario of translating chinese into english, a regular text input conversion model that converts chinese into english, and chinese into english, may be converted to obtain a conversion expression corresponding to chinese.
Illustratively, after the rule expression is stored in the rule base, if the rule expression in the rule base is changed, the original rule expression is directly updated to the latest rule expression, or in the process of processing the document data to be processed according to the rule expression, the preset rule base is executed, after a plurality of rule expressions are obtained by using a preset rule engine, the first hash value of the rule expression is calculated through a preset hash function, the obtained rule expression is cached, then the document data to be processed is processed according to the rule expression, and the document examination service of the time is completed. When the next order examination service is processed, a second hash value of the rule expression in the rule base needs to be calculated through a preset hash function, the second hash value is compared with the first hash value, if the second hash value is inconsistent with the first hash value, the rule expression in the rule base is updated, the updated rule expression in the rule base needs to be obtained again, if the second hash value is consistent with the first hash value, the rule expression in the rule base is not updated, the cached rule expression can be directly used, and the rule expression does not need to be obtained from the rule base. Because the number of the regular expressions is large, the change period is long, and the document data is processed by the cached regular expressions under the condition that the rule base is not changed through the processing of the step, the conversion time of the regular expressions can be effectively saved, and the overall auditing efficiency is improved.
207. Based on the standard syntactic information and the operator name, generating an analyzer corresponding to the rule expression through a preset language, and generating a rule engine for executing the analyzer; the operator names are used for representing the calculation logic and the analysis logic, and the standard grammar information is used for representing grammar information related to the operator names.
Illustratively, as shown in table 1 below, operator names are used for characterizing the computation logic and the parsing logic, the operator names include a plurality of operators, operator classes and descriptions (i.e., the computation logic and the parsing logic), standard syntax information characterizes syntax information about the operator names, g4 grammar files are written by using an Antlr (actuator for Language Recognition), the standard syntax information of the operators is defined, for example, when the operator names are related, a priority processing order of the operator names, and the like, after the g4 syntax is verified, the electronic device generates parsers corresponding to the regular expressions through a preset Language based on the standard syntax information and the operator names, the parsers include a lexer lexical parser and a parser, and also generates a pars treevisitor accessor, a pars treelister monitor, a rule checking Tool of the grammar parser, a lexical parser, a grammar parser, a processor, a processing device, the listener and the rule checking tool are both Java codes and generate a rule engine for executing a lexical parser, a grammar parser, an accessor, the listener and the rule checking tool.
In the process of generating the lexical parser, the syntactic parser, the accessor, the listener and the rule checking tool, the listener method does not return a value (the return type is void), and the traversal of the lexical parser is automatically completed. In the invention, in order to control the behavior of each child node in the syntax analysis tree, the self-defined accessor class is realized by inheriting the accessor base class method, and the code of a specific access node is completed in the self-defined accessor class. Considering that operators such as preset arithmetic operators, logic operators, comparison operators and the like exist in the javascript, and the lambda expression is supported, the use of the operators is convenient to expand, so that the specific logic of the operators is realized through the javascript function, and the purpose of simplifying the development workload is achieved. And setting the return value of the user-defined accessor class as a javascript statement, and calling node js by a Java program to complete the calculation process of the javascript when the rule engine runs.
TABLE 1
208. And determining data information of the first document data of the first node through a named entity identification method and a regular matching method, wherein the data information comprises a document name and a document value.
In one example, the document value includes an audit element and an element value corresponding to the audit element.
Illustratively, the electronic device determines data information of first document data of a first node through a Named Entity Recognition (NER) method and a regular matching method, wherein the data information includes a document name and a document value, the document value includes an audit element and an element value corresponding to the audit element, and key value pairs Json texts J1, J2, … and Jn using the audit element as a keyword can be generated.
For example, taking a bill of lading as an example, a Json text J1 includes a parent node, a first child node, a second child node, a grandchild node, a unique identification number of an audit element, and a value of the audit element. The father node is a bill name, the first child node is a bill category, the second child node is an auditing element extracted from the bill, and the grandchild node is a data type of the auditing element.
After obtaining a plurality of Json texts J1 and J2 …, the documents J1, J2, … and Jn can be input into a data integration module, and all the documents Json texts related to the same document examination service are spliced by the data integration module to obtain splicing result information. Furthermore, all document data to be processed of the same document examination service are quickly spliced by utilizing the characteristics of the Json format, and the purpose of batch audit can be achieved.
209. Determining an upper node corresponding to the name in the data information of the first document data as a second node in a preset database based on the corresponding relation between the preset document name and the upper node; wherein the second node has data information of the second document data.
For example, based on a corresponding relationship between a preset document name and a higher-level node, the electronic device may first query a preset database for the higher-level node corresponding to the name in the data information of the first document data, and then determine the higher-level node as a second node of the name in the data information of the first document data, where the second node has the data information of the second document data.
For example, all upper nodes of the data name in the data information of the first document data are inquired in the database and added as new keys, or all upper nodes of the data name in the data information of each first document data in the splicing result information are inquired in the database and added as new keys.
210. And replacing the bill value of the second bill data of the second node with the bill value of the first bill data of the first node to obtain the updated second bill data of the second node.
Illustratively, the electronic device assigns the bill value of the first bill data of the first node to the bill value of the second bill data of the second node, so as to obtain the updated second bill data of the second node.
211. Acquiring a plurality of rule expressions in a preset rule base by using a preset rule engine; the rule base comprises a plurality of rule expressions, and the rule engine is a program for executing the rule expressions.
For example, this step can be referred to as step 102 in fig. 1, and is not described again.
212. Converting the regular expression into a regular expression of a preset language; the rule expression of the preset language is used for running through a rule engine.
Illustratively, the preset language is java, the java language is the same as the language used by the program of the rule engine, and the electronic device converts the regular expression into a regular expression of a javascript statement, so that the regular expression of the preset language can be run through the rule engine.
213. And based on the corresponding relation between the document data to be processed and the regular expression, performing data processing on each document data to be processed through the regular expression corresponding to each document data to be processed to obtain auditing result information.
Illustratively, for each piece of document data to be processed, firstly, whether a corresponding regular expression exists is judged, and if the corresponding regular expression exists, each piece of document data to be processed is subjected to data processing through the regular expression of a javascript statement corresponding to each piece of document data to be processed, so that audit result information is obtained.
In the embodiment of the application, a plurality of document names are obtained. And determining an upper node corresponding to each document name, generating a corresponding relation between the document name and the upper node according to each document name and the upper node corresponding to each document name, and storing the corresponding relation in a database. Acquiring a plurality of rule texts; the rule text comprises a document name, a document value and an operator name. And determining the upper node corresponding to each bill name according to the corresponding relation between the bill name and the upper node. And training the initial pre-training language T5 model by a machine translation method based on the document name, the auditing element and the operator name to obtain a conversion model for generating a regular expression. And converting to obtain a regular expression corresponding to the document name, the auditing element and the operator name through the conversion model. Based on the standard syntactic information and the operator name, generating an analyzer corresponding to the rule expression through a preset language, and generating a rule engine for executing the analyzer; the operator names are used for representing the calculation logic and the analysis logic, and the standard grammar information is used for representing grammar information related to the operator names. And determining data information of the first document data of the first node through a named entity identification method and a regular matching method, wherein the data information comprises a document name and a document value. Determining an upper node corresponding to the name in the data information of the first document data as a second node in a preset database based on the corresponding relation between the preset document name and the upper node; wherein the second node has data information of the second document data. And replacing the bill value of the second bill data of the second node with the bill value of the first bill data of the first node to obtain the updated second bill data of the second node. Acquiring a plurality of rule expressions in a preset rule base by using a preset rule engine; the rule base comprises a plurality of rule expressions, and the rule engine is a program for executing the rule expressions. Converting the regular expression into a regular expression of a preset language; the rule expression of the preset language is used for running through a rule engine. And based on the corresponding relation between the document data to be processed and the regular expression, performing data processing on each document data to be processed through the regular expression corresponding to each document data to be processed to obtain auditing result information. Therefore, the rule expression in the rule base is automatically called through the rule engine, and then the document data to be processed is subjected to data processing through the rule expression, so that an automatic document examination process is formed, the time required by document examination is reduced, the efficiency and accuracy of document examination are greatly improved, and the technical problems of low accuracy and low efficiency of document examination service are solved.
Fig. 3 is a schematic structural diagram of a document data processing apparatus according to an embodiment of the present application, and as shown in fig. 3, the apparatus includes:
the data acquiring unit 31 is used for acquiring a plurality of documents to be processed; the document data to be processed comprises first document data of a first node and second document data of a second node, and the second node is an upper node of the first node;
an obtaining rule unit 32, configured to obtain a plurality of rule expressions in a preset rule base by using a preset rule engine; the rule base comprises a plurality of rule expressions, and the rule engine is a program for executing the rule expressions;
and the processing unit 33 is configured to perform data processing on each to-be-processed document data through the rule expression corresponding to each to-be-processed document data based on the corresponding relationship between the to-be-processed document data and the rule expression, so as to obtain audit result information.
The apparatus of this embodiment may execute the technical solution in the method, and the specific implementation process and the technical principle are the same, which are not described herein again.
Fig. 4 is a schematic structural diagram of another document data processing apparatus according to an embodiment of the present application, and based on the embodiment shown in fig. 3, as shown in fig. 4, the data obtaining unit 31 includes:
the first document data determining module 311 is configured to determine data information of the first document data of the first node by using a named entity identification method and a regular matching method, where the data information includes a document name and a document value.
A second node determining module 312, configured to determine, based on a corresponding relationship between a preset document name and an upper node, that the upper node corresponding to the name in the data information of the first document data is a second node in a preset database; wherein the second node has data information of the second document data.
The replacing module 313 is configured to replace the document value of the second document data of the second node with the document value of the first document data of the first node, so as to obtain the updated second document data of the second node.
In one example, the document value includes an audit element and an element value corresponding to the audit element.
In one example, the apparatus further comprises:
a document name acquiring unit 41 configured to acquire a plurality of document names.
And the storage relation unit 42 is configured to determine an upper node corresponding to each document name, generate a corresponding relation between the document name and the upper node according to each document name and the upper node corresponding to each document name, and store the corresponding relation in the database.
In one example, the apparatus further comprises:
an acquiring rule text unit 43 configured to acquire a plurality of rule texts; the rule text comprises a document name, a document value and an operator name.
And an upper node determining unit 44, configured to determine an upper node corresponding to each document name according to a correspondence between the document name and the upper node.
And a rule generating unit 45, configured to generate a rule expression corresponding to the upper node according to the document name, the audit element, and the operator name.
In one example, the rule generating unit 45 includes:
the training module 451 is configured to train an initial pre-training language T5 model by a machine translation method based on the document name, the audit element, and the operator name, to obtain a conversion model for generating a regular expression.
The generating module 452 is configured to obtain a rule expression corresponding to the document name, the audit element, and the operator name through conversion by using the conversion model.
In one example, the apparatus further comprises:
a conversion unit 46, configured to convert the rule expressions into rule expressions in a preset language after acquiring a plurality of rule expressions in a preset rule base by using a preset rule engine; the rule expression of the preset language is used for running through a rule engine.
In one example, the apparatus further comprises:
a generation rule engine unit 47, configured to generate, based on the standard syntax information and the operator name, a parser corresponding to the rule expression through a preset language, and generate a rule engine for executing the parser; the operator names are used for representing the calculation logic and the analysis logic, and the standard grammar information is used for representing grammar information related to the operator names.
The apparatus of this embodiment may execute the technical solution in the method, and the specific implementation process and the technical principle are the same, which are not described herein again.
Fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present application, and as shown in fig. 5, the electronic device includes: memory 51, processor 52.
The memory 51 has stored therein a computer program that is executable on the processor 52.
The processor 52 is configured to perform the methods provided in the embodiments described above.
The electronic device further comprises a receiver 53 and a transmitter 54. The receiver 53 is used for receiving commands and data transmitted from an external device, and the transmitter 54 is used for transmitting commands and data to an external device.
Fig. 6 is a block diagram of an electronic device, which may be a mobile phone, a computer, a digital broadcast terminal, a messaging device, a game console, a tablet device, a medical device, an exercise device, a personal digital assistant, etc., according to an embodiment of the present application.
The processing component 602 generally controls overall operation of the device 600, such as operations associated with display, telephone calls, data communications, camera operations, and recording operations. The processing component 602 may include one or more processors 620 to execute instructions to perform all or a portion of the steps of the methods described above. Further, the processing component 602 can include one or more modules that facilitate interaction between the processing component 602 and other components. For example, the processing component 602 can include a multimedia module to facilitate interaction between the multimedia component 608 and the processing component 602.
The memory 604 is configured to store various types of data to support operations at the apparatus 600. Examples of such data include instructions for any application or method operating on device 600, contact data, phonebook data, messages, pictures, videos, and so forth. The memory 604 may be implemented by any type or combination of volatile or non-volatile memory devices such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disks.
The multimedia component 608 includes a screen that provides an output interface between the device 600 and the user. In some embodiments, the screen may include a Liquid Crystal Display (LCD) and a Touch Panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive an input signal from a user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundary of a touch or slide action, but also detect the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 608 includes a front facing camera and/or a rear facing camera. The front camera and/or the rear camera may receive external multimedia data when the device 600 is in an operating mode, such as a shooting mode or a video mode. Each front camera and rear camera may be a fixed optical lens system or have a focal length and optical zoom capability.
The audio component 610 is configured to output and/or input audio signals. For example, audio component 610 includes a Microphone (MIC) configured to receive external audio signals when apparatus 600 is in an operational mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signal may further be stored in the memory 604 or transmitted via the communication component 616. In some embodiments, audio component 610 further includes a speaker for outputting audio signals.
The I/O interface 612 provides an interface between the processing component 602 and peripheral interface modules, which may be keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to: a home button, a volume button, a start button, and a lock button.
The sensor component 614 includes one or more sensors for providing status assessment of various aspects of the apparatus 600. For example, the sensor component 614 may detect an open/closed state of the device 600, the relative positioning of the components, such as a display and keypad of the device 600, the sensor component 614 may also detect a change in position of the device 600 or a component of the device 600, the presence or absence of user contact with the device 600, orientation or acceleration/deceleration of the device 600, and a change in temperature of the device 600. The sensor assembly 614 may include a proximity sensor configured to detect the presence of a nearby object without any physical contact. The sensor assembly 614 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 614 may also include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.
The communication component 616 is configured to facilitate communications between the apparatus 600 and other devices in a wired or wireless manner. The apparatus 600 may access a wireless network based on a communication standard, such as WiFi, 2G or 3G, or a combination thereof. In an exemplary embodiment, the communication component 616 receives broadcast signals or broadcast related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 616 further includes a Near Field Communication (NFC) module to facilitate short-range communications. For example, the NFC module may be implemented based on Radio Frequency Identification (RFID) technology, infrared data association (IrDA) technology, Ultra Wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.
In an exemplary embodiment, the apparatus 600 may be implemented by one or more Application Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), Programmable Logic Devices (PLDs), Field Programmable Gate Arrays (FPGAs), controllers, micro-controllers, microprocessors or other electronic components for performing the above-described methods.
In an exemplary embodiment, a non-transitory computer readable storage medium comprising instructions, such as the memory 604 comprising instructions, executable by the processor 620 of the apparatus 600 to perform the above-described method is also provided. For example, the non-transitory computer readable storage medium may be a ROM, a Random Access Memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, and the like.
Embodiments of the present application also provide a non-transitory computer-readable storage medium, where instructions in the storage medium, when executed by a processor of an electronic device, enable the electronic device to perform the method provided by the above embodiments.
An embodiment of the present application further provides a computer program product, where the computer program product includes: a computer program, stored in a readable storage medium, from which at least one processor of the electronic device can read the computer program, the at least one processor executing the computer program causing the electronic device to perform the solution provided by any of the embodiments described above.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This application is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
It will be understood that the present disclosure is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.
Claims (19)
1. A document data processing method is characterized by comprising the following steps:
acquiring a plurality of documents to be processed; the document data to be processed comprises first document data of a first node and second document data of a second node, and the second node is an upper node of the first node;
acquiring a plurality of rule expressions in a preset rule base by using a preset rule engine; the rule base comprises a plurality of rule expressions, and the rule engine is a program for executing the rule expressions;
and based on the corresponding relation between the document data to be processed and the regular expression, performing data processing on each document data to be processed through the regular expression corresponding to each document data to be processed to obtain auditing result information.
2. The method of claim 1, wherein obtaining a plurality of documents data to be processed comprises:
determining data information of first bill data of a first node through a named entity identification method and a regular matching method, wherein the data information comprises a bill name and a bill value;
determining an upper node corresponding to the name in the data information of the first document data as a second node in a preset database based on the corresponding relation between the preset document name and the upper node; wherein the second node has data information of second document data;
and replacing the bill value of the second bill data of the second node with the bill value of the first bill data of the first node to obtain the updated second bill data of the second node.
3. The method of claim 2, wherein the document value comprises an audit element and an element value corresponding to the audit element.
4. The method of claim 2, further comprising:
acquiring a plurality of document names;
determining an upper node corresponding to each bill name, generating a corresponding relation between the bill name and the upper node according to each bill name and the upper node corresponding to each bill name, and storing the corresponding relation in a database.
5. The method of claim 4, further comprising:
acquiring a plurality of rule texts; the rule text comprises a document name, a document value and an operator name;
determining an upper node corresponding to each bill name according to the corresponding relation between the bill name and the upper node;
and generating a regular expression corresponding to the upper node according to the bill name, the auditing element and the operator name.
6. The method of claim 5, wherein generating a regular expression corresponding to the upper node according to the document name, the audit element, and the operator name comprises:
training an initial pre-training language T5 model by a machine translation method based on the document name, the auditing elements and the operator name to obtain a conversion model for generating a regular expression;
and converting to obtain a regular expression corresponding to the bill name, the auditing element and the operator name through the conversion model.
7. The method according to any one of claims 1-6, wherein after obtaining the plurality of rule expressions in the predetermined rule base by using the predetermined rule engine, the method further comprises:
converting the regular expression into a regular expression of a preset language; and the rule expression of the preset language is used for running through the rule engine.
8. The method according to any one of claims 1-6, further comprising:
generating an analyzer corresponding to the rule expression through a preset language based on the standard grammar information and the operator name, and generating a rule engine for executing the analyzer; wherein the operator name is used for characterizing computation logic and parsing logic, and the standard grammar information is used for characterizing grammar information about the operator name.
9. A document data processing apparatus, comprising:
the data acquisition unit is used for acquiring a plurality of documents to be processed; the document data to be processed comprises first document data of a first node and second document data of a second node, and the second node is an upper node of the first node;
the rule obtaining unit is used for obtaining a plurality of rule expressions in a preset rule base by using a preset rule engine; the rule base comprises a plurality of rule expressions, and the rule engine is a program for executing the rule expressions;
and the processing unit is used for carrying out data processing on each document data to be processed through the regular expression corresponding to each document data to be processed based on the corresponding relation between the document data to be processed and the regular expression to obtain auditing result information.
10. The apparatus of claim 9, wherein the means for obtaining data comprises:
the first bill data determining module is used for determining data information of first bill data of a first node through a named entity identification method and a regular matching method, wherein the data information comprises a bill name and a bill value;
a second node determining module, configured to determine, in a preset database, an upper node corresponding to a name in the data information of the first document data as a second node based on a correspondence between the preset document name and the upper node; wherein the second node has data information of second document data;
and the replacing module is used for replacing the bill value of the second bill data of the second node with the bill value of the first bill data of the first node to obtain the updated second bill data of the second node.
11. The apparatus of claim 10, wherein the document value comprises an audit element and an element value corresponding to the audit element.
12. The apparatus of claim 10, further comprising:
the acquiring document name unit is used for acquiring a plurality of document names;
and the storage relation unit is used for determining the upper node corresponding to each bill name, generating the corresponding relation between the bill name and the upper node according to each bill name and the upper node corresponding to each bill name, and storing the corresponding relation in a database.
13. The apparatus of claim 12, further comprising:
the rule text acquiring unit is used for acquiring a plurality of rule texts; the rule text comprises a document name, a document value and an operator name;
the upper node determining unit is used for determining an upper node corresponding to each bill name according to the corresponding relation between the bill name and the upper node;
and the rule generating unit is used for generating a rule expression corresponding to the upper node according to the bill name, the audit element and the operator name.
14. The apparatus of claim 13, wherein the generation rule unit comprises:
the training module is used for training an initial pre-training language T5 model through a machine translation method based on the document name, the audit element and the operator name to obtain a conversion model for generating a regular expression;
and the generating module is used for converting the document name, the auditing element and the operator name to obtain a regular expression corresponding to the document name, the auditing element and the operator name through the conversion model.
15. The apparatus according to any one of claims 9-14, further comprising:
the conversion unit is used for converting a plurality of regular expressions into regular expressions of a preset language after the regular expressions are obtained by a preset rule engine in a preset rule base; and the rule expression of the preset language is used for running through the rule engine.
16. The apparatus according to any one of claims 9-14, further comprising:
the generating rule engine unit is used for generating a parser corresponding to the rule expression through a preset language based on the standard grammar information and the operator name and generating a rule engine for executing the parser; wherein the operator name is used for characterizing computation logic and parsing logic, and the standard grammar information is used for characterizing grammar information about the operator name.
17. An electronic device, comprising a memory, a processor, a computer program being stored in the memory and being executable on the processor, the processor implementing the method of any of the preceding claims 1-8 when executing the computer program.
18. A computer-readable storage medium having computer-executable instructions stored thereon, which when executed by a processor, perform the method of any one of claims 1-8.
19. A computer program product, characterized in that it comprises a computer program which, when being executed by a processor, carries out the method of any one of claims 1-8.
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