CN114266531A - Case examination method based on data security and element particle combination - Google Patents
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Abstract
The application relates to a case examination method, which comprises the following steps: receiving and storing case information of a preset case to obtain case material storage data of the case; analyzing the case material storage data by using a preset file analysis template to generate a case content data packet; identifying the case content data packet according to preset material file content particle attribute configuration to obtain content particle information, wherein the content particle information comprises at least one element of a preset case; comparing the content particle information according to a preset content particle comparison rule to obtain a comparison result; and integrating the comparison results to generate an audit result.
Description
Technical Field
The application belongs to the field of data automation, and particularly relates to a case examination method based on data security and element particle combination.
Background
In recent years, more and more people use credit cards and online financial platforms, so that financial dispute cases are rapidly increased. The ways of manually accepting case examination materials, semi-automatically accepting case examination materials, automatically accepting and curing case materials and the like in the court are far from meeting the fast-paced life style of people. With the continuous popularization of the internet +, the processing speed of business in various industries is increased, and the speed of case examination materials is increased at will.
In the traditional case examination, the content summary reading is carried out on the specified material type and content, the content summary reading is limited by the position mark or the font of the material content, the material content is identified according to the material resolution provided by the reading, and the like, so that the case material under examination is examined. The content inspection in such a manner is not accurate, and the source of the inspection material and the inspection result are easily forged and falsified.
Disclosure of Invention
Based on this, the application provides a case examination method, which comprises the following steps: receiving and storing case information of a preset case to obtain case material storage data of the case; analyzing the case material storage data by using a preset file analysis template to generate a case content data packet; identifying the case content data packet according to preset material file content particle attribute configuration to obtain content particle information, wherein the content particle information comprises at least one element of a preset case; comparing the content particle information according to a preset content particle comparison rule to obtain a comparison result; and integrating the comparison result, the content particle information and the case material receiving result information to generate an auditing result.
By using the method, the content of the case material can be automatically analyzed, and content particles can be extracted from the case material. The formal examination of case materials can be realized through comparison of content particles. The method can liberate manpower to a certain extent. Meanwhile, the method has high content inspection accuracy.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without exceeding the protection scope of the present application.
Fig. 1 shows a flowchart of an examination method of a case according to an embodiment of the present application.
Fig. 2 shows a schematic diagram of the components of another embodiment of the document review system of the present application.
FIG. 3 shows a block diagram of an electronic device according to an example embodiment.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some, but not all, embodiments of the present application. 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 application.
Fig. 1 shows a flowchart of an examination method of a case according to an embodiment of the present application.
As shown in fig. 1, in S110, case information of a preset case may be received and stored, and case material storage data of the case may be obtained. Alternatively, the case material storage data may be at least one file storing case information. Optionally, the file may include: at least one of an office file, a PDF file, and a picture file. The case material storage data may also not be limited to the above-described file form.
Optionally, case information may include json structured information and evidence package material. Optionally, the case information submitted by the user may be received in response to a user operation. Alternatively, case information from the user may be received through a network interface. Case information from the user may be received, for example, through an HTTP interface. Optionally, before receiving case information submitted by a user, the user may be authenticated to ensure the safety and reliability of the information. Optionally, the user may be authenticated by a mobile phone number + identity + name, may also be authenticated by a human face, may also be authenticated by a bank card + mobile phone number + name, and so on, which are not listed here.
Optionally, after receiving the case information, the case information may be stored, and the case material storage data is obtained. Alternatively, the case material storage data may be stored locally or in a remote server. Optionally, in S110, case material storage data may be sorted and sorted, so as to solve the problem of difficult sorting in practical situations. Further, case material storage data can be sorted and sorted in a regular matching mode. Optionally, case material storage data may be collated according to the contained information of the case. Optionally, the information may include: personnel information, material information, litigation information, and the like of the case. Alternatively, the aforementioned information may be used to store the case material storage data in a different table or a different file.
Optionally, in S110, case information may be visualized. Optionally, at least one of the case information, the collision result and the comparison result may be stored in at least one table. And the information can be classified and displayed. So that the operator can simply and visually acquire the information. And the adjustment of the content by an operator is convenient.
As shown in fig. 1, in S120, case material storage data may be read to generate a case content data package. Optionally, the case material storage data may be parsed by using a preset file parsing template, so as to generate a case content data package. Alternatively, the case content data package may be a collection of character strings extracted from case material storage data. For example, the content data packet may include a shape of "article four … …, original Zhang three complaints. Optionally, the set of strings may include strings that are capable of fully expressing semantics. Alternatively, the character string may be a sequence of characters separated by a specific character in the case material storage data. Alternatively, the character string may be a character string at a specific position in the case material storage data.
Optionally, S120 may include text extraction of case material storage data and semantic recognition of the extracted text. Optionally, the case material storage data may include at least one of an office format document, a PDF format document, and a picture document. Optionally, S120 may include obtaining a case content data package from the case material storage data by reading the office file content; or acquiring a case content data packet from case material storage data in a PDF file content reading mode; the method can also comprise the step of acquiring case content data packets from case material storage data in a mode of recognizing characters in the pictures.
Optionally, in S120, the case content data package may be obtained from the case material storage data according to the file parsing template. Alternatively, the file parsing template may be configured according to the content granule. Such as: for example, the content particle includes "source name". The file parsing template may be configured as follows: in case material storage data, a character string from the character string "original" to a space character is intercepted. The character string may be assigned to the content particle "source name" in a subsequent step.
Optionally, before S120, a file parsing template may be selected from a preset template library and configured to at least one file of the case material storage data. Or on the basis of the existing file analysis template, a certain modification can be made to obtain a new file analysis template, and the new file analysis template is configured in at least one file of the case material storage data. Alternatively, a file parsing template may be created according to a predetermined rule, and the file parsing template may be configured in at least one file of the case material storage data.
Further, the template may be parsed by a profile in response to manual manipulation by a user. Alternatively, the file parsing template may be automatically generated after being marked by a general AI program. Alternatively, different templates may be set according to differences in content, differences in file format, and differences in objects. Wherein the object may be a type of case, such as a case of credit card dispute. The content may be a further breakdown of the case, such as litigation of the dragon card business. The file form may include an office file, a PDF file, a picture file, and the like.
Optionally, a plurality of different file parsing templates may also be selected in S120. And a plurality of different case content data packets can be generated according to the plurality of different file analysis templates. One case content package may be preferentially determined among the plurality of different case content packages. If there are multiple parsing templates matching the preset case. A corresponding parsing result may be generated according to each parsing template, and a currently used preferred method is to use a result with the most element particle information obtained by depending on the template as a case content data packet. For example, the case content data packet may be compared with the corresponding file parsing template to obtain the similarity between the case content data packet and the corresponding file parsing template. Wherein the similarity may be the number of content particles assigned in the parsing result. The case content data package with higher similarity can be selected as the case.
As shown in fig. 1, in S130, the case content data packet may be intelligently identified to generate an identification result, and the identification result may include content grain information of the case. The case content data packet can be identified according to the case content particle attribute configuration, and the case content particle information can be obtained. Wherein the content particle information may comprise at least one element of the case. Optionally, at least one element may be obtained by decomposing at least one piece of material information in the case content data package. And assigning the at least one element to the corresponding content particle.
Alternatively, at least one character string in the case content data package generated in S120 may be acquired. A substring may be truncated from the at least one string. And may match the substring with at least one preset content particle and may assign the substring to the content particle.
For example, the content particles may include: original name, announced name, case name, … …. Correspondingly, the content grain information identified from the case content data packet according to the content grain may include: original name is zhang san, advertised name is li si, and case is credit card dispute … …. Alternatively, the content particles may not be limited to the above.
Optionally, before S130, the method may further include: and configuring the content particles of the preset cases. Alternatively, the content grain configuring the preset case may be provided before S120, or may be executed in parallel with S120. Alternatively, the content particle attributes of the preset case may be configured by responding to a manual operation of the user. Or automatically generating the content particle attributes of the preset cases after marking through a common AI program.
Alternatively, the file grain attribute configuration may correspond to a file attribute of at least one file in case material storage data of a preset case. For example, the file attributes may include: file name, file size, number of pages of file contents, etc.
As shown in fig. 1, in S140, the recognition result may be read, and a collision result may be generated with a preset comparison rule. Optionally, the alignment rule may be a content particle alignment rule. The collision result may include an alignment result of the content particles. The content particle information can be compared according to the content particle comparison rule to obtain a content particle comparison result. Optionally, the content particle comparison rule may include: a mutual comparison between at least two of the plurality of content grain information generated in S130. For example, the content particle comparison rule may include: comparing whether the original notice name is opposite to the original notice identity card name or not, and comparing whether the appeal-original notice identity card number is equal to the original notice identity card number or not. Alternatively, the content particle alignment result may be boolean data.
Optionally, before S140, the method may include: and configuring a content particle comparison rule. Alternatively, the content particle comparison rule may be configured in response to a manual operation of a user. The content particle alignment rules may also be generated after tagging by conventional AI programs.
Optionally in S140, the method may further include: it is determined whether at least one content particle has a value and the result of the determination is used as part of the collision result. Alternatively, the determination result may be boolean data. Such as: if the preset content particles have values, the judgment result can be recorded as true; if the preset content grain has no value, the judgment result can be recorded as false.
As shown in fig. 1, in S150, the collision result may be read and integrated to obtain a comparison result. Optionally, the content particle configuration and the content particle comparison rule may be collated, the foregoing collision result may be counted by using a comparison method, and the statistical result may be used as a basis for whether the audit is passed or not.
For example, the collision result may include a plurality of sets of alignment results of content particles, where each alignment result may be boolean data. Alternatively, the collision result with the value true and the collision result with the value false may be counted separately. Alternatively, it may be determined that the audit is not passed when the statistical number of collision results with a value of false or exceeds a threshold value.
Alternatively, the collision result may be managed in a hierarchical manner. For example, the collision result can be classified into important, general and secondary grades. And setting different thresholds for the statistical number of the collision results of different grades. The collision results of different grades can be respectively counted, and whether the audit is passed or not can be judged according to the corresponding threshold value.
Alternatively, different weights may be set for different levels of collision results. The product of the statistical number of the collision results of different grades and the corresponding weight can be accumulated and used as the judgment basis for whether the audit is passed or not. Alternatively, a ticket veto may be employed for important collision results. For example, if the important collision result is false, the audit is determined not to be passed.
Optionally, the content particle information generated in the integrating S130 may also be included in S150. Whether at least one content particle has a value or not may be taken as a boolean data and may be counted in a manner of counting collision results. Such as: when the preset content particles have values, the corresponding Boolean data can be recorded as true; when the preset content grain has no value, the corresponding boolean data may be denoted as false.
Optionally, after S150, the method may further include: generating an examination result according to the comparison result; and outputting the examination result. Optionally, the comparison result generated in S150 may be used as the examination result, or a part of the examination result. Optionally, the review result may further include at least one of the aforementioned content grain information and the aforementioned collision result.
Fig. 2 shows a schematic diagram of the components of another embodiment of the document review system of the present application.
As shown in fig. 2, the system 2000 may include: the system comprises a case receiving module 1, a case material processing module 4, a case material analyzing module 6, a case content collision module 7 and a case content comparison module.
The case receiving module 1 may be configured to receive and store case information of a preset case, and obtain storage data of the case. Alternatively, the case receiving module 1 may be connected with a plurality of merchants, and may receive case information from a plurality of merchants. Alternatively, the case receiving module 1 may receive the case information through an HTTP interface. Optionally, the case information may include at least one of json structured information and evidence package material. Alternatively, the storage data generated by the case receiving module 1 may include at least one of an office file, a PDF file, and a picture file.
Optionally, the case receiving module 1 may also be configured to sort the received case information. Optionally, the case receiving module 1 arranges the categorized case information by using a regular matching method.
The case material processing module 4 can be used for reading the content of the case information storage data to obtain a case content data packet. Alternatively, the case material processing module 4 may perform content reading of the case information storage data through multiple channels. The multiple channels may include, but are not limited to, ali, tench, self-research, etc. The case information may include, but is not limited to, complaints, legal identification, business licenses, financial licenses, and the like. Alternatively, the case material processing module 4 may support case information storage data of different material formats from the material type. The case information storage data may include, but is not limited to, complaints in formats such as pdf, word, docx, jpg, jpeg, etc., respectively. Optionally, the case material processing module 4 may adopt a diversified case material processing style, supporting most case information storage data processing modes.
The case material analyzing module 6 may be configured to identify the content data packet according to the content particle attribute of the case, so as to obtain the content particle information of the case. Wherein the content particle information may include at least one element of the case. Alternatively, the case material parsing module 6 may parse the content data packets respectively through two or more content particle attribute configurations. For example, different content particle attribute configurations can be adopted for appeal with different actual content, presentation forms and typesetting. The parsing method may include: element preferred judgment, element content half-and-half judgment, analysis result preferred judgment and the like. The element preference determination may include, but is not limited to, identification card format determination: number or number + X format. Element content halving decisions may include, but are not limited to, such as length of advertised name, surname decisions. The analysis result preferential judgment may include, but is not limited to, the analysis result being more perfect judgment. Optionally, the case material analyzing module 6 may determine the case material content in a high fault tolerance manner.
The case content collision module 7 may be configured to compare the content particle information according to a preset content particle comparison rule to obtain a collision result. Optionally, the case content collision module 7 may implement case content comparison in an element combination manner and output a collision result. A plurality of element identifiers (including but not limited to gender-generator, age-age, etc.) are preset to be combined with the matching rules and associated with the content particle information. And the content particle information from the case analysis module 6 collides with the preset element identifier and outputs a collision result. The module achieves the case material collision result and stores the result by adopting the advantages of independent elements, high control flexibility and the like.
The case content comparison module 8 can perform rule comparison on the case material collision result from the case content collision module 7 through presetting a comparison target source, and output the case content comparison result. Where the target source may include, but is not limited to, the complaint name-the license business name. The case content comparison module 8 can realize independent content output, dynamic real-time update and real-time output of optimal results for multiple merchants.
Optionally, the system 2000 may also include a merchant verification module 2. Optionally, the merchant verification module 2 may be used to verify the identity of the merchant to ensure the security and reliability of the information. Optionally, the merchant verification module 2 may verify the identity of the merchant through at least one piece of identity information of the merchant. For example, the merchant verification module 2 may verify the identity of the merchant by means of a mobile phone number + an identity card + a name. The merchant verification module 2 can also verify the identity of the merchant through the face information. The merchant verification module 2 can also verify the identity of the merchant in a mode of bank card + identity card + mobile phone number + name. The manner in which the merchant verification module 2 verifies the identity of the merchant may also not be limited to the above-listed manner. Alternatively, system 2000 disregards case information provided by merchants that fail to verify.
Optionally, the system 2000 may further include a case preprocessing module 3. Alternatively, system 2000 may be a distributed system. Optionally, the case preprocessing module 3 may be used for transmission and encryption processing of case information. Optionally, the case preprocessing module 3 may adopt data request docking authentication verification, data encryption transmission, data private key decryption, and data integrity verification on the case information received by the case receiving module 1 to ensure the security and reliability of the case basic information and case materials.
Optionally, the system 2000 may further comprise a public content reading module 5. Alternatively, the public content reading module 5 may be a preset program. And the public content reading module 5 is used for corresponding the information of the content data packet and the fields one by one according to the field information required by the database table structure by using the content data packet obtained by analyzing the file. Such as case names, title values, and the like.
Optionally, the system 2000 may further include a case content storage module 9. The content storage module 9 can store the data information in the program in the mysql database, the server text file, using a persistence framework (existing common technology).
Optionally, the system 2000 may further comprise a result output module 10. The result output module 10 outputs the information in the database and the file server to the front-end framework in a mode of using an HTTP protocol interface, and the front-end framework is displayed to the user in a form of a table.
FIG. 3 shows a block diagram of an electronic device according to an example embodiment.
An electronic device 200 according to this embodiment of the present application is described below with reference to fig. 3. The electronic device 200 shown in fig. 3 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present application.
As shown in fig. 3, the electronic device 200 is embodied in the form of a general purpose computing device. The components of the electronic device 200 may include, but are not limited to: at least one processing unit 210, at least one memory unit 220, a bus 230 connecting different system components (including the memory unit 220 and the processing unit 210), a display unit 240, and the like.
Wherein the storage unit stores program code executable by the processing unit 210 to cause the processing unit 210 to perform the methods according to various exemplary embodiments of the present application described herein. For example, the processing unit 210 may perform the method as shown in fig. 1.
The memory unit 220 may include readable media in the form of volatile memory units, such as a random access memory unit (RAM)2201 and/or a cache memory unit 2202, and may further include a read only memory unit (ROM) 2203.
The storage unit 220 may also include a program/utility 2204 having a set (at least one) of program modules 2205, such program modules 2205 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which, or some combination thereof, may comprise an implementation of a network environment.
The electronic device 200 may also communicate with one or more external devices 300 (e.g., keyboard, pointing device, bluetooth device, etc.), with one or more devices that enable a user to interact with the electronic device 200, and/or with any devices (e.g., router, modem, etc.) that enable the electronic device 200 to communicate with one or more other computing devices. Such communication may occur via an input/output (I/O) interface 250. Also, the electronic device 200 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network such as the Internet) via the network adapter 260. The network adapter 260 may communicate with other modules of the electronic device 200 via the bus 230. It should be appreciated that although not shown in the figures, other hardware and/or software modules may be used in conjunction with the electronic device 200, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.
By using the method, the content of the case material can be automatically analyzed, and content particles can be extracted from the case material. The formal examination of case materials can be realized through comparison of content particles. The method can liberate manpower to a certain extent. Meanwhile, the method has high content inspection accuracy.
As will be appreciated by one skilled in the art, aspects of the present application may be embodied as a system, method or computer program product. Accordingly, this application may take the form of an entirely hardware embodiment, an entirely software embodiment (including firmware, resident software, micro-code, etc.) or an embodiment combining software and hardware aspects that may all generally be referred to as a "circuit," module "or" system. Furthermore, the present application may take the form of a computer program product embodied in any tangible expression medium having computer-usable program code embodied in the medium.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable medium that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable medium produce an article of manufacture including instruction means which implement the function/act specified in the flowchart and/or block diagram block or blocks.
The computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In the foregoing embodiments, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments. The technical features of the embodiments may be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The foregoing detailed description of the embodiments of the present application has been presented to illustrate the principles and implementations of the present application, and the description of the embodiments is only intended to facilitate the understanding of the methods and their core concepts of the present application. Meanwhile, a person skilled in the art should, according to the idea of the present application, change or modify the embodiments and applications of the present application based on the scope of the present application. In view of the above, the description should not be taken as limiting the application.
Claims (10)
1. A method of auditing cases based on data security and elemental particle combinations, comprising:
receiving and storing case information of a preset case to obtain case material storage data of the case;
analyzing the case material storage data by using a preset file analysis template to generate a case content data packet;
identifying the case content data packet according to the content particle attribute configuration of the preset case to obtain content particle information, wherein the content particle information comprises at least one element of the preset case;
comparing the content particle information according to a preset content particle comparison rule to obtain a collision result;
and integrating the comparison collision results to generate comparison results.
2. The method as claimed in claim 1, wherein said receiving and storing case information of a preset case to obtain case material storage data of said case comprises:
and carrying out visualization processing on the case information.
3. The method as claimed in claim 1, wherein said receiving and storing case information of a preset case to obtain case material storage data of said case comprises:
authenticating the user identity according to the user identity information;
receiving case information of the preset case through the user;
and acquiring the case information of the preset case through an HTTP interface.
4. The method of claim 1, wherein the case information includes json structured information and evidence package material.
5. The method as claimed in claim 1, wherein said receiving and storing case information of a preset case to obtain case material storage data of said case comprises:
and sorting and classifying the case material storage data.
6. The method as claimed in claim 1, wherein said parsing said case material storage data using a preset file parsing template to generate a case content data package comprises:
analyzing the case material storage data in a file content reading mode;
the file content reading comprises: at least one of office file content reading, PDF content reading and picture OCR content acquisition.
7. The method according to claim 1, wherein before said parsing said case material storage data using a preset file parsing template to generate a case content data package, further comprising:
automatically generating the file analysis template through AI program marking; and/or
And responding to user operation, and configuring and generating the file analysis template.
8. The method of claim 1, wherein identifying the case content data package according to a preset material file content particle attribute configuration to obtain content particle information comprises:
and decomposing the case content data packet according to the material file content particle attribute, and matching the content particles to obtain content particle information.
9. An electronic device comprising a processor and a memory, and a program executable by the processor stored in the memory, the program, when executed, causing the processor to perform the method of any of claims 1-8.
10. A storage medium storing a program executable by a processor, the processor performing the method of any one of claims 1-8 when the program is executed.
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| CN109740869A (en) * | 2018-12-14 | 2019-05-10 | 平安国际融资租赁有限公司 | Data checking method, device, computer equipment and storage medium |
| CN110335180A (en) * | 2019-07-04 | 2019-10-15 | 北京市律典通科技有限公司 | Case is put on record material intelligence checking device |
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