CN114782952A - Traffic service processing method and device combining RPA and AI and electronic equipment - Google Patents

Traffic service processing method and device combining RPA and AI and electronic equipment Download PDF

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Publication number
CN114782952A
CN114782952A CN202210246327.1A CN202210246327A CN114782952A CN 114782952 A CN114782952 A CN 114782952A CN 202210246327 A CN202210246327 A CN 202210246327A CN 114782952 A CN114782952 A CN 114782952A
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key
key information
traffic
key field
information
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刘宝利
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Laiye Technology Beijing Co Ltd
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Laiye Technology Beijing Co Ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
    • G06F16/33—Querying
    • G06F16/332—Query formulation
    • G06F16/3329—Natural language query formulation or dialogue systems

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Abstract

The application relates to a traffic service processing method, a device and electronic equipment combining RPA and AI, relating to the technical field of RPA and AI, applied to an RPA robot, and comprising the following steps: acquiring traffic incident data to be processed; based on an OCR technology, identifying a certificate scanning piece and a paper document scanning piece of a person related to a traffic incident in traffic incident data to acquire first key information of at least one first key field in the certificate scanning piece and text information in the paper document scanning piece; extracting second key information of at least one second key field from the text information based on NLP technology; and inputting the first key information and the second key information into a traffic service system. The RPA robot acquires key information in traffic incident data based on OCR and NLP technologies and inputs the key information into a traffic service system, so that the traffic service information is accurately acquired and input, and the RPA robot replaces manpower to acquire and input information, so that the labor cost is reduced, and the traffic service processing efficiency is improved.

Description

Traffic service processing method and device combining RPA and AI and electronic equipment
Technical Field
The present application relates to the technical field of robot flow automation and artificial intelligence, and in particular, to a traffic service processing method and apparatus combining RPA and AI, and an electronic device.
Background
Robot Process Automation (RPA) is a Process task automatically executed according to rules by simulating human operations on a computer through specific robot software.
Artificial Intelligence (AI) is a technical science that studies and develops theories, methods, techniques and application systems for simulating, extending and expanding human Intelligence.
Currently, many cumbersome and repetitive services require manual handling. For example, in the process of traffic service processing, for some vehicles and drivers who violate traffic regulations, the staff of the integrated transportation law enforcement team needs to identify the required key information from the on-site record and certificate after checking and accepting the record and certificate on the site, and then enter the key information into the integrated law enforcement information system. This processing method requires a large amount of labor cost and time cost, and is likely to cause errors in the recognized information. How to improve the processing efficiency and accuracy of traffic services and reduce labor cost has become an urgent problem to be solved.
Disclosure of Invention
The application provides a traffic service processing method, a traffic service processing device and electronic equipment which are combined with RPA and AI, and aims to solve the technical problems of low processing efficiency, low accuracy and high labor cost of the traffic service processing method in the related technology.
An embodiment of a first aspect of the present application provides a traffic service processing method combining an RPA and an AI, which is applied to an RPA robot, and the method includes: acquiring traffic incident data to be processed, wherein the traffic incident data comprises certificate scanning pieces and paper document scanning pieces of traffic incident related personnel; identifying the certificate scanning piece based on an Optical Character Recognition (OCR) technology to acquire first key information of at least one first key field in the certificate scanning piece; based on an Optical Character Recognition (OCR) technology, recognizing the paper document scanning piece to obtain text information in the paper document scanning piece; extracting second key information of at least one second key field from the text information based on a Natural Language Processing (NLP) technology; and recording the first key information of at least one first key field and the second key information of at least one second key field into the traffic service system.
The embodiment of the second aspect of the present application provides a traffic service processing device combining RPA and AI, which is applied to an RPA robot, and the device includes: the first acquisition module is used for acquiring traffic incident data to be processed, wherein the traffic incident data comprises certificate scanning pieces and paper document scanning pieces of traffic incident related personnel; the first identification module is used for identifying the certificate scanning piece based on an Optical Character Recognition (OCR) technology so as to acquire first key information of at least one first key field in the certificate scanning piece; the second identification module is used for identifying the paper document scanning piece based on an Optical Character Recognition (OCR) technology so as to acquire text information in the paper document scanning piece; the extraction module is used for extracting second key information of at least one second key field from the text information based on a Natural Language Processing (NLP) technology; and the entry module is used for entering the first key information of at least one first key field and the second key information of at least one second key field into the traffic service system.
An embodiment of the third aspect of the present application provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the processor executes the computer program, the method according to the embodiment of the first aspect of the present application is implemented.
An embodiment of a fourth aspect of the present application provides a computer-readable storage medium, on which a computer program is stored, which, when executed by a processor, implements the method according to the embodiment of the first aspect of the present application.
An embodiment of the fifth aspect of the present application provides a computer program product, which includes a computer program, and when executed by a processor, the computer program implements the method according to the above embodiment of the first aspect of the present application.
The technical scheme provided by the embodiment of the application can have the following beneficial effects:
the RPA robot acquires key information in traffic incident data based on OCR and NLP technologies and inputs the key information into a traffic service system, so that the traffic service information is accurately acquired and input, the RPA robot replaces manpower to acquire and input information, the labor cost is reduced, and the traffic service processing efficiency is improved.
Additional aspects and advantages of the present application will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the present application.
Drawings
In the drawings, like reference characters designate like or similar parts or elements throughout the several views unless otherwise specified. The figures are not necessarily to scale. It is appreciated that these drawings depict only some embodiments in accordance with the disclosure and are not to be considered limiting of its scope.
Fig. 1 is a schematic flow chart of a traffic service processing method according to a first embodiment of the present application, which combines RPA and AI;
figures 2 and 3 are exemplary views of a credential scanner according to a first embodiment of the application;
FIG. 4 is an exemplary illustration of a paper document scanner according to a first embodiment of the application;
FIG. 5 is a flow chart illustrating a traffic service processing method according to a second embodiment of the present application in conjunction with RPA and AI;
FIG. 6 is an exemplary illustration of a credential scanner according to a second embodiment of the application;
FIG. 7 is an exemplary diagram of a service interface of a traffic service system according to a second embodiment of the present application;
fig. 8 is an exemplary diagram of first key information and second key information stored in a preset file type according to the second embodiment of the present application;
FIG. 9 is a flow chart illustrating a traffic service processing method according to a third embodiment of the present application, wherein the traffic service processing method combines RPA and AI;
fig. 10 is a schematic structural diagram of a traffic service processing device incorporating RPA and AI according to a fourth embodiment of the present application;
fig. 11 is a block diagram of an electronic device for implementing a traffic service processing method in conjunction with RPA and AI according to an embodiment of the present application.
Detailed Description
Reference will now be made in detail to the embodiments of the present application/disclosure, examples of which are illustrated in the accompanying drawings, wherein like reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are exemplary only for the purpose of explaining the present application/disclosure and are not to be construed as limiting the same.
These and other aspects of the embodiments of the present application/disclosure will be apparent with reference to the following description and attached drawings. In the description and drawings, particular embodiments of the present application/disclosed embodiments are disclosed in detail as being indicative of some of the ways in which the principles of the present application/disclosed embodiments may be employed, but it is understood that the scope of the embodiments is not limited thereby. Rather, the embodiments of the application/disclosure include all changes, modifications and equivalents coming within the spirit and terms of the claims appended hereto.
It should be noted that in the technical solution of the present disclosure, the acquisition, storage, application, and the like of the personal information of the related user all meet the regulations of the relevant laws and regulations, and do not violate the common customs of the public order.
In the description of the present application/disclosure, the term "plurality" means two or more.
The application provides a way of combining RPA and AI to replace manual automatic traffic service processing. The key information in the traffic incident data is acquired by combining the RPA robot with the AI technology and is input into the traffic service system, so that the traffic service information is accurately acquired and input, and the RPA robot replaces manpower to acquire and input information, thereby reducing the labor cost and improving the traffic service processing efficiency.
For the purpose of clearly explaining the embodiments of the present invention, terms related to the embodiments of the present invention will be explained first.
In the description of the present application, the "scanned article" refers to an electronic version document obtained by photographing and scanning a paper document by some equipment such as a high-speed camera or a scanner.
In the description of the present application, "traffic event data" refers to electronic data related to traffic services, such as scanned documents that may include identification cards of drivers, driver licenses, driving licenses of driven vehicles, and the like, which violate traffic regulations, and scanned documents of paper documents such as on-site notes of drivers, inquiry records, and the like. Accordingly, in the description of the present application, "traffic incident related person" may refer to a driver driving a vehicle.
In the description of the present application, the "traffic service system" refers to an online system handling traffic services, such as a comprehensive law enforcement information system that can manage information of vehicles and drivers violating traffic regulations, or a service system handling other traffic services, etc.
In the description of the present application, "key field" and "key information" are both fragments composed of a single character or a plurality of continuous characters, and "key field" and "key information" can be understood as an attribute item key and an attribute value, respectively, and have a corresponding relationship between the key field and the key information, and the key field and the corresponding key information together constitute a piece of structured data. For example, "zhang san" is the key information corresponding to the key field "name", and "name" and "zhang san" constitute a piece of structured data.
In the description of the present application, the "RPA robot" refers to a software robot that can automatically perform business processing in conjunction with AI technology and RPA technology. The RPA robot has two characteristics of 'connector' and 'non-invasion', and extracts, integrates and communicates data of different systems in a non-invasive mode on the premise of not changing an information system by simulating an operation method of a human.
In the description of the present application, "OCR (Optical Character Recognition)", specifically refers to a process in which an electronic device checks a Character printed on paper, determines its shape by detecting dark and light patterns, and then translates the shape into a computer text by a Character Recognition method; the method is characterized in that characters in a paper document are converted into an image file with a black-white dot matrix in an optical mode aiming at print characters, and the characters in the image are converted into a text format through recognition software for further editing and processing by word processing software.
In the embodiment of the application, the RPA robot can identify key information of key fields such as 'name', 'age', 'date of birth' and the like from certificate scanning pieces such as identity cards, driving licenses, business licenses and the like and paper document scanning pieces such as field notes by combining an OCR technology. Under various complex scenes such as shading, inclination, complex background, uneven illumination, shaking blur, mixed sticking and the like, the accuracy rate of print recognition can reach 99%. In addition, for the recognition of handwriting, a training algorithm model can be customized, so that the recognition accuracy rate of the model can meet the ideal requirement.
In the description of the present application, "NLP (Natural Language Processing)" refers to the ability of a machine to understand and interpret human writing and speech. The goal of NLP is to make computers/machines as intelligent as humans in understanding language, the task of NLP is to understand human language and convert it to machine language.
In the embodiment of the application, the RPA robot can understand the content of the text and make a decision by combining with the NLP technology, and extract structured data from the unstructured text, for example, can extract key information of key fields such as a business name, an address, a date and the like in the text. In addition, the user can extract the template by self-defining, the customized information extraction capability is rapidly realized, and the mixed mode of the model and the template is supported in a section of text.
In the description of the application, the man-machine cooperation platform refers to a platform for connecting the cooperation of an artificial and a robot, tasks needing manual judgment and decision can be distributed to the artificial in an automatic process by using the man-machine cooperation platform, and the artificial provides accurate input for the robot through operations such as form information input and information secondary check and confirmation, so that more and safer automatic opportunities are created.
In the description of the application, the "cooperative identification task" refers to an identification task that needs to be completed by a human-computer cooperation platform in cooperation with an RPA robot.
In the description of the present application, the "process control platform" refers to a process control platform of the RPA robot, and provides functions of designing and managing an execution process of the RPA robot belonging to the control platform.
In the description of the application, the "RPA robot control platform" refers to a management platform of an RPA robot, and provides functions of monitoring RPA robot clients belonging to the control platform, managing scheduled tasks, managing users and authorities, managing authorized permissions, and the like.
In the description of the present application, the "file transfer server" refers to a file server controlled by the RPA robot control platform, deployed on the RPA robot control platform, and capable of storing the RPA robot process generation and the required files.
In the description of the present application, "confidence", also referred to as reliability or confidence level, confidence coefficient, is used to indicate the reliability of the processing result, such as the text recognition result or the classification result. The higher the confidence, the more accurate the processing result.
A traffic service processing method, apparatus, electronic device, and storage medium according to the embodiments of the present application/disclosure are described below with reference to the accompanying drawings.
Fig. 1 is a flowchart of a traffic service processing method combining RPA and AI according to a first embodiment of the present application. As shown in fig. 1, the method may include the steps of:
step 101, traffic incident data to be processed is obtained, and the traffic incident data comprises certificate scanning pieces and paper document scanning pieces of people related to traffic incidents.
It should be noted that the traffic service processing method combining the RPA and the AI according to the embodiment of the present application may be executed by a traffic service processing device combining the RPA and the AI, and the traffic service processing device combining the RPA and the AI will be simply referred to as a traffic service processing device hereinafter. The traffic service processing device may be implemented by an RPA robot, for example, the traffic service processing device may be an RPA robot, or the traffic service processing device may be configured in an RPA robot, which is not limited in this application.
The RPA robot may be configured in an electronic device, and the electronic device may include, but is not limited to, a terminal device, a server, and the like. In the embodiment of the present application, a traffic service processing apparatus is taken as an example of an RPA robot installed in a terminal device.
The RPA robot in this embodiment may execute the method in real time in a specific time period or all day, which is not limited in this application. Wherein the specific time period can be set as desired.
Alternatively, the RPA robot may be activated upon receiving an activation instruction. For example, a worker handling traffic may trigger the above-mentioned start instruction for the RPA robot by means of a dialog. The triggering of the starting instruction for the RPA robot may be implemented in various ways, for example, the starting instruction for the RPA robot may be triggered in a manner of voice and/or text, and for example, the starting instruction for the RPA robot may also be triggered in a manner of triggering a specified control on a dialog interaction interface, which is not limited in this embodiment of the present application.
The certificate scanning component can comprise scanning components of certificates of identity cards, driving licenses and the like of persons related to traffic events; the paper document scanning piece can comprise a scanning piece of a paper document such as a site record, an inquiry record and the like of a person related to the traffic incident. The document scanning piece and the paper document scanning piece can be generated by scanning a document and a paper document through an image acquisition device such as a high-speed camera or a scanner.
For example, a worker who handles traffic services may scan documents such as identification cards of drivers, driver licenses, driving licenses of driven vehicles and the like, and documents such as on-site notes and inquiry records and the like, which violate traffic regulations, through a high-speed camera to generate traffic incident data to be processed, and then the RPA robot may automatically discover new traffic incident data to be processed.
Step 102, identifying the certificate scanned piece based on an Optical Character Recognition (OCR) technology to acquire first key information of at least one first key field in the certificate scanned piece.
Wherein, the first key field can be set as required. For example, all key fields that can be extracted from the certificate scanning component can be set; alternatively, certain key fields may be set, for example, the fields may be set to "name", "gender", "birth", etc., and the present application is not limited thereto.
In an embodiment of the application, since the certificate is usually in a fixed format, the RPA robot identifies the certificate scanned piece based on OCR technology, and can acquire the first key information of at least one first key field in the certificate scanned piece.
In addition, different first key fields can be set for scans of different certificates. For example, for a scanned part of an identity card, the first key field may be set to include: "name", "gender", "birth", "address", "citizen identification number"; for scans of business licenses, the first key field can be set to include: "unified social credit code", "name", "type", "legal representative", "registered capital", "expiration date", "business term", "residence".
For example, assuming that the document scanning component is a scanning component of an identity card, referring to fig. 2, the RPA robot identifies the document scanning component based on OCR technology, and can obtain first key information of "name", "gender", "birth" and "address" in the document scanning component, as shown in the first key field 201, and first key information of "citizen identity number" shown in the first key field 202.
Assuming that the certificate scanner is a scanning license, referring to fig. 3, the RPA robot identifies the certificate scanner based on OCR technology, and can obtain first key information of "unified social credit code" shown as a first key field 301 in fig. 3, first key information of "name", "type", "legal representative" shown as a first key field 302, and first key information of "registered capital", "established date", "business term", and "residence" shown as a first key field 303 in the certificate scanner.
And 103, identifying the paper document scanning piece based on an Optical Character Recognition (OCR) technology to acquire text information in the paper document scanning piece.
Wherein, steps 102 and 103 may be executed simultaneously, or step 102 may be executed first and then step 103 may be executed, or step 103 may be executed first and then step 102 may be executed, which is not limited in this application.
And 104, extracting second key information of at least one second key field from the text information based on a Natural Language Processing (NLP) technology.
And the second key field can be set as required. For example, all key fields that can be extracted from a paper document scanner may be set; alternatively, certain key fields may be set, such as "law enforcement place", "law enforcement time", etc., and the present application is not limited thereto.
In an embodiment of the application, the RPA robot may identify the paper document scanned piece based on OCR technology to obtain text information in the paper document scanned piece. Because the paper document usually does not have a fixed format, the RPA robot recognizes the paper document scanned object based on the OCR technology to obtain text information, usually unstructured data, and in order to be able to recognize the key information of the preset second key field from the paper document scanned object, the RPA robot may further extract the text information based on the NLP technology through context understanding to extract the second key information of each second key field from the text information. Each second key field and the corresponding second key information are a piece of structured data.
For example, assuming that the paper document scanned piece is a scanned piece of a field record, referring to fig. 4, the RPA robot identifies the scanned piece of the document based on OCR technology, and can identify text information in the scanned piece of the paper document, and further based on NLP technology, second key information of "law enforcement place" shown in a second key field 401 in fig. 4, second key information of "law enforcement time" shown in a second key field 402, second key information of "name" shown in a second key field 403, second key information of "identity number" shown in a second key field 404, second key information of "telephone" shown in a second key field 405, and second key information of "vehicle (ship) number" shown in a second key field 406 can be extracted from the text information.
And 105, inputting the first key information of at least one first key field and the second key information of at least one second key field into the traffic service system.
In the traffic service processing method combining the RPA and the AI provided in the embodiment of the application, the RPA robot acquires traffic event data to be processed, the traffic event data includes a certificate scanning piece and a paper document scanning piece of a person related to a traffic event, the certificate scanning piece is identified based on an OCR technology to acquire first key information of at least one first key field in the certificate scanning piece, the paper document scanning piece is identified based on the OCR technology to acquire text information in the paper document scanning piece, second key information of at least one second key field is extracted from the text information based on an NLP technology, and the first key information of the at least one first key field and the second key information of the at least one second key field are entered into a traffic service system. Therefore, the RPA robot acquires the key information in the traffic incident data based on the OCR and NLP technologies and inputs the key information into the traffic service system, so that the traffic service information is accurately acquired and input, the RPA robot replaces manpower to acquire and input the information, the labor cost is reduced, and the traffic service processing efficiency is improved.
The traffic service processing method combining the RPA and the AI provided in the embodiment of the present application is further described below with reference to fig. 5. Fig. 5 is a flowchart of a traffic service processing method combining RPA and AI according to a second embodiment of the present application, as shown in fig. 5, the method including:
step 501, traffic incident data to be processed is obtained, and the traffic incident data comprises certificate scanning pieces and paper document scanning pieces of traffic incident related personnel.
For a specific implementation manner and principle of step 501, reference may be made to the description of the foregoing embodiments, and details are not described here.
Step 502, based on the classification model, classifying each certificate image to obtain a target class to which the corresponding certificate image belongs.
Step 503, identifying the corresponding certificate images based on the text recognition models corresponding to the target categories to which the certificate images belong, so as to obtain first key information of at least one first key field.
The classification model may be any neural network model capable of realizing target classification, the text recognition model may be any neural network model capable of realizing text recognition, and the present application does not limit this.
In a possible implementation form, when scanning the documents, the staff handling the traffic service may scan different pages of the same document or different types of documents into the same scanned document, and accordingly, the same document scanned document may include a plurality of document images, each document image has a corresponding category, and the corresponding categories of the document images may be the same or different. For example, a document scanner may include both a front image and a back image of an identification card, or both a driver's license and a driver's license.
In order to improve the identification accuracy, in this embodiment of the application, each certificate image in the certificate scanned component may be classified based on the classification model, and then each certificate image may be identified by using the text identification model corresponding to the classification based on the classification result, so as to obtain the first key information of at least one first key field in each certificate image. The type corresponding to the certificate image can be set as required, for example, the type can be an identity card type, a driver's license type, a driving license type, a business license type, and the like.
For example, assuming that text recognition models corresponding to the identification card type, the driving license type, and the business license type are constructed in advance, the certificate scan unit is as shown in fig. 6. Then, based on the classification model, each certificate image certificate in fig. 6 is classified, it can be determined that the certificate images 601 and 602 belong to the driving license category and the certificate images 603 and 604 belong to the driving license category, and further, based on the text recognition model corresponding to the driving license category, the certificate images 601 and 602 can be recognized to obtain the first key information of at least one first key field in the certificate images 601 and 602, and based on the text recognition model corresponding to the driving license category, the certificate images 603 and 604 can be recognized to obtain the first key information of at least one first key field in the certificate images 603 and 604.
Step 504, based on the optical character recognition OCR technology, the paper document scanning element is recognized to obtain the text information in the paper document scanning element.
The steps 502 and 504 may be executed simultaneously, or the step 502 may be executed first and then the step 504 is executed, or the step 504 is executed first and then the step 502 is executed, which is not limited in this application.
And 505, extracting second key information of at least one second key field from the text information based on a Natural Language Processing (NLP) technology.
The specific implementation process and principle of steps 504-505 may refer to the description of the foregoing embodiments, and are not described herein again.
Step 506, according to each first key field and each target field in the service page of the traffic service system, filling the first key information of each first key field in the key information position of the first target key field corresponding to the first key field in the service page.
And 507, filling the second key information of each second key field into the key information position of the second target key field corresponding to the second key field in the service page according to each second key field and each target field in the service page of the traffic service system.
The target field is a key field which needs to input corresponding key information in a service page of the traffic service system.
For example, taking a traffic service system as an integrated law enforcement information system as an example, it is assumed that a service page of the traffic service system is as shown in fig. 7, and the service page of the traffic service system includes a plurality of target fields. Referring to fig. 7, for the first key field "name", since the first target key field corresponding to the first key field "name" in the business page is "name" shown in 701 of fig. 7, and the key information position of the first target key field 701 is the input box 702 shown in fig. 7, the first key information of the first key field "name" may be filled in the input box 702 in the business page; for the second key field "car (ship) number", since the second target key field corresponding to the second key field "car (ship) number" in the service page is the "car (ship) number" shown as 703 in fig. 7, and the key information position of the second target key field 703 is the input box 704 shown in fig. 7, the second key information of the second key field "car (ship) number" may be filled in the input box 704 in the service page.
The steps 506 and 507 may be executed simultaneously, or the step 506 may be executed first and then the step 507 is executed, or the step 507 is executed first and then the step 506 is executed, which is not limited in the present application, and the step 506 only needs to be executed after the step 503, and the step 507 only needs to be executed after the step 505.
Step 508, storing the first key information of the at least one first key field and the second key information of the at least one second key field in a preset file type.
The preset file type may be set according to a need, for example, the preset file type may be an Excel table file type, or a Word text file type, and the like, which is not limited in the present application.
In the embodiment of the present application, in order to backup the first key information of each first key field and the second key information of each second key field, the first key information of each first key field and the second key information of each second key field may also be stored in a preset file type.
Referring to fig. 8, in the embodiment of the present application, the first key information of each first key field and the second key information of each second key field may be stored in an Excel form file in a preset form, so as to backup the first key information of each first key field and the second key information of each second key field.
In summary, the traffic service processing method combining the RPA and the AI provided by the embodiment of the application obtains the key information in the traffic event data and inputs the key information into the traffic service system through the RPA robot based on the OCR and NLP technologies, so that the traffic service information is accurately obtained and input, and the RPA robot replaces manpower to obtain and input the information, thereby reducing the labor cost and improving the traffic service processing efficiency.
In a possible implementation form, the accuracy of the first key information of each first key field and/or the second key information of each second key field acquired by the RPA robot based on the OCR and NLP technologies may not be high, and in order to accurately enter the key information in the traffic event data into the traffic service system, in an embodiment of the present application, the first key information and the second key information acquired by the RPA robot based on the OCR and NLP technologies may be further confirmed. The traffic service processing method combining RPA and AI provided in the embodiment of the present application is further described below with reference to fig. 9.
Fig. 9 is a flowchart of a traffic service processing method combining RPA and AI according to a third embodiment of the present application, which may include, as shown in fig. 9:
step 901, traffic incident data to be processed is obtained, and the traffic incident data includes certificate scanning pieces and paper document scanning pieces of related personnel of the traffic incident.
Step 902, identifying the document scanning piece based on an Optical Character Recognition (OCR) technology to acquire first key information of at least one first key field in the document scanning piece.
Step 903, recognizing the paper document scanning piece based on an Optical Character Recognition (OCR) technology to obtain text information in the paper document scanning piece.
And 904, extracting second key information of at least one second key field from the text information based on a Natural Language Processing (NLP) technology.
The specific implementation process and principle of steps 901-904 may refer to the description of the foregoing embodiments, and are not described herein again.
And step 905, issuing a cooperative identification task associated with the traffic event data to the man-machine cooperative platform, and storing the traffic event data to a preset position.
The preset position is a preset file storage position and can be set according to needs, for example, the preset position can be a certain folder in the transit server.
In an embodiment of the present application, the RPA robot may save the traffic event data to a certain folder in the file transfer server by: and sending a file uploading request to the RPA robot control platform so that the RPA robot control platform receives the traffic event data based on the file uploading request and stores the traffic event data in the folder of the file transfer server.
Step 906, responding to the human-computer cooperation platform, processing the cooperation recognition task based on the traffic event data to obtain a recognition result of the traffic event data, storing the recognition result to a preset position, and obtaining the recognition result from the preset position.
In a possible implementation form, after the human-computer cooperation platform obtains the cooperation identification task, traffic event data associated with the cooperation identification task can be obtained from a preset position, and the cooperation identification task is processed based on the traffic event data to obtain an identification result of the traffic event data.
Specifically, after the human-computer cooperation platform acquires the cooperation identification task and the traffic event data associated with the cooperation identification task, a task notification can be sent to the staff of the human-computer cooperation platform, so that the staff of the human-computer cooperation platform logs in the human-computer cooperation platform to perform task processing. When a worker of the human-computer cooperation platform logs in the human-computer cooperation platform to perform task processing, the human-computer cooperation platform can display a task interface corresponding to the cooperative identification task, traffic event data and an input frame are displayed in the task interface, and the input frame is used for inputting a manual identification result of the traffic event data. After the staff identifies the traffic incident data and obtains an identification result, the identification result can be input in the input box, and then the man-machine cooperation platform can respond to the detected input information in the input box of the task interface and obtain the identification result which is manually input. Therefore, the man-machine cooperation platform can acquire the recognition result of the traffic incident data, and further can store the recognition result to a preset position. And responding to the situation that the man-machine cooperation platform stores the recognition result to a preset position, and the RPA robot can obtain the recognition result from the preset position.
In another possible implementation form, the RPA robot may further store the traffic event data, and the first key information and the second key information acquired by the RPA robot based on the OCR technology and the NLP technology to a preset position at the same time. After the man-machine cooperation platform obtains the cooperation identification task, traffic event data, first key information and second key information which are related to the cooperation identification task can be obtained from a preset position, and the cooperation identification task is processed based on the traffic event data, the first key information and the second key information, so that an identification result corresponding to the traffic event data is obtained.
Specifically, after the human-computer cooperation platform acquires the cooperation identification task, the traffic event data associated with the cooperation identification task, the first key information and the second key information, a task notification can be sent to the staff of the human-computer cooperation platform, so that the staff of the human-computer cooperation platform can log in the human-computer cooperation platform to perform task processing. When a worker of the human-computer cooperation platform logs in the human-computer cooperation platform to perform task processing, the human-computer cooperation platform can display a task interface corresponding to a cooperation recognition task, display traffic event data and a plurality of input boxes in the task interface, and display first key information of a corresponding first key field or second key information of a corresponding second key field in each input box. The staff can check whether the first key information or the second key information in each input frame is correct or not according to the traffic incident data, and when the first key information or the second key information is incorrect, the first key information or the second key information in the input frames can be modified, so that the man-machine cooperation platform can respond to the detected input information in the input frames of the task interface and obtain the identification result after manual input or check. Therefore, the man-machine cooperation platform can acquire the recognition result of the traffic incident data, and further can store the recognition result to a preset position. And responding to the situation that the man-machine cooperation platform stores the recognition result to a preset position, and the RPA robot can obtain the recognition result from the preset position.
In the embodiment of the application, in order to enable the human-computer cooperation platform to process the cooperation recognition task in the above manner to obtain the recognition result, a cooperation action may be created in the human-computer cooperation platform manually in advance. When the cooperative action is created, task contents that need to be processed in a manual cooperative manner in the cooperative identification task may be configured according to business needs, for example, which key fields in the form need to be identified manually, and field attribute information of the key fields to be identified. And moreover, task paths of the collaborative identification task can be configured according to business needs, namely the man-machine collaboration platform obtains traffic event data related to the collaborative identification task from which path, and stores the processing result of the task in which path after processing the collaborative identification task to obtain the processing result. In addition, a processing flow for processing the traffic service by combining the RPA robot and the man-machine cooperation platform can be compiled, in the process of the RPA robot executing the flow, a man-machine cooperation command is added and is associated to a corresponding cooperation action, so that the RPA robot executes the link needing the manual assistance in the process of processing the traffic service, the man-machine cooperation command can be triggered, a cooperation identification task associated with the cooperation action is generated on the man-machine cooperation platform, and the man-machine cooperation platform can acquire traffic event data from the configured task path when acquiring the cooperation identification task associated with the traffic event data, and the traffic event data and the configured input frames of the key information of each key field are displayed through a task interface of the cooperation identification task, so as to further process the input information in each input frame of the task interface, and acquiring an identification result. The field attribute information may include, for example, field identification of the field, field name, and other attribute information.
In the embodiment of the application, the human-computer collaboration platform may provide a collaboration action creation interface, and after manually selecting or inputting which key fields (i.e., target key fields) in the collaboration identification task need to be manually identified through the collaboration action creation interface, setting field attribute information of the target key fields, and a task path of the collaboration identification task, may trigger an action configuration command to create a collaboration action. Correspondingly, the human-computer cooperation platform can obtain an action configuration command, wherein the action configuration command comprises a field configuration command and a path configuration command, the field configuration command is used for configuring a target key field of the cooperation identification task and field attribute information corresponding to the target key field, the path configuration command is used for configuring a task path of the cooperation identification task, and further the human-computer cooperation platform can configure the target key field as the target key field needing to be identified by the cooperation identification task and configure the path corresponding to the preset position as the task path of the cooperation identification task based on the field configuration command and the path configuration command.
In the embodiment of the application, a man-machine cooperative command can be added to a link needing manual assistance in the process of executing the flow of the RPA robot through the flow management and control platform, and the man-machine cooperative command is associated with the corresponding cooperative action in the man-machine cooperative platform, so that when the RPA robot encounters the link needing manual assistance in the process of executing the flow, the man-machine cooperative command can be triggered, a cooperative identification task associated with the cooperative action is generated on the man-machine cooperative platform, and the cooperative identification task is processed through the man-machine cooperative platform.
Step 907 determines that the recognition result includes first key information of at least one first key field in the certificate scanning piece and second key information of at least one second key field in the paper document scanning piece.
Step 908, entering first key information of at least one first key field and second key information of at least one second key field into the traffic service system.
In the embodiment of the application, after the RPA robot acquires the recognition result, when it is determined that the recognition result includes first key information of at least one first key field in the certificate scanning piece and second key information of at least one second key field in the paper document scanning piece, that is, when the first key information and the second key information acquired by the RPA robot based on the OCR technology and the NLP technology are accurate key information, the first key information and the second key information can be entered into the traffic service system.
It should be noted that, when the first key information and/or the second key information acquired by the RPA robot based on the OCR technology and the NLP technology is incorrect, the first key information or the second key information acquired by the RPA robot based on the OCR technology and the NLP technology is different from the key information included in the recognition result acquired by the RPA robot from the preset position.
In addition, in a possible implementation form, in order to implement secondary confirmation of each first key information and each second key information, the RPA robot may further display, through the human-computer interaction interface, the first key information of the certificate scanning piece and at least one first key field therein, and display the second key information of the paper document scanning piece and at least one second key field therein. When the staff confirms that the first key information and the second key information displayed on the man-machine interaction interface are correct, a confirmation instruction for the first key information and the second key information can be triggered by clicking a confirmation button of the man-machine interaction interface and the like. After the RPA robot obtains the confirmation instruction, the correctness of each first key information and each second key information can be determined, and then each first key information and each second key information can be recorded into the traffic service system.
It should be noted that the RPA robot may directly issue the collaborative identification task associated with the traffic event data to the human-computer collaboration platform after acquiring the first key information and the second key information, or display the first key information and the second key information through the human-computer interaction interface, so as to manually confirm the first key information and the second key information, or manually confirm the first key information and the second key information when determining that the accuracy of the first key information and the second key information is not high.
Accordingly, before step 905, the method may further include: and determining that the confidence of the first key information and/or the second key information is less than a preset threshold.
The preset threshold may be set arbitrarily as needed, which is not limited in the present application.
It can be understood that, when the certificate scanning member is identified based on the OCR technology, in addition to the first key information of at least one first key field in the certificate scanning member, the confidence of each first key information can also be obtained. Similarly, when the second key information of at least one second key field in the paper document scanned part is obtained based on the OCR technology and the NLP technology, the confidence of each second key information can also be obtained at the same time. In the embodiment of the application, when the RPA robot determines that the confidence of each piece of first key information and/or each piece of second key information is smaller than a preset threshold, it may be determined that the accuracy of each piece of first key information and/or each piece of second key information is not high, so that a collaborative identification task related to traffic incident data may be issued to a human-computer collaborative platform, or each piece of first key information and each piece of second key information may be displayed through a human-computer interaction interface, so that each piece of first key information and each piece of second key information may be manually confirmed.
According to the traffic service processing method combining the RPA and the AI, after the RPA robot acquires the first key information and the second key information based on the OCR and NLP technologies, the first key information and the second key information are confirmed by combining the man-machine coordination center, and then the manually confirmed first key information and second key information are input into the traffic service system, so that the key information in traffic incident data is accurately input into the traffic service system, and the RPA robot replaces manpower to acquire and input information, so that the labor cost is reduced, and the traffic service processing efficiency is improved.
In order to implement the above embodiments, the present application further provides a traffic service processing device combining RPA and AI. Fig. 10 is a schematic structural diagram of a traffic service processing device combining RPA and AI according to a fourth embodiment of the present application.
As shown in fig. 10, the traffic service processing device 1000 combining RPA and AI, applied to an RPA robot, includes: a first obtaining module 1001, a first identifying module 1002, a second identifying module 1003, an extracting module 1004, and an entry module 1005.
The first acquisition module 1001 is configured to acquire traffic event data to be processed, where the traffic event data includes certificate scan pieces and paper document scan pieces of people related to a traffic event;
the first recognition module 1002 is configured to recognize the certificate scanned piece based on an Optical Character Recognition (OCR) technology to acquire first key information of at least one first key field in the certificate scanned piece;
the second identification module 1003 is configured to identify the scanned paper document based on an Optical Character Recognition (OCR) technique to obtain text information in the scanned paper document;
an extracting module 1004, configured to extract second key information of at least one second key field from the text information based on a natural language processing NLP technique;
an entry module 1005, configured to enter the first key information of the at least one first key field and the second key information of the at least one second key field into the traffic service system.
It should be noted that the traffic service processing device 1000 combining RPA and AI according to the embodiment of the present application may execute the traffic service processing method combining RPA and AI provided in the foregoing embodiment. The traffic service processing device 1000 combining RPA and AI may be implemented by an RPA robot, for example, the traffic service processing device 1000 combining RPA and AI may be an RPA robot, or the traffic service processing device 1000 combining RPA and AI may be configured in an RPA robot, which is not limited in this application.
The RPA robot may be configured in an electronic device, and the electronic device may include, but is not limited to, a terminal device, a server, and the like.
In one embodiment of the present application, a credential scanner includes a plurality of credential images, each credential image having a corresponding category;
a first identifying module 1002 for:
classifying each certificate image based on the classification model to obtain a target class to which the corresponding certificate image belongs;
and identifying the corresponding certificate image based on the text identification model corresponding to the target category to which each certificate image belongs to obtain first key information of at least one first key field.
In an embodiment of the present application, the entry module 1005 is configured to:
filling first key information of each first key field into a key information position of the first target key field corresponding to the first key field in the service page according to each first key field and each target field in the service page of the traffic service system;
and filling second key information of each second key field into a key information position of the second target key field corresponding to the second key field in the service page according to each second key field and each target field in the service page of the traffic service system.
In one embodiment of the present application, the traffic service processing device 1000 combining RPA and AI further includes:
the sending module is used for issuing a cooperative identification task related to the traffic event data to the man-machine cooperative platform and storing the traffic event data to a preset position;
the second acquisition module is used for responding to the human-computer cooperation platform and processing the cooperation recognition task based on the traffic event data to obtain a recognition result of the traffic event data, storing the recognition result to a preset position and acquiring the recognition result from the preset position;
and the first determining module is used for determining that the identification result comprises first key information of at least one first key field in the certificate scanning piece and second key information of at least one second key field in the paper document scanning piece.
In one embodiment of the present application, the traffic service processing device 1000 combining RPA and AI further includes:
and the second determining module is used for determining that the confidence degree of the first key information and/or the second key information is smaller than a preset threshold value.
In one embodiment of the present application, the traffic service processing device 1000 combining RPA and AI further includes:
and the storage module is used for storing the first key information of the at least one first key field and the second key information of the at least one second key field in a preset file type.
It should be noted that the foregoing explanation of the embodiment of the traffic service processing method combining the RPA and the AI is also applicable to the traffic service processing device combining the RPA and the AI in this embodiment, and details that are not published in the embodiment of the traffic service processing device combining the RPA and the AI in this application are not described herein again.
To sum up, the traffic service processing apparatus combining the RPA and the AI according to the embodiment of the present application obtains traffic event data to be processed, where the traffic event data includes a certificate scanning component and a paper document scanning component of a person related to a traffic event, identifies the certificate scanning component based on an optical character recognition OCR technology to obtain first key information of at least one first key field in the certificate scanning component, identifies the paper document scanning component based on the optical character recognition OCR technology to obtain text information in the paper document scanning component, extracts second key information of at least one second key field from the text information based on a natural language processing NLP technology, and records the first key information of the at least one first key field and the second key information of the at least one second key field in the traffic service system. Therefore, the RPA robot acquires the key information in the traffic incident data based on the OCR and NLP technologies and inputs the key information into the traffic service system, so that the traffic service information is accurately acquired and input, the RPA robot replaces manpower to acquire and input the information, the labor cost is reduced, and the traffic service processing efficiency is improved.
In order to implement the foregoing embodiments, an electronic device is further provided in an embodiment of the present application, and includes a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor executes the computer program to implement the traffic service processing method combining the RPA and the AI according to any one of the foregoing method embodiments.
In order to implement the foregoing embodiments, the present application further proposes a computer-readable storage medium, on which a computer program is stored, which when executed by a processor implements the traffic service processing method combining the RPA and the AI according to any of the foregoing method embodiments.
In order to implement the foregoing embodiments, the present application further proposes a computer program product, which when being executed by an instruction processor, implements the traffic service processing method combining RPA and AI according to any of the foregoing method embodiments.
FIG. 11 illustrates a block diagram of an exemplary electronic device suitable for use in implementing embodiments of the present application. The electronic device 1100 shown in fig. 11 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. 11, the electronic device 1100 is in the form of a general purpose computing device. The components of the electronic device 1100 may include, but are not limited to: one or more processors or processing units 16, a system memory 28, and a bus 18 that couples various system components including the memory 28 and the processing unit 16.
Bus 18 represents one or more of any of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, and a processor or local bus using any of a variety of bus architectures. By way of example, such architectures include, but are not limited to, Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MAC) bus, enhanced ISA bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus.
Electronic device 1100 typically includes a variety of computer system readable media. Such media may be any available media that is accessible by electronic device 1100 and includes both volatile and nonvolatile media, removable and non-removable media.
Memory 28 may include computer system readable media in the form of volatile Memory, such as Random Access Memory (RAM) 30 and/or cache Memory 32. The electronic device 1100 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, storage system 34 may be used to read from and write to non-removable, nonvolatile magnetic media (not shown in FIG. 11 and commonly referred to as a "hard drive"). Although not shown in FIG. 11, a disk drive for reading from and writing to a removable, nonvolatile magnetic disk (e.g., a "floppy disk") and an optical disk drive for reading from or writing to a removable, nonvolatile optical disk (e.g., a Compact disk Read Only Memory (CD-ROM), a Digital versatile disk Read Only Memory (DVD-ROM), or other optical media) may be provided. In these cases, each drive may be connected to bus 18 by one or more data media interfaces. Memory 28 may include at least one program product having a set (e.g., at least one) of program modules that are configured to carry out the functions of embodiments of the application.
A program/utility 40 having a set (at least one) of program modules 42 may be stored, for example, in memory 28, such program modules 42 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which examples or some combination thereof may comprise an implementation of a network environment. Program modules 42 generally perform the functions and/or methodologies of the embodiments described herein.
The electronic device 1100 may also communicate with one or more external devices 14 (e.g., keyboard, pointing device, display 24, etc.), one or more devices that enable a user to interact with the electronic device 1100, and/or any device (e.g., network card, modem, etc.) that enables the electronic device 1100 to communicate with one or more other computing devices. Such communication may be through an input/output (I/O) interface 22. Moreover, the electronic device 1100 may also 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 20. As shown in FIG. 11, the network adapter 20 communicates with the other modules of the electronic device 1100 via the bus 18. It should be appreciated that although not shown in FIG. 11, other hardware and/or software modules may be used in conjunction with the electronic device 1100, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, to name a few.
The processing unit 16 executes various functional applications and data processing, for example, implementing the methods mentioned in the foregoing embodiments, by executing programs stored in the memory 28.
In the description herein, reference to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the application. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Moreover, various embodiments or examples and features of various embodiments or examples described in this specification can be combined and combined by one skilled in the art without being mutually inconsistent.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or to implicitly indicate the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present application, "plurality" means at least two, e.g., two, three, etc., unless explicitly specified otherwise.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing steps of a custom logic function or process, and alternate implementations are included within the scope of the preferred embodiment of the present application in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present application.
The logic and/or steps represented in the flowcharts or otherwise described herein, such as an ordered listing of executable instructions that can be considered to implement logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Further, the computer-readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via for instance optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.
It should be understood that portions of the present application may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are well known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
It will be understood by those skilled in the art that all or part of the steps carried out in the method of implementing the above embodiments may be implemented by hardware related to instructions of a program, which may be stored in a computer readable storage medium, and the program, when executed, includes one or a combination of the steps of the method embodiments.
In addition, functional units in the embodiments of the present application may be integrated into one processing module, or each unit may exist alone physically, or two or more units are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a separate product, may also be stored in a computer-readable storage medium.
The storage medium mentioned above may be a read-only memory, a magnetic or optical disk, etc. Although embodiments of the present application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present application, and that variations, modifications, substitutions and alterations may be made to the above embodiments by those of ordinary skill in the art within the scope of the present application.

Claims (14)

1. A traffic service processing method combining robot flow automation (RPA) and Artificial Intelligence (AI), which is applied to an RPA robot, and comprises the following steps:
acquiring traffic incident data to be processed, wherein the traffic incident data comprises certificate scanning pieces and paper document scanning pieces of traffic incident related personnel;
based on an Optical Character Recognition (OCR) technology, recognizing the certificate scanning piece to acquire first key information of at least one first key field in the certificate scanning piece;
based on an Optical Character Recognition (OCR) technology, recognizing the paper document scanning piece to obtain text information in the paper document scanning piece;
extracting second key information of at least one second key field from the text information based on a Natural Language Processing (NLP) technology;
and inputting the first key information of the at least one first key field and the second key information of the at least one second key field into a traffic service system.
2. The method of claim 1, wherein the document scanner includes a plurality of document images therein, each document image having a corresponding category;
the OCR technology based on optical character recognition is used for recognizing the certificate scanning piece so as to acquire first key information of at least one first key field in the certificate scanning piece, and comprises the following steps:
classifying each certificate image based on a classification model to obtain a target class to which the corresponding certificate image belongs;
and identifying the corresponding certificate images based on the text identification models corresponding to the target categories to which the certificate images belong so as to acquire the first key information of the at least one first key field.
3. The method according to claim 1, wherein the entering of first key information of the at least one first key field and second key information of the at least one second key field into a traffic service system comprises:
filling first key information of each first key field into a key information position of a first target key field corresponding to the first key field in the service page according to each first key field and each target field in the service page of the traffic service system;
and filling second key information of each second key field in a key information position of a second target key field corresponding to the second key field in the service page according to each second key field and each target field in the service page of the traffic service system.
4. The method of claim 1, wherein before entering the first key information of the at least one first key field and the second key information of the at least one second key field into the traffic service system, further comprising:
issuing a cooperative identification task related to the traffic event data to a man-machine cooperative platform, and storing the traffic event data to a preset position;
responding to the human-computer cooperation platform, processing the cooperation recognition task based on the traffic event data to obtain a recognition result of the traffic event data, storing the recognition result to the preset position, and acquiring the recognition result from the preset position;
and determining that the identification result comprises first key information of at least one first key field in the certificate scanning piece and second key information of at least one second key field in the paper document scanning piece.
5. The method of claim 4, wherein before issuing the collaborative recognition task associated with the traffic event data to the human-computer collaborative platform and saving the traffic event data to a preset location, the method further comprises:
determining that the confidence of the first key information and/or the second key information is less than a preset threshold.
6. The method according to any one of claims 1-5, further comprising:
and storing the first key information of the at least one first key field and the second key information of the at least one second key field in a preset file type.
7. A traffic service processing apparatus combining RPA and AI, applied to an RPA robot, the apparatus comprising:
the system comprises a first acquisition module, a second acquisition module and a processing module, wherein the first acquisition module is used for acquiring traffic incident data to be processed, and the traffic incident data comprises certificate scanning pieces and paper document scanning pieces of traffic incident related personnel;
the first identification module is used for identifying the certificate scanning piece based on an Optical Character Recognition (OCR) technology so as to acquire first key information of at least one first key field in the certificate scanning piece;
the second recognition module is used for recognizing the paper document scanning piece based on an Optical Character Recognition (OCR) technology so as to acquire text information in the paper document scanning piece;
the extraction module is used for extracting second key information of at least one second key field from the text information based on a Natural Language Processing (NLP) technology;
and the entry module is used for entering the first key information of the at least one first key field and the second key information of the at least one second key field into the traffic service system.
8. The apparatus of claim 7, wherein the document scanner includes a plurality of document images therein, each document image having a corresponding category;
the first identification module is configured to:
classifying each certificate image based on a classification model to obtain a target class to which the corresponding certificate image belongs;
and identifying the corresponding certificate images based on the text identification models corresponding to the target categories to which the certificate images belong to so as to acquire the first key information of the at least one first key field.
9. The apparatus of claim 7, wherein the logging module is to:
filling first key information of each first key field into a key information position of the first target key field corresponding to the first key field in the service page according to each first key field and each target field in the service page of the traffic service system;
and filling second key information of each second key field into a key information position of a second target key field corresponding to the second key field in the service page according to each second key field and each target field in the service page of the traffic service system.
10. The apparatus of claim 7, further comprising:
the sending module is used for issuing the traffic event data-associated collaborative identification task to the man-machine collaborative platform and storing the traffic event data to a preset position;
the second acquisition module is used for responding to the human-computer cooperation platform, processing the cooperation identification task based on the traffic event data to obtain an identification result of the traffic event data, storing the identification result to the preset position, and acquiring the identification result from the preset position;
the first determining module is used for determining that the identification result comprises first key information of at least one first key field in the certificate scanning piece and second key information of at least one second key field in the paper document scanning piece.
11. The apparatus of claim 10, further comprising:
and the second determining module is used for determining that the confidence degree of the first key information and/or the second key information is smaller than a preset threshold value.
12. The apparatus of any one of claims 7-11, further comprising:
and the storage module is used for storing the first key information of the at least one first key field and the second key information of the at least one second key field in a preset file type.
13. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor implementing the method of any one of claims 1-6 when executing the computer program.
14. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 1-6.
CN202210246327.1A 2022-03-14 2022-03-14 Traffic service processing method and device combining RPA and AI and electronic equipment Pending CN114782952A (en)

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