CN113553842A - Work order processing method and device combining RPA and AI - Google Patents
Work order processing method and device combining RPA and AI Download PDFInfo
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Abstract
The embodiment of the application discloses a work order processing method and a device combining RPA and AI, wherein the work order processing method combining RPA and AI comprises the following steps: acquiring a work order to be processed; reading the work order information in the work order to be processed; and generating a classification result according to the work order information and the work order classification model, wherein the classification result comprises a plurality of undertaking units. By adopting the technical scheme, the RPA robot and the work order processing system are integrated, the work orders to be processed are automatically acquired, the classification result is automatically generated according to the work order information and the work order classification model, the work flow of work order processing is simplified, the processing efficiency and the accuracy are improved, the labor cost is reduced, and human errors are effectively avoided.
Description
Technical Field
The present disclosure relates to the technical field of Robot Process Automation (RPA) and Artificial Intelligence (AI), and in particular, to a method and an apparatus for processing a work order by combining RPA and AI.
Background
Robot Process Automation (RPA) is a Process task that simulates human operations on a computer by specific "robot software" and executes automatically according to rules.
Artificial Intelligence (AI) is a technical science that studies and develops theories, methods, techniques and application systems for simulating, extending and expanding human Intelligence.
The RPA and AI technology has the advantages of high automation degree, high accuracy and low cost. With the wide application of RPA robots, more and more human work is taken over by RPA robots. With the increasing demand of citizens, a large number of work orders to be distributed exist in 12345 central systems of various cities to be processed.
In the related technology, the work orders to be distributed need to be manually dispatched to the undertaking units, the manual operation process is complicated, the real-time performance of work order processing is low, the efficiency is low, a large amount of manpower is consumed, and errors are prone to occurring.
Disclosure of Invention
The present application is directed to solving, at least to some extent, one of the technical problems in the related art.
Therefore, a first objective of the present application is to provide a work order processing method combining RPA and AI, which simplifies the work flow of work order processing, improves the processing efficiency and accuracy, reduces the labor cost, and effectively avoids human errors.
A second object of the present application is to provide a work order processing apparatus that combines RPA and AI.
A third object of the present application is to propose a computing device.
A fourth object of the present application is to propose a computer readable storage medium.
In order to achieve the above object, an embodiment of a first aspect of the present application provides a method for processing a work order by combining RPA and AI, including: acquiring a work order to be processed; reading the work order information in the work order to be processed; and generating a classification result according to the work order information and the work order classification model, wherein the classification result comprises a plurality of undertaking units.
The work order processing method combining the RPA and the AI, provided by the embodiment of the application, includes the steps of obtaining a work order to be processed, reading work order information in the work order to be processed, and generating a classification result according to the work order information and a work order classification model, wherein the classification result comprises a plurality of undertaking units. In the embodiment, the RPA robot and the work order handling system are integrated, the work orders to be processed are automatically acquired, and the classification result is automatically generated according to the work order information and the work order classification model, so that the work flow of work order processing is simplified, the processing efficiency and accuracy are improved, the labor cost is reduced, and human errors are effectively avoided.
According to an embodiment of the present application, the work order processing method further includes: acquiring a undertaking unit selection instruction of a user, wherein the undertaking unit selection instruction comprises target undertaking units selected by the user from the plurality of undertaking units; filling the target undertaking unit on the work order system; and dispatching the work order to be processed to the target undertaking unit.
According to an embodiment of the present application, the work order classification model includes a keyword classification model and a deep learning classification model, and the generating of the classification result according to the work order information and the work order classification model includes: judging whether the work order information is matched with a keyword classification through Natural Language Processing (NLP) based on the keyword classification model; if the work order information is matched with the keyword classification, displaying the classification result output by the keyword classification model; and if the work order information is not matched with the keyword classification, generating and displaying the classification result through Natural Language Processing (NLP) according to the deep learning classification model and the work order information.
According to an embodiment of the present application, if the work order information matches a keyword classification, displaying the classification result output by the keyword classification model, including: if the work order information is matched with the keyword classification, displaying whether the first reminding information of the keyword classification model is adopted or not; responding to a first adoption instruction input by the user aiming at the first reminding information, and displaying the classification result output by the keyword classification model; and responding to a first skipping instruction input by the user aiming at the first reminding information, and generating and displaying the classification result through Natural Language Processing (NLP) according to the deep learning classification model and the work order information.
According to an embodiment of the present application, the generating and displaying the classification result through natural language processing NLP according to the deep learning classification model and the work order information includes: displaying second reminding information of whether the deep learning classification model is adopted or not; responding to a second adoption instruction input by the user aiming at the second reminding information, and generating and displaying the classification result through Natural Language Processing (NLP) according to the deep learning classification model and the work order information; and in response to a second skipping instruction input by the user for the second reminding information, classifying the work order information without adopting the deep learning classification model.
According to an embodiment of the present application, the work order processing method further includes: and recording the work order dispatching result of the work order to be processed, wherein the work order dispatching result comprises skipping or processed work orders.
According to an embodiment of the present application, the work order processing method further includes: displaying third reminding information whether to check the operation result; and responding to a viewing instruction input by the user aiming at the third reminding information, and displaying the work order dispatching result of the work order to be processed.
According to one embodiment of the present application, the work order information includes any one or combination of the following: title, affiliation, and appeal content.
In order to achieve the above object, a second embodiment of the present application provides a work order processing apparatus combining RPA and AI, including: the first acquisition module is used for acquiring a work order to be processed; the reading module is used for reading the work order information in the work order to be processed; and the generation module is used for generating a classification result according to the work order information and the work order classification model, and the classification result comprises a plurality of undertaking units.
The work order processing device combining the RPA and the AI, provided by the embodiment of the application, acquires a work order to be processed, reads work order information in the work order to be processed, and generates a classification result according to the work order information and a work order classification model, wherein the classification result comprises a plurality of undertaking units. In the embodiment, the RPA robot and the work order handling system are integrated, the work orders to be processed are automatically acquired, and the classification result is automatically generated according to the work order information and the work order classification model, so that the work flow of work order processing is simplified, the processing efficiency and accuracy are improved, the labor cost is reduced, and human errors are effectively avoided.
According to an embodiment of the present application, the work order processing apparatus further includes: a second obtaining module, configured to obtain a selection instruction of a undertaking unit of a user, where the selection instruction of the undertaking unit includes a target undertaking unit selected by the user in the plurality of undertaking units; the filling module is used for filling the target undertaking unit on the work order system; and the dispatching module is used for dispatching the work order to be processed to the target undertaking unit.
According to an embodiment of the application, the work order classification model includes a keyword classification model and a deep learning classification model, and the generation module includes: the judging unit is used for judging whether the work order information is matched with the keyword classification through Natural Language Processing (NLP) based on the keyword classification model; the display unit is used for displaying the classification result output by the keyword classification model if the work order information is matched with the keyword classification; and if the work order information is not matched with the keyword classification, generating and displaying the classification result through Natural Language Processing (NLP) according to the deep learning classification model and the work order information.
According to an embodiment of the present application, the display unit is specifically configured to: if the work order information is matched with the keyword classification, displaying whether the first reminding information of the keyword classification model is adopted or not; responding to a first adoption instruction input by the user aiming at the first reminding information, and displaying the classification result output by the keyword classification model; and responding to a first skipping instruction input by the user aiming at the first reminding information, and generating and displaying the classification result through Natural Language Processing (NLP) according to the deep learning classification model and the work order information.
According to an embodiment of the present application, the display unit is specifically configured to: displaying second reminding information of whether the deep learning classification model is adopted or not; responding to a second adoption instruction input by the user aiming at the second reminding information, and generating and displaying the classification result through Natural Language Processing (NLP) according to the deep learning classification model and the work order information; and in response to a second skipping instruction input by the user for the second reminding information, classifying the work order information without adopting the deep learning classification model.
According to an embodiment of the present application, the work order processing apparatus further includes: and the recording module is used for recording the work order dispatching result of the work order to be processed, wherein the work order dispatching result comprises skipping or processed work orders.
According to an embodiment of the present application, the work order processing apparatus further includes: the first display module is used for displaying third reminding information of whether the operation result is checked; and the second display module is used for responding to a viewing instruction input by the user aiming at the third reminding information and displaying the work order dispatching result of the work order to be processed.
According to one embodiment of the present application, the work order information includes any one or combination of the following: title, affiliation, and appeal content.
To achieve the above object, a third aspect of the present application provides a computing device, including: a memory storing executable program code; a processor coupled with the memory; the processor calls the executable program code stored in the memory to execute the work order processing method combining the RPA and the AI according to the embodiment of the first aspect of the present application.
To achieve the above object, a fourth aspect of the present application provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements a work order processing method combining RPA and AI as described in the first aspect of the present application.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings that are mostly needed to be used in the embodiments are briefly described below, and it is obvious that the drawings in the following description are only some of the embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a flowchart of a work order processing method combining RPA and AI according to an embodiment of the present disclosure;
FIG. 2 is a flowchart of a work order classification model training method for a work order processing method incorporating RPA and AI according to an embodiment of the present disclosure;
FIG. 3 is a flow chart of another work order processing method combining RPA and AI according to an embodiment of the present disclosure;
FIG. 4 is a flow chart of another work order processing method combining RPA and AI according to an embodiment of the present disclosure;
FIG. 5 is a flow chart of another work order processing method in conjunction with RPA and AI according to an embodiment of the present disclosure;
FIG. 6 is a flow chart of another work order processing method in conjunction with RPA and AI according to an embodiment of the present disclosure;
fig. 7 is an overall flowchart of a work order processing method combining RPA and AI according to an embodiment of the present application;
fig. 8 is a schematic diagram of a work order processing apparatus combining RPA and AI according to an embodiment of the present disclosure.
Detailed Description
Reference will now be made in detail to embodiments of the present application, examples of which are illustrated in the accompanying drawings, wherein like or similar 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 and intended to be used for explaining the present application and should not be construed as limiting the present application.
The following describes a work order processing method and apparatus in combination with RPA and AI according to an embodiment of the present application with reference to the drawings.
Fig. 1 is a flowchart of a RPA and AI combined work order processing method according to an embodiment of the present application, and as shown in fig. 1, the RPA and AI combined work order processing method includes the following steps:
and S101, acquiring a work order to be processed.
The main execution body of the RPA and AI combined work order processing method according to the embodiment of the present application may be the RPA and AI combined work order processing device according to the embodiment of the present application, and the RPA and AI combined work order processing device may be configured in an RPA system of any electronic device to execute the RPA and AI combined work order processing method according to the embodiment of the present application. Optionally, the RPA system may include an RPA robot. RPA is a software robot and artificial intelligence based business process automation technology. The RPA automatically processes high-frequency services with clear rules and batched through simulating manual operation of a keyboard and a mouse. The method is suitable for operation flows with definite business rules and structured input and output, and can be easily integrated on any system to process data across the system.
In the embodiment of the present application, the work orders to be processed are waiting for the work orders to be dispatched to the undertaking units for processing, such as various citizens' requirements in the 12345 systems of various regions. The RPA robot automatically acquires a to-be-processed work order, wherein the to-be-processed work order can specifically include but is not limited to office items stored in an Excel table created after a user logs in a work order processing system.
And S102, reading the work order information in the work order to be processed.
In the embodiment of the present application, the RPA robot automatically reads the work order information of the work order to be processed, which is acquired in step S101. The work order information may specifically include, but is not limited to, any one or a combination of more of the following information: title, belongings, and appeal content, etc.
And S103, generating a classification result according to the work order information and the work order classification model, wherein the classification result comprises a plurality of undertaking units.
In the embodiment of the present application, the RPA robot inputs the work order information obtained in step S102 into the work order classification model, and the work order classification model outputs a classification result including a plurality of undertaking units. The RPA robot may present a predetermined number of classification results to the user, for example, the top 5 classification results with the highest confidence. The work order classification model can be created through historical order dispatching data training, the work order classification model comprises a keyword classification model and a deep learning classification model, and a training flow chart of the work order classification model is shown in figure 2 and comprises the following steps: s201, investigating and researching a work flow of work order distribution; s202, collecting historical order dispatching data for more than three years; s203, training and testing the work order classification model based on Natural Language Processing (NLP) according to the historical dispatch data.
The work order processing method combining the RPA and the AI, provided by the embodiment of the application, includes the steps of obtaining a work order to be processed, reading work order information in the work order to be processed, and generating a classification result according to the work order information and a work order classification model, wherein the classification result comprises a plurality of undertaking units. In the embodiment, the RPA robot and the work order handling system are integrated, the work orders to be processed are automatically acquired, and the classification result is automatically generated according to the work order information and the work order classification model, so that the work flow of work order processing is simplified, the processing efficiency and accuracy are improved, the labor cost is reduced, and human errors are effectively avoided.
Fig. 3 is a flowchart of another work order processing method combining RPA and AI according to an embodiment of the present disclosure. As shown in fig. 3, on the basis of the embodiment shown in fig. 1, the work order processing method combining RPA and AI proposed in this embodiment specifically includes the following steps:
s301, acquiring a work order to be processed.
And S302, reading the work order information in the work order to be processed.
And S303, generating a classification result according to the work order information and the work order classification model, wherein the classification result comprises a plurality of undertaking units.
In the embodiment of the present application, steps S301 to S303 are the same as steps S101 to S103 in the above embodiment, and are not described herein again.
S304, acquiring a undertaking unit selection instruction of the user, wherein the undertaking unit selection instruction comprises target undertaking units selected by the user in the plurality of undertaking units.
In the embodiment of the present application, the target undertaking unit is a undertaking unit selected by the user from a plurality of undertaking units included in the classification result. The RPA robot displays the classification result including the plurality of undertaking units obtained in step S303 to the user, the user inputs a undertaking unit selection instruction by clicking the classification result, the undertaking unit selection instruction includes a target undertaking unit selected by the user from the plurality of undertaking units, and the RPA robot acquires the undertaking unit selection instruction of the user.
S305, filling the target undertaking unit on the work order system.
In this embodiment of the application, the RPA robot automatically fills the target undertaking unit on the work order system according to the target undertaking unit in the undertaking unit selection instruction obtained in step S304.
S306, dispatching the work order to be processed to the target undertaking unit.
In the embodiment of the present application, the RPA robot automatically extracts the dispatch flow according to the target undertaking unit filled in on the work order system in step S305, and dispatches the work order to be processed to the target undertaking unit.
Further, the work order processing method of the embodiment of the application may further include the following steps S307, S308, and S309.
S307, recording the work order dispatching result of the work order to be processed, wherein the work order dispatching result comprises skipping or processed work orders.
In the embodiment of the application, the RPA robot records the work order dispatching result of the work order to be processed, the work order dispatching result comprises skipping or processed work order dispatching result, if the user selects and confirms the target undertaking unit, the work order dispatching result is processed, and if the user skips selecting the target undertaking unit, the work order dispatching result is skipped.
S308, displaying third reminding information for checking the operation result.
In the embodiment of the application, after the RPA robot records the work order dispatching result of the work order to be processed, the third reminding information whether to check the operation result or not can be displayed to the user through the automatic pop-up window so as to prompt the user whether to check the operation result or not.
S309, responding to a checking instruction input by the user aiming at the third reminding information, and displaying the work order dispatching result of the work order to be processed.
In the embodiment of the application, the user can input an instruction whether to check the work order dispatching result or not through clicking operation on the third reminding information, if the user selects 'yes', the user inputs the checking instruction, the RPA robot displays the work order dispatching result of the work order to be processed, and if the user selects 'no', the work order dispatching process is ended.
The work order processing method combining the RPA and the AI, provided by the embodiment of the application, includes the steps of obtaining a work order to be processed, reading work order information in the work order to be processed, and generating a classification result according to the work order information and a work order classification model, wherein the classification result comprises a plurality of undertaking units. In the embodiment, the RPA robot is integrated with the work order handling system to automatically acquire the work orders to be processed, the classification results are generated according to the work order information and the work order classification model, the target undertaking units are automatically filled in the work order system according to the user undertaking unit selection instructions, and the work orders to be processed are dispatched to the target undertaking units, so that the work order processing workflow is simplified, the processing efficiency and accuracy are improved, the labor cost is reduced, the human errors are effectively avoided, the RPA robot automatically processes the work order dispatching workflow, the manual operation steps and links are reduced, and the rapid work order handling of the user is assisted.
Fig. 4 is a flowchart of another work order processing method combining RPA and AI according to an embodiment of the present disclosure. As shown in fig. 4, based on the embodiment shown in fig. 3, the step S303 "generate a classification result according to the work order information and the work order classification model, where the classification result includes a plurality of undertaking units" may specifically include:
s401, judging whether the work order information is matched with the keyword classification through Natural Language Processing (NLP) based on the keyword classification model.
In the embodiment of the application, the RPA robot judges whether the work order information is matched with the proper keyword classification through Natural Language Processing (NLP) based on the keyword classification model, specifically extracts the key information in the work order information, inputs the key information into the keyword classification model, and judges whether the work order information is matched with the proper keyword classification according to the output result of the keyword classification model.
S402, if the work order information is matched with the keyword classification, displaying the classification result output by the keyword classification model.
In the embodiment of the application, if the work order information is matched with the proper keyword classification, the classification result output by the keyword classification model can be displayed to the user through the automatic popup window.
And S403, if the work order information is not matched with the keyword classification, generating and displaying a classification result through Natural Language Processing (NLP) according to the deep learning classification model and the work order information.
In the embodiment of the application, if the work order information is not matched with the keyword classification, the work order information can be input into the deep learning classification model, and the classification result output by the deep learning classification model is displayed to a user through an automatic pop-up window.
The work order processing method combining the RPA and the AI, provided by the embodiment of the application, includes the steps of obtaining a work order to be processed, reading work order information in the work order to be processed, and generating a classification result according to the work order information and a work order classification model, wherein the classification result comprises a plurality of undertaking units. In the embodiment, the RPA robot and the work order processing system are integrated, the work orders to be processed are automatically acquired, the classification results are generated according to the work order information and the work order classification model, if the work order information is matched with the keyword classification, the keyword classification can be selected and also skipped, and the deep learning classification model is adopted for classification.
Fig. 5 is a flowchart of another work order processing method combining RPA and AI according to an embodiment of the present disclosure. As shown in fig. 5, based on the embodiment shown in fig. 4, step S402, "if the work order information matches the keyword classification, the classification result output by the keyword classification model is shown" may specifically include:
s501, if the work order information is matched with the keyword classification, displaying whether the first reminding information of the keyword classification model is adopted.
In the embodiment of the application, if the work order information is not matched with the keyword classification, whether the first reminding information of the keyword classification model is adopted or not can be displayed to a user through an automatic popup window.
S502, displaying the classification result output by the keyword classification model in response to a first adoption instruction input by the user aiming at the first reminding information.
In the embodiment of the application, the user can input an instruction whether to adopt the keyword classification model through clicking operation on the first reminding information, if the user selects 'yes', the first adopting instruction is input, and the RPA robot responds to the first adopting instruction and can display the classification result output by the keyword classification model to the user through automatic popup.
S503, responding to a first skipping instruction input by the user aiming at the first reminding information, and generating and displaying a classification result through Natural Language Processing (NLP) according to the deep learning classification model and the work order information.
In the embodiment of the application, a user can input an instruction whether to adopt a keyword classification model through clicking operation on the first reminding information, if the user selects 'no', the user inputs a first skipping instruction, the RPA robot responds to the first skipping instruction, the worksheet information is input into the deep learning classification model, and the classification result output by the deep learning classification model is displayed to the user through an automatic pop-up window.
The work order processing method combining the RPA and the AI, provided by the embodiment of the application, includes the steps of obtaining a work order to be processed, reading work order information in the work order to be processed, and generating a classification result according to the work order information and a work order classification model, wherein the classification result comprises a plurality of undertaking units. In the embodiment, the RPA robot and the work order processing system are integrated, the work orders to be processed are automatically acquired, the classification results are automatically generated according to the work order information and the work order classification model, if the work order information is matched with the keyword classification, the keyword classification can be selected and also can be skipped, and the deep learning classification model is adopted for classification, so that the work order processing work flow is simplified, the processing efficiency and the accuracy are improved, the labor cost is reduced, the human errors are effectively avoided, and more optional classification results are provided for users.
Fig. 6 is a flowchart of another work order processing method combining RPA and AI according to an embodiment of the present disclosure. As shown in fig. 6, based on the above-mentioned embodiments shown in fig. 4 and 5, the "generating and presenting a classification result through natural language processing NLP according to the deep learning classification model and the work order information" in step S403 and step S503 may specifically include:
s601, displaying whether the second reminding information of the deep learning classification model is adopted.
In the embodiment of the application, if the work order information is not matched with the keyword classification, or the work order information is matched with the keyword classification but the user inputs the first skipping instruction aiming at the first reminding information of whether the keyword classification model is adopted, the RPA robot can display the second reminding information of whether the deep learning classification model is adopted to the user through the automatic pop-up window.
And S602, responding to a second adoption instruction input by the user aiming at the second reminding information, and generating and displaying a classification result through Natural Language Processing (NLP) according to the deep learning classification model and the work order information.
In the embodiment of the application, the user can input an instruction whether to adopt the deep learning classification model through clicking operation on the second reminding information, if the user selects 'yes', the second adopting instruction is input, the RPA robot responds to the second adopting instruction, the work order information is input into the deep learning classification model, and the classification result output by the deep learning classification model is displayed to the user through an automatic pop-up window.
And S603, in response to a second skipping instruction input by the user for the second reminding information, classifying the work order information without adopting a deep learning classification model.
In the embodiment of the application, the user can input an instruction whether to adopt the deep learning classification model through clicking operation on the second reminding information, if the user selects 'no', the user inputs a second skipping instruction, and the RPA robot responds to the second skipping instruction and does not adopt the deep learning classification model to classify the work order information.
The work order processing method combining the RPA and the AI, provided by the embodiment of the application, includes the steps of obtaining a work order to be processed, reading work order information in the work order to be processed, and generating a classification result according to the work order information and a work order classification model, wherein the classification result comprises a plurality of undertaking units. In the embodiment, the RPA robot and the work order processing system are integrated, the work orders to be processed are automatically acquired, the classification result is automatically generated according to the work order information and the work order classification model, the deep learning classification model is adopted to classify the work order information which is not matched with the keyword classification, the work order processing work flow is simplified, the processing efficiency and the accuracy are improved, the labor cost is reduced, the human errors are effectively avoided, and more optional classification results are provided for users.
Fig. 7 is an overall flowchart of a work order processing method according to an embodiment of the present application, and as shown in fig. 7, the method includes the following steps:
s701, the user manually logs in the platform and creates an EXCEL table to store each office item.
S702, the RPA robot automatically enters a dispatch list.
And S703, the RPA robot acquires the work order to be processed of the current page.
And S704, reading the work order information in the work order to be processed by the RPA robot.
S705, the RPA robot judges whether the work order information is matched with a proper keyword classification based on the keyword classification model.
If the work order information matches the keyword classification, executing step S706; if the work order information does not match the keyword classification, step S708 is executed.
S706, the popup window displays whether the first reminding information of the keyword classification model is adopted.
If the user checks and confirms, executing step S707; if the user chooses to skip, step S708 is performed.
And S707, displaying the classification result generated according to the keyword classification model and the work order information through a popup window.
If the user checks and confirms the target undertaking unit in the classification result, executing S710-S711; if the user selects skip, S711 is performed.
And S708, displaying whether the second reminding information of the deep learning classification model is adopted or not through a popup window.
If the user selects "yes", then step S709 is executed; if the user selects "no", the work order information is classified without using the deep learning classification model, and step S711 is performed.
And S709, displaying the first 5 classification results generated according to the deep learning classification model and the work order information through a popup.
If the user checks and confirms the target undertaking unit in the classification result, executing S710-S711; if the user selects skip, S711 is performed.
And S710, the RPA robot automatically fills the target undertaking unit on the work order system and dispatches the work order to be processed to the target undertaking unit.
And S711, recording the work order dispatching result of the work order to be processed by the RPA robot.
If there is more data of the next page, go to step S703; if there is no next page data, step S712 is executed.
And S712, displaying whether the reminding information of the operation result is checked or not through a popup window.
If the user selects "yes", executing steps S713-S714; if the user selects "no", step S714 is performed.
And S713, displaying the work order dispatching result of the work order to be processed.
And S714, ending the work order dispatching process.
In order to implement the foregoing embodiment, an embodiment of the present application further provides a work order processing apparatus combining an RPA and an AI. As shown in fig. 8, the work order processing apparatus 800 according to the embodiment of the present application may specifically include: a first obtaining module 801, a reading module 802 and a generating module 803, wherein:
a first obtaining module 801, configured to obtain a work order to be processed.
The reading module 802 is configured to read work order information in a work order to be processed.
And the generating module 803 is configured to generate a classification result according to the work order information and the work order classification model, where the classification result includes a plurality of undertaking units.
Further, in a possible implementation manner of the embodiment of the present application, the work order processing apparatus 800 further includes: the second acquisition module is used for acquiring a undertaking unit selection instruction of the user, wherein the undertaking unit selection instruction comprises target undertaking units selected by the user from the plurality of undertaking units; the filling module is used for filling the target undertaking units on the work order system; and the dispatching module is used for dispatching the work orders to be processed to the target undertaking units.
Further, in a possible implementation manner of the embodiment of the present application, the work order classification model includes a keyword classification model and a deep learning classification model, and the generating module 803 includes: the judging unit is used for judging whether the work order information is matched with the keyword classification or not through Natural Language Processing (NLP) based on the keyword classification model; the display unit is used for displaying the classification result output by the keyword classification model if the work order information is matched with the keyword classification; and if the work order information is not matched with the keyword classification, generating and displaying a classification result through Natural Language Processing (NLP) according to the deep learning classification model and the work order information.
Further, in a possible implementation manner of the embodiment of the present application, the display unit is specifically configured to: if the work order information is matched with the keyword classification, displaying whether the first reminding information of the keyword classification model is adopted or not; displaying a classification result output by the keyword classification model in response to a first adoption instruction input by a user aiming at the first reminding information; and responding to a first skipping instruction input by the user aiming at the first reminding information, and generating and displaying a classification result through Natural Language Processing (NLP) according to the deep learning classification model and the work order information.
Further, in a possible implementation manner of the embodiment of the present application, the display unit is specifically configured to: displaying second reminding information of whether a deep learning classification model is adopted or not; responding to a second adoption instruction input by the user aiming at second reminding information, and generating and displaying a classification result through Natural Language Processing (NLP) according to the deep learning classification model and the work order information; and in response to a second skipping instruction input by the user for the second reminding information, classifying the work order information without adopting a deep learning classification model.
Further, in a possible implementation manner of the embodiment of the present application, the work order processing apparatus 800 further includes: and the recording module is used for recording the work order dispatching result of the work order to be processed, wherein the work order dispatching result comprises skipping or processed work orders.
Further, in a possible implementation manner of the embodiment of the present application, the work order processing apparatus 800 further includes: the first display module is used for displaying third reminding information of whether the operation result is checked; and the second display module is used for responding to a checking instruction input by the user aiming at the third reminding information and displaying the work order dispatching result of the work order to be processed.
Further, in a possible implementation manner of the embodiment of the present application, the work order information includes any one or a combination of more of the following information: title, affiliation, and appeal content.
It should be noted that the foregoing explanation of the embodiment of the RPA and AI combined work order processing method is also applicable to the RPA and AI combined work order processing apparatus of this embodiment, and is not repeated here.
The work order processing device combining the RPA and the AI, provided by the embodiment of the application, acquires a work order to be processed, reads work order information in the work order to be processed, and generates a classification result according to the work order information and a work order classification model, wherein the classification result comprises a plurality of undertaking units. In the embodiment, the RPA robot and the work order handling system are integrated to automatically acquire the work orders to be processed, the classification result is automatically generated according to the work order information and the work order classification model, if the work order information is matched with the keyword classification, the keyword classification can be selected, the keyword classification can also be skipped, the deep learning classification model is adopted for classification, if the work order information is not matched with the keyword classification, the deep learning classification model is adopted for classification, the target undertaking units are automatically filled in the work order system according to the undertaking unit selection instruction of the user, the work orders to be processed are distributed to the target undertaking units, the work flow of work order processing is simplified, the processing efficiency and the accuracy are improved, the labor cost is reduced, the human errors are effectively avoided, and more selectable classification results are provided for the user.
In various embodiments of the present application, it should be understood that the size of the serial number of each process described above does not mean that the execution sequence is necessarily sequential, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.
In the embodiments provided herein, it should be understood that "B corresponding to a" means that B is associated with a from which B can be determined. It should also be understood, however, that determining B from a does not mean determining B from a alone, but may also be determined from a and/or other information.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated units, if implemented as software functional units and sold or used as a stand-alone product, may be stored in a computer accessible memory. Based on such understanding, the technical solution of the present application, which is a part of or contributes to the prior art in essence, or all or part of the technical solution, may be embodied in the form of a software product, stored in a memory, including several requests for causing a computer device (which may be a personal computer, a server, a network device, or the like, and may specifically be a processor in the computer device) to execute part or all of the steps of the above-described method of the embodiments of the present application.
It will be understood by those skilled in the art that all or part of the steps in the methods of the embodiments described above may be implemented by hardware instructions of a program, and the program may be stored in a computer-readable storage medium, where the storage medium includes Read-Only Memory (ROM), Random Access Memory (RAM), Programmable Read-Only Memory (PROM), Erasable Programmable Read-Only Memory (EPROM), One-time Programmable Read-Only Memory (OTPROM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Compact Disc Read-Only Memory (CD-ROM), or other Memory, such as a magnetic disk, or a combination thereof, A tape memory, or any other medium readable by a computer that can be used to carry or store data.
The RPA and AI combined work order processing method and device disclosed in the embodiments of the present application are introduced in detail, and a specific example is applied to illustrate the principle and implementation manner of the present application, and the description of the embodiments is only used to help understanding the method and core ideas of the present application; meanwhile, for a person skilled in the art, according to the idea of the present application, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present application.
Claims (18)
1. A work order processing method combining RPA and AI is characterized by comprising the following steps:
acquiring a work order to be processed;
reading the work order information in the work order to be processed;
and generating a classification result according to the work order information and the work order classification model, wherein the classification result comprises a plurality of undertaking units.
2. The work order processing method as claimed in claim 1, further comprising:
acquiring a undertaking unit selection instruction of a user, wherein the undertaking unit selection instruction comprises target undertaking units selected by the user from the plurality of undertaking units;
filling the target undertaking unit on the work order system;
and dispatching the work order to be processed to the target undertaking unit.
3. The work order processing method of claim 1, wherein the work order classification model comprises a keyword classification model and a deep learning classification model, and the generating of the classification result according to the work order information and the work order classification model comprises:
judging whether the work order information is matched with a keyword classification through Natural Language Processing (NLP) based on the keyword classification model;
if the work order information is matched with the keyword classification, displaying the classification result output by the keyword classification model;
and if the work order information is not matched with the keyword classification, generating and displaying the classification result through Natural Language Processing (NLP) according to the deep learning classification model and the work order information.
4. The work order processing method as claimed in claim 3, wherein said displaying the classification result output by the keyword classification model if the work order information matches a keyword classification comprises:
if the work order information is matched with the keyword classification, displaying whether the first reminding information of the keyword classification model is adopted or not;
responding to a first adoption instruction input by the user aiming at the first reminding information, and displaying the classification result output by the keyword classification model;
and responding to a first skipping instruction input by the user aiming at the first reminding information, and generating and displaying the classification result through Natural Language Processing (NLP) according to the deep learning classification model and the work order information.
5. The work order processing method according to claim 3 or 4, wherein the generating and presenting the classification result through Natural Language Processing (NLP) according to the deep learning classification model and the work order information comprises:
displaying second reminding information of whether the deep learning classification model is adopted or not;
responding to a second adoption instruction input by the user aiming at the second reminding information, and generating and displaying the classification result through Natural Language Processing (NLP) according to the deep learning classification model and the work order information;
and in response to a second skipping instruction input by the user for the second reminding information, classifying the work order information without adopting the deep learning classification model.
6. The work order processing method as claimed in claim 2, further comprising:
and recording the work order dispatching result of the work order to be processed, wherein the work order dispatching result comprises skipping or processed work orders.
7. The work order processing method as claimed in claim 6, further comprising:
displaying third reminding information whether to check the operation result;
and responding to a viewing instruction input by the user aiming at the third reminding information, and displaying the work order dispatching result of the work order to be processed.
8. The work order processing method of claim 1, wherein the work order information comprises any one or a combination of more of the following information:
title, affiliation, and appeal content.
9. A work order processing apparatus that combines RPA and AI, comprising:
the first acquisition module is used for acquiring a work order to be processed;
the reading module is used for reading the work order information in the work order to be processed;
and the generation module is used for generating a classification result according to the work order information and the work order classification model, and the classification result comprises a plurality of undertaking units.
10. The work order processing apparatus of claim 9, further comprising:
a second obtaining module, configured to obtain a selection instruction of a undertaking unit of a user, where the selection instruction of the undertaking unit includes a target undertaking unit selected by the user in the plurality of undertaking units;
the filling module is used for filling the target undertaking unit on the work order system;
and the dispatching module is used for dispatching the work order to be processed to the target undertaking unit.
11. The work order processing apparatus of claim 9, wherein the work order classification model comprises a keyword classification model and a deep learning classification model, and the generation module comprises:
the judging unit is used for judging whether the work order information is matched with the keyword classification through Natural Language Processing (NLP) based on the keyword classification model;
the display unit is used for displaying the classification result output by the keyword classification model if the work order information is matched with the keyword classification; and if the work order information is not matched with the keyword classification, generating and displaying the classification result through Natural Language Processing (NLP) according to the deep learning classification model and the work order information.
12. The work order processing apparatus of claim 11, wherein the presentation unit is specifically configured to:
if the work order information is matched with the keyword classification, displaying whether the first reminding information of the keyword classification model is adopted or not;
responding to a first adoption instruction input by the user aiming at the first reminding information, and displaying the classification result output by the keyword classification model;
and responding to a first skipping instruction input by the user aiming at the first reminding information, and generating and displaying the classification result through Natural Language Processing (NLP) according to the deep learning classification model and the work order information.
13. The work order processing apparatus of claim 11 or 12, wherein the presentation unit is specifically configured to:
displaying second reminding information of whether the deep learning classification model is adopted or not;
responding to a second adoption instruction input by the user aiming at the second reminding information, and generating and displaying the classification result through Natural Language Processing (NLP) according to the deep learning classification model and the work order information;
and in response to a second skipping instruction input by the user for the second reminding information, classifying the work order information without adopting the deep learning classification model.
14. The work order processing apparatus of claim 10, further comprising:
and the recording module is used for recording the work order dispatching result of the work order to be processed, wherein the work order dispatching result comprises skipping or processed work orders.
15. The work order processing apparatus of claim 14, further comprising:
the first display module is used for displaying third reminding information of whether the operation result is checked;
and the second display module is used for responding to a viewing instruction input by the user aiming at the third reminding information and displaying the work order dispatching result of the work order to be processed.
16. The work order processing apparatus of claim 9, wherein the work order information comprises any one or combination of:
title, affiliation, and appeal content.
17. A computing device, comprising:
a memory storing executable program code;
a processor coupled with the memory;
the processor calls the executable program code stored in the memory to perform the combined RPA and AI work order processing method of any of claims 1-8.
18. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out a method of processing a work order combining RPA and AI according to any one of claims 1 to 8.
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