CN115935089A - RPA technology-based boarding point recommendation optimization method and system - Google Patents
RPA technology-based boarding point recommendation optimization method and system Download PDFInfo
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Abstract
The application provides a boarding point recommendation optimization method and system based on an RPA technology, which comprises the following steps: identifying a current state of the process automation robot; recommending, based on the current state, at least one pick-up recommendation point to a user of the process automation robot using a predictive learning model, wherein the predictive learning model is trained based on the user's actual selection of pick-up recommendation points; receiving a reply to the boarding recommendation point from the user; when the reply is to accept the at least one pick-up recommendation point, adding the at least one pick-up recommendation point to the process automation robot; based on the number of replied users, distributing confidence coefficient for each boarding recommendation point; and generating a suggested next batch of boarding recommendation points according to the boarding recommendation points and the confidence level for selection. The application innovatively applies the RPA technology to the taxi taking software testing process, and the recommended testing process of the boarding point is optimized.
Description
Technical Field
The application relates to the technical field of software testing, in particular to a boarding point recommendation optimization method and system based on an RPA technology.
Background
The RPA robot Process Automation technology, english is (Robotic Process Automation), is a Process file that integrates screen capture and service Process Automation management technologies, simulates operations of a user such as mouse click, keyboard input and the like, for example, opening an application program, processing an Excel form, logging in a management system and the like, and changes a service which has a certain rule and needs to be repeatedly executed into a section of automatically executable Process file. Then handed to the designated RPA robot 7 x 24 hours timed to perform as needed.
In the existing taxi taking software testing process of taxies, windmills and the like, a large amount of manual tests are often needed, operations such as clicking, keyboard input and the like are simulated for users, after a test result is analyzed, a backstage re-optimizes the user boarding point recommendation according to an optimization algorithm, and then the process is repeated by re-performing manual operations. This process often requires a significant expenditure of manual labor and time.
The existing RPA technology is often used in the industries of electronic commerce, finance, securities and the like, and the RPA technology is not used in the test process of taxi taking software.
Disclosure of Invention
In view of this, the present application aims to provide a pick-up point recommendation optimization method and system based on the RPA technology, which can specifically solve the existing problems.
Based on the above purpose, the present application provides an RPA technology-based boarding point recommendation optimization method, including:
identifying a current state of the process automation robot;
recommending at least one pick-up recommendation point to a user of the process automation robot using a predictive learning model based on the current state, wherein the predictive learning model is trained based on the user's actual selection of pick-up recommendation points;
receiving a reply to the boarding recommendation point from the user;
when the reply is to accept the at least one pick-up recommendation point, adding the at least one pick-up recommendation point to the process automation robot;
based on the number of replied users, distributing confidence coefficient for each boarding recommended point;
and generating a suggested next getting-on recommendation point for selection according to the getting-on recommendation point and the confidence level.
Further, the identifying a current state of the process automation robot includes:
responding to a current corresponding running picture screenshot of the associated RPA robot sent by any virtual network console server, and determining that the associated RPA robot is in a running state; responding to a natural language processing result corresponding to the connection state of the RPA robot associated with any virtual network console server and the console to indicate that the connection is disconnected, and determining that the RPA robot associated with any virtual network console server is in an offline state; alternatively, the first and second liquid crystal display panels may be,
and responding to the triggering of a preset control in an RPA monitoring interface, and determining the current running state of the RPA robot corresponding to each virtual network control console service end according to the data information currently acquired from each virtual network control console service end and the connection state of the RPA robot associated with each virtual network control console service end and the control console.
Further, the recommending at least one pick-up recommendation point to a user of the process automation robot using a predictive learning model includes:
modeling the preset real track data displaying the geographical position of the passenger to obtain a prediction learning model;
calculating the track similarity of the preset real track data, selecting the passenger geographical position corresponding to the preset real track data with the similarity higher than the preset value, and extracting semantic information of all the passenger geographical positions;
calculating semantic similarity according to the semantic information of the geographic position of the passenger;
and selecting a plurality of geographic positions according to the calculation result of the semantic similarity, and displaying the geographic positions on a map as boarding recommendation points for selection by a user of the process automation robot.
Further, the predictive learning model is trained by:
storing a list of commonly used pick-up points related to the RPA workflow;
storing a list of past pick-up points corresponding to the user;
the predictive learning model is updated based on the list of commonly used pick-up points, the list of past pick-up points, and the one or more monitored pick-up points.
Further, the adding the at least one pick-up recommended point to the process automation robot when the reply is to accept the at least one pick-up recommended point comprises:
receiving at least one boarding recommendation point accepted by a user from a user interface of the RPA;
adding a resource/process pair corresponding to the boarding recommendation point to an RPA command table, wherein the resource/process pair represents the boarding recommendation point selected from a user interface;
determining that a redundant resource/process pair is included in the command table, the redundant resource/process pair being a duplicate of the resource/process pair, and in response, deleting the redundant resource/process pair;
transmitting at least one command in the command table to an adapter of the process automation robot, the at least one command including a command to perform a process using a resource within an RPA platform;
alternatively, the first and second liquid crystal display panels may be,
launching a wizard component of a Customer Resource Management (CRM) component in the RPA;
generating, using the wizard component, a result of a match between the predictive learning model and a plurality of fields of the customer resource management component;
generating an RPA workflow based on a matching result of the wizard component, wherein the RPA workflow adds the at least one pick-up recommendation point to a combo box or a list box generated by the RPA workflow based on at least one of a plurality of fields of the customer resource management component.
Further, the assigning a confidence level to each boarding recommendation point based on the number of users responding includes:
identifying a number of users selecting each pick-up recommendation point;
determining a recommended event corresponding to each pick-up recommendation point based on the number of users selecting each pick-up recommendation point, wherein the recommended event comprises one or more event attributes determined based on one or more items;
determining an event confidence coefficient of a recommended event corresponding to each boarding recommendation point based on the event attributes;
alternatively, the first and second electrodes may be,
identifying a message of the user, wherein the message comprises the condition whether the user replies to accept each boarding recommended point;
determining a recommended event and an initial event confidence coefficient of the recommended event according to a message;
the initial event confidence is presented via a graphical interface, and a new event confidence is determined for the recommended event based on one or more additional computer-based actions of the user associated with the recommended event.
Further, the generating a suggested next batch of boarding recommendation points for selection according to the boarding recommendation points and the confidence level includes:
acquiring a selection standard of an RPA, and deleting the boarding recommended points of which the confidence level of the event is lower than the selection standard;
displaying an interface for adding a boarding recommendation point by a user;
obtaining a boarding recommendation point added by a user;
and combining the deleted boarding recommendation points with the boarding recommendation points added by the user to serve as next boarding recommendation points and displaying a selection interface.
Based on the above purpose, the present application further provides a pick-up point recommendation optimization system based on the RPA technology, including:
the state identification module is used for identifying the current state of the process automation robot;
a first recommendation module to recommend at least one pick-up recommendation point to a user of the process automation robot using a predictive learning model based on the current state, wherein the predictive learning model is trained based on the user's actual selection of pick-up recommendation points;
the reply receiving module is used for receiving a reply to the boarding recommendation point from the user;
an add referral point module that adds the at least one pick-up referral point to the process automation robot when the reply is an acceptance of the at least one pick-up referral point;
the confidence coefficient module is used for distributing confidence coefficient to each boarding recommendation point based on the number of replied users;
and the second recommending module generates a suggested next batch of boarding recommending points for selection according to the boarding recommending points and the confidence level.
In general, the advantages of the present application and the experience brought to the user are:
the application innovatively applies the RPA technology to the taxi taking software testing process, optimizes the recommended testing process of the taxi taking point and has the following advantages:
1. the running speed is high
The RPA performs repetitive operations based on explicit business rules, and has a faster execution speed compared with human, which can lead to shorter response time and more task amount.
2. Low cost of execution
The RPA robot can work for 7 × 24 hours without interruption for a long time, and human errors can not occur, so that the labor cost and the time cost are saved.
3. Without replacing existing systems
The PRA robot is mainly based on a screen capture technology, simulates operation of a user on a front-end interface, can quickly meet new service requirements without reconstructing an existing system, and reduces complexity and risk of IT deployment.
4. Strong expandability
The RPA robot can be deployed based on a physical machine and a virtual machine, and also supports the deployment and operation of different systems in different environments such as a windows system, a mac system, a linux system, a domestic system, a mobile terminal and the like, and when an enterprise has requirements, the service scene and the number of the robots can be expanded at any time.
5. Safety compliance
The RPA executes tasks based on clear business rules, thereby greatly improving the accuracy and the compliance of business processing and avoiding errors possibly generated by manual processing. The user can perform recording based on logs, screenshots, and video full-range audit trail RPA.
Drawings
In the drawings, like reference numerals refer to the same 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 therefore not to be considered limiting of its scope.
Fig. 1 shows a schematic diagram of the system architecture of the present application.
Fig. 2 shows a flowchart of an optimization method for vehicle boarding point recommendation based on RPA technology according to an embodiment of the present application.
FIG. 3 shows a schematic of an interface for first recommending pick-up points.
FIG. 4 shows a schematic view of a selection interface after a user first selects a recommended pick-up point.
FIG. 5 shows a schematic diagram of an interface for assigning confidence levels to different recommended pick-up points.
FIG. 6 shows a schematic diagram of an interface for assigning confidence levels to different recommended pick-up points.
Fig. 7 is a block diagram showing an upper vehicle point recommendation optimizing system based on RPA technology according to an embodiment of the present application.
Fig. 8 is a schematic structural diagram of an electronic device according to an embodiment of the present application;
fig. 9 is a schematic diagram of a storage medium according to an embodiment of the present application.
Detailed Description
The present application will be described in further detail with reference to the following drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the relevant invention and not restrictive of the invention. It should be noted that, for convenience of description, only the portions related to the related invention are shown in the drawings.
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the embodiments with reference to the attached drawings.
Fig. 1 shows a schematic diagram of the system architecture of the present application. In the embodiment of the application, a process automation robot is provided firstly, the current state of the process automation robot is identified, and then at least one boarding recommendation point is recommended to a user of the process automation robot; after receiving a reply to the boarding recommendation point from the user, judging whether the user accepts and approves the boarding recommendation point, and if the user approves, adding the at least one boarding recommendation point into the process automation robot; and the robot continuously allocates confidence to each boarding recommendation point according to the number of replied users, and then generates a suggested next boarding recommendation point for the user to select according to the boarding recommendation points and the confidence level.
Fig. 2 shows a flowchart of an upper vehicle point recommendation optimization method based on RPA technology according to an embodiment of the present application. As shown in fig. 2, the pick-up point recommendation optimization method based on the RPA technology includes:
s1, identifying the current state of the process automation robot;
s2, recommending at least one boarding recommendation point to a user of the process automation robot by using a predictive learning model based on the current state, wherein the predictive learning model is trained based on actual selection of the boarding recommendation points by the user;
s3, receiving a reply to the boarding recommended point from the user;
s4, when the reply is that the at least one boarding recommended point is accepted, adding the at least one boarding recommended point into the process automation robot;
s5, distributing confidence coefficient for each boarding recommended point based on the number of replied users;
and S6, generating a suggested next batch of boarding recommended points for selection according to the boarding recommended points and the confidence level.
VNC (Virtual Network Console) is an abbreviation for Virtual Network Console. The VNC is free open source software based on the UNIX and the Linux operating system, and is strong in remote control capability, efficient and practical. In Linux, VNC includes the following four commands: vncserver, vncviewer, vnpasswd, and vncconnect. In most cases the user only needs two of these commands: vncserver and vncviewer.
VNC is basically composed of two parts: an application (vncviewer) of which one part is a client; the other part is a server-side application program (vncserver). The server-side application program of the VNC has strong adaptability in UNIX and Linux operating systems, and the graphical user interface is very friendly. Any computer with a Linux platform on which a client-side application (vncviverer) is installed can be conveniently connected with a computer with a server-side application (vncserver) installed. In addition, a Java Web interface is also built in the server (vncserver), so that the operation of other computers by a user through the server can be displayed through Netscape, and the operation process and the display mode are visual and convenient. This application adopts VNC technology.
Specifically, in step S1, a specific implementation scheme for identifying the current state of the process automation robot includes:
responding to a running picture screenshot currently corresponding to a related RPA robot sent by any Virtual Network Console (VNC) server, and determining that the related RPA robot is currently in a running state; responding to a Natural Language Processing (NLP) result corresponding to the connection state of an RPA robot associated with any virtual network console server and a console to indicate disconnection, and determining that the RPA robot associated with any virtual network console server is in an offline state; alternatively, the first and second electrodes may be,
and responding to the triggering of a preset control in an RPA monitoring interface, and determining the current running state of the RPA robot corresponding to each virtual network control console service end according to the data information currently acquired from each virtual network control console service end and the connection state of the RPA robot associated with each virtual network control console service end and the control console.
In step S2, based on the current state, at least one boarding recommendation point is recommended to a user of the process automation robot using a predictive learning model. The prediction learning model is an artificial intelligence model consisting of a filtering model, a deep learning model and a sequencing model.
Recommending at least one pick-up recommendation point to a user of the process automation robot using a predictive learning model, comprising:
modeling preset real track data displaying the geographic position of a passenger to obtain a prediction learning model;
calculating the track similarity of the preset real track data, selecting the geographical position of the passenger corresponding to the preset real track data with the similarity higher than the preset value, and extracting semantic information of the geographical position of all the passengers;
calculating semantic similarity according to the semantic information of the geographical position of the passenger;
and selecting a plurality of geographic positions according to the calculation result of the semantic similarity, and displaying the geographic positions on a map as boarding recommendation points for selection by a user of the process automation robot.
As shown in FIG. 3, the recommended pick-up point A, B, C, D may also be further presented to the user in the form of a GUI interface. Of course, blank bars may also be designed for the user to further input new boarding points.
The predictive learning model is trained by:
storing a list of commonly used pick-up points related to the RPA workflow;
storing a list of past pick-up points corresponding to the user;
the predictive learning model is updated based on the list of commonly used pick-up points, the list of past pick-up points, and the one or more monitored pick-up points.
According to various embodiments described herein, the present application utilizes a predictive learning model (e.g., implemented based on artificial intelligence) to customize and personalize workflow design processes for a user. While the user is developing the RPA workflow, the user-selected pick-up points are monitored in real-time (or substantially real-time) and one or more recommended pick-up points are identified as candidate pick-up points for consideration by the user for use as pick-up points in a next workflow pick-up point sequence. The identification and generation of recommended pick-up points is also performed in real-time (or substantially real-time) using a predictive learning model.
This process is further personalized for the user as the predictive learning model is trained and retrained based on the actual selections made by the user (e.g., based on whether the user selects from recommended pick-up points). The identification of recommended pick-up points may be based on a variety of considerations. For example, personalization may take into account user-specific patterns from the user's past pick-up points selections (e.g., user preferences, user style, etc.). Customization may be accomplished by intelligent-based filtering of pick-up points that are most relevant (e.g., most popular, most common) to the workflow being designed, and so on.
In accordance with one or more embodiments, the system can include a design environment having a user interface that allows a user to easily drag and drop recommended pick-up points to conveniently build a workflow in an efficient and effective manner. For example, after placing a pick-up point in the workflow design window of the user interface, the pick-up point suggestion tab on the user interface will automatically update to the next set of suggestions identified by the predictive learning model. Initially, the system may filter recommended pick-up points, such as the most common pick-up points, based on popularity. Over time, the predictive learning model utilizes artificial intelligence functions to train and adjust the model to generate relevant suggestions that are more tailored to the style and preferences of the user.
As will be clear to those skilled in the art, any type of design environment may be used by RPA users, including but not limited to a GUI-based environment or a text-based environment. Further, in some implementations, users may record that they themselves or others perform a process to be automated. In such implementations, the design of the RPA flow does not occur in any particular environment. Thus, the present application may be configured to present its recommendations in any form suitable for a given environment.
For example, in a GUI-based design environment, the recommended flow operations may appear in a drop-down menu or as drag-and-drop icons. On the other hand, in a text-based design environment, the recommended flow operations may be displayed as "complete" half-typed "shaded text". (of course, text-based environments may also use drop-down menus and vice versa, depending on the implementation.) As another example, the recommended flow operation may be displayed as a "pop-up window" or notification on the designer's display device.
Further, depending on the implementation and process of design, the present application may include multiple neural networks.
For example, one neural network may be responsible for encoding context information into a single digital representation, while another machine learning sub-module may use the representation to recommend an operation based on the encoded context.
One example of a related neural network application is the use of well-known natural language processing techniques to recognize language-based (communication-related) actions. Once trained, the present application will receive as input the code for the current state.
In step S3, receiving a reply from the user to the boarding recommendation point, as shown in fig. 4, and obtaining a selected interface schematic diagram; the selected pick-up recommended point A, C, D may be highlighted by a different color highlight. Of course, a blank bar may be designed for the user to further input a new boarding point.
According to another aspect, the present application tracks certain metrics associated with pick-up points used by users in constructing an RPA workflow. Such information may be helpful in comparing the efficiency of user selection of recommended pick-up points, rather than pick-up points that are not recommended by the predictive learning model.
Indicators of the user's responses to the pick-up recommended points may include data or other information that may quantify or track parameters such as the number of mouse clicks, the amount of text the user enters to select a pick-up point, the amount of time to complete the task of adding the corresponding pick-up point, the completion of a workflow design that encompasses all pick-up points, and so forth. The indexes can also help a user to discover and predict the performance of the learning model according to the data and the information. These examples are illustrative only and are not intended to be limiting in any way.
In step S4, when the reply is to accept the at least one pick-up recommended point, adding the at least one pick-up recommended point to the process automation robot; the method includes the following two embodiments, and can be realized in one of the two embodiments.
The first embodiment is as follows:
receiving at least one boarding recommendation point accepted by a user from a user interface of the RPA;
adding a resource/process pair corresponding to the boarding recommendation point to an RPA command table, wherein the resource/process pair represents the boarding recommendation point selected from a user interface;
determining that a redundant resource/process pair is included in the command table, the redundant resource/process pair being a duplicate of the resource/process pair, and in response, deleting the redundant resource/process pair;
transmitting at least one command in the command table to an adapter of the process automation robot, the at least one command including a command to perform a process using a resource within an RPA platform.
For example, if the distance between two boarding recommendation points is too close among a plurality of boarding recommendation points selected by the user, one of the boarding recommendation points with no background is deleted, or the corresponding boarding recommendation point with a smaller number of users in the historical data is deleted, so as to further optimize the boarding recommendation points.
The second embodiment is as follows:
launching a wizard component of a Customer Resource Management (CRM) component in the RPA;
generating, using the wizard component, a match between the predictive learning model and a plurality of fields of the customer resource management component;
generating an RPA workflow based on the matching result of the wizard component, wherein the RPA workflow adds the at least one pick up recommendation point to a combo box or list box generated by the RPA workflow based on at least one of the plurality of fields of the customer resource management component.
In step S5, based on the number of replied users, a confidence is assigned to each boarding recommendation point.
For different users, the selected boarding recommendation points may be different, and therefore, when the number of times each boarding recommendation point is selected by the user is finally counted, the result is often different. How to measure the credibility of each boarding recommendation point is one of the technical problems to be solved by the application.
Specifically, step S5 can be implemented in one of the following two ways:
the first mode is as follows:
identifying a number of users selecting each pick-up recommendation point;
determining a recommended event corresponding to each pick-up recommendation point based on the number of users selecting each pick-up recommendation point, wherein the recommended event comprises one or more event attributes determined based on one or more items;
and determining the event confidence of the recommended event corresponding to each boarding recommendation point based on the event attributes.
In the first way, according to the number of users clicked at each boarding recommendation point, the confidence level can be determined in a proportional relationship with the confidence level. For example, if one hundred users reply to each recommendation point, recommendation point a may be set to have a confidence level of 90% if 90 users select it; if 80 users select the recommendation point C, the confidence level of the recommendation point C can be set to be 80%; if 70 users select the recommendation point D, the confidence level of the recommendation point D can be set to be 70%; and so on. This is well understood.
For example, as shown in fig. 5, in one or more embodiments, the predictive learning model as selected in fig. 4 predicts 3 pick-up recommendation points A, C, D as candidate pick-up recommendation points, and may add one (or more) additional pick-up recommendation points E (not yet in the set, predicted by the predictive learning model), assigning a confidence to each pick-up recommendation point based on the number of users that reply, the four pick-up recommendation points A, C, D, E having confidence levels of 90%, 80%, 70%, and 85%, respectively.
The second mode is as follows:
identifying a message of the user, wherein the message comprises the condition whether the user replies to accept each boarding recommended point;
determining a recommended event and an initial event confidence coefficient of the recommended event according to the message;
the initial event confidence is presented via a graphical interface, and a new event confidence is determined for the recommended event based on one or more additional computer-based actions of the user associated with the recommended event.
In a second manner, on the basis of the first manner, after the confidence level of each recommendation point is preliminarily determined by the number of replies from the user, the user is notified of the initially calculated confidence level. The user may challenge or question the initial confidence level, e.g., click, enter, etc. Thus, the opportunity of revising the initial confidence level is given, and the accuracy of the confidence can be further ensured.
In step S6, a suggested next batch of boarding recommendation points are generated according to the boarding recommendation points and the confidence level for selection.
Assume that the RPA has a selection criterion where the pick-up recommendation points with a confidence level below 85% will be eliminated. Thus, in evaluating the four pick-up recommendation points in FIG. 5, the RPA will remove/delete pick-up recommendation points D, C with 70%, 80% confidence levels from the set, plus pick-up point E that the user manually added.
For example, the predictive learning model may generate a suggested next pick-up recommendation point for selection by the user, including four pick-up recommendation points with a 70% or greater confidence level from the set, and an additional one, selected by the RPA based on the particular user consideration being served.
This simplified example is illustrative only and is not limiting in any way.
The application embodiment provides an RPA technology-based pick-up point recommendation optimization system, which is configured to execute the RPA technology-based pick-up point recommendation optimization method described in the above embodiment, as shown in fig. 7, and the system includes:
a state identification module 501, configured to identify a current state of the process automation robot;
a first recommendation module 502 that recommends, based on the current state, at least one pick-up recommendation point to a user of the process automation robot using a predictive learning model, wherein the predictive learning model is trained based on the user's actual selection of pick-up recommendation points;
a reply receiving module 503, configured to receive a reply to the boarding recommendation point from the user;
an add referral point module 504 configured to add the at least one pick-up referral point to the process automation robot when the reply is an acceptance of the at least one pick-up referral point;
a confidence module 505, which allocates confidence to each boarding recommendation point based on the number of replied users;
and a second recommending module 506, which generates a suggested next group of boarding recommended points for selection according to the boarding recommended points and the confidence level.
The pick-up point recommendation optimization system based on the RPA technology provided by the above embodiment of the present application and the pick-up point recommendation optimization method based on the RPA technology provided by the embodiment of the present application have the same inventive concept and have the same beneficial effects as the method adopted, operated or implemented by the application program stored in the pick-up point recommendation optimization system based on the RPA technology.
The embodiment of the present application further provides an electronic device corresponding to the pick-up point recommendation optimization method based on the RPA technology provided in the foregoing embodiment, so as to execute the pick-up point recommendation optimization method based on the RPA technology. The embodiments of the present application are not limited.
Please refer to fig. 8, which illustrates a schematic diagram of an electronic device according to some embodiments of the present application. As shown in fig. 8, the electronic device 20 includes: the system comprises a processor 200, a memory 201, a bus 202 and a communication interface 203, wherein the processor 200, the communication interface 203 and the memory 201 are connected through the bus 202; the memory 201 stores a computer program that can be executed on the processor 200, and the processor 200 executes the computer program to execute the RPA technology-based pick-up point recommendation optimization method provided by any one of the foregoing embodiments.
The Memory 201 may include a Random Access Memory (RAM) and may further include a non-volatile Memory (non-volatile Memory), such as at least one disk Memory. The communication connection between the network element of the system and at least one other network element is realized through at least one communication interface 203 (which may be wired or wireless), and the internet, a wide area network, a local network, a metropolitan area network, and the like can be used.
The processor 200 may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware or instructions in the form of software in the processor 200. The Processor 200 may be a general-purpose Processor, and includes a Central Processing Unit (CPU), a Network Processor (NP), and the like; but may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components. The various methods, steps, and logic blocks disclosed in the embodiments of the present application may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present application may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software module may be located in ram, flash memory, rom, prom, or eprom, registers, etc. storage media as is well known in the art. The storage medium is located in the memory 201, and the processor 200 reads the information in the memory 201 and completes the steps of the method in combination with the hardware thereof.
The electronic device provided by the embodiment of the application and the RPA technology-based boarding point recommendation optimization method provided by the embodiment of the application have the same inventive concept and have the same beneficial effects as the method adopted, operated or realized by the electronic device.
Referring to fig. 9, the computer-readable storage medium is an optical disc 30, and a computer program (i.e., a program product) is stored thereon, and when being executed by a processor, the computer program executes the RPA-technology-based pick-up point recommendation optimization method provided in any of the foregoing embodiments.
It should be noted that examples of the computer-readable storage medium may also include, but are not limited to, a phase change memory (PRAM), a Static Random Access Memory (SRAM), a Dynamic Random Access Memory (DRAM), other types of Random Access Memories (RAM), a Read Only Memory (ROM), an Electrically Erasable Programmable Read Only Memory (EEPROM), a flash memory, or other optical and magnetic storage media, which are not described in detail herein.
The computer-readable storage medium provided by the above embodiment of the present application and the pick-up point recommendation optimization method based on the RPA technology provided by the embodiment of the present application are based on the same inventive concept, and have the same beneficial effects as the method adopted, operated or implemented by the application program stored in the computer-readable storage medium.
It should be noted that:
the algorithms and displays presented herein are not inherently related to any particular computer, virtual machine, or other apparatus. Various general purpose systems may also be used with the teachings herein. The required structure for constructing such a system is apparent from the description above. In addition, this application is not directed to any particular programming language. It will be appreciated that a variety of programming languages may be used to implement the teachings of the present application as described herein, and any descriptions of specific languages are provided above to disclose the best modes of the present application.
In the description provided herein, numerous specific details are set forth. However, it is understood that embodiments of the application may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.
Similarly, it should be appreciated that in the foregoing description of exemplary embodiments of the application, various features of the application are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the application and aiding in the understanding of one or more of the various inventive aspects. However, the disclosed method should not be construed to reflect the intent: this application is intended to cover such departures from the present disclosure as come within known or customary practice in the art to which this invention pertains. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of this application.
Those skilled in the art will appreciate that the modules in the device in an embodiment may be adaptively changed and disposed in one or more devices different from the embodiment. The modules or units or components in the embodiments may be combined into one module or unit or component, and furthermore, may be divided into a plurality of sub-modules or sub-units or sub-components. All of the features disclosed in this specification (including any accompanying claims, abstract and drawings), and all of the processes or elements of any method or apparatus so disclosed, may be combined in any combination, except combinations where at least some of such features and/or processes or elements are mutually exclusive. Each feature disclosed in this specification (including any accompanying claims, abstract and drawings) may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.
Furthermore, those skilled in the art will appreciate that while some embodiments described herein include some features included in other embodiments, rather than other features, combinations of features of different embodiments are meant to be within the scope of the application and form different embodiments. For example, in the following claims, any of the claimed embodiments may be used in any combination.
The various component embodiments of the present application may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. Those skilled in the art will appreciate that a microprocessor or Digital Signal Processor (DSP) may be used in practice to implement some or all of the functions of some or all of the components in a virtual machine creation system according to embodiments of the present application. The present application may also be embodied as apparatus or system programs (e.g., computer programs and computer program products) for performing a portion or all of the methods described herein. Such programs implementing the present application may be stored on a computer readable medium or may be in the form of one or more signals. Such a signal may be downloaded from an internet website or provided on a carrier signal or in any other form.
It should be noted that the above-mentioned embodiments illustrate rather than limit the application, and that those skilled in the art will be able to design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The application may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several systems, several of these systems may be embodied by one and the same item of hardware. The usage of the words first, second and third, etcetera do not indicate any ordering. These words may be interpreted as names.
The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive various changes or substitutions within the technical scope of the present application, and these should be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (10)
1. A pick-up point recommendation optimization method based on RPA technology is characterized by comprising the following steps:
identifying a current state of the process automation robot;
recommending, based on the current state, at least one pick-up recommendation point to a user of the process automation robot using a predictive learning model, wherein the predictive learning model is trained based on the user's actual selection of pick-up recommendation points;
receiving a reply to the boarding recommendation point from the user;
when the reply is to accept the at least one pick-up recommendation point, adding the at least one pick-up recommendation point to the process automation robot;
based on the number of replied users, distributing confidence coefficient for each boarding recommendation point;
and generating a suggested next batch of boarding recommendation points according to the boarding recommendation points and the confidence level for selection.
2. The method of claim 1,
the identifying a current state of the process automation robot includes:
responding to a current corresponding running picture screenshot of a related RPA robot sent by any virtual network console server, and determining that the related RPA robot is in a running state; responding to a natural language processing result indication disconnection corresponding to a connection state of an RPA robot associated with any virtual network console server and a console, and determining that the RPA robot associated with any virtual network console server is in an offline state; alternatively, the first and second electrodes may be,
and responding to the triggering of a preset control in an RPA monitoring interface, and determining the current running state of the RPA robot corresponding to each virtual network control console service end according to the data information currently acquired from each virtual network control console service end and the connection state of the RPA robot associated with each virtual network control console service end and the control console.
3. The method of claim 2,
the recommending at least one pick-up recommendation point to a user of the process automation robot using a predictive learning model, comprising:
modeling preset real track data displaying the geographic position of a passenger to obtain a prediction learning model;
calculating the track similarity of the preset real track data, selecting the passenger geographical position corresponding to the preset real track data with the similarity higher than the preset value, and extracting semantic information of all the passenger geographical positions;
calculating semantic similarity according to the semantic information of the geographical position of the passenger;
and selecting a plurality of geographic positions according to the calculation result of the semantic similarity, and displaying the geographic positions on a map as boarding recommendation points for selection by a user of the process automation robot.
4. The method of claim 3,
the predictive learning model is trained by:
storing a list of commonly used pick-up points related to the RPA workflow;
storing a list of past pick-up points corresponding to the user;
the predictive learning model is updated based on the list of commonly used pick-up points, the list of past pick-up points, and the one or more monitored pick-up points.
5. The method of claim 4,
when the reply is to accept the at least one pick-up recommendation point, adding the at least one pick-up recommendation point to the process automation robot, comprising:
receiving at least one boarding recommendation point accepted by a user from a user interface of the RPA;
adding a resource/process pair corresponding to the boarding recommendation point to an RPA command table, wherein the resource/process pair represents the boarding recommendation point selected from a user interface;
determining that a redundant resource/process pair is included in the command table, the redundant resource/process pair being a duplicate of the resource/process pair, and in response, deleting the redundant resource/process pair;
transmitting at least one command in the command table to an adapter of the process automation robot, the at least one command including a command to perform a process using a resource within an RPA platform;
alternatively, the first and second electrodes may be,
launching a wizard component of a Customer Resource Management (CRM) component in the RPA;
generating, using the wizard component, a match between the predictive learning model and a plurality of fields of the customer resource management component;
generating an RPA workflow based on a matching result of the wizard component, wherein the RPA workflow adds the at least one pick-up recommendation point to a combo box or a list box generated by the RPA workflow based on at least one of a plurality of fields of the customer resource management component.
6. The method of claim 5,
the assigning a confidence to each pick-up recommended point based on the number of users responding comprises:
identifying a number of users selecting each pick-up recommendation point;
determining a recommended event corresponding to each pick-up recommendation point based on the number of users selecting each pick-up recommendation point, wherein the recommended event comprises one or more event attributes determined based on one or more items;
determining an event confidence coefficient of a recommended event corresponding to each boarding recommendation point based on the event attributes;
alternatively, the first and second electrodes may be,
identifying a message of the user, wherein the message comprises the condition whether the user replies to accept each boarding recommended point;
determining a recommended event and an initial event confidence coefficient of the recommended event according to a message;
the initial event confidence is presented via a graphical interface, and a new event confidence is determined for the recommended event based on one or more additional computer-based actions of the user associated with the recommended event.
7. The method of claim 6,
the step of generating suggested next boarding recommendation points for selection according to the boarding recommendation points and the confidence level comprises the following steps:
acquiring a selection standard of an RPA, and deleting the boarding recommended points of which the confidence level of the event is lower than the selection standard;
displaying an interface for adding a boarding recommendation point by a user;
obtaining a boarding recommendation point added by a user;
and combining the deleted boarding recommendation points with the boarding recommendation points added by the user to serve as next boarding recommendation points and displaying a selection interface.
8. A get-on point recommendation optimization system based on RPA technology is characterized by comprising:
the state identification module is used for identifying the current state of the process automation robot;
a first recommendation module to recommend at least one pick-up recommendation point to a user of the process automation robot using a predictive learning model based on the current state, wherein the predictive learning model is trained based on the user's actual selection of pick-up recommendation points;
the reply receiving module is used for receiving a reply to the boarding recommendation point from the user;
an add referral point module that adds the at least one pick-up referral point to the process automation robot when the reply is an acceptance of the at least one pick-up referral point;
the confidence coefficient module is used for distributing confidence coefficient to each boarding recommendation point based on the number of replied users;
and the second recommending module generates a suggested next batch of boarding recommending points for selection according to the boarding recommending points and the confidence level.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor executes the computer program to implement the method of any one of claims 1-7.
10. A computer-readable storage medium, on which a computer program is stored, characterized in that the program is executed by a processor to implement the method according to any of claims 1-7.
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