CN112241330A - Flow processing method, device, equipment and storage medium combining RPA and AI - Google Patents
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Abstract
The embodiment of the application discloses a flow processing method, a flow processing device, flow processing equipment and a storage medium which are combined with RPA and AI. Relates to the technical field of RPA and AI, wherein, the method is applied to the RPA execution end and comprises the following steps: acquiring RPA flow demand information of each RPA execution end; and sending the RPA flow requirement information to an RPA control terminal. Therefore, the flow demand information of each RPA execution end is acquired and sent to the RPA control end, so that the RPA flow is automatically rebuilt, the automation of the flow is further promoted, the manual participation rate is reduced, the flow efficiency is improved, and the workload of related workers is reduced.
Description
Technical Field
The present application relates to the technical field of robot Process Automation, and in particular, to a Process processing method, apparatus, device, and storage medium that combine RPA (robot Process Automation) and AI (artificialintellconference).
Background
Robot Process Automation (RPA) simulates the operation of a human on a computer through specific robot software and automatically executes Process tasks according to rules.
Artificial intelligence (Artificia lnternagence), abbreviated in english as AI. The method is a new technical science for researching and developing theories, methods, technologies and application systems for simulating, extending and expanding human intelligence. Artificial intelligence is a branch of computer science that attempts to understand the essence of intelligence and produce a new intelligent machine that can react in a manner similar to human intelligence, a field of research that includes robotics, speech recognition, image recognition, natural language processing, and expert systems.
At present, in the field of robot process automation, process automation can be realized, an RPA process execution end, an RPA process control end and an RPA process generation end are commonly used in a matching manner, wherein the RPA process control end is mainly used for controlling the RPA process execution end to execute the RPA process, but the RPA process control end at present usually needs to allocate the RPA process through manual control and then starts the RPA process execution, but after starting the RPA process execution, if the process execution of the PRA process execution end has problems, no corresponding automatic processing mode exists at present, which is not favorable for process automation.
Disclosure of Invention
The embodiment of the application discloses a flow processing method, a flow processing device, flow processing equipment and a storage medium which are combined with RPA and AI, and flow demand information of each RPA execution end is acquired and sent to an RPA control end, so that an RPA flow is automatically reconstructed, the automation of the flow is further promoted, the manual participation rate is reduced, the flow efficiency is improved, and the workload of related workers is reduced.
In a first aspect, an embodiment of the present application discloses a flow processing method combining an RPA and an AI, including:
acquiring RPA flow demand information of each RPA execution end;
and sending the RPA process requirement information to an RPA control end.
Optionally, the acquiring the RPA process requirement information of each RPA execution end includes: accessing each RPA execution end interface, and acquiring the execution feedback of the RPA process of each RPA execution end; natural language Processing (NLP for short) is carried out on the execution feedback of the RPA process of each RPA execution end, and the RPA execution end of the RPA process to be reconstructed is determined; and generating a flow reconstruction demand according to the RPA execution end of the RPA flow to be reconstructed and the RPA flow to be reconstructed.
Optionally, the generating a flow reconstruction requirement according to the RPA executing end of the RPA flow to be reconstructed and the RPA flow to be reconstructed includes: acquiring identification information of the RPA execution end of the RPA flow to be reconstructed and the RPA flow code to be reconstructed of the RPA execution end of the RPA flow to be reconstructed; and adding the identification information of the RPA execution end of the RPA flow needing to be reconstructed and the corresponding RPA flow code needing to be reconstructed into the RPA flow demand information in a correlated manner.
Optionally, the obtaining of the identification information of the RPA executing end of the RPA procedure to be reconstructed and the encoding of the RPA procedure to be reconstructed of the RPA executing end of the RPA procedure to be reconstructed include: acquiring identification information of the RPA execution end of the RPA flow needing to be reconstructed; and acquiring the RPA process code to be reconstructed corresponding to the identification information based on a preset corresponding relation.
Optionally, the method further includes: obtaining an authorized account number, an authorized password and a current flow code of an RPA control terminal from the RPA control terminal; the RPA process requirement information comprises: the authorized account number, the authorized password and the current flow code.
Optionally, the method includes: acquiring task parameter information required by an RPA process to be created from each RPA robot execution end interface; and adding the task parameter information into the RPA flow demand information.
Optionally, the method includes: receiving an RPA process creation task identifier issued by an RPA control terminal, an RPA execution terminal identifier corresponding to the creation task identifier, and a corresponding task parameter; based on the corresponding RPA robot execution end identification, the RPA process creation task identification and the corresponding task parameter are sent to the corresponding RPA robot execution end; and receiving feedback of the corresponding RPA robot execution end creation task.
Optionally, when the feedback of the corresponding RPA robot execution end to create the task is that the task creation fails, continuing to create the next task.
In order to achieve the above object, a second embodiment of the present application provides another flow processing apparatus combining RPA and AI, including: the first acquisition module is used for acquiring RPA process demand information of each RPA execution end; and the sending module is used for sending the RPA process requirement information to an RPA control terminal.
Optionally, the first obtaining module includes: the device comprises an acquisition unit, a processing unit and a feedback unit, wherein the acquisition unit is used for accessing each RPA execution end interface and acquiring the execution feedback of the RPA process of each RPA execution end; the determining unit is used for performing natural language processing on the execution feedback of the RPA process of each RPA execution end and determining the RPA execution end needing to be reconstructed; and the generating unit is used for generating a flow reconstruction demand according to the RPA execution end of the RPA flow to be reconstructed and the RPA flow to be reconstructed.
Optionally, the generating unit is specifically configured to: acquiring identification information of the RPA execution end of the RPA flow to be reconstructed and the RPA flow code to be reconstructed of the RPA execution end of the RPA flow to be reconstructed; and adding the identification information of the RPA execution end of the RPA flow needing to be reconstructed and the corresponding RPA flow code needing to be reconstructed into the RPA flow demand information in a correlated manner.
Optionally, the obtaining of the identification information of the RPA executing end of the RPA procedure to be reconstructed and the encoding of the RPA procedure to be reconstructed of the RPA executing end of the RPA procedure to be reconstructed include: acquiring identification information of the RPA execution end of the RPA flow needing to be reconstructed; and acquiring the RPA process code to be reconstructed corresponding to the identification information based on a preset corresponding relation.
Optionally, the apparatus further includes: the second acquisition module is used for acquiring the authorization account number, the authorization password and the current flow code of the RPA control terminal from the RPA control terminal; the RPA process requirement information comprises: the authorized account number, the authorized password and the current flow code.
Optionally, the apparatus further includes: a third acquisition module, configured to acquire task parameter information required by an RPA flow to be created from each RPA robot execution-side interface; and the adding module is used for adding the task parameter information into the RPA flow demand information.
Optionally, the apparatus includes: a first receiving module, configured to receive an RPA process creation task identifier issued by an RPA control end, an RPA execution end identifier corresponding to the creation task identifier, and a corresponding task parameter; the issuing module is used for issuing the RPA process creation task identifier and the corresponding task parameter to the corresponding RPA robot execution end based on the corresponding RPA robot execution end identifier; and the second receiving module is used for receiving feedback of the task created by the corresponding RPA robot execution end.
Optionally, when the feedback of the corresponding RPA robot execution end to create the task is that the task creation fails, continuing to create the next task.
In order to achieve the above object, an embodiment of a third aspect of the present application provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the processor executes the computer program, the processor implements the flow processing method combining the RPA and the AI as described in the above embodiment.
In order to achieve the above object, a fourth aspect of the present application provides a non-transitory computer-readable storage medium, on which a computer program is stored, and the computer program, when executed by a processor, implements the flow processing method combining RPA and AI as described in the above embodiments.
The technical scheme provided by the embodiment of the application at least has the following beneficial technical effects:
acquiring RPA flow demand information of each RPA execution end; and sending the RPA flow requirement information to an RPA control terminal. Therefore, the flow demand information of each RPA execution end is acquired and sent to the RPA control end, so that the RPA flow is automatically rebuilt, the automation of the flow is further promoted, the manual participation rate is reduced, the flow efficiency is improved, and the workload of related workers is reduced.
Additional aspects and advantages of the present application will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the present application.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
Fig. 1 is a schematic flowchart of a flow processing method combining RPA and AI according to an embodiment of the present disclosure;
FIG. 2 is a schematic flow chart of another flow processing method combining RPA and AI according to an embodiment of the present disclosure;
FIG. 3 is a schematic flow chart of another flow processing method combining RPA and AI according to the embodiment of the present application;
fig. 4 is a diagram illustrating an example of a flow processing method combining RPA and AI according to an embodiment of the present application;
fig. 5 is a schematic structural diagram of a flow processing apparatus combining RPA and AI according to an embodiment of the present application;
fig. 6 is a schematic structural diagram of another flow processing apparatus combining RPA and AI according to an embodiment of the present disclosure;
fig. 7 is a schematic structural diagram of another flow processing device combining RPA and AI according to an embodiment of the present application;
fig. 8 is a schematic structural diagram of another flow processing apparatus combining RPA and AI according to an embodiment of the present application;
fig. 9 is a schematic structural diagram of another flow processing apparatus combining an RPA and an AI according to an embodiment of the present application;
fig. 10 is a schematic hardware structure diagram of an electronic device according to an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
It is to be noted that the terms "comprises" and "comprising" and any variations thereof in the examples and figures of the present application are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements listed, but may alternatively include other steps or elements not listed, or inherent to such process, method, article, or apparatus.
A flow processing method, an apparatus, an electronic device, and a storage medium in conjunction with RPA and AI according to embodiments of the present application are described below with reference to the accompanying drawings.
Specifically, the present application is applied to an RPA (robot process automation) execution end, in this embodiment, the RPA execution end may obtain task parameter information required to create an RPA flow from each RPA robot execution end interface, then place the task parameter information into a corresponding data queue, then create a flow task corresponding to the task parameter information, and achieve the purpose of executing the flow task again that fails to be executed.
Fig. 1 is a schematic flowchart of a flow processing method combining RPA and AI according to an embodiment of the present disclosure.
As shown in fig. 1, the flow processing method includes the following steps:
And 102, sending the RPA process requirement information to an RPA control terminal.
Specifically, the RPA flow requirement information includes one or more of execution feedback (including execution success or failure information) of the RPA flow at each RPA execution end, an authorized account number and an authorized password at the RPA control end, a current flow code, and the like, and one or more of task parameter information and the like required by each RPA robot execution end interface to acquire the RPA flow to be created, and may be selectively set according to actual application needs.
Therefore, there are various ways to obtain the RPA flow requirement information of each RPA executing end, which are illustrated as follows:
in a first example, each RPA execution end interface is accessed, the execution feedback of the RPA process of each RPA execution end is obtained, the RPA execution end of the RPA process to be reconstructed is determined according to the execution feedback of the RPA process of each RPA execution end, and the process reconstruction requirement is generated according to the RPA execution end of the RPA process to be reconstructed and the RPA process to be reconstructed.
In a second example, an authorized account, an authorized password, and a current flow code of the RPA control end are obtained from the RPA control end.
In a third example, task parameter information required by the RPA process to be created is acquired from each RPA robot execution end interface.
Further, the RPA process requirement information is sent to the RPA control end, the process reconstruction requirement can be sent to the RPA control end, the authorized account number and the authorized password of the RPA control end, the current process code can be sent to the RPA control end, and the like, so that the RPA control end can create an RPA process creation task, namely an RPA execution end, and corresponding task parameters according to the RPA process requirement information, and the RPA robot execution end can execute corresponding tasks according to the task parameters, such as acquiring medical insurance data and the like.
To sum up, the RPA flow demand information of each RPA execution end is acquired by the flow processing method combining the RPA and the AI in the embodiment of the present application; and sending the RPA flow requirement information to an RPA control terminal. Therefore, the flow demand information of each RPA execution end is acquired and sent to the RPA control end, so that the RPA flow is automatically rebuilt, the automation of the flow is further promoted, the manual participation rate is reduced, the flow efficiency is improved, and the workload of related workers is reduced.
Based on the above description, the flow processing method combining RPA and AI according to the present application, applied to the RPA execution end, may determine whether to reconstruct the PRA flow according to the execution feedback of the RPA flow of each RPA execution end, so as to ensure that the PRA flow is successfully executed, and acquire target data, which is described in detail below with reference to fig. 2.
Fig. 2 is a schematic flow chart of another flow processing method combining RPA and AI according to an embodiment of the present disclosure.
As shown in fig. 2, the flow processing method combining RPA and AI includes the following steps:
Specifically, the execution feedback of the RPA flow of each RPA execution end can be obtained by accessing each RPA execution end interface, that is, two feedback results of whether the RPA flow of each RPA execution end is successfully executed or failed to be executed are obtained.
And 203, generating a flow reconstruction demand according to the RPA execution end of the RPA flow to be reconstructed and the RPA flow to be reconstructed, and sending the flow reconstruction demand to the RPA control end.
Specifically, after the execution feedback of the RPA flows of each RPA execution end is obtained, natural language processing is performed on the execution feedback, for example, semantic analysis is performed to obtain a task execution failure or execution success message, it is determined that the RPA execution end that failed execution is the RPA execution end that needs to be reconstructed, a corresponding RPA flow that needs to be reconstructed is obtained, and a flow reconstruction requirement is generated according to the RPA execution end that needs to be reconstructed and the RPA flow that needs to be reconstructed and sent to the RPA control end.
It can be understood that there are many ways to generate the flow reconstruction requirement according to the RPA executing end of the RPA flow to be reconstructed and the RPA flow to be reconstructed, and as a possible implementation way, the identification information of the RPA executing end of the RPA flow to be reconstructed and the RPA flow code to be reconstructed of the RPA executing end of the RPA flow to be reconstructed are obtained, and the identification information of the RPA executing end of the RPA flow to be reconstructed and the corresponding RPA flow code to be reconstructed are added to the RPA flow requirement information in a correlated manner.
Wherein, different RPA execution terminals correspond to different process codes, and as an example, the identification information of the RPA execution terminal needing to be rebuilt the RPA process is obtained; and acquiring the RPA process code to be reconstructed corresponding to the identification information based on the preset corresponding relation. That is to say, the corresponding relationship between the identification information of the RPA execution end and the RPA flow code to be reconstructed is established in advance, and the RPA flow code to be reconstructed corresponding to the identification information can be acquired through the identification information of the RPA execution end.
For example, there are multiple stores under one area, there are multiple RPA executives under one store, for example, there are 3 stores under a area, 2 RPA executives are used in the whole area, that is, there are 2 process codes (1 area 2 RPA executives, i.e., two process codes are written, where the process codes are not directly bound to a certain store); assuming that the 2 process codes are a001 and a002, when an OpenAPI dynamic creation task is called, RPA processes can be created according to the number of stores, that is, 6 RPA processes of a001+1 store, a001+2 store, a001+3 store, a002+1 store, a002+2 store and a002+3 store are performed in the form of stores when the RPA processes are scheduled, and each RPA process only performs a summary data collection reporting process of a certain store.
Therefore, the execution feedback of the RPA process of each RPA execution end is obtained by accessing each RPA execution end interface, the RPA execution end of the RPA process to be reconstructed is determined according to the execution feedback of the RPA process of each RPA execution end, the process reconstruction requirement is generated according to the RPA execution end of the RPA process to be reconstructed and is sent to the RPA control end, the RPA process is automatically reconstructed, the automation of the process is further promoted, the manual participation rate is reduced, the process efficiency is improved, and the workload of related workers is reduced.
Fig. 3 is a schematic flowchart of another flow processing method combining RPA and AI according to an embodiment of the present disclosure.
As shown in fig. 3, the flow processing method combining RPA and AI includes the following steps:
Specifically, a corresponding RPA flow may be created for each RPA robot execution end to achieve that each RPA robot execution end collects data as needed and provides the data to a user, so that task parameter information required by the RPA flow to be created, such as task parameter information of store numbers, store positions, payment categories, and the like, is acquired from each RPA robot execution end interface, the task parameter information is further added to the RPA flow demand information, and the RPA flow demand information is sent to the RPA control end. When the RPA process is created, a generalized process is usually created, and before the RPA process is started to be executed, the RPA control terminal determines the task parameter information according to the operation environment of the RPA process and the operation purpose of the RPA process. The task parameter information determined based on the operation environment can be store numbers and store positions, and the task parameter information determined based on the operation purpose can be payment types, payment account numbers and collection objects for a bill capture RPA process.
And 304, based on the corresponding RPA robot execution end identification, issuing the RPA process creation task identification and the corresponding task parameter to the corresponding RPA robot execution end.
And 305, receiving feedback of the corresponding RPA robot execution end creation task.
Specifically, the RPA control end may generate an RPA flow creation task identifier, an RPA execution end identifier corresponding to the creation task identifier, and corresponding task parameters according to task parameter information in the RPA flow demand information, and send the information to the RPA execution end, so that the RPA execution end receives the RPA flow creation task identifier, the RPA execution end identifier corresponding to the creation task identifier, and the corresponding task parameters delivered by the RPA control end, and thus, based on the corresponding RPA robot execution end identifier, issues the RPA flow creation task identifier and the corresponding task parameters to the corresponding RPA robot execution end, and the RPA robot execution end performs data acquisition according to the task parameters.
It should be noted that, when the feedback of the corresponding RPA robot execution end to create a task is that the creation of the task fails, the creation of the next task is continued. The task state is inquired according to the task identifier, the task identifier returned by the task interface is created and used for uniquely identifying one task, and the returned value (key field) can determine the task state (whether the task is successfully created).
Therefore, task parameter information required by the RPA process to be created is obtained from each RPA robot execution end interface, the task parameter information is added into the RPA process requirement information, the RPA process requirement information is sent to the RPA control end, the RPA process creation task identification issued by the RPA control end, the RPA execution end identification corresponding to the creation task identification and the corresponding task parameter are received, the RPA process creation task identification and the corresponding task parameter are issued to the corresponding RPA robot execution end based on the corresponding RPA robot execution end identification, and feedback of the creation task of the corresponding RPA robot execution end is received. So as to automatically rebuild the RPA flow, further promote the automation of the flow, reduce the manual participation rate, improve the flow efficiency and reduce the workload of related workers.
In order to make the above process more clear for those skilled in the art, as illustrated in fig. 4 below, the RPA execution end may obtain task parameter information required for creating an RPA flow from each RPA robot execution end interface, then place the task parameter information into a corresponding data queue, then create a flow task corresponding to the task parameter information, and implement the purpose of executing the flow task that fails to be executed again.
Specifically, as shown in fig. 4, (1) global variables are initialized, data (RPA process requirement information) related to all process blocks are loaded, and the like, and the output includes a dictionary type and data required by all process blocks, and key fields of the output dictionary include an authorized account number and an authorized password of an RPA control end, a current process code (unique identification of a process on the RPA control end), and a server address deployed by the RPA control end; (2) acquiring a store list, acquiring task parameter information to be created by calling an acquisition store information interface, wherein the array type which can be output is the task parameter information required by creating an RPA flow; (3) acquiring a piece of data in a list, and increasing the readability of the process; (4) adding data into a data queue, and putting task parameter information required by an RPA process into the data queue corresponding to an RPA control end, wherein the data required to be put into the data queue is data obtained by obtaining a store interface; (5) creating a data corresponding task, obtaining a current flow code to be executed according to the store information returned by the store information acquisition interface and the mapping relation, creating a flow task by using an open platform, and inputting the store information, the open platform authentication information of the RPA control end and the server address deployed by the RPA control end; (6) the task of creating the self flow is executed after all RPA flows are created, because the RPA flows may fail to execute due to the exception.
Therefore, the flow demand information of each RPA execution end is acquired and sent to the RPA control end, so that the RPA flow is automatically rebuilt, the automation of the flow is further promoted, the manual participation rate is reduced, the flow efficiency is improved, and the workload of related workers is reduced.
In order to implement the above embodiments, the present application further provides a flow processing device combining RPA and AI. Fig. 5 is a schematic structural diagram of a flow processing apparatus according to the present application, which combines RPA and AI, as shown in fig. 5, and includes: a first acquisition module 501 and a sending module 502, wherein,
a first obtaining module 501, configured to obtain RPA process requirement information of each RPA executing end.
A sending module 502, configured to send the RPA process requirement information to an RPA control end.
In an embodiment of the present application, as shown in fig. 6, on the basis of fig. 5, the first obtaining module 501 includes: the acquisition unit 5011, the determination unit 5012, and the generation unit 5013.
The obtaining unit 5011 is configured to access each RPA execution end interface, and obtain an execution feedback of an RPA procedure of each RPA execution end.
The determining unit 5012 is configured to perform natural language processing on the execution feedback of the RPA procedure at each RPA execution end, and determine an RPA execution end that needs to reconstruct the RPA procedure.
The generating unit 5013 is configured to generate a flow reconstruction requirement according to the RPA executing end of the RPA flow to be reconstructed and the RPA flow to be reconstructed.
In an embodiment of the application, the generating unit 5013 is specifically configured to: acquiring identification information of the RPA execution end of the RPA flow to be reconstructed and the RPA flow code to be reconstructed of the RPA execution end of the RPA flow to be reconstructed; and adding the identification information of the RPA execution end of the RPA flow needing to be reconstructed and the corresponding RPA flow code needing to be reconstructed into the RPA flow demand information in a correlated manner.
In an embodiment of the present application, acquiring identification information of an RPA executing end of the RPA procedure to be reconstructed and an RPA procedure code of the RPA executing end of the RPA procedure to be reconstructed include: acquiring identification information of the RPA execution end of the RPA flow needing to be reconstructed; and acquiring the RPA process code to be reconstructed corresponding to the identification information based on a preset corresponding relation.
In an embodiment of the present application, as shown in fig. 7, on the basis of fig. 5, the method further includes: a second obtaining module 503.
A second obtaining module 503, configured to obtain, from the RPA control end, an authorized account, an authorized password, and a current flow code of the RPA control end; the RPA process requirement information comprises: the authorized account number, the authorized password and the current flow code.
In an embodiment of the present application, as shown in fig. 8, on the basis of fig. 5, the method further includes: a third acquisition module 504 and an addition module 505.
A third obtaining module 504, configured to obtain task parameter information required by an RPA procedure to be created from each RPA robot execution-side interface.
An adding module 505, configured to add the task parameter information to the RPA flow requirement information.
In an embodiment of the present application, as shown in fig. 9, on the basis of fig. 5, the method further includes: a first receiving module 506, a sending down module 507 and a second receiving module 508.
A first receiving module 506, configured to receive an RPA process creation task identifier issued by an RPA control end, an RPA execution end identifier corresponding to the creation task identifier, and a corresponding task parameter.
And the issuing module 507 is configured to issue the RPA process creation task identifier and the corresponding task parameter to the corresponding RPA robot execution end based on the corresponding RPA robot execution end identifier.
A second receiving module 508, configured to receive feedback of task creation performed by the corresponding RPA robot executing end.
In an embodiment of the application, when the feedback of the corresponding RPA robot execution end to create the task is that the task creation fails, the creation of the next task is continued.
It should be noted that the foregoing explanation of the embodiment of the flow processing method is also applicable to the flow processing apparatus of the embodiment, and is not repeated here.
To sum up, the RPA and AI combined flow processing device of the embodiment of the present application obtains RPA flow demand information of each RPA execution end; and sending the RPA flow requirement information to an RPA control terminal. Therefore, the flow demand information of each RPA execution end is acquired and sent to the RPA control end, so that the RPA flow is automatically rebuilt, the automation of the flow is further promoted, the manual participation rate is reduced, the flow efficiency is improved, and the workload of related workers is reduced.
The electronic device provided in this embodiment may be used to implement the technical solutions of the above method embodiments, and the implementation principles and technical effects are similar, which are not described herein again.
Referring to fig. 10, a schematic structural diagram of an electronic device 900 suitable for implementing an embodiment of the present application is shown, where the electronic device 900 may be a terminal device or a server. Among them, the terminal Device may include, but is not limited to, a mobile terminal such as a mobile phone, a notebook computer, a digital broadcast receiver, a Personal Digital Assistant (PDA), a tablet computer (PAD), a Portable Media Player (PMP), a car terminal (e.g., car navigation terminal), etc., and a fixed terminal such as a digital TV, a desktop computer, etc. The electronic device shown in fig. 10 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present application.
As shown in fig. 10, the electronic device 900 may include a processing means (e.g., a central processing unit, a graphics processor, etc.) 901, which may perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 902 or a program loaded from a storage means 908 into a Random Access Memory (RAM) 903. In the RAM 903, various programs and data necessary for the operation of the electronic apparatus 900 are also stored. The processing apparatus 901, the ROM902, and the RAM 903 are connected to each other through a bus 904. An input/output (I/O) interface 905 is also connected to bus 904.
Generally, the following devices may be connected to the I/O interface 905: input devices 906 including, for example, a touch screen, touch pad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; an output device 907 including, for example, a Liquid Crystal Display (LCD), a speaker, a vibrator, and the like; storage 908 including, for example, magnetic tape, hard disk, etc.; and a communication device 909. The communication device 909 may allow the electronic apparatus 900 to perform wireless or wired communication with other apparatuses to exchange data. While fig. 10 illustrates an electronic device 900 having various means, it is to be understood that not all illustrated means are required to be implemented or provided. More or fewer devices may alternatively be implemented or provided.
In particular, according to embodiments of the application, the processes described above with reference to the flow diagrams may be implemented as computer software programs. For example, embodiments of the present application include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated by the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication device 909, or installed from the storage device 908, or installed from the ROM 902. The computer program, when executed by the processing apparatus 901, performs the above-described functions defined in the methods of the embodiments of the present application.
In order to implement the above embodiments, the present application also provides an electronic device, including: the processor executes the computer program to implement the flow processing method combining the RPA and the AI in the above embodiments.
In order to achieve the above embodiments, the present application also proposes a non-transitory computer-readable storage medium in which instructions, when executed by a processor, enable execution of the flow processing method combining RPA and AI in the above embodiments.
It should be noted that the computer readable medium mentioned above in the present application may be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present application, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In this application, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.
The computer readable medium may be embodied in the electronic device; or may exist separately without being assembled into the electronic device.
The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to perform the methods shown in the above embodiments.
Computer program code for carrying out operations for aspects of the present application may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of Network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units described in the embodiments of the present application may be implemented by software or hardware. Where the name of a unit does not in some cases constitute a limitation of the unit itself, for example, the first retrieving unit may also be described as a "unit for retrieving at least two internet protocol addresses".
The functions described herein above may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), systems on a chip (SOCs), Complex Programmable Logic Devices (CPLDs), and the like.
In the context of this application, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The above description is only a preferred embodiment of the application and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the disclosure herein is not limited to the particular combination of features described above, but also encompasses other arrangements formed by any combination of the above features or their equivalents without departing from the spirit of the disclosure. For example, the above features may be replaced with (but not limited to) features having similar functions disclosed in the present application.
Further, while operations are depicted in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order. Under certain circumstances, multitasking and parallel processing may be advantageous. Likewise, while several specific implementation details are included in the above discussion, these should not be construed as limitations on the scope of the application. Certain features that are described in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable subcombination.
Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing the claims.
Claims (11)
1. A flow processing method combining RPA and AI is characterized in that, applied to RPA execution end, it includes:
acquiring RPA flow demand information of each RPA execution end;
and sending the RPA process requirement information to an RPA control end.
2. The method according to claim 1, wherein said obtaining RPA procedure requirement information of each RPA executing end comprises:
accessing each RPA execution end interface, and acquiring the execution feedback of the RPA process of each RPA execution end;
performing natural language processing on the execution feedback of the RPA process of each RPA execution end, and determining the RPA execution end needing to be reconstructed;
and generating a flow reconstruction demand according to the RPA execution end of the RPA flow to be reconstructed and the RPA flow to be reconstructed.
3. The method according to claim 2, wherein generating a flow reconstruction requirement according to the RPA executing end of the RPA flow to be reconstructed and the RPA flow to be reconstructed comprises:
acquiring identification information of the RPA execution end of the RPA flow to be reconstructed and the RPA flow code to be reconstructed of the RPA execution end of the RPA flow to be reconstructed;
and adding the identification information of the RPA execution end of the RPA flow needing to be reconstructed and the corresponding RPA flow code needing to be reconstructed into the RPA flow demand information in a correlated manner.
4. The method according to claim 3, wherein the obtaining of the identification information of the RPA executing end of the RPA procedure to be reconstructed and the RPA procedure coding to be reconstructed of the RPA executing end of the RPA procedure to be reconstructed includes:
acquiring identification information of the RPA execution end of the RPA flow needing to be reconstructed;
and acquiring the RPA process code to be reconstructed corresponding to the identification information based on a preset corresponding relation.
5. The method of claim 1, further comprising:
obtaining an authorized account number, an authorized password and a current flow code of an RPA control terminal from the RPA control terminal;
the RPA process requirement information comprises: the authorized account number, the authorized password and the current flow code.
6. The method of claim 1, further comprising:
acquiring task parameter information required by an RPA process to be created from each RPA robot execution end interface;
and adding the task parameter information into the RPA flow demand information.
7. The method of claim 1, further comprising:
receiving an RPA process creation task identifier issued by an RPA control terminal, an RPA execution terminal identifier corresponding to the creation task identifier, and a corresponding task parameter;
based on the corresponding RPA robot execution end identification, the RPA process creation task identification and the corresponding task parameter are sent to the corresponding RPA robot execution end;
and receiving feedback of the corresponding RPA robot execution end creation task.
8. The method of claim 7,
and when the feedback of the corresponding RPA robot execution end for establishing the task is that the task is failed to be established, continuing to establish the next task.
9. A flow processing apparatus that combines RPA and AI, comprising:
the first acquisition module is used for acquiring RPA process demand information of each RPA execution end;
and the sending module is used for sending the RPA process requirement information to an RPA control terminal.
10. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor implementing the method of any one of claims 1-8 when executing the computer program.
11. A non-transitory computer readable storage medium having stored thereon a computer program, wherein the computer program, when executed by a processor, implements the method of any one of claims 1-8.
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