CN113159483A - Task scheduling method and device based on RPA and AI, robot and medium - Google Patents
Task scheduling method and device based on RPA and AI, robot and medium Download PDFInfo
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- CN113159483A CN113159483A CN202110024488.1A CN202110024488A CN113159483A CN 113159483 A CN113159483 A CN 113159483A CN 202110024488 A CN202110024488 A CN 202110024488A CN 113159483 A CN113159483 A CN 113159483A
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- G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
- G10L13/00—Speech synthesis; Text to speech systems
- G10L13/08—Text analysis or generation of parameters for speech synthesis out of text, e.g. grapheme to phoneme translation, prosody generation or stress or intonation determination
Abstract
According to the task scheduling method, device, robot and medium based on RPA and AI, task information of tasks to be scheduled, which is obtained by an RPA robot through query from a task scheduling system, is obtained, scheduling information is generated according to the task information, and the scheduling information is sent to a telephone robot, so that the telephone robot converts text data which is carried in the scheduling information and used for describing task content into voice to be played to a task executor, and the task executor executes the tasks according to the voice played by the telephone robot. According to the embodiment of the application, the robot replaces manpower to complete large-scale, repetitive and mechanical task scheduling work, so that labor force optimization is realized, the labor cost is saved, and the task scheduling efficiency is improved.
Description
Technical Field
The embodiment of the application relates to the technical field of robot application, in particular to a task scheduling method and device based on RPA and AI, a robot and a medium.
Background
RPA (robot Process Automation) simulates human operations on a computer through specific "robot software" and automatically executes Process tasks according to rules.
AI (Artificial Intelligence) is a new technical science for studying and developing theories, methods, techniques and application systems for simulating, extending and expanding human Intelligence.
RPA has unique advantages: low code, non-intrusive. The low code means that the RPA can be operated without high IT level, and business personnel who do not know programming can also develop the flow; non-invasively, the RPA can simulate human operation without opening the interface with a software system. However, conventional RPA has certain limitations: can only be based on fixed rules and application scenarios are limited. With the continuous development of the AI technology, the limitation of the traditional RPA is overcome by the deep fusion of the RPA and the AI, and the RPA + AI is a Hand work + Head work, which greatly changes the value of the labor force.
In the existing scheduling system, it is usually necessary to manually check the tasks on the scheduling system and process a large amount of scheduling instructions (such as system information confirmation, telephone notification, recording results, etc.) within a specified time. Especially, under the condition that a scheduling system is large and the task amount is large, the task amount of manual scheduling is large, the efficiency is low, and the problems of task scheduling delay, omission and the like are easy to occur.
Disclosure of Invention
The embodiment of the application provides a task scheduling method, a task scheduling device, a robot and a task scheduling medium based on RPA and AI, which are used for improving the task scheduling efficiency.
In a first aspect, an embodiment of the present application provides a task scheduling method based on RPA and AI, including:
acquiring task information of a task to be scheduled, which is obtained by a Robot Process Automation (RPA) robot by inquiring from a task scheduling system, wherein the task information comprises task content; generating scheduling information according to the task information, wherein the scheduling information comprises text data of the task content; and sending the scheduling information to a telephone robot so that the telephone robot converts the text data in the scheduling information into voice and plays the voice to a task performer, and the task performer performs a task based on the voice.
In one embodiment, before the conversation robot generates scheduling information according to the task information, the method further includes:
generating dialogue data based on the task content in the task information and outputting the dialogue data to a dispatcher; receiving an execution instruction fed back by the dispatcher; and verifying the execution instruction, and after the execution instruction is verified to be correct, executing the step of generating the scheduling information according to the task information.
In one embodiment, the execution instruction is a voice instruction or a text instruction.
In one embodiment, the scheduling information includes identification information of the telephone robot.
In one embodiment, transmitting the scheduling information to a telephone robot includes:
and sending the scheduling information to the RPA robot so that the RPA robot forwards the scheduling information to the telephone robot according to the identification information of the telephone robot.
In one embodiment, after transmitting the scheduling information to the telephone robot, the method further comprises:
receiving an execution result fed back by the RPA robot, and outputting the execution result to a dispatcher for confirmation; the execution result fed back by the RPA robot is fed back by the task performer after the task performer completes the task and is forwarded to the RPA robot by the telephone robot.
In an embodiment, the forwarding of the execution result to the RPA robot by the telephony robot is to convert the voice information fed back by the task performer into text through a preset automatic speech recognition engine ASR after the telephony robot receives the task execution result, so as to obtain the execution result described in text.
In one embodiment, the voice is obtained by converting the text data in the scheduling information through a preset voice and text bidirectional conversion engine TTS by the telephone robot.
In a second aspect, an embodiment of the present application provides a task scheduling device based on RPA and AI, including:
the acquisition module is used for acquiring task information of a task to be scheduled, which is obtained by the RPA robot through inquiring from a task scheduling system, wherein the task information comprises task content.
And the generating module is used for generating scheduling information according to the task information, wherein the scheduling information comprises text data of the task content.
And the sending module is used for sending the scheduling information to a telephone robot so that the telephone robot converts the text data in the scheduling information into voice and plays the voice to a task executor, and the task executor executes a task based on the voice.
In an embodiment, the apparatus may further include:
and the output module is used for generating dialogue data based on the task content in the task information and outputting the dialogue data to the dispatcher.
And the first receiving module is used for receiving the execution instruction fed back by the dispatcher.
And the generating module is used for verifying the execution instruction and executing the step of generating the scheduling information according to the task information after the execution instruction is verified to be correct.
In one embodiment, the execution instruction is a voice instruction or a text instruction
In one embodiment, the scheduling information includes identification information of the telephone robot.
In one embodiment, the transmission module is configured to transmit the scheduling information to the RPA robot, so that the RPA robot forwards the scheduling information to the telephony robot according to the identification information of the telephony robot.
In one embodiment, the apparatus may further include:
and the second receiving module is used for receiving an execution result fed back by the RPA robot, wherein the execution result fed back by the RPA robot is fed back to the telephone robot by the task executor after the task is completed, and is forwarded to the RPA robot by the telephone robot.
And the output module is also used for outputting the execution result to a dispatcher for confirmation.
In an embodiment, the forwarding of the execution result to the RPA robot by the telephony robot is to convert voice information fed back by a task performer into text through a preset automatic speech recognition engine ASR after the telephony robot receives the task execution result, so as to obtain an execution result described in text.
In one embodiment, the voice is obtained by converting the text data in the scheduling information through a preset voice and text bidirectional conversion engine TTS by the telephone robot.
In a third aspect, embodiments of the present application provide a conversation robot, including a memory and a processor; wherein the memory is configured to store executable instructions of the processor, and when the instructions are executed by the processor, the processor performs the method of the first aspect.
In a fourth aspect, embodiments of the present application provide a computer-readable storage medium, in which computer-executable instructions are stored, and when the computer-executable instructions are executed by a processor, the computer-readable storage medium is configured to implement the method according to the first aspect.
In a fifth aspect, an embodiment of the present application provides an electronic device, including a memory and a processor; wherein the memory is configured to store executable instructions of the processor, and when the instructions are executed by the processor, the processor performs the method of the first aspect.
According to the task scheduling method, device, robot and medium based on RPA and AI, task information of tasks to be scheduled, which is obtained by an RPA robot through query from a task scheduling system, is obtained, scheduling information is generated according to the task information, and the scheduling information is sent to a telephone robot, so that the telephone robot converts text data which is carried in the scheduling information and used for describing task content into voice to be played to a task executor, and the task executor executes the tasks according to the voice played by the telephone robot. According to the embodiment of the application, the robot replaces manpower to complete large-scale, repetitive and mechanical task scheduling work, so that labor force optimization is realized, the labor cost is saved, and the task scheduling efficiency is improved.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and together with the description, serve to explain the principles of the disclosure.
Fig. 1 is a flowchart of a task scheduling method based on RPA and AI according to an embodiment of the present disclosure;
fig. 2 is a schematic structural diagram of a scheduling system according to an embodiment of the present application;
fig. 3a is a flowchart of a task scheduling method based on RPA and AI according to an embodiment of the present disclosure;
fig. 3b is a screenshot of an interaction interface between a dispatcher and a conversation robot according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of a task scheduling device based on RPA and AI according to an embodiment of the present disclosure;
fig. 5 is a schematic structural diagram of a conversation robot according to an embodiment of the present application.
With the foregoing drawings in mind, certain embodiments of the disclosure have been shown and described in more detail below. These drawings and written description are not intended to limit the scope of the disclosed concepts in any way, but rather to illustrate the concepts of the disclosure to those skilled in the art by reference to specific embodiments.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the exemplary embodiments below are not intended to represent all implementations consistent with the present disclosure. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present disclosure, as detailed in the appended claims.
In the description of the present invention, the "interactive robot" refers to a computer program that can interact with human beings by voice or text, and may specifically be interactive robot software installed on communication software, an applet, or a web page, or may be a robot with an interactive function installed on a hardware robot entity.
In the description of the invention, TTS (TextToSpeech, from text to speech) is part of a human-machine dialog, capable of intelligently converting text into a natural speech stream.
In the description of the present invention, ASR (Automatic Speech Recognition) is a technology for converting human Speech into text.
The embodiment of the invention provides a task scheduling method, a task scheduling device, a robot and a task scheduling medium based on RPA and AI, and aims to improve task scheduling efficiency. The following provides a detailed description of embodiments of the invention.
Robot Process Automation (RPA) is a Process task that simulates human operations on a computer through specific robot software and automatically executes according to rules.
Ai (intellectual intelligence) is an english abbreviation for artificial intelligence, which is a new technical science for studying and developing theories, methods, techniques and application systems for simulating, extending and expanding human intelligence. The technical scheme provided by the embodiment of the invention mainly adopts a TTS technology and an ASR technology in an AI technology. By adopting TTS technology, the text data of the task content carried in the scheduling information can be converted into voice. By adopting the ASR technology, the voice information fed back by the task performer can be converted into characters, and an execution result described by the characters is obtained.
Fig. 1 is a flowchart of a task scheduling method based on RPA and AI according to an embodiment of the present application, and as shown in fig. 1, the method includes:
And 102, generating scheduling information according to the task information, wherein the scheduling information comprises text data of task content.
And 103, sending the scheduling information to the telephone robot so that the telephone robot converts the text data in the scheduling information into voice and plays the voice to the task performer, and the task performer performs the task based on the voice.
The method provided by the embodiment of the present application may be performed by a dispatch center, where the dispatch center may be a server or a platform, and in an implementation, the dispatch center may be a conversation robot. The conversation robot in the embodiment of the present application may be conversation robot software loaded on communication software, an applet, or a web page, or may be a robot with a conversation function loaded on a hardware robot entity.
For example, fig. 2 is a schematic structural diagram of a scheduling system provided in an embodiment of the present application, and as shown in fig. 2, the scheduling system shown in fig. 2 includes a scheduler 25, a conversation robot 20, an RPA robot 21, a scheduling system 22, a telephone robot 23, and a terminal device 24 held by a task performer, where the number of the conversation robot 20, the RPA robot 21, the telephone robot 23, and the device 24 may not be limited to one.
Referring to fig. 2, at least information of tasks to be scheduled and execution results of tasks that have completed scheduling are recorded in scheduling system 22. The RPA robot 21 checks the scheduling system 22 periodically (for example, at a fixed time every day), acquires task information of a task to be scheduled from the scheduling system 22 before the task starts, and pushes the task information to the conversation robot 20. The task information referred to in this embodiment may specifically be voice information or text information, and the task information at least may include task content. In other embodiments, the task information may also include task execution deadline, task performer identification, and other information. But it is considered that the data size of the text information is generally smaller than that of the voice information. The transmission speed of the text information in data transmission is fast, and the occupied transmission resources are small, so that the task information to be scheduled is specifically the text information in this embodiment.
The scheduling information in this embodiment may include task content described in voice or text, an identifier of the telephone robot to which the scheduling information object is transmitted, information of a task performer (for example, name, number, telephone number, and the like), and information of a task performance deadline.
The following methods may be used by the conversation robot 20 to generate scheduling information according to the task information and then send the scheduling information to the telephone robot 23:
in one transmission mode, the conversation robot 20 schedules the task, identifies an idle telephone robot from the system, and transmits the scheduling information directly to the idle telephone robot.
In another transmission mode, the conversation robot schedules the telephone robot, and transmits scheduling information carrying identification information of the telephone robot to the RPA robot 21, and the RPA robot 21 forwards the scheduling information to the corresponding telephone robot according to the identification information of the telephone robot carried in the scheduling information. In this transmission method, since the dialogue robot only needs to connect with the RPA robot and does not need to connect with the telephone robot, communication ports of the dialogue robot can be saved, and the dialogue robot can have more ports to connect with more RPA robots. When a large scheduling scene has a plurality of small scheduling aspects, one RPA robot can process the work of one small scheduling aspect, and the conversation robot coordinates and orchestrates a plurality of small scheduling aspects of tasks corresponding to the RPA robots, so as to avoid conflicts.
After receiving the scheduling information, the telephone robot 23 may convert text data of task content carried in the scheduling information into voice through a preset voice and text bidirectional conversion engine (TTS engine), and connect the telephone of the task performer in a dialing manner to play the voice to the task performer.
After receiving the call, the task performer performs the task according to the content of the call, and feeds back the task performance result to the telephone robot, wherein when the task performance result is fed back, any one of the following modes can be adopted:
in one mode, the task performer feeds back the results of partial performance of the task based on the voice query of the telepresence robot while performing the task
In another mode, after the task performer completes all the tasks, the telephone robot is connected through dialing, and the task performance result is fed back to the telephone robot.
After receiving the task execution result, the telephone robot can convert the voice information fed back by the task executor into characters through a preset Automatic Speech Recognition (ASR) engine to obtain an execution result described by the characters.
The RPA robot 21 receives the execution result fed back by the telephone robot 23. The execution result is recorded in the scheduling system 22, and the execution result is fed back to the conversation robot 20. The conversation robot 20 feeds back the execution result to the dispatcher through the front end notification page, so that the dispatcher confirms the execution result.
It should be understood that the above description is only an example of fig. 2, and is not intended to limit the present application solely.
In this embodiment, by acquiring task information of a task to be scheduled, which is obtained by querying the RPA robot from the task scheduling system, and generating scheduling information according to the task information, and sending the scheduling information to the telephone robot, the telephone robot converts text data used for describing task content carried in the scheduling information into voice to be played to a task performer, and the task performer performs the task according to the voice played by the telephone robot. According to the embodiment, the robot replaces manpower to complete large-scale, repetitive and mechanical task scheduling work, so that labor force optimization is realized, the labor cost is saved, and the task scheduling efficiency is improved.
Fig. 3a is a flowchart of a task scheduling method based on RPA and AI according to an embodiment of the present application, and as shown in fig. 3a, the method includes:
And step 302, generating a conversation number based on the task content in the task information and outputting the conversation number to a dispatcher.
As an example, the scheduling system shown in fig. 2 is still taken as an example. In fig. 2, the conversation robot can output the task content to the dispatcher in the form of voice or text for confirmation based on the front-end notification page. And after confirming that the task information is correct, the dispatcher feeds back an execution instruction to the conversation robot in a voice or text mode. That is, the execution command received by the conversation robot may be a voice command or a text command.
And step 303, receiving an execution instruction fed back by the dispatcher.
After receiving the execution instruction, if the execution instruction is a voice instruction, the conversation robot needs to convert the voice instruction into a text instruction according to a preset engine, analyze the semantics of the text instruction based on a semantic analysis model, verify whether a dispatcher confirms the task content, and if the dispatcher confirms the task content, confirm that the verification is correct. If the execution instruction is a character instruction, the character instruction can be directly analyzed based on the semantic analysis model, and the character instruction is verified based on the analysis result.
And 304, verifying the execution instruction, and generating scheduling information according to the task information after the execution instruction is verified to be correct, wherein the scheduling information comprises text data of task content.
Specifically, fig. 3b is a screenshot of an interaction interface between a dispatcher and a conversation robot according to the embodiment of the present application. As shown in fig. 3b, the task of the task to be scheduled, which is obtained by the RPA robot through querying from the task scheduling system, is "220 KV forest flat line is changed from operation to maintenance". After acquiring the task to be executed, the conversation robot displays the task information of the task on the interactive interface, generates conversation data to the dispatcher, and generates a request for confirming whether the information is wrong on the interactive interface as shown in fig. 3 b. The dispatcher can input an execution instruction of executing the 220KV forest flat line to be changed from operation to maintenance on the interactive interface according to the prompt of the conversation robot. After receiving the execution instruction, the interactive robot verifies the name of the execution instruction, and if the verification is passed, the interactive robot generates scheduling information according to the task information, such as step one and step two shown in fig. 3b, which specifically includes planning operation time, operation units, issuing persons, receiving persons, operation instructions, and the like.
And 305, sending the scheduling information to the telephone robot so that the telephone robot converts the text data in the scheduling information into voice and plays the voice to the task performer, and the task performer performs the task based on the voice.
According to the method and the device, before the scheduling information is generated, man-machine interaction is firstly carried out with the dispatcher, and the scheduling information is generated after the dispatcher confirms that the task content is correct, so that the occurrence of a mis-scheduling event can be avoided, and the accuracy of task scheduling is improved.
Fig. 4 is a schematic structural diagram of a task scheduling device based on RPA and AI according to an embodiment of the present application, where the task scheduling device may be embodied as a conversation robot in the foregoing embodiment, or a part of modules in the conversation robot. As shown in fig. 4, the apparatus 40 includes:
an obtaining module 41, configured to obtain task information of a task to be scheduled, where the task information is obtained by querying from a task scheduling system by an RPA robot, and the task information includes task content;
a generating module 42, configured to generate scheduling information according to the task information, where the scheduling information includes text data of the task content;
a sending module 43, configured to send the scheduling information to a telephone robot, so that the telephone robot converts the text data in the scheduling information into voice and plays the voice to a task performer, so that the task performer performs a task based on the voice.
In an embodiment, the apparatus 40 may further include:
and the output module is used for generating dialogue data based on the task content in the task information and outputting the dialogue data to the dispatcher.
And the first receiving module is used for receiving the execution instruction fed back by the dispatcher.
And the generating module is used for verifying the execution instruction and executing the step of generating the scheduling information according to the task information after the execution instruction is verified to be correct.
In one embodiment, the execution instruction is a voice instruction or a text instruction
In one embodiment, the scheduling information includes identification information of the telephone robot.
In one embodiment, the transmission module is configured to transmit the scheduling information to the RPA robot, so that the RPA robot forwards the scheduling information to the telephony robot according to the identification information of the telephony robot.
In one embodiment, the apparatus 40 may further include:
and the second receiving module is used for receiving an execution result fed back by the RPA robot, wherein the execution result fed back by the RPA robot is fed back to the telephone robot by the task executor after the task is completed, and is forwarded to the RPA robot by the telephone robot.
And the output module is also used for outputting the execution result to a dispatcher for confirmation.
In an embodiment, the forwarding of the execution result to the RPA robot by the telephony robot is to convert voice information fed back by a task performer into text through a preset automatic speech recognition engine ASR after the telephony robot receives the task execution result, so as to obtain an execution result described in text.
In one embodiment, the voice is obtained by converting the text data in the scheduling information through a preset voice and text bidirectional conversion engine TTS by the telephone robot.
The device provided by the embodiment can execute the method of any of the above embodiments, and the execution mode and the beneficial effects thereof are similar and are not described again here.
Fig. 5 is a schematic structural diagram of a conversation robot provided in an embodiment of the present application, which may be understood as the conversation robot in the above embodiment. The conversation robot 50 includes a memory 51 and a processor 52; wherein, the memory 51 is used for storing executable instructions of the processor 52, and when the instructions are executed by the processor 52, the processor 52 executes the method provided by the above method embodiment.
Embodiments of the present application also provide a computer-readable storage medium, in which computer-executable instructions are stored, and when the computer-executable instructions are executed by a processor, the computer-readable storage medium is used to implement the method described in the above method embodiments.
The embodiment of the application provides an electronic device, which comprises a memory and a processor; wherein the memory is used for storing executable instructions of the processor, and when the instructions are executed by the processor, the processor executes the method provided by the method embodiment.
It should be understood that the processor may be a Central Processing Unit (CPU), other general purpose processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, etc. A general purpose processor may be a microprocessor or any conventional processor or the like. It is noted that the processor may be an advanced reduced instruction set machine (ARM) architecture supported processor.
Further, optionally, the memory may include a read-only memory and a random access memory, and may further include a nonvolatile random access memory. The memory may be either volatile memory or nonvolatile memory, or may include both volatile and nonvolatile memory. The non-volatile memory may include a read-only memory (ROM), a Programmable ROM (PROM), an Erasable PROM (EPROM), an electrically Erasable EPROM (EEPROM), or a flash memory. Volatile memory can include Random Access Memory (RAM), which acts as external cache memory. By way of example, and not limitation, many forms of RAM are available. For example, Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), Synchronous Dynamic Random Access Memory (SDRAM), double data rate synchronous SDRAM (DDR SDRAM), Enhanced SDRAM (ESDRAM), synchlink DRAM (SLDRAM), and direct memory bus RAM (DR RAM).
In the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. The procedures or functions according to the present application are generated in whole or in part when the computer program instructions are loaded and executed on a computer. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the application. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present application, "a plurality" means two or more unless specifically limited otherwise.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This application is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
It will be understood that the present disclosure is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.
Claims (12)
1. A task scheduling method based on RPA and AI is characterized by comprising the following steps:
task information of a task to be scheduled, which is obtained by a robot process automation RPA robot through inquiring from a task scheduling system, is obtained, wherein the task information comprises task content;
generating scheduling information according to the task information, wherein the scheduling information comprises text data of the task content;
and sending the scheduling information to a telephone robot so that the telephone robot converts the text data in the scheduling information into voice and plays the voice to a task performer, and the task performer performs a task based on the voice.
2. The method of claim 1, wherein prior to generating scheduling information from the task information, the method further comprises:
generating dialogue data based on the task content in the task information and outputting the dialogue data to a dispatcher;
receiving an execution instruction fed back by the dispatcher;
and verifying the execution instruction, and after the execution instruction is verified to be correct, executing the step of generating the scheduling information according to the task information.
3. The method of claim 2, wherein the execution instruction is a voice instruction or a text instruction.
4. A method according to any of claims 1-3, characterized in that the scheduling information comprises identification information of the telephone robot.
5. The method of claim 4, wherein sending the scheduling information to a telephone robot comprises:
and sending the scheduling information to the RPA robot so that the RPA robot forwards the scheduling information to the telephone robot according to the identification information of the telephone robot.
6. The method of claim 1, wherein after transmitting the scheduling information to the telephone robot, the method further comprises:
receiving an execution result fed back by the RPA robot, and outputting the execution result to a dispatcher for confirmation;
the execution result fed back by the RPA robot is fed back by the task performer after the task performer completes the task and is forwarded to the RPA robot by the telephone robot.
7. The method as claimed in claim 6, wherein the telephone robot forwards the execution result of the RPA robot to the execution result described in text, and the execution result is obtained by converting the voice information fed back by the task performer into text through a preset automatic voice recognition engine ASR after the telephone robot receives the task execution result.
8. The method of claim 1, wherein the voice is obtained by converting the text data in the scheduling information by a telephone robot through a preset speech and text bi-directional conversion engine TTS.
9. A task scheduling device based on RPA and AI, comprising:
the system comprises an acquisition module, a task scheduling module and a scheduling module, wherein the acquisition module is used for acquiring task information of a task to be scheduled, which is obtained by an RPA robot from a task scheduling system through query, and the task information comprises task content;
the generating module is used for generating scheduling information according to the task information, wherein the scheduling information comprises text data of the task content;
and the sending module is used for sending the scheduling information to a telephone robot so that the telephone robot converts the text data in the scheduling information into voice and plays the voice to a task executor, and the task executor executes a task based on the voice.
10. A conversation robot, comprising: a memory and a processor;
wherein the memory is to store executable instructions of the processor, which when executed by the processor, the processor performs the method of any one of claims 1-8.
11. A computer-readable storage medium having computer-executable instructions stored therein, which when executed by a processor, are configured to implement the method of any one of claims 1-8.
12. An electronic device, comprising: a memory and a processor;
wherein the memory is to store executable instructions of the processor, which when executed by the processor, the processor performs the method of any one of claims 1-8.
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| CN114367995A (en) * | 2022-02-17 | 2022-04-19 | 来也科技(北京)有限公司 | High-density unmanned robot control method based on RPA and AI |
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