CN112070487A

CN112070487A - AI-based RPA process generation method, apparatus, device and medium

Info

Publication number: CN112070487A
Application number: CN202010981528.7A
Authority: CN
Inventors: 周津; 张曦; 胡一川; 汪冠春
Original assignee: Beijing Benying Network Technology Co Ltd; Beijing Laiye Network Technology Co Ltd
Current assignee: Beijing Benying Network Technology Co Ltd; Beijing Laiye Network Technology Co Ltd
Priority date: 2019-12-31
Filing date: 2020-09-17
Publication date: 2020-12-11
Anticipated expiration: 2040-09-17
Also published as: CN112070487B; CN112508518A

Abstract

The utility model provides a method, a device, equipment and a medium for generating robot process automation RPA process based on artificial intelligence AI, which relates to the field of AI and RPA, wherein, the method for generating the RPA process based on AI comprises the following steps: acquiring session data; and processing the session data to generate at least one RPA flow. By the method, the process is automatically excavated by depending on a machine, and the process is prevented from being excavated by depending on manpower, so that the process excavation efficiency is improved.

Description

AI-based RPA process generation method, apparatus, device and medium

Cross Reference to Related Applications

The present application claims priority of chinese patent application No. 201911423732.0, entitled "method for generating RPA procedure and corresponding device, computer-readable storage medium", filed by beijing benying network technologies, ltd, 12, 31, 2019.

Technical Field

The present disclosure relates to the field of Artificial Intelligence (AI) and Robot Process Automation (RPA), and more particularly, to a method, an apparatus, a device, and a medium for generating an RPA Process based on AI.

Background

Robot Process Automation (RPA) is a technology that simulates human operations on a computer through specific robot software, automatically executes Process tasks according to rules, and replaces repeated, regular and stable manual operations through robot operations. RPA has a very wide range of applications. Some of the iterative, standardization work may be done by the RPA.

Artificial Intelligence (AI) is a new technology science for researching and developing theories, methods, techniques and application systems for simulating, extending and expanding human Intelligence. Research in the field of artificial intelligence includes robotics, language recognition, image recognition, natural language processing, and expert systems, among others.

In order to assist the manual work through the RPA, the manual processes need to be mined out so that the RPA robot can execute the processes, thereby realizing the manual work. At present, the processes are basically mined manually, however, the manual mining process mode has the problem of low mining efficiency.

Disclosure of Invention

The object of the present disclosure is to solve at least to some extent one of the above-mentioned technical problems.

The disclosure provides a method, a device, equipment and a medium for generating an RPA flow based on AI, so as to realize automatic mining of the flow by means of a machine, avoid mining of the flow by means of manpower, and improve the efficiency of flow mining.

According to an aspect of the present disclosure, there is provided a method for generating an RPA procedure, including: acquiring session data; and processing the session data to generate at least one RPA process.

According to an example of the present disclosure, the method further includes: executing the at least one RPA procedure when it is determined to trigger execution of the at least one RPA procedure.

According to an example of the present disclosure, the processing the session data and generating at least one RPA procedure includes: carrying out session cutting on the session data to generate a plurality of sessions; clustering the plurality of conversations to generate a plurality of conversation classes; determining a recommendation process corresponding to each conversation class; and generating an RPA flow based on the recommended flow.

According to an example of the present disclosure, prior to the clustering the plurality of sessions, the method further comprises: denoising the plurality of sessions.

According to an example of the present disclosure, the determining, for each conversational class, a recommendation process corresponding to the conversational class includes: carrying out similarity calculation on events in each conversation in the conversation class to obtain a similarity calculation result; and determining a recommendation process corresponding to the conversation class based on the similarity calculation result.

According to an example of the present disclosure, the generating an RPA procedure based on the recommended procedure includes: displaying the recommended flow; determining a modification instruction for the recommended procedure; and generating an RPA flow according to the modification instruction.

According to an example of the present disclosure, the displaying the recommendation process includes: generating visual information corresponding to the recommended process; and displaying the visual information; wherein the generating an RPA flow according to the modification instruction includes: modifying the visual information according to the modification instruction; and generating an RPA flow based on the modified visualization information.

According to an example of the present disclosure, the obtaining session data includes: session data is obtained from at least one terminal.

According to another aspect of the present disclosure, there is provided an apparatus for generating an AI-based RPA procedure, including: an acquisition unit configured to acquire session data; and the generating unit is configured to process the session data and generate at least one RPA flow.

According to an example of the present disclosure, the apparatus further includes: an execution unit configured to execute the at least one RPA procedure when it is determined to trigger execution of the at least one RPA procedure.

According to an example of the present disclosure, the generating unit is configured to perform session cutting on the session data, and generate a plurality of sessions; clustering the plurality of conversations to generate a plurality of conversation classes; determining a recommendation process corresponding to each conversation class; and generating an RPA flow based on the recommended flow.

According to an example of the present disclosure, the apparatus further includes: a denoising unit configured to denoise the plurality of sessions before clustering the plurality of sessions.

According to an example of the present disclosure, the generating unit is configured to perform similarity calculation on events in each session in the session class to obtain a similarity calculation result; and determining a recommendation process corresponding to the conversation class based on the similarity calculation result.

According to an example of the present disclosure, wherein the generating unit is configured to display the recommendation flow; determining a modification instruction for the recommended procedure; and generating an RPA flow according to the modification instruction.

According to an example of the present disclosure, the apparatus further includes: a display unit configured to generate visual information corresponding to the recommended procedure; and displaying the visual information; wherein the generation unit is configured to modify the visualization information according to the modification instruction; and generating an RPA flow based on the modified visualization information.

According to an example of the present disclosure, the obtaining unit is configured to obtain session data from at least one terminal.

According to another aspect of the present disclosure, there is provided an AI-based RPA procedure generation apparatus including: a processor; and a memory in which a computer-executable program is stored, wherein the computer-executable program, when executed by the processor, performs the above-described method of generating an AI-based RPA procedure.

According to another aspect of the present disclosure, there is provided a computer-readable storage medium having stored thereon instructions that, when executed by a processor, cause the processor to perform the above-described method of generating an AI-based RPA procedure.

According to the method, the device, the equipment and the medium for generating the RPA flow based on the AI, the acquired session data can be processed to generate the RPA flow, so that the automatic mining of the flow by a machine is realized, the mining of the flow by manpower is avoided, and the efficiency of flow mining is improved.

Drawings

The above and other objects, features and advantages of the present disclosure will become more apparent by describing in more detail embodiments of the present disclosure with reference to the attached drawings. The accompanying drawings are included to provide a further understanding of the embodiments of the disclosure and are incorporated in and constitute a part of this specification, illustrate embodiments of the disclosure and together with the description serve to explain the principles of the disclosure and not to limit the disclosure. In the drawings, like reference numbers generally represent like parts or steps.

FIG. 1 is an architectural diagram of a system in which embodiments of the present disclosure may be applied.

Fig. 2 is a first flowchart of a method for generating an AI-based RPA flow according to an embodiment of the present disclosure.

Fig. 3 is a flowchart illustrating a method for processing session data to generate an RPA flow according to an embodiment of the disclosure.

Fig. 4 is a flowchart illustrating a method for determining a recommendation process corresponding to a conversational class based on a similarity calculation result according to an embodiment of the disclosure.

Fig. 5 is a flow diagram of a method of generating an RPA flow based on a recommended flow according to an embodiment of the present disclosure.

Fig. 6 is a second flowchart illustrating a method for generating an AI-based RPA flow according to an embodiment of the disclosure.

Fig. 7 shows a schematic structural diagram of an apparatus for performing the method shown in fig. 2 according to an embodiment of the present disclosure.

Fig. 8 illustrates a schematic structural diagram of an apparatus for performing the methods illustrated in fig. 2-6 according to an embodiment of the present disclosure.

Fig. 9 shows an architectural schematic of a device according to an embodiment of the disclosure.

Detailed Description

In order to make the objects, technical solutions and advantages of the present disclosure more apparent, example embodiments according to the present disclosure will be described in detail below with reference to the accompanying drawings. In the drawings, like reference numerals refer to like elements throughout. It should be understood that: the embodiments described herein are merely illustrative and should not be construed as limiting the scope of the disclosure.

First, an architecture diagram of a system in which embodiments of the present disclosure may be applied is described with reference to fig. 1. As shown in fig. 1, the system 100 may include a server 110 and a terminal 120. The terminal 120 may operate a client for collecting session data and collect session data through the client. The terminal 120 may then transmit the collected session data to the server 110. Server 110 may process the acquired session data to generate at least one RPA flow. By the method, the process excavation by the machine is realized, the process excavation by manpower is avoided, and the process excavation efficiency is improved.

Further, in the present disclosure, the terminal 120 may be an electronic device such as a smartphone, a tablet, a laptop portable computer, a desktop computer, a customer service terminal, and the like. The server 110 may be a device that establishes a communication link with the terminal 120 and generates an RPA procedure.

Further, it should be appreciated that although one server and one terminal are shown in FIG. 1, this is merely illustrative and the system shown in FIG. 1 may include multiple servers and/or multiple terminals.

A method of generating an AI-based RPA flow according to an embodiment of the present disclosure will be described below with reference to fig. 2. Fig. 2 is a flow diagram of a method 200 for generating an AI-based RPA flow according to an embodiment of the disclosure. Method 200 may be performed by the server of fig. 1.

As shown in fig. 2, the method 200 may include the steps of:

in step S201, session data is acquired.

For example, in step S201, the server may acquire session data from at least one terminal. In addition, the terminal can send the session data to the server in real time after collecting the session data. Alternatively, the terminal may transmit the session data to the server in non-real time after collecting the session data. For example, after the session data is collected, the terminal may store the session data in a storage module (e.g., a memory) of the terminal, and then send the session data to the server after a preset time.

According to an example of the present disclosure, the session data in step S201 may include start-stop identification information of each session. The start-stop identification information for each session may be, for example, a start identifier and/or an end identifier for the session.

According to another example of the present disclosure, the session data in step S201 may further include identification information of each session. The Identification information of each session may be, for example, a session Identification (ID).

According to another example of the present disclosure, the session data in step S201 may further include a plurality of events and timestamps corresponding to the respective events. For example, the terminal may determine timestamps corresponding to the respective events according to a conventional timestamp determination method, and transmit the timestamps corresponding to the respective events to the server, so that the session data acquired by the server includes the timestamps corresponding to the respective events.

According to another example of the present disclosure, the session data in step S201 may further include at least one of a page Uniform Resource Locator (URL), a page title, a page control element attribute, and the like corresponding to each event.

Then, in step S202, the session data is processed to generate at least one RPA flow.

It should be understood that the session data may include a plurality of sessions, categories of the plurality of sessions may be different, and sessions of different categories may correspond to different RPA flows, and therefore, in one possible implementation manner of the embodiment of the present disclosure, in order to improve reliability of the generated RPA flows, the plurality of sessions in the session data may be clustered to obtain a plurality of session classes, and then, for each session class, a recommendation flow corresponding to the session class may be determined, and at least one RPA flow may be generated based on the recommendation flow. The recommendation process may be a standard process or a formatting process corresponding to the session class.

In another possible implementation manner of the embodiment of the present disclosure, the session data may include a plurality of sessions, a vector corresponding to each session of the plurality of sessions may be determined, and then, at least one RPA procedure may be generated according to the vectors corresponding to the plurality of sessions.

In the present disclosure, for each of a plurality of sessions, a vector corresponding to the session may be determined according to a natural language processing method. Alternatively, in order to improve the accuracy of the vector calculation result corresponding to each session, the vector corresponding to each session may also be determined according to a deep learning technique and a machine learning method, which is not limited in this disclosure. For example, each session can be represented as a vector according to the mechanisms of Word2Vec and Glove in the natural language processing based on the weak supervised learning. It should be appreciated that the penalty function used in machine learning may also be optimized to improve the accuracy of vectoring events.

It should be noted that the above is only an exemplary embodiment, but the present disclosure is not limited thereto, and may also include other vector calculation methods known in the art as long as a vector corresponding to a session is obtained.

In the present disclosure, after determining vectors corresponding to the plurality of sessions, at least one RPA procedure may be generated from the vectors corresponding to the plurality of sessions. For example, a plurality of sessions may be clustered according to vectors corresponding to the plurality of sessions to generate a plurality of session classes, and then, for each session class, a recommendation flow corresponding to the session class may be determined, and at least one RPA flow may be generated based on the recommendation flow.

In yet another possible implementation manner of the embodiment of the present disclosure, the session data may include a plurality of sessions, each session may include a plurality of events, and a vector corresponding to each event in the session may be determined for each session, so that the vector corresponding to each session may be determined according to the vector corresponding to each event, and then, at least one RPA flow may be generated according to the vectors corresponding to the plurality of sessions. For example, a plurality of sessions may be clustered according to vectors corresponding to the plurality of sessions to generate a plurality of session classes, and then, for each session class, a recommendation flow corresponding to the session class may be determined, and at least one RPA flow may be generated based on the recommendation flow.

How the present disclosure clusters a plurality of sessions in session data, generates a plurality of conversational classes, and determines, for each conversational class, a recommendation flow corresponding to the conversational class to generate an RPA flow based on the recommendation flow will be described below with reference to fig. 3. Fig. 3 is a flow diagram illustrating a method 300 for processing session data to generate an RPA flow according to an embodiment of the disclosure. As shown in fig. 3, step S202 may include the following four sub-steps, which are step S301, step S302, step S303 and step S304 in fig. 3, respectively.

First, in step S301, session segmentation may be performed on session data to generate a plurality of sessions.

In the present disclosure, the clock of the terminal and the clock of the server may or may not be synchronized. When the clock of the terminal and the clock of the server are synchronized, the server does not need to align the timestamps of the respective events in the session data before session splitting the session data.

However, when the clock of the terminal and the clock of the server are not synchronized, the server needs to align the time stamps of the respective events in the session data before session division is performed on the session data. For example, the server may align timestamps of various events in the session data based on the terminal's clock and its own clock. Specifically, the server may determine a time difference between the terminal's clock and its own clock, and then align the timestamps of the respective events in the session data according to the time difference.

In addition, in step S301, the server may perform session segmentation on the session data according to the start/stop identification information of each session in the session data. For example, in an example where the start-stop identification information of the session is a start identifier of the session, the server may cut the session data according to the start identifier of each session. Specifically, the server may treat an event between two adjacent start identifiers as an event included in one session, thereby implementing session segmentation. For another example, in an example where the start-stop identification information of the session is an end identifier of the session, the server may cut the session data according to the end identifier of each session. Specifically, the server may treat an event between two adjacent end identifiers as an event included in one session, thereby implementing session segmentation. For another example, in an example where the start-stop identification information of the session is a start identifier and an end identifier of the session, the server may cut the session data according to the start identifier or the end identifier of each session.

In the present disclosure, a session may be a series of operations performed from a start page access to an end page access, and each operation in the series of operations may be referred to as an event. The event described herein may be an operation on a control (e.g., button, input box) in a page, such as clicking a button, entering text in an input box, etc. In other words, each generated session may include a plurality of events.

The following describes the sessions and events by taking an invoice processing flow as an example. For example, the invoice processing flow may include the following steps: (1) opening an invoice e-mail for the supplier; (2) creating a work item in the invoice management software; (3) checking whether the name of the supplier is correct; (4) checking whether the amount is correct; (5) if the tax is correct, calculating the tax; (6) inputting a name, an amount and a tax of a supplier; (7) the work item is closed. The invoice processing flow is a session, which includes seven events, which are the above steps (1) to (7), respectively.

In this disclosure, a session may also be referred to as a "flow". For convenience of use, sessions and flows may be used interchangeably hereinafter.

Then, in step S302, a plurality of sessions may be clustered, generating a plurality of session classes.

According to one example of the present disclosure, the server may denoise the plurality of sessions before clustering the plurality of sessions. For example, redundant events in multiple sessions can be considered as noise data, and thus, the server can remove the redundant events in the multiple sessions, thereby enabling denoising of the multiple sessions. In particular, when an event in the plurality of sessions occurs only in a single session in the plurality of sessions, the event may be considered a redundant event. Alternatively, the server may provide a redundant event repository, and when an event in the plurality of sessions matches an event in the redundant event repository, the event may be considered a redundant event.

In this example, the server may obtain an active session by de-noising a plurality of sessions. The server may then cluster the active sessions based on the vectors corresponding to the active sessions, thereby generating a plurality of conversational classes.

For example, the server may cluster the active sessions according to a conventional clustering algorithm to generate a plurality of conversational classes. Specifically, the input to the clustering algorithm may be a vector corresponding to an active conversation, and the output of the clustering algorithm may be a plurality of conversational classes. The aggregation algorithm described herein may be a K-Means (K-Means) clustering algorithm, a mean shift clustering algorithm, a density-based clustering method, a maximum expectation clustering algorithm with a Gaussian mixture model, a coacervation hierarchical clustering algorithm, a Graph Community Detection (Graph Community Detection) algorithm, and the like.

Further, in this example, the generated plurality of conversational classes may be conversational classes for a plurality of services. Or a conversational class of a plurality of sub-services of a service, for example, the generated conversational classes may include a conversational class relating to invoice process flow, a conversational class relating to personnel process flow, a conversational class relating to telephone customer service, and so on. Or the ticket booking process conversation class and the ticket refunding process conversation class in the ticket service telephone service.

Further, in this example, each conversational class may include at least one conversation. Furthermore, the individual sessions in each conversational class are related to the same service or sub-service. For example, when the conversation class is a conversation class related to an invoice processing flow, each conversation in the conversation class is related to the invoice processing flow.

Then, in step S303, for each conversational class, a recommendation flow corresponding to the conversational class may be determined. The recommended process may be a standard process or a formatted process corresponding to the conversational class.

According to an example of the present disclosure, the server may perform similarity calculation on events included in respective sessions in a session class to obtain a similarity calculation result. The similarity calculation result may include a similarity between any two events in the conversational class. For example, the server may calculate the similarity between any two events in the conversational class according to a conventional similarity calculation method.

Then, the server may determine a recommendation flow corresponding to the conversational class based on the similarity calculation result. A specific process of how the server determines the recommendation flow corresponding to the conversational class based on the similarity calculation result will be described in detail below with reference to fig. 4. Fig. 4 is a flowchart illustrating a method 400 for determining a recommendation process corresponding to a conversational class based on a similarity calculation result according to an embodiment of the disclosure.

As shown in fig. 4, in step S401, the server may determine a current event from events included in each session in one session class.

For example, the server may select an event from the events included in each session in the session class as the current event according to the start identifier of each session in the session class. Since the sessions in the conversational class belong to the same conversational class, it can be assumed that the first event in each session in the conversational class is the same. Under this assumption, the server may take the first event after the start identifier of any session in the session class as the current event.

Then, in step S402, the server may perform at least one loop process based on the similarity calculation result, wherein the loop process includes: judging whether a next event exists in other events except the current event in a plurality of events included in each session in the session class, wherein the similarity between the next event and the current event exceeds a threshold value; stopping executing the cyclic process when the next event does not exist in the other events; and when the next event exists in the other events, determining the next event from the other events, taking the next event as the current event in the next cycle process, and executing the next cycle process.

For example, the server may search for an event whose similarity to the current event exceeds a threshold from the similarity calculation result, and regard the searched event as a next event with respect to the current event.

In addition, the value of the threshold here may be set to a larger value, so that the similarity between the current event and the next event is higher.

In this disclosure, in a first loop process, the server may determine whether a next event exists in other events except the current event among a plurality of events included in each session in the session class, and when the next event does not exist in the other events, the server may determine a recommendation process corresponding to the session class according to the current event. That is, the recommendation flow corresponding to the conversation class includes only the current event.

In addition, when there is a next event among the other events, the server may determine the next event from among the other events, regard the next event as a current event in a next loop process, and execute the next loop process. That is to say, the server may repeatedly perform the step of determining whether there is a next event in other events, where the similarity of the next event to the current event adopted in the current cycle process exceeds the threshold, and when there is no next event in the other events, determining the recommended flow corresponding to the session class according to the current event adopted in the current cycle process, and when there is the next event in the other events, adopting the next event as the current event in the next cycle process, and executing the next cycle process until there is no next event in the other events, and stopping executing the cycle process.

Then, in step S403, the server may determine a recommended procedure corresponding to the conversation class according to the current event adopted in the last loop process.

In this disclosure, the server may determine the recommended flow corresponding to the conversation class according to the current event adopted in the last cycle process. Specifically, the server may determine the number of times of executing the loop process, determine the recommended flow corresponding to the session class according to the current event if the number of times of executing the loop process is one, and determine the recommended flow corresponding to the session class according to the current event adopted in the last loop process and the current event adopted in each previous loop process if the number of times of executing the loop process is greater than one, that is, the server may determine the recommended flow corresponding to the session class according to the current event adopted in each loop process. For example, the recommendation flow corresponding to the conversational class may include the current event taken by each looping process.

A specific example corresponding to the method 400 is given below. Assume that a session class includes three sessions and the first event of the three sessions is the same, and the first session includes three events, respectively denoted as P₁、P₁₂、P₁₃And the second session includes three events,are respectively represented as P₁、P₂₂、P₂₃The third session includes four events, respectively denoted as P₁、P₃₂、P₃₃、P₃₄. According to the method 400, in step S401, an event P may be determined₁Is the current event. Then, in step S402, in the first loop, the P-division included in the conversational class may be determined based on the similarity calculation result₁Remaining events (P) other than₁₂、P₁₃、P₂₂、P₂₃、P₃₂、P₃₃、P₃₄) Whether there is a next event. When there is a next event, the next event can be selected from the remaining events (P)₁₂、P₁₃、P₂₂、P₂₃、P₃₂、P₃₃、P₃₄) In determining the next event, e.g. determining the next event as event P₂₂. Then, the event P can be₂₂As the current event adopted for the second cycle process, and performing the second cycle process, i.e., repeating the above-described step S402, it can be determined with respect to the event P₂₂The next event of (2) is an event P₂₃And will event P₂₃As the current event employed by the third cyclic process. Then, by repeating the above step S402, it can be determined that the event P is related to₂₃The next event of (2) is an event P₃₄And will event P₃₄As the current event employed by the fourth cyclic process. Then, the above step S402 is repeated, and the event P cannot be found₃₄The next event of (2). Then, in step S403, the current event, i.e., event P, which may be taken according to the fourth round of the loop process₃₄And the current event adopted by the first cyclic process, i.e. event P₁The current event adopted in the second cycle, i.e. event P₂₂The current event adopted in the third cycle process, namely the event P₂₃To determine a recommendation process corresponding to the conversation class, which may include an event P₁Event P₂₂Event P₂₃And event P₃₄。

Further, according to another example of the present disclosure, the server may mine a session having a session score higher than a first threshold from a session class, and determine a recommendation process corresponding to the session class according to the session having the mined session score higher than the first threshold.

Specifically, first, the server may mine sessions from each conversational class that occur with a frequency above a second threshold. For example, the server may count the frequency of occurrence of each session in the class of sessions and then select sessions from the class of sessions that occur with a frequency above the second threshold. For another example, the server may mine sessions having an occurrence frequency higher than the second threshold from each conversational class according to a conventional Recurrent Neural Network (RNN) deep learning. The RNN deep learning described herein may be a conventional Long Short-Term Memory network (LSTM) model or the like.

In examples where the server mines sessions from each conversational class that occur with a frequency above a second threshold according to the RNN, the server may determine the network characteristics of the RNN according to the second threshold. The network characteristics of the RNN may include one or more of network parameters, number of network nodes, and the like.

The server may then mine sessions with session scores above the first threshold from among sessions mined with a frequency of occurrence above a second threshold. For example, for sessions mined to occur with a frequency above the second threshold, the server may determine a session score for each of those sessions and then select a session from those sessions having a session score above the first threshold.

In the present disclosure, the session score may be a function of at least one of a priority of the session, an input-output ratio of the session, and the like. For example, the session score may be a priority of the session or an input-output ratio of the session. As another example, the session score may be a weighted average of the priority of the session and the input-output ratio of the session.

Further, the input-output ratio of the session may be determined according to at least one of the number of events included in the session, the frequency of occurrence of the session, the time taken for the session, and the like. For example, the input-output ratio of a session may be proportional to at least one of the number of events included in the session, the frequency of occurrence of the session, and the time spent by the session. For example, the input-output ratio of a session may be the product of the number of events that the session includes, the frequency of occurrence of the session, and the time spent by the session. The server may then treat at least one of the sessions mined for which the session score is above the first threshold as a recommendation flow corresponding to the session class. For example, when the number of sessions for which the mined session score is higher than the first threshold is one, the session for which the mined session score is higher than the first threshold may be regarded as the recommendation flow corresponding to the session class. When the number of sessions with the mined session score higher than the first threshold is plural, one session may be selected as the recommendation flow corresponding to the session class from among the sessions with the mined session score higher than the first threshold.

Returning to fig. 3, in step S304, an RPA flow may be generated based on the recommended flow.

The specific flow of the server generating the RPA flow based on the recommended flow will be specifically described below with reference to fig. 5. Fig. 5 is a flowchart illustrating a method 500 for generating an RPA flow based on a recommended flow by a server according to an embodiment of the disclosure.

As shown in fig. 5, in step S501, a recommendation flow may be displayed. For example, the server may generate visualization information corresponding to the recommended procedure and display the visualization information. The visualization information described herein may be at least one of an image (e.g., a flowchart), an audio file, a video file, etc., corresponding to the recommended procedure.

Then, in step S502, a modification instruction for the recommended flow may be determined. For example, the server may receive a modification instruction for the recommended procedure through an input device (e.g., mouse, keyboard, etc.) of the server.

Then, in step S503, an RPA flow may be generated according to the modification instruction.

Specifically, the server may modify the visualization information according to the modification instruction to obtain the modified visualization information. For example, in an example where the visualization information is a flowchart, the server may delete, modify, or adjust certain steps in the flowchart according to the modification instruction to obtain a modified flowchart.

The server may then generate an RPA procedure based on the modified visualization information. For example, in an example where the visualization information is a flow diagram, the server may generate an RPA flow based on the modified flow diagram.

Further, referring to fig. 6, on the basis of the embodiment shown in fig. 2, after step S202, the method 200 may further include step S203 and step S204.

Specifically, in step S203, it may be determined whether execution of the generated at least one RPA procedure is triggered. For example, a flow label corresponding to the RPA flow may be generated. It may then be determined whether an operation (e.g., a click) has been performed on the flow label to determine whether to trigger execution of the generated RPA flow.

When it is determined that the at least one RPA procedure is triggered to be executed, step S204 may be performed, that is, the at least one RPA procedure is executed. When it is determined that the at least one RPA procedure is not triggered to be performed, step S204, that is, the at least one RPA procedure is not performed.

The specific processes for performing the method 200 by the server are described above. The method 200 may also be performed by the terminal of fig. 1, according to another example of the present disclosure. It should be appreciated that the process of performing the method 200 by the terminal is similar to the process of performing the method 200 by the server above, with the main differences described below.

Specifically, in the example of the method 200 executed by the terminal, in step S201, the terminal may collect session data to obtain the session data, and may also receive session data from other terminals to obtain the session data.

Furthermore, when the terminal collects session data to acquire the session data, the terminal does not need to align the time stamps of the respective events in the session data before performing session division (i.e., step S301) on the session data. When the terminal receives session data from other terminals to acquire the session data, the terminal may need to align time stamps of respective events in the session data before performing session segmentation on the session data (i.e., step S301).

According to the generation method disclosed by the embodiment of the invention, the acquired session data can be processed to generate the RPA flow, so that the automatic mining flow depending on a machine is realized, the mining flow depending on manual work is avoided, and the efficiency of flow mining is further improved.

Hereinafter, an apparatus corresponding to the method illustrated in fig. 2 according to an embodiment of the present disclosure is described with reference to fig. 7. Fig. 7 shows a schematic block diagram of an apparatus 600 for performing the method shown in fig. 2 according to an embodiment of the present disclosure. The apparatus 600 may be the server 110 in fig. 1. Alternatively, the apparatus 600 may also be the terminal 120 in fig. 1. Since the function of the apparatus 600 is the same as the details of the method described above with reference to fig. 2, a detailed description of the same is omitted here for the sake of simplicity. As shown in fig. 7, the apparatus 600 includes: an acquisition unit 610 configured to acquire session data; a generating unit 620 configured to process the session data and generate at least one RPA flow. The apparatus 600 may include other components in addition to the two units, however, since these components are not related to the contents of the embodiments of the present disclosure, illustration and description thereof are omitted herein.

For example, the acquisition unit 610 may acquire session data from at least one terminal. In addition, the terminal can send the session data to the server in real time after collecting the session data. Alternatively, the terminal may transmit the session data to the server in non-real time after collecting the session data. For example, after the session data is collected, the terminal may store the session data in a storage module (e.g., a memory) of the terminal, and then send the session data to the server after a preset time.

According to one example of the present disclosure, the session data may include start-stop identification information of the respective sessions. The start-stop identification information for each session may be, for example, a start identifier and/or an end identifier for the session.

According to another example of the present disclosure, the session data may further include identification information of the respective sessions. The Identification information of each session may be, for example, a session Identification (ID).

According to another example of the present disclosure, the session data may further include a plurality of events and timestamps corresponding to the respective events. For example, the terminal may determine timestamps corresponding to the respective events according to a conventional timestamp determination method, and transmit the timestamps corresponding to the respective events to the server, so that the session data acquired by the server includes the timestamps corresponding to the respective events.

According to another example of the present disclosure, the session data may further include at least one of a Uniform Resource Locator (URL), a page title, a page control element attribute, and the like corresponding to each event.

A specific procedure of processing the session data by the generation unit 620 to generate the RPA flow will be described below.

First, the generation unit 620 may perform session segmentation on the session data to generate a plurality of sessions.

In the present disclosure, the clock of the terminal and the clock of the server may or may not be synchronized. When the clock of the terminal and the clock of the server are synchronized, the generation unit 620 does not need to align the time stamps of the respective events in the session data before session division is performed on the session data.

However, when the clock of the terminal and the clock of the server are not synchronized, the generation unit 620 needs to align the time stamps of the respective events in the session data before session division is performed on the session data. For example, the generating unit 620 may align the time stamps of the respective events in the session data based on the clock of the terminal and the clock itself. Specifically, the generation unit 620 may determine a time difference between the clock of the terminal and the clock itself, and then align the time stamps of the respective events in the session data according to the time difference.

In addition, the generating unit 620 may perform session segmentation on the session data according to the start-stop identification information of each session in the session data. For example, in an example in which the start-stop identification information of the session is a start identifier of the session, the generating unit 620 may cut the session data according to the start identifier of each session. Specifically, the generation unit 620 may treat an event between two adjacent start identifiers as an event included in one session, thereby implementing session segmentation. For another example, in an example where the start-stop identification information of the session is an end identifier of the session, the generating unit 620 may cut the session data according to the end identifier of each session. Specifically, the generation unit 620 may treat an event between two adjacent end identifiers as an event included in one session, thereby implementing session segmentation. For another example, in an example where the start-stop identification information of the session is a start identifier and an end identifier of the session, the generating unit 620 may cut the session data according to the start identifier or the end identifier of each session.

According to an example of the present disclosure, referring to fig. 8, on the basis of the embodiment shown in fig. 7, the apparatus 600 may further include a denoising unit 630 configured to denoise the plurality of sessions before clustering the plurality of sessions. For example, redundant events in multiple sessions can be regarded as noise data, and thus, the denoising unit 630 can remove the redundant events in the multiple sessions, thereby implementing denoising of the multiple sessions. In particular, when an event in the plurality of sessions occurs only in a single session in the plurality of sessions, the event may be considered a redundant event. Alternatively, the server may provide a redundant event repository, and when an event in the plurality of sessions matches an event in the redundant event repository, the event may be considered a redundant event.

In this example, the denoising unit 630 may obtain an effective conversation by denoising a plurality of conversations. Then, the generating unit 620 may cluster the active sessions based on the vectors corresponding to the active sessions, thereby generating a plurality of conversational classes.

For example, the generating unit 620 may perform clustering processing on the active sessions according to a conventional clustering algorithm to generate a plurality of conversational classes. Specifically, the input to the clustering algorithm may be a vector corresponding to an active conversation, and the output of the clustering algorithm may be a plurality of conversational classes. The aggregation algorithm described herein may be a K-Means (K-Means) clustering algorithm, a mean shift clustering algorithm, a density-based clustering method, a maximum expectation clustering algorithm with a Gaussian mixture model, a coacervation hierarchical clustering algorithm, a Graph Community Detection (Graph Community Detection) algorithm, and the like.

Further, in this example, the generated plurality of conversational classes may be conversational classes for a plurality of services or a plurality of sub-services under one service. For example, the generated plurality of conversation classes may include a conversation class related to invoice process flows, a conversation class related to industrial pipelines, a conversation class related to personnel process flows, a conversation class related to telephone customer services, and the like. Or the ticket booking process conversation class and the ticket refunding process conversation class in the ticket service telephone service.

Further, in this example, each conversational class may include at least one conversation. Furthermore, the individual sessions in each conversational class are related to the same service or sub-service. For example, when the conversation class is a conversation class related to an invoice processing flow, each conversation in the conversation class is related to the invoice processing flow. For another example, when the conversation class is about a booking flow conversation class, each conversation in the conversation class is related to an invoice processing flow.

Further, in the present disclosure, the recommendation flow may be a standard flow or a formatted flow corresponding to the conversation class.

According to an example of the present disclosure, the generating unit 620 may perform similarity calculation on events included in respective sessions in a session class to obtain a similarity calculation result. The similarity calculation result may include a similarity between any two events in the conversational class. For example, the generating unit 620 may calculate the similarity between any two events in the conversational class according to a conventional similarity calculation method.

Then, the generating unit 620 may determine a recommendation flow corresponding to the conversation class based on the similarity calculation result. A specific procedure of how the generation unit 620 determines the recommendation flow corresponding to the conversation class based on the similarity calculation result will be described in detail below.

First, the generating unit 620 may determine a current event from events included in each session in one session class. For example, the generating unit 620 may select one event from the events included in each session in the session class as the current event according to the start identifier of each session in the session class. Since the sessions in the conversational class belong to the same conversational class, it can be assumed that the first event in each session in the conversational class is the same. Under this assumption, the generating unit 620 may take the first event after the start identifier of any session in the session class as the current event.

Then, the generating unit 620 may perform at least one loop process based on the similarity calculation result, wherein the loop process includes: judging whether a next event exists in other events except the current event in a plurality of events included in each session in the session class, wherein the similarity between the next event and the current event exceeds a threshold value; stopping executing the cyclic process when the next event does not exist in the other events; and when the next event exists in the other events, determining the next event from the other events, taking the next event as the current event in the next cycle process, and executing the next cycle process.

For example, the generation unit 620 may search for an event whose similarity with the current event exceeds a threshold from the similarity calculation result, and regard the searched event as a next event with respect to the current event.

In this disclosure, in a first loop process, the generating unit 620 may determine whether a next event exists in other events except for the current event in the plurality of events included in each session in the session class, and when the next event does not exist in the other events, the generating unit 620 may determine a recommendation flow corresponding to the session class according to the current event. That is, the recommendation flow corresponding to the conversation class includes only the current event.

Further, when there is a next event among the other events, the generating unit 620 may determine the next event from the other events, regard the next event as a current event in a next loop process, and execute the next loop process. That is to say, the generating unit 620 may repeatedly perform the step of determining whether there is a next event in other events, where the similarity of the next event with the current event adopted in the current cycle process exceeds the threshold, and when there is no next event in the other events, determining the recommended flow corresponding to the session class according to the current event adopted in the current cycle process, and when there is the next event in the other events, adopting the next event as the current event in the next cycle process, and executing the next cycle process until there is no next event in the other events, and stopping executing the cycle process.

Then, the generating unit 620 may determine a recommended procedure corresponding to the conversation class according to the current event adopted in the last loop process.

In this disclosure, the generating unit 620 may determine the recommended flow corresponding to the conversation class according to the current event adopted in the last cycle process. Specifically, the generating unit 620 may determine the number of times of executing the loop process, determine the recommended flow corresponding to the session class according to the current event if the number of times of executing the loop process is one, and determine the recommended flow corresponding to the session class according to the current event adopted in the last loop process and the current event adopted in each previous loop process if the number of times of executing the loop process is greater than one, that is, the generating unit 620 may determine the recommended flow corresponding to the session class according to the current event adopted in each loop process. For example, the recommendation flow corresponding to the conversational class may include the current event taken by each looping process.

Further, according to another example of the present disclosure, the generating unit 620 may mine a session having a session score higher than a first threshold from one session class, and determine a recommendation flow corresponding to the session class according to the session having the mined session score higher than the first threshold.

Specifically, first, the generation unit 620 may mine sessions whose occurrence frequency is higher than the second threshold from each of the conversational classes. For example, the generating unit 620 may count the occurrence frequency of each session in the session class, and then select a session from the session class whose occurrence frequency is higher than the second threshold. For another example, the generating unit 620 may mine sessions from each conversational class that occur with a frequency above the second threshold according to conventional RNN deep learning. The RNN deep learning described herein may be a conventional LSTM model or the like.

In examples where the generation unit 620 mines sessions from each conversational class that occur with a frequency above the second threshold according to the RNN, the generation unit 620 may determine the network characteristics of the RNN according to the second threshold. The network characteristics of the RNN may include one or more of network parameters, number of network nodes, and the like.

Then, the generating unit 620 may mine sessions having a session score higher than the first threshold from among the mined sessions having the occurrence frequency higher than the second threshold. For example, for sessions mined with a frequency of occurrence above the second threshold, the generation unit 620 may determine a session score for each of the sessions and then select a session from the sessions with a session score above the first threshold.

Further, the input-output ratio of the session may be determined according to at least one of the number of events included in the session, the frequency of occurrence of the session, the time taken for the session, and the like. For example, the input-output ratio of a session may be proportional to at least one of the number of events included in the session, the frequency of occurrence of the session, and the time spent by the session. For example, the input-output ratio of a session may be the product of the number of events that the session includes, the frequency of occurrence of the session, and the time spent by the session.

Then, the generating unit 620 may regard at least one of the sessions with the mined session score higher than the first threshold as the recommendation flow corresponding to the session class. For example, when the number of sessions for which the mined session score is higher than the first threshold is one, the session for which the mined session score is higher than the first threshold may be regarded as the recommendation flow corresponding to the session class. When the number of sessions with the mined session score higher than the first threshold is plural, one session may be selected as the recommendation flow corresponding to the session class from among the sessions with the mined session score higher than the first threshold.

The specific flow of the generation unit 620 generating the RPA flow based on the recommended flow will be specifically described below.

Referring first to fig. 8, on the basis of the embodiment shown in fig. 7, the apparatus 600 may further include a display unit 640 configured to display a recommendation flow. For example, the display unit 640 may generate visualized information corresponding to the recommended procedure and display the visualized information. The visualization information described herein may be at least one of an image (e.g., a flowchart), an audio file, a video file, etc., corresponding to the recommended procedure.

The generation unit 620 may then determine a modification instruction for the recommended flow. For example, the generation unit 620 may receive a modification instruction for the recommended procedure through an input device (e.g., a mouse, a keyboard, etc.) of the server.

The generation unit 620 may then generate an RPA flow according to the modification instruction.

In particular, the generation unit 620 may modify the visualization information according to the modification instruction to obtain the modified visualization information. For example, in the example where the visualized information is a flowchart, the generating unit 620 may delete, modify or adjust some steps in the flowchart according to the modification instruction to obtain a modified flowchart.

The generating unit 620 may then generate an RPA procedure based on the modified visualization information. For example, in an example where the visualization information is a flowchart, the generating unit 620 may generate the RPA flow based on the modified flowchart.

Further, referring to fig. 8, on the basis of the embodiment shown in fig. 7, the apparatus 600 may further include an executing unit 650 configured to execute the at least one RPA procedure when it is determined that the execution of the at least one RPA procedure is triggered.

In particular, the execution unit 650 may determine whether to trigger execution of the generated at least one RPA flow. For example, a flow label corresponding to the RPA flow may be generated. It may then be determined whether an operation (e.g., a click) has been performed on the flow label to determine whether to trigger execution of the generated RPA flow.

When determining to trigger execution of the at least one RPA procedure, the execution unit 650 may execute the at least one RPA procedure. When it is determined that the at least one RPA procedure is not triggered to be performed, the performing unit 650 may not perform the at least one RPA procedure.

According to the generation device disclosed by the embodiment of the disclosure, the acquired session data can be processed to generate the RPA flow, so that the flow is mined by a machine, the flow is prevented from being mined manually, and the efficiency of flow mining is improved.

Furthermore, devices (e.g., servers, terminals, etc.) according to embodiments of the present disclosure may also be implemented by way of the architecture of a computing device shown in fig. 9. Fig. 9 illustrates an architecture of the computing device. As shown in fig. 9, computing device 700 may include a bus 710, one or more CPUs 720, a Read Only Memory (ROM)730, a Random Access Memory (RAM)740, a communication port 750 to connect to a network, input/output components 760, a hard disk 770, and the like. Storage devices in the computing device 700, such as the ROM 730 or the hard disk 770, may store various data or files used in computer processing and/or communications as well as program instructions executed by the CPU. Computing device 700 may also include a user interface 780. Of course, the architecture shown in FIG. 9 is merely exemplary, and one or more components of the computing device shown in FIG. 9 may be omitted when implementing different devices, as desired.

Embodiments of the present disclosure may also be implemented as a computer-readable storage medium. A computer readable storage medium according to an embodiment of the present disclosure has computer readable instructions stored thereon. The computer readable instructions, when executed by a processor, may perform a method according to embodiments of the present disclosure described with reference to the above figures. The computer-readable storage medium includes, but is not limited to, volatile memory and/or non-volatile memory, for example. The volatile memory may include, for example, Random Access Memory (RAM), cache memory (cache), and/or the like. The non-volatile memory may include, for example, Read Only Memory (ROM), hard disk, flash memory, etc.

Those skilled in the art will appreciate that the disclosure of the present disclosure is susceptible to numerous variations and modifications. For example, the various devices or components described above may be implemented in hardware, or may be implemented in software, firmware, or a combination of some or all of the three.

Furthermore, as used in this disclosure and in the claims, the terms "a," "an," "the," and/or "the" are not intended to be inclusive in the singular, but rather are inclusive in the plural, unless the context clearly dictates otherwise. The use of "first," "second," and similar terms in this disclosure is not intended to indicate any order, quantity, or importance, but rather is used to distinguish one element from another. Likewise, the word "comprising" or "comprises", and the like, means that the element or item listed before the word covers the element or item listed after the word and its equivalents, but does not exclude other elements or items. The terms "connected" or "coupled" and the like are not restricted to physical or mechanical connections, but may include electrical connections, whether direct or indirect.

Furthermore, flow charts are used in this disclosure to illustrate operations performed by systems according to embodiments of the present disclosure. It should be understood that the preceding or following operations are not necessarily performed in the exact order in which they are performed. Rather, various steps may be processed in reverse order or simultaneously. Meanwhile, other operations may be added to the processes, or a certain step or several steps of operations may be removed from the processes.

Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.

While the present disclosure has been described in detail above, it will be apparent to those skilled in the art that the present disclosure is not limited to the embodiments described in the present specification. The present disclosure can be implemented as modifications and variations without departing from the spirit and scope of the present disclosure defined by the claims. Accordingly, the description of the present specification is for the purpose of illustration and is not intended to be in any way limiting of the present disclosure.

Claims

1. A method for generating an automatic RPA flow of a robot flow based on artificial intelligence AI comprises the following steps:

acquiring session data; and

and processing the session data to generate at least one RPA flow.

2. The method of claim 1, further comprising:

executing the at least one RPA procedure when it is determined to trigger execution of the at least one RPA procedure.

3. The method of claim 1, wherein said processing said session data to generate at least one RPA flow comprises:

carrying out session cutting on the session data to generate a plurality of sessions;

clustering the plurality of conversations to generate a plurality of conversation classes;

determining a recommendation process corresponding to each conversation class; and

and generating an RPA flow based on the recommended flow.

4. The method of claim 3, prior to said clustering said plurality of sessions, said method further comprising:

denoising the plurality of sessions.

5. The method of claim 3, wherein the determining, for each conversational class, a recommendation flow corresponding to the conversational class comprises:

carrying out similarity calculation on events in each conversation in the conversation class to obtain a similarity calculation result; and

and determining a recommendation process corresponding to the conversation class based on the similarity calculation result.

6. The method of any of claims 3 to 5, wherein said generating an RPA procedure based on said recommended procedure comprises:

displaying the recommended flow;

determining a modification instruction for the recommended procedure; and

and generating an RPA flow according to the modification instruction.

7. The method of claim 6, wherein the first and second light sources are selected from the group consisting of a red light source, a green light source, and a blue light source,

wherein the displaying the recommended process includes:

generating visual information corresponding to the recommended process; and

displaying the visual information;

wherein the generating an RPA flow according to the modification instruction includes:

modifying the visual information according to the modification instruction; and

and generating an RPA flow based on the modified visual information.

8. The method of claim 1, wherein the obtaining session data comprises:

session data is obtained from at least one terminal.

9. An apparatus for generating an automated RPA procedure for a robotic procedure based on Artificial Intelligence (AI), comprising:

an acquisition unit configured to acquire session data; and

and the generating unit is configured to process the session data and generate at least one RPA flow.

10. An apparatus for generating an automated RPA procedure for a robotic procedure based on Artificial Intelligence (AI), comprising:

a processor; and

a memory, wherein the memory has stored therein a computer-executable program that, when executed by the processor, performs the method of generating an AI-based RPA procedure of any one of claims 1-8.

11. A computer-readable storage medium having stored thereon instructions that, when executed by a processor, cause the processor to perform the method of generating an AI-based RPA procedure of any one of claims 1-8.