Efficiency evaluation method and device combining RPA and AI, and electronic equipment

Abstract

The present disclosure provides an efficiency evaluation method and device combining RPA and AI, and an electronic device, relating to the technical fields of RPA and AI. The method comprises: recording an execution duration of an automation process during execution of the automation process by an RPA system; obtaining a reference duration corresponding to each operation command in the automation process; determining a total reference duration corresponding to the automation process according to the reference duration corresponding to each operation command; and determining an efficiency of the RPA system in executing the automation process according to the total reference duration and the execution duration. The combination of RPA and AI realizes the fusion of automatic execution and intelligent decision-making, effectively improving the automation and intelligent degree of the RPA system in simulating human business decision-making and business processing. The present application realizes quantitative statistics and evaluation of the efficiency of the RPA system in executing the automation process, and provides support for accurate calculation of the benefits generated by the RPA system.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the technical field of computer, in particular to the field of Artificial Intelligence (AI) and Robotic Process Automation (RPA), and especially relates to an efficiency evaluation method and device combining RPA and AI, an electronic device and a storage medium. BACKGROUND

[0002] Robotic Process Automation (RPA) is a specific "robotic software" that simulates human operations on a computer and automatically executes process tasks according to rules.

[0003] Artificial Intelligence (AI) is a technical science that studies and develops theories, methods, technologies and application systems for simulating, extending and expanding human intelligence.

[0004] In existing businesses, many tedious and repetitive processes need to be handled by humans. For example, for some data systems, a human needs to log in to the system website at regular intervals, then open multiple tables in the system one by one, and copy the data in the tables to the corresponding forms. When the amount of data in the table is large, the time length and error rate of human processing will increase. With the development of RPA robotic technology, more and more human work is taken over by RPA robots and widely used in various fields. However, the degree of improvement of the work efficiency of the RPA robot relative to the human efficiency, and the time cost and labor cost saved by the RPA robot instead of human work, are not directly reflected by specific quantitative indicators, so it is impossible to provide a reference for the decision of enterprises to use RPA systems to replace human work. Therefore, it is of great significance to study how to quantitatively analyze the execution efficiency of RPA robots. SUMMARY

[0005] The efficiency evaluation method and device combining RPA and AI, the electronic device and the storage medium provided by the present disclosure are used to solve the problem of accurately evaluating the execution efficiency of RPA robots.

[0006] The efficiency evaluation method combining RPA and AI provided by an aspect of the present disclosure comprises:

[0007] During the execution of the automation process by the RPA system, the execution time length of the automation process is recorded;

[0008] The reference time length corresponding to each operation command in the automation process is obtained;

[0009] determine a total reference duration corresponding to the automation process according to the reference duration corresponding to each operation command;

[0010] determine the efficiency of the RPA system in executing the automation process according to the total reference duration and the execution duration.

[0011] Another aspect of the present disclosure provides an efficiency evaluation device combining RPA and AI, comprising:

[0012] a first recording module configured to record an execution duration of the automation process during execution of the automation process by the RPA system;

[0013] a first obtaining module configured to obtain a reference duration corresponding to each operation command in the automation process;

[0014] a first determining module configured to determine a total reference duration corresponding to the automation process according to the reference duration corresponding to each operation command;

[0015] a second determining module configured to determine the efficiency of the RPA system in executing the automation process according to the total reference duration and the execution duration.

[0016] Another aspect of the present disclosure provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the efficiency evaluation method combining RPA and AI as described above when executing the program.

[0017] Another aspect of the present disclosure provides a computer readable storage medium having a computer program stored thereon, wherein the program is executable on a processor to implement the efficiency evaluation method combining RPA and AI as described above.

[0018] Another aspect of the present disclosure provides a computer program product comprising a computer program, wherein the computer program is executable on a processor to implement the efficiency evaluation method combining RPA and AI as described above.

[0019] The efficiency evaluation method, device, electronic device, computer readable storage medium and computer program product combining RPA and AI provided by the present disclosure have the following technical effects:

[0020] First, the execution duration of the automation process is recorded during the execution of the automation process by the RPA system; then, the reference duration corresponding to each operation command in the automation process is obtained; then, the total reference duration corresponding to the automation process is determined according to the reference duration corresponding to each operation command; and finally, the efficiency of the RPA system in executing the automation process is determined according to the total reference duration and the execution duration. The combination of RPA and AI realizes the integration of automatic execution and intelligent decision-making, effectively improving the automation and intelligent degree of the RPA system in simulating human business decision-making and business processing. The present application realizes the quantitative statistics and evaluation of the efficiency of the RPA system in executing the automation process, and provides support for accurately calculating the benefits generated by the RPA system.

[0021] Additional aspects and advantages of the present disclosure will be in part apparent and in part pointed out hereinafter. BRIEF DESCRIPTION OF DRAWINGS

[0022] The above and / or additional aspects and advantages of the present disclosure will become apparent and be readily appreciated from the following description, taken in conjunction with the accompanying drawings, in which:

[0023] Figure 1 A flowchart of an efficiency evaluation method combining RPA and AI provided by an embodiment of the present disclosure;

[0024] Figure 2 A flowchart of an efficiency evaluation method combining RPA and AI provided by another embodiment of the present disclosure;

[0025] Figure 3 A structural diagram of an efficiency evaluation device combining RPA and AI provided by an embodiment of the present disclosure;

[0026] Figure 4 A structural diagram of an efficiency evaluation device combining RPA and AI provided by another embodiment of the present disclosure;

[0027] Figure 5 A structural diagram of an electronic device provided by an embodiment of the present disclosure. DETAILED DESCRIPTION

[0028] Embodiments of the present disclosure are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals represent the same or similar elements throughout. The embodiments described below by reference to the accompanying drawings are exemplary and are intended to explain the present disclosure, and cannot be understood as a limitation of the present disclosure.

[0029] The embodiments of the present disclosure aim at the quantitative statistics and evaluation of the efficiency of the RPA system in executing the automation process, and propose an efficiency evaluation method combining RPA and AI.

[0030] It should be noted that the RPA (Robotic Process Automation) technology can intelligently understand the existing application of the electronic device through the user interface, automate the repetitive, rule-based and large-volume routine operations, such as automatically reading emails, reading Office components, operating databases and web pages, client software, collecting data and performing tedious calculations, and generating files and reports in large quantities, so that the RPA technology can greatly reduce the input of human cost and effectively improve the office efficiency.

[0031] The AI (Artificial Intelligence) technology can react to stimuli in a way similar to human reaction and learn from it, which is a simulation of human consciousness and thinking information process, and the application fields include robots, language recognition, image recognition, natural language processing and expert systems.

[0032] In the process of executing any type of automation process, the RPA system and the AI model can be configured in the electronic device for efficiency evaluation, so that the RPA system can automatically collect and process data according to the set program to realize the evaluation of execution efficiency.

[0033] In the description of the present application, the term "execution time" refers to the time used by the RPA system when executing the automation process. For example, the time for the RPA system to log in to the system to read the table data, the time for the RPA system to recognize the invoice information and enter the system, etc.

[0034] In the description of the present application, the term "reference time" refers to the time used by other types of subjects to complete the operation action. For example, the operation time for manually opening the specified system website, the operation time for manually inputting the login username, etc.

[0035] In the description of the present application, the term "efficiency" represents the relationship between the time used by the RPA system to execute the automation process and the time used by the artificial to complete the corresponding task.

[0036] In the description of the present application, the term "value parameter" represents the unit value amount brought by the RPA system executing the automation process. For example, the hourly wage of the corresponding post personnel when the RPA system replaces the artificial to execute a certain type of task.

[0037] In the description of the present application, the term "reference value amount" represents the total value amount brought by the RPA system executing the automation process. For example, the daily wage or monthly wage of the corresponding post personnel when the RPA system replaces the artificial to execute a certain type of task.

[0038] The efficiency evaluation method, device, electronic equipment and storage medium combining RPA and AI provided by the present disclosure are described in detail below with reference to the accompanying drawings.

[0039] Figure 1 A flowchart of the efficiency evaluation method combining RPA and AI provided by an embodiment of the present disclosure.

[0040] The efficiency evaluation method combining RPA and AI of the present embodiment can be executed by the efficiency evaluation device combining RPA and AI provided by the present embodiment, which can be configured in an electronic device.

[0041] As shown in the efficiency evaluation method combining RPA and AI, the following steps are included: Figure 1

[0042] Step 101: During the execution of an automation process by an RPA system, record the execution duration of the automation process.

[0043] The automation process can be a process for the RPA system to perform any type of task. For example, it can be a process for the RPA system to automatically log in to a system to read office components, or a process for the RPA system to automatically operate client software, etc., which is not limited by the present disclosure.

[0044] It can be understood that the RPA system will generate a certain execution duration when executing the automation process. The execution duration can be related to the complexity of the automation process and the amount of tasks completed.

[0045] For example, when the automation process is to log in to a system to read table data, the execution duration can be short; when the automation process is to identify invoice information and enter the system, the execution duration can be long.

[0046] Alternatively, when the automation process is to log in to a system to read one table data, the execution duration can be short; when the automation process is to log in to a system to read multiple table data, the execution duration can be long.

[0047] In the present embodiment, the execution duration of the automation process can be recorded by recording the time when the RPA system starts to execute the automation process and the time when the RPA system finishes executing the automation process. Then, the execution duration of the automation process is obtained according to the start time and the end time of the automation process.

[0048] Step 102: Obtain the reference duration corresponding to each operation command in the automation process.

[0049] It can be understood that the automation process can be composed of a series of continuous operation commands.

[0050] ​For example, when the automated process is to read form data from a login system, the operation commands can include opening a specified system URL, entering a login username, entering a login password, filling in a verification code, clicking login, opening a form, reading data, and the like.

[0051] For each operation command, the corresponding operation can be completed by the RPA system or by a human, and the time required can be different. Therefore, when evaluating the efficiency of the RPA system in executing the automated process, the time for human operation can be used as a reference time.

[0052] For example, when the automated process is to read form data from a login system, the operation time for a human to open a specified system URL can be set to 1 second, the operation time for entering a login username can be set to 2 seconds, the operation time for entering a login password can be set to 2 seconds, and the operation time for filling in a verification code can be set to 3 seconds, and the like.

[0053] The time for human operation can be determined according to the actual operation time of the corresponding step. When designing the automated process of the RPA system, a reference time field can be added to each operation command of the automated process, and the specific value of the reference time field can be set using the corresponding human operation time.

[0054] For example, for an operation command of clicking a certain position on the screen with a mouse, the operation command can include a screen coordinate x field, a screen coordinate y field, a mouse left button field, a reference time field, and the like.

[0055] Step 103: Determine the total reference time corresponding to the automated process according to the reference time corresponding to each operation command.

[0056] The total reference time corresponding to the automated process can be obtained by summing the reference times corresponding to all operation commands.

[0057] It should be noted that since the reference time corresponding to each operation command can be the time required for a human to complete the corresponding operation, the total reference time corresponding to the automated process can represent the total time required for a human to complete the same process.

[0058] Step 104: Determine the efficiency of the RPA system in executing the automated process according to the total reference time and the execution time.

[0059] The efficiency of the RPA system in executing the automated process can be determined by calculating the total reference time and the execution time using any desired calculation method.

[0060] For example, a ratio of the total reference duration to the execution duration can be taken as the efficiency of the RPA system in executing the automation process. Alternatively, a difference between the total reference duration and the execution duration can be calculated, and a ratio of the difference to the execution duration can be taken as the efficiency of the RPA system in executing the automation process, which is not limited in the present disclosure.

[0061] In the embodiments of the present disclosure, first, the execution duration of the automation process is recorded during the execution of the automation process by the RPA system; then, the reference duration corresponding to each operation command in the automation process is obtained; then, the total reference duration corresponding to the automation process is determined according to the reference duration corresponding to each operation command; and finally, the efficiency of the RPA system in executing the automation process is determined according to the total reference duration and the execution duration. The combination of RPA and AI realizes the integration of automatic execution and intelligent decision-making, effectively improving the automation and intelligent degree of the RPA system in simulating human business decision-making and business processing. The present application realizes the quantitative statistics and evaluation of the efficiency of the RPA system in executing the automation process, and provides support for accurately calculating the benefits generated by the RPA system.

[0062] Figure 2 A flowchart of an efficiency evaluation method combining RPA and AI provided by another embodiment of the present disclosure.

[0063] As shown in Figure 2 , the efficiency evaluation method combining RPA and AI includes the following steps:

[0064] Step 201, recording the execution duration of the automation process during the execution of the automation process by the RPA system.

[0065] Step 202, obtaining the reference duration corresponding to each operation command in the automation process.

[0066] Step 203, determining the total reference duration corresponding to the automation process according to the reference duration corresponding to each operation command.

[0067] Step 204, determining the efficiency of the RPA system in executing the automation process according to the total reference duration and the execution duration.

[0068] The specific implementation of the above steps 201-204 can refer to the detailed description of other embodiments of the present disclosure, which will not be repeated here.

[0069] Step 205, recording the initial time and the end time corresponding to each operation command during the execution of the automation process by the RPA system.

[0070] Step 206, determining the execution sub-duration corresponding to each operation command according to the initial time and the end time corresponding to each operation command.

[0071] It can be understood that the duration of the RPA system in executing different operation commands can be shorter than that of the human or longer than that of the human.

[0072] For example, when semantic analysis, image recognition and other processes are involved in the automated process, the RPA system needs to call the corresponding service for processing, and therefore the execution duration can be longer than that of the human.

[0073] Or, when only reading, recording and other operations of a large amount of data are involved in the automated process, the execution duration of the RPA system can be shorter than that of the human operation.

[0074] Therefore, the efficiency of the RPA system in executing each operation command is statistically evaluated, which can provide a basis for the improvement and optimization of the RPA system for specific operation commands.

[0075] Among them, by recording the initial time and the end time corresponding to each operation command, the execution sub-duration corresponding to each operation command can be determined.

[0076] For example, when the operation command is to input a username, the RPA system can record the time when the keyboard starts to be struck and the time when the keyboard is struck, and the difference between the end time and the start time is taken as the execution sub-duration of the operation command of inputting the username.

[0077] Step 207, according to the execution sub-duration corresponding to each operation command and the corresponding reference duration, determine the sub-efficiency of the RPA system in executing each operation command.

[0078] Among them, any calculation method can be selected as needed to calculate the execution sub-duration corresponding to each operation command and the corresponding reference duration to determine the sub-efficiency of the RPA system in executing each operation command.

[0079] For example, the ratio of the reference duration corresponding to each operation command and the corresponding execution sub-duration can be taken as the sub-efficiency of the RPA system in executing each operation command.

[0080] Or, the difference between the reference duration corresponding to each operation command and the corresponding execution sub-duration can be calculated, and the ratio of the difference and the corresponding execution sub-duration is taken as the efficiency of the RPA system in executing each operation command, which is not limited in the present disclosure.

[0081] Step 208, in response to the sub-efficiency of any operation command being less than a threshold value, determining that any operation command is an operation command to be updated.

[0082] For example, if the sub-efficiency of the operation command for recognizing the verification code is less than the threshold value when the RPA system executes the operation command, the operation command for recognizing the verification code can be marked as an operation command to be updated, so as to optimize and update the image recognition service called by the operation command later.

[0083] The setting of the threshold value can be determined according to the calculation manner of the sub-efficiency of the operation command, which is not limited in the present disclosure.

[0084] In the embodiments of the present disclosure, the initial time and the end time corresponding to the execution of each operation command by the RPA system are recorded, and then the sub-execution time corresponding to each operation command is determined according to the initial time and the end time corresponding to each operation command. Then, the sub-efficiency of the RPA system executing each operation command is determined according to the sub-execution time corresponding to each operation command and the corresponding reference time, and then the operation command to be updated and optimized is selected. Thus, the improvement and optimization of the RPA system for a specific operation command are provided.

[0085] In step 209, the operation type to which each operation command belongs is determined.

[0086] In step 210, the total execution time and the first total reference time corresponding to each operation type are determined according to the operation type to which each operation command belongs, the sub-execution time corresponding to each operation command, and the reference time.

[0087] It can be understood that when the RPA system executes the automation process, it may need to execute tens, hundreds or even more operation commands, and multiple operation commands may belong to the same type.

[0088] For example, when the automation process involves a large amount of data reading, recording and other operations, the RPA system needs to repeatedly execute the copy and paste command several times. Then, when the sub-efficiency of each operation command is calculated, a large amount of repeated data may be obtained.

[0089] Therefore, by determining the operation type to which each operation command belongs, the sum of the sub-execution time corresponding to the operation commands belonging to the same type can be calculated to obtain the total execution time corresponding to the operation commands of the type, and the sum of the reference time corresponding to the operation commands belonging to the same type can be calculated to obtain the first total reference time corresponding to the operation commands of the type.

[0090] For example, the operation command involving natural language processing can be regarded as a type of operation command to calculate the total execution time and the first total reference time corresponding to the operation commands of the type.

[0091] Specifically, when the RPA system is executing the automation process, the time for each operation to be completed by calling the natural language processing technology can be recorded, and the time required for each operation to be completed by human can be set. After the RPA system completes the task, the sum of the time for each operation to be completed by calling the natural language processing technology is calculated as the total execution time, and the sum of the time for each operation to be completed by human is calculated as the first total reference time.

[0092] Natural Language Processing (NLP) is a computer science and linguistics cross-discipline, which is also commonly known as computational linguistics. Since natural language is the fundamental mark of human beings distinguishing from other animals. Without language, human thinking cannot be discussed. Therefore, natural language processing reflects the highest task and realm of artificial intelligence. That is, only when the computer has the ability to process natural language, the machine can be considered to achieve true intelligence.

[0093] Alternatively, the operation command related to optical character recognition can be taken as a type of operation command to calculate the total execution time and the first total reference time corresponding to the type of operation command.

[0094] Specifically, when the RPA system is executing the automation process, the time for each operation to be completed by calling the natural language processing technology can be recorded, and the time required for each operation to be completed by human can be set. After the RPA system completes the task, the sum of the time for each operation to be completed by calling the natural language processing technology is calculated as the total execution time, and the sum of the time for each operation to be completed by human is calculated as the first total reference time.

[0095] Optical Character Recognition (OCR) is a computer input technology that converts the text of various bills, newspapers, books, documents and other printed matter into image information through scanning and other optical input methods, and then converts the image information into usable computer input technology using text recognition technology. It can be applied to the input and processing of bank bills, large amounts of text data, archive files, and case files. It is suitable for automatic scanning and identification of large amounts of bills and forms in the banking, tax and other industries, and long-term storage.

[0096] It should be noted that the above examples are only illustrative and cannot be used as a limitation on the types of operation commands in the embodiments of the present disclosure.

[0097] Step 211, according to the total execution time and the first total reference time corresponding to each operation type, determine the efficiency of the RPA system in executing the operation command of each operation type.

[0098] The total execution time and the first total reference time corresponding to each operation type can be calculated according to any calculation method as needed to determine the efficiency of the RPA system in executing the operation command of each operation type.

[0099] For example, the ratio of the first total reference time corresponding to each operation type to the corresponding total execution time can be taken as the efficiency of the RPA system in executing the operation command of each operation type.

[0100] Alternatively, the difference between the first total reference time corresponding to each operation type and the corresponding total execution time can be calculated, and the ratio of the difference to the corresponding total execution time can be taken as the efficiency of the RPA system in executing the operation command of each operation type, which is not limited in the present disclosure.

[0101] In the embodiments of the present disclosure, the total execution time and the first total reference time corresponding to each type of operation command are first determined according to the execution sub-time and the reference time corresponding to the operation command belonging to the same type, and then the efficiency of the RPA system in executing the operation command of each operation type is determined according to the total execution time and the first total reference time corresponding to each operation type. Thereby, the improvement and optimization of the RPA system for a specific type of operation command are provided.

[0102] Step 212, obtaining a value parameter corresponding to the RPA system.

[0103] The value parameter can be any parameter that can reflect the value brought by the RPA system in executing the automation process.

[0104] For example, a monthly average wage can be set for the specific scene and task type of the RPA system in executing the automation process, and then the labor cost per working hour can be calculated according to the monthly average wage, and the labor cost per working hour can be taken as the corresponding value parameter.

[0105] It should be noted that since the complexity and difficulty of different tasks are different, different value parameters can be set for different types of RPA systems.

[0106] Step 213, determining a reference value amount of the RPA system according to the value parameter and the total reference time corresponding to the automation process.

[0107] The reference value amount of the RPA system can be the labor cost saved by the RPA system in executing the automation process.

[0108] For example, the value parameter is the labor cost per hour for personnel in the corresponding position, and the total reference time for the automated process is the total time required for a person to complete the corresponding operation. The product of the value parameter and the total reference time for the automated process can be used as the reference value of the RPA system to obtain the total labor cost saved by the RPA system.

[0109] It should be noted that the reference value of an RPA system can be determined based on the total reference time corresponding to the execution of an automated process by the RPA system once, or by accumulating the total reference time corresponding to the execution of multiple automated processes by the RPA system. This disclosure does not limit this.

[0110] In this embodiment of the disclosure, by obtaining the value parameters corresponding to the RPA system and determining the reference value of the RPA system based on the value parameters and the total reference time corresponding to the automated process, the benefits generated by the RPA system are reflected intuitively and accurately, providing support for the application of the RPA system.

[0111] To achieve the above embodiments, this disclosure also proposes an efficiency evaluation device that combines RPA and AI.

[0112] Figure 3 This is a schematic diagram of an efficiency evaluation device combining RPA and AI, provided as an embodiment of the present disclosure.

[0113] like Figure 3 As shown, the efficiency evaluation device 300 combining RPA and AI includes: a first recording module 310, a first acquisition module 320, a first determination module 330 and a second determination module 340.

[0114] The first recording module 310 is used to record the execution time of the automated process during the execution of the automated process in the RPA system.

[0115] The first acquisition module 320 is used to acquire the reference duration corresponding to each operation command in the automated process.

[0116] The first determining module 330 is used to determine the total reference duration of the automated process based on the reference duration corresponding to each operation command.

[0117] The second determining module 340 is used to determine the efficiency of the RPA system when executing automated processes based on the total reference time and execution time.

[0118] It should be noted that the functions and specific implementation principles of the modules described above in this disclosure embodiment can be referred to the above method embodiments, and will not be repeated here.

[0119] The efficiency evaluation device combining RPA and AI provided by the embodiment of the present disclosure firstly records the execution duration of the automation process during the execution of the automation process by the RPA system; then acquires the reference duration corresponding to each operation command in the automation process; then determines the total reference duration corresponding to the automation process according to the reference duration corresponding to each operation command; and finally determines the efficiency of the RPA system in executing the automation process according to the total reference duration and the execution duration. The combination of RPA and AI realizes the fusion of automatic execution and intelligent decision-making, effectively improving the automation and intelligent degree of the RPA system in simulating human business decision-making and business processing. The present application realizes the quantitative statistics and evaluation of the efficiency of the RPA system in executing the automation process, and provides support for accurately calculating the benefits generated by the RPA system.

[0120] Figure 4 The structure diagram of the efficiency evaluation device combining RPA and AI provided by another embodiment of the present disclosure is shown in Figure 4 The efficiency evaluation device combining RPA and AI 400 includes a first recording module 410, a first acquisition module 420, a first determination module 430, a second determination module 440, a second recording module 450, a third determination module 460, a fourth determination module 470, a fifth determination module 480, a sixth determination module 490, a seventh determination module 4100, an eighth determination module 4110, a second acquisition module 4120, and a ninth determination module 4130.

[0121] The first recording module 410 is configured to record the execution duration of the automation process during the execution of the automation process by the RPA system.

[0122] The first acquisition module 420 is configured to acquire the reference duration corresponding to each operation command in the automation process.

[0123] The first determination module 430 is configured to determine the total reference duration corresponding to the automation process according to the reference duration corresponding to each operation command.

[0124] The second determination module 440 is configured to determine the efficiency of the RPA system in executing the automation process according to the total reference duration and the execution duration.

[0125] The second recording module 450 is configured to record the initial time and the end time corresponding to each operation command during the execution of the automation process by the RPA system.

[0126] The third determination module 460 is configured to determine the execution sub-duration corresponding to each operation command according to the initial time and the end time corresponding to each operation command.

[0127] The fourth determination module 470 is used to determine the sub-efficiency of the RPA system when executing each operation command based on the execution sub-duration and the corresponding reference duration of each operation command.

[0128] The fifth determining module 480 is used to determine any operation command as an operation command to be updated in response to the sub-efficiency corresponding to any operation command being less than a threshold.

[0129] The sixth determination module 490 is used to determine the operation type to which each operation command belongs.

[0130] The seventh determining module 4100 is used to determine the total execution time and the first total reference time for each operation type based on the operation type to which each operation command belongs, and the execution sub-duration and reference duration corresponding to each operation command.

[0131] The eighth determination module 4110 is used to determine the efficiency of the RPA system when executing operation commands for each operation type based on the total execution time corresponding to each operation type and the first total reference time.

[0132] The second acquisition module 4120 is used to acquire the value parameters corresponding to the RPA system.

[0133] The ninth determination module 4130 is used to determine the reference value of the RPA system based on the value parameters and the total reference time corresponding to the automated process.

[0134] In some embodiments of this disclosure, the operation commands include at least one of natural language processing and optical character recognition.

[0135] It is understood that this embodiment is accompanied by Figure 4 The efficiency evaluation device 400 in the above embodiment is the same as the efficiency evaluation device 300 in the above embodiment, the first recording module 410 is the same as the first recording module 310 in the above embodiment, the first acquisition module 420 is the same as the first acquisition module 320 in the above embodiment, the first determination module 430 is the same as the first determination module 330 in the above embodiment, and the second determination module 440 is the same as the second determination module 340 in the above embodiment. They can have the same functions and structures.

[0136] It should be noted that the functions and specific implementation principles of the modules described above in this disclosure embodiment can be referred to the above method embodiments, and will not be repeated here.

[0137] The efficiency evaluation device combining RPA and AI provided in this embodiment obtains the value parameters corresponding to the RPA system and determines the reference value of the RPA system based on the value parameters and the total reference time corresponding to the automated process. This enables an intuitive and accurate representation of the benefits generated by the RPA system and provides support for the application of RPA systems.

[0138] To achieve the above-mentioned embodiments, the present disclosure further provides an electronic device.

[0139] Figure 5 A structural schematic diagram of the electronic device of the efficiency evaluation method combining RPA and AI according to the embodiments of the present disclosure.

[0140] As Figure 5 shown, the electronic device 500 includes:

[0141] The memory 510 and the processor 520, the bus 530 connecting different components (including the memory 510 and the processor 520), the memory 510 stores a computer program, and the processor 520 executes the program to realize the efficiency evaluation method combining RPA and AI according to the embodiments of the present disclosure.

[0142] The bus 530 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus using any of a variety of bus structures. For example, these architectures include but are not limited to industry standard architecture (ISA) bus, micro channel architecture (MAC) bus, enhanced ISA bus, video electronics standards association (VESA) local bus, and peripheral component interconnect (PCI) bus.

[0143] The electronic device 500 typically includes a variety of electronic device readable media. These media can be any available media that can be accessed by the electronic device 200, including volatile and nonvolatile media, removable and non-removable media.

[0144] The memory 510 can also include computer system readable media in the form of volatile memory, such as random access memory (RAM) 540 and / or cache memory 550. The electronic device 500 can further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, a storage system 560 can be used for reading from and writing to non-removable, non-volatile magnetic media (e.g., a "hard drive"). Figure 5 not shown, a magnetic hard disk drive, for reading from and writing to a non-removable, non-volatile magnetic media (not shown and typically called a "hard drive"). Although Figure 5 not shown, a magnetic hard disk drive, for reading from and writing to a non-removable, non-volatile magnetic media (not shown and typically called a "hard drive"). Although

[0145] Program / utility 580 having a set of program modules 570 can be stored in memory 510, such as RAM, ROM, EEPROM, flash or other non-volatile memory. Such program modules 570 can include an operating system, one or more application programs, other program modules, and program data, each of which or a combination thereof, can include implementation of the network environment in each of these examples or some combination thereof. Program modules 570 generally carry out the functions and / or methodologies described in the embodiments of the disclosure.

[0146] The electronic device 500 can also communicate with one or more external devices 590 such as a keyboard or pointing device, a display 591, etc.; one or more devices that enable a user to interact with the electronic device 500; and / or one or more devices that enable the electronic device 500 to communicate with one or more other computing devices. Such communication can occur via Input / Output (I / O) interface(s) 592. Still yet, the electronic device 500 can communicate with one or more networks such as a local area network (LAN), a general wide area network (WAN), and / or a public network (e.g., the Internet) via network adapter 593. As depicted, network adapter 593 communicates with the other components of the electronic device 500 via bus 530. It should be appreciated that although not shown, other hardware and / or software components could be used in conjunction with the electronic device 500. These include, but are not limited to, microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data archival storage systems, etc.

[0147] The processor 520 performs various function applications and data processing by running programs stored in the memory 510.

[0148] It should be noted that the implementation process and technical principles of the electronic device of the embodiment are described above in the explanation of the combination of RPA and AI efficiency evaluation method of the embodiment of the disclosure, which will not be described here.

[0149] The electronic device provided by the embodiment of the disclosure can execute the combination of RPA and AI efficiency evaluation method as described above. First, the execution duration of the automation process is recorded during the execution of the automation process in the RPA system. Then, the reference duration corresponding to each operation command in the automation process is obtained. Then, the total reference duration corresponding to the automation process is determined according to the reference duration corresponding to each operation command. Finally, the efficiency of the RPA system in executing the automation process is determined according to the total reference duration and the execution duration. Thus, the quantitative statistics and evaluation of the efficiency of the RPA system in executing the automation process are realized, which provides support for accurately calculating the benefits generated by the RPA system.

[0150] In order to realize the above-mentioned embodiments, the disclosure further provides a computer readable storage medium.

[0151] The computer readable storage medium, on which the computer program is stored, is configured to enable a processor to implement the method for efficiency evaluation combined with RPA and AI according to the embodiments of the present disclosure.

[0152] To achieve the above-mentioned embodiments, the present disclosure further provides a computer program, which is configured to enable a processor to implement the method for efficiency evaluation combined with RPA and AI according to the embodiments of the present disclosure.

[0153] In an alternative implementation form, the embodiments can employ any combination of one or more computer readable media. The computer readable media can be a computer readable signal medium or a computer readable storage medium. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus or device, or any suitable combination of the above. More specific examples (a non-exhaustive list) of the computer readable storage medium include an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this document, the computer readable storage medium can be any tangible medium that contains or stores a program used by or in connection with an instruction execution system, apparatus or device.

[0154] The computer readable signal medium can include a data signal traveling in baseband or an analog carrier wave traveling in the baseband over a propagation medium. The program code embodied on the computer readable signal medium can be transmitted using any appropriate medium, including but not limited to wireless, wired, optical fiber cable, RF, etc., or any suitable combination of the above.

[0155] The program code contained on the computer readable medium can be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber cable, RF, etc., or any suitable combination of the above.

[0156] Computer program code for carrying out operations of the present disclosure can be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C++ or the like, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code can execute entirely on the user's electronic device, partly on the user's electronic device, as a stand-alone software package, partly on the user's electronic device and partly on a remote electronic device or entirely on the remote electronic device or server. In the latter scenario, the remote electronic device can be connected to the user's electronic device through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external electronic device (for example, through the Internet using an Internet Service Provider). The application is not limited to the use of any particular programming language. Program code can be any combination of machine language or source code.

[0157] According to the technical solution of the present disclosure, first, the execution duration of the automation process is recorded during the execution of the automation process in the RPA system; then the reference duration corresponding to each operation command in the automation process is obtained; then the total reference duration corresponding to the automation process is determined according to the reference duration corresponding to each operation command; finally, the efficiency of the RPA system in executing the automation process is determined according to the total reference duration and the execution duration. Thus, the quantitative statistics and evaluation of the efficiency of the RPA system in executing the automation process are realized, which provides support for accurately calculating the benefits generated by the RPA system.

[0158] Other embodiments of the present disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the application disclosed herein. The present disclosure is intended to cover any variations, uses or adaptations of the present disclosure following, in general, the principles of the present disclosure and including such departures from the present disclosure that come within known or customary practice in the art to which the present disclosure pertains. The specification and examples are to be regarded as exemplary only, and the true scope and spirit of the present disclosure are indicated by the appended claims.

[0159] It should be understood that the present disclosure is not limited to the precise structures herein described and illustrated in the drawings, and that various modifications and changes can be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. An efficiency evaluation method combining RPA and AI, characterized in that, The method comprises the following steps: During execution of an automation process by a robot process automation (RPA) system, a duration of execution of the automation process is recorded; A reference duration corresponding to each operation command in the automation process is obtained, wherein the reference duration is a duration of completing each operation command by a human being; A total reference duration corresponding to the automation process is determined according to the reference duration corresponding to each operation command; An efficiency of the RPA system in executing the automation process is determined according to the total reference duration and the duration of execution; During execution of the automation process by the RPA system, an initial time and an end time corresponding to each operation command are recorded; An execution sub-duration corresponding to each operation command is determined according to the initial time and the end time corresponding to each operation command; A sub-efficiency of the RPA system in executing each operation command is determined according to the execution sub-duration corresponding to each operation command and the reference duration corresponding to each operation command; After the S7, the method further comprises: in response to a sub-efficiency of any operation command being less than a threshold value, determining that the any operation command is an operation command to be updated; The method further comprises: obtaining a value parameter corresponding to the RPA system; and determining a reference value amount of the RPA system according to the value parameter and the total reference duration corresponding to the automation process.

2. The method of claim 1, wherein, The method further comprises: Determining an operation type to which each operation command belongs; Determining a total execution duration and a first total reference duration corresponding to each operation type according to the operation type to which each operation command belongs, and the execution sub-duration and the reference duration corresponding to each operation command; Determining an efficiency of the RPA system in executing operation commands of each operation type according to the total execution duration and the first total reference duration corresponding to each operation type.

3. The method of claim 2, wherein, The operation command comprises at least one of natural language processing (NLP) and optical character recognition (OCR).

4. An efficiency evaluation device combining RPA and AI, characterized in that, The method comprises the following steps: A first recording module is configured to record a duration of execution of an automation process during execution of the automation process by an RPA system; A first obtaining module is configured to obtain a reference duration corresponding to each operation command in the automation process, wherein the reference duration is a duration of completing each operation command by a human being; A first determining module is configured to determine a total reference duration corresponding to the automation process according to the reference duration corresponding to each operation command; A second determining module is configured to determine an efficiency of the RPA system in executing the automation process according to the total reference duration and the duration of execution; A second recording module is configured to record an initial time and an end time corresponding to each operation command during execution of the automation process by the RPA system; A third determining module is configured to determine an execution sub-duration corresponding to each operation command according to the initial time and the end time corresponding to each operation command; A fourth determining module is configured to determine a sub-efficiency of the RPA system in executing each operation command according to the execution sub-duration corresponding to each operation command and the reference duration corresponding to each operation command; The method further includes: a fifth determining module, configured to determine any operation command as an operation command to be updated in response to a sub-efficiency corresponding to the operation command being less than a threshold value; A second obtaining module is configured to obtain a value parameter corresponding to the RPA system; A ninth determining module is configured to determine a reference value amount of the RPA system according to the value parameter and a total reference duration corresponding to the automation process.

5. The apparatus of claim 4, wherein, The method further includes: A sixth determining module is configured to determine an operation type to which each operation command belongs; A seventh determining module is configured to determine a total execution duration and a first total reference duration corresponding to each operation type according to an operation type to which each operation command belongs, an execution sub-duration corresponding to each operation command, and a reference duration; An eighth determining module is configured to determine an efficiency of the RPA system in executing operation commands of each operation type according to the total execution duration and the first total reference duration corresponding to each operation type.

6. An electronic device, comprising: A memory, a processor, and a program stored in the memory and executable on the processor, and characterized in that the processor implements the efficiency evaluation method combining RPA and AI according to any one of claims 1-3 when executing the program.

7. A computer readable storage medium having stored thereon a computer program, characterized in that, The program is executed by the processor to implement the efficiency evaluation method combining RPA and AI according to any one of claims 1-3.

8. A computer program product comprising a computer program which, when executed by a processor, implements the efficiency evaluation method combining RPA and AI according to any one of claims 1-3.

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