RPA system operation guarantee method for visual process monitoring and adaptive optimization

Abstract

The invention relates to the field of RPA systems, and discloses a visual process monitoring and self-adaptive optimization RPA system operation guarantee method, which comprises the following steps of: firstly, performing evaluation analysis on task completion efficiency based on task completion condition data of each process robot in a last period; secondly, operation evaluation analysis of the process robots is carried out based on the operation conditions of the process robots in the last period, and finally the next-period task load distribution is carried out based on the evaluation analysis result of the task completion efficiency and the operation evaluation analysis result of the process robots, so that the process state can be monitored in real time, and the abnormal conditions can be quickly responded; and distribution of task load is continuously adjusted and optimized according to system operation data, so that continuous and efficient uninterrupted operation is realized.

Description

Technical Field

[0001] This application belongs to the field of RPA systems, specifically, an RPA system operation assurance method with visual process monitoring and adaptive optimization. Background Art

[0002] RPA system is a technology that uses software robots to simulate and execute repetitive and regular business processes performed by humans in digital systems. RPA system automates a series of business tasks through software robots. These tasks usually include data input, file transfer, form filling, report generation, etc. They are routine work in business processes. Generally speaking, RPA system provides enterprises with an efficient and economical method to automate routine business processes, thereby improving operational efficiency and service quality. When ensuring the operation of RPA system, an RPA system operation assurance method with visual process monitoring and adaptive optimization is required; when ensuring the operation of RPA system, the prior art cannot accurately analyze the abnormal operation data of RPA system, resulting in the inability to effectively adjust and optimize the allocation of task volume according to the system operation data, thereby resulting in unreasonable task volume allocation causing system operation failures; in order to solve the problems raised by this background technology, this application designs an RPA system operation assurance method with visual process monitoring and adaptive optimization. Summary of the invention

[0003] In order to address the deficiencies in the prior art mentioned in the background technology, the present application proposes first to evaluate and analyze the task completion efficiency based on the task completion data of each process robot in the previous cycle, and secondly to evaluate and analyze the operation of the process robots based on the operation status of each process robot in the previous cycle, and finally to allocate the task volume for the next cycle based on the evaluation and analysis results of the task completion efficiency and the operation evaluation and analysis results of the process robots. The present application can monitor the process status in real time, respond quickly to abnormal situations, and continuously adjust and optimize the allocation of task volume according to the system operation data to achieve continuous, efficient and uninterrupted operation, which not only improves the reliability of the system, but also greatly improves the operation and maintenance efficiency and the enterprise's workflow management capabilities.

[0004] To achieve the above objectives, the present application provides the following technical solutions: First, the present application provides an RPA system operation assurance method for visual process monitoring and adaptive optimization, which includes the following specific steps:

[0005] S1. Obtain the task completion data and operation status of each process robot in the previous cycle, and obtain the task volume data that needs to be assigned in this cycle;

[0006] S2. Evaluate and analyze the task completion efficiency based on the task completion data of each process robot in the previous cycle;

[0007] S3. Perform operation evaluation and analysis of the process robots based on the operation status of each process robot in the previous cycle;

[0008] S4. Allocate the task volume for the next cycle based on the evaluation and analysis results of the task completion efficiency and the operation evaluation and analysis results of the process robot.

[0009] As an optimal technical solution for the RPA system operation assurance method with visual process monitoring and adaptive optimization, the task completion status data in S1 specifically includes the data volume data of the previous cycle tasks, the error rate data of various tasks and the task processing time data, and the operation status in S1 is the data on the amount of resources occupied when performing various tasks.

[0010] As a preferred technical solution for the RPA system operation assurance method of visual process monitoring and adaptive optimization, the evaluation and analysis of the task completion efficiency in S2 includes the following specific steps:

[0011] S21, based on the data volume data of the previous cycle task, the error rate data of various tasks and the processing time data of the task, the success coefficient of the previous cycle task is evaluated, which includes the following specific contents:

[0012] S211, obtaining data volume data of the tasks completed in the previous period, and obtaining difficulty data of each completed task after calculating the inverse of the data volume data;

[0013] S212, obtain the difficulty data of each completed task, the error rate data of various tasks and the processing time data of the task, and substitute them into the calculation formula of the success coefficient of the previous cycle task completion to calculate the success coefficient of the previous cycle task completion, wherein the calculation formula of the success coefficient of the previous cycle task completion of the p-th process robot is: Among them, np is the number of tasks completed by the p-th process robot in the previous cycle, ci is the error rate of the i-th task completed by the p-th process robot in the previous cycle. Since the success coefficient is required here, 1-ci needs to be substituted. Ti is the data volume of the i-th task completed by the p-th process robot in the previous cycle, ti is the completion time of the i-th task completed by the p-th process robot in the previous cycle, Vm is the average speed of historical task completion, exp() is the power of the natural constant e. In this formula, the completion status of the tasks in the previous cycle is analyzed through the completion speed of the tasks in the previous cycle, the success rate of task completion and the task completion speed;

[0014] S22, evaluating the completion stability coefficient of the tasks in the previous cycle based on the data volume data of the tasks at each stage of the previous cycle and the processing time data of the tasks;

[0015] The evaluation of the stability coefficient of the task completion in the previous cycle includes the following specific steps:

[0016] Obtain the data volume data and task processing time data of each stage of the previous cycle, import the data volume data and task processing time data of each stage of the previous cycle into the calculation formula of the stability coefficient of the task completion of the previous cycle to calculate the stability coefficient of the task completion of the previous cycle. Among them, the calculation formula of the stability coefficient of the task completion of the pth process robot in the previous cycle is: in, In this formula, the stability of task completion is analyzed by the deviation of the processing speed of each task;

[0017] S23. Obtain the task completion efficiency by performing weighted summation based on the success coefficient of task completion in the previous cycle and the stability coefficient of task completion in the previous cycle.

[0018] As a preferred technical solution for the RPA system operation assurance method of visual process monitoring and adaptive optimization, the operation evaluation and analysis of the process robot includes the following specific steps:

[0019] S31, obtaining resource volume data and data volume data of various tasks of each process robot, and setting the ratio of resource volume data and data volume data of corresponding tasks as the consumption coefficient;

[0020] S32, obtaining the consumption coefficients of various tasks of each process robot and substituting them into the operation evaluation value calculation formula to calculate the operation evaluation value, wherein the calculation formula for the operation evaluation value of the pth process robot on the previous cycle is: Where a is the weight of the abnormal consumption coefficient ratio, Hi is the consumption coefficient of the i-th task in the p-th process robot in the previous period, so the operation of the process robot is evaluated by the consumption coefficient of the consumed resources and the fluctuation value of the consumption coefficient.

[0021] As a preferred technical solution for the RPA system operation guarantee method of visual process monitoring and adaptive optimization, the allocation of the next cycle task volume in S4 includes the following specific contents:

[0022] S41, obtaining the previous cycle operation evaluation value and task completion efficiency of each process robot, and obtaining the allocation coefficient of each process robot after weighted summation, and obtaining the task volume proportion of the corresponding process robot by dividing the allocation coefficient of all process robots by the allocation coefficients of all process robots, and obtaining the task volume data allocated to the corresponding process robot by multiplying the task volume proportion of the corresponding process robot by the task volume data to be allocated in this cycle;

[0023] S42. Allocate the task volume according to the task volume data assigned to the corresponding process robot, and present it visually in real time.

[0024] In a second aspect, the present application further provides an electronic device, comprising: a processor and a memory, wherein the memory stores a computer program that can be called by the processor;

[0025] The processor executes the above-mentioned RPA system operation assurance method of visual process monitoring and adaptive optimization by calling the computer program stored in the memory.

[0026] In a third aspect, the present application also provides a computer-readable storage medium storing instructions, which, when executed on a computer, enables the computer to execute the RPA system operation assurance method of visual process monitoring and adaptive optimization as described above.

[0027] Compared with the prior art, the beneficial effects of the present application are as follows: the present application first evaluates and analyzes the task completion efficiency based on the task completion data of each process robot in the previous cycle, and secondly evaluates and analyzes the operation of the process robot based on the operation status of each process robot in the previous cycle, and finally allocates the task volume for the next cycle based on the evaluation and analysis results of the task completion efficiency and the operation evaluation and analysis results of the process robot. The present application can monitor the process status in real time, respond quickly to abnormal situations, and continuously adjust and optimize the allocation of task volume according to the system operation data to achieve continuous, efficient and uninterrupted operation, which not only improves the reliability of the system, but also greatly improves the operation and maintenance efficiency and the enterprise's workflow management capabilities. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Other features, objects and advantages of the present application will become more apparent by reading the detailed description of non-limiting embodiments made with reference to the following drawings;

[0029] Figure 1 This is a schematic diagram of the overall process of the RPA system operation assurance method for visual process monitoring and adaptive optimization for this application;

[0030] Figure 2 This is a schematic diagram of step S2 of the RPA system operation assurance method for visual process monitoring and adaptive optimization of this application;

[0031] Figure 3 This is a schematic diagram of step S21 of the RPA system operation assurance method for visual process monitoring and adaptive optimization of this application;

[0032] Figure 4 A schematic diagram of an electronic device of the present application. DETAILED DESCRIPTION

[0033] In order to better understand the present application, various aspects of the present application will be described in more detail with reference to the accompanying drawings. It should be understood that these detailed descriptions are only descriptions of exemplary embodiments of the present application and do not limit the scope of the present application in any way. Throughout the specification, the same reference numerals refer to the same elements. The expression "and / or" includes any and all combinations of one or more of the associated listed items.

[0034] In the accompanying drawings, the size, dimensions and shape of the elements have been slightly adjusted for ease of explanation. The accompanying drawings are only examples and are not strictly drawn to scale. As used herein, the terms "substantially", "approximately" and similar terms are used as terms to indicate approximation, not as terms to indicate degree, and are intended to illustrate the inherent deviations in measured or calculated values ​​that will be recognized by those of ordinary skill in the art. In addition, in the present application, the order in which the steps are processed does not necessarily represent the order in which these processes occur in actual operation, unless otherwise clearly defined or can be derived from the context. It should also be understood that expressions such as "including", "including", "having", "including" and / or "including" are open rather than closed expressions in this specification, which indicate the presence of the stated features, elements and / or components, but do not exclude the presence of one or more other features, elements, components and / or combinations thereof. In addition, when expressions such as "at least one of..." appear after a list of listed features, they modify the entire list of features, rather than just modifying the individual elements in the list. In addition, when describing the embodiments of the present application, "may" is used to represent "one or more embodiments of the present application". Furthermore, the term "exemplary" is intended to refer to an example or illustration. Unless otherwise specified, all words used herein (including engineering terms and scientific and technological terms) have the same meaning as those commonly understood by those of ordinary skill in the art to which this application belongs. It should also be understood that, unless otherwise clearly stated in this application, words defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant technology, and should not be interpreted in an idealized or overly formal sense.

[0035] Example 1

[0036] In order to solve the technical problems raised in the background technology, the present application provides a preferred embodiment: Figure 1-Figure 3 As shown in the figure, the RPA system operation guarantee method with visual process monitoring and adaptive optimization includes the following specific steps:

[0037] S1. Obtain the task completion data and operation status of each process robot in the previous cycle, and obtain the task volume data that needs to be assigned in this cycle;

[0038] In this embodiment, the task completion status data in S1 specifically includes the data volume data of the previous cycle task, the error rate data of various tasks and the processing time data of the task, and the operation status in S1 is the resource volume data occupied when performing various tasks, wherein the data volume data of the previous cycle task, the error rate data of various tasks and the processing time data of the task can be obtained by obtaining the task log; and after obtaining the data, the data can be selected to be stored in the corresponding storage components respectively;

[0039] S2. Evaluate and analyze the task completion efficiency based on the task completion data of each process robot in the previous cycle;

[0040] In this embodiment, if Figure 2 As shown, the evaluation and analysis of the task completion efficiency in S2 includes the following specific steps:

[0041] S21, based on the data volume data of the previous period task, the error rate data of various tasks and the processing time data of the task, the success coefficient of the previous period task is evaluated. In this embodiment, Figure 3 As shown in the figure, the evaluation of the success coefficient of the previous cycle task completion includes the following specific contents:

[0042] S211, obtaining data volume data of the tasks completed in the previous period, and obtaining difficulty data of each completed task after calculating the inverse of the data volume data;

[0043] S212, obtain the difficulty data of each completed task, the error rate data of various tasks and the processing time data of the task, and substitute them into the calculation formula of the success coefficient of the previous cycle task completion to calculate the success coefficient of the previous cycle task completion, wherein the calculation formula of the success coefficient of the previous cycle task completion of the p-th process robot is: Among them, np is the number of tasks completed by the p-th process robot in the previous cycle, ci is the error rate of the i-th task completed by the p-th process robot in the previous cycle. Since the success coefficient is required here, 1-ci needs to be substituted. Ti is the data volume of the i-th task completed by the p-th process robot in the previous cycle, ti is the completion time of the i-th task completed by the p-th process robot in the previous cycle, Vm is the average speed of historical task completion, exp() is the power of the natural constant e. In this formula, the completion status of the tasks in the previous cycle is analyzed through the completion speed of the tasks in the previous cycle, the success rate of task completion and the task completion speed;

[0044] S22, evaluating the completion stability coefficient of the tasks in the previous cycle based on the data volume data of the tasks at each stage of the previous cycle and the processing time data of the tasks;

[0045] In this embodiment, evaluating the stability coefficient of task completion in the previous period includes the following specific steps:

[0046] Obtain the data volume data and task processing time data of each stage of the previous cycle, import the data volume data and task processing time data of each stage of the previous cycle into the calculation formula of the stability coefficient of the task completion of the previous cycle to calculate the stability coefficient of the task completion of the previous cycle. Among them, the calculation formula of the stability coefficient of the task completion of the pth process robot in the previous cycle is: in, In this formula, the stability of task completion is analyzed by the deviation of the processing speed of each task;

[0047] S23, performing weighted summation based on the success coefficient of task completion in the previous cycle and the stability coefficient of task completion in the previous cycle to obtain task completion efficiency;

[0048] It should be noted in this embodiment that the setting parameters and weights of this embodiment can be set by a person skilled in the art or obtained through experiments. The setting parameters and weights in this embodiment can be obtained through experiments such as: obtaining the task completion data and operation status of each process robot in the previous cycle and substituting them into each process of this embodiment to obtain the allocation of the task volume in the next cycle, and at the same time obtaining the task success rate after the historical allocation, and importing the maximum value of the task success rate and the allocation of the task volume in the next cycle into the fitting software to obtain the values ​​of each setting parameter that meets the maximum success rate;

[0049] S3. Perform operation evaluation and analysis of the process robots based on the operation status of each process robot in the previous cycle;

[0050] In this embodiment, the operation evaluation and analysis of the process robot includes the following specific steps:

[0051] S31, obtaining resource volume data and data volume data of various tasks of each process robot, and setting the ratio of resource volume data and data volume data of corresponding tasks as the consumption coefficient;

[0052] S32, obtaining the consumption coefficients of various tasks of each process robot and substituting them into the operation evaluation value calculation formula to calculate the operation evaluation value, wherein the calculation formula for the operation evaluation value of the pth process robot on the previous cycle is: Among them, a is the weight of the abnormal consumption coefficient, Hi is the consumption coefficient of the i-th task of the p-th process robot in the previous period. In this step, the operation of the process robot is evaluated by the consumption coefficient of the consumed resources and the fluctuation value of the consumption coefficient. In this formula, is the average value of the consumption coefficient of the task resources, representing the amount of resources consumed by the task. The sum of the differences between the consumption coefficient of each task and the average value represents the volatility of the consumption coefficient;

[0053] S4. Allocate the task volume for the next cycle based on the evaluation and analysis results of the task completion efficiency and the operation evaluation and analysis results of the process robot;

[0054] In this embodiment, the allocation of the next period task amount in S4 includes the following specific contents:

[0055] S41, obtaining the previous cycle operation evaluation value and task completion efficiency of each process robot, and obtaining the allocation coefficient of each process robot after weighted summation, and obtaining the task volume proportion of the corresponding process robot by dividing the allocation coefficient of all process robots by the allocation coefficients of all process robots, and obtaining the task volume data allocated to the corresponding process robot by multiplying the task volume proportion of the corresponding process robot by the task volume data to be allocated in this cycle;

[0056] S42, allocating the task amount according to the task amount data allocated to the corresponding process robots, and presenting it visually in real time. In this embodiment, the content of the real-time visual presentation includes the task completion status of each process robot, the allocation amount of the next cycle task of each process robot, and the operation status of each process robot;

[0057] In one of the specific embodiments, step S4 may also include the following specific contents before allocating the task volume: comparing the last cycle operation evaluation value and task completion efficiency of the process robot with their respective set qualified values; if the last cycle operation evaluation value and task completion efficiency of the process robot are both less than or equal to the corresponding qualified values, it means that the corresponding process robot has a fault and needs to be repaired, the corresponding faulty process robot is removed, and then the task volume is allocated, and a maintenance instruction for the corresponding faulty process robot is issued to the staff;

[0058] This embodiment may also include a system embodiment, which is implemented based on the above-mentioned visual process monitoring and adaptive optimization RPA system operation assurance method. The functions of the corresponding modules are the same as the method steps in the method embodiment, and will not be described in detail here.

[0059] It should be noted in this example that this embodiment has the following advantages over the prior art: first, an evaluation and analysis of the task completion efficiency is performed based on the task completion data of each process robot in the previous cycle; secondly, an operation evaluation and analysis of the process robot is performed based on the operation status of each process robot in the previous cycle; finally, the task volume for the next cycle is allocated based on the evaluation and analysis results of the task completion efficiency and the operation evaluation and analysis results of the process robot. This allows real-time monitoring of the process status, rapid response to abnormal situations, and continuous adjustment and optimization of the allocation of task volume based on system operation data to achieve continuous, efficient and uninterrupted operation, which not only improves the reliability of the system, but also greatly improves the operation and maintenance efficiency and the enterprise's workflow management capabilities.

[0060] Example 2

[0061] This embodiment provides an electronic device, such as Figure 4 As shown, Figure 4 Schematic diagram of an electronic device of the present application, the electronic device comprising: a processor and a memory, wherein the memory stores a computer program that can be called by the processor;

[0062] The processor executes the above-mentioned RPA system operation assurance method of visual process monitoring and adaptive optimization by calling the computer program stored in the memory.

[0063] The electronic device may have relatively large differences due to different configurations or performances, and may include one or more processors and one or more memories, wherein the memory stores at least one computer program, which is loaded and executed by the processor to implement the RPA system operation assurance method for visual process monitoring and adaptive optimization provided in the above method embodiment. The electronic device may also include other components for implementing the functions of the device. For example, the electronic device may also have components such as a wired or wireless network interface and an input and output interface to input and output data. This embodiment will not be described in detail here.

[0064] Example 3

[0065] This embodiment provides a computer-readable storage medium having a rewritable computer program stored thereon;

[0066] When the computer program runs on a computer device, the computer device executes the above-mentioned RPA system operation assurance method of visual process monitoring and adaptive optimization.

[0067] For example, the computer readable storage medium can be a read-only memory, a random access memory, a read-only CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, and the like.

[0068] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When a computer instruction or computer program is loaded or executed on a computer, a process or function according to an embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network or other programmable device. Computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, computer instructions can be transmitted from a website site, a computer, a server or a data center to another website site, a computer, a server or a data center through a wired network or / and a wireless network. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or a data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0069] The above is a detailed introduction to the RPA system operation assurance method for visual process monitoring and adaptive optimization provided by the present application. The various embodiments in the specification are described in a progressive manner. Each embodiment focuses on the differences from other embodiments. The same and similar parts between the embodiments can refer to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple. For the relevant parts, refer to the method part description. It should be pointed out that for ordinary technicians in this technical field, without departing from the principles of the present application, several improvements and modifications can be made to the present application, and these improvements and modifications also fall within the scope of protection of the claims of the present application.

[0070] It should also be noted that, in this specification, relational terms such as first and second, etc. are merely used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or apparatus that includes a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or apparatus. In the absence of further restrictions, the elements defined by the sentence "comprises one..." do not exclude the presence of other identical elements in the process, method, article or apparatus that includes the elements.

Claims

1. The RPA system operation guarantee method with visual process monitoring and adaptive optimization is characterized by: It includes the following specific steps: S1. Obtain the task completion data and operation status of each process robot in the previous cycle, and obtain the task volume data that needs to be assigned in this cycle; S2. Evaluate and analyze the task completion efficiency based on the task completion status data of each process robot in the previous cycle; S3. Perform operation evaluation and analysis of the process robots based on the operation status of each process robot in the previous cycle; S4. Allocate the task volume for the next cycle based on the evaluation and analysis results of the task completion efficiency and the operation evaluation and analysis results of the process robot.

2. The RPA system operation guarantee method for visual process monitoring and adaptive optimization according to claim 1 is characterized in that: The evaluation and analysis of the task completion efficiency in S2 includes the following specific steps: S21, evaluating the success coefficient of the task completion in the previous cycle based on the data volume data of the task in the previous cycle, the error rate data of various tasks and the processing time data of the tasks; S22, evaluating the completion stability coefficient of the tasks in the previous cycle based on the data volume data of the tasks at each stage of the previous cycle and the processing time data of the tasks; S23. Obtain the task completion efficiency by performing weighted summation based on the success coefficient of task completion in the previous cycle and the stability coefficient of task completion in the previous cycle.

3. The RPA system operation guarantee method for visual process monitoring and adaptive optimization as claimed in claim 2 is characterized in that: The operation evaluation and analysis of the process robot includes the following specific steps: S31, obtaining resource volume data and data volume data of various tasks of each process robot, and setting the ratio of resource volume data and data volume data of corresponding tasks as the consumption coefficient; S32, obtaining the consumption coefficients of various tasks of each process robot and substituting them into the operation evaluation value calculation formula to calculate the operation evaluation value, wherein the calculation formula for the operation evaluation value of the pth process robot on the previous cycle is: Among them, a is the weight of the abnormal consumption coefficient, and Hi is the consumption coefficient of the i-th task in the previous cycle of the p-th process robot.

4. The RPA system operation guarantee method for visual process monitoring and adaptive optimization as claimed in claim 3 is characterized in that: The allocation of the next cycle task volume in S4 includes the following specific contents: S41, obtaining the previous cycle operation evaluation value and task completion efficiency of each process robot, and obtaining the allocation coefficient of each process robot after weighted summation, and obtaining the task volume proportion of the corresponding process robot by dividing the allocation coefficient of all process robots by the allocation coefficients of all process robots, and obtaining the task volume data allocated to the corresponding process robot by multiplying the task volume proportion of the corresponding process robot by the task volume data to be allocated in this cycle; S42. Allocate the task volume according to the task volume data assigned to the corresponding process robot, and present it visually in real time.

5. The RPA system operation guarantee method for visual process monitoring and adaptive optimization as claimed in claim 4 is characterized in that: The evaluation of the success coefficient of the previous cycle task completion includes the following specific contents: S211, obtaining data volume data of the tasks completed in the previous period, and obtaining difficulty data of each completed task after calculating the inverse of the data volume data; S212, obtain the difficulty data of each completed task, the error rate data of various tasks and the processing time data of the task, and substitute them into the calculation formula of the success coefficient of the previous cycle task completion to calculate the success coefficient of the previous cycle task completion, wherein the calculation formula of the success coefficient of the previous cycle task completion of the p-th process robot is: Among them, np is the number of tasks completed by the p-th process robot in the previous cycle, ci is the error rate of the i-th task completed by the p-th process robot in the previous cycle, Ti is the data volume of the i-th task completed by the p-th process robot in the previous cycle, ti is the completion time of the i-th task completed by the p-th process robot in the previous cycle, Vm is the average speed of historical task completion, and exp() is the power of the natural constant e.

6. The RPA system operation guarantee method for visual process monitoring and adaptive optimization according to claim 5 is characterized in that: The evaluation of the stability coefficient of the task completion of the previous cycle includes the following specific steps: obtaining the data volume data and the task processing time data of each stage of the task in the previous cycle, importing the data volume data and the task processing time data of each stage of the task in the previous cycle into the calculation formula of the stability coefficient of the task completion of the previous cycle to calculate the stability coefficient of the task completion of the previous cycle, wherein the calculation formula of the stability coefficient of the task completion of the previous cycle of the pth process robot is: Among them, Vz is the mean of task processing speed.

7. The RPA system operation guarantee method for visual process monitoring and adaptive optimization according to claim 6 is characterized in that: The task completion status data in S1 specifically includes the data volume data of the previous cycle task, the error rate data of various tasks and the processing time data of the task. The operation status in S1 is the resource volume data occupied when performing various tasks.

8. An electronic device comprising: A processor and a memory, wherein the memory stores a computer program that can be called by the processor; It is characterized in that the processor executes the RPA system operation assurance method for visual process monitoring and adaptive optimization as described in any one of claims 1 to 7 by calling the computer program stored in the memory.

9. A computer-readable storage medium, characterized in that: Instructions are stored, and when the instructions are executed on a computer, the computer executes the RPA system operation assurance method for visual process monitoring and adaptive optimization as described in any one of claims 1 to 7.

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