RPA task exception handling method and device, server and storage medium

Abstract

The invention relates to the field of data processing, and discloses an RPA task exception handling method and device, a server and a storage medium. The method comprises the steps of obtaining execution feature information corresponding to an RPA task, wherein the execution feature information comprises execution time, an error rate, user intervention frequency and system response time; if it is judged that the RPA task is abnormal according to the execution feature information, obtaining a repair strategy according to the execution feature information; and executing an exception repair operation according to the repair strategy. In the embodiment of the invention, the repair strategy is generated according to the collected execution feature information. Therefore, each exception handling is a solution customized for specific conditions instead of an invariable handling mode, and the success rate of problem solving is improved.

Description

Technical Field

[0001] The present invention relates to the field of data processing, and in particular to an exception handling method, device, server and storage medium for RPA tasks. Background Art

[0002] In the current RPA (Robotic Process Automation) task processing, there is a challenge of lack of efficient and intelligent exception handling mechanism.

[0003] Traditional methods mainly rely on simple threshold judgment or manual intervention. It is difficult to obtain and analyze key feature information in real time when executing RPA tasks, which limits the efficiency of the automation process and increases operating costs. A new technical solution is needed to solve the above technical problems. Summary of the invention

[0004] The main purpose of the present invention is to solve the technical problem of ZZZZ.

[0005] A first aspect of the present invention provides an exception handling method for an RPA task, the exception handling method for an RPA task comprising:

[0006] Obtaining execution characteristic information corresponding to the RPA task, wherein the execution characteristic information includes execution time, error rate, user intervention frequency, and system response time;

[0007] If the RPA task is determined to be abnormal according to the execution characteristic information, a repair strategy is obtained according to the execution characteristic information;

[0008] According to the repair strategy, an abnormal repair operation is performed.

[0009] Optionally, in a first implementation manner of the first aspect of the present invention, if it is determined that the RPA task is abnormal according to the execution feature information, the step of acquiring a repair strategy according to the execution feature information includes:

[0010] If the RPA task is determined to be abnormal according to the execution characteristic information, problem cause information is generated according to the execution characteristic information;

[0011] Generate solution information corresponding to the problem cause information;

[0012] The repair strategy is generated according to the solution information.

[0013] Optionally, in a second implementation of the first aspect of the present invention, the step of generating the repair strategy according to the solution information includes:

[0014] Searching in a preset solution database whether there is the repair strategy corresponding to the solution information;

[0015] If the repair strategy does not exist, a prompt message indicating that manual intervention is required is output.

[0016] Optionally, in a third implementation manner of the first aspect of the present invention, after the step of generating solution information corresponding to the problem cause information, the method further includes:

[0017] Generate repair information corresponding to the abnormal repair operation, and generate an abnormality report according to the repair information, the execution feature information, the problem cause information and the solution information;

[0018] The exception report is output.

[0019] Optionally, in a fourth implementation manner of the first aspect of the present invention, the step of obtaining execution feature information corresponding to the RPA task includes:

[0020] Obtaining execution information of the RPA task, wherein the execution information includes log data, performance indicators, and output results;

[0021] Perform feature extraction on the execution information to obtain the execution feature information.

[0022] Optionally, in a fifth implementation manner of the first aspect of the present invention, after the step of performing an abnormal repair operation according to the repair strategy, the method further includes:

[0023] When the abnormal repair operation is completed, requesting input of feedback information corresponding to the abnormal repair operation;

[0024] When the feedback information is obtained, the repair strategy is updated according to the feedback information.

[0025] Optionally, in a sixth implementation manner of the first aspect of the present invention, if it is determined that the RPA task is abnormal according to the execution feature information, before the step of acquiring a repair strategy according to the execution feature information, the method further includes:

[0026] The execution feature information is input into a pre-trained neural network model, wherein the neural network model determines whether the RPA task is abnormal based on the execution feature information.

[0027] A second aspect of the present invention provides an exception handling device for an RPA task, comprising a memory and a processor, wherein the processor is used to execute program instructions stored in the memory so that the exception handling device for the RPA task performs the above-mentioned exception handling method for the RPA task.

[0028] A third aspect of the present invention provides a server, characterized in that it includes a memory and a processor, and the processor is used to execute program instructions stored in the memory so that the server executes the above-mentioned RPA task exception handling method.

[0029] A fourth aspect of the present invention provides a computer-readable storage medium, in which instructions are stored. When the computer-readable storage medium is executed on a computer, the computer executes the above-mentioned RPA task exception handling method.

[0030] In an embodiment of the present invention, the execution feature information corresponding to the RPA task is obtained, and the execution feature information includes execution time, error rate, user intervention frequency, and system response time; if the RPA task is determined to be abnormal based on the execution feature information, a repair strategy is obtained based on the execution feature information; and an abnormal repair operation is performed based on the repair strategy. Whether a task is abnormal is determined by automatically collecting execution feature information (such as execution time, error rate, user intervention frequency, and system response time). The reliance on manual intervention is reduced, and the accuracy of exception handling is improved. A repair strategy is generated based on the collected execution feature information. Each exception handling is a solution tailored to the specific situation, rather than a one-size-fits-all approach, which improves the success rate of problem solving. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 This is a schematic diagram of an embodiment of a method for handling an exception of an RPA task in an embodiment of the present invention;

[0032] Figure 2 This is a schematic diagram of a specific embodiment of step 102 of the method for handling an exception of an RPA task in an embodiment of the present invention;

[0033] Figure 3 This is a schematic diagram of a specific embodiment of step 1023 of the method for handling an exception of an RPA task in an embodiment of the present invention;

[0034] Figure 4 A schematic diagram of an embodiment of an exception handling device for an RPA task in an embodiment of the present invention;

[0035] Figure 5 FIG. 1 is a schematic diagram of an embodiment of a server in an embodiment of the present invention. DETAILED DESCRIPTION

[0036] Embodiments of the present invention provide an RPA task exception handling method, device, server, and storage medium.

[0037] The embodiments disclosed in the present invention will be described in more detail below with reference to the accompanying drawings. Although certain embodiments disclosed in the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be construed as being limited to the embodiments described herein, which are instead provided for a more thorough and complete understanding of the present invention. It should be understood that the drawings and embodiments disclosed in the present invention are only for exemplary purposes and are not intended to limit the scope of protection disclosed in the present invention.

[0038] In the description of the embodiments disclosed in the present invention, the term "including" and similar terms should be understood as open inclusion, that is, "including but not limited to". The term "based on" should be understood as "based at least in part on". The term "one embodiment" or "the embodiment" should be understood as "at least one embodiment". The terms "first", "second", etc. may refer to different or the same objects. Other explicit and implicit definitions may also be included below.

[0039] For ease of understanding, the specific process of the embodiment of the present invention is described below. Figure 1 , an embodiment of the method for handling an exception of an RPA task in an embodiment of the present invention includes:

[0040] 101. Obtain execution characteristic information corresponding to the RPA task, where the execution characteristic information includes execution time, error rate, user intervention frequency, and system response time;

[0041] Specifically, the RPA system, or Robotic Process Automation system, is a technology that uses software robots or "robot workers" to simulate and perform repetitive, regular, and predictable tasks that humans perform in daily business processes. These software robots can interact with applications or systems by simulating the way humans operate computers, use mice, keyboard input, read and process data, etc. The core function of the RPA system is to automate the execution of preset business processes to improve efficiency, reduce errors, and free up human resources.

[0042] RPA tasks refer to specific business processes or operations defined and configured in the RPA system. These tasks are usually repetitive, regular, and predictable, and are suitable for execution through automation. RPA tasks can cover multiple aspects such as data input, processing, interaction, and decision-making, such as data extraction, conversion, task allocation, and communication. In actual applications, RPA tasks are usually designed to be automatically executed according to predefined instructions and processes without human intervention.

[0043] The RPA system provides the necessary environment and tools for the execution of RPA tasks. Through the RPA system, users can define, configure and manage various RPA tasks to ensure that they can be automatically executed as expected.

[0044] In this embodiment, the RPA task exception handling device collects data related to the RPA task execution, including but not limited to log data, performance indicators and output results. The collected data can be obtained in real time from the running RPA task to ensure the timeliness and accuracy of the data.

[0045] Extract key execution feature information from the collected data, such as execution time, error rate, user intervention frequency, and system response time. This is used for subsequent abnormality judgment. The feature extraction process may include data preprocessing, such as standardization and normalization, to adapt to subsequent analysis.

[0046] 102. If the RPA task is determined to be abnormal according to the execution characteristic information, a repair strategy is obtained according to the execution characteristic information;

[0047] Specifically, if a task is judged to be abnormal, further analyze the cause of the problem, including using machine learning models or rule base methods to infer possible causes of the problem from execution feature information. The rule base is a set of predefined rules used for decision support.

[0048] Based on the analyzed cause of the problem, corresponding solution information is generated, including querying the preset solution database to find out whether there is a matching repair strategy.

[0049] Optionally, the execution feature information is input into a pre-trained neural network model, wherein the neural network model determines whether the RPA task is abnormal based on the execution feature information. Specifically, the execution feature information is input into a pre-trained neural network model to determine whether the RPA task is abnormal. The neural network model is trained based on historical data and known abnormal patterns and can identify data that deviates from normal operation.

[0050] 103. Perform an abnormal repair operation according to the repair strategy.

[0051] Specifically, according to the obtained repair strategy, specific abnormal repair operations are implemented, which may include restarting tasks, modifying data input, adjusting process parameters, etc.

[0052] After the repair operation is completed, relevant feedback information will be requested to evaluate the effectiveness of the repair operation.

[0053] In an embodiment of the present invention, the execution feature information corresponding to the RPA task is obtained, and the execution feature information includes execution time, error rate, user intervention frequency, and system response time; if the RPA task is determined to be abnormal based on the execution feature information, a repair strategy is obtained based on the execution feature information; and an abnormal repair operation is performed based on the repair strategy. Whether a task is abnormal is determined by automatically collecting execution feature information (such as execution time, error rate, user intervention frequency, and system response time). The reliance on manual intervention is reduced, and the accuracy of exception handling is improved. A repair strategy is generated based on the collected execution feature information. Each exception handling is a solution tailored to the specific situation, rather than a one-size-fits-all approach, which improves the success rate of problem solving.

[0054] Reference Figure 2 , Figure 2 FIG. 1 is a schematic diagram of a specific embodiment of step 102 of the method for handling an exception of an RPA task in an embodiment of the present invention. Step 102 also includes the following specific implementation methods:

[0055] 1021. If it is determined that the RPA task is abnormal according to the execution characteristic information, problem cause information is generated according to the execution characteristic information;

[0056] Specifically, based on the collected execution feature information, such as execution time, error rate, user intervention frequency, and system response time, an analysis algorithm or neural network model is used to determine whether there are any abnormalities in the RPA task.

[0057] When the anomaly is confirmed, the cause analysis is automatically triggered. In this stage, data mining and pattern recognition technology are used to generate problem cause information from execution features. This can be achieved based on association rule learning, decision tree analysis and other technologies.

[0058] 1022. Generate solution information corresponding to the problem cause information;

[0059] Specifically, based on the cause of the problem obtained through analysis, the knowledge base and historical data are used to generate targeted solution information. Machine learning models can be combined to provide evidence-based solution strategies.

[0060] 1023. Generate the repair strategy according to the solution information.

[0061] Specifically, the cause of the problem and the solution information are comprehensively considered to generate the final repair strategy. If the corresponding strategy does not exist in the preset solution database, it is prompted that manual intervention is required.

[0062] In steps 1021-1023, through automated analysis and problem solving, the reliance on manual intervention is reduced, and the automation and efficiency of the entire RPA system are improved. This optional embodiment can self-learn and adjust, and over time, the frequency and severity of errors are reduced, and long-term self-learning can reduce maintenance costs.

[0063] For the exception handling of RPA tasks, a good judgment mechanism is needed to determine in which cases automatic processing is sufficient to solve the problem and in which cases manual intervention is required. Over-reliance on automatic processing may ignore the uniqueness of complex problems, while over-reliance on manual intervention may affect efficiency. Based on this, this embodiment provides an optional embodiment.

[0064] Reference Figure 3 , Figure 3 FIG. 1 is a schematic diagram of a specific implementation of step 1023 of the method for handling an exception of an RPA task in an embodiment of the present invention. Step 1023 also includes the following specific implementation methods:

[0065] 10231. Searching a preset solution database to determine whether the repair strategy corresponding to the solution information exists;

[0066] Specifically, a preset solution database is searched to find solution information that matches the generated problem cause information.

[0067] Check whether there is a corresponding repair policy. If so, the policy will be used for the exception repair operation.

[0068] 10232. If the repair strategy does not exist, output a prompt message indicating that manual intervention is required.

[0069] Specifically, if there is no appropriate preset repair strategy, it will be prompted that manual intervention is required to deal with the current abnormal situation.

[0070] In steps 10231-10232, automatically retrieving the repair strategy from the preset solution database can quickly and accurately handle RPA task exceptions, reducing errors and delays. It can automatically adjust its processing strategy to adapt to different abnormal situations. If there is no suitable strategy, timely introduction of manual processing can enable the smooth completion of the task.

[0071] It is necessary to ensure that the information in the generated abnormal report is accurate, and any error or omission may affect the diagnosis and subsequent processing of the problem. Based on this, this embodiment provides an optional embodiment.

[0072] Step 101 also includes the following specific implementations:

[0073] 1011. Generate repair information corresponding to the abnormal repair operation, and generate an abnormality report according to the repair information, the execution feature information, the problem cause information and the solution information;

[0074] Specifically, after determining the cause of the problem and the solution, detailed repair information required for the repair operation is generated. A detailed exception report is generated based on the repair information, execution feature information, problem cause information and solution information.

[0075] 1013. Output the abnormality report.

[0076] In steps 1011-1012, by generating a detailed exception report, relevant personnel can clearly understand the cause of the exception, the measures taken, and the final result. The exception report provides key information to help analyze and optimize the execution process of the RPA task.

[0077] It is crucial to ensure that the collected execution information is complete and accurate, and any missing or incorrect data may lead to incorrect feature extraction and subsequent analysis. Based on this, this embodiment provides an optional embodiment.

[0078] Step 101 also includes the following specific implementations:

[0079] 1011. Obtain execution information of the RPA task, where the execution information includes log data, performance indicators, and output results.

[0080] Specifically, the execution information of RPA tasks is collected, including log data, performance indicators, and output results.

[0081] 1013. Perform feature extraction on the execution information to obtain the execution feature information.

[0082] Specifically, from the collected execution information, key execution feature information is extracted, such as execution time, error rate, user intervention frequency, and system response time.

[0083] In steps 1011 - 1012 , by extracting key and detailed execution feature information, the performance and potential problems in task execution can be analyzed more accurately.

[0084] In traditional RPA task exception processing, the repair strategy is often fixed or based on limited experience. As a result, when faced with complex and changing exception problems, the repair strategy may not be flexible or effective enough. Based on this, this embodiment provides an optional embodiment.

[0085] Step 101 also includes the following specific implementations:

[0086] 1011. When the abnormal repair operation is completed, request input of feedback information corresponding to the abnormal repair operation;

[0087] Specifically, the RPA task exception is repaired according to the obtained repair strategy.

[0088] After the abnormal repair operation is completed, a request is triggered to input feedback information corresponding to the abnormal repair operation. The feedback information may include whether the repair is successful, whether new problems arise, difficulties encountered during the repair process, and suggestions for modifying the repair strategy.

[0089] Receive feedback from users or automatic detection tools. Analyze the feedback. Make necessary adjustments to the existing repair strategy based on the analysis results of the feedback. This may include modifying the repair steps, adding new repair measures, or deleting invalid repair methods.

[0090] 1013. When the feedback information is obtained, update the repair strategy according to the feedback information.

[0091] Specifically, the updated repair strategy is stored in the system's knowledge base or solution database so that similar problems can be solved more efficiently in the future.

[0092] In steps 1011-1012, by collecting and analyzing feedback information, the repair strategy can be continuously optimized, invalid or inefficient repair attempts can be reduced, and the overall repair efficiency can be improved. Feedback information provides first-hand information about the repair effect, which can accurately evaluate the effectiveness of the repair strategy and make necessary adjustments accordingly.

[0093] It is easy to understand that the terms involved in the exception handling method of RPA tasks include but are not limited to:

[0094] RPA tasks: refers to specific business processes or workflows that are automatically executed using RPA technology. RPA software can simulate human user operations on computers, such as clicking, typing, copying and pasting, to automate business processes.

[0095] Execution characteristic information: Execution characteristic information refers to various data collected during the execution of RPA tasks that can reflect the execution status or performance of tasks. In the present invention, execution characteristic information includes execution time, error rate, user intervention frequency, system response time, etc.

[0096] Repair strategy: refers to the solution or measures formulated for RPA task abnormalities. Repair strategies may include re-executing tasks, adjusting task parameters, modifying task processes, etc.

[0097] Abnormal repair operation: refers to the process of handling RPA task abnormalities according to the repair strategy. It includes steps such as identifying the cause of the abnormality, executing repair measures, and verifying the repair effect.

[0098] Problem cause information: refers to the specific reasons that lead to RPA task exceptions obtained by analyzing execution feature information. This information may involve task configuration errors, insufficient system resources, external dependency issues, etc.

[0099] Solution information: refers to specific measures or suggestions formulated based on the cause of the problem, aimed at resolving RPA task anomalies. These measures may include modifying task configuration, optimizing system resource allocation, adjusting external dependencies, etc.

[0100] Solution database: refers to a database that stores known problems and corresponding solutions. In RPA task exception processing, you can quickly find a repair strategy for a specific problem by searching the solution database.

[0101] Exception report: refers to a document that records the occurrence of RPA task exceptions, the cause of the problem, the solution and the repair result.

[0102] Feature extraction: Feature extraction refers to the process of extracting valuable information or features from raw data for problem judgment. In RPA task exception processing, feature extraction usually involves analyzing and processing execution information to extract feature information that can reflect the task execution status or performance.

[0103] Neural network model: A neural network model is a machine learning model based on an artificial neural network that can simulate the connection and information transmission process between neurons in the human brain. In RPA task exception processing, the neural network model can be used to analyze and judge execution feature information to identify whether the task is abnormal.

[0104] Reference Figure 4, the embodiment of the present disclosure provides an exception handling device 400 for RPA tasks, including an exception handling device body for RPA tasks, and an exception handling device 405 for RPA tasks. The exception handling device 405 for RPA tasks includes a processor (processor) 404 and a memory (memory) 401 storing program instructions. Optionally, the device may also include a communication interface (Communication Interface) 402 and a bus 403. Among them, the processor 404, the communication interface 402, and the memory 401 can complete mutual communication through the bus 403. The communication interface 402 can be used for information transmission. The processor 404 can call the program instructions in the memory 401 to execute the exception handling method for the RPA task of the above embodiment. In addition, the logic instructions in the above memory 401 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. The memory 401, as a computer-readable storage medium, can be used to store software programs, computer executable programs, such as program instructions / modules corresponding to the method in the embodiment of the present disclosure. The processor 404 executes the function application and data processing by running the program instructions / modules stored in the memory 401, that is, the exception handling method of the RPA task in the above embodiment is implemented. The memory 401 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and an application required for at least one function; the data storage area may store data created according to the use of the terminal device, etc. In addition, the memory 401 may include a high-speed random access memory and may also include a non-volatile memory.

[0105] Reference Figure 5 , the embodiment of the present disclosure provides a server 500, including a processor (processor) 504 and a memory (memory) 501 storing program instructions. Optionally, the server may also include a communication interface (CommunicationInterface) 502 and a bus 503. Among them, the processor 504, the communication interface 502, and the memory 501 can communicate with each other through the bus 503. The communication interface 502 can be used for information transmission. The processor 504 can call the program instructions in the memory 501 to execute the exception handling method of the RPA task of the above embodiment.

[0106] In addition, the logic instructions in the above-mentioned memory 501 can be implemented in the form of software functional units and can be stored in a computer-readable storage medium when sold or used as an independent product. The memory 501, as a computer-readable storage medium, can be used to store software programs, computer executable programs, such as program instructions / modules corresponding to the method in the embodiment of the present disclosure. The processor 504 executes functional applications and data processing by running the program instructions / modules stored in the memory 501, that is, the exception handling method of the RPA task in the above-mentioned embodiment is implemented. The memory 501 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and at least one application required for a function; the data storage area may store data created according to the use of the terminal device, etc. In addition, the memory 501 may include a high-speed random access memory and may also include a non-volatile memory.

[0107] The technical solution of the embodiment of the present disclosure can be embodied in the form of a software product, which is stored in a storage medium and includes one or more instructions for enabling a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in the embodiment of the present disclosure. The aforementioned storage medium may be a non-transient storage medium, including: a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and other media that can store program codes, or a transient storage medium.

[0108] The above description and the accompanying drawings fully illustrate the embodiments of the present disclosure so that those skilled in the art can practice them. Other embodiments may include structural, logical, electrical, process and other changes. The embodiments represent only possible changes. Unless explicitly required, separate components and functions are optional, and the order of operation may vary. The parts and features of some embodiments may be included in or replace the parts and features of other embodiments. Moreover, the words used in this application are only used to describe the embodiments and are not used to limit the claims. As used in the description of the embodiments and the claims, unless the context clearly indicates, the singular forms of "a", "an" and "the" are intended to include plural forms as well. Similarly, the term "and / or" as used in this application refers to any and all possible combinations of listings containing one or more associated ones. In addition, when used in the present application, the term "comprise" and its variants "comprises" and / or comprising refer to the presence of stated features, wholes, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or groups thereof. In the absence of further restrictions, the elements defined by the sentence "comprising a ..." do not exclude the presence of other identical elements in the process, method or device comprising the elements. In this article, each embodiment may focus on the differences from other embodiments, and the same and similar parts between the various embodiments may refer to each other. For the methods, products, etc. disclosed in the embodiments, if they correspond to the method part disclosed in the embodiments, then the relevant parts can refer to the description of the method part.

[0109] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software may depend on the specific application and design constraints of the technical solution. The technicians may use different methods for each specific application to implement the described functions, but such implementations should not be considered to exceed the scope of the embodiments of the present disclosure. The technicians may clearly understand that, for the convenience and simplicity of description, the specific working processes of the systems, devices and units described above may refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here.

[0110] In the embodiments disclosed herein, the disclosed methods and products (including but not limited to devices, equipment, etc.) can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units can be only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between each other shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms. The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the units may be selected according to actual needs to implement this embodiment. In addition, each functional unit in the embodiment of the present disclosure may be integrated in a processing unit, or each unit may exist physically alone, or two or more units may be integrated in one unit.

[0111] The flowchart and block diagram in the accompanying drawings show the possible architecture, function and operation of the system, method and computer program product according to the embodiment of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of the code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. In some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, which can depend on the functions involved. In the description corresponding to the flowchart and the block diagram in the accompanying drawings, the operations or steps corresponding to different boxes can also occur in a different order from the order disclosed in the description, and sometimes there is no specific order between different operations or steps. For example, two consecutive operations or steps can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, which can depend on the functions involved. Each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented by a dedicated hardware-based system that performs the specified functions or actions, or may be implemented by a combination of dedicated hardware and computer instructions.

Claims

1. A method for handling exceptions in an RPA task, characterized in that: The exception handling method of the RPA task includes: Obtaining execution characteristic information corresponding to the RPA task, wherein the execution characteristic information includes execution time, error rate, user intervention frequency, and system response time; If the RPA task is determined to be abnormal according to the execution characteristic information, a repair strategy is obtained according to the execution characteristic information; According to the repair strategy, an abnormal repair operation is performed.

2. The RPA task exception handling method according to claim 1, characterized in that: If the RPA task is determined to be abnormal according to the execution feature information, the step of obtaining a repair strategy according to the execution feature information includes: If the RPA task is determined to be abnormal according to the execution characteristic information, problem cause information is generated according to the execution characteristic information; Generate solution information corresponding to the problem cause information; The repair strategy is generated according to the solution information.

3. The RPA task exception handling method according to claim 2, characterized in that: The step of generating the repair strategy according to the solution information includes: Searching in a preset solution database whether there is the repair strategy corresponding to the solution information; If the repair strategy does not exist, a prompt message indicating that manual intervention is required is output.

4. The RPA task exception handling method according to claim 2, characterized in that: After the step of generating solution information corresponding to the problem cause information, the method further includes: Generate repair information corresponding to the abnormal repair operation, and generate an abnormality report according to the repair information, the execution feature information, the problem cause information and the solution information; The exception report is output.

5. The RPA task exception handling method according to claim 1, characterized in that: The step of obtaining execution feature information corresponding to the RPA task includes: Obtaining execution information of the RPA task, wherein the execution information includes log data, performance indicators, and output results; Perform feature extraction on the execution information to obtain the execution feature information.

6. The RPA task exception handling method according to claim 1, characterized in that: After the step of performing the abnormal repair operation according to the repair strategy, the method further includes: When the abnormal repair operation is completed, requesting input of feedback information corresponding to the abnormal repair operation; When the feedback information is obtained, the repair strategy is updated according to the feedback information.

7. The RPA task exception handling method according to claim 1, characterized in that: If it is determined that the RPA task is abnormal according to the execution feature information, before the step of obtaining a repair strategy according to the execution feature information, the method further includes: The execution feature information is input into a pre-trained neural network model, wherein the neural network model determines whether the RPA task is abnormal based on the execution feature information.

8. An exception handling device for RPA tasks, characterized in that: The system comprises a memory and a processor, wherein the processor is used to execute program instructions stored in the memory to implement the RPA task exception handling method according to any one of claims 1 to 7.

9. A server, characterized in that: The system comprises a memory and a processor, wherein the processor is used to execute program instructions stored in the memory to implement the RPA task exception handling method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the exception handling method for an RPA task according to any one of claims 1 to 7 is implemented.

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