A cross-system cross-platform RPA digital employee process execution system

Abstract

The application discloses a cross-system cross-platform RPA digital employee process execution system and relates to the technical field of RPA digital employees.The application comprises a platform construction module, a task planning module, an execution module, a report generation module and an optimization iteration module.The platform construction module is used for constructing a HyperAgent intelligent agent platform and integrating the platform with RPA capability in a decoupled mode, so that an intelligent agent+RPA collaborative execution environment is realized, the workload of manual task allocation and scheduling is greatly reduced, the flexibility and adaptability of task execution are improved, the diversified and dynamic business demands of enterprises are adapted, the complexity and failure rate of manual operation are avoided, the execution efficiency and standardization level of cross-system business processes are improved, the system development, operation and use threshold is greatly reduced, the efficiency of enterprise landing application is improved, and the stability and universality of system execution are improved.

Description

Technical Field

[0001] This invention relates to the field of RPA digital employee technology, and specifically to a cross-system, cross-platform RPA digital employee process execution system. Background Technology

[0002] With the comprehensive advancement of enterprise digital transformation, internal business operations are gradually exhibiting a complex situation of cross-departmental, cross-level, and cross-system collaboration. Daily business processes generally involve multiple heterogeneous terminals and business carriers, such as web-based management systems, desktop clients, office documents, email communications, SAP enterprise management systems, databases, and various industry-specific business platforms. These systems are independently built, have inconsistent data standards, and varying degrees of interface openness, resulting in a large number of scattered and independent business silos. Cross-platform and cross-system business connections heavily rely on manual offline operations, leading to low overall process efficiency. Therefore, it is necessary to analyze a cross-system and cross-platform RPA digital employee process execution system.

[0003] Existing technology, such as the invention application patent with publication number CN117371772A, discloses a digital employee workflow system based on RPA technology, comprising: an RPA automation control platform including a workflow identification module, an intelligent analysis module, a development and deployment module, an execution module, a monitoring and management module, and an intelligent optimization module. This invention allows users to input workflows via an operating terminal. The intelligent analysis module converts the identified workflows into understandable and executable automation rules and specific operation instructions. The development and deployment module develops software based on the automation rules analyzed by the intelligent analysis module and deploys it to the corresponding work environment. Simultaneously, the monitoring and management module monitors and manages the developed software to ensure its normal operation and promptly corrects errors and abnormal data. The intelligent optimization module analyzes and adjusts data obtained during actual use to improve the system's performance and effectiveness.

[0004] Existing technologies can meet the basic requirements for a cross-system, cross-platform RPA digital employee workflow execution system, but they also have some potential defects and challenges, specifically in the following aspects: First, traditional RPA is only suitable for executing fixed processes in a single scenario and system, with weak cross-platform adaptability. It is difficult to integrate with older systems without open API interfaces or privately deployed systems, making it difficult to achieve truly unified cross-system and cross-terminal execution. Second, existing RPA solutions lack top-level intelligent agent coordination and scheduling capabilities, and can only execute preset fixed scripts. They lack the ability to parse tasks based on natural language business objectives, autonomously decompose tasks, and intelligently schedule tasks. First, the existing automation solutions lack the ability to independently divide sub-tasks and sort out execution dependencies based on business logic, resulting in extremely poor process flexibility and adaptability. Second, traditional RPA platforms have closed architectures with high development and configuration thresholds, lacking unified development and maintenance capabilities that are no-code and visual. They are also deeply disconnected from capabilities such as RAG knowledge bases and intelligent data analysis, and can only complete basic simulation operations, affecting the integration and analysis of execution data and the output of standardized reports. Third, existing automation solutions lack unified monitoring, anomaly tracing, and closed-loop optimization mechanisms for the entire process, making it impossible to track and control the entire process of task scheduling, process execution, and data interaction, and making it difficult to locate process bottlenecks and achieve dynamic iterative upgrades. Summary of the Invention

[0005] The purpose of this invention is to provide a cross-system, cross-platform RPA digital employee workflow execution system, which solves the problems existing in the background technology.

[0006] To solve the above technical problems, the present invention adopts the following technical solution: The present invention provides a cross-system and cross-platform RPA digital employee process execution system, including a platform construction module, a task planning module, an execution module, a report generation module, and an optimization iteration module.

[0007] The platform building module is used to build the HyperAgent intelligent agent platform and complete the RPA capability integration based on enterprise business needs and system environment, so as to obtain an intelligent agent + RPA collaborative execution environment.

[0008] The task planning module is used to autonomously decompose and schedule business tasks based on the collaborative execution environment to obtain a standardized sequence of subtasks.

[0009] The execution module is used to call the RPA execution module based on the subtask sequence to automate cross-system and cross-platform business operations and obtain process execution results.

[0010] The report generation module is used to integrate and analyze the data of process execution results to generate standardized business reports.

[0011] The optimization and iteration module is used to monitor and optimize the collaborative execution environment, task planning logic, and process execution effect to obtain an iterative automated execution solution.

[0012] Furthermore, the construction of the HyperAgent intelligent agent platform and the integration of RPA capabilities to obtain an intelligent agent + RPA collaborative execution environment are specifically analyzed as follows: based on the enterprise's existing IT architecture and business systems, the deployment method and scope of the intelligent agent platform are determined to obtain a platform deployment plan.

[0013] Based on the platform deployment scheme, a HyperAgent intelligent agent platform consisting of three major modules—Agent, Studio, and Server—was built to obtain the basic intelligent agent operating environment.

[0014] Based on the basic intelligent agent operating environment, an enhanced intelligent agent environment is obtained by integrating the RAG knowledge base, Insight data analysis, and RPA action execution core capabilities.

[0015] Based on the enhanced agent environment, the decoupled integration of RPA tools and agent platform is completed, supporting operation calls from systems without API interfaces, so as to obtain an agent + RPA collaborative execution environment.

[0016] Furthermore, the method for autonomously decomposing and scheduling business tasks based on the collaborative execution environment to obtain a standardized sub-task sequence is as follows: based on the natural language business objectives input by the user, the task requirements are parsed and the intent is identified to obtain explicit task instructions.

[0017] Based on clear task instructions, the overall task is broken down into independently executable subtasks according to business logic to obtain a preliminary set of subtasks.

[0018] Based on the execution dependencies and resource allocation of subtasks, the set of subtasks is sorted and scheduled to obtain a standardized subtask sequence.

[0019] Furthermore, the method for automatically executing cross-system and cross-platform business operations based on subtask sequences to obtain process execution results is as follows: based on standardized subtask sequences, the corresponding RPA execution template and operation node are matched to obtain the subtask execution configuration.

[0020] Based on the subtask execution configuration, RPA is used to simulate manual operations on the execution object to obtain single-step execution data.

[0021] Based on single-step execution data, complete the process actions across system data to obtain the process execution result.

[0022] Furthermore, the specific analysis method for integrating and analyzing the process execution results to generate a standardized business report is as follows: based on the process execution results, multi-source heterogeneous data is cleaned, summarized, and filtered to obtain a standardized analysis dataset.

[0023] Based on standardized analysis datasets, data mining and pattern extraction are performed using the Insight data analysis module to obtain data insights.

[0024] Based on data insights, standardized business reports are generated according to a preset format.

[0025] Furthermore, the specific analysis method for monitoring and optimizing the collaborative execution environment, task planning logic, and process execution effect to obtain the iterative automated execution scheme is as follows: based on the unified monitoring module, the status of the entire process of agent scheduling, RPA execution, and data interaction is tracked to obtain the process operation log.

[0026] Based on the process operation logs, execution bottlenecks, abnormal nodes, and efficiency shortcomings can be identified to find areas for process optimization.

[0027] Based on process optimization points, the task planning rules, RPA execution process, and platform configuration parameters are adjusted to obtain an iterative automated execution solution.

[0028] Furthermore, the aforementioned platform deployment scheme establishes a HyperAgent intelligent agent platform comprising three major modules: Agent, Studio, and Server, to obtain a basic intelligent agent operating environment. The specific analysis method includes: building the Studio module based on a unified development framework, service management, and interface standards to obtain a no-code visual development environment.

[0029] The Server module enables unified deployment, operation, and monitoring to provide the platform's operating environment.

[0030] The Agent module is used to implement business interactions and task triggering to obtain the basic intelligent agent operating environment.

[0031] Furthermore, the specific analysis method for obtaining single-step execution data by simulating manual operation through the execution object based on the subtask execution configuration includes: using non-intrusive technology, RPA is integrated into the existing system without changing the original architecture to obtain a stable access state.

[0032] Based on low-code configuration, RPA execution rules and operation paths are set to obtain precise execution logic.

[0033] Based on precise execution logic, simulated operations are performed to obtain single-step execution data.

[0034] Furthermore, the method for adjusting task planning rules, RPA execution processes, and platform configuration parameters based on process optimization points to obtain an iterative automated execution scheme is as follows: based on changes in business requirements, update the intelligent agent task decomposition rules and scheduling strategies to obtain optimized planning logic.

[0035] Based on the requirements of execution efficiency, we simplify redundant RPA steps and optimize node configuration to obtain an efficient execution process.

[0036] Based on the platform's operating status, resource allocation and interface configuration are adjusted to obtain an iterative automated execution solution.

[0037] Furthermore, the optimization iteration module has a built-in instruction conversion unit that converts trigger instructions from different terminals into unified JSON format execution instructions, ensuring that the backend execution logic of the same type of task initiated by each terminal is completely consistent, and the execution results are fed back to the user in a form adapted to the corresponding terminal, thereby realizing the unified instruction control function of the multi-terminal normalization access module.

[0038] The beneficial effects of this invention are as follows: First, by constructing a HyperAgent intelligent agent platform through a platform building module and decoupledly integrating it with RPA capabilities, a collaborative execution environment of intelligent agent + RPA is achieved. This supports operation calls from systems without API interfaces and is adaptable to various execution objects such as web pages, desktop applications, Excel, email, SAP, and databases. It effectively breaks down the barriers of heterogeneous multi-system architecture and data silos within enterprises, without requiring changes to the existing IT architecture. It enables unified automated execution of cross-system and cross-platform business processes, solving the technical pain points of poor cross-platform adaptability and difficulty in accessing systems without interfaces in traditional RPA. Second, with the help of the task planning module, based on user-input natural language business objectives, it can autonomously complete task parsing, intent recognition, task decomposition, and scheduling planning, generating standardized sub-task sequences. This eliminates the need for manually pre-setting fixed scripts and allows for the autonomous organization of sub-task dependencies and optimization of execution order based on business logic. This significantly reduces the workload of manual task allocation and scheduling, improves the flexibility and adaptability of task execution, and meets the diverse and dynamic business needs of enterprises. Third, the execution module is based on a standardized subtask sequence and matches the corresponding RPA execution template. Through non-intrusive access and low-code configuration, it simulates manual operation and accurately completes cross-system data reading, verification, entry, download, and reporting processes. This avoids the tedium and error rate of manual operation and improves the execution efficiency and standardization of cross-system business processes. Attached Figure Description

[0039] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0040] Figure 1 This is a schematic diagram of the system structure connection of the present invention.

[0041] Figure 2 This is a schematic diagram of the system structure connection of the present invention. Detailed Implementation

[0042] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0043] Reference Figure 1 As shown, the present invention provides a cross-system, cross-platform RPA digital employee process execution system, including: a platform construction module, a task planning module, an execution module, a report generation module, and an optimization iteration module.

[0044] The platform building module is used to build the HyperAgent intelligent agent platform and complete the RPA capability integration based on enterprise business needs and system environment, so as to obtain an intelligent agent + RPA collaborative execution environment.

[0045] The task planning module is used to autonomously decompose and schedule business tasks based on the collaborative execution environment to obtain a standardized sequence of subtasks.

[0046] The execution module is used to call the RPA execution module based on the subtask sequence to automate cross-system and cross-platform business operations and obtain process execution results.

[0047] The report generation module is used to integrate and analyze the data of process execution results to generate standardized business reports.

[0048] The optimization and iteration module is used to monitor and optimize the collaborative execution environment, task planning logic, and process execution effect to obtain an iterative automated execution solution.

[0049] In the above embodiments, the specific analysis method for constructing the HyperAgent intelligent agent platform and completing RPA capability integration to obtain an intelligent agent + RPA collaborative execution environment is as follows: based on the enterprise's existing IT architecture and business systems, determine the deployment method and scope of the intelligent agent platform to obtain a platform deployment plan.

[0050] Based on the platform deployment scheme, a HyperAgent intelligent agent platform consisting of three major modules—Agent, Studio, and Server—was built to obtain the basic intelligent agent operating environment.

[0051] Based on the basic intelligent agent operating environment, an enhanced intelligent agent environment is obtained by integrating the RAG knowledge base, Insight data analysis, and RPA action execution core capabilities.

[0052] Based on the enhanced agent environment, the decoupled integration of RPA tools and agent platform is completed, supporting operation calls from systems without API interfaces, so as to obtain an agent + RPA collaborative execution environment.

[0053] In the above embodiments, the specific analysis method for autonomously decomposing and scheduling business tasks based on the collaborative execution environment to obtain a standardized sub-task sequence is as follows: based on the natural language business objectives input by the user, the task requirements are parsed and the intent is identified to obtain clear task instructions.

[0054] Based on clear task instructions, the overall task is broken down into independently executable subtasks according to business logic to obtain a preliminary set of subtasks.

[0055] Based on the execution dependencies and resource allocation of subtasks, the set of subtasks is sorted and scheduled to obtain a standardized subtask sequence.

[0056] In the above embodiments, the method of calling the RPA execution module based on the subtask sequence to automate cross-system and cross-platform business operations and obtain process execution results is as follows: based on the standardized subtask sequence, the corresponding RPA execution template and operation node are matched to obtain the subtask execution configuration.

[0057] Based on the subtask execution configuration, RPA is used to simulate manual operations on the execution object to obtain single-step execution data.

[0058] Based on single-step execution data, complete the process actions across system data to obtain the process execution result.

[0059] It should be noted that the objects to be executed include web pages, desktop applications, Excel, email, SAP, databases, etc.

[0060] It should be noted that the process includes reading, verifying, entering, downloading, and reporting.

[0061] In the above embodiments, the specific analysis method for integrating and analyzing the process execution results to generate a standardized business report is as follows: based on the process execution results, multi-source heterogeneous data is cleaned, summarized, and filtered to obtain a standardized analysis dataset.

[0062] Based on standardized analysis datasets, data mining and pattern extraction are performed using the Insight data analysis module to obtain data insights.

[0063] Based on data insights, standardized business reports are generated according to a preset format.

[0064] It should be noted that standardized business reports include Excel, Word, PPT, industry reports, etc.

[0065] In the above embodiments, the specific analysis method for monitoring and optimizing the collaborative execution environment, task planning logic, and process execution effect to obtain the iterative automated execution scheme is as follows: based on the unified monitoring module, the status of the entire process of agent scheduling, RPA execution, and data interaction is tracked to obtain the process operation log.

[0066] Based on the process operation logs, execution bottlenecks, abnormal nodes, and efficiency shortcomings can be identified to find areas for process optimization.

[0067] Based on process optimization points, the task planning rules, RPA execution process, and platform configuration parameters are adjusted to obtain an iterative automated execution solution.

[0068] In the above embodiments, the HyperAgent intelligent agent platform, which includes three major modules: Agent, Studio, and Server, is built based on the platform deployment scheme to obtain the basic intelligent agent operating environment. The specific analysis method includes: building the Studio module based on a unified development framework, service management, and interface standards to obtain a no-code visual development environment.

[0069] The Server module enables unified deployment, operation, and monitoring to provide the platform's operating environment.

[0070] The Agent module is used to implement business interactions and task triggering to obtain the basic intelligent agent operating environment.

[0071] In the above embodiments, the specific analysis method for obtaining single-step execution data by simulating manual operation through the execution object based on the subtask execution configuration includes: based on non-intrusive technology, RPA is integrated into the existing system without changing the original architecture to obtain a stable access state.

[0072] Based on low-code configuration, RPA execution rules and operation paths are set to obtain precise execution logic.

[0073] Based on precise execution logic, simulated operations are performed to obtain single-step execution data.

[0074] It should be noted that the simulated operations include clicking, typing, copying, pasting, downloading, and uploading.

[0075] It should be noted that the execution targets include RPA applications such as web pages, desktop applications, Excel files, email, SAP systems, and databases.

[0076] In the above embodiments, the specific analysis method for adjusting the task planning rules, RPA execution process and platform configuration parameters based on process optimization points to obtain the iterative automated execution scheme is as follows: based on changes in business requirements, update the intelligent agent task decomposition rules and scheduling strategies to obtain the optimized planning logic.

[0077] Based on the requirements of execution efficiency, we simplify redundant RPA steps and optimize node configuration to obtain an efficient execution process.

[0078] Based on the platform's operating status, resource allocation and interface configuration are adjusted to obtain an iterative automated execution solution.

[0079] In the above embodiments, the optimization iteration module has a built-in instruction conversion unit. The instruction conversion unit converts the trigger instructions of different terminals into unified JSON format execution instructions, ensuring that the backend execution logic of the same type of task initiated by each terminal is completely consistent. The execution results are fed back to the user in a form adapted to the corresponding terminal, thereby realizing the unified instruction control function of the multi-terminal normalization access module.

[0080] The above content is merely an example and illustration of the concept of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described, or use similar methods to replace them, as long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, and all such modifications or additions should fall within the protection scope of the present invention.

Claims

1. A cross-system, cross-platform RPA digital employee workflow execution system, characterized in that, include: The platform building module is used to build the HyperAgent intelligent agent platform and complete the RPA capability integration based on enterprise business needs and system environment, so as to obtain an intelligent agent + RPA collaborative execution environment; The task planning module is used to autonomously decompose and schedule business tasks based on the collaborative execution environment to obtain a standardized sequence of subtasks. The execution module is used to call the RPA execution module based on the subtask sequence to automate cross-system and cross-platform business operations and obtain process execution results; The report generation module is used to integrate and analyze the data of process execution results to generate standardized business reports; The optimization and iteration module is used to monitor and optimize the collaborative execution environment, task planning logic, and process execution effect to obtain an iterative automated execution solution.

2. The cross-system, cross-platform RPA digital employee workflow execution system according to claim 1, characterized in that, The construction of the HyperAgent intelligent agent platform and the integration of RPA capabilities to obtain an intelligent agent + RPA collaborative execution environment are specifically analyzed as follows: Based on the enterprise's existing IT architecture and business systems, determine the deployment method and scope of the intelligent agent platform to obtain a platform deployment plan; Based on the platform deployment scheme, a HyperAgent intelligent agent platform including three major modules: Agent, Studio, and Server is built to obtain the basic intelligent agent operating environment; Based on the basic intelligent agent operating environment, the RAG knowledge base, Insight data analysis, and RPA action execution core capabilities are integrated to obtain an enhanced intelligent agent environment; Based on the enhanced agent environment, the decoupled integration of RPA tools and agent platform is completed, supporting operation calls from systems without API interfaces, so as to obtain an agent + RPA collaborative execution environment.

3. The cross-system, cross-platform RPA digital employee workflow execution system according to claim 2, characterized in that, The method for autonomously decomposing and scheduling business tasks based on a collaborative execution environment to obtain a standardized sub-task sequence is as follows: Based on the natural language business objectives input by the user, the task requirements are parsed and the intent is identified in order to obtain clear task instructions; Based on clear task instructions, the overall task is broken down into independently executable subtasks according to business logic to obtain a preliminary set of subtasks; Based on the execution dependencies and resource allocation of subtasks, the set of subtasks is sorted and scheduled to obtain a standardized subtask sequence.

4. The cross-system, cross-platform RPA digital employee workflow execution system according to claim 1, characterized in that, The method for automating cross-system and cross-platform business operations by calling the RPA execution module based on subtask sequences to obtain process execution results is as follows: Based on the standardized subtask sequence, the corresponding RPA execution template and operation node are matched to obtain the subtask execution configuration; Based on the subtask execution configuration, RPA is used to simulate manual operations on the execution object to obtain single-step execution data; Based on single-step execution data, complete the process actions across system data to obtain the process execution result.

5. The cross-system, cross-platform RPA digital employee workflow execution system according to claim 1, characterized in that, The specific analysis method for integrating and analyzing the process execution results to generate standardized business reports is as follows: Based on the process execution results, multi-source heterogeneous data are cleaned, summarized, and filtered to obtain a standardized analysis dataset; Based on standardized analysis datasets, data mining and pattern extraction are performed using the Insight data analysis module to obtain data insight conclusions; Based on data insights, standardized business reports are generated according to a preset format.

6. The cross-system, cross-platform RPA digital employee workflow execution system according to claim 1, characterized in that, The specific analysis method for monitoring and optimizing the collaborative execution environment, task planning logic, and process execution effect to obtain an iterative automated execution solution is as follows: Based on the unified monitoring module, the status of the entire process of agent scheduling, RPA execution, and data interaction is tracked to obtain process operation logs; Based on the process operation log, identify execution bottlenecks, abnormal nodes, and efficiency shortcomings to find areas for process optimization. Based on process optimization points, the task planning rules, RPA execution process, and platform configuration parameters are adjusted to obtain an iterative automated execution solution.

7. The cross-system, cross-platform RPA digital employee workflow execution system according to claim 1, characterized in that, The aforementioned platform deployment scheme establishes a HyperAgent intelligent agent platform comprising three main modules: Agent, Studio, and Server, to obtain the basic intelligent agent operating environment. The specific analysis methods include: Based on a unified development framework, service management, and interface standards, the Studio module is built to provide a no-code visual development environment. The Server module is used to complete unified deployment, operation and maintenance and monitoring to obtain the platform's operating environment; The Agent module is used to implement business interactions and task triggering to obtain the basic intelligent agent operating environment.

8. The cross-system, cross-platform RPA digital employee workflow execution system according to claim 1, characterized in that, The subtask-based execution configuration, which simulates manual operation through the execution object to obtain single-step execution data, includes the following specific analysis methods: Based on non-intrusive technology, RPA can be integrated into existing systems without changing the original architecture, thus achieving a stable access state. Based on low-code configuration, RPA execution rules and operation paths are set to obtain precise execution logic; Based on precise execution logic, simulated operations are performed to obtain single-step execution data.

9. The cross-system, cross-platform RPA digital employee workflow execution system according to claim 1, characterized in that, The method for adjusting task planning rules, RPA execution processes, and platform configuration parameters based on process optimization points to obtain an iterative automated execution solution is as follows: Based on changes in business requirements, update the intelligent agent task decomposition rules and scheduling strategies to obtain optimized planning logic; Based on the requirements of execution efficiency, we simplify redundant RPA steps and optimize node configuration to obtain an efficient execution process; Based on the platform's operating status, resource allocation and interface configuration are adjusted to obtain an iterative automated execution solution.

10. A cross-system, cross-platform RPA digital employee workflow execution system according to claim 1, characterized in that, The optimization iteration module has a built-in instruction conversion unit, which converts the trigger instructions from different terminals into unified JSON format execution instructions. This ensures that the backend execution logic of the same type of task initiated by each terminal is completely consistent, and the execution results are fed back to the user in a form adapted to the corresponding terminal, thereby realizing the unified instruction control function of the multi-terminal normalization access module.

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