RPA data processing method, apparatus, computer equipment and storage medium

Abstract

This application belongs to the field of big data and relates to an RPA data processing method, apparatus, computer equipment, and storage medium. The method includes: acquiring an RPA data packet, the RPA data packet including RPA data of multiple data dimensions of RPA components; acquiring a data processing task corresponding to the RPA data in the RPA data packet, the data processing task including a primary statistical task and an advanced evaluation task; running the data processing task to process the RPA data, obtaining component statistical results and component evaluation results; and generating a data processing result of the RPA data based on the component statistical results and the component evaluation results. This application systematically processes RPA data, reducing resource waste of RPA data.

Description

Technical Field

[0001] This application relates to the field of big data technology, and in particular to an RPA data processing method, apparatus, computer equipment and storage medium. Background Technology

[0002] Robotic Process Automation (RPA) is a business process automation technology based on software robots and artificial intelligence (AI). It's an application that can automatically complete highly repetitive, rule-based tasks, thereby reducing manual labor intensity, error rates, accelerating business processes, and achieving automation and intelligence in work. Currently, RPA is widely used in finance, taxation, supply chain, human resources, information maintenance, and many other fields.

[0003] RPA products generate a large amount of data during operation, and this data has certain informational value. However, there is currently no systematic processing for RPA data, resulting in a waste of RPA data resources. Summary of the Invention

[0004] The purpose of this application is to provide an RPA data processing method, apparatus, computer equipment, and storage medium to reduce the waste of RPA data.

[0005] To address the aforementioned technical problems, this application provides an RPA data processing method, employing the following technical solution:

[0006] Obtain RPA data packets, which include RPA data from multiple data dimensions of the RPA component;

[0007] Obtain the data processing task corresponding to the RPA data in the RPA data packet, the data processing task including a primary statistical task and an advanced evaluation task;

[0008] Run the data processing task to process the RPA data and obtain component statistical results and component evaluation results;

[0009] The data processing results of the RPA data are generated based on the component statistics and the component evaluation results.

[0010] To address the aforementioned technical problems, this application also provides an RPA data processing apparatus, which employs the following technical solution:

[0011] A data packet acquisition module is used to acquire RPA data packets, which include RPA data from multiple data dimensions of the RPA component;

[0012] The task acquisition module is used to acquire data processing tasks corresponding to the RPA data in the RPA data packet. The data processing tasks include primary statistical tasks and advanced evaluation tasks.

[0013] The data processing module is used to run the data processing task to process the RPA data and obtain component statistical results and component evaluation results.

[0014] The result generation module is used to generate data processing results for the RPA data based on the component statistical results and the component evaluation results.

[0015] To address the aforementioned technical problems, this application also provides a computer device that employs the following technical solution:

[0016] Obtain RPA data packets, which include RPA data from multiple data dimensions of the RPA component;

[0017] Obtain the data processing task corresponding to the RPA data in the RPA data packet, the data processing task including a primary statistical task and an advanced evaluation task;

[0018] Run the data processing task to process the RPA data and obtain component statistical results and component evaluation results;

[0019] The data processing results of the RPA data are generated based on the component statistics and the component evaluation results.

[0020] To address the aforementioned technical problems, this application also provides a computer-readable storage medium, employing the technical solution described below:

[0021] Obtain RPA data packets, which include RPA data from multiple data dimensions of the RPA component;

[0022] Obtain the data processing task corresponding to the RPA data in the RPA data packet, the data processing task including a primary statistical task and an advanced evaluation task;

[0023] Run the data processing task to process the RPA data and obtain component statistical results and component evaluation results;

[0024] The data processing results of the RPA data are generated based on the component statistics and the component evaluation results.

[0025] Compared with the prior art, the embodiments of this application have the following advantages: First, it acquires RPA data packets, which include RPA data from multiple data dimensions of RPA components, ensuring data richness. The RPA data packets are generated with encryption, ensuring data security. Second, it acquires preset data processing tasks corresponding to the RPA data, including primary statistical tasks and advanced evaluation tasks. The primary statistical tasks perform statistical summarization of the RPA data, while the advanced evaluation tasks perform evaluation and analysis of the RPA components. Third, it runs the data processing tasks to perform different levels of data processing on the RPA data, obtaining component statistical results and component evaluation results respectively, and generating data processing results for the RPA data. This achieves systematic processing of RPA data and reduces resource waste of RPA data. Attached Figure Description

[0026] To more clearly illustrate the solutions in this application, the accompanying drawings used in the description of the embodiments of this application will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0027] Figure 1 This is an exemplary system architecture diagram to which this application can be applied;

[0028] Figure 2 This is a flowchart of an embodiment of the RPA data processing method according to this application;

[0029] Figure 3 This is a schematic diagram of the structure of one embodiment of the RPA data processing apparatus according to this application;

[0030] Figure 4 This is a schematic diagram of the structure of one embodiment of the computer device according to this application. Detailed Implementation

[0031] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein in the specification of the application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application; the terms "comprising" and "having," and any variations thereof, in the specification, claims, and foregoing drawings of this application, are intended to cover non-exclusive inclusion. The terms "first," "second," etc., in the specification, claims, or foregoing drawings of this application are used to distinguish different objects, not to describe a particular order.

[0032] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0033] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings.

[0034] like Figure 1 As shown, system architecture 100 may include terminal devices 101, 102, and 103, a network 104, and a server 105. Network 104 serves as the medium for providing communication links between terminal devices 101, 102, and 103 and server 105. Network 104 may include various connection types, such as wired or wireless communication links, or fiber optic cables, etc.

[0035] Users can use terminal devices 101, 102, and 103 to interact with server 105 via network 104 to receive or send messages, etc. Various communication client applications can be installed on terminal devices 101, 102, and 103, such as web browser applications, shopping applications, search applications, instant messaging tools, email clients, social media platform software, etc.

[0036] Terminal devices 101, 102, and 103 can be various electronic devices with displays and support web browsing, including but not limited to smartphones, tablets, e-book readers, MP3 players (Moving Picture Experts Group Audio Layer III), MP4 players (Moving Picture Experts Group Audio Layer IV), laptops, and desktop computers, etc.

[0037] Server 105 can be a server that provides various services, such as a backend server that supports the pages displayed on terminal devices 101, 102, and 103.

[0038] It should be noted that the RPA data processing method provided in this application embodiment is generally executed by a server, and correspondingly, the RPA data processing device is generally located in the server.

[0039] It should be understood that Figure 1The number of terminal devices, networks, and servers shown is merely illustrative. Depending on implementation needs, any number of terminal devices, networks, and servers can be included.

[0040] Continue to refer to Figure 2 A flowchart of an embodiment of the RPA data processing method according to this application is shown. The RPA data processing method includes the following steps:

[0041] Step S201: Obtain RPA data packets, which include RPA data from multiple data dimensions of the RPA component.

[0042] In this embodiment, the RPA data processing method runs on an electronic device (e.g., Figure 1 The server shown can communicate with the terminal via wired or wireless connection. It should be noted that the aforementioned wireless connection methods may include, but are not limited to, 3G / 4G / 5G connections, WiFi connections, Bluetooth connections, WiMAX connections, Zigbee connections, UWB (ultra wideband) connections, and other currently known or future wireless connection methods.

[0043] Specifically, this application includes an executor and a processing device for an RPA component. The executor can be located in a terminal device, responsible for executing the RPA component, collecting relevant data from the RPA component to obtain RPA data, and sending the RPA data to the processing device in the form of RPA data packets. The processing device can be located in a server, responsible for processing the RPA data.

[0044] The processing device first acquires an RPA data packet, which contains RPA data. The RPA data can be data related to the RPA component collected from multiple preset data dimensions.

[0045] Furthermore, before step S201 above, the process may include: collecting RPA data from the RPA component through the runner, wherein the RPA data includes basic data and runtime data, and the basic data and runtime data each have multiple data dimensions; and encrypting the RPA data through the encryptor in the runner to obtain an RPA data packet.

[0046] Specifically, RPA data of the RPA component can be collected in advance through the runner in the terminal device. RPA data includes basic data and runtime data, each with multiple data dimensions. Basic data is typically generated during RPA component initialization or updates, such as the component name, display name, code, version number, category, creation date, and creator. Runtime data is typically generated during RPA component operation, including the number of runs, runtime status, and runtime logs (data related to component operation, including logs). The runner can automatically collect RPA data from the RPA component according to pre-defined data dimensions.

[0047] The runner contains an encryptor, and the processing device contains a decryptor. The encryptor encrypts RPA data to obtain RPA data packets, and the decryptor decrypts the RPA data packets to obtain the original RPA data. The runner and processing device can pre-agree on a key, and then generate encryptor code and decryptor code based on the key and its algorithm. The key and algorithm are contained within the encryptor and decryptor code. The encryptor code on the runner side is generated based on the encryptor code, and the decryptor code on the processing device side is generated based on the decryptor code. By storing the key in the code, encryption / decryption can be performed at runtime, and the key cannot be directly obtained externally, thus improving key security and consequently enhancing data transmission security. After generating the RPA data packet, the runner sends it to the processing device on the server.

[0048] In this embodiment, the runner collects RPA data from the RPA component. The RPA data includes basic data and operational data, and each of the basic data and operational data has multiple data dimensions, ensuring the richness of the collected RPA data. The RPA data is encrypted by the encryptor in the runner to obtain RPA data packets, ensuring the security of RPA data packet transmission.

[0049] Step S202: Obtain the data processing task corresponding to the RPA data in the RPA data packet. The data processing task includes a primary statistical task and an advanced evaluation task.

[0050] Specifically, the processing device acquires the RPA data from the RPA data packet, and then obtains the data processing task corresponding to the pre-configured RPA data. The data processing task can perform specific logical data processing on the RPA data.

[0051] Data processing tasks can be a collective term for multiple tasks, including basic statistical tasks and advanced evaluation tasks.

[0052] Furthermore, the steps described above for obtaining the data processing task corresponding to the RPA data in the RPA data packet may include: inputting the RPA data packet into a decryptor and running the decryptor to decrypt the RPA data packet to obtain the RPA data; extracting the RPA component identifier from the RPA data; and querying the data processing task corresponding to the RPA data based on the RPA component identifier.

[0053] Specifically, the RPA data packet is input into the decryptor in the processing device, and the decryptor is run to decrypt the RPA data packet to obtain the RPA data. Then, the RPA component identifier is extracted from the RPA data. The RPA component identifier uniquely identifies the RPA component; it can be the component name, display name, or component code. Based on the RPA component identifier, the corresponding data processing task is searched in the data processing task list, and the searched data processing task is used as the data processing task corresponding to the RPA data.

[0054] In this embodiment, the RPA data packet is input into the decryptor, and the decryptor is run to decrypt the RPA data packet to obtain RPA data in plaintext form, ensuring that subsequent data processing can be performed; the RPA component identifier is extracted from the RPA data, and the data processing task is queried according to the RPA component identifier so that the RPA data can be systematically processed according to the data processing task.

[0055] Step S203: Run a data processing task to process the RPA data and obtain component statistical results and component evaluation results.

[0056] Specifically, the task involves processing equipment operation data. This data processing includes primary statistical tasks and advanced evaluation tasks. Primary statistical tasks are mainly used to statistically analyze and summarize RPA operation data, while advanced evaluation tasks are used to evaluate and analyze RPA components. Executing the primary statistical tasks yields component statistical results, and executing the advanced evaluation tasks yields component evaluation results.

[0057] Furthermore, step S203 may include: running a primary statistical task in the data processing task to process the RPA data based on the primary statistical task and obtain component statistical results; and running an advanced evaluation task in the data processing task based on the RPA data and component statistical results to obtain component evaluation results.

[0058] Specifically, the primary statistical task in the data processing task is run to perform statistical and inductive data processing on the RPA data based on the primary statistical task, and to obtain component statistical results.

[0059] Advanced evaluation tasks can be performed based on primary statistical tasks. That is, advanced evaluation tasks are run based on RPA data and component statistical results to obtain component evaluation results.

[0060] In this embodiment, a primary statistical task is first performed to process data at the statistical and inductive levels to obtain component statistical results; then, an advanced evaluation task is run based on the RPA data and component statistical results to evaluate and analyze the RPA components and obtain component evaluation results, thus realizing comprehensive and systematic processing of RPA data.

[0061] Furthermore, the primary statistical task in the aforementioned data processing task, which processes RPA data based on the primary statistical task to obtain component statistical results, may include: extracting first feature data from RPA data based on the primary statistical task in the data processing task; obtaining first standard data corresponding to the first feature data from the primary statistical task; comparing the first feature data with the first standard data; and generating component statistical results based on the comparison results.

[0062] Specifically, the primary statistical task requires specific feature data during runtime; this feature data is data from a certain dimension of the RPA data. The primary statistical task extracts the first feature data from the RPA data, and then obtains the first standard data corresponding to the first feature data from the primary statistical task.

[0063] The first standard data acts as a benchmark. The first feature data is compared with the first standard data to obtain a comparison result. Based on the comparison result, it is determined whether the first feature data is normal, thereby determining whether the RPA component is functioning correctly and generating component statistical results. It should be noted that "normal" here refers to normality measured by the first feature data.

[0064] For example, the number of times a stage of an RPA component is run (the first characteristic data) is counted and then compared with a threshold for the number of runs (the first standard data). The success rate of a stage of an RPA component is also counted and then compared with a threshold for the success rate (the first standard data). If the number of stage runs reaches 90% of the threshold for the number of runs and the success rate of the stage runs reaches 90% of the threshold for the success rate, it means that the stage of the RPA component is running as expected. If both are below 70% of the threshold, the RPA component may have defects.

[0065] In this embodiment, first feature data is extracted from RPA data, and first standard data corresponding to the first feature data is obtained from the primary statistical task; the first feature data is compared with the first standard data, and component statistical results are generated based on the comparison results, thereby realizing the statistics and summarization of RPA data and realizing the first level of RPA data processing.

[0066] Furthermore, the steps described above, which involve running an advanced evaluation task within a data processing task based on RPA data and component statistical results to obtain component evaluation results, may include: determining an advanced evaluation task within the data processing task based on the component statistical results; the advanced evaluation task may include an anomaly detection task, a component expansion task, or a component optimization task; obtaining the second feature data corresponding to the advanced evaluation task from the RPA data and component statistical results; and running the advanced evaluation task based on the second feature data to obtain the component evaluation results.

[0067] Specifically, advanced evaluation tasks can be selectively performed based on the component statistical results. Data processing tasks can include multiple advanced evaluation tasks, and at least one advanced evaluation task can be selected from multiple advanced evaluation tasks based on the component statistical results.

[0068] Advanced evaluation tasks can include anomaly detection, component expansion, or component optimization. Each advanced evaluation task can extract data from RPA data and component statistics to obtain the required second feature data. Then, the advanced evaluation task is run based on this second feature data to obtain the corresponding component evaluation results. The second feature data and the first feature data can contain data of the same dimension.

[0069] Anomaly detection tasks can involve inputting second-type feature data into an anomaly detection model. This model can be built based on a neural network. Inputting the second-type feature data into the anomaly detection model allows for a deeper assessment of whether an RPA component exhibits anomalies, or whether an RPA component will experience anomalies in the future, thus obtaining anomaly detection results. Compared to the first-type feature data, the second-type feature data is more subtle in indicating whether an RPA component is functioning correctly. An RPA component may experience anomalies in the future, which may not be immediately apparent from the first-type feature data. In such cases, anomaly detection tasks can be used to detect these anomalies. In this context, the second type of data can include the CPU (central processing unit) resources, memory resources, disk resources, network resources, runtime, and response time consumed by the RPA component during runtime.

[0070] Based on the component statistics, we can assess how to further improve the RPA component, and then execute component extension tasks or component optimization tasks. These can be executed automatically by the program, manually, or by a combination of program and manual execution. If the component statistics indicate that the RPA component is performing well on a certain metric, we can execute the corresponding component extension task to further improve the component's performance; if the component statistics indicate that the RPA component is performing poorly on a certain metric, we can execute the corresponding component optimization task to improve the component's performance.

[0071] For example, based on the component statistics, if the number of times an RPA component runs a stage is greater than or equal to M1% of the run count threshold (M1 is an integer from 0 to 100, for example, M1 = 90), or if the success rate of an RPA component's stage runs is greater than or equal to N1% of the success rate threshold (N1 is an integer from 0 to 100, for example, N1 = 90), it indicates that the RPA component is running well and its application scenarios can be further expanded, and the scenario guidance content can be enriched. At this time, the corresponding component expansion task can be executed.

[0072] Based on the component statistics, if the number of times an RPA component runs a stage is less than M2% of the run count threshold (M2 is an integer from 0 to 100, and M2 is less than M1, for example, M2 = 70), or if the success rate of an RPA component's stage runs is less than N2% of the success rate threshold (N2 is an integer from 0 to 100, and N2 is less than N1, for example, N2 = 70), it indicates that the RPA component is performing poorly. A preliminary assessment suggests that the RPA component may have application scenario defects. Component optimization tasks could include: conducting user surveys to determine the reasons for the poor performance; testing and troubleshooting the RPA component; automatically generating or reminding developers to compile RPA component usage guidance documents to guide users in using the RPA component; or directly initiating assistance for users in using the RPA component.

[0073] In this embodiment, an advanced evaluation task is determined in the data processing task based on the component statistical results. The advanced evaluation task includes anomaly detection task, component expansion task, or component optimization task. The second feature data required for the advanced evaluation task is obtained from the RPA data and component statistical results. Then, the advanced evaluation task is run to realize the evaluation and analysis of the RPA component, so as to improve the RPA component and realize the second level of RPA data processing.

[0074] Step S204: Generate data processing results for RPA data based on component statistical results and component evaluation results.

[0075] Specifically, the data processing results are generated based on the component statistics and component evaluation results. These data processing results can systematically display the RPA data and the operating status of the RPA components.

[0076] Furthermore, step S204 may include: processing component statistical results and component evaluation results through a graphical tool to obtain data processing results of RPA data; displaying the data processing results through the target terminal; and storing the RPA data and data processing results in a database.

[0077] Specifically, component statistical results and component evaluation results can be processed using pre-defined graphical tools to obtain RPA data processing results. These results can include multi-dimensional visualizations such as graphs, tables, and data. The processing device then sends the data processing results to the target terminal, allowing developers to better view and monitor the operational status of the RPA components.

[0078] The processing device can also store RPA data and data processing results in a database to achieve persistent data storage.

[0079] In this embodiment, the data processing results are obtained by processing the component statistical results and component evaluation results through graphical tools. The data processing results can be displayed in the form of charts, etc., so that developers can better view and monitor the operation of RPA components, and the RPA data and data processing results can be persistently saved through the database.

[0080] In this embodiment, an RPA data packet is acquired, which includes RPA data from multiple data dimensions of the RPA component, ensuring data richness. The RPA data packet is generated with encryption to ensure data security. Pre-defined data processing tasks corresponding to the RPA data are acquired. These tasks include a primary statistical task and an advanced evaluation task. The primary statistical task performs statistical summarization of the RPA data, while the advanced evaluation task performs evaluation and analysis of the RPA components. The data processing tasks are run to perform different levels of data processing on the RPA data, obtaining component statistical results and component evaluation results respectively, and generating the RPA data processing results. This achieves systematic processing of RPA data and reduces resource waste of RPA data.

[0081] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by instructing related hardware with computer-readable instructions. These computer-readable instructions can be stored in a computer-readable storage medium. When executed, the program can include the processes of the embodiments of the above methods. The aforementioned storage medium can be a non-volatile storage medium such as a magnetic disk, optical disk, or read-only memory (ROM), or random access memory (RAM).

[0082] It should be understood that although the steps in the flowcharts of the accompanying figures are shown sequentially as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the accompanying figures may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0083] Further reference Figure 3 As a response to the above Figure 2 To implement the method shown, this application provides an embodiment of an RPA data processing apparatus, which is similar to... Figure 2 Corresponding to the method embodiments shown, this device can be specifically applied to various electronic devices.

[0084] like Figure 3 As shown, the RPA data processing device 300 described in this embodiment includes: a data packet acquisition module 301, a task acquisition module 302, a data processing module 303, and a result generation module 304, wherein:

[0085] The data packet acquisition module 301 is used to acquire RPA data packets, which include RPA data from multiple data dimensions of the RPA component.

[0086] The task acquisition module 302 is used to acquire the data processing tasks corresponding to the RPA data in the RPA data packet. The data processing tasks include primary statistical tasks and advanced evaluation tasks.

[0087] The data processing module 303 is used to run data processing tasks to process RPA data and obtain component statistical results and component evaluation results.

[0088] The result generation module 304 is used to generate data processing results of RPA data based on component statistical results and component evaluation results.

[0089] In this embodiment, an RPA data packet is acquired, which includes RPA data from multiple data dimensions of the RPA component, ensuring data richness. The RPA data packet is generated with encryption to ensure data security. Pre-defined data processing tasks corresponding to the RPA data are acquired. These tasks include a primary statistical task and an advanced evaluation task. The primary statistical task performs statistical summarization of the RPA data, while the advanced evaluation task performs evaluation and analysis of the RPA components. The data processing tasks are run to perform different levels of data processing on the RPA data, obtaining component statistical results and component evaluation results respectively, and generating the RPA data processing results. This achieves systematic processing of RPA data and reduces resource waste of RPA data.

[0090] In some optional implementations of this embodiment, the RPA data processing device 300 may further include: a data acquisition module and a data encryption module, wherein:

[0091] The data acquisition module is used to collect RPA data from the RPA component through the runner. The RPA data includes basic data and runtime data, each with multiple data dimensions.

[0092] The data encryption module is used to encrypt RPA data through the encryptor in the runner to obtain RPA data packets.

[0093] In this embodiment, the runner collects RPA data from the RPA component. The RPA data includes basic data and operational data, and each of the basic data and operational data has multiple data dimensions, ensuring the richness of the collected RPA data. The RPA data is encrypted by the encryptor in the runner to obtain RPA data packets, ensuring the security of RPA data packet transmission.

[0094] In some optional implementations of this embodiment, the task acquisition module 302 may include: a data packet decryption submodule, an identifier extraction submodule, and a task query submodule, wherein:

[0095] The packet decryption submodule is used to input RPA packets into the decryptor and run the decryptor to decrypt the RPA packets and obtain RPA data.

[0096] The identifier extraction submodule is used to extract RPA component identifiers from RPA data.

[0097] The task query submodule is used to query the data processing tasks corresponding to RPA data based on the RPA component identifier.

[0098] In this embodiment, the RPA data packet is input into the decryptor, and the decryptor is run to decrypt the RPA data packet to obtain RPA data in plaintext form, ensuring that subsequent data processing can be performed; the RPA component identifier is extracted from the RPA data, and the data processing task is queried according to the RPA component identifier so that the RPA data can be systematically processed according to the data processing task.

[0099] In some optional implementations of this embodiment, the data processing module 303 may include: a primary operation submodule and an evaluation operation submodule, wherein:

[0100] The primary execution submodule is used to run the primary statistical tasks in the data processing tasks, so as to process the RPA data based on the primary statistical tasks and obtain the component statistical results.

[0101] The evaluation and execution submodule is used to run advanced evaluation tasks in the data processing task based on RPA data and component statistics to obtain component evaluation results.

[0102] In this embodiment, a primary statistical task is first performed to process data at the statistical and inductive levels to obtain component statistical results; then, an advanced evaluation task is run based on the RPA data and component statistical results to evaluate and analyze the RPA components and obtain component evaluation results, thus realizing comprehensive and systematic processing of RPA data.

[0103] In some optional implementations of this embodiment, the primary operation submodule may include: a first extraction unit, a standard acquisition unit, and a data comparison unit, wherein:

[0104] The first extraction unit is used to extract first feature data from RPA data based on the primary statistical task in the data processing task.

[0105] The standard acquisition unit is used to acquire the first standard data corresponding to the first feature data from the primary statistical task.

[0106] The data comparison unit is used to compare the first feature data with the first standard data and generate component statistical results based on the comparison results.

[0107] In this embodiment, first feature data is extracted from RPA data, and first standard data corresponding to the first feature data is obtained from the primary statistical task; the first feature data is compared with the first standard data, and component statistical results are generated based on the comparison results, thereby realizing the statistics and summarization of RPA data and realizing the first level of RPA data processing.

[0108] In some optional implementations of this embodiment, the evaluation and execution submodule may include: a task determination unit, a second acquisition unit, and an evaluation and execution unit, wherein:

[0109] The task determination unit is used to determine advanced evaluation tasks in the data processing tasks based on the component statistical results. Advanced evaluation tasks include anomaly detection tasks, component expansion tasks, or component optimization tasks.

[0110] The second acquisition unit is used to acquire the second feature data corresponding to the advanced evaluation task from RPA data and component statistical results.

[0111] The evaluation execution unit is used to run advanced evaluation tasks based on the second feature data to obtain component evaluation results.

[0112] In this embodiment, an advanced evaluation task is determined in the data processing task based on the component statistical results. The advanced evaluation task includes anomaly detection task, component expansion task, or component optimization task. The second feature data required for the advanced evaluation task is obtained from the RPA data and component statistical results. Then, the advanced evaluation task is run to realize the evaluation and analysis of the RPA component, so as to improve the RPA component and realize the second level of RPA data processing.

[0113] In some optional implementations of this embodiment, the result generation module 304 may include: a result processing submodule, a result display submodule, and a data storage submodule, wherein:

[0114] The results processing submodule is used to process component statistical results and component evaluation results through graphical tools to obtain the data processing results of RPA data.

[0115] The results display submodule is used to display the data processing results through the target terminal.

[0116] The data storage submodule is used to store RPA data and data processing results in the database.

[0117] In this embodiment, the data processing results are obtained by processing the component statistical results and component evaluation results through graphical tools. The data processing results can be displayed in the form of charts, etc., so that developers can better view and monitor the operation of RPA components, and the RPA data and data processing results can be persistently saved through the database.

[0118] To address the aforementioned technical problems, embodiments of this application also provide a computer device. Please refer to [link / reference needed]. Figure 4 , Figure 4 This is a basic structural block diagram of the computer device in this embodiment.

[0119] The computer device 4 includes a memory 41, a processor 42, and a network interface 43 that are interconnected via a system bus. It should be noted that only the computer device 4 with components 41-43 is shown in the figure; however, it should be understood that it is not required to implement all the shown components, and more or fewer components can be implemented alternatively. Those skilled in the art will understand that the computer device described here is a device capable of automatically performing numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes, but is not limited to, microprocessors, application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), digital signal processors (DSPs), embedded devices, etc.

[0120] The computer device can be a desktop computer, laptop, handheld computer, or cloud server, etc. The computer device can interact with the user via a keyboard, mouse, remote control, touchpad, or voice control.

[0121] The memory 41 includes at least one type of readable storage medium, including flash memory, hard disk, multimedia card, card-type memory (e.g., SD or DX memory), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 41 may be an internal storage unit of the computer device 4, such as the hard disk or memory of the computer device 4. In other embodiments, the memory 41 may also be an external storage device of the computer device 4, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the computer device 4. Of course, the memory 41 may also include both the internal storage unit and its external storage device of the computer device 4. In this embodiment, the memory 41 is typically used to store the operating system and various application software installed on the computer device 4, such as computer-readable instructions for RPA data processing methods. In addition, the memory 41 can also be used to temporarily store various types of data that have been output or will be output.

[0122] In some embodiments, the processor 42 may be a central processing unit (CPU), controller, microcontroller, microprocessor, or other data processing chip. The processor 42 is typically used to control the overall operation of the computer device 4. In this embodiment, the processor 42 is used to execute computer-readable instructions stored in the memory 41 or to process data, for example, to execute computer-readable instructions of the RPA data processing method.

[0123] The network interface 43 may include a wireless network interface or a wired network interface, which is typically used to establish communication connections between the computer device 4 and other electronic devices.

[0124] The computer device provided in this embodiment can execute the above-described RPA data processing method. Here, the RPA data processing method can be any of the RPA data processing methods described in the various embodiments above.

[0125] In this embodiment, an RPA data packet is acquired, which includes RPA data from multiple data dimensions of the RPA component, ensuring data richness. The RPA data packet is generated with encryption to ensure data security. Pre-defined data processing tasks corresponding to the RPA data are acquired. These tasks include a primary statistical task and an advanced evaluation task. The primary statistical task performs statistical summarization of the RPA data, while the advanced evaluation task performs evaluation and analysis of the RPA components. The data processing tasks are run to perform different levels of data processing on the RPA data, obtaining component statistical results and component evaluation results respectively, and generating the RPA data processing results. This achieves systematic processing of RPA data and reduces resource waste of RPA data.

[0126] This application also provides another embodiment, namely, providing a computer-readable storage medium storing computer-readable instructions that can be executed by at least one processor to cause the at least one processor to perform the steps of the RPA data processing method described above.

[0127] In this embodiment, an RPA data packet is acquired, which includes RPA data from multiple data dimensions of the RPA component, ensuring data richness. The RPA data packet is generated with encryption to ensure data security. Pre-defined data processing tasks corresponding to the RPA data are acquired. These tasks include a primary statistical task and an advanced evaluation task. The primary statistical task performs statistical summarization of the RPA data, while the advanced evaluation task performs evaluation and analysis of the RPA components. The data processing tasks are run to perform different levels of data processing on the RPA data, obtaining component statistical results and component evaluation results respectively, and generating the RPA data processing results. This achieves systematic processing of RPA data and reduces resource waste of RPA data.

[0128] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0129] Obviously, the embodiments described above are only some embodiments of this application, not all embodiments. The accompanying drawings show preferred embodiments of this application, but do not limit the patent scope of this application. This application can be implemented in many different forms; rather, the purpose of providing these embodiments is to provide a more thorough and comprehensive understanding of the disclosure of this application. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing specific embodiments, or make equivalent substitutions for some of the technical features. Any equivalent structures made using the content of this application's specification and drawings, directly or indirectly applied to other related technical fields, are similarly within the scope of patent protection of this application.

Claims

1. An RPA data processing method, characterized in that, Includes the following steps: Obtain an RPA data packet, which includes RPA data of multiple data dimensions of an RPA component. The RPA data includes basic data and runtime data, each with multiple data dimensions. The basic data includes the component name, component display name, component code, component version number, component category, component creation date, and component creator. The runtime data includes the number of times the component has been run, the component's runtime status, and runtime record data. Obtain the data processing task corresponding to the RPA data in the RPA data packet. The data processing task includes a primary statistical task and an advanced evaluation task. The primary statistical task realizes the statistical summarization of RPA data, and the advanced evaluation task realizes the evaluation and analysis of RPA components. Run the data processing task to process the RPA data and obtain component statistical results and component evaluation results; Data processing results for the RPA data are generated based on the component statistical results and the component evaluation results. The steps of running the data processing task to process the RPA data and obtain component statistical results and component evaluation results include: Run the primary statistical task in the data processing task to process the RPA data based on the primary statistical task and obtain component statistical results; Based on the RPA data and the component statistics, the advanced evaluation task in the data processing task is run to obtain the component evaluation results; The step of running the primary statistical task in the data processing task to process the RPA data based on the primary statistical task and obtain component statistical results includes: Based on the primary statistical task in the data processing task, first feature data is extracted from the RPA data; Obtain the first standard data corresponding to the first feature data from the primary statistical task; The first feature data is compared with the first standard data, and component statistical results are generated based on the comparison results. The step of running the advanced evaluation task in the data processing task based on the RPA data and the component statistical results to obtain the component evaluation results includes: Based on the component statistics, an advanced evaluation task is determined in the data processing task. The advanced evaluation task includes anomaly detection task, component expansion task, or component optimization task. Obtain the second feature data corresponding to the advanced evaluation task from the RPA data and the component statistical results; The advanced evaluation task is run based on the second feature data to obtain the component evaluation results.

2. The RPA data processing method according to claim 1, characterized in that, Prior to the step of obtaining RPA packets, the following is also included: The RPA data of the RPA component is collected through the runner. The RPA data includes basic data and operational data, and the basic data and operational data each have multiple data dimensions. The RPA data is encrypted using the encryptor in the runner to obtain an RPA data packet.

3. The RPA data processing method according to claim 2, characterized in that, The step of obtaining the data processing task corresponding to the RPA data in the RPA data packet includes: The RPA data packet is input into the decryptor and the decryptor is run to decrypt the RPA data packet to obtain the RPA data; Extract the RPA component identifier from the RPA data; Based on the RPA component identifier, query the data processing task corresponding to the RPA data.

4. The RPA data processing method according to claim 1, characterized in that, The step of generating the data processing result of the RPA data based on the component statistical results and the component evaluation results includes: The component statistical results and component evaluation results are processed using graphical tools to obtain the data processing results of the RPA data; The data processing results are displayed on the target terminal; and... The RPA data and the data processing results are stored in a database.

5. An RPA data processing device, characterized in that, include: The data packet acquisition module is used to acquire RPA data packets. The RPA data packets include RPA data of multiple data dimensions of the RPA component. The RPA data includes basic data and runtime data. The basic data and the runtime data each have multiple data dimensions. The basic data includes the component name, component display name, component code, component version number, component category, component creation date, and component creator of the RPA component. The runtime data includes the number of times the component has been run, the component running status, and the running record data. The task acquisition module is used to acquire the data processing task corresponding to the RPA data in the RPA data packet. The data processing task includes a primary statistical task and an advanced evaluation task. The primary statistical task realizes the statistical summary of RPA data, and the advanced evaluation task realizes the evaluation and analysis of RPA components. The data processing module is used to run the data processing task to process the RPA data and obtain component statistical results and component evaluation results. The result generation module is used to generate data processing results for the RPA data based on the component statistical results and the component evaluation results. The data processing module includes: a primary operation submodule and an evaluation operation submodule, wherein: The primary operation submodule is used to run the primary statistical task in the data processing task, so as to process the RPA data based on the primary statistical task and obtain component statistical results. The evaluation operation submodule is used to run the advanced evaluation task in the data processing task based on the RPA data and the component statistical results to obtain the component evaluation results. The operation submodule includes: a first extraction unit, a standard acquisition unit, and a data comparison unit, wherein: The first extraction unit is used to extract first feature data from the RPA data based on the primary statistical task in the data processing task; The standard acquisition unit is used to acquire the first standard data corresponding to the first feature data from the primary statistical task; The data comparison unit is used to compare the first feature data with the first standard data, and generate component statistical results based on the comparison results. The evaluation and operation submodule includes: a task determination unit, a second acquisition unit, and an evaluation and operation unit, wherein: The task determination unit is used to determine an advanced evaluation task in the data processing task based on the component statistical results. The advanced evaluation task includes an anomaly detection task, a component expansion task, or a component optimization task. The second acquisition unit is used to acquire the second feature data corresponding to the advanced evaluation task from the RPA data and the component statistical results; The evaluation operation unit is used to run the advanced evaluation task based on the second feature data to obtain the component evaluation results.

6. A computer device comprising a memory and a processor, the memory storing computer-readable instructions, wherein the processor, when executing the computer-readable instructions, implements the steps of the RPA data processing method as described in any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-readable instructions, which, when executed by a processor, implement the steps of the RPA data processing method as described in any one of claims 1 to 4.

Images