Intelligent collaborative office automation RPA system and data interaction processing method

Abstract

The present application discloses an intelligent collaborative office automation RPA system and a data interaction processing method, which belongs to the field of collaborative office. The present application sorts tasks based on task urgency and task matching degree. In terms of task scheduling and data interaction, the present application innovatively designs a task priority adjustment algorithm, intelligently optimizes the execution order according to the task data volume, task urgency and system load matching, and improves the office efficiency of the execution software. At the same time, when the present application judges the task urgency and system load matching, it evaluates similar tasks based on the historical task processing information of the execution software and the data information of the execution software accepting tasks, thereby accurately obtaining the task urgency and system load matching, further improving the office efficiency of the execution software.

Description

Technical Field

[0001] This application belongs to the field of collaborative office, specifically an intelligent collaborative office automation RPA system and a data interaction processing method. Background Art

[0002] RPA systems, or Robotic Process Automation systems, use software to simulate manual human work on computers. They record and replay software operations according to pre-set automation rules. RPA systems are primarily used in software robots. By simulating manual human work on computers, they automate repetitive, tedious, and rule-based tasks in software operations. Collaborative office work often requires RPA systems to handle some office tasks. However, when processing multiple tasks together, existing RPA systems can typically only sort tasks based on the time they were issued or the person who issued them. They cannot use historical processing tasks to accurately analyze task urgency and system load matching, resulting in an inability to accurately plan the order of task execution and low software execution efficiency. Most existing technologies suffer from these problems.

[0003] In order to solve the problems raised by this background technology, this application designs an intelligent collaborative office automation RPA system and a data interaction processing method. Summary of the Invention

[0004] In order to address the deficiencies in the existing technology mentioned in the background technology, the present application proposes an intelligent collaborative office automation RPA system and a data interaction processing method. The present application evaluates the urgency of tasks based on the processing status of similar tasks, analyzes the task matching degree based on the historical processing step data of the task and the data information of the execution software accepting the task, and sorts tasks based on the task urgency and task matching degree. In terms of task scheduling and data interaction, an innovative task priority adjustment algorithm is designed to intelligently optimize the execution order based on the task data volume, task urgency and system load matching, thereby improving the office efficiency of the execution software. At the same time, when the present application judges the task urgency and system load matching, it evaluates similar tasks based on the historical processing task information of the execution software and the data information of the execution software accepting the task, thereby accurately obtaining the task urgency and system load matching, thereby further improving the office efficiency of the execution software.

[0005] To achieve the above objectives, the present application provides the following technical solutions: First, the present application provides an intelligent collaborative office automation data interaction processing method, which includes the following specific steps:

[0006] S1. Obtain data information about tasks accepted by the execution software, information about the task issuing manager, and information about the execution software's historical task processing;

[0007] S2. Evaluate similar tasks based on the historical task processing information of the execution software and the data information of the tasks accepted by the execution software, and evaluate the urgency of the task based on the processing status of similar tasks;

[0008] S3, performing task matching analysis based on the historical processing step data of the task and the data information of the execution software accepting the task;

[0009] S4. Sort tasks based on task urgency and task matching;

[0010] S5. Process the tasks according to the task sorting results.

[0011] As a preferred technical solution for the intelligent collaborative office automation data interaction processing method, the specific content of step S1 is:

[0012] S11, obtaining content data of the task accepted by the execution software, wherein the content data includes the task title, text, and nature data of the task;

[0013] S12. Obtaining management authority data of the information of the administrator who issues the task accepted by the execution software;

[0014] S13. Obtaining the duration data of executing the software historical processing related tasks and the setting time limit data of the historical processing tasks.

[0015] As a preferred technical solution for the intelligent collaborative office automation data interaction processing method, the evaluation of similar tasks based on the historical task processing information of the executing software and the data information of the task accepted by the executing software in S2 includes the following specific steps:

[0016] S21. Obtain the titles, text, and nature data of previously completed tasks, and simultaneously obtain the titles, text, and nature data of currently accepted tasks;

[0017] S22. Import the property data of the currently accepted j-th task and the property data of the historically completed i-th task into the property data similarity calculation formula to calculate the property data similarity, wherein the property data similarity calculation formula is: Wherein, Tij is the similarity between the property data of the currently accepted j-th task and the i-th task completed in the past, m() is the number of elements in the set, ki is the set consisting of the property data types of the i-th task completed in the past, kj is the set consisting of the property data types of the currently accepted j-th task, ∩ is the intersection symbol of the set, and ∪ is the union symbol of the set;

[0018] S23. The calculated property data similarity, the titles and texts of previously completed tasks, and the titles and texts of currently accepted tasks are introduced into the task similarity calculation formula to calculate the task similarity. The task similarity calculation formula between the currently accepted jth task and the historically completed ith task is: Among them, a is the text similarity weight, mi is the number of character types in the title and text of the i-th task completed in the past, mj is the number of character types in the title and text of the currently accepted j-th task, Qi z is the number of occurrences of the z-th character type in the title and text of the i-th task completed in the past, and Qjz is the number of occurrences of the z-th character type in the title and text of the currently accepted j-th task. In this way, the similarity of tasks is comprehensively analyzed based on the content and nature of the tasks;

[0019] S24. Obtain the task similarity between each currently accepted task and each historically completed task, obtain the historically completed tasks whose task similarity with the accepted task is greater than or equal to a similarity threshold, and set them as similar tasks to the accepted task. It should be noted that the similarity threshold here is set according to the required accuracy.

[0020] As a preferred technical solution for the intelligent collaborative office automation data interaction processing method, the evaluation of task urgency based on the processing status of similar tasks in step S2 includes the following specific steps:

[0021] S25. Obtain the set time limit data of similar tasks corresponding to the currently accepted task, and import the data into the task urgency value calculation formula to calculate the task urgency value. The calculation formula for the task urgency value of the currently accepted j-th task is: In this formula, Hj is the number of similar tasks currently accepting the jth task, Xjc is the task similarity between the currently accepting jth task and the cth similar task, tic is the time limit set for the cth similar task currently accepting the jth task, and tm is the average time limit of all historical tasks. δ In order to issue management authority priority data for management personnel information, this formula evaluates the urgency of the current task by the time limit of historical similar tasks, and at the same time, weights different tasks by similarity to accurately evaluate the task urgency.

[0022] As a preferred technical solution for the intelligent collaborative office automation data interaction processing method, the task matching degree analysis based on the historical processing step data of the task and the data information of the execution software accepting the task in step S3 includes the following specific contents:

[0023] Obtain the duration data of similar tasks related to the execution software's historical processing and the data size of the tasks and import them into the task matching degree calculation formula to calculate the task matching degree. The task matching degree calculation formula for the currently accepted j-th task is: Among them, tjcx is the processing time of the cth similar task of the current jth task, tcm is the average processing time, Qjc is the data volume of the cth similar task of the current jth task, and Qm is the average task data volume. In this way, the processing difficulty of the execution software for the corresponding task is evaluated by the processing time of the historical similar tasks of the task, and the matching status of the execution software for the corresponding task is analyzed based on the data volume of this task.

[0024] As a preferred technical solution for the intelligent collaborative office automation data interaction processing method, the sorting of tasks based on task urgency, task matching degree, and task issuing manager information in step S4 includes the following specific contents:

[0025] Obtain the task urgency values ​​and task matching degrees of the tasks accepted by the execution software, first sort the tasks in descending order according to their task urgency values. If tasks with the same task urgency values ​​are encountered, sort them in descending order according to the task matching degrees to obtain a task completion sorting table.

[0026] On the second aspect, the present application provides an intelligent collaborative office automation RPA system, which is implemented based on the above-mentioned intelligent collaborative office automation data interaction processing method, and specifically includes a data acquisition module, a similar task acquisition module, a task urgency assessment module, a task matching degree assessment module and a task sorting module; wherein, the data acquisition module is used to obtain data information of the execution software accepting tasks, information of the task issuing manager and historical task processing information of the execution software; the similar task acquisition module evaluates similar tasks based on the historical task processing information of the execution software and the data information of the execution software accepting tasks; the task urgency assessment module assesses the urgency of tasks based on the processing status of similar tasks; the task matching degree assessment module performs task matching degree analysis based on the historical processing step data of the task and the data information of the execution software accepting tasks; the task sorting module sorts tasks based on task urgency and task matching degree, and processes tasks according to the sorting results of the tasks.

[0027] In a third aspect, the present application provides an electronic device, comprising: a processor and a memory, wherein the memory stores a computer program that can be called by the processor;

[0028] The processor executes the above-mentioned intelligent collaborative office automation data interaction processing method by calling the computer program stored in the memory.

[0029] In a fourth aspect, the present application provides a computer-readable storage medium storing instructions, which, when executed on a computer, enables the computer to execute the intelligent collaborative office automation data interaction processing method as described above.

[0030] Compared with the existing technology, the beneficial effects of the present application are: 1. The present application evaluates similar tasks based on the historical task processing information of the executing software and the data information of the tasks accepted by the executing software, evaluates the task urgency based on the processing status of similar tasks, analyzes the task matching degree based on the historical processing step data of the task and the data information of the tasks accepted by the executing software, sorts tasks based on the task urgency and task matching degree, and in terms of task scheduling and data interaction, innovatively designs a task priority adjustment algorithm, intelligently optimizes the execution order according to the task data volume, task urgency and system load matching, and improves the office efficiency of the executing software.

[0031] 2. At the same time, when this application judges the urgency of the task and the matching of the system load, it evaluates similar tasks based on the historical task processing information of the execution software and the data information of the execution software accepting tasks, and then accurately obtains the urgency of the task and the matching of the system load, further improving the office efficiency of the execution software. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Other features, objects and advantages of the present application will become more apparent by reading the detailed description of non-limiting embodiments made with reference to the following drawings;

[0033] Figure 1 This is a schematic diagram of the overall process of the intelligent collaborative office automation data interaction processing method of this application;

[0034] Figure 2 This is a schematic diagram of step S2 of the intelligent collaborative office automation data interaction processing method of this application;

[0035] Figure 3 This is a schematic diagram of the overall framework of the intelligent collaborative office automation RPA system for this application. DETAILED DESCRIPTION

[0036] To better understand the present application, various aspects of the present application will be described in more detail with reference to the accompanying drawings. It should be understood that these detailed descriptions are merely descriptions of exemplary embodiments of the present application and are not intended to limit the scope of the present application in any way. Throughout the specification, the same reference numerals refer to the same elements. The expression "and / or" includes any and all combinations of one or more of the associated listed items.

[0037] In the accompanying drawings, the size, dimensions, and shapes of elements have been slightly adjusted for ease of illustration. The drawings are for illustration only and are not drawn strictly to scale. As used herein, the terms "substantially," "approximately," and similar terms are intended to indicate approximation, not degree, and are intended to illustrate the inherent variations in measured or calculated values ​​that would be recognized by one of ordinary skill in the art. Furthermore, in this application, the order in which the various steps are described does not necessarily represent the order in which these steps would occur in actual operation, unless otherwise specified or inferred from the context. It should also be understood that expressions such as "comprises," "including," "having," "includes," and / or "comprising" are open-ended, not closed-ended, expressions in this specification, indicating the presence of the stated features, elements, and / or components, but do not exclude the presence of one or more other features, elements, components, and / or combinations thereof. Furthermore, when expressions such as "at least one of..." appear after a list of listed features, they modify the entire list of features, not just the individual elements in the list. Furthermore, when describing embodiments of the present application, the use of "may" means "one or more embodiments of the present application." Furthermore, the term "exemplary" is intended to refer to an example or illustration. Unless otherwise specified, all words used herein (including engineering terms and scientific and technological terms) have the same meaning as commonly understood by those skilled in the art to which this application belongs. It should also be understood that, unless otherwise specified in this application, words defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant technology, and should not be interpreted in an idealized or overly formal sense.

[0038] Example 1

[0039] In order to solve the technical problems raised in the background technology, this application provides a preferred embodiment: Figure 1-Figure 2 As shown, the intelligent collaborative office automation data interaction processing method includes the following specific steps:

[0040] S1. Obtain data information about tasks accepted by the execution software, information about the task issuing manager, and information about the execution software's historical task processing;

[0041] In this embodiment, the specific contents of step S1 are:

[0042] S11. Acquire content data of a task accepted by the execution software, wherein the content data includes a task title, task text, and attribute data. In this embodiment, examples of types of task attribute data are provided. For example, if the execution software needs to perform document verification, the attribute data in this case is document verification. For example, if the execution software needs to perform office debugging on a document, the attribute data in this case is office debugging.

[0043] S12. Obtaining management authority data of the administrator who issues the task accepted by the execution software. In this embodiment, a priority must be set for the administrator. For example, the higher the management authority level, the greater the priority. This factor must be taken into account when sorting tasks during the execution of the software. The number of contacts of the administrator is preferably used.

[0044] S13. Obtaining execution time data of tasks related to historical software processing and set time limit data of historical processing tasks. In this embodiment, it should be noted that the execution time data of tasks related to historical processing is obtained by statistically analyzing historical processing situations. As for the set time limit data of historical processing tasks, some tasks may have set time limits while others may not. In this case, it is necessary to estimate the time limits of the tasks based on historical data.

[0045] S2. Evaluate similar tasks based on the historical task processing information of the execution software and the data information of the tasks accepted by the execution software, and evaluate the urgency of the task based on the processing status of similar tasks;

[0046] In this embodiment, the evaluation of similar tasks based on the historical task processing information of the executing software and the data information of the tasks accepted by the executing software in S2 includes the following specific steps:

[0047] S21. Obtain the titles, text, and nature data of previously completed tasks, and simultaneously obtain the titles, text, and nature data of currently accepted tasks;

[0048] S22. Import the property data of the currently accepted j-th task and the property data of the historically completed i-th task into the property data similarity calculation formula to calculate the property data similarity, wherein the property data similarity calculation formula is: Wherein, Tij is the similarity between the property data of the currently accepted j-th task and the i-th task completed in the past, m() is the number of elements in the set, ki is the set consisting of the property data types of the i-th task completed in the past, kj is the set consisting of the property data types of the currently accepted j-th task, ∩ is the intersection symbol of the set, and ∪ is the union symbol of the set;

[0049] S23. The calculated property data similarity, the titles and texts of previously completed tasks, and the titles and texts of currently accepted tasks are introduced into the task similarity calculation formula to calculate the task similarity. The task similarity calculation formula between the currently accepted jth task and the historically completed ith task is: Wherein, a is the text similarity weight, obtained through expert scoring or experiments, and the text similarity weights of different tasks are definitely different. mi is the number of character types in the title and text of the i-th task completed in the past, mj is the number of character types in the title and text of the j-th task currently being accepted, Qi z is the number of occurrences of the z-th character type in the title and text of the i-th task completed in the past, and Qjz is the number of occurrences of the z-th character type in the title and text of the j-th task currently being accepted. In this embodiment, the purpose of this setting is to comprehensively analyze the similarity of tasks based on their content and nature.

[0050] S24. Obtain task similarities between each currently accepted task and each previously completed task, obtain previously completed tasks whose task similarity to the accepted task is greater than or equal to a similarity threshold, and set them as similar tasks to the accepted task. In this embodiment, it should be noted that the similarity threshold is set according to the required accuracy, obtained through expert scoring or experiments, and is preferably 80%. If the similarity threshold is selected to be small, the number of similar tasks to the accepted task will increase, which is not conducive to subsequent calculation and analysis. If the similarity threshold is selected to be large, the number of similar tasks to the accepted task will decrease, resulting in increased randomness in subsequent calculations.

[0051] At the same time, the evaluation of task urgency based on the processing status of similar tasks in step S2 includes the following specific steps:

[0052] S25. Obtain the set time limit data of similar tasks corresponding to the currently accepted task, and import the data into the task urgency value calculation formula to calculate the task urgency value. The calculation formula for the task urgency value of the currently accepted j-th task is: In this formula, Hj is the number of similar tasks currently accepting the jth task, Xjc is the task similarity between the currently accepting jth task and the cth similar task, tic is the time limit set for the cth similar task currently accepting the jth task, and tm is the average time limit of all historical tasks. δ The management authority priority data for issuing management personnel information is set as follows: in the above formula, the time limit of similar historical tasks is used to evaluate the urgency of the current task, and different tasks are weighted by similarity to accurately assess the urgency of the task;

[0053] S3, performing task matching analysis based on the historical processing step data of the task and the data information of the execution software accepting the task;

[0054] In this embodiment, the task matching degree analysis based on the historical processing step data of the task and the data information of the execution software accepting the task in step S3 includes the following specific contents:

[0055] Obtain the duration data of similar tasks related to the execution software's historical processing and the data size of the tasks and import them into the task matching degree calculation formula to calculate the task matching degree. The task matching degree calculation formula for the currently accepted j-th task is: Among them, tjcx is the processing time of the cth similar task of the current jth task, tcm is the average processing time, Qjc is the data volume of the cth similar task of the current jth task, and Qm is the average task data volume. The advantage of this setting is that it can evaluate the processing difficulty of the execution software for the corresponding task by the processing time of the historical similar tasks of the task, and at the same time analyze the matching status of the execution software for the corresponding task based on the data volume of the current task;

[0056] S4. Sort tasks based on task urgency and task matching;

[0057] In this embodiment, the sorting of tasks based on task urgency, task matching degree, and task dispatching manager information in step S4 includes the following specific contents:

[0058] Obtain the task urgency values ​​and task matching degrees of the tasks accepted by the execution software, first sort the tasks in descending order according to their task urgency values. If tasks with the same task urgency values ​​are encountered, sort them in descending order according to their task matching degrees to obtain a task completion ranking table;

[0059] S5. The execution software processes the tasks according to the task sorting results and executes the tasks in the order of the task completion sorting table;

[0060] At the same time, it is necessary to explain that the setting parameters in this embodiment are obtained through a large number of experiments as a control group. The specific acquisition method is: obtaining at least 500 sets of data information on the execution software accepting tasks, information on the task issuing management personnel, and information on the execution software's historical processing of tasks for control experiments, substituting the obtained data sets into the task urgency value calculation formula and the task matching degree calculation formula to calculate the task urgency value and the task matching degree, obtaining the expert's judgment results on the execution order of these tasks, importing the calculation results and judgment results into the fitting software, and outputting the value of the setting parameters that meets the maximum judgment accuracy;

[0061] Arrange the calculated maintenance values ​​of all structures in descending order and perform structure maintenance in the order of arrangement.

[0062] It should be noted in this example that this embodiment has the following advantages over the prior art: task urgency is evaluated based on the processing status of similar tasks, task matching degree is analyzed based on the historical processing step data of the task and the data information of the execution software accepting the task, tasks are sorted based on task urgency and task matching degree, and in terms of task scheduling and data interaction, an innovative task priority adjustment algorithm is designed to intelligently optimize the execution order based on the task data volume, task urgency and system load matching, thereby improving the office efficiency of the execution software; at the same time, when the present application judges the task urgency and system load matching, similar tasks are evaluated based on the historical processing task information of the execution software and the data information of the execution software accepting the task, thereby accurately obtaining the task urgency and system load matching, thereby further improving the office efficiency of the execution software.

[0063] Example 2

[0064] like Figure 3 As shown, the intelligent collaborative office automation RPA system is implemented based on the above-mentioned intelligent collaborative office automation data interaction processing method, which specifically includes a data acquisition module, a similar task acquisition module, a task urgency assessment module, a task matching degree assessment module and a task sorting module; wherein the data acquisition module is used to obtain data information of the execution software accepting the task, information of the task issuing manager and historical task processing information of the execution software; the similar task acquisition module evaluates similar tasks based on the historical task processing information of the execution software and the data information of the execution software accepting the task; the task urgency assessment module evaluates the urgency of the task based on the processing status of similar tasks; the task matching degree assessment module performs task matching degree analysis based on the historical processing step data of the task and the data information of the execution software accepting the task; the task sorting module sorts the tasks based on the task urgency and task matching degree, and processes the tasks according to the task sorting results; at the same time, the data transmission direction of each module in this embodiment is as follows Figure 3 As shown by the arrow direction in the figure, the specific steps of each module in this embodiment have been described in detail in the above method embodiment and will not be repeated here.

[0065] Example 3

[0066] This embodiment provides an electronic device, comprising: a processor and a memory, wherein the memory stores a computer program that can be called by the processor;

[0067] The processor executes the above-mentioned intelligent collaborative office automation data interaction processing method by calling the computer program stored in the memory.

[0068] The electronic device may vary significantly due to different configurations or performance, and may include one or more processors and one or more memories, wherein the memories store at least one computer program, which is loaded and executed by the processor to implement the intelligent collaborative office automation data interaction processing method provided by the above method embodiment. The electronic device may also include other components for implementing device functions. For example, the electronic device may also have components such as a wired or wireless network interface and an input / output interface to facilitate data input and output. This embodiment will not be described in detail here.

[0069] Example 4

[0070] This embodiment provides a computer-readable storage medium having a rewritable computer program stored thereon;

[0071] When the computer program runs on a computer device, the computer device is caused to execute the above-mentioned intelligent collaborative office automation data interaction processing method.

[0072] For example, the computer readable storage medium can be a read-only memory, a random access memory, a read-only CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, and the like.

[0073] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the process or function according to the embodiments of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via a wired network or / and a wireless network. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0074] The terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed or inherent to such process, method, article, or apparatus.

[0075] The above description is merely a preferred embodiment of the present application and an illustration of the technical principles employed. Those skilled in the art should understand that the scope of application involved in this application is not limited to the technical solutions formed by a specific combination of the above-mentioned technical features, but should also cover other technical solutions formed by any combination of the above-mentioned technical features or their equivalents without departing from the aforementioned application concept. For example, a technical solution formed by replacing the above-mentioned features with (but not limited to) technical features with similar functions applied for in this application.

Claims

1. An intelligent collaborative office automation data interaction processing method, characterized in that: It includes the following specific steps: S1. Obtain data information about tasks accepted by the execution software, information about the task issuing manager, and information about the execution software's historical task processing; S2. Evaluate similar tasks based on the historical task processing information of the execution software and the data information of the tasks accepted by the execution software, and evaluate the urgency of the task based on the processing status of similar tasks; S3, performing task matching analysis based on the historical processing step data of the task and the data information of the execution software accepting the task; The task matching degree analysis based on the historical processing step data of the task and the data information of the execution software accepting the task in step S3 includes the following specific contents: obtaining the duration data of the execution software's historical processing of similar tasks and the data size of the tasks, and importing them into the task matching degree calculation formula to calculate the task matching degree, wherein the task matching degree calculation formula for the currently accepted j-th task is: , where tjcx is the processing time of the cth similar task of the current jth task, tcm is the average processing time, Qjc is the data volume of the cth similar task of the current jth task, Qm is the average task data volume, where Xjc is the task similarity between the current jth task and the cth similar task, and Hj is the number of similar tasks of the current jth task; S4. Sort tasks based on task urgency and task matching; S5. Process the tasks according to the task sorting results.

2. The intelligent collaborative office automation data interaction processing method according to claim 1, characterized in that: The evaluation of similar tasks based on the historical task processing information of the execution software and the data information of the tasks accepted by the execution software in S2 includes the following specific steps: Get the title, text, and nature of the tasks completed in the past, and also get the title, text, and nature of the tasks currently being accepted; Import the property data of the currently accepted j-th task and the property data of the historically completed i-th task into the property data similarity calculation formula to calculate the property data similarity, wherein the property data similarity calculation formula is: , where Tij is the similarity between the property data of the currently accepted j-th task and the i-th task completed in the past, m() is the number of elements in the set, ki is the set consisting of the property data types of the i-th task completed in the past, and kj is the set consisting of the property data types of the currently accepted j-th task. is the intersection symbol of the sets, is the union symbol of sets; The calculated property data similarity, the titles and texts of historically completed tasks, and the titles and texts of currently accepted tasks are imported into the task similarity calculation formula to calculate task similarity; Obtain the task similarity between each currently accepted task and each previously completed task, obtain the previously completed tasks whose task similarity with the accepted task is greater than or equal to the similarity threshold, and set them as similar tasks to the accepted task.

3. The intelligent collaborative office automation data interaction processing method according to claim 2, characterized in that: The calculation formula for the task similarity between the currently accepted j-th task and the historically completed i-th task is: , where a is the text similarity weight, mi is the number of character types in the title and text of the i-th task completed in the past, mj is the number of character types in the title and text of the j-th task currently accepted, Qiz is the number of occurrences of the z-th character type in the title and text of the i-th task completed in the past, and Qjz is the number of occurrences of the z-th character type in the title and text of the j-th task currently accepted.

4. The intelligent collaborative office automation data interaction processing method according to claim 3, characterized in that: The step S2 of evaluating the urgency of a task based on the processing status of similar tasks includes the following specific steps: Obtain the set time limit data of similar tasks corresponding to the currently accepted task, and import it into the task urgency value calculation formula to calculate the task urgency value. The calculation formula for the task urgency value of the currently accepted j-th task is: In this formula, Hj is the number of similar tasks currently accepting the jth task, Xjc is the task similarity between the currently accepting jth task and the cth similar task, tjc is the time limit for the cth similar task currently accepting the jth task, and tm is the average time limit of all historical tasks. It is the management authority priority data for issuing management personnel information.

5. The intelligent collaborative office automation data interaction processing method according to claim 4, characterized in that: The sorting of tasks based on task urgency and task matching degree in step S4 includes the following specific contents: obtaining the task urgency value and task matching degree of the execution software to accept the task, first sorting the accepted tasks in descending order according to the task urgency value, and if tasks with the same task urgency value are encountered, sorting the tasks with the same task urgency value in descending order according to the task matching degree to obtain a task completion sorting table.

6. The intelligent collaborative office automation data interaction processing method according to claim 5, characterized in that: The specific contents of the S1 step are: Obtaining content data of a task accepted by the execution software, wherein the content data includes a task title, text, and property data of the task; Obtain the management authority data of the administrator who issues the task accepted by the execution software; Obtain the duration data of executing tasks related to software historical processing and the set time limit data of historical processing tasks.

7. An intelligent collaborative office automation (RPA) system, implemented based on the intelligent collaborative office automation data interaction processing method according to any one of claims 1 to 6, characterized in that: It specifically includes a data acquisition module, a similar task acquisition module, a task urgency assessment module, a task matching degree assessment module and a task sorting module; wherein, the data acquisition module is used to obtain data information of the execution software accepting tasks, information of the task issuing manager and historical task processing information of the execution software; the similar task acquisition module evaluates similar tasks based on the historical task processing information of the execution software and the data information of the execution software accepting tasks; the task urgency assessment module assesses the urgency of tasks based on the processing status of similar tasks; the task matching degree assessment module performs task matching degree analysis based on the historical processing step data of the task and the data information of the execution software accepting tasks; the task sorting module sorts tasks based on task urgency and task matching degree, and processes tasks according to the sorting results.

8. An electronic device comprising: A processor and a memory, wherein the memory stores a computer program that can be called by the processor; It is characterized in that the processor executes the intelligent collaborative office automation data interaction processing method according to any one of claims 1 to 6 by calling the computer program stored in the memory.

9. A computer-readable storage medium, characterized in that Instructions are stored, and when the instructions are run on a computer, the computer is caused to execute the intelligent collaborative office automation data interaction processing method according to any one of claims 1 to 6.

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