Task automation processing method, system, apparatus, and electronic device

Abstract

Embodiments of the present application provide a task automation processing method, system, device and electronic equipment, related to the field of financial technology. The method responds to a task creation operation, obtains task data, and performs identification analysis on the task data to obtain a task attribute set. Then, at least one to-be-assigned object is matched from a pre-constructed organizational structure knowledge graph based on the task attribute set. The task processing flow and task scheduling parameters are generated according to the task attribute set and the at least one to-be-assigned object, and the task processing flow is executed based on the task scheduling parameters. The method can reduce the dependence on manual input, manual assignment and manual tracking in the task processing process, improve the pertinence of task assignment and the coherence of process execution, and thus improve the business processing efficiency and accuracy.

Description

Technical Field

[0001] This application relates to the field of financial technology, and in particular to a method, system, device and electronic device for automating task processing. Background Technology

[0002] Existing document systems are widely used in enterprise and government office scenarios, and typically use a combination of process-based approval and manual operation to complete document circulation, distribution, tracking and archiving.

[0003] In existing technologies, document entry, approval and assignment, and process follow-up all rely on manual operation. Approval and comments need to be read, classified and sorted manually one by one. The entire business process is time-consuming, and manual processing is prone to problems such as process delays and processing errors. Summary of the Invention

[0004] This application provides a method, system, apparatus, and electronic device for automating task processing, in order to construct an automated business processing flow, thereby reducing reliance on manual labor in the business processing process and improving business processing efficiency and accuracy.

[0005] In a first aspect, embodiments of this application provide a method for automating task processing, the method comprising:

[0006] In response to a task creation operation, retrieve task data;

[0007] The task data is identified and analyzed to obtain a set of task attributes;

[0008] Based on the set of task attributes, at least one object to be assigned is matched from a pre-built organizational structure knowledge graph;

[0009] Based on the set of task attributes and at least one object to be assigned, a task processing flow is generated; the task processing flow includes the task scheduling parameters of each flow node.

[0010] The task processing flow is executed based on the task scheduling parameters.

[0011] In one possible embodiment, the task data is identified and analyzed to obtain a set of task attributes, including:

[0012] The task data is identified and analyzed to obtain the task type, urgency level, and security level;

[0013] Extract task keywords from task data and construct task tags;

[0014] The processing time limit is determined based on the urgency level, predefined task time limit rules, and / or time-sensitive words in the task data;

[0015] A set of task attributes is generated based on task type, urgency, confidentiality level, task tags, and processing time limit.

[0016] In one possible embodiment, the objects to be assigned include: processing departments and the target responsible persons of the processing departments. Based on a set of task attributes, at least one object to be assigned is matched from a pre-built organizational structure knowledge graph, including:

[0017] Based on task type and task tag, at least one processing department and at least one candidate responsible person for each processing department are identified from the organizational structure knowledge graph; each candidate responsible person has corresponding attribute information; the attribute information includes: department, skill tag, permission data, number of pending tasks and historical processing efficiency;

[0018] When there are multiple candidates for responsibility in a processing department, the comprehensive score of each candidate is calculated by weighting the matching degree between the attribute information of the multiple candidates and the task attribute set.

[0019] The candidate responsible for the highest overall score will be designated as the target responsible person for the handling department.

[0020] In one possible embodiment, the attribute information includes: skill tags, number of pending tasks, and task processing efficiency; based on the matching degree between the attribute information of multiple candidate responsible persons and the task attribute set, a weighted comprehensive score is calculated for each candidate responsible person, including:

[0021] The skill tags and task tags are matched to obtain the first score;

[0022] The second score is determined based on the number of tasks to be completed;

[0023] Map task processing efficiency to a third rating;

[0024] Based on the urgency of the task attribute set, the weights corresponding to the first, second, and third scores are determined, and the first, second, and third scores are weighted and summed based on the weights to obtain the comprehensive score.

[0025] In one possible embodiment, the task scheduling parameters include: node execution parameters, and timeout thresholds, reminder delivery channels, reminder triggering timings, and escalation rules for each process node; based on the task attribute set and at least one object to be assigned, a task processing flow and task scheduling parameters are generated, including:

[0026] Identify the target process template in the process template library that matches the task type;

[0027] Each object to be assigned is associated with a corresponding process node in the task processing flow. Each process node is associated with a node execution parameter, which includes: the next process node and the initiation condition of the next process node. The association rules are defined inside the template.

[0028] Based on the urgency and processing time limit, determine the timeout threshold, reminder delivery channel, reminder triggering time, and escalation rules for each process node in the target process template; the escalation rules are used to indicate the adjustment of the urgency level according to the task processing time and timeout duration.

[0029] In one possible embodiment, the task processing flow is executed based on task scheduling parameters, including:

[0030] The automated execution engine drives the script according to preset rules and executes the task processing flow according to the task scheduling parameters.

[0031] In one possible embodiment, the automated execution engine includes: a preset rule-driven script, comprising:

[0032] If there is a form to be filled in associated with the current process node, the value to be filled is obtained from the system corresponding to the data source type of the field to be filled in the form, and the value to be filled is then filled into the form.

[0033] When the current process node is the approval completion node, the approval result and related attachments corresponding to the task processing process will be synchronized to the preset system; the preset system includes the human resources system and the document management system;

[0034] When the current process node is the process initiation node, monitor the execution status of the current process node, and when the execution status changes to the end status, initiate the next process node in the target process template corresponding to the task data.

[0035] In one possible embodiment, the method further includes:

[0036] During the execution of the task processing flow, monitor the processing time and timeout duration of the current process node;

[0037] If the processing time and timeout duration meet the escalation rules, adjust the urgency level according to the escalation rules;

[0038] Based on the adjusted urgency level, update the task scheduling parameters of the task processing flow.

[0039] Secondly, embodiments of this application provide a task automation processing apparatus, the apparatus comprising:

[0040] The acquisition module is used to retrieve task data in response to task creation operations;

[0041] The processing module is used to identify and analyze task data to obtain a set of task attributes;

[0042] The matching module is used to match at least one object to be assigned from a pre-built organizational knowledge graph based on a set of task attributes.

[0043] The generation module is used to generate a task processing flow based on a set of task attributes and at least one object to be assigned; the task processing flow includes task scheduling parameters for each process node.

[0044] The execution module is used to execute the task processing flow based on task scheduling parameters.

[0045] In one possible embodiment, the above-described apparatus includes: a determining module;

[0046] The processing module is specifically used to identify and analyze task data to obtain task type, urgency level, and security level;

[0047] The processing module is also used to extract task keywords from task data and construct task tags;

[0048] The determination module is used to determine the processing time limit based on the urgency level, predefined task time limit rules, and / or time-sensitive words in the task data;

[0049] The generation module is also used to generate a set of task attributes based on task type, urgency, confidentiality level, task tags, and processing time limit.

[0050] In one possible embodiment, the objects to be assigned include: processing departments and target responsible persons of the processing departments. Based on the task attribute set, the determination module is also used to determine at least one processing department and at least one candidate responsible person of each processing department from the organizational structure knowledge graph based on task type and task tag. Each candidate responsible person has corresponding attribute information. The attribute information includes: department, skill tag, permission data, number of pending tasks and historical processing efficiency.

[0051] The processing module is also used to calculate the comprehensive score of each candidate responsible for a task based on the matching degree between the attribute information of the multiple candidates responsible for the task and the task attribute set when there are multiple candidate responsible for the task.

[0052] The determination module is also used to identify the candidate responsible person corresponding to the highest comprehensive score as the target responsible person of the handling department.

[0053] In one possible embodiment, the attribute information includes: skill tags, number of pending tasks, and task processing efficiency; the processing module is also used to match the skill tags with the task tags to obtain a first score;

[0054] The processing module is also used to obtain a second score based on the number of pending tasks;

[0055] The processing module is also used to map task processing efficiency to a third rating;

[0056] The determination module is also used to determine the weights of the first, second, and third scores based on the urgency of the task attribute set.

[0057] The processing module is also used to perform a weighted summation of the first, second, and third scores based on their respective weights to obtain a comprehensive score.

[0058] In one possible embodiment, the task scheduling parameters include: node execution parameters, as well as timeout thresholds, reminder delivery channels, reminder triggering timing, and escalation rules for each process node; the determination module is also used to determine the target process template in the process template library that matches the task type;

[0059] The processing module is also used to associate each object to be assigned with the corresponding process node in the task processing flow. The process node is associated with node execution parameters, which include: the next process node and the initiation conditions of the next process node; the association rules are defined inside the template.

[0060] The determination module is also used to determine the timeout threshold, reminder delivery channel, reminder triggering time, and escalation rules for each process node in the target process template based on the urgency and processing time limit; the escalation rules are used to indicate the adjustment of the urgency level based on the task processing time and timeout duration.

[0061] In one possible embodiment, the execution module is specifically used to drive the script according to preset rules and execute the task processing flow according to the task scheduling parameters through an automated execution engine.

[0062] In one possible embodiment, the automated execution engine includes: a preset rule-driven script, comprising:

[0063] If there is a form to be filled in associated with the current process node, the value to be filled is obtained from the system corresponding to the data source type of the field to be filled in the form, and the value to be filled is then filled into the form.

[0064] When the current process node is the approval completion node, the approval result and related attachments corresponding to the task processing process will be synchronized to the preset system; the preset system includes the human resources system and the document management system;

[0065] When the current process node is the process initiation node, monitor the execution status of the current process node, and when the execution status changes to the end status, initiate the next process node in the target process template corresponding to the task data.

[0066] In one possible embodiment, the above-described apparatus further includes: a monitoring module;

[0067] The monitoring module is used to monitor the processing time and timeout duration of the current process node during the execution of the task processing flow.

[0068] The processing module is also used to adjust the urgency level according to the upgrade rules if the processing time and timeout time meet the upgrade rules.

[0069] The processing module is also used to update the task scheduling parameters of the task processing flow based on the adjusted urgency level.

[0070] Thirdly, embodiments of this application provide a task automation processing system, comprising a business service layer and an engine and scheduling layer; the business service layer and the engine and scheduling layer are communicatively connected.

[0071] The business service layer is used to respond to task creation operations, obtain task data, and identify and analyze the task data to obtain a set of task attributes;

[0072] The business service layer is also used to match at least one object to be assigned from a pre-built organizational structure knowledge graph based on a set of task attributes.

[0073] The business service layer is also used to generate a task processing flow based on the task attribute set and at least one object to be assigned; the task processing flow includes the task scheduling parameters of each process node.

[0074] The engine, scheduling layer, and business service layer execute task processing procedures based on task scheduling parameters.

[0075] Thirdly, embodiments of this application provide an electronic device, including: a memory and a processor; the memory stores computer-executable instructions; the processor executes the computer-executable instructions stored in the memory, causing the processor to perform the first aspect and / or various possible implementations of the first aspect as described above.

[0076] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the first aspect and / or various possible implementations of the first aspect.

[0077] Fifthly, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the first aspect and / or various possible implementations of the first aspect.

[0078] The task automation processing method, system, device, and electronic device provided in this application, in response to a task creation operation, acquires task data, identifies and analyzes the task data to obtain a task attribute set, then matches at least one object to be assigned from a pre-built organizational structure knowledge graph based on the task attribute set, and generates a task processing flow and task scheduling parameters based on the task attribute set and at least one object to be assigned, and executes the task processing flow based on the task scheduling parameters. This method can reduce the reliance on manual input, manual assignment, and manual tracking in the task processing process, improve the targeting of task assignment and the coherence of process execution, thereby improving business processing efficiency and accuracy. Attached Figure Description

[0079] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0080] Figure 1 This is a schematic diagram of the structure of a document processing system provided in an embodiment of this application;

[0081] Figure 2 Flowchart of the task automation processing method provided in this application Figure 1 ;

[0082] Figure 3 Flowchart of the task automation processing method provided in this application Figure 2 ;

[0083] Figure 4 A schematic diagram of the structure of a business service layer provided in this application;

[0084] Figure 5 This is a schematic diagram of the structure of the task automation processing device provided in the embodiments of this application;

[0085] Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0086] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0087] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0088] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, storage, use, processing, transmission, provision, disclosure, and application of the relevant data all comply with the relevant laws, regulations, and standards of the relevant countries and regions, have taken necessary confidentiality measures, do not violate public order and good morals, and provide corresponding operation access points for users to choose to authorize or refuse.

[0089] Furthermore, the technical solution involved in this application, which involves big data analysis of user information (including but not limited to personal biometrics, identity data, consumption data, asset data, electronic terminal operation data, etc.) and the use of artificial intelligence technology for automated decision-making, and makes decisions that have a significant impact on personal rights based on the results of automated decision-making, provides users with corresponding operation entry points for users to choose to agree to or reject the results of automated decision-making; if the user chooses to reject, the process will proceed to the expert decision-making process.

[0090] In this application, the terms "exemplary" or "for example" are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" or "for example" in this application should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of terms such as "exemplary" or "for example" is intended to present the relevant concepts in a specific manner.

[0091] In the embodiments of this application, the use of terms such as "first" and "second" is to distinguish between identical or similar items that have essentially the same function and effect. For example, "first electronic device" and "second electronic device" are merely used to distinguish different electronic devices and do not limit their order of execution. Those skilled in the art will understand that the terms "first" and "second" do not limit the quantity or execution order, and that "first" and "second" do not necessarily imply that they are different.

[0092] In this application embodiment, "at least one" refers to one or more, and "more than one" refers to two or more. "And / or" describes the relationship between associated objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following associated objects have an "or" relationship.

[0093] It should be noted that the task automation processing method, system, device and electronic device provided in this application can be used in the field of fintech, or in any field outside of fintech. The application field of the task automation processing method, system, device and electronic device in this application is not limited.

[0094] Automated document processing technology is widely used in various industries, covering multiple stages such as task initiation, task assignment, approval process, processing tracking, result feedback, and archiving.

[0095] Existing business systems typically employ a combination of pre-set approval templates and manual judgment to process tasks. In these systems, document entry, approval assignment, and process tracking all rely on manual operation, requiring manual reading and categorization of approvals and comments. In high-frequency collaborative environments, this manual-judgment-centric processing model amplifies delays and instability. When the workload increases rapidly, staff must meticulously sift through numerous pending items, identifying attributes, finding suitable recipients, and configuring processes. This heavy workload and reliance on personal experience negatively impacts overall processing efficiency and reduces the predictability of process execution.

[0096] Therefore, existing technologies suffer from time-consuming business processing procedures and are prone to technical problems such as process delays and processing errors.

[0097] In view of this, the task automation processing method provided in this application, in response to the task creation operation, after acquiring task data, first identifies and analyzes the task data to obtain a set of task attributes reflecting the task category, urgency, responsibility, and processing requirements; then, based on this set of task attributes, it matches at least one object to be assigned from a pre-built organizational structure knowledge graph, so that the task content corresponds to the personnel relationships, job division, and responsibility structure within the organization; subsequently, it generates a task processing flow and task scheduling parameters according to the set of task attributes and the object to be assigned, and executes the task processing flow based on the task scheduling parameters, thereby linking task identification, object matching, flow formation, and flow execution into an integrated processing link, reducing reliance on human experience judgment, improving the targeting of task assignment and the adaptability of the processing flow, and improving business processing efficiency and accuracy.

[0098] The technical solutions of this application will be described in detail below with reference to specific embodiments. The specific embodiments described below can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of this application will be described below with reference to the accompanying drawings.

[0099] Figure 1 This is a schematic diagram of the structure of a task automation processing system provided in an embodiment of this application. Figure 1 As shown, the task automation processing system includes a business service layer 101 and an engine and scheduling layer 102;

[0100] The business service layer communicates with the engine and scheduling layer. The business service layer 101 is used to complete task data analysis, object matching, process generation and execution. The engine and scheduling layer 105 drives task flow, automatic execution and time-series scheduling in a unified manner through the process engine, task scheduling engine, RPA (Robotic Process Automation) execution engine and time scheduling engine, ensuring the automated operation of the system.

[0101] Specifically, the business service layer 101 responds to a task creation operation by acquiring task data, analyzing and identifying the task data to obtain a set of task attributes, and then matching at least one object to be assigned from a pre-built organizational structure knowledge graph based on the task attribute set. Subsequently, the business service layer 101 also generates a task processing flow based on the task attribute set and at least one object to be assigned, the task processing flow including task scheduling parameters for each process node. After obtaining the task processing flow, the engine, scheduling layer 102, and business service layer 101 execute the task processing flow based on the task scheduling parameters.

[0102] Understandably, an automated task processing system can also include an access layer, a gateway layer, and a data storage layer. The access layer serves as the unified access point for the document processing system, handling task creation requests and data interactions submitted from PCs (Personal Computers), mobile devices, and business system interfaces, thus achieving unified access management across multiple devices.

[0103] The gateway layer is responsible for request authentication, traffic control, routing and forwarding, and security interception, and uniformly manages service access entry points to ensure system access security and orderly distribution of requests.

[0104] The data storage layer is used to store task data and organizational structure knowledge graphs.

[0105] During the automated task processing, natural language commands and official document texts submitted by users from PCs or mobile devices are first uniformly received and encapsulated into standard requests by the access layer. Subsequently, after authentication, traffic control, and security interception by the gateway layer, they are routed to the business service layer 101. The business service layer 101 performs intent recognition and slot extraction on the natural language and extracts key information and performs structured mapping on the official document text to obtain standardized task data. Further, object matching and process generation are completed by combining the organizational structure knowledge graph read from the data storage layer, and then the executable process is distributed to the engine and scheduling layer 102. The process engine, task scheduling engine, RPA execution engine, and timed scheduling engine in the engine and scheduling layer 102 uniformly drive the task flow, automatic execution, and time-series scheduling.

[0106] During automated task processing, the business service layer 101 and the engine and scheduling layer 102 read and write task data and graph information in the data storage layer 104 in real time, completing a complete closed loop from request access to automated execution.

[0107] Figure 2 Flowchart of the task automation processing method provided in this application Figure 1 The executing entity of this application can be, for example, an electronic device deploying a business system, wherein the business system deploys a task automation processing program corresponding to the task automation processing method. This method can be applied to, for example... Figure 1 The document processing system shown. (For example...) Figure 2 As shown, the method includes:

[0108] S201: In response to a task creation operation, retrieve task data.

[0109] Among them, the task creation operation refers to the task generation behavior initiated by the user terminal, business system interface or automatic triggering mechanism, which corresponds to the creation event in the scenarios of document processing, request for instructions, approval application, joint signing, supervision and issuance or other pending items entry.

[0110] Specifically, in response to any of the aforementioned task creation operations, raw task data is obtained. This raw task data includes at least two types of data: natural language input by the user and official document text. Official document text includes, for example, financial reimbursement forms and notices / announcements, while natural language includes, for example, business instructions, security descriptions, and approval opinions input by the user. Further, both types of data are preprocessed to obtain task data. This task data includes, but is not limited to, the main body of the document, approval messages, table fields, attachment descriptions, initiator information, submission time, source system identifier, and contextual information associated with the task creation operation. Task data is the pending business information used to trigger subsequent automated processing and serves as the input foundation for the entire automated task processing chain.

[0111] It is understandable that official document texts undergo processing such as format parsing, key information extraction, content classification, and structured mapping to obtain structured business fields. Format parsing includes, but is not limited to, table extraction and paragraph segmentation. Key information may include, for example, amount, initiator information, date, and number. The document text itself may include, for example, the document type, urgency level, and drafting department selected by the user from a dropdown menu.

[0112] For natural language, text cleaning, word segmentation and part-of-speech tagging, syntactic analysis, semantic role labeling, intent recognition and slot extraction are performed to obtain standardized semantic representations of user commands.

[0113] For example, a business instruction could be "The first letter is very urgent, urge the relevant person in charge to handle it," and the corresponding user instruction semantic representation could be "Document type = letter, urgency level = urgent, trigger expediting." A security description could be "This document involves undisclosed financial data, keep it confidential," and the corresponding user instruction semantic representation could be "Risk tag: High confidentiality; Instruction: Restrict attachment download permissions." An approval opinion could be "This contract amount is quite large, it is recommended that the legal department review it," and the corresponding user instruction semantic representation could be "Risk tag: Legal intervention; Instruction: Add legal approval node."

[0114] Optionally, the original task data may originate from a document system and / or related systems. Related systems may include, for example, email systems, human resources systems, and office systems.

[0115] In one possible embodiment, this step is collaboratively completed by the access layer and business service layer deployed in the document processing environment. The access layer receives task creation requests from PC web interfaces, mobile office terminals, and third-party business system interfaces. When a user clicks the submit button on the creation interface, the front end encapsulates the basic fields, body content, attachment index, countersigning options, remarks, and user identity credentials from the page form into a creation message and sends it to the server. After receiving the creation message, the server first performs a request validity check, including verifying the user's identity, access permissions, request timestamp, message signature, and the completeness of required fields. After the verification passes, the server parses the message content and extracts it to form a task data object. For task creation requests from business interfaces, JSON (JavaScript Object Notation), XML (Extensible Markup Language), or table-structured data can be received through a predefined interface protocol and converted into standard task data according to a unified field model. For inputs from email, document exchange interfaces, or other heterogeneous systems, an adapter program can be used to map the original content to a unified task data structure, thereby ensuring the consistency of data from different sources.

[0116] In the actual implementation process, the task data includes not only explicitly entered information, but also information automatically supplemented by the business system. For example, the initiator's identifier, department, job code, and terminal type can be automatically written based on the login status; the creation time can be written based on the unified time service; the source business number, item number, or associated process number can be written based on the business system context; and the attachment storage address, file type, summary value, and file size can be written based on the attachment service.

[0117] For the main body and attachments of official documents, text extraction processing can be performed simultaneously during the text acquisition stage. For example, for various types of documents, images, or scanned documents, document parsing engines and text recognition engines can be invoked to convert the main text, text descriptions in images, and field content in tables into analyzable text, which is then written to the cache or database as part of the task data.

[0118] If there is a risk of duplicate submissions when creating a task, a task fingerprint can be generated based on the initiator, title, time window, task number, and text summary. This fingerprint is used to perform duplicate detection during creation and prevent the same task from becoming multiple conflicting tasks.

[0119] Furthermore, to ensure the stability of subsequent analysis, preprocessing operations can be performed after acquiring the task data. These preprocessing operations include, but are not limited to, character encoding standardization, date format standardization, attachment naming standardization, sensitive field desensitization, null value filling, and abnormal symbol cleaning. For semi-structured or unstructured data such as natural language descriptions in approval opinions, vague expressions in requests, and technical terminology paragraphs in attachments, both the original content and the parsed text can be retained. This serves two purposes: firstly, for subsequent semantic recognition, and secondly, for manual traceability and audit documentation.

[0120] Understandably, after acquiring task data, a task data record corresponding to this task creation operation is generated, and this record is written into the task library, index library, or message queue as input for the next step of identification and analysis.

[0121] This step enables the stable collection and unified organization of task data from multiple input environments, reducing data loss and format inconsistencies caused by differences in sources, and making subsequent task attribute extraction more complete and accurate.

[0122] S202: Identify and analyze the task data to obtain a set of task attributes.

[0123] Specifically, the business system deploys an identification module. After acquiring task data, this module is activated to analyze the task data across three levels: structured data identification, semi-structured data extraction, and unstructured semantic analysis. This results in multiple task attributes, which are then used to generate a task attribute set using a unified data structure. This task attribute set includes, but is not limited to, task category, urgency, confidentiality level, task tags, processing time limit, responsibility attribution, and other information characterizing task features. It serves as the direct basis for subsequent matching of objects to be assigned from the organizational structure knowledge graph and for generating task processing flows and task scheduling parameters.

[0124] For example, for structured data such as form fields, source system identifiers, and application type codes, explicit attributes such as task category, application type, initiating department, and proposed processing level are determined based on field mapping rules. For tables, templated documents, or fixed-format request forms in attachments, attributes such as amount, project name, contract number, deadline, and confidentiality identifier are extracted using layout recognition and key field location technologies.

[0125] Furthermore, for unstructured text such as main body, approval opinions, and remarks, semantic attributes are extracted using a combination of word segmentation, named entity recognition, keyword extraction, syntactic analysis, rule matching, and text classification models. For example, the statement "Please complete the feedback before the end of today's workday" is identified as having the highest urgency level and a processing deadline of "before 5:30 PM"; the statement "Involving budget increases and special fund allocations" is identified as having a financial approval task attribute; and the statement "For internal circulation only, not for external distribution" is identified as having an internal confidentiality level.

[0126] This step reduces the impact of human comprehension bias on task assignment by extracting stable and usable decision features from complex task data, and provides a structured basis for subsequent automatic matching and dynamic process generation, thereby helping to improve the accuracy and efficiency of business processing.

[0127] S203: Based on the task attribute set, match at least one object to be assigned from a pre-built organizational knowledge graph.

[0128] The organizational structure knowledge graph includes departmental relationships, job relationships, hierarchical relationships, responsibility boundaries, personnel capability tags, authorization scope, and historical processing records. The objects to be assigned are candidate objects for undertaking task processing, including the processing department and the person responsible for the task, and may also include collaborators, countersigners, reviewers, or monitoring nodes.

[0129] Understandably, the nodes in the pre-built organizational structure knowledge graph include department nodes, position nodes, personnel nodes, role nodes, capability tag nodes, task type nodes, and permission scope nodes. The edge relationships include subordinate relationships, reporting relationships, delegation relationships, approval relationships, responsibility coverage relationships, professional matching relationships, and historical collaboration relationships. This knowledge graph can be jointly constructed by the organizational structure system, human resources system, unified identity platform, job description library, permission management system, and historical document processing records, and its timeliness is maintained through regular synchronization and event-driven updates.

[0130] After obtaining the task attribute set, the task intelligent scheduling and assignment module deployed in the business system maps the task category, responsibility, urgency, confidentiality level, processing time limit, and tag set in the task attribute set to graph query conditions, obtaining at least one object to be assigned. For example, when the task attributes indicate that it belongs to budget approval and involves a specific project, the system first locates the financial management department, the project management department, and the set of positions with relevant approval authority in the graph, and then determines the primary responsible department by combining the task initiating department, the matter's attribution, and the approval level.

[0131] After identifying the processing departments, the responsible personnel for each department are further determined based on attributes such as task category and responsibility in the task attribute set. These responsible personnel and their corresponding departments are then designated as the objects to be assigned.

[0132] This step maps task content to actual job divisions and responsibilities by associating task attribute sets with structured personnel relationships within the organization. This solves the problems of misassignment, omission, and unreasonable assignment caused by relying solely on manual search of the responsible person or rough routing according to fixed templates in traditional systems.

[0133] Optionally, the organizational structure knowledge graph employs knowledge graph triple modeling, graph database storage, entity linking, and multi-factor weighted scoring methods. It establishes a knowledge network linking organizational structure, departments, positions, personnel, skills, permissions, and workloads, enabling personnel matching, approval chain generation, and real-time permission verification. The organizational structure knowledge graph supports document dispatch and access control.

[0134] This organizational structure diagram constructs a comprehensive network of relationships among departments, positions, personnel, skills, and permissions, enabling the visualization and real-time accessibility of organizational information, as well as the construction of a panoramic view of personnel capabilities.

[0135] S204: Generate a task processing flow based on the task attribute set and at least one object to be assigned.

[0136] The task processing flow is a process structure used to define the subsequent execution path and node flow of a task. It reflects the sequence of steps, node roles, and connections between nodes from task acceptance, assignment, review, countersigning, approval, feedback to archiving.

[0137] The task processing flow includes task scheduling parameters for each node. These parameters are control data used to manage the task processing flow. Examples of these parameters include the node handler, earliest trigger time, latest completion time, reminder threshold, timeout escalation target, processing method restrictions, and allowed rollback range.

[0138] Specifically, the business system deploys a process generation engine that obtains a standard task process template based on a set of task attributes and objects to be assigned. The standard task process template includes multiple process nodes, each associated with node attributes and configurable parameters. Node attributes are static information about the node, including but not limited to node identifier, node name, node execution order, and node type. Configurable parameters are dynamic and adjustable parameters for the node.

[0139] After obtaining the standard task flow template corresponding to the task data, the parameters to be configured for each process node are filled in by combining multiple task attributes in the task attribute set, and the filled standard task flow template is used as the task processing flow.

[0140] For example, the parameters to be configured include a responsible person field, and the population rule associated with the responsible person field can be, for example, "filter the target responsible persons from at least one object to be assigned whose job responsibilities match the node type".

[0141] For example, process generation is achieved by combining template instantiation with dynamic node orchestration. For common types of matters within the organization, basic process templates are pre-defined, such as budget approval processes, follow-up process processes, contract countersigning processes, and risk reporting processes.

[0142] Upon receiving the task attribute set, the corresponding basic template is first selected. Then, based on the multiple task attributes in the task attribute set, nodes are added or deleted, their order is adjusted, and conditional branches are configured. Furthermore, a joint analysis of the node attributes and task attribute sets is performed. For example, for ordinary budget approval tasks, the process may include: handling acceptance, departmental review, financial review, supervisor approval, and archiving; for budget tasks involving cross-departmental fund coordination, a collaborative signing node is inserted after departmental review; for urgent tasks, the time limits of intermediate nodes are shortened, and some nodes are allowed to be processed in parallel to meet processing time requirements.

[0143] This step maps the task attribute set and the objects to be assigned together into a processing path and execution control rules oriented towards the current task scenario, thereby solving the problem that the process template is fixed in the traditional solution and cannot be dynamically adjusted according to task characteristics and personnel situation.

[0144] S205: Execute the task processing flow based on task scheduling parameters.

[0145] This step drives the task processing flow through task scheduling parameters, which can link the aforementioned task data acquisition, task attribute identification, object matching to be assigned, and process generation results in a closed loop, forming an automated processing link from creation to completion.

[0146] In this stage, task scheduling parameters are used not only to start the process, but also to control the pace of process advancement, node triggering conditions, status change logic, reminder and reminder strategies, and exception escalation paths. Therefore, their role extends throughout the entire task lifecycle.

[0147] Specifically, after receiving the task processing flow, the process execution engine in the business system first creates a task instance context, writes the current status, current node, main responsible object, cooperating object, deadline and priority and other execution information, and then starts the task according to the process starting node and the triggering rules in the task scheduling parameters of the starting node.

[0148] If the starting node is an automatic assignment node, the pending tasks will be pushed to the responsible person's workbench, mobile to-do list, or departmental processing pool according to the information of the person to be assigned. If the starting node is an automatic review node, such as form integrity verification or attachment legality check, the corresponding business rule service will be called first to complete the automatic processing, and the task will be pushed to the manual node after the processing is successful.

[0149] During node execution, the process engine continuously reads task scheduling parameters to determine when to send reminders, when to trigger timeout reminders, when to escalate nodes, and when to allow rework or transfer for assistance. For example, for nodes with reminder thresholds, a reminder is sent via in-system message, SMS, mobile push, or unified message center when the remaining processing time of the node is below the threshold; for nodes that have timed out, reminder records are automatically added according to the timeout policy, and the task is escalated to the superior approver or substitute responsible person.

[0150] During the process, the task state machine is maintained synchronously. The state machine can include at least the following states: created, awaiting assignment, processing, awaiting countersigning, awaiting approval, feedback received, completed, returned, and suspended. Whenever a node is completed, the processor submits comments, countersigning ends, or an approval conclusion is reached, the process engine updates the task status based on the task scheduling parameters and process definition, and decides whether to trigger the next node.

[0151] During the task processing flow, for parallel nodes, the rule of "flowing after all nodes are completed" or "flowing after any key node is completed" is adopted according to the task scheduling parameters. For conditional branch nodes, the subsequent path is determined based on the approval result, amount threshold, risk level, or other task attributes. If an anomaly occurs during execution, such as the processor's account being deactivated, external service call failure, attachments being unreadable, or a node being blocked for a long time, retry, agent switching, and alarm operations are performed according to the anomaly recovery strategy in the task scheduling parameters. In addition, when the state machine of a process node indicates an abnormal state, the task processing flow can be transferred to the manual intervention queue.

[0152] Understandably, throughout the entire task processing flow, all operations during execution are logged and traced to meet the needs of auditing, review, and statistical analysis. When the task processing flow reaches the termination node, closing operations are performed based on the flow results. These closing operations include, but are not limited to, generating completion records, summarizing approval opinions, archiving task data, and outputting processing result notifications.

[0153] This application provides an automated task processing method that, in response to a task creation operation, acquires task data, identifies and analyzes the task data to obtain a set of task attributes, and then matches at least one object to be assigned from a pre-built organizational structure knowledge graph based on the task attribute set. Subsequently, a task processing flow is generated based on the task attribute set and at least one object to be assigned, and then the task processing flow is executed based on the task scheduling parameters in the task processing flow. This method uses task scheduling parameters to run through and drive the execution of the task processing flow, forming a closed-loop automated processing link from data acquisition, attribute extraction, object matching, flow orchestration to execution, improving the targeting of task assignment and the coherence of flow execution, reducing the manual dependence on task processing, and thus improving business processing efficiency and accuracy.

[0154] Figure 3 Flowchart of the task automation processing method provided in this application Figure 2 The embodiments of this application are... Figure 1-2 Based on the embodiments, a possible method for automating task processing is described in detail. For example... Figure 3 As shown, the method includes:

[0155] S301: Retrieves task data in response to a task creation operation.

[0156] This step is similar to the explanation of step S201 above, and will not be repeated here.

[0157] S302: Identify and analyze task data to obtain task type, urgency, and confidentiality level; extract task keywords from task data to construct task tags; determine processing time limits based on urgency, predefined task time limit rules, and / or time-sensitive words in task data.

[0158] As explained in step S201 above, the task data includes structured business fields from official document texts and standardized user instruction semantic representations. The input to this step is these structured business fields and standardized user instruction semantic representations. The structured business fields include document type fields, such as "request," "report," and "letter," etc., and the standardized user instruction semantic representations include intent tags, such as "initiate approval" and "query progress," etc.

[0159] Specifically, semantic analysis is performed on the document type field and document body in the structured business fields, and / or the intent tags in the standardized user instruction semantic representation, to identify the task type corresponding to the task data. This task type package could include, for example, document receipt processing, document issuance approval, procurement approval, financial reimbursement, personnel appointments and dismissals, meeting minutes, notices and announcements, etc.

[0160] Furthermore, based on the urgency and security classification fields in the structured data, and / or time-sensitive words and security classification keywords parsed from the standardized semantic representation of user instructions, the urgency and security level of the task are determined. The urgency field can be, for example, "normal," "important," or "urgent," while time-sensitive words can be, for example, "hurry up," "as soon as possible," "before next week," or "overdue." Security classification fields and keywords can be, for example, "internal view" or "confidential." Additionally, if the task data includes document data identified from documents, security classification labels can be extracted from this document data, and the security level can be determined based on these labels.

[0161] Understandably, if the security levels identified from structured fields, user command semantics, and documentation differ, the highest security level will be determined as the security level for this task. After determining the security level, it can also be sent to the task submitter for confirmation.

[0162] For task tag generation, word segmentation, part-of-speech tagging, and domain dictionary comparison are used to extract task keywords that can represent the subject, object, and key actions of the task from the task data in step S301 above, and task tags are generated based on these task keywords. If multiple task keywords exist, task tags are generated according to word frequency, weight, and semantic relevance. Task keywords can be, for example, "server" or "budget," and may also include department names and risk indicators.

[0163] The task completion time limit is determined by a combination of urgency and / or the aforementioned time-sensitive terms. In one implementation, if the task data contains a specific deadline, that deadline is used as the task completion time limit; if the task data contains relative time terms (such as "within three days" or "next week"), the specific date is calculated based on the system's current time; if neither is present, the task completion time limit corresponding to the urgency level is determined based on predefined task time limit rules. These rules include the correspondence between urgency level, the first task type, and the task completion time limit. For example, if the urgency level is the highest, the task completion time limit for the first task type at the highest urgency level is determined from the task time limit rules.

[0164] This step can be implemented by the semantic understanding and task recognition module deployed in the business service layer. Figure 4 This application provides a schematic diagram illustrating the module interaction between the business service layer and the engine and scheduling layer. For example... Figure 4 As shown, the business service layer includes a multi-source information acquisition module 401, a semantic understanding and task recognition module 402, a task intelligent scheduling and assignment module 403, an intelligent reminder delivery module 404, a process execution and feedback closed-loop module 405, and a security control and vulnerability detection module 406. The engine and scheduling layer includes a process engine 407 and an RPA execution engine 408.

[0165] The multi-source information acquisition module 401 is used to proactively retrieve raw data from multiple heterogeneous data sources, such as official document systems, approval processes, system logs, configuration files, and traffic data collection points, according to preset cycles or event triggering conditions, and also covers structured data generated by PCs and mobile devices. After completing the parsing, cleaning, and structuring of the data, the multi-source information acquisition module 401 sends the standardized task data to the semantic understanding and task recognition module 402 to drive subsequent intelligent task scheduling and process execution.

[0166] Event triggering conditions can include, for example, new document release events, approval process status change events, log file addition events, configuration file modification events, or traffic data threshold alarm events. The preset period is set based on task timeliness requirements, data source update frequency, or system resource usage strategies; the preset period can be, for example, a fixed time point or a period of low business activity.

[0167] S303: Generate a set of task attributes based on task type, urgency, confidentiality level, task tags, and processing time limit.

[0168] The task type, urgency, confidentiality level, task tags, and processing time limit are structured and combined to form a unified set of task attributes. For example... Figure 4 As shown, after the semantic understanding and task recognition module 402 generates a task attribute set, it sends the task attribute set to the task intelligent scheduling and dispatch module 403 and the intelligent reminder delivery module 404.

[0169] S304: Based on task type and task tag, identify at least one processing department and at least one candidate responsible person for each processing department from the organizational structure knowledge graph.

[0170] Specifically, a joint search of the organizational structure knowledge graph is performed based on task type and task tags. First, at least one processing department with the qualification to handle the task is identified according to the task type. If there is only one processing department, it is the lead department. If there are multiple processing departments, they include at least one lead department and at least one supporting department. Subsequently, the task permissions corresponding to the task type are obtained, and these permissions are compared with the job permissions of the candidate responsible persons to filter out personnel with matching permissions, forming at least one candidate responsible person for each processing department.

[0171] It should be noted that each candidate responsible has corresponding attribute information, which comes from the organizational structure knowledge graph. The attribute information includes, but is not limited to, department, skill tags, permission data, number of pending tasks, and historical processing efficiency.

[0172] This step can be performed by Figure 4 The task intelligent scheduling and assignment module 403 shown is executed. Based on the organizational knowledge graph, the task intelligent scheduling and assignment module 403 determines at least one processing department to handle this task and at least one candidate person in charge of each processing department based on the task attribute set sent by the semantic understanding and task recognition module 402.

[0173] S305: When there are multiple candidate responsible persons in the processing department, calculate the comprehensive score of each candidate responsible person by weighting the matching degree between the attribute information of the multiple candidate responsible persons and the task attribute set; and determine the candidate responsible person corresponding to the highest comprehensive score as the target responsible person of the processing department.

[0174] When there are multiple candidate responsible persons within a processing department, the matching degree of skill tags and task tags, the conformity of permission data and task handling permissions, the matching degree of the number of pending tasks and the urgency of tasks, and the correlation between historical processing efficiency and task processing time limit of each candidate responsible person are mapped as scoring items, and then weighted and summed according to preset weights to obtain the comprehensive score of each candidate responsible person.

[0175] It should be noted that the weights of the above scoring items are jointly constrained by the task type, confidentiality level, and urgency, making time-sensitive tasks more focused on efficiency factors and specialized tasks more focused on skill factors.

[0176] In one possible implementation, the attribute information of each candidate responsible includes skill tags, the number of pending tasks, and task processing efficiency. Skill tags are matched with task tags to obtain a first score; the number of pending tasks is used to obtain a second score; task processing efficiency is mapped to a third score; based on the urgency of the task attribute set, the weights corresponding to the first, second, and third scores are determined, and the first, second, and third scores are weighted and summed to obtain a comprehensive score.

[0177] For example, the matching degree between the candidate's skill tags and task tags is calculated to obtain the first score. Based on the number of pending tasks currently held by the candidate, a workload coefficient is calculated and mapped to the second score; the lower the workload coefficient, the higher the score. Furthermore, the candidate's historical processing efficiency for this task type is mapped to the third score. The historical processing efficiency is based on the average historical processing time corresponding to this task type in the attribute information; the higher the historical processing efficiency, the higher the third score.

[0178] After obtaining the first, second, and third scores, the weight of each score item is determined based on the urgency level in the task attribute set. For example, if the urgency level is the highest, the weights corresponding to the first and third scores are higher than the weight corresponding to the second score. The specific weight values ​​are preset according to the business scenario, and this application does not limit them.

[0179] Understandably, this step and step S306 below are both implemented by the task intelligent scheduling and assignment module 403, and after obtaining each processing department and the target responsible person of each processing department, the module associates each processing department and the target responsible person of each processing department with the task identifier in the task data.

[0180] This method jointly evaluates candidate personnel based on skill level, current workload, and historical efficiency, and dynamically adjusts the scoring weights according to the urgency of the task, ensuring that the matching results simultaneously reflect task suitability and personnel capacity. This guarantees accurate and prioritized assignment of high-urgency tasks while reducing assignment bias caused by uneven personnel workload, thus improving the accuracy, timeliness, and stability of task assignment.

[0181] S306: If there is a candidate responsible person in the processing department, the candidate responsible person shall be identified as the target responsible person in the processing department.

[0182] S307: Determine the target process template in the process template library that matches the task type.

[0183] After obtaining the task attribute set and the object to be assigned, the corresponding template is retrieved from the process template library according to the task type, and the template is loaded as the target process template for this task.

[0184] Understandably, the process template library is pre-stored in the task processing system's storage module. Each process template in the library defines the sequence of nodes a task must traverse from start to finish, as well as the processing role for each node (e.g., "drafter," "department head," "supervisor"). The node sequence could be, for example, "drafting → reviewing → countersigning → approval → stamping → issuing → archiving," and the processing roles could be, for example, "drafter," "department head," or "supervisor."

[0185] S308: Associate each object to be assigned with the corresponding process node in the target process template; based on the urgency and processing time limit, determine the timeout threshold, reminder delivery channel, reminder triggering time and escalation rules for each process node in the target process template to obtain the task processing flow.

[0186] It should be noted that the task scheduling parameters include node execution parameters, as well as the timeout threshold, reminder delivery channel, reminder triggering timing, and escalation rules for each process node. The escalation rules are used to indicate the adjustment of urgency based on the task processing time and timeout duration. Furthermore, the aforementioned process nodes are associated with node execution parameters, which include the next process node and the initiation conditions for the next process node. These node execution parameters consist of the node attributes and configurable parameters explained in step S204 above. The node attributes include the next process node and the initiation conditions for the next process node. The configurable parameters include the matching rules for the node handlers, which can be defined by factors such as department, position, and permissions.

[0187] Specifically, after obtaining the target process template, based on the matching rules associated with the node handler field in the execution parameters of each process node in the target process template, the node handler of each process node is obtained from the above-mentioned objects to be assigned, and the attribute information of the node handler is written into the node handler field of the corresponding process node.

[0188] Understandably, node execution parameters can be written into the configuration table of the process template and saved through conditional expressions within the template, making it easy for different task types to reuse the same node structure without changing the template backbone.

[0189] For timeout thresholds, the estimated processing time for each process node in the target process template is determined. Combining the estimated processing time with the processing time limits in the task attribute set, the node processing time limit for each process node is determined. Based on the node processing time limit and urgency, an initial timeout threshold is calculated, and this initial timeout threshold is adjusted by referring to the node's estimated processing time to obtain the timeout threshold for each process node. The timeout threshold for a process node is less than its node processing time limit. The estimated processing time for each node is determined based on the historical average processing time of each process node.

[0190] In some embodiments, the processing time limit is multiplied by an urgency coefficient that is negatively correlated with the urgency level to obtain an initial timeout threshold. It is then determined whether the estimated processing time of the process node meets a preset timeout threshold correction condition. If the estimated processing time meets the preset timeout threshold correction condition, the initial timeout threshold is corrected to obtain the timeout threshold for the process node. Otherwise, the initial timeout threshold is used as the timeout threshold for that process node.

[0191] The above-mentioned timeout threshold correction conditions include: when the estimated processing time of a process node is greater than the first preset time, the timeout threshold is increased by the first preset time until the node processing time limit of the process node is reached; when the estimated processing time of a process node is less than the second preset time, the timeout threshold is decreased by the second preset time until the preset lower limit of the timeout threshold is reached.

[0192] It should be noted that the first preset duration is longer than the second preset duration. This application sets the first preset duration based on the historical processing time distribution of the task type, the importance of the process node in the process, or the system's fault tolerance and redundancy requirements. The second preset duration is set based on the lower quartile of the node's normal processing time, the task's real-time requirements, or the user's acceptable waiting threshold. The lower limit of the timeout threshold is set based on the task's minimum service quality guarantee, business compliance requirements, or the system's default minimum response time. This application does not impose any restrictions on the specific values ​​of the first preset duration, the second preset duration, and the lower limit of the timeout threshold.

[0193] For example, urgency levels include urgent tasks, important tasks, and ordinary tasks. The timeout coefficient for urgent tasks is 0.5, for important tasks it is 0.7, and for ordinary tasks it is 1. That is, if the task is urgent, its timeout threshold is half of the node processing time, that is, the actual node processing time is compressed to half of the predicted node processing time.

[0194] Regarding the notification channels and triggering times, the system matches corresponding channels and triggering times from a preset notification rule library based on the urgency of the task. The preset notification rule library can be user-defined; this application does not impose any restrictions on it.

[0195] For example, for a task with an urgency level of "Urgent," the corresponding reminder triggering time is: "A reminder will begin 2 hours before the timeout threshold is reached, and will be sent every 30 minutes. Upon timeout, the first-level timeout rule in the escalation rules will be triggered immediately." For a task with an urgency level of "Important," the corresponding reminder triggering time is: "A reminder will begin 1 business day before the timeout threshold is reached, and will be sent daily. Upon timeout, the reminder will be escalated and copied." For a task with an urgency level of "Normal," the corresponding reminder triggering time is: "A reminder will begin 1 day before the timeout threshold is reached, and will be sent only once. Upon timeout, a reminder will be sent again."

[0196] The escalation rules are pre-configured in the task processing system. The escalation rules include: when the first level of timeout is reached, a reminder is sent to the person responsible for the task, and a copy is sent to the person's direct supervisor; when the second level of timeout is reached, a copy is sent to the two levels of leaders above, and a supervision item is generated in the automatic report of the department meeting; when the third level of timeout is reached, a special supervision task is generated and assigned to the supervision and inspection department for intervention.

[0197] The timeout is categorized into three levels: Level 1 timeout (the first time a task exceeds its processing time limit for the first time), Level 2 timeout (the task remains unprocessed after a Level 1 timeout reminder, and the timeout duration reaches a first preset threshold; for example, more than one business day after a Level 1 timeout), and Level 3 timeout (the task remains unprocessed after a Level 2 timeout, and the timeout duration reaches a second preset threshold; the second preset threshold could be, for example, three business days). The first and second preset thresholds are set based on task processing requirements, and this application does not limit their specific values.

[0198] After obtaining the timeout threshold, reminder delivery channels, reminder triggering timing, and escalation rules, these information are associated with the corresponding process nodes as the basis for task processing.

[0199] Understandably, for tasks requiring multi-level approval, the target process template includes the approval chain for the task. The approval chain is dynamically corrected based on the task data to obtain the target approval chain. Multiple process nodes in the target process template are then serialized based on the approval chain. After associating the corresponding task scheduling parameters with these multiple process nodes, the final executable task processing flow is obtained.

[0200] The approval process can be structured as follows: "Applicant's Department Head → Purchasing Department Head → Deputy General Manager in Charge". If the purchase amount in the task data exceeds a preset purchase amount threshold, the approval process is modified to "Applicant's Department Head → Purchasing Department Head → Deputy General Manager in Charge → General Manager". Each approver in this approval process is determined in real time based on the current employees in the organizational structure knowledge graph, and automatic replacement of proxy approvers is supported.

[0201] This step can be performed by Figure 4 The intelligent reminder delivery module 404 is implemented in the process of subsequent task processing. Based on the task node, overdue date, priority and the online status of the responsible person, the intelligent reminder delivery module 404 delivers reminders through multiple channels such as official documents, emails and OA system.

[0202] S309: Through the automated execution engine, the script is driven according to preset rules to execute the task processing flow according to the task scheduling parameters.

[0203] The automated execution engine is Figure 1 As shown in the engine and scheduling layer 105, after the business service layer generates the task processing flow, it sends the task processing flow to the engine and scheduling layer 105. The process engine 407 in the engine and scheduling layer 105 drives the script according to the preset rules, calls the process execution and feedback closed-loop module 405 of the business service layer, and the RPA execution engine 408 executes the task processing flow according to the task scheduling parameters.

[0204] The automated execution engine is the main execution entity in the task processing system. It is used to call the business service layer's process execution and feedback closed-loop module 405 and RPA execution engine 408 based on rule-driven scripts to complete form filling, result archiving, and process triggering. The preset rule-driven scripts are used to carry the execution logic under different node conditions. When a node is associated with a form to be filled, the script determines the source of the value based on the data source type of the field to be filled and establishes a data access connection with the corresponding system to complete the field backfilling.

[0205] The Workflow Engine 407, based on a Finite State Machine (FSM), priority queue scheduling, timed polling, and timeout triggering algorithms, enables full-process status control, timeout reminders, and automated scheduling of official documents (drafting, review, countersigning, approval, stamping, issuance, circulation, archiving, etc.). As the central driver of the entire process, the Workflow Engine undertakes the core responsibilities of status control and time scheduling. For example, it uniformly manages the triggering conditions and jump rules between different states, ensuring a clear and controllable flow logic from submission to archiving. Furthermore, the Workflow Engine can automatically set processing time limits based on document type and urgency, and automatically trigger multi-channel reminder mechanisms at key process nodes to ensure documents progress within the specified time. The Workflow Engine categorizes urgent, important, and routine documents into hierarchical queues, prioritizing urgent matters to ensure rapid circulation of high-priority documents. Moreover, in the event of a task timeout, the Workflow Engine automatically triggers follow-up and escalation notifications, recording the entire process status and operation logs in detail, achieving full traceability and no delays.

[0206] The RPA Execution Engine 408 employs rule-driven scripting, OCR (Optical Character Recognition) control image recognition, and multi-threaded asynchronous concurrency technologies to automate cross-system operations, form filling, format validation, and security hardening by simulating manual operations. The RPA Execution Engine is responsible for automated operations at the execution layer, efficiently resolving cross-system and repetitive tasks.

[0207] Specifically, the RPA execution engine 408 is responsible for automatically synchronizing data between the official document system and other business systems, eliminating errors from manual copying and pasting, accurately identifying interface controls and form fields of each business system, automatically filling in data, validating format specifications, and performing standardized typesetting to ensure compliance with specifications. The RPA execution engine 408 can also perform system interface health checks, permission rule hardening, system vulnerability scanning, and operation log retention, realizing the automated execution of security operation and maintenance processes.

[0208] In addition, the RPA execution engine 408 supports the simultaneous processing of multiple documents, significantly improving the throughput and execution efficiency of document processing, and is suitable for scenarios involving large-volume document processing.

[0209] For example, the preset rule-driven script includes: when the current process node is associated with a form to be filled, obtaining the value to be filled from the system corresponding to the data source type of the field to be filled in the form, and filling the form with the value to be filled; when the current process node is an approval completion node, synchronizing the approval result and related attachments corresponding to the task processing process to the preset system; when the current process node is a process initiation node, monitoring the execution status of the current process node, and initiating the next process node in the target process template corresponding to the task data when the execution status changes to the completion status.

[0210] Understandably, during the execution of the task processing flow, the processing time and timeout duration of the current process node are monitored; if the processing time and timeout duration meet the escalation rules, the urgency level is adjusted according to the escalation rules; and the task scheduling parameters of the task processing flow are updated based on the adjusted urgency level.

[0211] The processing time represents the processing time consumed since the current process node entered the execution state, while the timeout duration represents the maximum allowed processing time preset in the scheduling configuration for this process node. Both can be continuously collected based on the node timer of the process engine during task processing operations, and the real-time processing time can be compared with the timeout duration.

[0212] Once the current process node begins processing, a timer record is established, and the timeout duration of the corresponding node is read from the task scheduling data. When the processing time reaches the trigger condition set by the escalation rule, the urgency level is adjusted to a higher level according to the rule, and the reminder frequency, follow-up intensity, or scheduling priority of the task in the process is recalculated accordingly. Subsequently, the adjusted urgency level is written back to the scheduling object corresponding to the task processing flow, completing the update of the task scheduling data, so that the task can be executed according to the new urgency level in subsequent pending sorting, message push, manual follow-up, and node transfer. For different business scenarios, the threshold of the escalation rule can be configured in association with task type, processing time limit, and node role.

[0213] Understandably, this method is... Figure 4 The security management and vulnerability detection module 406 shown is implemented. This method can dynamically identify timeout risks based on real-time processing time during task processing and automatically increase the task urgency level, thereby driving the scheduling data to change synchronously. This gives high-urgency tasks higher priority and stronger reminder capabilities, reduces the probability of node delays and timeout backlogs, and improves the timeliness, controllability, and stability of document processing workflows.

[0214] This application provides an automated task processing method that, in response to a task creation operation, acquires task data and analyzes it to obtain the task type, urgency, and confidentiality level. It also extracts task keywords from the task data to construct task tags, and then determines the processing time limit based on the urgency, predefined task time limit rules, and / or time-sensitive words in the task data. Subsequently, a task attribute set is generated based on the task type, urgency, confidentiality level, task tags, and processing time limit. This method automatically completes multi-dimensional attribute identification after task creation, reducing the workload of manually determining task categories and time limits, and ensuring that the attribute results can be stably used for subsequent assignment and circulation. Since the task type, urgency, and confidentiality level are uniformly identified, and task tags and processing time limits are also generated synchronously, the consistency and interpretability of task attributes can be significantly improved, reducing misjudgments, omissions, and inaccurate time limit settings, thereby improving the accuracy and efficiency of document processing.

[0215] Furthermore, based on task type and task tags, at least one processing department and at least one candidate responsible person for each processing department are identified from the organizational structure knowledge graph. When multiple candidate responsible persons exist for a processing department, a weighted comprehensive score is calculated based on the matching degree between the attribute information of multiple candidate responsible persons and the task attribute set. The candidate responsible person with the highest comprehensive score is then identified as the target responsible person for the processing department. Additionally, if only one candidate responsible person exists for a processing department, that candidate responsible person is identified as the target responsible person for that department. This method automatically maps organizational structure, job capabilities, and task attributes to the organizational structure knowledge graph, enabling continuous linkage between the identification of processing departments and the selection of target responsible persons. This reduces biases caused by manual judgment and ensures that the final assignment result simultaneously considers responsibility attribution, authorization legitimacy, professional matching, and current workload status. This improves task assignment accuracy, reduces the probability of misassignment and omission, and enhances task flow efficiency and overall processing stability in multi-department collaborative scenarios.

[0216] After identifying the tasks to be assigned, a target workflow template matching the task type is determined from the workflow template library. Each task to be assigned is then associated with a corresponding workflow node in the target workflow template. Based on urgency and processing time limits, the timeout threshold, reminder channels, reminder triggering timing, and escalation rules for each workflow node in the target workflow template are determined, resulting in the task processing flow. This method integrates workflow templates, personnel associations, and scheduling parameters into task scheduling data, achieving unified configuration of task flow paths and expediting mechanisms. This allows tasks of different urgency levels to automatically match corresponding processing rhythms and maintains consistency in the flow relationships, reminder methods, and escalation strategies between nodes. Because the escalation rules can dynamically adjust the urgency based on actual processing time, this method can promptly enhance supervision when tasks are nearing or exceeding their processing time limits, reducing omissions and delays, and improving the continuity, controllability, and automation of task processing.

[0217] Ultimately, the automated execution engine drives scripts according to preset rules, executing task processing flows according to task scheduling parameters. This reduces time consumption caused by manual data entry, synchronization, and follow-up, improving the continuity and consistency of the process. Because script actions are constrained by task scheduling parameters, the processing results remain consistent with the process template and node status, reducing the probability of incorrect data entry, omissions, and duplicate processing, and enhancing execution stability in complex document scenarios.

[0218] Figure 5 This is a schematic diagram of the structure of the task automation processing device provided in the embodiments of this application, as shown below. Figure 5 As shown, this application embodiment provides a task automation processing device, the device 50 including:

[0219] The acquisition module 501 is used to acquire task data in response to a task creation operation;

[0220] Processing module 502 is used to identify and analyze task data to obtain a set of task attributes;

[0221] Matching module 503 is used to match at least one object to be assigned from a pre-built organizational knowledge graph based on a set of task attributes.

[0222] The generation module 504 is used to generate a task processing flow based on the task attribute set and at least one object to be assigned; the task processing flow includes the task scheduling parameters of each process node.

[0223] Execution module 505 is used to execute the task processing flow based on task scheduling parameters.

[0224] In one possible embodiment, the above-described apparatus includes: a determining module 506;

[0225] The processing module 502 is specifically used to identify and analyze task data to obtain task type, urgency and security level;

[0226] Processing module 502 is also used to extract task keywords from task data and construct task tags;

[0227] The determination module 506 is used to determine the processing time limit based on the urgency level, predefined task time limit rules, and / or time-sensitive words in the task data;

[0228] The generation module 504 is also used to generate a set of task attributes based on task type, urgency, confidentiality level, task tags, and processing time limit.

[0229] In one possible embodiment, the objects to be assigned include: processing departments and target responsible persons of the processing departments. Based on the task attribute set, the determination module 506 is further used to determine at least one processing department and at least one candidate responsible person of each processing department from the organizational structure knowledge graph based on the task type and task tags. Each candidate responsible person has corresponding attribute information. The attribute information includes: department, skill tags, permission data, number of pending tasks and historical processing efficiency.

[0230] The processing module 502 is also used to calculate the comprehensive score of each candidate responsible person by weighting the matching degree between the attribute information of the multiple candidate responsible persons and the task attribute set when there are multiple candidate responsible persons in the processing department.

[0231] The determination module 506 is also used to determine the candidate responsible person corresponding to the highest comprehensive score as the target responsible person of the processing department.

[0232] In one possible embodiment, the attribute information includes: skill tags, number of pending tasks, and task processing efficiency; the processing module 502 is also used to match the skill tags with the task tags to obtain a first score;

[0233] The processing module 502 is also used to obtain a second score based on the number of pending tasks;

[0234] The processing module 502 is also used to map task processing efficiency to a third rating;

[0235] The determination module 506 is also used to determine the weights corresponding to the first score, the second score, and the third score based on the urgency of the task attribute set.

[0236] The processing module 502 is also used to perform a weighted summation of the first score, the second score, and the third score based on the weights to obtain a comprehensive score.

[0237] In one possible embodiment, the task scheduling parameters include: node execution parameters, as well as timeout thresholds, reminder delivery channels, reminder triggering timing, and escalation rules for each process node; the determination module 506 is also used to determine the target process template in the process template library that matches the task type;

[0238] The processing module 502 is also used to associate each object to be assigned with the corresponding process node in the task processing flow. The process node is associated with node execution parameters, which include: the next process node and the initiation conditions of the next process node; the association rules are defined inside the template.

[0239] The determination module 506 is also used to determine the timeout threshold, reminder delivery channel, reminder triggering time, and escalation rules for each process node in the target process template based on the urgency and processing time limit; the escalation rules are used to indicate the adjustment of the urgency level based on the task processing time and timeout duration.

[0240] In one possible embodiment, the execution module 505 is specifically used to drive the script according to preset rules and execute the task processing flow according to the task scheduling parameters through an automated execution engine.

[0241] In one possible embodiment, the automated execution engine includes: a preset rule-driven script, comprising:

[0242] If there is a form to be filled in associated with the current process node, the value to be filled is obtained from the system corresponding to the data source type of the field to be filled in the form, and the value to be filled is then filled into the form.

[0243] When the current process node is the approval completion node, the approval result and related attachments corresponding to the task processing process will be synchronized to the preset system; the preset system includes the human resources system and the document management system;

[0244] When the current process node is the process initiation node, monitor the execution status of the current process node, and when the execution status changes to the end status, initiate the next process node in the target process template corresponding to the task data.

[0245] In one possible embodiment, the above-described apparatus further includes: a monitoring module 507;

[0246] The monitoring module 507 is used to monitor the processing time and timeout duration of the current process node during the execution of the task processing flow.

[0247] The processing module 502 is also used to adjust the urgency level according to the upgrade rules if the processing time and timeout time meet the upgrade rules;

[0248] The processing module 502 is also used to update the task scheduling parameters of the task processing flow based on the adjusted urgency level.

[0249] The task automation processing device provided in this application embodiment can be used to execute the technical solution of the task automation processing method in any of the above embodiments of this application. Its implementation principle and technical effect are similar, and will not be described again here.

[0250] Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application, such as... Figure 6 As shown, the electronic device 60 of this embodiment may include at least one processor 601 and a memory 602. Optionally, the device 60 may also include a communication component 603. The processor 601, memory 602, and communication component 603 are connected via a bus 604.

[0251] In a specific implementation, at least one processor 601 executes computer execution instructions stored in memory 602, causing at least one processor 601 to perform the above-described method.

[0252] The specific implementation process of processor 601 can be found in the above method embodiments, and its implementation principle and technical effect are similar. It will not be repeated here.

[0253] In the above embodiments, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.

[0254] The memory may include random access memory (RAM) and may also include non-volatile memory (NVM), such as at least one disk storage device.

[0255] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.

[0256] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0257] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the above-described method.

[0258] The aforementioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0259] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and the readable storage medium can exist as discrete components in the device.

[0260] The division of units is merely a logical functional division; in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0261] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0262] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0263] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0264] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0265] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily essential to this application.

[0266] It should be further noted that although the steps in the flowchart are shown sequentially according to 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 flowchart 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. The execution order of these sub-steps or stages 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.

[0267] It should be understood that the above-described device embodiments are merely illustrative, and the device of this application can also be implemented in other ways. For example, the division of units / modules in the above embodiments is only a logical functional division, and there may be other division methods in actual implementation. For example, multiple units, modules, or components may be combined, or integrated into another system, or some features may be ignored or not executed.

[0268] Furthermore, unless otherwise specified, the functional units / modules in the various embodiments of this application can be integrated into one unit / module, or each unit / module can exist physically separately, or two or more units / modules can be integrated together. The integrated units / modules described above can be implemented in hardware or in the form of software program modules.

[0269] When integrated units / modules are implemented in hardware, the hardware can be digital circuits, analog circuits, etc. The physical implementation of the hardware structure includes, but is not limited to, transistors, memristors, etc. Unless otherwise specified, the processor can be any suitable hardware processor, such as a CPU, GPU, FPGA, DSP, and ASIC, etc. Unless otherwise specified, the storage unit can be any suitable magnetic or magneto-optical storage medium, such as Resistive Random Access Memory (RRAM), Dynamic Random Access Memory (DRAM), Static Random Access Memory (SRAM), Enhanced Dynamic Random Access Memory (EDRAM), High-Bandwidth Memory (HBM), Hybrid Memory Cube (HMC), etc.

[0270] If the integrated unit / module is implemented as a software program module and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), portable hard drive, magnetic disk, or optical disk.

[0271] In the above embodiments, the descriptions of each embodiment have their own emphasis. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments. The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as the combination of these technical features does not contradict each other, it should be considered within the scope of this specification.

[0272] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this application are indicated by the following claims.

[0273] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is limited only by the appended claims.

Claims

1. A method for automating task processing, characterized in that, include: In response to a task creation operation, retrieve task data; The task data is identified and analyzed to obtain a set of task attributes; Based on the set of task attributes, at least one object to be assigned is matched from the pre-built organizational structure knowledge graph; Based on the set of task attributes and the at least one object to be assigned, a task processing flow is generated. The task processing flow includes task scheduling parameters for each process node; The task processing flow is executed based on the task scheduling parameters.

2. The method according to claim 1, characterized in that, The process of identifying and analyzing the task data to obtain a set of task attributes includes: The task data is identified and analyzed to obtain the task type, urgency level, and security level. Extract task keywords from the task data and construct task tags; The processing time limit is determined based on the urgency level, predefined task time limit rules, and / or time-sensitive words in the task data; A set of task attributes is generated based on the task type, the urgency level, the confidentiality level, the task tag, and the processing time limit.

3. The method according to claim 2, characterized in that, The objects to be assigned include: the processing department and the target responsible person of the processing department. The process of matching at least one object to be assigned from the pre-built organizational structure knowledge graph based on the task attribute set includes: Based on the task type and the task tag, at least one processing department and at least one candidate responsible person for each processing department are determined from the organizational structure knowledge graph; each candidate responsible person has corresponding attribute information; the attribute information includes: department, skill tag, permission data, number of pending tasks and historical processing efficiency; When there are multiple candidate responsible persons in the processing department, the comprehensive score of each candidate responsible person is calculated by weighting the matching degree between the attribute information of the multiple candidate responsible persons and the task attribute set. The candidate responsible person corresponding to the highest comprehensive score is determined as the target responsible person of the processing department; If there is a candidate person in charge in the processing department, the candidate person in charge will be identified as the target person in charge of the processing department.

4. The method according to claim 3, characterized in that, The attribute information includes: skill tags, number of pending tasks, and task processing efficiency; the weighted calculation of the comprehensive score of each candidate responsible person based on the matching degree between the attribute information of the multiple candidate responsible persons and the task attribute set includes: The skill tags and task tags are matched to obtain a first score; A second score is obtained based on the number of tasks to be done; The task processing efficiency is mapped to a third score; Based on the urgency of the task attribute set, the weights corresponding to the first score, the second score, and the third score are determined respectively, and the first score, the second score, and the third score are weighted and summed based on the weights to obtain the comprehensive score.

5. The method according to claim 3, characterized in that, The task scheduling parameters include: node execution parameters, and timeout thresholds, reminder delivery channels, reminder triggering times, and escalation rules for each process node; the step of generating a task processing flow based on the task attribute set and the at least one object to be assigned includes: Identify the target process template in the process template library that matches the task type; Each of the objects to be assigned is associated with a corresponding process node in the target process template. The process node is associated with node execution parameters, which include: the next process node and the initiation conditions of the next process node. Based on the urgency level and the processing time limit, determine the timeout threshold, reminder delivery channel, reminder triggering time, and escalation rules for each process node in the target process template; the escalation rules are used to indicate the adjustment of the urgency level according to the task processing time and timeout duration.

6. The method according to any one of claims 2-5, characterized in that, The execution of the task processing flow based on the task scheduling parameters includes: The automated execution engine drives the script according to preset rules and executes the task processing flow according to the task scheduling parameters.

7. The method according to claim 6, characterized in that, The preset rule-driven script includes: When there is a form to be filled associated with the current process node, the value to be filled is obtained from the system corresponding to the data source type of the field to be filled in the form, and the value to be filled is filled into the form. If the current process node is an approval completion node, the approval result and related attachments corresponding to the task processing process will be synchronized to the preset system; When the current process node is a process initiation node, the execution status of the current process node is monitored, and when the execution status changes to the end status, the next process node in the target process template corresponding to the task data is initiated.

8. The method according to claim 5, characterized in that, The method further includes: During the execution of the task processing flow, monitor the processing time and timeout duration of the current process node; If the processing time and the timeout time meet the escalation rules, the urgency level is adjusted according to the escalation rules; Based on the adjusted urgency level, update the task scheduling parameters of the task processing flow.

9. A task automation processing system, characterized in that, include: Business service layer and engine and scheduling layer; The business service layer is communicatively connected to the engine and scheduling layer; The business service layer is used to respond to the task creation operation, obtain task data, and identify and analyze the task data to obtain a set of task attributes; The business service layer is also used to match at least one object to be assigned from a pre-built organizational structure knowledge graph based on the task attribute set. The business service layer is also used to generate a task processing flow based on the task attribute set and the at least one object to be assigned. The task processing flow includes task scheduling parameters for each process node; The engine and scheduling layer and the business service layer execute the task processing flow based on the task scheduling parameters.

10. A task automation processing device, characterized in that, include: The acquisition module is used to retrieve task data in response to task creation operations; The identification module is used to identify and analyze the task data to obtain a set of task attributes; The matching module is used to match at least one object to be assigned from a pre-built organizational structure knowledge graph based on the set of task attributes. The generation module is used to generate a task processing flow based on the task attribute set and the at least one object to be assigned. The task processing flow includes task scheduling parameters for each process node; The execution module is used to execute the task processing flow based on the task scheduling parameters.

11. An electronic device, characterized in that, include: Memory, processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory, causing the processor to perform the method as described in any one of claims 1-8.

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