Cooperative office hierarchical management method and system based on workflow model

Abstract

The invention provides a collaborative office hierarchical management method and system based on a workflow model, and relates to the technical field of office management, and the method comprises the steps: constructing a hierarchical task scheduling framework based on a pre-constructed workflow model; obtaining office task data, adjusting a hierarchical scheduling rule in the hierarchical task scheduling framework according to the office task data, and generating an adjusted rule; based on the input terminal parameters in the hierarchical task scheduling framework, collecting and processing office task related data submitted or processed by each employee through a touch input device under the high-concurrency office task, and generating verification passing data; setting and adjusting bandwidth basic allocation logic according to task priority identifiers in the data passing verification, generating a bandwidth allocation result, and executing hierarchical management of office tasks by a target processing node of a hierarchical task scheduling framework in combination with the data passing verification and processing nodes and processing time limits corresponding to three pieces of task priority information. And refined hierarchical management of task processing in a high-concurrency office environment is realized.

Description

Technical Field

[0001] This application relates to the field of office management technology, and in particular to a collaborative office hierarchical management method and system based on a workflow model. Background Technology

[0002] In modern enterprise collaborative work environments, as organizations expand and business processes become more complex, cross-departmental and multi-role task processing places higher demands on system response efficiency and resource scheduling capabilities. Especially in high-concurrency scenarios, when a large number of tasks are submitted or processed simultaneously, traditional unified scheduling mechanisms struggle to balance the urgency of tasks, the professional matching of processing nodes, and the real-time nature of user interactions, leading to delays in critical tasks, severe resource contention, and a degraded user experience.

[0003] Currently, some advanced collaborative office platforms have introduced task scheduling schemes based on rule engines and static priority queues. These schemes categorize tasks using preset business rules and set fixed priority thresholds based on historical processing data, distributing tasks to appropriate processing queues. Upon receiving a task request, the system performs a preliminary assessment based on preset rules and allocates tasks according to the load status of processing nodes, while reserving a certain resource quota for high-priority queues to ensure the response speed of critical tasks. However, existing solutions exhibit significant limitations in adaptability to dynamically changing office workloads and diverse task characteristics. For example, relying on pre-set static rules prevents dynamic adjustments based on real-time emergency task response pressure, task backlog, or the actual matching efficiency of processing nodes, leading to rigid resource allocation. High-priority tasks may still be delayed due to bandwidth contention. Furthermore, the system lacks the ability to perceive user behavior when submitting data through interactive devices, making it difficult to identify operational bottlenecks under high concurrency. The lack of a dynamic feedback and adaptive adjustment mechanism can easily lead to low resource utilization and delayed response to critical tasks in complex office scenarios. Summary of the Invention

[0004] The purpose of this application is to provide a collaborative office hierarchical management method and system based on a workflow model, in order to solve the problems of rigid resource allocation, difficulty in identifying operational bottlenecks under high concurrency in existing technologies, and the tendency to cause low resource utilization and delayed response to critical tasks in complex office scenarios.

[0005] To address the aforementioned technical problems, firstly, this application provides a collaborative office hierarchical management method based on a workflow model, comprising:

[0006] A hierarchical task scheduling framework is built based on a pre-built workflow model;

[0007] The system acquires office task data, adjusts the hierarchical scheduling rules in the hierarchical task scheduling framework based on the office task data, and generates adjusted rules. The office task data includes emergency task response time limit, ordinary task queuing time, original task priority information, and processing node matching rate. The adjusted rules include three task priority information and corresponding processing nodes, processing time limits, and data transmission formats.

[0008] Based on the input terminal parameters in the hierarchical task scheduling framework, data related to office tasks submitted or processed by each employee through touch input devices under high-concurrency office tasks are collected. According to the adjusted rules, the data related to office tasks is marked, added, and verified to generate verified data.

[0009] Based on the task priority identifier in the verified data, set up basic bandwidth allocation logic corresponding to the three task priority information respectively, and adjust the basic bandwidth allocation logic to generate bandwidth allocation results;

[0010] Based on the bandwidth allocation results, combined with the verification passed data, the processing nodes and processing time limits corresponding to the three task priority information, the target processing node of the hierarchical task scheduling framework performs hierarchical management of office tasks.

[0011] Optionally, based on a pre-built workflow model, a hierarchical task scheduling framework is constructed, including:

[0012] By using a pre-built workflow model, we define various common tasks and their corresponding standard workflow paths in collaborative office scenarios, forming a basic task workflow framework. The various common tasks include approval tasks, document tasks, and meeting tasks. The standard workflow path includes the node connection relationship between the task initiator, intermediate processing end, and final execution end.

[0013] Based on the node connection relationship in the basic task flow framework, the hardware resource mapping relationship is configured to record the maximum number of tasks that each task processing node can process and the upper limit of network access bandwidth, forming a resource configuration list.

[0014] Based on the task processing needs in collaborative office scenarios, description fields are set to distinguish the urgency of tasks, and weight values ​​are assigned to each description field to form a set of priority evaluation dimensions. The description fields include a task deadline sub-field, a number of participants involved sub-field, and the importance of related business sub-field.

[0015] Based on the node logic of the basic task flow framework, the resource configuration list, and the priority evaluation dimension set, a hierarchical scheduling logic component is constructed. The hierarchical scheduling logic component is added to the basic task flow framework to obtain an intermediate task flow framework. The hierarchical scheduling logic component includes a task receiving module, a task allocation module, and a task monitoring module.

[0016] The intermediate task flow framework is configured with a framework interaction interface to form a hierarchical task scheduling framework. The framework interaction interface includes a task input interface, a node communication interface, and a status feedback interface.

[0017] Optionally, the hierarchical scheduling rules in the hierarchical task scheduling framework are adjusted based on the office task data to generate adjusted rules. The office task data includes emergency task response time limits, ordinary task queuing time, original task priority information, and processing node matching rate. The adjusted rules include three task priority information items and corresponding processing nodes, processing time limits, and data transmission formats, including:

[0018] Based on the task urgency description of the original task priority information in the office task data, the original task priority information is divided into three task priority information, which include first-level task priority information, second-level task priority information and third-level task priority information.

[0019] Calculate the time difference between the emergency task response time limit and the waiting time for ordinary tasks in the office task data. Based on the hierarchical requirements of the three task priority information, divide the time difference into two parts. The sum of the smaller part and the emergency task response time limit is used as the processing time limit for the second-level task priority information. The emergency task response time limit is used as the processing time limit for the first-level task priority information. The waiting time for ordinary tasks is used as the processing time limit for the third-level task priority information.

[0020] The average processing node matching rate of all processing nodes in the office task data is calculated as the baseline matching rate. The processing node matching rate of all processing nodes is compared with the baseline matching rate to determine the candidate processing node list corresponding to the three task priority information respectively.

[0021] Based on the order of priority of the three task information, the data transmission format corresponding to the three task priority information is determined respectively. The data transmission format of the first-level task priority information is an encrypted transmission format with digital signature; the data transmission format of the second-level task priority information is a compressed transmission format with verification code; and the data transmission format of the third-level task priority information is a standard transmission format without additional information.

[0022] Replace the corresponding content of the hierarchical scheduling rules in the hierarchical task scheduling framework with the three task priority information, the processing time limit, the candidate processing node list, and the data transmission format corresponding to the three task priority information, and generate the adjusted rules.

[0023] Optionally, according to the adjusted rules, the office task-related data is labeled and validated to generate validated data, including:

[0024] The task urgency description in the office task-related data is matched with three task priority information to determine the target priority information corresponding to the office task-related data;

[0025] Add a task priority identifier corresponding to the target priority information to the office task-related data to form identified office task data. The task priority identifier includes the level number of the target priority information and the corresponding priority level identifier.

[0026] Check whether the data transmission format of the tagged office task data conforms to the target data transmission format corresponding to the target priority information in the adjusted rules, and integrate the tagged office task data whose data transmission format conforms to the corresponding target data transmission format into a temporary verification dataset;

[0027] The labeled office task data in the temporary verification dataset are sorted to form verification-passed data.

[0028] Optionally, based on the task priority identifier in the verified data, bandwidth basic allocation logic corresponding to the three task priority information is set, and the bandwidth basic allocation logic is adjusted to generate bandwidth allocation results, including:

[0029] Based on the task priority identifier in the verified data, the verified data is divided into three groups of data to be assigned, corresponding to the three task priority information. The number of office tasks in each group of data to be assigned is marked as the total number of office tasks. The total amount of data of all office tasks in each group of data to be assigned is marked as the total amount of task data. The average amount of data per task in each group of data to be assigned is calculated.

[0030] Based on the total network bandwidth value in the hierarchical task scheduling framework, determine the available total network bandwidth and set the basic bandwidth allocation logic corresponding to the priority information of the three tasks respectively.

[0031] Select online processing nodes that are in the online state from the candidate processing node list corresponding to the three task priority information in the adjusted rules, count the number of tasks executed by each online processing node, and calculate the average load rate of all online processing nodes under the corresponding task priority information by combining the corresponding maximum number of tasks that can be processed.

[0032] Based on the processing time limits corresponding to the three task priority information in the adjusted rules, the submitted time of each office task is calculated, and the average time of all submitted times is calculated in groups. Combined with the processing time limits of the corresponding task priority information, the average remaining processing time of each group of data to be allocated is obtained.

[0033] Based on the total number of office tasks, the average load rate, and the average remaining processing time, the bandwidth basic allocation logic is adjusted to obtain the final bandwidth allocation ratio.

[0034] Based on the final bandwidth allocation ratio, the total actual bandwidth allocation value for each group of data to be allocated is calculated. Combined with the total number of office tasks and the total amount of task data for each group of data to be allocated, the bandwidth allocation result is generated.

[0035] Optionally, based on the final bandwidth allocation ratio, the total actual bandwidth allocation value for each group of data to be allocated is calculated. Combining this with the total number of office tasks and the total data volume of each group of data to be allocated, a bandwidth allocation result is generated, including:

[0036] Based on the total available network bandwidth and the final bandwidth allocation ratio, calculate the basic bandwidth allocation value corresponding to each group of data to be allocated;

[0037] Based on the average remaining processing time of each group of data to be allocated, determine the data transmission urgency corresponding to each group of data to be allocated.

[0038] Based on the data transmission urgency and average data volume per task of each group of data to be allocated, the basic bandwidth allocation value is adjusted to obtain the actual total bandwidth allocation value of each group of data to be allocated. The adjustment process includes a first upward adjustment of the basic bandwidth allocation value corresponding to the data to be allocated whose average data volume per task is higher than the average average data volume per task of all data to be allocated, and a second upward adjustment of the basic bandwidth allocation value corresponding to the data to be allocated whose data transmission urgency is higher than a preset urgency threshold. The sum of the bandwidth allocation values ​​of all data to be allocated shall not exceed the total available network bandwidth.

[0039] Based on the load rate of all online processing nodes under the corresponding task priority information, the total actual bandwidth allocation value of each group of data to be allocated is split to form an initial task bandwidth association record. The total amount of office tasks and total data volume of each group of data to be allocated are then integrated with the corresponding initial task bandwidth association record to generate the bandwidth allocation result.

[0040] Optionally, based on the bandwidth allocation result, combined with the verification passed data, the processing nodes corresponding to the three task priority information, and the processing time limits, the target processing node of the hierarchical task scheduling framework performs hierarchical management of office tasks, including:

[0041] Based on the initial task bandwidth association record in the bandwidth allocation result, and combined with the task priority identifier in the verification passed data, the online processing node and the corresponding node allocated bandwidth in the initial task bandwidth association record are bound to the office task in the verification passed data to obtain task node association data.

[0042] Select the processing node that exists simultaneously in the candidate processing node list corresponding to the three task priority information and the initial task bandwidth association record, and whose remaining processing capacity meets the standard, as the target processing node.

[0043] Based on the processing time limits corresponding to the three task priority information and the preset task distribution sorting rules, the office tasks in the task node association data are distributed to the target processing node. At the same time, bandwidth is allocated to the nodes in the initial task bandwidth association record for the target processing node, and task execution initial state data is generated.

[0044] Collect the task processing progress of each target processing node during the execution of office tasks, and send task processing reminders to target processing nodes whose task processing progress is lower than the preset progress threshold, so as to form an intervention record in the task processing process;

[0045] Obtain the task processing results after each target processing node completes its office tasks. Integrate the task processing results with the task node's associated data, the processing time limits corresponding to the three task priority information, and the intervention records to form a single task hierarchical management record. Summarize the single task hierarchical management records of all target processing nodes to generate a hierarchical management master record for high-concurrency office tasks, thus completing the hierarchical management of office tasks.

[0046] Secondly, this application provides a collaborative office hierarchical management method and system based on a workflow model, including:

[0047] The building module is used to construct a hierarchical task scheduling framework based on a pre-built workflow model;

[0048] The acquisition module is used to acquire office task data, adjust the hierarchical scheduling rules in the hierarchical task scheduling framework according to the office task data, and generate the adjusted rules. The office task data includes emergency task response time limit, ordinary task queuing time, original task priority information and processing node matching rate. The adjusted rules include three task priority information and corresponding processing nodes, processing time limits and data transmission formats.

[0049] The processing module is used to collect data related to office tasks submitted or processed by each employee through touch input devices under high-concurrency office tasks based on the input terminal parameters in the hierarchical task scheduling framework, and to perform identification, addition and verification processing on the data related to office tasks according to the adjusted rules, and generate verification passed data.

[0050] The adjustment module is used to set the bandwidth basic allocation logic corresponding to the three task priority information respectively according to the task priority identifier in the verification passed data, and adjust the bandwidth basic allocation logic to generate bandwidth allocation results.

[0051] The management module is used to perform hierarchical management of office tasks by the target processing node of the hierarchical task scheduling framework based on the bandwidth allocation results, the verification pass data, the processing nodes corresponding to the three task priority information and the processing time limits.

[0052] Thirdly, this application provides an electronic device, comprising:

[0053] Memory, used to store computer programs;

[0054] A processor, used to execute the computer program to implement the steps of a workflow-based collaborative office hierarchical management method as described in the first aspect above.

[0055] Fourthly, this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, can implement the steps of a workflow-based collaborative office hierarchical management method as described in the first aspect above.

[0056] This application provides a workflow-based hierarchical management method for collaborative office work. By establishing a hierarchical task scheduling framework based on a pre-built workflow model, it achieves systematization and standardization of office task processing structure. Combined with real-time acquired office task data, it dynamically adjusts scheduling rules, enabling the system to adaptively generate optimized scheduling strategies based on multiple factors such as emergency task response pressure, backlog of ordinary tasks, original priority, and processing node matching efficiency. This improves the accuracy of task classification and the flexibility of scheduling logic. Furthermore, by collecting operational data from employees on touch input devices in high-concurrency scenarios and applying updated rules, it further classifies tasks... Intelligent verification based on context enhances the system's ability to perceive user behavior and improves the reliability of data processing. Differentiated allocation of bandwidth resources based on task priority, and real-time correction of the basic allocation logic by introducing dynamic factors such as total task volume, node load, and remaining processing time limits, achieves elastic allocation of network resources. Finally, by combining verified task data, matched processing nodes, and response time limits, hierarchical execution is completed, improving the efficiency of high-priority tasks and the timeliness of critical business responses. This alleviates system bottlenecks caused by resource contention and rigid scheduling, enhancing the stability and overall operational efficiency of the collaborative office system in complex, high-load environments. Furthermore, by grouping verified tasks by priority and analyzing the task size, data load, and online status and load level of processing nodes in each group, the actual execution pressure of each priority task is comprehensively assessed. Simultaneously, by combining the task submission time and the specified processing time limit, the urgency is quantified, and the bandwidth allocation ratio of each priority queue is dynamically adjusted accordingly. This ensures that resource allocation not only relies on preset rules but also responds to real-time business pressure and system status changes, thus achieving an evolution from static quotas to dynamic adaptation. It overcomes the rigidity of resource allocation caused by relying on fixed rules in traditional solutions. By dynamically adjusting bandwidth based on multiple dimensions such as task load, node capabilities, and time constraints, it avoids delays in processing high-priority tasks due to insufficient underlying resources, improves the transmission guarantee capability of critical tasks in high-concurrency environments, optimizes the overall network resource utilization efficiency, prevents system performance imbalance caused by excess or congestion of some queue resources, and enhances the adaptability of scheduling strategies and the responsiveness of collaborative office systems. Attached Figure Description

[0057] To more clearly illustrate the technical solutions of the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0058] Figure 1A flowchart illustrating a collaborative office hierarchical management method based on a workflow model, provided in an embodiment of this application;

[0059] Figure 2 A schematic diagram illustrating a specific implementation of a workflow-based collaborative office hierarchical management method provided in this application embodiment;

[0060] Figure 3 This is a schematic diagram of the structure of a collaborative office hierarchical management system based on a workflow model, provided in an embodiment of this application. Detailed Implementation

[0061] To address the shortcomings of existing collaborative office systems' static rule-based task scheduling mechanisms in adapting to dynamic business pressures and accurately responding to changes in task urgency and resource matching under high-concurrency scenarios, this application constructs a hierarchical task scheduling framework based on a workflow model. It introduces real-time data collection and analysis capabilities for multi-dimensional office task data, including response time limits for urgent tasks, waiting times for ordinary tasks, original priorities, and processing node matching rates. This enables dynamic generation and updating of scheduling rules, improving the timeliness and accuracy of task classification. Furthermore, by combining input terminal parameters with employee interaction behavior on touch devices, the submitted task data is context-sensitively identified and validated, enhancing the system's adaptability to high-concurrency interaction scenarios. A task priority-driven bandwidth allocation mechanism is further employed, differentiating network resources according to priority queues and dynamically adjusting them based on task load, processing node status, and time constraints. This ensures high-priority tasks receive sufficient transmission guarantees. Finally, the target processing nodes within the scheduling framework complete accurate task distribution and time-limited processing, thereby transforming office task management from static configuration to dynamic adaptation and improving the system's resource utilization efficiency and critical business response capabilities in complex environments.

[0062] To enable those skilled in the art to better understand the present application, the present application will be further described in detail below with reference to the accompanying drawings and specific embodiments. Obviously, the described embodiments are merely some embodiments of the present application, and not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0063] The core of this application is to provide a collaborative office hierarchical management method based on a workflow model, and a flowchart of one specific implementation is shown below. Figure 1 As shown, the method includes:

[0064] Step 101: Based on the pre-built workflow model, construct a hierarchical task scheduling framework.

[0065] In this step, the pre-built workflow model refers to a model pre-built based on common collaborative office business scenarios (such as approval processes, document circulation, and meeting organization). This model includes basic task types, task flow logic, and node interaction relationships, providing underlying process support for the subsequent construction of a hierarchical task scheduling framework. It is designed based on the enterprise's historical office process data and industry-standard office practices. The hierarchical task scheduling framework, based on the pre-built workflow model, integrates hardware resource configuration, priority evaluation dimensions, scheduling logic components, and interaction interfaces. It is a system framework for implementing hierarchical scheduling and management of office tasks, reflecting the full-process management logic of office tasks from collection, allocation, processing to result aggregation, and supporting the efficient flow of office tasks of different priorities in high-concurrency scenarios.

[0066] Step 102: Obtain office task data, adjust the hierarchical scheduling rules in the hierarchical task scheduling framework according to the office task data, and generate the adjusted rules. The office task data includes emergency task response time limit, ordinary task queuing waiting time, original task priority information and processing node matching rate. The adjusted rules include three task priority information and corresponding processing nodes, processing time limits and data transmission formats.

[0067] In this step, "office task data" refers to the various data sets related to office task processing obtained from the office system. It reflects the urgency of office tasks, processing wait times, initial priorities, and node adaptability, and is used to adjust the hierarchical scheduling rules to adapt to actual office needs. "Hierarchical scheduling rules" refers to the set of rules pre-set in the hierarchical task scheduling framework, used to guide office task classification, node allocation, and resource configuration. Designed based on general office scenarios, they are used to initially standardize task scheduling logic and can be adjusted based on office task data to generate more suitable adjusted rules. "Adjusted rules" refers to the rules formed after adjusting the hierarchical scheduling rules based on office task data. These rules include three task priority information and their corresponding processing nodes, processing time limits, and data transmission formats, reflecting a task scheduling standard that dynamically adapts to actual office needs. They are used to guide subsequent operations such as office task data verification, bandwidth allocation, and task distribution. "Urgent task response time limit" refers to the maximum time threshold from task submission to the start of processing that the office system must meet when handling urgent office tasks. It reflects the time-sensitive requirements of urgent tasks and is set based on the enterprise's response efficiency requirements for key office operations. It is used to determine the processing time limit for high-priority tasks. The average waiting time for a regular task refers to the time from when a regular office task is submitted and enters the processing queue until processing begins. It reflects the congestion in regular task processing and is calculated based on historical regular task processing waiting data. This information is used to determine the processing time for low-priority tasks. The original task priority information (e.g., urgent, moderate, lenient) is included when an office task is submitted, initially describing its urgency level. It reflects the task's initial importance level and is based on the initiating employee's judgment of the task's urgency or the default business type setting. This information is used to subsequently categorize the task into three priority levels. The processing node matching rate is the proportion of office tasks of the corresponding type successfully processed by a single processing node out of the total number of tasks of that type received by that node. It reflects the adaptability of a processing node to a specific task type and is used to determine the candidate processing node list for each task priority. The three task priority levels are the first, second, and third priority levels obtained by refining the original task priority information according to urgency. Level 1 is the most urgent, level 2 is moderate, and level 3 is the most lenient. This reflects the differentiated urgency requirements of office tasks and guides the subsequent priority order of bandwidth allocation and task distribution. Processing nodes refer to hardware devices (such as servers and terminal hosts) used to process office tasks. These include candidate processing nodes, online processing nodes, and target processing nodes, which are used to carry out the actual processing operations of office tasks and are selected from the office system's hardware devices based on hardware resource mapping relationships. Processing time limits refer to the maximum allowed time from submission to completion for office tasks with corresponding priority information. Level 1 corresponds to the response time limit for urgent tasks, Level 2 corresponds to the sum of the response time limit and a small portion of the time difference for urgent tasks, and Level 3 corresponds to the queuing time for ordinary tasks, reflecting the time constraints for tasks of different priorities.Data transmission format refers to the format standard followed by office task data during transmission. The three task priority information correspond to the encrypted transmission format with digital signature (Level 1, ensuring security), the compressed transmission format with verification code (Level 2, balancing security and efficiency), and the standard transmission format without additional information (Level 3, ensuring efficiency), which are used to standardize the security and efficiency of task data transmission.

[0068] In this embodiment of the application, office task data containing emergency task response time limits, ordinary task queuing time, original task priority information, and processing node matching rate are obtained through the interaction interface of the hierarchical task scheduling framework or the associated office system database. Based on the office task data, the original hierarchical scheduling rules in the hierarchical task scheduling framework are adjusted to generate the adjusted rules.

[0069] Step 103: Based on the input terminal parameters in the hierarchical task scheduling framework, collect the office task-related data submitted or processed by each employee through touch input devices under high-concurrency office tasks, and perform identification, addition, and verification processing on the office task-related data according to the adjusted rules to generate verification-passed data.

[0070] In this step, the input terminal parameters refer to the configuration information related to the task input terminal in the hierarchical task scheduling framework. This includes the device identifier of the touch input device, data transmission protocol, access permissions, and data acquisition frequency. These parameters are used to standardize the data acquisition logic for office tasks in high-concurrency scenarios and are set based on the hardware characteristics of the touch input device and system access requirements. High-concurrency office tasks refer to scenarios where a large number of office tasks (far exceeding the normal processing volume) are submitted or processed simultaneously within the same time period, reflecting the high-load operation of the office system. This is common during peak business periods such as monthly / quarterly settlements and large-scale project collaborations. Touch input devices refer to hardware devices used by employees to submit or process office tasks, supporting manual touch operation. They enable interaction between employees and the office system and are configured based on the convenient operation needs of the office scenario. They are the main input carrier for office task-related data. Office task-related data refers to the raw data directly related to the office task submitted or processed by employees through touch input devices in high-concurrency scenarios. This data includes task content, submission time, initiating employee identifier, task type, etc., reflecting the basic information of the office task. Verified data refers to compliant data obtained after adding task priority identifiers, verifying transmission formats, and sorting office task-related data. It includes office task information that meets the requirements of the adjusted rules and the corresponding task priority identifiers, reflecting the effective task data that has been filtered and optimized, and is used for the setting and adjustment of subsequent bandwidth allocation logic.

[0071] In this embodiment, input terminal parameters (such as device identifier, data transmission protocol, access permissions, etc. of touch input devices) are extracted from the hierarchical task scheduling framework. Based on these parameters, the data acquisition module collects data related to office tasks submitted or processed by each employee through touch input devices (such as touch computers, tablets, and other manually operable input devices) in high-concurrency office task scenarios (such as task content, submission time, initiating employee information, etc.). Subsequently, these office task-related data are labeled and verified according to the adjusted rules to generate verified data.

[0072] Step 104: Based on the task priority identifier in the verified data, set the bandwidth basic allocation logic corresponding to the three task priority information respectively, and adjust the bandwidth basic allocation logic to generate bandwidth allocation results.

[0073] In this step, the task priority identifier refers to the mark added to the office task-related data to identify the priority of the task. It includes the level number (e.g., 1, 2, 3) and priority level identifier (e.g., urgent, medium, lenient) corresponding to the three task priority information, reflecting the priority attribute of the office task and used to distinguish data to be allocated based on different priorities. The bandwidth basic allocation logic refers to the initial bandwidth allocation rules set based on the differences in the urgency of the three task priority information. It specifies the initial allocation ratio of the available total network bandwidth among the three priority data to be allocated, providing an initial basis for bandwidth allocation. The bandwidth allocation result refers to the result obtained after adjustment by the bandwidth basic allocation logic, including the actual total bandwidth allocation value corresponding to each group of data to be allocated, the total number of office tasks, and the total data volume of the tasks. It reflects the bandwidth resource configuration scheme for office task data transmission and is used to guide the subsequent distribution and bandwidth configuration of office tasks.

[0074] Step 105: Based on the bandwidth allocation result, combined with the verification passed data, the processing nodes and processing time limits corresponding to the three task priority information, the target processing node of the hierarchical task scheduling framework performs hierarchical management of office tasks.

[0075] In this step, the target processing node refers to the processing node selected from the candidate processing node list corresponding to the three task priority information. It is the node that exists in the initial task bandwidth association record and has sufficient remaining processing capacity. It is the node that actually performs office task processing and is used to carry out the specific processing operations of office tasks.

[0076] This application's hierarchical task scheduling framework integrates hardware resources, priority evaluation, scheduling components, and interaction interfaces, providing underlying support for hierarchical task management and avoiding the shortcomings of traditional frameworks where resources are disconnected from tasks. Adjusted rules can optimize priorities, processing times, and transmission formats based on actual data such as emergency task response time limits and processing node matching rates. In high-concurrency scenarios, data is collected based on input terminal parameters and verified using adjusted rules, ensuring the compliance and validity of task data and avoiding the shortcomings of traditional verification methods that are limited to format checks. By combining task priority identifiers, node load, and remaining processing time to set and adjust basic bandwidth allocation logic, on-demand allocation and dynamic optimization of bandwidth resources are achieved. Hierarchical management through target processing nodes, including task distribution, progress monitoring, and result integration, ensures efficient flow of high-priority tasks and improves the utilization rate of processing node resources.

[0077] This application provides a specific embodiment. Step 101 involves constructing a hierarchical task scheduling framework based on a pre-built workflow model, specifically including the following steps:

[0078] Step 111: Using a pre-built workflow model, define various common tasks and their corresponding standard workflow paths in collaborative office scenarios to form a basic task workflow framework. The various common tasks include approval tasks, document tasks, and meeting tasks. The standard workflow path includes the node connection relationship between the task initiator, intermediate processing end, and final execution end.

[0079] In this step, the collaborative office scenario refers to the application environment in which multiple departments and roles in an enterprise or organization jointly participate in handling office tasks. It covers the entire process of task initiation, workflow, processing, and archiving, reflecting the collaborative attributes of office work. It clarifies the construction goals and application boundaries of the hierarchical task scheduling framework and is derived based on the enterprise's organizational structure, business processes, and personnel collaboration models. Various common tasks refer to a set of frequently occurring office tasks with typical business attributes in collaborative office scenarios, including approval tasks, document tasks, and meeting tasks. They reflect the core types of office tasks and provide a clear basis for task definition in the basic task workflow framework. They are derived based on the enterprise's historical office task data statistics and business needs analysis. The standard workflow path corresponding to various common tasks refers to the pre-defined fixed process logic from task initiation to completion for each type of common task. It includes the node connection relationships between the task initiator, intermediate processing end, and final execution end, reflecting the task workflow rules. It standardizes the transmission direction and processing order of tasks within the framework and is designed based on the enterprise's actual office process optimization and efficiency requirements. The basic task workflow framework refers to a preliminary framework with basic task workflow capabilities, formed by defining various common tasks and their corresponding standard workflow paths. It reflects the core workflow logic of office tasks and provides underlying structural support for subsequent hardware resource mapping and scheduling logic component construction. It is derived from a pre-built workflow model and the task requirements of collaborative office scenarios. Approval tasks refer to office tasks in collaborative office scenarios that require multi-role level review and confirmation before completion, such as expense reimbursement approval, project initiation review, and contract signing approval. It reflects the review attributes of tasks and clarifies the types of tasks requiring multi-node collaborative review in the basic task workflow framework. It is derived from enterprise management systems and compliance requirements. Document tasks refer to office tasks in collaborative office scenarios that revolve around the document lifecycle, such as document creation, editing, review, transmission, and archiving, such as work report writing, product manual revision, and contract document transmission. It reflects the document association attributes of tasks and clarifies the types of tasks that need to process document data in the basic task workflow framework. It is derived from office data processing requirements and document management standards. Meeting-related tasks refer to office tasks related to meeting organization and execution in collaborative work scenarios, such as meeting scheduling, participant notification, agenda creation, meeting minutes compilation, and meeting material distribution. These tasks reflect the meeting support attributes of the tasks and are used to clarify the types of tasks serving meeting collaboration within the basic task workflow framework. They are derived from enterprise meeting management processes and collaboration needs. The task initiation point refers to the initial logical node where employees submit office tasks in a collaborative work scenario. It is usually associated with hardware terminals such as touch input devices and reflects the starting point of the task. It is used to clarify the task access point within the basic task workflow framework and is designed based on employee task submission behavior and device access requirements.The intermediate processing end refers to the logical node in a collaborative office scenario that undertakes and processes office tasks. It is typically associated with high-performance task processing nodes (such as servers), reflecting the core processing stages of the task. It clarifies the main processing carrier of the task within the basic task flow framework and is derived based on task processing performance requirements and resource allocation. The final execution end refers to the logical node in a collaborative office scenario that completes the finishing work of office tasks (such as result archiving and feedback push). It is typically associated with storage-type task processing nodes (such as archive servers), reflecting the task's endpoint. It clarifies the final processing destination of the task within the basic task flow framework and is derived based on task result management requirements and resource function division. Node connection relationships refer to the task transmission logic and data interaction relationships between the task initiator, intermediate processing end, and final execution end within the basic task flow framework. Examples include the transmission path from the task initiator to the intermediate processing end and the result feedback path from the intermediate processing end to the final execution end. This reflects the task flow topology and standardizes the flow direction of tasks within the framework, designed based on office workflow efficiency and data interaction requirements.

[0080] In this embodiment, the pre-built workflow model (which already includes basic collaborative office workflow logic) is used in conjunction with the business needs of actual collaborative office scenarios. First, various common tasks in collaborative office scenarios are clearly defined, including approval tasks, document tasks, and meeting tasks. Then, for each common task, the standard flow path from initiation to completion is outlined, clarifying the node connection relationship between the task initiation end (such as the logic end corresponding to the touch input device), the intermediate processing end (such as the department-level processing server), and the final execution end (such as the connection logic of the task being transmitted to the intermediate processing end after submission from the initiation end, and then flowing to the final execution end after processing). Through the above definition of task types and flow paths, a basic task flow framework that can cover core office scenarios and clarify task flow logic is formed.

[0081] Step 112: Based on the node connection relationship in the basic task flow framework, configure the hardware resource mapping relationship to record the maximum number of tasks that each task processing node can process and the upper limit of network access bandwidth, and form a resource configuration list.

[0082] In this step, the hardware resource mapping relationship refers to the functional matching relationship between physical hardware devices (i.e., task processing nodes) in the office system and the task initiation end, intermediate processing end, and final execution end in the basic task flow framework. It reflects the adaptation logic between hardware resources and framework nodes, and is used to support task flow through hardware resources. It is obtained based on the functional requirements of framework nodes and the performance parameters of hardware devices. A task processing node refers to the physical hardware device in the office system used to carry out office task processing, such as a server or terminal host. It has functions such as task reception, processing, and storage, reflecting the hardware execution carrier of the framework, and is used to actually execute office tasks. It is obtained based on hardware device performance testing and framework node requirement screening. The maximum number of tasks that can be processed refers to the total number of office tasks that a single task processing node can stably process per unit of time, reflecting the upper limit of the task processing node's processing capacity. It is used to record node performance in the resource configuration list and is obtained based on stress testing of the node (continuously investing different numbers of tasks and recording stable processing thresholds). The maximum network access bandwidth refers to the maximum network data transmission rate that a single task processing node can access, reflecting the upper limit of the node's network connection capacity. It is used to record node network performance in the resource configuration list and is obtained based on speed testing of the node using network speed testing tools. The resource configuration list is a structured document that integrates hardware resource mapping relationships, the maximum number of tasks that each task processing node can process, and the upper limit of network access bandwidth. It reflects the hardware resources that the framework can call upon and is used to provide resource data support for the hierarchical scheduling logic components. It is obtained based on hardware resource mapping configuration and node performance test results.

[0083] In this embodiment, firstly, hardware devices (such as physical servers and terminal hosts) that can be used to process tasks in the office system are identified and defined as task processing nodes. Then, based on the functional requirements and node connection relationships of the task initiation end, intermediate processing end, and final execution end in the basic task flow framework, task processing nodes with different functions are mapped to their corresponding ends (e.g., mapping high-performance servers to intermediate processing ends to carry core processing work, and mapping storage servers to final execution ends to achieve task result archiving). At the same time, by performing performance tests on each task processing node (e.g., continuously inputting tasks and recording the maximum stable processing volume), the maximum number of tasks that each node can process is determined and recorded. The network access capability of each node is detected by a network speed test tool, and its network access bandwidth limit is recorded. Finally, the mapping relationship between task processing nodes and each end, the maximum number of tasks that each node can process, and the network access bandwidth limit are integrated into a structured document to form a resource configuration list.

[0084] Step 113: Based on the task processing requirements in the collaborative office scenario, set description fields to distinguish the urgency of tasks, and set weight values ​​for each description field to form a set of priority evaluation dimensions. The description fields include a task deadline sub-field, a number of participants involved sub-field, and the importance of related business sub-field.

[0085] In this step, "task processing requirements" refers to the core demands for handling office tasks in a collaborative work scenario, such as rapid response to urgent tasks, priority processing of important tasks, and efficient resource utilization. It reflects the processing goals of office tasks and guides the construction of the priority assessment dimension set, derived from enterprise business goals and user experience requirements. "Task urgency" refers to the degree of urgency at which an office task needs to be processed. Tasks with higher urgency require priority resource allocation, reflecting the time-sensitive nature of the task. It serves as the basis for task allocation in the hierarchical scheduling logic component and is comprehensively evaluated based on dimensions such as task deadline, number of participants, and importance of related business. "Description fields" are key assessment dimensions used to differentiate task urgency, reflecting the core information for assessing task urgency. They are used to construct the priority assessment dimension set and are derived from the analysis of task processing requirements and factors influencing urgency. "Weight values" are numerical values ​​(summing up to 100%) assigned to each description field to reflect its influence on task urgency assessment. They are used to quantify task urgency in the priority assessment dimension set and are determined based on the enterprise's emphasis on each assessment dimension. The priority assessment dimension set refers to an assessment system that integrates descriptive fields and corresponding weight values. It reflects a quantitative assessment logic for task urgency, used by the hierarchical scheduling logic component to calculate task priorities, and is derived based on task processing requirements and the weight configuration of descriptive fields. The task deadline sub-segment refers to the dimension in the descriptive field that records the final time the task must be completed, reflecting the task's time constraint and used to assess task urgency. It is derived based on the business requirements set when the task was initiated. The number of participants sub-segment refers to the dimension in the descriptive field that records the number of employees or departments involved in task processing, reflecting the scope of task collaboration and used to assess task urgency. It is derived based on statistics of task collaboration requirements. The importance of related business sub-segment refers to the dimension in the descriptive field that records the importance of the task's related business to the enterprise, reflecting the task's business value and used to assess task urgency. It is derived based on the enterprise's business hierarchy.

[0086] In this embodiment, key dimensions strongly correlated with task urgency are first identified. These include a task deadline sub-segment (the final time the task must be completed, directly related to urgency; the closer the deadline, the higher the urgency), a number of participants sub-segment (the number of employees or departments involved in the task; more participants may have a wider impact and should be prioritized), and related business importance sub-segment (the importance of the business associated with the task to the enterprise; for example, tasks related to core businesses have higher urgency). Next, based on the enterprise's emphasis on different dimensions, weight values ​​are assigned to each descriptive field (e.g., the task deadline sub-segment is weighted at 50%, the number of participants sub-segment at 30%, and the related business importance sub-segment at 20%, with a total weight of 100%). These weight values ​​reflect the influence of each field on the task urgency assessment. Finally, the three descriptive fields and their corresponding weight values ​​are integrated to form a priority assessment dimension set.

[0087] Step 114: Based on the node logic of the basic task flow framework, the resource configuration list, and the priority evaluation dimension set, construct a hierarchical scheduling logic component, add the hierarchical scheduling logic component to the basic task flow framework to obtain an intermediate task flow framework. The hierarchical scheduling logic component includes a task receiving module, a task allocation module, and a task monitoring module.

[0088] In this step, node logic refers to the functional division, data transmission protocol, and interaction rules of each node (including the task initiator, intermediate processing, and final execution) in the basic task flow framework. It reflects the operational logic of the framework nodes and serves as the functional adaptation basis for building the hierarchical scheduling logic component, derived from the design rules of the basic task flow framework. The hierarchical scheduling logic component refers to a logical unit that integrates the task receiving module, task allocation module, and task monitoring module, possessing functions such as task receiving, priority evaluation, node allocation, and status monitoring. It reflects the core scheduling capabilities of the framework and is used to achieve hierarchical scheduling of office tasks. It is constructed based on node logic, resource configuration lists, and priority evaluation dimension sets. The intermediate task flow framework refers to the framework formed by embedding the hierarchical scheduling logic component into the basic task flow framework, possessing basic flow and scheduling capabilities. It reflects the transitional form of the framework from merely flowable to schedulable, providing a foundation for subsequent configuration of interaction interfaces. It is derived from the fusion of the basic task flow framework and the hierarchical scheduling logic component. The task receiving module, within the hierarchical scheduling logic component, is responsible for receiving tasks. It acquires relevant data from the task initiator, extracts key task information (such as deadline and number of participants), reflecting the framework's task access capabilities. It provides raw task data to the task allocation module and is designed based on the interaction rules of the task initiator in the node logic. The task allocation module, also within the hierarchical scheduling logic component, is responsible for allocating tasks. It calculates the task urgency based on the information extracted by the task receiving module and the priority evaluation dimension set, and allocates tasks to suitable nodes in conjunction with the resource configuration list. It reflects the framework's core task scheduling capabilities and is used to match tasks with resources. It is designed based on priority evaluation logic and resource status analysis. The task monitoring module, within the hierarchical scheduling logic component, is responsible for monitoring task and node status. It collects real-time task processing progress, node load, and bandwidth usage, reflecting the framework's status awareness capabilities. It provides dynamic resource data to the task allocation module and feedback information to the status feedback interface. It is designed based on node communication rules and data collection requirements.

[0089] In this embodiment, three core modules of the component are first constructed, including a task receiving module, a task allocation module, and a task monitoring module. Then, the constructed hierarchical scheduling logic component is technically integrated with the basic task flow framework (such as connecting the module interface of the component with the node interface of the basic framework to ensure data interaction). The component is embedded in the basic task flow framework to obtain an intermediate task flow framework that integrates scheduling logic.

[0090] Step 115: Configure the framework interaction interface in the intermediate task flow framework to form a hierarchical task scheduling framework. The framework interaction interface includes a task input interface, a node communication interface, and a status feedback interface.

[0091] In this step, the framework interaction interface refers to the set of interfaces configured in the intermediate task flow framework used to realize information interaction between the framework and external devices and internal nodes. It includes task input interfaces, node communication interfaces, and status feedback interfaces, reflecting the framework's interactive capabilities and ensuring smooth transmission of task data, instructions, and status. It is designed based on the interaction requirements between the framework and external devices / nodes. The task input interface is the interface in the framework interaction interface used to receive external task data. It connects the touch input device and the task receiving module, supporting stable access to task data in high-concurrency scenarios. It reflects the framework's task entry capability and is used to allow data related to office tasks submitted by employees to enter the framework. It is designed based on the transmission protocol and data format adaptation of touch input devices. The node communication interface is the interface in the framework interaction interface used to realize data interaction between the framework and task processing nodes. It connects the task allocation module, task monitoring module, and task processing nodes, responsible for issuing task allocation instructions and uploading node status data. It reflects the communication capabilities between the framework and nodes and ensures efficient transmission of scheduling instructions and status data. It is designed based on the communication protocol of the task processing nodes. The status feedback interface refers to the interface in the framework's interaction interface used to provide feedback on the status of tasks and nodes. It connects the task monitoring module with the touch input device / office management platform and is responsible for pushing task processing progress and node running status to users or administrators. It reflects the framework's status feedback capability and is used to improve the transparency of office task processing. It is designed based on users' needs for obtaining status information.

[0092] In this embodiment, a task input interface, a node communication interface, and a status feedback interface are first configured. Through the configuration of these three types of interfaces, the intermediate task flow framework has complete interactive capabilities of task input, scheduling processing, and status feedback, ultimately forming a fully functional hierarchical task scheduling framework.

[0093] This application's embodiments avoid the shortcomings of traditional frameworks, such as tasks lacking fixed flow direction and low processing efficiency, through a basic task flow framework, providing structural support for orderly task flow; the resource configuration list enables precise matching of hardware resources and task flow nodes; the priority evaluation dimension set compensates for the shortcomings of traditional frameworks, such as the lack of clear task priority evaluation standards and the inability to prioritize urgent tasks, providing a quantitative basis for hierarchical scheduling; the hierarchical scheduling logic component endows the framework with core scheduling capabilities of receiving, allocating, and monitoring; and the configuration of three types of interaction interfaces enables the framework to have complete interaction capabilities, solving the problems of poor communication and inability to provide status feedback between traditional frameworks and external devices / nodes.

[0094] This application provides a specific embodiment. Step 102 involves adjusting the hierarchical scheduling rules in the hierarchical task scheduling framework based on the office task data to generate adjusted rules. The office task data includes emergency task response time limits, ordinary task queuing time, original task priority information, and processing node matching rate. The adjusted rules include three task priority information items and corresponding processing nodes, processing time limits, and data transmission formats. Specifically, the following steps are included:

[0095] Step 201: Based on the task urgency description of the original task priority information in the office task data, divide the original task priority information into three task priority information, which include first-level task priority information, second-level task priority information, and third-level task priority information.

[0096] In this step, the task urgency description refers to the text or label content (such as immediate response, routine processing, delayed execution) in the original task priority information of the office task data, which intuitively reflects the urgency of the task. It reflects the initial urgency attribute of the task and serves as the core basis for dividing the three task priority information. It is extracted from the original task priority information in the office task data. Level 1 task priority information refers to the highest priority category among the three task priority information, corresponding to office tasks requiring immediate response. It is used to identify critical tasks that require priority resource allocation and rapid processing, and is divided based on the urgency description of immediate response tasks in the original task priority information. Level 2 task priority information refers to the medium priority category among the three task priority information, corresponding to office tasks requiring routine processing. It is used to identify ordinary tasks that require balancing resource usage and processing efficiency, and is divided based on the urgency description of routine processing tasks. Level 3 task priority information refers to the lowest priority category among the three task priority information, corresponding to office tasks that need delayed execution. It is used to identify less demanding tasks that can be processed during off-peak hours and prioritize resources for higher-priority tasks, and is divided based on the urgency description of delayed execution tasks.

[0097] In this embodiment, raw task priority information is extracted from office task data, and then task urgency descriptions (such as immediate response, routine processing, delayed execution, etc.) are filtered out from the raw task priority information. Then, based on the differences in urgency descriptions and the enterprise's priority management needs for office tasks, the raw task priority information is divided into three levels of task priority information. The task urgency description is corresponding to the first-level task priority information (most urgent, requiring priority resource allocation) for immediate response, the task urgency description is corresponding to the second-level task priority information (medium urgency, balancing resources and efficiency) for routine processing, and the task urgency description is corresponding to the third-level task priority information (most lenient, allowing for off-peak processing). The core distinguishing criteria for the three priorities are clearly defined.

[0098] Step 202: Calculate the time difference between the emergency task response time limit and the waiting time for ordinary tasks in the office task data. Based on the hierarchical requirements of the three task priority information, divide the time difference into two parts. The sum of the smaller part and the emergency task response time limit is used as the processing time limit for the secondary task priority information. The emergency task response time limit is used as the processing time limit for the primary task priority information. The waiting time for ordinary tasks is used as the processing time limit for the tertiary task priority information.

[0099] In this step, the hierarchical requirements for the three task priority information refer to the core principles to be followed when dividing the three task priority information (Level 1 requires the shortest processing time and highly adaptable nodes, Level 2 requires medium processing time and adaptable nodes, and Level 3 requires the longest processing time and basic nodes). This reflects the differentiated processing requirements of tasks with different priorities and guides the calculation of processing time limits and the selection of candidate processing nodes. It is based on the resource allocation and efficiency requirements of enterprise collaborative work. The two parts refer to the time difference between the response time of urgent tasks and the queuing time of ordinary tasks, divided according to the hierarchical requirements of the three task priority information into two numerical segments. These segments are used to calculate the processing time limit for Level 2 task priority information and are based on the enterprise's time tolerance for medium-urgent tasks. The processing time limit for Level 2 task priority information refers to the maximum allowed time from task submission to completion set for Level 2 task priority information, reflecting the time constraints of medium-urgent tasks and used to regulate the processing rhythm of Level 2 tasks. The processing time limit for Level 1 task priority information refers to the maximum allowed time from task submission to completion set for Level 1 task priority information, reflecting the strict time requirements of the most urgent tasks and used to ensure rapid processing of critical tasks. The processing time limit for Level 3 task priority information refers to the maximum allowed time from task submission to completion for Level 3 task priority information. It directly adopts the queuing waiting time of ordinary tasks in the office task data, reflecting the flexible time requirements of lenient tasks and adapting to the needs of off-peak processing.

[0100] In this embodiment, the emergency task response time and the waiting time for ordinary tasks are extracted from the office task data. The time difference between the emergency task response time and the ordinary task waiting time is calculated by subtracting the ordinary task waiting time from the emergency task response time (if the result is negative, the absolute value is taken). Then, based on the hierarchical requirements of the three task priority information and the company's time tolerance for medium-urgent tasks, the time difference is divided into two parts (e.g., divided at a ratio of 1:2, or divided according to a standard of not exceeding 50% of the emergency task response time). Subsequently, the smaller value of the two parts is selected, and this value is added to the emergency task response time. The sum is used as the processing time limit for the secondary task priority information (ensuring it is between the first and third levels). At the same time, the emergency task response time in the office task data is directly used as the processing time limit for the first-level task priority information (ensuring the fastest processing of the most urgent tasks), and the waiting time for ordinary tasks is used as the processing time limit for the third-level task priority information (adapting to the time requirements of lenient tasks), thus clarifying the time constraints of the three priorities.

[0101] Step 203: Calculate the average processing node matching rate of all processing nodes in the office task data as the baseline matching rate, and compare the processing node matching rate of all processing nodes with the baseline matching rate to determine the candidate processing node list corresponding to the three task priority information respectively.

[0102] In this step, the baseline matching rate refers to the average matching rate of all processing nodes in the office task data, reflecting the average adaptability of all processing nodes to handle tasks, and is used as a reference standard for screening candidate processing nodes. The candidate processing node list refers to the set of processing nodes adapted to the processing of tasks of each priority (divided into first-level, second-level, and third-level lists), reflecting the node resources that can be called for tasks of different priorities, and is used for node selection during subsequent task distribution.

[0103] In this embodiment, the matching rate of each processing node is extracted from the office task data. The matching rates of all processing nodes are summed and then divided by the total number of processing nodes. The average value is used as the baseline matching rate (reflecting the average adaptability of all nodes). Then, the matching rate of each processing node is compared with the baseline matching rate one by one. Taking into account the processing requirements of the three task priority information (level 1 tasks require highly adaptable nodes, level 2 tasks require medium adaptable nodes, and level 3 tasks require basic adaptable nodes), processing nodes with a matching rate 10% or higher than the baseline matching rate are selected as the candidate processing node list corresponding to the level 1 task priority information. Processing nodes with a matching rate between the baseline matching rate and ±10% are selected as the candidate processing node list corresponding to the level 2 task priority information. Processing nodes with a matching rate 10% or lower than the baseline matching rate but still have processing capabilities are selected as the candidate processing node list corresponding to the level 3 task priority information, ensuring the adaptability of nodes to task priorities.

[0104] Step 204: Based on the order of priority of the three task information, determine the data transmission format corresponding to the three task priority information respectively. The data transmission format of the first-level task priority information is an encrypted transmission format with digital signature, the data transmission format of the second-level task priority information is a compressed transmission format with verification code, and the data transmission format of the third-level task priority information is a standard transmission format without additional information.

[0105] In this step, the priority order refers to the ranking of the three task priority information according to their urgency (Level 1 > Level 2 > Level 3), reflecting the primary and secondary relationship of task priorities. This order determines the data transmission format and resource allocation order for tasks of different priorities, and is based on the differences in urgency among the three task priority information. The encrypted transmission format with digital signature is designed for Level 1 task priority information. It first encrypts the task data (to prevent data theft) and then adds the sender's digital signature (to prevent data tampering), ensuring the security and integrity of high-urgency task data transmission. It is designed based on the high security requirements of Level 1 tasks. The compressed transmission format with checksum is designed for Level 2 task priority information. It first compresses the task data and then generates a data checksum, balancing transmission efficiency and data security for medium-urgency tasks. It is designed based on the efficiency and security requirements of Level 2 tasks. The standard transmission format without additional information is designed for Level 3 task priority information. It transmits directly according to a general data format without additional encryption, compression, or checksum processing, prioritizing the transmission efficiency of low-urgency tasks. It is designed based on the efficiency priority requirements of Level 3 tasks.

[0106] In this embodiment, the priority levels of the three tasks are first defined as follows: Level 1 task priority information > Level 2 task priority information > Level 3 task priority information (the higher the level, the higher the urgency and security requirements of the task). Based on this order, and considering the transmission requirements of different priority tasks (high priority requires security assurance, medium priority requires a balance between security and efficiency, and low priority requires efficiency first), the corresponding data transmission formats are determined. Specifically, the data transmission format for Level 1 task priority information is an encrypted transmission format with a digital signature; the data transmission format for Level 2 task priority information is a compressed transmission format with a verification code; and the data transmission format for Level 3 task priority information is a standard transmission format without additional information, thus clarifying the transmission standards for different priority tasks.

[0107] Step 205: Replace the corresponding content of the hierarchical scheduling rules in the hierarchical task scheduling framework with the three task priority information, the processing time limit, the candidate processing node list, and the data transmission format corresponding to the three task priority information, and generate the adjusted rules.

[0108] In this step, the corresponding content of the hierarchical scheduling rule refers to the old configuration content related to task priority information, processing time limit, processing node, and data transmission format in the original hierarchical scheduling rule of the hierarchical task scheduling framework. It is used as the object to be replaced and provides the basis for generating the adjusted rule, which is obtained based on the initial configuration of the hierarchical task scheduling framework.

[0109] In this embodiment, the existing hierarchical scheduling rules are retrieved from the hierarchical task scheduling framework. The corresponding content of the hierarchical scheduling rules related to task priority information, processing time limits, processing nodes, and data transmission formats is located. Then, the three task priority information (level 1, level 2, and level 3) obtained in the first step, the three processing time limits obtained in the second step (corresponding to levels 1, 2, and 3 respectively), the three candidate processing node lists obtained in the third step (corresponding to levels 1, 2, and 3 respectively), and the three data transmission formats obtained in the fourth step (corresponding to levels 1, 2, and 3 respectively) are used to replace the corresponding old content in the hierarchical scheduling rules one by one. After the replacement is completed, the new rules are logically verified (to ensure that the correspondence between priority, time limit, node, and format is consistent). After verification, the adjusted rules are generated to provide a dynamically adapted rule basis for subsequent office task data verification, bandwidth allocation, and task distribution.

[0110] This application's embodiments address the problem that traditional rules, where all tasks share the same time limit, cannot meet varying urgency requirements, ensuring rapid processing of high-priority tasks. The candidate processing node list overcomes the shortcomings of traditional rules, such as blind node allocation and low adaptability, improving task-node matching efficiency. Determining differentiated data transmission formats based on priority balances security and efficiency, avoiding the problems of traditional rules' single transmission format, lack of security for high-security tasks, or low efficiency for low-urgency tasks. Replacing and adjusting rules improves adaptability and the ability to respond to changes in office workload. It enhances the scheduling flexibility and task processing effectiveness of the collaborative office system in high-concurrency scenarios.

[0111] This application provides a specific embodiment. Step 103 involves adding and verifying the office task-related data according to the adjusted rules to generate verified data. This specifically includes the following steps:

[0112] Step 301: Match the task urgency description in the office task-related data with the three task priority information to determine the target priority information corresponding to the office task-related data.

[0113] In this step, the target priority information refers to the unique priority category (level 1, level 2, or level 3 task priority information) determined by matching the office task-related data with the task urgency description and the three task priority information. It reflects the specific urgency level of the office task and is used to guide the addition of task priority labels.

[0114] In this embodiment, a description of the urgency of a task (such as immediate response, routine processing, or delayed execution) is extracted from the data related to office tasks. This description is then compared one by one with the three task priority information (level 1, level 2, and level 3 task priority information) defined in the adjusted rules. If the task urgency description is immediate response, then level 1 task priority information is matched as the target priority information corresponding to the data related to the office task. If the description is routine processing, then level 2 task priority information is matched as the target priority information. If the description is delayed execution, then level 3 task priority information is matched as the target priority information, ensuring that each piece of data related to an office task can be assigned a clear priority category.

[0115] Step 302: Add a task priority identifier corresponding to the target priority information to the office task-related data to form identified office task data. The task priority identifier includes the level number of the target priority information and the corresponding priority level identifier.

[0116] In this step, the labeled office task data refers to the dataset formed after adding task priority labels to office task-related data. It includes both the original office task information (such as task content and submission time) and the level number and priority hierarchy label corresponding to the target priority information, reflecting the task's priority attribute and used for subsequent data transmission format verification. The level number of the target priority information refers to the numerical symbol used in the task priority label to indicate the priority level (e.g., 1, 2, 3 correspond to levels one, two, and three respectively), intuitively reflecting the task priority ranking and used to quickly distinguish tasks of different priorities. It is determined based on the hierarchical order of the target priority information. The priority hierarchy label refers to the textual symbol used in the task priority label to describe the priority attribute (e.g., urgent, medium, lenient), reflecting the semantic features of the task's urgency and used to aid in understanding the task priority. It is determined based on the urgency description of the target priority information.

[0117] In this embodiment, based on the target priority information, a task priority identifier is added to the corresponding office task-related data. The target priority information level number is identified by a number (e.g., level 1 corresponds to 1, level 2 corresponds to 2, and level 3 corresponds to 3), and the priority level identifier is identified by a text description (e.g., level 1 corresponds to urgent, level 2 corresponds to medium, and level 3 corresponds to lenient). The level number and priority level identifier are appended to the office task-related data in a preset format (e.g., adding a priority: level number-priority level identifier field to the data header) using a data tagging tool, forming labeled office task data that contains both the original task information and the priority identifier, facilitating rapid identification of task priorities in subsequent steps.

[0118] Step 303: Check whether the data transmission format of the tagged office task data conforms to the target data transmission format corresponding to the target priority information in the adjusted rules, and integrate the tagged office task data whose data transmission format conforms to the corresponding target data transmission format into a temporary verification dataset.

[0119] In this step, the target data transmission format refers to the standard transmission format corresponding to the three task priority information in the adjusted rules (Level 1 is an encrypted transmission format with digital signature, Level 2 is a compressed transmission format with checksum, and Level 3 is a standard transmission format without additional information). This reflects the data transmission requirements of tasks with different priorities and is used to verify the format compliance of tagged office task data. The temporary verification dataset refers to an intermediate dataset formed by integrating tagged office task data whose data transmission format conforms to the target data transmission format. It only contains compliant task data, reflects the valid task information after initial screening, and is used for subsequent sorting processing to generate verified data.

[0120] In this embodiment, the target data transmission format corresponding to the target priority information is first obtained from the adjusted rules (Level 1 corresponds to an encrypted transmission format with a digital signature, Level 2 corresponds to a compressed transmission format with a verification code, and Level 3 corresponds to a standard transmission format without additional information). Then, a format verification tool is used to check whether the data transmission format of the tagged office task data conforms to the target data transmission format: for Level 1 tasks, the data is verified to be encrypted and contains a digital signature; for Level 2 tasks, the data is verified to be compressed and includes a verification code; for Level 3 tasks, the data is verified to conform to the standard transmission format without additional information. All tagged office task data that have passed the format check are classified and integrated according to priority category to form a temporary verification dataset to ensure that the data format entering the subsequent process is compliant.

[0121] Step 304: Sort the labeled office task data in the temporary verification dataset to form verification passed data.

[0122] In this embodiment, the labeled office task data in the temporary verification dataset is sorted: firstly, it is sorted by the level number in the task priority identifier (level 1 tasks are ranked first, followed by level 2, and finally level 3); for labeled office task data of the same priority, it is further sorted by the task submission time (earlier submission times are ranked first); after sorting, the data is organized into a structured dataset in order to form verification passed data, providing an orderly and compliant task data foundation for the subsequent setting and adjustment of bandwidth allocation logic.

[0123] This application's embodiments visualize task priorities by adding task priority identifiers containing level numbers and hierarchical identifiers, thus solving the problems of unclear task attributes and lack of scheduling basis. Strict validation based on the target data transmission format filters out compliant task data to form a temporary validation dataset, overcoming the shortcomings of traditional validation which only performs basic checks and suffers from transmission errors due to format chaos, ensuring data transmission stability. Through dual sorting by priority and submission time, validation-passed data is ordered, solving the problem of disordered task data accumulation and low processing efficiency in high-concurrency scenarios.

[0124] This application provides a specific embodiment, such as Figure 2 As shown, step 104 involves setting up basic bandwidth allocation logic corresponding to the three task priority information based on the task priority identifier in the verified data, and adjusting the basic bandwidth allocation logic to generate bandwidth allocation results. This specifically includes the following steps:

[0125] Step 401: Based on the task priority identifier in the verified data, divide the verified data into three groups of data to be assigned, corresponding to the three task priority information. Mark the number of office tasks in each group of data to be assigned as the total number of office tasks. Mark the total amount of data of all office tasks in each group of data to be assigned as the total amount of data of tasks. Calculate the average amount of data per task in each group of data to be assigned.

[0126] In this step, the data to be allocated refers to the three groups of data that have been split from the verified data according to task priority identifiers, each corresponding to one of the three task priority information groups. These groups reflect the classification of office tasks with different priorities and are used for subsequent targeted bandwidth allocation calculations. The number of office tasks refers to the number of individual office tasks contained in each group of data to be allocated, reflecting the basic scale of tasks in each group and used to calculate the total number of office tasks. This is calculated based on the task list of each group of data to be allocated. The total number of office tasks refers to the sum of all office tasks in each group of data to be allocated, reflecting the overall scale of tasks under this priority and serving as the basis for determining bandwidth requirements. The total data volume of all office tasks refers to the sum of the data volume of each office task in each group of data to be allocated, reflecting the overall volume of task data under this priority. The total task data volume refers to the result after marking the total data volume of all office tasks in each group of data to be allocated. This is the official identifier for the total data volume of all office tasks, reflecting the total scale of task data under this priority and used as a data volume reference for subsequent bandwidth allocation. The average data volume per task refers to the total data volume of each group of tasks to be allocated divided by the total number of office tasks in that group. It reflects the average data volume of a single task under this priority and is used to determine the bandwidth requirements of a single task.

[0127] Step 402: Based on the total network bandwidth value in the hierarchical task scheduling framework, determine the available total network bandwidth and set the basic bandwidth allocation logic corresponding to the priority information of the three tasks respectively.

[0128] In this step, the total network bandwidth refers to the maximum data transmission rate that the entire network accessed by the hierarchical task scheduling framework can provide. It reflects the total scale of network resources that the framework can call upon and is used to determine the available total network bandwidth. It is extracted from the network configuration parameters of the hierarchical task scheduling framework. The available total network bandwidth refers to the bandwidth remaining after deducting the fixed bandwidth occupied by the framework's basic operation and system maintenance from the total network bandwidth value. It reflects the bandwidth resources that can actually be used for office task data transmission and is the core basis for subsequent bandwidth allocation. It is calculated based on the total network bandwidth value minus the fixed bandwidth occupied.

[0129] Step 403: Select the online processing nodes that are in the online state from the candidate processing node list corresponding to the three task priority information in the adjusted rules, and count the number of tasks executed by each online processing node. Combined with the corresponding maximum number of tasks that can be processed, calculate the average load rate of all online processing nodes under the corresponding task priority information.

[0130] In this step, "online processing nodes" refers to the processing nodes currently connected to the network and capable of receiving and processing tasks, selected from the candidate processing node list corresponding to the three task priority information in the adjusted rules. This reflects the node resources that can be actually accessed and is used for subsequent load rate calculations and bandwidth allocation. "Number of tasks being executed" refers to the number of office tasks currently being processed by each online processing node, reflecting the node's current task capacity and used to calculate the node load rate. This is obtained through statistics from the task monitoring module of the hierarchical task scheduling framework. "Maximum number of tasks that can be processed" refers to the maximum number of tasks that each online processing node can stably process per unit time, reflecting the node's upper limit of task processing capacity and used to calculate the node load rate. This is extracted from the resource configuration list. "Corresponding task priority information" refers to a specific priority among the three task priority information (e.g., level 1, level 2, or level 3), reflecting the priority range limitation for subsequent operations and used to clarify the targeting of operations such as load rate and bandwidth allocation. Average load rate refers to the average load rate of all online processing nodes under the corresponding task priority information. The load rate of a single node is the number of tasks executed by that node divided by its maximum number of tasks that can be processed. The average load rate reflects the overall load level of the nodes under that priority and is used to adjust the bandwidth allocation logic.

[0131] Step 404: Based on the processing time limits corresponding to the three task priority information in the adjusted rules, calculate the submitted time of each office task, and calculate the average time of all submitted times in groups. Combined with the processing time limits of the corresponding task priority information, obtain the average remaining processing time of each group of data to be allocated.

[0132] In this step, the submitted duration refers to the time interval from submission to the current time for each office task, reflecting the time the task has been waiting to be processed, and is used to calculate the average remaining processing time. The average remaining processing time refers to the average of the submitted durations of all office tasks under the corresponding task priority information. Subtracting this average from the processing time limit of that priority reflects the overall remaining processing time of tasks under that priority, and is used to determine the urgency of bandwidth transmission.

[0133] Step 405: Based on the total number of office tasks, the average load rate, and the average remaining processing time, adjust the bandwidth basic allocation logic to obtain the final bandwidth allocation ratio.

[0134] In this step, the final bandwidth allocation ratio refers to the proportion of the total available network bandwidth occupied by the three task priority information determined after adjusting the basic bandwidth allocation logic. It reflects the allocation weight of bandwidth resources among different priorities and is used to calculate the basic bandwidth of each group of data to be allocated.

[0135] Step 406: Based on the final bandwidth allocation ratio, calculate the total actual bandwidth allocation value for each group of data to be allocated, and generate the bandwidth allocation result by combining the total number of office tasks and the total amount of task data for each group of data to be allocated.

[0136] In this step, the actual total bandwidth allocation value refers to the final bandwidth value that can be used by each group of data after adjusting the basic bandwidth allocation value of each group to be allocated. It reflects the actual bandwidth resources obtained under this priority and is used for subsequent allocation to online processing nodes.

[0137] Optionally, step 406 involves calculating the total actual bandwidth allocation value for each group of data to be allocated based on the final bandwidth allocation ratio, and generating a bandwidth allocation result by combining the total number of office tasks and the total amount of task data for each group of data to be allocated. This specifically includes the following steps:

[0138] Step 411: Calculate the basic bandwidth allocation value corresponding to each group of data to be allocated based on the total available network bandwidth and the final bandwidth allocation ratio.

[0139] In this step, the basic bandwidth allocation value refers to the initial usable bandwidth value for each group of data to be allocated, obtained by multiplying the total available network bandwidth by the final bandwidth allocation ratio. It reflects the initial bandwidth allocation amount under this priority and is used for subsequent adjustments to obtain the actual total bandwidth allocation value.

[0140] Step 412: Determine the data transmission urgency corresponding to each group of data to be allocated based on the average remaining processing time of each group of data to be allocated.

[0141] In this step, data transmission urgency refers to the degree of urgency of data transmission for a group of tasks, determined based on the average remaining processing time of each group of data to be allocated. The shorter the average remaining processing time, the higher the urgency, reflecting the urgent bandwidth demand of the task under this priority, and is used to adjust the basic bandwidth allocation value.

[0142] Step 413: Adjust the basic bandwidth allocation value according to the data transmission urgency and average data volume per task of each group of data to be allocated, to obtain the actual total bandwidth allocation value of each group of data to be allocated. The adjustment process includes making an upward adjustment to the basic bandwidth allocation value corresponding to the data to be allocated whose average data volume per task is higher than the average average data volume per task of all data to be allocated, and making a second upward adjustment to the basic bandwidth allocation value corresponding to the data to be allocated whose data transmission urgency is higher than a preset urgency threshold. The sum of the bandwidth allocation values ​​of all data to be allocated shall not exceed the total available network bandwidth.

[0143] In this step, the average data volume per task refers to the sum of the average data volumes of the data to be allocated corresponding to the three task priority information, divided by 3. This reflects the average data volume benchmark of a single task among all the data to be allocated, and is used to determine whether the data volume of a single task in a certain group of data to be allocated is too high. The first upward adjustment refers to the bandwidth allocation operation performed on the base bandwidth allocation value for the data to be allocated whose average data volume per task is higher than the average data volume per task. This reflects the bandwidth tilt towards tasks with larger data volumes, used to meet the transmission needs of this group of tasks. The preset urgency threshold is a pre-set critical value used to determine whether the urgency of data transmission is too high. It reflects the judgment standard for the urgency of data transmission and is used to trigger the second upward adjustment. It is set based on the time sensitivity requirements of task processing in collaborative office scenarios. The second upward adjustment refers to the further increase in bandwidth allocation value (or the bandwidth value after the first upward adjustment) for the data to be allocated whose data transmission urgency is higher than the preset urgency threshold. This reflects the bandwidth priority guarantee for urgent tasks, used to ensure the rapid transmission of urgent tasks. The total bandwidth allocation value refers to the sum of the actual bandwidth allocation values ​​of the data to be allocated corresponding to the three task priority information. It reflects the total amount of bandwidth resources actually allocated and is used to ensure that the total available network bandwidth is not exceeded.

[0144] Step 414: Based on the load rate of all online processing nodes under the corresponding task priority information, split the total actual bandwidth allocation value of each group of data to be allocated to form an initial task bandwidth association record, and integrate the total amount of office tasks and the total amount of task data of each group of data to be allocated with the corresponding initial task bandwidth association record to generate the bandwidth allocation result.

[0145] In this step, the load rate refers to the number of tasks executed by a single online processing node divided by its maximum number of tasks that can be processed. It reflects the task saturation level of a single node and is used to split the total actual bandwidth allocation for each group of data to be allocated. The initial task bandwidth association record refers to the correspondence record between online processing nodes and node-assigned bandwidth formed after splitting the total actual bandwidth allocation for each group of data to be allocated according to the load rate of all online processing nodes under the corresponding priority. It reflects the binding relationship between nodes and bandwidth and is used for subsequent task-node-bandwidth binding. It is obtained based on the splitting of the total actual bandwidth allocation for each group of data to be allocated and the load rate of the corresponding online processing node.

[0146] In this embodiment, the task priority identifier corresponding to each data point is extracted from the verified data. Based on the identifier, the verified data is split into three groups of data to be allocated, corresponding to first-level, second-level, and third-level task priority information, respectively. For each group of data to be allocated, the office tasks contained therein are enumerated one by one and the number is counted. This number is marked as the number of office tasks. Then, the total number of office tasks in the group is summed to obtain the total number of office tasks. At the same time, the data volume of each office task in each group of data to be allocated is counted and summed to obtain the total data volume of all office tasks. This total data volume is marked as the total data volume of tasks. Finally, the total data volume of tasks in each group of data to be allocated is divided by the total number of office tasks to calculate the average data volume of a single task in each group. Secondly, step 402 determines the basic resources and initial logic for bandwidth allocation: the total network bandwidth value is extracted from the network configuration module of the hierarchical task scheduling framework. Based on the framework's daily operation monitoring data, the bandwidth fixedly occupied by basic system services and background maintenance is deducted to determine the total available network bandwidth. Combining the differences in urgency of the three task priority information (Level 1 is the most urgent, Level 2 is medium, and Level 3 is the most lenient), the basic bandwidth allocation logic corresponding to the three priorities is set. For example, the basic bandwidth ratio of Level 1 priority is higher than that of Level 2, and Level 2 is higher than that of Level 3. Next, step 403 obtains the online node resources and load status: the candidate processing node list corresponding to the three task priority information is extracted from the adjusted rules. A status detection command is sent to each candidate node through the node communication interface of the hierarchical task scheduling framework, and nodes that return an online status are selected as online processing nodes. The number of tasks currently being processed by each online processing node is counted through the task monitoring module. The maximum number of tasks that each online processing node can process is extracted from the resource configuration list. The load rate of a single node is obtained by dividing the number of tasks being processed by each node by its maximum number of tasks that can be processed. Then, the average load rate of all online processing nodes under the corresponding priority is calculated to obtain the average load rate of that priority. Then, in step 404, the remaining processing time of the task is calculated: the processing time limits corresponding to the three task priority information are extracted from the adjusted rules; the submission time of each office task in each group of pending data is obtained through the task monitoring module; the submission time of each task is obtained by subtracting the submission time from the current time; the submission time of all tasks in each group of pending data is added together and divided by the total number of office tasks in that group to obtain the average submission time of that group; finally, the average remaining processing time of each group of pending data is obtained by subtracting the average submission time from the processing time limit of the corresponding priority.Next, the bandwidth allocation ratio is optimized through step 405: by comprehensively analyzing the total amount of office tasks (the larger the total amount, the higher the bandwidth requirement), average load rate (the lower the load rate, the higher the bandwidth that can be carried), and average remaining processing time (the shorter the time, the more urgent the bandwidth requirement), the basic bandwidth allocation logic set in step 402 is adjusted. For example, if a certain priority has a large total amount of office tasks, a low average load rate, and a short average remaining processing time, the bandwidth ratio of that priority is increased, and finally the final bandwidth allocation ratios corresponding to the three priorities are obtained. Finally, step 406 completes the generation of bandwidth allocation results. Step 411 first calculates the base bandwidth: multiplying the total available network bandwidth by the final bandwidth allocation ratio to obtain the base bandwidth allocation value for each group of data to be allocated. Step 412 determines the transmission urgency: based on the average remaining processing time of each group of data to be allocated, the shorter the average remaining processing time, the higher the data transmission urgency, thus clarifying the urgency level of each group. Step 413 adjusts the bandwidth to the actual total value: calculating the average data volume per task for the three priority groups of data to be allocated; if the average data volume per task for a group is higher than this average, its base bandwidth allocation value is adjusted upwards. Simultaneously, if the data transmission urgency of a group is higher than a preset urgency threshold, its... The bandwidth value (either the initial upward value or the base value) is adjusted upwards a second time. During the adjustment process, the three sets of bandwidth values ​​are accumulated in real time to ensure that the total bandwidth allocation value does not exceed the total available network bandwidth, and finally the actual bandwidth allocation value of each group is obtained. Step 414: Split bandwidth and integrate the results: According to the load rate of all online processing nodes under the corresponding priority, the actual bandwidth allocation value of each group of data to be allocated is split according to the load rate ratio (nodes with low load rates are allocated more bandwidth), forming an initial task bandwidth association record that records the node-bandwidth correspondence. The total amount of office tasks and total data volume of each group of data to be allocated are integrated with the corresponding initial task bandwidth association record to generate a bandwidth allocation result that includes priority-task parameters-node-bandwidth.

[0147] Assuming Company A's collaborative office system operates based on a hierarchical task scheduling framework, the first step is to execute step 401: extract task priority identifiers from the verified data and split them into three groups of data to be allocated: Level 1 (10 office tasks, total office task count 10, total data size of all office tasks 1000MB, marked as total task data size 1000MB, average data size per task 100MB), Level 2 (20 office tasks, total office task count 20, total office task count 1200MB, total task data size 1200MB, average data size per task 60MB), and Level 3 (30 office tasks, total office task count 30, total office task count 1500MB, total task data size 1500MB, average data size per task 50MB). Next, step 402 is executed: extract the total network bandwidth value of 1000Mbps from the framework, deduct the system's basic usage of 200Mbps, determine the available total network bandwidth as 800Mbps, and set the bandwidth allocation logic as Level 1 40%, Level 2 30%, and Level 3 30%. Then, step 403 is executed: from the candidate list of the adjusted rules, three Level 1 online processing nodes (with 2, 3, and 2 tasks executed respectively, a maximum of 5 tasks that can be processed each, individual load rates of 40%, 60%, and 40%, and an average load rate of 46.7%), five Level 2 online processing nodes (average load rate of 50%), and four Level 3 online processing nodes (average load rate of 55%) are selected. Next, step 404 is executed: the processing time limits for Level 1 (2 hours), Level 2 (4 hours), and Level 3 (8 hours) are extracted. The average submitted time for Level 1 tasks is calculated to be 0.5 hours (average remaining processing time 1.5 hours), for Level 2 tasks to be 1 hour (average remaining processing time 3 hours), and for Level 3 tasks to be 2 hours (average remaining processing time 6 hours). Then, step 405 is executed: because Level 1 tasks have the shortest average remaining processing time and the lowest average load rate, the final bandwidth allocation ratio is adjusted to Level 1 45%, Level 2 32%, and Level 3 23%.Finally, step 406 is executed: Step 411 calculates the basic bandwidth allocation values ​​for Level 1 (800 × 45%) = 360Mbps, Level 2 (256Mbps), and Level 3 (184Mbps); Step 412 determines the data transmission urgency for Level 1 (highest), Level 2 (medium), and Level 3 (lowest); Step 413 calculates the average data volume per task as (100 + 60 + 50) / 3 ≈ 70MB. Since the average data volume per task for Level 1 is higher than the average, it is increased by 20Mbps to 380Mbps. Furthermore, since the urgency of Level 1 is higher than the preset threshold, it is increased by another 20Mbps. The bandwidth allocation ranges from Mbps to 400Mbps, with no adjustments for Level 2 and Level 3. The actual total bandwidth allocation values ​​are 400Mbps, 256Mbps, and 184Mbps (totaling 800Mbps). Step 414 splits the Level 1 400Mbps bandwidth according to the load rates of the three nodes (160Mbps is allocated to nodes with a load rate of 40%, 120Mbps to nodes with a load rate of 60%, and 120Mbps to nodes with a load rate of 40%), forming an initial task bandwidth association record. Then, the total amount of office tasks and the total amount of task data in each group are integrated to generate the bandwidth allocation result.

[0148] This application's embodiments address the problems of traditional static bandwidth allocation, neglecting task priority and node load, and difficulty in adapting to transmission urgency by constructing a dynamic bandwidth allocation process. By prioritizing data, calculating node load, and adapting to urgency, bandwidth is allocated on demand, ensuring high-priority tasks, improving bandwidth utilization, and supporting system transmission stability and task processing efficiency under high concurrency.

[0149] This application provides a specific embodiment. Step 105 involves, based on the bandwidth allocation result, and in conjunction with the verification passed data, the processing nodes corresponding to the three task priority information, and the processing time limits, the target processing node of the hierarchical task scheduling framework performs hierarchical management of office tasks. This specifically includes the following steps:

[0150] Step 501: Based on the initial task bandwidth association record in the bandwidth allocation result and the task priority identifier in the verification passed data, bind the online processing node and the corresponding node allocated bandwidth in the initial task bandwidth association record with the office task in the verification passed data to obtain task node association data.

[0151] In this step, node allocated bandwidth refers to the bandwidth resource quota pre-allocated to each online processing node in the initial task bandwidth association record for transmitting office task data. It reflects the network transmission resources available to the online processing node and is used to ensure stable transmission of task data. Task node association data refers to the dataset formed by binding the online processing nodes and node allocated bandwidth in the initial task bandwidth association record with the office tasks in the verified data according to task priority identifiers. It reflects the correspondence between office tasks, processing nodes, and bandwidth and is used for subsequent task distribution and resource configuration.

[0152] In this embodiment of the application, the office tasks in the verified data are then matched with online processing nodes of the same priority in the initial task bandwidth association record according to the task priority identifier (e.g., an office task with a first-level priority is matched with an online processing node corresponding to the first-level priority); finally, the successfully matched online processing nodes, the node allocation bandwidth corresponding to the node, and the corresponding office tasks are bound one by one to form task node association data containing the correspondence between office tasks, online processing nodes, and node allocation bandwidth.

[0153] Step 502: Select the processing node that exists simultaneously in the candidate processing node list corresponding to the three task priority information and the initial task bandwidth association record, and whose remaining processing capacity meets the standard, as the target processing node.

[0154] In this step, remaining processing capacity refers to the number of additional office tasks that a processing node can currently handle, reflecting the node's idle processing resources and used to determine whether the node can handle more tasks. Meeting the remaining processing capacity standard means that the processing node's remaining processing capacity is not less than the number of office tasks that the node needs to handle, reflecting that the processing node has sufficient idle resources to handle task distribution and is used to screen target processing nodes.

[0155] In this embodiment, the candidate processing node list (derived from the adjusted rules) corresponding to the three task priority information and the online processing node list in the initial task bandwidth association record are obtained respectively. The processing nodes that exist in both lists are filtered out by the intersection operation to obtain the candidate processing nodes. Then, the remaining processing capacity of each candidate processing node is calculated (by subtracting the number of currently executed tasks from the maximum number of tasks that the node can process), and it is determined whether the remaining processing capacity meets the standard (meeting the standard means that the remaining processing capacity is not less than the number of office tasks that the node needs to handle). The candidate processing nodes with the remaining processing capacity meet the standard are determined as the target processing nodes.

[0156] Step 503: Based on the processing time limits corresponding to the three task priority information and the preset task distribution sorting rules, distribute the office tasks in the task node association data to the target processing node, and at the same time allocate bandwidth to the nodes in the initial task bandwidth association record for the target processing node, and generate task execution initial state data.

[0157] In this step, the preset task distribution sorting rules refer to the pre-defined rules used to determine the order of office task distribution. These rules reflect the priority logic of task distribution and are used to standardize the task distribution order. They are set based on the processing time limits and urgency requirements of the three task priority information. The initial task execution status data refers to a dataset recording the initial information such as the distributed office tasks, target processing nodes, configured node bandwidth allocation, and processing time limits. This dataset reflects the basic state of the task immediately after distribution and is used for subsequent task progress monitoring and result traceability.

[0158] In this embodiment, the processing time limits corresponding to the three task priority information are extracted from the adjusted rules to clarify the time constraints of tasks with different priorities. Then, the preset task distribution sorting rules (such as sorting according to the rule that the shorter the average remaining processing time, the higher the distribution priority) are called to sort the office tasks in the task node association data. Then, according to the sorting results, the office tasks are distributed to the corresponding target processing nodes one by one. At the same time, the node allocation bandwidth corresponding to the target processing node is extracted from the initial task bandwidth association record and the bandwidth resource is configured for it. Finally, the distributed office tasks, target processing nodes, configured node allocation bandwidth, processing time limits and other information are recorded to generate task execution initial state data.

[0159] Step 504: Collect the task processing progress of each target processing node during the execution of office tasks, and send task processing reminders to target processing nodes whose task processing progress is lower than the preset progress threshold, so as to form an intervention record in the task processing process.

[0160] In this step, task processing progress refers to the proportion of office tasks completed by the target processing node within a certain period of time out of the total number of tasks distributed to that node. It reflects the node's task processing efficiency and is used to determine whether a processing reminder needs to be sent. It is calculated based on the number of completed tasks at the target processing node and the total number of distributed tasks. The preset progress threshold is a pre-set critical proportion used to determine whether the target processing node's task processing progress is lagging behind. It reflects the minimum progress requirement for task processing and is used to trigger task processing reminders. It is set based on the processing time limits and efficiency requirements of office tasks. Task processing reminders are prompts (such as text or pop-up notifications) sent to the target processing node when its task processing progress falls below the preset progress threshold. They reflect intervention actions on lagging nodes and are used to avoid task processing delays. They are generated and sent based on progress comparison results. Intervention records during task processing refer to records of the time the task processing reminder was sent, the target processing node, the lagging progress, and the reminder content. They reflect intervention actions during task processing and are used for subsequent task result analysis and process optimization.

[0161] In this embodiment, the task monitoring module of the hierarchical task scheduling framework collects the task processing progress of each target processing node in real time (calculated by dividing the number of office tasks completed by the node by the total number of office tasks distributed to the node); based on a preset progress threshold (such as 30% of the total number of tasks to be completed within a unit of time), the task processing progress of each target processing node is compared with the preset progress threshold; if the progress of a target processing node is lower than the preset progress threshold, a task processing reminder is sent to the node through the node communication interface, such as a text prompt indicating that the progress is lagging and requesting faster processing; the reminder sending time, target processing node, lagging progress, and other information are recorded to form an intervention record in the task processing process.

[0162] Step 505: Obtain the task processing result after each target processing node completes its office task, integrate the task processing result with the task node associated data, the processing time limit corresponding to the three task priority information and the intervention record to form a single task hierarchical management record, and summarize the single task hierarchical management records of all target processing nodes to generate a hierarchical management master record of high-concurrency office tasks, thus completing the hierarchical management of office tasks.

[0163] In this step, the task processing result refers to the result data generated after the target processing node completes the office task, including the task completion status (success / failure), completion time, processing details, etc., reflecting the final processing status of the task, and used to integrate and form a single task hierarchical management record. The single task hierarchical management record refers to the record formed by integrating the task processing result of a single office task, the corresponding task node association data, the processing time limit of its priority, and intervention records (if any), reflecting the full-process information of a single task, and used to summarize and generate a hierarchical management master record. The summary of single task hierarchical management records for all target processing nodes refers to the operation of collecting all single task hierarchical management records generated by all target processing nodes, classifying and organizing them according to dimensions such as task priority, processing node, and completion time, reflecting the integration action of all task records, and used to generate a comprehensive hierarchical management master record. The hierarchical management master record for high-concurrency office tasks refers to the master record formed after summarizing all single task hierarchical management records, covering the full-process information of all office tasks in this high-concurrency scenario, reflecting the overall processing status of this high-concurrency task, and used for subsequent review and system optimization.

[0164] In this embodiment, after each target processing node completes all distributed office tasks, the task processing results (such as task completion status, completion time, and processing result details) of that node are obtained through the node communication interface. The task processing results of each office task are integrated with the task node association data corresponding to the task, the processing time limit of the priority of the task, and the corresponding intervention records (if any) to form a single task hierarchical management record containing the entire process information of a single office task. All single task hierarchical management records generated by the target processing nodes are collected and classified and summarized according to the task priority, processing node, and other dimensions to generate a hierarchical management master record for this high-concurrency office task.

[0165] This application's embodiments solve the problems of disordered node matching and uncontrolled progress in traditional management, ensure high-priority task resources, improve the orderliness and efficiency of task processing under high concurrency, and provide a complete basis for office management review.

[0166] Figure 3 This application provides a schematic diagram of a specific implementation of a workflow-based collaborative office hierarchical management system, with reference to... Figure 3 The system may include:

[0167] Module 21 is used to build a hierarchical task scheduling framework based on a pre-built workflow model;

[0168] The acquisition module 22 is used to acquire office task data, adjust the hierarchical scheduling rules in the hierarchical task scheduling framework according to the office task data, and generate the adjusted rules. The office task data includes emergency task response time limit, ordinary task queuing time, original task priority information and processing node matching rate. The adjusted rules include three task priority information and corresponding processing nodes, processing time limits and data transmission formats.

[0169] Processing module 23 is used to collect data related to office tasks submitted or processed by each employee through touch input devices under high-concurrency office tasks based on the input terminal parameters in the hierarchical task scheduling framework, and to perform identification, addition and verification processing on the data related to office tasks according to the adjusted rules, and generate verification passed data.

[0170] The adjustment module 24 is used to set the bandwidth basic allocation logic corresponding to the three task priority information respectively according to the task priority identifier in the verification passed data, and adjust the bandwidth basic allocation logic to generate bandwidth allocation results.

[0171] Management module 25 is used to perform hierarchical management of office tasks by the target processing node of the hierarchical task scheduling framework based on the bandwidth allocation result, the verification pass data, the processing nodes corresponding to the three task priority information and the processing time limit.

[0172] This application provides an embodiment of a workflow-based collaborative office hierarchical management system to implement the aforementioned workflow-based collaborative office hierarchical management method. Therefore, the specific implementation of the workflow-based collaborative office hierarchical management system can be found in the embodiment section of the workflow-based collaborative office hierarchical management method described above. The specific implementation can be referred to the description of the corresponding embodiments, which will not be repeated here.

[0173] This application also provides an electronic device, comprising: a memory for storing a computer program; and a processor for executing the computer program to implement the steps of the workflow-based collaborative office hierarchical management method described above.

[0174] This application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of any of the above-described workflow-based collaborative office hierarchical management methods.

[0175] In one exemplary embodiment, the aforementioned computer-readable storage medium may include, but is not limited to, various media capable of storing computer programs, such as USB flash drives, read-only memory, random access memory, portable hard drives, magnetic disks, or optical disks.

[0176] The embodiments of this application also provide a computer program product, which includes a computer program that, when executed by a processor, implements the steps in any of the above embodiments of the workflow-based collaborative office hierarchical management method.

[0177] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0178] The above provides a detailed description of a workflow-based collaborative office hierarchical management method and system provided in this application. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the embodiments above are merely for the purpose of helping to understand the method and its core ideas. It should be noted that those skilled in the art can make various improvements and modifications to this application without departing from its principles, and these improvements and modifications also fall within the protection scope of this application.

Claims

1. A collaborative office hierarchical management method based on a workflow model, characterized in that, include: A hierarchical task scheduling framework is built based on a pre-built workflow model; The system acquires office task data, adjusts the hierarchical scheduling rules in the hierarchical task scheduling framework based on the office task data, and generates adjusted rules. The office task data includes emergency task response time limit, ordinary task queuing time, original task priority information, and processing node matching rate. The adjusted rules include three task priority information and corresponding processing nodes, processing time limits, and data transmission formats. Based on the input terminal parameters in the hierarchical task scheduling framework, data related to office tasks submitted or processed by each employee through touch input devices under high-concurrency office tasks are collected. According to the adjusted rules, the data related to office tasks is marked, added, and verified to generate verified data. Based on the task priority identifier in the verified data, set up basic bandwidth allocation logic corresponding to the three task priority information respectively, and adjust the basic bandwidth allocation logic to generate bandwidth allocation results; Based on the bandwidth allocation results, combined with the verification passed data, the processing nodes and processing time limits corresponding to the three task priority information, the target processing node of the hierarchical task scheduling framework performs hierarchical management of office tasks.

2. The method according to claim 1, characterized in that, Based on a pre-built workflow model, a hierarchical task scheduling framework is constructed, including: By using a pre-built workflow model, we define various common tasks and their corresponding standard workflow paths in collaborative office scenarios, forming a basic task workflow framework. The various common tasks include approval tasks, document tasks, and meeting tasks. The standard workflow path includes the node connection relationship between the task initiator, intermediate processing end, and final execution end. Based on the node connection relationship in the basic task flow framework, the hardware resource mapping relationship is configured to record the maximum number of tasks that each task processing node can process and the upper limit of network access bandwidth, forming a resource configuration list. Based on the task processing needs in collaborative office scenarios, description fields are set to distinguish the urgency of tasks, and weight values ​​are assigned to each description field to form a set of priority evaluation dimensions. The description fields include a task deadline sub-field, a number of participants involved sub-field, and the importance of related business sub-field. Based on the node logic of the basic task flow framework, the resource configuration list, and the priority evaluation dimension set, a hierarchical scheduling logic component is constructed. The hierarchical scheduling logic component is added to the basic task flow framework to obtain an intermediate task flow framework. The hierarchical scheduling logic component includes a task receiving module, a task allocation module, and a task monitoring module. The intermediate task flow framework is configured with a framework interaction interface to form a hierarchical task scheduling framework. The framework interaction interface includes a task input interface, a node communication interface, and a status feedback interface.

3. The method according to claim 1, characterized in that, The hierarchical scheduling rules in the hierarchical task scheduling framework are adjusted based on the office task data to generate adjusted rules. The office task data includes emergency task response time limits, ordinary task queuing time, original task priority information, and processing node matching rate. The adjusted rules include three task priority information items and their corresponding processing nodes, processing time limits, and data transmission formats, including: Based on the task urgency description of the original task priority information in the office task data, the original task priority information is divided into three task priority information, which include first-level task priority information, second-level task priority information and third-level task priority information. Calculate the time difference between the emergency task response time limit and the waiting time for ordinary tasks in the office task data. Based on the hierarchical requirements of the three task priority information, divide the time difference into two parts. The sum of the smaller part and the emergency task response time limit is used as the processing time limit for the second-level task priority information. The emergency task response time limit is used as the processing time limit for the first-level task priority information. The waiting time for ordinary tasks is used as the processing time limit for the third-level task priority information. The average processing node matching rate of all processing nodes in the office task data is calculated as the baseline matching rate. The processing node matching rate of all processing nodes is compared with the baseline matching rate to determine the candidate processing node list corresponding to the three task priority information respectively. Based on the order of priority of the three task information, the data transmission format corresponding to the three task priority information is determined respectively. The data transmission format of the first-level task priority information is an encrypted transmission format with digital signature; the data transmission format of the second-level task priority information is a compressed transmission format with verification code; and the data transmission format of the third-level task priority information is a standard transmission format without additional information. Replace the corresponding content of the hierarchical scheduling rules in the hierarchical task scheduling framework with the three task priority information, the processing time limit, the candidate processing node list, and the data transmission format corresponding to the three task priority information, and generate the adjusted rules.

4. The method according to claim 1, characterized in that, According to the adjusted rules, the data related to the office tasks are labeled, validated, and validated to generate validated data, including: The task urgency description in the office task-related data is matched with three task priority information to determine the target priority information corresponding to the office task-related data; Add a task priority identifier corresponding to the target priority information to the office task-related data to form identified office task data. The task priority identifier includes the level number of the target priority information and the corresponding priority level identifier. Check whether the data transmission format of the tagged office task data conforms to the target data transmission format corresponding to the target priority information in the adjusted rules, and integrate the tagged office task data whose data transmission format conforms to the corresponding target data transmission format into a temporary verification dataset; The labeled office task data in the temporary verification dataset are sorted to form verification-passed data.

5. The method according to claim 1, characterized in that, Based on the task priority identifier in the verified data, bandwidth basic allocation logic is set up corresponding to the three task priority information respectively, and the bandwidth basic allocation logic is adjusted to generate bandwidth allocation results, including: Based on the task priority identifier in the verified data, the verified data is divided into three groups of data to be assigned, corresponding to the three task priority information. The number of office tasks in each group of data to be assigned is marked as the total number of office tasks. The total amount of data of all office tasks in each group of data to be assigned is marked as the total amount of task data. The average amount of data per task in each group of data to be assigned is calculated. Based on the total network bandwidth value in the hierarchical task scheduling framework, determine the available total network bandwidth and set the basic bandwidth allocation logic corresponding to the priority information of the three tasks respectively. Select online processing nodes that are in the online state from the candidate processing node list corresponding to the three task priority information in the adjusted rules, count the number of tasks executed by each online processing node, and calculate the average load rate of all online processing nodes under the corresponding task priority information by combining the corresponding maximum number of tasks that can be processed. Based on the processing time limits corresponding to the three task priority information in the adjusted rules, the submitted time of each office task is calculated, and the average time of all submitted times is calculated in groups. Combined with the processing time limits of the corresponding task priority information, the average remaining processing time of each group of data to be allocated is obtained. Based on the total number of office tasks, the average load rate, and the average remaining processing time, the bandwidth basic allocation logic is adjusted to obtain the final bandwidth allocation ratio. Based on the final bandwidth allocation ratio, the total actual bandwidth allocation value for each group of data to be allocated is calculated. Combined with the total number of office tasks and the total amount of task data for each group of data to be allocated, the bandwidth allocation result is generated.

6. The method according to claim 5, characterized in that, Based on the final bandwidth allocation ratio, the total actual bandwidth allocation value for each group of data to be allocated is calculated. Combined with the total number of office tasks and the total data volume of each group of data to be allocated, a bandwidth allocation result is generated, including: Based on the total available network bandwidth and the final bandwidth allocation ratio, calculate the basic bandwidth allocation value corresponding to each group of data to be allocated; Based on the average remaining processing time of each group of data to be allocated, determine the data transmission urgency corresponding to each group of data to be allocated. Based on the data transmission urgency and average data volume per task of each group of data to be allocated, the basic bandwidth allocation value is adjusted to obtain the actual total bandwidth allocation value of each group of data to be allocated. The adjustment process includes a first upward adjustment of the basic bandwidth allocation value corresponding to the data to be allocated whose average data volume per task is higher than the average average data volume per task of all data to be allocated, and a second upward adjustment of the basic bandwidth allocation value corresponding to the data to be allocated whose data transmission urgency is higher than a preset urgency threshold. The sum of the bandwidth allocation values ​​of all data to be allocated shall not exceed the total available network bandwidth. Based on the load rate of all online processing nodes under the corresponding task priority information, the total actual bandwidth allocation value of each group of data to be allocated is split to form an initial task bandwidth association record. The total amount of office tasks and total data volume of each group of data to be allocated are then integrated with the corresponding initial task bandwidth association record to generate the bandwidth allocation result.

7. The method according to claim 1, characterized in that, Based on the bandwidth allocation results, combined with the verification passed data, the processing nodes corresponding to the three task priority information, and the processing time limits, the target processing node of the hierarchical task scheduling framework performs hierarchical management of office tasks, including: Based on the initial task bandwidth association record in the bandwidth allocation result, and combined with the task priority identifier in the verification passed data, the online processing node and the corresponding node allocated bandwidth in the initial task bandwidth association record are bound to the office task in the verification passed data to obtain task node association data. Select the processing node that exists simultaneously in the candidate processing node list corresponding to the three task priority information and the initial task bandwidth association record, and whose remaining processing capacity meets the standard, as the target processing node. Based on the processing time limits corresponding to the three task priority information and the preset task distribution sorting rules, the office tasks in the task node association data are distributed to the target processing node. At the same time, bandwidth is allocated to the nodes in the initial task bandwidth association record for the target processing node, and task execution initial state data is generated. Collect the task processing progress of each target processing node during the execution of office tasks, and send task processing reminders to target processing nodes whose task processing progress is lower than the preset progress threshold, so as to form an intervention record in the task processing process; Obtain the task processing results after each target processing node completes its office tasks. Integrate the task processing results with the task node's associated data, the processing time limits corresponding to the three task priority information, and the intervention records to form a single task hierarchical management record. Summarize the single task hierarchical management records of all target processing nodes to generate a hierarchical management master record for high-concurrency office tasks, thus completing the hierarchical management of office tasks.

8. A collaborative office hierarchical management system based on a workflow model, characterized in that, include: The building module is used to construct a hierarchical task scheduling framework based on a pre-built workflow model; The acquisition module is used to acquire office task data, adjust the hierarchical scheduling rules in the hierarchical task scheduling framework according to the office task data, and generate the adjusted rules. The office task data includes emergency task response time limit, ordinary task queuing time, original task priority information and processing node matching rate. The adjusted rules include three task priority information and corresponding processing nodes, processing time limits and data transmission formats. The processing module is used to collect data related to office tasks submitted or processed by each employee through touch input devices under high-concurrency office tasks based on the input terminal parameters in the hierarchical task scheduling framework, and to perform identification, addition and verification processing on the data related to office tasks according to the adjusted rules, and generate verification passed data. The adjustment module is used to set the bandwidth basic allocation logic corresponding to the three task priority information respectively according to the task priority identifier in the verification passed data, and adjust the bandwidth basic allocation logic to generate bandwidth allocation results. The management module is used to perform hierarchical management of office tasks by the target processing node of the hierarchical task scheduling framework based on the bandwidth allocation results, the verification pass data, the processing nodes corresponding to the three task priority information and the processing time limits.

9. A computing device, characterized in that, It includes a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are invoked and executed by the processing component to implement a workflow-based collaborative office hierarchical management method as described in any one of claims 1 to 7.

10. A computer storage medium, characterized in that, The system contains a computer program that, when executed by a computer, implements a workflow-based hierarchical management method for collaborative office work as described in any one of claims 1 to 7.

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