Design simulation task collaboration and data management method and system based on process engine

Abstract

The invention belongs to the technical field of project collaborative management, and particularly relates to a design simulation task collaboration and data management method and system based on a process engine, and the system comprises a process engine module, a task management module, a data management module, a user interface presetting module and an analysis and optimization module. The process engine module is specifically composed of a task modeling engine, a scheduling and execution engine and a monitoring and control engine and is used for being responsible for task modeling, dynamic scheduling and cooperative control. Through clearer steps and modularized system design, a team can complete management tasks more efficiently, work delay caused by information asymmetry is reduced, meanwhile, effective resource scheduling is carried out, task execution is more efficient, and management time is saved; and meanwhile, the system can automatically schedule and redistribute resources according to task priorities and resource conditions, so that the resource use efficiency is maximized, and the bottleneck and deficiency of resource use can be identified by analyzing historical data.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of project collaborative management, in particular to a design simulation task collaboration and data management method and system based on a process engine. BACKGROUND

[0002] Project collaborative management is a mode of integrated management of multiple associated and parallel projects, which realizes efficient operation through coordination of enterprise strategy and project objectives and optimization of resource allocation. This mode takes human resources, financial resources, material resources and project knowledge as the core management elements, emphasizes the collaboration mechanism of project managers and senior managers, and reduces resource conflicts and improves execution efficiency through cross-project collaboration capabilities; Its management framework includes strategic planning, resource integration and other elements, and adopts the organization form of project management department coordinating cross-functional teams. In practice, it needs to deal with project priority conflicts and cross-department coordination problems, and realizes real-time monitoring of resources and task collaboration through tools such as Worktile and PingCode. The Multi-Project Management (MPM) theory provides methodological support, focusing on solving resource constraint scheduling algorithms and project portfolio optimization and other core problems; With the development of economy and the intensification of market competition, project management has gradually become an indispensable ability in various industries. Standardized project management methods help organizations improve the success rate and efficiency of projects.

[0003] The prior art has the following defects or problems: The existing management method and system have information islands within the organization, lack effective information sharing between departments, resulting in poor communication and duplication of work; traditional task management often relies on manual input and tracking, which is prone to errors and time-consuming; there is a lack of real-time monitoring and adjustment mechanism, resulting in idle or insufficient resources, affecting project progress.

[0004] It should be noted that the above content belongs to the technical cognition of the inventor and does not necessarily constitute prior art. SUMMARY

[0005] In view of the deficiencies of the prior art, the present application provides a design simulation task collaboration and data management method and system based on a process engine, which solves the existing problems.

[0006] To achieve the above purpose, the present application provides the following technical scheme: a design simulation task collaboration and data management system based on a process engine, comprising: A process engine module, which is specifically composed of a task modeling engine, a scheduling and execution engine and a monitoring and control engine, is used for task modeling, dynamic scheduling and collaborative control; Task management module, which is specifically composed of task creation and distribution module and progress tracking and feedback module, is used to realize task creation, distribution and state tracking; Data management module, which is specifically composed of data management layer, version control system and data access management module, is used to provide data storage, version management and security control; User interface preset module, which is internally provided with task view engine and data view engine, provides friendly operation interface for users, facilitating task management and data query; The user interface preset module is signal connected with a touch display screen, thereby providing an overall project progress overview, showing task status, time schedule, resource usage and other important indicators, helping users quickly understand the project status; Analysis and optimization module, which is internally provided with data analysis tool and optimization suggestion module, is used to provide decision support and realize optimization suggestion after task execution through data analysis.

[0007] In some embodiments, the task modeling engine is used to define the process of design simulation task, which is composed of the following sub-modules: Task decomposition sub-module, the task modeling engine supports to decompose a single complex design simulation task into multiple sub-tasks, forming a hierarchical task structure, in addition, users can redefine the name, description, estimated time and required resources of each sub-task; At the same time, each sub-task can be formulated during task decomposition, to ensure that the correct order is followed during execution; Input-output relationship establishment sub-module, the task modeling engine can define the input and output data types and formats of each sub-task decomposed by the task decomposition sub-module, to clarify how data flows between tasks, while customers can upload sample data and templates for subsequent task use, while detecting the validity of input data to ensure that all data can meet the expected requirements before task execution, reducing execution failure caused by data format error; Execution condition setting sub-module, the execution condition setting sub-module can set specific execution conditions for each sub-task, including specific resource availability, time window and dependent pre-task completion flag, such conditions can automatically determine the start and stop of the task; During task execution, the task modeling engine can support dynamic adjustment of execution effect, and reschedule tasks according to new conditions.

[0008] In some embodiments, the scheduling and execution engine is responsible for automatic task scheduling, dynamically allocating tasks according to preset rules and current resource status, and supports setting and adjusting task priorities. It consists of the following sub-modules: The task self-scheduling submodule determines the scheduling order and execution time of tasks based on the rules set by the pre-defined execution conditions submodule. It can also monitor the status of current system resources in real time and automatically adjust the allocation of tasks according to resource availability. The priority management submodule allows users to set priorities for different tasks, thereby ensuring that high-priority tasks can obtain the necessary resources and execution conditions. At the same time, it dynamically adjusts priorities according to real-time conditions and task changes to ensure the overall efficiency of the system is optimized. The priority management module can implement multiple priority scheduling strategies and select the most suitable scheduling algorithm by evaluating the impact when the task is completed. The elastic scaling scheduling submodule dynamically adjusts the allocation of computing resources based on task load, supporting efficient utilization of the elastic resources of the cloud computing environment.

[0009] In some embodiments, the monitoring and control engine can monitor the task execution process in real time, collect status information and provide feedback, and it consists of the following sub-modules: The real-time status monitoring submodule monitors the execution progress of each task in real time to ensure that the latest status of the task can be obtained at any time. The execution progress includes start time, execution status, priority and completion time. In addition, the system resources consumed by the task during execution are monitored and relevant statistical information is generated to ensure the transparency of resource availability. The task status information collection submodule can collect status information of different tasks and resources, thereby generating a comprehensive report to provide a basis for the monitoring and control engine to perform performance analysis and optimize its strategies. It can also save relevant historical data of task execution to provide a reference for subsequent performance evaluation. The early warning and notification submodule is used to set anomaly thresholds. Once an anomaly in task execution is detected or the predefined anomaly threshold is exceeded, an early warning is immediately triggered. At the same time, the anomaly can be notified to relevant personnel via email, SMS and instant messaging to ensure that they can respond quickly.

[0010] In some embodiments, the task creation and assignment module can create new tasks through an interface and automatically assign them to suitable team members based on task complexity and required resources. It consists of the following sub-modules: The task creation submodule supports users in creating and using task templates. It also includes a user information entry module, through which users can fill in the task name, description, deadline, and priority remarks. The task creation submodule allows users to categorize tasks by project, status, and type, facilitating subsequent management and retrieval. The resource and complexity assessment submodule allows users to define the required resources when creating a task, including human and technical support. It can also automatically assess the complexity of a task based on its description and requirements, and provide a complexity score for the task based on historical data to help with subsequent allocation. The self-allocation mechanism submodule can select suitable members for task allocation based on each team member's skills, available time, and current workload. Priority is considered during this process, and resource allocation is adjusted when there is an urgent need for a task to ensure that the urgent task starts and is completed in a timely manner. The progress tracking and feedback module can update the progress of each person in real time, recording their start time, estimated completion time, and actual completion time. Users can adjust their work plans based on the feedback information. It consists of the following sub-modules: The progress update and feedback submodule allows you to create a simple task update form that requires team members to fill in relevant task information each time they update, while also providing options for task status. While team members update task progress, record their feedback on task execution, including problems encountered, support needed, and resources. Set a feedback cycle, and use group meetings and individual interviews for detailed discussions on progress to ensure that every team member has the opportunity to express their opinions and suggestions. The progress update submodule can determine the time nodes for regular progress updates and track the progress of each task in real time through project management software; The data analysis and adjustment submodule allows project managers to flexibly adjust work plans based on real-time feedback and progress, including reallocating resources and adjusting task priorities, to ensure timely project delivery. The collected progress data can be used for analysis to identify which tasks were completed on time and which tasks were delayed and the reasons for it, thus providing a basis for future project planning.

[0011] In some embodiments, the data management layer uses a database to store all data in the design simulation process, including design models, simulation results, and task records, in order to ensure data security and consistency. The version control system is used to track version changes of each data record, ensuring that users can easily access historical versions to prevent accidental data loss and erroneous modifications; The version control system is specifically a distributed control system, which allows each user to store a complete history in the storage unit, thereby accelerating local operations. At the same time, it allows users to restore to a previous version in the event of an error or accidental data loss, ensuring that the recovery process is simple and feasible and reducing the risk of misoperation. The data access management module manages user access permissions to data through an access control mechanism to ensure the privacy and security of sensitive information and prevent unauthorized access. It includes the following sub-modules: The access control submodule provides more flexible access management based on attributes, allowing access permissions to be controlled according to user attributes, resource attributes, and environmental conditions. The identity verification submodule verifies user authentication through single sign-on, allowing access to multiple task content with a single user credential, thus simplifying the authentication process. The data classification and grading module can effectively classify data based on public, internal, confidential, and top secret, and set different access control policies according to the category. It also considers the use, storage, and destruction of data to ensure effective management of data access throughout the entire lifecycle.

[0012] In some embodiments, the data analysis tool can use statistical analysis and mining techniques to perform in-depth analysis of historical project data, and discover bottlenecks and potential optimization points in the design process; The optimization suggestion module can provide specific optimization suggestions based on the analysis results, including resource reallocation and process redesign, to improve overall performance. It consists of the following sub-modules: The resource reallocation submodule can assess the match between team members' skills and current tasks, reallocate tasks to the most suitable candidates to improve work efficiency, and analyze the potential return on investment of each task to transfer funds from inefficient projects to efficient projects to ensure the maximum effectiveness of fund utilization. The data and feedback mechanism module has built-in data-driven decision-making capabilities. It can identify bottlenecks through data analysis, monitor and evaluate optimization effects using actual data, ensure that the measures taken are based on facts rather than subjective judgments, and establish a regular feedback mechanism to collect suggestions from employees and customers. Through continuous feedback, it improves optimization suggestions and implementation effects, forming a virtuous cycle of improvement.

[0013] Another technical problem this invention aims to solve is to propose a design simulation task collaboration and data management method based on a process engine, comprising the following steps: Step 1: Users enter information such as task name, description, due date, and priority through the task creation submodule. Users can also select existing task templates to quickly create new tasks. Step 2: Use the task decomposition submodule to break down the complex task into multiple subtasks, define the name, description, estimated time and required resources for each subtask, and set dependencies for each subtask to ensure the correct execution order; Step 3: Use input / output relationships to create sub-modules, ensure the data types and formats of input and output for each sub-task, and upload sample data and templates; Step 4: By using the execution condition setting submodule, set conditions such as resource availability, time window, and completion flags for preceding tasks for each subtask; Step 5: The scheduling and execution engine automatically schedules tasks based on preset rules and current resource status, monitors resource status in real time, and dynamically adjusts task allocation. During this process, users can set priorities for different tasks to ensure that high-priority tasks receive the necessary resources. Step Six: Once all conditions are met, the task will automatically enter the execution state. This process is tracked in real time by the monitoring and control engine. Team members need to update the task progress regularly and record the start time, scheduled completion time, and actual completion time. Step 7: The monitoring and control engine collects status information during task execution, generates a comprehensive report and provides feedback, and sets an anomaly threshold. When an anomaly occurs, an early warning is immediately triggered and relevant personnel are notified. Step 8: Store all data in the design simulation process through the data management layer and use a version control system to track changes to each data record. The data access management module ensures that only authorized users can access specific data, implementing fine-grained access control. Step 9: Use data analysis tools to conduct in-depth analysis of historical project data, identify bottlenecks and potential optimization points, and then the optimization suggestion module provides specific optimization solutions based on the analysis results, including resource reallocation and process redesign; Step 10: Establish a channel for regularly collecting suggestions from employees and customers through a data domain feedback mechanism to improve and optimize measures. Then, based on the feedback and analysis results, flexibly adjust the work plan to ensure timely project delivery.

[0014] Compared with existing technologies, this invention provides a method and system for collaborative design simulation tasks and data management based on a process engine, which has the following beneficial effects: This application presents a process engine-based design simulation task collaboration and data management method and system. Through clearer steps and modular system design, it enables teams to complete management tasks more efficiently, reduces work delays caused by information asymmetry, and enables effective resource scheduling, making task execution more efficient and saving management time. At the same time, the system can automatically schedule and reallocate resources based on task priority and resource status, thereby maximizing resource utilization efficiency. It can also identify bottlenecks and deficiencies in resource utilization by analyzing historical data, thereby enabling targeted optimization. This management approach involves a clearer allocation of team responsibilities, with each task and subtask having a designated person in charge. Team members can update task status in real time, promoting communication and collaboration, ensuring that everyone has a clear understanding of the project's progress, and helping to improve team members' sense of responsibility and efficiency. This application ensures that only authorized personnel can access specific data through fine-grained access control, thereby improving data security and the ability to protect sensitive information. The version control function for data and files ensures that important information is not lost, facilitates the tracing of historical changes, and reduces the risk of errors. Attached Figure Description

[0015] Figure 1 This is a schematic diagram of the design simulation task collaboration and data management system based on the process engine of the present invention. Detailed Implementation

[0016] The technical solutions of the present invention will be clearly and completely described below with reference to the embodiments and accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0017] It should be understood that the step numbers used in the text are for ease of description only and are not intended to limit the order in which the steps are performed.

[0018] It should be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0019] The terms “comprising” and “including” indicate the presence of the described feature, whole, step, operation, element and / or component, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or collections thereof.

[0020] The term “and / or” refers to any combination of one or more of the associated listed items, as well as all possible combinations, and includes these combinations.

[0021] Please see Figure 1In this implementation plan, a design simulation task collaboration and data management system based on a process engine includes: The process engine module consists of a task modeling engine, a scheduling and execution engine, and a monitoring and control engine. It is responsible for task modeling, dynamic scheduling, and collaborative control. The task modeling engine is used to define the flow of design simulation tasks, and it consists of the following sub-modules: The task decomposition submodule of the task modeling engine supports the decomposition of a single complex design simulation task into multiple subtasks, forming a hierarchical task structure. In addition, users can redefine the name, description, estimated time, and required resources of each subtask. At the same time, each subtask can have its dependencies defined during the task decomposition process to ensure that it is executed in the correct order; The input-output relationship establishment submodule allows the task modeling engine to define the input and output data types and formats for each subtask decomposed by the task decomposition submodule. This clarifies how data flows between tasks. Customers can also upload sample data and templates for use in subsequent tasks. The engine also checks the validity of the input data to ensure that all data meets the expected requirements before task execution, reducing execution failures caused by incorrect data formats. The execution condition setting submodule allows you to set unique execution conditions for each subtask. These conditions include specific resource availability, time windows, and completion flags of dependent prerequisite tasks. These conditions can automatically determine the start and stop of the task. During task execution, when circumstances change, the task modeling engine can dynamically adjust the execution effect and reschedule the task according to the new conditions. The scheduling and execution engine is responsible for the automatic scheduling of tasks, dynamically allocating tasks according to preset rules and current resource status, and supports setting and adjusting task priorities. It consists of the following sub-modules: The task self-scheduling submodule determines the scheduling order and execution time of tasks based on the rules set by the pre-defined execution conditions submodule. It can also monitor the status of current system resources in real time and automatically adjust the allocation of tasks according to resource availability. The priority management submodule allows users to set priorities for different tasks, thereby ensuring that high-priority tasks can obtain the necessary resources and execution conditions. At the same time, it dynamically adjusts priorities according to real-time conditions and task changes to ensure the overall efficiency of the system is optimized. The priority management module can implement various priority scheduling strategies and select the most suitable scheduling algorithm by evaluating the impact when the task is completed. The elastic scaling scheduling submodule dynamically adjusts the allocation of computing resources based on task load, supporting the efficient utilization of elastic resources in the cloud computing environment. The monitoring and control engine can monitor the task execution process in real time, collect status information and provide feedback. It consists of the following sub-modules: The real-time status monitoring submodule monitors the execution progress of each task in real time, ensuring that the latest status of the task can be obtained at any time. The execution progress includes start time, execution status, priority, and completion time. In addition, the system resources consumed by the task during execution are monitored and relevant statistical information is generated to ensure the transparency of resource availability. The task status information collection submodule can collect status information of different tasks and resources, thereby generating a comprehensive report to provide a basis for the monitoring and control engine to perform performance analysis and optimize its strategies. It can also save relevant historical data of task execution to provide a reference for subsequent performance evaluation. The early warning and notification submodule is used to set anomaly thresholds. Once an anomaly in task execution is detected or the predetermined anomaly threshold is exceeded, an early warning is immediately triggered. At the same time, the anomaly can be notified to relevant personnel via email, SMS and instant messaging to ensure that they can respond quickly. The task management module consists of a task creation and allocation module and a progress tracking and feedback module, which are used to realize task creation, allocation and status tracking. The task creation and assignment module can create new tasks through an interface and automatically assign them to suitable team members based on task complexity and required resources. It consists of the following sub-modules: The task creation submodule supports users in creating and using task templates. It also includes a user information entry module, through which users can fill in the task name, description, deadline, and priority remarks. The task creation submodule allows users to categorize tasks by project, status, and type for easier management and retrieval later. The resource and complexity assessment submodule allows users to define the required resources when creating a task, including human and technical support. It can also automatically assess the complexity of a task based on its description and requirements, and provide a complexity score for the task based on historical data to help with subsequent allocation. The self-allocation mechanism submodule can select suitable members for task allocation based on each team member's skills, available time, and current workload. Priority is considered during this process, and resource allocation is adjusted when there is an urgent need for a task to ensure that the urgent task starts and is completed in a timely manner. The progress tracking and feedback module can update the progress of each person in real time, recording their start time, estimated completion time, and actual completion time. Users can adjust their work plans based on the feedback information. It consists of the following sub-modules: The progress update and feedback submodule allows you to create a simple task update form that requires team members to fill in relevant task information each time they update, while also providing options for task status. While team members update task progress, record their feedback on task execution, including problems encountered, support needed, and resources. Set a feedback cycle, and use group meetings and individual interviews for detailed discussions on progress to ensure that every team member has the opportunity to express their opinions and suggestions. The progress update submodule can determine the time nodes for regular progress updates and track the progress of each task in real time through project management software; The data analysis and adjustment submodule allows project managers to flexibly adjust work plans based on real-time feedback and progress, including reallocating resources and adjusting task priorities, to ensure timely project delivery. The collected progress data can be used for analysis to identify which tasks are completed on time and which tasks are delayed and the reasons for them, thus providing a basis for future project planning. The data management module consists of a data management layer, a version control system, and a data access management module. It is used to provide data storage, version management, and security control. The data management layer uses a database to store all data during the design simulation process, including design models, simulation results, and task records, in order to ensure data security and consistency. Version control systems are used to track version changes of each data record, ensuring that users can easily access historical versions to prevent accidental data loss and erroneous modifications; Version control systems are specifically distributed control systems, which allow each user to store a complete history in the storage unit, thereby accelerating local operations. At the same time, they allow users to restore to a previous version in the event of an error or accidental data loss, ensuring that the recovery process is simple and feasible and reducing the risk of misoperation. The data access management module manages user access permissions to data through an access control mechanism, ensuring the privacy and security of sensitive information and preventing unauthorized access. It includes the following sub-modules: The access control submodule provides more flexible access management based on attributes, allowing access permissions to be controlled according to user attributes, resource attributes, and environmental conditions. The identity verification submodule verifies user authentication through single sign-on, allowing access to multiple task content with a single user credential, thus simplifying the authentication process. The data classification and grading module can effectively classify data based on public, internal, confidential, and top secret, and set different access control policies according to the category. It also considers the use, storage, and destruction of data to ensure effective management of data access throughout the entire lifecycle. The user interface preset module has a built-in task view engine and data view engine, which provides users with a user-friendly interface for easy task management and data query. The user interface is pre-connected to a touch screen to provide an overall overview of the project progress, displaying important indicators such as task status, time progress, and resource usage, helping users quickly understand the project status. The analysis and optimization module includes built-in data analysis tools and optimization suggestion modules, which are used to provide decision support and provide optimization suggestions after task execution through data analysis. Data analysis tools can use statistical analysis and mining techniques to conduct in-depth analysis of historical project data, and discover bottlenecks and potential optimization points in the design process; The optimization suggestion module provides specific optimization suggestions based on the analysis results, including resource reallocation and process redesign, to improve overall performance. It consists of the following sub-modules: The resource reallocation submodule can assess the match between team members' skills and current tasks, reallocate tasks to the most suitable candidates to improve work efficiency, and analyze the potential return on investment of each task to transfer funds from inefficient projects to efficient projects to ensure the maximum effectiveness of fund utilization. The data and feedback mechanism module has built-in data-driven decision-making capabilities. It can identify bottlenecks through data analysis, monitor and evaluate optimization effects using actual data, ensure that the measures taken are based on facts rather than subjective judgments, and establish a regular feedback mechanism to collect suggestions from employees and customers. Through continuous feedback, it improves optimization suggestions and implementation effects, forming a virtuous cycle of improvement.

[0022] Based on the aforementioned workflow engine-based design simulation task collaboration and data management system, a workflow engine-based design simulation task collaboration and data management method is proposed, including the following steps: Step 1: Users enter information such as task name, description, due date, and priority through the task creation submodule. Users can also select existing task templates to quickly create new tasks. Step 2: Use the task decomposition submodule to break down the complex task into multiple subtasks, define the name, description, estimated time and required resources for each subtask, and set dependencies for each subtask to ensure the correct execution order; Step 3: Use input / output relationships to create sub-modules, ensure the data types and formats of input and output for each sub-task, and upload sample data and templates; Step 4: By using the execution condition setting submodule, set conditions such as resource availability, time window, and completion flags for preceding tasks for each subtask; Step 5: The scheduling and execution engine automatically schedules tasks based on preset rules and current resource status, monitors resource status in real time, and dynamically adjusts task allocation. During this process, users can set priorities for different tasks to ensure that high-priority tasks receive the necessary resources. Step Six: Once all conditions are met, the task will automatically enter the execution state. This process is tracked in real time by the monitoring and control engine. Team members need to update the task progress regularly and record the start time, scheduled completion time, and actual completion time. Step 7: The monitoring and control engine collects status information during task execution, generates a comprehensive report and provides feedback, and sets an anomaly threshold. When an anomaly occurs, an early warning is immediately triggered and relevant personnel are notified. Step 8: Store all data in the design simulation process through the data management layer and use a version control system to track changes to each data record. The data access management module ensures that only authorized users can access specific data, implementing fine-grained access control. Step 9: Use data analysis tools to conduct in-depth analysis of historical project data, identify bottlenecks and potential optimization points, and then the optimization suggestion module provides specific optimization solutions based on the analysis results, including resource reallocation and process redesign; Step 10: Establish a channel for regularly collecting suggestions from employees and customers through a data domain feedback mechanism to improve and optimize measures. Then, based on the feedback and analysis results, flexibly adjust the work plan to ensure timely project delivery.

[0023] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments.

[0024] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A design simulation task collaboration and data management system based on a process engine, characterized in that, include: The process engine module consists of a task modeling engine, a scheduling and execution engine, and a monitoring and control engine, and is responsible for task modeling, dynamic scheduling, and collaborative control. The task management module consists of a task creation and allocation module and a progress tracking and feedback module, which are used to realize task creation, allocation and status tracking. The data management module, specifically composed of a data management layer, a version control system, and a data access management module, is used to provide data storage, version management, and security control. The user interface preset module has a built-in task view engine and data view engine, which provides users with a user-friendly operating interface to facilitate task management and data query. The user interface preset module is connected to a touch screen to provide an overall overview of the project progress, displaying important indicators such as task status, time progress, and resource usage, helping users quickly understand the project status; The analysis and optimization module includes built-in data analysis tools and optimization suggestion modules, which are used to provide decision support and provide optimization suggestions after task execution through data analysis.

2. The design simulation task collaboration and data management system based on a process engine according to claim 1, characterized in that, The task modeling engine is used to define the flow of design simulation tasks, and it consists of the following sub-modules: The task decomposition submodule allows the task modeling engine to decompose a single complex design simulation task into multiple subtasks, forming a hierarchical task structure. In addition, users can redefine the name, description, estimated time, and required resources of each subtask. At the same time, each subtask can have its dependencies defined during the task decomposition process to ensure that it is executed in the correct order; The input-output relationship establishment submodule allows the task modeling engine to define the input and output data types and formats for each subtask decomposed by the task decomposition submodule, thereby clarifying how data flows between tasks. Customers can also upload sample data and templates for use in subsequent tasks, while checking the validity of input data to ensure that all data meets the expected requirements before task execution, reducing execution failures caused by incorrect data formats. The execution condition setting submodule can set unique execution conditions for each subtask. These execution conditions include specific resource availability, time windows, and completion flags of dependent prerequisite tasks. Such conditions can automatically determine the start and stop of the task. During task execution, when circumstances change, the task modeling engine can dynamically adjust the execution effect and reschedule the task according to the new conditions.

3. The design simulation task collaboration and data management system based on a process engine according to claim 1, characterized in that, The scheduling and execution engine is responsible for the automatic scheduling of tasks, dynamically allocating tasks according to preset rules and current resource status, and supports the setting and adjustment of task priorities. It consists of the following sub-modules: The task self-scheduling submodule determines the scheduling order and execution time of tasks based on the rules set by the pre-defined execution conditions submodule. It can also monitor the status of current system resources in real time and automatically adjust the allocation of tasks according to resource availability. The priority management submodule allows users to set priorities for different tasks, thereby ensuring that high-priority tasks can obtain the necessary resources and execution conditions. At the same time, it dynamically adjusts priorities according to real-time conditions and task changes to ensure the overall efficiency of the system is optimized. The priority management module can implement multiple priority scheduling strategies and select the most suitable scheduling algorithm by evaluating the impact when the task is completed. The elastic scaling scheduling submodule dynamically adjusts the allocation of computing resources based on task load, supporting efficient utilization of the elastic resources of the cloud computing environment.

4. The design simulation task collaboration and data management system based on a process engine according to claim 1, characterized in that, The monitoring and control engine can monitor the task execution process in real time, collect status information and provide feedback. It consists of the following sub-modules: The real-time status monitoring submodule monitors the execution progress of each task in real time to ensure that the latest status of the task can be obtained at any time. The execution progress includes start time, execution status, priority and completion time. In addition, the system resources consumed by the task during execution are monitored and relevant statistical information is generated to ensure the transparency of resource availability. The task status information collection submodule can collect status information of different tasks and resources, thereby generating a comprehensive report to provide a basis for the monitoring and control engine to perform performance analysis and optimize its strategies. It can also save relevant historical data of task execution to provide a reference for subsequent performance evaluation. The early warning and notification submodule is used to set anomaly thresholds. Once an anomaly in task execution is detected or the predefined anomaly threshold is exceeded, an early warning is immediately triggered. At the same time, the anomaly can be notified to relevant personnel via email, SMS and instant messaging to ensure that they can respond quickly.

5. The design simulation task collaboration and data management system based on a process engine according to claim 1, characterized in that: The task creation and assignment module can create new tasks through an interface and automatically assign them to suitable team members based on task complexity and required resources. It consists of the following sub-modules: The task creation submodule supports users in creating and using task templates. It also includes a user information entry module, through which users can fill in the task name, description, deadline, and priority remarks. The task creation submodule allows users to categorize tasks by project, status, and type, facilitating subsequent management and retrieval. The resource and complexity assessment submodule allows users to define the required resources when creating a task, including human and technical support. It can also automatically assess the complexity of a task based on its description and requirements, and provide a complexity score for the task based on historical data to help with subsequent allocation. The self-allocation mechanism submodule can select suitable members for task allocation based on each team member's skills, available time, and current workload. Priority is considered during this process, and resource allocation is adjusted when there is an urgent need for a task to ensure that the urgent task starts and is completed in a timely manner. The progress tracking and feedback module can update the progress of each person in real time, recording their start time, estimated completion time, and actual completion time. Users can adjust their work plans based on the feedback information. It consists of the following sub-modules: The progress update and feedback submodule allows you to create a simple task update form that requires team members to fill in relevant task information each time they update, while also providing options for task status. While team members update task progress, record their feedback on task execution, including problems encountered, support needed, and resources. Set a feedback cycle, and use group meetings and individual interviews for detailed discussions on progress to ensure that every team member has the opportunity to express their opinions and suggestions. The progress update submodule can determine the time nodes for regular progress updates and track the progress of each task in real time through project management software; The data analysis and adjustment submodule allows project managers to flexibly adjust work plans based on real-time feedback and progress, including reallocating resources and adjusting task priorities, to ensure timely project delivery. The collected progress data can be used for analysis to identify which tasks were completed on time and which tasks were delayed and the reasons for it, thus providing a basis for future project planning.

6. The design simulation task collaboration and data management system based on a process engine according to claim 1, characterized in that, The data management layer uses a database to store all data during the design simulation process, including design models, simulation results, and task records, in order to ensure data security and consistency. The version control system is used to track version changes of each data record, ensuring that users can easily access historical versions to prevent accidental data loss and erroneous modifications; The version control system is specifically a distributed control system, which allows each user to store a complete history in the storage unit, thereby accelerating local operations. At the same time, it allows users to restore to a previous version in the event of an error or accidental data loss, ensuring that the recovery process is simple and feasible and reducing the risk of misoperation. The data access management module manages user access permissions to data through an access control mechanism to ensure the privacy and security of sensitive information and prevent unauthorized access. It includes the following sub-modules: The access control submodule provides more flexible access management based on attributes, allowing access permissions to be controlled according to user attributes, resource attributes, and environmental conditions. The identity verification submodule verifies user authentication through single sign-on, allowing access to multiple task content with a single user credential, thus simplifying the authentication process. The data classification and grading module can effectively classify data based on public, internal, confidential, and top secret, and set different access control policies according to the category. It also considers the use, storage, and destruction of data to ensure effective management of data access throughout the entire lifecycle.

7. The design simulation task collaboration and data management system based on a process engine according to claim 1, characterized in that, The data analysis tool can use statistical analysis and mining techniques to conduct in-depth analysis of historical project data, and discover bottlenecks and potential optimization points in the design process; The optimization suggestion module can provide specific optimization suggestions based on the analysis results, including resource reallocation and process redesign, to improve overall performance. It consists of the following sub-modules: The resource reallocation submodule can assess the match between team members' skills and current tasks, reallocate tasks to the most suitable candidates to improve work efficiency, and analyze the potential return on investment of each task to transfer funds from inefficient projects to efficient projects to ensure the maximum effectiveness of fund utilization. The data and feedback mechanism module has built-in data-driven decision-making capabilities. It can identify bottlenecks through data analysis, monitor and evaluate optimization effects using actual data, ensure that the measures taken are based on facts rather than subjective judgments, and establish a regular feedback mechanism to collect suggestions from employees and customers. Through continuous feedback, it improves optimization suggestions and implementation effects, forming a virtuous cycle of improvement.

8. A design simulation task collaboration and data management method based on a process engine, characterized in that, Includes the following steps: Step 1: Users enter information such as task name, description, due date, and priority through the task creation submodule. Users can also select existing task templates to quickly create new tasks. Step 2: Use the task decomposition submodule to break down the complex task into multiple subtasks, define the name, description, estimated time and required resources for each subtask, and set dependencies for each subtask to ensure the correct execution order; Step 3: Use input / output relationships to create sub-modules, ensure the data types and formats of input and output for each sub-task, and upload sample data and templates; Step 4: By using the execution condition setting submodule, set conditions such as resource availability, time window, and completion flags for preceding tasks for each subtask; Step 5: The scheduling and execution engine automatically schedules tasks based on preset rules and current resource status, monitors resource status in real time, and dynamically adjusts task allocation. During this process, users can set priorities for different tasks to ensure that high-priority tasks receive the necessary resources. Step Six: Once all conditions are met, the task will automatically enter the execution state. This process is tracked in real time by the monitoring and control engine. Team members need to update the task progress regularly and record the start time, scheduled completion time, and actual completion time. Step 7: The monitoring and control engine collects status information during task execution, generates a comprehensive report and provides feedback, and sets an anomaly threshold. When an anomaly occurs, an early warning is immediately triggered and relevant personnel are notified. Step 8: Store all data in the design simulation process through the data management layer and use a version control system to track changes to each data record. The data access management module ensures that only authorized users can access specific data, implementing fine-grained access control. Step 9: Use data analysis tools to conduct in-depth analysis of historical project data, identify bottlenecks and potential optimization points, and then the optimization suggestion module provides specific optimization solutions based on the analysis results, including resource reallocation and process redesign; Step 10: Establish a channel for regularly collecting suggestions from employees and customers through a data domain feedback mechanism to improve and optimize measures. Then, based on the feedback and analysis results, flexibly adjust the work plan to ensure timely project delivery.

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