Intelligent task management method and system based on visual process arrangement

Abstract

The invention relates to the technical field of intelligent task management, in particular to an intelligent task management method and system based on visual process orchestration, and the method comprises the following steps: S1, task definition and decomposition: defining basic attributes and requirements of a task in the system, and decomposing a complex task into a plurality of subtasks; and S2, flow arrangement and visualization: arranging task flows by utilizing a visualization tool, setting an execution sequence, a dependency relationship and parallel or serial execution of the tasks. According to the intelligent task management method and system based on visual flow arrangement, the problems that in the prior art, an intelligent task management system cannot perform visual flow arrangement in the actual use process and lacks an intuitive visual interface, so that information such as task progress, resource allocation and flow state is difficult to be clear at a glance, and the task management efficiency is improved are solved. A large amount of time needs to be spent on looking up documents or carrying out verbal communication so as to know the latest dynamic problem of the task.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent task management, and in particular to an intelligent task management method and system based on visual process orchestration. Background Art

[0002] The intelligent task management method and system is a management tool that integrates modern information technology, aiming to improve the efficiency and quality of task execution. The intelligent task management method is a task management process based on artificial intelligence technology, which optimizes task allocation, execution and monitoring through automation and intelligent means. The intelligent task management method and system is an efficient and intelligent task management tool, which optimizes task allocation, execution and monitoring through automation and intelligent means, and improves the efficiency and quality of task execution. At the same time, it can also enhance team collaboration, optimize resource utilization and provide decision support, injecting strong impetus into the successful development of the organization.

[0003] However, the intelligent task management system in the prior art cannot perform visual process orchestration during actual use and lacks an intuitive visual interface, making it difficult to understand information such as task progress, resource allocation, and process status at a glance. It takes a lot of time to consult documents or communicate verbally to understand the latest developments of the task. To address the above problems, an intelligent task management method and system based on visual process orchestration are provided. Summary of the invention

[0004] The purpose of the present invention is to provide an intelligent task management method and system based on visual process arrangement to solve the problems raised in the above background technology. To achieve the above purpose, the present invention provides the following technical solution: an intelligent task management method based on visual process arrangement, comprising the following steps:

[0005] S1, Task definition and decomposition, defining the basic attributes and requirements of tasks in the system, and decomposing complex tasks into multiple subtasks;

[0006] S2, process arrangement and visualization, use visualization tools to arrange task processes, set the execution order, dependencies, parallel or serial execution of tasks;

[0007] S3, Intelligent task allocation and prioritization, the system intelligently allocates tasks based on team members' work habits, capabilities and current workloads, and automatically prioritizes tasks based on deadlines, importance and dependencies;

[0008] S4, real-time monitoring and feedback, the system monitors the execution of tasks in real time and sends task reminders and deadline notifications in a timely manner;

[0009] S5. Data analysis and optimization: Collect and analyze relevant data on task management, such as task completion rate and resource utilization rate. Based on the analysis results, optimize and improve the task management process.

[0010] Preferably, the basic attributes of the task include task name, description, priority, and deadline.

[0011] Preferably, the pipeline data in S4 includes the progress, resource consumption, and potential risks of the task.

[0012] Preferably, the intelligent task allocation and priority sorting are completed by using genetic algorithm, particle swarm algorithm or simulated annealing algorithm.

[0013] Preferably, the genetic algorithm includes the following steps:

[0014] S1. Initialization: Determine the genetic parameters;

[0015] S2. Determine the coding scheme: Use a random method or other methods to generate an initial population composed of N chromosomes, and at the same time set the inherited generation number k = 0;

[0016] S3. According to the fitness function, calculate the fitness of each individual in the population;

[0017] S4. If the termination condition set by the algorithm is met, output the result and stop the algorithm; otherwise, continue to execute the following steps;

[0018] S5. Perform selection operations according to a suitable selection method until a new generation of population with a population size of N is generated;

[0019] S6. If the crossover probability P C > Random(0,1), then perform crossover operations on the new generation of population obtained by selection. After crossover, a new population is formed, where Random(0,1) is used to generate a floating-point number between [0,1];

[0020] S7. If the mutation probability P m > Random(0,1), then perform mutation operations on each chromosome in the population generated by the crossover operation until a new population with a population size of N is formed;

[0021] S8. k = k + 1, return to step S1.

[0022] Preferably, the particle swarm algorithm includes:

[0023] S1. There are multiple particle populations in this model, and each population will search the solution space;

[0024] S2. The collaborative cooperation and knowledge sharing among particles are divided into two categories: information interaction within the same population and between different populations;

[0025] S3. The information exchanged between each population is the historical optimal fitness value and the corresponding position vector of each population;

[0026] S4. The position update formula of the particle remains unchanged, while the velocity update formula of the particle becomes:

[0027]

[0028] where P G =(P G1 , P G2 , …, P Gn ) is the optimal solution among all populations;

[0029] S5. Different inertia parameters w, learning factors c 1 , c 2 , and random numbers r 1 , r 2 , reflect the differences between populations to a certain extent.

[0030] Preferably, the basic steps of the algorithm model are as follows:

[0031] S1. Randomly initialize multiple populations and each particle in the population;

[0032] S2. Judge whether the algorithm termination condition is satisfied. If so, exit the algorithm; otherwise, continue the following steps;

[0033] S3. For each particle, calculate the fitness value of the particle and judge whether to update the historical optimal fitness value and position vector of the particle.

[0034] Preferably, the algorithm model further includes:

[0035] S1. Select the particle with the optimal fitness value in the population to which the particle belongs and judge whether to update the historical optimal fitness value and position vector of the population to which the particle belongs;

[0036] S2. Select the particle with the optimal fitness value among all populations and judge whether to update the historical optimal fitness value and position vector of all populations;

[0037] S3. Update the velocity and position of each particle according to the formula and jump to S2.

[0038] Preferably, it includes a task management module, which is coupled to a team collaboration module, which is coupled to an intelligent analysis module, which is coupled to a reporting and visualization module.

[0039] Preferably, the team collaboration module includes an instant messaging module, which is coupled to a file sharing and document management module, which is coupled to a task management and project management module, which is coupled to an information publishing and sharing platform module, which is coupled to a version control module.

[0040] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0041] 1. By defining the basic attributes and requirements of tasks in the system, decomposing complex tasks into multiple subtasks, using visualization tools to choreograph the task process, setting the execution order, dependencies, parallel or serial execution of tasks, the system intelligently assigns tasks according to the work habits, capabilities and current workload of team members, automatically sorts the priorities of tasks according to the deadlines, importance and dependencies of tasks, the system monitors the execution status of tasks in real time, sends task reminders and deadline notifications in a timely manner, collects and analyzes relevant data on task management, such as task completion rate and resource utilization rate, and optimizes and improves the task management process according to the data analysis results, thus solving the problem that the intelligent task management system in the prior art cannot perform visual process choreography and lacks an intuitive visual interface in the actual use process, making it difficult to see at a glance information such as task progress, resource allocation and process status, and requiring a lot of time to consult documents or conduct oral communication to understand the latest dynamics of tasks. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 It is a flowchart of an intelligent task management method and system based on visual process choreography according to the present invention;

[0043] Figure 2 It is a flowchart of an algorithm model of an intelligent task management method and system based on visual process choreography according to the present invention;

[0044] Figure 3 It is a system block diagram of an intelligent task management method and system based on visual process choreography according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0045] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0046] Please refer to Figures 1 to 3 , the present invention provides a technical solution: an intelligent task management method and system based on visual process orchestration, including an intelligent task management method based on visual process orchestration, which includes the following steps:

[0047] S1. Task definition and decomposition. Define the basic attributes and requirements of tasks in the system, and decompose complex tasks into multiple subtasks. Task definition helps to clarify the specific goals of tasks, enabling the project team to have a clear understanding of the tasks, thereby ensuring that tasks can be carried out according to the established goals. In visual process orchestration, task definition is the basis for building the process. Clear task definition helps to determine the execution order, dependencies, and required resources of tasks, thereby ensuring the accuracy and effectiveness of the process. Through clear task definition, the project team can allocate tasks, monitor progress, and evaluate performance more effectively, thus improving the overall efficiency of task management. Task decomposition is the process of breaking down complex tasks or projects into smaller, more specific, and more manageable subtasks. By breaking down large tasks into multiple small tasks, the complexity of the tasks can be reduced, making the tasks easier to understand and execute. Task decomposition helps to clarify the responsible persons and executors of each subtask, thereby ensuring that tasks can be effectively executed. According to the results of task decomposition, the project team can allocate human resources, material resources, and time resources more reasonably to improve the utilization efficiency of resources. Through task decomposition, the project team can adjust the execution order and priority of tasks more flexibly to cope with uncertainties and changes in the project. Task decomposition makes the progress and status of each subtask clearer, helping the project team to detect problems in a timely manner and take corresponding risk management measures;

[0048] S2. Process Orchestration and Visualization: Use visualization tools to orchestrate task processes, set the execution order, dependencies, parallel or serial execution of tasks. Process orchestration refers to organizing and arranging a series of tasks according to specific logic and order to ensure that tasks can be completed efficiently and accurately. Through reasonable process orchestration, the execution order and dependencies of tasks can be optimized, waiting time and resource conflicts between tasks can be reduced, thereby improving the efficiency of task execution. Process orchestration makes task management more orderly and controllable. The project team can clearly see the execution progress and status of tasks to promptly identify problems and take corresponding measures for adjustment. For complex tasks, process orchestration can break them down into multiple subtasks and clarify the responsible persons and executors of each subtask. At the same time, through process orchestration, the collaborative work between each subtask can be ensured to achieve the successful completion of the overall task. Visualization means presenting complex data and information in intuitive forms such as graphs, images, animations, etc. so that people can better understand and analyze. Through visualization, the project team can intuitively see information such as the execution progress of tasks, resource allocation, and relationships between tasks, thereby enhancing the transparency of task management. Visualization provides intuitive data display and analysis tools. The project team can use these tools to conduct in-depth analysis of task execution to better formulate and adjust decisions. Visualization enables members in the project team to better understand the work progress and status of each other, thereby strengthening teamwork and communication. At the same time, visualization can also help team members better understand the overall situation and goals of the project to better collaborate. In the intelligent task management system, process orchestration and visualization are interdependent and mutually reinforcing. On the one hand, process orchestration provides the basic data and structural support for visualization. Through process orchestration, the relationships and execution order between tasks can be clarified, thus providing a clear data framework and logical structure for visualization. On the other hand, visualization provides intuitive display and analysis tools for process orchestration. Through visualization, the project team can more intuitively understand the execution situation and status of tasks to better optimize and adjust the process;

[0049] S3. Intelligent task assignment and priority sorting. The system intelligently assigns tasks based on the work habits, capabilities, and current workloads of team members, and automatically sorts the priorities of tasks according to the deadlines, importance, and dependencies of the tasks. Intelligent task assignment refers to the use of advanced algorithms and artificial intelligence technologies to automatically assign tasks to the most suitable employees based on factors such as the nature and complexity of the tasks, the skills, experience, geographical location, and current workload of the employees. The intelligent task assignment system can comprehensively consider multiple factors to ensure that tasks are assigned to employees with the corresponding skills and experience, thereby improving the accuracy and quality of task completion. Through intelligent task assignment, the system can monitor the workload of employees in real time, avoid situations of task overload or idleness, and achieve the optimal allocation of resources. Intelligent task assignment can automatically assign tasks to the most suitable employees, reducing the time and cost of manual assignment, and thus improving the overall work efficiency. Through intelligent task assignment, employees can receive tasks that match their skills and experience, which helps to enhance employees' work enthusiasm and satisfaction. Priority sorting refers to sorting tasks according to factors such as the importance, urgency, resource availability, and dependencies of the tasks to determine the order of task execution. Through priority sorting, it can be ensured that key tasks that have a direct impact on the success of the project are given priority, thereby reducing the risk of project failure. Priority sorting helps the project team to focus more on the core goals, reduce unnecessary work interference, optimize the work process, and improve the overall work efficiency. Through priority sorting, resources can be reasonably allocated to ensure that resources are used for the most important tasks, thereby enhancing the utilization efficiency of resources. Priority sorting enables the project team to clearly see the order and status of task execution, thereby enhancing the controllability of project management;

[0050] S4. Real-time monitoring and feedback. The system monitors the execution of tasks in real time, sends task reminders and deadline notifications in a timely manner, and real-time monitoring can continuously track the progress of tasks to ensure that tasks are carried out according to the predetermined schedule and plan. Through the visual interface, project managers can clearly see the current status of each task, so as to find problems in time and take corrective measures. Real-time monitoring helps the project team better understand the allocation and utilization of resources. Through monitoring data, the team can identify idle or overused resources, and make adjustments and optimizations accordingly, thereby improving the utilization efficiency of resources. Real-time monitoring makes the project management process more transparent. Project team members can understand the overall progress of the project and the completion of each task in real time, which helps to enhance trust and collaboration among team members. Real-time monitoring provides project managers with real-time data and information support, enabling them to make dynamic decisions based on the latest situation. This decision-making method is more flexible and efficient, which helps project teams better cope with various challenges and changes. The feedback mechanism can promptly discover problems and obstacles in the execution of tasks. Through the visual interface and real-time monitoring data, project managers can quickly identify potential problems and take appropriate measures to solve them, thereby preventing the problems from expanding or affecting the overall progress of the project. Feedback data can provide an important basis for optimizing the task execution process. By analyzing feedback data, the project team can identify bottlenecks and deficiencies in the process and make improvements and optimizations accordingly, thereby improving the efficiency and quality of task execution;

[0051] S5. Data analysis and optimization: collect and analyze relevant data on task management, such as task completion rate and resource utilization rate, and optimize and improve the task management process based on the data analysis results.

[0052] In this embodiment, the basic attributes of a task include task name, description, priority, and deadline.

[0053] In this embodiment, the line management data in S4 includes the progress, resource consumption and potential risks of the task.

[0054] In this embodiment, the intelligent task allocation and priority sorting are performed using a genetic algorithm, a particle swarm algorithm or a simulated annealing algorithm.

[0055] In this embodiment, the genetic algorithm includes the following steps:

[0056] S1, initialization, determination of genetic parameters;

[0057] S2. Determine the coding scheme, use a random method or other method to generate an initial population consisting of N chromosomes, and set the number of generations that have been inherited k=0;

[0058] S3. Calculate the fitness of each individual in the population according to the fitness function;

[0059] S4. If the termination condition set by the algorithm is satisfied, output the result and stop the algorithm; otherwise, continue to execute the following steps;

[0060] S5. Perform a selection operation according to a suitable selection method until a new generation of population with a population size of N is generated;

[0061] S6. If the crossover probability P C > Random(0,1), perform a crossover operation on the new generation of population obtained by selection. After crossover, a new population is formed, where Random(0,1) is used to generate a floating-point number between [0,1];

[0062] S7. If the mutation probability P m > Random(0,1), perform a mutation operation on each chromosome in the population generated by the crossover operation until a new population with a population size of N is formed;

[0063] S8. k = k + 1, and return to step S1.

[0064] In this embodiment, the particle swarm algorithm includes:

[0065] S1. There are multiple particle populations in the model, and each population searches the solution space;

[0066] S2. The collaborative cooperation and knowledge sharing among particles are divided into two categories: information interaction within the same population and information interaction between different populations;

[0067] S3. The information exchanged between each population is the historical optimal fitness value and the corresponding position vector of each population;

[0068] S4. The position update formula of the particle remains unchanged, while the velocity update formula of the particle becomes:

[0069]

[0070] where P G = (P G1 , P G2 , …, P Gn ) is the optimal solution in all populations;

[0071] S5. Different inertia parameters w, learning factors c 1 , c 2 , and random numbers r 1 , r 2 , reflect the differences between populations to a certain extent.

[0072] In this embodiment, the basic steps of the algorithm model are as follows:

[0073] S1. Randomly initialize multiple populations and each particle in the populations;

[0074] S2. Determine whether the algorithm termination condition is met. If so, exit the algorithm; otherwise, continue with the following steps;

[0075] S3. For each particle, calculate the fitness value of the particle and determine whether to update the historical best fitness value and position vector of the particle.

[0076] In this embodiment, the algorithm model further includes:

[0077] S1. Select the particle with the best fitness value in the population to which the particle belongs and determine whether to update the historical best fitness value and position vector of the population to which the particle belongs;

[0078] S2. Select the particle with the best fitness value among all populations and determine whether to update the historical best fitness value and position vector of all populations;

[0079] S3. Update the velocity and position of each particle according to the formula and jump to S2.

[0080] In this embodiment, a task management module is included. The task management module is coupled to a team collaboration module. The team collaboration module is coupled to an intelligent analysis module. The intelligent analysis module is coupled to a report and visualization module.

[0081] In this embodiment, the team collaboration module includes an instant messaging module. The instant messaging module is coupled to a file sharing and document management module. The file sharing and document management module is coupled to a task management and project management module. The task management and project management module is coupled to an information publishing and sharing platform module. The information publishing and sharing platform module is coupled to a version control module.

[0082] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art of this industry should understand that the present invention is not limited by the above embodiments. The above embodiments and the descriptions in the specification are only preferred examples of the present invention and are not used to limit the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements fall within the scope of the present invention claimed. The scope of protection claimed by the present invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent task management method based on visual process orchestration, characterized in that: The steps include: S1, Task definition and decomposition, defining the basic attributes and requirements of tasks in the system, and decomposing complex tasks into multiple subtasks; S2, process arrangement and visualization, use visualization tools to arrange task processes, set the execution order, dependencies, parallel or serial execution of tasks; S3, Intelligent task allocation and prioritization, the system intelligently allocates tasks based on team members' work habits, capabilities and current workloads, and automatically prioritizes tasks based on deadlines, importance and dependencies; S4, real-time monitoring and feedback, the system monitors the execution of tasks in real time and sends task reminders and deadline notifications in a timely manner; S5. Data analysis and optimization: collect and analyze relevant data on task management, such as task completion rate and resource utilization rate, and optimize and improve the task management process based on the data analysis results.

2. The intelligent task management method based on visual process arrangement according to claim 1 is characterized in that: The basic attributes of the task include task name, description, priority, and deadline.

3. The intelligent task management method based on visual process arrangement according to claim 1 is characterized in that: The S4 midline management data includes the progress of the task, resource consumption and potential risks.

4. The intelligent task management method based on visual process arrangement according to claim 1 is characterized in that: The intelligent task allocation and priority sorting are performed using genetic algorithm, particle swarm algorithm or simulated annealing algorithm.

5. The intelligent task management method based on visual process arrangement according to claim 1 is characterized in that: The genetic algorithm comprises the following steps: S1, initialization, determination of genetic parameters; S2. Determine the coding scheme, use a random method or other method to generate an initial population consisting of N chromosomes, and set the number of generations that have been inherited k=0; S3. Calculate the fitness of each individual in the population according to the fitness function; S4. If the termination condition set by the algorithm is met, the result is output and the algorithm stops. Otherwise, continue to execute the following steps; S5. Perform a selection operation according to a suitable selection method until a new generation population with a population size of N is generated; S6. If the crossover probability P C > Random(0,1), then a crossover operation is performed on the selected new generation population to form a new population after crossover, where Random(0,1) is used to generate floating point numbers between [0,1]; S7, if the mutation probability P m >Random(0,1), then each chromosome in the population generated by the crossover operation is mutated until a new population of size N is formed; S8, k=k+1, return to step S1.

6. The intelligent task management method based on visual process arrangement according to claim 1 is characterized in that: The particle swarm algorithm comprises: S1. There are multiple particle populations in the model, and each population searches the solution space; S2. Collaboration and knowledge sharing among particles can be divided into two categories: information interaction within the same population and between different populations; S3. The information exchanged between each population is the historical optimal fitness value of each population and the corresponding position vector; S4. The particle position update formula does not change, but the particle velocity update formula becomes: Where P G =(P G1 ,P G2 ,…,P Gn ) is the optimal solution among all populations; S5, different inertia parameters w, learning factors c1, c2 are used between populations, and random numbers r1, r2, To a certain extent, it reflects the differences between populations.

7. The intelligent task management method based on visual process arrangement according to claim 6 is characterized in that: The basic steps of the algorithm model are as follows: S1, randomly initialize multiple populations and each particle in the population; S2. Determine whether the algorithm termination condition is met. If so, exit the algorithm; otherwise, continue with the following steps; S3. For each particle, calculate the fitness value of the particle and determine whether to update the historical optimal fitness value and position vector of the particle.

8. The intelligent task management system based on visual process arrangement according to claim 1 is characterized by: The algorithm model also includes: S1, select the particle with the best fitness value in the population to which the particle belongs, and determine whether to update the historical best fitness value and position vector of the population to which the particle belongs; S2, select the particle with the best fitness value among all populations, and determine whether to update the historical best fitness value and position vector of all populations; S3. Update the speed and position of each particle according to the formula and jump to S2.

9. Intelligent task management system based on visual process orchestration, characterized by: It includes a task management module, the task management module is coupled to a team collaboration module, the team collaboration module is coupled to an intelligent analysis module, and the intelligent analysis module is coupled to a report and visualization module.

10. The intelligent task management system based on visual process arrangement according to claim 1 is characterized in that: The team collaboration module includes an instant messaging module, the instant messaging module is coupled to a file sharing and document management module, the file sharing and document management module is coupled to a task management and project management module, the task management and project management module is coupled to an information publishing and sharing platform module, and the information publishing and sharing platform module is coupled to a version control module.

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