Artificial intelligence task allocation management processing system

Abstract

The invention relates to the technical field of task allocation management, and particularly discloses an artificial intelligence task allocation management processing system, which comprises a task receiving module used for receiving an artificial intelligence task to be processed; the task analysis module is used for analyzing the received task and determining the type, complexity and required computing resources of the task; the resource management module is used for managing available computing resources in the system; according to the artificial intelligence task allocation management processing system, processing is carried out through the task receiving module, the task analysis module, the resource management module, the task allocation module, the task execution module and the result feedback module, manual intervention and rule presetting are not needed, and when the system faces large-scale and diversified tasks, the task allocation efficiency is improved. According to the invention, efficient and accurate distribution can be realized, the cost consumption is reduced, the system distribution is reasonable, and the overall efficiency of system use is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of task allocation management, and in particular relates to an artificial intelligence task allocation management processing system. Background Art

[0002] With the development of cloud computing technology, more and more cloud services need to begin to incorporate multiple computing resource pools to improve the processing efficiency of cloud computing tasks. How to effectively plan the allocation and compatibility between cloud computing tasks and computing resource pools has become an important technical issue.

[0003] With the rapid development of artificial intelligence technology, its application in various fields is becoming more and more extensive, including industry, agriculture, service industry, etc. In these fields, artificial intelligence task allocation management and processing system has become an important means to improve production efficiency and optimize resource allocation. However, the existing task allocation system still has some shortcomings and challenges when dealing with complex and changeable artificial intelligence tasks.

[0004] Artificial intelligence task allocation management refers to the process of using artificial intelligence technology to optimize task allocation in order to improve work efficiency and resource utilization. Artificial intelligence task allocation management is the process of using artificial intelligence technology to reasonably allocate and schedule tasks. It automatically allocates tasks to the most suitable individuals or teams by analyzing team members' skills, workload, historical performance, and task requirements and characteristics, thereby ensuring that tasks can be completed efficiently and accurately.

[0005] Artificial intelligence can comprehensively consider multiple factors, such as team members' skill level, work experience, current workload, etc., to ensure that tasks are assigned to the most appropriate personnel; through automated task allocation, manual intervention and decision-making time can be reduced, allowing team members to get to work more quickly, thereby improving overall work efficiency; artificial intelligence can reasonably allocate resources according to task requirements and the actual situation of team members, avoiding waste and overuse of resources; through data analysis, artificial intelligence can predict potential risks and problems, and take corresponding measures in advance to prevent them, thereby reducing the risk of project failure.

[0006] Traditional task allocation systems often rely on manual intervention and preset rules, which makes it difficult for the system to achieve efficient and accurate allocation when faced with large-scale and diversified tasks. Manual intervention not only increases costs, but may also lead to unreasonable allocation due to human factors, affecting overall efficiency. Therefore, an artificial intelligence task allocation management and processing system is proposed. Summary of the invention

[0007] The purpose of the present invention is to provide an artificial intelligence task allocation management and processing system to solve the problem that traditional task allocation systems often rely on manual intervention and preset rules, which makes it difficult for the system to achieve efficient and accurate allocation when faced with large-scale and diversified tasks. Manual intervention not only increases costs, but may also lead to unreasonable allocation due to human factors, affecting the overall efficiency.

[0008] To achieve the above object, the present invention provides the following technical solutions:

[0009] An artificial intelligence task allocation management processing system, comprising:

[0010] A task receiving module, used to receive artificial intelligence tasks to be processed;

[0011] The task analysis module is used to analyze the received tasks and determine the type, complexity and required computing resources of the tasks;

[0012] Among them, the task analysis module includes a task type identification submodule, a complexity assessment submodule and a resource demand prediction submodule. The task type identification submodule is used to identify the specific type of the task, and the specific type of the task is image, natural language and machine learning model. The complexity assessment submodule is used to assess the complexity of the task. The complexity assessment content of the task includes the amount of calculation, data scale and processing time. The resource demand prediction submodule is used to predict the type and quantity of computing resources required for the task according to the type and complexity of the task;

[0013] Resource management module, used to manage the computing resources available in the system;

[0014] The resource management module includes a resource monitoring submodule and a resource scheduling submodule. The resource monitoring submodule is used to monitor the status of each computing resource in the system in real time. The status of the computing resources includes utilization rate, idle rate, failure rate, etc. The resource scheduling submodule is used to dynamically adjust the allocation and scheduling of computing resources according to the requirements of the task allocation module and the information of the resource monitoring submodule.

[0015] The task allocation module allocates tasks to appropriate computing resources based on the results of the task analysis module and the information of the resource management module;

[0016] Wherein, the task allocation module includes a task priority sorting submodule and a resource matching submodule;

[0017] The task priority sorting submodule is used to sort the tasks according to their urgency and importance, and the resource matching submodule is used to select appropriate computing resources from the resource management module for allocation according to the priority of the task and the required computing resources;

[0018] The task execution module is used to execute the assigned tasks and monitor the execution progress and status of the tasks;

[0019] Wherein, the task execution module includes a task execution engine submodule and an exception handling submodule;

[0020] The task execution engine submodule is used to execute the assigned tasks, and the exception handling submodule is used to handle the abnormal situations in the execution process of the monitoring tasks. The abnormal situations in the exception handling submodule include calculation errors and insufficient resources.

[0021] The result feedback module is used to feed back the result of task execution to the task requester;

[0022] The result feedback module includes a result formatting submodule and a result storage submodule. The result formatting submodule is used to format the result of the task execution, and the result storage submodule is used to store the result of the task execution in a specified location.

[0023] As can be seen from the above, the artificial intelligence task allocation management processing system is processed through the task receiving module, task analysis module, resource management module, task allocation module, task execution module and result feedback module, and has the following effects:

[0024] The task receiving module can receive and enter various types of task information to ensure that the system has a comprehensive understanding of the tasks, and can automatically receive tasks, reducing the time and errors of manual entry, ensuring the accuracy and completeness of task information, and providing a reliable basis for subsequent analysis.

[0025] The task analysis module can conduct in-depth analysis of the received tasks to understand the task requirements, priorities and complexity. It can also provide suitable candidate resources for task allocation through accurate understanding of task requirements, adjust the processing flow according to the complexity of the task, and ensure that the tasks are completed efficiently and orderly.

[0026] Resource management module: various resources that can be used to perform tasks in the management system, such as human resources, equipment resources, etc., and through dynamic resource management, ensure full utilization and efficient allocation of resources, avoid resource waste, and reduce project costs;

[0027] The task allocation module can allocate tasks to the most appropriate resources based on the results of task analysis and resource management, and ensure that tasks are completed by the most suitable resources, thereby improving overall work efficiency and ensuring that tasks can be completed with high quality through reasonable allocation;

[0028] The task execution module can monitor and manage the execution of tasks to ensure that tasks are carried out as planned, and can promptly identify problems and make adjustments to ensure the smooth completion of tasks. It can also adjust the task execution strategy according to actual conditions and improve the flexibility of task execution.

[0029] The result feedback module collects the results of task execution, conducts evaluation and feedback, and provides reference for the system's subsequent task allocation. Through the feedback mechanism, it continuously optimizes the task allocation and execution process, accumulates task execution experience and data, and provides more accurate reference for subsequent tasks.

[0030] To sum up, the artificial intelligence task allocation management and processing system of the present invention is processed through the task receiving module, task analysis module, resource management module, task allocation module, task execution module and result feedback module, without the need for human intervention and preset rules, so that the system can achieve efficient and accurate allocation when facing large-scale and diversified tasks, reduce cost consumption, make the system allocation reasonable, and improve the overall efficiency of system use.

[0031] As can be seen from the above, the artificial intelligence task allocation management processing system is processed through the resource monitoring submodule, resource scheduling submodule, task priority sorting submodule, resource matching submodule, task execution engine submodule, exception handling submodule, result formatting submodule and result storage submodule, which has the following benefits:

[0032] The resource monitoring submodule can collect and analyze resource status information in real time, such as CPU usage, memory usage, disk space, etc., to ensure effective use of resources. By monitoring resource usage, potential resource bottlenecks can be discovered in a timely manner, and corresponding measures can be taken to prevent them, so as to avoid affecting task execution.

[0033] The resource scheduling submodule can automatically adjust resource allocation according to task requirements and resource availability to ensure that tasks can run efficiently. Through reasonable resource scheduling, it can reduce resource waste and idleness and improve the overall utilization of resources.

[0034] The task priority sorting submodule can intelligently sort tasks according to their urgency and importance to ensure that key tasks are given priority. By giving priority to key tasks, the overall project cycle can be shortened and the efficiency of task execution can be improved.

[0035] The resource matching submodule makes accurate matches based on the task requirements and resource characteristics to ensure that the task can be completed by the most appropriate resource. Accurate resource matching can improve the quality of task completion and reduce errors and delays caused by resource mismatch.

[0036] The task execution engine submodule can automatically receive tasks and execute them without human intervention, which improves the execution speed and accuracy of tasks; it can handle multiple tasks at the same time, which improves the concurrent processing capability of the system;

[0037] The exception handling submodule can monitor the execution of tasks in real time. Once an exception is found, it will be handled immediately to avoid affecting the normal execution of the task. Through effective exception handling, the stability and reliability of the system can be improved to ensure the smooth completion of the task.

[0038] The result formatting submodule can format the results of task execution to meet specific format requirements and facilitate subsequent analysis and processing. The formatted results can improve the readability and usability of the data, making it easier for users to understand and use.

[0039] The result storage submodule can safely store the results of task execution to ensure data integrity and security; the stored results can facilitate subsequent query and analysis to provide support for decision-making.

[0040] Compared with the prior art, the present invention has the following beneficial effects:

[0041] The artificial intelligence task allocation management and processing system of the present invention is processed through a task receiving module, a task analysis module, a resource management module, a task allocation module, a task execution module and a result feedback module, without the need for human intervention and preset rules, so that the system can achieve efficient and accurate allocation when facing large-scale and diversified tasks, reducing cost consumption, making the system allocation reasonable, and improving the overall efficiency of system use. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 It is a module flow chart of the present invention. DETAILED DESCRIPTION

[0043] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0044] like Figure 1 As shown, an artificial intelligence task allocation management and processing system includes:

[0045] A task receiving module, used to receive artificial intelligence tasks to be processed;

[0046] The task analysis module is used to analyze the received tasks and determine the type, complexity and required computing resources of the tasks;

[0047] Among them, the task analysis module includes a task type identification submodule, a complexity assessment submodule and a resource demand prediction submodule. The task type identification submodule is used to identify the specific type of the task, and the specific type of the task is image, natural language and machine learning model. The complexity assessment submodule is used to assess the complexity of the task. The complexity assessment content of the task includes the amount of calculation, data scale and processing time. The resource demand prediction submodule is used to predict the type and quantity of computing resources required for the task according to the type and complexity of the task;

[0048] Resource management module, used to manage the computing resources available in the system;

[0049] The resource management module includes a resource monitoring submodule and a resource scheduling submodule. The resource monitoring submodule is used to monitor the status of each computing resource in the system in real time. The status of the computing resources includes utilization rate, idle rate, failure rate, etc. The resource scheduling submodule is used to dynamically adjust the allocation and scheduling of computing resources according to the requirements of the task allocation module and the information of the resource monitoring submodule.

[0050] The task allocation module allocates tasks to appropriate computing resources based on the results of the task analysis module and the information of the resource management module;

[0051] Wherein, the task allocation module includes a task priority sorting submodule and a resource matching submodule;

[0052] The task priority sorting submodule is used to sort the tasks according to their urgency and importance, and the resource matching submodule is used to select appropriate computing resources from the resource management module for allocation according to the priority of the task and the required computing resources;

[0053] The task execution module is used to execute the assigned tasks and monitor the execution progress and status of the tasks;

[0054] Wherein, the task execution module includes a task execution engine submodule and an exception handling submodule;

[0055] The task execution engine submodule is used to execute the assigned tasks, and the exception handling submodule is used to handle the abnormal situations in the execution process of the monitoring tasks. The abnormal situations in the exception handling submodule include calculation errors and insufficient resources.

[0056] The result feedback module is used to feed back the result of task execution to the task requester;

[0057] The result feedback module includes a result formatting submodule and a result storage submodule. The result formatting submodule is used to format the result of the task execution, and the result storage submodule is used to store the result of the task execution in a specified location.

[0058] As can be seen from the above, the artificial intelligence task allocation management processing system is processed through the task receiving module, task analysis module, resource management module, task allocation module, task execution module and result feedback module, and has the following effects:

[0059] The task receiving module can receive and enter various types of task information to ensure that the system has a comprehensive understanding of the tasks, and can automatically receive tasks, reducing the time and errors of manual entry, ensuring the accuracy and completeness of task information, and providing a reliable basis for subsequent analysis.

[0060] The task analysis module can conduct in-depth analysis of the received tasks to understand the task requirements, priorities and complexity. It can also provide suitable candidate resources for task allocation through accurate understanding of task requirements, adjust the processing flow according to the complexity of the task, and ensure that the tasks are completed efficiently and orderly.

[0061] Resource management module: various resources that can be used to perform tasks in the management system, such as human resources, equipment resources, etc., and through dynamic resource management, ensure full utilization and efficient allocation of resources, avoid resource waste, and reduce project costs;

[0062] The task allocation module can allocate tasks to the most appropriate resources based on the results of task analysis and resource management, and ensure that tasks are completed by the most suitable resources, thereby improving overall work efficiency and ensuring that tasks can be completed with high quality through reasonable allocation;

[0063] The task execution module can monitor and manage the execution of tasks to ensure that tasks are carried out as planned, and can promptly identify problems and make adjustments to ensure the smooth completion of tasks. It can also adjust the task execution strategy according to actual conditions and improve the flexibility of task execution.

[0064] The result feedback module collects the results of task execution, conducts evaluation and feedback, and provides reference for the system's subsequent task allocation. Through the feedback mechanism, it continuously optimizes the task allocation and execution process, accumulates task execution experience and data, and provides more accurate reference for subsequent tasks.

[0065] To sum up, the artificial intelligence task allocation management and processing system of the present invention is processed through the task receiving module, task analysis module, resource management module, task allocation module, task execution module and result feedback module, without the need for human intervention and preset rules, so that the system can achieve efficient and accurate allocation when facing large-scale and diversified tasks, reduce cost consumption, make the system allocation reasonable, and improve the overall efficiency of system use.

[0066] As can be seen from the above, the artificial intelligence task allocation management processing system is processed through the resource monitoring submodule, resource scheduling submodule, task priority sorting submodule, resource matching submodule, task execution engine submodule, exception handling submodule, result formatting submodule and result storage submodule, which has the following benefits:

[0067] The resource monitoring submodule can collect and analyze resource status information in real time, such as CPU usage, memory usage, disk space, etc., to ensure effective use of resources. By monitoring resource usage, potential resource bottlenecks can be discovered in a timely manner, and corresponding measures can be taken to prevent them, so as to avoid affecting task execution.

[0068] The resource scheduling submodule can automatically adjust resource allocation according to task requirements and resource availability to ensure that tasks can run efficiently. Through reasonable resource scheduling, it can reduce resource waste and idleness and improve the overall utilization of resources.

[0069] The task priority sorting submodule can intelligently sort tasks according to their urgency and importance to ensure that key tasks are given priority. By giving priority to key tasks, the overall project cycle can be shortened and the efficiency of task execution can be improved.

[0070] The resource matching submodule makes accurate matches based on the task requirements and resource characteristics to ensure that the task can be completed by the most appropriate resource. Accurate resource matching can improve the quality of task completion and reduce errors and delays caused by resource mismatch.

[0071] The task execution engine submodule can automatically receive tasks and execute them without human intervention, which improves the execution speed and accuracy of tasks; it can handle multiple tasks at the same time, which improves the concurrent processing capability of the system;

[0072] The exception handling submodule can monitor the execution of tasks in real time. Once an exception is found, it will be handled immediately to avoid affecting the normal execution of the task. Through effective exception handling, the stability and reliability of the system can be improved to ensure the smooth completion of the task.

[0073] The result formatting submodule can format the results of task execution to meet specific format requirements and facilitate subsequent analysis and processing. The formatted results can improve the readability and usability of the data, making it easier for users to understand and use.

[0074] The result storage submodule can safely store the results of task execution to ensure data integrity and security; the stored results can facilitate subsequent query and analysis to provide support for decision-making.

[0075] Furthermore, this design application is applied to artificial intelligence task allocation management and processing, which is processed through a task receiving module, a task analysis module, a resource management module, a task allocation module, a task execution module and a result feedback module. There is no need for human intervention and preset rules, so that the system can achieve efficient and accurate allocation when facing large-scale and diversified tasks, reducing cost consumption, making the system allocation reasonable, and improving the overall efficiency of system use.

[0076] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An artificial intelligence task allocation management and processing system, characterized in that: include: A task receiving module, used to receive artificial intelligence tasks to be processed; The task analysis module is used to analyze the received tasks and determine the type, complexity and required computing resources of the tasks; Resource management module, used to manage the computing resources available in the system; The task allocation module allocates tasks to appropriate computing resources based on the results of the task analysis module and the information of the resource management module; The task execution module is used to execute the assigned tasks and monitor the execution progress and status of the tasks; The result feedback module is used to feed back the result of task execution to the task requester.

2. The artificial intelligence task allocation management processing system according to claim 1, characterized in that: The task analysis module includes a task type identification submodule, a complexity assessment submodule and a resource requirement prediction submodule. The task type identification submodule is used to identify the specific type of the task, and the specific types of the task are image, natural language and machine learning model. The complexity assessment submodule is used to evaluate the complexity of the task. The complexity assessment content of the task includes the amount of calculation, data scale and processing time. The resource requirement prediction submodule is used to predict the type and quantity of computing resources required for the task according to the type and complexity of the task.

3. The artificial intelligence task allocation management processing system according to claim 1, characterized in that: The resource management module includes a resource monitoring submodule and a resource scheduling submodule. The resource monitoring submodule is used to monitor the status of each computing resource in the system in real time. The status of the computing resources includes utilization rate, idle rate, failure rate, etc. The resource scheduling submodule is used to dynamically adjust the allocation and scheduling of computing resources according to the requirements of the task allocation module and the information of the resource monitoring submodule.

4. The artificial intelligence task allocation management processing system according to claim 1, characterized in that: The task allocation module includes a task priority sorting submodule and a resource matching submodule.

5. The artificial intelligence task allocation management processing system according to claim 4, characterized in that: The task priority sorting submodule is used to prioritize tasks according to their urgency and importance, and the resource matching submodule is used to select appropriate computing resources from the resource management module for allocation according to the priority of the task and the required computing resources.

6. The artificial intelligence task allocation management processing system according to claim 1, characterized in that: The task execution module includes a task execution engine submodule and an exception handling submodule.

7. The artificial intelligence task allocation management processing system according to claim 6, characterized in that: The task execution engine submodule is used to execute the assigned tasks, and the exception handling submodule is used to handle abnormal situations during the execution of the monitoring tasks. One of the situations in the exception handling submodule includes calculation errors and insufficient resources.

8. The artificial intelligence task allocation management processing system according to claim 1, characterized in that: The result feedback module includes a result formatting submodule and a result storage submodule. The result formatting submodule is used to format the result of task execution, and the result storage submodule is used to store the result of task execution in a designated location.

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