Full-cycle scheduling instruction management system
Through dynamic decision-making model and machine learning optimization algorithm, combined with distributed architecture and cloud computing platform, the scheduling flexibility and data processing capabilities of the full-cycle scheduling instruction management system in complex environments is solved, and efficient and flexible task execution and safe and reliable system operation are achieved.
Patent Information
- Application Number
- CN202510460521.3
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-04-14
- Publication Date
- 2025-08-26
AI Technical Summary
The existing full-cycle scheduling instruction management system has poor scheduling decision-making flexibility when facing complex dynamic environments, and it is difficult to deal with emergencies in real time. It has limited processing capabilities under high concurrent tasks and large-scale data, which affects the real-time and accuracy of scheduling.
Adopting dynamic decision-making models, machine learning-based instruction generation, distributed architecture, blockchain technology, deep learning feedback mechanism and cloud computing platform, we optimize scheduling strategies and instruction management to improve system flexibility and data processing capabilities.
It realizes efficient and flexible scheduling decisions in complex environments, improves the accuracy and response speed of task execution, enhances the scalability and data processing capabilities of the system, and ensures the timely completion and security of tasks.
Smart Images
Figure CN120540703A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of scheduling instruction management, and in particular to a full-cycle scheduling instruction management system. Background Art
[0002] The full-cycle scheduling instruction management system is mainly composed of a scheduling control module, an instruction generation module, an instruction management module, an execution monitoring module, and a feedback adjustment module. The working principle of the system is to classify and schedule tasks through the scheduling control module, and the instruction generation module generates specific operation instructions according to the scheduling strategy, and uniformly manages and schedules them through the instruction management module. The execution monitoring module is responsible for real-time monitoring of the progress and results of instruction execution, and the feedback adjustment module adjusts subsequent scheduling strategies based on execution feedback to ensure the efficient operation of the system and the smooth completion of tasks. The core of this system is to optimize the task scheduling process, especially in a multi-tasking environment. It can dynamically adjust the scheduling strategy based on factors such as priority, resource usage, and task urgency, making the execution of various tasks more balanced and efficient.
[0003] While the full-cycle scheduling and command management system offers certain advantages in scheduling and task management, it still suffers from several drawbacks. The system's scheduling decisions lack flexibility in complex and dynamic environments, making it difficult to respond to emergencies or unconventional tasks in real time. This can lead to delayed system response times. The system's ability to process large amounts of data is limited, especially in the case of highly concurrent tasks and large amounts of data. This can lead to bottlenecks, impacting the real-time and accuracy of scheduling. Summary of the Invention
[0004] In response to the shortcomings of the existing technology, the present invention provides a full-cycle scheduling instruction management system, which solves the problems of poor scheduling decision-making flexibility and difficulty in responding to emergencies in real time; the system has limited processing capabilities for large amounts of data, which affects the real-time and accuracy of scheduling.
[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: a full-cycle scheduling instruction management system, comprising:
[0006] Scheduling control module, used to generate scheduling plans based on task priority, resource availability, and urgency;
[0007] An instruction generation module is used to generate specific operation instructions according to the scheduling plan;
[0008] Instruction management module, used for unified management, storage, scheduling and distribution of instructions;
[0009] Execution monitoring module, used to monitor the execution progress and results of instructions in real time;
[0010] The feedback adjustment module is used to adjust the subsequent scheduling strategy according to the execution feedback to ensure the efficient operation of the system.
[0011] Preferably, the scheduling control module further includes a dynamic decision model for automatically adjusting the scheduling strategy based on environmental changes. The mathematical formula of the dynamic decision model is as follows:
[0012]
[0013] in,
[0014] S is the optimal scheduling solution;
[0015] X is the task scheduling decision vector;
[0016] w i is the weight of task i;
[0017] Cost i (X) is the execution cost of task i under scheduling scheme X;
[0018] λ is the scheduling penalty coefficient;
[0019] Penalty(X) is the penalty term of scheduling scheme X, which is used to consider the processing of burst tasks.
[0020] Preferably, the instruction generation module adopts an optimization algorithm based on machine learning to intelligently generate optimal instructions according to task characteristics. The mathematical formula of the optimization algorithm is as follows:
[0021]
[0022] in,
[0023] Y is the generated operation instruction;
[0024] Accuracy(Y) is the execution accuracy of instruction set Y;
[0025] α is the coefficient that weighs execution accuracy and execution time;
[0026] ExecutionTime(Y) is the execution time of instruction set Y.
[0027] Preferably, the feedback adjustment module adopts a feedback mechanism based on deep learning to automatically optimize the scheduling algorithm according to the execution data. The deep learning optimization formula is:
[0028]
[0029] in,
[0030] L loss is the loss function;
[0031] y i Feedback for actual implementation;
[0032] is the predicted value;
[0033] w j is the weight of the neural network;
[0034] λ is the regularization parameter.
[0035] Preferably, the instruction management module adopts a distributed architecture, which can efficiently share instruction data among multiple nodes, reduce system load and improve processing capabilities. The instruction generation module adopts an optimization algorithm based on machine learning to intelligently generate optimal instructions according to task characteristics.
[0036] Preferably, the instruction management module optimizes instruction storage and access paths through an intelligent cache mechanism, thereby improving the response speed of instruction scheduling.
[0037] Preferably, the system adopts a data recording and verification mechanism based on blockchain technology to ensure the security and non-tamperability of instructions. The execution monitoring module can conduct real-time evaluation of execution progress through multi-dimensional analysis, automatically predict task completion time, and adjust resource allocation in advance.
[0038] Preferably, the system adopts a resource scheduling and distribution platform based on cloud computing, which can flexibly schedule and process tasks among multiple virtual nodes, thereby improving the scalability of the system.
[0039] The present invention provides a full-cycle scheduling instruction management system. It has the following beneficial effects:
[0040] This full-cycle scheduling instruction management system automatically adjusts scheduling strategies through a dynamic decision-making model, enabling the system to make flexible scheduling decisions based on environmental changes and task priorities, effectively improving the flexibility of scheduling decisions, especially in the face of emergencies, enabling timely responses and adjustments. In addition, the instruction generation module uses a machine learning-based optimization algorithm to intelligently generate optimal instructions based on task characteristics. This innovation not only improves execution accuracy, but also makes reasonable trade-offs based on task complexity and execution time to ensure the efficient operation of the system. Through these optimizations, the system can maintain efficient scheduling in complex environments and ensure timely and accurate execution of tasks.
[0041] In addition to optimizing the flexibility of scheduling decisions, the system also has significant advantages in data processing capabilities and stability when handling high-concurrency tasks. The instruction management module optimizes instruction storage and access paths through a distributed architecture and intelligent caching mechanism, significantly improving the response speed of instruction scheduling and reducing system load. In addition, the data recording and verification mechanism based on blockchain technology ensures the security and non-tamperability of instructions, providing a strong guarantee for the reliability of the system. The system also adopts a cloud computing-based resource scheduling and distribution platform, which can flexibly schedule and process tasks between multiple virtual nodes, greatly improving the system's scalability and ability to handle large-scale data. Therefore, the present invention can maintain efficient and accurate scheduling in a large-scale, high-concurrency task environment, while providing a solid foundation for future expansion and optimization. BRIEF DESCRIPTION OF THE DRAWINGS
[0042] Figure 1 It is a structural schematic diagram of the present invention. DETAILED DESCRIPTION
[0043] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.
[0044] Example 1
[0045] like Figure 1 As shown, an embodiment of the present invention provides a full-cycle scheduling instruction management system, including a scheduling control module for generating a scheduling plan based on the priority, resource status, and urgency of the task. The scheduling control module further includes a dynamic decision model for automatically adjusting the scheduling strategy based on environmental changes. The mathematical formula of the dynamic decision model is as follows:
[0046]
[0047] in,
[0048] S is the optimal scheduling solution.
[0049] X is the task scheduling decision vector.
[0050] w i is the weight of task i.
[0051] Cost i (X) is the execution cost of task i under scheduling scheme X.
[0052] λ is the scheduling penalty coefficient.
[0053] Penalty(X) is the penalty term of scheduling scheme X, which is used to consider the processing of burst tasks.
[0054] The instruction generation module is used to generate specific operation instructions according to the scheduling plan. The instruction generation module uses an optimization algorithm based on machine learning to intelligently generate optimal instructions based on task characteristics. The mathematical formula of the optimization algorithm is as follows:
[0055]
[0056] in,
[0057] Y is the generated operation instruction.
[0058] Accuracy(Y) is the execution accuracy of instruction set Y.
[0059] α is the coefficient that weighs execution accuracy and execution time.
[0060] ExecutionTime(Y) is the execution time of instruction set Y. The instruction management module adopts a distributed architecture, which can efficiently share instruction data among multiple nodes, reduce system load and improve processing capabilities.
[0061] The instruction management module is used to uniformly manage, store, schedule and distribute instructions. The instruction management module adopts a distributed architecture, which can efficiently share instruction data among multiple nodes, reduce system load and improve processing capabilities. The instruction management module optimizes instruction storage and access paths through an intelligent caching mechanism, thereby improving the response speed of instruction scheduling.
[0062] The execution monitoring module is used to monitor the execution progress and results of instructions in real time.
[0063] The feedback adjustment module is used to adjust the subsequent scheduling strategy based on execution feedback to ensure the efficient operation of the system. The feedback adjustment module adopts a feedback mechanism based on deep learning to automatically optimize the scheduling algorithm based on execution data. The deep learning optimization formula is:
[0064]
[0065] in,
[0066] L loss is the loss function.
[0067] y i Feedback for actual implementation.
[0068] is the predicted value.
[0069] w j is the weight of the neural network.
[0070] λ is the regularization parameter.
[0071] The instruction management module optimizes instruction storage and access paths through an intelligent caching mechanism, improving the response speed of instruction scheduling. The system adopts a cloud computing-based resource scheduling and distribution platform, which can flexibly schedule and process tasks among multiple virtual nodes, improving the scalability of the system.
[0072] Experimental Examples
[0073] To fully validate the performance of the proposed full-cycle scheduling instruction management system in a high-concurrency, large-scale task scheduling environment, this example used a larger task set and conducted detailed recording and analysis of the experimental data. This experiment incorporated actual computational and task scheduling data to evaluate the system's performance in complex environments.
[0074] Experimental setup
[0075] Experimental Tasks and Environment: This experiment used a scheduling environment containing 5,000 tasks. Task execution time and resource requirements were simulated using real workloads. Task execution times ranged from 10 to 60 minutes. Task priority, resource requirements (including CPU, memory, and bandwidth), and task type were dynamically configured based on actual business scenarios. Tasks were divided into three priority categories:
[0076] Low-priority tasks: 2,000, execution time 10-30 minutes, low resource requirements;
[0077] Medium priority tasks: 1,500 tasks, execution time 20-40 minutes, medium resource requirements;
[0078] High-priority tasks: 1,500 tasks, execution time 30-60 minutes, high resource requirements.
[0079] 2. System configuration:
[0080] The scheduling control module uses a dynamic decision-making model based on reinforcement learning. The execution cost of each task is calculated by its resource requirements and task execution time. The reinforcement learning model makes real-time scheduling decisions based on the following formula:
[0081]
[0082] Among them, α=0.1,γ=0.9, the immediate reward of the task r t+1 Dynamic calculation is performed based on the success rate of task execution and resource consumption.
[0083] The instruction generation module uses an optimization algorithm based on a deep neural network (DNN) to intelligently generate operation instructions based on task characteristics, optimizing execution time and accuracy. The optimization formula used is:
[0084]
[0085] Among them, α=0.5 is the weight coefficient.
[0086] The instruction management module uses a distributed architecture and intelligent caching mechanism to optimize instruction storage and access paths, ensuring efficient system response. By sharing task instruction data across multiple nodes, system load is reduced and excessive latency is avoided.
[0087] The execution monitoring module monitors task execution in real time, including task completion time, resource usage, and execution accuracy. Monitoring data is evaluated in real time using a multi-dimensional analysis model.
[0088] The feedback adjustment module uses a deep learning mechanism to automatically adjust the scheduling strategy to optimize the accuracy and efficiency of task execution. The optimization process is performed using the following loss function:
[0089]
[0090] Among them, λ = 0.01 is the regularization parameter.
[0091] Experimental data and calculation results
[0092] 1. Task scheduling execution time
[0093] In a scheduling environment with 5,000 tasks, the system optimized the scheduling through a dynamic scheduling strategy based on reinforcement learning. The experimental data is as follows:
[0094] Before scheduling: If all tasks are executed in the default order, the total scheduling time is 25,000 minutes.
[0095] After scheduling: After adopting the dynamic decision-making model, the total scheduling time was optimized to 20,000 minutes, and the task scheduling efficiency was improved by 20%.
[0096] The specific task execution time comparison is as follows:
[0097]
[0098] 2. Execution accuracy and execution time of instruction generation
[0099] The instruction generation module generates operation instructions through optimization algorithms. Experimental results show that:
[0100] Before optimization: The instruction set execution accuracy was 87%, and the average execution time was 18 minutes.
[0101] After optimization: The instruction set execution accuracy increased to 93%, the average execution time was reduced to 15 minutes, and the optimization range was about 17%.
[0102]
[0103] 3. Resource allocation and load balancing
[0104] The system optimizes resource allocation in a high-concurrency environment through a distributed architecture and intelligent caching mechanism. The following is a summary of resource usage under different task loads:
[0105] Without optimization:
[0106] CPU usage: 80%
[0107] Memory usage: 85%
[0108] System delay: average delay 15 minutes
[0109] After optimization:
[0110] CPU usage: 70%
[0111] Memory usage: 75%
[0112] System delay: Average delay reduced to 10 minutes, an improvement of approximately 33%.
[0113] 4. Feedback adjustment and optimization
[0114] The feedback adjustment module optimizes the scheduling strategy through deep learning, reducing task delays and resource waste. The following is the change in the loss function in the experiment:
[0115] Initial loss value: 0.45
[0116] Loss after 1000 iterations: 0.12
[0117] Optimization extent: Losses were reduced by 73%, and the scheduling strategy was significantly optimized.
[0118] 5. Cloud computing and virtual node scheduling
[0119] The system uses a cloud computing-based resource scheduling and distribution platform, which further improves the scalability of the system through cross-node scheduling and task allocation. The following is the resource scheduling effect under different task loads:
[0120] Task load 5000:
[0121] Scheduling efficiency: 100%
[0122] System response time: 10 minutes
[0123] Task load 10000:
[0124] Scheduling efficiency: 95%
[0125] System response time: 20 minutes
[0126] Experimental Conclusion
[0127] The following conclusions can be drawn from this experiment:
[0128] Scheduling efficiency is significantly improved: The system significantly improves the efficiency of task scheduling through reinforcement learning scheduling strategies, and task scheduling time is reduced by 20%.
[0129] Instruction execution optimization: Through machine learning optimization algorithms, the system achieves a better balance between execution accuracy and execution time, and instruction execution efficiency is improved by 17%.
[0130] Strong high-concurrency processing capabilities: The system optimizes resource allocation through distributed architecture and intelligent caching mechanism, successfully reducing system load and latency, and performs well in high-concurrency environments.
[0131] The deep learning optimization feedback mechanism is effective: the feedback adjustment module effectively optimized the scheduling strategy, reducing the loss value by 73%, demonstrating that the system has strong adaptive capabilities.
[0132] High scalability: The system uses the cloud computing platform's resource scheduling to maintain high scheduling efficiency and low system response time when processing more tasks.
[0133] While 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 these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.
Claims
1. A full-cycle scheduling instruction management system, characterized in that: include: Scheduling control module, used to generate scheduling plans based on task priority, resource availability, and urgency; An instruction generation module is used to generate specific operation instructions according to the scheduling plan; Instruction management module, used for unified management, storage, scheduling and distribution of instructions; Execution monitoring module, used to monitor the execution progress and results of instructions in real time; The feedback adjustment module is used to adjust the subsequent scheduling strategy according to the execution feedback to ensure the efficient operation of the system.
2. A full-cycle scheduling instruction management system according to claim 1, characterized in that: The scheduling control module further includes a dynamic decision model, the mathematical formula of which is as follows: in, S is the optimal scheduling solution; X is the task scheduling decision vector; w i is the weight of task i; Cost i (X) is the execution cost of task i under scheduling scheme X; λ is the scheduling penalty coefficient; Penalty(X) is the penalty term of scheduling scheme X, which is used to consider the processing of burst tasks.
3. A full-cycle scheduling instruction management system according to claim 1, characterized in that: The instruction generation module uses an optimization algorithm based on machine learning to intelligently generate optimal instructions based on task characteristics. The mathematical formula of the optimization algorithm is as follows: in, Y is the generated operation instruction; Accuracy(Y) is the execution accuracy of instruction set Y; α is the coefficient that weighs execution accuracy and execution time; ExecutionTime(Y) is the execution time of instruction set Y.
4. A full-cycle scheduling instruction management system according to claim 1, characterized in that: The feedback adjustment module uses a deep learning-based feedback mechanism to automatically optimize the scheduling algorithm based on execution data. The deep learning optimization formula is: in, L loss is the loss function; y i Feedback for actual implementation; is the predicted value; w j is the weight of the neural network; λ is the regularization parameter.
5. The full-cycle scheduling instruction management system according to claim 1, characterized in that: The instruction management module adopts a distributed architecture, which can efficiently share instruction data among multiple nodes, and the instruction generation module adopts an optimization algorithm based on machine learning.
6. A full-cycle scheduling instruction management system according to claim 1, characterized in that: The instruction management module optimizes instruction storage and access paths through an intelligent cache mechanism.
7. The full-cycle scheduling instruction management system according to claim 1, characterized in that: The system uses a data recording and verification mechanism based on blockchain technology to ensure the security and non-tamperability of instructions. The execution monitoring module can conduct real-time evaluation of execution progress through multi-dimensional analysis, automatically predict task completion time, and adjust resource allocation in advance.
8. The full-cycle scheduling instruction management system according to claim 1, characterized in that: The system adopts a cloud computing-based resource scheduling and distribution platform, which can flexibly schedule and process tasks among multiple virtual nodes, thereby improving the scalability of the system.