Complex project-oriented task priority adaptive adjustment system and method

Abstract

The invention relates to the technical field of task scheduling, in particular to a task priority self-adaptive adjustment system and method for a complex project, and provides the following scheme that a plurality of dependency graphs are constructed by obtaining a plurality of task data sets of a target project and extracting multi-dimensional features of time, resources and dependency relationships; and the tasks are divided into a first-layer task, a second-layer task and a third-layer task. And calculating the priority of each layer of tasks in sequence, and generating a global task execution sequence in combination with the time urgency of the first priority, the resource demand of the second priority and the dependency relationship of the third priority. And when an adjustment condition is triggered, sequentially adjusting the priorities according to the priority levels, and dynamically updating a task execution sequence. The task scheduling efficiency and the global adaptability of the complex project can be improved, and the production efficiency and the resource utilization rate are improved.

Description

Technical Field

[0001] The present application relates to the field of task scheduling technology, and in particular to a system and method for adaptively adjusting task priorities for complex projects. Background Art

[0002] In the task management and scheduling process of complex projects, especially in dynamic environments involving multiple tasks, multiple resources, and multiple constraints, effectively determining the execution order of tasks is a long-standing challenge. Prioritization of tasks is typically based on a single-dimensional static model, such as determining priority based on time urgency or resource requirements. However, this single-dimensional priority calculation method struggles to fully reflect the multidimensional attributes and global relevance of tasks in complex project environments, resulting in scheduling solutions that often lack flexibility and global optimization capabilities.

[0003] The problem raised by this background technology is: the existing scheduling technology lacks a dynamic adjustment mechanism for priority, resulting in the inability of task execution order to adapt to actual production needs. To solve the above problem, this application designs a task priority adaptive adjustment system and method for complex projects. Summary of the Invention

[0004] The technical problem to be solved by this application is to address the deficiencies of the existing technology and provide a system and method for adaptive adjustment of task priorities for complex projects. By acquiring multiple task data sets of the target project, extracting multi-dimensional features of time, resources and dependencies, constructing multiple dependency graphs, and dividing tasks into first-level tasks, second-level tasks and third-level tasks. The priorities of tasks at each level are calculated in turn, and the global task execution order is generated by combining the time urgency of the first priority, the resource requirements of the second priority and the dependencies of the third priority. When the adjustment conditions are triggered, the priorities are adjusted in turn according to the priority levels, and the task execution order is dynamically updated. This application can improve the task scheduling efficiency and global adaptability of complex projects, and improve production efficiency and resource utilization.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A method for adaptively adjusting task priorities for complex projects, the method comprising:

[0007] Obtain initial data for multiple tasks based on the target project and generate multiple task datasets;

[0008] The tasks are layered according to the task data set, a plurality of priorities are calculated in sequence, each of the plurality of priorities represents a priority of a feature dimension, and the order of task execution is determined according to the plurality of priorities;

[0009] Adjusting the plurality of priorities according to a preset priority adjustment trigger condition to obtain corresponding update priorities;

[0010] The task execution order is updated according to the update priority.

[0011] The step of stratifying the tasks according to the task data set includes:

[0012] Extracting data features of the task data set, wherein the data features include a first dependency graph, a second dependency graph, and a third dependency graph, wherein the first dependency graph, the second dependency graph, and the third dependency graph have different dependency dimensions;

[0013] According to the data features, the tasks are divided into first-layer tasks, second-layer tasks and third-layer tasks according to the dependency dimensions of the first dependency graph, the second dependency graph and the third dependency graph.

[0014] The extracting data features of the task data set includes:

[0015] Extracting a time field from the task data set, and calculating a first dependency graph based on the time field;

[0016] Matching task elements from the task data set according to predefined task execution rules, and calculating a second dependency graph according to the task elements;

[0017] Directed associations between the task data sets are extracted, and a third dependency graph is calculated using a topological sorting algorithm.

[0018] The multiple priorities are calculated in sequence, including:

[0019] Calculating a first priority based on the time contribution of the first-tier tasks to the target project;

[0020] Calculate a second priority based on the task requirements of the second-tier task in resource allocation and the first priority;

[0021] A third priority is calculated according to the execution order of the third-level tasks in the task dependency chain and in combination with the first priority and the second priority.

[0022] The calculating the first priority includes:

[0023] Define the urgency, importance, and completion indicators for the first-tier tasks;

[0024] Converting the urgency index, importance index, and completion index into a fuzzy judgment matrix in the form of triangular fuzzy numbers, and performing a fuzzy consistency test on the fuzzy judgment matrix;

[0025] After the matrix consistency check passes, the fuzzy judgment matrix is ​​converted into a fuzzy weight vector, and the first priority is calculated according to the fuzzy weight vector;

[0026] The calculating the second priority in combination with the first priority includes:

[0027] Initializing a reinforcement learning environment, wherein the reinforcement learning environment includes an agent, actions, a state space, and rewards;

[0028] Iteratively updating the agent according to the action, state space, and reward, wherein the iterative update includes a policy update, an update step size of which is based on the first priority and is updated according to a policy error;

[0029] After the number of iterations reaches a preset number or the strategy error is less than an error threshold, a second priority is calculated according to the strategy error.

[0030] The calculating a third priority by combining the first priority and the second priority includes:

[0031] Constructing a task dependency chain according to the third dependency graph, wherein each node in the task dependency chain represents a third-layer task, and directed edges between nodes represent pre-dependencies or post-dependencies between tasks;

[0032] Calculate the initial priority based on the first priority, second priority and depth of the node;

[0033] The initial priority of the current node is updated through the propagation mechanism, and the initial priority of each node is used as the third priority.

[0034] The adjusting the multiple priorities to obtain corresponding update priorities includes:

[0035] According to the triggered adjustment condition, if the first priority is adjusted, the second priority and the third priority are adjusted in sequence after the first priority is adjusted to generate an updated priority;

[0036] If the second priority is adjusted, the third priority is adjusted after the second priority is adjusted to generate an updated priority;

[0037] If the third priority is adjusted, after the third priority is adjusted, the third priorities of the remaining upstream third-level tasks are updated according to the position of the adjusted third-level task in the task dependency chain to generate an updated priority.

[0038] When multiple adjustment conditions are triggered simultaneously, the priorities are adjusted in sequence according to the first priority, the second priority, and the third priority to generate an updated priority.

[0039] A task priority adaptive adjustment system for complex projects, comprising a task layering module, a priority calculation module, a priority update module, and a task scheduling module;

[0040] The task stratification module is used to obtain initial data of multiple tasks according to the target project, generate multiple task data sets, and stratify the tasks according to the task data sets;

[0041] The priority calculation module is used to calculate the priority corresponding to each layer according to the stratification result;

[0042] The priority updating module is configured to adjust the plurality of priorities according to a preset priority adjustment trigger condition to obtain corresponding updated priorities;

[0043] The task scheduling module is used to generate a global task scheduling solution according to the priority, or adjust the task execution order according to the updated priority.

[0044] Compared with the prior art, the present invention has the following beneficial effects:

[0045] 1. The present invention obtains multi-dimensional data features of tasks, divides tasks into layers, calculates the priorities of tasks in each layer in turn, generates a global task execution order, and combines a dynamic adjustment mechanism to achieve real-time update of task priorities. It can effectively solve the problems of single task priority calculation, lack of scheduling flexibility and global optimization capabilities in the existing technology.

[0046] 2. The present invention comprehensively considers multiple characteristic dimensions such as the time urgency, resource requirements and dependencies of tasks, so that the task scheduling plan is more in line with actual production needs; and when the project changes dynamically, it can adjust the priority and update the task order in sequence, thereby improving the adaptability and global optimization capabilities of the scheduling plan. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Other features, objects and advantages of the present invention will become more apparent upon reading the detailed description of non-limiting embodiments made with reference to the following drawings:

[0048] Figure 1 This is a flow chart of a method for adaptively adjusting task priorities for complex projects according to embodiment 1 of the present invention;

[0049] Figure 2 Schematic diagram of the MES system priority calculation process in Example 1 of the present invention;

[0050] Figure 3 This is a schematic diagram of the MES system priority adjustment process in Example 1 of the present invention;

[0051] Figure 4 This is a schematic diagram of the data feature extraction process in Example 1 of the present invention;

[0052] Figure 5 This is a module diagram of a system for adaptively adjusting task priorities for complex projects according to embodiment 2 of the present invention. DETAILED DESCRIPTION

[0053] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0054] Example 1:

[0055] See also Figure 1 The present invention provides an embodiment: a method for adaptively adjusting task priorities for complex projects, the specific steps of the method are as follows:

[0056] S1: Obtain the initial data of multiple tasks according to the target project and generate multiple task datasets;

[0057] In this embodiment, the target project may be a production order received by a manufacturing management system (MES), or may be other types of complex task management projects, such as supply chain planning, engineering construction projects, or R&D management projects, without limitation here.

[0058] In this embodiment, initial data can be configured based on the specifics of the target project. For example, for an MES system, initial data includes the production order's task details, delivery time, resource requirements (equipment, personnel, and materials), and inter-task dependencies. For a supply chain planning project, initial data can include purchase orders, logistics time, warehouse capacity, and supplier constraints. For an engineering construction project, initial data can include construction node plans, required machinery and equipment, personnel arrangements, and cross-stage task coordination relationships. Specifically, the initial data for a target project can be summarized as information on the three core dimensions of the target project's time constraints, resource requirements, and dependencies.

[0059] In this embodiment, the target project can be divided into several subtasks with independent execution conditions. The subtasks after task division also include task data sets with time nodes, resource allocation requirements and inter-task dependency logic.

[0060] S2: stratifying the tasks according to the task data set and calculating multiple priorities in sequence;

[0061] In this embodiment, task stratification aims to refine task management within complex projects by clarifying their roles and characteristics within the overall project, thereby providing a structured foundation for dynamic priority calculation. The core logic of stratification lies in breaking down tasks based on time, resources, and dependencies to ensure that scheduling solutions meet global objectives while efficiently utilizing local resources.

[0062] It should be noted that the task hierarchy in this embodiment is targeted at each subtask rather than the overall target project. The first-level tasks primarily consider the impact of tasks on the global goal. For example, in an MES system, the first-level tasks are reflected in the delivery time and key process plans for each production order. The second-level tasks mainly analyze the resource dimension and calculate the priority of each task in resource allocation. The third-level tasks mainly calculate priorities based on the task dependency chain and execution order, focusing on the logical relationship between tasks and the local scheduling order.

[0063] Specifically, the layering process is a structured modeling tool for complex scheduling scenarios. After layering, tasks are prioritized at three levels, reflecting their multidimensional characteristics within the global, resource, and dependency chains. For example, in an MES system, a time-sensitive task with low resource utilization may have a high priority at the factory level but a low priority at the production line level, allowing the scheduling system to accurately allocate resources.

[0064] S3: adjusting the plurality of priorities according to a preset priority adjustment trigger condition to obtain a corresponding update priority;

[0065] In this embodiment, priority adjustment is centered around the sequential adjustment and dynamic adaptation of multiple levels of priority. This is driven by trigger conditions and combined with the logic of hierarchical priority levels to complete the update in sequence. Trigger conditions may include, but are not limited to, changes in global goals, abnormal resource status, and changes in task status.

[0066] In this embodiment, when a trigger condition is activated, the priority of the first-tier tasks is adjusted first. For example, in the scenario of a global goal change, the time urgency weight of the task is adjusted to recalculate the time priority of the task, thereby updating the priority of the first-tier tasks. Adjusting the priority of the first-tier tasks provides a guiding benchmark for subsequent priority adjustments based on the global goal.

[0067] In this embodiment, the priorities of second-tier tasks are adjusted based on the priorities of first-tier tasks. The second-tier task priorities are combined with the adjusted results of the first-tier tasks to optimize the resource dimension. Adjusting resource priorities can include reassessing the matching relationship between tasks and resources. For example, for tasks with urgent resource needs but lower time priorities, the resource usage weight can be reduced, while at the same time, resources can be reallocated to tasks with higher time priorities. The adjusted results are then used in a resource utilization optimization model to dynamically evaluate resource allocation efficiency and conflicts, ensuring the overall rationality of resource allocation.

[0068] In this embodiment, the priorities of third-tier tasks are adjusted based on the updated priorities of second-tier tasks. Third-tier tasks primarily depend on the execution order of tasks and the logical relationships within the dependency chain. The adjustment process is based on the directed graph structure of the task dependency chain and dynamically optimizes the order of tasks within the dependency chain by analyzing the priorities of predecessor and successor tasks. For example, when the resource priority of a task increases significantly, the execution order of its subsequent tasks is automatically adjusted to reduce logical conflicts in scheduling. The priority propagation mechanism within the dependency chain ensures consistency between the logical order of tasks and the global goal.

[0069] S4: Update the task execution order according to the update priority.

[0070] See also Figure 2 and Figure 3, which are respectively a schematic diagram of the MES system priority calculation process and a schematic diagram of the MES system priority adjustment process of an embodiment of the present invention. Specifically, this embodiment proposes a method for adaptively adjusting task priorities for complex projects. Taking the MES system as an example, after receiving a production order through the factory's MES system, the production order is decomposed into multiple production tasks and a task data set is generated. By extracting multi-dimensional features from the task data set, a time task dependency graph, a resource task dependency graph, and a sequence task dependency graph are constructed. The production tasks are divided into three levels: factory-level tasks, production line-level tasks, and workstation-level tasks, and the priorities of tasks at each level are calculated in turn. The factory-level priority is calculated by defining time urgency, order importance, and completion indicators, and converting them into fuzzy weight vectors using fuzzy mathematical methods; the production line-level priority is based on the reinforcement learning method, and dynamically optimizes the matching of tasks and resources by constructing an intelligent agent and resource allocation strategy; the workstation-level priority constructs a directed graph through the task dependency chain, and calculates the initial priority by combining the factory-level priority, production line-level priority, and node depth, and dynamically updates the initial priority through the feature propagation mechanism. After priority calculations are complete, the order of task execution is determined based on multiple priorities. When changes in task status or resource availability are detected, priorities are adjusted sequentially based on the triggered adjustment conditions, ensuring dynamic adaptability of global scheduling goals. When trigger conditions involve adjustments to global goals, factory-level priorities are prioritized, followed by production line-level and then workstation-level priorities. When trigger conditions involve resource allocation issues or changes in task status, the relevant priorities are adjusted in a hierarchical manner and propagated to other tasks. The updated priorities are used to adjust and update the order of task execution in real time, ultimately achieving global optimization and dynamic adaptation of task scheduling in complex projects, improving production efficiency and resource utilization.

[0071] For example, an MES system receives a production order that contains multiple closely related subtasks. These subtasks involve different time nodes (such as production deadlines), resource requirements (such as equipment, materials, and personnel), and dependencies between tasks (such as a task must be performed after the previous task is completed). When faced with such complex projects, traditional technologies often only schedule tasks based on a single-dimensional static priority, ignoring the dynamic changes of multi-dimensional factors. In this embodiment, by layering the subtasks of the production order, the first-level tasks calculate the priority based on the time dimension to ensure that time-critical tasks can be completed first; the second-level tasks calculate the priority based on the resource dimension, and adjust the resource allocation plan based on the first-level priority to maximize resource utilization efficiency; the third-level tasks calculate the priority through dependency relationships to optimize the consistency of the execution order of tasks.

[0072] For example, suppose a production order contains three subtasks: Subtask A must be completed first to meet deadlines, Subtask B requires equipment reallocation due to resource constraints, and Subtask C depends on the completion of Subtask A to start. In this case, the time dimension priority is first calculated to increase the global priority of Subtask A. Next, the priority is adjusted based on the resource dimension to reallocate resources to Subtask B. Finally, the start time of Subtask C is rescheduled based on the dependencies to ensure logical consistency of the schedule and efficient resource utilization.

[0073] Furthermore, when changes in task status or resource conditions are detected, this embodiment can dynamically adjust priorities through triggering conditions. For example, assuming that the completion time of subtask A is delayed, it will trigger the recalculation of the time dimension priority, thereby adjusting its impact on subsequent subtasks B and C; then, the resource dimension priority will update the resource allocation weight of the task based on the result of the time adjustment to ensure the dynamic adaptability of resource allocation; finally, the dependency dimension priority is propagated through the optimization of the task dependency chain to adjust the execution order of all related tasks. This sequential adjustment mechanism enables task scheduling to quickly respond to changes in the dynamic environment, improving production efficiency and the reliability of order delivery.

[0074] The specific steps of S2 are as follows:

[0075] S2.1: Extracting data features of the task data set, wherein the data features include a first dependency graph, a second dependency graph, and a third dependency graph;

[0076] It can be understood that, in this embodiment, the first dependency graph may be a time task dependency graph, the second dependency graph may be a resource task dependency graph, and the third dependency graph may be a sequence task dependency graph;

[0077] Specifically, by constructing the first, second, and third dependency graphs, the time, resource, and dependency characteristics of tasks are visualized and structured. The first dependency graph extracts the time nodes and related constraint information of the task based on the time field, such as the start time, deadline, and estimated duration of the task, and combines the dynamic programming method to calculate the time association weights between tasks to form the time urgency relationship of the tasks. The second dependency graph extracts resource demand information from the data set through predefined task association rules, analyzes the matching relationship between tasks and equipment, personnel, and materials, and combines the Internet of Things technology to obtain the availability and load rate of resources in real time to generate a dynamic mapping relationship between tasks and resources. The third dependency graph constructs a directed graph of tasks based on the pre- or post-dependencies between tasks, identifies the critical path through topological sorting, and analyzes the execution logic of tasks in the dependency chain. Through the construction of these three dependency graphs, the time urgency, resource requirements, and execution order characteristics of tasks can be accurately described.

[0078] See also Figure 4 , a schematic diagram of the data feature extraction process according to an embodiment of the present invention, the specific steps of S2.1 are as follows:

[0079] S2.1.1: Extracting a time field from the task dataset, and calculating a first dependency graph based on the time field;

[0080] Specifically, the time fields extracted from the task dataset include constraint information such as the task's start time, end time, duration requirement, earliest start time, and latest finish time. These time fields can be automatically extracted using predefined parsing rules. For example, natural language processing techniques can be used to identify time keywords from task descriptions, or standardized time fields can be directly read from a database.

[0081] In this embodiment, the first dependency graph is a directed acyclic graph (DAG), where nodes represent tasks and edges represent the temporal dependencies between tasks. The temporal dependencies are calculated using a dynamic programming algorithm, quantified based on the time spans between tasks (e.g., the difference between the earliest task start time and the latest task completion time).

[0082] S2.1.2: Match task elements from the task dataset using predefined task execution rules, and calculate a second dependency graph based on the task elements;

[0083] Specifically, by analyzing the resource types and allocation constraints required for a task, a mapping relationship between tasks and resources is formed. Task elements include, but are not limited to, information such as equipment requirements, personnel requirements, and material requirements. This information is extracted from the task dataset and linked using predefined task execution rules. For example, equipment requirements can be automatically extracted from the task description or resource allocation table, personnel requirements can be determined based on job information and process requirements, and material requirements are dynamically obtained from the inventory database using IoT technology.

[0084] In this embodiment, the nodes in the second dependency graph represent tasks, while the edges represent the mapping relationship between tasks and resources. By analyzing resource availability, resource load, and resource sharing, weights are assigned to the dependencies between tasks and resources. The weights reflect the urgency or resource utilization of the tasks. To avoid resource conflicts, an optimization algorithm based on conflict resolution rules is used during the construction process to adjust the order of task and resource allocation.

[0085] S2.1.3: Extract the directed associations between the task data sets and calculate the third dependency graph using a topological sorting algorithm;

[0086] Specifically, directed relationships between tasks can be identified by analyzing task preconditions, postconditions, and execution order. For example, logical information such as "must be started after a certain task is completed" or "triggered after a certain task is completed" can be extracted from task descriptions, and related information can be automatically identified using natural language processing (NLP) and keyword rule extraction techniques.

[0087] In this embodiment, a directed graph (DAG) of task dependency chains is constructed, where nodes represent tasks and edges represent the logical relationships between tasks. A topological sorting algorithm is used to sort the directed graph and generate a task execution sequence.

[0088] S2.2: Divide tasks into first-tier tasks, second-tier tasks, and third-tier tasks based on data characteristics;

[0089] It can be understood that, in this embodiment, if the target is an MES system, the first-level tasks may be factory-level tasks, the second-level tasks may be production-line-level tasks, and the third-level tasks may be workstation-level tasks;

[0090] Furthermore, in supply chain management projects, the first-tier tasks can be logistics scheduling tasks, which primarily reflect the time objectives and delivery requirements of the entire supply chain. For example, if a supply chain needs to complete the full chain delivery of an order at a specific time point, procurement planning tasks and transportation scheduling tasks will be prioritized in the first tier. The second-tier tasks can be regional warehousing tasks, which primarily focus on resource allocation, such as the allocation of warehousing capacity and optimizing inbound and outbound efficiency. The third-tier tasks can be picking or sorting tasks, which reflect the local execution order of tasks in material handling. This layered approach allows supply chain management to be refined down to the operations of each warehousing node based on global scheduling, improving efficiency and accuracy.

[0091] Going further, in an R&D project, the first-tier tasks might be those for major R&D phases, such as conceptual design, prototyping, or testing. These tasks focus on the overall project timeline and key milestone delivery requirements. The second-tier tasks might be the assignments to each R&D team, such as hardware design, software development, or testing for the testing team, primarily reflecting resource allocation and team collaboration. The third-tier tasks might be specific R&D activities, such as designing a module, writing code, or running specific test cases, primarily reflecting the logical order and dependencies between tasks. This layered approach effectively organizes the R&D process, ensuring efficient collaboration across teams and alignment of task objectives.

[0092] Specifically, task layering is to decompose the scheduling problem of complex projects into manageable sub-problems, so that priority calculations can be carried out in a targeted manner at different levels. The first-level tasks are based on the time-task dependency graph, which represents the contribution of tasks to the global time goal, and the priority is centered on the time urgency of the task; the second-level tasks are based on the resource-task dependency graph, which reflects the task's demand and conflict for resource allocation, and refines the scheduling constraints through the resource dimension; the third-level tasks are based on the sequential task dependency graph, which describes the position and logical order of tasks in the dependency chain, and plays a key role in the local consistency and execution logic optimization of the scheduling scheme. It decouples the multi-dimensional scheduling goals into independent optimization problems at different levels, and reduces the complexity of scheduling calculations through the divide-and-conquer approach.

[0093] S2.3: Calculate the first priority based on the time contribution of the first-tier tasks to the target project;

[0094] In this embodiment, the calculation of the first priority is quantitatively analyzed using three core factors: the urgency index, the target project's importance index, and the task completion index. The urgency index measures the urgency of the task time, reflecting the dynamic ratio of the remaining time to the deadline. The target project's importance index assesses the task's contribution to the overall project goal, highlighting the task's strategic position within the overall project. The completion index quantifies the ratio of the completed to the uncompleted portion of the task, reflecting the actual impact of the task's current execution status on scheduling.

[0095] Specifically, to address the bias in weight allocation caused by subjective judgment and the inability to scientifically handle indicator ambiguity in traditional priority calculations, this embodiment uses fuzzy mathematics to construct a fuzzy judgment matrix, quantifying the relative importance of indicators through triangular fuzziness. Fuzzy calculations incorporate consistency checks and defuzzification techniques to ensure the logical rationality and scientific nature of indicator weights. Dynamic weight adjustments are also implemented to adapt to real-time changes in task status.

[0096] The specific steps of S2.3 are as follows:

[0097] S2.3.1: Define the urgency, importance, and completion indicators for the first-tier tasks;

[0098] In this embodiment, the urgency index is quantified by calculating the ratio of the remaining time of the task to the deadline. Specifically, the remaining time represents the time difference between the current time point and the task deadline. For example, if the remaining time of a task is close to the deadline, its urgency index value will increase significantly, ensuring that the scheduling system can give priority to time-sensitive tasks.

[0099] In this embodiment, the importance index is measured from the perspective of the target project, quantified based on the target project's contribution to the overall business goal. For example, for a manufacturing project, certain tasks may be on the critical path for order delivery, and their importance index will be higher because delays in these tasks will directly impact the overall delivery target. For a supply chain management project, the importance index may be linked to the order fulfillment capabilities of key suppliers, and tasks related to these suppliers will be given a higher importance index value to ensure that the supply of key materials is not affected by delays.

[0100] In this embodiment, the completion index is used to measure the gap between the actual progress of the task and the expected progress. For example, when the task completion rate is low, the completion index is low, thereby prompting the scheduling system to give priority to such tasks.

[0101] S2.3.2: Convert the urgency index, importance index, and completion index into a fuzzy judgment matrix in the form of triangular fuzzy numbers, perform a fuzzy consistency test on the fuzzy judgment matrix, and calculate the fuzzy consistency index;

[0102] Specifically, the urgency index, importance index and completion index are defined as triangular fuzzy numbers ,in, Indicates the minimum possible value of the indicator, reflecting the weight of the indicator in the least important case, Indicates the maximum possible value of the indicator, reflecting the weight of the indicator in the most important case, It represents the most likely value of the indicator and reflects the weight of the indicator under typical conditions;

[0103] Construct language evaluation indicators. Language evaluation includes equally important, slightly important, significantly important, very important and extremely important. The corresponding fuzzy values ​​are: 、 、 、 and .

[0104] Furthermore, based on the time urgency of the task (urgency index), the criticality of the target project (importance index), and the progress of task completion (completion index), a triangular fuzzy judgment matrix was constructed by comparing them pairwise based on expert experience and combining them with the language evaluation scale. The specific form of the matrix is ​​as follows:

[0105] ,

[0106] Where M represents the triangular fuzzy judgment matrix, Each element in the matrix represents the relative importance of indicator i and indicator j, and is assigned according to the language evaluation scale.

[0107] For example, if time urgency is slightly more important than the criticality of the target project, it is assigned the value of “slightly important”, and its corresponding triangular fuzzy number is , the inverse relationship is recorded as If time urgency is significantly more important than task completion progress, it is assigned the value of “significantly important”, and its corresponding triangular fuzzy number is , the inverse relationship is recorded as If the criticality of the target project is slightly more important than the task completion progress, it is assigned as “slightly important”, and its corresponding triangular fuzzy number is , the inverse relationship is recorded as , finally, the triangular fuzzy judgment matrix is ​​recorded as:

[0108] .

[0109] By constructing a triangular fuzzy judgment matrix, the relative importance of time urgency, target project criticality, and task completion progress can be quantified;

[0110] Furthermore, the purpose of the fuzzy consistency test is to ensure that the comparison relationship in the judgment matrix satisfies the consistency principle, that is, if urgency is more important than importance, and importance is more important than completion, then urgency should be more important than completion. First, calculate the fuzzy maximum eigenvalue of the fuzzy judgment matrix , compare the fuzzy maximum eigenvalue with the fuzzy judgment matrix dimension n, and calculate the fuzzy consistency weight of each indicator , calculate the consistency ratio based on the fuzzy consistency weight and random consistency index RI , where RI is the value obtained from a predefined table according to the matrix dimension. For example, when n=3, RI=0.58. When CR is less than 0.1, the judgment matrix passes the consistency test, otherwise the triangular fuzzy judgment matrix needs to be readjusted.

[0111] S2.3.3: After the matrix consistency check passes, the fuzzy judgment matrix is ​​converted into a fuzzy weight vector, and the first priority is calculated based on the fuzzy weight vector;

[0112] Specifically, each column of the fuzzy judgment matrix is ​​summed up to obtain the fuzzy sum of each column. Each element in the matrix is ​​divided by the fuzzy sum of its column to complete the matrix normalization process;

[0113] Furthermore, in the normalized fuzzy matrix, the mean of each row is calculated as the fuzzy weight vector for that indicator. For example, the fuzzy weight values ​​for time urgency, target project criticality, and task completion progress are calculated by taking the mean of the minimum possible value, most likely value, and maximum possible value of each row to form the fuzzy weight vector.

[0114] Furthermore, the fuzzy weight vector is defuzzified by the centroid method, and the triangular fuzzy number is converted into an urgency determination value, a criticality determination value, and a completion determination value. The first priority is calculated based on the three single determination values. The calculation formula for the first priority is:

[0115] ,

[0116] in, Indicates the first priority, Indicates the urgency indicator of the first-level tasks, Represents the importance index of the first-level task, Indicates the completion index of the first-level task, represents the urgency determination value of the urgency indicator, Indicates the critical determination value of the importance indicator, Indicates the completion determination value of the completion indicator.

[0117] S2.4: Calculate the second priority based on the task requirements of the second-tier tasks in resource allocation and the first priority;

[0118] For example, taking the MES system as an example, the calculation of the second-level task priority needs to solve the following problems: competitiveness of resource allocation, dependencies between multiple tasks, and global coordination of time goals. The action of the production task agent is defined as selecting suitable resources, while the action of the resource agent is defined as allocating to appropriate tasks. In the state space of reinforcement learning, the resource requirements of the production task (such as equipment type, material requirements, and staffing) and the real-time load of resources (such as current occupancy and available time window) are used as core state variables. The reward function is designed to maximize resource utilization and the timeliness of task completion. For example, when the resource allocation efficiency of the production task is high or the time urgency goal is met, the agent will receive a higher positive reward. If there is a conflict in resource allocation or the resources cannot meet the task requirements, the agent will be punished.

[0119] The specific steps of S2.4 are as follows:

[0120] S2.4.1: Initialize a reinforcement learning environment, wherein the reinforcement learning environment includes an agent, actions, a state space, and rewards;

[0121] In this embodiment, the reinforcement learning environment is initialized to simulate the interaction process between tasks and resource allocation, and to improve resource utilization efficiency and the accuracy of task priority calculation through dynamic optimization. In the reinforcement learning environment, the reinforcement learning environment is defined as a resource allocation optimization model, in which each task and resource is regarded as an agent. The action is defined as the task agent selecting the required resources based on the current resource status, and the resource agent responding to the request for task allocation and adjusting its load. The state space describes dynamic information such as the current availability of resources, the resource requirements and load conditions of the task, while the reward is defined as the degree of achievement of the optimization goal, such as maximizing resource utilization and improving the completion efficiency of high-priority tasks. The construction of this environment transforms the resource allocation problem into a dynamic optimization problem, and the agent can improve its strategy through continuous learning, thereby achieving an improvement in global resource utilization and task completion efficiency.

[0122] S2.4.2: Iteratively updating the agent based on the action, state space, and reward, wherein the iterative updating includes updating the policy, with an update step size based on the first priority and a policy error;

[0123] In this embodiment, the agent's iterative updates are accomplished through a reinforcement learning algorithm, incorporating the task's first-priority weights to optimize its resource selection strategy. In each iteration, the task agent selects the optimal resource allocation action based on the current state, while the resource agent responds to the allocation decision based on the load status and provides real-time reward feedback. The size of the reward depends on the rationality of resource allocation, such as the degree of resource load balance and the efficiency of completing high-priority tasks. During the iteration process, the agent adjusts its decision parameters based on the policy error (i.e., the deviation between the current allocation strategy and the optimal strategy). The update step size is based on the first-priority priority, with higher-priority tasks receiving larger step sizes to accelerate the policy optimization process. For example, in one example scenario, if a task has a high time priority but the current resource selection strategy is inefficient, the system will accelerate the convergence of the task's strategy by increasing the step size, ensuring rapid optimization of the high-priority task.

[0124] S2.4.3: After the number of iterations reaches a preset number or the policy error is less than the error threshold, a second priority is calculated based on the policy error.

[0125] In this embodiment, the size of the policy error reflects the quality of the resource allocation strategy. Due to the influence of the weight, high-priority tasks usually converge to a better strategy, so their second priority will also increase accordingly. By combining the policy error with the first priority to calculate the second priority, dynamic optimization of task priorities can be achieved. For example, in a production line scenario, after multiple rounds of iterative optimization of the allocation strategy for a task with a high first priority, its resource matching efficiency is significantly improved, so its second priority will also be higher than other tasks. Through this method, the dual requirements of time urgency and resource allocation efficiency can be effectively guaranteed, while improving the overall scheduling efficiency of the production line.

[0126] The calculation formula for the second priority is:

[0127] ,

[0128] in, Indicates the second priority, represents the impact weight coefficient of the strategy error, Indicates the impact weight coefficient of resource utilization, represents the reinforcement learning algorithm, represents the state space, represents the action space, represents the reward function, Indicates the resource utilization of the second-level tasks;

[0129] The calculation formula for the update step size is:

[0130] ,

[0131] in, represents the update step size, Indicates the current step size, represents the basic update rate in the reinforcement learning algorithm, Represents the value function, at the current step Next, execute the strategy The cumulative expected rewards that can be obtained, Represents the policy gradient of the strategy at the current step size;

[0132] S2.5: Calculate the third priority based on the execution order of the third-level tasks in the task dependency chain and the combination of the first priority and the second priority;

[0133] The specific steps of S2.5 are as follows:

[0134] S2.5.1: Construct a task dependency chain based on the third-level dependency graph, where each node in the task dependency chain represents a third-level task, and directed edges between nodes represent pre- or post-dependencies between tasks.

[0135] In this embodiment, the task dependency chain is a directed graph structure constructed based on the third dependency graph, which is used to describe the execution logic and dependency relationship between the third-layer tasks. Each task node represents a specific subtask, and the directed edges between the nodes are used to represent the dependency constraints between tasks. For example, Task B can only be started after Task A is completed, so there is a directed edge from Task A to Task B. The construction of the dependency chain is based on the task logical relationship parsed in the task data set, and is combined with a topological sorting algorithm to generate an ordered chain of task execution. In addition, in order to improve the accuracy of the dependency chain, the dependency relationship will be dynamically updated according to the actual progress of the task, the nodes corresponding to the completed tasks will be deleted, and the pre-dependencies of the related tasks will be adjusted.

[0136] S2.5.2: Calculate the initial priority based on the node's first priority, second priority, and node depth;

[0137] Specifically, the global importance of each task in the dependency chain is quantified by combining the node's first priority, second priority, and node depth. The first priority reflects the task's time urgency, the second priority reflects the task's resource requirements and allocation status, and the node depth indicates the task's hierarchical position in the dependency chain. Node depth is calculated based on the structure of the dependency chain and determined using a depth-first search (DFS) or breadth-first search (BFS) algorithm. A greater depth indicates a task's further from the final goal and a greater weakening factor on its initial priority. In the priority calculation, these three factors are weighted and combined by setting weight coefficients to form the task's initial priority.

[0138] S2.5.3: Traverse the directed graph to extract the longest path, and update the initial priority of each node in the longest path based on the average initial priority of the longest path;

[0139] In this embodiment, the longest path is extracted to identify the critical path in the task dependency chain that has the greatest impact on global scheduling. The longest path is extracted by traversing a directed graph. During the traversal process, the depth of the task nodes along each path is accumulated, and the chain with the longest path length is selected as the critical path. The initial priority of each node on the critical path is adjusted based on the average initial priority of the path to ensure that the tasks on the critical path are completed first. For example, if the longest path contains tasks A, B, and C, and their initial priorities are 0.8, 0.6, and 0.7, respectively, their average initial priority is 0.7. In this case, the priority of each task node on the critical path will be adjusted. The priority update method can set an enhancement factor, such as weighting the average priority of the critical path to the initial priority of the node, to ensure that the critical path tasks have a higher priority during the scheduling process. Alternatively, the influence of the critical path tasks can be strengthened by applying a nonlinear priority enhancement strategy to the priorities of the critical path nodes. For example, for the task nodes on the longest path, an exponential priority growth method is used to dynamically adjust the priority value based on the initial priority of the node and the global importance index of the path.

[0140] Specifically, the priority of each node on the path is not only determined by its own initial priority, but also adaptively adjusted based on the priority distribution of other nodes along the path, ensuring that tasks along the entire critical path are completed with higher priority. This avoids the risk of overall path delays due to the low priority of a single node. Furthermore, a nonlinear improvement strategy enhances the global impact of critical path tasks, further improving the global goal achievement rate and local optimization effectiveness of the task scheduling solution.

[0141] S2.5.4: Each node receives characteristic information from its predecessor node and updates the initial priority based on the characteristic information, wherein the characteristic information includes the first priority, the second priority, and the initial priority of the predecessor node;

[0142] In this embodiment, the update of task priority is based on the feature propagation mechanism of the predecessor node in the task dependency chain. Each task node receives feature information from its direct predecessor node, including the first priority, second priority and initial priority of the predecessor node. For example, if the predecessor node of task B is task A, the priority update formula of task B can be dynamically adjusted according to the feature information of task A, such as transferring the time urgency weight of task A to task B, and performing weighted correction based on the characteristics of task B itself. The principle of this feature propagation mechanism is that each task in the task chain is affected by the status of the predecessor task, and the transitivity of the task status in the dependency chain can be dynamically reflected through feature propagation. For example, if the time priority of the predecessor task A is increased due to delay, the priorities of the subsequent tasks B and C will also be adjusted accordingly to ensure the consistency of the scheduling logic. Through feature transmission between tasks, the task priorities in the dependency chain can be dynamically optimized to ensure that the scheduling scheme can adapt to changes in task status in real time.

[0143] S2.5.5: After feature propagation is complete, use each node’s initial priority as the third priority.

[0144] In this embodiment, after the feature propagation of all tasks in the dependency chain is completed, the initial priority of each task node is officially determined to be the third priority. At this point, the third priority not only reflects the time urgency, resource status, and dependency chain position of the task, but also combines the impact of other tasks on it during the feature propagation process to form a dynamically adjusted priority result. For example, when the time and resource status of Task C's predecessor tasks A and B change, feature propagation can transmit this change to Task C in real time, so that Task C's third priority is consistent with the actual scheduling requirements.

[0145] The specific steps for S3 are as follows:

[0146] S3.1: Based on the triggered adjustment condition, if the first priority needs to be adjusted, the first priority is adjusted, and then the second and third priorities are adjusted in sequence to generate an updated priority.

[0147] In this embodiment, if the triggering condition involves a change in the global objective (e.g., an earlier or later delivery date for a production order), the first priority level needs to be adjusted. Specifically, the adjustment of the first priority level focuses on the time dimension, dynamically adjusting the priority level by analyzing the deadline, remaining time, and task progress status in the task time field. For example, if the delivery date of a production order is earlier, the time urgency weight of the associated tasks will increase, resulting in a recalculation of the first priority level. The adjusted first priority level directly serves as input to the second priority level, reassessing the priority of the tasks in resource allocation. Through resource mapping and dynamic allocation models, the adjusted first priority level is combined with the resource load to update the resource load, for example, prioritizing critical equipment and personnel for urgent tasks. Subsequently, the third priority level is adjusted based on the updated second priority level. In a dependency chain, the logical order of tasks is affected by the interaction of the priorities of the first two levels. For example, if a task needs to be executed earlier due to an increase in time priority, the dependencies of subsequent tasks need to be dynamically adjusted. This layer-by-layer adjustment mechanism ensures global consistency in priority adjustments, effectively avoiding task execution conflicts caused by adjustments at a single level, thereby improving the robustness and global optimization of the scheduling solution.

[0148] S3.2: If the second priority level needs to be adjusted, adjust the third priority level after adjusting the second priority level to generate an updated priority level.

[0149] In this embodiment, if the triggering condition involves resource allocation issues (such as equipment failure, resource overload, or staff shortages), the second priority level needs to be adjusted. Specifically, by real-time monitoring of resource availability and load, the matching relationship between tasks and resources is re-evaluated in conjunction with the resource dependency graph. This adjustment process dynamically lowers the priority of tasks with significant resource conflicts while increasing the priority of tasks with high resource utilization efficiency. The adjusted second priority level directly affects the update of the third priority level, and through the optimized propagation of the dependency chain, the execution order of tasks is replanned. For example, when a task requires resource reallocation due to a device priority adjustment, the execution time of its subsequent dependent tasks also needs to be adjusted to ensure the logical order consistency of the dependency chain. By dynamically optimizing resource allocation, not only can resource conflicts be avoided, but also the execution of time-critical tasks can be prioritized in resource shortages, thereby improving resource utilization efficiency and task scheduling flexibility.

[0150] S3.3: If the third priority needs to be adjusted, after adjusting the third priority, update the third priority of the remaining upstream third-level tasks according to the position of the adjusted third-level task in the task dependency chain to generate an updated priority;

[0151] In this embodiment, if the triggering condition involves a change in the task status (such as task delay, decreased completion rate, or broken dependency chain), the third priority level needs to be adjusted. Specifically, through directed graph analysis of the task dependency chain, the predecessor and successor tasks of the affected task are identified, and their priorities are re-evaluated. For example, when a critical task is delayed, the priority of its successor task will automatically increase, and the critical path in the dependency chain will be recalculated to optimize the execution order of the tasks. At the same time, the task adjustment will be propagated upstream, such as adjusting the remaining priority of the predecessor task to avoid the impact of local adjustments on the global plan. Through this optimized propagation mechanism of the dependency chain, it can be ensured that the adjusted priority level can maintain logical consistency on a global scale, especially in scenarios with frequent dynamic changes, and can quickly adapt to environmental changes to ensure the stability and reliability of task scheduling.

[0152] S3.4: When multiple adjustment conditions are triggered simultaneously, the priorities are adjusted in the order of the first priority, the second priority, and the third priority to generate an updated priority.

[0153] In this embodiment, when multiple adjustment conditions are triggered simultaneously, such as when order delivery is advanced, resource allocation conflicts occur, and task delays occur simultaneously, the order of priority adjustments follows a logical progression from first to third priority. First, the first priority is adjusted with the global goal as the core, for example, by dynamically updating the time urgency weight to ensure that the global time goal is achieved. The adjusted first priority is then passed as input to the second priority, which dynamically optimizes the resource allocation strategy, for example, by prioritizing resources for urgent tasks to resolve resource conflicts. Finally, the third priority is updated based on the first two levels of priority, and the task execution order is replanned through dependency chain analysis to ensure the logical consistency of the adjustment results. For example, when a production line task is delayed due to equipment failure, resources are reallocated by adjusting the time priority, and the logical order of related tasks in the dependency chain is adjusted to minimize the impact of the delay. This sequential adjustment method ensures global consistency of priority adjustments, avoids priority conflicts that may arise when multiple adjustment conditions are triggered simultaneously, and ensures the flexibility and robustness of the scheduling solution.

[0154] In this embodiment, the task execution order is determined and updated based on multiple priorities, specifically including:

[0155] The overall priority of each task is determined based on a weighted model of three priorities: time, resources, and dependencies. The weight of the time priority determines the importance of the task in meeting the global delivery target, while the weight of the resource priority is dynamically adjusted based on the current resource status (such as equipment occupancy and material inventory). The dependency priority further calibrates the logical order between tasks to ensure the rationality and efficiency of the final execution order. For example, for a production order in the MES system, when subtask A has a higher time priority than subtask B, but subtask A has a lower resource priority than subtask B, the comprehensive priority model will combine the priority values ​​of the two and dynamically adjust their execution order based on the global optimization goals of the project.

[0156] The specific process of updating the execution order of tasks is based on a multi-level progressive logic. First, the global urgent tasks are recalculated based on the adjustment of the time dimension priority. For example, when the order delivery time is advanced, the system will immediately increase the priority of the urgent task and propagate this adjustment result backward in the dependency chain to reorder the execution order of related tasks. Subsequently, based on the priority adjustment of the resource dimension, the allocation of key resources will be optimized. For example, through a dynamic allocation algorithm, high-priority tasks are assigned to equipment or materials with priority availability, thereby avoiding scheduling delays caused by resource conflicts. Finally, based on the adjustment mechanism of task dependencies, the task order is updated in the dependency chain directed graph. For example, when a task is postponed, the system will readjust the order of its subsequent tasks by updating the topology of the graph to ensure the logical consistency of the entire scheduling plan.

[0157] Example 2:

[0158] See also Figure 5 The present invention provides an embodiment: a task priority adaptive adjustment system for complex projects, the system comprising a task layering module, a priority calculation module, a priority update module and a task scheduling module;

[0159] The task stratification module is used to obtain initial data of multiple tasks according to the target project, generate multiple task data sets, and stratify the tasks according to the task data sets;

[0160] The priority calculation module is used to calculate the priority corresponding to each layer according to the stratification result;

[0161] The priority updating module is configured to adjust the plurality of priorities according to a preset priority adjustment trigger condition to obtain corresponding updated priorities;

[0162] The task scheduling module is used to generate a global task scheduling solution according to the priority, or adjust the task execution order according to the updated priority.

[0163] Although the embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and are not to be construed as limitations on the present invention. A person skilled in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. A method for adaptively adjusting task priorities for complex projects, characterized by: The method comprises: Obtain initial data for multiple tasks based on the target project and generate multiple task datasets; The tasks are layered according to the task data set, a plurality of priorities are calculated in sequence, each of the plurality of priorities represents a priority of a feature dimension, and the order of task execution is determined according to the plurality of priorities; Adjusting the plurality of priorities according to a preset priority adjustment trigger condition to obtain corresponding update priorities; The task execution order is updated according to the update priority.

2. The method for adaptively adjusting task priorities for complex projects according to claim 1, characterized in that: The step of stratifying the tasks according to the task data set includes: Extracting data features of the task data set, wherein the data features include a first dependency graph, a second dependency graph, and a third dependency graph, wherein the first dependency graph, the second dependency graph, and the third dependency graph have different dependency dimensions; According to the data features, the tasks are divided into first-layer tasks, second-layer tasks and third-layer tasks according to the dependency dimensions of the first dependency graph, the second dependency graph and the third dependency graph.

3. The method for adaptively adjusting task priorities for complex projects according to claim 2, characterized in that: The extracting data features of the task data set includes: Extracting a time field from the task data set, and calculating a first dependency graph based on the time field; Matching task elements from the task data set according to predefined task execution rules, and calculating a second dependency graph according to the task elements; Directed associations between the task data sets are extracted, and a third dependency graph is calculated using a topological sorting algorithm.

4. The method for adaptively adjusting task priorities for complex projects according to claim 2, characterized in that: The multiple priorities are calculated in sequence, including: Calculating a first priority based on the time contribution of the first-tier tasks to the target project; Calculate a second priority based on the task requirements of the second-tier task in resource allocation and the first priority; A third priority is calculated according to the execution order of the third-level tasks in the task dependency chain and in combination with the first priority and the second priority.

5. The method for adaptively adjusting task priorities for complex projects according to claim 4, characterized in that: The calculating the first priority includes: Define the urgency, importance, and completion indicators for the first-tier tasks; Converting the urgency index, importance index, and completion index into a fuzzy judgment matrix in the form of triangular fuzzy numbers, and performing a fuzzy consistency test on the fuzzy judgment matrix; After the matrix consistency check is passed, the fuzzy judgment matrix is ​​converted into a fuzzy weight vector, and the first priority is calculated according to the fuzzy weight vector.

6. The method for adaptively adjusting task priorities for complex projects according to claim 4, characterized in that: The calculating the second priority in combination with the first priority includes: Initializing a reinforcement learning environment, wherein the reinforcement learning environment includes an agent, actions, a state space, and rewards; Iteratively updating the agent according to the action, state space, and reward, wherein the iterative update includes a policy update, an update step size of which is based on the first priority and is updated according to a policy error; After the number of iterations reaches a preset number or the strategy error is less than an error threshold, a second priority is calculated according to the strategy error.

7. The method for adaptively adjusting task priorities for complex projects according to claim 4, characterized in that: The calculating a third priority by combining the first priority and the second priority includes: Constructing a task dependency chain according to the third dependency graph, wherein each node in the task dependency chain represents a third-layer task, and directed edges between nodes represent pre-dependencies or post-dependencies between tasks; Calculate the initial priority based on the first priority, second priority and depth of the node; The initial priority of the current node is updated through the propagation mechanism, and the initial priority of each node is used as the third priority.

8. The method for adaptively adjusting task priorities for complex projects according to claim 4, characterized in that: The adjusting the multiple priorities to obtain corresponding update priorities includes: According to the triggered adjustment condition, if the first priority is adjusted, the second priority and the third priority are adjusted in sequence after the first priority is adjusted to generate an updated priority; If the second priority is adjusted, the third priority is adjusted after the second priority is adjusted to generate an updated priority; If the third priority is adjusted, after the third priority is adjusted, the third priorities of the remaining upstream third-level tasks are updated according to the position of the adjusted third-level task in the task dependency chain to generate an updated priority.

9. The method for adaptively adjusting task priorities for complex projects according to claim 4, characterized in that: When multiple adjustment conditions are triggered simultaneously, adjusting the multiple priorities to obtain corresponding updated priorities further includes: The priorities are adjusted in sequence according to the first priority, the second priority, and the third priority to generate an updated priority.

10. A system for adaptively adjusting task priorities for complex projects, which is used to implement the method for adaptively adjusting task priorities for complex projects according to any one of claims 1 to 9, characterized in that: The system includes a task layering module, a priority calculation module, a priority update module and a task scheduling module; The task stratification module is used to obtain initial data of multiple tasks according to the target project, generate multiple task data sets, and stratify the tasks according to the task data sets; The priority calculation module is used to calculate the priority corresponding to each layer according to the stratification result; The priority updating module is configured to adjust the plurality of priorities according to a preset priority adjustment trigger condition to obtain corresponding updated priorities; The task scheduling module is used to generate a global task scheduling solution according to the priority, or adjust the task execution order according to the updated priority.

Images