CN113283692A - Intelligent man-machine cooperation scheduling method and system for monitoring resource allocation of bulk commodity trading market - Google Patents
Intelligent man-machine cooperation scheduling method and system for monitoring resource allocation of bulk commodity trading market Download PDFInfo
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Abstract
The invention discloses an intelligent man-machine cooperation scheduling method and system aiming at monitoring resource allocation of a large commodity trading market, wherein the method is vital to intelligently schedule and allocate human and machine resources in real time according to task requirements so as to improve task completion rate, and when a task arrives, a task processing module performs solving of part of key path sets and allocation calculation of deadline time according to topological graph relations formed by different links of the task to obtain a task to-be-executed queue and a task pool; secondly, intelligent real-time allocation of man and machine resources is carried out through a resource scheduling module, and the execution sequence and specific starting time of tasks are output; and automatically adjusting a scheduling distribution strategy according to the execution information fed back by the tasks in real time, so that as many tasks as possible are completed before the deadline. The scheduling method gives consideration to the influence of task execution uncertainty, and has the advantages of high scheduling plan robustness and low task delay risk compared with the traditional 'first come first serve' and single fixed allocation mode.
Description
Technical Field
The invention relates to an intelligent man-machine cooperation scheduling technology, and belongs to an intelligent man-machine cooperation scheduling method and system aiming at monitoring resource allocation of a large commodity trading market.
Background
With the rapid development of internet technology, the electronic commerce market in the bulk commodity field develops rapidly, and the number of trading platforms increases day by day. The large commodity trading market involves national strategic materials such as energy, mineral products, cotton, grain and oil and the like, and has the characteristics of large trading quantity, large price fluctuation, large trading risk, large influence on radiation and the like. In recent years, a series of risk events and industry disordering of a bulk commodity electronic commerce market reflect the common current situation and severe problems of difficult market supervision and poor platform service at the present stage.
At present, a bulk commodity electronic commerce platform mainly provides commodity transaction related services for customers and is matched with government departments (mainly clearinghouses) to provide supervision functions such as transaction data submission and the like. Although many functions have been substantially intelligent and automated, some processes remain semi-automated and require the participation of specialized personnel. Professional background knowledge is utilized to combine with high-efficiency performance of computing resources to jointly complete tasks such as wind control intervention, daily final settlement, reconciliation and the like. Under the current nationwide scene of a large number of supervision tasks, the number of workers of a supervision department or a supervision platform is limited, and how to improve the efficiency of a man-machine cooperation mode (as shown in figure 1) so as to achieve the purpose of improving the completion quality of the supervision tasks and reduce the possible loss caused by task lag has certain research significance.
Generally, a supervision task is composed of a plurality of different links with precedence relationship, and relates to the participation and execution of personnel in different departments and platforms, and each task has its own starting time, deadline, estimated execution time and required executive personnel attributes. All tasks form a task flow diagram, and each task needs to be dispatched and distributed to different personnel for execution, as shown in fig. 1, the tasks have complex topological structures, and the matching relationship between the personnel and the tasks, the task execution sequence and the specific start time need to be clarified. But the completion time of the task is uncertain and the time follows a certain normal distribution (which can be fitted by historical data). As shown in example fig. 2, each task has an average completion time μ, a completion time variance σ, and a required resource r, nodes 1, 2, and 3 are different links of the same task, nodes 4 and 5 are different links of the same task, node 0 and node 6 are added start and end nodes, and all attribute values are 0. It can be seen that in the formed task topology, the task represented by each node must be completed before it can begin execution. The uncertainty of task completion time and the limited resources may result in delayed completion of the task, which may result in significant economic loss to the mass market. Therefore, the scheduling plan is expected to have good robustness, can deal with risks brought by delayed completion of tasks, distributes the task scheduling to suitable personnel as far as possible before the deadline comes, and improves the efficiency and quality of the execution of the supervision task scheduling.
Disclosure of Invention
The technical problem is as follows: the invention aims to provide an intelligent man-machine cooperation scheduling method and system for supervising resource allocation in a large commodity trading market. The method gives consideration to the influence of uncertainty of task execution, and avoids the problems of low robustness of a scheduling plan and high task delay risk caused by 'first come first served'.
The technical scheme is as follows: in a supervision resource allocation system of a bulk commodity trading market, a dispatching center node and a plurality of human and machine resources exist. Each task has a different topology structure, each node in the topology structure represents a different subtask and has a corresponding resource requirement, and therefore, how to schedule and allocate different human and machine resources to participate in executing the supervision task is the key of the technical scheme. The main technical scheme of the scheduling method is as follows:
the resources existing in the supervisory resource allocation system can be roughly divided into two categories, heterogeneous human resources with limited amount of professional skills and high-performance computing resources which can be regarded as no upper limit. When a task for supervising a large commodity trading market occurs, task execution time and efficiency are often determined by human resources, because the time efficiency of data processing by computing resources is far higher than the working time efficiency of people, and meanwhile, many links need the participation of professional skills of operators, such as wind control intervention, daily final settlement and the like. Therefore, the key of the technical scheme is the cooperative scheduling of human and machine resources. Uncertainty of execution time is brought when the personnel participate in the execution of the task, how to schedule limited personnel in real time is achieved, and the fact that the task is completed on time to the maximum extent becomes an important index for measuring the rationality of the method.
An intelligent man-machine cooperation scheduling method and system aiming at monitoring resource allocation of a large commodity trading market comprises the following steps:
(1) when the task reaches a dispatching center, the task is disassembled into a topological structure according to a supervision flow, and a source point and a sink point are added to form a directed acyclic graph formed by all the tasks;
(2) judging whether available resources related to tasks exist according to all human and machine resources available in the system, and fitting the mean value and variance of the completion time of different types of tasks and the resource demand according to historical data;
(3) establishing an optimization target of task delay risk and constraints such as time, resources and the like according to the task topological graph, and judging whether the task arrives in real time;
(4) when the task is reached in real time, solving a part of key path sets, calculating the deadline of the child node, adding the executable task to a waiting queue, and placing the rest in a task pool; calculating the task priority of the waiting queue, and performing scheduling distribution according to the attributes of idle human resources and idle machine resources; then according to the feedback information of the task execution condition, releasing occupied human and machine resources, updating the task deadline and the priority, and moving the executable task from the task pool to a waiting queue;
(5) and when the task does not reach the threshold in real time, judging whether the scale of the task and the constraint variable exceed the threshold, if so, quickly solving according to a preset heuristic algorithm or an evolutionary algorithm, if not, calculating an accurate solution by using a branch-and-bound or integer programming solver, then obtaining a scheduling scheme with low delay risk and high robustness, and sequentially executing the task according to the scheduling scheme and feeding back the execution condition.
An intelligent man-machine cooperation scheduling method and system for supervising resource allocation aiming at a large commodity trading market, wherein the system comprises the following steps:
the task processing module is used for decomposing the task into subtasks when the task reaches the scheduling center, and then solving a part of key path sets and calculating the deadline of the subtasks according to a topological graph relation formed by the subtasks so as to obtain a task to-be-executed queue and a task pool (subtasks which cannot be executed immediately);
the resource scheduling module is used for intelligently allocating idle human resources and machine resources in real time along with the execution of the tasks, and determining the matching relation between operators and the tasks, the execution sequence of the tasks and the specific starting time;
and the feedback adjusting module is used for automatically adjusting the scheduling distribution strategy according to the specific execution information (completion time, execution condition and the like) fed back by the task in real time. By adjusting the scheduling strategy in real time, the tasks are distributed to appropriate resources as many as possible and can be completed before the deadline time, and the risk and loss of the trading market caused by the task delay are avoided.
As a preferred technical solution of the present invention, the present invention is characterized in that: in the system, n supervision tasks are counted, and each supervision task can be represented by a five-tuple: t isi＝{Vi,Ei,Ai,Wi,Di}，
Vi,EiRespectively representing subtask point and edge set in directed acyclic graph, n supervision tasks are represented by G < V, E > and TiIn AiTime of arrival, WiRepresenting the set of expected working times of each subtask, DiRepresents the latest completion time of the task, Bi＝num(Vi) Representing a task TiThe number of subtasks. Assuming that the supervisory resource has multiple types RiEach subtask node vij,
All require some type of resource rijEstimated time of operation wijStandard deviation of σij. Each policing resource may be represented by a triplet: ri＝{TYPEi,NUMi,OFFLOADiRepresents the type of resource (human resources represent its work skills, computing resources represent its computing power), quantity and execution time load, respectively. In the directed acyclic graph formed by all tasks, the nodes (tasks) can be continuously completed only after all predecessor nodes (tasks) are completed.
Under uncertain conditions, assume task completion time di,jIs a mean value and standard deviation of mu (d)i,j) And σ (d)i,j) Normal distribution of (c), sigma (d) of taski,j) The larger the instability of its completion time, the greater the risk of delay. Defining a deferred risk weight for a task as
Since the actual start time and completion time of any predecessor task are directly affected after the completion time of the predecessor task is delayed, that is, the delay risk of the current task is increased due to the predecessor activity, and the cumulative delay risk of the task is
The edge (i, n) belongs to the task topological graph set G < V, E >, the robustness optimization index is
Si,jRepresentative is task vi,jActual start time of si,jRepresented is the start time of the task-based scheduling plan, Ni,jRepresentative is task vi,jThe number of predecessor tasks.
As a preferred technical solution of the present invention, the present invention is characterized in that: all tasks in the step (1) form a directed acyclic graph, a partial critical path set solving algorithm is adopted, a part of critical paths in the directed acyclic graph are reversely marked from a sink until no unallocated precursor nodes exist, a partial critical path is obtained, the process is repeated until all nodes are marked, and a critical path set is obtained. Then traversing all subtask nodes in the key path set, wherein the current node is vi,jAccording to the formula
The calculation of its deadline is performed for each node. Mest，Meft，MlctThe earliest starting time, the earliest finishing time and the latest finishing time are respectively calculated according to the topological relation of each node of the directed acyclic graph. v. ofi,1Representing the earliest starting task in a critical path, vi,kRepresenting the task that started the latest. And after the calculation of the deadline is completed, all the subtasks are added to the task pool to be allocated, and the resource allocation is waited.
As a preferred technical solution of the present invention, the present invention is characterized in that: the tasks which can be executed and distributed in the step (4) exist in the task queue, the degree of criticality of the tasks is calculated according to the task deadline, the scheduling center matches each idle human resource with a suitable task with high priority, the task execution is assisted by the calculation resources to assist an operator in executing the tasks, and after one task is completed, the subsequent tasks which can be executed are added into the waiting queue from the task pool.
As a preferred technical solution of the present invention, the present invention is characterized in that: and (4) before the resource scheduling process in the step (4), priority calculation needs to be carried out on the tasks in the waiting queue. All the time stored in the waiting queue is the task that can be allocated resources to start executing, i.e. the predecessor tasks are all completed. Assume that the current system time is tsystem，ω(wi,j) Representing the initial estimated execution time, sigma being the standard deviation of the average execution time, the subtask vi,jPriority P ofi,jDefining:
the emergency degree of the tasks is sequenced according to the priority, the system scheduling center schedules a resource scheduling algorithm, real-time idle resources in the system are distributed according to the priority, and task nodes v of the resources are obtainedi,jRemoved from the queue, the corresponding operator (human resources) will be assigned to task vi,jAnd performing subsequent execution, and simultaneously setting the current resource to be in an occupied state.
As a preferred technical solution of the present invention, the present invention is characterized in that: after each round of resource scheduling is finished, the corresponding operator performs specific execution of the task, and when the task is actually finished, the actual task completion time is required to readjust ssub(vi,j) And adds all new tasks whose predecessor tasks have completed execution to the wait queue. Suppose a current task vi,jThe real time of completion of execution is λ (v)i,j) Then the maximum execution time of the previously reserved subtasks needs to be adjusted to
δ(vi,j)＝dsub(vi,j)-Mest(vi,j)，
Corresponding updated earliest start time
vi,pRepresents vi,jThe predecessor node of (1).
Then the task pool and the subsequent subtask deadline in the waiting queue need to be recalculated,
for a task v that is currently executing completioni,jOf any subsequent task of the current task vi,kIf task vi,kAll predecessor tasks of (d) have been completed except for the need to use λ (v)i,j) To update the deadlines of other subtasks, and also to update vi,kRemoved from the task pool and added to the wait queue, v at this pointi,kResource allocation may be performed.
As a preferred technical solution of the present invention, the present invention is characterized in that: when the tasks are completed, the resources are idle, and the feedback regulation and resource scheduling functions are called successively until all the tasks are allocated with corresponding resources and are executed. When a new task comes, the task needs to be decomposed, corresponding data needs to be calculated, and subtasks are added to a waiting queue or a task pool. When a task is completed, the occupation state of the supervision resources is converted into an idle state, the signal is given to trigger feedback regulation to update and adjust the task states of the task pool and the waiting queue, and then the allocation of the calling resources is triggered to allocate the resources in the idle state.
Assuming that the initial task number in the system is n, each task flow can be decomposed into subtask flows with a topological structure, different subtasks require different resources, and all task links can form a directed acyclic graph. According to historical data, the completion time and the characteristics and the quantity of required human and machine resources can be estimated for each subtask link.
Has the advantages that:
(1) and the task completion rate is improved: the man-machine cooperation scheduling method can dynamically adjust resources in the whole system according to characteristics and requirements of tasks, and improve the task completion rate through timely feedback adjustment of the tasks and reasonable reservation of task delay time.
(2) The resource utilization efficiency is improved: because the supervision resources (mainly manpower, high-performance computing resources are relatively abundant) in the large commodity trading market are limited in quantity and have certain technical characteristics, the task execution is critical, and because the resources are distributed to a large number of supervision tasks in a dynamic scheduling manner all the time, the resources can be better utilized, and the idle waste of the resources is avoided.
(3) High robustness and low risk of postponement: in a large commodity transaction market supervision resource allocation system, the supervision task flow is complex, multiple links are needed, different personnel use professional skills, and high-performance computing resources are used for supervision and examination of transactions. The man-machine cooperation scheduling method effectively reduces task execution uncertainty caused by personnel participation, and reduces delay risks through online scheduling.
Drawings
FIG. 1 is a human-machine collaboration mode
FIG. 2 is a schematic diagram of a task topology
FIG. 3 is a schematic diagram of the main functions of a dispatch system
FIG. 4 is a principal schematic diagram of the method of the present invention
Detailed Description
The invention relates to an intelligent man-machine cooperation scheduling method and system for supervising resource allocation of a large commodity trading market. As shown in fig. 2-4. The method comprises the following steps:
(1) assuming that n supervision tasks are counted in the system, the task decomposition is performed after the task arrives, and each supervision task can be represented by a five-tuple: t isi＝{Vi,Ei,Ai,Wi,Di}，
Vi,EiRespectively representing subtask point and edge set in directed acyclic graph, n supervision tasks are represented by G < V, E > and TiIn AiTime of arrival, WiRepresenting the set of expected working times of each subtask, DiRepresents the latest completion time of the task, Bi＝num(Vi) Representing a task TiThe number of subtasks. Assuming that the supervisory resource has multiple types RiEach subtask node vij,
All require some type of resource rijEstimated time of operation wijStandard deviation of σij. Each policing resource may be represented by a triplet: ri＝{TYPEi,NUMi,OFFLOADiRepresents the type of resource (human resources represent its work skills, computing resources represent its computing power), quantity and execution time load, respectively. In the directed acyclic graph formed by all tasks, the nodes (tasks) can be continuously completed only after all predecessor nodes (tasks) are completed. As shown in fig. 2, it is shown that one task is composed of three subtasks of nodes 1, 2, and 3, another task is composed of nodes 4 and 5, nodes 0 and 6 are added source points and sink points, arrows between the tasks represent a precedence dependency relationship, and three values of each node represent an average value, a standard deviation, and required resource attributes of the task estimated work time, respectively.
Under uncertain conditions, assume task completion time di,jIs a mean value and standard deviation of mu (d)i,j) And σ (d)i,j) Normal distribution of (c), sigma (d) of taski,j) The larger the instability of its completion time, the greater the risk of delay. Defining a deferred risk weight for a task as
Since the actual start time and completion time of any predecessor task are directly affected after the completion time of the predecessor task is delayed, that is, the delay risk of the current task is increased due to the predecessor activity, and the cumulative delay risk of the task is
The edge (i, n) belongs to the task topological graph set G < V, E >, the robustness optimization index is
Si,jRepresentative is task vi,jActual start time of si,jRepresented is the start time of the task-based scheduling plan, Ni,jRepresentative is task vi,jThe number of predecessor tasks.
(2) A certain deferrable time is reserved considering the assignment of a task deadline to each subtask in the topology. Since all tasks constitute a directed acyclic graph, first consider how to reasonably allocate deadlines to different subtasks. And reversely marking part of the key paths in the directed acyclic graph from the sink point by using a part of key path set solving algorithm until no unallocated precursor node exists to obtain a part of key paths, and repeating the process until all nodes are marked to obtain a key path set. Then traversing all subtask nodes in the key path set, wherein the current node is vi,jWhen the cutoff time is calculated as
Mest，Meft，MlctThe earliest starting time, the earliest finishing time and the latest finishing time are respectively calculated according to the topological relation of each node of the directed acyclic graph. v. ofi,1Representing the earliest starting task in a critical path, vi,kRepresenting the task that started the latest. And after the calculation of the deadline is completed, all the subtasks are added to the task pool to be allocated, and the resource allocation is waited. As in fig. 2, the first addition to the wait queue is task nodes 1 and 4 (node 0 is the source of the active addition), and nodes 2, 3, 5, 6 in the task pool.
(3) Before the resource scheduling process, priority calculation needs to be carried out on tasks in the waiting queue. All the time stored in the waiting queue is the task that can be allocated resources to start executing, i.e. the predecessor tasks are all completed. Assume that the current system time is tsystem，ω(wi,j) Representing the execution time of the initial estimate, σ being the averageStandard deviation of execution time, subtask vi,jPriority P ofi,jDefining:
the emergency degree of the tasks is sequenced according to the priority, the system scheduling center schedules a resource scheduling algorithm, real-time idle resources in the system are distributed according to the priority, and task nodes v of the resources are obtainedi,jRemoved from the queue, the corresponding operator (human resources) will be assigned to task vi,jAnd performing subsequent execution, and simultaneously setting the current resource to be in an occupied state. As shown in fig. 3, the waiting queue (i.e., the prepared task queue) is a task set that can be executed immediately after processing tasks, the urgency of the tasks is determined according to the priority, and then the immediately available resources (operator resources and computing resources) are allocated to the task with the highest urgency by resource scheduling, and the corresponding tasks and resources form a corresponding relationship, as shown in the figure, the resource state is occupied (green).
(4) After each round of resource scheduling is completed, the corresponding operator performs specific execution of the task, however, the specific completion condition and completion time of the task cannot be accurately estimated. When the task is actually completed, the real task completion time is needed to readjust dsub(vi,j) And adds all new tasks whose predecessor tasks have completed execution to the wait queue. Suppose a current task vi,jThe real time of completion of execution is λ (v)i,j) Then the maximum execution time of the previously reserved subtasks needs to be adjusted to
δ(vi,j)＝dsub(vi,j)-Mest(vi,j)，
Corresponding updated earliest start time
vi,pRepresents vi,jThe predecessor node of (1). Then the deadlines of the subsequent subtasks in the task pool and the waiting queue need to be renewedThe calculation is updated in such a way that,
for a task v that is currently executing completioni,jOf any subsequent task of the current task vi,kIf task vi,kAll predecessor tasks of (d) have been completed except for the need to use λ (v)i,j) To update the deadlines of other subtasks, and also to update vi,kRemoved from the task pool and added to the wait queue, v at this pointi,kResource allocation may be performed. As shown in fig. 2, after the subtask nodes 1 and 4 are scheduled and allocated to the corresponding operators for execution, if the node 1 is actually executed, the corresponding operators may be allocated, and the expected deadline of the subtask nodes 2 and 3 needs to be recalculated according to the actual completion time of the node 1, and meanwhile, the nodes 2 and 3 need to be removed from the task pool and added to the waiting queue to wait for resource scheduling and allocation.
(5) And (4) when the tasks are finished and the resources are idle, successively calling the feedback regulation in the step (4) and the resource scheduling in the step (3) until all the tasks are allocated with the corresponding resources and are executed. And when a new task arrives, the step (1) is required to be called, corresponding data are decomposed and calculated for the task, and subtasks are added to a waiting queue or a task pool. As shown in fig. 3, when a task is completed, the resource occupancy state (green) is changed to an idle state (white), the signal is given to trigger feedback adjustment to update and adjust the task states of the task pool and the waiting queue, and then resource allocation is triggered and called to allocate the idle state resources.
Claims (8)
1. An intelligent man-machine cooperation scheduling method and system aiming at monitoring resource allocation of a large commodity trading market is characterized in that: the method comprises the following steps:
(1) when the task reaches a dispatching center, the task is disassembled into a topological structure according to a supervision flow, and a source point and a sink point are added to form a directed acyclic graph formed by all the tasks;
(2) judging whether available resources related to tasks exist according to all human and machine resources available in the system, and fitting the mean value and variance of the completion time of different types of tasks and the resource demand according to historical data;
(3) establishing an optimization target of task delay risk and constraints such as time, resources and the like according to the task topological graph, and judging whether the task arrives in real time;
(4) when the task is reached in real time, solving a part of key path sets, calculating the deadline of the child node, adding the executable task to a waiting queue, and placing the rest in a task pool; calculating the task priority of the waiting queue, and performing scheduling distribution according to the attributes of idle human resources and idle machine resources; then according to the feedback information of the task execution condition, releasing occupied human and machine resources, updating the task deadline and the priority, and moving the executable task from the task pool to a waiting queue;
(5) and when the task does not reach the threshold in real time, judging whether the scale of the task and the constraint variable exceed the threshold, if so, quickly solving according to a preset heuristic algorithm or an evolutionary algorithm, if not, calculating an accurate solution by using a branch-and-bound or integer programming solver, then obtaining a scheduling scheme with low delay risk and high robustness, and sequentially executing the task according to the scheduling scheme and feeding back the execution condition.
2. An intelligent man-machine cooperation scheduling method and system aiming at monitoring resource allocation of a large commodity trading market is characterized in that: the system comprises:
the task processing module is used for decomposing the task into subtasks when the task reaches the scheduling center, and then solving a part of key path sets and calculating the deadline of the subtasks according to a topological graph relation formed by the subtasks so as to obtain a task to-be-executed queue and a task pool (subtasks which cannot be executed immediately);
the resource scheduling module is used for intelligently allocating idle human resources and machine resources in real time along with the execution of the tasks, and determining the matching relation between operators and the tasks, the execution sequence of the tasks and the specific starting time;
and the feedback adjusting module is used for automatically adjusting the scheduling distribution strategy according to the specific execution information (completion time, execution condition and the like) fed back by the task in real time. By adjusting the scheduling strategy in real time, the tasks are distributed to appropriate resources as many as possible and can be completed before the deadline time, and the risk and loss of the trading market caused by the task delay are avoided.
3. The intelligent human-machine cooperation scheduling method and system for regulation resource deployment in the market for mass commodity transaction according to claim 2, wherein: in the system, n supervision tasks are counted, and each supervision task can be represented by a five-tuple: t isi＝{Vi，Ei，Ai，Wi，Di}，
Vi，EiRespectively representing subtask point and edge set in directed open-loop graph, and directed acyclic graph formed by n supervision tasks represented by G < V, E > and task TiIn AiTime of arrival, WiRepresenting the set of expected working times of each subtask, DiRepresents the latest completion time of the task, Bi＝num(Vi) Representing a task TiThe number of subtasks. Assuming that the supervisory resource has multiple types RiEach subtask node
All require some type of resource rijEstimated time of operation wijStandard deviation of σij. Each policing resource may be represented by a triplet: ri＝{TYPEi，NUMi，OFFLOADiRepresents the type of resource (human resources represent its work skills, computing resources represent its computing power), quantity and execution time load, respectively. In the directed acyclic graph formed by all tasks, the nodes can continue to finish the process only after all predecessor nodes (tasks) finishBecomes the node (task).
Under uncertain conditions, assume task completion time di，jIs a mean value and standard deviation of mu (d)i，j) And σ (d)i，j) Normal distribution of (c), sigma (d) of taski，j) The larger the instability of its completion time, the greater the risk of delay. Defining a deferred risk weight for a task as
Since the actual start time and completion time of any predecessor task are directly affected after the completion time of the predecessor task is delayed, that is, the delay risk of the current task is increased due to the predecessor activity, and the cumulative delay risk of the task is
The edge (i, n) belongs to the task topological graph set G < V, E >, the robustness optimization index is
Si，jRepresentative is task vi，jActual start time of si，jRepresented is the start time of the task-based scheduling plan, Ni，jRepresentative is task vi，jThe number of predecessor tasks.
4. The intelligent human-machine cooperation scheduling method and system for regulation resource deployment in the market for mass commodity transaction according to claim 1, wherein: all tasks in the step (1) form a directed acyclic graph, and a part of key paths in the directed acyclic graph are reversely marked from a sink through a part of key path set solving algorithm until the part of key paths are not storedAnd obtaining a part of key paths at the unallocated precursor nodes, and repeating the process until all the nodes are marked to obtain a key path set. Then traversing all subtask nodes in the key path set, wherein the current node is vi，jAccording to the formula
The calculation of its deadline is performed for each node. Mest，Meft，MlctThe earliest starting time, the earliest finishing time and the latest finishing time are respectively calculated according to the topological relation of each node of the directed acyclic graph. v. ofi，1Representing the earliest starting task in a critical path, vi，kRepresenting the task that started the latest. And after the calculation of the deadline is completed, all the subtasks are added to the task pool to be allocated, and the resource allocation is waited.
5. The intelligent human-machine cooperation scheduling method and system for regulation resource deployment in the market for mass commodity transaction according to claim 1, wherein: the tasks which can be executed and distributed in the step (4) exist in the task queue, the degree of criticality of the tasks is calculated according to the task deadline, the scheduling center matches each idle human resource with a suitable task with high priority, the task execution is assisted by the calculation resources to assist an operator in executing the tasks, and after one task is completed, the subsequent tasks which can be executed are added into the waiting queue from the task pool.
6. The intelligent human-machine cooperation scheduling method and system for regulation resource deployment in the market for mass commodity transaction according to claim 2, wherein: and (4) before the resource scheduling process in the step (4), priority calculation needs to be carried out on the tasks in the waiting queue. All the time stored in the waiting queue is the task that can be allocated resources to start executing, i.e. the predecessor tasks are all completed. Assume that the current system time is tsystem，ω(wi，j) Representing execution of initial predictionsTime, σ is the standard deviation of the average execution time, subtask vi，jPriority P ofi，jDefining:
the emergency degree of the tasks is sequenced according to the priority, the system scheduling center schedules a resource scheduling algorithm, real-time idle resources in the system are distributed according to the priority, and task nodes v of the resources are obtainedi，jRemoved from the queue, the corresponding operator (human resources) will be assigned to task vi，jAnd performing subsequent execution, and simultaneously setting the current resource to be in an occupied state.
7. The intelligent human-machine cooperation scheduling method and system for regulation resource deployment in the market for mass commodity transaction according to claim 1, wherein: after each round of resource scheduling is finished, the corresponding operator performs specific execution of the task, and when the task is actually finished, the real task completion time is required to readjust dsub(vi，j) And adds all new tasks whose predecessor tasks have completed execution to the wait queue. Suppose a current task vi，jThe real time of completion of execution is λ (v)i，j) Then the maximum execution time of the previously reserved subtasks needs to be adjusted to
δ(vi，j)＝dsub(vi，j)-Mest(vi，j)，
Corresponding updated earliest start time
vi，pRepresents vi，jThe predecessor node of (1).
Then the task pool and the subsequent subtask deadline in the waiting queue need to be recalculated,
for a task v that is currently executing completioni，jOf any subsequent task of the current task vi，kIf task vi，kAll predecessor tasks of (d) have been completed except for the need to use λ (v)i，j) To update the deadlines of other subtasks, and also to update vi，kRemoved from the task pool and added to the wait queue, v at this pointi，kResource allocation may be performed.
8. The intelligent human-machine cooperation scheduling method and system for regulation resource deployment in the market for mass commodity transaction according to claim 1, wherein: when the tasks are completed, the resources are idle, and the feedback regulation and resource scheduling functions are called successively until all the tasks are allocated with corresponding resources and are executed. When a new task comes, the task needs to be decomposed, corresponding data needs to be calculated, and subtasks are added to a waiting queue or a task pool. When a task is completed, the occupation state of the supervision resources is converted into an idle state, the signal is given to trigger feedback regulation to update and adjust the task states of the task pool and the waiting queue, and then the allocation of the calling resources is triggered to allocate the resources in the idle state.
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113918323A (en) * | 2021-09-17 | 2022-01-11 | 中标慧安信息技术股份有限公司 | High-energy-efficiency computing task allocation method and device in edge computing |
| CN115392814A (en) * | 2022-11-01 | 2022-11-25 | 中科雨辰科技有限公司 | Data processing system for configuration task |
| CN115423603A (en) * | 2022-08-31 | 2022-12-02 | 厦门国际银行股份有限公司 | Wind control model establishing method and system based on machine learning and storage medium |
| CN115936331A (en) * | 2022-11-01 | 2023-04-07 | 中科雨辰科技有限公司 | Data processing system for obtaining target task object based on distance |
| CN117095804A (en) * | 2023-10-19 | 2023-11-21 | 江苏健康无忧网络科技有限公司 | Medical examination idle period scheduling method |
| WO2023242942A1 (en) * | 2022-06-14 | 2023-12-21 | 日本電信電話株式会社 | Schedule execution assistance device, method, and program |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108345501A (en) * | 2017-01-24 | 2018-07-31 | 全球能源互联网研究院 | A kind of distributed resource scheduling method and system |
| CN108920261A (en) * | 2018-05-23 | 2018-11-30 | 中国航天系统科学与工程研究院 | A kind of two-stage self-adapting dispatching method suitable for large-scale parallel data processing task |
| CN108958919A (en) * | 2018-07-13 | 2018-12-07 | 湘潭大学 | More DAG task schedule expense fairness assessment models of limited constraint in a kind of cloud computing |
-
2021
- 2021-03-19 CN CN202110296445.9A patent/CN113283692B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108345501A (en) * | 2017-01-24 | 2018-07-31 | 全球能源互联网研究院 | A kind of distributed resource scheduling method and system |
| CN108920261A (en) * | 2018-05-23 | 2018-11-30 | 中国航天系统科学与工程研究院 | A kind of two-stage self-adapting dispatching method suitable for large-scale parallel data processing task |
| CN108958919A (en) * | 2018-07-13 | 2018-12-07 | 湘潭大学 | More DAG task schedule expense fairness assessment models of limited constraint in a kind of cloud computing |
Non-Patent Citations (1)
| Title |
|---|
| 郭美云;杨博;陈志刚;: "基于网格资源超图模型的可信任务调度", 计算机工程, no. 13 * |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113918323A (en) * | 2021-09-17 | 2022-01-11 | 中标慧安信息技术股份有限公司 | High-energy-efficiency computing task allocation method and device in edge computing |
| WO2023242942A1 (en) * | 2022-06-14 | 2023-12-21 | 日本電信電話株式会社 | Schedule execution assistance device, method, and program |
| CN115423603A (en) * | 2022-08-31 | 2022-12-02 | 厦门国际银行股份有限公司 | Wind control model establishing method and system based on machine learning and storage medium |
| CN115392814A (en) * | 2022-11-01 | 2022-11-25 | 中科雨辰科技有限公司 | Data processing system for configuration task |
| CN115936331A (en) * | 2022-11-01 | 2023-04-07 | 中科雨辰科技有限公司 | Data processing system for obtaining target task object based on distance |
| CN115936331B (en) * | 2022-11-01 | 2023-10-27 | 中科雨辰科技有限公司 | Data processing system for acquiring target task object based on distance |
| CN117095804A (en) * | 2023-10-19 | 2023-11-21 | 江苏健康无忧网络科技有限公司 | Medical examination idle period scheduling method |
| CN117095804B (en) * | 2023-10-19 | 2023-12-29 | 江苏健康无忧网络科技有限公司 | Medical examination idle period scheduling method |
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