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CN-121981478-A - Collinear workshop element dynamic reconstruction method based on space-time urgency deduction

CN121981478ACN 121981478 ACN121981478 ACN 121981478ACN-121981478-A

Abstract

The invention provides a collinear workshop element dynamic reconstruction method based on space-time urgency deduction, and belongs to the technical field of intelligent manufacturing. Aiming at the problems of response lag and system oscillation caused by order fluctuation in a multi-variety variable batch collinear production scene, the method constructs a manufacturing service collaborative network model with embedded task batch and workload intensity. The method comprises the steps of calculating the local resource urgency of each community in real time, calculating the network coupling urgency based on the betweenness centrality and the upstream neighbor state, constructing a risk-sensitive and trend-aware space-time urgency deduction evaluation function by combining the first-order momentum trend and the historical fluctuation risk of the urgency, and finally executing a constraint evolution game mechanism with hysteresis characteristics to drive dynamic resource reconstruction under the condition that an evolution threshold value is met and the constraint is reconstructed. The invention effectively solves the problem of mismatch between supply and demand through a multidimensional urgency deduction and hysteresis buffer mechanism, and realizes self-adaptive regulation and global optimization of workshop productivity.

Inventors

  • CHENG YING
  • LIU HONGTING
  • CHEN JIAHAO
  • TAO FEI

Assignees

  • 北京航空航天大学

Dates

Publication Date
20260505
Application Date
20260126

Claims (10)

  1. 1. A collinear workshop element dynamic reconstruction method based on space-time urgency deduction is characterized by comprising the following steps: Constructing a co-linear workshop process network, namely constructing a manufacturing service collaborative network model embedded with task batches, and defining network nodes, communities, association relation edges and production task dynamic operation rules; Step 2, based on real-time running state data of a network model, calculating accumulated waiting workload and idle node quantity of each community, obtaining workload rate and idle rate through normalization, and quantifying resource space-time urgency; Step3, analyzing the network space-time coupling urgency, namely constructing a risk-sensitive and trend-aware space-time urgency deduction analysis method, and integrating instantaneous benefits, trend prediction and risk assessment; Step 4, analyzing a network dynamic reconfiguration evolution game mechanism, namely calculating net evolution benefits among communities when the mechanism is executed, judging whether an evolution threshold and a reconfiguration cost constraint are met, and driving resources to be orderly reconfigured among communities and updating states; And step 5, generating and executing a workshop resource reconstruction scheme, namely iteratively updating the global state, including task processing progress promotion, reconstruction node activation and network topology adjustment, and circularly executing urgency deduction and evolution decision until a termination condition is met.
  2. 2. The method of claim 1, wherein in step 1, the building of the collaborative network model for manufacturing services of the embedded task lot includes three parts, namely network structure modeling, core matrix building and dynamic network rule formulation, specifically: the modeling of a network structure, namely defining three core elements of nodes, communities and edges of the network, and defining the topological association relationship among the elements; establishing a node state matrix and an edge connection matrix, and quantitatively describing the connection attribute of the node state and the edge; And setting node state conversion, task time sequence constraint and quantity conservation rules, and guaranteeing the rationality of the dynamic operation of the network.
  3. 3. The method for dynamically reconstructing a network structure according to claim 2, wherein the modeling of the network structure is implemented by: The network model is expressed as Wherein For the purpose of node bonding, Is an edge set; Involving rigid node subsets And flexible node subset ; Corresponding to fixed production service equipment/personnel contained in the rigid production line, the nodes are provided with fixed positions and strict sequence constraints; The nodes have high maneuverability and reconfigurability corresponding to movable production service equipment/personnel contained in the flexible manufacturing area; Dividing into rigid edge subsets With flexible edge subsets ; The fixed directional edge with invariable direction in the rigid production line ), The dynamic edges which can change direction at any time in the flexible manufacturing area comprise undirected edges among nodes in the production community and variable directed edges among the production community; Community of people Formed by aggregation of nodes with the same manufacturing capacity at the same time, the attribution relation between the nodes and communities meets the following conditions The network comprises a plurality of communities with different functions ; Manufacturing task Decomposition into modular subtasks constrained by context constraints, functionally, each class subtask Defined as Wherein For the task Is a subtask sequence number of (1) representing a subtask Required to pass through Working procedure, batch production Is a product of (a).
  4. 4. The method for dynamically reconstructing according to claim 2, wherein the state matrix is constructed by the following specific implementation manner: node state matrix Time of day Node The state matrix of (2) is Wherein 0 Indicates idle/available, 1 indicates occupied; A remaining time unit representing completion of the current activity; representing the community ID to which the node belongs; Edge connection matrix Node of the design And (3) with Is connected by the edges of Wherein -1 Represents a non-directional connection with a capability node, 0 represents a non-connection, 1 represents a directional connection with a sequential relationship; The task switching time is represented, and the value is 0 when no connection or no directional connection exists; indicating the cost of the logistics switch.
  5. 5. The method for dynamically reconstructing according to claim 2, wherein the dynamic network rule is formulated by the following specific implementation manner: The node state transition rule is that when the node accepts subtasks or starts reconstruction, Change from 0 to 1 when In the time-course of which the first and second contact surfaces, From 1 to 0; task timing rules, subtasks Execution time of (a) Wherein For a batch of tasks, Is a community Internal unit product processing time, subtask start time should be satisfied And the completion time is ; Quantity conservation rules-the sum of the number of all community nodes is equal to the total size of the network, i.e Wherein each community maintains at least one active node, i.e The number of products in the community is not more than the number of available nodes, namely , wherein, Representing communities of people At the moment of time Is defined by the number of nodes of the (a), Is the number of subtasks currently processed in the community.
  6. 6. The method for dynamically reconstructing according to claim 1, wherein the implementation of the "shop production resource local urgency analysis" in step 2 is as follows: calculating the accumulated waiting workload of the community, namely the community At the moment of time Is set as a waiting subtask of (1) Accumulating waiting workload Network total waiting workload ; Calculating the number of idle nodes of community Is the number of idle nodes Total idle node number of network ; Calculating community normalization index, namely working load rate Idle rate ; Quantifying resource urgency: the value range is Positive values indicate urgent demands for community manufacture, negative values indicate surplus resources within the community.
  7. 7. The method for dynamically reconstructing according to claim 1, wherein the implementation of the "network coupling urgency analysis" in step 3 is as follows: while taking into account instantaneous urgency Trend of urgency change Risk of urgency fluctuation The content of the three aspects; Wherein the instantaneous urgency Deriving network coupling urgency brought by network topology based on urgency at current time I.e. Wherein Rigid nodes as network vulnerability multipliers Flexible node ; Is the mesogenic centrality of the community; in order for the dynamic coupling coefficient to be a function of, Is a collection of upstream neighboring communities; trend of urgency change Characterizing the degree of change in the degree of urgency of network coupling; risk of urgency fluctuation Wherein In order to view the size of the window, Is a moving average of the urgency within the window; community space-time coupling urgency deduction function Wherein And (2) and , Is a risk avoidance factor.
  8. 8. The method for dynamically reconstructing according to claim 1, wherein in step 4, the specific implementation process of the "network dynamic reconstruction evolution game mechanism" is as follows: setting evolution parameters to reconstruct the book Threshold of evolution Delay in switching time ; Calculating evolution benefits for source communities With the target community Net benefit of ; Node reconstruction decisions when the net benefit exceeds a set benefit threshold, i.e And is also provided with When an idle node exists, the reconfiguration is triggered, the node state is set as 'in reconfiguration', The target community is set as ; Community belonging update-locking participation in evolution And (3) with Avoid repeated decision until reaching maximum evolution times Or combinations of communities that do not meet the condition.
  9. 9. The method for dynamically reconstructing as recited in claim 1, wherein the implementation of the "generating and executing workshop resource reconstruction scheme" in step 5 is as follows: Advancing task processing progress, namely updating all working nodes When (when) When the current task is completed and the task is converted into an idle state; activating reconstruction node, updating the node in' reconstruction When (when) At this time, the node is activated and the target community is added, Turning to idle; Adjusting network topology, namely updating edge connection matrix according to node community attribution change The connection relation of the two; And (3) performing iteration: returning to the step 2 to recalculate the resource urgency and the urgency value, and cycling until Reaching the maximum simulation time Or the system supply and demand reaches dynamic balance.
  10. 10. The method of dynamic reconfiguration of claim 3, wherein the variable directed edges between the production communities represent that a stream of material is available between the two nodes.

Description

Collinear workshop element dynamic reconstruction method based on space-time urgency deduction Technical Field The invention belongs to the technical field of intelligent manufacturing, and relates to a collinear workshop element dynamic reconstruction method based on space-time urgency deduction, which is particularly suitable for flexible collinear production environments with multiple varieties and variable batches. Background With the continuous upgrade of intelligent production and large-scale customization demands, traditional shop rigid production models have been difficult to adapt to dynamically changing manufacturing environments. The traditional production line adopts a fixed layout and static resource allocation mode, and lacks flexible response capability to task types and batch fluctuation. In the intelligent production scene, the manufacturing process is transformed towards digitization and service, and the self-shared manufacturing intelligent main body gradually replaces fixed equipment to form a modularized manufacturing service cooperation mode. However, the conventional production management method omits the randomness of task demands, the switching cost and time delay of resource reconstruction due to service matching and scheduling in a multi-focus static environment, has insufficient autonomy consideration on intelligent manufacturing resources, is difficult to cope with community bottleneck or idle problems caused by supply and demand mismatch, and particularly has the problems of system jitter, low efficiency and the like caused by frequent heavy structure when facing complex interferences such as batch mutation, dynamic change of cooperative relation and the like, and cannot realize dynamic balance and global optimization of resource supply and demand. Disclosure of Invention The invention aims to overcome the defects that a resource reconstruction mechanism is static, the reconstruction cost and hysteresis thereof are ignored, the consideration of demand trend and fluctuation risk is lacked, and system jitter is easy to cause in the existing manufacturing service collaboration, and provides a collinear workshop element dynamic reconstruction method based on space-time urgency deduction. The method aims at constructing a high-fidelity manufacturing service cooperative network model with embedded workload, and provides basis for dynamic balanced configuration and efficient cooperative scheduling of flexible workshop resources through real-time monitoring of task supply and demand matching states, quantitative calculation of resource urgency, comprehensive evaluation of multidimensional urgency (current state-trend prediction-risk evaluation) and constrained network evolution intelligent decision, so that the resource utilization rate and dynamic adaptability of a manufacturing service cooperative system, and the running stability and global optimization capability in a resource reconstruction process are improved. According to the collinear workshop element dynamic reconstruction method based on space-time urgency deduction, a high-efficiency system for supply-demand perception, trend prejudgment and dynamic resource reconstruction is constructed through a manufacturing service collaborative network model embedded in a working batch, a multi-dimensional urgency deduction method and a constraint evolution game mechanism with hysteresis characteristics. The method can integrate task batch, node state, community load and other multidimensional data in real time, construct a high-fidelity dynamic network model and realize full-period dynamic management and control of the manufacturing service collaborative process. The method comprises the steps of capturing supply and demand tension relations through quantifying resource urgency, predicting future demand trend through combining workload momentum, avoiding unstable risks through fluctuation assessment, optimizing reasonable flow of resources among communities through net evolution income calculation and reconstruction constraint control, and guaranteeing global supply and demand balance. The method can effectively inhibit system jitter caused by frequent heavy load, improves the resource utilization rate, dynamic adaptability and operation stability of a flexible workshop, provides an intelligent resource allocation solution for complex and changeable manufacturing environments under large-scale customization, and has important practicability and popularization value. The technical scheme adopted by the invention for solving the technical problems is that the method for dynamically reconstructing the collinear workshop elements based on space-time urgency deduction comprises the following steps: the method comprises the steps of 1, constructing a Manufacturing Service Cooperation (MSC) network model embedded with workload, defining definition and association rules of nodes (manufacturing services), communities (functional service cluste