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CN-122022390-A - Intelligent dynamic scheduling and resource allocation system and method for hardware production

CN122022390ACN 122022390 ACN122022390 ACN 122022390ACN-122022390-A

Abstract

The invention relates to the technical field of process control, in particular to an intelligent dynamic scheduling and resource allocation system and method for hardware production, comprising the steps of firstly acquiring production equipment operation information, task queuing information, mould occupation information, material distribution information and order delivery information, and establishing a resource association relation; and finally, generating a resource allocation correction scheme according to the order delay risk, and outputting an order delay result prevention and control result by combining execution feedback. The invention can reduce the degree of resource mismatch, alleviate the conflict of die occupation and compress the order delay risk.

Inventors

  • Lin Quanmin

Assignees

  • 杭州煜品科技有限公司

Dates

Publication Date
20260512
Application Date
20260409

Claims (10)

  1. 1. An intelligent dynamic scheduling and resource allocation system for hardware production, which is characterized by comprising: The data construction module is used for acquiring production equipment operation information, task queuing information, die occupation information, material distribution information and order delivery information, and establishing resource association relations among equipment, tasks, dies and materials; The deviation evaluation module is used for extracting equipment load characteristics and task priority characteristics from the resource association relationship, grouping production equipment by adopting k-means clustering to obtain equipment grouping results, and determining priority matching deviation indexes according to the equipment grouping results and the resource association relationship, wherein the priority matching deviation indexes are used for representing matching deviation between equipment load and task priority; The scheduling generation module is used for determining the die occupation conflict information according to the resource association relation and the priority matching deviation index, optimizing a die transfer path by adopting a genetic algorithm when the deviation index corresponding to the die occupation conflict information is higher than a preset conflict threshold value, and generating an updated scheduling scheme according to the die occupation conflict information; and the feedback correction module is used for determining order delay risks according to the updated scheduling scheme and the order delivery information, generating a resource allocation correction scheme according to the order delay risks, acquiring execution feedback information corresponding to the resource allocation correction scheme, and outputting an order delay result prevention and control result according to the execution feedback information.
  2. 2. The system of claim 1, wherein the data construction module is configured to time align the production equipment operation information, the task queuing information, the die occupation information, the material distribution information, and the order delivery information, and to establish a correspondence between equipment, tasks, dies, and materials according to a production equipment identifier, a task identifier, a die identifier, and a material lot identifier, to obtain the resource association.
  3. 3. The system of claim 1, wherein the deviation evaluation module is configured to extract a device load feature and a task priority feature from the resource association relationship, and group production devices by using k-means clustering, so as to obtain the device grouping result.
  4. 4. The system of claim 3, wherein the deviation evaluation module is configured to determine a device load level according to the device load characteristics corresponding to each device group in the device grouping result, determine a task priority level according to the task priority characteristics corresponding to each device group in the resource association relationship, and determine the priority matching deviation index according to a deviation degree between the device load level and the task priority level.
  5. 5. The system of claim 4, wherein the bias evaluation module is further configured to determine a corresponding device group as an abnormal device group when the priority matching bias index is higher than a preset bias threshold, and send the resource association relationship corresponding to the abnormal device group to the schedule generation module.
  6. 6. The system of claim 1, wherein the schedule generating module is configured to determine an overlapping relationship of occupation times of a plurality of tasks for a same mold according to a task processing period and a mold occupation period corresponding to the resource association relationship, and determine the mold occupation conflict information according to the overlapping relationship of occupation times.
  7. 7. The system of claim 6, wherein the schedule generation module is configured to optimize a die transfer path using a genetic algorithm according to the die occupation conflict information, the priority matching deviation index, and a process matching relationship between a die and a production facility when a deviation index corresponding to the die occupation conflict information is higher than a preset conflict threshold, and to adjust at least one of a die use order, a task processing order, and an allocation relationship between a task and the production facility according to the optimized die transfer path, to generate the updated schedule.
  8. 8. The system of claim 1, wherein the feedback correction module is configured to determine an order delay risk for a task to be completed based on the updated scheduling scheme and the order delivery information, and to generate the resource allocation correction scheme based on the order delay risk.
  9. 9. The system of claim 8, wherein the feedback correction module is further configured to obtain the execution feedback information corresponding to the resource allocation correction scheme, verify the order delay risk according to the execution feedback information, and output the order delay result prevention and control result according to a verification result.
  10. 10. An intelligent dynamic scheduling and resource allocation method for hardware production, which is applied to the intelligent dynamic scheduling and resource allocation system for hardware production according to any one of claims 1 to 9, and is characterized by comprising the following steps: Acquiring production equipment operation information, task queuing information, die occupation information, material distribution information and order delivery information, and establishing resource association relations among equipment, tasks, dies and materials; Extracting equipment load characteristics and task priority characteristics from the resource association relationship, grouping production equipment by adopting k-means clustering to obtain equipment grouping results, and determining priority matching deviation indexes according to the equipment grouping results and the resource association relationship, wherein the priority matching deviation indexes are used for representing matching deviation between equipment load and task priority; determining die occupation conflict information according to the resource association relation and the priority matching deviation index, and optimizing a die transfer path by adopting a genetic algorithm when the deviation index corresponding to the die occupation conflict information is higher than a preset conflict threshold value to generate an updated scheduling scheme; Determining order delay risks according to the updated scheduling scheme and the order delivery information, generating a resource allocation correction scheme according to the order delay risks, acquiring execution feedback information corresponding to the resource allocation correction scheme, and outputting an order delay result prevention and control result according to the execution feedback information.

Description

Intelligent dynamic scheduling and resource allocation system and method for hardware production Technical Field The invention relates to the technical field of process control, in particular to an intelligent dynamic scheduling and resource allocation system and method for hardware production. Background Hardware manufacturing generally involves multi-equipment parallel processing, multi-die repeated calling, multi-batch material distribution and multi-order cross production, and has the advantages of tight production beat, strong resource coupling and direct influence on capacity release, delivery stability and production cost by a process control level. In the prior art, the arrangement and resource allocation depend on fixed rules or manual experience, equipment running states, task queuing states, mold occupation states, material in-place states and order delivery constraints are often dispersed with each other, unified data association is difficult to form, when equipment loads are not matched with task urgency, an effective deviation recognition mechanism is usually lacking in the existing scheme, when the same mold is occupied by multiple tasks in competition, conflict recognition and dynamic adjustment are difficult to be completed in time in the existing scheme, meanwhile, feedback after the adjustment is performed in most schemes is underutilized, so that abnormality cannot be corrected in a closed loop in time, and equipment idle running, mold waiting, material mismatching and order delay are further caused. Disclosure of Invention The invention provides an intelligent dynamic scheduling and resource allocation system and method for hardware production, which are used for at least solving the problems of resource mismatch and order delay caused by lack of linkage control among equipment load, task priority, die occupation and order delivery in the existing production scheduling. In a first aspect, the present invention provides an intelligent dynamic scheduling and resource allocation system for hardware production, including: The data construction module is used for acquiring production equipment operation information, task queuing information, die occupation information, material distribution information and order delivery information, and establishing resource association relations among equipment, tasks, dies and materials; the deviation evaluation module is used for extracting equipment load characteristics and task priority characteristics from the resource association relationship, grouping production equipment by adopting k-means clustering to obtain equipment grouping results, determining priority matching deviation indexes according to the equipment grouping results and the resource association relationship, wherein the priority matching deviation indexes are used for representing matching deviation between equipment load and task priority; The scheduling generation module is used for determining the die occupation conflict information according to the resource association relation and the priority matching deviation index, optimizing a die transfer path by adopting a genetic algorithm when the deviation index corresponding to the die occupation conflict information is higher than a preset conflict threshold value, and generating an updated scheduling scheme according to the die occupation conflict information; The feedback correction module is used for determining order delay risks according to the updated scheduling scheme and the order delivery information, generating a resource allocation correction scheme according to the order delay risks, acquiring execution feedback information corresponding to the resource allocation correction scheme, and outputting an order delay result prevention and control result according to the execution feedback information. In one possible implementation manner, the data construction module is configured to time align production equipment operation information, task queuing information, mold occupation information, material distribution information and order delivery information, and establish a correspondence between equipment, tasks, molds and materials according to the production equipment identifier, task identifier, mold identifier and material batch identifier, so as to obtain a resource association relationship. In one possible implementation, the deviation evaluation module is configured to extract a device load feature and a task priority feature from the resource association relationship, and group production devices by adopting k-means clustering to obtain a device grouping result. In one possible implementation manner, the deviation evaluation module is configured to determine a device load level according to a device load feature corresponding to each device group in the device grouping result, determine a task priority level according to a task priority feature corresponding to each device group in the resource association relationship,