CN113837532B - Dynamic scheduling system for job shop - Google Patents
Dynamic scheduling system for job shop Download PDFInfo
- Publication number
- CN113837532B CN113837532B CN202110934754.4A CN202110934754A CN113837532B CN 113837532 B CN113837532 B CN 113837532B CN 202110934754 A CN202110934754 A CN 202110934754A CN 113837532 B CN113837532 B CN 113837532B
- Authority
- CN
- China
- Prior art keywords
- platform
- module
- data
- production
- scheduling
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000004519 manufacturing process Methods 0.000 claims abstract description 90
- 238000004891 communication Methods 0.000 claims abstract description 27
- 238000004458 analytical method Methods 0.000 claims abstract description 10
- 230000002457 bidirectional effect Effects 0.000 claims abstract description 3
- 230000005540 biological transmission Effects 0.000 claims abstract description 3
- 238000007726 management method Methods 0.000 claims description 43
- 238000003860 storage Methods 0.000 claims description 35
- 239000002994 raw material Substances 0.000 claims description 31
- 230000002159 abnormal effect Effects 0.000 claims description 25
- 239000011265 semifinished product Substances 0.000 claims description 25
- 239000000047 product Substances 0.000 claims description 17
- 238000013480 data collection Methods 0.000 claims description 11
- 238000000034 method Methods 0.000 claims description 8
- 238000012545 processing Methods 0.000 claims description 8
- 238000009826 distribution Methods 0.000 claims description 6
- 238000012544 monitoring process Methods 0.000 claims description 5
- 238000013523 data management Methods 0.000 claims description 3
- 230000007423 decrease Effects 0.000 claims description 3
- 238000004148 unit process Methods 0.000 claims description 3
- 238000013461 design Methods 0.000 abstract description 6
- 230000000694 effects Effects 0.000 description 5
- 238000010586 diagram Methods 0.000 description 2
- 230000008676 import Effects 0.000 description 2
- 238000003780 insertion Methods 0.000 description 2
- 230000037431 insertion Effects 0.000 description 2
- 238000012423 maintenance Methods 0.000 description 2
- 239000000463 material Substances 0.000 description 2
- 238000010923 batch production Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000010485 coping Effects 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06312—Adjustment or analysis of established resource schedule, e.g. resource or task levelling, or dynamic rescheduling
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/10—Office automation; Time management
- G06Q10/103—Workflow collaboration or project management
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/04—Manufacturing
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/30—Computing systems specially adapted for manufacturing
Abstract
The invention discloses a job shop dynamic scheduling system, and relates to the technical field of scheduling systems. The system comprises a terminal, a task platform, a model building module, a workshop platform, a management platform, an area cooperative platform and a cloud server, wherein a database, an autonomous analysis module and a scheduling platform are respectively mounted on the cloud server, a data port of the terminal performs bidirectional data transmission with the area cooperative platform through the task platform, one end of a data output port of the terminal is in communication connection with the scheduling platform through a 4G/5G/WIFi network, and one end of a data output port of the scheduling platform is respectively connected with a dynamic constraint module and a task allocation module. According to the invention, through the design of the dispatching platform and the management platform, the device can efficiently complete equipment dispatching and equipment dispatching work of a production workshop, and the system is provided with the acquisition module for workshop production data on the basis of a traditional dispatching system.
Description
Technical Field
The invention belongs to the technical field of dispatching systems, and particularly relates to a job shop dynamic dispatching system.
Background
With rapid development of science and technology and increasing market competition, the production mode of most enterprises is changed from traditional single, large-batch production to multi-variety, small-batch or single-piece production, the structure type of the production process is further complicated, and the traditional production scheduling method cannot adapt to the demand of highly-variable orders.
The high variability and unpredictability of manufacturer group orders between suppliers and suppliers on the supply chain makes their production scheduling problem particularly complex, with the main problems of conventional scheduling for existing enterprises:
1. the load of the factory equipment is uneven, part of equipment cannot be operated at full load, even at lower load, and part of equipment is operated at overload;
2. the capability of dealing with equipment faults and emergency bill insertion is poor, and part of product orders cannot be delivered on schedule;
3. the equipment failure rate and the capacity estimation capability of each piece of equipment are weak;
based on this, the present invention provides a job shop dynamic scheduling system to solve the above-mentioned problems in the background art.
Disclosure of Invention
The invention aims to provide a job shop dynamic scheduling system, which solves the problems of poor scheduling effect and estimation prediction capability of the existing shop dynamic scheduling system through the design of processes such as a regional collaboration platform, a scheduling platform and the like.
In order to solve the technical problems, the invention is realized by the following technical scheme:
the invention relates to a job shop dynamic scheduling system, which comprises a terminal, a task platform, a model building module, a shop platform, a management platform, an area cooperative platform and a cloud server, wherein a database, an autonomous analysis module and a scheduling platform are respectively carried on the cloud server;
the automatic control system is characterized in that one end of the workshop platform is provided with an acquisition module, a data output end of the acquisition module is connected with a progress prediction module, a communication end of the progress prediction module is connected with an early warning module, communication ports of the early warning module are respectively connected with a big data collection module and an AI allocation module, a data output end of the big data collection module is respectively connected with a model building module and a database, one end of the model building module is in communication connection with a management platform, and one end of a communication output port of the AI allocation module or a dispatching platform is connected with a dynamic constraint module.
Preferably, the terminal is any one of a PC/mobile phone terminal, the terminal outputs a scheduling instruction to a scheduling platform, the terminal performs related workshop processing or scheduling task browsing and receiving operation in a task platform through a wireless network, the task platform and a region collaboration platform are any one of a WEB page, a chat group, an exchange community and APP integrated software, the region collaboration platform provides a communication platform for a plurality of factories with similar production equipment, similar production workshops and similar production quality in a region, and the factories perform workshop demand sending, idle production workshop information sending, equipment maintenance method sharing communication, equipment production efficiency report sharing communication, workshop task allocation and optimizing method sharing communication, unsold product type and product information sharing and unused raw materials and semi-finished product information sharing through the region collaboration platform.
Preferably, the scheduling platform is any one of a WEB page, a chat group and an APP, the scheduling platform distributes and distributes tasks to the workshop platform through a task distribution module, the scheduling platform distributes tasks which cannot be completed according to needs and are completed according to quality to the regional collaboration platform through the task distribution module so as to seek collaboration and further meet production requirements and production requirements, the scheduling platform performs updating of algorithms, adding, deleting and maintaining of information by factory management staff, and an instruction sent by the scheduling platform is backed up by a database.
Preferably, the workshop platform respectively comprises a logistics unit, a storage unit and a production unit, wherein the logistics unit conveys finished products processed by the production unit into the storage unit for storage, the logistics unit conveys unprocessed semi-finished products and processed raw materials from the storage unit to the production unit for processing, the production unit processes unfinished semi-finished products and processed raw materials into finished products, the logistics unit conveys finished products, semi-finished products and raw materials processed in the storage unit to a designated place, and operators in the logistics unit, the storage unit and the production unit all hold data uploading terminals.
Preferably, the collecting module collects the image data information and the dynamic event information in the logistics unit, the storage unit and the production unit in real time through the camera, the collecting module collects the workshop information data uploaded by the data uploading terminal in real time, the collecting module transmits all kinds of collected data to the management platform and the progress prediction module in real time, the progress prediction module is a data analysis and prediction algorithm, the progress prediction module monitors the production data or the production efficiency in the unit time or the set time according to the collecting data of the collecting module, the progress prediction module predicts the producible data of the production unit in the set time according to the production data in the unit time of the production unit, the progress prediction module predicts the raw material or the semi-finished product consumption data in the unit time or the set time according to the collecting data of the collecting module, the raw material or the semi-finished product consumption data in the unit time and the purchase data of the semi-finished product in the set time are simultaneously obtained according to the collecting data of the collecting module, the progress prediction module predicts the producible data of the raw material or the semi-finished product in the set time in the unit time and the transportation unit time according to the collecting data of the collecting module, and the transportation efficiency in the unit time of the logistics unit in the set time and the transportation module can be predicted according to the collecting data in the unit time of the collecting module.
Preferably, the progress prediction module synchronously outputs the collected related data and the predicted data to the early warning module, the early warning module is a numerical comparison algorithm, when the early warning module monitors that the production data in the unit time of the production unit, the transportation data in the unit time of the logistics unit, the raw material consumption data in the unit time of the storage unit and the purchasing data are lower than a threshold value or a set value, the early warning module synchronously outputs an abnormal alarm signal to the scheduling platform and the AI allocation module, after receiving the early warning signal of the early warning module, the AI allocation module or the scheduling platform specifically restricts the production efficiency, the raw material purchasing efficiency or the logistics efficiency in the next unit time to each unit in the workshop platform through the dynamic restriction module, when the acquisition module monitors that the related data in the next unit time still meet the threshold value, the scheduling platform increases or decreases the specific value of the related unit efficiency restriction in the workshop platform according to the predicted data of the progress prediction module, and sends an instruction to the management platform, after receiving the instruction by the management platform, the related unit is subjected to the cooperative task allocation after the workshop restriction module.
Preferably, the management platform monitors abnormal production data and abnormal events of the workshop platform in real time according to the early warning data of the acquisition early warning module and the monitoring data of the acquisition module, when the management platform monitors the related abnormal production data or the abnormal events, the management platform maintains, updates, discards, adds, arranges and produces related production apparatuses or manages related specific workshop personnel, the management platform enables the workshop platform to recover the platform in a manual intervention mode after receiving the abnormal events, the management platform simultaneously receives control instructions of the scheduling platform to prompt and manage each unit of the workshop platform, and the workshop platform can also independently report the related abnormal data or the abnormal events to the management platform through the autonomous feedback module.
Preferably, the progress prediction module, the early warning module, the AI allocation module or the scheduling platform and the autonomous analysis module are integrated on the scheduling platform and updated and maintained by a user of the scheduling platform, the database is used for backup storage of scheduling instruction data of the scheduling platform and backup storage of data collected by the collecting module, the data in the database is synchronously uploaded to the cloud server, and the autonomous analysis module provides data support and algorithm support for the AI allocation module or the scheduling platform through a data management allocation algorithm.
The invention has the following beneficial effects:
1. according to the invention, the device can efficiently complete equipment scheduling and equipment scheduling work of a production workshop through the design of the scheduling platform and the management platform, the system is additionally provided with the acquisition module for workshop production data on the basis of a traditional scheduling system, the production data and the production efficiency of the workshop can be monitored in real time through the increase of the acquisition module, the production efficiency and the production data in the workshop can be intelligently predicted through the design of the progress prediction module, and the scheduling effect and the use uniformity of the workshop equipment of the system are effectively improved through the realization of the intelligent prediction effect.
2. According to the invention, through the design of the regional collaboration platform, the single-plant working mode of the traditional factory scheduling system is changed into the multi-plant collaborative working mode, the performance of the system for coping with emergency bill insertion is further improved through multi-plant collaboration, the operation efficiency of workshops can be dynamically adjusted and restrained in real time through the design of the dynamic restraint module, and the completable effect and the completable degree of the system during operation and scheduling are effectively improved and ensured through the realization of the dynamic adjustment effect.
Of course, it is not necessary for any one product to practice the invention to achieve all of the advantages set forth above at the same time.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed for the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow diagram of a job shop dynamic scheduling system;
fig. 2 is a schematic flow structure diagram of the scheduling platform and the acquisition module.
In the drawings, the list of components represented by the various numbers is as follows:
1. a regional collaboration platform; 2. a dispatching platform; 3. a workshop platform; 4. a management platform; 5. a cloud server; 6. a database; 7. a model building module; 8. a dynamic constraint module; 9. an autonomous feedback module; 10. an acquisition module; 11. a progress prediction module; 12. an early warning module; 13. a big data collection module; 14. and an AI allocation module.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1-2, the invention discloses a job shop dynamic scheduling system, which comprises a terminal, a task platform, a model building module 7, a shop platform 3, a management platform 4, an area cooperative platform 1 and a cloud server 5, wherein a database 6, an autonomous analysis module and a scheduling platform 2 are respectively carried on the cloud server 5, and a data port of the terminal performs bidirectional data transmission with the area cooperative platform 1 through the task platform;
one end of a terminal data output port is in communication connection with a dispatching platform 2 through a 5G network, one end of the dispatching platform 2 data output port is respectively connected with a dynamic constraint module 8 and a task allocation module, the dispatching platform 2 dynamically adjusts the specific constraint values according to the workshop production task and the production efficiency which are output to the workshop platform 3 through the dynamic constraint module 8, and the dispatching platform 2 dynamically adjusts the specific constraint values according to the acquired data of an acquisition module 10;
the task allocation port of the dispatching platform 2 is respectively connected with the workshop platform 3 and the regional collaboration platform 1 through a task allocation module, one end of the communication port of the workshop platform 3 is respectively connected with the dispatching platform 2 and the management platform 4 through an autonomous feedback module 9, and one ends of the communication interfaces of the management platform 4 and the dynamic constraint module 8 are both communicated with the workshop platform 3;
one end of the workshop platform 3 is matched with an acquisition module 10, a data output end of the acquisition module 10 is connected with a progress prediction module 11, a communication end of the progress prediction module 11 is connected with an early warning module 12, communication ports of the early warning module 12 are respectively connected with a big data collection module 13 and an AI allocation module 14, the data output end of the big data collection module 13 is respectively connected with a model building module 7 and a database 6, one end of the model building module 7 is in communication connection with the management platform 4, and one end of a communication output port of the AI allocation module 14 or the scheduling platform 2 is connected with the dynamic constraint module 8.
The terminal outputs a scheduling instruction to the scheduling platform 2, the terminal browses and receives operations of related workshop processing or scheduling tasks in the task platform through a wireless network, the task platform and the regional collaboration platform 1 are any one of WEB webpages, chat groups, communication communities and APP integrated software, the regional collaboration platform 1 provides a communication platform for a plurality of factories with similar production devices, similar production workshops and similar production quality in the region, and the factories send out workshop demands, send out information of idle production workshops, share and communicate an instrument maintenance method, share and communicate an instrument production efficiency report, share and communicate workshop task allocation and optimizing methods, share and communicate unsold finished product types and finished product information and share unused raw materials and semi-finished product information through the regional collaboration platform 1.
The scheduling platform 2 is any one of WEB pages, chat groups and APP, the scheduling platform 2 distributes and delivers tasks to the workshop platform 3 through a task distribution module, the scheduling platform 2 distributes tasks which cannot be completed according to needs and are completed according to quality to the regional collaboration platform 1 through the task distribution module so as to seek collaboration and further meet production requirements and production requirements, the scheduling platform 2 performs algorithm updating, information adding, deleting and maintaining through factory management staff, and an instruction sent by the scheduling platform 2 is backed up by the database 6.
The workshop platform 3 comprises a logistics unit, a storage unit and a production unit, wherein the logistics unit conveys finished products processed by the production unit into the storage unit for storage, the logistics unit conveys unprocessed semi-finished products and processed raw materials from the storage unit into the production unit for processing, the production unit processes unfinished semi-finished products and processed raw materials into finished products, the logistics unit conveys finished products, semi-finished products and raw materials processed in the storage unit to a designated place, and operators in the logistics unit, the storage unit and the production unit all hold data uploading terminals.
The collecting module 10 collects image data information and dynamic event information in the logistics unit, the storage unit and the production unit in real time through the camera, the collecting module 10 collects workshop information data uploaded by the data uploading terminal in real time, the collecting module 10 transmits various collected data to the management platform 4 and the progress predicting module 11 in real time, the progress predicting module 11 analyzes and predicts data and predicts algorithms, the progress predicting module 11 monitors production data or production efficiency in unit time or set time according to the collected data of the collecting module 10, and the progress predicting module 11 predicts producible data of the production unit in set follow-up unit time according to the production data in a plurality of unit time of the production unit;
the progress prediction module 11 monitors raw material or semi-finished product consumption data in unit time or set time of the storage unit and purchase and import data of the raw material or semi-finished product in unit time or set time of the storage unit according to the acquired data of the acquisition module 10, and the progress prediction module 11 predicts raw material or semi-finished product consumption trend and purchase demand trend of the storage unit in the follow-up unit time according to the raw material or semi-finished product consumption data and the purchase and import data of the raw material or semi-finished product in a plurality of unit time of the storage unit;
the progress prediction module 11 monitors the transportation efficiency and transportation data of the logistics unit in unit time or in a set time according to the collected data of the collection module 10, and the progress prediction module 11 predicts and sets the transportable efficiency and transportable data of the logistics unit in the subsequent unit time according to the transportation efficiency and transportation data of the logistics unit in a plurality of unit time.
The progress prediction module 11 synchronously outputs the collected related data and the predicted data to the early warning module 12, the early warning module 12 is a numerical comparison algorithm, when the early warning module 12 monitors that the production data in the unit time of the production unit, the transportation data in the unit time of the logistics unit, the raw material consumption data in the unit time of the storage unit and the purchasing data are lower than a threshold value or a set value, the early warning module 12 synchronously outputs an abnormal alarm signal to the scheduling platform 2 and the AI allocation module 14, the AI allocation module 14 or the scheduling platform 2 receives the early warning signal of the early warning module 12, the AI allocation module 14 or the scheduling platform 2 specifically restricts the production efficiency, the raw material purchasing efficiency or the logistics efficiency in the unit time of the next unit time to each unit in the workshop platform 3 through the dynamic restriction module 8, when the acquisition module 10 monitors that the related data in the unit time of the next unit time meet the threshold value, the scheduling platform 2 increases or decreases the specific value of the related unit efficiency restriction in the workshop platform 3 according to the predicted data of the progress prediction module 11, and sends a control command to the other side 4 to the other side, after the control platform receives the control command to the other side, the control platform 2 receives the control command to coordinate the work area, and the control platform is adjusted through the control platform 2.
The management platform 4 monitors abnormal production data and abnormal events of the workshop platform 3 in real time according to the early warning data of the acquisition early warning module 12 and the monitoring data of the acquisition module 10, when the management platform 4 monitors the related abnormal production data or the abnormal events, the management platform 4 maintains, updates, discards, adds, arranges and produces related production apparatuses or manages related specific workshop personnel, the management platform 4 enables the workshop platform 3 to recover the platform in a manual intervention mode after receiving the abnormal events, the management platform 4 simultaneously receives control instructions of the scheduling platform 2 to supervise and promote each unit of the workshop platform 3, and the workshop platform 3 can also report related abnormal data or abnormal events to the management platform 4 independently through the independent feedback module 9.
The progress prediction module 11, the early warning module 12, the AI allocation module 14 or the scheduling platform 2 and the autonomous analysis module are integrated on the scheduling platform 2, and are updated and maintained by a user of the scheduling platform 2, the database 6 is used for backup storage of scheduling instruction data of the scheduling platform 2 and backup storage of data acquired by the acquisition module 10, the data in the database 6 is synchronously uploaded to the cloud server 5, and the autonomous analysis module provides data support and algorithm support for the AI allocation module 14 or the scheduling platform 2 through a data management allocation algorithm.
In the description of the present specification, the descriptions of the terms "one embodiment," "example," "specific example," and the like, mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The preferred embodiments of the invention disclosed above are intended only to assist in the explanation of the invention. The preferred embodiments are not exhaustive or to limit the invention to the precise form disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best understand and utilize the invention. The invention is limited only by the claims and the full scope and equivalents thereof.
Claims (1)
1. The utility model provides a job shop dynamic scheduling system, includes terminal station, its characterized in that: the system comprises a workshop platform (3), a management platform (4), a regional collaboration platform (1) and a cloud server (5), wherein a database (6), an autonomous analysis module and a scheduling platform (2) are respectively loaded on the cloud server (5), a data port of a terminal is in bidirectional data transmission with the regional collaboration platform (1) through the task platform, one end of a data output port of the terminal is in communication connection with the scheduling platform (2) through a 4G/5G/WIFi network, one end of the data output port of the scheduling platform (2) is respectively connected with a dynamic constraint module (8) and a task allocation module, the task allocation port of the scheduling platform (2) is respectively connected with the workshop platform (3) and the regional collaboration platform (1) through the task allocation module, one end of a communication port of the workshop platform (3) is respectively in communication connection with the scheduling platform (2) and the management platform (4) through an autonomous feedback module (9), and one end of a communication interface of the management platform (4) and the dynamic constraint module (8) are both in communication with the workshop platform (3); one end of a workshop platform (3) is provided with an acquisition module (10), the data output end of the acquisition module (10) is connected with a progress prediction module (11), the communication end of the progress prediction module (11) is connected with an early warning module (12), the communication port of the early warning module (12) is respectively connected with a big data collection module (13) and an AI allocation module (14), the data output end of the big data collection module (13) is respectively connected with a model building module (7) and a database (6), one end of the model building module (7) is in communication connection with a management platform (4), one end of a communication output port of the AI allocation module (14) or a scheduling platform (2) is connected with a dynamic constraint module (8), the terminal is any one of a PC/mobile phone end, the terminal outputs scheduling instructions to the scheduling platform (2), the terminal performs related workshop processing or scheduling tasks and receiving operations in a task platform through a wireless network, the task platform and an area collaboration platform (1) are WEB pages, chat groups, communication software, an APP (APP) are integrated software, and the devices in the same type of the factory collaboration platform and the same type (1) send out quality information to a plurality of similar factory production platforms (1) in a same type, and a same type of factory production platform and a plurality of factory production platform and a same type of production platform and a plurality of production platform and a same type of production platform are in a same type, and a same type of production platform and a production platform is provided with a production platform The utility model provides a method for sharing and exchanging instrument production efficiency reports, sharing and exchanging workshop task allocation and optimizing methods, sharing unsold finished product types and finished product information, sharing unused raw materials and semi-finished product information, wherein a dispatching platform (2) is any one of WEB pages, chat groups and APP, the dispatching platform (2) distributes and distributes tasks to a workshop platform (3) through a task distribution module, the dispatching platform (2) distributes tasks which cannot be completed according to needs and are completed according to quality to an area cooperative platform (1) through the task distribution module so as to seek cooperation and further meet production requirements and production requirements, the dispatching platform (2) carries out algorithm updating, information adding, deleting and maintaining through factory management staff, the automatic backup of the dispatching platform (2) is carried out by a database (6), the workshop platform (3) respectively comprises a logistics unit, a storage unit and a production unit, the logistics unit conveys finished products processed by the production unit into the storage unit for storage, the logistics unit conveys unprocessed semi-finished products and processed raw materials into the production unit for processing at the same time from the storage unit, the production unit processes the unprocessed semi-finished products and the processed raw materials into finished products, the logistics unit conveys the finished products processed in the storage unit, the semi-finished products and the raw materials to a designated place at the same time, operators in the logistics unit, the storage unit and the production unit all hold data uploading terminals, and the acquisition module (10) acquires the logistics unit, the processing raw materials and the processing raw materials in real time through a camera, the collecting module (10) collects the workshop information data uploaded by the data uploading terminal in real time at the same time, the collecting module (10) transmits the collected various data to the management platform (4) and the progress predicting module (11) in real time, the progress predicting module (11) analyzes and predicts the algorithm for the data, the progress predicting module (11) monitors the production data or the production efficiency in the unit time or the set time according to the collecting data of the collecting module (10), the progress predicting module (11) simultaneously predicts the producible data of the unit time in the subsequent unit time according to the production data in the unit time of the production, the progress predicting module (11) monitors the raw material or semi-finished product consumption data in the unit time or the set time of the storage according to the collecting data of the collecting module (10), the raw material or the semi-finished product consumption data in the unit time or the set time of the storage, the progress predicting module (11) simultaneously collects the raw material or the semi-finished product consumption data in the unit time or the set time according to the collecting trend data in the collecting module (10) and the purchasing trend data in the subsequent unit time or the set time of the storage unit and the semi-finished product consumption data in the set time or the set time of the storage module according to the collecting trend data in the collecting module (10), the progress prediction module (11) synchronously outputs abnormal alarm signals to the scheduling platform (2) and the AI allocation module (14) according to the transportation efficiency and the transportation data of a plurality of logistics units in unit time so as to predict and set the transportable efficiency and the transportable data of the logistics units in the subsequent unit time, the progress prediction module (11) synchronously outputs the collected related data and the predicted data to the early warning module (12), the early warning module (12) is a numerical comparison algorithm, when the early warning module (12) monitors the production data, the transportation data in the logistics unit time, the raw material consumption data and the purchasing data in the storage unit time in the production unit time and the transportation data in the logistics unit time in the unit time to be lower than a threshold value or a set value, the early warning module (12) synchronously outputs abnormal alarm signals to the scheduling module (2) and the AI allocation module (14), the AI allocation module (14) or the scheduling module (2) specifically constrains the production efficiency, the raw material efficiency or the logistics efficiency in the next unit time to each unit in the workshop platform (3) through the dynamic constraint module (8), when the collected related data in the unit time in the monitoring unit time in the next unit time (10) is not met, and the early warning module (10) still receives the related data in the threshold value in the unit time when the monitoring module (10) is not met, the dispatching platform (2) increases or decreases the specific value of the efficiency constraint of the relevant units in the workshop platform (3) according to the predicted data of the progress predicting module (11), and sends an instruction to the management platform (4), the management platform (4) supervises and manages the relevant units in the workshop platform (3) after receiving the instruction, the dispatching platform (2) distributes the redundant task amount to the regional collaboration platform (1) to seek collaboration after adjusting the constraint, the management platform (4) monitors the abnormal production data and the abnormal event of the workshop platform (3) in real time according to the early warning data of the acquisition early warning module (12) and the monitoring data of the acquisition module (10), when the management platform (4) monitors the relevant abnormal production data or the abnormal event, the management platform (4) maintains, updates, discards, adds, retires, arranges or manages the relevant specific workshop personnel, the management platform (4) after receiving the abnormal event, recovers the platform (3) through a manual intervention mode, the management platform (4) can receive the abnormal event and simultaneously report the abnormal production data of the workshop platform (3) to the independent control platform (3) through the independent control platform (2), the big data collection module (13) collects data of the early warning frequency of the early warning module (12), the work completion degree, the work average efficiency, the work quality and the work capacity of relevant specific workshop staff of relevant units in the workshop platform (3) are researched and judged through the data collection, the big data collection module (13) establishes an operation efficiency model, a work quality model and a work completion degree model of each unit in the workshop platform (3) according to a model establishment algorithm in the model establishment module (7) after collecting the data, the model establishment module (7) synchronously outputs the produced models to the management platform (4), the management platform (4) synchronously outputs the produced models to the management platform (4) according to an establishment model of the model establishment module (7) so as to optimize the management of the workshop platform (3), the progress prediction module (11), the early warning module (12), the AI deployment module (14) or the scheduling platform (2) and the autonomous analysis module are integrated on the scheduling platform (2) and are updated and maintained by a backup operator of the scheduling platform (2), the database (6) is used for scheduling command data of the scheduling platform (2) to be stored in the cloud data collection module (5), the autonomous analysis module provides data support and algorithm support for the AI deployment module (14) or the scheduling platform (2) through a data management allocation algorithm.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110934754.4A CN113837532B (en) | 2021-08-16 | 2021-08-16 | Dynamic scheduling system for job shop |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110934754.4A CN113837532B (en) | 2021-08-16 | 2021-08-16 | Dynamic scheduling system for job shop |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113837532A CN113837532A (en) | 2021-12-24 |
| CN113837532B true CN113837532B (en) | 2024-01-26 |
Family
ID=78960586
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110934754.4A Active CN113837532B (en) | 2021-08-16 | 2021-08-16 | Dynamic scheduling system for job shop |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113837532B (en) |
Families Citing this family (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114022054B (en) * | 2022-01-05 | 2022-04-12 | 机科发展科技股份有限公司 | AGV operation automatic scheduling system and MES system interface integration method |
| CN116360375B (en) * | 2023-05-26 | 2023-08-11 | 江苏中超新能源科技有限公司 | Control method and system for repeatable manufacturing of solar photovoltaic module |
| CN116562599A (en) * | 2023-07-10 | 2023-08-08 | 深圳凯升联合科技有限公司 | Efficient intelligent factory scheduling system based on deep learning |
| CN116579589A (en) * | 2023-07-12 | 2023-08-11 | 北京云尚汇信息技术有限责任公司 | Sanitation vehicle operation condition intelligent supervision system |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110837981A (en) * | 2019-11-18 | 2020-02-25 | 广东工业大学 | Scheduling and control cooperative processing method and related equipment |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20150046363A1 (en) * | 2013-08-07 | 2015-02-12 | Flextronics Ap, Llc | Method and Apparatus for Managing, Displaying, Analyzing, Coordinating, and Optimizing Innovation, Engineering, Manufacturing, and Logistics Infrastructures |
-
2021
- 2021-08-16 CN CN202110934754.4A patent/CN113837532B/en active Active
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110837981A (en) * | 2019-11-18 | 2020-02-25 | 广东工业大学 | Scheduling and control cooperative processing method and related equipment |
Non-Patent Citations (1)
| Title |
|---|
| 基于工业大数据的柔性作业车间动态调度;汤洪涛等;计算机集成制造系统;第26卷(第9期);第2497-2510页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113837532A (en) | 2021-12-24 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN113837532B (en) | Dynamic scheduling system for job shop | |
| CN110568829B (en) | Mine full production chain intelligence management and control system | |
| CN105352542B (en) | A kind of current conversion station panorama condition monitoring system | |
| CN105824300A (en) | Heavy type intelligent factory system based on IoT (Internet of Things) technology and digital management technology | |
| CN110535845A (en) | A kind of GROUP OF HYDROPOWER STATIONS remote date transmission method, system, terminal and storage medium based on Internet of Things | |
| CN105629864A (en) | Energy consumption monitoring application system and energy consumption monitoring method | |
| CN104635684A (en) | Cluster optimization control system for air compressor | |
| CN106559501B (en) | Flat type industrial control method and system | |
| CN105678504A (en) | XML-based field layer, MES, ERP integrated information integration method | |
| KR20220142176A (en) | Smart factory managing system and operating method thereof | |
| CN111353690A (en) | Block chain enabled production scheduling edge calculation method | |
| CN116011759A (en) | Intelligent workshop management system based on Internet of things | |
| CN105005252A (en) | Intelligent power generation monitoring method and system for hydropower station | |
| CN107598925A (en) | A kind of robot cluster control method | |
| CN101739010A (en) | Embedded data acquisition device | |
| CN116755404A (en) | Discrete manufacturing workshop management system and method based on edge calculation | |
| CN101447039A (en) | Production enterprise computer management system | |
| CN108700325A (en) | A kind of interactive managing system controlling air-conditioning based on Internet of Things wisdom | |
| CN206833223U (en) | A kind of district cooling station operation management system based on WEB technologies | |
| CN104865941A (en) | Implementation method for process control station task scheduling | |
| CN115081679A (en) | CDC-based industrial data dynamic change automatic perception processing system | |
| CN112465485A (en) | Intelligent printing and dyeing management system | |
| CN201812186U (en) | Distributed server module for monitoring car loading | |
| CN102789182A (en) | Wind turbine generator gearbox lubricating oil on-line monitor and control platform based on ARM (advanced RISC machine) | |
| CN113589770A (en) | Production system based on MES |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |