CN113837532A - Dynamic scheduling system for job shop - Google Patents
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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, a regional coordination platform and a cloud server, wherein a database, an autonomous analysis module and a scheduling platform are respectively loaded on the cloud server, a data port of the terminal carries out bidirectional data transmission with the regional coordination 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 scheduling platform and the management platform, the device can efficiently complete equipment scheduling and equipment scheduling work of a production workshop, and the system is additionally provided with a workshop production data acquisition module on the basis of the traditional scheduling system.
Description
Technical Field
The invention belongs to the technical field of scheduling systems, and particularly relates to a job shop dynamic scheduling system.
Background
With the rapid development of scientific technology and the increasingly fierce market competition, the production mode of most enterprises is changed from the traditional single and large-batch production to the multi-variety, small-batch or single-piece production, the structural type of the production process is further complicated, and the traditional production scheduling method cannot adapt to the highly-changed order requirements.
The high variability and unpredictability of the manufacturer group orders on the supply chain between suppliers and warehousers makes the production scheduling problem become extremely complex, and the main problems of conventional scheduling of existing enterprises are:
1. the load of the plant equipment is uneven, part of the equipment cannot run at full load, even runs at lower load, and part of the equipment runs at overload;
2. the capacity of coping with equipment failure and emergency order insertion is poor, and partial product orders cannot be delivered according to the date;
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 problems in the background art.
Disclosure of Invention
The invention aims to provide a job shop dynamic scheduling system, which solves the problem of poor scheduling effect and estimation and prediction capability of the existing shop dynamic scheduling system through the design of processes such as a regional cooperation 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 dynamic scheduling system of a job shop, which comprises a terminal, a task platform, a model building module, a shop platform, a management platform, a regional cooperative platform and a cloud server, wherein the cloud server is respectively provided with a database, an autonomous analysis module and a scheduling platform, a data port of the terminal is used for carrying out bidirectional data transmission with the regional 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, one end of a data output port of the scheduling platform is respectively connected with a dynamic constraint module and a task allocation module, a task allocation port of the scheduling platform is respectively connected with the shop platform and the regional cooperative platform through the task allocation module, one end of a communication port of the shop platform is in communication connection with the scheduling platform and the management platform through an autonomous feedback module, one end of the management platform and one end of the dynamic constraint module communication interface are both communicated with a workshop platform;
the system comprises a workshop platform, a data output end of the workshop platform and a data output end of a scheduling platform, wherein the workshop platform is provided with an acquisition module in a matched mode, the data output end of the acquisition module is connected with a progress prediction module, the communication end of the progress prediction module is connected with an early warning module, the communication port of the early warning module is respectively connected with a big data collection module and an AI allocation module, the 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 the AI allocation module or a communication output port of a scheduling 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 the scheduling platform, the terminal browses and receives the operation of the related workshop processing or scheduling task from the task platform through a wireless network, the task platform and the regional collaboration platform are any one of WEB pages, chat groups, communication communities and APP integrated software, the regional collaboration platform provides a communication platform for a plurality of factories with similar production instruments, similar production workshops and similar production quality in a region, and the plurality of factories send workshop requirements, send information of idle production workshops, share and exchange an instrument maintenance method, share and exchange an instrument production efficiency report, share and exchange a workshop task allocation and optimization method, share of types of unsold finished products and finished product information and share of unused raw materials and semi-finished product information through the regional collaboration platform.
Preferably, the dispatch platform is any one of WEB webpage, chat group, APP, the dispatch platform carries out the distribution of task and assigns to the workshop platform through task distribution module, the dispatch platform simultaneously through task distribution module with the workshop platform can't accomplish as required, task distribution that the quality was accomplished to regional collaborative platform in order to seek in coordination and then satisfy production demand and production requirement, the dispatch platform carries out the update of algorithm, the addition of information, deletion and maintenance by factory management personnel, the instruction that the dispatch platform sent is backed up by the database by oneself.
Preferably, the workshop platform comprises a logistics unit, a storage unit and a production unit respectively, the logistics unit transports finished products processed by the production unit to the storage unit for storage, the logistics unit transports unprocessed semi-finished products and processing raw materials from the storage unit to the production unit for processing, the production unit processes the unprocessed semi-finished products and the processing raw materials into finished products, the logistics unit transports the finished products, the semi-finished products and the raw materials which are processed in the storage unit to a specified place, and operators in the logistics unit, the storage unit and the production unit all hold data uploading terminals.
Preferably, the acquisition module acquires 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 acquisition module simultaneously acquires workshop information data uploaded by the data uploading terminal in real time, the acquisition module transmits various acquired 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 production data or production efficiency of the production unit in unit time or set time according to the acquired data of the acquisition module, the progress prediction module predicts producible data of the production unit in subsequent unit time according to the production data of the production unit in a plurality of unit times, and the progress prediction module monitors raw material or semi-finished product consumption data and storage unit time or set time according to the acquired data of the acquisition module The system comprises a progress prediction module, a logistics unit and a logistics unit, wherein the progress prediction module is used for predicting and setting the consumption trend and the purchasing demand trend of the raw materials or the semi-finished products of the logistics unit in a subsequent unit time according to the consumption data of the raw materials or the semi-finished products in the storage unit in a plurality of unit times and the purchasing input data of the raw materials or the semi-finished products in the subsequent unit time, monitoring the transportation efficiency and the transportation data of the logistics unit in the unit time or in the set time according to the acquired data of the acquisition module, and predicting and setting the transportability efficiency and the transportability data of the logistics unit in the subsequent unit time according to the transportation efficiency and the transportation data of the logistics unit in the plurality of unit times.
Preferably, the progress prediction module synchronously outputs the collected related data and the prediction 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 unit time of the production unit, the transportation data in unit time of the logistics unit, the raw material consumption data in unit time of the storage unit and the purchase data are lower than a threshold value or a set value, the early warning module synchronously outputs an abnormal warning signal to the scheduling platform and the AI allocation module, after the AI allocation module or the scheduling platform receives 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 purchase efficiency or the logistics efficiency in the next unit time to each unit in the workshop platform through the dynamic restriction module, when the collection module monitors that the related data in the next unit time meets the threshold value, the early warning module closes the early warning signal, when the acquisition module monitors that the efficiency of the relevant units in the next unit time still does not meet the threshold value, the scheduling platform increases or decreases the specific efficiency constraint value of the relevant units in the workshop platform according to the prediction data of the progress prediction module on one hand, and sends an instruction to the management platform on the other hand, the management platform supervises and urges the relevant units in the workshop platform and relevant operation and maintenance work after receiving the instruction, and the scheduling platform allocates redundant task quantity to the regional cooperation platform through the task allocation module to seek cooperation after regulating the constraint.
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 related abnormal production data or abnormal events, the management platform maintains, updates, discards, adds, stops, arranges production or manages related specific workshop personnel, the management platform restores the workshop platform after receiving the abnormal events in a manual intervention mode, the management platform simultaneously receives control instructions of the scheduling platform to supervise and manage each unit of the workshop platform, and the workshop platform can also report related abnormal constant data or abnormal events to the management platform independently 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 all integrated on the scheduling platform and are 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 acquired by the acquisition module, 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 system, the scheduling platform and the management platform are designed, so that the device can efficiently complete equipment scheduling and equipment scheduling work of a production workshop, the system is additionally provided with an 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 addition of the acquisition module, the production efficiency and the production data in the workshop can be intelligently predicted through the acquired data through the design of a progress prediction module, and the production scheduling effect of the system and the use uniformity of workshop equipment are effectively improved through the realization of an intelligent prediction effect.
2. The system changes a single-plant working mode of the traditional plant scheduling system into a multi-plant cooperative working mode through the design of a regional cooperative platform, further improves the emergency bill insertion performance of the system through the multi-plant cooperation, can perform real-time dynamic adjustment and dynamic restriction on the operation efficiency of a workshop through the design of a dynamic restriction module, and effectively improves and guarantees the accomplishment effect and the accomplishment degree of the system during operation and scheduling through the realization of the dynamic adjustment effect.
Of course, it is not necessary for any product in which the invention is practiced to achieve all of the above-described advantages at the same time.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic diagram of a flow structure of a job shop dynamic scheduling system;
fig. 2 is a schematic view of a flow structure of a scheduling platform and an acquisition module.
In the drawings, the components represented by the respective reference numerals are listed below:
1. a regional collaboration platform; 2. a scheduling 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 allocating module.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1-2, the invention relates to a job shop dynamic scheduling system, which comprises a terminal, wherein the terminal is a PC, and further comprises a task platform, a model building module 7, a shop platform 3, a management platform 4, an area coordination platform 1 and a cloud server 5, wherein the cloud server 5 is respectively provided with a database 6, an autonomous analysis module and a scheduling platform 2, and a data port of the terminal performs bidirectional data transmission with the area coordination platform 1 through the task platform;
one end of a terminal data output port is in communication connection with the dispatching platform 2 through a 5G network, one end of the data output port of the dispatching platform 2 is respectively connected with the dynamic constraint module 8 and the task allocation module, the dispatching platform 2 outputs a workshop production task with a specific value and production efficiency to the workshop platform 3 through the dynamic constraint module 8, and the dispatching platform 2 dynamically adjusts the specific constraint value according to the acquired data of the acquisition module 10;
a task allocation port of the dispatching platform 2 is respectively connected with the workshop platform 3 and the regional collaborative platform 1 through a task allocation module, one end of a communication port of the workshop platform 3 is respectively in communication connection with the dispatching platform 2 and the management platform 4 through an autonomous feedback module 9, and one ends of communication interfaces of the management platform 4 and the dynamic constraint module 8 are both in communication with the workshop platform 3;
one end of the workshop platform 3 is matched 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, and one end of the AI allocation module 14 or a communication output port of a dispatching platform 2 is connected with a dynamic constraint module 8.
The terminal outputs a scheduling instruction to the scheduling platform 2, the terminal browses and receives related workshop processing or scheduling tasks from 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 instruments, similar production workshops and similar production quality in regions, and the plurality of factories send out workshop requirements, send out information of idle production workshops, share communication of instrument maintenance methods, share communication of instrument production efficiency reports, share communication of workshop task allocation and optimization methods, share communication of types of unsold finished products and finished product information, share of unused raw materials and semi-finished product information through the regional collaboration platform 1.
Wherein, the scheduling platform 2 is any one of WEB webpage, chat group, APP, the scheduling platform 2 carries out the distribution and issue of task to workshop platform 3 through the task allocation module, the scheduling platform 2 simultaneously through the task allocation module with workshop platform 3 can't accomplish as required, the task of accomplishing by the quality distributes to regional cooperation platform 1 in order to seek the cooperation and then satisfy production demand and production requirement, the scheduling platform 2 carries out the renewal of algorithm, the addition of information, delete and maintain by factory management personnel, the instruction that the scheduling platform 2 sent carries out the self-backup by database 6.
Wherein, workshop platform 3 includes the commodity circulation unit respectively, storage unit and production unit, the commodity circulation unit is saved in transporting the finished product of production unit processing to the storage unit, the commodity circulation unit is processed in transporting unprocessed semi-manufactured goods and processing raw materials to the production unit by the storage unit simultaneously, the production unit is processed into the finished product with uncompleted semi-manufactured goods and processing raw materials, the commodity circulation unit is with the finished product of processing in the storage unit simultaneously, semi-manufactured goods and raw materials transportation to appointed place, the commodity circulation unit, the operating personnel in storage unit and the production unit all hold the data and upload the terminal.
The system comprises an acquisition module 10, a progress prediction module 11, a data analysis and prediction algorithm, a data analysis and prediction module 11, a production unit, a storage unit and a production unit, wherein the acquisition module 10 acquires image data information and dynamic event information in a logistics unit, the storage unit and the production unit in real time through a camera, the acquisition module 10 simultaneously acquires workshop information data uploaded by a data uploading terminal in real time, the acquisition module 10 transmits various acquired data to a management platform 4 and the progress prediction module 11 in real time, the progress prediction module 11 monitors production data or production efficiency of the production unit in unit time or in set time according to the acquired data of the acquisition module 10, and the progress prediction module 11 simultaneously predicts and sets producible data of the production unit in subsequent unit time according to the production data of the production unit in a plurality of unit times;
the progress prediction module 11 monitors the consumption data of the raw materials or the semi-finished products in unit time or set time of the storage unit and the purchase remittance data of the raw materials or the semi-finished products in unit time or set time of the storage unit according to the acquisition data of the acquisition module 10, and the progress prediction module 11 predicts the consumption trend and the purchase demand trend of the raw materials or the semi-finished products of the storage unit in subsequent unit time according to the consumption data of the raw materials or the semi-finished products in a plurality of unit times of the storage unit and the purchase remittance data of the raw materials or the semi-finished products;
the schedule prediction module 11 monitors the transportation efficiency and the transportation data of the logistics unit in unit time or within a set time according to the data collected by the collection module 10, and the schedule prediction module 11 predicts the transportable efficiency and the transportable data of the logistics unit in a set subsequent unit time according to the transportation efficiency and the transportation data of the logistics unit in a plurality of unit times.
Wherein, the schedule predicting module 11 synchronously outputs the collected related data and the prediction 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 unit time of the production unit, the transportation data in unit time of the logistics unit, the raw material consumption data in unit time of the storage unit and the purchase data are lower than a threshold value or a set value, the early warning module 12 synchronously outputs an abnormal alarm signal to the dispatching platform 2 and the AI dispatching module 14, after the AI dispatching module 14 or the dispatching platform 2 receives the early warning signal of the early warning module 12, the AI dispatching module 14 or the dispatching platform 2 specifically restricts the production efficiency, the raw material purchase efficiency or the logistics efficiency in the next unit time to each unit in the workshop platform 3 through the dynamic restricting module 8, when the collecting module 10 monitors that the related data in the next unit time meets the threshold value, the early warning module 12 closes the early warning signal, when the acquisition module 10 monitors that the efficiency in the relevant unit in the next unit time still does not meet the threshold value, the scheduling platform 2 increases or decreases the specific value of the efficiency constraint of the relevant unit in the workshop platform 3 according to the prediction data of the progress prediction module 11 on one hand, and sends an instruction to the management platform 4 on the other hand, after receiving the instruction, the management platform 4 supervises and urges the relevant unit in the workshop platform 3 and relevant operation and maintenance work, and after adjusting the constraint, the scheduling platform 2 distributes redundant task quantity to the regional collaboration platform 1 through the task distribution module to seek collaboration.
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 related abnormal production data or abnormal events, the management platform 4 maintains, updates, discards, adds, stops, arranges relevant production instruments 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 receives control instructions of the scheduling platform 2 at the same time to supervise and manage each unit of the workshop platform 3, and the workshop platform 3 can also report related abnormal constant data or abnormal events to the management platform 4 through the autonomous 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 all 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, 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 herein, references to the description of "one embodiment," "an example," "a specific example" or the like are intended to 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 invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. 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 to be illustrative only. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise embodiments 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 utilize the invention. The invention is limited only by the claims and their full scope and equivalents.
Claims (9)
1. A job shop dynamic scheduling system, including the terminal, its characterized in that:
the system is characterized by further comprising a task platform, a model establishing module (7), a workshop platform (3), a management platform (4), a regional cooperation 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 the terminal is in bidirectional data transmission with the regional cooperation platform (1) through the task platform, one end of a terminal data output port is in communication connection with the scheduling platform (2) through a 4G/5G/WIFi network, one end of a data output port of the scheduling platform (2) is respectively connected with a dynamic constraint module (8) and a task allocation module, a task allocation port of the scheduling platform (2) is respectively connected with the workshop platform (3) and the regional cooperation platform (1) through the task allocation module, one end of a communication port of the workshop platform (3) is respectively connected with the scheduling platform (2) and the regional cooperation platform (1) through an autonomous feedback module (9) The platform (4) is in communication connection, and one end of a communication interface of the management platform (4) and one end of a communication interface of the dynamic constraint module (8) are both in communication with the workshop platform (3);
one end of the workshop platform (3) is matched 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), and one end of the communication output port of the AI allocation module (14) or a scheduling platform (2) is connected with a dynamic constraint module (8).
2. The dynamic job shop scheduling system according to claim 1, wherein the terminal is any one of PC/mobile phone, the terminal outputs a scheduling command to the scheduling platform (2), the terminal browses and receives related workshop processing or scheduling tasks from the task platform through a wireless network, the task platform and the regional collaboration platform (1) are any one of WEB pages, chat groups, communication communities and APP integration software, the regional collaboration platform (1) provides a communication platform for a plurality of factories having similar production apparatuses, similar production workshops and similar production quality in a region, and the plurality of factories send out workshop demands, send out information of idle production workshops, share communication of apparatus maintenance methods, share communication report forms of apparatus production efficiency through the regional collaboration platform (1), Sharing and exchanging of the workshop task allocation and optimization method, sharing of the types of unsold finished products and the information of the finished products, and sharing of the information of unused raw materials and semi-finished products.
3. The dynamic job shop scheduling system according to claim 1, wherein the scheduling platform (2) is any one of a WEB page, a chat group and an APP, the scheduling platform (2) distributes and issues tasks to the job shop platform (3) through the task distribution module, the scheduling platform (2) simultaneously distributes the tasks that the job shop platform (3) cannot be completed as required and are completed according to quality to the regional collaboration platform (1) through the task distribution module to seek collaboration and further meet production requirements and production requirements, the scheduling platform (2) updates the algorithm, adds, deletes and maintains information by factory managers, and the instructions sent by the scheduling platform (2) are automatically backed up by the database (6).
4. The dynamic scheduling system of job shops according to claim 1, wherein the shop platform (3) comprises a logistics unit, a storage unit and a production unit, the logistics unit transports finished products processed by the production unit to the storage unit for storage, the logistics unit transports unprocessed semi-finished products and raw materials from the storage unit to the production unit for processing, the production unit processes the unprocessed semi-finished products and raw materials into finished products, the logistics unit transports the finished products, semi-finished products and raw materials processed in the storage unit to a designated location, and the operators in the logistics unit, the storage unit and the production unit all hold data uploading terminals.
5. The dynamic job shop scheduling system according to claim 1, wherein the collection module (10) collects the image data information and the dynamic event information in the logistics unit, the warehousing unit and the production unit in real time through a camera, the collection module (10) collects the shop information data uploaded by the data uploading terminal in real time at the same time, the collection module (10) transmits the collected various data to the management platform (4) and the progress prediction module (11) in real time, the progress prediction module (11) is a data analysis and prediction algorithm, the progress prediction module (11) monitors the production data or the production efficiency of the production unit in unit time or set time according to the collected data of the collection module (10), the progress prediction module (11) predicts the production data of the production unit in the set subsequent unit time according to the production data of the production unit in a plurality of unit times, the progress prediction module (11) monitors the consumption data of the raw materials or semi-finished products in unit time or set time of the storage unit and the purchase and import data of the raw materials or semi-finished products in unit time or set time of the storage unit according to the acquisition data of the acquisition module (10), the progress prediction module (11) predicts and sets the consumption trend and the purchasing demand trend of the raw materials or the semi-finished products of the storage unit in the subsequent unit time according to the consumption data of the raw materials or the semi-finished products in a plurality of unit time of the storage unit and the purchasing import data of the raw materials or the semi-finished products at the same time, the progress prediction module (11) monitors the transportation efficiency and the transportation data of the logistics unit in unit time or within a set time according to the acquisition data of the acquisition module (10), the progress prediction module (11) predicts and sets the transportable efficiency and the transportable data of the logistics unit in the subsequent unit time according to the transportation efficiency and the transportation data of the logistics unit in a plurality of unit times.
6. The dynamic job shop scheduling system according to claim 1, wherein the progress prediction module (11) synchronously outputs the collected related data and the collected predicted data to the pre-warning module (12), the pre-warning module (12) is a numerical comparison algorithm, when the pre-warning module (12) monitors that the production data in unit time of the production unit, the transportation data in unit time of the logistics unit, the raw material consumption data in unit time of the storage unit and the purchase data are lower than a threshold value or a set value, the pre-warning module (12) synchronously outputs an abnormal alarm signal to the scheduling platform (2) and the AI scheduling module (14), and after the AI scheduling module (14) or the scheduling platform (2) receives the pre-warning signal of the pre-warning module (12), the AI scheduling module (14) or the scheduling platform (2) specifically restricts the production efficiency in the next unit time to each unit in the workshop platform (3) through the dynamic restriction module (8), The raw material purchasing efficiency or the logistics efficiency, when the acquisition module (10) monitors that the related data in the next unit time meets the threshold value, the early warning module (12) closes the early warning signal, when the acquisition module (10) monitors that the efficiency in the relevant unit in the next unit time still does not meet the threshold value, on one hand, the scheduling platform (2) increases or decreases the specific value of the efficiency constraint of the relevant units in the workshop platform (3) according to the prediction data of the progress prediction module (11), on the other hand, the scheduling platform sends an instruction to the management platform (4), after the management platform (4) receives the instruction, supervising and urging related units in the workshop platform (3) and related operation and maintenance work, after the dispatching platform (2) adjusts the constraint, and distributing redundant task quantity to the regional collaboration platform (1) through the task distribution module to seek collaboration.
7. The dynamic job shop scheduling system according to claim 1, wherein the management platform (4) monitors abnormal production data and abnormal events of the shop 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 or manages related specific shop personnel, the management platform (4) enables the shop platform (3) to recover the platform in a manual intervention mode after receiving the abnormal events, the management platform (4) receives the control instruction of the scheduling platform (2) at the same time to supervise and manage each unit of the shop platform (3), and the shop platform (3) can also use the autonomous feedback module (9) to supervise and manage related abnormal constant data or abnormal events And reporting to the management platform (4) automatically.
8. The dynamic job shop scheduling system according to claim 1, the big data collection module (13) collects 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 related specific workshop personnel of related units in the workshop platform (3) are researched and judged through data acquisition, the big data collection module (13) establishes an operation efficiency model, an operation quality model and an operation completion degree model of each unit in the workshop platform (3) according to a model establishment algorithm in the model establishment module (7) after data are collected, the model building module (7) synchronously outputs the produced models to the management platform (4), the management platform (4) optimizes the management of the workshop platform (3) according to the established model of the model establishing module (7).
9. The dynamic job shop scheduling system according to claim 1, wherein the progress prediction module (11), the early warning module (12), the AI scheduling module (14) or the scheduling platform (2) and the autonomous analysis module are all integrated on the scheduling platform (2) and are updated and maintained by a user of the scheduling platform (2) through an algorithm, the database (6) is used for backup storage of scheduling instruction data of the scheduling platform (2) and backup storage of data collected by the collection module (10), data in the database (6) are synchronously uploaded to the cloud server (5), and the autonomous analysis module provides data support and algorithm support for the AI scheduling module (14) or the scheduling platform (2) through a data management and distribution algorithm.
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