CN115511285A - Production scheduling system based on big data - Google Patents

Abstract

The invention discloses a production scheduling system based on big data, which comprises a production planning module, an unmanned design module and a scheduling analysis module, wherein the production planning module is used for planning a production flow, the unmanned design module is used for carrying out intelligent unmanned deployment on a production link, and the scheduling analysis module is used for deeply analyzing a processing scheme and a manual deployment condition; the unmanned design module comprises a conveying unmanned design module, a loading and unloading unmanned design module, a storage unmanned design module and a management unmanned design module, wherein the conveying unmanned design module is used for carrying out unmanned design and deployment on a conveying process, the loading and unloading unmanned design module is used for carrying out unmanned automatic deployment on a loading and unloading link, and the storage unmanned design module is used for carrying out unmanned control on a logistics end.

Description

A production scheduling system based on big data

technical field

The invention relates to the technical field of big data, in particular to a production scheduling system based on big data.

Background technique

Gears are widely used transmission parts in various mechanical instruments, and the gear manufacturing industry occupies an unshakable position in my country's manufacturing industry. However, the gear market is becoming increasingly saturated and the competitive environment is becoming increasingly fierce. Enterprises can only occupy the market by constantly updating their manufacturing processes and improving their core competitiveness. It is an inevitable trend for gear processing to be automated, intelligent, and unmanned. At present, most of the production methods of gears in China are semi-manual, which requires a lot of manpower, and each person's business ability is different, which brings a great test to the development of the conscience of the enterprise. Therefore, it is necessary to design an unmanned and efficient production scheduling system based on big data.

Contents of the invention

The purpose of the present invention is to provide a production scheduling system based on big data, so as to solve the problems raised in the above-mentioned background technology.

In order to solve the above technical problems, the present invention provides the following technical solutions: a production scheduling system based on big data, including a production planning module, an unmanned design module, and a scheduling analysis module, and the production planning module is used to plan the production process , the unmanned design module is used for intelligent unmanned deployment of the production link, the scheduling analysis module is used for in-depth analysis of processing schemes and manual deployment conditions; the unmanned design module includes the delivery unmanned design module , unmanned loading and unloading design module, unmanned warehousing design module, unmanned management design module, the unmanned transportation design module is used for unmanned design and deployment of the transportation process, the unmanned loading and unloading design The module is used for unmanned automatic deployment of the loading and unloading link. The unmanned storage design module is used for unmanned control of the logistics end. The unmanned management design module is used for unmanned manual operation of the management link. Humanized substitution, the unmanned design module for conveying, the unmanned design module for loading and unloading are electrically connected to the unmanned storage design module, and the unmanned management design module is electrically connected to the unmanned storage design module. Unmanned deployment for production is the key to completing industrial upgrading and improving competitiveness. At the same time, automatic control of inventory and production shipments is carried out to control production and inventory, reduce redundant expenditures, increase investment in industrial upgrading, and reduce additional costs. .

According to the above technical solution, the production planning module includes an inventory update module, a shipment balance module, a special order arrangement module, and a production capacity estimation module. The inventory update module is used to update the inventory of warehouse inventory. It is used to balance order demand and shipment arrangement, the special order arrangement module is used to deal with production arrangements under special circumstances, the capacity estimation module is used to use big data for capacity estimation, and the inventory update module It is electrically connected with the shipment balance module, and the special order arrangement module is electrically connected with the production capacity estimation module.

According to the above technical solution, the scheduling analysis module includes a user login module, a processing scheme generation module, and an instruction sending module, the user login module is used to provide management personnel with login authority, and the processing scheme generation module is For the generation of processing plans, the instruction sending module is used to send production instructions to the production line, and the user login module is electrically connected to the processing plan generation module and the instruction sending module.

According to the above technical solution, the operating method of the system specifically includes the following steps:

Step S1: Carry out production plan deployment, specifically inventory inventory update, shipment balance, special production order scheduling, and capacity estimation; production deployment is extremely important in the production operation of an enterprise, and production deployment can balance inventory and output, and reduce factors Additional losses caused by the imbalance between inventory and production;

Step S2: Carry out unmanned design, specifically, unmanned transportation design, unmanned loading and unloading design, unmanned storage design, and unmanned management design;

Step S3: Establish a scheduling analysis model based on the production plan and unmanned design plan; the scheduling analysis model is based on the production plan and unmanned design plan. After the model is established, the subsequent production plan can be automatically generated only by filling in data according to the actual situation , where the production scheme includes material usage and production planning;

Step S4: Conduct semi-manual trial production according to the scheduling analysis model. The scheduling analysis model will be unstable in the early stage, so semi-manual trial production is required to continuously adjust the scheduling strategy and optimize the scheduling logic.

According to the above technical solution, in the step S2, the unmanned design specifically includes the following features:

S21: Unmanned conveying design combines automatic conveying line and RFID reading system, and a flexible connection buffer zone is set between the equipment of the automatic conveying line; the automatic conveying line completes the transmission of materials between devices along the fixed conveying line, It adopts flexible connection and has a buffer zone. When the process beat is inconsistent, it can play a buffer role to prevent production disconnection. At the same time, the RFID reading system is used to read the label of each material. The reading result is connected with the inventory balance to improve the supply chain;

S22: Unmanned loading and unloading design combined with industrial computer and multi-joint robot; industrial computer combined with big data technology to control the current production, and send instructions to the central processor of multi-joint robot to realize up and down, left and right, forward and backward movement, And can flexibly grab gear materials;

S23: Unmanned storage design is divided into hardware equipment and software equipment. The combination of software and hardware realizes unmanned storage; hardware equipment includes three-dimensional high-rise shelves, pallet carriers and aisle stackers, and software equipment includes inbound and outbound transportation systems and communication systems. , Stacker control system, computer monitoring system. After the goods arrive, they are sorted and packed first, and then barcode scanning and storage verification are carried out. After the completion, they are transported to the corresponding roadway entrance by the conveyor belt and ready for storage;

S24: Unmanned management design is divided into planning layer, control layer and base layer design; planning layer design realizes unmanned scheduling, formulates scheduling plans according to production work orders and workshop conditions, arranges gear processing sequence and processing machine operation, and controls The layer manages and monitors according to the information fed back from the workshop, and the base layer realizes the collection and transmission of workshop data, and sends it to the planning layer for production scheduling.

According to the above technical solution, in the step S24, the unmanned planning layer, control layer and base layer form a closed loop, and the planning layer performs production scheduling according to the workshop data collected by the base layer, and sends operation instructions to the control layer. When any part fails, the closed loop will be disconnected, all parts will suspend work, and a maintenance reminder will be sent to prevent the production of defective products due to partial failures, waste of production materials, and improve the operating efficiency of the production process.

According to the above technical solution, the unmanned conveying design in step S21 sets up a buffer area between the equipment of the automatic conveying line, and adopts a flexible connection. When the process beat is inconsistent, it can play a buffering role and prevent production disconnection . The specific process is as follows: after the pallets to be put into storage are allocated by the system, they are transported to the corresponding storage space. If the capacity of the storage buffer is not full, they will enter the buffer for queuing; otherwise, they will continue to wait on the conveyor belt. The pallets in the storage buffer need to be Wait until the storage is free before sending it to the storage area, and finally wait until the stacker is free before moving the goods to the designated location to prevent the production line from stagnating due to the accumulation of goods.

According to the above technical solution, the system also includes a production scheduling model, and the method for establishing the model includes the following steps:

Step A: Create a new model, select the OEM workpiece according to the production order, and input the estimated production volume and delivery date of the workpiece as the new model;

Step B: Select an existing model, and you can view work order information, processing unit information, and process preparation time in the existing model;

Step C: After the scheduling model is selected, the scheduling model is solved by calling the static scheduling algorithm; the genetic algorithm is used to solve the model, including large data scale, discarding ratio, and cross selection probability parameters. The genetic algorithm can be used to realize automatic filling The function of entering parameters can reduce manual intervention and better reflect the production scheduling in the big data environment;

Step D: Perform dynamic simulation in the established scheduling model. Dynamic simulation can dynamically regulate different emergencies, including five emergencies such as processing time change, machine failure repair time change, delivery date advance or delay, and material loss, and realize the simulation in the known scheduling model after the input event , and send the output to the manual inspection station in the form of a Gantt chart.

According to the above technical solution, in the step A, the establishment environment of the dispatching model is VB language, and the mode of adding controls is connected with the database, and various operations in the database are synchronously connected with the dispatching model, so as to improve the establishment of the dispatching analysis model process to ensure that the calculated data in the model are more accurate.

Compared with the prior art, the beneficial effects achieved by the present invention are: the present invention balances and predicts inventory and production capacity by providing a production planning module, improves supply scheduling, and provides unmanned design modules for transportation, Loading, unloading, warehousing, and management links are designed unmanned and closed-loop management. When any part fails, the closed loop will be disconnected, all parts will suspend work, and maintenance reminders will be sent to prevent defective products from being produced due to partial failures. Waste production raw materials and improve the operating efficiency of the production process.

Description of drawings

The accompanying drawings are used to provide a further understanding of the present invention, and constitute a part of the description, and are used together with the embodiments of the present invention to explain the present invention, and do not constitute a limitation to the present invention. In the attached picture:

Fig. 1 is a schematic diagram of the composition of the system modules of the present invention.

detailed description

The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

Please refer to Figure 1, the present invention provides a technical solution: a production scheduling system based on big data, including a production planning module, an unmanned design module, and a scheduling analysis module. The production planning module is used to plan the production process, and the unmanned The design module is used for intelligent unmanned deployment of the production link, and the scheduling analysis module is used for in-depth analysis of processing schemes and manual deployment conditions; the unmanned design module includes the unmanned design module for conveying, unmanned loading and unloading Humanized design module, unmanned management design module, unmanned transportation design module is used for unmanned design and deployment of the transportation process, unmanned loading and unloading design module is used for unmanned automatic deployment of loading and unloading links, warehousing The unmanned design module is used for unmanned management and control of the logistics terminal, and the unmanned management design module is used for unmanned replacement of manual operations in the management link. The unmanned design module for transportation, unmanned loading and unloading design module and The unmanned storage design module is electrically connected, and the unmanned management design module is electrically connected to the unmanned storage design module. Unmanned deployment for production is the key to completing industrial upgrading and improving competitiveness. At the same time, automatic control of inventory and production shipments is carried out to control production and inventory, reduce redundant expenditures, increase investment in industrial upgrading, and reduce additional costs. .

The production planning module includes an inventory update module, a shipment balance module, a special order arrangement module, and a production capacity estimation module. The inventory update module is used to update the inventory of the warehouse inventory, and the shipment balance module is used to balance the order demand and shipment arrangement. , the special ordering module is used to deal with production arrangements under special circumstances, the production capacity estimation module is used to use big data to estimate production capacity, the inventory update module is electrically connected to the shipment balance module, and the special ordering module is connected to the capacity estimation module electrical connection.

The scheduling analysis module includes a user login module, a processing plan generation module, and an instruction sending module. The user login module is used to provide login authority for managers, the processing plan generation module is used to generate processing plans according to the production plan, and the instruction sending module is used to send The production order is sent to the production line, and the user login module is electrically connected with the processing plan generation module and the order sending module.

The operating method of the system specifically includes the following steps:

Step S1: Carry out production plan deployment, specifically inventory inventory update, shipment balance, special production order scheduling, and capacity estimation; production deployment is extremely important in the production operation of an enterprise, and production deployment can balance inventory and output, and reduce factors Additional losses caused by the imbalance between inventory and production;

Step S2: Carry out unmanned design, specifically, unmanned transportation design, unmanned loading and unloading design, unmanned storage design, and unmanned management design;

Step S3: Establish a scheduling analysis model based on the production plan and unmanned design plan; the scheduling analysis model is based on the production plan and unmanned design plan. After the model is established, the subsequent production plan can be automatically generated only by filling in data according to the actual situation , where the production scheme includes material usage and production planning;

Step S4: Conduct semi-manual trial production according to the scheduling analysis model. The scheduling analysis model will be unstable in the early stage, so semi-manual trial production is required to continuously adjust the scheduling strategy and optimize the scheduling logic.

In step S2, the unmanned design specifically includes the following features:

S21: Unmanned conveying design combines automatic conveying line and RFID reading system, and a flexible connection buffer zone is set between the equipment of the automatic conveying line; the automatic conveying line completes the transmission of materials between devices along the fixed conveying line, It adopts flexible connection and has a buffer zone. When the process beat is inconsistent, it can play a buffer role to prevent production disconnection. At the same time, the RFID reading system is used to read the label of each material. The reading result is connected with the inventory balance to improve the supply chain;

S22: Unmanned loading and unloading design combined with industrial computer and multi-joint robot; industrial computer combined with big data technology to control the current production, and send instructions to the central processor of multi-joint robot to realize up and down, left and right, forward and backward movement, And can flexibly grab gear materials;

S23: Unmanned storage design is divided into hardware equipment and software equipment. The combination of software and hardware realizes unmanned storage; hardware equipment includes three-dimensional high-rise shelves, pallet carriers and aisle stackers, and software equipment includes inbound and outbound transportation systems and communication systems. , Stacker control system, computer monitoring system. After the goods arrive, they are sorted and packed first, and then barcode scanning and storage verification are carried out. After the completion, they are transported to the corresponding roadway entrance by the conveyor belt and ready for storage;

S24: Unmanned management design is divided into planning layer, control layer and base layer design; planning layer design realizes unmanned scheduling, formulates scheduling plans according to production work orders and workshop conditions, arranges gear processing sequence and processing machine operation, and controls The layer manages and monitors according to the information fed back from the workshop, and the base layer realizes the collection and transmission of workshop data, and sends it to the planning layer for production scheduling.

In step S24, the unmanned planning layer, control layer and base layer form a closed loop, and the planning layer performs production scheduling based on the workshop data collected by the base layer, and sends operation instructions to the control layer. When any part fails, the closed loop will be disconnected, all parts will suspend work, and a maintenance reminder will be sent to prevent the production of defective products due to partial failures, waste of production materials, and improve the operating efficiency of the production process.

In the unmanned conveying design in step S21, a buffer area is set between the equipment of the automatic conveying line, and flexible connections are used. When the process beats are inconsistent, it can play a buffering role and prevent production disconnection. The specific process is as follows: after the pallets to be put into storage are allocated by the system, they are transported to the corresponding storage space. If the capacity of the storage buffer is not full, they will enter the buffer for queuing; otherwise, they will continue to wait on the conveyor belt. The pallets in the storage buffer need to be Wait until the storage is free before sending it to the storage area, and finally wait until the stacker is free before moving the goods to the designated location to prevent the production line from stagnating due to the accumulation of goods.

The system also includes a production scheduling model, and the method for establishing the model includes the following steps:

Step A: Create a new model, select the OEM workpiece according to the production order, and input the estimated production volume and delivery date of the workpiece as the new model;

Step B: Select an existing model, and you can view work order information, processing unit information, and process preparation time in the existing model;

Step C: After the scheduling model is selected, the scheduling model is solved by calling the static scheduling algorithm; the genetic algorithm is used to solve the model, including large data scale, discarding ratio, and cross selection probability parameters. The genetic algorithm can be used to realize automatic filling The function of entering parameters can reduce manual intervention and better reflect the production scheduling in the big data environment;

Step D: Perform dynamic simulation in the established scheduling model. Dynamic simulation can dynamically regulate different emergencies, including five emergencies such as processing time change, machine failure repair time change, delivery date advance or delay, and material loss, and realize the simulation in the known scheduling model after the input event , and send the output to the manual inspection station in the form of a Gantt chart.

In step A, the environment for establishing the dispatching model is VB language, and connects with the database by adding controls, and synchronously connects various operations in the database with the dispatching model, perfects the establishment process of the dispatching analysis model, and ensures that in the model The calculated data is more accurate.

It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply that there is a relationship between these entities or operations. There is no such actual relationship or order between them. Furthermore, the term "comprises", "comprises" or any other variation thereof is intended to cover a non-exclusive inclusion such that a process, method, article, or apparatus comprising a set of elements includes not only those elements, but also includes elements not expressly listed. other elements of or also include elements inherent in such a process, method, article, or device.

Finally, it should be noted that: the above is only a preferred embodiment of the present invention, and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, for those skilled in the art, it still The technical solutions recorded in the foregoing embodiments may be modified, or some technical features thereof may be equivalently replaced. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.

Claims (9)

1. A production scheduling system based on big data, comprising a production planning module, an unmanned design module, and a scheduling analysis module, characterized in that: the production planning module is used to plan the production process, and the unmanned design The module is used for intelligent unmanned deployment of the production link, and the scheduling analysis module is used for in-depth analysis of processing schemes and manual deployment conditions; the unmanned design module includes the unmanned design module for transportation and the unmanned design module for loading and unloading , unmanned storage design module, and unmanned management design module, the unmanned transportation design module is used for unmanned design and deployment of the transportation process, and the unmanned loading and unloading design module is used for unmanned loading and unloading Unmanned automatic deployment, the unmanned storage design module is used for unmanned management and control of the logistics end, the unmanned management design module is used for unmanned replacement of manual operations in the management link, the transportation The unmanned design module, the unmanned loading and unloading design module are electrically connected to the unmanned storage design module, and the unmanned management design module is electrically connected to the unmanned storage design module. 2. A production scheduling system based on big data according to claim 1, characterized in that: the production planning module includes an inventory update module, a shipment balance module, a special order arrangement module, and a production capacity estimation module, and the The inventory update module is used to update the inventory of the warehouse inventory, the shipment balance module is used to balance the order demand and the shipment arrangement, the special order arrangement module is used to deal with the production arrangement under special circumstances, and the production capacity The estimation module is used for capacity estimation using big data, the inventory update module is electrically connected to the shipment balance module, and the special order arrangement module is electrically connected to the capacity estimation module. 3. A production scheduling system based on big data according to claim 2, characterized in that: the scheduling analysis module includes a user login module, a processing plan generation module, and an instruction sending module, and the user login module is used for The management personnel provide login authority, the processing plan generation module is used to generate the processing plan according to the production plan, the instruction sending module is used to send the production order to the production line, the user login module and the processing plan generation module, The instruction sending module is electrically connected. 4. A kind of big data-based production scheduling system according to claim 3, characterized in that: the operating method of the system specifically comprises the following steps: Step S1: Carry out production plan deployment, specifically inventory inventory update, shipment balance, special production order, and capacity estimation; Step S2: Carry out unmanned design, specifically, unmanned transportation design, unmanned loading and unloading design, unmanned storage design, and unmanned management design; Step S3: Establish a scheduling analysis model according to the production plan and unmanned design plan; Step S4: Conduct semi-manual trial production according to the scheduling analysis model. 5. A production scheduling system based on big data according to claim 4, characterized in that: in said step S2, the unmanned design specifically includes the following features: S21: The unmanned conveying design combines the automatic conveying line and RFID reading system, and sets up a flexible connection buffer between the equipment of the automatic conveying line; S22: Unmanned loading and unloading design combined with industrial computer and multi-joint robot; S23: The design of unmanned storage is divided into hardware equipment and software equipment, and the combination of software and hardware realizes unmanned storage; S24: Unmanned management design is divided into planning layer, control layer and basic layer design. 6. A production scheduling system based on big data according to claim 5, characterized in that: in said S24, the unmanned management planning layer, control layer and base layer form a closed loop, and the planning layer collects data according to the base layer Received workshop data, perform production scheduling, and send operation instructions to the control layer. 7. A production scheduling method based on big data according to claim 6, characterized in that: in the unmanned design of conveying in S21, a buffer area is set between the equipment of the automatic conveying line, and a flexible connection is adopted, When the process beat is inconsistent, it can play a buffer role to prevent production breakage. 8. a kind of production scheduling system based on big data according to claim 7, is characterized in that, described this system also comprises a kind of production scheduling model, and the establishment method of this model comprises the following steps: Step A: Create a new model, select the OEM workpiece according to the production order, and input the estimated production volume and delivery date of the workpiece as the new model; Step B: Select an existing model, and you can view work order information, processing unit information, and process preparation time in the existing model; Step C: After the scheduling model is selected, solve the scheduling model by calling the static scheduling algorithm; Step D: Perform dynamic simulation in the established scheduling model. 9. a kind of production scheduling system based on big data according to claim 8, is characterized in that: in described step A, the establishment environment of scheduling model is VB language, and is connected with database by the mode of adding control, will Various operations in the database are connected synchronously with the scheduling model, which improves the establishment process of the scheduling analysis model and ensures that the calculation data in the model is more accurate.

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