CN117952567A - Production management method and system based on MES intelligent manufacturing - Google Patents
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Abstract
The application discloses a production management method and a system based on MES intelligent manufacturing, and relates to the field of data processing, wherein the method comprises the steps of receiving order tasks, generating an order material model, wherein the order material model comprises a procedure control, a material control and a material and product scheduling plan; associating and issuing an order material model to corresponding production equipment, and generating a capacity combination model according to the production efficiency of the production equipment and the scheduling plan of materials and products; the capacity combination model plans materials to execute idle time working procedures on equipment according to working procedure control pieces and equipment occupancy rate, and obtains scheduling plans of other materials corresponding to other products or the same product according to the materials control pieces, the constituent materials of the products and the stock of the materials; the capacity combination model and the order material model are matched to control the product combination with the maximum output yield value of the MES in unit time. According to the application, the datamation and modularization production line is realized, the data association is constructed, the optimal coupling model is analyzed and combined according to the historical production data, the decoupling and the coupling are intelligently scheduled and executed, the efficient production is realized, and the production cost is reduced.
Description
Technical Field
The application relates to the field of data processing, in particular to a production management method and system based on MES intelligent manufacturing.
Background
The traditional MES system mostly adopts two production planning modes of on-demand production and pre-casting stock;
The traditional MES system is defined as a production execution system which receives the requirement planning information from the ERP or sales front end order system to execute production arrangement, and the stock production part adopts a manual experience method to carry out the issuing of the stock pre-casting plan. Products that are not pre-dosed are either out of stock or are forced to be discarded in the event of an emergency order because the production cycle conditions are not met. Therefore, in actual MES production management, the production plan is difficult to be tightly combined with an MES system due to frequent plan change and frequent production replacement, and the real-time monitoring and scheduling advantages of the MES cannot be fully exerted, so that the production cost is high, the management difficulty is complex, the customer satisfaction is low, and the risk of inventory cost is increased.
In a specific PVC production process, as for interaction between production and sales systems, due to inconvenient transfer of a list among various MES systems and the condition of unequal and error-prone data transfer caused by artificial links, enterprises often sell at the front end and produce at the rear end, the results brought by market guidance cannot be corrected in time, and timely and effective order transferring strategies and intelligent manufacturing management scheduling plans cannot be corrected;
Therefore, there is a need for a joint method of MES intelligent manufacturing line management and order scheduling policy analysis to realize the collaborative operation of the front-end order system and the back-end MES intelligent manufacturing system.
Disclosure of Invention
The application discloses a production management method and a system based on MES intelligent manufacturing, which can solve the problems in the prior art.
In a first aspect, the present application provides a production management method based on MES intelligent manufacturing, which is characterized by comprising:
Acquiring historical order data, and analyzing association rules between materials and products, wherein the association rules comprise the constituent materials of the products, the storage quantity of the products and the storage quantity of the materials;
Dynamically collecting cost data of materials and value data of products, and analyzing change data of material cost and change data of value of the products;
Receiving an order task, and generating an order material model, wherein the order material model comprises a procedure control, a material control and a material and product scheduling plan;
Associating and issuing an order material model to corresponding production equipment, and generating a capacity combination model according to the production efficiency of the production equipment and the scheduling plan of materials and products;
The capacity combination model plans materials to execute idle time working procedures on equipment according to working procedure control pieces and equipment occupancy rate, and obtains scheduling plans of other materials corresponding to other products or the same product according to the materials control pieces, the constituent materials of the products and the stock of the materials;
And the productivity combination model and the order material model are matched to control the product combination with the maximum output production value of the MES in unit time.
Further, the acquiring the historical order data, analyzing the association rule between the materials and the products, including the constituent materials of the products, the stock of the products and the stock of the materials, includes:
decoupling material data in the historical orders, forming materials of different types into material controls, associating corresponding materials to corresponding products according to material combinations, and analyzing the stock change of multiple materials and products caused by orders;
The method further comprises the steps of analyzing the occupancy rate of the incomplete order data to equipment, obtaining the types and the amounts of the incomplete order balance materials, and obtaining the completion time and the scheduling plan of the remaining products of the incomplete order.
Further, the dynamic collection of cost data of the material and value data of the product, analysis of change data of the material cost and change data of the value of the product, includes:
accessing market and purchase data, writing material cost data into corresponding material controls, and writing value data of products into material controls of related materials;
and fitting the change data of the cost of the material and the change data of the value of the product of the next acquisition period according to the mathematical model.
Further, the order receiving task generates an order material model, wherein the order material model comprises a procedure control, a material and product scheduling plan, and specifically further comprises:
The order material model is used for dispersing an order task into a plurality of material controls, correspondingly acquiring products from the plurality of material controls through association rules of procedure controls, wherein the procedure controls also comprise production equipment for matching and identifying corresponding procedures;
the order material model analyzes order tasks, generates stock difference data of material controls in unit time, and generates occupancy rate of equipment corresponding to the procedure controls in unit time.
Further, the stock difference data of the material control in unit time is the quantity fluctuation difference of corresponding materials in the material control in unit production time; the occupancy rate of the equipment corresponding to the generated procedure control in unit time is the saturation of the equipment corresponding to the procedure in the procedure control in the unit production time in the process of executing the change of the stock difference of the material control;
wherein, the unit production time is the production time of one product.
Further, for materials in the order task, the method comprises the following steps: consumption material and output material;
the consumable material is a material consumed by the production order task product;
the output material is a material generated by producing order task products;
The association and order material model issuing are carried out on corresponding production equipment, and the capacity combination model is generated according to the production efficiency of the production equipment and the scheduling plan of materials and products, and specifically comprises the following steps:
According to the corresponding production equipment in the procedure control, decoupling the material control and the procedure control in the order task to the corresponding production equipment, copying the material control of the corresponding material to be issued to the corresponding production equipment when one material is associated with a plurality of procedure controls, copying the corresponding procedure control when a plurality of materials are associated with one procedure control, and associating different materials with the same procedure control;
The production schedule according to the production efficiency of the production equipment and the materials and products comprises the following steps: accessing the occupancy rate of the unfinished order data to the equipment, acquiring the material class and quantity data of unfinished order balance, and accessing the association rule data between the materials and the products;
and (3) carrying out iterative training in the following conditions through a random forest model, selecting and setting the materials and the types and the quantity of products in the capacity combined model, controlling equipment occupied by corresponding process controls of the products to be lower than the equipment saturation work occupancy rate, controlling the stock difference direction of the corresponding materials of the products to be opposite to the stock difference direction of the order tasks, and outputting at least two different products for the products in the combined production capacity combined model and the products in the order tasks, wherein the total time of outputting at least two different products for the products in the combined production capacity combined model and the products in the order tasks is smaller than the sum of the time of separately producing and producing one product in the combined production capacity combined model and one product in the order tasks.
Further, a random forest model is output backwards, and a plurality of capacity combination model strategies meeting iteration conditions are obtained;
accessing the change data of the material cost and the change data of the value of the product;
calculating the difference value of the sum of product values, the consumption material cost and the output material cost in unit time, which are output by different product combinations and different production capacity combination model strategies under the same production time reference;
and selecting a capacity combination model strategy with the maximum difference value output in different capacity combination model strategies.
Further, according to the capacity combination model strategy of the maximum difference value and the order material model, scheduling of materials and products is performed, and the scheduling is checked:
Checking whether equipment occupation conflict exists when materials are produced under the process control, whether equipment occupation rate is higher than 100% in unit time exists, whether production enterprises do not have relevant material corresponding equipment exists, if yes, alarming is conducted, iteration indexes and parameters of a random forest model are corrected manually, and iteration training is conducted again.
In a second aspect, the present application provides a production management system based on MES intelligent manufacturing, for implementing a production management method based on MES intelligent manufacturing as in the first aspect, including: the system comprises a database module, a price reference module, a market-end order acquisition module, an analysis module and a production scheduling module;
the database module is used for acquiring historical order data, analyzing association rules between materials and products, including the constituent materials of the products, the stock of the products and the stock of the materials;
The price reference module is used for dynamically collecting cost data of materials and value data of products, analyzing change data of material cost and change data of value of the products;
The market-end order acquisition module is used for receiving order tasks and generating an order material model, wherein the order material model comprises a procedure control, a material control and a material and product scheduling plan;
the analysis module is used for associating and issuing an order material model to corresponding production equipment, and generating a capacity combination model according to the production efficiency of the production equipment and the scheduling plan of materials and products;
The production scheduling module comprises: the productivity combination model is used for planning other materials to execute idle time working procedures on the same equipment according to the occupancy rate of the working procedure control equipment, and is also used for acquiring scheduling plans of other materials corresponding to other products or the same product according to the selection of the materials control, the constituent materials of the products and the stock of the materials; and the productivity combination model and the order material model are matched to control the product combination with the maximum output production value of the MES in unit time.
According to the production management method and system based on MES intelligent manufacturing, a market prediction model is built, training is carried out by using historical data, a continuously growing prediction production plan is output, stock backlog is reduced, and turnover rate is improved;
According to the application, the datamation and modularization production line is realized, the data association is constructed, the optimal coupling model is analyzed and combined according to the historical production data, the decoupling and the coupling are intelligently scheduled and executed, the efficient production is realized, and the production cost is reduced.
Drawings
The accompanying drawings, which are included to provide a further understanding of embodiments of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain the principles of the application. In the drawings:
FIG. 1 is a flow chart of a method for manufacturing management based on MES intelligent manufacturing according to an exemplary embodiment of the present application.
Detailed Description
Reference will now be made in detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The implementations described in the following exemplary examples do not represent all implementations consistent with the application. Rather, they are merely examples of apparatus and methods consistent with aspects of the application as detailed in the accompanying claims.
Problems in MES applications in the prior art, conventional production planning model: each enterprise can select a corresponding proper planning mode according to the self industry characteristics to reduce risks and improve efficiency. For example, large machinery manufacturing, on-demand production models are commonly used, as these industries are more customizable; the industries characterized by quick-elimination products generally adopt stock production, because the products in the industries are generally high in standardization degree and large in market fluctuation;
A significant portion of businesses employ a hybrid model, both in order production and in stock production. Products with lower sales volume, higher value and high customization degree are produced in an on-demand mode; the stock production is adopted by other products with high standardization degree and large sales volume, manual intervention is added in the stock production, high-value stock backlog is reduced to a certain extent, and certain production flexibility is provided, but the problems of standards, accuracy, intervention period and the like of manual intervention still plague decision makers, because necessary data association cannot be found by manpower from massive historical data, market research data, evaluation reports of industry experts/sales personnel, marketing strategies and other market data, and the system has a large-amplitude promotion space in the aspects of production period, supply guarantee, production cost, stock backlog and the like.
The conventional MES system is defined as a production execution system, receives the demand plan information from the ERP or sales front end order system to execute production arrangement and stock production, and adopts a manual experience method to issue a stock pre-cast plan. Products that are not pre-dosed are either out of stock or are forced to be discarded in the event of an emergency order because the production cycle conditions are not met. Therefore, in actual MES production management, the production plan is difficult to be tightly combined with an MES system due to frequent plan change and frequent production replacement, and the real-time monitoring and scheduling advantages of the MES cannot be fully exerted, so that the production cost is high, the management difficulty is complex, the customer satisfaction is low, and the risk of inventory cost is increased.
Noun interpretation:
Stock production, also known as "pre reborn production," is centered on an enterprise organizing production activities according to preset stock levels. The key of the production mode is to maintain a certain product stock so as to cope with the fluctuation of market demands, ensure timely supply and meet the demands of customers.
The advantage of stock production: continuous supply guarantee: stock production ensures continuous supply of product and reduces backorder due to production interruption or supply chain problems. This is particularly important for industries that require a stable supply.
Meets the requirements of clients: by maintaining a certain amount of inventory, the enterprise can more quickly respond to customer needs, shortening the delivery cycle, and improving customer satisfaction.
Balancing production load: the stock production can balance the production load to a certain extent, so that the production process is more stable. When market demand fluctuates, enterprises can utilize the inventory to adjust production capacity, avoiding waste of production resources.
Stock production has the following disadvantages:
High inventory cost: maintaining a certain amount of inventory requires investment, including costs for purchase of inventory items, inventory costs, inventory management, and the like. If the stock quantity is too large, a large amount of mobile funds of the enterprise are occupied, and the operation cost of the enterprise is increased.
Inventory backlog risk: changes in market demand may lead to stock backlog. If market demand drops, excessive inventory may lead to product sales and even require a price reduction process, resulting in losses to the enterprise.
Market demand prediction difficulty: accurate prediction of market demand is critical to mass production. However, market demand is affected by a variety of factors, such as economic environment, consumer preference, etc., which makes market demand prediction difficult. If the predictions are inaccurate, it may result in insufficient or excessive inventory.
Product outdated risk: for some products, the market changes faster and outdated products may lose value quickly. In this case, stock production may result in product outages, with losses to the enterprise.
On-demand production, also known as "order production," is a way to organize production according to actual demand. The core is that the customer starts to produce the product after placing the order.
The production according to the need has the advantages that: the flexibility is high: on-demand production allows enterprises to schedule production campaigns according to real-time market demand. This means that the enterprise can quickly respond to market changes, adjusting the production plan to meet the customer's personalized needs.
The stock cost is low: the on-demand production model reduces reliance on large amounts of inventory, reducing risk of inventory backlog. This helps to reduce inventory costs and avoid capital occupation and wastage.
Waste is reduced: as the production according to the demand is carried out according to the actual order, the over-production and the product backlog can be avoided, and the waste of resources and raw materials is reduced.
Customer satisfaction is improved: the production on demand can more accurately meet the personalized demands of customers, and the product quality and customer satisfaction are improved. This helps establish a long-term stable customer relationship;
On demand production has the following disadvantages:
The production period is long: on-demand production may require longer production cycles because the enterprise needs to schedule production according to orders, which may require time to acquire and process. This may lead to extended lead times, affecting the customer experience.
Supply chain risk: on demand production requires high stability of the supply chain. If a problem occurs in the supply chain, such as a supplier delays delivery or a shortage of raw materials, production breaks may result, affecting execution of the production plan. It is also possible that the cost fluctuation of raw materials after order placement will create a certain risk of cost fluctuation.
High cost: on-demand production may require higher equipment and human input to meet the production requirements of the personalized demand. This may result in increased production costs and reduced profit margins for the enterprise.
The management difficulty increases: the on-demand production model involves more production lots and more frequent production adjustments, increasing the complexity and difficulty of production management. Enterprises need to establish a perfect information system and a production scheduling mechanism to ensure the smooth proceeding of the production process.
In the conventional production and scheduling manner, elements such as productivity and working conditions (such as a mold, a recipe, a team, etc.) of each device in a production line are used as fixed conditions, and in an MES system, the elements to be managed have corresponding management manners, for example: mold physical management, BOM formula management and team personnel basic data management. In actual production scheduling, the system can automatically combine corresponding conditions according to production plan targets, and take the satisfaction of the conditions as production tasks for execution.
Traditional scheduling is a way of scheduling production by preset scheduling rules based on existing resources and capabilities. It is of primary concern how to maximize production efficiency using limited resources such as equipment, manpower, and materials.
The advantages are that:
Stability and reliability: the stability and the reliability of the traditional MES scheduling system are fully verified through long-time development and application. In a normal operation environment, the system can stably operate, and smooth execution of a production plan is ensured.
Accuracy: the traditional MES scheduling focuses on each element achieving the production condition, establishes corresponding scheduling rules according to different production condition requirements, and can accurately deliver a scheduling plan according to the preset scheduling rules.
Disadvantages:
Uncertainty: the conventional preset production rule is that simple conditions meet logic operation, elements of production conditions participate in production and production in an efficiency state of a fixed value, and actual conditions of the elements of the conditions have certain change states, for example: the states of life cycle aging, failure rate, qualification rate, efficiency and the like are not constant, and the comprehensive efficiency of various different condition combinations is also different, so that the traditional preset scheduling rules have larger uncertain risks on production results, and serious conditions can lead to the consequences of rising production cost, delay of a period of time and the like, so that the maximum production efficiency is not realized.
The application has the specific application scene of MES intelligent manufacturing.
The technical conception of the application is as follows:
Quantifying links and factors influencing a production plan, pre-training by adopting a proper model framework, and guiding and predicting production by using a trained model;
The specific technical concept also comprises the following steps:
Technical point 1, collecting historical sales, ex-warehouse data, production data, market demand data, etc.
The data is cleaned to remove outliers and duplicate entries. Data conversion and normalization are performed to facilitate model processing.
Technical point 2, identifies key features related to the production plan from various data, such as seasonal demands, market trends, promotional campaigns, supply chain conditions, etc. Variables reflecting these features are extracted and created for model input.
The technical point 3 selects a proper prediction algorithm according to the characteristics and the complexity of the characteristic data, and specifically, the method can comprise time sequence distribution state analysis, algorithm (such as a neural network, a Support Vector Machine (SVM), random forest and the like) or a deep learning model. The application adopts random forests.
And 4, training a model by using historical data, and optimizing the prediction performance by adjusting parameters of the model. The performance of the model is evaluated by using cross-validation and other technologies, so that the model is ensured to have good generalization capability.
Technical point 5, collecting actual production data and market feedback for evaluating the predictive effect of the model. And iterating and improving the model according to the evaluation result to improve the accuracy and reliability of prediction.
Technical point 6, combine concrete PVC production, sales link, in concrete PVC production process, to the interaction between production, sales system, because the transmission of list among each MES system is inconvenient, in addition the artificial link leads to the unequal, the mistake-prone condition of data transmission, often enterprise is two sets of systems in front end sales and rear end production, can't in time revise the result that brings because of market direction, can't in time, effective transmission order tactics and the management scheduling plan of modification intelligent manufacturing are done.
According to the application, through certain market front-end data collection, the intelligent prediction model is used for accurately and efficiently predicting the most suitable market demand in the longest period to guide the delivery of the production plan.
Therefore, the production plan with high accuracy is realized, the stock allowance is reduced to the maximum extent, the minimum amount of manual intervention is reduced, the production period is shortened, the management difficulty is reduced, and the risk of a supply chain is reduced.
The application provides a production management method and system based on MES intelligent manufacturing, and aims to solve the technical problems in the prior art.
The following describes the technical scheme of the present application and how the technical scheme of the present application solves the above technical problems in detail with specific embodiments. The following embodiments may be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments. Embodiments of the present application will be described below with reference to the accompanying drawings.
Example 1:
a production management method based on MES intelligent manufacturing, as shown in fig. 1, includes:
S1, acquiring historical order data, and analyzing association rules between materials and products, wherein the association rules comprise the constituent materials of the products, the storage quantity of the products and the storage quantity of the materials;
decoupling material data in the historical orders, forming materials of different types into material controls, associating corresponding materials to corresponding products according to material combinations, and analyzing the stock change of multiple materials and products caused by orders;
The method further comprises the steps of analyzing the occupancy rate of the incomplete order data to equipment, obtaining the types and the amounts of the incomplete order balance materials, and obtaining the completion time and the scheduling plan of the remaining products of the incomplete order.
S2, dynamically collecting cost data of materials and value data of products, and analyzing change data of material cost and change data of value of the products;
accessing market and purchase data, writing material cost data into corresponding material controls, and writing value data of products into material controls of related materials;
and fitting the change data of the cost of the material and the change data of the value of the product of the next acquisition period according to the mathematical model.
S3, receiving order tasks, and generating an order material model, wherein the order material model comprises a procedure control, a material control and a material and product scheduling plan;
The order material model is used for dispersing an order task into a plurality of material controls, correspondingly acquiring products from the plurality of material controls through association rules of procedure controls, wherein the procedure controls also comprise production equipment for matching and identifying corresponding procedures;
the order material model analyzes order tasks, generates stock difference data of material controls in unit time, and generates occupancy rate of equipment corresponding to the procedure controls in unit time.
The stock difference data of the material control in unit time is the quantity fluctuation difference of corresponding materials in the material control in unit production time; the occupancy rate of the equipment corresponding to the generated procedure control in unit time is the saturation of the equipment corresponding to the procedure in the procedure control in the unit production time in the process of executing the change of the stock difference of the material control; wherein, the unit production time is the production time of one product.
S4, associating and issuing an order material model to corresponding production equipment, and generating a capacity combination model according to the production efficiency of the production equipment and the scheduling plan of materials and products;
For materials in an order task, comprising: consumption material and output material;
the consumable material is a material consumed by the production order task product;
the output material is a material generated by producing order task products;
The association and order material model issuing are carried out on corresponding production equipment, and the capacity combination model is generated according to the production efficiency of the production equipment and the scheduling plan of materials and products, and specifically comprises the following steps:
According to the corresponding production equipment in the procedure control, decoupling the material control and the procedure control in the order task to the corresponding production equipment, copying the material control of the corresponding material to be issued to the corresponding production equipment when one material is associated with a plurality of procedure controls, copying the corresponding procedure control when a plurality of materials are associated with one procedure control, and associating different materials with the same procedure control;
The production schedule according to the production efficiency of the production equipment and the materials and products comprises the following steps: accessing the occupancy rate of the unfinished order data to the equipment, acquiring the material class and quantity data of unfinished order balance, and accessing the association rule data between the materials and the products;
And (3) carrying out iterative training in the following conditions through a random forest model, selecting and setting the materials and the types and the quantity of products in the capacity combined model, controlling equipment occupied by corresponding process controls of the products to be lower than the equipment saturation work occupancy rate, controlling the stock difference direction of the corresponding materials of the products to be opposite to the stock difference direction of the order tasks, and outputting at least two different products for the products in the combined production capacity combined model and the products in the order tasks, wherein the total time of outputting at least two different products for the products in the combined production capacity combined model and the products in the order tasks is smaller than the sum of the time of separately producing and producing one product in the combined production capacity combined model and one product in the order tasks. Backward outputting a random forest model, and acquiring a plurality of capacity combination model strategies meeting iteration conditions; accessing the change data of the material cost and the change data of the value of the product; calculating the difference value of the sum of product values, the consumption material cost and the output material cost in unit time, which are output by different product combinations and different production capacity combination model strategies under the same production time reference; and selecting a capacity combination model strategy with the maximum difference value output in different capacity combination model strategies. According to the capacity combination model strategy of the maximum difference value and the order material model, scheduling of materials and products is carried out, and the scheduling is checked: checking whether equipment occupation conflict exists when materials are produced under the process control, whether equipment occupation rate is higher than 100% in unit time exists, whether production enterprises do not have relevant material corresponding equipment exists, if yes, alarming is conducted, iteration indexes and parameters of a random forest model are corrected manually, and iteration training is conducted again.
S5, planning materials to execute idle time working procedures on equipment according to working procedure control and equipment occupancy rate by the productivity combination model, and acquiring scheduling plans of other materials corresponding to other products or the same product according to the materials control, the constituent materials of the products and the stock of the materials.
And S6, controlling the product combination with the maximum output yield value of the MES in unit time by matching the productivity combination model with the order material model.
In a second aspect, the present application provides a production management system based on MES intelligent manufacturing, for implementing a production management method based on MES intelligent manufacturing as in the first aspect, including: the system comprises a database module, a price reference module, a market-end order acquisition module, an analysis module and a production scheduling module;
the database module is used for acquiring historical order data, analyzing association rules between materials and products, including the constituent materials of the products, the stock of the products and the stock of the materials;
The price reference module is used for dynamically collecting cost data of materials and value data of products, analyzing change data of material cost and change data of value of the products;
The market-end order acquisition module is used for receiving order tasks and generating an order material model, wherein the order material model comprises a procedure control, a material control and a material and product scheduling plan;
the analysis module is used for associating and issuing an order material model to corresponding production equipment, and generating a capacity combination model according to the production efficiency of the production equipment and the scheduling plan of materials and products;
The production scheduling module comprises: the productivity combination model is used for planning other materials to execute idle time working procedures on the same equipment according to the occupancy rate of the working procedure control equipment, and is also used for acquiring scheduling plans of other materials corresponding to other products or the same product according to the selection of the materials control, the constituent materials of the products and the stock of the materials; and the productivity combination model and the order material model are matched to control the product combination with the maximum output production value of the MES in unit time.
In the several embodiments provided by the present application, it should be understood that the disclosed systems and methods may be implemented in other ways. For example, the system embodiments described above are merely illustrative, e.g., the division of the modules is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple modules or components may be combined or integrated into another system, or some features may be omitted or not performed.
The modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical modules, i.e., may be located in one place, or may be distributed over a plurality of network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist alone physically, or two or more modules may be integrated into one module. The integrated modules may be implemented in hardware or in hardware plus software functional modules.
It is to be understood that the application is not limited to the precise arrangements and instrumentalities shown in the drawings, which have been described above, and that various modifications and changes may be effected without departing from the scope thereof. The scope of the application is limited only by the appended claims.
It will be appreciated by those skilled in the art that embodiments of the invention may be provided as methods or systems. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises an element.
The foregoing is merely exemplary of the present application and is not intended to limit the present application. Various modifications and variations of the present application will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. which come within the spirit and principles of the application are to be included in the scope of the claims of the present application.
Other embodiments of the application will be apparent to those skilled in the art from consideration of the specification and practice of the application disclosed herein. This application is intended to cover any variations, uses, or adaptations of the application following, in general, the principles of the application and including such departures from the present disclosure as come within known or customary practice within the art to which the application pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the application being indicated by the following claims.
It is to be understood that the application is not limited to the precise arrangements and instrumentalities shown in the drawings, which have been described above, and that various modifications and changes may be effected without departing from the scope thereof. The scope of the application is limited only by the appended claims.
Claims (9)
1. The production management method based on the MES intelligent manufacturing is characterized by comprising the following steps of:
Acquiring historical order data, and analyzing association rules between materials and products, wherein the association rules comprise the constituent materials of the products, the storage quantity of the products and the storage quantity of the materials;
Dynamically collecting cost data of materials and value data of products, and analyzing change data of material cost and change data of value of the products;
Receiving an order task, and generating an order material model, wherein the order material model comprises a procedure control, a material control and a material and product scheduling plan;
Associating and issuing an order material model to corresponding production equipment, and generating a capacity combination model according to the production efficiency of the production equipment and the scheduling plan of materials and products;
The capacity combination model plans materials to execute idle time working procedures on equipment according to working procedure control pieces and equipment occupancy rate, and obtains scheduling plans of other materials corresponding to other products or the same product according to the materials control pieces, the constituent materials of the products and the stock of the materials;
And the productivity combination model and the order material model are matched to control the product combination with the maximum output production value of the MES in unit time.
2. The method of claim 1, wherein the step of obtaining historical order data, analyzing association rules between materials and products, including constituent materials of the products, stock of the products, and stock of the materials, comprises:
decoupling material data in the historical orders, forming materials of different types into material controls, associating corresponding materials to corresponding products according to material combinations, and analyzing the stock change of multiple materials and products caused by orders;
The method further comprises the steps of analyzing the occupancy rate of the incomplete order data to equipment, obtaining the types and the amounts of the incomplete order balance materials, and obtaining the completion time and the scheduling plan of the remaining products of the incomplete order.
3. The method for production management based on MES intelligent manufacturing according to claim 2, wherein the dynamically collecting cost data of materials and value data of products, analyzing change data of material cost and change data of value of products, includes:
accessing market and purchase data, writing material cost data into corresponding material controls, and writing value data of products into material controls of related materials;
and fitting the change data of the cost of the material and the change data of the value of the product of the next acquisition period according to the mathematical model.
4. A method of manufacturing management based on MES intelligent manufacturing as claimed in claim 3, wherein said receiving an order task generates an order material model, the order material model including a process control, a material control, and a scheduling plan for materials and products, and specifically further comprising:
The order material model is used for dispersing an order task into a plurality of material controls, correspondingly acquiring products from the plurality of material controls through association rules of procedure controls, wherein the procedure controls also comprise production equipment for matching and identifying corresponding procedures;
the order material model analyzes order tasks, generates stock difference data of material controls in unit time, and generates occupancy rate of equipment corresponding to the procedure controls in unit time.
5. The production management method based on MES intelligent manufacturing according to claim 4, wherein stock difference data of the material control in unit time is a fluctuation difference value of quantity of corresponding materials in the material control in unit production time; the occupancy rate of the equipment corresponding to the generated procedure control in unit time is the saturation of the equipment corresponding to the procedure in the procedure control in the unit production time in the process of executing the change of the stock difference of the material control;
wherein, the unit production time is the production time of one product.
6. The method of claim 5, wherein for materials in the order task, comprising: consumption material and output material;
the consumable material is a material consumed by the production order task product;
the output material is a material generated by producing order task products;
The association and order material model issuing are carried out on corresponding production equipment, and the capacity combination model is generated according to the production efficiency of the production equipment and the scheduling plan of materials and products, and specifically comprises the following steps:
According to the corresponding production equipment in the procedure control, decoupling the material control and the procedure control in the order task to the corresponding production equipment, copying the material control of the corresponding material to be issued to the corresponding production equipment when one material is associated with a plurality of procedure controls, copying the corresponding procedure control when a plurality of materials are associated with one procedure control, and associating different materials with the same procedure control;
The production schedule according to the production efficiency of the production equipment and the materials and products comprises the following steps: accessing the occupancy rate of the unfinished order data to the equipment, acquiring the material class and quantity data of unfinished order balance, and accessing the association rule data between the materials and the products;
and (3) carrying out iterative training in the following conditions through a random forest model, selecting and setting the materials and the types and the quantity of products in the capacity combined model, controlling equipment occupied by corresponding process controls of the products to be lower than the equipment saturation work occupancy rate, controlling the stock difference direction of the corresponding materials of the products to be opposite to the stock difference direction of the order tasks, and outputting at least two different products for the products in the combined production capacity combined model and the products in the order tasks, wherein the total time of outputting at least two different products for the products in the combined production capacity combined model and the products in the order tasks is smaller than the sum of the time of separately producing and producing one product in the combined production capacity combined model and one product in the order tasks.
7. The method according to claim 6, wherein the method comprises outputting backward a random forest model, and obtaining a plurality of capacity combination model strategies meeting iteration conditions;
accessing the change data of the material cost and the change data of the value of the product;
calculating the difference value of the sum of product values, the consumption material cost and the output material cost in unit time, which are output by different product combinations and different production capacity combination model strategies under the same production time reference;
and selecting a capacity combination model strategy with the maximum difference value output in different capacity combination model strategies.
8. The method of claim 7, wherein the scheduling of materials and products is performed according to the capacity combination model strategy of the maximum difference and the order material model, and the scheduling is checked:
Checking whether equipment occupation conflict exists when materials are produced under the process control, whether equipment occupation rate is higher than 100% in unit time exists, whether production enterprises do not have relevant material corresponding equipment exists, if yes, alarming is conducted, iteration indexes and parameters of a random forest model are corrected manually, and iteration training is conducted again.
9. A production management system based on MES intelligent manufacturing, for implementing a production management method based on MES intelligent manufacturing as claimed in any one of claims 1 to 8, comprising: the system comprises a database module, a price reference module, a market-end order acquisition module, an analysis module and a production scheduling module;
the database module is used for acquiring historical order data, analyzing association rules between materials and products, including the constituent materials of the products, the stock of the products and the stock of the materials;
The price reference module is used for dynamically collecting cost data of materials and value data of products, analyzing change data of material cost and change data of value of the products;
The market-end order acquisition module is used for receiving order tasks and generating an order material model, wherein the order material model comprises a procedure control, a material control and a material and product scheduling plan;
the analysis module is used for associating and issuing an order material model to corresponding production equipment, and generating a capacity combination model according to the production efficiency of the production equipment and the scheduling plan of materials and products;
The production scheduling module comprises: the productivity combination model is used for planning other materials to execute idle time working procedures on the same equipment according to the occupancy rate of the working procedure control equipment, and is also used for acquiring scheduling plans of other materials corresponding to other products or the same product according to the selection of the materials control, the constituent materials of the products and the stock of the materials; and the productivity combination model and the order material model are matched to control the product combination with the maximum output production value of the MES in unit time.
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Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| DE10258655A1 (en) * | 2001-12-22 | 2003-07-03 | Abb Research Ltd | Process and system for dynamic, model-based planning and optimization of production processes, as well as for the creation of a schedule |
| CN106408113A (en) * | 2016-08-31 | 2017-02-15 | 广州亿澳斯软件股份有限公司 | Production order scheduling management method and system |
| CN110580026A (en) * | 2019-09-18 | 2019-12-17 | 工业云制造(四川)创新中心有限公司 | intelligent manufacturing MES system |
| CN111626532A (en) * | 2019-02-28 | 2020-09-04 | 湖南师范大学 | Intelligent scheduling method for steelmaking production plan based on big data rule self-learning |
| CN116307008A (en) * | 2021-12-17 | 2023-06-23 | 中国科学院沈阳自动化研究所 | APS (APS) scheduling method in MTO (methyl thiazolyl tetrazolium) and MTS mixed production mode |
| WO2023130709A1 (en) * | 2022-01-06 | 2023-07-13 | 青岛海尔科技有限公司 | Production scheduling method and apparatus for household appliance device, device, and storage medium |
| CN116976948A (en) * | 2023-07-27 | 2023-10-31 | 中原工学院 | Method and system for generating dynamic feedback flow diagram of full value chain of manufacturing enterprise |
| CN117350492A (en) * | 2023-10-13 | 2024-01-05 | 宿迁云瑞国科信息技术有限公司 | MES operation management system capable of intelligently controlling comparison historical data |
| CN117557078A (en) * | 2024-01-12 | 2024-02-13 | 诺彩科技(江苏)有限公司 | Intelligent production plan management method and system based on time nodes |
-
2024
- 2024-03-25 CN CN202410345004.7A patent/CN117952567B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| DE10258655A1 (en) * | 2001-12-22 | 2003-07-03 | Abb Research Ltd | Process and system for dynamic, model-based planning and optimization of production processes, as well as for the creation of a schedule |
| CN106408113A (en) * | 2016-08-31 | 2017-02-15 | 广州亿澳斯软件股份有限公司 | Production order scheduling management method and system |
| CN111626532A (en) * | 2019-02-28 | 2020-09-04 | 湖南师范大学 | Intelligent scheduling method for steelmaking production plan based on big data rule self-learning |
| CN110580026A (en) * | 2019-09-18 | 2019-12-17 | 工业云制造(四川)创新中心有限公司 | intelligent manufacturing MES system |
| CN116307008A (en) * | 2021-12-17 | 2023-06-23 | 中国科学院沈阳自动化研究所 | APS (APS) scheduling method in MTO (methyl thiazolyl tetrazolium) and MTS mixed production mode |
| WO2023130709A1 (en) * | 2022-01-06 | 2023-07-13 | 青岛海尔科技有限公司 | Production scheduling method and apparatus for household appliance device, device, and storage medium |
| CN116976948A (en) * | 2023-07-27 | 2023-10-31 | 中原工学院 | Method and system for generating dynamic feedback flow diagram of full value chain of manufacturing enterprise |
| CN117350492A (en) * | 2023-10-13 | 2024-01-05 | 宿迁云瑞国科信息技术有限公司 | MES operation management system capable of intelligently controlling comparison historical data |
| CN117557078A (en) * | 2024-01-12 | 2024-02-13 | 诺彩科技(江苏)有限公司 | Intelligent production plan management method and system based on time nodes |
Non-Patent Citations (2)
| Title |
|---|
| ZHOU, JH: "iDiagnosis & Prognosis - An Intelligent Platform for Complex Manufacturing", 2009 IEEE/ASME INTERNATIONAL CONFERENCE ON ADVANCED INTELLIGENT MECHATRONICS, vol. 1, 1 January 2009 (2009-01-01), pages 405 * |
| 余阿东: "复杂产品人工智能生产调度研究", 机械设计与制造, 15 September 2023 (2023-09-15), pages 1 - 5 * |
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