MES-based management method and related equipment

Abstract

The invention provides an MES-based management method and related equipment, and is applied to the technical field of data processing. The method comprises the steps of obtaining target park information and target product order information; processing the order information of the target product to generate a production process set of the target product; the target park information is processed based on the production process set of the target product, the production priority and production material information of the production equipment are generated, and the production material information comprises other order excess material information matched with the target product and immediate excess material information matched with the target product; processing the production priority of the production equipment, and generating target production equipment and a target automated guided vehicle matched with the target production equipment; obtaining a target order early warning model; and processing the target production equipment, the target automated guided vehicle and the production material information based on the target order early warning model to generate order production information.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a management method based on MES and related equipment. Background Art

[0002] With the development of computer technology, the business system of enterprises obtains a large number of product orders through the network and produces products according to the product orders. After obtaining the product orders, how to schedule production according to the product orders to optimize production capacity and improve production efficiency has become an urgent problem to be solved. In traditional technology, production scheduling is performed manually based on the order information in the product order. The manual scheduling process consumes a lot of time and is inefficient.

[0003] If we still use the traditional order processing method and produce according to the order requirements submitted by customers, it will be difficult to cope with multiple orders, complex and diverse orders, and changing order situations. It may cause the final product processing to exceed the delivery period, which will directly affect the order delivery plan.

[0004] It should be noted that the information disclosed in the above background technology section is only used to enhance the understanding of the background of the present disclosure, and therefore may include information that does not constitute the prior art known to ordinary technicians in the field. Summary of the invention

[0005] The purpose of this application is to provide a management method and related equipment and systems based on MES, which at least to some extent overcomes the problems of the prior art and collects information on production areas, equipment and AGVs in the park. For the order generation process set, optimize material use, reduce waste, use the order early warning model, predict production anomalies, monitor progress in real time, and ensure delivery. Through intelligent data processing, improve production efficiency and provide innovative management solutions for carton manufacturing.

[0006] Other features and advantages of the present application will become apparent from the following detailed description, or may be learned in part by the practice of the present invention.

[0007] According to one aspect of the present application, a management method based on MES is provided, including: obtaining target park information and target product order information, wherein the target park information includes information of different production areas, production equipment information matching the production area information, and automatic guided vehicle information matching the production area information; processing the target product order information to generate a set of production procedures for the target product, wherein the set of production procedures for the target product is used to characterize the procedures required for carton production; processing the target park information based on the production procedure set of the target product to generate production priority and production material information of the production equipment, wherein the production material information includes other order surplus material information matching the target product and expiring surplus material information matching the target product; processing the production priority of the production equipment to generate target production equipment and a target automatic guided vehicle matching the target production equipment; obtaining a target order early warning model; processing the target production equipment, target automatic guided vehicle and production material information based on the target order early warning model to generate order production information.

[0008] In one embodiment of the present application, obtaining a preset order warning model includes: obtaining a training sample set and an initial order warning model, wherein the initial order warning model is generated based on historical product order information of a target park; preprocessing the training sample set to generate a training sample set with target feature data; and processing the initial order warning model based on the training sample set with target feature data to generate a target order warning model.

[0009] In one embodiment of the present application, the training sample set is preprocessed to generate a training sample set with target feature data, including: extracting features from the training sample set to determine an original feature library; dividing each feature data set according to the original feature library to generate a training set and a verification set; using a classifier to predict each verification set divided from the original feature library to generate a prediction result; training each training set divided from the original feature library based on a preset algorithm to obtain a verification set class prediction result; processing the prediction result and the verification set class prediction result to generate target feature data.

[0010] In one embodiment of the present application, the initial order warning model is processed based on the training sample set with the target feature data to generate a target order warning model, including: extracting multiple data groups from the training set, wherein each data group contains a preset number of data samples, wherein at least one data sample includes target feature data; training the initial order warning model based on the data samples in the multiple data groups to generate a trained order warning model; processing the trained evaluation index model based on the validation set to generate a test result; if the data sample containing the target feature data in the test result is used to characterize the existence of an abnormality in the production order, the trained order warning model is used as the target order warning model.

[0011] In one embodiment of the present application, the target park information is processed based on the production process set of the target product to generate the production priority and production material information of the production equipment, including: processing the target park information based on the production process set of the target product to generate a production equipment set, an automatic guided vehicle set, a production progress information matching the production equipment set, and a work progress information matching the automatic guided vehicle set, wherein the production equipment set is used to process the production process set of the target product; processing the production equipment set based on the production progress information matching the production equipment set to generate production task information of the production equipment, real-time material information of the production equipment, and residual material information of the production equipment, wherein the real-time material information and the residual material information are materials matching the target product; processing the production task information of the production equipment to generate the production priority of the production equipment; processing the real-time material information of the production equipment and the residual material information of the production equipment to generate production material information, wherein the production material information includes residual material information of other orders matching the target product and expiring residual material information matching the target product.

[0012] In one embodiment of the present application, the production priority of the production equipment is processed to generate target production equipment and a target automated guided vehicle matching the target production equipment, including: processing the automated guided vehicle set based on work progress information matching the automated guided vehicle set to generate handling task information of the automated guided vehicle; processing the production priority of the production equipment to generate target production equipment; acquiring location information of the target production equipment; processing the handling task information of the automated guided vehicle and the automated guided vehicle set based on the location information of the target production equipment and the production material information to generate a target automated guided vehicle.

[0013] In one embodiment of the present application, the target production equipment, target automatic guided vehicle and production material information are processed based on the target order early warning model to generate order production information, including: processing the target production equipment and production material information to generate production progress information corresponding to each production process; processing the production progress information corresponding to each production process based on the target automatic guided vehicle to generate order production progress information; processing the order production progress information based on the target order early warning model to generate order production information.

[0014] Another aspect of the present application is a management device based on MES, characterized in that it includes: an acquisition module, used to acquire target park information and target product order information, wherein the target park information includes different production area information, production equipment information matching the production area information, and automatic guided vehicle information matching the production area information; acquiring a target order early warning model; a processing module, used to process the target product order information to generate a set of production process for the target product, wherein the set of production process for the target product is used to characterize the process required for carton production; based on the production process set of the target product, the target park information is processed to generate production priority and production material information of the production equipment, wherein the production material information includes other order surplus material information matching the target product and expiring surplus material information matching the target product; the production priority of the production equipment is processed to generate target production equipment and a target automatic guided vehicle matching the target production equipment; based on the target order early warning model, the target production equipment, target automatic guided vehicle and production material information are processed to generate order production information.

[0015] According to another aspect of the present application, an electronic device is provided, characterized in that it comprises: a first processor; and a memory for storing executable instructions of the first processor; wherein the first processor is configured to implement the above-mentioned MES-based management method by executing the executable instructions.

[0016] According to another aspect of the present application, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a second processor, the above-mentioned MES-based management method is implemented.

[0017] According to another aspect of the present application, a computer program product is provided, including a computer program, characterized in that when the computer program is executed by a third processor, the above-mentioned MES-based management method is implemented.

[0018] This application provides a management method based on MES and related equipment, which collects information about production areas, equipment and AGVs in the park and builds a database to support the production process. Generate a set of process steps for orders to optimize material use and reduce waste. Use the order warning model to predict production anomalies, monitor progress in real time, and ensure delivery. Through intelligent data processing, improve production efficiency, reduce uncertainty, and provide innovative management solutions for carton manufacturing.

[0019] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 A flowchart of a management method based on MES provided in one embodiment of the present application is shown; Figure 2 A schematic diagram of the structure of a management device based on MES provided in one embodiment of the present application is shown; Figure 3 A schematic diagram of the structure of an electronic device provided by an embodiment of the present application is shown; Figure 4 A schematic diagram of a storage medium provided in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0021] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0022] Combine the following Figure 1 To describe the MES-based management method according to the exemplary embodiment of the present application. It should be noted that the following application scenarios are only shown to facilitate understanding of the spirit and principles of the present application, and the embodiments of the present application are not limited in this regard. On the contrary, the embodiments of the present application can be applied to any applicable scenario.

[0023] In one implementation, the present application also proposes a management method and related equipment based on MES. Figure 1 The following schematically shows a flow chart of a management method based on MES according to an embodiment of the present application. Figure 1 As shown, the method is applied to a server, comprising: S101, obtaining target park information and target product order information.

[0024] In one implementation, in the field of paper product manufacturing, such as paper bags, paper boxes, paper cups and other products, the implementation of a management method based on MES (Manufacturing Execution System) can improve production efficiency, quality and flexibility. The following is an implementation of the MES-based management method in paper product manufacturing: The target park information includes production area information, production equipment information and automatic guided vehicle information. The production area information is used to record the specific location, equipment type, production capacity and other information of key production areas such as bag making area, printing area, die cutting area and packaging area. The production equipment information is used to record in detail the specifications, production capacity, operating status, maintenance cycle and current tasks of key equipment such as printing machines, die cutting machines, folder gluers and folding machines. The automatic guided vehicle (AGV) information is used to track the location, task status, route and load of the AGV, and the transportation of raw materials, semi-finished products and finished products.

[0025] The server manages the detailed information of each order, including product type (paper bags, paper boxes, paper cups), size, printing requirements, quantity, delivery time, etc. According to the order requirements, the required raw materials (such as paper, ink, glue, etc.) and auxiliary materials are calculated to generate an accurate material requirement plan. According to the order requirements and equipment capacity, a detailed production plan is formulated, including equipment allocation, production batches, production sequence, etc. Considering the market urgency and profit margin, priorities are set for different orders to optimize production scheduling.

[0026] Monitor raw material inventory, automatically trigger raw material replenishment requests, ensure uninterrupted production, dispatch AGVs to transport raw materials and semi-finished products in a timely manner according to production plans and material requirements, reduce waiting time, collect key quality parameters such as paper thickness, printing quality, glue bonding strength, etc., and conduct real-time monitoring and control. In addition, the production process of each batch of products will be tracked, including information such as the raw materials used, production equipment, and production time, to facilitate quality control and problem tracing, realize the automation of the production process, reduce manual intervention, improve production efficiency and consistency, collect production data, analyze and report, and support production decisions and continuous improvement. Through this MES-based management method, paper product manufacturers can realize the automation, informatization and intelligence of the production process, improve production efficiency, reduce costs, and ensure product quality and environmental safety.

[0027] In the paper product manufacturing process, the application of AGV (Automated Guided Vehicle) can significantly improve the efficiency and accuracy of material handling. The AGV system usually includes a ground control system and a vehicle control system. The ground control system is responsible for task allocation, vehicle scheduling, path management, etc., while the vehicle control system is responsible for the specific navigation calculation and walking operation of the AGV.

[0028] S102: Process the target product order information to generate a production process set of the target product.

[0029] In one embodiment, in the production process of paper products, such as paper bags, paper boxes, paper cups, etc., the processing of target product order information is also crucial. First, detailed information of the paper product order is obtained from the ERP system or order management module, including size, material, printing requirements, quantity, delivery date, etc. According to the order requirements, the necessary production processes are defined. Typical paper product production processes may include but are not limited to the following: Material preparation: Select appropriate paper, ink and other auxiliary materials.

[0030] Printing: Printing patterns, text or logos on materials.

[0031] Die-cutting: Die-cut the printed paper into the desired shape according to the design.

[0032] Folding and forming: Fold the die-cut paper into the shape of a box or cup.

[0033] Gluing: To glue or other adhesives together the seams of paper products.

[0034] Quality inspection: Check whether the printing quality, size, bonding strength, etc. meet the standards.

[0035] Packaging and storage: Qualified paper products are packaged and stored in the warehouse waiting for shipment.

[0036] According to the defined process, the target product production process set is generated to characterize the process required for paper product production. According to the production process set, the required production resources are allocated, including raw materials, production equipment, personnel, and AGV, etc. The production task center module in the MES system is used to formulate a detailed production plan, including the start time, end time, responsible equipment and personnel of each process, etc., analyze the production process set, identify bottleneck processes and potential improvement points, and improve production efficiency by optimizing process sequence, equipment utilization or personnel allocation.

[0037] During the production execution process, the real-time monitoring function of the server is used to track the progress and quality of each process, and the production plan is adjusted in time to deal with any deviations. The production data of each process, including production time, output, scrap rate, etc., are collected for subsequent efficiency analysis and continuous improvement. The quality traceability management module in the server is used to record the production details of each process to ensure the quality traceability of the product. Through the above steps, each process of paper product production is effectively managed, thereby improving production efficiency, ensuring product quality, and meeting customer delivery requirements.

[0038] S103: Process the target park information based on the production process set of the target product to generate production priority and production material information of production equipment.

[0039] In one embodiment, in the production process of paper products such as paper bags, paper boxes, paper cups, etc., processing target park information and generating related production information can be completed by the following steps. Utilize the production model configuration module of the MES system to determine the required production equipment set based on the production process set of paper products. For paper bags, paper boxes, paper cups, etc., this may include paper cutting machines, printing machines, die-cutting machines, folding machines, bonding machines, etc. At the same time, AGV carts are integrated through the IoT module to form an automatic guided vehicle set for automatic transportation of materials. According to the configuration of the production equipment set and the automatic guided vehicle set, the production progress information and work progress information that match them are generated. This information will guide the operation sequence of the production equipment and the transportation tasks of the AGV carts to ensure the smoothness of the production process.

[0040] Assign tasks to the production equipment set, generate production task information for the production equipment, collect real-time material information and surplus material information of the production equipment, which will be used for subsequent production scheduling and material management. According to the urgency of the production task, order priority, equipment capacity and other parameters, process the production task information in the production task center module and generate the priority of each production equipment. Combined with real-time material information and surplus material information, it is processed in the workshop material control module to generate detailed production material information, including other order surplus material information and expiring surplus material information that match the target product, in order to optimize material use and reduce waste.

[0041] Collect the status and location information of all equipment and AGVs related to paper product production in the target park, monitor the material usage of production equipment in real time, generate real-time material information and residual material information, assign production priority to each device in the production task center module, and integrate real-time material information and residual material information in the workshop material control module to generate comprehensive production material information. Ensure that each process of paper product production is effectively managed, thereby improving production efficiency, ensuring product quality, and meeting customer delivery requirements. At the same time, by optimizing material usage, reducing waste, and reducing production costs, the automation, informatization, and intelligence of the production process can be realized, and the competitiveness of paper product manufacturing can be improved.

[0042] In another embodiment, in the production process of paper products such as paper bags, paper boxes, paper cups, etc., similar steps can be used to process the target park information and generate relevant production information. The following are the specific steps for these products: Use the production model configuration module of the MES system to determine the required production equipment set based on the production process set of paper products, which may include paper cutting machines, flexographic printing machines, screen printing machines, paper box forming machines, bonding machines, etc. Integrate AGV carts through the IoT module to form an automatic guided vehicle set for the automatic transportation of raw materials, semi-finished products and finished products.

[0043] According to the configuration of the production equipment set and the automatic guided vehicle set, the production progress information and work progress information that match them are generated to ensure the smoothness of the production process, assign tasks to the production equipment set, generate production task information for the production equipment, and collect real-time material information and residual material information of the production equipment. In the production task center module, according to the urgency of the production task, order priority, equipment capacity and other parameters, a priority is generated for each production equipment, combined with real-time material information and residual material information, processed in the workshop material control module, and detailed production material information is generated to optimize material use and reduce waste.

[0044] The MES system collects the status and location information of all equipment and AGVs related to paper product production in the target park, monitors the material usage of production equipment in real time, integrates real-time material information and surplus material information in the workshop material control module, and generates comprehensive production material information, including surplus materials and expiring surplus materials from other orders. Ensure that each process of paper product production is effectively managed, improve production efficiency, ensure product quality, and meet customer delivery requirements. At the same time, by optimizing material use, reducing waste, and reducing production costs, the overall competitiveness of paper product manufacturing can be improved.

[0045] In another implementation, adding a "scheduling job" function to the MES system can be achieved through the following steps: In the production model configuration module of the MES system, determine the required set of production equipment based on the production process set of cartons (or other paper products). This includes cardboard cutting machines, printing machines, folding and gluing machines, die-cutting machines, etc. At the same time, the AGV cart is integrated through the IoT module to form an automatic guided vehicle set for automatic transportation of materials. Use the "pre-scheduled Kanban" function of the MES system to view the corresponding pre-scheduled Gantt chart. This Kanban can show the scheduled start and end times of production tasks, as well as the tasks assigned to specific equipment, to help monitor production progress and detect potential bottlenecks or delays in advance.

[0046] The planning department can send the scheduled jobs to the unassigned task pool through the "dispatch" function of the MES system. In this way, the manufacturing department can perform specific dispatch operations based on the preset machines and scheduled dates. If the scheduled jobs need to be adjusted, the planning department can use the "change schedule" function to batch readjust the scheduled machines. This allows for rapid rescheduling of production tasks in the face of urgent changes or optimization needs. The MES system is able to collect real-time material information and residual material information of production equipment, which will be used for subsequent production scheduling and material management. Through the workshop material control module, the material usage of production equipment can be monitored in real time, and real-time material information and residual material information can be generated. In the production task center module, production priorities are assigned to each device based on the urgency and importance of the production task. This helps ensure that high-priority tasks are given priority in resources and processing.

[0047] Integrate real-time material information and surplus material information to generate comprehensive production material information, including surplus materials from other orders and expiring surplus materials, to optimize material use and reduce waste. It can effectively manage the production process of cartons or other paper products, improve production efficiency, ensure product quality, and meet customer delivery requirements. At the same time, by optimizing material use, reducing waste and lowering production costs.

[0048] S104, processing the production priority of the production equipment to generate a target production equipment and a target automatic guided vehicle matching the target production equipment.

[0049] In one implementation, the automated guided vehicle set is processed based on work progress information that matches the automated guided vehicle set to generate handling task information for the automated guided vehicles, the production priority of the production equipment is processed to generate target production equipment, the location information of the target production equipment is acquired, and the handling task information of the automated guided vehicle and the automated guided vehicle set are processed based on the location information of the target production equipment and the production material information to generate a target automated guided vehicle.

[0050] The generation of the handling task information of the automated guided vehicle and the target production equipment, as well as the collaborative work between them, can be achieved through the following steps: According to the matching of the AGV set and the work progress information, the handling task information of each AGV is generated, including the starting position, the target position, the type and quantity of the materials to be handled, the estimated handling time, etc. According to the production priority of the production equipment, the most urgent or important production task is selected and the target production equipment is generated. The target production equipment refers to the equipment that needs to carry out production activities or preparation activities immediately.

[0051] Obtain the real-time location information of the target production equipment in the park, combine the location information of the target production equipment and the production material information, and process the handling task information and the set of automated guided vehicles through the IOT module. Determine the position relationship between each AGV and the target production equipment, as well as the relationship between the AGV and the location of the remaining materials, generate the target automated guided vehicle, and determine which AGVs need to be dispatched to a specific location to perform tasks.

[0052] In a large paper product manufacturing plant, a variety of paper products are produced, such as paper bags, paper boxes, paper cups, etc. There are multiple production areas inside the factory, each equipped with specific production equipment, such as paper cutting machines, printing machines, die-cutting machines, folding and gluing machines, etc. At the same time, the factory uses multiple AGVs to transport raw materials and semi-finished products, such as roll paper or printed roll paper materials. The handling of finished products is completed by automated equipment such as manipulators, automatic box pressing and sealing, and automatic conveyor lines.

[0053] The following example can be used to illustrate this: According to the production plan and work progress, the system identifies and arranges material handling tasks. For example, a batch of roll paper needs to be transported from the warehouse to the printing area at 2 pm. The system assigns specific handling tasks to the AGV, including the starting position, target position, type of material to be handled, quantity, and estimated handling time. For example, the task order may indicate that 5 tons of roll paper is to be transported from warehouse location A to printing area location B, with an estimated handling time of 30 minutes. Based on the urgency of the production order and the equipment capacity, the system determines that the printing press C in the printing area is the most urgent production equipment at present, and production activities need to be carried out immediately to prepare to print a batch of paper boxes with specific patterns. Through the IoT module, the system obtains the real-time location information of the printing press C and confirms that it is located at a specific location in the printing area.

[0054] The system analyzes the current position of the AGV and the position of the printing press C, determines the AGV closest to the printing press C, and assigns the paper transportation task to the closest AGV to ensure that the materials can be delivered in time. Considering the storage location of the remaining materials, if there is unused paper in the printing area, the system will instruct the AGV to transport the remaining materials back to the warehouse or transfer them to other required areas after completing the transportation task of the new paper. The transportation of finished products does not involve AGV, but is completed by automated equipment such as manipulators, automatic box pressing and sealing, and automatic conveyor lines to improve transportation efficiency and reduce manual intervention.

[0055] Through the above process, paper product manufacturers can ensure the efficient operation of the production process. At the same time, through precise material management and AGV scheduling, the overall production efficiency and the accuracy of material management can be improved. This intelligent production management method helps to reduce costs, improve product quality, and meet the diverse needs of customers.

[0056] In another implementation, in the first material handling task, the manual trigger system issues a guidance task order, and the AGV performs material handling from the warehouse to the production line according to the requirements of the task order. In this process, the system generates a transfer order based on the relevant information of the work order previously opened by the planned subject, such as the type, quantity, destination, etc. of the material. The front-line manual only needs to trigger the start instruction according to the handling requirements, and the MES system will automatically generate a task order based on the work order information and instructions and issue it to the AGV. After receiving the task order, the AGV uses its navigation system, such as electromagnetic guidance, magnetic tape guidance or laser navigation, to reach the designated location along the preset path to perform the handling task. For the handling of subsequent materials, the AGV system can be automatically triggered. This means that once the materials on the production line are consumed to a certain extent, the system will automatically generate a handling task and assign it to the AGV without manual intervention. According to the system instructions, the AGV will automatically go to the warehouse to pick up materials and transport them to the production line to realize the automation of material replenishment.

[0057] AGV has various navigation and guidance methods, including electromagnetic guidance, magnetic tape guidance, optical guidance, etc. Different guidance methods are suitable for different application scenarios. For example, electromagnetic guidance is suitable for environments where the path is fixed and not easy to change, while optical guidance provides higher flexibility and precision. In paper product manufacturing plants, the application of AGV trolleys is mainly concentrated on the handling of raw materials and semi-finished products, especially roll paper materials. For example, AGV trolleys are used to transport roll paper raw materials from warehouses to production lines. For example, the roll paper is transported from the storage area to the cutting machine or printing machine. After printing, die-cutting and other processes, the AGV trolley transports the printed roll paper material to the next production link, such as the folding and gluing machine. AGV trolleys reduce the need for manual handling through automated handling, optimize material flow, and improve production efficiency. According to production plans and real-time needs, AGV trolleys can flexibly adjust handling tasks to adapt to production changes. AGV trolleys automatically travel on predetermined paths, reduce conflicts between people and logistics in the workshop, and improve workplace safety. By reducing manual handling and optimizing material handling paths, AGVs help reduce production costs, improve the level of production automation, optimize logistics management, and ensure production continuity and efficiency.

[0058] In summary, the application of AGV system in paper product manufacturing improves production efficiency, reduces labor costs, and enhances production flexibility and response speed through automated material handling and intelligent scheduling. With the continuous advancement of technology, the application of AGV system in the paper product manufacturing industry will be more extensive and in-depth.

[0059] S105, obtaining a target order warning model.

[0060] In one implementation, a training sample set and an initial order warning model are obtained, wherein the initial order warning model is generated based on historical product order information of a target park. The training sample set is preprocessed to generate a training sample set with target feature data, and the initial order warning model is processed based on the training sample set with target feature data to generate a target order warning model.

[0061] The generation and optimization of the order warning model is an important function that can help the park more effectively predict and manage problems that may arise during the order production process. The following are the steps to generate the target order warning model starting from the training sample set and the initial order warning model: First, collect historical product order information from the target park, including order details, production data, delivery time, customer feedback, etc., to ensure that the sample set contains enough historical data to cover different production scenarios and potential order problems. Using historical order data, develop an initial order warning model. This model can be based on statistical analysis, machine learning, or deep learning technology. The initial model may include simple rules, such as automatically marking orders with historical delays as high risk.

[0062] Perform data cleaning on the training sample set, including removing incomplete or erroneous data records and handling missing values, possibly by interpolation, deletion, or using default values. Extract useful features from the original data, such as order size, delivery period, seasonal factors, historical delay rate, etc., and create new features, such as remaining production time calculated from order date and delivery date. Standardize or normalize the data to eliminate the impact of different magnitudes and dimensions.

[0063] Use the preprocessed training sample set to train the initial model. This may include adjusting model parameters, selecting the best algorithm and evaluation metrics, and applying machine learning algorithms such as random forests, support vector machines, neural networks, etc. Use cross-validation or retain a validation set to evaluate the performance of the model and adjust model parameters to optimize performance. Based on the training results, generate the final order warning model. This model can predict potential production delays, quality issues, or other risks based on real-time order data, enabling the server to receive order data in real time and perform early warning analysis.

[0064] Regularly collect the model's prediction results and actual events, analyze the model's accuracy and reliability, and continuously adjust and optimize the model based on feedback from actual applications. As new orders are completed, new data is continuously added to the training sample set to maintain the timeliness and accuracy of the model. Build an effective order early warning model to help the park identify and deal with potential production problems in advance, thereby improving customer satisfaction and production efficiency.

[0065] In another implementation, the training sample set is preprocessed to generate a training sample set with target feature data, which specifically includes the following contents: feature extraction of the training sample set, determination of the original feature library, collection of historical order data, including order details, production data, customer information, historical problem records, etc., identification of various features that may affect order fulfillment from the original data, such as order size, delivery deadline, customer credit rating, historical order fulfillment status, etc., integration of all selected features into a feature library, and providing a basis for subsequent data processing and model training.

[0066] Divide each feature data set according to the original feature library to generate training sets and validation sets. Randomly divide the data in the original feature library into training sets and validation sets. The common ratio is 70% training set and 30% validation set. Ensure that each data point has the correct label, which is crucial for the training of supervised learning models. Use the classifier to predict each validation set divided by the original feature library and generate prediction results. Select the appropriate machine learning classifier according to the nature of the problem, such as logistic regression, decision tree, support vector machine, etc. Use the classifier to predict the validation set and generate preliminary prediction results. Divide each training set in the original feature library based on the preset algorithm for training to obtain the validation set class prediction results. Use the training set data to train the selected classifier, use the validation set to evaluate the performance of the model, and adjust the model parameters to optimize the prediction accuracy.

[0067] Process the prediction results and the validation set prediction results to generate target feature data, analyze the prediction results of the classifier on the validation set, identify the strengths and weaknesses of the model performance, and based on the prediction results, you may need to return to the feature engineering stage to further optimize feature selection or engineering, such as adding new features or removing irrelevant features. Determine the final feature set, which will be used to build the final order warning model. Continuously iterate the model based on the target feature data until satisfactory performance is achieved, ensuring that the order warning model can be trained and predicted based on the most relevant and predictive features, thereby improving the accuracy and practicality of the model.

[0068] In another embodiment, the initial order warning model is processed based on the training sample set with target feature data to generate the target order warning model, which specifically includes the following contents: extract multiple data groups from the training set, wherein each data group contains a preset number of data samples, wherein at least one data sample includes target feature data, and randomly extract multiple data groups from the training set, each group contains a preset number of data samples, ensuring that each data group contains at least one data sample with target features, and these samples can represent possible abnormal situations. Label each data sample, especially those containing target features, to ensure that the model can learn abnormal features.

[0069] The initial order warning model is trained based on data samples in multiple data sets to generate a trained order warning model, and a suitable machine learning algorithm is selected as the initial order warning model, such as random forest, gradient boosting machine (GBM), neural network, etc. The initial model is trained using multiple data sets, and model parameters such as learning rate, tree depth (for tree model), number and size of hidden layers (for neural network) are adjusted to optimize model performance. The cross-validation method is used to evaluate the stability and generalization ability of the model to ensure that the model performs well on different data sets. The trained evaluation index model is processed based on the validation set to generate test results, and the trained model is tested using the validation set to generate test results. Evaluation indicators such as accuracy, recall, F1 score, ROC-AUC, etc. are calculated to evaluate the prediction performance of the model.

[0070] If the data samples containing the target feature data in the test results are used to characterize the existence of abnormalities in production orders, the trained order warning model is used as the target order warning model. Analyze the test results, paying special attention to the prediction results of the samples containing the target feature data. If the test results show that the samples containing the target feature data can be accurately identified as abnormal by the model, the model training is considered successful. Confirm the trained model as the final target order warning model and prepare to deploy it to the production environment. Integrate the target order warning model into the sMES system so that it can monitor and warn real-time order data. After the model is deployed, continue to monitor its performance and make necessary adjustments and optimizations based on feedback from actual applications.

[0071] Through the above steps, it can be ensured that the target order early warning model can accurately identify abnormal situations in production orders, so as to give early warning and take corresponding measures to reduce production delays and improve customer satisfaction.

[0072] S106, processing the target production equipment, target automatic guided vehicle and production material information based on the target order warning model to generate order production information.

[0073] In one implementation, in the MES system, the analysis of historical data is crucial to help companies identify and prevent potential production problems, including major accidents. The MES system can provide in-depth insights into the production process through real-time monitoring and historical data analysis. The system can record and analyze various data in the production process, including equipment operating status, production capacity, material usage, quality control results, etc. For queries on "whether there are major accidents in historical data", the MES system can provide detailed accident records and analysis reports. For example, the system can record events such as equipment failures, production interruptions, quality deviations, and provide detailed analysis of the causes, effects, and treatment results of these events. These data can be used for future accident prevention and improvement measures.

[0074] The accident recording function of the MES system can help enterprises track and analyze past production accidents, so as to avoid similar incidents in future production. Through in-depth analysis of historical data, the MES system can identify key risk factors in the production process and predict potential production delays or problems through early warning models. When implementing the MES system, enterprises should ensure that the system can integrate and analyze various data from the production line, including equipment status, production tasks, material information, etc. In this way, the MES system can not only provide real-time production monitoring, but also optimize the production process and improve production efficiency through analysis of historical data.

[0075] In summary, the MES system plays an important role in processing historical data and preventing major accidents. Through real-time monitoring and historical data analysis, the MES system can help enterprises promptly discover and solve problems in the production process, thereby improving production safety and efficiency.

[0076] In another implementation, the target production equipment and production material information are processed to generate production progress information corresponding to each production process, collect the status information of the target production equipment, including equipment operation status, production capacity, current tasks, etc., and collect production material information, including material type, quantity, location, supply status, etc. According to the production equipment and material information, the estimated start time, end time and current progress of each production process are calculated, and the production progress information of each process is generated in combination with the actual production data and the planned data, and the production progress information is updated in real time to reflect any changes in the production process, such as equipment failure, material delay, etc.

[0077] Based on the target automatic guided vehicle, the production progress information corresponding to each production process is processed to generate order production progress information, integrate the real-time position and task status information of AGV, adjust the material supply and production scheduling plan according to the transportation efficiency and path of AGV, and combine the progress information of each production process with the material transportation progress of AGV to generate the overall order production progress information.

[0078] The order production progress information is processed based on the target order warning model to generate order production information. The target order warning model is used to analyze the order production progress information and predict potential production delays or problems. The model can identify key risk factors in the production process based on historical data and machine learning algorithms. The analysis results of the order warning model are integrated into the order production information to provide a comprehensive view of the order status and generate easy-to-understand reports and dashboards for management and relevant staff.

[0079] Ensure the accuracy and timeliness of production reporting through real-time data collection and analysis, and use technologies such as barcodes, RFID or mobile applications to simplify the data entry process. Collect key production efficiency indicators such as equipment utilization rate, production cycle time, material flow rate, etc., analyze these data, and identify production bottlenecks and improvement opportunities. Through real-time monitoring and early warning systems, quickly respond to abnormal situations in production, reduce waste, implement lean production strategies, optimize production processes, and reduce unnecessary steps and delays. Through precise production planning and material management, reduce the accumulation of work-in-progress, optimize production scheduling, ensure timely supply of materials, and avoid production interruptions.

[0080] Comprehensively monitor and manage production resources, including equipment, materials, manpower, etc., implement effective resource allocation strategies, improve resource utilization, and reduce costs. It can help companies optimize the production process, improve production efficiency and product quality, and reduce costs and risks.

[0081] This application collects information on different production areas, relevant production equipment and AGV information of the target park by the server, and builds a comprehensive park information database. Then, for the target product order, the system processes and generates a set of production processes, and clarifies the various steps required for production. The production process set is used to process park information, assign priorities to production equipment, and generate detailed production material information, including surplus and expiring surplus information that matches other orders. This step ensures the effective use of materials and reduces waste. The target production equipment is determined based on the production priority of the production equipment, and the corresponding AGVs are matched to these equipment to optimize the handling and flow of materials.

[0082] In order to identify and respond to possible problems in the production process in advance, an order early warning model was introduced. The model is trained based on historical order data and can predict whether there are abnormalities in production orders. Through preprocessing and feature extraction of the training sample set, the system generates a training sample set with target features, and uses these data to train and optimize the initial early warning model. With the help of the early warning model, the production progress can be monitored in real time, the production plan can be adjusted in time, and the production efficiency and accuracy of order delivery can be ensured. Finally, the production equipment, AGV and material information are comprehensively processed to generate detailed order production information, which provides strong support for production decisions. Through intelligent data processing and prediction models, the efficiency and response speed of production management are significantly improved, the uncertainty and waste in production are reduced, and innovative management solutions are brought to the carton manufacturing industry.

[0083] In one embodiment, if Figure 2 As shown, the present application also provides a management device based on MES, including: The acquisition module 201 is used to acquire target park information and target product order information, wherein the target park information includes different production area information, production equipment information matching the production area information, and automatic guided vehicle information matching the production area information; and acquire a target order early warning model; Processing module 202 is used to process the target product order information and generate a set of production process for the target product, wherein the set of production process for the target product is used to characterize the process required for carton production; based on the set of production process for the target product, the target park information is processed to generate the production priority and production material information of the production equipment, wherein the production material information includes other order surplus material information matching the target product and expiring surplus material information matching the target product; the production priority of the production equipment is processed to generate the target production equipment and the target automatic guided vehicle matching the target production equipment; based on the target order early warning model, the target production equipment, the target automatic guided vehicle and the production material information are processed to generate order production information.

[0084] In another embodiment of the present application, the processing module 202 is configured to obtain a preset order warning model, including: Obtaining a training sample set and an initial order warning model, wherein the initial order warning model is generated based on historical product order information of the target park; Preprocessing the training sample set to generate a training sample set with target feature data; The initial order warning model is processed based on the training sample set with target feature data to generate a target order warning model.

[0085] In another embodiment of the present application, the processing module 202 is configured to pre-process the training sample set to generate a training sample set with target feature data, including: Extracting features from the training sample set to determine an original feature library; Divide each feature data set according to the original feature library to generate a training set and a validation set; Use the classifier to divide the original feature library into various verification sets for prediction and generate prediction results; Based on the preset algorithm, the original feature library is divided into various training sets for training, and the prediction results of the validation set are obtained; The prediction result and the validation set prediction result are processed to generate target feature data.

[0086] In another embodiment of the present application, the processing module 202 is configured to process the initial order warning model based on the training sample set with target feature data to generate a target order warning model, including: Extracting a plurality of data groups from the training set, wherein each data group comprises a preset number of data samples, wherein at least one data sample comprises target feature data; Training the initial order warning model based on data samples in the plurality of data groups to generate a trained order warning model; Processing the trained evaluation index model based on the validation set to generate a test result; If the data sample containing the target feature data in the test result is used to characterize the existence of an abnormality in the production order, the trained order warning model is used as the target order warning model.

[0087] In another embodiment of the present application, the processing module 202 is configured to process the target park information based on the production process set of the target product to generate production priority and production material information of the production equipment, including: Processing the target park information based on the production process set of the target product to generate a production equipment set, an automatic guided vehicle set, production progress information matching the production equipment set, and work progress information matching the automatic guided vehicle set, wherein the production equipment set is used to process the production process set of the target product; Processing the production equipment set based on the production progress information matching the production equipment set to generate production task information of the production equipment, real-time material information of the production equipment, and residual material information of the production equipment, wherein the real-time material information and the residual material information are materials matching the target product; Processing the production task information of the production equipment to generate a production priority of the production equipment; The real-time material information of the production equipment and the surplus material information of the production equipment are processed to generate production material information, wherein the production material information includes other order surplus material information matching the target product and expiring surplus material information matching the target product.

[0088] In another embodiment of the present application, the processing module 202 is configured to process the production priority of the production equipment, generate a target production equipment and a target automatic guided vehicle matching the target production equipment, including: Processing the automated guided vehicle set based on the work progress information matched by the automated guided vehicle set to generate handling task information of the automated guided vehicles; Processing the production priority of the production equipment to generate target production equipment; Obtain location information of target production equipment; The handling task information of the automated guided vehicle and the automated guided vehicle set are processed based on the location information of the target production equipment and the production material information to generate a target automated guided vehicle.

[0089] In another embodiment of the present application, the processing module 202 is configured to process the target production equipment, the target automatic guided vehicle and the production material information based on the target order warning model to generate order production information, including: Process the target production equipment and production material information to generate production progress information corresponding to each production process; Processing the production progress information corresponding to each production process based on the target automatic guided vehicle to generate order production progress information; The order production progress information is processed based on the target order early warning model to generate order production information.

[0090] The present application embodiment provides an electronic device, such as Figure 3 As shown, the electronic device 3 includes a first processor 300, a memory 301, a bus 302 and a communication interface 303, and the first processor 300, the communication interface 303 and the memory 301 are connected via the bus 302; the memory 301 stores a computer program that can be run on the first processor 300, and when the first processor 300 runs the computer program, the MES-based management method provided in any of the aforementioned embodiments of the present application is executed.

[0091] The memory 301 may include a high-speed random access memory (RAM), and may also include a non-volatile memory, such as at least one disk memory. The communication connection between the system network element and at least one other network element is realized through at least one communication interface 303 (which may be wired or wireless), and the Internet, wide area network, local area network, metropolitan area network, etc. may be used.

[0092] The bus 302 may be an ISA bus, a PCI bus, or an EISA bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. The memory 301 is used to store programs, and the first processor 300 executes the programs after receiving the execution instruction. The MES-based management method disclosed in any implementation of the above-mentioned embodiment of the present application may be applied to the first processor 300, or implemented by the first processor 300.

[0093] The first processor 300 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the hardware integrated logic circuit or software instructions in the first processor 300. The above-mentioned first processor 300 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a readily available programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present application can be implemented or executed. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc. The steps of the method disclosed in the embodiments of the present application can be embodied as being executed by a hardware decoding processor, or by a combination of hardware and software modules in a decoding processor. The software module can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory 301, and the first processor 300 reads the information in the memory 301 and completes the steps of the above method in combination with its hardware.

[0094] The electronic device provided by the above-mentioned embodiments of the present application and the MES-based management method provided by the embodiments of the present application are based on the same inventive concept and have the same beneficial effects as the methods adopted, run or implemented by the application programs stored therein.

[0095] The present application embodiment provides a computer-readable storage medium, such as Figure 4 As shown, the computer-readable storage medium stores 401 a computer program, and when the computer program is read and executed by the second processor 402, the aforementioned MES-based management method is implemented.

[0096] The technical solution of the embodiment of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for enabling an electronic device (which may be an air conditioner, a refrigeration device, a personal computer, a server, or a network device, etc.) or a processor to execute all or part of the steps of the method described in the embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a ROM, a RAM, a magnetic disk, or an optical disk.

[0097] The computer-readable storage medium provided in the above-mentioned embodiments of the present application and the MES-based management method provided in the embodiments of the present application are based on the same inventive concept and have the same beneficial effects as the methods adopted, run or implemented by the application programs stored therein.

[0098] An embodiment of the present application provides a computer program product, including a computer program, wherein the computer program is executed by a third processor to implement the method described above.

[0099] The computer program product provided by the above-mentioned embodiments of the present application and the MES-based management method provided by the embodiments of the present application are based on the same inventive concept and have the same beneficial effects as the methods adopted, run or implemented by the application programs stored therein.

[0100] It should be noted that, in this application, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or still includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise one..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0101] Each embodiment in this application is described in a related manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the evaluation of the MES-based management method, electronic device, electronic device, and readable storage medium embodiment, since it is basically similar to the above-mentioned MES-based management method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the above-mentioned MES-based management method embodiment.

[0102] Although the present application is disclosed as above, the present application is not limited thereto. Any person skilled in the art can make various changes and modifications without departing from the spirit and scope of the present application. Therefore, the protection scope of the present application shall be subject to the scope defined by the claims.

Claims

1. A management method based on MES, characterized in that: include: Acquire target park information and target product order information, wherein the target park information includes different production area information, production equipment information matching the production area information, and automatic guided vehicle information matching the production area information; Processing the target product order information to generate a production process set of the target product, wherein the production process set of the target product is used to represent the processes required for carton production; Processing the target park information based on the production process set of the target product to generate production priority and production material information of the production equipment, wherein the production material information includes other order surplus material information matching the target product and expiring surplus material information matching the target product; Processing the production priority of the production equipment to generate a target production equipment and a target automatic guided vehicle matching the target production equipment; Obtain target order warning model; Based on the target order early warning model, the target production equipment, target automatic guided vehicle and production material information are processed to generate order production information.

2. The method according to claim 1, characterized in that Get the preset order warning model, including: Obtaining a training sample set and an initial order warning model, wherein the initial order warning model is generated based on historical product order information of the target park; Preprocessing the training sample set to generate a training sample set with target feature data; The initial order warning model is processed based on the training sample set with target feature data to generate a target order warning model.

3. The method according to claim 2, characterized in that Preprocessing the training sample set to generate a training sample set with target feature data includes: Extracting features from the training sample set to determine an original feature library; Divide each feature data set according to the original feature library to generate a training set and a validation set; Use the classifier to divide the original feature library into various verification sets for prediction and generate prediction results; Based on the preset algorithm, the original feature library is divided into various training sets for training, and the prediction results of the validation set are obtained; The prediction result and the validation set prediction result are processed to generate target feature data.

4. The method according to claim 3, characterized in that The initial order warning model is processed based on the training sample set with target feature data to generate a target order warning model, including: Extracting a plurality of data groups from the training set, wherein each data group comprises a preset number of data samples, wherein at least one data sample comprises target feature data; Training the initial order warning model based on data samples in the plurality of data groups to generate a trained order warning model; Processing the trained evaluation index model based on the validation set to generate a test result; If the data sample containing the target feature data in the test result is used to characterize the existence of an abnormality in the production order, the trained order warning model is used as the target order warning model.

5. The method according to claim 1, characterized in that The target park information is processed based on the production process set of the target product to generate production priority and production material information of the production equipment, including: Processing the target park information based on the production process set of the target product to generate a production equipment set, an automatic guided vehicle set, production progress information matching the production equipment set, and work progress information matching the automatic guided vehicle set, wherein the production equipment set is used to process the production process set of the target product; Processing the production equipment set based on the production progress information matching the production equipment set to generate production task information of the production equipment, real-time material information of the production equipment, and residual material information of the production equipment, wherein the real-time material information and the residual material information are materials matching the target product; Processing the production task information of the production equipment to generate a production priority of the production equipment; The real-time material information of the production equipment and the surplus material information of the production equipment are processed to generate production material information, wherein the production material information includes other order surplus material information matching the target product and expiring surplus material information matching the target product.

6. The method according to claim 5, characterized in that Processing the production priority of the production equipment to generate a target production equipment and a target automatic guided vehicle matching the target production equipment includes: Processing the automated guided vehicle set based on the work progress information matched by the automated guided vehicle set to generate handling task information of the automated guided vehicles; Processing the production priority of the production equipment to generate target production equipment; Obtain location information of target production equipment; The handling task information of the automated guided vehicle and the automated guided vehicle set are processed based on the location information of the target production equipment and the production material information to generate a target automated guided vehicle.

7. The method according to claim 6, characterized in that Based on the target order warning model, the target production equipment, target automatic guided vehicle and production material information are processed to generate order production information, including: Process the target production equipment and production material information to generate production progress information corresponding to each production process; Processing the production progress information corresponding to each production process based on the target automatic guided vehicle to generate order production progress information; The order production progress information is processed based on the target order early warning model to generate order production information.

8. A management device based on MES, characterized in that: The device comprises: An acquisition module is used to acquire target park information and target product order information, wherein the target park information includes different production area information, production equipment information matching the production area information, and automatic guided vehicle information matching the production area information; and acquire a target order early warning model; A processing module is used to process the target product order information and generate a set of production process for the target product, wherein the set of production process for the target product is used to characterize the process required for carton production; based on the set of production process for the target product, the target park information is processed to generate the production priority and production material information of the production equipment, wherein the production material information includes other order surplus material information matching the target product and expiring surplus material information matching the target product; the production priority of the production equipment is processed to generate the target production equipment and the target automatic guided vehicle matching the target production equipment; based on the target order early warning model, the target production equipment, the target automatic guided vehicle and the production material information are processed to generate order production information.

9. An electronic device, characterized in that: include: a first processor; and a memory for storing executable instructions of the first processor; The first processor is configured to execute the MES-based management method according to any one of claims 1 to 7 by executing the executable instructions.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the second processor, the MES-based management method according to any one of claims 1 to 7 is implemented.

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