Production order management method and system based on artificial intelligence

Abstract

The invention provides a production order management method and system based on artificial intelligence, and relates to the technical field of production management. The method comprises the steps of obtaining predicted order information according to historical order information and industry trend information; according to the customer order information, the prediction order information and the product inventory information, generating supplementary library order information; according to the customer order information, the research and development order information and the replenishment order information, determining an order execution sequence and a production process sequence of the order; according to the order execution sequence, the production process sequence and the equipment capacity, a production equipment sequence and a production plan are determined; if the order insertion information is received, the order execution sequence, the production equipment sequence and the production plan are updated according to the information of the orders being executed, the order execution sequence and the order insertion information; if the production plan of the production equipment needs to be updated, the real-time capacity of the production equipment is determined, and the production equipment sequence and the production plan are updated. And the flexibility and response speed of production order management are improved.

Description

Technical Field

[0001] This invention relates to the field of production management technology, and more specifically, to a production order management method and system based on artificial intelligence. Background Technology

[0002] Production order management is crucial for a company's production and operations; it's like the "lifeline" of the production process, impacting costs, efficiency, quality, and even customer satisfaction. By rigorously controlling the order lifecycle, production factors such as materials, personnel, and machinery can be effectively coordinated. Its core function is to ensure efficient, orderly, and traceable production activities, directly supporting the achievement of core operational goals such as on-time delivery, quality assurance, and cost control. It serves as a key bridge connecting the planning and execution levels.

[0003] Currently, the technical solutions supporting production order management mainly include two systems: the production module of an Enterprise Resource Planning (ERP) system and a Manufacturing Execution System (MES). ERP systems typically have built-in production order management functions, focusing on order creation, material requirements calculation, Bill of Materials (BOM), cost aggregation, and other functions, handling key data and business logic at the planning level. MES systems, on the other hand, focus more on real-time execution and monitoring at the shop floor level, managing production steps, work instructions, drawings, and equipment data collection. In recent years, integrated platforms that combine ERP and MES capabilities, as well as solutions that utilize AI / big data for predictive analytics and optimized scheduling, are becoming increasingly popular.

[0004] Despite continuous technological advancements, existing production order management solutions still face numerous challenges and drawbacks. Traditional systems are rigid and struggle to adapt flexibly to complex and ever-changing scenarios such as production scheduling, order insertions, and rework. These shortcomings limit the maximization of production order management efficiency and hinder further optimization of enterprise production efficiency, response speed, and costs. Summary of the Invention

[0005] The problem that this invention aims to solve is that existing production order management methods are not timely or flexible.

[0006] To address the aforementioned problems, in a first aspect, the present invention provides a production order management method based on artificial intelligence, comprising:

[0007] Obtain historical order information and industry trend information;

[0008] Based on historical order information and industry trend information, predicted order information is obtained;

[0009] Based on existing customer order information, forecasted order information, and product inventory information, replenishment order information is generated;

[0010] Based on customer order information, internal R&D order information, and replenishment order information, determine the order execution sequence and the corresponding production process sequence for each order;

[0011] Based on the order execution sequence, production process sequence, and equipment capacity, select production equipment and determine the production equipment sequence for each order and the production plan for each production equipment.

[0012] If an order insertion is received, update the order execution sequence, the production equipment sequence for each order, and the production plan for the production equipment based on the information of the orders currently being executed, the order execution sequence, and the order insertion information.

[0013] By comparing the output of the production equipment uploaded by the production monitoring equipment with the production plan of that production equipment, the production progress of the equipment can be obtained.

[0014] Determine whether the production plan for the production equipment needs to be updated based on the equipment's production progress;

[0015] If it is necessary to update the production plan of the production equipment, the real-time capacity of the production equipment is determined, and the production equipment sequence for each order and the production plan for each production equipment are updated in combination with the information of the orders being executed, the order execution sequence and the production process sequence corresponding to the orders.

[0016] Optionally, obtaining the predicted order information based on historical order information and industry trend information includes:

[0017] Based on the aforementioned industry trend information, determine the projected growth rate of the product;

[0018] Based on the historical order information and the predicted growth rate, the predicted order information is obtained.

[0019] Optionally, generating replenishment order information based on existing customer order information, forecasted order information, and product inventory information includes:

[0020] Based on existing customer order information and forecasted order information, determine the production idle time period;

[0021] Based on the product inventory information and forecast order information, determine the products to be produced and the quantity of products.

[0022] Based on the pre-production products, product quantities, and production idle time periods, replenishment order information is determined.

[0023] Optionally, determining the order execution sequence and the corresponding production process sequence based on customer order information, internal R&D order information, and replenishment order information includes:

[0024] Based on customer order information, internal R&D order information, and replenishment order information, extract the product quantity, product type, delivery date, and customer information for each order. Each customer's order corresponds to a specific order importance, with the order importance levels in the following order order: Level 1, Level 2, Level 3, and Level 4.

[0025] Based on the product quantity, product type, delivery date, and order importance of each order, determine the priority and production process sequence for each order;

[0026] All orders are sorted according to their priority to obtain the order execution sequence.

[0027] Optionally, determining the priority and production process sequence for each order based on the product quantity, product type, delivery date, and order importance includes:

[0028] Based on the product type of the order, the corresponding production process sequence is determined. The production process sequence includes multiple process types, and each process type is executed by the corresponding production equipment.

[0029] The estimated production time is determined based on the number of products, the number of production equipment, and the equipment capacity.

[0030] Determine the surplus duration based on the expected production duration, delivery date, and current date;

[0031] Order priority is determined based on product quantity, order importance, and surplus duration.

[0032] Optionally, the priority:

[0033]

[0034] Where H represents priority; T1 represents delivery date; T0 represents current date; T represents normalized duration; A represents product quantity; n represents number of production equipment; a i Let A0 represent the equipment capacity of the i-th production equipment; C represents the normalized quantity; C0 represents the order importance; and C0 represents the preset importance.

[0035] Optionally, the step of selecting production equipment based on the order execution sequence, production process sequence, and equipment capacity, and determining the production equipment sequence for each order and the production plan for each production equipment includes:

[0036] Based on the order execution sequence, the corresponding production process sequence of the order is picked up sequentially.

[0037] Based on the process type in the production process sequence, multiple production equipment information corresponding to the process type are sequentially picked up. The production equipment information includes the production scheduling end date and equipment capacity. Equipment with a capacity greater than the preset capacity value is high-production equipment, and equipment with a capacity less than the preset capacity value is low-production equipment.

[0038] When the number of products in an order is greater than or equal to the preset number of products, at least one production equipment is selected from multiple high-yield equipment corresponding to each process type as the execution equipment for that process type. The selected production equipment is the one with the smallest production schedule end date among the high-yield equipment.

[0039] When the number of products in an order is less than or equal to the preset number of products, the production equipment with the smallest production end date is selected from multiple low-production equipment corresponding to each process type as the execution equipment for that process type.

[0040] Add the production of the order sequentially to the production plan of the equipment to obtain the production equipment sequence of the order and the production plan of the production equipment.

[0041] Optionally, if an order insertion information is received, updating the order execution sequence, the production equipment sequence for each order, and the production plan for the production equipment based on the currently executing order information, the order execution sequence, and the order insertion information includes:

[0042] Based on the order insertion information, determine the priority of the order insertion information;

[0043] The priority of the inserted order information is compared with the priority of unexecuted orders in the order execution sequence to determine the position of the inserted order in the order execution sequence, and the inserted order is added to the order execution sequence to obtain the updated order execution sequence;

[0044] Starting with the insertion of an order, based on the updated order execution sequence, production process sequence, and equipment capacity, the production equipment is re-acquired, and the production equipment sequence and production plan for each order are updated.

[0045] Optionally, the step of determining whether the production plan for the production equipment needs to be updated based on the equipment's production progress is as follows:

[0046] Based on the production progress and production plan of the production equipment, determine whether the production lag time of the production equipment is greater than the allowable time;

[0047] If the production delay time is greater than the allowable time, it is determined that the production plan for the production equipment needs to be updated.

[0048] Secondly, the present invention also provides an artificial intelligence-based production order management system, comprising:

[0049] The information acquisition module is used to acquire historical order information and industry trend information;

[0050] The order forecasting module is used to obtain predicted order information based on historical order information and industry trend information;

[0051] The replenishment order generation module is used to generate replenishment order information based on existing customer order information, forecast order information, and product inventory information;

[0052] The order analysis module is used to determine the order execution sequence and the corresponding production process sequence based on customer order information, internal R&D order information, and replenishment order information.

[0053] The production scheduling module is used to select production equipment and determine the production equipment sequence for each order and the production plan for each production equipment based on the order execution sequence, production process sequence and equipment capacity.

[0054] The order insertion processing module is used to update the order execution sequence, the production equipment sequence for each order, and the production plan for the production equipment based on the order information being executed, the order execution sequence, and the order insertion information when an order insertion message is received.

[0055] The progress monitoring module is used to compare the output of the production equipment uploaded by the production monitoring equipment with the production plan of the production equipment to obtain the production progress of the equipment.

[0056] The delay detection module is used to determine whether the production plan of the production equipment needs to be updated based on the equipment's production progress.

[0057] The delay processing module is used to determine the real-time capacity of the production equipment if it is necessary to update the production plan of the production equipment, and update the production equipment sequence of each order and the production plan of each production equipment in combination with the information of the orders being executed, the order execution sequence and the production process sequence corresponding to the orders.

[0058] This invention provides a production order management method and system based on artificial intelligence. Compared with existing technologies, it has the following advantages:

[0059] Based on historical order information and industry trend information, predicted order information is obtained, thereby forecasting future order volume and order timing. Then, based on existing customer order information, predicted order information, and product inventory information, replenishment order information is generated. This allows for replenishment of inventory during production line downtime or advance production of upcoming customer orders, thereby shortening order waiting time and improving production fault tolerance. Based on customer order information, internal R&D order information, and replenishment order information, the order execution sequence and the corresponding production process sequence for each order are determined. This reveals the order of production scheduling and the process routes involved in each order's products. Then, based on the order execution sequence, production process sequence, and equipment capacity, production equipment is selected, and the production equipment sequence and production plan for each production equipment for each order are determined. Finally, the production equipment is implemented on the production equipment, and the operation of the production equipment and personnel scheduling are arranged according to the production plan. If an order insertion is received, the order execution sequence, production equipment sequence for each order, and production plan for each production equipment are updated based on the information of currently executing orders, the order execution sequence, and the insertion information. This inserts the insertion into the order execution sequence while minimizing impact on the delivery of other orders, thus improving the flexibility of production scheduling. Furthermore, during production, the progress of each production piece of equipment needs to be monitored. The output of each production piece of equipment is compared with its production plan to determine its production progress. Based on this progress, it is determined whether the production plan for that equipment needs updating. If an update is required, the real-time capacity of the production equipment is determined, and the production equipment sequence and production plan for each order are updated in conjunction with the information of currently executing orders, the order execution sequence, and the corresponding production process sequence. This timely adjustment of the production equipment sequence prevents delays in all orders related to a particular production piece due to equipment failure, wear and tear, or rework. This improves the flexibility and responsiveness of production order management, optimizes production line scheduling, and maximizes production efficiency. Attached Figure Description

[0060] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0061] Figure 1 A flowchart illustrating a production order management method based on artificial intelligence, provided as an embodiment of the present invention;

[0062] Figure 2 This is a schematic diagram of the structure of an artificial intelligence-based production order management system provided in an embodiment of the present invention. Detailed Implementation

[0063] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions in the embodiments of this application are described clearly and completely. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0064] To better understand the above technical solutions, the following will provide a detailed explanation of the technical solutions in conjunction with the accompanying drawings and specific implementation methods.

[0065] like Figure 1 As shown in the embodiment of this application, a production order management method based on artificial intelligence is provided, including:

[0066] S1: Obtain historical order information and industry trend information.

[0067] S2: Based on historical order information and industry trend information, predictive order information is obtained.

[0068] S3: Generate replenishment order information based on existing customer order information, forecast order information, and product inventory information.

[0069] S4: Based on customer order information, internal R&D order information, and replenishment order information, determine the order execution sequence and the corresponding production process sequence for each order.

[0070] S5: Based on the order execution sequence, production process sequence, and equipment capacity, pick the production equipment, and determine the production equipment sequence for each order and the production plan for each production equipment.

[0071] S6: If an order insertion message is received, update the order execution sequence, the production equipment sequence for each order, and the production plan for the production equipment based on the order information being executed, the order execution sequence, and the order insertion message.

[0072] S7: Compare the output of the production equipment uploaded by the production monitoring equipment with the production plan of the production equipment to obtain the equipment production progress.

[0073] S8: Determine whether the production plan for the production equipment needs to be updated based on the equipment production progress.

[0074] S9: If it is necessary to update the production plan of the production equipment, determine the real-time capacity of the production equipment, and update the production equipment sequence of each order and the production plan of each production equipment in combination with the order information being executed, the order execution sequence and the production process sequence corresponding to the order.

[0075] In this optional embodiment, predicted order information is obtained based on historical order information and industry trend information, thereby predicting future order volume and order occurrence time in advance. Then, based on existing customer order information, predicted order information, and product inventory information, replenishment order information is generated. This allows for replenishment of inventory during production line downtime or advance production of upcoming customer orders, thereby shortening order waiting time and improving production fault tolerance. Based on customer order information, internal R&D order information, and replenishment order information, the order execution sequence and the corresponding production process sequence for each order are determined, revealing the order of production scheduling and the process routes involved in each order product. Then, based on the order execution sequence, production process sequence, and equipment capacity, production equipment is selected, determining the production equipment sequence for each order and the production plan for each production equipment. Finally, this is implemented on the production equipment, and the operation of the production equipment and personnel scheduling are arranged according to the production plan. If an order insertion is received, the order execution sequence, production equipment sequence for each order, and production plan for each production equipment are updated based on the information of currently executing orders, the order execution sequence, and the insertion information. This inserts the insertion into the order execution sequence while minimizing impact on the delivery of other orders, thus improving the flexibility of production scheduling. Furthermore, during production, the progress of each production piece of equipment needs to be monitored. The output of each production piece of equipment is compared with its production plan to determine its production progress. Based on this progress, it is determined whether the production plan for that equipment needs updating. If an update is required, the real-time capacity of the production equipment is determined, and the production equipment sequence and production plan for each order are updated in conjunction with the information of currently executing orders, the order execution sequence, and the corresponding production process sequence. This timely adjustment of the production equipment sequence prevents delays in all orders related to a particular production piece due to equipment failure, wear and tear, or rework. This improves the flexibility and responsiveness of production order management, optimizes production line scheduling, and maximizes production efficiency.

[0076] The following is a detailed description of each step.

[0077] S1: Obtain historical order information and industry trend information.

[0078] Specifically, historical order information is obtained from past databases, including order signing time, order completion time, and customer information. Industry trend information can include industry demand trends for the product, positive policy information for the industry in which the product is located, and information on the development of customers over the years.

[0079] S2: Based on historical order information and industry trend information, obtain predicted order information. This step includes the following:

[0080] S21: Determine the projected growth rate of the product based on the industry trend information.

[0081] Specifically, by analyzing the industry's demand trends for products from industry trend information, we can predict the potential demand for products in the future and thus forecast the product's growth rate.

[0082] S22: Based on the historical order information and the predicted growth rate, obtain the predicted order information.

[0083] Specifically, based on order information from the past two or three years and the projected growth rate, it is possible to predict the orders that may be generated in the future, forming projected order information. Based on historical order information, it is possible to roughly predict the peak or trough periods of future orders, as well as the demand for product types and quantities in future orders.

[0084] S3: Generate replenishment order information based on existing customer order information, forecast order information, and product inventory information. This step includes the following:

[0085] S31: Determine the production idle time period based on existing customer order information and forecast order information.

[0086] Specifically, based on existing customer order information and predicted order information, it is possible to basically know the future production idle periods. During these periods, the production line will have idle moments, which can be fully utilized to increase the output and utilization rate of the production line and reduce costs.

[0087] S32: Determine the pre-production products and quantities based on the product inventory information and forecast order information.

[0088] Specifically, since frequently ordered products are prepared in advance in the warehouse, which can cope with sudden or urgent orders, the missing products in the warehouse can be replenished based on product inventory information and predicted order information, thereby obtaining the pre-produced products and their quantities.

[0089] S33: Determine replenishment order information based on the pre-production products, product quantity, and production idle time period.

[0090] Specifically, the production time for replenishment orders can be set during idle production periods. The pre-production products and quantities can be calculated by working backward from the idle production equipment and its output during the idle production periods, ultimately forming replenishment order information.

[0091] S4: Based on customer order information, internal R&D order information, and replenishment order information, determine the order execution sequence and the corresponding production process sequence for each order. This step includes the following:

[0092] S41: Based on customer order information, internal R&D order information, and replenishment order information, extract the product quantity, product type, delivery date, and customer information for each order. Each customer's order corresponds to a specific order importance, with the order importance levels in the order of Level 1, Level 2, Level 3, and Level 4.

[0093] Specifically, since replenishment orders are prepared for peak order periods and are considered internally generated virtual orders, they can be classified as lower importance, such as Level 1. Orders from key customers can be classified as the highest importance, Level 4. Orders from general customers can be classified as the next lower importance, such as Level 3 or Level 2. R&D orders generated internally determine the company's product update and iteration speed, technological innovation, and market appeal; therefore, R&D orders can be classified as Level 3.

[0094] S42: Determine the priority and production process sequence for each order based on the product quantity, product type, delivery date, and order importance. This step specifically includes the following:

[0095] S421: Determine the corresponding production process sequence based on the product type of the order. The production process sequence includes multiple process types, and each process type is executed by the corresponding production equipment.

[0096] Specifically, the production of a piece of machinery requires sheet metal housings, shaft components, pipe fittings, and other purchased components. These parts need to be produced through different processes. Some processes can be carried out simultaneously, while others can only be completed in a specific order. Therefore, for each product type in each order, there is a corresponding production process sequence. Since some processes are not sequential or their order can be changed, a product can have multiple production process sequences.

[0097] S422: Determine the estimated production time based on the product quantity, the number of production equipment, and the equipment capacity.

[0098] S423: Determine the surplus duration based on the estimated production duration, delivery date, and current date.

[0099] Specifically, the expected production time The surplus duration ΔT = (T1 - T0) - t; the first priority item h = 1 - ΔT / T.

[0100] Where A represents the number of products; n represents the number of production equipment; a i T represents the equipment capacity of the i-th production equipment; T1 represents the delivery date; T0 represents the current date; and T represents the normalized duration.

[0101] S424: Determine order priority based on product quantity, order importance, and surplus duration.

[0102] Specifically, the priority is calculated using the following formula:

[0103]

[0104] Where H represents priority; T1 represents delivery date; T0 represents current date; T represents normalized duration; A represents product quantity; n represents number of production equipment; a i A0 represents the equipment capacity of the i-th production equipment; A0 represents the normalized quantity, which is a value greater than the maximum product quantity in historical orders; C represents the order importance; C0 represents the preset importance, and C0 can be set to 3 to increase the priority of the most important customers.

[0105] S43: Sort all orders according to the priority to obtain the order execution sequence.

[0106] Specifically, priorities can be sorted from highest to lowest, and corresponding orders can be arranged according to their priority to form an order execution sequence.

[0107] S5: Based on the order execution sequence, production process sequence, and equipment capacity, select production equipment and determine the production equipment sequence for each order and the production plan for each production equipment. This step includes the following:

[0108] S51: According to the order execution sequence, pick up the corresponding production process sequence of the order in sequence.

[0109] S52: Based on the process type in the production process sequence, pick up multiple production equipment information corresponding to the process type in sequence. The production equipment information includes the production scheduling end date and equipment capacity. Equipment with a capacity greater than the preset capacity value is high-production equipment, and equipment with a capacity lower than the preset capacity value is low-production equipment.

[0110] Specifically, for example, the sheet metal equipment is selected for the rib reinforcement process, the welding equipment is selected for the welding process, and so on, selecting the corresponding production equipment and its information according to the process type.

[0111] S53: When the number of products in an order is greater than or equal to the preset number of products, it means that the number of products to be produced in the order is large. Priority is given to arranging production on production equipment with a large capacity. In this way, the production equipment only needs to produce this type of product, thereby increasing the operator's proficiency and reducing the defect rate. Pick at least one production equipment from multiple high-capacity equipment corresponding to each process type as the execution equipment for that process type. Among them, the selected production equipment is the production equipment with the smallest production end date among the high-capacity equipment.

[0112] Specifically, if the required production time for a selected production device exceeds the delivery date from the current date, then another production device with the shortest production end date needs to be selected from the remaining high-yield devices. The required production time is then calculated repeatedly to determine whether the theoretical delivery date exceeds the actual delivery date. This process continues until the theoretical delivery date does not exceed the actual delivery date, and multiple execution devices are selected to produce the products for the order.

[0113] S54: When the quantity of products in an order is less than or equal to the preset quantity, it indicates that the quantity of products purchased in the order is small. The production equipment with the shortest production end date can be selected from among the multiple low-production equipment corresponding to each process type as the execution equipment for that process type. Since the quantity of products is small, one execution equipment can be selected for production. Although the production time is extended, it can still meet the customer's delivery date requirements.

[0114] S55: Add the production of this order sequentially to the production plan of the executing equipment to obtain the production equipment sequence of the order and the production plan of the production equipment.

[0115] Specifically, based on the production process sequence, the corresponding execution equipment for each process type is selected, thereby obtaining the production equipment sequence and the production plan for each production equipment.

[0116] S6: If an order insertion is received, update the order execution sequence, the production equipment sequence for each order, and the production plan for the production equipment based on the information of the orders currently being executed, the order execution sequence, and the order insertion information. This step includes the following:

[0117] S61: Determine the priority of the order insertion information based on the inserted order information.

[0118] Specifically, the priority of the order is calculated using the priority calculation formula shown in S424.

[0119] S62: Compare the priority of the inserted order information with the priority of unexecuted orders in the order execution sequence to determine the position of the inserted order in the order execution sequence, and add the inserted order to the order execution sequence to obtain the updated order execution sequence.

[0120] S63: Starting with the insertion of an order, based on the updated order execution sequence, production process sequence, and equipment capacity, re-pick up the production equipment and update the production equipment sequence and production plan for each order.

[0121] Specifically, due to the addition of rush orders, the order execution order changes from the start of the rush order. According to the specific steps of step S5, the production equipment sequence corresponding to the rush order and subsequent orders is analyzed, and the production plan of the production equipment is updated. This solves the management chaos caused by temporary rush orders, makes production order management orderly and flexible, and improves the adaptability and accuracy of the production order management system.

[0122] S7: Compare the output of the production equipment uploaded by the production monitoring equipment with the production plan of the production equipment to obtain the equipment production progress.

[0123] Specifically, during the order production process, it is necessary to monitor the equipment. Each production equipment terminal is equipped with a production monitoring device to obtain the output of the production equipment in real time. There is no need for manual input of output, avoiding delays in data upload. It can monitor the status of the production equipment in real time and ensure the real-time progress of equipment production.

[0124] S8: Determine whether the production plan for the production equipment needs to be updated based on the equipment's production progress. This step includes the following:

[0125] S81: Based on the production progress and production plan of the production equipment, determine whether the production delay time of the production equipment is greater than the allowable time.

[0126] S82: If the production delay time is greater than the allowable time, it is determined that the production plan of the production equipment needs to be updated.

[0127] For example, if the production equipment was planned to produce 100 products as of yesterday, but only 60 products were actually produced yesterday, and those 60 products should have been completed two days ago according to the production plan, then the production is two days behind schedule. The allowable time is set to one day, which is considered a serious delay and the production plan of the production equipment needs to be updated.

[0128] S9: If it is necessary to update the production plan of the production equipment, determine the real-time capacity of the production equipment, and update the production equipment sequence of each order and the production plan of each production equipment in combination with the order information being executed, the order execution sequence and the production process sequence corresponding to the order.

[0129] Specifically, if there is a discrepancy between the theoretical capacity and the real-time capacity of a production device, the discrepancy may be due to factors such as equipment failure, equipment aging, or equipment processing rework products, which may cause delays in the production plan. In order to ensure timely delivery of orders, it is necessary to update the equipment capacity of the production device to the real-time capacity, and then update the production equipment sequence and the production plan of each production device according to S52-S55. This will solve the problem of sudden delays caused by equipment aging, failure, etc., and improve the flexibility of production order management.

[0130] like Figure 2 As shown in the illustration, an artificial intelligence-based production order management system provided in this application includes:

[0131] The information acquisition module 10 is used to acquire historical order information and industry trend information.

[0132] The order prediction module 20 is used to obtain predicted order information based on historical order information and industry trend information.

[0133] The replenishment order generation module 30 is used to generate replenishment order information based on existing customer order information, forecast order information, and product inventory information.

[0134] The order analysis module 40 is used to determine the order execution sequence and the corresponding production process sequence based on customer order information, internal R&D order information, and replenishment order information.

[0135] The production scheduling module 50 is used to pick up production equipment based on the order execution sequence, production process sequence and equipment capacity, and determine the production equipment sequence for each order and the production plan for each production equipment.

[0136] The order insertion processing module 60 is used to update the order execution sequence, the production equipment sequence for each order, and the production plan for the production equipment based on the order information being executed, the order execution sequence, and the order insertion information if it receives an order insertion message.

[0137] The progress monitoring module 70 is used to compare the output of the production equipment uploaded by the production monitoring equipment with the production plan of the production equipment to obtain the production progress of the equipment.

[0138] The delay discrimination module 80 is used to determine whether the production plan of the production equipment needs to be updated based on the equipment production progress.

[0139] The delay processing module 90 is used to determine the real-time capacity of the production equipment if it is necessary to update the production plan of the production equipment, and update the production equipment sequence of each order and the production plan of each production equipment in combination with the order information being executed, the order execution sequence and the production process sequence corresponding to the order.

[0140] In this embodiment, the beneficial effects of the AI-based production order management system are similar to those of the AI-based production order management method described above, and will not be repeated here.

[0141] An electronic device provided in this application includes a memory and a processor; the memory is used to store a computer program; the processor is used to implement the artificial intelligence-based production order management method described above when the computer program is executed.

[0142] This application provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it implements the artificial intelligence-based production order management method described above.

[0143] In this embodiment, the beneficial effects of the electronic device and the computer-readable storage medium are similar to those of the artificial intelligence-based production order management method described above, and will not be repeated here.

[0144] The present invention describes electronic devices that can serve as servers or clients of this application, which are examples of hardware devices that can be applied to various aspects of this application. Electronic devices are intended to represent various forms of digital electronic computer devices, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. Electronic devices can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the application described and / or claimed herein.

[0145] Electronic devices include a computing unit that can perform various appropriate actions and processes based on a computer program stored in read-only memory (ROM) or loaded from a storage unit into random access memory (RAM). The RAM can also store various programs and data required for device operation. The computing unit, ROM, and RAM are interconnected via a bus. Input / output (I / O) interfaces are also connected to the bus.

[0146] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc. In this application, the units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of the embodiments of this application according to actual needs. Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated units can be implemented in hardware or as software functional units.

[0147] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0148] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A production order management method based on artificial intelligence, characterized in that, include: Obtain historical order information and industry trend information; Based on historical order information and industry trend information, predicted order information is obtained; Based on existing customer order information, forecasted order information, and product inventory information, replenishment order information is generated; Based on customer order information, internal R&D order information, and replenishment order information, determine the order execution sequence and the corresponding production process sequence for each order; Based on the order execution sequence, production process sequence, and equipment capacity, select production equipment and determine the production equipment sequence for each order and the production plan for each production equipment. If an order insertion is received, update the order execution sequence, the production equipment sequence for each order, and the production plan for the production equipment based on the information of the orders currently being executed, the order execution sequence, and the order insertion information. By comparing the output of the production equipment uploaded by the production monitoring equipment with the production plan of that production equipment, the production progress of the equipment can be obtained. Determine whether the production plan for the production equipment needs to be updated based on the equipment's production progress; If it is necessary to update the production plan of the production equipment, the real-time capacity of the production equipment is determined, and the production equipment sequence for each order and the production plan for each production equipment are updated in combination with the information of the orders being executed, the order execution sequence and the production process sequence corresponding to the orders.

2. The production order management method based on artificial intelligence as described in claim 1, characterized in that, The process of obtaining predicted order information based on historical order information and industry trend information includes: Based on the aforementioned industry trend information, determine the projected growth rate of the product; Based on the historical order information and the predicted growth rate, the predicted order information is obtained.

3. The production order management method based on artificial intelligence as described in claim 1, characterized in that, The process of generating replenishment order information based on existing customer order information, forecasted order information, and product inventory information includes: Based on existing customer order information and forecasted order information, determine the production idle time period; Based on the product inventory information and forecast order information, determine the products to be produced and the quantity of products. Based on the pre-production products, product quantities, and production idle time periods, replenishment order information is determined.

4. The production order management method based on artificial intelligence as described in claim 1, characterized in that, The process of determining the order execution sequence and the corresponding production process sequence based on customer order information, internal R&D order information, and replenishment order information includes: Based on customer order information, internal R&D order information, and replenishment order information, extract the product quantity, product type, delivery date, and customer information for each order. Each customer's order corresponds to a specific order importance, with the order importance levels in the following order order: Level 1, Level 2, Level 3, and Level 4. Based on the product quantity, product type, delivery date, and order importance of each order, determine the priority and production process sequence for each order; All orders are sorted according to their priority to obtain the order execution sequence.

5. The production order management method based on artificial intelligence as described in claim 4, characterized in that, The process of determining the priority and production process sequence for each order based on the product quantity, product type, delivery date, and order importance includes: Based on the product type of the order, the corresponding production process sequence is determined. The production process sequence includes multiple process types, and each process type is executed by the corresponding production equipment. The estimated production time is determined based on the number of products, the number of production equipment, and the equipment capacity. Determine the surplus duration based on the expected production duration, delivery date, and current date; Order priority is determined based on product quantity, order importance, and surplus duration.

6. The production order management method based on artificial intelligence as described in claim 5, characterized in that, The priority: Where H represents priority; T1 represents delivery date; T0 represents current date; T represents normalized duration; A represents product quantity; n represents number of production equipment; a i Let A0 represent the equipment capacity of the i-th production equipment; C represents the normalized quantity; C0 represents the order importance; and C0 represents the preset importance.

7. The production order management method based on artificial intelligence as described in claim 1, characterized in that, The step of selecting production equipment based on the order execution sequence, production process sequence, and equipment capacity, and determining the production equipment sequence for each order and the production plan for each production equipment includes: Based on the order execution sequence, the corresponding production process sequence of the order is picked up sequentially. Based on the process type in the production process sequence, multiple production equipment information corresponding to the process type are sequentially picked up. The production equipment information includes the production scheduling end date and equipment capacity. Equipment with a capacity greater than the preset capacity value is high-production equipment, and equipment with a capacity less than the preset capacity value is low-production equipment. When the number of products in an order is greater than or equal to the preset number of products, at least one production equipment is selected from multiple high-yield equipment corresponding to each process type as the execution equipment for that process type. The selected production equipment is the one with the smallest production schedule end date among the high-yield equipment. When the number of products in an order is less than or equal to the preset number of products, the production equipment with the smallest production end date is selected from multiple low-production equipment corresponding to each process type as the execution equipment for that process type. Add the production of the order sequentially to the production plan of the equipment to obtain the production equipment sequence of the order and the production plan of the production equipment.

8. The production order management method based on artificial intelligence as described in claim 7, characterized in that, If an order insertion is received, updating the order execution sequence, the production equipment sequence for each order, and the production plan for the production equipment based on the currently executing order information, the order execution sequence, and the order insertion information includes: Based on the order insertion information, determine the priority of the order insertion information; The priority of the inserted order information is compared with the priority of unexecuted orders in the order execution sequence to determine the position of the inserted order in the order execution sequence, and the inserted order is added to the order execution sequence to obtain the updated order execution sequence; Starting with the insertion of an order, based on the updated order execution sequence, production process sequence, and equipment capacity, the production equipment is re-acquired, and the production equipment sequence and production plan for each order are updated.

9. The production order management method based on artificial intelligence as described in claim 1, characterized in that, The step of determining whether the production plan for the production equipment needs to be updated based on the equipment's production progress is as follows: Based on the production progress and production plan of the production equipment, determine whether the production lag time of the production equipment is greater than the allowable time; If the production delay time is greater than the allowable time, it is determined that the production plan for the production equipment needs to be updated.

10. A production order management system based on artificial intelligence, characterized in that, include: The information acquisition module is used to acquire historical order information and industry trend information; The order forecasting module is used to obtain predicted order information based on historical order information and industry trend information; The replenishment order generation module is used to generate replenishment order information based on existing customer order information, forecast order information, and product inventory information; The order analysis module is used to determine the order execution sequence and the corresponding production process sequence based on customer order information, internal R&D order information, and replenishment order information. The production scheduling module is used to select production equipment and determine the production equipment sequence for each order and the production plan for each production equipment based on the order execution sequence, production process sequence and equipment capacity. The order insertion processing module is used to update the order execution sequence, the production equipment sequence for each order, and the production plan for the production equipment based on the order information being executed, the order execution sequence, and the order insertion information when an order insertion message is received. The progress monitoring module is used to compare the output of the production equipment uploaded by the production monitoring equipment with the production plan of the production equipment to obtain the production progress of the equipment. The delay detection module is used to determine whether the production plan of the production equipment needs to be updated based on the equipment's production progress. The delay processing module is used to determine the real-time capacity of the production equipment if it is necessary to update the production plan of the production equipment, and update the production equipment sequence of each order and the production plan of each production equipment in combination with the information of the orders being executed, the order execution sequence and the production process sequence corresponding to the orders.

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