Production scheduling method and system, computer readable storage medium and product, and electronic device

Abstract

The application provides a production scheduling and dispatching method, which comprises the following steps: obtaining purchase order information, the purchase order information comprising at least two purchase order numbers and product types, product quantities and delivery dates corresponding to the purchase order numbers; obtaining sales order information based on the purchase order information, and splitting and issuing the sales order information, the sales order information comprising a plurality of sales orders, each sales order comprising a sales order number, a product quantity and a delivery date corresponding to each product type; obtaining inventory quantities corresponding to product types corresponding to each sales order, and determining whether the inventory quantities corresponding to the sales orders are less than the product quantities; collecting the sales orders with the inventory quantities less than the product quantities, and obtaining production cycle information corresponding to the product types corresponding to the sales orders; determining priorities of the sales orders based on the inventory quantities, the sales orders and the production cycle information; and scheduling and dispatching based on the priorities of the sales orders.

Description

Technical Field

[0001] This specification relates to the field of semiconductor material manufacturing, and more specifically, to a scheduling method and system for silicon wafers, computer-readable storage media and products, and electronic devices. Background Technology

[0002] In the semiconductor manufacturing industry, silicon wafers, as a core basic material, have a complex production process with numerous steps, and customer orders are characterized by multiple batches and variations. Currently, silicon wafer production scheduling relies heavily on manual processes combined with experience, requiring coordination of information from various aspects such as sales orders, inventory status, and production capacity. However, manual scheduling suffers from inefficiency and strong subjectivity, making it difficult to quickly adapt to dynamically changing customer needs. Furthermore, it is prone to wasting production resources or delaying product delivery due to untimely information transmission and unreasonable prioritization, thus affecting customer satisfaction and hindering the large-scale and intelligent development of the silicon wafer manufacturing industry.

[0003] Therefore, there is an urgent need for an efficient and accurate production scheduling and dispatching solution to achieve precise matching between demand and production, and improve scheduling efficiency and delivery reliability. Summary of the Invention

[0004] The first aspect of this application provides a production scheduling method for silicon wafers, comprising: obtaining purchase order information, wherein the purchase order information includes at least two purchase order numbers and product type, product quantity, and delivery date corresponding to the purchase order numbers; obtaining sales order information based on the purchase order information, and splitting and distributing the sales order information, wherein the sales order information includes multiple sales orders, and each sales order includes a sales order number, product quantity, and delivery date corresponding to each product type; obtaining the inventory quantity corresponding to the product type of each sales order, and determining whether the inventory quantity corresponding to the sales order is less than the product quantity; summarizing the sales orders whose inventory quantity is less than the product quantity, and obtaining the production cycle information corresponding to the product type of the sales order; determining the priority of each sales order based on the inventory quantity, sales orders, and production cycle information; and scheduling production based on the priority of the sales orders.

[0005] In the above solution, by converting purchase order information into divisible sales order information, and combining it with inventory status to filter sales orders that need to be supplemented with production, and by determining the priority of the product type corresponding to the sales order through production cycle information and executing production scheduling, a direct link between demand and production can be established. Through layered processing, the orderly connection from order receipt to production can be achieved, which can completely change the disorderliness of traditional manual control, greatly improve production scheduling efficiency, and reduce the risk of delivery delays caused by process disruptions from the source.

[0006] In one specific embodiment of the first aspect of this application, the steps of obtaining sales order information based on purchase order information and splitting and distributing the sales order information may include: establishing an order database based on purchase order information, wherein the order database stores customer information and purchase order information corresponding to the purchase orders; determining sales order information based on the order database, so as to split the sales order information into sales orders and distribute them.

[0007] In the above solution, an order database is established to store customer information and order information corresponding to purchase orders. Sales orders are then determined and split based on this order database. In this way, the order database can be used to achieve standardized storage and reuse of purchase order information, providing unified data support for the splitting of sales orders. This avoids omissions or mismatches caused by fragmented information when splitting sales orders, ensuring that each split sales order can accurately correspond to the purchase requirements, and improving the traceability and standardization of order execution.

[0008] In one specific embodiment of the first aspect of this application, the step of establishing an order database based on purchase order information may include: reading information from purchase order information and generating sales orders based on a trained order model, wherein the order model is used to arrange the order features corresponding to the purchase orders in the sales orders in a preset order, and the order features include customer information, purchase order number, product type, product quantity and delivery date.

[0009] In the above solution, the standardized arrangement rules of order features are solidified by the order model, realizing the automated conversion of purchase order information into sales orders. This eliminates the tedious process of manually sorting out order features and arranging sales orders, and quickly generates sales orders that meet production execution requirements, significantly reducing manual operation costs and time losses.

[0010] In one specific embodiment of the first aspect of this application, the step of establishing an order database based on purchase order information may further include: retrieving a pre-stored order model corresponding to customer information based on the order model, comparing it with the order characteristics of the sales order, and verifying the sales order, wherein the order model includes pre-stored product types corresponding to customer information.

[0011] In the above solution, after the order model generates a sales order, the order model corresponding to the customer information is called for feature comparison and verification, which effectively improves the accuracy of sales order information, avoids the subsequent production schedule from being out of touch with customer needs due to errors in key data such as product type and quantity, and ensures the reliability of the basic production schedule data.

[0012] In one specific embodiment of the first aspect of this application, the step of determining the priority of each sales order based on inventory quantity, sales order and production cycle information may include: calculating the production capacity of each sales order before the delivery date based on the process cycle required for the wafer corresponding to the sales order and the utilization rate of the production line, and determining the priority of the sales order based on the production capacity; wherein, process cycle a is the processing time for a single site to produce a single wafer, and utilization rate b is the percentage of production operation time of the equipment used to prepare the wafer.

[0013] In the above solution, order demand is directly linked to the actual capacity of the production system. Quantitative indicators replace subjective judgment, making the determination of sales order priority in line with the actual production capacity of the production line, rather than relying on human experience. This avoids equipment overload or resource idleness caused by blind production scheduling and improves the utilization rate of production resources.

[0014] In one specific embodiment of the first aspect of this application, the step of calculating the production capacity of each sales order before the delivery date based on the process cycle required for the wafer corresponding to the sales order and the utilization rate of the production line, and determining the priority of the sales orders based on the production capacity, may include: obtaining the process cycle a and the utilization rate b; taking the difference between the delivery date corresponding to the sales order and the current date as the delivery cycle c, and obtaining the remaining effective processing time d = c × b for the product type corresponding to the sales order at each site; calculating the remaining effective processing output quantity e of the wafer corresponding to the product type of the sales order at a single site and based on the process cycle a and the remaining effective processing time d, where e = d / a; obtaining the inventory shortage quantity f of the wafer corresponding to the sales order, and taking the difference between the remaining effective processing output quantity e and the inventory shortage quantity f as the sorting value; and sorting the production priority of each sales order based on the magnitude of the sorting value.

[0015] In the above scheme, a calculation model for prioritization is constructed using multi-dimensional quantitative indicators to more accurately match order demand with remaining capacity.

[0016] In one specific embodiment of the first aspect of this application, the step of sorting the production priority of each sales order based on the numerical value of the sorting value may include: sorting the production priority of each sales order from largest to smallest based on the numerical value of the sorting value.

[0017] In the above scheme, sales orders are prioritized according to their ranking values ​​from largest to smallest, which can minimize the number of overdue orders and reduce the pressure of order delivery.

[0018] In one specific embodiment of the first aspect of this application, the step of scheduling production based on the priority of sales orders may include: parsing each sales order, wherein the parsed sales orders have been prioritized based on a sorting value, and the parsing content includes determining the association between the sorting value, the sales order, and the part number, wherein the part number corresponds to the product type in the sales order; sorting the wafer work-in-process matching each sales order according to the association and the priority order of the sales orders corresponding to the sorting value, so as to generate a wafer work-in-process scheduling list that matches the order of the sorting value; reading the work-in-process scheduling list, converting the scheduling list into standardized transfer instructions, and issuing the transfer instructions; receiving the transfer instructions and executing the handling operation, transferring the wafer work-in-process according to the order of the sorting value corresponding to the wafer work-in-process scheduling list, and starting processing.

[0019] In the above solution, by parsing sales orders with determined priorities, scheduling and sorting wafer work-in-process, generating standardized transfer instructions and executing transfer operations, the abstract priority sorting is transformed into specific production execution actions, achieving seamless connection from production scheduling to production processing. No manual intervention is required throughout the process, which not only improves production execution efficiency but also avoids time loss and operational errors in the manual scheduling process.

[0020] In one specific embodiment of the first aspect of this application, the purchase order information includes a purchase order, the purchase order includes at least two purchase order numbers, and the at least two purchase order numbers correspond to different product types.

[0021] In another specific embodiment of the first aspect of this application, the purchase order information includes at least two purchase orders, each purchase order including at least one purchase order number, and for all purchase orders, there are at least two purchase order numbers that correspond to different product types.

[0022] The above solution clarifies that a purchase order can contain multiple different product type numbers under a single order, or at least two different product type numbers under multiple orders. This allows the solution to adapt to the order placement habits of different customers and avoid process adaptation problems caused by inconsistent order formats.

[0023] A second aspect of this application provides a production scheduling and dispatching system for silicon wafers. The system includes: an acquisition module configured to acquire purchase order information, wherein the purchase order information includes at least two purchase order numbers and the product type, product quantity, and delivery date corresponding to each purchase order number; a splitting module configured to acquire sales order information based on the purchase order information and split and distribute the sales order information, wherein the sales order information includes multiple sales orders, and each sales order includes a sales order number, product quantity, and delivery date corresponding to each product type; a judgment module configured to acquire the inventory quantity corresponding to the product type of each sales order and determine whether the inventory quantity corresponding to the sales order is less than the product quantity; a summarization module configured to summarize sales orders where the inventory quantity is less than the product quantity and acquire the production cycle information corresponding to the product type of the sales order; a determination module configured to determine the priority of each sales order based on the inventory quantity, sales order, and production cycle information; and a dispatching module configured to perform production scheduling and dispatching based on the priority of the sales orders.

[0024] In the above solution, by converting purchase order information into divisible sales order information, and combining it with inventory status to filter sales orders that need to be supplemented with production, and by determining the priority of the product type corresponding to the sales order through production cycle information and executing production scheduling, a direct link between demand and production can be established. Through layered processing, the orderly connection from order receipt to production can be achieved, which can completely change the disorderliness of traditional manual control, greatly improve production scheduling efficiency, and reduce the risk of delivery delays caused by process disruptions from the source.

[0025] A third aspect of this application provides a computer-readable storage medium having computer-executable instructions stored thereon, which, when executed by a processor, implement the scheduling and dispatching method described in the first aspect above.

[0026] The principle behind storing the computer-executable instructions that implement the above production scheduling and dispatching methods is to convert the methods into computer-readable program code, which facilitates long-term storage and retrieval. The technical effect is to provide a flexible storage medium for the methods, support cross-device reuse, and reduce the hardware adaptation cost of technology implementation.

[0027] The fourth aspect of this application provides an electronic device including a processor and a memory, the memory being used to store processor-executable instructions, and the processor being used to execute the scheduling and dispatching method described in the first aspect above.

[0028] The above-mentioned production scheduling and dispatching method is implemented by the processor executing instructions stored in the memory. Its principle is to use the computing and execution capabilities of hardware devices to transform program code into actual operations. The technical effect is that the production scheduling and dispatching method can operate stably with the help of industrial-grade electronic equipment, meeting the real-time and reliability requirements in the silicon wafer production scenario.

[0029] The fifth aspect of this application provides a computer program product, which includes a computer program that, when executed by a processor, implements the scheduling and dispatching method described in the first aspect above.

[0030] The principle behind executing the above production scheduling and dispatching method through computer programs is to encapsulate the method into a standardized program product, which facilitates rapid deployment and updates. The technical effect is to simplify the process of promoting and applying the technology, enabling different companies to quickly introduce this production scheduling and dispatching scheme and promote the automation upgrade of the silicon wafer manufacturing industry. Attached Figure Description

[0031] Figure 1 The diagram shown is one of the flowcharts for a scheduling and dispatching method for silicon wafers provided in an embodiment of this application.

[0032] Figure 2 The following is a flowchart of a scheduling and dispatching method for silicon wafers provided in an embodiment of this application.

[0033] Figure 3 The following is a flowchart of a scheduling and dispatching method for silicon wafers provided in an embodiment of this application.

[0034] Figure 4 The diagram shown is a schematic diagram of a sales order based on an order model provided in an embodiment of this application.

[0035] Figure 5 The following is a flowchart of a scheduling and dispatching method for silicon wafers provided in an embodiment of this application.

[0036] Figure 6 The diagram shown is a fifth flowchart of a scheduling and dispatching method for silicon wafers provided in an embodiment of this application.

[0037] Figure 7 The following is a flowchart of a scheduling and dispatching method for silicon wafers provided in an embodiment of this application.

[0038] Figure 8 The following is a flowchart (seventh) of a scheduling and dispatching method for silicon wafers provided in an embodiment of this application.

[0039] Figure 9 The diagram shown is a schematic block diagram of a production scheduling and dispatching system provided in an embodiment of this application.

[0040] Figure 10 The diagram shown is a block diagram of an electronic device provided in an embodiment of this application. Detailed Implementation

[0041] The technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this specification, and not all embodiments. Based on the embodiments in this specification, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this specification.

[0042] With the rapid development of the semiconductor industry, the market demand for large-size silicon wafers (such as 300mm) continues to grow, placing higher demands on the accuracy and efficiency of production scheduling and dispatching. Currently, the delivery model for silicon wafers is mainly based on scheduled delivery according to customer production plans, resulting in scattered and unstable delivery dates. In addition, the long production cycle of silicon wafers and the strong correlation between the production sequence of equipment in each process further increase the difficulty of production scheduling and dispatching.

[0043] For example, in a production scheduling scenario for silicon wafers, the scheduling work is mainly completed manually by the sales department and the production management department. After receiving a customer's purchase order, the sales department manually organizes information such as the order number, product type, quantity, and delivery date and passes it on to the production department. Then, the production department combines manually compiled data such as inventory quantity and production cycle of each process, and uses experience to determine production priorities and formulate a production schedule.

[0044] The aforementioned production scheduling and dispatching model presents several problems, including: First, inconsistent customer purchase order formats lead to omissions and errors during manual information processing, compromising order data accuracy. Second, inventory, capacity, and order demand data are scattered across different systems or documents, resulting in low efficiency for manual analysis and potential misalignment between production plans and actual capacity due to information asymmetry. Third, production prioritization relies entirely on manual experience, lacking quantitative basis, which can lead to situations where high-pressure orders are not prioritized while low-priority orders occupy critical capacity, causing product delivery delays and increasing the risk of customer complaints. Fourth, manual scheduling of work-in-process transfers and equipment allocation is prone to operational errors and time losses, impacting production execution efficiency. These problems not only result in low production scheduling efficiency and insufficient delivery reliability in silicon wafer production but also waste production resources, severely restricting the company's market competitiveness.

[0045] In view of this, this application proposes a scheduling method and system for silicon wafers, a computer-readable storage medium and product, and an electronic device to at least solve these technical problems. Figure 1As shown, the production scheduling and dispatching method may include the following steps S100 to S600.

[0046] S100, Obtain purchase order information, wherein the purchase order information includes at least two purchase order numbers and the product type, product quantity and delivery date corresponding to the purchase order number.

[0047] S200 obtains sales order information based on purchase order information and splits and distributes the sales order information. The sales order information includes multiple sales orders, and each sales order includes a sales order number, product quantity, and delivery date corresponding to each product type.

[0048] S300: Obtain the inventory quantity corresponding to the product type for each sales order, and determine whether the inventory quantity corresponding to the sales order is less than the product quantity.

[0049] S400 aggregates sales orders where the inventory quantity is less than the product quantity and obtains the production cycle information corresponding to the product type of the sales order.

[0050] S500 determines the priority of each sales order based on inventory quantity, sales order, and production cycle information.

[0051] The S600 schedules production and dispatches tasks based on the priority of sales orders.

[0052] In traditional silicon wafer production, there is a lack of direct and efficient linkage between purchase order demand and production execution. After receiving orders, the sales department must manually organize and pass them on to the production department, which then determines the production sequence based on experience. This process is not only cumbersome and time-consuming but also prone to problems such as discrepancies in order information transmission and unreasonable production priority determination. In the scheduling and dispatching method in steps S100 to S600 above, purchase order information is transformed into divisible sales order information. This information is then combined with inventory status to filter sales orders requiring additional production. Furthermore, the priority of the product type corresponding to each sales order is determined based on production cycle information, and production scheduling is executed accordingly. This establishes a direct link between demand and production, achieving an orderly connection from order receipt to production through layered processing. This completely changes the disorderliness of traditional manual control, significantly improves scheduling efficiency, and reduces the risk of delivery delays caused by process disruptions from the outset. Specifically, through a layered processing flow of "purchase order → sales order splitting → inventory screening → priority determination → production scheduling execution," the scattered purchase order information is first transformed into structured, splittable sales orders, ensuring that the production department can accurately obtain the specific needs of each product type. Then, through inventory status screening, orders that need to be supplemented with production are focused on to avoid ineffective production scheduling. Finally, the production priority of the product type corresponding to the sales order is determined by combining production cycle information, so that production execution has a clear basis. The whole process forms a closed loop linkage between demand and production, completely eliminating the reliance on manual experience. This not only improves the orderliness and efficiency of production scheduling, but also avoids delivery delays caused by information gaps and decision-making errors from the process design.

[0053] For example, in a silicon wafer production scheduling scenario, a silicon wafer manufacturing company receives two customer purchase orders simultaneously, and assumes: Purchase order number PO-20250901-001 corresponds to 300mm N-type silicon wafers, with a quantity of 800 wafers and a delivery date of October 20, 2025; Purchase order 2, numbered PO-20250901-002, corresponds to 300mm P-type silicon wafers, with a quantity of 500 wafers and a delivery date of October 10, 2025.

[0054] First, the system obtains complete information for these two purchase orders, including order number, product type, quantity, and delivery date. Then, it converts these into corresponding sales orders SO-20250901-001 (800 N-type silicon wafers, 300mm, delivery date October 20, 2025) and SO-20250901-002 (500 P-type silicon wafers, 300mm, delivery date October 10, 2025), and then uses Enterprise Distributed Application Service (IDS) to process these orders. The Service (EDAS) system is split and distributed to the production scheduling module; then, the inventory is checked. Assuming there are 300 N-type silicon wafers and 100 P-type silicon wafers in stock, both less than the order demand, the system aggregates these two sales orders requiring additional production and obtains that the production cycle for 300mm N-type silicon wafers is 72 hours per batch (i.e., the production cycle for a single N-type silicon wafer is 72 hours), and the production cycle for 300mm P-type silicon wafers is 60 hours per batch; then... Based on the inventory gap (500 N-type silicon wafers and 400 P-type silicon wafers), delivery date, and production cycle, priority was calculated, and SO-20250901-002 was determined to have a higher priority than SO-20250901-001. Finally, production scheduling was initiated according to the priority order, first arranging the production of 300mm P-type silicon wafers, and then proceeding with the production of 300mm N-type silicon wafers, to ensure that both orders were delivered on schedule. In the above process, no manual intervention was required, so the production scheduling efficiency was significantly improved compared to the traditional manual method.

[0055] In at least one embodiment of this application, such as Figure 2 As shown, step S200 above, namely, obtaining sales order information based on purchase order information and splitting and distributing the sales order information, may also include the following steps S210 and S220.

[0056] S210: Establish an order database based on purchase order information. The order database stores customer information and purchase order information corresponding to purchase orders.

[0057] S220: Based on the order database, determine the sales order information, then split the sales order information into sales orders and issue them.

[0058] In the production scheduling and dispatching methods of steps S210 and S220 above, an order database is established to store customer information and order information corresponding to purchase orders. Based on this order database, sales orders are determined and split for distribution. This allows for the standardized storage and reuse of purchase order information through the order database, providing unified data support for the splitting of sales orders. This avoids omissions or mismatches caused by fragmented information during sales order splitting, ensuring that each split sales order accurately corresponds to the purchase requirements and improving the traceability and standardization of order execution. Specifically, in traditional order processing models, customer information and product requirement information for purchase orders are often scattered across different documents or systems. When sales orders need to be generated and split for distribution, staff must search and summarize information from multiple channels, which is not only inefficient but also prone to problems such as information omissions (e.g., omitting special customer delivery requirements) and data mismatches (e.g., matching customer A's order information to customer B's sales order). This solution establishes a unified order database, centrally and systematically storing basic customer information (such as name, contact person, and contact information) and core order information (such as order number, product type, quantity, delivery date, and special requirements) for each purchase order, forming a complete order data archive. When splitting sales orders, the complete and unified data source is directly retrieved from the order database, eliminating the need for cross-channel verification. This ensures that the splitting process for sales orders is supported by clear and consistent data, avoiding errors caused by fragmented information and ensuring that each sales order can be accurately linked to the original purchase order, thereby significantly improving the standardization and traceability of order processing.

[0059] For example, in a silicon wafer production scheduling scenario, customer A issues a purchase order containing customer information (name: Jia Electronics Technology Co., Ltd., contact person: Manager Zhang, phone: 138XXXX1234) and order information (order number PO-20250902-001, product type: 600 300mm (8-inch) silicon wafers and 300 450mm (12-inch) silicon wafers, delivery date: November 5, 2025, special requirement: anti-static packaging required). After receiving the purchase order, the system associates the customer information with the order information and stores it in the order database, forming a standardized data record. When a sales order needs to be generated, the system retrieves the complete data of the purchase order from the order database and splits it into two sales orders according to product type: SO-20250902-001 (600 300mm wafers, delivery date November 5, 2025, special requirements: such as anti-static packaging) and SO-20250902-002 (300 450mm silicon wafers, delivery date November 5, 2025, special requirements: such as anti-static packaging). These sales orders are then issued to the production workshops responsible for 8-inch and 12-inch silicon wafers respectively through the EDAS system. During production, staff can quickly trace the customer information and special requirements of the original purchase order in the order database using the sales order number, ensuring production is carried out as required. No information omissions or mismatches occur throughout the process, and compared to manual queries, order tracing time is significantly reduced.

[0060] In at least one embodiment of this application, such as Figure 3 and Figure 4 As shown, step S210 above, namely, the step of establishing an order database based on purchase order information, may include the following step S211.

[0061] S211, based on the trained order model, reads information from the purchase order information and generates a sales order. The order model is used to arrange the order features corresponding to the purchase order in the sales order in a preset order. The order features include customer information, purchase order number, product type, product quantity and delivery date.

[0062] In step S211 above, the standardized arrangement rules of order features are solidified through the order model, realizing the automated conversion of purchase order information into sales orders. This eliminates the tedious process of manually organizing order features and formatting sales orders, quickly generating sales orders that meet production execution requirements, and significantly reducing manual operation costs and time consumption. Specifically, the formats of purchase orders from different customers vary greatly. Some customers list product quantities at the top of the order page, while others list them at the bottom, and the presentation of customer information also differs. In the traditional model, staff need to read each purchase order, manually extract key features such as customer information, purchase order number, and product type, and then reformat them according to the company's internal production requirements to generate sales orders. This process is not only time-consuming and labor-intensive, but also prone to errors in feature extraction or layout chaos due to human negligence, affecting subsequent production execution. In the above solution of this application, the trained order model pre-solidifies the standardized arrangement rules of order features, which can automatically identify key features in purchase orders of different formats, and can organize feature information and generate sales orders in a preset order without manual intervention. For example, Figure 4 As shown, in each sales order, customer information (including buyer's company name, address, etc.), purchase order number, product type, product quantity, delivery date, etc., can be arranged sequentially from left to right and then from top to bottom. This eliminates the tedious process of manual sorting and formatting, ensures the uniformity and standardization of the sales order format, and allows the production department to quickly receive and identify order requirements, significantly reducing manual operation costs and time consumption.

[0063] For example, in a silicon wafer production scheduling scenario, a company collects purchase order samples from 100 mainstream customers, trains and generates an order model, and pre-sets the order feature arrangement order as customer name (e.g., ...). Figure 4 (buyer in the list), purchase order number (e.g.) Figure 4 Order number, product type, product quantity, delivery date (e.g.) Figure 4 (estimated delivery date), special requirements (e.g.) Figure 4(Notes in the text). When a customer, B, issues a purchase order in PDF or image format, the customer name is located in the upper right corner of the first page, the purchase order number in the upper left corner, the product type and quantity are listed in a table in the middle, and the delivery date is at the end of the order. After receiving the purchase order, the system inputs it into a trained order model. The model automatically identifies and extracts key features such as the customer name "B Semiconductor Co., Ltd.", the purchase order number "PO-20250903-001", the product type "300mm 200nm Dumm silicon wafers", the product quantity "500 pieces", and the delivery date "October 30, 2025". It then automatically formats and generates a standardized sales order, SO-20250903-001, according to a preset order. Because the system automatically identifies and organizes information based on a trained model, the processing time is greatly reduced compared to manual processing, and the sales order format is kept consistent, avoiding risks such as formatting and information entry errors.

[0064] In at least one embodiment of this application, such as Figure 5 As shown, step S210, which is the step of establishing an order database based on purchase order information, may also include the following step S212.

[0065] S212, based on the order model, retrieve the pre-stored order model corresponding to the customer information, compare it with the order characteristics of the sales order, and verify the sales order. The order model includes the pre-stored product type corresponding to the customer information.

[0066] In step S212 above, after the order model generates a sales order, it calls the order model corresponding to the customer information for feature comparison and verification. This effectively improves the accuracy of the sales order information and avoids discrepancies between subsequent production plans and customer needs due to errors in key data such as product type and quantity, ensuring the reliability of basic production scheduling data. Specifically, although the order model can automatically convert purchase order information into sales orders, in practical applications, factors such as blurry purchase order images or abnormal formats may cause model recognition errors, such as recognizing "500 pieces" as "50 pieces," or mistakenly matching other customers' product types to the current order. If this erroneous data is directly used for production scheduling, it will lead to a serious mismatch between the quantity and type of products produced and customer needs, resulting in wasted production resources and delivery delays. The solution described in step S212 above allows for the verification of customer order data. Since the order database pre-stores historical order data for each customer, including frequently purchased product types and typical order quantity ranges, when a sales order is generated by the order model, the system automatically retrieves the corresponding customer's model (corresponding sales order) and compares the product type, quantity, and other characteristics in the sales order with the pre-stored information to determine if the data matches. For example, if the data exceeds the normal range or is mismatched, the data can be reread to regenerate the sales order, and / or an alert can be triggered prompting manual review. This effectively filters out errors, improves the accuracy of sales order information, and provides reliable foundational data for subsequent production scheduling.

[0067] For example, in a silicon wafer production scheduling scenario, the order database stores customer C's order information, recording that the product types purchased by this customer are "300mm 14nm silicon wafers" and "300mm 7nm silicon wafers," with a single batch order quantity ranging from 300 to 1000 wafers. After the system identifies the purchase order issued by customer C through the order model, it generates a sales order SO-20250904-001, in which the product type is "300mm 5nm silicon wafers" and the quantity is 150 wafers. Subsequently, the system retrieves customer C's data (such as the image corresponding to the purchase order) for verification and finds that the product type "300mm 5nm silicon wafers" is not in customer C's purchase order data archive, and the quantity of 150 wafers is less than the purchase range of 300 wafers, thus immediately triggering an alert to remind manual review. For example, after review, staff discovered that the product type in the purchase order was actually "300mm 7nm silicon wafers" because the image of the entered purchase order was blurry, leading to a model recognition error. Furthermore, the quantity was incorrectly listed as "450 wafers" instead of "150 wafers." After correcting the data, the sales order was regenerated and passed verification. This demonstrates that the verification process successfully prevented invalid production due to data errors, improving the accuracy of sales order information.

[0068] In at least one embodiment of this application, such as Figure 6 As shown, step 500 above, which is the step of determining the priority of each sales order based on inventory quantity, sales order and production cycle information, may include the following step S510.

[0069] S510 calculates the production capacity for each sales order before the delivery date based on the required process cycle and production line utilization rate of the wafers corresponding to the sales orders, and determines the priority of the sales orders based on the production capacity. Process cycle a is the processing time for a single site to produce a single wafer, and utilization rate b is the percentage of production operating time of the equipment used to prepare the wafers.

[0070] In step S510 above, order demand is directly linked to the actual capacity of the production system. Quantitative indicators replace subjective judgment, ensuring that the priority of sales orders aligns with the actual production capacity of the production line, rather than relying on manual experience. This avoids equipment overload or resource idleness caused by blind scheduling, thus improving the utilization rate of production resources. Specifically, in the scheduling process that prioritizes sales orders based on manual experience, only the proximity of the order delivery date and the importance of the customer may be considered, while ignoring the actual production capacity of the production line. For example, if an order's delivery date is approaching, but the corresponding production line's utilization rate is already 100%, the remaining capacity cannot meet the order's demand. Forced scheduling would lead to equipment overload and increase the risk of failure. Conversely, another order with a slightly later delivery date may have idle capacity on the corresponding production line, but it is not prioritized, resulting in resource idleness. By introducing two core quantitative indicators, process cycle a and utilization rate b, the solution in step S510 directly links order demand with the actual production line capacity. Process cycle a reflects the production time of a single product, while utilization rate b reflects the actual operating efficiency of the production line. These two indicators can accurately calculate the number of orders (production quantity of each type of silicon wafer) that the production line can fulfill before the delivery date, i.e., the production capacity. Priority is then determined based on the production capacity, making the priority determination more scientific and objective. This avoids equipment overload caused by blind production scheduling and makes full use of idle capacity, thereby improving the overall utilization rate of production resources.

[0071] For example, in a silicon wafer production scheduling scenario, a silicon wafer manufacturing workshop has two dedicated production lines, A and B. Production line A focuses on producing 300mm low-to-medium process silicon wafers, compatible with two specifications: 300mm 200nm Dumm silicon wafers with a process cycle of a1 = 1.2 hours / wafer and 300mm 100nm silicon wafers with a process cycle of a2 = 2.4 hours / wafer, with a current utilization rate of b1 = 85% (20.4 hours of effective daily operation). Production line B focuses on producing 300mm advanced process silicon wafers, compatible with two specifications: 3 The process cycle for 300mm 50nm silicon wafers is a3 = 3.8 hours / wafer, and the process cycle for 300mm 7nm silicon wafers is a4 = 5.2 hours / wafer. The current utilization rate is b2 = 90% (21.6 hours of effective daily operation). The workshop is simultaneously receiving five sales orders: Order 1 (160 300mm 200nm 7nm silicon wafers, delivery date 10 days later), Order 2 (110 300mm 100nm silicon wafers, delivery date 14 days later), Order 3 (70 300mm 50nm silicon wafers, delivery date 10 days later), and Order 4 (70 300mm 50nm silicon wafers, delivery date 14 days later). Orders 1 and 5 (300mm 7nm silicon wafers, 18 days after delivery date) and 60 (300mm 200nm Dumm silicon wafers, 8 days after delivery date) are listed below. First, considering the compatibility of each production line with the specifications and the process cycles for different specifications, the corresponding production capacity is calculated. For production line A, for orders 1 and 5 (200nm Dumm, a1 = 1.2 hours / wafer), the production capacity within 8 days is (8 × 20.4) / 1.2 = 136 wafers, and the production capacity within 10 days is (10 × 20.4) / 1.2 = 136 wafers. 4) / 1.2 = 170 pieces. For order 2 (100nm power devices, a2 = 2.4 hours / piece), the production capacity within 14 days = (14 × 20.4) / 2.4 = 119 pieces. For order 3 (50nm logic chips, a3 = 3.8 hours / piece), the production capacity within 12 days = (12 × 21.6) / 3.8 ≈ 68.2 pieces. For order 4 (7nm advanced packaging, a4 = 5.2 hours / piece), the production capacity within 18 days = (18 × 21.6) / 5.2 ≈ 73.8 pieces.

[0072] If manual scheduling is used, and staff do not consider production line adaptability and specification process cycles, but only arrange production according to the order of order receipt, the following operations may occur: Order 2 production is started first. Order 2 is 100nm power device silicon wafers. Production line A's daily capacity is 20.4 / 2.4≈8.5 wafers. Only 85 wafers can be completed in the first 10 days. The remaining 25 wafers need to continue production for about 3 days, occupying a total of 13 days of production line A. Although order 2 is eventually completed within the 14-day delivery cycle, production line A has no remaining time to respond to the 8-day delivery requirement of order 5. Order 5 is forced to be delayed until order 2 is completed and can only complete 42.5 wafers in the following 5 days. The remaining 87.5 wafers are completed 2 days late. Order 1 is then started after order 5 is delivered. Production has commenced. With the 10-day delivery cycle over, production line A can only complete 59.5 wafers in the remaining 7 days, leaving 100.5 wafers 3 days behind schedule. Production line B, on the other hand, starts production of order 4, which consists of 7nm silicon wafers. Line B's daily capacity is approximately 4.15 wafers (21.6 / 5.2). Only 33.2 wafers were completed in the first 8 days, leaving 26.8 wafers to be produced for approximately 6.5 more days, occupying a total of 14.5 days on line B. Production of order 3 was pushed back to begin 8 days after order 4 started. Within the 12-day delivery cycle, only approximately 16.6 wafers (4.15 × 4) could be completed, leaving 53.4 wafers 7 days behind schedule. Ultimately, order 1 was 3 days behind schedule, order 3 was 7 days behind schedule, and order 5 was 2 days behind schedule, resulting in an on-time delivery rate of only 40%.

[0073] Using the aforementioned scheme of this application, if priority is determined based on the number of overdue orders (assuming the goal is to reduce the number and extent of overdue orders) and the production capacity of the production line, order 5 (8-day delivery) has the shortest delivery period and the highest priority; order 3 (12-day delivery) has the next shortest delivery period and the second highest priority; order 1 (10-day delivery) has a delivery period earlier than orders 2 and 4 and the third highest priority; order 2 (14-day delivery) has a delivery period later than the previous three and the fourth highest priority; and order 4 (18-day delivery) has the longest delivery period and the lowest priority. Production is then scheduled according to this priority. Production line A prioritizes order 5, completing 136 pieces within 8 days, which perfectly meets the 130-piece requirement of order 5, and order 5 is delivered on schedule. Then, order 1 is seamlessly followed, with production completed within 10 days. Production line A, with a capacity of 170 pieces, fully covered the 160-piece requirement of Order 1, and Order 1 was delivered on schedule. Next, production of Order 2 was initiated. Within 14 days, production line A produced 119 pieces, meeting the 110-piece requirement of Order 2, and Order 2 was delivered on schedule. Production line B simultaneously advanced production of Order 3, producing approximately 68.2 pieces within 12 days, leaving a shortfall of 1.8 pieces from the 70-piece requirement of Order 3. Order 3 was completed one day late. Finally, production line B operated at full capacity for Order 4, producing 73.8 pieces within 18 days, fully covering the 60-piece requirement, and Order 4 was delivered on schedule. Ultimately, of the five orders, Orders 1, 2, 4, and 5 were all delivered on schedule, with only Order 3 being one day late. This significantly reduced the number of late orders and the duration of late orders compared to manual scheduling, resulting in a substantial improvement in overall delivery reliability.

[0074] In at least one embodiment of this application, such as Figure 7 As shown, step S510 above, namely, calculating the production capacity of each sales order before the delivery date based on the process cycle required for the wafer corresponding to the sales order and the utilization rate of the production line, and determining the priority of the sales orders based on the production capacity, may include the following steps S511 to S515.

[0075] S511, obtain process cycle a and utilization rate b.

[0076] S512, take the difference between the delivery date corresponding to the sales order and the current date as the delivery period c, and obtain the remaining effective processing time d=c×b for the product type corresponding to the sales order at each site.

[0077] S513, calculate the remaining effective processing output quantity e of the wafers corresponding to the product type of a single site and sales order based on the process cycle a and the remaining effective processing time d, where e=d / a.

[0078] S514, obtain the inventory shortage quantity f of the wafers corresponding to the sales order, and use the difference between the remaining effective processing output quantity e and the inventory shortage quantity f as the sorting value.

[0079] S515 sorts the production priority of each sales order based on the numerical value of the sorting value.

[0080] In steps S511 to S515 above, a calculation model for priority ranking is constructed using multi-dimensional quantitative indicators to more accurately match order demand with remaining capacity. Specifically, key factors such as process cycle, utilization rate, delivery cycle, and inventory gap are transformed into calculable specific indicators, and ranking values ​​are derived step by step. Among them, the remaining effective processing time d comprehensively considers the delivery cycle and utilization rate, reflecting the actual available production time of the production line within the specified time; the remaining effective processing output quantity e is based on the process cycle and the remaining effective processing time, quantifying the number of products that the production line can produce; the ranking value, by comparing the remaining effective processing output quantity with the inventory gap, intuitively reflects the fit between production line capacity and order demand. Through this series of quantitative calculations, the originally vague priority determination is transformed into a clear numerical comparison, which can accurately distinguish the priority of different orders and ensure a high degree of matching between production scheduling and remaining capacity.

[0081] For example, in a silicon wafer production scheduling scenario, there are three sales orders, all for 300mm silicon wafer products, with the following details: Sales order A: Product type 300mm N type, quantity 500 pieces, delivery date 15 days later, inventory shortage quantity f1=400 pieces; corresponding site process cycle a=2 hours / piece, utilization rate b=85%.

[0082] Sales order B: Product type 300mm P type, quantity 300 pieces, delivery date 10 days later, inventory shortage quantity f2=100 pieces; corresponding site process cycle a=1.8 hours / piece, utilization rate b=90%.

[0083] Sales order C: Product type 300mm 200nm Dumm, quantity 400 pieces, delivery date 12 days later, inventory shortage quantity f3=100 pieces; corresponding site process cycle a=2.2 hours / piece, utilization rate b=80%.

[0084] If manual production scheduling is used, and production is scheduled sequentially according to the order of order receipt (A, B, C), then: When producing order A, the effective daily working hours = 24 × 85% = 20.4 hours, and the daily output = 20.4 ÷ 2 = 10.2 pieces. The total time to complete the 400-piece gap is 400 ÷ 10.2 ≈ 39.2 days, far exceeding the 15-day delivery cycle. Order A is overdue by 39.2 - 15 = 24.2 days. After completing order A, production of order B begins. The effective daily working hours = 24 × 90% = 21.6 hours, and the daily output = 21.6 ÷ 1.8 = 12 pieces. The total time to complete the 100-piece gap is... The total time taken was approximately 8.3 days (100 ÷ 12). Order B started on day 39.2, exceeding the deadline by 39.2 + 8.3 - 10 = 37.5 days. After completing Order B, production of Order C began. The effective working hours per day were 24 × 80% = 19.2 hours, and the daily output was approximately 8.7 pieces (19.2 ÷ 2.2). The total time taken to complete the 100-piece shortfall was approximately 11.5 days (100 ÷ 8.7). Order C started on day 39.2 + 8.3 = 47.5, exceeding the deadline by 47.5 + 11.5 - 12 = 47 days. Ultimately, all three orders were severely overdue, and none were delivered on time.

[0085] When using the scheme of this application, the calculation process for the ranking value corresponding to each sales order is as follows: Sales order A: Delivery cycle c1 = 15 days, remaining effective processing time d1 = 15 × 24 × 85% = 306 hours, remaining effective processing output quantity e1 = 306 ÷ 2 = 153 pieces, sorting value = 153 - 400 = -247.

[0086] Sales order B: Delivery cycle c2 = 10 days, remaining effective processing time d2 = 10 × 24 × 90% = 216 hours, remaining effective processing output quantity e2 = 216 ÷ 1.8 = 120 pieces, sorting value = 120 - 100 = 20.

[0087] Sales order C: Delivery cycle c2=12 days, remaining effective processing time d3=12×24×80%=230.4 hours, remaining effective processing output quantity e3=230.4÷2.2≈104.7 pieces, sorting value=104.7-100=4.7.

[0088] By comparing the sorting values, order B (20) > order C (4.7) > order A (-247). If in actual production, priority should be given to reducing the number of overdue orders, then the priority order is order B > order C > order A. Based on this priority scheduling, the production line prioritizes the production needs of Order B, completing 120 pieces within a 10-day delivery cycle to cover the 100-piece shortfall, ensuring on-time delivery of Order B. Next, production of Order C begins, completing approximately 104.7 pieces within a 12-day delivery cycle to meet the 100-piece shortfall, also ensuring on-time delivery of Order C. The total production time for Orders B and C is 10 + 2 = 12 days (Order C only needs 2 days to make up for the remaining output after Order B is completed). Only then does the production line start production of Order A. Order A still needs 39.2 days to cover the 400-piece shortfall, for a total completion time of 12 + 39.2 = 51.2 days. Order A is 36.2 days overdue. Thus, only Order A is overdue, significantly reducing the number of overdue orders and reducing the pressure of communicating with customers regarding extensions or negotiating order scheduling deadlines. Furthermore, this method provides time for the production line to accept new orders, facilitating the planning of subsequent orders and production scheduling.

[0089] It should be noted that the embodiments in this application only list the situation where orders are overdue when the production line is operating at full capacity. In actual scenarios, there may also be situations where the production line's production capacity completely covers the orders. In this case, the solution of this application can calculate the time node when the production line completes all orders in real time when receiving orders, which makes it easier to plan the subsequent order receiving situation. For example, the customer can be informed of the appointment time and delivery cycle of the new order, thereby ensuring the delivery deadline while maintaining production efficiency.

[0090] In at least one embodiment of this application, step S515, namely, the step of prioritizing each sales order based on the numerical value of the ranking value, may include: prioritizing each sales order from largest to smallest based on the numerical value of the ranking value. This can reduce the number of overdue orders, thereby ensuring the on-time delivery rate of orders. Thus, explicitly prioritizing sales orders by ranking value from largest to smallest can minimize the number of overdue orders and reduce order delivery pressure. For specific details, please refer to the relevant descriptions in the foregoing embodiments, which will not be repeated here.

[0091] In at least one embodiment of this application, such as Figure 8 As shown, step S600, namely, the step of scheduling production and dispatching work based on the priority of sales orders, may include the following steps S610 to S640.

[0092] S610, parse each sales order, wherein the parsed sales orders have been sorted based on the sorting value to determine the priority. The parsing content includes determining the relationship between the sorting value, the sales order and the part number, and the part number corresponds to the product type in the sales order.

[0093] S620: Based on the relationship and the priority order of the sales orders corresponding to the sorting values, the wafer work-in-process that matches each sales order is sorted in scheduling order to generate a wafer work-in-process scheduling list that matches the order of the sorting values.

[0094] S630 reads the work-in-process scheduling list, converts the scheduling list into standardized transfer instructions, and issues the transfer instructions.

[0095] S640 receives the transfer instruction and executes the transfer operation. According to the sorting value corresponding to the wafer work-in-process schedule, the wafer work-in-process is transferred and the processing is started.

[0096] In steps S610 to S640 above, by parsing sales orders with determined priorities, scheduling and sorting wafer work-in-process, generating standardized transfer instructions, and executing transfer operations, the abstract priority ranking is transformed into specific production execution actions. This achieves seamless integration from production scheduling to production processing, without any manual intervention. This improves production execution efficiency and avoids time losses and operational errors in manual scheduling. Specifically, in traditional production scheduling, after manually determining priorities, the production plan needs to be manually converted into scheduling instructions, and then staff or equipment need to be arranged to transfer work-in-process. The entire process involves multiple manual handovers, which is not only time-consuming but also prone to problems such as incorrect scheduling order (e.g., transferring work-in-process of low-priority orders first) and instruction transmission deviations, resulting in low production execution efficiency and affecting the implementation of the production schedule. The proposed solution employs fully automated scheduling. First, it parses the sorted sales orders, clarifying the relationship between orders and part numbers. Then, it schedules and sorts work-in-process according to priority, generating a standardized list. This list is then converted into machine-readable transfer instructions. Finally, automated transfer equipment executes the transfer operation. The entire process requires no manual intervention, achieving seamless integration from production planning to manufacturing. This not only reduces time lost through manual handover but also avoids human error, ensuring a high degree of consistency between scheduling actions and production plans, significantly improving production efficiency and accuracy.

[0097] For example, in a silicon wafer production scheduling scenario, assuming the production workshop has determined the sales order priority order as order 2 > order 3 > order 1 > order 4, the corresponding part numbers are L002, L003, L001, and L004, and the matching wafer work-in-process are WIP-002, WIP-003, WIP-001, and WIP-004, respectively. First, the RTD (Real-Time Dispatching System) parses these sales orders, determining the sequence value, the relationship between the sales order number and the part number (order 2-L002, order 3-L003, order 1-L001, order 4-L004). Then, based on priority, the work-in-process is scheduled and sorted, generating a scheduling list (WIP-002, WIP-003, WIP-001, WIP-004 in sequence). The MCS (Material Control System) reads this list and converts it into standardized transport instructions, such as "Instruction 1: Transfer WIP-002 from buffer B2 to process equipment M1, execute L002 part number processing; Instruction 2: Transfer WIP-003 from buffer B3 to process equipment M2, execute L003 part number processing," etc., and issues them to AMHS (Automated Material Handling System). The AMHS system controls AGVs (Automated Guided Vehicles) to perform transfer operations according to instructions. After the transfer is completed, it triggers the start of the processing equipment.

[0098] In some embodiments of this application, the purchase order information includes a single purchase order, which includes at least two purchase order numbers, and the at least two purchase order numbers correspond to different product types. Alternatively, in other embodiments of this application, the purchase order information includes at least two purchase orders, each purchase order including at least one purchase order number, and for all purchase orders, at least two purchase order numbers correspond to different product types. Thus, a purchase order can contain multiple different product type numbers under a single order, or at least two different product type numbers under multiple orders, enabling the above-mentioned solution of this application to adapt to the order placement habits of different customers and avoid process adaptation problems caused by inconsistent order formats. Specifically, different customers have different order placement habits. Some customers, for ease of management, will include multiple different product type purchase requirements in a single purchase order and assign an independent purchase order number to each product type. If the production scheduling system only supports a single order corresponding to a single product type and number, the orders of such customers need to be manually split before being entered into the system, which not only increases manual operation costs but also easily leads to splitting errors. This solution explicitly supports a single purchase order containing at least two product type numbers. The system can automatically identify and process such orders without manual splitting, directly converting them into corresponding sales orders. This adapts to customers' centralized ordering habits, avoids process adaptation issues caused by inconsistent order formats, and improves the flexibility and efficiency of order processing.

[0099] For example, in a silicon wafer production scheduling scenario, customer Ding, in order to simplify the ordering process, issues a purchase order (main order number PO-20250905-001). This order contains two sub-purchase order numbers (PO-20250905-001-01 and PO-20250905-001-02), corresponding to 400 300mm 12nm silicon wafers and 200 300mm 5nm silicon wafers, respectively, with a delivery date of November 10, 2025. After receiving the purchase order, the system automatically identifies the two sub-order numbers and their corresponding product types under the single master order. Without manual splitting, it directly adds them to the order database and generates two corresponding sales orders: SO-20250905-001 (PO-20250905-001-01, 400 300mm 12nm silicon wafers) and SO-20250905-002 (PO-20250905-001-02, 200 300mm 5nm silicon wafers). Subsequently, inventory assessment, priority calculation, and production scheduling are performed according to the normal process.

[0100] At least one embodiment of this application provides a scheduling and dispatching system for silicon wafers, such as... Figure 9As shown, the production scheduling and dispatching system includes an acquisition module 10, a splitting module 20, a judgment module 30, a summarizing module 40, a determination module 50, and a dispatching module 60. The acquisition module 10 is configured to acquire purchase order information, which includes at least two purchase order numbers and the corresponding product type, quantity, and delivery date. The splitting module 20 is configured to acquire sales order information based on the purchase order information and split and distribute the sales order information, which includes multiple sales orders, each including a sales order number, quantity, and delivery date corresponding to each product type. The judgment module 30 is configured to acquire the inventory quantity corresponding to the product type of each sales order and determine whether the inventory quantity corresponding to the sales order is less than the product quantity. The summarizing module 40 is configured to summarize sales orders where the inventory quantity is less than the product quantity and acquire the production cycle information corresponding to the product type of the sales order. The determination module 50 is configured to determine the priority of each sales order based on the inventory quantity, sales order, and production cycle information. The dispatch module 60 is configured to schedule production and dispatch based on the priority of sales orders.

[0101] This production scheduling system transforms purchase order information into divisible sales order information, filters sales orders requiring additional production based on inventory status, and prioritizes the product types corresponding to each sales order using production cycle information before executing production scheduling. This establishes a direct link between demand and production, achieving an orderly connection from order receipt to production through layered processing. This fundamentally changes the disorderly nature of traditional manual control, significantly improving scheduling efficiency and reducing the risk of delivery delays due to process disruptions from the outset. The production scheduling method corresponding to this system can be found in the relevant descriptions in the aforementioned embodiments, and will not be repeated here.

[0102] At least one embodiment of this application provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the production scheduling and dispatching method described in the above embodiments.

[0103] At least one embodiment of this application provides an electronic device, such as... Figure 10 As shown, the electronic device 1 includes a processor 2 (which may be at least one) and a memory 3. The memory 3 is used to store executable instructions (e.g., application programs) of the processor 2. The application programs stored in the memory 3 may include one or more modules, each corresponding to a set of instructions. The processor 2 is configured to execute instructions for performing the scheduling and dispatching method in the above embodiments.

[0104] Electronic device 1 may also include a power supply component configured to perform power management of electronic device 1, a wired or wireless network interface configured to connect electronic device 1 to a network, and an input / output (I / O) interface. Electronic device 1 may operate on an operating system stored in memory 3, such as Windows Server™, Mac OS X™, Unix™, Linux™, FreeBSD™, or similar.

[0105] At least one embodiment of this application provides a computer program product, which includes a computer program that, when executed by a processor, implements the production scheduling and dispatching method described in the above embodiments. Specifically, the computer program can be stored in a computer-readable storage medium. When the above production scheduling and dispatching method needs to be executed, the processor can read the computer program from the computer-readable storage medium and load it into memory for execution, so as to drive the relevant hardware devices to complete operations such as control of the cutting process, parameter adjustment, wear compensation (band saw thickness compensation), and thickness monitoring according to the steps, processes, and logic described in the above embodiments.

[0106] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0107] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0108] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0109] 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 this embodiment according to actual needs.

[0110] In addition, the functional units in the various embodiments of the present invention 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.

[0111] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program verification codes, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0112] Furthermore, it should be noted that the combination of the various technical features in this case is not limited to the combination methods described in the claims of this case or the combination methods described in the specific embodiments. All technical features described in this case can be freely combined or combined in any way, unless they contradict each other.

[0113] It should be noted that the above examples are merely specific embodiments of the present invention, and the present invention is obviously not limited to the above embodiments, with many similar variations. All modifications that can be directly derived or conceived by those skilled in the art from the content disclosed in this invention should fall within the protection scope of this invention.

[0114] It should be understood that the terms "first," "second," etc., mentioned in the embodiments of the present invention are merely for the purpose of more clearly describing the use of the technical solutions in the embodiments of the present invention, and are not intended to limit the scope of protection of the present invention.

[0115] The above description is merely a preferred embodiment of this specification and is not intended to limit this specification. Any modifications or equivalent substitutions made within the spirit and principles of this specification should be included within the scope of protection of this specification.

Claims

1. A scheduling and dispatching method for silicon wafers, characterized in that, include: Obtain purchase order information, wherein the purchase order information includes at least two purchase order numbers and the product type, product quantity and delivery date corresponding to the purchase order number; Sales order information is obtained based on the purchase order information, and the sales order information is split and distributed. The sales order information includes multiple sales orders, and each sales order includes a sales order number corresponding to each product type, the product quantity, and the delivery date. Obtain the inventory quantity corresponding to the product type for each sales order, and determine whether the inventory quantity corresponding to the sales order is less than the product quantity; Summarize the sales orders whose inventory quantity is less than the product quantity, and obtain the production cycle information corresponding to the product type of the sales order; The priority of each sales order is determined based on the inventory quantity, the sales order, and the production cycle information; and Production scheduling and work assignment are based on the priority of the sales orders.

2. The production scheduling and work assignment method according to claim 1, characterized in that, The step of obtaining sales order information based on the purchase order information and splitting and distributing the sales order information includes: An order database is established based on the purchase order information, and the order database stores customer information corresponding to the purchase orders and the purchase order information; and Based on the order database, sales order information is determined, and the sales order information is split into sales orders and issued.

3. The production scheduling and work assignment method according to claim 2, characterized in that, The step of establishing an order database based on the purchase order information includes: Based on the trained order model, information is read from the purchase order information and the sales order is generated. The order model is used to arrange the order features corresponding to the purchase order in the sales order in a preset order. The order features include the customer information, the purchase order number, the product type, the product quantity, and the delivery date.

4. The production scheduling and work assignment method according to claim 3, characterized in that, The step of establishing an order database based on the purchase order information also includes: Based on the order model, a pre-stored order model corresponding to the customer information is retrieved and compared with the order characteristics of the sales order to verify the sales order. The order model includes the pre-stored product type corresponding to the customer information.

5. The production scheduling and work assignment method according to claim 3 or 4, characterized in that, Determining the priority of each sales order based on the inventory quantity, the sales order, and the production cycle information includes: Based on the process cycle required for the wafers corresponding to the sales orders and the utilization rate of the production lines, calculate the production capacity of each sales order before the delivery date, and determine the priority of the sales orders based on the production capacity; Wherein, the process cycle a is the processing time for a single site to produce a single wafer, and the utilization rate b is the percentage of production operating time of the equipment used to prepare the wafer.

6. The production scheduling and work assignment method according to claim 5, characterized in that, The step of calculating the production capacity of each sales order before the delivery date based on the process cycle required for the wafer corresponding to the sales order and the production line utilization rate, and determining the priority of the sales orders based on the production capacity, includes: Obtain the process cycle a and the utilization rate b; The difference between the delivery date corresponding to the sales order and the current date is used as the delivery period c, so as to obtain the remaining effective processing time d = c × b for the product type corresponding to the sales order at each site; Based on the process cycle a and the remaining effective processing time d, the remaining effective processing output quantity e of the wafers corresponding to the product type of the single site and the sales order is calculated, where e = d / a; Obtain the shortage quantity f of the wafers corresponding to the sales order, and use the difference between the remaining effective processing output quantity e and the shortage quantity f as the sorting value; and Based on the numerical value of the sorting value, the production priority of each sales order is sorted.

7. The production scheduling and work assignment method according to claim 6, characterized in that, The process of prioritizing production for each sales order based on the numerical value of the sorting value includes: Based on the numerical value of the sorting value, the production priority of each sales order is sorted from largest to smallest.

8. The production scheduling and work assignment method according to claim 6, characterized in that, The production scheduling and work assignment based on the priority of the sales orders includes: Each of the sales orders is parsed, wherein the parsed sales orders have been sorted according to the priority determined based on the sorting value, and the parsing content includes determining the association between the sorting value, the sales order and the part number, wherein the part number corresponds to the product type in the sales order; Based on the association and the priority order of the sales orders corresponding to the sorting value, the wafer work-in-process matching each sales order is sorted in scheduling order to generate a wafer work-in-process scheduling list that matches the order of the sorting value. Read the work-in-process scheduling list, convert the scheduling list into standardized transfer instructions, and issue the transfer instructions. Upon receiving the transfer instruction and executing the transfer operation, the wafer work-in-process is transferred according to the sorting value corresponding to the wafer work-in-process scheduling list, and processing is initiated.

9. The production scheduling and work assignment method according to any one of claims 1 to 4, characterized in that, The purchase order information includes one purchase order, which includes at least two purchase order numbers, and the at least two purchase order numbers correspond to different product types; or The purchase order information includes at least two purchase orders, each purchase order includes at least one purchase order number, and for all the purchase orders, there are at least two purchase order numbers that correspond to different product types.

10. A scheduling and dispatching system for silicon wafers, characterized in that, include: The acquisition module is configured to acquire purchase order information, wherein the purchase order information includes at least two purchase order numbers and the product type, product quantity and delivery date corresponding to the purchase order numbers; The splitting module is configured to obtain sales order information based on the purchase order information, and split and distribute the sales order information. The sales order information includes multiple sales orders, and each sales order includes a sales order number corresponding to each product type, the product quantity, and the delivery date. The judgment module is configured to obtain the inventory quantity corresponding to the product type for each sales order, and to determine whether the inventory quantity corresponding to the sales order is less than the product quantity; The aggregation module is configured to aggregate sales orders where the inventory quantity is less than the product quantity, and obtain the production cycle information corresponding to the product type of the sales order; The determining module is configured to determine the priority of each sales order based on the inventory quantity, the sales order, and the production cycle information; and The dispatch module is configured to schedule production and dispatch based on the priority of the sales orders.

11. A computer-readable storage medium having computer-executable instructions stored thereon, characterized in that, When the executable instructions are executed by the processor, they implement the scheduling and dispatching method as described in any one of claims 1 to 9.

12. An electronic device, characterized in that, include: processor; Memory used to store the processor's executable instructions; The processor is used to execute the production scheduling and dispatching method as described in any one of claims 1 to 9.

13. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the scheduling and dispatching method as described in any one of claims 1 to 9.

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