Intelligent management and control system and method for production plan

Abstract

The invention discloses an intelligent management and control system and method for a production plan. The intelligent management and control system comprises a data acquisition module, a data processing module and an intelligent management and control module, relates to the technical field of production plan management and control, and solves the technical problem of low product order delivery efficiency of an enterprise caused by difficulty in timely adjustment of a production plan in a scheme in the prior art. According to the method, by integrating multi-dimensional data such as production orders, zero accessory composition, workshop drawings and equipment parameters, a high-precision data twinning model is constructed, and virtual simulation and dynamic optimization of the production process are achieved. The production order is disassembled into the zero accessory work order, and the data twinborn model is combined for simulated production, so that the process feasibility can be verified in advance, the zero accessory consumption and the quality inspection result can be predicted, and the trial and error cost and the production risk can be remarkably reduced. And on the basis of a closed-loop feedback mechanism of simulation data, the production plan can be adjusted in real time, the resource utilization rate and the production rhythm matching degree are improved, and the product order delivery efficiency of an enterprise is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of production plan control, and specifically relates to an intelligent control system and method for production plans. Background Art

[0002] In modern industrial production and supply chain management, production planning and control are key links to ensure the efficient operation of enterprises, optimize the allocation of resources, and the speed of market response. With the intensification of global competition, the increasing diversification of consumer demands, and the shortening of product life cycles, traditional production planning management models are facing unprecedented challenges. Traditional methods often rely on manual experience, fixed rules, or simple spreadsheets for plan formulation and adjustment, which not only consume time and effort but also are difficult to adapt to the rapidly changing market environment and production conditions.

[0003] The existing technology obtains the equipment operation parameters of each link in the production workshop in real time and controls the production plan according to the equipment operation parameters. However, in actual situations, the actual delivery time of the production plan is affected by the inventory of product parts and components and the product yield rate. The existing technology solutions are difficult to control the product inventory and yield rate during the product production process and timely adjust the production plan, resulting in a low delivery efficiency of the enterprise's product orders.

[0004] The present invention provides an intelligent control system and method for production plans to solve the above technical problems. Summary of the Invention

[0005] The present invention aims to solve at least one of the technical problems existing in the prior art; for this purpose, the present invention proposes an intelligent control system and method for production plans to solve the technical problem that the existing technology solutions are difficult to timely adjust the production plan, resulting in a low delivery efficiency of the enterprise's product orders.

[0006] To achieve the above object, the first aspect of the present invention provides an intelligent control system for production plans, including: a data processing module, and a data acquisition module and an intelligent control module connected thereto;

[0007] The data acquisition module: is used to obtain the production order information, component information of parts and components, drawing information, and equipment parameter information of the production workshop of the target product within a set time period; collect the historical inbound quantity and historical consumption quantity of several parts and components in the production workshop within a set time period;

[0008] The data processing module: is used to split the production order information based on the component information of parts and components of the target product to obtain production work orders for several parts and components; determine the inventory prediction value for the set time period based on the historical inbound quantity and historical consumption quantity of several parts and components; and,

[0009] Construct a digital twin model of the production workshop based on the drawing information and equipment parameter information; perform simulated production on the target product based on the production order, inventory prediction value, and digital twin model to obtain simulated spare part consumption data and simulated quality inspection data of the target product;

[0010] The intelligent control module: is used to control the production plan based on the simulated spare part consumption data and simulated quality inspection data.

[0011] Preferably, the splitting of the production order information based on the spare part composition information of the target product includes:

[0012] Extract the production order information and the spare part composition information of a single target product; among them, the production order information includes the delivery quantity and delivery time of the target product; the spare part composition information includes raw material information and quantity information;

[0013] Calculate the difference between the delivery time and the current time to obtain the planned production days; calculate the ratio of the delivery quantity to the planned production days to obtain the daily output;

[0014] Multiply the daily output by the quantity information of several spare parts in a single target product respectively to obtain the daily production volume of each spare part; integrate the raw material information and the daily production volume of several spare parts into a production work order.

[0015] Preferably, the determination of the inventory prediction value for a set time period based on the historical inbound quantity and historical consumption quantity of several spare parts includes:

[0016] Extract the historical inbound quantity and historical consumption quantity of several spare parts; perform linear fitting on the historical inbound quantity and historical consumption quantity respectively to obtain the inbound quantity change curve and the consumption quantity change curve; perform integral calculation on the inbound quantity change curve to obtain the cumulative inbound quantity value; perform integral calculation on the consumption quantity change curve to obtain the cumulative consumption quantity value; calculate the difference between the cumulative inbound quantity value and the cumulative consumption quantity value to obtain the inventory prediction value.

[0017] Preferably, the construction of the digital twin model of the production workshop based on the drawing information and equipment parameter information includes:

[0018] Extract the drawing information and equipment parameter information of the production workshop; among them, the drawing information includes the structure information and location information of the production equipment in the production workshop; the equipment parameter information includes the operating parameters of the production equipment, raw material processing information, and product processing rate;

[0019] Input the drawing information of the production workshop into 3D model software and create a 3D simulation model of the production workshop; synchronize the equipment parameter information to the 3D simulation model, and mark the synchronized 3D simulation model as the digital twin model.

[0020] Preferably, the simulated production of the target product based on the production work order, inventory forecast value and data twin model includes:

[0021] Extract the production work orders and inventory forecast values of several parts and accessories; input the production orders and inventory forecast values of several parts and accessories into the data twin model, and simulate production through the data twin model; record the simulated parts and accessories consumption data and the simulated quality inspection data of the target products during the simulated production process; among them, simulated production includes product processing and product quality inspection.

[0022] Preferably, the control of the production plan based on the simulated parts and accessories consumption data and the simulated quality inspection data includes:

[0023] Extract simulated parts and accessories consumption data and simulated quality inspection data;

[0024] Determine the defect level of each part and accessory based on simulated quality inspection data, and count the number of parts and accessories with each defect level; defect levels include level 1 defects and level 2 defects.

[0025] Calculate the total rework time required to process defective products based on the number of parts and accessories for each defect level and the corresponding rework time;

[0026] Calculate the delivery time forecast value of the production order based on the total rework time and the number of unscheduled target products;

[0027] Determine whether the delivery time forecast value is earlier than the delivery time specified in the production order; if so, continue to produce the target product according to the production plan; if not, adjust the production plan to ensure that the corresponding number of target products are produced before the delivery time specified in the production order.

[0028] It should be noted that parts and accessories with level one defects need to be scrapped and redone, while parts and accessories with level two defects can be repaired and made into qualified parts and accessories.

[0029] Preferably, the determining of the defect level of each component and accessory based on the simulated quality inspection data includes:

[0030] Extract the simulated quality inspection data of each part and accessory; the simulated quality inspection data includes the dimensional deviation and performance compliance rate of each part and accessory;

[0031] Determine whether the simulated quality inspection data of the parts and accessories is greater than the corresponding preset threshold; if yes, mark the defect level of the corresponding parts and accessories as a first-level defect; if not, mark the defect level of the corresponding parts and accessories as a second-level defect.

[0032] Preferably, the calculation of the total rework time required to process the defective products based on the number of parts and accessories of each defect level and the corresponding rework time includes:

[0033] Extract the quantity of spare parts and the rework duration for each defect level; calculate the total rework duration SGS required to process a number of defective products through the formula SGS = T1×N1 + T2×N2; where T1 is the rework duration of spare parts with first-level defects, T2 is the rework duration of spare parts with second-level defects, N1 is the quantity of spare parts with first-level defects, and N2 is the quantity of spare parts with second-level defects.

[0034] Preferably, calculating the predicted delivery time of the production order based on the total rework duration and the quantity of un-scheduled target products includes:

[0035] Extract the total rework duration and the quantity of un-scheduled target products; calculate the product of the quantity of un-scheduled target products and the processing duration of a single product to obtain the remaining duration to be completed; calculate the sum of the current time, the remaining duration to be completed, and the total rework duration to obtain the predicted delivery time.

[0036] The second aspect of the present invention provides an intelligent control method for production planning, including:

[0037] S1: Obtain the production order information, spare part composition information of the target product, as well as the drawing information and equipment parameter information of the production workshop within a set time period; collect the historical inbound quantity and historical consumption quantity of several spare parts in the production workshop within the set time period;

[0038] S2: Split the production order information based on the spare part composition information of the target product to obtain production work orders for several spare parts;

[0039] S3: Determine the predicted inventory value for the set time period based on the historical inbound quantity and historical consumption quantity of several spare parts;

[0040] S4: Construct a digital twin model of the production workshop based on the drawing information and equipment parameter information;

[0041] S5: Simulate the production of the target product based on the production work orders, predicted inventory value, and digital twin model to obtain simulated spare part consumption data and simulated quality inspection data of the target product;

[0042] S6: Control the production plan based on the simulated spare part consumption data and simulated quality inspection data.

[0043] Compared with the prior art, the beneficial effects of the present invention are:

[0044] 1. The present invention integrates multi-dimensional data such as production orders, component compositions, workshop drawings, and equipment parameters to construct a high-precision data twin model, realizing virtual simulation and dynamic optimization of the production process. This method uses historical inbound and consumption data to establish an inventory forecasting model, effectively balancing material supply and consumption and avoiding the risks of inventory redundancy or shortage. By disassembling production orders into component work orders and simulating production in combination with the data twin model, the feasibility of the process can be verified in advance, the consumption of components and the quality inspection results can be predicted, significantly reducing the trial-and-error cost and production risks. Based on the closed-loop feedback mechanism of the simulation data, the production plan can be adjusted in real time, improving the resource utilization rate and the matching degree of the production rhythm, and ultimately achieving improved product quality stability, shortened production cycle, and reduced comprehensive costs. This solution significantly enhances the executability, flexibility, and global optimization ability of the production plan through digital and intelligent means.

[0045] 2. The present invention extracts and analyzes the component consumption and quality inspection data of the simulated production, accurately determines the defect levels and quantities of each component, and calculates the total rework duration required to process the defects accordingly. This method combines the rework duration with the quantity of un-scheduled products to effectively predict the actual delivery time of the production order, thereby realizing the dynamic adjustment and optimization of the production plan. When it is found that the predicted delivery time is later than the order requirement, the plan is adjusted in time to ensure on-time delivery, avoiding the risk of delayed delivery caused by improper defect handling or unreasonable planning. This method not only improves the accuracy and efficiency of product quality control, but also enhances the flexibility and adaptability of the production plan, contributing to improving resource utilization rate, reducing waste, shortening the production cycle, and ultimately achieving efficient and reliable production management. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0047] Figure 1 It is the overall flowchart of the intelligent control method for production planning of the present invention;

[0048] Figure 2 It is the schematic diagram of the principle of the intelligent control system for production planning of the present invention;

[0049] Figure 3 It is the flowchart of controlling the production plan in the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0050] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0051] Please refer to Figures 1-3 , an embodiment of the first aspect of the present invention provides an intelligent control system for production planning, including: a data processing module, and a data acquisition module and an intelligent control module connected thereto;

[0052] Data acquisition module: used to obtain the production order information, spare part composition information of the target product, as well as the drawing information and equipment parameter information of the production workshop within a set time period; collect the historical storage quantity and historical consumption quantity of several spare parts in the production workshop within a set time period;

[0053] Data processing module: used to split the production order information based on the spare part composition information of the target product to obtain production work orders for several spare parts; determine the inventory prediction value for the set time period based on the historical storage quantity and historical consumption quantity of several spare parts; and,

[0054] Construct a digital twin model of the production workshop based on the drawing information and equipment parameter information; simulate the production of the target product based on the production work orders, inventory prediction value and digital twin model to obtain simulated spare part consumption data and simulated quality inspection data of the target product;

[0055] Intelligent control module: used to control the production plan based on the simulated spare part consumption data and simulated quality inspection data.

[0056] In this embodiment, splitting the production order information based on the spare part composition information of the target product includes:

[0057] Extract the production order information and the spare part composition information of a single target product; wherein, the production order information includes the delivery quantity and delivery time of the target product; the spare part composition information includes raw material information and quantity information;

[0058] Calculate the difference between the delivery time and the current time to obtain the planned production days; calculate the ratio of the delivery quantity to the planned production days to obtain the daily output;

[0059] Multiply the daily output by the quantity information of several spare parts in a single target product respectively to obtain the daily spare part production quantity of each spare part; integrate the raw material information and the daily spare part production quantity of several spare parts into production work orders.

[0060] It should be noted that the daily working hours of the production workshop are 8 hours.

[0061] Exemplarily, set the delivery quantity of the target product in the production order information to 100 pieces, the delivery time to August 20, 2020, and the current time to August 11, 2020; calculate the difference between the delivery time and the current time to obtain the planned production days as 10 days; calculate the ratio of the delivery quantity to the planned production days to obtain the daily output as 10 pieces per day;

[0062] Assume that the target product is a stainless steel insulated bucket, and the component information of a single target product is shown in the following table:

[0063] Spare parts Housing Barrel cover Sealing ring Inner liner Raw material information 304 stainless steel 304 stainless steel Rubber Glass Quantity information (pcs) 1 1 1 1

[0064] Multiply the daily output by the quantity information of several components in a single target product respectively to obtain the daily component production quantities of the shell, bucket lid, sealing ring, and inner liner as 10 pieces per day; integrate the raw material information of several components and the daily component production quantities into a production work order.

[0065] The present invention extracts and analyzes key data in the production order and component composition information, accurately calculates the planned production days and daily output, and realizes the refined decomposition of production tasks. This method can accurately calculate the quantity of each component to be produced daily according to the specific requirements of the components of a single product, and then generate a detailed production work order to ensure the accuracy and timeliness of material preparation and production arrangement. This process not only improves the scientificity and operability of the production plan, but also effectively avoids problems such as resource waste and overproduction or underproduction, and helps to improve production efficiency, optimize inventory management, and shorten the delivery cycle.

[0066] In this embodiment, determining the inventory prediction value for a set time period based on the historical inbound quantity and historical consumption quantity of several components includes:

[0067] Extract the historical inbound quantity and historical consumption quantity of several components; perform linear fitting on the historical inbound quantity and historical consumption quantity respectively to obtain the inbound quantity change curve and consumption quantity change curve; perform integral calculation on the inbound quantity change curve to obtain the cumulative inbound quantity value; perform integral calculation on the consumption quantity change curve to obtain the cumulative consumption quantity value; calculate the difference between the cumulative inbound quantity value and the cumulative consumption quantity value to obtain the inventory prediction value.

[0068] In this embodiment, constructing a digital twin model of the production workshop based on the drawing information and equipment parameter information includes:

[0069] Extract the drawing information and equipment parameter information of the production workshop; among them, the drawing information includes the structural information and location information of the production equipment in the production workshop; the equipment parameter information includes the operating parameters of the production equipment, the raw material information for processing, and the product processing rate;

[0070] Input the drawing information of the production workshop into 3D modeling software and create a 3D simulation model of the production workshop; synchronize the equipment parameter information to the 3D simulation model, and mark the synchronized 3D simulation model as a digital twin model.

[0071] The present invention extracts and integrates key information such as the structure, location, and operating parameters of production equipment, and uses 3D modeling software to create a highly realistic digital twin model of the workshop. This method not only realizes the accurate mapping of the physical production workshop in the virtual environment, but also ensures the authenticity and real-time nature of the production process simulation by synchronizing equipment parameters to the 3D simulation model. The digital twin model supports virtual commissioning and optimization, helps to detect potential problems in advance, reduces the downtime of actual production, and improves equipment utilization and production efficiency.

[0072] In this embodiment, based on the production order, inventory prediction value, and digital twin model, the target product is simulated for production, including:

[0073] Extract the production orders and inventory prediction values of several spare parts; input the production orders and inventory prediction values of several spare parts into the digital twin model, and perform simulated production through the digital twin model; record the simulated consumption data of the spare parts and the simulated quality inspection data of the target product during the simulated production process; wherein, the simulated production includes product processing and product quality inspection.

[0074] In this embodiment, based on the simulated spare part consumption data and simulated quality inspection data, the production plan is controlled, including:

[0075] Extract the simulated spare part consumption data and simulated quality inspection data;

[0076] Based on the simulated quality inspection data, determine the defect levels of each spare part, and count the number of spare parts at each defect level; wherein, the defect levels include first-level defects and second-level defects;

[0077] Based on the number of spare parts at each defect level and the corresponding rework duration, calculate the total rework duration required to process defective products;

[0078] Based on the total rework duration and the number of un-scheduled target products, calculate the predicted delivery time of the production order;

[0079] Judge whether the predicted delivery time is earlier than the delivery time specified in the production order; if so, continue to produce the target product according to the production plan; if not, adjust the production plan to ensure that the corresponding number of target products is produced before the delivery time specified in the production order.

[0080] The present invention extracts and analyzes the consumption and quality inspection data of the accessories in the simulated production, accurately determines the defect levels and quantities of each accessory, and calculates the total rework time required to process the defects accordingly. This method combines the rework time with the quantity of un-scheduled products, effectively predicts the actual delivery time of the production order, and thus realizes the dynamic adjustment and optimization of the production plan. When it is found that the predicted delivery time is later than the order requirement, the plan is adjusted in time to ensure on-time delivery, avoiding the risk of delayed delivery caused by improper defect handling or unreasonable plan. This method not only improves the accuracy and efficiency of product quality control, but also enhances the flexibility and adaptability of the production plan, helps to improve resource utilization rate, reduce waste, shorten the production cycle, and finally achieve efficient and reliable production management.

[0081] Exemplarily, set the predicted delivery time to August 21, 2020, and the delivery time specified in the production order is August 20, 2020; since the predicted delivery time is later than the delivery time specified in the production order, the production plan is adjusted to ensure that the corresponding quantity of target products is produced before the delivery time specified in the production order.

[0082] It should be noted that the accessories with first-level defects need to be scrapped and remade, and the accessories with second-level defects can be repaired to become qualified accessories.

[0083] In this embodiment, determining the defect levels of each accessory based on the simulated quality inspection data includes:

[0084] Extracting the simulated quality inspection data of each accessory; wherein, the simulated quality inspection data includes the dimensional deviation and performance compliance rate of each accessory;

[0085] Judging whether the simulated quality inspection data of the accessory is greater than the corresponding preset threshold; if so, marking the defect level of the corresponding accessory as a first-level defect; if not, marking the defect level of the corresponding accessory as a second-level defect.

[0086] In this embodiment, calculating the total rework time required to process defective products based on the quantity of accessories of each defect level and the corresponding rework time includes:

[0087] Extracting the quantity of accessories of each defect level and the rework time; calculating the total rework time SGS required to process a number of defective products through the formula SGS = T1×N1 + T2×N2; where, T1 is the rework time of the accessories with first-level defects, T2 is the rework time of the accessories with second-level defects, N1 is the quantity of the accessories with first-level defects, and N2 is the quantity of the accessories with second-level defects.

[0088] Exemplarily, set the rework duration T1 of first-level defective zero parts to 0.5 hours, the rework duration T2 of second-level defective zero parts to 0.2 hours, the number N1 of first-level defective zero parts to 4, and the number N2 of second-level defective zero parts to 10; calculate the total rework duration SGS required for several defective products to be 4 hours through the formula.

[0089] In this embodiment, based on the total rework duration and the number of un-scheduled target products, calculate the predicted delivery time of the production order, including:

[0090] Extract the total rework duration and the number of un-scheduled target products; calculate the product of the number of un-scheduled target products and the processing duration of a single product to obtain the duration to be completed; calculate the sum of the current time, the duration to be completed, and the total rework duration to obtain the predicted delivery time.

[0091] Exemplarily, set the total rework duration to 8 hours, the number of un-scheduled target products to 60 pieces, the processing duration of a single product to 1.2 hours, and the current time to August 12, 2020; calculate the duration to be completed to be 60 hours; calculate the sum of the current time, the duration to be completed, and the total rework duration to obtain the predicted delivery time of August 21, 2020.

[0092] The second aspect of the embodiment of the present invention provides an intelligent control method for production planning, including:

[0093] S1: Obtain the production order information, zero part composition information of the target product, drawing information and equipment parameter information of the production workshop within the set time period; collect the historical inbound quantity and historical consumption quantity of several zero parts in the production workshop within the set time period;

[0094] S2: Split the production order information based on the zero part composition information of the target product to obtain production work orders for several zero parts;

[0095] S3: Determine the predicted inventory value for the set time period based on the historical inbound quantity and historical consumption quantity of several zero parts;

[0096] S4: Construct a digital twin model of the production workshop based on the drawing information and equipment parameter information;

[0097] S5: Simulate the production of the target product based on the production work orders, predicted inventory value and digital twin model to obtain simulated zero part consumption data and simulated quality inspection data of the target product;

[0098] S6: Control the production plan based on the simulated zero part consumption data and simulated quality inspection data.

[0099] Some of the data in the above formula is calculated by removing the dimension and taking its numerical value. The formula is obtained by software simulation of a large amount of collected data to get a formula closest to the actual situation. The preset parameters and preset thresholds in the formula are set by those skilled in the art according to the actual situation or obtained through simulation of a large amount of data.

[0100] The working principle of the present invention:

[0101] The present invention obtains the production order information, spare part composition information of the target product, as well as the drawing information and equipment parameter information of the production workshop within a set time period; collects the historical inbound quantity and historical consumption quantity of several spare parts in the production workshop within the set time period; splits the production order information based on the spare part composition information of the target product to obtain production work orders for several spare parts; determines the inventory prediction value for the set time period based on the historical inbound quantity and historical consumption quantity of several spare parts; constructs a digital twin model of the production workshop based on the drawing information and equipment parameter information; conducts simulated production of the target product based on the production work orders, inventory prediction value and digital twin model to obtain simulated spare part consumption data and simulated quality inspection data of the target product; controls the production plan based on the simulated spare part consumption data and simulated quality inspection data.

[0102] The above embodiments are only used to illustrate the technical method of the present invention and not to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical method of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical method of the present invention.

Claims

1. An intelligent control system for production planning, comprising: A data processing module, as well as a data acquisition module and an intelligent control module connected thereto; characterized in that The data acquisition module: is used to obtain the production order information, spare part composition information of the target product within a set time period, as well as the drawing information and equipment parameter information of the production workshop; collect the historical inbound quantity and historical consumption quantity of several spare parts in the production workshop within a set time period; The data processing module: is used to split the production order information based on the spare part composition information of the target product to obtain production work orders for several spare parts; determine the inventory prediction value for the set time period based on the historical inbound quantity and historical consumption quantity of several spare parts; and Construct a digital twin model of the production workshop based on the drawing information and equipment parameter information; perform simulated production on the target product based on the production work orders, inventory prediction value, and digital twin model to obtain simulated spare part consumption data and simulated quality inspection data of the target product; The intelligent control module: is used to control the production plan based on the simulated spare part consumption data and simulated quality inspection data.

2. The intelligent control system for production planning according to claim 1, wherein The splitting of the production order information based on the spare part composition information of the target product includes: Extract the production order information and the spare part composition information of a single target product; wherein, the production order information includes the delivery quantity and delivery time of the target product; the spare part composition information includes raw material information and quantity information; Calculate the difference between the delivery time and the current time to obtain the planned production days; calculate the ratio of the delivery quantity to the planned production days to obtain the daily output; Multiply the daily output by the quantity information of several spare parts in a single target product respectively to obtain the daily spare part production quantity of each spare part; integrate the raw material information and the daily spare part production quantity of several spare parts into a production work order.

3. An intelligent control system for production planning according to claim 1, characterized in that, The determination of the inventory prediction value for the set time period based on the historical inbound quantity and historical consumption quantity of several spare parts includes: Extract the historical inbound quantity and historical consumption quantity of several spare parts; perform linear fitting on the historical inbound quantity and historical consumption quantity respectively to obtain an inbound quantity change curve and a consumption quantity change curve; perform integral calculation on the inbound quantity change curve to obtain an inbound quantity cumulative value; perform integral calculation on the consumption quantity change curve to obtain a consumption quantity cumulative value; calculate the difference between the inbound quantity cumulative value and the consumption quantity cumulative value to obtain the inventory prediction value.

4. An intelligent control system for production planning according to claim 1, characterized in that, The construction of the digital twin model of the production workshop based on the drawing information and equipment parameter information includes: Extract the drawing information and equipment parameter information of the production workshop; wherein, the drawing information includes the structure information and location information of the production equipment in the production workshop; the equipment parameter information includes the operating parameters of the production equipment, raw material processing information, and product processing rate; Input the drawing information of the production workshop into 3D modeling software and create a 3D simulation model of the production workshop; synchronize the equipment parameter information to the 3D simulation model, and mark the synchronized 3D simulation model as a digital twin model.

5. An intelligent control system for production planning according to claim 1, characterized in that The simulated production of the target product based on the production work orders, inventory prediction value, and digital twin model includes: Extract the production work orders and inventory forecast values of several spare parts; input the production orders and inventory forecast values of several spare parts into the digital twin model, and conduct simulated production through the digital twin model; record the simulated consumption data of the spare parts and the simulated quality inspection data of the target product during the simulated production process; wherein, the simulated production includes product processing and product quality inspection.

6. An intelligent control system for production planning according to claim 2, characterized in that, The control of the production plan based on the simulated spare part consumption data and simulated quality inspection data includes: Extract the simulated spare part consumption data and simulated quality inspection data; Determine the defect levels of each spare part based on the simulated quality inspection data, and count the number of spare parts at each defect level; wherein, the defect levels include first-level defects and second-level defects; Calculate the total rework time required to process defective products based on the number of spare parts at each defect level and the corresponding rework duration; Calculate the predicted delivery time of the production order based on the total rework time and the number of un-scheduled target products; Judge whether the predicted delivery time is earlier than the delivery time specified in the production order; if so, continue to produce the target product according to the production plan; if not, adjust the production plan to ensure that the corresponding number of target products are produced before the delivery time specified in the production order.

7. An intelligent control system for production planning according to claim 6, characterized in that, The determination of the defect levels of each spare part based on the simulated quality inspection data includes: Extract the simulated quality inspection data of each spare part; wherein, the simulated quality inspection data includes the dimensional deviation and performance compliance rate of each spare part; Judge whether the simulated quality inspection data of the spare part is greater than the corresponding preset threshold; if so, mark the defect level of the corresponding spare part as a first-level defect; if not, mark the defect level of the corresponding spare part as a second-level defect.

8. An intelligent control system for production planning according to claim 6, characterized in that, The calculation of the total rework time required to process defective products based on the number of spare parts at each defect level and the corresponding rework duration includes: Extract the number of spare parts at each defect level and the rework duration; calculate the total rework time SGS required to process several defective products through the formula SGS = T1×N1 + T2×N2; wherein, T1 is the rework duration of first-level defect spare parts, T2 is the rework duration of second-level defect spare parts, N1 is the number of first-level defect spare parts, and N2 is the number of second-level defect spare parts.

9. An intelligent control system for production planning according to claim 6, characterized in that, The calculation of the predicted delivery time of the production order based on the total rework time and the number of un-scheduled target products includes: Extract the total rework time and the number of un-scheduled target products; calculate the product of the number of un-scheduled target products and the processing duration of a single product to obtain the remaining work duration; calculate the sum of the current time, the remaining work duration, and the total rework time to obtain the predicted delivery time.

10. An intelligent control method for production planning, which operates based on the intelligent control system and method for production planning according to any one of claims 1-9, characterized in that, Includes: Obtain the production order information, spare part composition information of the target product, as well as the drawing information and equipment parameter information of the production workshop within the set time period; Collect the historical inbound quantity and historical consumption quantity of several spare parts in the production workshop within the set time period; Split the production order information based on the spare part composition information of the target product to obtain the production work orders of several spare parts; Determine the inventory forecast value for the set time period based on the historical inbound quantity and historical consumption quantity of several spare parts; Construct a digital twin model of the production workshop based on the drawing information and equipment parameter information; Based on the production work order, the predicted inventory quantity, and the digital twin model, simulate the production of the target product to obtain the simulated spare part consumption data and the simulated quality inspection data of the target product; Control the production plan based on the simulated spare part consumption data and the simulated quality inspection data.

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