Scheduling system between semi-finished products and finished products based on intelligent manufacturing management

Abstract

The application discloses a semi-finished product and finished product inter-scheduling system based on intelligent manufacturing management and belongs to the technical field of intelligent manufacturing. The system comprises a data acquisition module, a scheduling calculation module, an execution control module and a data storage module. The modules are connected through an industrial bus protocol. The output end of the data acquisition module is connected with the input end of the scheduling calculation module and the input end of the data storage module. The input end of the execution control module receives the output signal of the scheduling calculation module. The output end of the execution control module is connected with a stamping device. The output end of the execution control module is connected with the input end of the data storage module. The data storage module and the scheduling calculation module realize bidirectional communication. The application can solve the supply-demand matching problem between semi-finished products and finished products.

Description

Technical Field

[0001] The present invention belongs to the technical field of intelligent manufacturing, and in particular relates to a scheduling system between semi-finished products and finished products based on intelligent manufacturing management. Background Art

[0002] In the field of metal product production scheduling, existing technologies commonly suffer from difficulties integrating heterogeneous data from multiple sources, delayed dynamic scheduling responses, and insufficient execution accuracy. As the hardware industry transitions to a high-variety, low-batch production model, traditional scheduling systems are unable to meet the real-time decision-making requirements under complex process constraints.

[0003] Chinese patent CN202211494021.4 hot processing production scheduling method and production scheduling device adopts K-means clustering algorithm to pre-process and classify forging production data, and screens out the production data of forgings in the heating link. The production data includes: forging number, process of forging number, furnace entry temperature and furnace exit temperature corresponding to the process of forging number; according to the production data, a greedy thermal algorithm or an exhaustive algorithm is adopted to obtain the optimal solution for the heating link sorting of the forging; and the production scheduling of the forgings in the heating link is performed according to the optimal solution for the heating process sorting.

[0004] Chinese patent CN202111295813.4 dynamic scheduling method and production scheduling system for multi-model small-batch production lines, the dynamic scheduling method includes: obtaining an order set; obtaining the model information and quantity information of each order from the set; obtaining the corresponding process information from the model information; obtaining the process flow of the model and the required personnel information and equipment information from the process information; obtaining the optimal production sequence and the allocation results of personnel and equipment through the planning engine; the production scheduling system includes a basic data module, an order management module and a production scheduling module.

[0005] However, some problems still exist, such as the coexistence of backlogs and shortages of semi-finished products. Due to the lack of an accurate inventory control model, excessive stockpiling of raw materials often occurs, and downstream processes are forced to stop and wait due to lack of materials, forming a vicious cycle of "the front process is full and the back process is out of supply." Summary of the Invention

[0006] The purpose of the present invention is to provide a scheduling system between semi-finished products and finished products based on intelligent manufacturing management to solve the supply and demand matching problem between semi-finished products and finished products.

[0007] In order to achieve the above technical objectives, the technical solutions adopted by the present invention are as follows:

[0008] The scheduling system between semi-finished products and finished products based on intelligent manufacturing management includes a data acquisition module, a scheduling calculation module, an execution control module, and a data storage module; each module is connected through an industrial bus protocol, the output end of the data acquisition module is connected to the input end of the scheduling calculation module and the input end of the data storage module, the input end of the execution control module receives the output signal of the scheduling calculation module, the output end of the execution control module is connected to the stamping equipment, the output end of the execution control module is connected to the input end of the data storage module, and the data storage module and the scheduling calculation module communicate in a two-way manner.

[0009] The data acquisition module includes a semi-finished product inventory monitoring unit, an equipment status monitoring unit, a finished product order data interface, and an industrial intelligent gateway; the equipment status monitoring unit is used to collect the operating parameters of the stamping equipment, the finished product order data interface obtains order demand information in real time, and the industrial intelligent gateway realizes the protocol conversion of each device. The semi-finished product inventory monitoring unit includes an RFID tag reader and a laser scanner. The RFID tag reader is used to track the shelf storage status in real time; the laser scanner is used for inventory statistics. It is used to obtain the semi-finished product inventory, finished product order demand, current finished product inventory and the status of the stamping equipment in real time, and realize the collection of semi-finished product inventory, finished product order demand fluctuations, and stamping equipment operating parameters. Its working steps are as follows:

[0010] S101: Real-time collection of production parameters of stamping equipment and process parameters of finished products;

[0011] S102: Scan shelf labels in real time and update semi-finished product inventory data to the local database;

[0012] S103: Pull the finished product order from the ERP system. The data fields include order ID, product model, demand quantity, and latest delivery time.

[0013] S104: Calculate the matching between the current inventory of finished products and the demand for finished product orders;

[0014] S105: Determine whether the remaining inventory of finished products meets the safety stock requirement;

[0015] S106: The processed data is transmitted to the scheduling calculation module. The message format adopts the JSON standardized structure and includes a timestamp, stamping equipment ID, and data value fields.

[0016] Compared with existing technologies, this data acquisition module has the following advantages:

[0017] 1. Real-time acquisition of key parameters of stamping equipment such as pressure, temperature, and speed, tracking of workpiece flow status, and automatic identification of sheet metal at the beginning and finished products at the end of the line, achieving full transparency of the input-output process, enabling managers to quickly respond to equipment anomalies or process deviations to avoid production interruptions.

[0018] 2. Based on the collected real-time data (such as equipment utilization, order progress, and mold change time), the system can automatically disassemble the production plan, dynamically adjust the production sequence based on inventory and stamping equipment status, and optimize production batches by analyzing mold change time and stamping cycle data to reduce downtime and wait times, significantly improving the overall efficiency of the equipment.

[0019] 3. Automatically record the dimensions and surface defects of each batch of stamped parts. When quality issues arise, they can be quickly traced back to the specific equipment, operator, and raw material batch, achieving full supply chain traceability.

[0020] 4. Automated data collection reduces manual entry errors and significantly improves production rhythm accuracy.

[0021] The scheduling calculation module integrates the semi-finished product inventory, finished product order demand, finished product current inventory and real-time stamping equipment status data, uses the dynamic priority algorithm to perform optimization calculations, generates scheduling instructions that adapt to the production scenario, and sends them to the execution control module; its working steps are as follows:

[0022] S201: Receive real-time data stream from the data acquisition module and perform data verification;

[0023] S202: Converting heterogeneous data of stamping equipment into a unified dimension;

[0024] S203: When an order is received, the scheduling mechanism is started and a scheduling instruction is generated;

[0025] S204: Send the scheduling instruction to the execution control module;

[0026] Compared with the existing technology, this scheduling calculation module has the following advantages:

[0027] 1. Based on the production plan issued by the data acquisition module, the system automatically breaks down the demand plan into an executable stamping production sequence, taking into account constraints such as equipment availability, minimum production batch size, and workstation equipment capacity, taking into account the equipment capacity, etc., and uses algorithms to perform detailed scheduling of process stations and generate process route guidance. The scheduling calculation module can also dynamically adjust the production sequence to address emergencies such as urgent orders or equipment failures, thereby reducing idle stamping equipment.

[0028] 2. By connecting to a data acquisition module, key parameters such as the press line's operating status, die change time, and stop signals are monitored in real time. When a press machine is detected to be down or a die change timeout occurs, the production plan is immediately revised, automatically extending the buffer time for subsequent processes to avoid delays. Furthermore, by combining semi-finished product inventory, finished product order demand, and current finished product inventory, the raw material input rhythm is dynamically adjusted to prevent material shortages or backlogs.

[0029] The execution control module parses the parameter package generated by the scheduling calculation module, converts the order and stamping equipment start and stop sequence instructions into recognizable binary control codes, and realizes the conversion and execution of scheduling instructions to stamping equipment actions; its working steps are as follows:

[0030] S301: After receiving the instruction output by the scheduling calculation module, start instruction compliance verification;

[0031] S302: Check the availability of stamping equipment and the completeness of matching materials. If there are abnormal instructions, dynamic rescheduling requests will be automatically triggered.

[0032] S303: Encapsulate instructions according to the system communication protocol characteristics: Use a time window synchronization mechanism to ensure multi-device collaborative operation;

[0033] S304: Establish a monitoring matrix: collect current and vibration spectrum to predict faults; calculate overall equipment efficiency;

[0034] Compared with the existing technology, the execution control module has the following advantages:

[0035] 1. By receiving the production plan issued by the scheduling calculation module, combined with parameters such as inventory level and stamping equipment status, it automatically disassembles and generates workshop-level task instructions based on workshop needs, and automatically allocates production batches for the stamping line to ensure seamless connection between production preparation and execution.

[0036] 2. Reduce waste and downtime losses caused by human error.

[0037] The data storage module stores historical scheduling data. Its working steps are as follows:

[0038] S401: Receive real-time streaming data from the data acquisition module through the industrial protocol conversion gateway and complete data packet verification at the edge node;

[0039] S402: Add a timestamp to the instruction sequence generated by the scheduling calculation module;

[0040] S403: aligning the timing of the instruction execution status data fed back by the execution control module with the original instruction;

[0041] S404: Establish a cross-library association index to quickly retrieve the mapping relationship between semi-finished product inventory and finished product order requirements;

[0042] S405: Respond to the data call request of the scheduling calculation module.

[0043] Compared with the existing technology, this data storage module has the following advantages:

[0044] 1. It can collect and integrate stamping equipment operating parameters (such as pressure, speed, and displacement), process parameters (such as mold switching status and stamping cycle), quality data, and energy consumption in real time, record material inventory, consumption, and flow status, and provide dynamic constraints for production scheduling. In addition, key events such as equipment failure alarms and downtime during the production process are also stored, forming a complete production history record, providing a basis for subsequent analysis.

[0045] 2. This not only serves real-time production scheduling but also provides data support for process optimization and predictive maintenance. By accumulating stamping equipment operating data, maintenance work orders can be triggered in advance. By linking production batches, mold parameters, and test results, quality issues can be quickly identified.

[0046] 3. Seamlessly connect with the execution control module through standardized interfaces, so that the production plan can be dynamically adjusted based on real-time inventory, stamping equipment status and other data to optimize the production scheduling logic.

[0047] Further defined, the dynamic priority algorithm satisfies the formula: P = α·Q t +β·D d , where P is the dynamic priority, Q t is the real-time inventory quantity (pieces), D d is the remaining time of the order (hours), α and β are weight coefficients.

[0048] Further defined, the formula for calculating the matching between the current inventory of finished products and the demand for finished product orders in step S104 is: where Q s is the current inventory of finished products, is the demand for the i-th order. When ΔQ < safety stock, a replenishment signal is triggered. This allows for timely replenishment of inventory to prevent stockouts.

[0049] It is further defined that the data storage module adopts a time series database, and the storage period satisfies: T s =5log2(N r ), where T s is the data storage period (days), N r The number of data records for a single day.

[0050] It is further defined that the execution control module includes an abnormal fuse mechanism, when the stamping equipment fails for a time T f satisfy: Automatically switch to standby stamping equipment, among which W t is the total task volume of the day (pieces), R nis the normal production capacity of the stamping equipment (pieces / hour), and η is the system redundancy coefficient. The system redundancy coefficient η is dynamically adjusted based on historical failure data, and the adjustment formula is: where η n is the new system redundancy coefficient, η o is the historical system redundancy coefficient, T d T is the total downtime of the equipment last month (hours), t is the total running time (hours).

[0051] It is further defined that the industrial bus protocol is one of Profinet, EtherCAT or Modbus TCP.

[0052] Stamping parts manufacturers often encounter a mismatch between raw material procurement volumes and production needs during production. For example, if the procurement volume is too large, raw materials can pile up, increasing warehouse storage pressure and costs. If the procurement volume is too small, urgent orders with large demands can lead to raw material shortages and production halts. Furthermore, if orders are received from important customers, production plans cannot be adjusted immediately, causing production disruptions.

[0053] Compared with the prior art, the present invention has the following advantages:

[0054] 1. Real-time data collection of stamping equipment operating status, production progress, and other data is collected. It analyzes abnormal situations (such as equipment failures and production deviations) and automatically adjusts production plans or equipment parameters. For example, if a stamping line stops abnormally, the system automatically records the downtime and triggers the manual intervention interface to ensure a quick response to the problem.

[0055] 2. The system intelligently allocates resources such as manpower, molds, and energy based on production plans, improving equipment utilization. For example, after receiving a production plan, the system automatically generates a detailed production plan for the stamping shop based on inventory and mold status, and enables precise material scheduling to avoid resource conflicts.

[0056] 3. Monitor the status of stamping equipment, collect data such as stamping equipment stroke frequency and mold temperature in real time, predict stamping equipment failures and perform maintenance in advance, and dynamically optimize production scheduling.

[0057] 4. Real-time monitoring of each stamping part's process parameters ensures dimensional accuracy and consistency, enabling precise defect tracing and improving quality traceability efficiency. When quality issues arise, they can be quickly traced back to specific equipment, operators, and raw material batches, enabling full-chain traceability. When material shortages or equipment overload are detected, the system automatically triggers an alarm and sends it to the manager's mobile device, minimizing downtime. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] The present invention can be further illustrated by the non-limiting examples given in the accompanying drawings;

[0059] Figure 1 It is a connection diagram of the present invention;

[0060] Figure 2 This is a workflow diagram of the data acquisition module in the present invention;

[0061] Figure 3 This is a workflow diagram of the scheduling calculation module in the present invention;

[0062] Figure 4 This is a workflow diagram for executing the control module in the present invention;

[0063] Figure 5 This is a workflow diagram of the data storage module in the present invention;

[0064] The main component symbols are described as follows:

[0065] Data acquisition module 1, scheduling calculation module 2, execution control module 3, data storage module 4, stamping equipment 5. DETAILED DESCRIPTION

[0066] In order to enable those skilled in the art to better understand the present invention, the technical solution of the present invention is further described below with reference to the accompanying drawings and embodiments.

[0067] like Figure 1 As shown, the scheduling system for semi-finished products and finished products based on intelligent manufacturing management of the present invention includes a data acquisition module 1, a scheduling calculation module 2, an execution control module 3, and a data storage module 4; each module is connected via an industrial bus protocol, which is one of Profinet, EtherCAT, or Modbus TCP. The output of the data acquisition module 1 is connected to the input of the scheduling calculation module 2 and the input of the data storage module 4. The input of the execution control module 3 receives the output signal of the scheduling calculation module 2. The output of the execution control module 3 is connected to the stamping equipment 5. The output of the execution control module 3 is connected to the input of the data storage module 4. The data storage module 4 and the scheduling calculation module 2 communicate bidirectionally.

[0068] Data acquisition module 1 includes a semi-finished product inventory monitoring unit, an equipment status monitoring unit, a finished product order data interface, and an industrial intelligent gateway. The semi-finished product inventory monitoring unit uses an RFID tag reader to track shelf storage status in real time; a laser scanner is used for inventory statistics. The equipment status monitoring unit uses vibration sensors, temperature sensors, and other sensors to collect the operating parameters of the stamping equipment. The finished product order data interface integrates the API interface of the company's ERP system to obtain order demand information (including specifications, quantity, and delivery time) in real time. The industrial intelligent gateway supports dual Ethernet and RS232 / 485 interfaces and is compatible with industrial protocols such as ModbusTCP to achieve protocol conversion for each device.

[0069] like Figure 2 As shown, the data acquisition module 1 is used to obtain the semi-finished product inventory, finished product order demand, current finished product inventory and the status of the stamping equipment in real time, and realize the collection of semi-finished product inventory, finished product order demand fluctuations and stamping equipment operating parameters. Its working steps are as follows:

[0070] S101: Real-time collection of production parameters of stamping equipment and process parameters of finished products;

[0071] S102: The RFID tag reader scans the shelf tag in real time and updates the semi-finished product inventory data to the local database;

[0072] S103: Pull the finished product order from the ERP system. The data fields include order ID, product model, demand quantity, and latest delivery time.

[0073] S104: Matching calculation of current inventory of finished products with demand for finished product orders; remaining inventory of finished products Among them, Q s is the current inventory of finished products, is the demand quantity of the i-th order;

[0074] S105: Determine whether the remaining inventory of finished products meets the safety stock requirement;

[0075] The remaining inventory of finished products, ΔQ, is the inventory of finished products after the i-th order is delivered. When the remaining inventory of finished products, ΔQ, is less than the safety stock, a replenishment signal is triggered. When the remaining inventory of finished products, ΔQ, is greater than or equal to the safety stock, no replenishment is required, and the data is directly sent to the data storage module. The safety stock is determined based on the company's actual production situation. The calculation process is as follows:

[0076] Statistical historical data is used to obtain the average monthly consumption (μ = 8,000 pieces), the maximum monthly consumption (Max = 11,000 pieces), and the procurement lead time (L = 15 days) of a certain specification of stamped sheet metal parts in the past 12 months.

[0077]

[0078] S106: The processed data is transmitted to the scheduling calculation module. The message format adopts the JSON standardized structure and includes a timestamp, stamping equipment ID, and data value fields.

[0079] like Figure 3 As shown, the scheduling calculation module 2 integrates the semi-finished product inventory, finished product order demand, finished product current inventory and real-time stamping equipment status data, uses the dynamic priority algorithm to perform optimization calculations, generates scheduling instructions that adapt to the production scenario, and sends them to the execution control module 3; its working steps are as follows:

[0080] S201: Receive the real-time data stream from the data acquisition module, use the CRC32 check mechanism to filter abnormal data, and perform data verification;

[0081] S202: Converting heterogeneous data of stamping equipment into a unified dimension;

[0082] S203: When an order is received, the scheduling mechanism is started and a scheduling instruction is generated;

[0083] S204: Send the scheduling instruction to the execution control module.

[0084] The above dynamic priority algorithm satisfies the formula: P = α·Q t +β·D d , where P is the dynamic priority, Q t is the real-time inventory quantity (pieces), D d is the remaining time of the order (hours), α and β are weight coefficients.

[0085] The weight coefficients α and β can be determined according to actual conditions, for example:

[0086] α reflects the pressure of order delivery and is positively correlated with customer level and contract penalty rate;

[0087] β represents the equipment operating efficiency, which needs to take into account production constraints such as mold replacement costs and downtime losses;

[0088] The importance of each of the sub-indicators of α and β is scored (1-9 scale method), and a judgment matrix is ​​constructed for the α factor:

[0089] index Delivery time compression rate Customer Level Order amount Delivery time compression rate 1 3 5 Customer Level 1 / 3 1 2 Order amount 1 / 5 1 / 2 1

[0090] Table 1

[0091] Calculated by the eigenvector method: The α weight W1 = 0.62 and the β weight W2 = 0.38 are obtained by the square root method. The production data of the past 12 months are extracted to calculate the dispersion of the indicators:

[0092] The coefficient of variation of order urgency CVα = σ (order delivery deviation rate) / μ (delivery cycle) = 0.45;

[0093] Coefficient of variation of equipment utilization CVβ = σ(equipment OEE) / μ(OEE) = 0.28;

[0094] Objective weight calculation: W1' = CVα / (CVα+CVβ) = 0.62; W2' = CVβ / (CVα+CVβ) = 0.38;

[0095] Use multiplication synthesis method to eliminate subjective and objective deviations:

[0096]

[0097] like Figure 4 As shown, the execution control module 3 is configured with a programmable logic controller group and a distributed I / O module, interacts with the system through the industrial Ethernet protocol, parses the parameter package generated by the scheduling calculation module, converts the order and stamping equipment start and stop timing instructions into recognizable binary control codes, and realizes the conversion and execution of scheduling instructions to stamping equipment actions; its working steps are as follows:

[0098] S301: After receiving the instruction output by the scheduling calculation module, start instruction compliance verification;

[0099] S302: Check the availability of stamping equipment and the completeness of matching materials. If there are abnormal instructions, dynamic rescheduling requests will be automatically triggered.

[0100] S303: Encapsulate instructions according to the system communication protocol characteristics: Use a time window synchronization mechanism to ensure multi-device collaborative operation;

[0101] S304: Establish a monitoring matrix: collect current and vibration spectrum to achieve fault prediction; calculate overall equipment efficiency.

[0102] The execution control module 3 includes an abnormal fuse mechanism. When the device fails for a time T f satisfy: Automatically switch to backup equipment when W t is the total task volume of the day (pieces), R n is the normal production capacity of the equipment (pieces / hour), and η is the system redundancy coefficient.

[0103] The system redundancy coefficient η is dynamically adjusted based on historical fault data, and the adjustment formula is: where η n is the new system redundancy coefficient, η o is the historical system redundancy coefficient, T d T is the total downtime of the equipment last month (hours), t is the total running time (hours).

[0104] The data storage module 4 uses a time series database, and the storage period meets the following requirements:

[0105] T s =γlog2(N r ), where T s is the data storage period (days), N r is the number of data records per day, and γ is the standard coefficient.

[0106] For the production of stamping parts, assuming

[0107] n r =f 采样 ·t 运行 =10Hz×86400s=8.64×10 5 Articles / day

[0108] According to ISO9001 quality system clause 7.5.3, process data must be retained for the product life cycle + 3 years. If the stamping part life cycle is 10 years, then:

[0109]

[0110] According to the VDI 2862 metal processing equipment standard, a 30% redundancy is required for the storage cycle: γ = 3.4 × 1.3 = 4.4, rounded up to γ ​​= 5.

[0111] like Figure 5 As shown, the data storage module 4 stores historical scheduling data. The data storage module 4 stores the following contents: semi-finished product inventory three-dimensional coordinate mapping table, finished product order demand matrix, and equipment performance history records. The working steps of the data storage module 4 are:

[0112] S401: Receive real-time streaming data from the data acquisition module through the industrial protocol conversion gateway and complete data packet verification at the edge node;

[0113] S402: Add a timestamp to the instruction sequence generated by the scheduling calculation module;

[0114] S403: aligning the timing of the instruction execution status data fed back by the execution control module with the original instruction;

[0115] S404: Establish a cross-library association index to quickly retrieve the mapping relationship between semi-finished product inventory and finished product order requirements;

[0116] S405: Respond to the data call request of the scheduling calculation module.

[0117] Through comparative tests, the performance improvement of this system compared with the original technology is mainly reflected in the following table:

[0118]

[0119]

[0120] The comparison of key indicators in Table 2 is as follows:

[0121]

[0122]

[0123] Table 3

[0124] The above is a detailed introduction to the scheduling system between semi-finished products and finished products based on intelligent manufacturing management provided by the present invention. The description of the specific embodiments is only intended to help understand the method and core concept of the present invention. It should be noted that for those skilled in the art, without departing from the principles of the present invention, several improvements and modifications can be made to the present invention, and such improvements and modifications also fall within the scope of protection of the claims of the present invention.

Claims

1. A scheduling system between semi-finished products and finished products based on intelligent manufacturing management, characterized by: It includes a data acquisition module, a scheduling calculation module, an execution control module, and a data storage module; each module is connected through an industrial bus protocol, the output end of the data acquisition module is connected to the input end of the scheduling calculation module and the input end of the data storage module, the input end of the execution control module receives the output signal of the scheduling calculation module, the output end of the execution control module is connected to the stamping equipment, the output end of the execution control module is connected to the input end of the data storage module, and the data storage module and the scheduling calculation module communicate in a two-way manner; in The data acquisition module is used to obtain the semi-finished product inventory, finished product order demand, current finished product inventory and stamping equipment status in real time, and realize the collection of semi-finished product inventory, finished product order demand fluctuations and stamping equipment operating parameters. Its working steps are as follows: S101: Real-time collection of production parameters of stamping equipment and process parameters of finished products; S102: Scan shelf labels in real time and update semi-finished product inventory data to the local database; S103: Pull the finished product order from the ERP system. The data fields include order ID, product model, demand quantity, and latest delivery time. S104: Calculate the matching between the current inventory of finished products and the demand for finished product orders; S105: Determine whether the remaining inventory of finished products meets the safety stock requirement; S106: The processed data is transmitted to the scheduling calculation module; The scheduling calculation module integrates semi-finished product inventory, finished product order demand, current finished product inventory, and real-time stamping equipment status data, uses a dynamic priority algorithm to optimize calculations, generates scheduling instructions that adapt to production scenarios, and sends them to the execution control module; The working steps are: S201: Receive real-time data stream from the data acquisition module and perform data verification; S202: Converting heterogeneous data of stamping equipment into a unified dimension; S203: When an order is received, the scheduling mechanism is started and a scheduling instruction is generated; S204: Send the scheduling instruction to the execution control module; The execution control module parses the parameter package generated by the scheduling calculation module, converts the order and stamping equipment start and stop sequence instructions into recognizable binary control codes, and realizes the conversion and execution of scheduling instructions to stamping equipment actions; The working steps are: S301: After receiving the instruction output by the scheduling calculation module, start instruction compliance verification; S302: Verify the availability of stamping equipment and the completeness of matching materials; S303: Encapsulate instructions according to the system communication protocol characteristics: Use a time window synchronization mechanism to ensure multi-device collaborative operation; S304: Establish a monitoring matrix: collect current and vibration spectrum to predict faults; calculate overall equipment efficiency; Data storage module, stores historical scheduling data.

2. The scheduling system between semi-finished products and finished products based on intelligent manufacturing management according to claim 1 is characterized by: The data acquisition module includes a semi-finished product inventory monitoring unit, an equipment status monitoring unit, a finished product order data interface, and an industrial intelligent gateway; the equipment status monitoring unit is used to collect the operating parameters of the stamping equipment, the finished product order data interface obtains order demand information in real time, and the industrial intelligent gateway realizes protocol conversion of each device.

3. The scheduling system between semi-finished products and finished products based on intelligent manufacturing management according to claim 2 is characterized by: The semi-finished product inventory monitoring unit includes an RFID tag reader and a laser scanner. The RFID tag reader is used to track shelf storage status in real time; the laser scanner is used for inventory statistics.

4. The scheduling system between semi-finished products and finished products based on intelligent manufacturing management according to claim 1 is characterized by: The dynamic priority algorithm satisfies the formula: P=α·Q t +β·D d , Where P is the dynamic priority, Q t is the real-time inventory quantity (pieces), D d is the remaining time of the order (hours), α and β are weight coefficients.

5. The scheduling system between semi-finished products and finished products based on intelligent manufacturing management according to claim 1 is characterized by: The formula for calculating the matching between the current inventory of finished products and the demand for finished product orders in step S104 is: where Q s is the current inventory of finished products, is the demand quantity of the i-th order, and the replenishment signal is triggered when ΔQ < safety stock.

6. The scheduling system between semi-finished products and finished products based on intelligent manufacturing management according to claim 1 is characterized by: The working steps of the data storage module are: S401: Receive real-time streaming data from the data acquisition module through the industrial protocol conversion gateway and complete data packet verification at the edge node; S402: Add a timestamp to the instruction sequence generated by the scheduling calculation module; S403: aligning the timing of the instruction execution status data fed back by the execution control module with the original instruction; S404: Establish a cross-library association index to quickly retrieve the mapping relationship between semi-finished product inventory and finished product order requirements; S405: Respond to the data call request of the scheduling calculation module.

7. The scheduling system between semi-finished products and finished products based on intelligent manufacturing management according to claim 1 is characterized by: The data storage module uses a time series database, and the storage period meets the following requirements: T s =5log2(N r ), Where T s is the data storage period (days), N r The number of data records for a single day.

8. The scheduling system between semi-finished products and finished products based on intelligent manufacturing management according to claim 1 is characterized by: The execution control module includes an abnormal fuse mechanism. When the stamping equipment fails for a time T f satisfy: Automatically switch to standby stamping equipment, where W t is the total task volume of the day (pieces), R n is the normal production capacity of the stamping equipment (pieces / hour), and η is the system redundancy coefficient.

9. The scheduling system between semi-finished products and finished products based on intelligent manufacturing management according to claim 8, characterized in that: The system redundancy coefficient η is dynamically adjusted based on historical fault data, and the adjustment formula is: Among them, η n is the new system redundancy coefficient, η o is the historical system redundancy coefficient, T d T is the total downtime of equipment last month (hours), t is the total running time (hours).

10. The scheduling system between semi-finished products and finished products based on intelligent manufacturing management according to claim 1, characterized in that: The industrial bus protocol is one of Profinet, EtherCAT or ModbusTCP.

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