Injection workshop production resource data optimization management method and system

Abstract

This invention relates to the technical field of production resource data optimization management, and provides a method and system for optimizing production resource data management in injection molding workshops. The method includes: predicting material demand timeline information for candidate production scheduling schemes within a preset time period; generating material supply capacity information based on current material inventory, material in transit, and material replenishment cycle information; comparing the material demand timeline information with the material supply capacity information to assess material shortage risk; generating a corresponding material shortage risk score for each candidate production scheduling scheme; and determining the optimal production scheduling scheme based on the material shortage risk score for each candidate production scheduling scheme. This invention improves data accuracy and comprehensiveness.

Description

Technical Field

[0001] This invention relates to the technical field of production resource data optimization management, specifically to a method and system for optimizing production resource data management in an injection molding workshop. Background Technology

[0002] In modern manufacturing environments, especially in production workshops like injection molding plants, the pursuit of high efficiency and on-time delivery is a common goal. Many production plants have introduced advanced management systems in hopes of better allocating resources, ensuring full utilization of equipment, timely material supply, and smooth production planning. These systems typically collect data in real time from various equipment in the workshop, such as machines, material handling lines, and warehouses, and then generate a production plan that appears to be the most reasonable.

[0003] However, the actual operating environment of the workshop is far more complex than the ideal model envisioned by the system. For example, when the workshop suddenly receives an urgent additional order from an important customer, requiring the production of a small batch of precision parts using special engineering plastics within a very short time, this order has a very high priority and must be immediately inserted into the existing production schedule. Upon receiving this instruction, the optimization management system immediately activates its dynamic scheduling program. The system quickly checks all running equipment and finds that the regular production task being performed by injection molding machine A is about to be completed, and it is expected to be freed up in half an hour. At the same time, the system checks the inventory information, showing that there is enough special engineering plastic in the warehouse to produce this urgent order. Based on these two pieces of information, the system makes what seems to be the most reasonable decision: postpone the regular production task B that injection molding machine A is about to perform, and then insert this urgent order into the time slot when machine A will be freed up. After the instruction is issued, the support staff next to machine A begin to prepare for the subsequent mold change and material change work.

[0004] Half an hour later, Machine A completed its previous task on time. Workshop technicians quickly moved in and, following instructions, replaced the precision mold used for the urgent order. The entire mold change process went smoothly. Next, the material handler, based on the requisition form generated by the system, went to the designated location in the warehouse to collect the special engineering plastic. However, an unexpected problem arose. The material handler discovered that the actual remaining quantity of material in the location where the system indicated sufficient inventory was far lower than the recorded value, insufficient to complete this urgent order. Further investigation revealed that the technical department had temporarily used some of this material for new product trial molding the previous week, but due to a procedural oversight, the material requisition information had not been entered into the management system in a timely manner, resulting in a significant discrepancy between the system's inventory data and the actual inventory.

[0005] This "ghost inventory" in the data directly led to the disruption of the production process. Urgent orders could not be started due to a lack of raw materials, and injection molding machine A, which had just completed mold changes and was ready, was forced to stop and wait. This machine, which should have been one of the most utilized pieces of equipment in the workshop, was now an idle resource. Worse still, this sudden situation triggered a chain reaction. Regular production task B, which had been postponed by the system, could not start production on machine A on time, disrupting the entire subsequent production chain. The workshop scheduler had to intervene manually in an emergency, contacting the purchasing department to find out how long it would take to replenish this special material, and reassessing the entire workshop's production plan to find alternative solutions. This would undoubtedly consume a lot of time and could potentially delay other orders. The initial "optimal" decision, intended to improve efficiency, was based on inaccurate data and instead caused more chaos and waste of resources than if the system had not been optimized.

[0006] To address the aforementioned issues, existing technologies urgently need improvement. Summary of the Invention

[0007] This application discloses a method and system for optimizing production resource data management in injection molding workshops, aiming to solve the problem that in existing production management systems, inaccurate key data in complex production sites leads to a disconnect between production plans and actual conditions, thereby affecting production efficiency and on-time delivery.

[0008] The technical solution of this application is as follows: In a first aspect, this application discloses a method for optimizing and managing production resource data in an injection molding workshop, comprising the following steps: Obtain production resource status information; production resource status information includes at least injection molding machine operating status information, mold availability information, current material inventory information, material in transit information, and material replenishment cycle information; Based on the production resource status information, multiple candidate production scheduling schemes are generated; each candidate production scheduling scheme is used to define the production tasks to be executed by the injection molding machine within a preset time period, the mold information used, and the expected start time and expected end time information; For each candidate production scheduling scheme, based on the production task information, the bill of materials information corresponding to the production task, and the single-item material consumption information and scrap rate information contained in the bill of materials information, the material demand timeline information of the candidate production scheduling scheme within the preset time period is predicted. Based on current material inventory, in-transit material quantity, and material replenishment cycle information, material supply capacity information is generated. Material demand timeline information is compared with material supply capacity information to assess material shortage risk. Corresponding material shortage risk score information is generated for each candidate production scheduling scheme. Material demand timeline information includes demand points and demand quantities sorted by time. Material supply capacity information includes available supply points and available supply quantities sorted by time. Based on the material shortage risk score information corresponding to each candidate production scheduling scheme, the optimal production scheduling scheme information is determined.

[0009] This technical solution can comprehensively consider the various production resource statuses in the injection molding workshop, generate and evaluate multiple candidate production scheduling schemes, and effectively identify potential material supply problems through a material shortage risk assessment mechanism, thereby optimizing production scheduling, improving production efficiency and material utilization, and solving the problem of production plans being out of sync with reality due to inaccurate data.

[0010] Secondly, this application also discloses an injection molding workshop production resource data optimization management system for performing injection molding workshop production resource data optimization management, including: The resource status acquisition module is used to acquire production resource status information. The production resource status information includes at least the injection molding machine operating status information, mold availability information, current material inventory information, material in transit information, and material replenishment cycle information. The candidate scheme generation module is used to generate multiple candidate production scheduling schemes based on production resource status information. Each candidate production scheduling scheme is used to define the production tasks to be executed by the injection molding machine within a preset time period, the mold information used, and the expected start time and expected end time information. The demand time forecasting module is used to predict the material demand timeline information of each candidate production scheduling scheme within a preset time period, based on the production task information, the bill of materials information corresponding to the production task, and the single-item material consumption information and scrap rate information contained in the bill of materials information. The shortage risk scoring module generates material supply capacity information based on current material inventory, material in transit, and material replenishment cycle information. It compares material demand timeline information with material supply capacity information to determine material shortage risk and generates corresponding material shortage risk scores for each candidate production scheduling scheme. The material demand timeline information includes demand points and demand quantities sorted by time; the material supply capacity information includes available supply points and available supply quantities sorted by time. The production scheduling optimization module is used to determine the optimal production scheduling scheme based on the material shortage risk score information corresponding to each candidate production scheduling scheme.

[0011] This technical solution provides an integrated system that, through modular design, enables optimized management of production resources in the injection molding workshop. It automates data acquisition, solution generation, risk assessment, and scheduling optimization, thereby improving the efficiency and accuracy of production management.

[0012] Beneficial Effects: The injection molding workshop production resource data optimization management method disclosed in this application provides a solid data foundation for subsequent production scheduling by acquiring comprehensive production resource status information, including injection molding machine operating status, mold availability, material inventory, and replenishment cycle. Based on this, the method can generate multiple candidate production scheduling schemes and, for each scheme, accurately predict the material demand timeline based on information such as production tasks, bill of materials, unit material consumption, and scrap rate. More importantly, this method compares the material demand timeline with material supply capacity information to assess material shortage risk and generates a corresponding material shortage risk score for each candidate scheme. Finally, based on these risk scores, the optimal production scheduling scheme is determined.

[0013] Through the above technical solution, this application effectively solves the problem in the prior art where inaccurate key data leads to a disconnect between production plans and actual conditions. Specifically, this method can: 1. Improve data accuracy and comprehensiveness: By acquiring multi-dimensional and real-time production resource status information, the data foundation for scheduling decisions is ensured to be more accurate and comprehensive, avoiding decision-making biases caused by information lag or incompleteness.

[0014] 2. Optimize material management and risk warning: By predicting material demand timelines and comparing them with material supply capacity, potential material shortage risks can be identified in advance, and risk scores can be quantified, enabling managers to take timely countermeasures and effectively avoid production interruptions and material waste.

[0015] 3. Improve the scientific nature and adaptability of production scheduling: By generating multiple candidate solutions and conducting risk assessments, this method can examine production plans from multiple perspectives and select the solution with the lowest risk in terms of material supply, thereby making production scheduling more scientific, reasonable, and adaptable, and better able to cope with the complexity and uncertainty of the production site.

[0016] 4. Reduce production costs and improve delivery efficiency: By optimizing material supply and production scheduling, downtime due to material shortages was reduced, equipment utilization and production efficiency were improved, thereby reducing production costs and ensuring on-time order delivery.

[0017] In summary, this application significantly improves the optimization level of production resource management in injection molding workshops through refined data management and intelligent decision support, overcomes the shortcomings of existing technologies, and provides strong technical support for achieving efficient and stable production operations. Attached Figure Description

[0018] Figure 1 This is a flowchart of a method for optimizing and managing production resource data in an injection molding workshop, as described in one embodiment of the present invention. Figure 2 This is a flowchart of a method for optimizing and managing production resource data in an injection molding workshop, as described in another embodiment of the present invention. Figure 3 This is a system block diagram of a production resource data optimization and management system for an injection molding workshop according to another embodiment of the present invention; Explanation of reference numerals in the attached figures: 1. Injection Molding Workshop Production Resource Data Optimization Management System; 11. Resource Status Acquisition Module; 12. Candidate Solution Generation Module; 13. Demand Time Forecasting Module; 14. Shortage Risk Scoring Module; 15. Production Scheduling Optimization Module. Detailed Implementation

[0019] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments. The components of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0020] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0021] This application proposes a method for optimizing and managing production resource data in injection molding workshops, combining... Figure 1 As shown, it includes: S1, Obtain production resource status information; the production resource status information includes at least the injection molding machine operating status information, mold availability information, current material inventory information, material in transit information, and material replenishment cycle information; S2, based on production resource status information, generates multiple candidate production scheduling schemes; each candidate production scheduling scheme is used to define the production tasks to be executed by the injection molding machine within a preset time period, the mold information used, and the expected start time and expected end time information; S3, for each candidate production scheduling scheme information, based on the production task information, the bill of materials information corresponding to the production task, and the single-piece material consumption information and scrap rate information contained in the bill of materials information, predict the material demand timeline information of the candidate production scheduling scheme information within the preset time period. S4 generates material supply capacity information based on current material inventory, in-transit material quantity, and material replenishment cycle information. It then compares the material demand timeline information with the material supply capacity information to assess material shortage risk and generates a corresponding material shortage risk score for each candidate production scheduling scheme. The material demand timeline information includes demand points and demand quantities sorted by time; the material supply capacity information includes available supply points and available supply quantities sorted by time. S5. Based on the material shortage risk score information corresponding to each candidate production scheduling scheme, determine the optimal production scheduling scheme information.

[0022] To better understand the technical solution proposed in this application, we will first explain some key terms and their implementation environment. "Production resource status information" refers to the data set of various resources directly related to production activities during the injection molding process, including their current operating status, availability, and quantity. This information reflects the overall resource allocation of the production system at a given moment and is a crucial basis for production scheduling optimization and resource allocation decisions. Production resource status information includes at least injection molding machine operating status information, mold availability information, current material inventory information, material in transit information, and material replenishment cycle information.

[0023] Specifically, "injection molding machine operating status information" refers to the operating status data generated by the injection molding equipment during production, such as equipment on / off status, real-time load rate, operating cycle time, fault alarm records, and maintenance plans. This information can come from real-time data acquisition from the equipment control system or from a historical production record database. "Mold availability information" refers to the current usability status of mold resources in the production system. This includes the mold's current location, whether it is in use, its maintenance or repair status, remaining usable life, and the type and quantity of molds available for production. Continuous tracking of mold status can prevent mold resource conflicts or duplicate allocations during scheduling.

[0024] "Current Material Inventory Information" refers to the real-time inventory quantity of various production materials in the warehouse, including raw materials, semi-finished products, and auxiliary materials. This data typically comes from a warehouse management system or inventory database. "Materials in Transit Information" refers to the quantity of materials for which purchase orders have been completed but have not yet entered the warehouse, along with their corresponding estimated arrival times. This information reflects the material resources that can be added to the inventory system in the future. "Material Replenishment Cycle Information" refers to the time required from issuing a replenishment request to the actual receipt of materials into the warehouse. This cycle can be obtained through statistical analysis of historical purchase data or determined by the delivery cycle provided by the supplier.

[0025] "Candidate production scheduling information" refers to a set of multiple selectable production plans generated by the system based on the current production resource status and order demand. Each candidate production scheduling plan details the production tasks that the injection molding machine will execute within a preset time period, the mold information used, and the expected start and end times of the tasks, thus forming a complete production task time arrangement structure.

[0026] "Material Requirements Timeline Information" refers to a time-series data structure formed by predicting the demand for various materials at different points in time under a candidate production scheduling scheme. This timeline includes not only the required quantity of each material, but also the specific time points when the demand occurs and the cumulative trend of demand changes. By constructing material requirements timeline information, the material consumption of different production tasks can be intuitively reflected over time, thus providing basic data for subsequent material supply capacity assessment.

[0027] "Material supply capacity information" refers to the information on the quantity of materials that the system can provide at various future time points based on current inventory, materials in transit, and replenishment cycles. This information is also represented in time series form, including the available inventory quantity and the quantity of materials that can be replenished at different future time points, thus forming a predictive curve of material supply capacity changes over time. By comparing the material demand timeline information with the material supply capacity information, potential material supply and demand imbalances can be identified.

[0028] The "Material Shortage Risk Score" is an indicator used to quantify the potential risk level of each candidate production scheduling plan in terms of material supply. This score comprehensively considers factors such as the quantity of the material in short supply, the timing of the shortage, the duration of the shortage, and the criticality of the material in production. For example, the risk score is significantly higher when a critical raw material is in short supply before production begins; a lower risk score is obtained if the shortage is small or occurs later in the day.

[0029] "Optimal Production Scheduling Plan Information" refers to the optimal production plan selected by the system after comprehensively analyzing multiple candidate production scheduling plans. This plan effectively reduces the risk of material shortages while meeting production efficiency and delivery cycle requirements.

[0030] The implementation environment of this application is typically a smart manufacturing execution system (MES) or enterprise resource planning system (ERP) deployed on a server or cloud platform in the injection molding workshop. This system interacts with workshop equipment, warehousing system and procurement system through industrial network to form a complete production data management and decision support platform.

[0031] In the specific implementation process, the first step is to acquire production resource status information. This information includes at least the injection molding machine's operating status, mold availability, current material inventory, material in transit, and material replenishment cycle. For example, the injection molding machine's operating current, pressure, and temperature can be collected in real time through an equipment data acquisition system, generating corresponding injection molding machine operating status information. Simultaneously, molds are tracked and managed using RFID tags or barcode systems to obtain mold availability information. Current material inventory and material in transit information can be synchronously obtained through data interfaces with the warehouse management system and the procurement management system.

[0032] Subsequently, based on the acquired production resource status information, multiple candidate production scheduling schemes are generated. The system can generate multiple scheduling schemes that satisfy equipment capacity constraints, mold matching constraints, and order priority rules through heuristic algorithms or combinatorial optimization algorithms. For example, multiple scheduling results can be generated under different task sorting methods using genetic algorithms or simulated annealing algorithms, and each result contains task arrangement information for each injection molding machine within a future preset time period.

[0033] For each candidate production scheduling plan, based on production task information, corresponding bill of materials (BOM) information, unit material consumption information, and scrap rate information, the system predicts the material demand timeline information for that plan within a preset time period. Specifically, the system first queries the product's BOM information, then adjusts the material demand calculation based on the production task quantity and scrap rate to obtain the demand quantity of each material at different time points. By summarizing all task demands in chronological order, a complete material demand timeline information is formed.

[0034] Next, material supply capacity information is generated based on current inventory levels, in-transit material quantities, and replenishment cycle information. The system predicts future inventory changes based on current inventory levels and estimated arrival times of in-transit materials, and generates a time-ordered sequence of available supply quantities.

[0035] Subsequently, the material demand timeline information is compared with the material supply capacity information at each time point to identify potential material shortages. When demand exceeds supply capacity at a certain point in time, a shortage risk is identified. The system generates material shortage risk scoring information based on factors such as the shortage quantity, the time of occurrence, and the criticality of the material.

[0036] Finally, the optimal production scheduling scheme is determined based on the material shortage risk score information corresponding to each candidate production scheduling scheme. For example, a risk threshold can be set to prioritize the screening of schemes with lower risks. On this basis, a comprehensive evaluation is carried out in combination with production efficiency, equipment utilization rate and delivery time targets to finally select the optimal production scheduling scheme.

[0037] Through the above steps of data collection, scheduling generation, demand forecasting, supply capacity assessment and risk scoring, this application can identify potential material shortages in advance during the production planning stage, thereby avoiding downtime or production delays caused by insufficient materials during the production process, and improving the efficiency of production resource utilization and the stability of production plans in the injection molding workshop.

[0038] Optional, combined Figure 2 As shown, the steps for determining material shortage risk and generating a corresponding material shortage risk score for each candidate production scheduling scheme include: A1, obtain information on fine-tuning the process parameters of the injection molding machine; A2, retrieve the physical property data of the material batch used in the current production task; A3, based on material physical property data and process parameter fine-tuning information, infers the impact of process parameter fine-tuning on single-piece material consumption information and scrap rate information, and generates consumption and scrap correction parameter information; A4. Based on the consumption and scrap correction parameter information, correct the material demand timeline information to obtain the corrected material demand timeline information; A5 compares the revised material demand timeline information with the material supply capacity information to assess the material shortage risk and obtain the assessment result; based on the assessment result, it generates corresponding material shortage risk score information for each candidate production scheduling scheme.

[0039] Specifically, acquiring process parameter fine-tuning information for injection molding machines refers to the data records generated during the production process when making minor adjustments to key molding parameters. This process parameter fine-tuning information includes at least the changes in injection pressure, injection speed, holding time, mold temperature, and cooling time. This data can be acquired in real-time through the injection molding machine control system's data interface, or manually entered and recorded by the operator on the human-machine interface. Continuously recording this fine-tuning data reflects the dynamic changes in the actual process status during production. These changes often directly affect the actual material consumption and the probability of defective products, and therefore need to be incorporated into the material demand forecasting calculation process.

[0040] Obtaining material physical property data for the batches of materials used in the current production task refers to collecting key physical or physicochemical property information for specific batches of materials involved in the current production task, such as melt flow index, density, moisture content, shrinkage rate, flowability parameters, and thermal stability. This material physical property data can come from batch inspection reports provided by material suppliers, or it can be obtained through sampling inspections using material testing equipment installed in the workshop, such as melt flow indexers, moisture analyzers, or density measuring devices. Since different batches of raw materials may have certain performance differences during the production process, these differences directly affect the flow characteristics, filling efficiency, and shrinkage behavior during injection molding, thereby further affecting the actual material consumption and scrap rate of a single product. Therefore, these differences need to be comprehensively considered during production scheduling and forecasting.

[0041] In practical applications, the system can infer the impact of changes in process parameters on the consumption of individual materials and the scrap rate based on acquired material physical property data and process parameter fine-tuning information, and generate corresponding consumption and scrap correction parameters. This inference process can utilize empirical models, statistical regression models, or pre-set expert rule bases built from historical production data for analysis, thereby establishing the correlation between process parameter fine-tuning, changes in material physical properties, and actual material consumption and scrap rate. The consumption and scrap correction parameters can be expressed as correction coefficients, correction ratios, or correction increments, used to dynamically adjust the individual material consumption and scrap rate used in the initial prediction stage, thus making the prediction results closer to actual production conditions.

[0042] Furthermore, the system corrects the original material demand timeline information based on the generated consumption and scrap correction parameters, resulting in a corrected material demand timeline. This correction process involves recalculating the material demand quantity for each demand time point based on the original material demand timeline. For example, if the correction parameters indicate that the actual consumption of a single unit of material is lower than the initial forecast, the material demand quantity for each time point is reduced accordingly; if the scrap rate increases, additional compensation needs to be added to the demand calculation to ensure the smooth completion of production tasks. In this way, the system can construct a material demand change curve that more closely reflects the actual production process.

[0043] Subsequently, the revised material demand timeline information is compared with the material supply capacity information to assess the risk of material shortages. Based on the assessment results, a corresponding material shortage risk score is generated for each candidate production scheduling scheme. Since the revised demand timeline has comprehensively considered the impact of changes in process parameters and batch differences in materials, using this timeline for supply and demand comparison can obtain more accurate risk assessment results, thereby improving the reliability of production scheduling decisions.

[0044] In some preferred embodiments, the process can be illustrated using a specific production scenario. For example, an injection molding workshop is performing a production task for a plastic casing product. The initial prediction phase sets the material consumption per unit at 10 grams and the scrap rate at 2%. During actual production, due to changes in the workshop's ambient temperature, the operator fine-tunes the injection pressure of the injection molding machine, increasing it by 0.5 MPa. Simultaneously, a newly arrived batch of raw materials is found to have a melt flow index approximately 5% higher than the standard value. The system first acquires the aforementioned process parameter fine-tuning information and the corresponding batch material's physical properties data. Subsequently, based on a correlation model established from historical production data, the system comprehensively analyzes these changes and infers that when the injection pressure increases by 0.5 MPa and the melt flow index increases by 5%, the actual material consumption per unit will decrease by approximately 0.2 grams, while the scrap rate will increase slightly by 0.1%. These inference results are recorded as consumption and scrap correction parameter information.

[0045] After obtaining the corrected parameters, the system updates the original material demand timeline information. For example, if the original plan was to require 1000 kg of raw materials at a certain production time point, this requirement is revised to approximately 980 kg after considering the reduction in unit consumption and changes in scrap rate. The system then compares the revised material demand timeline information with the material supply capacity information time-by-time to obtain a new material shortage risk assessment result. For example, without the correction calculation, the system might determine that the production scheduling plan has a slight material shortage risk during a certain period; however, after reassessing using the revised demand timeline, the system might determine that the shortage risk has been significantly reduced or even eliminated. In this way, the production scheduling system can be provided with more accurate and reliable material shortage risk scoring information, thereby improving the scientific nature of scheduling decisions and the efficiency of production resource utilization.

[0046] Optionally, the steps for assessing material shortage risk and generating a corresponding material shortage risk score for each candidate production scheduling scheme include: The material demand timeline information of each candidate production scheduling plan is compared with the material supply capacity information to identify whether there is a risk of material shortage within the preset time period. When a material shortage risk is identified, obtain the criticality level information of the shortage material; Obtain the time interval between the time of the shortage occurrence and the current time, and obtain the shortage quantity information; Based on the criticality level information, time interval information, and shortage quantity information of the scarce materials, a comprehensive risk score is calculated; Based on the comprehensive risk score, material shortage risk score information is generated for corresponding candidate production scheduling schemes.

[0047] Specifically, after identifying a material shortage risk, the system further obtains information on the criticality level of the shortage materials. This criticality level information can be understood as the material's importance in the production process or the severity of its impact on production. For example, materials can be classified into different levels such as A, B, and C based on factors such as cost, substitutability, impact on core product functionality, and supply chain stability. Level A materials may represent core, high-value, or difficult-to-substitute materials, whose shortage would lead to severe production disruptions; Level C materials may represent auxiliary, low-value, or easily substitutable materials, whose shortage would have a relatively smaller impact. The purpose is to differentiate the potential impact of shortages of different materials.

[0048] Simultaneously, the system also acquires the time interval information between the time of the shortage occurrence and the current time. This time interval refers to the length of time from the current moment to the expected time of the material shortage, and its purpose is to assess the urgency of the shortage. The shorter the time interval, the more imminent the shortage, and the greater the likelihood of needing emergency measures.

[0049] In addition, the system also acquires information on the quantity of shortages. This shortage quantity information refers to the difference between the demand and supply of materials at a specific point in time, aiming to quantify the severity of the shortage. A larger shortage quantity usually means a wider impact on production planning, potentially requiring more resources to compensate.

[0050] After obtaining the above information, a comprehensive risk score is calculated based on the criticality level, time interval, and quantity of the shortage material. The comprehensive risk score can be understood as a quantitative assessment of the material shortage risk, and its calculation can employ various methods such as weighted summation, table lookup, or machine learning models. For example, different weights can be assigned to the criticality level, time interval, and quantity of the shortage, and then the quantified values ​​of these factors can be weighted and combined to obtain a comprehensive risk score. The purpose is to integrate multiple dimensions of risk factors into a unified numerical value to facilitate subsequent comparison and decision-making.

[0051] Finally, based on the calculated comprehensive risk score, material shortage risk assessment information is generated for the corresponding candidate production scheduling schemes. This material shortage risk assessment information is an indicator used to characterize the potential risk level of each candidate production scheduling scheme in terms of material supply, aiming to provide a more comprehensive and refined risk assessment basis for optimizing production scheduling.

[0052] Optionally, the step of determining the optimal production scheduling scheme based on the material shortage risk score information corresponding to each candidate production scheduling scheme includes: Obtain the order business target parameter information corresponding to each candidate production scheduling scheme; the order business target parameter information includes order delivery date deviation information and order expected profit contribution information, which characterize the time deviation of the planned completion time from the expected delivery time of the order. Obtain the production resource impact assessment information corresponding to each candidate production scheduling plan; the production resource impact assessment information includes mold replacement frequency information and expected overall equipment efficiency information. Based on the order business target parameter information and the production resource impact assessment information, calculate the business resource benefit score information; By integrating material shortage risk scoring information with business resource efficiency scoring information, a comprehensive decision-making score information is obtained for each candidate production scheduling scheme. Based on comprehensive decision-making scoring information, the optimal production scheduling scheme is determined.

[0053] Specifically, order business target parameter information refers to key indicators related to orders and used to measure business performance. Among these, order delivery date deviation information quantifies the difference between the planned completion time and the customer's expected delivery time; a smaller difference generally indicates higher customer satisfaction. Order projected profit contribution information directly reflects the potential economic benefits of the order upon completion. Obtaining these parameters aims to ensure that production scheduling decisions are aligned with the company's overall business objectives.

[0054] Furthermore, production resource impact assessment information is used to measure the impact of different production scheduling schemes on the efficiency of production resource utilization in the injection molding workshop. Mold changeover frequency information refers to the frequency of mold changes required for a specific production scheduling scheme within a preset time period; fewer mold changes generally mean shorter downtime and higher production efficiency. Projected Overall Equipment Effectiveness (OEE) information is a comprehensive indicator used to assess the availability, performance, and product quality of key equipment such as injection molding machines; a higher value indicates better equipment utilization and production efficiency. By evaluating this information, it can be ensured that production scheduling schemes meet production needs while maximizing resource allocation and utilization.

[0055] The business resource efficiency score is calculated by comprehensively considering order business objective parameters and production resource impact assessment information. This score aims to quantify the overall performance of each candidate production scheduling scheme at both the business and resource utilization levels. For example, weighted average or other multi-criteria decision-making methods can be used to integrate factors such as order delivery date deviation, expected order profit contribution, mold changeover frequency, and expected overall equipment efficiency to arrive at a unified efficiency assessment score.

[0056] In practical applications, material shortage risk assessment information and business resource efficiency assessment information are integrated to obtain a comprehensive decision score for each candidate production scheduling plan. This integration process typically involves a weighted sum of the two scores, where the weights can be dynamically adjusted based on current production strategies, market demand, or corporate priorities. For example, when material supply is tight, the weight of the material shortage risk assessment can be appropriately increased; while when pursuing high profits or high efficiency, the weight of the business resource efficiency assessment can be increased accordingly. In this way, the comprehensive decision score can fully reflect the performance of a production scheduling plan across multiple dimensions, including material supply, business efficiency, and resource utilization.

[0057] Therefore, based on the comprehensive decision-making score information, the optimal production scheduling scheme is finally determined. Typically, the candidate scheme with the highest comprehensive decision-making score will be selected as the optimal production scheduling scheme.

[0058] Optionally, the steps for obtaining the order business target parameter information corresponding to each candidate production scheduling plan include: Obtain initial expected delivery time information and initial projected profit contribution information for the order; Continuously monitor external events related to orders; Assess the impact of external event information on expected delivery time information or projected profit contribution information for orders, and generate updated business target information for orders. Update the order business target parameter information based on the order business target update information; The updated order business target parameter information is sent to the scheduling decision engine module to correct the business resource efficiency score information.

[0059] Specifically, when obtaining initial expected delivery time information and initial projected profit contribution information for an order, these can be directly extracted from the order management system or enterprise resource planning (ERP) system. This information is usually defined when the order is created and serves as the baseline target for order execution.

[0060] Furthermore, continuous monitoring of external events related to orders refers to the system's uninterrupted collection and analysis of various external data that may affect order fulfillment or profitability. These external events may include, but are not limited to, changes in market demand, delivery delays from raw material suppliers, urgent change requests from customers, logistical disruptions, sudden policy adjustments, or natural disasters. Monitoring can be achieved by integrating multiple data sources, such as supply chain management systems, customer relationship management systems, market intelligence systems, and external news or weather services.

[0061] The assessment of the impact of external events on expected delivery times or projected profit contributions, and the generation of updated order business objectives, refers to the system's analysis of monitored external events and quantification of their impact on key order business objectives. For example, when a raw material supplier delays delivery, the system assesses the degree of impact on the expected delivery time of related orders based on factors such as the delay duration and the material's criticality. It may also calculate the cost increases resulting from emergency procurement or expedited shipping, thereby affecting projected profit contributions. Updated order business objectives represent this assessment result quantitatively, for example, as adjustments to expected delivery times or correction coefficients for projected profit contributions.

[0062] Therefore, updating the order business target parameters based on the updated order business target information means applying the evaluated updated information to the initial expected delivery time and projected profit contribution information to obtain the order business target parameters reflecting the latest situation. This update can be additive, such as adding a delay day to the expected delivery time; or it can be multiplicative, such as multiplying the projected profit contribution by an impact coefficient.

[0063] Finally, the updated order business objective parameters are sent to the scheduling decision engine module to correct the business resource efficiency score, ensuring that the input data for scheduling decisions is always up-to-date. When calculating the business resource efficiency score, the scheduling decision engine module uses these dynamically updated order business objective parameters, enabling the final production scheduling plan to better adapt to changes in the external environment.

[0064] Optional steps for continuously monitoring external events related to orders include: Configure the data conversion module; the data conversion module is used to convert the raw data format from different external event data sources into the system's preset standard data format; Use the data conversion module to convert the raw data format of external event information into a standard data format; Identify and extract key event structured information from the standard data format of external event information; key event structured information includes at least event type information, occurrence time information, order number information involved, and description of the impact on expected delivery time or expected profit contribution; The system stores structured information about key events and performs real-time indexing on this information to create a searchable external event index for continuous monitoring.

[0065] The data conversion module is configured to process raw data from various external data sources, such as logistics updates from suppliers, order change requests from customers, market price fluctuation information, or notifications of sudden events. Since these data sources may have different data formats and structures, the core function of the data conversion module is to uniformly convert this heterogeneous data into a system-preset standard data format. This aims to eliminate processing obstacles caused by data format inconsistencies, ensuring the efficiency and accuracy of subsequent data processing.

[0066] Furthermore, after the raw data format of external event information is converted into a standard data format, the system will conduct in-depth analysis of this standardized data to identify and extract key event structured information. This key information forms the basis for assessing the impact of external events on order business objectives, specifically including event type information (e.g., material delays, order cancellations, price adjustments, etc.), event occurrence time information, affected order number information, and a detailed description of the impact on expected delivery time or projected profit contribution. By extracting this structured information, clear and quantifiable input can be provided for subsequent automated evaluation and decision-making.

[0067] Furthermore, to achieve continuous monitoring and rapid response to external events, the extracted structured information of key events will be stored and indexed in real time. By constructing a searchable index of external events, the system can efficiently query, filter, and analyze historical and real-time event data. This real-time indexing mechanism ensures that when new external events occur, the system can quickly identify, process, and incorporate their impact into the order business objective update process, thereby achieving true continuous monitoring.

[0068] Optionally, the steps for assessing the impact of external event information on expected delivery time information or projected profit contribution information to generate updated order business target information include: Configure the event impact mapping rule base; the event impact mapping rule base defines the mapping relationship between different event type information, event intensity information and order expected delivery time information or expected profit contribution information; the mapping relationship is used to output the impact increment or impact coefficient; When external event information is received, the event type and event intensity information of the external event information are identified; Based on event type information, event intensity information, and event impact mapping rule base, query or calculate the preliminary impact value of external events; Identify the order quantity information associated with external event information and the order priority information for each order; Based on order quantity and order priority information, the initial impact value of external events is weighted and assigned to obtain differentiated impact value information; The differentiated impact value information is used as the order business target update information and output to the scheduling decision engine module.

[0069] Specifically, the event impact mapping rule base can be understood as a predefined set of logic aimed at transforming the characteristics of various external events (such as event type and event intensity) into specific, quantifiable impacts on order business objectives (such as expected delivery time or projected profit contribution). For example, event type information may include "material supplier delays," "sudden equipment failures," "urgent customer orders," and "rework due to quality issues"; event intensity information may be a quantifiable description of the event's severity, such as "minor," "moderate," "severe," or specific delay days or failure duration. The mapping relationship can be represented as a lookup table, mathematical formula, or based on a machine learning model, used to output the impact increment or impact coefficient.

[0070] When the system receives external event information, it first needs to parse it to accurately identify its event type and intensity. For example, it may extract key information from the event description using natural language processing techniques or match it using preset event codes.

[0071] In practical applications, based on the identified event type information, event intensity information, and event impact mapping rule base, a preset mapping table can be queried or corresponding calculation logic can be executed to obtain the preliminary impact value of the external event. This preliminary impact value is a general impact assessment derived from the characteristics of the event itself.

[0072] Furthermore, to make the impact assessment more targeted, it is necessary to identify the order quantity information associated with the external event and the order priority information for each order. The order quantity information refers to the total number of orders affected by the external event, while the order priority information reflects the importance of each affected order to the business, such as "urgent," "critical," and "normal."

[0073] Based on this, the initial impact value of external events is weighted according to order quantity and priority information. For example, high-priority orders can be assigned higher weights, making their impact more significant during updates; for cases with a large number of affected orders, aggregation or averaging methods can be used. This weighting allocation yields differentiated impact value information, which more accurately reflects the actual impact of external events on the business objectives of different orders.

[0074] Finally, the differentiated impact value information is used as the updated information for order business objectives and output to the scheduling decision engine module. The scheduling decision engine module will use this updated information to revise the business resource efficiency score information, thereby enabling a more accurate balance of various factors in the subsequent process of determining the optimal production scheduling plan.

[0075] Optionally, the steps for integrating material shortage risk assessment information with the aforementioned business resource efficiency assessment information include: Obtain production scenario characteristic information; production scenario characteristic information includes the current order urgency, the current workshop equipment utilization rate, and the current overall material inventory level; Based on the characteristics of the production scenario, select the fusion weight configuration information that matches the current production scenario from the preset fusion weight set; The material shortage risk score and business resource benefit score of each candidate production scheduling scheme are weighted and fused using the fusion weight configuration information to obtain the corresponding comprehensive decision score information.

[0076] Specifically, production scenario characteristic information refers to a set of key indicators used to describe the overall operational status and business priorities of the current injection molding workshop. This information aims to capture macro-environmental factors that influence production scheduling decisions. Among these, the current order urgency can be understood as the overall priority or delivery pressure of all pending orders, which can be obtained, for example, by statistically analyzing the proportion of urgent orders, average remaining delivery time, or weighted average priority. The current workshop equipment utilization rate refers to the ratio of the actual operating time of all injection molding machines in the workshop to the total available time within a specific time period, reflecting equipment utilization efficiency and production load. The current overall material inventory level refers to the ratio of the current total inventory of all materials in the workshop to their safety stock or maximum inventory level, reflecting the adequacy of material supply. These characteristic information collectively depict a comprehensive picture of the current production environment, aiming to provide contextual basis for subsequent decisions.

[0077] Furthermore, the pre-defined set of fusion weights refers to a database or rule set containing various fusion weight configuration information. Each fusion weight configuration predefines the relative importance of material shortage risk scoring information and business resource efficiency scoring information in the calculation of comprehensive decision-making scoring information under a specific production scenario. For example, when the urgency of an order is high, business resource efficiency scoring information (especially order delivery time deviation) may be given a higher weight; when the material inventory level is low, material shortage risk scoring information may be given a higher weight. The construction of this set can be trained and optimized through expert experience, historical data analysis, or machine learning models to ensure that each configuration effectively reflects the decision preferences under specific scenarios.

[0078] Specifically, selecting the fusion weight configuration information that matches the current production scenario refers to the system searching or calculating the weight combination that best represents the decision-making preferences of the current production environment from a preset set of fusion weights based on the production scenario feature information acquired in real time. For example, a series of rules can be defined, such as "if the order urgency is high and the material inventory level is medium, then select weight configuration A", or the most suitable weight can be dynamically determined through advanced algorithms such as fuzzy matching and cluster analysis.

[0079] In practical applications, using fusion weight configuration information to weight and fuse the material shortage risk score and business resource benefit score of each candidate production scheduling plan involves applying selected weights to these two scores and calculating the final comprehensive decision score through weighted summation or other weighted averaging methods. For example, the comprehensive decision score = (weight_risk * material shortage risk score) + (weight_benefit * business resource benefit score). The purpose is to unify the evaluation results from different dimensions onto a comparable scale and adjust them according to the priorities of the current production scenario, thereby obtaining a comprehensive and adaptable decision-making basis.

[0080] Optionally, the steps for obtaining production scenario feature information include: Configure a high-frequency data acquisition strategy for injection molding machine operation to acquire injection molding machine operation status data at a preset frequency and form an injection molding machine operation status dataset; Configure an event-triggered data collection strategy for material inventory data. When a material inbound / outbound event or a replenishment event occurs, update the current inventory level of the material and generate an inventory event dataset. Configure an order urgency priority change monitoring mechanism to obtain the updated order urgency identifier information when the order priority changes. The injection molding machine operating status dataset, inventory event dataset, and order urgency identification information are timestamped to obtain timestamped data. The data after timestamp alignment is aggregated to calculate workshop equipment utilization rate, overall material inventory level, and order urgency, and then integrated into production scenario feature information within a unified time window.

[0081] Specifically, configuring a high-frequency data acquisition strategy for injection molding machines refers to automatically collecting operating status data of the injection molding machine at preset, short time intervals (e.g., every minute or every few seconds) by deploying sensors on the machine or integrating it with existing control systems. This data includes start / stop status, production cycle time, and fault codes. These data are continuously recorded and aggregated into an injection molding machine operating status dataset, the purpose of which is to reflect the actual operating status of the equipment in the workshop in real time and provide basic data for calculating the workshop equipment utilization rate.

[0082] The event-triggered data collection strategy for material inventory can be understood as follows: when key events such as material receipt, issuance, or replenishment occur, the system can immediately respond and update the current inventory information of the materials. For example, by scanning material barcodes, RFID tags, or integrating with a warehouse management system (WMS), the real-time accuracy of material inventory data is ensured. These event-driven data updates form an inventory event dataset, the purpose of which is to accurately track the dynamic changes of materials and provide a basis for assessing the overall inventory level.

[0083] In practical applications, configuring an order urgency priority change monitoring mechanism involves establishing a system module that continuously monitors any changes in order priority within the order management system. Once the priority of an order is adjusted (e.g., from "normal" to "urgent"), the mechanism immediately captures and retrieves the updated order urgency status information. The purpose is to promptly detect changes in order demand and ensure the real-time nature of order urgency information.

[0084] Furthermore, timestamp alignment of the injection molding machine operating status dataset, inventory event dataset, and order urgency identifier information involves synchronizing data from different data sources and with different collection frequencies using a unified timestamp. For example, frequently collected injection molding machine data can be matched with event-triggered inventory data and priority change data on the same timeline to ensure that all data reflects the true situation at the same point in time during analysis. The purpose is to eliminate time discrepancies caused by heterogeneous data sources and ensure the accuracy of subsequent aggregation analysis.

[0085] Therefore, aggregating timestamp-aligned data involves statistically analyzing and calculating the aligned data within a unified time window. For example, within a specific time window, the workshop equipment utilization rate (e.g., the proportion of actual operating time to total time) can be calculated based on injection molding machine operating status data; the overall material inventory level (e.g., the ratio of total material inventory to total safety stock) can be calculated based on current material inventory information; and the number or proportion of current urgent orders can be statistically analyzed based on order urgency indicators, thus obtaining the order urgency level. These calculation results are integrated into production scenario characteristic information within a unified time window, aiming to provide a comprehensive snapshot of the current production environment.

[0086] This application also discloses a production resource data optimization and management system for injection molding workshops, used to perform production resource data optimization and management in injection molding workshops, combined with... Figure 3 As shown, the injection molding workshop production resource data optimization management system 1 includes: The resource status acquisition module 11 is used to acquire production resource status information; the production resource status information includes at least the injection molding machine operating status information, mold availability information, current material inventory information, material in transit information, and material replenishment cycle information. The candidate scheme generation module 12 is used to generate multiple candidate production scheduling scheme information based on production resource status information; each candidate production scheduling scheme information is used to define the production task information to be executed by the injection molding machine within a preset time period, the mold information used, and the expected start time and expected end time information; The demand time prediction module 13 is used to predict the material demand timeline information of each candidate production scheduling scheme within a preset time period, based on the production task information, the bill of materials information corresponding to the production task, and the single-piece material consumption information and scrap rate information contained in the bill of materials information. The shortage risk scoring module 14 is used to generate material supply capacity information based on the current inventory information, the amount of material in transit information, and the material replenishment cycle information. It compares the material demand timeline information with the material supply capacity information to judge the material shortage risk and generates corresponding material shortage risk score information for each candidate production scheduling scheme. The material demand timeline information includes the demand time points and demand quantities sorted by time; the material supply capacity information includes the available supply time points and available supply quantities sorted by time. The production scheduling optimization module 15 is used to determine the optimal production scheduling scheme based on the material shortage risk score information corresponding to each candidate production scheduling scheme.

[0087] This application achieves comprehensive acquisition, unified management, and in-depth analysis of production resource data in injection molding workshops by constructing a modular system architecture. Within this architecture, various types of production resource data are continuously collected and structured. Based on this, refined forecasting of material demand, dynamic assessment of material supply capacity, and quantitative analysis of material shortage risks effectively overcome the problems of inaccurate scheduling decisions caused by scattered data sources, untimely information updates, or insufficient analytical dimensions in traditional production management systems facing complex production environments. Through the collaborative operation of various modules within the system, multiple candidate production scheduling schemes can be comprehensively compared, and the production scheduling scheme with higher robustness in terms of material supply stability can be selected. This significantly improves the accuracy, stability, and execution efficiency of production plans in injection molding workshops, further ensuring that production tasks can be completed smoothly according to plan and reducing the risk of material shortages during production operations.

[0088] In some embodiments of this application, the aforementioned injection molding workshop production resource data optimization management system achieves a clearly defined system architecture with a modular internal structure. Each module can independently execute specific data processing tasks and also achieve collaborative operation through a unified data interface. The resource status acquisition module can be configured to connect to different data sources through multiple data interfaces. For example, it can directly read sensor data from the injection molding machine control system through an industrial communication interface, or it can synchronize data with existing manufacturing execution systems, enterprise resource planning systems, or warehouse management systems through a software interface, thereby acquiring data such as injection molding machine operating status information, mold usage status information, and material inventory information. In specific implementations, this module can adopt an asynchronous data acquisition mechanism based on message queues to achieve real-time acquisition of high-frequency data; or it can use a timed polling method to periodically read updated data from a specified database to adapt to the data acquisition needs of different system environments.

[0089] The candidate solution generation module can be implemented as an independent computing service to generate multiple candidate production scheduling schemes that meet basic production constraints based on production resource status information. This module can encapsulate various production scheduling algorithms; for example, it can use a rule-based expert system to initially screen production tasks, or it can generate diverse scheduling schemes through combinatorial optimization algorithms. For instance, in some implementations, this module can utilize genetic algorithms or simulated annealing algorithms to optimize the ordering of production tasks and generate multiple feasible production scheduling schemes under conditions such as equipment capacity constraints, mold matching constraints, and order priority constraints. This module can be deployed in a server environment with high computing power to support the generation and evaluation of a large number of candidate schemes in a short period of time.

[0090] The demand forecasting module can be designed as a data processing engine. Its core function is to predict material demand in the future production cycle based on the bill of materials (BOM) information and material consumption parameters corresponding to the production tasks. This module can calculate the required material quantities at different time points by parsing the correspondence between production task information and the product BOM, and generate a material demand timeline. At the system implementation level, this module can perform demand forecasting in batch mode at fixed time intervals, or it can automatically trigger the demand forecasting process when a new production task or scheduling plan is received. The module can also include a material consumption model to comprehensively consider factors such as scrap rate, process adjustments, and production losses when calculating material demand, thereby improving the accuracy of the forecast results.

[0091] The shortage risk scoring module can be implemented as an independent risk assessment service. Its main responsibility is to compare and analyze material demand timeline information with material supply capacity information, and to quantitatively assess the potential material shortage risk. This module can calculate a risk score based on multiple factors such as the shortage quantity, the timing of the shortage, and the importance of the shortage material. For example, when the shortage material is a critical raw material and the shortage occurs close to the start time of the production task, its risk score will be significantly higher. This module can be configured to automatically perform risk assessments whenever a new candidate production schedule is generated or material supply capacity information changes, thereby keeping the risk assessment results updated in real time.

[0092] The production scheduling optimization module can be designed as a decision support unit. Its main function is to comprehensively evaluate scheduling schemes based on material shortage risk scores and other business objectives, and ultimately determine the optimal production scheduling scheme. In some implementations, this module can integrate multi-objective decision-making algorithms, such as using analytic hierarchy process (AHP) or fuzzy comprehensive evaluation methods to comprehensively assess multiple indicators such as production efficiency, material risk, equipment utilization, and delivery cycle, thereby obtaining a comprehensive score result and selecting the optimal production scheduling scheme based on the score result. Simultaneously, this module can also provide an operation interface, allowing operators to manually review or make partial adjustments to the system-recommended scheduling schemes, thus maintaining the advantages of automated decision-making while incorporating practical experience from the production floor.

[0093] Compared with existing technologies, the core improvement of the injection molding workshop production resource data optimization management system proposed in this application lies in the decoupling design of production resource information, scheduling generation process, and material risk assessment process through a modular architecture. Traditional production management systems typically adopt a highly coupled structure, with various data processing and scheduling logics concentrated in the same module, making it difficult for the system to adapt quickly when the production environment changes. This application divides functions such as resource status acquisition, candidate solution generation, demand time prediction, shortage risk scoring, and production scheduling optimization into independent modules, enabling the system to achieve flexible expansion and maintenance while maintaining overall coordinated operation.

[0094] Furthermore, this application employs a shortage risk scoring module to proactively and quantitatively assess material supply risks, enabling the system to identify potential material shortages during the production scheduling phase and prioritize lower-risk scheduling options from multiple candidate solutions. This scheduling optimization mechanism, based on a combination of data prediction and risk assessment, effectively reduces the risk of production interruptions caused by unstable material supply, while also minimizing additional costs associated with emergency procurement and temporary scheduling. Through the synergistic effect of the aforementioned system structure and decision-making mechanism, this application achieves more stable, efficient, and reliable production management in the complex and ever-changing production environment of injection molding workshops.

[0095] The above are merely embodiments of this application and are not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for optimizing and managing production resource data in an injection molding workshop, characterized in that, include: Obtain production resource status information; The production resource status information includes at least the injection molding machine operating status information, mold availability information, current material inventory information, material in transit information, and material replenishment cycle information; Based on the production resource status information, multiple candidate production scheduling schemes are generated; each candidate production scheduling scheme is used to define the production task information to be executed by the injection molding machine within a preset time period, the mold information used, and the expected start time and expected end time information; For each candidate production scheduling scheme, based on the production task information, the bill of materials information corresponding to the production task, and the single-item material consumption information and scrap rate information contained in the bill of materials information, the material demand timeline information of the candidate production scheduling scheme within a preset time period is predicted. Based on the current inventory information, the amount of materials in transit, and the replenishment cycle information, material supply capacity information is generated. The material demand timeline information is compared with the material supply capacity information to assess the risk of material shortage. A corresponding material shortage risk score is generated for each candidate production scheduling scheme. The material demand timeline information includes demand points and demand quantities sorted by time. The material supply capacity information includes available supply points and available supply quantities sorted by time. Based on the material shortage risk score information corresponding to each candidate production scheduling scheme, the optimal production scheduling scheme information is determined.

2. The method for optimizing and managing production resource data in an injection molding workshop according to claim 1, characterized in that, The step of assessing material shortage risk and generating a corresponding material shortage risk score for each candidate production scheduling scheme includes: Obtain information on fine-tuning the process parameters of the injection molding machine; Obtain the physical property data of the material batches used in the current production task; Based on the material physical property data and the process parameter fine-tuning information, the impact of process parameter fine-tuning on the single-piece material consumption information and scrap rate information is inferred, and consumption and scrap correction parameter information is generated. Based on the consumption and scrap correction parameter information, the material demand timeline information is corrected to obtain the corrected material demand timeline information. The revised material demand timeline information is compared with the material supply capacity information to determine the material shortage risk and obtain the determination result; based on the determination result, corresponding material shortage risk score information is generated for each candidate production scheduling scheme information.

3. The method for optimizing and managing production resource data in an injection molding workshop according to claim 1, characterized in that, The step of assessing material shortage risk and generating a corresponding material shortage risk score for each candidate production scheduling scheme includes: The material demand timeline information of each candidate production scheduling plan is compared with the material supply capacity information to identify whether there is a risk of material shortage within the preset time period. When a material shortage risk is identified, obtain the criticality level information of the shortage material; Obtain the time interval between the time of the shortage occurrence and the current time, and obtain the shortage quantity information; Based on the criticality level information of the scarce materials, the time interval information, and the shortage quantity information, a comprehensive risk score is calculated; Based on the comprehensive risk score, material shortage risk score information is generated for the corresponding candidate production scheduling scheme information.

4. The method for optimizing and managing production resource data in an injection molding workshop according to claim 1, characterized in that, The step of determining the preferred production scheduling scheme based on the material shortage risk score information corresponding to each candidate production scheduling scheme includes: Obtain the order business target parameter information corresponding to each candidate production scheduling scheme; the order business target parameter information includes order delivery date deviation information and order expected profit contribution information, which characterize the time deviation of the planned completion time from the expected delivery time of the order. Obtain the production resource impact assessment information corresponding to each candidate production scheduling scheme; the production resource impact assessment information includes mold replacement frequency information and expected overall equipment efficiency information. Based on the order business target parameter information and the production resource impact assessment information, calculate the business resource benefit score information; By integrating the material shortage risk score information and the business resource efficiency score information, a comprehensive decision score information for each candidate production scheduling scheme is obtained; Based on the comprehensive decision-making scoring information, the optimal production scheduling scheme is determined.

5. The method for optimizing and managing production resource data in an injection molding workshop according to claim 4, characterized in that, The steps for obtaining the order business target parameter information corresponding to each candidate production scheduling scheme include: Obtain initial expected delivery time information and initial projected profit contribution information for the order; Continuously monitor external events related to orders; Assess the impact of the external event information on the expected delivery time of the order or the projected profit contribution information, and generate updated order business objectives information; Update the order business target parameter information according to the order business target update information; The updated order business target parameter information is sent to the scheduling decision engine module to correct the business resource efficiency score information.

6. The method for optimizing and managing production resource data in an injection molding workshop according to claim 5, characterized in that, The steps for continuously monitoring external event information related to orders include: Configure a data conversion module; the data conversion module is used to convert the raw data format from different external event data sources into the system's preset standard data format; The data conversion module is used to convert the raw data format of external event information into a standard data format. Key event structured information is identified and extracted from the standard data format of external event information; the key event structured information includes at least event type information, occurrence time information, order number information involved, and description information of its impact on expected delivery time or expected profit contribution; The key event structured information is stored and indexed in real time to form searchable external event index information, so as to achieve continuous monitoring.

7. The method for optimizing and managing production resource data in an injection molding workshop according to claim 5, characterized in that, The step of assessing the impact of the external event information on the expected delivery time information or projected profit contribution information of the order, and generating updated order business target information, includes: Configure an event impact mapping rule base; the event impact mapping rule base defines the mapping relationship between different event type information, event intensity information and order expected delivery time information or expected profit contribution information; the mapping relationship is used to output the impact increment or impact coefficient; When external event information is received, the event type information and event intensity information of the external event information are identified; Based on the event type information, event intensity information, and the event impact mapping rule base, query or calculate the preliminary impact value of the external event; Identify the order quantity information associated with the external event information and the order priority information of each order; Based on the order quantity information and order priority information, the initial impact value of the external event is weighted and assigned to obtain differentiated impact value information; The differentiated impact value information is used as the order business target update information and output to the scheduling decision engine module.

8. The method for optimizing and managing production resource data in an injection molding workshop according to claim 4, characterized in that, The step of integrating the material shortage risk score information and the business resource efficiency score information to obtain the comprehensive decision score information for each candidate production scheduling scheme includes: Acquire production scenario characteristic information; the production scenario characteristic information includes the current order urgency, the current workshop equipment utilization rate, and the current overall material inventory level; Based on the production scenario feature information, select fusion weight configuration information that matches the current production scenario from the preset fusion weight set; The material shortage risk score and business resource benefit score of each candidate production scheduling scheme are weighted and fused using the fusion weight configuration information to obtain the corresponding comprehensive decision score information.

9. The method for optimizing and managing production resource data in an injection molding workshop according to claim 8, characterized in that, The steps for obtaining production scene feature information include: Configure a high-frequency data acquisition strategy for injection molding machine operation to acquire injection molding machine operation status data at a preset frequency and form an injection molding machine operation status dataset; Configure an event-triggered data collection strategy for material inventory data. When a material inbound / outbound event or a replenishment event occurs, update the current inventory level of the material and generate an inventory event dataset. Configure an order urgency priority change monitoring mechanism to obtain the updated order urgency identifier information when the order priority changes. The injection molding machine operating status dataset, the inventory event dataset, and the order urgency identification information are timestamped to obtain timestamped data. The data after timestamp alignment is aggregated to calculate workshop equipment utilization rate, overall material inventory level, and order urgency, and then integrated into production scenario feature information within a unified time window.

10. A production resource data optimization management system for injection molding workshops, used to perform production resource data optimization management in injection molding workshops, characterized in that, include: The resource status acquisition module is used to acquire production resource status information; The production resource status information includes at least the injection molding machine operating status information, mold availability information, current material inventory information, material in transit information, and material replenishment cycle information; The candidate scheme generation module is used to generate multiple candidate production scheduling schemes based on the production resource status information; each candidate production scheduling scheme is used to define the production task information to be executed by the injection molding machine within a preset time period, the mold information used, and the expected start time and expected end time information. The demand time forecasting module is used to predict the material demand timeline information of each candidate production scheduling scheme within a preset time period, based on the production task information, the bill of materials information corresponding to the production task, and the single-piece material consumption information and scrap rate information contained in the bill of materials information. The shortage risk scoring module is used to generate material supply capacity information based on the current inventory information, the amount of material in transit information, and the material replenishment cycle information. It then compares the material demand timeline information with the material supply capacity information to determine the material shortage risk and generates a corresponding material shortage risk score for each candidate production scheduling scheme. The material demand timeline information includes demand points and demand quantities sorted by time; the material supply capacity information includes available supply points and available supply quantities sorted by time. The production scheduling optimization module is used to determine the optimal production scheduling scheme information based on the material shortage risk score information corresponding to each candidate production scheduling scheme information.

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