Lace sharing manufacturing system and method

Abstract

The invention relates to the field of textile manufacturing, and discloses a lace sharing manufacturing system and method.According to the system, similar orders are intelligently integrated through an order merging module, and the machine changing cost is reduced based on rules such as lace types and pattern attributes; the intelligent production scheduling module generates an optimization scheme in combination with the equipment capacity and the order priority, so that the equipment utilization rate is improved; the quality control module monitors the production state in real time to ensure stable product quality; the supply chain management module integrates upstream and downstream resources to reduce the purchase cost; the visual monitoring module is combined with a virtual reality technology to realize real-time display of production data; the cloud computing platform is connected in series with the scattered factories to construct a capacity sharing pool, and the Internet of Things module realizes cross-enterprise data interaction. According to the method, intelligent control of the production process is realized through the steps of order splitting, intelligent matching, dynamic production scheduling, real-time monitoring and the like.

Description

Technical Field

[0001] The present invention relates to the field of textile lace product production, and in particular to a lace shared manufacturing system and method. Background Art

[0002] In the textile industry, order-based production of lace products has always been a crucial process. However, with the ever-changing market demand and soaring order volumes, traditional distribution capabilities are no longer able to meet existing production needs. Existing technologies for processing lace orders present numerous challenges.

[0003] First, the rapid growth in order volume posed a severe challenge to traditional distribution capabilities. Traditional manual distribution methods proved inadequate amidst the surge in orders, resulting in inefficient order processing and an inability to meet the urgent market demand for lace products. This not only impacted production schedules but also potentially led to customer churn and a loss of market share.

[0004] Secondly, the mismatch between small orders and the factory's larger orders is becoming increasingly prominent. Existing lace production equipment is often optimized for high-volume production and lacks flexibility for small orders. Due to the disparity in production requirements between small and large orders, traditional production methods struggle to achieve rapid response and efficient production for small orders. This often results in lengthy waiting times during the production process for small orders, increasing production costs and delivery times.

[0005] Furthermore, lace production equipment is expensive to modify. Changes to the lace pattern require retooling to accommodate the new production requirements. However, this process is lengthy and costly, taking one or two days to complete, making it uneconomical for small orders. Consequently, many factories are forced to forgo small orders or bear the significant cost of retooling, severely restricting the diversification and personalization of lace products.

[0006] To address these issues, existing order-based lace production methods are no longer able to meet market demand. Therefore, a new shared lace manufacturing system and method are urgently needed to address these issues. This system and method aims to achieve rapid order processing and efficient production coordination through intelligent and automated means, thereby meeting market demand for diverse and personalized lace products. Summary of the Invention

[0007] The present invention aims to provide a lace shared manufacturing system and method. A lace shared manufacturing system, comprising: The Order Merger Module is used to merge similar, but not identical, lace orders based on pre-set rules to improve production efficiency. These rules include, but are not limited to, lace type, pattern attributes, material requirements, and production equipment compatibility. The module first analyzes the attributes of received orders, including key information such as lace type, pattern, and size. It then uses an intelligent algorithm to match orders with similar attributes and merges those that successfully match into new production orders, thereby optimizing the allocation of production resources.

[0008] The intelligent production scheduling module generates a production schedule based on consolidated production orders and constraints. This module uses IoT sensing devices to collect real-time production site data, including equipment status and production progress. It also analyzes orders, taking into account factors such as order priority and delivery dates. Based on constraints such as equipment capacity, it applies operations research and intelligent algorithms to generate the optimal production schedule. It then intelligently allocates and adjusts production tasks to ensure efficient and orderly production.

[0009] Quality Control Module: This module monitors the production process in real time to ensure product quality meets preset standards. It includes a quality inspection unit and a quality feedback unit. The quality inspection unit, equipped with sensors and image recognition technology, inspects semi-finished and finished products during production, identifying defective products in real time. The quality feedback unit transmits inspection results to the production department, enabling timely adjustments to production processes and equipment parameters to ensure consistent product quality.

[0010] Supply Chain Management Module: used to integrate upstream and downstream supply chain resources to realize the procurement of raw materials and distribution of products, including supplier management unit, procurement management unit, inventory management unit and logistics management unit; the supplier management unit is responsible for evaluating and selecting suitable suppliers; the procurement management unit generates purchase orders based on production plans and inventory status; the inventory management unit monitors inventory levels in real time to avoid backlogs and waste; the logistics management unit cooperates with logistics companies to ensure that products are delivered to customers on time.

[0011] Visual monitoring module: Utilizes virtual reality and data visualization technologies to provide real-time and comprehensive display of production data. This module integrates data from various modules and uses virtual reality and data visualization technologies to present production data to managers in an intuitive and easy-to-understand manner, assisting them in making quick decisions.

[0012] Cloud computing platform: This connects physically dispersed textile factories, creating a shared capacity pool and matching order demand with idle capacity. The platform provides infrastructure, platform, and software services to support the informatization and digital transformation of textile factories. Through the cloud computing platform, factories can share capacity and resources, achieving intelligent order matching and efficient production.

[0013] IoT Data Collection Module: Deploy IoT sensing devices to establish a cross-enterprise production data collection system, providing real-time communication on equipment status, material inventory, and process parameters. This module uses IoT sensing devices deployed at production sites to collect real-time data on equipment status, material inventory, and process parameters. This data is transmitted to the cloud computing platform via wired or wireless channels, providing data support for modules such as intelligent production scheduling and quality control.

[0014] Preferably, the order merging module further includes: User input interface: used to receive user order information, the detailed parameters of which include the material, pattern and size of the lace products; this interface provides a user-friendly interactive interface, receives the detailed parameters of the lace products entered by the user, and provides basic data for subsequent order merging.

[0015] Order splitting processing unit: preliminarily splits the order according to the user input content and identifies the elements that can be merged, which include the same lace type, similar patterns or shared materials; this unit preliminarily splits the received orders and identifies the order elements that can be merged based on factors such as lace type, pattern complexity, material composition, etc., providing a basis for subsequent intelligent matching.

[0016] Merger Decision Unit: This unit intelligently matches pre-split orders based on rules to form a merged production order, ensuring that the merged order can be produced with minimal or no machine modification. This unit intelligently matches pre-split orders based on preset rules to form a merged production order. During the matching process, priority is given to orders with the same lace type, similar patterns, or shared materials, ensuring that the merged order can be produced with minimal or no machine modification.

[0017] Preferably, the intelligent production scheduling module further includes: Multi-process collaborative optimization unit: This unit uses operations research and intelligent algorithms to achieve collaborative optimization of multiple processes based on the characteristics of textile processes, reducing production bottlenecks. By optimizing process sequence and resource allocation, it reduces production bottlenecks and improves overall production efficiency.

[0018] Real-time Scheduling Response Unit: Leveraging edge computing and IoT technologies, this unit responds to changes in production progress and equipment status in real time, dynamically adjusting production task allocation. This unit utilizes edge computing and IoT technologies to monitor production progress and equipment status in real time. Upon detecting an anomaly, such as a production delay or equipment failure, the scheduling mechanism is immediately triggered, dynamically adjusting production task allocation to ensure smooth execution of the production plan.

[0019] Deep reinforcement learning algorithm: Utilizing a deep reinforcement learning algorithm, combined with historical production data, we continuously optimize production scheduling, improving efficiency and equipment utilization. This algorithm learns from patterns and regularities in historical production data to predict future trends in production demand. Based on this, we continuously optimize scheduling strategies, improving efficiency and equipment utilization.

[0020] Preferably, the constraints include equipment capacity and order priority; when generating a production scheduling plan, factors such as equipment capacity and order priority are fully considered to ensure the rationality and feasibility of the production scheduling plan.

[0021] Preferably, the quality control module further comprises: Quality Inspection Unit: Equipped with sensors and image recognition technology, this unit conducts quality inspections on semi-finished and finished products during the production process, identifying defective products in real time. This unit utilizes high-precision sensors and image recognition technology to inspect the quality of semi-finished and finished products during the production process. By monitoring key parameters and appearance quality in real time, defective products can be identified promptly, ensuring that product quality meets preset standards.

[0022] Quality Feedback Unit: Feedback the quality inspection results to the production department to adjust the production process or equipment parameters to ensure stable product quality. This unit will promptly feedback the quality inspection results to the production department so that the production department can adjust the production process or equipment parameters according to the feedback results to ensure stable product quality.

[0023] Preferably, the system further supports: Multi-factory collaborative production: Through the cloud computing platform, production task collaboration and resource sharing among multiple factories can be achieved; through the cloud computing platform, multiple factories can be connected in series to achieve production task collaboration and resource sharing, thereby improving overall capacity utilization and production efficiency.

[0024] Customized production service: According to user needs, we provide customized lace product design and production services to meet user's personalized needs; According to user's specific needs, we provide customized lace product design and production services to meet user's personalized needs and improve user satisfaction and market competitiveness.

[0025] Preferably, the multi-factory collaborative production further includes: Factory Capacity Assessment: The production capacity, equipment status, and technical level of factories joining the system are assessed to ensure that the factories can meet production needs; a comprehensive assessment is conducted on factories applying to join the system, including production capacity, equipment status, and technical level, to ensure that the factories can meet production needs and guarantee production quality and efficiency.

[0026] Production task allocation: Based on the factory capacity assessment results and order requirements, production tasks are intelligently allocated to the most suitable factory for production, ensuring efficient execution and on-time delivery of production tasks.

[0027] Production progress tracking: Through the IoT data collection module, the production progress of each factory can be tracked in real time to ensure that orders are delivered on time; using the IoT data collection module, the production progress of each factory can be tracked in real time to ensure that orders can be delivered to customers on time, thereby improving customer satisfaction and market reputation.

[0028] A lace shared manufacturing method, based on the above-mentioned lace shared manufacturing system, comprises the following steps: S1. Receive user order information, perform preliminary splitting of the order, and identify elements that can be merged; receive user order information through a user input interface, and then use an order splitting processing unit to perform preliminary splitting of the order and identify elements that can be merged, such as the same lace type, similar patterns, or shared materials.

[0029] S2. Intelligently match the preliminarily split orders according to preset rules to form a merged production order; use the merge judgment unit to intelligently match the preliminarily split orders according to preset rules to form a merged production order, ensuring that the merged order can be produced without changing the machine or with only minimal change.

[0030] S3. Generate the optimal production scheduling plan based on the merged production orders, combined with constraints including equipment capacity and order priority; the intelligent production scheduling module generates the optimal production scheduling plan based on the merged production orders, combined with constraints such as equipment capacity and order priority.

[0031] S4. Real-time monitoring of production progress and equipment status, intelligent allocation and real-time adjustment of production tasks; real-time monitoring of production progress and equipment status through the Internet of Things data acquisition module. Once an abnormal situation is detected, the scheduling mechanism is immediately triggered to intelligently allocate and adjust production tasks in real time.

[0032] S5. Through the visual monitoring module, real-time and comprehensive production data display is provided to assist managers in making quick decisions. The visual monitoring module is used to display production data to managers in an intuitive and easy-to-understand manner to assist them in making quick decisions and ensure the smooth progress of the production process.

[0033] Preferably, the step S2 further includes: Use deep neural network models to analyze order product processes and predict production difficulty and required equipment. During the intelligent matching process, further use deep neural network models to analyze order product processes and predict production difficulty and required equipment, providing strong support for subsequent order consolidation and production scheduling plan generation.

[0034] Match the order product process with the production equipment to ensure that the combined order can be produced without or with minimal machine changes; based on the analysis results of the deep neural network model, match the order product process with the production equipment to ensure that the combined order can be produced without or with minimal machine changes, thereby reducing production costs and improving production efficiency.

[0035] Based on the matching results, orders are further optimized and merged to improve production efficiency; based on the matching results, orders are further optimized and merged to maximize production efficiency and reduce production costs.

[0036] Preferably, the step S3 further includes: Collect and analyze data from the production process to optimize order merging rules and intelligent production scheduling algorithms; by collecting and analyzing data from the production process, continuously optimize order merging rules and intelligent production scheduling algorithms to improve the intelligence level and production efficiency of the system.

[0037] Regularly evaluate system performance, upgrade and improve the system based on the evaluation results, and continuously improve system performance; Regularly evaluate system performance, upgrade and improve the system based on the evaluation results to ensure that the system can continue to meet production needs and continuously improve system performance and production efficiency.

[0038] In summary, the lace shared manufacturing system provided by the present invention realizes the optimized configuration and efficient utilization of production resources through the synergistic effect of the order merging module, the intelligent production scheduling module, the quality control module, the supply chain management module, the visual monitoring module, the cloud computing platform and the Internet of Things data acquisition module. The system can intelligently merge similar orders, reduce the number and cost of machine changes, and improve production efficiency; at the same time, through intelligent production scheduling, it optimizes the production process, improves equipment utilization, and quickly responds to market demand. In addition, the system also strengthens supply chain collaboration, reduces procurement costs, and ensures that product quality meets preset standards by real-time monitoring of production status. The application of the cloud computing platform and the Internet of Things data acquisition module further realizes resource sharing and real-time data interaction, providing strong support for the system's intelligent decision-making.

[0039] The method based on the lace shared manufacturing system realizes intelligent management and optimization of the production process through the steps of receiving user order information, preliminary splitting of orders, intelligent matching to form merged orders, generating the optimal production scheduling plan, real-time monitoring of production progress and equipment status, and providing real-time production data display. This method can significantly improve production efficiency, reduce production delays and resource waste, and ensure stable product quality. Through real-time monitoring and rapid decision support, the method can also help managers promptly discover and solve problems in the production process, improve management level and market responsiveness. This method provides enterprises with an efficient, flexible and intelligent lace product manufacturing solution. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 This is a system block diagram of a lace shared manufacturing system of the present invention.

[0041] Figure 2 It is a step diagram of a lace sharing manufacturing method of the present invention. DETAILED DESCRIPTION

[0042] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0043] See also Figure 1 The present invention provides a technical solution, a lace shared manufacturing system, including: an order merging module, an intelligent production scheduling module, a quality control module, a supply chain management module, a visual monitoring module, a cloud computing platform and an Internet of Things data acquisition module.

[0044] The order merging module of this system realizes the intelligent merging of similar but not identical lace product orders through refined rule determination, thereby significantly improving production efficiency. The specific implementation is as follows: User Input Interface: The system provides a user-friendly interface for receiving user order information. Users are required to enter detailed parameters of the lace product, including material type (such as cotton, silk, and synthetic fibers), pattern characteristics (such as floral, geometric, and abstract patterns), dimensions (such as width, length, and density), and special process requirements (such as embroidery, printing, and hot stamping).

[0045] The order consolidation module of the lace shared manufacturing system uses refined rule-based judgments, comprehensively considering multiple dimensions such as lace type (such as material), pattern attributes (type, complexity, and similarity), material requirements (size specifications, special processes, color and color fastness), and production equipment compatibility (equipment capabilities, status, and adjustment costs), to achieve intelligent and efficient order consolidation, thereby reducing waiting time and idle time in the production process, improving production efficiency, and reducing costs caused by frequent machine changes.

[0046] Order Splitting Processing Unit: After the system receives a user's order, the Order Splitting Processing Unit automatically splits the order into its initial components. This splitting is based on factors such as lace type, pattern complexity, and material composition. For example, orders of the same type but with slightly different patterns can be split into mergeable components, such as those with the same lace base material or similar pattern styles.

[0047] Merge Decision Unit: Based on pre-defined, detailed rules, the Merge Decision Unit intelligently matches the initially split orders. These rules include lace type, pattern attributes, material requirements, and production equipment compatibility. The system uses algorithms to analyze similarities between orders and determine which orders can be merged for production, with minimal or no adjustments to production equipment. These merged orders are then generated into new production instructions and sent to manufacturers for production.

[0048] By implementing the order consolidation module, the system can intelligently consolidate small orders into larger ones, thus avoiding the high costs associated with frequent adjustments to machine parameters. Order consolidation also helps improve equipment utilization, reducing waiting and idle time during the production process, further boosting production efficiency.

[0049] The intelligent production scheduling module of this system combines constraints such as equipment capacity and order priority to generate the optimal production scheduling plan to ensure the efficient execution of production tasks. The specific implementation is as follows: Multi-Process Collaborative Optimization Unit: This unit utilizes operations research and intelligent algorithms based on the characteristics of textile processes to achieve collaborative optimization of multiple processes. The system considers factors such as the logical relationships between processes, equipment capabilities, and staffing, and uses algorithmic models to calculate the optimal process sequence and resource allocation. This helps reduce production bottlenecks and improve overall production efficiency.

[0050] Real-time Scheduling Response Unit: Leveraging edge computing and IoT technologies, the real-time scheduling response unit responds to changes in production progress and equipment status in real time. Using IoT sensing devices, the system collects real-time data from the production site, such as equipment status, material inventory, and process parameters. Upon detecting an anomaly, such as a production delay or equipment failure, the system immediately triggers the scheduling mechanism, dynamically adjusting production task allocation to ensure smooth execution of the production plan.

[0051] Deep reinforcement learning algorithm: The intelligent production scheduling module uses a deep reinforcement learning algorithm, combined with historical production data, to continuously optimize production scheduling plans. By learning patterns and regularities from historical production data, the system predicts future trends in production demand. Based on this, the algorithm model automatically adjusts scheduling strategies to improve scheduling efficiency and equipment utilization. For example, the system can intelligently assign production tasks to the most appropriate production equipment based on factors such as order priority, equipment capacity, and delivery date.

[0052] Through the implementation of the intelligent production scheduling module, the system significantly improves equipment utilization, shortens production cycles, and reduces production delays. Furthermore, the real-time scheduling response mechanism ensures flexibility and resilience in the production process, enabling rapid response to various abnormal situations on the production site. The application of deep reinforcement learning algorithms empowers the system with self-learning and optimization capabilities, continuously improving scheduling efficiency and equipment utilization.

[0053] The quality control module of this system is used to monitor the production process in real time to ensure that the product quality meets the preset standards; the quality control module includes a quality detection unit and a quality feedback unit; Quality Inspection Unit: Equipped with high-precision sensors and image recognition technology, this unit performs quality inspections on semi-finished and finished products during the production process. Sensors monitor key process parameters such as temperature, humidity, and tension in real time, ensuring the production environment meets process requirements. Image recognition technology automatically inspects product appearance and identifies defects such as color difference, blemishes, and dimensional deviations.

[0054] Quality Feedback Unit: This unit promptly provides quality inspection results to the production department, enabling adjustments to production processes and equipment parameters. If a quality issue is detected, the system immediately triggers an alarm, notifying production personnel to address it. Furthermore, the system automatically adjusts production process parameters and equipment configuration based on the quality inspection results to ensure consistent product quality.

[0055] By implementing the quality control module, the system can achieve real-time monitoring and quality control of the production process, ensuring that product quality meets preset standards. This helps reduce defective and return rates, improving customer satisfaction and brand reputation.

[0056] The supply chain management module of this system is used to integrate upstream and downstream supply chain resources, realize timely procurement of raw materials and rapid distribution of products, and reduce inventory costs. It includes supplier management unit, procurement management unit, inventory management unit and logistics management unit.

[0057] Supplier Management Unit: Integrates upstream and downstream supply chain resources, establishes a supplier evaluation system, and regularly evaluates and selects suppliers. The system intelligently selects the most suitable suppliers for collaboration based on factors such as product quality, delivery time, and price.

[0058] Procurement Management Unit: Intelligently generates purchase orders based on production plans and inventory levels, enabling timely procurement of raw materials. The system monitors raw material inventory in real time and automatically triggers the procurement process if inventory falls below the safety stock level, ensuring the timely supply of raw materials required for production.

[0059] Inventory Management Unit: IoT sensing devices monitor inventory status in real time, enabling precise inventory management. The system can intelligently adjust inventory levels based on production plans and sales forecasts to avoid inventory backlogs and waste.

[0060] Logistics Management Unit: Establish partnerships with logistics companies to achieve rapid distribution and delivery of products. The system can intelligently select the most appropriate logistics solution based on order requirements and the transportation capacity of the logistics company to ensure that products are delivered to customers on time.

[0061] Through the implementation of the supply chain management module, the system can integrate and optimize the allocation of supply chain resources, reduce inventory costs, and improve the responsiveness and flexibility of the supply chain. This helps reduce inventory backlogs and waste, improves capital utilization efficiency, and ensures that products are delivered to customers on time, thereby improving customer satisfaction.

[0062] The visual monitoring module of this system uses virtual reality and data visualization technology to provide real-time and comprehensive production data display, helping managers make quick decisions. The specific implementation is as follows: Data Collection and Integration: IoT sensing devices collect real-time production site data, such as equipment status, material inventory, and process parameters. The system also integrates data from multiple modules, including the order management system, production scheduling system, and quality control system, to form a comprehensive view of production data.

[0063] Data visualization: Utilizing virtual reality and data visualization technologies, production data can be presented to management personnel in an intuitive and understandable manner. For example, 3D visualization can be used to display the production site layout and equipment status; dynamic charts can be used to display key performance indicators (KPIs) such as production progress, equipment utilization, and quality indicators.

[0064] Interaction and decision support: Managers can interact with production data through the visual monitoring module, such as filtering data for specific time periods, viewing detailed production logs, and simulating different production scenarios. This helps managers gain a deeper understanding of production conditions, identify potential problems, and develop targeted improvement measures.

[0065] By implementing a visual monitoring module, the system provides managers with a real-time, comprehensive view of production data, enabling them to make quick decisions. This helps improve the transparency and efficiency of production management, reducing information asymmetry and decision-making errors. Furthermore, the visual monitoring module helps enhance managers' decision-making capabilities and responsiveness, ensuring smooth production processes.

[0066] The cloud computing platform of this system connects physically dispersed textile factories, builds a production capacity sharing pool, and realizes intelligent matching of order demand and idle production capacity. The specific implementation method is as follows: Infrastructure as a Service (IaaS): This provides computing, storage, networking, and other infrastructure resources to support the informatization and digital transformation of textile factories. Through virtualization technology, it enables dynamic resource allocation and flexible expansion to meet the needs of textile factories at different production stages.

[0067] Platform as a Service (PaaS): This platform provides application development, testing, and deployment services, enabling textile factories to quickly build and deploy customized application systems. For example, textile factories can use PaaS to quickly develop order management systems and production scheduling systems tailored to their production needs.

[0068] Software as a Service (SaaS): This model provides standardized software services, such as ERP, CRM, and SCM, to support textile factories in standardizing and automating their business processes. Through the SaaS model, textile factories can obtain high-quality software services at a lower cost, improving business processing efficiency and accuracy.

[0069] Capacity Sharing Pool: Through the cloud computing platform, physically dispersed textile factories are connected in series to form a capacity sharing pool. The system intelligently matches orders with factories based on order demand and their respective production capacity, achieving optimized capacity allocation and efficient utilization.

[0070] By implementing a cloud computing platform, the system can break the limitations of physical space and achieve capacity sharing and collaborative production among textile factories. This helps improve overall capacity utilization, reduce production costs, and enhance the market competitiveness and responsiveness of textile factories.

[0071] The IoT data collection module of this system deploys IoT sensing devices to establish a cross-enterprise production data collection system, and interacts with equipment status, material inventory, and process parameters in real time. The specific implementation is as follows: Deployment of sensing devices: Various IoT sensing devices, such as sensors, RFID readers, and cameras, are deployed at the production site of textile factories. These devices can monitor key parameters of the production process in real time, such as equipment status, material inventory, and process parameters.

[0072] Data collection and transmission: IoT sensing devices transmit collected data to the cloud computing platform via wired or wireless means. Encryption technology is used during data transmission to ensure data security and integrity.

[0073] Data processing and analysis: The cloud computing platform cleans, integrates, and analyzes collected data to extract valuable information. For example, data analysis can reveal equipment operating conditions, material consumption, and the stability of process parameters.

[0074] Real-time interaction and sharing: The IoT data collection module enables real-time data interaction and sharing across enterprises. This helps textile factories achieve production collaboration and resource sharing, improving overall production efficiency.

[0075] By implementing IoT data collection modules, the system achieves comprehensive awareness and real-time monitoring of the production process. This helps promptly identify anomalies in the production process, such as equipment failures and material shortages, and enables prompt action to address them. Furthermore, real-time interaction and sharing mechanisms facilitate production collaboration and resource sharing among textile factories, improving overall production efficiency and market competitiveness.

[0076] See also Figure 2 A lace sharing manufacturing method, based on the above-mentioned lace sharing manufacturing system, includes the following steps: S1. Receive user order information, perform preliminary splitting of the order, and identify elements that can be merged; Users place orders through the user-friendly interface provided by the system. During the ordering process, users are required to enter detailed parameters for the lace product, including but not limited to material type (e.g., cotton, silk, polyester), pattern design (e.g., floral, geometric, abstract art), dimensions (e.g., width, length, density), and any special crafting requirements (e.g., embroidery, hot stamping, printing, etc.). The system supports multiple ordering methods, including but not limited to web, mobile app, and API, to meet the needs of diverse users.

[0077] After the system receives a user's order, the order splitting unit within the order consolidation module automatically performs a preliminary split. This split is based primarily on key factors such as lace type, pattern complexity, and material composition. For example, the system will split orders of the same type but with slightly different patterns into mergeable elements, such as those with the same lace base material or similar pattern styles. This step lays the foundation for subsequent intelligent matching and merging operations.

[0078] S2. Intelligently match the initially split orders based on preset detailed rules to form a merged production order; Based on this initial split, the merging decision unit intelligently matches orders based on pre-defined, sophisticated rules. These include, but are not limited to, lace type, pattern attributes, material requirements, and production equipment compatibility. Using advanced algorithms, the system analyzes similarities between orders and determines which ones can be consolidated for production, with minimal or no adjustments to production equipment.

[0079] During the intelligent matching process, the system utilizes a deep neural network model to conduct in-depth analysis of the order's product process. This model predicts production difficulty and required equipment, ensuring that the combined order can be produced with minimal or no machine modification. Through this intelligent matching, the system significantly improves production efficiency and reduces the costs associated with frequent machine changes.

[0080] S3. Generate the optimal production scheduling plan based on the merged production orders, combined with constraints such as equipment capacity and order priority; The intelligent production scheduling module generates an optimal production plan based on the merged production orders, taking into account key constraints such as equipment capacity and order priority. This module utilizes multi-process collaborative optimization algorithms and deep reinforcement learning algorithms to ensure the efficiency and feasibility of the production plan.

[0081] Multi-process collaborative optimization algorithms comprehensively consider the complexity of textile manufacturing processes, enabling coordinated optimization across multiple processes, thereby reducing production bottlenecks and improving overall production efficiency. Deep reinforcement learning algorithms continuously learn from historical production data to optimize scheduling strategies to address future changes in production demand. Through the combined application of these algorithms, the system generates an optimal scheduling plan that meets both equipment capacity constraints and order priority requirements.

[0082] S4. Real-time monitoring of production progress and equipment status, intelligent allocation and real-time adjustment of production tasks; During the production process, the IoT data collection module monitors production progress and equipment status in real time. Through IoT sensing devices deployed at the production site, the system collects key data such as equipment status, material inventory, and process parameters in real time. If anomalies such as production delays, equipment failures, or material shortages are detected, the system immediately triggers the scheduling mechanism, intelligently allocating and adjusting production tasks in real time.

[0083] This real-time monitoring and intelligent adjustment mechanism ensures the smooth execution of production plans and improves the flexibility and resilience of the production process. The system can quickly respond to various changes in the production site and ensure that production tasks are completed on time.

[0084] S5. Through the visual monitoring module, it provides real-time and comprehensive production data display to assist managers in making quick decisions; The visual monitoring module utilizes virtual reality and data visualization technologies to provide managers with a real-time and comprehensive display of production data. Through this module, managers can intuitively understand key information such as the production site layout, equipment status, and production progress.

[0085] Data visualization uses a variety of formats, including dynamic charts and 3D visualization, to provide managers with a deeper understanding of production conditions, identify potential issues, and develop targeted improvement measures. Furthermore, managers can interact with production data through the visual monitoring module, filtering data for specific time periods, viewing detailed production logs, and simulating different production scenarios. This interactive and decision-support feature further enhances managers' decision-making capabilities and response speed.

[0086] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0087] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A lace shared manufacturing system, characterized in that: include: Order merging module: used to merge similar but not identical lace product orders based on pre-set rules to improve production efficiency; the rules include but are not limited to lace type, pattern attributes, material requirements, and production equipment compatibility; Intelligent production scheduling module: used to generate production scheduling plans based on the merged production orders and constraints; Quality control module: used to monitor the production process in real time to ensure that product quality meets preset standards; the quality control module includes a quality detection unit and a quality feedback unit; Supply chain management module: used to integrate upstream and downstream supply chain resources to realize the procurement of raw materials and the distribution of products, including supplier management unit, procurement management unit, inventory management unit and logistics management unit; Visual monitoring module: uses virtual reality and data visualization technology to provide real-time and comprehensive production data display; Cloud computing platform: Connect physically dispersed textile factories, build a capacity sharing pool, and match order demand with idle capacity; IoT data collection module: Deploy IoT sensing devices, establish a cross-enterprise production data collection system, and interact with equipment status, material inventory, and process parameters in real time.

2. The lace shared manufacturing system according to claim 1, characterized in that: The order merging module further includes: User input interface: used to receive user order information, the detailed parameters of the order information include the material, pattern and size of the lace product; Order splitting processing unit: preliminarily splits the order according to the user input content and identifies elements that can be merged, such as the same lace type, similar pattern or common materials; Merger judgment unit: Intelligently match the initially split orders based on rules to form merged production orders, ensuring that the merged orders can be produced without or with minimal machine changes.

3. The lace shared manufacturing system according to claim 1, characterized in that: The intelligent production scheduling module further includes: Multi-process collaborative optimization unit: used to achieve collaborative optimization of multiple processes and reduce production bottlenecks based on the characteristics of textile technology by applying operations research and intelligent algorithms; Real-time scheduling response unit: Leveraging edge computing and IoT technologies, it responds to changes in production progress and equipment status in real time, dynamically adjusting production task allocation; Deep reinforcement learning algorithm: Using deep reinforcement learning algorithm, combined with historical production data, we continuously optimize production scheduling plans, improve scheduling efficiency and equipment utilization.

4. The lace shared manufacturing system according to any one of claim 1, characterized in that: The constraints include equipment capacity and order priority.

5. The lace shared manufacturing system according to claim 1, characterized in that: The quality control module further comprises: Quality inspection unit: Equipped with sensors and image recognition technology, it performs quality inspections on semi-finished and finished products during the production process and identifies defective products in real time; Quality feedback unit: Feedback quality test results to the production department to adjust production processes or equipment parameters to ensure stable product quality.

6. The lace shared manufacturing system according to claim 1, characterized in that: The system also supports: Multi-factory collaborative production: Through the cloud computing platform, production task collaboration and resource sharing among multiple factories can be achieved; Customized production service: According to user needs, we provide customized lace product design and production services to meet user's personalized needs.

7. The lace shared manufacturing system according to claim 1, characterized in that: The multi-factory collaborative production further includes: Factory Capacity Assessment: Evaluate the production capacity, equipment status, technical level, etc. of the factories that join the system to ensure that the factories can meet production needs; Production task allocation: Based on the factory capacity assessment results and order requirements, production tasks are allocated to the most suitable factory for production; Production progress tracking: Through the IoT data collection module, the production progress of each factory can be tracked in real time to ensure that orders are delivered on time.

8. A lace shared manufacturing method, based on a lace shared manufacturing system according to any one of claims 1 to 7, characterized in that: The following steps are involved: S1. Receive user order information, perform preliminary splitting of the order, and identify elements that can be merged; S2. Match the initially split orders according to preset rules to form a merged production order; S3. Generate a production schedule based on the merged production orders and constraints, including equipment capacity and order priority. S4. Real-time monitoring of production progress and equipment status, allocation and real-time adjustment of production tasks; S5. Through the visual monitoring module, production data display is provided to assist managers in making quick decisions.

9. The lace sharing manufacturing method according to claim 8, characterized in that: The step S2 further includes: Use deep neural network models to analyze order product processes and predict production difficulty and required equipment; Match the order product process with the production equipment to ensure that the combined order can be produced without or with minimal machine modification; Based on the matching results, orders are further optimized and merged to improve production efficiency.

10. The lace sharing manufacturing method according to claim 8, characterized in that: The step S3 further includes: Collect and analyze data from the production process to optimize order consolidation rules and intelligent production scheduling algorithms; Evaluate system performance, upgrade and improve the system based on the evaluation results, and enhance system performance.

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