技术领域Technical Field
本发明涉及家纺制品生产控制技术领域，尤其涉及一种基于PLC编程自动控制的家纺制品生产方法及系统。The present invention relates to the technical field of home textile product production control, and in particular to a home textile product production method and system based on PLC programming automatic control.
背景技术Background technique
PLC，全称可编程逻辑控制器(Programmable Logic Controller)，是一种专为工业环境中的数字运算操作而设计的电子设备。它采用可编程的存储器，用于其内部存储执行逻辑运算、顺序控制、计时、计数和算术运算等指令，并能通过数字或模拟输入/输出接口，控制各种类型的机械设备或生产过程。通过将PLC与家纺制品生产设备相连，实现了生产线上各工序的自动控制，包括原材料处理、编织、染色、定型、裁剪和包装等环节。PLC根据预设的程序执行指令，通过收集来自传感器的实时数据，动态调整设备参数，确保生产过程的高效、稳定运行。然而，传统的家纺制品生产方法需要人工观测家纺制品原料以进行筛选，使得对家纺制品原料的筛选效果较差且耗费人力资源，并且需要人工控制家纺制品生产过程中生产设备的控制参数，使得生产成本过大，不能保证家纺制品的产品质量以及生产过程的响应速度。PLC, the full name of which is Programmable Logic Controller, is an electronic device designed for digital computing operations in industrial environments. It uses a programmable memory to store instructions such as logical operations, sequential control, timing, counting and arithmetic operations, and can control various types of mechanical equipment or production processes through digital or analog input/output interfaces. By connecting PLC to home textile production equipment, automatic control of each process on the production line is achieved, including raw material processing, weaving, dyeing, shaping, cutting and packaging. PLC executes instructions according to preset programs, and dynamically adjusts equipment parameters by collecting real-time data from sensors to ensure efficient and stable operation of the production process. However, the traditional home textile production method requires manual observation of home textile raw materials for screening, which makes the screening effect of home textile raw materials poor and consumes human resources, and requires manual control of the control parameters of the production equipment during the production of home textile products, which makes the production cost too high and cannot guarantee the product quality of home textile products and the response speed of the production process.
发明内容Summary of the invention
基于此，本发明提供一种基于PLC编程自动控制的家纺制品生产方法及系统，以解决至少一个上述技术问题。Based on this, the present invention provides a method and system for producing home textile products based on PLC programming and automatic control to solve at least one of the above technical problems.
为实现上述目的，一种基于PLC编程自动控制的家纺制品生产方法，包括以下步骤：To achieve the above purpose, a method for producing home textile products based on PLC programming and automatic control comprises the following steps:
步骤S1：根据监控扫描设备进行家纺制品原料扫描处理，生成家纺制品原料扫描数据；对家纺制品原料扫描数据进行目标家纺制品原料扫描数据采集，以得到目标家纺制品原料扫描数据；Step S1: scanning the home textile product raw material according to the monitoring scanning device to generate home textile product raw material scanning data; collecting target home textile product raw material scanning data on the home textile product raw material scanning data to obtain the target home textile product raw material scanning data;
步骤S2：根据传感器集成设备进行家纺制品生产数据实时采集处理，生成家纺制品生产数据，其中所述家纺制品生产数据包括生产设备状态数据以及家纺制品状态数据；Step S2: collecting and processing the home textile product production data in real time according to the sensor integrated device to generate the home textile product production data, wherein the home textile product production data includes the production equipment status data and the home textile product status data;
步骤S3：对目标家纺制品原料扫描数据以及家纺制品生产数据进行数据匹配处理，生成家纺制品原料-生产匹配数据；Step S3: performing data matching processing on the target home textile product raw material scanning data and the home textile product production data to generate home textile product raw material-production matching data;
步骤S4：根据预设的支持向量机算法以及家纺制品原料-生产匹配数据进行家纺制品生产质量的优化预测模型建立，生成优化家纺制品生产质量预测模型；根据优化家纺制品生产质量预测模型进行PLC生产控制逻辑设计，生成PLC生产控制逻辑数据，并将PLC生产控制逻辑数据反馈至终端执行家纺制品自动控制生产作业。Step S4: Establish an optimization prediction model for the production quality of home textile products based on the preset support vector machine algorithm and the home textile product raw material-production matching data, and generate an optimized home textile product production quality prediction model; design PLC production control logic based on the optimized home textile product production quality prediction model, generate PLC production control logic data, and feed back the PLC production control logic data to the terminal to execute the automatic control production operation of home textile products.
本发明通过监控扫描设备进行家纺制品原料的扫描处理，实现了对原料信息的快速、准确采集，确保了生产过程中使用的原料质量和规格符合预定标准，通过对原料数据的精确采集，有效避免因原料问题导致的生产缺陷，提高最终产品的质量，精确的原料数据还支持生产过程中的物料跟踪、供应链管理和成本控制，为高效和可持续的生产提供数据支持。利用传感器集成设备实时采集家纺制品的生产数据，包括生产设备状态和家纺制品状态，能够实现对生产过程的实时监控和控制，有助于即时发现生产过程中的任何异常或偏差，如设备故障或产品质量问题，从而迅速采取措施进行调整或修正。实时数据采集和分析还能优化生产调度和资源配置，提高生产效率和灵活性，减少浪费，最终实现成本节约和生产力的提升。对目标家纺制品原料扫描数据及家纺制品生产数据进行精确的数据匹配处理，生成的家纺制品原料-生产匹配数据能够为生产过程提供更为准确的数据支持，这种匹配处理确保了原料选择与实际生产需求之间的最佳匹配，减少了因原料不匹配造成的生产效率下降和产品质量问题，支持了更精细化的生产管理，比如原料的优化利用和生产过程的精细调控，从而提升了资源利用效率和生产过程的可控性。通过预设的支持向量机(SVM)算法和家纺制品原料-生产匹配数据，建立的优化家纺制品生产质量预测模型能够有效预测并优化生产质量，不仅显著提高了产品质量和生产效率，还通过精确的质量控制减少了浪费，提升了整体的生产经济效益。利用该模型进行PLC生产控制逻辑设计，并将控制逻辑数据反馈至生产终端，实现了生产过程的自动化和智能化控制。这种基于数据驱动的控制逻辑，使生产过程更加灵活、响应速度更快，能够有效应对生产过程中的各种变化和不确定性，保证生产过程的稳定性和产品质量的一致性。The present invention scans and processes the raw materials of home textile products through monitoring scanning equipment, thereby realizing rapid and accurate collection of raw material information, ensuring that the quality and specifications of the raw materials used in the production process meet the predetermined standards. Through the accurate collection of raw material data, production defects caused by raw material problems can be effectively avoided, and the quality of the final product can be improved. Accurate raw material data also supports material tracking, supply chain management and cost control in the production process, and provides data support for efficient and sustainable production. Using sensor integrated equipment to collect production data of home textile products in real time, including the status of production equipment and home textile products, can achieve real-time monitoring and control of the production process, which helps to immediately discover any anomalies or deviations in the production process, such as equipment failures or product quality problems, so as to quickly take measures to adjust or correct them. Real-time data collection and analysis can also optimize production scheduling and resource allocation, improve production efficiency and flexibility, reduce waste, and ultimately achieve cost savings and productivity improvements. The target home textile product raw material scanning data and home textile product production data are accurately matched, and the generated home textile product raw material-production matching data can provide more accurate data support for the production process. This matching process ensures the best match between raw material selection and actual production needs, reduces the decline in production efficiency and product quality problems caused by raw material mismatch, and supports more refined production management, such as the optimal utilization of raw materials and fine regulation of the production process, thereby improving resource utilization efficiency and controllability of the production process. Through the preset support vector machine (SVM) algorithm and home textile product raw material-production matching data, the optimized home textile product production quality prediction model established can effectively predict and optimize production quality, which not only significantly improves product quality and production efficiency, but also reduces waste through precise quality control, and improves the overall production economic benefits. The model is used to design PLC production control logic, and the control logic data is fed back to the production terminal, realizing the automation and intelligent control of the production process. This data-driven control logic makes the production process more flexible and responsive, and can effectively respond to various changes and uncertainties in the production process, ensuring the stability of the production process and the consistency of product quality.
优选地，步骤S1包括以下步骤：Preferably, step S1 comprises the following steps:
步骤S11：根据监控扫描设备进行家纺制品原料扫描处理，生成家纺制品原料扫描数据；Step S11: Scanning the raw materials of home textile products according to the monitoring scanning device to generate scanning data of the raw materials of home textile products;
步骤S12：对家纺制品原料扫描数据进行多维原料质量检测处理，生成多维原料质量检测数据；Step S12: performing multi-dimensional raw material quality detection processing on the home textile product raw material scanning data to generate multi-dimensional raw material quality detection data;
步骤S13：根据多维原料质量检测数据进行PLC原料筛选逻辑分析，生成PLC原料筛选逻辑数据；Step S13: performing PLC raw material screening logic analysis according to the multi-dimensional raw material quality detection data to generate PLC raw material screening logic data;
步骤S14：基于PLC原料筛选逻辑数据对多维原料质量检测数据进行有效原料质量检测数据筛选，以得到有效原料质量检测数据；Step S14: Screening the effective raw material quality detection data of the multi-dimensional raw material quality detection data based on the PLC raw material screening logic data to obtain effective raw material quality detection data;
步骤S15：基于有效原料质量检测数据对家纺制品原料扫描数据进行目标家纺制品原料扫描数据采集，以得到目标家纺制品原料扫描数据。Step S15: collecting target home textile product raw material scanning data on the home textile product raw material scanning data based on the effective raw material quality detection data to obtain the target home textile product raw material scanning data.
本发明通过监控扫描设备进行家纺制品原料的扫描处理，直接生成家纺制品原料扫描数据，自动化地收集了原料的详细信息，包括类型、质量、尺寸等，为后续的原料筛选和质量检测提供了第一手数据，这样的自动化扫描减少了人为错误，提高了数据收集的效率和准确性。对多维原料质量检测数据进行多维原料质量检测处理，生成的多维原料质量检测数据能够全面反映原料的质量状态，通过多角度、多指标地评估原料，能够揭示出原料的综合质量情况，包括但不限于强度、纯度、颜色等各种重要参数，确保筛选出来的原料在各个重要维度上都符合生产要求。根据多维原料质量检测数据进行PLC原料筛选逻辑分析，生成PLC原料筛选逻辑数据，实现了原料筛选的自动化和智能化，使得原料筛选过程更加精准、高效，通过预设的筛选逻辑自动排除不符合质量标准的原料，确保了生产使用的原料都是符合要求的，从而直接提升了产品质量和生产效率。基于PLC原料筛选逻辑数据，对多维原料质量检测数据进行有效原料质量检测数据筛选，以得到有效原料质量检测数据，提高了原料质量检测的准确度和效率，这种筛选机制减少了因原料质量不达标导致的生产问题和成品缺陷。基于有效原料质量检测数据，对家纺制品原料扫描数据进行目标家纺制品原料扫描数据采集，进一步提炼和确保了所采集原料数据的目标性和有效性，确保符合生产要求的原料数据才会被用于后续生产过程，优化了原料的选择过程，从而在提高生产效率的同时，也最大化地保障了产品的质量。The present invention performs scanning processing of home textile product raw materials through monitoring scanning equipment, directly generates home textile product raw material scanning data, automatically collects detailed information of raw materials, including type, quality, size, etc., provides first-hand data for subsequent raw material screening and quality inspection, and such automated scanning reduces human errors and improves the efficiency and accuracy of data collection. Multidimensional raw material quality inspection data is subjected to multidimensional raw material quality inspection processing, and the generated multidimensional raw material quality inspection data can fully reflect the quality status of raw materials. By evaluating raw materials from multiple angles and multiple indicators, the comprehensive quality of raw materials can be revealed, including but not limited to various important parameters such as strength, purity, color, etc., to ensure that the screened raw materials meet production requirements in all important dimensions. PLC raw material screening logic analysis is performed according to multidimensional raw material quality inspection data to generate PLC raw material screening logic data, realizes the automation and intelligence of raw material screening, makes the raw material screening process more accurate and efficient, and automatically excludes raw materials that do not meet quality standards through preset screening logic, ensuring that the raw materials used in production meet the requirements, thereby directly improving product quality and production efficiency. Based on the PLC raw material screening logic data, the multi-dimensional raw material quality inspection data is screened for effective raw material quality inspection data to obtain effective raw material quality inspection data, which improves the accuracy and efficiency of raw material quality inspection. This screening mechanism reduces production problems and finished product defects caused by substandard raw material quality. Based on the effective raw material quality inspection data, the home textile product raw material scanning data is collected for target home textile product raw material scanning data, further refining and ensuring the target and effectiveness of the collected raw material data, ensuring that raw material data that meets production requirements will be used in subsequent production processes, optimizing the raw material selection process, thereby improving production efficiency while maximizing product quality.
优选地，步骤S12包括以下步骤：Preferably, step S12 comprises the following steps:
对多维原料质量检测数据进行维度指标赋权处理，生成原料质量维度指标权重数据；根据预设的多维原料质量评估决策以及原料质量维度指标权重数据进行PLC原料筛选逻辑设计，生成PLC原料筛选逻辑数据。The multi-dimensional raw material quality inspection data is processed by dimensional indicator weighting to generate raw material quality dimensional indicator weight data; the PLC raw material screening logic is designed according to the preset multi-dimensional raw material quality assessment decision and raw material quality dimensional indicator weight data to generate PLC raw material screening logic data.
本发明对多维原料质量检测数据进行维度指标赋权处理，生成的原料质量维度指标权重数据，合理反映出各个质量指标对最终产品质量的影响程度，通过为不同的质量维度指定不同的权重，确保了在原料筛选过程中能够更加精准地评价原料的适用性和优劣。基于预设的多维原料质量评估决策以及原料质量维度指标权重数据进行的PLC原料筛选逻辑设计，实现原料筛选过程的自动化和智能化，不仅提高了筛选的效率和准确性，还使得原料的选择过程更加客观和合理，通过精确控制原料的质量，可以直接影响到生产流程的顺畅进行和产品质量的稳定性，确保生产出的家纺制品能够满足高标准的质量要求。The present invention performs dimensional index weighting processing on multi-dimensional raw material quality detection data, and the generated raw material quality dimensional index weight data reasonably reflects the degree of influence of each quality index on the quality of the final product. By specifying different weights for different quality dimensions, it is ensured that the applicability and quality of the raw materials can be more accurately evaluated during the raw material screening process. The PLC raw material screening logic design based on the preset multi-dimensional raw material quality assessment decision and the raw material quality dimensional index weight data realizes the automation and intelligence of the raw material screening process, which not only improves the efficiency and accuracy of the screening, but also makes the raw material selection process more objective and reasonable. By accurately controlling the quality of the raw materials, it can directly affect the smooth progress of the production process and the stability of the product quality, ensuring that the produced home textile products can meet high standards of quality requirements.
优选地，步骤S2包括以下步骤：Preferably, step S2 comprises the following steps:
步骤S21：根据预设的家纺制品生产监测决策进行传感器配置数据分析，生成传感器配置数据；Step S21: Analyze sensor configuration data according to preset home textile product production monitoring decisions to generate sensor configuration data;
步骤S22：基于传感器配置数据对传感器集成设备执行配置调节，并通过配置调节后的传感器集成设备进行初始家纺制品生产数据实时监测处理，生成初始家纺制品生产数据，其中所述初始家纺制品生产数据包括初始生产设备状态数据以及初始家纺制品状态数据；Step S22: performing configuration adjustment on the sensor integrated device based on the sensor configuration data, and performing real-time monitoring and processing of initial home textile product production data through the sensor integrated device after the configuration adjustment to generate initial home textile product production data, wherein the initial home textile product production data includes initial production device status data and initial home textile product status data;
步骤S23：根据初始家纺制品生产数据进行生产设备异常模式识别处理，生成生产设备异常模式数据；Step S23: performing production equipment abnormal pattern recognition processing according to the initial home textile product production data to generate production equipment abnormal pattern data;
步骤S24：根据生产设备异常模式数据进行生产设备修复参数分析处理，生成生产设备修复参数；Step S24: Analyze and process production equipment repair parameters according to the production equipment abnormality mode data to generate production equipment repair parameters;
步骤S25：基于生产设备修复参数对初始家纺制品生产数据进行迭代更新处理，生成家纺制品生产数据。Step S25: performing iterative updating processing on the initial home textile product production data based on the production equipment repair parameters to generate home textile product production data.
本发明根据预设的家纺制品生产监测决策进行传感器配置数据分析，可以精确指导传感器集成设备的配置，确保传感器布置最佳匹配生产需求，优化了传感器的配置，使其能够更加精确地监测生产过程中的关键参数，从而提高生产过程的监控效率和准确性。适当的传感器配置对于实现高质量生产至关重要，因为它直接影响到数据收集的质量和全面性。基于传感器配置数据对传感器集成设备执行配置调节，并通过配置调节后的传感器集成设备进行初始家纺制品生产数据的实时监测处理，确保了生产过程从一开始就被实时监控，允许及时发现并处理任何潜在的生产问题，从而减少生产中断的风险，提高生产线的稳定性和产品的一致性。对初始家纺制品生产数据进行生产设备异常模式识别处理，帮助及时识别生产过程中的异常情况，如设备故障或性能下降，提高了对生产问题的响应速度和处理效率，确保了生产过程的连续性和产品质量的稳定性。根据生产设备异常模式数据进行生产设备修复参数分析处理，生成的生产设备修复参数指导设备的及时维修和调整，不仅减少了设备停机时间，还确保了设备在恢复生产后能够以最佳状态运行，从而提高了生产效率和设备的使用寿命。基于生产设备修复参数对初始家纺制品生产数据进行迭代更新处理，反映了设备维修和调整后的生产状态，确保了生产数据的持续更新和优化，为持续改进生产过程提供了数据支持，更好地监控生产过程的各个环节，实现对生产过程的精细控制，从而提升整体的生产质量和效率。The present invention analyzes sensor configuration data according to preset home textile production monitoring decisions, can accurately guide the configuration of sensor integrated equipment, ensure that the sensor layout best matches the production requirements, optimize the configuration of the sensor, and enable it to more accurately monitor the key parameters in the production process, thereby improving the monitoring efficiency and accuracy of the production process. Appropriate sensor configuration is crucial to achieving high-quality production because it directly affects the quality and comprehensiveness of data collection. Based on the sensor configuration data, the sensor integrated equipment is configured and adjusted, and the initial home textile production data is monitored in real time by the sensor integrated equipment after configuration adjustment, ensuring that the production process is monitored in real time from the beginning, allowing any potential production problems to be discovered and handled in a timely manner, thereby reducing the risk of production interruption, and improving the stability of the production line and the consistency of the product. The initial home textile production data is processed by abnormal pattern recognition of production equipment, which helps to timely identify abnormal situations in the production process, such as equipment failure or performance degradation, improves the response speed and processing efficiency to production problems, and ensures the continuity of the production process and the stability of product quality. The production equipment repair parameter analysis and processing is performed based on the abnormal mode data of the production equipment. The generated production equipment repair parameters guide the timely maintenance and adjustment of the equipment, which not only reduces the equipment downtime, but also ensures that the equipment can operate in the best state after resuming production, thereby improving production efficiency and the service life of the equipment. The initial home textile product production data is iteratively updated based on the production equipment repair parameters, reflecting the production status after equipment maintenance and adjustment, ensuring the continuous updating and optimization of production data, providing data support for the continuous improvement of the production process, better monitoring of each link of the production process, and achieving fine control of the production process, thereby improving the overall production quality and efficiency.
优选地，步骤S23包括以下步骤：Preferably, step S23 includes the following steps:
根据初始家纺制品状态数据进行异常家纺制品状态数据提取处理，生成异常家纺制品状态数据；Extracting and processing abnormal home textile product status data according to the initial home textile product status data to generate abnormal home textile product status data;
基于异常家纺制品状态数据对初始生产设备状态数据进行异常生产设备状态数据标记处理，生成异常生产设备状态数据；Based on the abnormal home textile product status data, the initial production equipment status data is subjected to abnormal production equipment status data marking processing to generate abnormal production equipment status data;
根据异常生产设备状态数据进行生产设备周期性异常分析处理，生成生产设备周期性异常数据；Perform periodic abnormal analysis and processing of production equipment based on abnormal production equipment status data to generate periodic abnormal data of production equipment;
根据生产设备周期性异常数据进行生产设备异常模式识别处理，生成生产设备异常模式数据。The abnormal pattern recognition processing of the production equipment is performed based on the periodic abnormal data of the production equipment to generate the abnormal pattern data of the production equipment.
本发明对初始家纺制品状态数据进行异常数据提取处理，快速识别生产过程中的质量问题，有助于及时发现产品质量偏差，比如尺寸不一致、颜色差异过大或其他质量缺陷，从而实现对问题产品的早期识别。基于异常家纺制品状态数据对初始生产设备状态数据进行异常状态标记处理，精确地定位生产问题的来源。这个过程通过标记与产品质量问题相关联的设备状态异常，为诊断和解决生产问题提供了重要线索，确保了问题可以被迅速地识别和定位，减少了问题解决的时间，提高了生产线的稳定性和效率。根据异常生产设备状态数据进行周期性异常分析处理，揭示出设备故障或性能下降的周期性模式，识别可能由于设备磨损或定期维护不足导致的问题，从而实现对生产设备的预防性维护和优化调整，通过提前识别和解决周期性问题，显著减少了生产中断的风险，确保了生产流程的连续性和生产效率的提高。根据生产设备周期性异常数据进行异常模式识别处理，能够为生产设备的维护和修复提供明确的指导，减少设备故障导致的生产损失，这种基于数据的识别和预测机制大大提高了生产设备的可靠性和生产线的稳定运行。The present invention performs abnormal data extraction processing on the initial home textile product status data, quickly identifies quality problems in the production process, and helps to timely discover product quality deviations, such as inconsistent size, excessive color difference or other quality defects, thereby realizing early identification of problem products. Based on the abnormal home textile product status data, the initial production equipment status data is subjected to abnormal status marking processing to accurately locate the source of the production problem. This process provides important clues for diagnosing and solving production problems by marking the abnormal equipment status associated with product quality problems, ensuring that the problems can be quickly identified and located, reducing the time for problem solving, and improving the stability and efficiency of the production line. Periodic abnormal analysis processing is performed based on the abnormal production equipment status data to reveal the periodic pattern of equipment failure or performance degradation, identify problems that may be caused by equipment wear or insufficient regular maintenance, thereby realizing preventive maintenance and optimization adjustment of production equipment, and significantly reducing the risk of production interruption by identifying and solving periodic problems in advance, ensuring the continuity of the production process and the improvement of production efficiency. Abnormal pattern recognition processing is performed based on the periodic abnormal data of production equipment, which can provide clear guidance for the maintenance and repair of production equipment and reduce production losses caused by equipment failure. This data-based recognition and prediction mechanism greatly improves the reliability of production equipment and the stable operation of the production line.
优选地，步骤S3包括以下步骤：Preferably, step S3 comprises the following steps:
步骤S3 1：根据目标家纺制品原料扫描数据以及家纺制品状态数据进行家纺制品数据匹配处理，生成家纺制品匹配数据；Step S31: performing home textile product data matching processing according to the target home textile product raw material scanning data and the home textile product status data to generate home textile product matching data;
步骤S32：根据目标家纺制品原料扫描数据以及生产设备状态数据进行生产时序匹配处理，生成生产时序匹配数据；Step S32: Perform production timing matching processing according to the target home textile product raw material scanning data and the production equipment status data to generate production timing matching data;
步骤S33：根据家纺制品匹配数据以及生产时序匹配数据进行家纺制品生产匹配节点分析处理，生成家纺制品生产匹配节点数据；Step S33: performing home textile product production matching node analysis and processing according to the home textile product matching data and the production time sequence matching data to generate home textile product production matching node data;
步骤S34：根据家纺制品生产匹配节点数据对目标家纺制品原料扫描数据以及家纺制品生产数据进行数据匹配处理，生成家纺制品原料-生产匹配数据。Step S34: performing data matching processing on the target home textile product raw material scanning data and the home textile product production data according to the home textile product production matching node data to generate home textile product raw material-production matching data.
本发明根据目标家纺制品原料扫描数据及家纺制品状态数据进行家纺制品数据匹配处理，确保原料选择与最终产品状态之间的高度一致性，有效地连接了原料的特性与产品的质量要求，确保使用的原料最适合预定的产品标准，从而提高了产品质量和满足客户需求的能力。根据目标家纺制品原料扫描数据及生产设备状态数据进行生产时序匹配处理，优化生产流程的时间安排和资源分配，确保了生产活动与设备状态的同步，从而最大化生产效率，实现了更流畅和高效的生产调度。通过根据家纺制品匹配数据及生产时序匹配数据进行家纺制品生产匹配节点分析处理，识别生产过程中的关键节点，如原料输入、加工过程、和质量控制点，通过精确分析和优化生产关键节点，提高了生产过程的管理效率和产品质量控制的准确性，为顺利完成生产任务提供了重要支撑。根据家纺制品生产匹配节点数据对目标家纺制品原料扫描数据及家纺制品生产数据进行最终的数据匹配处理，为生产过程提供了全面的数据支持，能够确保生产决策和控制逻辑基于准确和全面的信息，从而最大化地提升生产过程的效率和产品质量，同时减少资源浪费，优化生产成本。The present invention performs home textile product data matching processing based on the target home textile product raw material scanning data and home textile product status data, ensures a high degree of consistency between raw material selection and final product status, effectively connects the characteristics of the raw materials with the quality requirements of the product, ensures that the used raw materials are most suitable for the predetermined product standards, thereby improving product quality and the ability to meet customer needs. Production timing matching processing is performed based on the target home textile product raw material scanning data and production equipment status data, optimizes the time arrangement and resource allocation of the production process, ensures the synchronization of production activities and equipment status, thereby maximizing production efficiency and achieving smoother and more efficient production scheduling. By performing home textile product production matching node analysis and processing based on home textile product matching data and production timing matching data, key nodes in the production process, such as raw material input, processing process, and quality control points, are identified. By accurately analyzing and optimizing key production nodes, the management efficiency of the production process and the accuracy of product quality control are improved, providing important support for the smooth completion of production tasks. The final data matching processing is performed on the target home textile product raw material scanning data and home textile product production data according to the home textile product production matching node data, which provides comprehensive data support for the production process and can ensure that production decisions and control logic are based on accurate and comprehensive information, thereby maximizing the efficiency of the production process and product quality, while reducing resource waste and optimizing production costs.
优选地，步骤S4包括以下步骤：Preferably, step S4 comprises the following steps:
步骤S41：根据预设的支持向量机算法建立生产设备状态以及家纺制品状态的映射关系，以生成初步家纺制品生产质量预测模型；Step S41: establishing a mapping relationship between the production equipment status and the home textile product status according to a preset support vector machine algorithm to generate a preliminary home textile product production quality prediction model;
步骤S42：根据家纺制品原料-生产匹配数据中的生产设备状态数据作为输入数据以及家纺制品原料-生产匹配数据中的家纺制品状态数据作为输出数据进行模型训练样本设计，以生成模型训练样本；Step S42: Designing a model training sample according to the production equipment status data in the home textile product raw material-production matching data as input data and the home textile product status data in the home textile product raw material-production matching data as output data, so as to generate a model training sample;
步骤S43：根据模型训练样本对初步家纺制品生产质量预测模型进行模型训练优化处理，生成优化家纺制品生产质量预测模型；Step S43: performing model training optimization processing on the preliminary home textile product production quality prediction model according to the model training samples to generate an optimized home textile product production quality prediction model;
步骤S44：根据优化家纺制品生产质量预测模型进行PLC生产控制逻辑设计，生成PLC生产控制逻辑数据，并将PLC生产控制逻辑数据反馈至终端执行家纺制品自动控制生产作业。Step S44: Design the PLC production control logic according to the optimized home textile product production quality prediction model, generate PLC production control logic data, and feed back the PLC production control logic data to the terminal to execute the automatic control production operation of home textile products.
本发明利用预设的支持向量机(SVM)算法建立生产设备状态与家纺制品状态的映射关系，生成的初步家纺制品生产质量预测模型准确地预测生产过程中的质量变化，这种映射关系的建立允许在生产早期阶段即可预测和识别潜在的产品质量问题，为提前采取改进措施提供了可能，从而提高最终产品的质量。通过设计模型训练样本，即将家纺制品原料-生产匹配数据中的生产设备状态数据作为输入，以及相应的家纺制品状态数据作为输出，这一过程为深入训练和优化质量预测模型提供了精确的数据基础，确保了模型能够准确捕捉到生产过程中的关键特征和规律，从而提高预测模型的准确性和可靠性。通过对初步家纺制品生产质量预测模型进行模型训练优化处理，生成的优化家纺制品生产质量预测模型能够更准确地预测生产质量，特别是在复杂的生产环境中，这种优化处理通过不断地学习和调整，使预测模型更加精细化和适应性强，能够有效指导生产过程的调整，确保产品质量持续优化。根据优化家纺制品生产质量预测模型进行的PLC生产控制逻辑设计及其数据反馈，实现了生产过程的自动化和智能化控制，通过精确控制生产参数和流程，确保生产活动能够根据预测模型的指导以及PLC的自动化程序自动调整生产设备的控制参数，从而最大程度上提高生产效率和产品质量，这种基于数据和模型的智能化生产控制机制为制造业提供了一种高效、灵活且可持续的生产解决方案。The present invention uses a preset support vector machine (SVM) algorithm to establish a mapping relationship between the state of production equipment and the state of home textile products. The generated preliminary home textile product production quality prediction model accurately predicts the quality changes in the production process. The establishment of this mapping relationship allows the prediction and identification of potential product quality problems in the early stage of production, which provides the possibility for taking improvement measures in advance, thereby improving the quality of the final product. By designing a model training sample, that is, taking the production equipment state data in the home textile product raw material-production matching data as input, and the corresponding home textile product state data as output, this process provides an accurate data basis for in-depth training and optimization of the quality prediction model, ensuring that the model can accurately capture the key features and laws in the production process, thereby improving the accuracy and reliability of the prediction model. By performing model training optimization processing on the preliminary home textile product production quality prediction model, the generated optimized home textile product production quality prediction model can more accurately predict the production quality, especially in a complex production environment. This optimization processing makes the prediction model more refined and adaptable through continuous learning and adjustment, and can effectively guide the adjustment of the production process to ensure continuous optimization of product quality. The PLC production control logic design and its data feedback based on the optimized home textile product production quality prediction model realize the automation and intelligent control of the production process. By precisely controlling the production parameters and processes, it ensures that production activities can automatically adjust the control parameters of production equipment according to the guidance of the prediction model and the PLC automation program, thereby maximizing production efficiency and product quality. This data and model-based intelligent production control mechanism provides the manufacturing industry with an efficient, flexible and sustainable production solution.
优选地，步骤S43包括以下步骤：Preferably, step S43 includes the following steps:
将模型训练样本进行数据划分，分别生成模型训练集、模型验证集、模型测试集；Divide the model training samples into data to generate model training set, model verification set, and model test set respectively;
利用模型训练集对初步家纺制品生产质量预测模型进行模型训练处理，生成家纺制品生产质量预测模型；Using the model training set to perform model training processing on the preliminary home textile product production quality prediction model, and generating the home textile product production quality prediction model;
基于模型验证集对家纺制品生产质量预测模型进行模型验证评估处理，生成模型验证评估数据；Based on the model validation set, the model validation and evaluation processing of the home textile product production quality prediction model is carried out to generate model validation and evaluation data;
对模型验证评估数据进行生产链路评估数据的阶段划分出来，生成生产链路阶段评估数据；Divide the model validation evaluation data into stages of production link evaluation data to generate production link stage evaluation data;
根据生产链路阶段评估数据进行生产链路瓶颈优化参数分析，生成生产链路瓶颈优化参数；Analyze the bottleneck optimization parameters of the production link based on the production link stage evaluation data, and generate the production link bottleneck optimization parameters;
通过生产链路瓶颈优化参数对家纺制品生产质量预测模型进行模型优化调节，生成优化调节后的家纺制品生产质量预测模型，并利用模型测试集对优化调节后的家纺制品生产质量预测模型进行模型测试，生成优化家纺制品生产质量预测模型。The home textile product production quality prediction model is optimized and adjusted by optimizing the production link bottleneck parameters to generate an optimized home textile product production quality prediction model. The optimized home textile product production quality prediction model is tested using a model test set to generate an optimized home textile product production quality prediction model.
本发明将模型训练样本进行数据划分，生成模型训练集、模型验证集、模型测试集的做法，确保了机器学习模型的训练、验证、和测试过程互不干扰，公正地评估模型性能，有助于有效避免模型过拟合，确保模型具有良好的泛化能力。利用模型训练集对初步家纺制品生产质量预测模型进行模型训练处理，使模型能够学习到生产过程中的关键质量影响因素及其内在关系，从而提高了模型预测生产质量的准确性。基于模型验证集对家纺制品生产质量预测模型进行模型验证评估处理，在不影响最终测试结果的前提下检验模型的性能，生成的模型验证评估数据有助于调整和优化模型参数，进一步提升模型的准确率和可靠性。对模型验证评估数据进行生产链路评估数据的阶段划分，能够细致地识别出生产过程中的各个阶段及其对产品质量的影响，有助于定位质量问题的根源，为后续的优化提供指导。根据生产链路阶段评估数据进行的生产链路瓶颈优化参数分析，能够明确指出生产过程中的瓶颈和低效环节，对于改进生产流程、提升生产效率和产品质量至关重要。通过生产链路瓶颈优化参数对家纺制品生产质量预测模型进行模型优化调节，并利用模型测试集进行测试，不仅提高了模型的预测精度，还确保了模型在实际生产环境中的适用性和有效性，更准确地指导生产，有效提升生产质量和效率。The present invention divides the model training samples into data, generates the model training set, model verification set, and model test set, ensures that the training, verification, and test processes of the machine learning model do not interfere with each other, fairly evaluates the model performance, helps to effectively avoid model overfitting, and ensures that the model has good generalization ability. The model training set is used to perform model training processing on the preliminary home textile product production quality prediction model, so that the model can learn the key quality influencing factors and their internal relationships in the production process, thereby improving the accuracy of the model prediction production quality. Based on the model verification set, the home textile product production quality prediction model is subjected to model verification and evaluation processing, and the performance of the model is tested without affecting the final test results. The generated model verification and evaluation data helps to adjust and optimize the model parameters, and further improves the accuracy and reliability of the model. The stage division of the production link evaluation data for the model verification evaluation data can carefully identify the various stages in the production process and their impact on product quality, help locate the root cause of quality problems, and provide guidance for subsequent optimization. The production link bottleneck optimization parameter analysis based on the production link stage evaluation data can clearly point out the bottlenecks and inefficient links in the production process, which is crucial for improving production processes, improving production efficiency and product quality. The home textile product production quality prediction model is optimized and adjusted by optimizing the production link bottleneck parameters and tested using the model test set. This not only improves the model's prediction accuracy, but also ensures the model's applicability and effectiveness in the actual production environment, guides production more accurately, and effectively improves production quality and efficiency.
优选地，所述根据生产链路阶段评估数据进行生产链路瓶颈优化参数分析包括以下步骤：Preferably, the performing of production link bottleneck optimization parameter analysis according to production link stage evaluation data comprises the following steps:
根据生产链路阶段评估数据进行生产链路阶段决策因素识别，生成生产链路阶段决策因素数据；Identify decision factors at the production link stage based on the production link stage evaluation data and generate decision factor data at the production link stage;
根据生产链路阶段决策因素数据进行树节点参数分析，生成树节点参数；根据树节点参数进行生产链路优化决策树模型建立，生成生产链路优化决策树模型；Perform tree node parameter analysis based on the decision factor data of the production link stage to generate tree node parameters; establish a production link optimization decision tree model based on the tree node parameters to generate a production link optimization decision tree model;
根据生产链路优化决策树模型进行生产链路瓶颈优化参数分析，生成生产链路瓶颈优化参数。According to the production link optimization decision tree model, the production link bottleneck optimization parameter analysis is performed to generate the production link bottleneck optimization parameters.
本发明根据生产链路阶段评估数据进行生产链路阶段决策因素识别，能够精确地指出影响生产链路效率和产品质量的关键因素，有助于明确生产过程中的优化焦点，为生产链路的改进提供了数据支持和方向指导，确保优化活动能够针对性地解决实际问题。根据生产链路阶段决策因素数据进行的树节点参数分析及生成的树节点参数，为构建生产链路优化决策树模型提供了基础参数，这些参数反映了生产链路各阶段的决策逻辑和可能的优化路径，有助于精确模拟和分析生产过程，从而发现并优化生产流程中的关键节点。根据树节点参数建立的生产链路优化决策树模型，系统地分析和评估生产链路的各个环节和决策点，为识别和分析生产链路的瓶颈问题提供了一种直观且系统的方法，有助于更深入地理解生产流程中的复杂关系和影响因素，为生产流程的优化提供了科学的决策。根据生产链路优化决策树模型进行的生产链路瓶颈优化参数分析能够精确地识别生产流程中的瓶颈环节，并提出具体的优化建议和参数，有助于针对性地解决生产过程中的效率低下和质量问题，提高整个生产链路的效率和产品质量，确保生产活动能够更加顺畅且高效地进行，通过这些优化参数的应用，实现生产过程的持续改进和优化。The present invention identifies the decision factors of the production link stage according to the production link stage evaluation data, can accurately point out the key factors that affect the efficiency of the production link and the quality of the product, helps to clarify the optimization focus in the production process, provides data support and direction guidance for the improvement of the production link, and ensures that the optimization activities can solve practical problems in a targeted manner. The tree node parameter analysis and generated tree node parameters based on the production link stage decision factor data provide basic parameters for constructing a production link optimization decision tree model. These parameters reflect the decision logic and possible optimization paths of each stage of the production link, and help to accurately simulate and analyze the production process, thereby discovering and optimizing the key nodes in the production process. The production link optimization decision tree model established according to the tree node parameters systematically analyzes and evaluates each link and decision point of the production link, provides an intuitive and systematic method for identifying and analyzing the bottleneck problem of the production link, helps to more deeply understand the complex relationships and influencing factors in the production process, and provides scientific decision-making for the optimization of the production process. The production chain bottleneck optimization parameter analysis based on the production chain optimization decision tree model can accurately identify the bottleneck links in the production process and put forward specific optimization suggestions and parameters, which helps to solve the inefficiency and quality problems in the production process in a targeted manner, improve the efficiency and product quality of the entire production chain, and ensure that production activities can be carried out more smoothly and efficiently. Through the application of these optimization parameters, continuous improvement and optimization of the production process can be achieved.
本说明书中提供一种基于PLC编程自动控制的家纺制品生产系统，用于执行如上述所述的基于PLC编程自动控制的家纺制品生产方法，该基于PLC编程自动控制的家纺制品生产系统包括：This specification provides a home textile product production system based on PLC programming automatic control, which is used to execute the home textile product production method based on PLC programming automatic control as described above. The home textile product production system based on PLC programming automatic control includes:
目标家纺制品原料扫描模块，用于根据监控扫描设备进行家纺制品原料扫描处理，生成家纺制品原料扫描数据；对家纺制品原料扫描数据进行目标家纺制品原料扫描数据采集，以得到目标家纺制品原料扫描数据；The target home textile product raw material scanning module is used to perform home textile product raw material scanning processing according to the monitoring scanning device to generate home textile product raw material scanning data; the home textile product raw material scanning data is subjected to target home textile product raw material scanning data collection to obtain the target home textile product raw material scanning data;
家纺制品生产数据采集模块，用于根据传感器集成设备进行家纺制品生产数据实时采集处理，生成家纺制品生产数据，其中所述家纺制品生产数据包括生产设备状态数据以及家纺制品状态数据；A home textile product production data acquisition module, which is used to collect and process home textile product production data in real time according to the sensor integrated device to generate home textile product production data, wherein the home textile product production data includes production equipment status data and home textile product status data;
家纺制品原料-生产匹配模块，用于对目标家纺制品原料扫描数据以及家纺制品生产数据进行数据匹配处理，生成家纺制品原料-生产匹配数据；A home textile product raw material-production matching module is used to perform data matching processing on target home textile product raw material scanning data and home textile product production data to generate home textile product raw material-production matching data;
PLC生产控制逻辑分析模块，用于根据预设的支持向量机算法以及家纺制品原料-生产匹配数据进行家纺制品生产质量的优化预测模型建立，生成优化家纺制品生产质量预测模型；根据优化家纺制品生产质量预测模型进行PLC生产控制逻辑设计，生成PLC生产控制逻辑数据，并将PLC生产控制逻辑数据反馈至终端执行家纺制品自动控制生产作业。The PLC production control logic analysis module is used to establish an optimization prediction model for the production quality of home textile products based on the preset support vector machine algorithm and the home textile product raw material-production matching data, and generate an optimized home textile product production quality prediction model; design the PLC production control logic based on the optimized home textile product production quality prediction model, generate PLC production control logic data, and feed back the PLC production control logic data to the terminal to execute the automatic control production operation of home textile products.
本申请有益效果在于，本发明的基于PLC编程自动控制的家纺制品生产方法可通过PLC自动筛选出目标的家纺制品原料，保障了对家纺制品原料的筛选效果及客观性，并对采集到的原料扫描数据和生产数据进行精确匹配处理，用于分析生产设备的控制参数，以及根据决策树模型分析生产设备中每个阶段的控制参数对应的生产瓶颈，从而精准调节家纺制品生产过程中生产设备的控制参数，通过分析出的生产设备的控制参数设计为PLC的控制逻辑参数，以实现家访制品生产的自动控制，提高了家访制品生产的智能化水平，减少了人工干预，降低了生产成本，能够在保证产品质量的同时，提升生产的灵活性和响应速度。The beneficial effects of the present application lie in that the home textile product production method based on PLC programming and automatic control of the present invention can automatically screen out target home textile product raw materials through PLC, thereby ensuring the screening effect and objectivity of the home textile product raw materials, and accurately matching the collected raw material scanning data and production data for analyzing the control parameters of the production equipment, and analyzing the production bottlenecks corresponding to the control parameters of each stage in the production equipment according to the decision tree model, so as to accurately adjust the control parameters of the production equipment in the production process of home textile products, and design the control logic parameters of the PLC through the analyzed control parameters of the production equipment to realize the automatic control of home textile product production, thereby improving the intelligence level of home textile product production, reducing manual intervention, and reducing production costs, and being able to improve production flexibility and response speed while ensuring product quality.
附图说明BRIEF DESCRIPTION OF THE DRAWINGS
图1为本发明一种基于PLC编程自动控制的家纺制品生产方法的步骤流程示意图；FIG1 is a schematic flow chart of the steps of a method for producing home textile products based on PLC programming and automatic control according to the present invention;
图2为图1中步骤S4的详细实施步骤流程示意图；FIG2 is a schematic diagram of a detailed implementation process of step S4 in FIG1 ;
本发明目的的实现、功能特点及优点将结合实施例，参照附图做进一步说明。The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings.
具体实施方式Detailed ways
下面结合附图对本发明的技术方法进行清楚、完整的描述，显然，所描述的实施例是本发明的一部分实施例，而不是全部的实施例。基于本发明中的实施例，本领域所属的技术人员在没有做出创造性劳动前提下所获得的所有其他实施例，都属于本发明保护的范围。The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are 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 technicians in this field without creative work are within the scope of protection of the present invention.
此外，附图仅为本发明的示意性图解，并非一定是按比例绘制。图中相同的附图标记表示相同或类似的部分，因而将省略对它们的重复描述。附图中所示的一些方框图是功能实体，不一定必须与物理或逻辑上独立的实体相对应。可以采用软件形式来实现功能实体，或在一个或多个硬件模块或集成电路中实现这些功能实体，或在不同网络和/或处理器方法和/或微控制器方法中实现这些功能实体。In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and/or processor methods and/or microcontroller methods.
应当理解的是，虽然在这里可能使用了术语“第一”、“第二”等等来描述各个单元，但是这些单元不应当受这些术语限制。使用这些术语仅仅是为了将一个单元与另一个单元进行区分。举例来说，在不背离示例性实施例的范围的情况下，第一单元可以被称为第二单元，并且类似地第二单元可以被称为第一单元。这里所使用的术语“和/或”包括其中一个或更多所列出的相关联项目的任意和所有组合。It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and/or" used herein includes any and all combinations of one or more of the listed associated items.
为实现上述目的，请参阅图1至图2，本发明提供一种基于PLC编程自动控制的家纺制品生产方法，包括以下步骤：To achieve the above object, please refer to Figures 1 to 2. The present invention provides a method for producing home textile products based on PLC programming automatic control, comprising the following steps:
步骤S1：根据监控扫描设备进行家纺制品原料扫描处理，生成家纺制品原料扫描数据；对家纺制品原料扫描数据进行目标家纺制品原料扫描数据采集，以得到目标家纺制品原料扫描数据；Step S1: scanning the home textile product raw material according to the monitoring scanning device to generate home textile product raw material scanning data; collecting target home textile product raw material scanning data on the home textile product raw material scanning data to obtain the target home textile product raw material scanning data;
步骤S2：根据传感器集成设备进行家纺制品生产数据实时采集处理，生成家纺制品生产数据，其中所述家纺制品生产数据包括生产设备状态数据以及家纺制品状态数据；Step S2: collecting and processing the home textile product production data in real time according to the sensor integrated device to generate the home textile product production data, wherein the home textile product production data includes the production equipment status data and the home textile product status data;
步骤S3：对目标家纺制品原料扫描数据以及家纺制品生产数据进行数据匹配处理，生成家纺制品原料-生产匹配数据；Step S3: performing data matching processing on the target home textile product raw material scanning data and the home textile product production data to generate home textile product raw material-production matching data;
步骤S4：根据预设的支持向量机算法以及家纺制品原料-生产匹配数据进行家纺制品生产质量的优化预测模型建立，生成优化家纺制品生产质量预测模型；根据优化家纺制品生产质量预测模型进行PLC生产控制逻辑设计，生成PLC生产控制逻辑数据，并将PLC生产控制逻辑数据反馈至终端执行家纺制品自动控制生产作业。Step S4: Establish an optimization prediction model for the production quality of home textile products based on the preset support vector machine algorithm and the home textile product raw material-production matching data, and generate an optimized home textile product production quality prediction model; design PLC production control logic based on the optimized home textile product production quality prediction model, generate PLC production control logic data, and feed back the PLC production control logic data to the terminal to execute the automatic control production operation of home textile products.
本发明通过监控扫描设备进行家纺制品原料的扫描处理，实现了对原料信息的快速、准确采集，确保了生产过程中使用的原料质量和规格符合预定标准，通过对原料数据的精确采集，有效避免因原料问题导致的生产缺陷，提高最终产品的质量，精确的原料数据还支持生产过程中的物料跟踪、供应链管理和成本控制，为高效和可持续的生产提供数据支持。利用传感器集成设备实时采集家纺制品的生产数据，包括生产设备状态和家纺制品状态，能够实现对生产过程的实时监控和控制，有助于即时发现生产过程中的任何异常或偏差，如设备故障或产品质量问题，从而迅速采取措施进行调整或修正。实时数据采集和分析还能优化生产调度和资源配置，提高生产效率和灵活性，减少浪费，最终实现成本节约和生产力的提升。对目标家纺制品原料扫描数据及家纺制品生产数据进行精确的数据匹配处理，生成的家纺制品原料-生产匹配数据能够为生产过程提供更为准确的数据支持，这种匹配处理确保了原料选择与实际生产需求之间的最佳匹配，减少了因原料不匹配造成的生产效率下降和产品质量问题，支持了更精细化的生产管理，比如原料的优化利用和生产过程的精细调控，从而提升了资源利用效率和生产过程的可控性。通过预设的支持向量机(SVM)算法和家纺制品原料-生产匹配数据，建立的优化家纺制品生产质量预测模型能够有效预测并优化生产质量，不仅显著提高了产品质量和生产效率，还通过精确的质量控制减少了浪费，提升了整体的生产经济效益。利用该模型进行PLC生产控制逻辑设计，并将控制逻辑数据反馈至生产终端，实现了生产过程的自动化和智能化控制。这种基于数据驱动的控制逻辑，使生产过程更加灵活、响应速度更快，能够有效应对生产过程中的各种变化和不确定性，保证生产过程的稳定性和产品质量的一致性。The present invention scans and processes the raw materials of home textile products through monitoring scanning equipment, thereby realizing rapid and accurate collection of raw material information, ensuring that the quality and specifications of the raw materials used in the production process meet the predetermined standards. Through the accurate collection of raw material data, production defects caused by raw material problems can be effectively avoided, and the quality of the final product can be improved. Accurate raw material data also supports material tracking, supply chain management and cost control in the production process, and provides data support for efficient and sustainable production. Using sensor integrated equipment to collect production data of home textile products in real time, including the status of production equipment and home textile products, can achieve real-time monitoring and control of the production process, which helps to immediately discover any anomalies or deviations in the production process, such as equipment failures or product quality problems, so as to quickly take measures to adjust or correct them. Real-time data collection and analysis can also optimize production scheduling and resource allocation, improve production efficiency and flexibility, reduce waste, and ultimately achieve cost savings and productivity improvements. The target home textile product raw material scanning data and home textile product production data are accurately matched, and the generated home textile product raw material-production matching data can provide more accurate data support for the production process. This matching process ensures the best match between raw material selection and actual production needs, reduces the decline in production efficiency and product quality problems caused by raw material mismatch, and supports more refined production management, such as the optimal utilization of raw materials and fine regulation of the production process, thereby improving resource utilization efficiency and controllability of the production process. Through the preset support vector machine (SVM) algorithm and home textile product raw material-production matching data, the optimized home textile product production quality prediction model established can effectively predict and optimize production quality, which not only significantly improves product quality and production efficiency, but also reduces waste through precise quality control, and improves the overall production economic benefits. The model is used to design PLC production control logic, and the control logic data is fed back to the production terminal, realizing the automation and intelligent control of the production process. This data-driven control logic makes the production process more flexible and responsive, and can effectively respond to various changes and uncertainties in the production process, ensuring the stability of the production process and the consistency of product quality.
作为本发明的一个实施例，参考图1所述，为本发明一种基于PLC编程自动控制的家纺制品生产方法的步骤流程示意图，在本实施例中，所述智能用药管理方法包括以下步骤：As an embodiment of the present invention, referring to FIG. 1 , which is a schematic flow chart of a method for producing home textile products based on PLC programming automatic control, in this embodiment, the intelligent medication management method includes the following steps:
步骤S1：根据监控扫描设备进行家纺制品原料扫描处理，生成家纺制品原料扫描数据；对家纺制品原料扫描数据进行目标家纺制品原料扫描数据采集，以得到目标家纺制品原料扫描数据；Step S1: scanning the home textile product raw material according to the monitoring scanning device to generate home textile product raw material scanning data; collecting target home textile product raw material scanning data on the home textile product raw material scanning data to obtain the target home textile product raw material scanning data;
本发明实施例中，在原料入库阶段安装监控扫描设备，包括条码扫描器、图像识别摄像头等，用于捕捉原料的详细信息，如类型、尺寸、颜色、纹理等。当家纺制品原料通过扫描区域时，启动扫描设备自动扫描并捕获原料信息，将信息数字化成家纺制品原料扫描数据。基于预先定义的原料需求标准(如质量、尺寸范围等)，从扫描得到的数据中筛选出满足生产需求的目标家纺制品原料扫描数据，并将这些数据保存至数据库供后续使用。In the embodiment of the present invention, monitoring scanning equipment is installed during the raw material storage stage, including a barcode scanner, an image recognition camera, etc., to capture detailed information of the raw materials, such as type, size, color, texture, etc. When the home textile product raw materials pass through the scanning area, the scanning equipment is started to automatically scan and capture the raw material information, and the information is digitized into home textile product raw material scanning data. Based on the pre-defined raw material demand standards (such as quality, size range, etc.), the target home textile product raw material scanning data that meets the production requirements is screened from the scanned data, and the data is saved in the database for subsequent use.
步骤S2：根据传感器集成设备进行家纺制品生产数据实时采集处理，生成家纺制品生产数据，其中所述家纺制品生产数据包括生产设备状态数据以及家纺制品状态数据；Step S2: collecting and processing the home textile product production data in real time according to the sensor integrated device to generate the home textile product production data, wherein the home textile product production data includes the production equipment status data and the home textile product status data;
本发明实施例中，在家纺制品生产线的关键位置安装传感器，包括温湿度传感器、速度传感器、压力传感器等，用于实时监测生产环境和生产过程的关键参数，传感器集成设备实时监测生产设备的运行状态(如速度、温度、压力等)和家纺制品的状态(如尺寸、重量、质地等)，并将这些数据实时传输至中央数据处理系统，中央数据处理系统接收来自各传感器的数据，对数据进行整理、分析，生成结构化的家纺制品生产数据，包括具体的生产设备状态数据和家纺制品状态数据，用于监控生产过程的质量和效率，以及为后续的数据匹配和生产质量优化提供基础。In an embodiment of the present invention, sensors are installed at key positions of a home textile product production line, including temperature and humidity sensors, speed sensors, pressure sensors, etc., for real-time monitoring of key parameters of the production environment and the production process. The sensor integration equipment monitors the operating status of the production equipment (such as speed, temperature, pressure, etc.) and the status of the home textile products (such as size, weight, texture, etc.) in real time, and transmits these data to a central data processing system in real time. The central data processing system receives data from each sensor, organizes and analyzes the data, and generates structured home textile product production data, including specific production equipment status data and home textile product status data, which is used to monitor the quality and efficiency of the production process and provide a basis for subsequent data matching and production quality optimization.
步骤S3：对目标家纺制品原料扫描数据以及家纺制品生产数据进行数据匹配处理，生成家纺制品原料-生产匹配数据；Step S3: performing data matching processing on the target home textile product raw material scanning data and the home textile product production data to generate home textile product raw material-production matching data;
本发明实施例中，将筛选得到的目标家纺制品原料扫描数据与家纺制品生产数据集成到一起，包括原料的详细信息(如材质、尺寸等)以及相应的生产过程信息(如设备状态、产品质量检测数据等)，使用数据处理软件或定制开发的算法对整合后的数据进行匹配分析，识别原料属性与生产过程参数之间的相关性，目的是找出影响产品质量的关键原料特性和生产条件，基于匹配分析的结果，生成家纺制品原料-生产匹配数据，详细描述哪些原料特性与生产过程中的哪些参数密切相关，为生产质量预测和优化提供依据。In an embodiment of the present invention, the screened target home textile product raw material scanning data and the home textile product production data are integrated together, including detailed information of the raw materials (such as material, size, etc.) and corresponding production process information (such as equipment status, product quality inspection data, etc.), and the integrated data are matched and analyzed using data processing software or a custom-developed algorithm to identify the correlation between raw material properties and production process parameters, with the aim of finding out key raw material characteristics and production conditions that affect product quality. Based on the results of the matching analysis, home textile product raw material-production matching data is generated, which describes in detail which raw material characteristics are closely related to which parameters in the production process, providing a basis for production quality prediction and optimization.
步骤S4：根据预设的支持向量机算法以及家纺制品原料-生产匹配数据进行家纺制品生产质量的优化预测模型建立，生成优化家纺制品生产质量预测模型；根据优化家纺制品生产质量预测模型进行PLC生产控制逻辑设计，生成PLC生产控制逻辑数据，并将PLC生产控制逻辑数据反馈至终端执行家纺制品自动控制生产作业。Step S4: Establish an optimization prediction model for the production quality of home textile products based on the preset support vector machine algorithm and the home textile product raw material-production matching data, and generate an optimized home textile product production quality prediction model; design PLC production control logic based on the optimized home textile product production quality prediction model, generate PLC production control logic data, and feed back the PLC production control logic data to the terminal to execute the automatic control production operation of home textile products.
本发明实施例中，利用支持向量机(SVM)算法，基于家纺制品原料-生产匹配数据，建立初步的家纺制品生产质量预测模型，需要编程实现SVM算法，并选择合适的核函数、调整参数以最佳化模型性能，对模型进行训练，使用一部分已知结果的数据作为训练集，以调整模型参数，确保模型能够准确预测生产质量。使用另一部分数据作为验证集，评估模型的准确性和泛化能力，必要时进行调优，提高预测准确率，完成模型优化后，使用测试集数据对模型进行最终测试，确认模型的预测性能满足生产需求，最终生成优化家纺制品生产质量预测模型。基于优化家纺制品生产质量预测模型，设计PLC(可编程逻辑控制器)的生产控制逻辑，包括定义如何根据新的监测数据以及对应的模型预测结果自动调整生产参数(如设备速度、温度、压力等)，以优化产品质量，将设计好的PLC生产控制逻辑编程到PLC控制系统中，并部署至生产线，确保控制逻辑能够准确执行，实现对生产过程的自动控制，在生产过程中实时收集生产数据，反馈至PLC控制系统，根据预测模型和控制逻辑持续优化生产条件，确保产品质量。In the embodiment of the present invention, a support vector machine (SVM) algorithm is used to establish a preliminary home textile product production quality prediction model based on home textile product raw material-production matching data. It is necessary to program the SVM algorithm, select a suitable kernel function, adjust parameters to optimize model performance, train the model, and use a part of the data with known results as a training set to adjust the model parameters to ensure that the model can accurately predict production quality. Use another part of the data as a validation set to evaluate the accuracy and generalization ability of the model, and perform optimization when necessary to improve the prediction accuracy. After completing the model optimization, use the test set data to perform a final test on the model to confirm that the model's prediction performance meets production requirements, and finally generate an optimized home textile product production quality prediction model. Based on the optimization of the home textile product production quality prediction model, the production control logic of the PLC (programmable logic controller) is designed, including defining how to automatically adjust the production parameters (such as equipment speed, temperature, pressure, etc.) according to the new monitoring data and the corresponding model prediction results to optimize product quality. The designed PLC production control logic is programmed into the PLC control system and deployed to the production line to ensure that the control logic can be accurately executed and realize automatic control of the production process. During the production process, production data is collected in real time and fed back to the PLC control system. The production conditions are continuously optimized according to the prediction model and control logic to ensure product quality.
优选地，步骤S1包括以下步骤：Preferably, step S1 comprises the following steps:
步骤S11：根据监控扫描设备进行家纺制品原料扫描处理，生成家纺制品原料扫描数据；Step S11: Scanning the raw materials of home textile products according to the monitoring scanning device to generate scanning data of the raw materials of home textile products;
步骤S12：对家纺制品原料扫描数据进行多维原料质量检测处理，生成多维原料质量检测数据；Step S12: performing multi-dimensional raw material quality detection processing on the home textile product raw material scanning data to generate multi-dimensional raw material quality detection data;
步骤S13：根据多维原料质量检测数据进行PLC原料筛选逻辑分析，生成PLC原料筛选逻辑数据；Step S13: performing PLC raw material screening logic analysis according to the multi-dimensional raw material quality detection data to generate PLC raw material screening logic data;
步骤S14：基于PLC原料筛选逻辑数据对多维原料质量检测数据进行有效原料质量检测数据筛选，以得到有效原料质量检测数据；Step S14: Screening the effective raw material quality detection data of the multi-dimensional raw material quality detection data based on the PLC raw material screening logic data to obtain effective raw material quality detection data;
步骤S15：基于有效原料质量检测数据对家纺制品原料扫描数据进行目标家纺制品原料扫描数据采集，以得到目标家纺制品原料扫描数据。Step S15: collecting target home textile product raw material scanning data on the home textile product raw material scanning data based on the effective raw material quality detection data to obtain the target home textile product raw material scanning data.
本发明通过监控扫描设备进行家纺制品原料的扫描处理，直接生成家纺制品原料扫描数据，自动化地收集了原料的详细信息，包括类型、质量、尺寸等，为后续的原料筛选和质量检测提供了第一手数据，这样的自动化扫描减少了人为错误，提高了数据收集的效率和准确性。对多维原料质量检测数据进行多维原料质量检测处理，生成的多维原料质量检测数据能够全面反映原料的质量状态，通过多角度、多指标地评估原料，能够揭示出原料的综合质量情况，包括但不限于强度、纯度、颜色等各种重要参数，确保筛选出来的原料在各个重要维度上都符合生产要求。根据多维原料质量检测数据进行PLC原料筛选逻辑分析，生成PLC原料筛选逻辑数据，实现了原料筛选的自动化和智能化，使得原料筛选过程更加精准、高效，通过预设的筛选逻辑自动排除不符合质量标准的原料，确保了生产使用的原料都是符合要求的，从而直接提升了产品质量和生产效率。基于PLC原料筛选逻辑数据，对多维原料质量检测数据进行有效原料质量检测数据筛选，以得到有效原料质量检测数据，提高了原料质量检测的准确度和效率，这种筛选机制减少了因原料质量不达标导致的生产问题和成品缺陷。基于有效原料质量检测数据，对家纺制品原料扫描数据进行目标家纺制品原料扫描数据采集，进一步提炼和确保了所采集原料数据的目标性和有效性，确保符合生产要求的原料数据才会被用于后续生产过程，优化了原料的选择过程，从而在提高生产效率的同时，也最大化地保障了产品的质量。The present invention performs scanning processing of home textile product raw materials through monitoring scanning equipment, directly generates home textile product raw material scanning data, automatically collects detailed information of raw materials, including type, quality, size, etc., provides first-hand data for subsequent raw material screening and quality inspection, and such automated scanning reduces human errors and improves the efficiency and accuracy of data collection. Multidimensional raw material quality inspection data is subjected to multidimensional raw material quality inspection processing, and the generated multidimensional raw material quality inspection data can fully reflect the quality status of raw materials. By evaluating raw materials from multiple angles and multiple indicators, the comprehensive quality of raw materials can be revealed, including but not limited to various important parameters such as strength, purity, color, etc., to ensure that the screened raw materials meet production requirements in all important dimensions. PLC raw material screening logic analysis is performed according to multidimensional raw material quality inspection data to generate PLC raw material screening logic data, realizes the automation and intelligence of raw material screening, makes the raw material screening process more accurate and efficient, and automatically excludes raw materials that do not meet quality standards through preset screening logic, ensuring that the raw materials used in production meet the requirements, thereby directly improving product quality and production efficiency. Based on the PLC raw material screening logic data, the multi-dimensional raw material quality inspection data is screened for effective raw material quality inspection data to obtain effective raw material quality inspection data, which improves the accuracy and efficiency of raw material quality inspection. This screening mechanism reduces production problems and finished product defects caused by substandard raw material quality. Based on the effective raw material quality inspection data, the home textile product raw material scanning data is collected for target home textile product raw material scanning data, further refining and ensuring the target and effectiveness of the collected raw material data, ensuring that raw material data that meets production requirements will be used in subsequent production processes, optimizing the raw material selection process, thereby improving production efficiency while maximizing product quality.
本发明实施例中，在原料处理区域安装监控扫描设备，如条码扫描仪、RFID读取器、视觉识别系统等，用于自动识别和记录原料信息，当家纺原料通过扫描区时，自动触发扫描设备，收集原料的基本信息，如类型、批次、规格等，生成家纺制品原料扫描数据。在原料检测区域配置多维质量检测设备，包括重量、尺寸、颜色、纹理等检测仪器，原料通过检测区域时，自动进行多维度的质量检测，并记录检测结果，生成多维原料质量检测数据。根据生产需求，预先设定原料的质量筛选标准，如重量范围、颜色一致性标准等，利用PLC系统对多维原料质量检测数据进行逻辑分析，根据筛选标准生成PLC原料筛选逻辑数据。基于PLC原料筛选逻辑数据，自动从多维原料质量检测数据中筛选出满足质量标准的数据，剔除不合格的数据，将通过筛选的数据确定为有效原料质量检测数据，用于后续的生产流程。将有效的原料质量检测数据与原始的家纺制品原料扫描数据进行匹配整合，基于整合后的数据，进一步筛选出符合特定生产目标需求的原料数据，作为目标家纺制品原料扫描数据，为具体的生产批次准备精确的原料信息。In an embodiment of the present invention, a monitoring scanning device, such as a barcode scanner, an RFID reader, a visual recognition system, etc., is installed in the raw material processing area to automatically identify and record raw material information. When the home textile raw material passes through the scanning area, the scanning device is automatically triggered to collect basic information of the raw material, such as type, batch, specification, etc., and generate home textile product raw material scanning data. Multidimensional quality detection equipment is configured in the raw material detection area, including detection instruments such as weight, size, color, texture, etc. When the raw material passes through the detection area, multidimensional quality detection is automatically performed, and the detection results are recorded to generate multidimensional raw material quality detection data. According to production requirements, the quality screening standards of the raw materials are pre-set, such as weight range, color consistency standard, etc., and the PLC system is used to perform logical analysis on the multidimensional raw material quality detection data, and PLC raw material screening logic data is generated according to the screening standards. Based on the PLC raw material screening logic data, data that meets the quality standard is automatically screened out from the multidimensional raw material quality detection data, and unqualified data is eliminated. The screened data is determined as effective raw material quality detection data for subsequent production processes. Match and integrate the effective raw material quality inspection data with the original home textile product raw material scanning data. Based on the integrated data, further screen out the raw material data that meets the specific production target requirements as the target home textile product raw material scanning data to prepare accurate raw material information for specific production batches.
优选地，步骤S12包括以下步骤：Preferably, step S12 comprises the following steps:
对多维原料质量检测数据进行维度指标赋权处理，生成原料质量维度指标权重数据；根据预设的多维原料质量评估决策以及原料质量维度指标权重数据进行PLC原料筛选逻辑设计，生成PLC原料筛选逻辑数据。The multi-dimensional raw material quality inspection data is processed by dimensional indicator weighting to generate raw material quality dimensional indicator weight data; the PLC raw material screening logic is designed according to the preset multi-dimensional raw material quality assessment decision and raw material quality dimensional indicator weight data to generate PLC raw material screening logic data.
本发明对多维原料质量检测数据进行维度指标赋权处理，生成的原料质量维度指标权重数据，合理反映出各个质量指标对最终产品质量的影响程度，通过为不同的质量维度指定不同的权重，确保了在原料筛选过程中能够更加精准地评价原料的适用性和优劣。基于预设的多维原料质量评估决策以及原料质量维度指标权重数据进行的PLC原料筛选逻辑设计，实现原料筛选过程的自动化和智能化，不仅提高了筛选的效率和准确性，还使得原料的选择过程更加客观和合理，通过精确控制原料的质量，可以直接影响到生产流程的顺畅进行和产品质量的稳定性，确保生产出的家纺制品能够满足高标准的质量要求。The present invention performs dimensional index weighting processing on multi-dimensional raw material quality detection data, and the generated raw material quality dimensional index weight data reasonably reflects the degree of influence of each quality index on the quality of the final product. By specifying different weights for different quality dimensions, it is ensured that the applicability and quality of the raw materials can be more accurately evaluated during the raw material screening process. The PLC raw material screening logic design based on the preset multi-dimensional raw material quality assessment decision and the raw material quality dimensional index weight data realizes the automation and intelligence of the raw material screening process, which not only improves the efficiency and accuracy of the screening, but also makes the raw material selection process more objective and reasonable. By accurately controlling the quality of the raw materials, it can directly affect the smooth progress of the production process and the stability of the product quality, ensuring that the produced home textile products can meet high standards of quality requirements.
本发明实施例中，根据生产要求和质量标准，评估原料质量的各个维度(如重量、尺寸、颜色、纹理等)对最终产品质量的影响程度，确定每个维度的重要性，根据评估结果，为每个维度指标分配权重，确保对产品质量影响较大的指标在原料筛选过程中具有更高的决策权重，将所有维度指标及其对应权重整合成原料质量维度指标权重数据表，供后续原料筛选逻辑设计使用。依据产品质量要求和原料特性，制定一套包含所有质量维度的原料评估决策规则，套规则应当考虑各维度之间的相互作用及其对最终产品质量的综合影响，将原料质量维度指标权重数据集成到多维原料质量评估决策规则中，确保原料筛选逻辑能够反映出各质量维度的重要性，利用PLC编程语言和工具，根据上述评估决策规则和权重数据，设计PLC原料筛选逻辑，包括编写能够自动计算每批原料综合质量评分的程序，以及根据评分自动决定是否接受该批原料的逻辑控制流程，完成PLC程序设计后，生成一套可供PLC执行的原料筛选逻辑数据，将直接应用于生产线上的PLC系统中，实现原料的自动化质量筛选。In the embodiment of the present invention, according to the production requirements and quality standards, the influence of each dimension of raw material quality (such as weight, size, color, texture, etc.) on the quality of the final product is evaluated, the importance of each dimension is determined, and a weight is assigned to each dimensional indicator according to the evaluation results to ensure that the indicator with a greater impact on product quality has a higher decision weight in the raw material screening process, and all dimensional indicators and their corresponding weights are integrated into a raw material quality dimensional indicator weight data table for subsequent raw material screening logic design. According to the product quality requirements and raw material characteristics, a set of raw material evaluation decision rules including all quality dimensions is formulated, and the set of rules should consider the interaction between the dimensions and their comprehensive impact on the quality of the final product, and the raw material quality dimensional indicator weight data is integrated into the multidimensional raw material quality evaluation decision rules to ensure that the raw material screening logic can reflect the importance of each quality dimension. Using PLC programming language and tools, according to the above evaluation decision rules and weight data, the PLC raw material screening logic is designed, including writing a program that can automatically calculate the comprehensive quality score of each batch of raw materials, and a logic control process that automatically decides whether to accept the batch of raw materials according to the score. After completing the PLC program design, a set of raw material screening logic data that can be executed by the PLC is generated, which will be directly applied to the PLC system on the production line to realize the automated quality screening of raw materials.
优选地，步骤S2包括以下步骤：Preferably, step S2 comprises the following steps:
步骤S21：根据预设的家纺制品生产监测决策进行传感器配置数据分析，生成传感器配置数据；Step S21: Analyze sensor configuration data according to preset home textile product production monitoring decisions to generate sensor configuration data;
步骤S22：基于传感器配置数据对传感器集成设备执行配置调节，并通过配置调节后的传感器集成设备进行初始家纺制品生产数据实时监测处理，生成初始家纺制品生产数据，其中所述初始家纺制品生产数据包括初始生产设备状态数据以及初始家纺制品状态数据；Step S22: performing configuration adjustment on the sensor integrated device based on the sensor configuration data, and performing real-time monitoring and processing of initial home textile product production data through the sensor integrated device after the configuration adjustment to generate initial home textile product production data, wherein the initial home textile product production data includes initial production device status data and initial home textile product status data;
步骤S23：根据初始家纺制品生产数据进行生产设备异常模式识别处理，生成生产设备异常模式数据；Step S23: performing production equipment abnormal pattern recognition processing according to the initial home textile product production data to generate production equipment abnormal pattern data;
步骤S24：根据生产设备异常模式数据进行生产设备修复参数分析处理，生成生产设备修复参数；Step S24: Analyze and process production equipment repair parameters according to the production equipment abnormality mode data to generate production equipment repair parameters;
步骤S25：基于生产设备修复参数对初始家纺制品生产数据进行迭代更新处理，生成家纺制品生产数据。Step S25: performing iterative updating processing on the initial home textile product production data based on the production equipment repair parameters to generate home textile product production data.
本发明根据预设的家纺制品生产监测决策进行传感器配置数据分析，可以精确指导传感器集成设备的配置，确保传感器布置最佳匹配生产需求，优化了传感器的配置，使其能够更加精确地监测生产过程中的关键参数，从而提高生产过程的监控效率和准确性。适当的传感器配置对于实现高质量生产至关重要，因为它直接影响到数据收集的质量和全面性。基于传感器配置数据对传感器集成设备执行配置调节，并通过配置调节后的传感器集成设备进行初始家纺制品生产数据的实时监测处理，确保了生产过程从一开始就被实时监控，允许及时发现并处理任何潜在的生产问题，从而减少生产中断的风险，提高生产线的稳定性和产品的一致性。对初始家纺制品生产数据进行生产设备异常模式识别处理，帮助及时识别生产过程中的异常情况，如设备故障或性能下降，提高了对生产问题的响应速度和处理效率，确保了生产过程的连续性和产品质量的稳定性。根据生产设备异常模式数据进行生产设备修复参数分析处理，生成的生产设备修复参数指导设备的及时维修和调整，不仅减少了设备停机时间，还确保了设备在恢复生产后能够以最佳状态运行，从而提高了生产效率和设备的使用寿命。基于生产设备修复参数对初始家纺制品生产数据进行迭代更新处理，反映了设备维修和调整后的生产状态，确保了生产数据的持续更新和优化，为持续改进生产过程提供了数据支持，更好地监控生产过程的各个环节，实现对生产过程的精细控制，从而提升整体的生产质量和效率。The present invention analyzes sensor configuration data according to preset home textile production monitoring decisions, can accurately guide the configuration of sensor integrated equipment, ensure that the sensor layout best matches the production requirements, optimize the configuration of the sensor, and enable it to more accurately monitor the key parameters in the production process, thereby improving the monitoring efficiency and accuracy of the production process. Appropriate sensor configuration is crucial to achieving high-quality production because it directly affects the quality and comprehensiveness of data collection. Based on the sensor configuration data, the sensor integrated equipment is configured and adjusted, and the initial home textile production data is monitored in real time by the sensor integrated equipment after configuration adjustment, ensuring that the production process is monitored in real time from the beginning, allowing any potential production problems to be discovered and handled in a timely manner, thereby reducing the risk of production interruption, and improving the stability of the production line and the consistency of the product. The initial home textile production data is processed by abnormal pattern recognition of production equipment, which helps to timely identify abnormal situations in the production process, such as equipment failure or performance degradation, improves the response speed and processing efficiency to production problems, and ensures the continuity of the production process and the stability of product quality. The production equipment repair parameter analysis and processing is performed based on the abnormal mode data of the production equipment. The generated production equipment repair parameters guide the timely maintenance and adjustment of the equipment, which not only reduces the equipment downtime, but also ensures that the equipment can operate in the best state after resuming production, thereby improving production efficiency and the service life of the equipment. The initial home textile product production data is iteratively updated based on the production equipment repair parameters, reflecting the production status after equipment maintenance and adjustment, ensuring the continuous updating and optimization of production data, providing data support for the continuous improvement of the production process, better monitoring of each link of the production process, and achieving fine control of the production process, thereby improving the overall production quality and efficiency.
本发明实施例中，审查家纺制品的生产流程，确定需要监测的关键参数，如温度、湿度、速度、压力等，根据监测需求，选择适合的传感器类型，并确定它们在生产线上的最佳安装位置，以确保能有效收集所需数据，将决定的传感器类型和安装位置等信息整理为传感器配置数据，为下一步的传感器安装和调节提供指导。根据传感器配置数据，对生产线上的传感器集成设备进行安装和配置调节，确保每个传感器都能准确地捕获数据，启动调节后的传感器集成设备，对生产过程进行实时监测，收集初始生产设备状态数据和家纺制品状态数据，将收集到的数据整理为结构化的初始家纺制品生产数据，为后续分析和优化提供基础。对收集到的初始家纺制品生产数据进行分析，识别出可能的生产设备异常模式，如设备运行不稳、性能下降等，将识别出的异常模式整理为生产设备异常模式数据，以便进行进一步的分析和处理。根据生产设备异常模式数据，分析确定导致异常的原因，并制定相应的修复措施。根据修复措施，确定需要调整的生产设备参数，如调节温度设置、改变生产速度等，生成生产设备修复参数。根据生产设备修复参数，对生产线上的设备进行调整或修复，以解决识别出的异常问题，在应用了修复参数后，再次通过传感器集成设备对生产过程进行监测，收集更新后的生产数据，将更新后的监测数据整理更新，生成反映了设备修复和参数调整后的家纺制品生产数据，为持续的生产监控和质量控制提供依据。In the embodiment of the present invention, the production process of home textile products is reviewed, and the key parameters that need to be monitored, such as temperature, humidity, speed, pressure, etc., are determined. According to the monitoring requirements, suitable sensor types are selected, and their optimal installation positions on the production line are determined to ensure that the required data can be effectively collected. The determined sensor type and installation position and other information are organized into sensor configuration data to provide guidance for the next step of sensor installation and adjustment. According to the sensor configuration data, the sensor integrated equipment on the production line is installed and configured to ensure that each sensor can accurately capture data, and the adjusted sensor integrated equipment is started to monitor the production process in real time, collect initial production equipment status data and home textile product status data, and organize the collected data into structured initial home textile product production data to provide a basis for subsequent analysis and optimization. The collected initial home textile product production data is analyzed to identify possible abnormal modes of production equipment, such as unstable equipment operation, performance degradation, etc., and the identified abnormal modes are organized into production equipment abnormal mode data for further analysis and processing. According to the production equipment abnormal mode data, the cause of the abnormality is analyzed and determined, and corresponding repair measures are formulated. According to the repair measures, the production equipment parameters that need to be adjusted are determined, such as adjusting the temperature setting, changing the production speed, etc., and the production equipment repair parameters are generated. According to the production equipment repair parameters, the equipment on the production line is adjusted or repaired to solve the identified abnormal problems. After the repair parameters are applied, the production process is monitored again through the sensor integrated device to collect updated production data, and the updated monitoring data is sorted and updated to generate home textile product production data reflecting the equipment repair and parameter adjustment, providing a basis for continuous production monitoring and quality control.
优选地，步骤S23包括以下步骤：Preferably, step S23 includes the following steps:
根据初始家纺制品状态数据进行异常家纺制品状态数据提取处理，生成异常家纺制品状态数据；Extracting and processing abnormal home textile product status data according to the initial home textile product status data to generate abnormal home textile product status data;
基于异常家纺制品状态数据对初始生产设备状态数据进行异常生产设备状态数据标记处理，生成异常生产设备状态数据；Based on the abnormal home textile product status data, the initial production equipment status data is subjected to abnormal production equipment status data marking processing to generate abnormal production equipment status data;
根据异常生产设备状态数据进行生产设备周期性异常分析处理，生成生产设备周期性异常数据；Perform periodic abnormal analysis and processing of production equipment based on abnormal production equipment status data to generate periodic abnormal data of production equipment;
根据生产设备周期性异常数据进行生产设备异常模式识别处理，生成生产设备异常模式数据。The abnormal pattern recognition processing of the production equipment is performed based on the periodic abnormal data of the production equipment to generate the abnormal pattern data of the production equipment.
本发明对初始家纺制品状态数据进行异常数据提取处理，快速识别生产过程中的质量问题，有助于及时发现产品质量偏差，比如尺寸不一致、颜色差异过大或其他质量缺陷，从而实现对问题产品的早期识别。基于异常家纺制品状态数据对初始生产设备状态数据进行异常状态标记处理，精确地定位生产问题的来源。这个过程通过标记与产品质量问题相关联的设备状态异常，为诊断和解决生产问题提供了重要线索，确保了问题可以被迅速地识别和定位，减少了问题解决的时间，提高了生产线的稳定性和效率。根据异常生产设备状态数据进行周期性异常分析处理，揭示出设备故障或性能下降的周期性模式，识别可能由于设备磨损或定期维护不足导致的问题，从而实现对生产设备的预防性维护和优化调整，通过提前识别和解决周期性问题，显著减少了生产中断的风险，确保了生产流程的连续性和生产效率的提高。根据生产设备周期性异常数据进行异常模式识别处理，能够为生产设备的维护和修复提供明确的指导，减少设备故障导致的生产损失，这种基于数据的识别和预测机制大大提高了生产设备的可靠性和生产线的稳定运行。The present invention performs abnormal data extraction processing on the initial home textile product status data, quickly identifies quality problems in the production process, and helps to timely discover product quality deviations, such as inconsistent size, excessive color difference or other quality defects, thereby realizing early identification of problem products. Based on the abnormal home textile product status data, the initial production equipment status data is subjected to abnormal status marking processing to accurately locate the source of the production problem. This process provides important clues for diagnosing and solving production problems by marking the abnormal equipment status associated with product quality problems, ensuring that the problems can be quickly identified and located, reducing the time for problem solving, and improving the stability and efficiency of the production line. Periodic abnormal analysis processing is performed based on the abnormal production equipment status data to reveal the periodic pattern of equipment failure or performance degradation, identify problems that may be caused by equipment wear or insufficient regular maintenance, thereby realizing preventive maintenance and optimization adjustment of production equipment, and significantly reducing the risk of production interruption by identifying and solving periodic problems in advance, ensuring the continuity of the production process and the improvement of production efficiency. Abnormal pattern recognition processing is performed based on the periodic abnormal data of production equipment, which can provide clear guidance for the maintenance and repair of production equipment and reduce production losses caused by equipment failure. This data-based recognition and prediction mechanism greatly improves the reliability of production equipment and the stable operation of the production line.
本发明实施例中，从初始家纺制品生产数据中，提取与家纺制品状态相关的数据，分析家纺制品状态数据，根据预设的正常运行参数范围(如尺寸、重量、色差等标准)，识别出不符合标准的异常数据，将识别出的异常家纺制品状态数据整理汇总，生成异常家纺制品状态数据报告。对异常家纺制品状态数据进行深入分析，以确定导致这些异常的生产设备状态问题，基于分析结果，对初始生产设备状态数据进行检查和标记，准确指出哪些设备状态数据与家纺制品的异常状态相关联，以得到异常生产设备状态数据。整理并收集一段时间内所有标记的异常生产设备状态数据，利用统计分析工具或软件，分析这些异常数据的发生模式，识别是否存在周期性异常，例如识别出生产设备具有突发性异常状况以及持续性的异常状况，将分析得到的周期性异常模式整理成报告，生成生产设备周期性异常数据。对生产设备周期性异常数据进行详细分析，结合生产过程和设备工作原理，确定异常模式的根本原因，基于分析结果，识别生产设备的具体异常模式，如某个部件的磨损、温度控制失准等，将识别的生产设备异常模式详细记录，生成生产设备异常模式数据报告，为后续的设备修复和参数调整提供依据。In an embodiment of the present invention, data related to the status of home textile products are extracted from the initial home textile product production data, the home textile product status data is analyzed, and according to the preset normal operating parameter range (such as size, weight, color difference and other standards), abnormal data that does not meet the standards is identified, and the identified abnormal home textile product status data is sorted and summarized to generate an abnormal home textile product status data report. The abnormal home textile product status data is deeply analyzed to determine the production equipment status problems that cause these abnormalities. Based on the analysis results, the initial production equipment status data is checked and marked, and it is accurately pointed out which equipment status data is associated with the abnormal status of home textile products to obtain abnormal production equipment status data. All marked abnormal production equipment status data within a period of time are sorted and collected, and the occurrence pattern of these abnormal data is analyzed using statistical analysis tools or software to identify whether there are periodic anomalies, such as identifying that the production equipment has sudden abnormal conditions and continuous abnormal conditions, and the periodic abnormal patterns obtained by analysis are sorted into reports to generate periodic abnormal data of production equipment. Conduct a detailed analysis of the periodic abnormal data of production equipment, combine the production process and the working principle of the equipment to determine the root cause of the abnormal pattern. Based on the analysis results, identify the specific abnormal pattern of the production equipment, such as wear of a certain component, inaccurate temperature control, etc., record the identified abnormal pattern of the production equipment in detail, and generate a production equipment abnormal pattern data report to provide a basis for subsequent equipment repair and parameter adjustment.
优选地，步骤S3包括以下步骤：Preferably, step S3 comprises the following steps:
步骤S3 1：根据目标家纺制品原料扫描数据以及家纺制品状态数据进行家纺制品数据匹配处理，生成家纺制品匹配数据；Step S31: performing home textile product data matching processing according to the target home textile product raw material scanning data and the home textile product status data to generate home textile product matching data;
步骤S32：根据目标家纺制品原料扫描数据以及生产设备状态数据进行生产时序匹配处理，生成生产时序匹配数据；Step S32: Perform production timing matching processing according to the target home textile product raw material scanning data and the production equipment status data to generate production timing matching data;
步骤S33：根据家纺制品匹配数据以及生产时序匹配数据进行家纺制品生产匹配节点分析处理，生成家纺制品生产匹配节点数据；Step S33: performing home textile product production matching node analysis and processing according to the home textile product matching data and the production time sequence matching data to generate home textile product production matching node data;
步骤S34：根据家纺制品生产匹配节点数据对目标家纺制品原料扫描数据以及家纺制品生产数据进行数据匹配处理，生成家纺制品原料-生产匹配数据。Step S34: performing data matching processing on the target home textile product raw material scanning data and the home textile product production data according to the home textile product production matching node data to generate home textile product raw material-production matching data.
本发明根据目标家纺制品原料扫描数据及家纺制品状态数据进行家纺制品数据匹配处理，确保原料选择与最终产品状态之间的高度一致性，有效地连接了原料的特性与产品的质量要求，确保使用的原料最适合预定的产品标准，从而提高了产品质量和满足客户需求的能力。根据目标家纺制品原料扫描数据及生产设备状态数据进行生产时序匹配处理，优化生产流程的时间安排和资源分配，确保了生产活动与设备状态的同步，从而最大化生产效率，实现了更流畅和高效的生产调度。通过根据家纺制品匹配数据及生产时序匹配数据进行家纺制品生产匹配节点分析处理，识别生产过程中的关键节点，如原料输入、加工过程、和质量控制点，通过精确分析和优化生产关键节点，提高了生产过程的管理效率和产品质量控制的准确性，为顺利完成生产任务提供了重要支撑。根据家纺制品生产匹配节点数据对目标家纺制品原料扫描数据及家纺制品生产数据进行最终的数据匹配处理，为生产过程提供了全面的数据支持，能够确保生产决策和控制逻辑基于准确和全面的信息，从而最大化地提升生产过程的效率和产品质量，同时减少资源浪费，优化生产成本。The present invention performs home textile product data matching processing based on the target home textile product raw material scanning data and home textile product status data, ensures a high degree of consistency between raw material selection and final product status, effectively connects the characteristics of the raw materials with the quality requirements of the product, ensures that the used raw materials are most suitable for the predetermined product standards, thereby improving product quality and the ability to meet customer needs. Production timing matching processing is performed based on the target home textile product raw material scanning data and production equipment status data, optimizes the time arrangement and resource allocation of the production process, ensures the synchronization of production activities and equipment status, thereby maximizing production efficiency and achieving smoother and more efficient production scheduling. By performing home textile product production matching node analysis and processing based on home textile product matching data and production timing matching data, key nodes in the production process, such as raw material input, processing process, and quality control points, are identified. By accurately analyzing and optimizing key production nodes, the management efficiency of the production process and the accuracy of product quality control are improved, providing important support for the smooth completion of production tasks. The final data matching processing is performed on the target home textile product raw material scanning data and home textile product production data according to the home textile product production matching node data, which provides comprehensive data support for the production process and can ensure that production decisions and control logic are based on accurate and comprehensive information, thereby maximizing the efficiency of the production process and product quality, while reducing resource waste and optimizing production costs.
本发明实施例中，将目标家纺制品原料扫描数据和家纺制品状态数据导入到数据分析平台上。这些数据包括原料的详细信息(如类型、质量等)以及家纺制品在生产过程中的各项状态数据(如成品尺寸、质量等)，运用数据分析工具，对导入的原料数据和家纺制品状态数据进行对比分析，分析目的是找出原料属性与成品状态之间的相关性，例如某种原料特性与成品质量高低之间的关系，基于匹配分析的结果，生成一组家纺制品匹配数据，详细描述了原料特性与家纺制品最终状态之间的关联关系，为生产过程中的原料选择和使用提供依据。将目标家纺制品原料扫描数据和生产设备状态数据集中处理，涉及的数据包括原料的使用时间点、使用批次以及与这些时间点相匹配的生产设备的状态信息(如设备运行速度、温度等)，利用时间序列分析方法，研究原料使用的时序与设备状态变化之间的关系，识别出在特定的设备状态下，使用某些特定原料能否带来更优的生产效果，根据时序分析的结果，生成生产时序匹配数据，展示了在不同的生产时段内，哪些原料与哪些设备状态之间存在较强的匹配性，为优化生产流程和提高生产效率提供参考。将家纺制品匹配数据和生产时序匹配数据收集到一起，识别出生产过程中的关键匹配节点，这些节点是指那些原料特性与设备状态匹配度高，且对最终产品质量有显著影响的生产环节，例如将家纺制品匹配数据对应生产过程中每个阶段的生产时序匹配数据建立匹配节点，该匹配节点包含了中途生产出的产品以及对应的生产设备中的控制参数，提取并记录这些关键匹配节点的具体信息，生成家纺制品生产匹配节点数据。这些数据详细描述了各个匹配节点的特征，包括原料类型、设备状态、时间点等。将家纺制品生产匹配节点数据与目标家纺制品原料扫描数据及家纺制品生产数据进行进一步整合，将匹配节点的具体信息与原料的详细特性以及生产过程的实际数据结合起来。对整合后的数据进行深度分析，重点识别哪些原料特性与哪些生产条件在特定的匹配节点，以确保分析结果的准确性和可靠性。根据匹配分析的结果，生成家纺制品原料-生产匹配数据，记录了生产过程中的关键原料使用和生产条件调整的信息，为制定生产策略和优化生产流程提供了数据支撑。In an embodiment of the present invention, the target home textile product raw material scanning data and home textile product status data are imported into the data analysis platform. These data include detailed information of the raw materials (such as type, quality, etc.) and various status data of the home textile products during the production process (such as finished product size, quality, etc.). The imported raw material data and home textile product status data are compared and analyzed using data analysis tools. The purpose of the analysis is to find out the correlation between the raw material properties and the finished product status, such as the relationship between a certain raw material characteristic and the quality of the finished product. Based on the results of the matching analysis, a set of home textile product matching data is generated, which describes in detail the correlation between the raw material characteristics and the final state of the home textile products, and provides a basis for the selection and use of raw materials in the production process. The target home textile product raw material scanning data and production equipment status data are processed together. The data involved include the time point of raw material use, the batch of raw materials used, and the status information of the production equipment matching these time points (such as equipment running speed, temperature, etc.). The time series analysis method is used to study the relationship between the timing of raw material use and the change of equipment status, and identify whether the use of certain specific raw materials can bring better production results under specific equipment status. According to the results of the timing analysis, the production timing matching data is generated, which shows which raw materials have a strong match with which equipment status in different production periods, providing a reference for optimizing the production process and improving production efficiency. The home textile product matching data and the production timing matching data are collected together to identify the key matching nodes in the production process. These nodes refer to the production links where the raw material characteristics have a high degree of match with the equipment status and have a significant impact on the quality of the final product. For example, the home textile product matching data corresponds to the production timing matching data of each stage in the production process to establish a matching node. The matching node contains the products produced in the middle and the control parameters in the corresponding production equipment. The specific information of these key matching nodes is extracted and recorded to generate the home textile product production matching node data. These data describe the characteristics of each matching node in detail, including raw material type, equipment status, time point, etc. The home textile product production matching node data is further integrated with the target home textile product raw material scanning data and home textile product production data, and the specific information of the matching node is combined with the detailed characteristics of the raw materials and the actual data of the production process. The integrated data is deeply analyzed, focusing on identifying which raw material characteristics and which production conditions are at specific matching nodes to ensure the accuracy and reliability of the analysis results. Based on the results of the matching analysis, the home textile product raw material-production matching data is generated, which records the information on the use of key raw materials and the adjustment of production conditions in the production process, providing data support for formulating production strategies and optimizing production processes.
优选地，步骤S4包括以下步骤：Preferably, step S4 comprises the following steps:
步骤S41：根据预设的支持向量机算法建立生产设备状态以及家纺制品状态的映射关系，以生成初步家纺制品生产质量预测模型；Step S41: establishing a mapping relationship between the production equipment status and the home textile product status according to a preset support vector machine algorithm to generate a preliminary home textile product production quality prediction model;
步骤S42：根据家纺制品原料-生产匹配数据中的生产设备状态数据作为输入数据以及家纺制品原料-生产匹配数据中的家纺制品状态数据作为输出数据进行模型训练样本设计，以生成模型训练样本；Step S42: Designing a model training sample according to the production equipment status data in the home textile product raw material-production matching data as input data and the home textile product status data in the home textile product raw material-production matching data as output data, so as to generate a model training sample;
步骤S43：根据模型训练样本对初步家纺制品生产质量预测模型进行模型训练优化处理，生成优化家纺制品生产质量预测模型；Step S43: performing model training optimization processing on the preliminary home textile product production quality prediction model according to the model training samples to generate an optimized home textile product production quality prediction model;
步骤S44：根据优化家纺制品生产质量预测模型进行PLC生产控制逻辑设计，生成PLC生产控制逻辑数据，并将PLC生产控制逻辑数据反馈至终端执行家纺制品自动控制生产作业。Step S44: Design the PLC production control logic according to the optimized home textile product production quality prediction model, generate PLC production control logic data, and feed back the PLC production control logic data to the terminal to execute the automatic control production operation of home textile products.
本发明利用预设的支持向量机(SVM)算法建立生产设备状态与家纺制品状态的映射关系，生成的初步家纺制品生产质量预测模型准确地预测生产过程中的质量变化，这种映射关系的建立允许在生产早期阶段即可预测和识别潜在的产品质量问题，为提前采取改进措施提供了可能，从而提高最终产品的质量。通过设计模型训练样本，即将家纺制品原料-生产匹配数据中的生产设备状态数据作为输入，以及相应的家纺制品状态数据作为输出，这一过程为深入训练和优化质量预测模型提供了精确的数据基础，确保了模型能够准确捕捉到生产过程中的关键特征和规律，从而提高预测模型的准确性和可靠性。通过对初步家纺制品生产质量预测模型进行模型训练优化处理，生成的优化家纺制品生产质量预测模型能够更准确地预测生产质量，特别是在复杂的生产环境中，这种优化处理通过不断地学习和调整，使预测模型更加精细化和适应性强，能够有效指导生产过程的调整，确保产品质量持续优化。根据优化家纺制品生产质量预测模型进行的PLC生产控制逻辑设计及其数据反馈，实现了生产过程的自动化和智能化控制，通过精确控制生产参数和流程，确保生产活动能够根据预测模型的指导以及PLC的自动化程序自动调整生产设备的控制参数，从而最大程度上提高生产效率和产品质量，这种基于数据和模型的智能化生产控制机制为制造业提供了一种高效、灵活且可持续的生产解决方案。The present invention uses a preset support vector machine (SVM) algorithm to establish a mapping relationship between the state of production equipment and the state of home textile products. The generated preliminary home textile product production quality prediction model accurately predicts the quality changes in the production process. The establishment of this mapping relationship allows the prediction and identification of potential product quality problems in the early stage of production, which provides the possibility for taking improvement measures in advance, thereby improving the quality of the final product. By designing a model training sample, that is, taking the production equipment state data in the home textile product raw material-production matching data as input, and the corresponding home textile product state data as output, this process provides an accurate data basis for in-depth training and optimization of the quality prediction model, ensuring that the model can accurately capture the key features and laws in the production process, thereby improving the accuracy and reliability of the prediction model. By performing model training optimization processing on the preliminary home textile product production quality prediction model, the generated optimized home textile product production quality prediction model can more accurately predict the production quality, especially in a complex production environment. This optimization processing makes the prediction model more refined and adaptable through continuous learning and adjustment, and can effectively guide the adjustment of the production process to ensure continuous optimization of product quality. The PLC production control logic design and its data feedback based on the optimized home textile product production quality prediction model realize the automation and intelligent control of the production process. By precisely controlling the production parameters and processes, it ensures that production activities can automatically adjust the control parameters of production equipment according to the guidance of the prediction model and the PLC automation program, thereby maximizing production efficiency and product quality. This data and model-based intelligent production control mechanism provides the manufacturing industry with an efficient, flexible and sustainable production solution.
作为本发明的一个实施例，参考图2所示，为图1中步骤S4的详细实施步骤流程示意图，在本实施例中所述步骤S4包括：As an embodiment of the present invention, referring to FIG. 2 , which is a schematic flow chart of detailed implementation steps of step S4 in FIG. 1 , in this embodiment, step S4 includes:
步骤S41：根据预设的支持向量机算法建立生产设备状态以及家纺制品状态的映射关系，以生成初步家纺制品生产质量预测模型；Step S41: establishing a mapping relationship between the production equipment status and the home textile product status according to a preset support vector machine algorithm to generate a preliminary home textile product production quality prediction model;
本发明实施例中，分析生产设备状态数据和家纺制品状态数据，确定二者之间的关系。这包括识别哪些设备状态参数对产品质量有显著影响，使用软件工具或编程语言(如Python、R等)预先设计支持向量机算法，包括定义模型的核函数、惩罚参数等，建立一个初步的生产质量预测模型，该模型能够根据输入的生产设备状态数据预测家纺制品的质量状态。In the embodiment of the present invention, the production equipment status data and the home textile product status data are analyzed to determine the relationship between the two. This includes identifying which equipment status parameters have a significant impact on product quality, using software tools or programming languages (such as Python, R, etc.) to pre-design a support vector machine algorithm, including defining the kernel function and penalty parameters of the model, and establishing a preliminary production quality prediction model that can predict the quality status of home textile products based on the input production equipment status data.
步骤S42：根据家纺制品原料-生产匹配数据中的生产设备状态数据作为输入数据以及家纺制品原料-生产匹配数据中的家纺制品状态数据作为输出数据进行模型训练样本设计，以生成模型训练样本；Step S42: Designing a model training sample according to the production equipment status data in the home textile product raw material-production matching data as input data and the home textile product status data in the home textile product raw material-production matching data as output data, so as to generate a model training sample;
本发明实施例中，从家纺制品原料-生产匹配数据中提取生产设备状态数据作为模型的输入数据(特征向量)，以及对应的家纺制品状态数据(如质量等级)作为输出数据(标签)，根据提取的数据，设计训练样本。确保样本覆盖各种设备状态和产品质量的组合，以提高模型的泛化能力，对训练样本进行必要的预处理，如标准化、归一化处理，以满足SVM模型训练的需求，将整理好的数据集划分为训练集、验证集和测试集，训练集用于模型训练，验证集用于模型参数调优，测试集用于评估模型性能，以生成模型训练样本，准备用于模型训练和优化过程。In the embodiment of the present invention, the production equipment status data is extracted from the home textile product raw material-production matching data as the input data (feature vector) of the model, and the corresponding home textile product status data (such as quality grade) is used as the output data (label), and the training samples are designed according to the extracted data. Ensure that the samples cover various combinations of equipment status and product quality to improve the generalization ability of the model, perform necessary preprocessing on the training samples, such as standardization and normalization, to meet the requirements of SVM model training, divide the sorted data set into training set, validation set and test set, the training set is used for model training, the validation set is used for model parameter tuning, and the test set is used to evaluate model performance, so as to generate model training samples, ready for model training and optimization process.
步骤S43：根据模型训练样本对初步家纺制品生产质量预测模型进行模型训练优化处理，生成优化家纺制品生产质量预测模型；Step S43: performing model training optimization processing on the preliminary home textile product production quality prediction model according to the model training samples to generate an optimized home textile product production quality prediction model;
本发明实施例中，使用步骤S42中设计的模型训练样本，通过软件工具或编程环境(如Python的scikit-learn库)对初步的家纺制品生产质量预测模型进行训练，包括输入训练集数据，让模型学习如何根据生产设备状态预测家纺制品的质量状态，在训练过程中，通过验证集评估模型的性能，根据验证结果，调整模型参数(如SVM的C参数和核参数)，使用决策树算法寻找最优的参数组合，使用测试集数据对模型进行最终评估，确认模型的预测性能达到预期目标，评估指标可以包括准确率、召回率、F1分数等，完成训练、参数调优和性能评估后，得到优化家纺制品生产质量预测模型，用于实际生产过程中的质量控制和预测。In an embodiment of the present invention, the model training samples designed in step S42 are used to train a preliminary home textile product production quality prediction model through a software tool or a programming environment (such as Python's scikit-learn library), including inputting training set data to allow the model to learn how to predict the quality status of home textile products based on the status of production equipment. During the training process, the performance of the model is evaluated through a validation set, and the model parameters (such as the C parameter and kernel parameter of SVM) are adjusted according to the validation results. The decision tree algorithm is used to find the optimal parameter combination, and the model is finally evaluated using the test set data to confirm that the prediction performance of the model reaches the expected goal. The evaluation indicators may include accuracy, recall rate, F1 score, etc. After completing training, parameter tuning and performance evaluation, an optimized home textile product production quality prediction model is obtained, which is used for quality control and prediction in the actual production process.
步骤S44：根据优化家纺制品生产质量预测模型进行PLC生产控制逻辑设计，生成PLC生产控制逻辑数据，并将PLC生产控制逻辑数据反馈至终端执行家纺制品自动控制生产作业。Step S44: Design the PLC production control logic according to the optimized home textile product production quality prediction model, generate PLC production control logic data, and feed back the PLC production control logic data to the terminal to execute the automatic control production operation of home textile products.
本发明实施例中，分析优化后的预测模型输出结果，确定模型预测家纺制品质量状态的准确性和可靠性，基于这些信息设计PLC控制逻辑，以实现生产过程的自动化调整，根据模型预测结果和生产质量要求，设计PLC控制逻辑，包括制定规则和程序，以便在生产过程中根据模型预测的质量状态自动调整生产设备的运行参数，如调节速度、温度等，将设计的控制逻辑编程到PLC系统中，使用PLC编程工具，按照设计逻辑进行编码，配置生产线上的PLC系统，将配置好的PLC控制逻辑部署到生产线的实际操作中，确保生产线能够根据控制逻辑和模型预测结果自动调节生产参数，优化生产过程和产品质量，在生产过程中持续监控PLC控制逻辑的执行效果，例如实时监测新的原料数据，并根据新的原料数据预测出最佳的生产设备控制参数，使得自动化的家纺制品生产产品能达到优秀的标准。In an embodiment of the present invention, the output results of the optimized prediction model are analyzed to determine the accuracy and reliability of the model's prediction of the quality status of home textile products. Based on this information, a PLC control logic is designed to achieve automated adjustment of the production process. According to the model prediction results and production quality requirements, the PLC control logic is designed, including formulating rules and procedures so that the operating parameters of the production equipment can be automatically adjusted according to the quality status predicted by the model during the production process, such as adjusting the speed and temperature. The designed control logic is programmed into the PLC system, and a PLC programming tool is used to encode according to the designed logic, configure the PLC system on the production line, and deploy the configured PLC control logic to the actual operation of the production line to ensure that the production line can automatically adjust the production parameters according to the control logic and the model prediction results, optimize the production process and product quality, and continuously monitor the execution effect of the PLC control logic during the production process, such as real-time monitoring of new raw material data, and predicting the optimal production equipment control parameters based on the new raw material data, so that the automated home textile product production products can meet excellent standards.
优选地，步骤S43包括以下步骤：Preferably, step S43 includes the following steps:
将模型训练样本进行数据划分，分别生成模型训练集、模型验证集、模型测试集；Divide the model training samples into data to generate model training set, model verification set, and model test set respectively;
利用模型训练集对初步家纺制品生产质量预测模型进行模型训练处理，生成家纺制品生产质量预测模型；Using the model training set to perform model training processing on the preliminary home textile product production quality prediction model, and generating the home textile product production quality prediction model;
基于模型验证集对家纺制品生产质量预测模型进行模型验证评估处理，生成模型验证评估数据；Based on the model validation set, the model validation and evaluation processing of the home textile product production quality prediction model is carried out to generate model validation and evaluation data;
对模型验证评估数据进行生产链路评估数据的阶段划分出来，生成生产链路阶段评估数据；Divide the model validation evaluation data into stages of production link evaluation data to generate production link stage evaluation data;
根据生产链路阶段评估数据进行生产链路瓶颈优化参数分析，生成生产链路瓶颈优化参数；Analyze the bottleneck optimization parameters of the production link based on the production link stage evaluation data, and generate the production link bottleneck optimization parameters;
通过生产链路瓶颈优化参数对家纺制品生产质量预测模型进行模型优化调节，生成优化调节后的家纺制品生产质量预测模型，并利用模型测试集对优化调节后的家纺制品生产质量预测模型进行模型测试，生成优化家纺制品生产质量预测模型。The home textile product production quality prediction model is optimized and adjusted by optimizing the production link bottleneck parameters to generate an optimized home textile product production quality prediction model. The optimized home textile product production quality prediction model is tested using a model test set to generate an optimized home textile product production quality prediction model.
本发明将模型训练样本进行数据划分，生成模型训练集、模型验证集、模型测试集的做法，确保了机器学习模型的训练、验证、和测试过程互不干扰，公正地评估模型性能，有助于有效避免模型过拟合，确保模型具有良好的泛化能力。利用模型训练集对初步家纺制品生产质量预测模型进行模型训练处理，使模型能够学习到生产过程中的关键质量影响因素及其内在关系，从而提高了模型预测生产质量的准确性。基于模型验证集对家纺制品生产质量预测模型进行模型验证评估处理，在不影响最终测试结果的前提下检验模型的性能，生成的模型验证评估数据有助于调整和优化模型参数，进一步提升模型的准确率和可靠性。对模型验证评估数据进行生产链路评估数据的阶段划分，能够细致地识别出生产过程中的各个阶段及其对产品质量的影响，有助于定位质量问题的根源，为后续的优化提供指导。根据生产链路阶段评估数据进行的生产链路瓶颈优化参数分析，能够明确指出生产过程中的瓶颈和低效环节，对于改进生产流程、提升生产效率和产品质量至关重要。通过生产链路瓶颈优化参数对家纺制品生产质量预测模型进行模型优化调节，并利用模型测试集进行测试，不仅提高了模型的预测精度，还确保了模型在实际生产环境中的适用性和有效性，更准确地指导生产，有效提升生产质量和效率。The present invention divides the model training samples into data, generates the model training set, model verification set, and model test set, ensures that the training, verification, and test processes of the machine learning model do not interfere with each other, fairly evaluates the model performance, helps to effectively avoid model overfitting, and ensures that the model has good generalization ability. The model training set is used to perform model training processing on the preliminary home textile product production quality prediction model, so that the model can learn the key quality influencing factors and their internal relationships in the production process, thereby improving the accuracy of the model prediction production quality. Based on the model verification set, the home textile product production quality prediction model is subjected to model verification and evaluation processing, and the performance of the model is tested without affecting the final test results. The generated model verification and evaluation data helps to adjust and optimize the model parameters, and further improves the accuracy and reliability of the model. The stage division of the production link evaluation data for the model verification evaluation data can carefully identify the various stages in the production process and their impact on product quality, help locate the root cause of quality problems, and provide guidance for subsequent optimization. The production link bottleneck optimization parameter analysis based on the production link stage evaluation data can clearly point out the bottlenecks and inefficient links in the production process, which is crucial for improving production processes, improving production efficiency and product quality. The home textile product production quality prediction model is optimized and adjusted by optimizing the production link bottleneck parameters and tested using the model test set. This not only improves the model's prediction accuracy, but also ensures the model's applicability and effectiveness in the actual production environment, guides production more accurately, and effectively improves production quality and efficiency.
本发明实施例中，将整理好的模型训练样本分为三个部分：模型训练集、模型验证集、模型测试集。比例通常按照70％(训练集)、15％(验证集)、15％(测试集)来分配，以确保模型能在各种数据上都进行训练和评估，使用模型训练集数据对初步家纺制品生产质量预测模型进行训练。这一步骤通常涉及选择合适的学习率、迭代次数等超参数，完成训练过程后，得到家纺制品生产质量预测模型。使用模型验证集对训练好的模型进行验证评估，分析模型的预测性能和准确性，生成模型验证评估数据。根据模型验证评估数据，判断模型是否过拟合或欠拟合，并评估其在未见数据上的表现，将模型验证评估数据按照生产过程的不同阶段进行划分，以识别模型在哪个生产阶段的预测性能最好或最差，生成生产链路阶段评估数据。根据生产链路阶段评估数据，分析确定生产过程中的瓶颈环节，即模型预测性能较差的部分，针对识别出的瓶颈环节，分析如何调整模型参数或生产工艺参数以优化生产过程，生成生产链路瓶颈优化参数。根据生产链路瓶颈优化参数，对家纺制品生产质量预测模型进行优化调节，包括调整模型结构、优化算法参数等，使用模型测试集对优化调节后的模型进行最终测试，验证模型优化后的预测性能是否提高，生成优化后的家纺制品生产质量预测模型。In an embodiment of the present invention, the organized model training samples are divided into three parts: a model training set, a model validation set, and a model test set. The proportions are usually allocated according to 70% (training set), 15% (validation set), and 15% (test set) to ensure that the model can be trained and evaluated on various data, and the model training set data is used to train the preliminary home textile product production quality prediction model. This step usually involves selecting appropriate hyperparameters such as learning rate and number of iterations. After completing the training process, a home textile product production quality prediction model is obtained. The trained model is verified and evaluated using the model validation set, the prediction performance and accuracy of the model are analyzed, and model validation evaluation data is generated. According to the model validation evaluation data, it is determined whether the model is overfitting or underfitting, and its performance on unseen data is evaluated. The model validation evaluation data is divided according to different stages of the production process to identify which production stage has the best or worst prediction performance of the model, and the production link stage evaluation data is generated. According to the evaluation data of the production chain stage, the bottleneck link in the production process is analyzed and determined, that is, the part with poor model prediction performance. For the identified bottleneck link, how to adjust the model parameters or production process parameters to optimize the production process is analyzed to generate the production chain bottleneck optimization parameters. According to the production chain bottleneck optimization parameters, the home textile product production quality prediction model is optimized and adjusted, including adjusting the model structure, optimizing algorithm parameters, etc. The optimized and adjusted model is finally tested using the model test set to verify whether the prediction performance of the model is improved after optimization, and the optimized home textile product production quality prediction model is generated.
优选地，所述根据生产链路阶段评估数据进行生产链路瓶颈优化参数分析包括以下步骤：Preferably, the performing of production link bottleneck optimization parameter analysis according to production link stage evaluation data comprises the following steps:
根据生产链路阶段评估数据进行生产链路阶段决策因素识别，生成生产链路阶段决策因素数据；Identify decision factors at the production link stage based on the production link stage evaluation data and generate decision factor data at the production link stage;
根据生产链路阶段决策因素数据进行树节点参数分析，生成树节点参数；根据树节点参数进行生产链路优化决策树模型建立，生成生产链路优化决策树模型；Perform tree node parameter analysis based on the decision factor data of the production link stage to generate tree node parameters; establish a production link optimization decision tree model based on the tree node parameters to generate a production link optimization decision tree model;
根据生产链路优化决策树模型进行生产链路瓶颈优化参数分析，生成生产链路瓶颈优化参数。According to the production link optimization decision tree model, the production link bottleneck optimization parameter analysis is performed to generate the production link bottleneck optimization parameters.
本发明根据生产链路阶段评估数据进行生产链路阶段决策因素识别，能够精确地指出影响生产链路效率和产品质量的关键因素，有助于明确生产过程中的优化焦点，为生产链路的改进提供了数据支持和方向指导，确保优化活动能够针对性地解决实际问题。根据生产链路阶段决策因素数据进行的树节点参数分析及生成的树节点参数，为构建生产链路优化决策树模型提供了基础参数，这些参数反映了生产链路各阶段的决策逻辑和可能的优化路径，有助于精确模拟和分析生产过程，从而发现并优化生产流程中的关键节点。根据树节点参数建立的生产链路优化决策树模型，系统地分析和评估生产链路的各个环节和决策点，为识别和分析生产链路的瓶颈问题提供了一种直观且系统的方法，有助于更深入地理解生产流程中的复杂关系和影响因素，为生产流程的优化提供了科学的决策。根据生产链路优化决策树模型进行的生产链路瓶颈优化参数分析能够精确地识别生产流程中的瓶颈环节，并提出具体的优化建议和参数，有助于针对性地解决生产过程中的效率低下和质量问题，提高整个生产链路的效率和产品质量，确保生产活动能够更加顺畅且高效地进行，通过这些优化参数的应用，实现生产过程的持续改进和优化。The present invention identifies the decision factors of the production link stage according to the production link stage evaluation data, can accurately point out the key factors that affect the efficiency of the production link and the quality of the product, helps to clarify the optimization focus in the production process, provides data support and direction guidance for the improvement of the production link, and ensures that the optimization activities can solve practical problems in a targeted manner. The tree node parameter analysis and generated tree node parameters based on the production link stage decision factor data provide basic parameters for constructing a production link optimization decision tree model. These parameters reflect the decision logic and possible optimization paths of each stage of the production link, and help to accurately simulate and analyze the production process, thereby discovering and optimizing the key nodes in the production process. The production link optimization decision tree model established according to the tree node parameters systematically analyzes and evaluates each link and decision point of the production link, provides an intuitive and systematic method for identifying and analyzing the bottleneck problem of the production link, helps to more deeply understand the complex relationships and influencing factors in the production process, and provides scientific decision-making for the optimization of the production process. The production chain bottleneck optimization parameter analysis based on the production chain optimization decision tree model can accurately identify the bottleneck links in the production process and put forward specific optimization suggestions and parameters, which helps to solve the inefficiency and quality problems in the production process in a targeted manner, improve the efficiency and product quality of the entire production chain, and ensure that production activities can be carried out more smoothly and efficiently. Through the application of these optimization parameters, continuous improvement and optimization of the production process can be achieved.
本发明实施例中，分析生产链路阶段评估数据，这些数据反映了生产过程中的不同阶段及其性能，基于生产链路阶段评估数据，识别出影响生产链路阶段性能的关键决策因素，如原料质量、设备效率、操作参数等，整理识别出的决策因素，生成生产链路阶段决策因素数据。这些数据详细描述了每个生产阶段的关键影响因素。对收集的生产链路阶段决策因素数据进行详细分析，识别出影响生产效率和产品质量的关键因素，对每个决策因素评估其对生产过程的影响程度，基于此评估为每个因素分配权重。权重的分配可以依据专家经验或数据分析结果来进行，将每个决策因素及其权重整合为树节点参数。这些参数将用于构建生产链路优化决策树模型，反映不同决策因素在生产过程中的重要性。基于树节点参数，设计优化决策树的结构，包括确定树的深度、各节点代表的决策因素、节点分裂的条件等，对于决策树中的每个节点，使用相应的树节点参数(包括决策因素和权重)来决定其分裂路径。分裂条件基于决策因素对生产效率和质量的具体影响，如某参数超过阈值时分裂至特定子节点，依据设计的结构和分裂条件，使用决策树算法(可能涉及编程实现)构建生产链路优化决策树模型，需要迭代调整模型结构，以确保模型能准确反映生产过程的决策路径，完成决策树的构建和验证后，得到最终的生产链路优化决策树模型。该模型能够指导如何根据不同的生产条件做出优化决策，以改善生产链路的效率和产品质量。利用建立的生产链路优化决策树模型，分析整个生产过程，特别是那些被模型指示为关键影响点的阶段，识别模型中指出的生产瓶颈，即那些对生产质量和效率影响最大的决策因素所在的阶段，针对识别出的生产瓶颈，分析需要调整或优化的参数，这些优化参数旨在改进瓶颈阶段的生产效率和产品质量，以生成生产链路瓶颈优化参数，为实施生产过程优化提供了具体的指导。In an embodiment of the present invention, the production link stage evaluation data is analyzed, and these data reflect the different stages and their performance in the production process. Based on the production link stage evaluation data, the key decision factors affecting the performance of the production link stage, such as raw material quality, equipment efficiency, operating parameters, etc., are identified, and the identified decision factors are sorted out to generate production link stage decision factor data. These data describe the key influencing factors of each production stage in detail. The collected production link stage decision factor data are analyzed in detail to identify the key factors affecting production efficiency and product quality, and each decision factor is evaluated for its degree of influence on the production process, and a weight is assigned to each factor based on this evaluation. The weight allocation can be based on expert experience or data analysis results, and each decision factor and its weight are integrated into tree node parameters. These parameters will be used to construct a production link optimization decision tree model to reflect the importance of different decision factors in the production process. Based on the tree node parameters, the structure of the optimized decision tree is designed, including determining the depth of the tree, the decision factors represented by each node, the conditions for node splitting, etc. For each node in the decision tree, the corresponding tree node parameters (including decision factors and weights) are used to determine its split path. The splitting condition is based on the specific impact of the decision factors on production efficiency and quality. For example, when a parameter exceeds the threshold, it is split to a specific sub-node. According to the designed structure and splitting conditions, a decision tree algorithm (may involve programming implementation) is used to build a production chain optimization decision tree model. The model structure needs to be iteratively adjusted to ensure that the model can accurately reflect the decision path of the production process. After completing the construction and verification of the decision tree, the final production chain optimization decision tree model is obtained. This model can guide how to make optimization decisions according to different production conditions to improve the efficiency and product quality of the production chain. Using the established production chain optimization decision tree model, the entire production process is analyzed, especially those stages indicated as key influencing points by the model, and the production bottlenecks pointed out in the model are identified, that is, the stages where the decision factors that have the greatest impact on production quality and efficiency are located. For the identified production bottlenecks, the parameters that need to be adjusted or optimized are analyzed. These optimization parameters are aimed at improving the production efficiency and product quality of the bottleneck stage to generate the production chain bottleneck optimization parameters, which provides specific guidance for the implementation of production process optimization.
本说明书中提供一种基于PLC编程自动控制的家纺制品生产系统，用于执行如上述所述的基于PLC编程自动控制的家纺制品生产方法，该基于PLC编程自动控制的家纺制品生产系统包括：This specification provides a home textile product production system based on PLC programming automatic control, which is used to execute the home textile product production method based on PLC programming automatic control as described above. The home textile product production system based on PLC programming automatic control includes:
目标家纺制品原料扫描模块，用于根据监控扫描设备进行家纺制品原料扫描处理，生成家纺制品原料扫描数据；对家纺制品原料扫描数据进行目标家纺制品原料扫描数据采集，以得到目标家纺制品原料扫描数据；The target home textile product raw material scanning module is used to perform home textile product raw material scanning processing according to the monitoring scanning device to generate home textile product raw material scanning data; the home textile product raw material scanning data is subjected to target home textile product raw material scanning data collection to obtain the target home textile product raw material scanning data;
家纺制品生产数据采集模块，用于根据传感器集成设备进行家纺制品生产数据实时采集处理，生成家纺制品生产数据，其中所述家纺制品生产数据包括生产设备状态数据以及家纺制品状态数据；A home textile product production data acquisition module, which is used to collect and process home textile product production data in real time according to the sensor integrated device to generate home textile product production data, wherein the home textile product production data includes production equipment status data and home textile product status data;
家纺制品原料-生产匹配模块，用于对目标家纺制品原料扫描数据以及家纺制品生产数据进行数据匹配处理，生成家纺制品原料-生产匹配数据；A home textile product raw material-production matching module is used to perform data matching processing on target home textile product raw material scanning data and home textile product production data to generate home textile product raw material-production matching data;
PLC生产控制逻辑分析模块，用于根据预设的支持向量机算法以及家纺制品原料-生产匹配数据进行家纺制品生产质量的优化预测模型建立，生成优化家纺制品生产质量预测模型；根据优化家纺制品生产质量预测模型进行PLC生产控制逻辑设计，生成PLC生产控制逻辑数据，并将PLC生产控制逻辑数据反馈至终端执行家纺制品自动控制生产作业。The PLC production control logic analysis module is used to establish an optimization prediction model for the production quality of home textile products based on the preset support vector machine algorithm and the home textile product raw material-production matching data, and generate an optimized home textile product production quality prediction model; design the PLC production control logic based on the optimized home textile product production quality prediction model, generate PLC production control logic data, and feed back the PLC production control logic data to the terminal to execute the automatic control production operation of home textile products.
本申请有益效果在于，本发明的基于PLC编程自动控制的家纺制品生产方法可通过PLC自动筛选出目标的家纺制品原料，保障了对家纺制品原料的筛选效果及客观性，并对采集到的原料扫描数据和生产数据进行精确匹配处理，用于分析生产设备的控制参数，以及根据决策树模型分析生产设备中每个阶段的控制参数对应的生产瓶颈，从而精准调节家纺制品生产过程中生产设备的控制参数，通过分析出的生产设备的控制参数设计为PLC的控制逻辑参数，以实现家访制品生产的自动控制，提高了家访制品生产的智能化水平，减少了人工干预，降低了生产成本，能够在保证产品质量的同时，提升生产的灵活性和响应速度。The beneficial effects of the present application lie in that the home textile product production method based on PLC programming and automatic control of the present invention can automatically screen out target home textile product raw materials through PLC, thereby ensuring the screening effect and objectivity of the home textile product raw materials, and accurately matching the collected raw material scanning data and production data for analyzing the control parameters of the production equipment, and analyzing the production bottlenecks corresponding to the control parameters of each stage in the production equipment according to the decision tree model, so as to accurately adjust the control parameters of the production equipment in the production process of home textile products, and design the control logic parameters of the PLC through the analyzed control parameters of the production equipment to realize the automatic control of home textile product production, thereby improving the intelligence level of home textile product production, reducing manual intervention, and reducing production costs, and being able to improve production flexibility and response speed while ensuring product quality.
因此，无论从哪一点来看，均应将实施例看作是示范性的，而且是非限制性的，本发明的范围由所附权利要求而不是上述说明限定，因此旨在将落在申请文件的等同要件的含义和范围内的所有变化涵括在本发明内。Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.
以上所述仅是本发明的具体实施方式，使本领域技术人员能够理解或实现本发明。对这些实施例的多种修改对本领域的技术人员来说将是显而易见的，本文中所定义的一般原理可以在不脱离本发明的精神或范围的情况下，在其它实施例中实现。因此，本发明将不会被限制于本文所示的这些实施例，而是要符合与本文所发明的原理和新颖特点相一致的最宽的范围。The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.
| Application Number | Priority Date | Filing Date | Title |
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| CN202410516526.9ACN118426421B (en) | 2024-04-26 | 2024-04-26 | Household textile product production method and system based on PLC programming automatic control |
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| CN202410516526.9ACN118426421B (en) | 2024-04-26 | 2024-04-26 | Household textile product production method and system based on PLC programming automatic control |
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| CN116485348A (en)* | 2023-05-17 | 2023-07-25 | 深圳元臻织智能科技有限公司 | Knitted product operation management system based on supply chain |
| CN116971109A (en)* | 2023-08-09 | 2023-10-31 | 山东福特尔新材料科技有限公司 | Method and system for accurately controlling yarn in tufted carpet production process |
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| CN114331146A (en)* | 2021-12-29 | 2022-04-12 | 深圳市智能制造软件开发有限公司 | Textile mill supply chain management system based on big data prediction |
| CN115577936A (en)* | 2022-10-08 | 2023-01-06 | 深圳市广德教育科技股份有限公司 | A clothing workshop data information statistics system and method based on industrial internet of things |
| CN116485348A (en)* | 2023-05-17 | 2023-07-25 | 深圳元臻织智能科技有限公司 | Knitted product operation management system based on supply chain |
| CN116971109A (en)* | 2023-08-09 | 2023-10-31 | 山东福特尔新材料科技有限公司 | Method and system for accurately controlling yarn in tufted carpet production process |
| CN117193212A (en)* | 2023-10-17 | 2023-12-08 | 上海东尚信息科技股份有限公司 | Automatic software control system for production |
| CN117115147A (en)* | 2023-10-19 | 2023-11-24 | 山东华盛创新纺织科技有限公司 | Textile detection method and system based on machine vision |
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