Blockchain-based method for tracing quality of polymer materials in down clothing

Abstract

The application provides a method for tracing the quality of high polymer materials in down garments based on a blockchain. The method belongs to the technical field of garment quality tracing and information management. The method comprises the following steps: labeling all high polymer raw materials by using the Internet of Things, creating digital assets for each batch of raw materials by using the blockchain technology, uploading key data to the blockchain, and forming raw material archives; monitoring relevant data of a production line in real time, uploading the relevant data to a cloud platform in real time through an Internet of Things gateway; assigning a unique logistics label to finished down garments by using RFID and GPS, recording logistics information, linking the logistics information with the raw material archives and production records on the blockchain, and forming a complete supply chain view. Through the blockchain technology, the entire process of all high polymer raw materials from procurement to finished down garments is recorded on the blockchain, forming an unalterable data chain.

Description

Technical Field

[0001] This invention proposes a blockchain-based method for tracing the quality of polymer materials in down garments, belonging to the field of garment quality traceability and information management technology. Background Technology

[0002] Traditional down apparel quality traceability systems largely rely on centralized databases, which suffer from data silos, susceptibility to tampering, and low traceability efficiency. With consumers increasingly focused on product quality, safety, and environmental attributes, building a system that can both guarantee data authenticity and provide in-depth traceability information is particularly urgent. Furthermore, existing technologies lack detailed recording of environmental parameters and energy consumption levels during the production process of polymer materials, limiting the comprehensive assessment of product quality. Summary of the Invention

[0003] This invention provides a blockchain-based method for tracing the quality of polymer materials in down garments, thereby addressing the problems mentioned in the background section.

[0004] The present invention proposes a blockchain-based method for tracing the quality of polymer materials in down garments, the method comprising:

[0005] S1. All polymer raw materials are tagged with IoT, and blockchain technology is used to create digital assets for each batch of raw materials. Key data is uploaded to the blockchain to form raw material files.

[0006] S2. Monitor relevant data on the production line in real time and upload the relevant data to the cloud platform in real time through the IoT gateway;

[0007] S3. Assign unique logistics tags to finished down garments using RFID and GPS, record logistics information, and link the logistics information with raw material files and production records on the blockchain to form a complete supply chain view.

[0008] S4. Access the blockchain platform by scanning the QR code on the clothing or entering the unique ID to view the complete traceability chain;

[0009] S5. Based on big data analytics, identify potential risks in the supply chain and use machine learning models to predict future quality issues based on historical data.

[0010] The beneficial effects of this invention are as follows: Through blockchain technology, the entire process of all polymer raw materials, from procurement to finished down garments, is recorded on the blockchain, forming an immutable data chain. This makes every link in the supply chain highly transparent, allowing consumers and regulatory agencies to easily access complete product traceability information by scanning a QR code or entering a unique ID. This transparent traceability system helps improve consumer trust and reduce the circulation of counterfeit and substandard products. Utilizing IoT sensors and smart contracts to monitor various data points in the production line and logistics links in real time, combined with machine learning models for data analysis and risk warning, the system automatically triggers an early warning mechanism and locks the relevant batches upon detecting anomalies, ensuring that problems are discovered and addressed promptly, thereby significantly improving the quality control level of raw materials and products. Through smart contracts and big data analytics, abnormal patterns in the production process can be automatically identified, potential future quality problems can be predicted, and corresponding emergency response plans can be initiated. This not only helps reduce waste and losses in the production process but also improves the company's production efficiency and management level. Simultaneously, path optimization algorithms based on historical data and real-time information can automatically plan the optimal logistics route, reducing transportation costs and improving logistics efficiency. By embedding augmented reality technology into QR codes, consumers can view detailed information about clothing, including the production process and the source of raw materials, through their mobile phone cameras. This not only enhances the consumer shopping experience but also provides greater transparency of product information. Furthermore, through an interactive traceability platform, consumers can gain a deeper understanding of the complete traceability chain of the clothing, increasing interaction and trust between brands and consumers. This technological solution is based on a multi-party collaboration mechanism, allowing upstream and downstream enterprises in the supply chain to jointly participate in the formulation and execution of smart contracts. This collaborative mechanism not only promotes cooperation among all parties in the supply chain but also integrates resources to jointly address risks and challenges in the supply chain, enhancing the stability and resilience of the entire supply chain. Through the self-learning and optimization mechanism of the machine learning model, the system can continuously train and optimize the model based on newly collected data, improving prediction accuracy and decision support capabilities. Simultaneously, a feedback mechanism is established to collect data feedback and user evaluations during actual transportation, continuously optimizing the route planning algorithm to ensure the efficiency and reliability of the logistics process. Attached Figure Description

[0011] Figure 1 This is a diagram illustrating the steps of the method described in this invention. Detailed Implementation

[0012] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0013] One embodiment of the present invention, such as Figure 1As shown, a blockchain-based method for tracing the quality of polymer materials in down garments includes the following steps:

[0014] S1. All polymer raw materials are tagged with IoT tags. Each tag contains basic information such as a unique identifier (UID), material type, place of origin, and batch number. Using blockchain technology, a digital asset is created for each batch of raw materials, and key data is uploaded to the blockchain. The key data includes basic information and the first quality inspection report. An immutable raw material file is formed.

[0015] S2. Deploy sensors and smart devices on the production line to monitor relevant data in real time. The relevant data includes the production environment (such as temperature and humidity), equipment operating status, energy consumption, etc., and upload the relevant data to the cloud platform in real time through an IoT gateway. The cloud platform preprocesses the collected data and associates the timestamps, operation records, and quality inspection results of key production events (such as start-up, shutdown, material change, etc.) with the raw material files, and uploads them to the blockchain.

[0016] S3. Assign a unique logistics tag to the finished down garment using RFID and GPS, and record the logistics information, including the time of entry and exit from the warehouse, location changes, and temperature and humidity conditions; link the logistics information with the raw material files and production records on the blockchain to form a complete supply chain view;

[0017] S4. Consumers can access the blockchain platform by scanning the QR code on the clothing or entering a unique ID to view the complete traceability chain from raw materials to finished products.

[0018] S5. Based on big data analytics, identify potential risk points in the supply chain, including quality fluctuation trends and abnormal energy consumption patterns; use machine learning models to predict potential quality problems based on historical data, take proactive measures, and achieve intelligent quality management; deploy smart contracts on the blockchain to define rules for handling quality problems, including automatically triggering return processes and compensation mechanisms.

[0019] The working principle of the above technical solution is as follows: Each batch of polymer raw materials is tagged with IoT tags, including bio-based materials. Each tag contains basic information such as a unique identifier (UID), material type, origin, and batch number. These tags serve as digital proof of the raw material's identity. Blockchain technology is used to create a corresponding digital asset for each batch of raw materials. This asset contains basic information about the raw materials and key data such as the initial quality inspection report. The characteristics of blockchain ensure the immutability of this data, thus forming a "digital ID card" for the raw materials. Sensors and smart devices are deployed on the production line to monitor key data such as the production environment (e.g., temperature, humidity), equipment operating status, and energy consumption in real time. The monitored data is uploaded to the cloud platform in real time via an IoT gateway. The cloud platform preprocesses this data, extracting timestamps, operation records, and quality inspection results for key production events (e.g., startup, shutdown, material change). The processed key production event information is then associated with the raw material file and uploaded to the blockchain. In this way, the production process of each batch of raw materials is fully digitally recorded. Using RFID and GPS technology, unique logistics tags are assigned to finished down garments, recording logistics information including entry and exit times, location changes, and temperature and humidity conditions. This logistics information is linked to raw material files and production records on the blockchain, forming a complete traceability chain from raw materials to finished products. This provides consumers with a convenient way to check the product's origin and production process. Consumers can access the blockchain platform by scanning the QR code on the garment or entering a unique ID. On the blockchain platform, consumers can view the complete traceability chain information from raw material procurement, production process to logistics and distribution, ensuring product transparency and credibility. Based on big data analytics, data in the supply chain is deeply mined to identify potential quality fluctuation trends and abnormal energy consumption patterns. Machine learning models are used to predict future quality problems using historical data and take preventative measures in advance, including evaluating material quality under multiple coupled conditions such as humidity and sunlight exposure. Smart contracts are deployed on the blockchain to define rules for handling quality issues. When a quality problem occurs, the smart contract can automatically trigger a return process or compensation mechanism to ensure timely and fair handling of the issue.

[0020] The aforementioned technical solution achieves the following results: By applying advanced technologies such as IoT tagging, blockchain, RFID, and GPS, end-to-end traceability from raw materials to finished down garments is realized. Consumers can easily access the blockchain platform by scanning a QR code or entering a unique ID to view the complete traceability chain of the product, achieving full lifecycle quality traceability based on standardized dimensions. This not only improves supply chain transparency but also enhances consumer trust in the products. The distributed ledger and encryption algorithms of blockchain technology ensure the authenticity and immutability of data uploaded to the blockchain. Once data is recorded on the blockchain, it cannot be modified or deleted, thus guaranteeing the accuracy and reliability of data such as raw material files, production records, and logistics information. Through the application of big data analytics and machine learning models, potential risk points in the supply chain can be identified, future quality problems can be predicted, and intervention measures can be taken in advance. This not only improves the efficiency of quality management but also reduces losses caused by quality problems. Meanwhile, deploying smart contracts on the blockchain defines rules for handling quality issues, enabling automated processing and further improving the intelligence level of quality management. Real-time monitoring of production line data, including the production environment, equipment operating status, and energy consumption, helps companies promptly identify problems and bottlenecks in the production process, optimize processes, and improve efficiency. Furthermore, big data analytics can uncover abnormal energy consumption patterns, helping companies develop energy-saving and emission-reduction measures, reduce production costs, and achieve sustainable development. Implementing quality traceability methods, through quality assessments based on hazardous substance analysis and tracking, not only helps improve product quality and safety but also demonstrates a company's responsible attitude towards consumers and its strict control over product quality. This contributes to enhancing the company's brand image and market competitiveness, attracting more consumer attention and trust.

[0021] In one embodiment of the present invention, S1 includes:

[0022] S11. Based on the polymer raw material information collection standard, RFID technology is used in combination with QR codes to record tag information; the RFID tag has a built-in UID, and the QR code contains visual information, which facilitates quick identification and verification.

[0023] S12. Introduce a blockchain hash algorithm to encrypt raw material information and generate a unique hash value as an anti-counterfeiting label; combine the decentralized nature of blockchain to generate a unique digital asset ID for each batch of raw materials.

[0024] S13. Deploy smart contracts on the blockchain. Based on the preset threshold of raw material quality standards, if a quality problem is detected, an early warning mechanism will be automatically triggered and the relevant batch will be locked.

[0025] S14. Combining multi-source data, including IoT sensors and third-party testing institutions, to cross-verify raw material information, and based on the dynamic update mechanism of raw material archives, submitting update requests under specific conditions (such as batch replacement, quality improvement, etc.), and seamlessly updating information after verification by the blockchain consensus mechanism.

[0026] The working principle of the above technical solution is as follows: Based on the polymer raw material information collection standard, key information of the material, such as material composition, environmental protection level, and manufacturer qualifications, is comprehensively collected. This information is the foundation for ensuring the quality of raw materials. RFID technology, combined with QR codes, is used to record the collected tag information. The RFID tag has a built-in unique identifier (UID) for rapid identification of raw materials in the Internet of Things environment. The QR code contains visual information, such as the basic attributes of the raw material and manufacturer information, facilitating rapid manual identification and verification. A blockchain hash algorithm is introduced to encrypt the raw material information and generate a unique hash value. This hash value serves as an anti-counterfeiting mark for the tag, effectively preventing information from being tampered with or forged. Combining the decentralized nature of blockchain, a unique digital asset ID is generated for each batch of raw materials. This ID is mapped one-to-one with the physical raw material, ensuring the uniqueness and traceability of the asset on the blockchain. The initial quality inspection report not only includes traditional test results but also incorporates the digital signature of a third-party testing agency. The digital signature ensures the authority and immutability of the data, making the quality inspection report more credible. Smart contracts are deployed on the blockchain to preset thresholds for raw material quality standards. These thresholds are set according to industry standards or company requirements to determine whether raw materials are qualified. If the quality of raw materials is found to be non-compliant with preset standards, the smart contract will automatically trigger an early warning mechanism. This includes locking the relevant batch to prevent it from entering the production line and sending alarm information to relevant personnel. Cross-validation of raw material information is performed using multi-source data from IoT sensors and third-party testing institutions. This ensures the accuracy and completeness of the information and improves the reliability of the raw material archive. Based on the dynamic update mechanism of the raw material archive, update requests are submitted under specific conditions (such as batch replacement, quality improvement, etc.). These requests are verified by the blockchain consensus mechanism to ensure the legality and validity of the updates. Once the update request is verified, the blockchain will automatically and seamlessly update the information. This ensures that the raw material archive always remains consistent with the actual situation, providing a reliable foundation for subsequent traceability and quality management.

[0027] The above technical solution achieves the following results: By using a standard for collecting information on polymer raw materials, it covers key information such as material composition, environmental protection level, and manufacturer qualifications, ensuring the comprehensiveness and accuracy of raw material information. The use of RFID technology combined with QR codes for tag recording not only improves the efficiency and convenience of information reading but also enables rapid identification and verification of raw materials through the UID of the RFID tag and the visual information of the QR code, effectively avoiding information omissions or errors. The introduction of a blockchain hash algorithm to encrypt raw material information generates a unique hash value as an anti-counterfeiting identifier for the tag, greatly enhancing the anti-counterfeiting capability of the raw materials. Simultaneously, leveraging the decentralized nature of blockchain, a unique digital asset ID is generated for each batch of raw materials, ensuring a one-to-one mapping between assets and physical raw materials, achieving full traceability from raw materials to finished products. This not only helps combat counterfeit and substandard products but also provides consumers with more transparent and reliable product information. The initial quality inspection report not only includes traditional test results but also incorporates the digital signature of a third-party testing agency. The introduction of digital signatures ensures the authority and immutability of the quality inspection report, allowing consumers and relevant departments to trust and rely on these reports more effectively. This helps enhance consumer trust in products and provides strong support for product quality supervision. Deploying smart contracts on the blockchain, based on preset thresholds for raw material quality standards, enables an intelligent quality early warning mechanism. Once a quality problem is detected, the smart contract automatically triggers an alert and locks the relevant batch to prevent it from entering the production line. This not only improves the efficiency and accuracy of quality management but also effectively prevents quality problems from escalating, protecting the company's reputation and consumers' rights. Combining multi-source data, including IoT sensors and third-party testing institutions, cross-validates raw material information, ensuring its accuracy and reliability. Simultaneously, based on a dynamic update mechanism for raw material archives, update requests are submitted under specific conditions and seamlessly updated after verification by the blockchain consensus mechanism. This not only guarantees the timeliness and accuracy of raw material information but also improves the flexibility and convenience of information management.

[0028] In one embodiment of the present invention, S13 includes:

[0029] Based on preset raw material quality standards (including but not limited to component ratio, environmental protection level, physical properties, etc.), the logical rules of the smart contract are constructed; the rules are used as the benchmark for raw material quality monitoring, and an early warning is triggered once the data deviates from the preset threshold; the constructed logical rules are encoded into a smart contract using a smart contract programming language.

[0030] Before deployment, the smart contract is tested, including unit testing, integration testing, and security auditing; the tested smart contract is deployed to the blockchain network and linked to the raw material information management system.

[0031] By deploying IoT sensors at key stages, key quality parameters of raw materials are collected in real time, pre-processed, and then uploaded to the blockchain.

[0032] The smart contract analyzes the uploaded quality data in real time according to preset threshold rules; once an abnormality is detected, it immediately triggers an early warning mechanism and sends warning information to relevant personnel.

[0033] Once the warning is triggered, the smart contract automatically locks the batch of raw materials involved in the quality problem; at the same time, through the immutability of the blockchain, the information of the problematic batch is permanently recorded.

[0034] Based on the type and severity of the warning, the smart contract automatically initiates the corresponding emergency response plan; based on the monitoring data and warning records, the smart contract automatically generates a raw material quality monitoring report; and shares the quality monitoring report with all relevant parties (including manufacturers, suppliers, customers, etc.) through the blockchain network.

[0035] The working principle of the above technical solution is as follows: Based on preset raw material quality standards (such as component ratio, environmental protection level, physical properties, etc.), the logical rules of the smart contract are constructed; the logical rules are encoded into a smart contract using a smart contract programming language (such as Solidity); before deployment, the smart contract undergoes comprehensive testing, including unit testing, integration testing, and security auditing, to ensure its correctness and security; the tested smart contract is deployed on the blockchain network and linked with the raw material information management system to achieve real-time data interaction; IoT sensors are deployed in key stages such as raw material storage and processing to collect key quality parameters of raw materials in real time (such as temperature, humidity, weight, etc.); the collected data is preprocessed to meet the input requirements of the smart contract; the preprocessed data is uploaded to the blockchain for access and analysis by the smart contract; the smart contract analyzes the uploaded quality data in real time according to preset threshold rules; once data anomalies are detected (such as exceeding preset thresholds), an early warning mechanism is immediately triggered to send warning information to relevant personnel; the warning... The information includes detailed information on abnormal data, potential impacts, and suggested emergency response measures. Upon triggering an alert, the smart contract automatically locks the batch of raw materials involved in the quality issue to prevent it from entering the production line. Leveraging the immutability of blockchain, information on the problematic batch is permanently recorded, including batch number, abnormal data, and alert time. This facilitates subsequent traceability and analysis of the problematic batch to determine the cause and solution. Based on the alert type and severity, the smart contract automatically initiates the corresponding emergency response plan. This plan includes notifying third-party testing agencies for re-inspection, initiating raw material recall procedures, and adjusting production plans. The smart contract automatically generates a raw material quality monitoring report based on monitoring data and alert records. The report includes quality data trend analysis, details of the alert event, and emergency response measures, providing comprehensive quality monitoring information to relevant personnel. The quality monitoring report is shared with all relevant parties (including manufacturers, suppliers, and customers) through the blockchain network, enhancing supply chain transparency and traceability and increasing consumer trust in the products.

[0036] The above technical solution achieves the following effects: By constructing smart contracts, it automates and intelligently monitors raw material quality. Smart contracts can analyze uploaded quality data in real time according to preset raw material quality standards, and immediately trigger an early warning mechanism upon detecting data anomalies. This not only significantly improves the response speed of quality management but also reduces human intervention and the risk of human error. Based on blockchain technology, smart contracts are tamper-proof. This means that once data is uploaded to the blockchain, it cannot be modified or deleted. Therefore, smart contracts ensure the accuracy and reliability of raw material quality monitoring data, providing strong evidence for tracing quality issues. Through smart contracts, raw material quality data is uploaded to the blockchain in real time and is accessible to all relevant parties. This greatly enhances the transparency and traceability of the supply chain, enabling manufacturers, suppliers, and customers to understand the quality status of raw materials in real time, strengthening trust and cooperation within the supply chain. Smart contracts can automatically activate corresponding emergency response plans based on the type and severity of the early warning. This includes notifying third-party testing agencies for re-inspection, initiating raw material recall procedures, and adjusting production plans. By optimizing emergency response processes, enterprises can address quality issues more quickly, reduce quality risks, and protect consumer rights. Smart contracts can automatically generate raw material quality monitoring reports based on monitoring data and early warning records. These reports include quality data trend analysis, details of early warning events, and emergency response measures, providing comprehensive quality monitoring information for relevant personnel. This information helps enterprises better understand the quality status of raw materials, supports decision-making, and improves product quality and competitiveness. This technical solution applies blockchain technology and smart contracts to raw material quality management, providing new ideas and directions for the application and development of blockchain technology in the field of quality management. This not only helps promote the popularization and application of blockchain technology but also fosters innovation and development in the field of quality management.

[0037] In one embodiment of the present invention, S2 includes:

[0038] S21. Through monitoring equipment, including high-precision temperature sensors, humidity sensors and energy consumption monitoring equipment, the subtle changes in the production environment are monitored in real time, and the operating status of the equipment is monitored in real time through the health management system equipped with the intelligent equipment.

[0039] S22. Based on the event association algorithm, production events are intelligently matched with relevant data to form a complete event chain; based on the machine learning algorithm, a production anomaly detection model is built to automatically identify anomaly patterns in the production process.

[0040] S23. Based on the preset early warning response level, automatically trigger the corresponding emergency handling process according to the degree of abnormality, such as suspending production or adjusting process parameters.

[0041] The working principle of the above technical solution is as follows: High-precision temperature sensors, humidity sensors, and energy consumption monitoring equipment are deployed in the production environment to monitor subtle changes in the production environment (such as minor fluctuations in temperature and humidity) and the operating status of equipment (such as energy consumption and vibration) in real time. The health management system equipped with the intelligent equipment receives data from the monitoring equipment and performs real-time analysis to monitor the health status of the equipment. Through data analysis, the health management system can predict and prevent potential equipment failures, such as predicting motor failures by monitoring abnormal increases in energy consumption, or predicting mechanical wear by analyzing vibration data. The algorithm intelligently matches production events (such as equipment downtime, quality anomalies, etc.) with relevant data to form a complete event chain. Relevant data includes raw material files (such as batch numbers, composition, and source) and quality inspection reports (such as test results and anomaly records). Based on a large amount of historical data, a production anomaly detection model is constructed. This model can automatically identify abnormal patterns in the production process, such as identifying potential quality problems or decreased production efficiency by analyzing abnormal fluctuations in production data. It presets different early warning response levels based on the severity of the anomaly, such as minor, moderate, and severe anomalies. Once the anomaly detection model identifies an abnormal pattern in the production process and determines the severity based on the early warning response level, it will automatically trigger the corresponding emergency handling procedures. For example, for minor anomalies, it may only require adjusting process parameters or performing simple equipment maintenance; for moderate anomalies, it may require suspending production for further inspection; and for severe anomalies, it may require immediately stopping production and initiating a comprehensive troubleshooting and repair process.

[0042] The effects of the above technical solution are as follows: Through monitoring equipment such as high-precision temperature sensors, humidity sensors, and energy consumption monitoring devices, subtle changes in the production environment and the operating status of equipment can be monitored in real time. This real-time monitoring not only helps to promptly detect potential problems in the production environment, such as abnormal fluctuations in temperature or humidity, but also enables the health management system equipped with intelligent equipment to predict and prevent potential equipment failures. This preventative maintenance measure can significantly improve the stability and efficiency of the production line and reduce production interruptions caused by equipment failures. Based on event correlation algorithms, production events are intelligently matched with relevant data to form a complete event chain. This intelligent matching can reveal the inherent connections between production events, providing a more comprehensive perspective for problem identification. Simultaneously, the production anomaly detection model built through machine learning algorithms can automatically identify abnormal patterns in the production process, improving the accuracy and timeliness of problem identification. This intelligent detection capability helps enterprises discover, analyze, and resolve problems more quickly. Based on preset early warning response levels, corresponding emergency handling procedures are automatically triggered according to the severity of the anomaly. This automated emergency handling mechanism can quickly respond to abnormal situations in the production process, reducing the delay and uncertainty of manual intervention. By promptly suspending production and adjusting process parameters, enterprises can effectively control the spread of abnormal situations and reduce losses caused by production anomalies. The aforementioned technical solutions not only help improve current production management levels but also provide strong support for enterprises' intelligent transformation. By introducing high-precision monitoring equipment, intelligent algorithms, and automated emergency response mechanisms, enterprises can gradually build an intelligent production management system, laying a solid foundation for future sustainable development.

[0043] In one embodiment of the present invention, step S22 includes:

[0044] S221. Preprocess the large amount of raw data collected from monitoring equipment and equipment health management system, and use feature engineering technology to extract key event features from the preprocessed data, such as temperature fluctuation range, humidity change trend, and abnormal energy consumption peak.

[0045] S222. Combine raw material files (such as batch number, component ratio, supplier information, etc.) and quality inspection reports (defect type, frequency of occurrence, degree of impact, etc.) to construct a multi-dimensional event feature set;

[0046] S223. Based on the event association algorithm, it automatically identifies the potential causal relationships and time series correlations between different data sources, and intelligently matches various events in production (such as equipment failure warnings, raw material quality fluctuations, abnormal environmental parameters, etc.) with relevant data to form a continuous and complete event chain.

[0047] S224. An unsupervised learning method is used to train the integrated multi-dimensional event feature set to learn the data distribution characteristics under normal production conditions and automatically identify data points that deviate from the normal pattern, i.e., potential production anomalies.

[0048] S225. For the abnormal patterns identified by the model, cluster analysis is used to further analyze their inherent laws and influencing factors, and on this basis, a production anomaly knowledge graph is constructed.

[0049] The working principle of the above technical solution is as follows: First, the large amount of raw data collected from monitoring equipment (such as high-precision temperature sensors, humidity sensors, energy consumption monitoring equipment, etc.) and equipment health management systems is preprocessed, including data cleaning, noise reduction, and normalization, to ensure data quality and consistency. Next, using feature engineering techniques, key event features are extracted from the preprocessed data, such as temperature fluctuation range, humidity change trends, and abnormal energy consumption peaks. These features reflect the operating status of the production environment and equipment. Combined with raw material records (such as batch numbers, component ratios, supplier information, etc.) and quality inspection reports (defect types, frequency of occurrence, degree of impact, etc.), the key event features are integrated with raw material information and quality inspection results to construct a multi-dimensional event feature set. This feature set not only includes the status information of the production environment and equipment but also covers the quality and defect status of raw materials, providing a comprehensive data foundation for subsequent anomaly detection. Based on event association algorithms, potential causal relationships and time-series correlations between different data sources are automatically identified. By intelligently matching various events in production (such as equipment failure warnings, raw material quality fluctuations, and abnormal environmental parameters) with relevant data, a continuous and complete event chain is formed. This event chain reveals the inherent connections between production events, helping to understand the background and causes of anomalies. An unsupervised learning method is employed to train the integrated multi-dimensional event feature set. By learning the data distribution characteristics under normal production conditions, the model can automatically identify data points that deviate from normal patterns, i.e., potential production anomalies. This method does not require pre-labeling of anomalous data, making it more flexible and efficient in practical applications. For the anomaly patterns identified by the model, cluster analysis is used to further analyze their inherent patterns and influencing factors. Cluster analysis can group similar anomaly patterns together, revealing the commonalities and differences between anomalies. Based on this, a production anomaly knowledge graph is constructed, visually displaying anomaly types, frequencies, potential causes, and scope of impact. The knowledge graph not only provides a visual representation of anomaly patterns but also offers decision-makers deep insights, helping them better understand anomalies in the production process and formulate corresponding response strategies.

[0050] The above technical solution offers the following advantages: By preprocessing and extracting features from the data collected by monitoring equipment, key parameters in the production process, such as temperature, humidity, and energy consumption, can be monitored in real time. If these parameters experience abnormal fluctuations, the system can immediately issue warnings, enabling the production team to respond quickly and take measures to avoid potential production failures and quality problems. Combined with raw material archives and quality inspection reports, a multi-dimensional event feature set can be constructed, and causal relationships and time-series correlations between different data sources can be automatically identified through event correlation algorithms. This helps to accurately locate abnormal links in production, reduce investigation time, and improve problem-solving efficiency. Unsupervised learning methods are used to train the integrated multi-dimensional event feature set, learning the data distribution characteristics under normal production conditions. By automatically identifying data points that deviate from normal patterns, the system can provide data support for decision-makers, enabling them to make more informed production decisions based on facts. Through cluster analysis of abnormal patterns and knowledge graph construction, bottlenecks and potential risks in the production process can be revealed. This helps enterprises optimize resource allocation, such as adjusting production plans, improving processes, or strengthening quality control, thereby improving overall production efficiency and profitability. The implementation of the aforementioned technical solutions helps enterprises enhance their level of intelligence, achieving automated, digital, and intelligent management of the production process. This not only improves production efficiency and quality but also reduces labor costs and safety risks. Real-time monitoring and early warning systems help enterprises promptly identify and resolve environmental issues in the production process, such as abnormal energy consumption. This helps enterprises reduce energy consumption and emissions, improve resource utilization efficiency, and thus enhance sustainable development capabilities. Comprehensive monitoring and anomaly warnings of the production process help enterprises improve product quality, reduce defect rates, and increase customer satisfaction and loyalty. High-quality products and efficient production management help enterprises build a positive brand image and enhance market competitiveness. This not only attracts more customers but also promotes the long-term development of the enterprise.

[0051] In one embodiment of the present invention, S223 includes:

[0052] Information from multiple data sources is integrated; natural language processing (NLP) and machine learning techniques are used to perform preliminary event identification on the integrated data;

[0053] By employing time series analysis techniques, we can identify the time series characteristics of events from different data sources, and use causal relationship mining algorithms to automatically explore potential causal relationships between events and construct causal chains between events.

[0054] By combining key event features extracted from the raw data with multi-dimensional information from raw material archives and quality inspection reports, event features are fused.

[0055] By employing feature selection, dimensionality reduction, and fusion algorithms, a high-dimensional, complex, and comprehensive multi-dimensional event feature set is constructed.

[0056] Based on the event feature set and causal relationship chain constructed above, a matching algorithm is used to automatically and intelligently match various events in production with relevant data; at the same time, association optimization technology is used to further optimize and adjust the event chain.

[0057] The constructed event chain is then visualized.

[0058] The working principle of the above technical solution is as follows: First, information from multiple data sources, including monitoring equipment, equipment health management systems, raw material archive systems, and quality inspection reports, is integrated. To ensure data consistency and comparability, data standardization techniques, such as data cleaning, format conversion, and missing value handling, are employed to provide a high-quality data foundation for subsequent analysis. Natural Language Processing (NLP) and machine learning techniques are then used to perform preliminary event identification on the integrated data. By defining a set of event keywords and rules, key event information, such as equipment failure types and descriptions of raw material quality problems, is automatically extracted from the text data. These events are then preliminarily classified to provide a clear framework for subsequent correlation analysis. Finally, time series analysis techniques, such as Autoregressive Moving Average (ARIMA) and Long Short-Term Memory (LSTM) networks, are used to identify the time series characteristics of events from different data sources, including periodicity, trends, and seasonality. Simultaneously, causal relationship mining algorithms, such as Granger causality tests and Bayesian network-based causal inference, are employed to automatically explore potential causal relationships between events and construct causal chains. Key event features extracted from raw data (such as temperature fluctuation ranges and humidity trends) are combined with multi-dimensional information from raw material archives and quality inspection reports to fuse event features. Through feature selection, dimensionality reduction, and fusion algorithms, such as Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and deep learning models, a high-dimensional, complex, and comprehensive multi-dimensional event feature set is constructed, providing a rich information foundation for subsequent anomaly detection and pattern recognition. Based on the constructed event feature set and causal relationship chains, advanced matching algorithms, such as similarity-based matching and graph theory-based matching, are used to automatically and intelligently match various events in production with relevant data. Simultaneously, association optimization techniques, such as local optimization based on greedy algorithms and global optimization based on genetic algorithms, are used to further optimize and adjust the event chain, ensuring its continuity and integrity. Finally, the constructed event chain is visualized, intuitively presenting the relationships between events through timelines, causal chains, and event nodes. Furthermore, interpretable machine learning techniques are introduced to provide interpretability support for the event chain, helping users better understand the logic and basis of event associations.

[0059] The above technical solution achieves the following results: By integrating information from multiple data sources, data silos are broken down, achieving comprehensive data aggregation. This not only improves data utilization but also provides a rich data foundation for subsequent event identification and correlation analysis. Utilizing Natural Language Processing (NLP) and machine learning techniques, preliminary event identification is performed on the integrated data, significantly improving the intelligence level of event identification. Simultaneously, through model training and optimization, various key events in production can be accurately identified, providing accurate event information for subsequent analysis. Time series analysis techniques are employed to identify the time series characteristics of events from different data sources, which helps in understanding the dynamic changes and trends of events. Furthermore, causal relationship mining algorithms are used to automatically explore potential causal relationships between events, constructing causal chains between events and providing strong support for a deeper understanding of event correlations in the production process. Event features are fused by combining key event features extracted from raw data with multi-dimensional information from raw material archives and quality inspection reports. This not only enriches the content of event features but also improves their comprehensiveness and complexity, providing a more comprehensive information foundation for subsequent pattern recognition and anomaly detection. Through feature selection, dimensionality reduction, and fusion algorithms, a high-dimensional, complex, and comprehensive multi-dimensional event feature set is constructed. This not only improves the quality of the feature set but also provides an efficient foundation for subsequent intelligent matching. Simultaneously, the matching algorithm automatically and intelligently matches various events in production with relevant data, significantly improving matching efficiency and accuracy. Furthermore, association optimization technology further optimizes and adjusts the event chain, ensuring its accuracy and consistency. Finally, the constructed event chain is visualized, making the relationships between events more intuitive and easier to understand, providing strong support for production process monitoring and management.

[0060] In one embodiment of the present invention, S3 includes:

[0061] S31. Encrypt RFID tags and GPS trackers, record the corresponding information, and deploy edge computing devices at logistics nodes to process logistics data in real time.

[0062] S32. Based on big data visualization technology, the supply chain view is presented in a visual form such as dynamic charts and 3D maps;

[0063] S33. Based on historical logistics data and real-time traffic information, develop a route optimization algorithm to automatically plan the optimal logistics route;

[0064] S34. By combining blockchain smart contracts, risk warning thresholds are set in the logistics process. If a warning is triggered, the preset response strategy is automatically executed.

[0065] The working principle of the above technical solution is as follows: Sensitive information in RFID tags and GPS trackers is encrypted to ensure data security and privacy during the logistics process; encryption technologies may include symmetric encryption algorithms (such as AES) or asymmetric encryption algorithms (such as RSA), as well as dynamic encryption technologies, to enhance the security of data transmission and storage; basic environmental parameters such as inbound and outbound times, location changes, temperature, humidity, and vibration are recorded, providing important data for quality control during the logistics process; real-time monitoring through RFID tags and GPS trackers ensures the accuracy and timeliness of data; edge computing devices are deployed at logistics nodes to process and analyze logistics data in real time; edge computing devices can reduce data transmission latency, improve data processing efficiency, and reduce the load on the central server; big data visualization technology is used to present the supply chain view in the form of dynamic charts, 3D maps, etc.; this visualization method helps managers intuitively understand the operating status of the supply chain, promptly identify potential problems, and make corresponding decision adjustments; based on historical logistics data and real-time data... This system collects and analyzes relevant data on logistics routes, including road congestion, traffic control information, and vehicle performance parameters. It develops route optimization algorithms to automatically plan the optimal logistics route. These algorithms may employ intelligent algorithms (such as Dijkstra's algorithm or ant colony optimization) that comprehensively consider multiple factors (such as distance, time, and cost) to find the optimal path. Based on the algorithm-planned optimal path, it arranges logistics transportation routes. During transportation, it dynamically adjusts the route according to real-time traffic information to ensure smooth operation. It also incorporates blockchain smart contract technology to set risk warning thresholds during the logistics process. Smart contracts automatically monitor logistics data and execute preset response strategies when these thresholds are triggered. These strategies may include adjusting logistics plans, activating backup warehouses, and notifying relevant personnel. Through the automatic execution of smart contracts, effective management and control of risks during the logistics process are achieved, helping to reduce uncertainty and improve efficiency and safety.

[0066] The effects of the above technical solutions are as follows: By encrypting RFID tags and GPS trackers, the illegal interception or tampering of logistics data during transmission can be effectively prevented, thus ensuring the integrity and authenticity of the data. This encryption measure provides strong protection for information security in the logistics process; deploying edge computing devices at logistics nodes enables real-time processing and analysis of logistics data. Compared with traditional centralized data processing methods, edge computing can reduce data transmission latency and improve data processing efficiency, thereby responding more quickly to various needs in the logistics process; based on big data visualization technology, the supply chain view is presented in the form of dynamic charts, 3D maps, and other visualizations, allowing managers to more intuitively understand the overall situation and operational status of the supply chain. This visualization management method helps managers quickly identify problems, make decisions, and optimize supply chain management processes; through visualization technology, managers can obtain various data and information of the supply chain in real time, such as inventory status, transportation progress, and environmental parameters. This data and information provides managers with rich decision support, helping to improve the efficiency and accuracy of decision-making; the path optimization algorithm developed based on historical logistics data and real-time traffic information can automatically plan the optimal logistics route. The application of this algorithm helps reduce transportation time, lower transportation costs, and improve logistics efficiency. The route optimization algorithm can continuously adjust and optimize logistics routes based on real-time traffic information and historical data, thus adapting to various complex and changing logistics environments. Combined with blockchain smart contract technology, risk warning thresholds can be set during the logistics process. When actual data triggers these warning thresholds, the smart contract will automatically execute preset response strategies, such as adjusting the logistics plan and activating backup warehouses. The automated execution characteristic of smart contracts makes risk response faster and more accurate. This helps reduce delays and errors caused by human intervention and improves the stability and reliability of the logistics process.

[0067] In one embodiment of the present invention, S33 includes:

[0068] S331. Integrate multi-source data and preprocess the integrated multi-source data. The multi-source data includes historical logistics data (such as transportation time, distance, cost, historical traffic conditions, etc.), real-time traffic information (such as road conditions, congestion, traffic accidents, etc.), weather forecast data (such as rainfall, strong winds, snow, and other weather factors that may affect transportation), geographic information (such as road grade, bridge and tunnel restrictions, restricted areas, etc.), and vehicle status data (such as fuel consumption, tire wear, load conditions, etc.).

[0069] S332. Based on historical logistics data, a preliminary set of optimal routes is generated using a route planning algorithm; and combined with real-time traffic information and vehicle status data, the preliminary routes are adjusted in real time using a machine learning model.

[0070] S333. Incorporate multi-objective optimization strategies into path planning, and use genetic algorithms to solve for the optimal solution set through multi-objective optimization functions;

[0071] S334. Use logistics simulation software to simulate the optimized route and test its performance in different scenarios; verify the effectiveness and reliability of the route optimization algorithm by comparing the simulation results with the actual situation.

[0072] S335. Establish a feedback mechanism to collect data feedback and user evaluations during the actual transportation process and continuously optimize the route planning algorithm;

[0073] S336. By combining real-time traffic information and vehicle status data, identify potential transportation risks, such as severe congestion, traffic accidents, and severe weather; based on the risk warning results, automatically trigger preset emergency response strategies, such as adjusting logistics plans, activating backup warehouses, and changing transportation methods; at the same time, use machine learning models to evaluate the effectiveness of different strategies in real time and select the optimal emergency plan.

[0074] The working principle of the above technical solution is as follows: First, the system collects data from multiple sources, including historical logistics data, real-time traffic information, weather forecast data, geographic information, and vehicle status data. The collected multi-source data undergoes preprocessing, including data cleaning, format conversion, and outlier handling, to ensure data accuracy and consistency. Based on historical logistics data, the system uses path planning algorithms (such as Dijkstra's algorithm) to generate a preliminary set of optimal paths. These path sets consider factors such as transportation time, distance, and cost. Combining real-time traffic information and vehicle status data, the system uses a machine learning model to adjust the preliminary paths in real time. The model can predict future road condition changes, assess the potential risks and costs of different paths, and thus dynamically select the optimal path. In path planning, the system incorporates a multi-objective optimization strategy, simultaneously considering multiple objectives such as transportation cost, time efficiency, and carbon emissions. Through a multi-objective optimization function, the system uses methods such as genetic algorithms to find the optimal solution set that satisfies multiple objectives from multiple possible paths. Using logistics simulation software, the system simulates the optimized paths to test their performance in different scenarios. By comparing the simulation results with actual conditions, the system verifies the effectiveness and reliability of the path optimization algorithm. Based on the verification results, the system will further optimize and adjust the algorithm; the system establishes a feedback mechanism to collect data feedback and user evaluations during actual transportation; based on the collected feedback and evaluations, the system continuously optimizes the route planning algorithm to improve its accuracy and practicality; combining real-time traffic information and vehicle status data, the system can identify potential transportation risks, such as severe congestion, traffic accidents, and severe weather; based on risk warning results, the system automatically triggers preset emergency response strategies, such as adjusting logistics plans, activating backup warehouses, and changing transportation methods. Simultaneously, the system uses machine learning models to evaluate the effectiveness of different strategies in real time and select the optimal emergency response plan.

[0075] The effects of the above technical solutions are as follows: By integrating and preprocessing multi-source data, applying route planning algorithms, and employing multi-objective optimization strategies, more scientific and rational logistics routes can be generated, reducing waiting time, transshipment frequency, and transportation distance, thereby improving logistics efficiency. Optimized logistics routes can reduce expenses such as fuel consumption, vehicle wear and tear, and labor costs, thus lowering transportation costs. Simultaneously, the application of risk warning and emergency response mechanisms can avoid additional costs caused by unforeseen events. Timely delivery and reliable logistics services can enhance customer satisfaction. The application of route planning algorithms and real-time adjustment mechanisms ensures goods are delivered within the customer's required timeframe, improving delivery accuracy and reliability. Through the application of logistics route planning and optimization systems, enterprises can improve logistics efficiency, reduce costs, and enhance customer satisfaction, thereby strengthening their brand image and market competitiveness. Integrating environmental indicators such as carbon emissions into route planning, and applying multi-objective optimization strategies, can reduce carbon emissions while meeting transportation needs, promoting sustainable development for enterprises.

[0076] In one embodiment of the present invention, S332 includes:

[0077] Transportation data is extracted from historical logistics databases and used as input to a path planning algorithm to generate a preliminary set of optimal paths.

[0078] Real-time traffic information and vehicle status data are obtained from external data sources through API interfaces or IoT technology, integrated in real time, and updated to the route planning system in real time.

[0079] Using machine learning models, the initial set of routes is dynamically adjusted; future traffic conditions are predicted, and routes are dynamically adjusted based on real-time data.

[0080] During the route adjustment process, the impact of vehicle status data is fully considered; by constructing an evaluation model of vehicle status and route adaptability, the adjusted route is quantitatively evaluated.

[0081] The dynamically adjusted route planning results are fed back to the transportation management system in real time for dispatchers or autonomous driving systems to reference. Simultaneously, a real-time feedback mechanism for route optimization results is established to collect actual data during the transportation process; this data is then used to iteratively optimize the route planning algorithm.

[0082] The working principle of the above technical solution is as follows: Rich transportation data, including but not limited to transportation time, distance, cost, and historical traffic conditions, is extracted from historical logistics databases. This data serves as input to a route planning algorithm, which uses advanced algorithms (such as Dijkstra's algorithm) to generate a preliminary set of optimal routes. These route sets, based on historical data, reflect the optimal choices under normal traffic conditions. Real-time traffic information (such as current road conditions, congestion, and traffic accident reports) is integrated with vehicle status data (such as fuel consumption, tire wear, and load limits). This information is acquired from external data sources via API interfaces or IoT technology and updated in real time to the route planning system. Machine learning models (such as deep learning networks and reinforcement learning models) are used to dynamically adjust the preliminary route set. These models can learn the changing patterns of traffic conditions, predict possible future traffic conditions, and dynamically adjust routes based on real-time data to avoid congested sections or potential risk areas. The impact of vehicle status data is fully considered during the route adjustment process. For example, routes that reduce fuel consumption are selected based on vehicle fuel consumption; sections that cause significant tire wear are avoided based on tire wear; routes that can safely carry goods and comply with traffic regulations are selected based on load limits; by constructing an evaluation model of vehicle state and route adaptability, the adjusted routes are quantitatively evaluated to ensure that the routes not only conform to the actual state of the vehicles but also meet the requirements of transportation efficiency and safety; the dynamically adjusted route planning results are fed back to the transportation management system in real time for dispatchers or autonomous driving systems to refer to. Simultaneously, a real-time feedback mechanism for route optimization results is established to collect actual data during transportation (such as actual travel time, fuel consumption, and changes in vehicle state); this actual data is used to iteratively optimize the route planning algorithm, continuously improving its accuracy and adaptability. Through continuous learning and improvement, the route planning algorithm can better adapt to complex and changing traffic environments and vehicle state changes.

[0083] The effects of the above technical solution are as follows: By extracting rich transportation data from historical logistics databases and utilizing advanced route planning algorithms, a series of preliminary optimal route sets can be generated. These route sets, based on historical data and considering various factors (such as transportation time, distance, and cost), provide a reliable foundation for subsequent dynamic adjustments. Traffic information and vehicle status data are acquired in real time from external data sources via API interfaces or IoT technology and integrated into the route planning system. This real-time information integration enables route planning to dynamically adapt to changes in traffic conditions, improving the practicality and accuracy of the routes. Machine learning models are used to dynamically adjust the preliminary routes, predicting possible future traffic conditions and adjusting routes based on real-time data. This prediction and adjustment mechanism helps avoid congested sections, reduce transportation time, and improve transportation efficiency. During route adjustment, the impact of vehicle status data, such as fuel consumption, tire wear, and load limitations, is fully considered. By constructing an evaluation model for vehicle status and route adaptability, the adjusted routes are quantitatively evaluated, ensuring that the routes not only conform to the actual vehicle status but also meet the requirements of transportation efficiency and safety. The dynamically adjusted route planning results are fed back to the transportation management system in real time for dispatchers or autonomous driving systems to reference. This real-time feedback mechanism helps dispatchers or the system make timely decisions and optimize transportation plans. It establishes a real-time feedback mechanism for route optimization results, collects actual data during transportation, and uses this data to iteratively optimize the route planning algorithm. This continuous optimization mechanism helps improve the accuracy and adaptability of the route planning algorithm, enabling it to better adapt to complex and changing traffic environments and vehicle status changes. Overall, this technical solution significantly improves transportation efficiency and safety through the integration of real-time information, dynamic adjustment, vehicle status and route adaptability assessment, and real-time feedback and iterative optimization. It helps reduce transportation time, lower costs, increase customer satisfaction, and bring greater economic and social benefits to enterprises.

[0084] In one embodiment of the present invention, step S4 includes:

[0085] S41. Embed augmented reality technology in the QR code, so that consumers can scan it and view the clothing information through their mobile phone camera, and provide multilingual support for the QR code and unique ID;

[0086] S42. Consumers can learn about the complete traceability chain of clothing by operating the platform, including clicking and dragging; the traceability chain includes environmental certification of polymer materials, transparency of the production process, and logistics path.

[0087] The working principle of the above technical solution is as follows: Augmented reality technology is embedded in the QR code of the clothing. This means that the QR code is no longer just a simple link or information carrier, but can trigger an AR experience on the mobile phone camera. When consumers use their mobile phones to scan the QR code, the mobile phone camera will capture the QR code information and launch the AR application. After launching the AR application, consumers can view the 3D model of the clothing through the mobile phone camera. This 3D model is based on the actual size and style of the clothing, providing consumers with a near-fitting experience. In addition to the 3D model, consumers can view detailed clothing information such as material, size, and washing instructions. QR codes and unique IDs offer multilingual support, meaning that consumers from any country or region can use their own language to view clothing information and try it on. This greatly increases consumer convenience and satisfaction, while also enhancing the brand's international image. Consumers can explore the complete traceability chain of the clothing on the interactive traceability platform through clicks and drags. This traceability chain includes every step of the clothing's journey from raw material procurement to production and logistics. First, the traceability chain shows whether the polymer materials used in the clothing have undergone environmental certification, helping consumers understand the clothing's environmental performance and sustainability. Next, the traceability chain shows the clothing's production process, including manufacturing techniques and quality control. This helps consumers understand the quality and manufacturing process of the clothing. Finally, the traceability chain shows the logistics path of the clothing from the factory to the consumer, including shipping, transportation, and customs clearance. This helps consumers understand the shipping status and estimated arrival time of clothing; through the interactive traceability platform, consumers can freely click and drag elements on the page to gain a deeper understanding of the aspects they are interested in; this interactive experience not only increases consumer engagement but also enhances their trust and loyalty to the brand.

[0088] The effects of the above technical solution are as follows: By embedding augmented reality technology into QR codes, consumers can simply scan the QR code to view 3D models and try-on effects of the clothing using their mobile phone cameras. This intuitive and vivid display method greatly enhances the consumer shopping experience, allowing them to better understand the product's appearance and effect before purchasing. The 3D model and try-on effect provide consumers with a near-fitting experience, helping to reduce return rates due to unsuitable sizes or dissatisfaction with the style. Simultaneously, this novel display method attracts more consumer attention and interest, thereby promoting sales conversion. Providing multilingual support for QR codes and unique IDs can meet the needs of consumers in different countries and regions. This international design enables brands to reach a wider range of global consumers, enhancing brand influence and market share. Through the interactive traceability platform, consumers can understand the complete traceability chain of the clothing, including environmental certifications of polymer materials, transparency in the production process, and information on logistics routes. This transparency helps enhance consumer trust in the brand, making them more confident in purchasing products. Providing complete traceability chain information not only demonstrates the brand's commitment to product quality and environmental performance but also reflects the brand's social responsibility and integrity. This helps enhance brand image, strengthen consumer loyalty, and generate positive word-of-mouth. By showcasing the environmental certifications of polymer materials and the transparency of the production process, brands can guide consumers to pay more attention to environmental and sustainable development issues. This guidance helps drive the entire industry towards a more environmentally friendly and sustainable direction. Although this is not directly mentioned in the technical solution, complete traceability information can also provide brands with valuable data on inventory management and supply chain optimization. By analyzing this data, brands can more accurately predict demand, optimize inventory levels, and reduce operating costs.

[0089] In one embodiment of the present invention, step S5 includes:

[0090] S51. Based on deep learning algorithms, construct complex models such as quality prediction and fault early warning; combine upstream and downstream supply chain data to conduct multi-dimensional analysis and identify potential quality problems and risk points; and continuously train and optimize the model based on newly collected data according to the model's self-learning and optimization mechanism.

[0091] S52. Combine machine learning models with intelligent decision-making systems to provide managers with intelligent decision-making suggestions, and introduce complex event processing algorithms into smart contracts to handle complex events and abnormal situations in the supply chain.

[0092] S53. Based on a multi-party collaboration mechanism, it allows upstream and downstream enterprises in the supply chain to jointly participate in the formulation and execution of smart contracts.

[0093] The working principle of the above technical solution is as follows: It utilizes deep learning algorithms to construct complex models for quality prediction and fault early warning. These models can process and analyze massive amounts of supply chain data to identify potential quality problems and risks; they combine data from upstream and downstream of the supply chain for multi-dimensional analysis. This includes data from various links such as raw material suppliers, manufacturers, and distributors to ensure a comprehensive understanding of the supply chain; by analyzing this data, the model can identify key factors and potential risks that may lead to quality problems; based on newly collected data, the model can continuously learn and optimize itself. This means that over time, the model's predictive and early warning capabilities will gradually improve, more accurately reflecting the actual situation of the supply chain; combining machine learning models with intelligent decision-making systems provides managers with intelligent decision-making suggestions. These suggestions are based on the model's in-depth analysis of supply chain data and aim to help managers make more informed and efficient decisions; complex event handling algorithms are introduced into smart contracts to handle complex events and anomalies in the supply chain. These algorithms can monitor the operational status of the supply chain in real time, and will automatically trigger preset response mechanisms once an anomaly or complex event is detected; based on a multi-party collaboration mechanism, upstream and downstream enterprises in the supply chain are allowed to jointly participate in the formulation and execution of smart contracts. This means all stakeholders have the opportunity to comment on the content and execution of smart contracts to ensure their fairness and feasibility; once a smart contract is finalized, it will automatically execute pre-defined rules and processes. This includes various aspects such as data sharing, transaction confirmation, and handling of quality issues. The execution of smart contracts will significantly improve the transparency and efficiency of the supply chain.

[0094] The effects of the above technical solutions are as follows: The quality prediction and fault warning models built using deep learning algorithms can accurately identify potential quality problems and risks in the supply chain. This helps companies take proactive measures to avoid or reduce losses caused by quality issues. Multi-dimensional analysis combined with upstream and downstream supply chain data helps companies comprehensively understand the operational status of the supply chain and identify potential problems and hidden dangers. This provides strong support for companies to formulate targeted improvement measures. The model can continuously learn and optimize based on newly collected data, thereby continuously improving the accuracy of predictions and warnings. This means that over time, companies' supply chain quality management and risk control capabilities will continue to improve. Combining machine learning models with intelligent decision-making systems provides managers with intelligent decision-making suggestions. This helps managers make informed decisions quickly, improving the responsiveness and flexibility of the supply chain. Introducing complex event processing algorithms into smart contracts can automatically handle complex events and anomalies in the supply chain. This reduces the need for manual intervention and improves the automation level of the supply chain. Based on a multi-party collaboration mechanism, upstream and downstream companies in the supply chain can jointly participate in the formulation and execution of smart contracts. This helps enhance trust and cooperation among all participants in the supply chain, promoting information sharing and collaborative work. The introduction and execution of smart contracts make the operational status and transaction information of the supply chain more transparent. This helps businesses identify and resolve issues promptly, reducing the risk of information asymmetry in the supply chain. Through accurate forecasting, early warning, and automated processing, businesses can reduce costs associated with rework and returns due to quality problems. Simultaneously, intelligent decision-making and multi-party collaboration help reduce operational and communication costs. Automated processing and intelligent decision-making significantly improve the responsiveness and operational efficiency of the supply chain. This helps businesses meet market demands more quickly, improving customer satisfaction and competitiveness. By optimizing supply chain management and improving resource utilization efficiency, businesses can reduce their environmental impact and enhance supply chain sustainability. This helps businesses fulfill their social responsibilities and enhance their brand image and reputation.

[0095] In one embodiment of the present invention, S51 includes:

[0096] Multi-source heterogeneous data is collected from upstream and downstream enterprises in the supply chain. The multi-source heterogeneous data includes raw material quality, production process parameters, logistics information, and market demand feedback. The collected multi-source heterogeneous data is preprocessed, including noise removal, missing value filling, and data standardization.

[0097] Based on domain knowledge and statistical methods, we extract feature variables that are crucial for quality prediction and fault early warning, such as time series trends, periodic fluctuations, and outlier detection indicators; we use a long short-term memory network architecture to process time series data and predict the quality trend of products at different production stages.

[0098] By combining convolutional neural networks and recurrent neural networks, a hybrid model is constructed to comprehensively analyze image data and sensor data and identify potential fault modes.

[0099] By combining supply chain network graph theory analysis, key nodes and vulnerable links are identified, and the risk of supply chain disruption is assessed; unsupervised learning algorithms are used to continuously monitor supply chain data and dynamically identify new risk points and potential quality problems.

[0100] Based on the feedback mechanism between the model's prediction results and the actual quality situation, the model parameters are continuously adjusted through reinforcement learning.

[0101] The working principle of the above technical solution is as follows: It collects diverse and heterogeneous data from upstream and downstream enterprises in the supply chain, including raw material quality data, production process parameters, logistics information, and market demand feedback. This data originates from different systems, equipment, and platforms, exhibiting diversity, complexity, and heterogeneity. The collected heterogeneous data is preprocessed to remove noise, fill in missing values, and standardize the data. This preprocessing step ensures data quality and consistency, providing a reliable foundation for subsequent data analysis. Based on domain knowledge and statistical methods, it extracts feature variables crucial for quality prediction and fault warning. These feature variables may include time-series trends, periodic fluctuations, and outlier detection indicators. A Long Short-Term Memory (LSTM) network architecture is used to process the time-series data, predicting the quality trend of the product at different production stages. LSTM networks can capture long-term dependencies in time-series data, making them suitable for complex quality prediction tasks. Combined with convolutional... Hybrid models using Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) are constructed to comprehensively analyze image data (such as screenshots from equipment monitoring videos) and sensor data. CNNs are used to extract local features from image data, while RNNs are used to capture temporal dependencies in time-series data. Through comprehensive analysis of the hybrid models, potential failure modes are identified and early warning signals are issued. Combined with supply chain network graph theory analysis, key nodes and vulnerable links are identified, and the risk of supply chain disruption is assessed. This helps enterprises take proactive measures to address potential risks and challenges. Unsupervised learning algorithms (such as cluster analysis and anomaly detection) are used to continuously monitor supply chain data. Through these algorithms, new risk points and potential quality problems are dynamically identified, ensuring the robust operation of the supply chain. Based on the feedback mechanism between model prediction results and actual quality conditions, reinforcement learning is used to continuously adjust model parameters. Reinforcement learning enables the model to self-optimize based on real-time feedback, improving prediction accuracy and robustness.

[0102] The effects of the above technical solution are as follows: It extensively collects multi-source heterogeneous data from upstream and downstream enterprises in the supply chain, covering aspects such as raw material quality, production process parameters, logistics information, and market demand feedback. This comprehensive data collection approach helps enterprises gain a deeper understanding of each link in the supply chain, providing a solid foundation for subsequent data analysis and decision support. The collected multi-source heterogeneous data undergoes preprocessing, including noise removal, missing value imputation, and data standardization. These preprocessing operations significantly improve data quality and usability, providing a reliable data foundation for subsequent data analysis and model training. Based on domain knowledge and statistical methods, it extracts feature variables crucial for quality prediction and fault early warning. These feature variables reflect key information and potential risks in the supply chain, providing targeted input for subsequent data analysis and model training. It employs a Long Short-Term Memory (LSTM) network architecture to process time-series data, predicting product quality trends at different production stages. The LSTM network can capture long-term dependencies in time-series data, improving the accuracy and stability of quality prediction. Finally, it combines Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) to construct a hybrid model for comprehensive analysis of image and sensor data, identifying potential fault modes. This hybrid model fully leverages the advantages of different data types to improve the accuracy and timeliness of fault warnings. Combined with supply chain network graph theory analysis, it identifies critical nodes and vulnerable links, assessing supply chain disruption risks. This analytical approach helps companies proactively identify potential supply chain risks and take corresponding preventative and response measures. Unsupervised learning algorithms (such as cluster analysis and anomaly detection) are used to continuously monitor supply chain data, dynamically identifying new risk points and potential quality issues. This monitoring method reflects the real-time operational status of the supply chain, helping companies promptly identify and address potential problems. Based on a feedback mechanism between model predictions and actual quality conditions, reinforcement learning continuously adjusts model parameters. This feedback mechanism allows the model to gradually improve its prediction accuracy and robustness through continuous learning and optimization, thus better adapting to the actual operation of the supply chain.

[0103] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A blockchain-based method for tracing the quality of polymer materials in down garments, characterized in that, The method includes: S1. All polymer raw materials are tagged with IoT, and blockchain technology is used to create digital assets for each batch of raw materials. Key data is uploaded to the blockchain to form raw material files. S2. Monitor relevant data on the production line in real time and upload the relevant data to the cloud platform in real time through the IoT gateway; S3. Assign unique logistics tags to finished down garments using RFID and GPS, record logistics information, and link the logistics information with raw material files and production records on the blockchain to form a complete supply chain view. S4. Access the blockchain platform by scanning the QR code on the clothing or entering the unique ID to view the complete traceability chain; S5. Based on big data analytics, identify potential risks in the supply chain and use machine learning models to predict future quality issues based on historical data. S2 includes S22, S22 includes S223, and S223 includes: Information from multiple data sources is integrated; natural language processing and machine learning techniques are used to perform preliminary event identification on the integrated data. By employing time series analysis techniques, we can identify the time series characteristics of events from different data sources, and use causal relationship mining algorithms to automatically explore potential causal relationships between events and construct causal chains between events. By combining key event features extracted from the raw data with multi-dimensional information from raw material archives and quality inspection reports, event features are fused. By employing feature selection, dimensionality reduction, and fusion algorithms, a high-dimensional, complex, and comprehensive multi-dimensional event feature set is constructed. Based on the event feature set and causal relationship chain constructed above, a matching algorithm is used to automatically and intelligently match various events in production with relevant data; at the same time, association optimization technology is used to further optimize and adjust the event chain. The constructed event chain is then visualized.

2. The blockchain-based method for tracing the quality of polymer materials in down garments according to claim 1, characterized in that, S1 includes: S11. Based on the polymer raw material information collection standard, RFID technology is used in conjunction with QR codes to record tag information; S12. Introduce a blockchain hash algorithm to encrypt the raw material information and generate a unique hash value as a label anti-counterfeiting identifier; combine the decentralized characteristics of blockchain to generate a unique digital asset ID for each batch of raw materials. S13. Deploy smart contracts on the blockchain. Based on the preset threshold of raw material quality standards, if a quality problem is detected, an early warning mechanism will be automatically triggered and the relevant batch will be locked. S14. Combining multi-source data, cross-validating raw material information, and based on the dynamic update mechanism of raw material archives, submitting update requests under specific conditions, and seamlessly updating information after verification by the blockchain consensus mechanism.

3. The blockchain-based method for tracing the quality of polymer materials in down garments according to claim 2, characterized in that, S13 includes: Based on the preset raw material quality standards, construct the logical rules of the smart contract; use a smart contract programming language to encode the constructed logical rules into a smart contract; Before deployment, the smart contract is tested, and the smart contract that passes the test is deployed to the blockchain network and linked to the raw material information management system. By deploying IoT sensors at key stages, key quality parameters of raw materials are collected in real time, pre-processed, and then uploaded to the blockchain. The smart contract analyzes the uploaded quality data in real time according to preset threshold rules; once an abnormality is detected, it immediately triggers an early warning mechanism and sends warning information to relevant personnel. Once the warning is triggered, the smart contract automatically locks the batch of raw materials involved in the quality problem. At the same time, the information of the problematic batch is permanently recorded through the immutability of the blockchain. Based on the type and severity of the warning, the smart contract automatically initiates the corresponding emergency response plan; based on the monitoring data and warning records, the smart contract automatically generates a raw material quality monitoring report; and shares the quality monitoring report with all relevant parties through the blockchain network.

4. The blockchain-based method for tracing the quality of polymer materials in down garments according to claim 1, characterized in that, The S2 includes: S21. Through monitoring equipment, monitor subtle changes in the production environment in real time, and through the health management system equipped with intelligent equipment, monitor the operating status of the equipment in real time. S22. Based on the event association algorithm, production events are intelligently matched with relevant data to form a complete event chain; based on the machine learning algorithm, a production anomaly detection model is built to automatically identify anomaly patterns in the production process. S23. Based on the preset early warning response level, the corresponding emergency handling process is automatically triggered according to the degree of abnormality.

5. The blockchain-based method for tracing the quality of polymer materials in down garments according to claim 4, characterized in that, S22 includes: S221. The raw data collected from the monitoring equipment and the equipment health management system is preprocessed, and key event features are extracted from the preprocessed data using feature engineering techniques. S222. Construct a multi-dimensional event feature set by combining raw material archives and quality inspection reports; S223. Based on the event association algorithm, it automatically identifies the potential causal relationships and time series correlations between different data sources, and intelligently matches various events in production with relevant data to form a continuous and complete event chain. S224. An unsupervised learning method is used to train the integrated multi-dimensional event feature set, learn the data distribution characteristics under normal production conditions, and automatically identify data points that deviate from the normal pattern. S225. For the abnormal patterns identified by the model, cluster analysis is used to further analyze their inherent laws and influencing factors, and on this basis, a production anomaly knowledge graph is constructed.

6. The blockchain-based method for tracing the quality of polymer materials in down garments according to claim 1, characterized in that, The S3 includes: S31. Encrypt RFID tags and GPS trackers, record the corresponding information, and deploy edge computing devices at logistics nodes to process logistics data in real time. S32. Based on big data visualization technology, present the supply chain view in a visual form; S33. Based on historical logistics data and real-time traffic information, develop a route optimization algorithm to automatically plan the optimal logistics route; S34. By combining blockchain smart contracts, risk warning thresholds are set in the logistics process. If a warning is triggered, the preset response strategy is automatically executed.

7. The blockchain-based method for tracing the quality of polymer materials in down garments according to claim 6, characterized in that, S33 includes: Integrate multi-source data and preprocess the integrated multi-source data; Based on historical logistics data, a preliminary set of optimal routes is generated using route planning algorithms; and combined with real-time traffic information and vehicle status data, the preliminary routes are adjusted in real time using machine learning models. Multi-objective optimization strategies are incorporated into path planning, and the optimal solution set is obtained by using a genetic algorithm through a multi-objective optimization function. The optimized route was simulated using logistics simulation software to test its performance in different scenarios; the route optimization algorithm was verified by comparing the simulation results with the actual situation. Establish a feedback mechanism to collect data feedback and user evaluations during the actual transportation process, and continuously optimize the route planning algorithm; By combining real-time traffic information and vehicle status data, potential transportation risks are identified. Based on the risk warning results, preset emergency response strategies are automatically triggered. At the same time, machine learning models are used to evaluate the effectiveness of different strategies in real time and select the optimal emergency plan.

8. The blockchain-based method for tracing the quality of polymer materials in down garments according to claim 1, characterized in that, The S4 includes: S41. Embed augmented reality technology in the QR code, so that consumers can scan it and view the clothing information through their mobile phone camera, and provide multilingual support for the QR code and unique ID; S42. Consumers can learn about the complete traceability chain of clothing by operating and based on the interactive traceability platform.

9. The blockchain-based method for tracing the quality of polymer materials in down garments according to claim 1, characterized in that, The S5 includes: S51. Based on deep learning algorithms, construct complex models; combine upstream and downstream supply chain data to conduct multi-dimensional analysis, identify potential quality problems and risk points; and continuously train and optimize the model based on newly collected data according to the model's self-learning and optimization mechanism. S52. Combine machine learning models with intelligent decision-making systems to provide managers with intelligent decision-making suggestions, and introduce complex event processing algorithms into smart contracts to handle complex events and abnormal situations in the supply chain. S53. Based on a multi-party collaboration mechanism, it allows upstream and downstream enterprises in the supply chain to jointly participate in the formulation and execution of smart contracts.

10. The blockchain-based method for tracing the quality of polymer materials in down garments according to claim 8, characterized in that, S51 includes: Multi-source heterogeneous data is collected and preprocessed, including noise removal, missing value filling and data standardization. Based on domain knowledge and statistical methods, feature variables that are crucial for quality prediction and fault early warning are extracted. A long short-term memory network architecture is used to process time series data and predict the quality trend of products at different production stages. By combining convolutional neural networks and recurrent neural networks, a hybrid model is constructed to comprehensively analyze image data and sensor data and identify potential fault modes. By combining supply chain network graph theory analysis, key nodes and vulnerable links are identified, and the risk of supply chain disruption is assessed; unsupervised learning algorithms are used to continuously monitor supply chain data and dynamically identify new risk points and potential quality problems. Based on the feedback mechanism between the model's prediction results and the actual quality situation, the model parameters are continuously adjusted through reinforcement learning.

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