A method and system for tracing the production quality and safety of smart vests

Abstract

The present invention relates to the technical field of production quality and safety traceability, and in particular to a method and system for tracing the production quality and safety of smart vests. The method comprises the following steps: obtaining data on the production link of smart vests, wherein the data on the production link of smart vests include data on the raw material procurement stage, data on the monitoring and manufacturing stage, data on the quality inspection stage, and data on the packaging stage; extracting material information from the raw material procurement stage data to obtain material information data of smart vests; analyzing the operation of production equipment from the monitoring and manufacturing stage data to generate production equipment operation data; conducting vest quality inspection on the quality inspection stage data to generate vest inspection data; and analyzing the packaging status of finished products from the packaging stage data to generate finished product packaging status data. The present invention improves the multi-dimensional and multi-level data comprehensive analysis problem of data analysis through Internet of Things technology, radio frequency identification technology, data analysis technology, and blockchain technology.

Description

Technical Field

[0001] The present invention relates to the technical field of production quality safety traceability, and in particular to a method and system for tracing the production quality safety of smart vests. Background Art

[0002] With the rapid development of intelligent manufacturing and Internet of Things technology, more and more companies have realized that traditional production management methods can no longer meet the complex needs of modern industry. Therefore, more and more companies have begun to actively seek to apply intelligent devices and advanced technologies to production quality management in order to comprehensively improve the automation, intelligence and information level of the production process. As a new type of wearable device, smart vests are gradually becoming an important part of this trend. By integrating a variety of advanced sensors, positioning modules and data processing units on the vest, smart vests can monitor various parameters in the production environment and the operating status of workers during work in real time. These sensors can include temperature and humidity sensors, accelerometers, gyroscopes, heart rate monitors, etc. The integration of these technologies not only enables smart vests to comprehensively collect environmental and worker data, but also processes and analyzes these data in real time to provide accurate production data support. However, the application of existing smart vests in production quality traceability is still in its infancy, and the data analysis is not mature enough. At present, most of the data collected by smart vests are basic parameters, lacking in-depth multi-dimensional data sources, and the existing data analysis mainly stays on single-dimensional data processing, lacking comprehensive analysis of multi-dimensional and multi-level data, and it is difficult to provide efficient production quality traceability support. Summary of the invention

[0003] Based on this, it is necessary to provide a method and system for tracing the quality and safety of smart vest production to solve at least one of the above technical problems.

[0004] To achieve the above object, the present invention provides a method for tracing the quality and safety of smart vest production, the method comprising the following steps:

[0005] Step S1: Acquire the data of the smart vest production process, wherein the data of the smart vest production process includes raw material procurement stage data, monitoring and manufacturing stage data, quality inspection stage data and packaging stage data; extract material information from the raw material procurement stage data to obtain the smart vest material information data; perform production equipment operation analysis on the monitoring and manufacturing stage data to generate production equipment operation data; perform vest quality inspection on the quality inspection stage data to generate vest inspection data; perform finished product packaging status analysis on the packaging stage data to generate finished product packaging status data;

[0006] Step S2: transmitting the real-time collected material information data, production equipment operation data, vest inspection data and finished product packaging status data to the central control system through the wireless communication module for data integration to generate an integrated data set for the production link of the smart vest; unifying the data format of the integrated data set for the production link of the smart vest to generate a unified data set for the production link of the smart vest; encrypting and storing the unified data set for the production link of the smart vest to generate encrypted storage data for the production link of the smart vest;

[0007] Step S3: preprocessing the encrypted and stored data in the smart vest production process to obtain a preprocessed smart vest production process data set; extracting key features from the preprocessed smart vest production process data set to generate a smart vest production process data feature vector;

[0008] Step S4: Perform potential quality analysis on the feature vector of the smart vest production process data to generate smart vest production quality analysis result data; perform production anomaly detection on the feature vector of the smart vest production process data to generate smart vest production anomaly detection data;

[0009] Step S5: Perform intelligent quality control using the smart vest production quality analysis result data and the smart vest production anomaly detection data to generate smart vest production quality control data; construct a control strategy model based on the smart vest production quality control data to obtain a smart vest production quality adjustment control strategy model;

[0010] Step S6: According to the smart vest production quality adjustment control strategy model, the smart vest production link data is traced to the vest production quality product, and a production quality report and traceability record data are generated; the production quality report and traceability record data are uploaded to the blockchain network for distributed storage to perform the smart vest production quality and safety traceability operation.

[0011] The present invention obtains data on the production process of smart vests, wherein the data on the production process of smart vests includes data from the raw material procurement stage, data from the monitoring and manufacturing stage, data from the quality inspection stage, and data from the packaging stage, to ensure that each key step of the production process has data support. By extracting material information from the raw material procurement stage data, various raw material information data required for the smart vest can be accurately obtained. The monitoring and manufacturing stage data is used to perform production equipment operation analysis to generate detailed production equipment operation data, which can monitor the operating status and efficiency of the equipment in real time. The quality inspection stage data is used to perform vest quality inspection to generate vest inspection data, provide the quality status of each smart vest, and help reduce the defective product rate. The packaging stage data is used to perform finished product packaging. Status analysis generates finished product packaging status data, which can provide the final packaging status of the smart vest. The real-time collected material information data, production equipment operation data, vest inspection data and finished product packaging status data are transmitted to the central control system through the wireless communication module for data integration, and an integrated data set for the production link of the smart vest is generated, ensuring the timeliness and integrity of each data source in the production link. The data format of the integrated data set of the smart vest production link is unified to generate a unified data set for the smart vest production link, ensuring the consistency and comparability of the data within the central control system. The unified data set of the smart vest production link is encrypted and stored to generate encrypted storage data for the smart vest production link, ensuring the data is stored and transmitted. The confidentiality and integrity of the data transmission process can be effectively prevented from unauthorized access and tampering. The data is preprocessed by encrypting and storing the data in the smart vest production process link to obtain the preprocessed smart vest production process data set, which helps to improve the quality and consistency of the data and lay the foundation for subsequent data analysis and feature extraction. The key features of the preprocessed smart vest production process data set are extracted to generate the smart vest production process data feature vector, which effectively describes all aspects and key indicators of the production process and converts a large amount of complex production data into a concise feature vector. The potential quality analysis is performed through the smart vest production process data feature vector to generate the smart vest production quality analysis result data, which can identify and evaluate the production process. The potential quality problems and trends in the production process are helpful to discover and correct potential quality problems early, perform production anomaly detection on the characteristic vector of the smart vest production process data, generate smart vest production anomaly detection data, and timely discover abnormal conditions and bad trends in the production process. The smart vest production quality analysis result data and the smart vest production anomaly detection data are used for intelligent quality control, and the smart vest production quality control data is generated to obtain detailed information about product quality and production status. The control strategy model is constructed according to the smart vest production quality control data to obtain the smart vest production quality adjustment control strategy model, which realizes real-time production monitoring of the production process and adjusts the production process in an automated and intelligent manner.According to the smart vest production quality adjustment control strategy model, the vest production quality product traceability is performed on the data of the smart vest production link, and the production quality report and traceability record data are generated. Through accurate recording and analysis, the production details and quality status of each production batch can be traced. The production quality report provides real-time evaluation and feedback on the quality management effect in the production process, helping the production team to quickly discover problems and take measures to improve them. The production quality report and traceability record data are uploaded to the blockchain network for distributed storage to ensure the credibility and long-term preservation of production quality data. Through blockchain storage, data tampering and loss can be effectively prevented, and the reliability and transparency of smart vest production quality safety traceability data can be improved. Therefore, the present invention improves the multi-dimensional and multi-level data comprehensive analysis problems of data analysis through Internet of Things technology, radio frequency identification technology, data analysis technology and blockchain technology. ,

[0012] Preferably, step S1 comprises the following steps:

[0013] Step S11: using sensors to obtain data from the production process of smart vests in real time, wherein the data from the production process of smart vests includes data from the raw material procurement stage, data from the monitoring and manufacturing stage, data from the quality inspection stage, and data from the packaging stage;

[0014] Step S12: extracting material record information from the raw material procurement stage data to obtain smart vest material information data;

[0015] Step S13: locate the production equipment based on the monitoring manufacturing stage data to obtain the production equipment location data; deploy sensors on the production equipment based on the production equipment location data to obtain the production equipment sensor deployment data; construct a distributed network based on the production equipment sensor deployment data to obtain a distributed monitoring network for production equipment; collect production equipment data using the distributed monitoring network for production equipment to obtain production equipment operation data;

[0016] Step S14: Perform vest quality inspection on the quality inspection stage data to generate vest inspection data, wherein the vest quality inspection includes appearance inspection, function inspection, size inspection and durability inspection;

[0017] Step S15: Perform a finished product packaging status analysis on the packaging stage data to generate finished product packaging status data, wherein the finished product packaging status analysis includes packaging material integrity analysis and packaging process environmental condition analysis.

[0018] The present invention uses sensors to obtain real-time data on the production process of smart vests. The sensors obtain key data on the production process in real time, including raw material procurement, manufacturing monitoring, quality inspection and packaging status. These data provide the necessary information basis for subsequent steps, and ensure the complete recording and traceability of the materials for the extracted raw material information data, laying a foundation for quality control. The location data of the production equipment is accurately recorded, and sensors are deployed on the production equipment according to the location data of the production equipment to form a distributed monitoring network. The operating status of the production equipment can be monitored in real time, and real-time data collection of the production process is realized. The vest quality inspection is performed on the quality inspection stage data. The vest quality inspection covers multiple aspects of appearance, function, size and durability, and the vest inspection data is generated. The finished product packaging status analysis is performed on the packaging stage data. The finished product packaging status analysis focuses on the integrity of the packaging materials and the environmental conditions to ensure the safety and integrity of the product during transportation and storage, and the finished product packaging status data is generated.

[0019] Preferably, step S2 comprises the following steps:

[0020] Step S21: transmitting material information data, production equipment operation data, vest inspection data and finished product packaging status data to the central control system through the wireless communication module for data integration to generate an integrated data set for the production of smart vests;

[0021] Step S22: converting the integrated data set of the smart vest production link into a standard format to obtain a converted data set of the smart vest production link; performing structured unified processing on the converted data set of the smart vest production link to generate a unified data set of the smart vest production link;

[0022] Step S23: encrypting the unified data set of the smart vest production link to obtain encrypted format data; storing the encrypted format data to generate encrypted storage data of the smart vest production link.

[0023] The present invention transmits material information data, production equipment operation data, vest inspection data and finished product packaging status data to the central control system through a wireless communication module for data integration, and generates an integrated data set for the production link of smart vests, effectively realizing the real-time capture and integration of various key data in the production process, ensuring the integrity and timeliness of information in the production link, converting the integrated data set of the production link of smart vests into a standard format, so that information from different data sources can be processed and analyzed in a unified format, and obtaining a converted data set of the production link of smart vests, thereby improving the readability and utilization efficiency of the data, encrypting the unified data set of the production link of smart vests to obtain encrypted format data, ensuring the security and confidentiality of important data generated in the production process during transmission, storing the encrypted format data, generating encrypted storage data for the production link of smart vests, and effectively preventing the risk of information leakage and data tampering during the storage process.

[0024] Preferably, step S3 comprises the following steps:

[0025] Step S31: performing data preprocessing on the encrypted and stored data in the smart vest production process to generate a preprocessed smart vest production process data set, wherein the data preprocessing includes data decryption, data cleaning, data denoising and data standardization;

[0026] Step S32: performing time series extraction on the preprocessed smart vest production process data set to obtain a smart vest production process time series data set; performing spectrum conversion on the smart vest production process time series data set to generate a smart vest production process spectrum graph; performing frequency domain feature extraction on the smart vest production process spectrum graph to generate a smart vest production process data feature vector.

[0027] The present invention uses the encrypted and stored data in the production link of the smart vest to perform decryption processing, decrypts the encrypted and stored production data into readable original data, ensures the security of the data while allowing further analysis and processing of the data, then cleans the decrypted data in the production link of the smart vest to ensure the consistency and accuracy of the data, performs denoising processing on the cleaned data in the production link of the smart vest to ensure that the data is clean and accurate, which is conducive to subsequent analysis, and standardizes the denoised data in the production link of the smart vest to make the data comparable and consistent between different sources, and performs time series extraction on the preprocessed data set. The columns can reflect the changes of key indicators in the vest production process over time. Through time series analysis, trends and periodic changes in the production process can be identified, which helps to optimize production plans and resource allocation. The time series data set of the smart vest production process is converted into a spectrum diagram. The spectrum diagram shows the energy distribution of signals at different frequencies. The spectrum diagram of the smart vest production process is generated, which helps to discover the vibration frequency or periodic frequency domain characteristics in the vest production process. The frequency domain features of the spectrum diagram of the smart vest production process are extracted to generate the data feature vector of the smart vest production process, which can help identify and analyze the key frequency domain information in the production process and provide a basis for subsequent anomaly detection.

[0028] Preferably, step S4 comprises the following steps:

[0029] Step S41: extracting production process features according to the feature vector of the smart vest production process data to obtain the smart vest production process data; analyzing the potential quality problems of the smart vest production process data to generate smart vest production quality analysis result data;

[0030] Step S42: extract abnormal features according to the feature vector of the smart vest production process data to obtain abnormal data of the smart vest production process; perform abnormal production detection on the abnormal data of the smart vest production process to generate abnormal production detection data of the smart vest.

[0031] The present invention extracts production process characteristics from the characteristic vector of the smart vest production process data, wherein the production process characteristics include production speed, temperature change and material consumption rate, and the production process characteristics reflect the production process quality problems existing in the production process. The potential quality problem analysis of the smart vest production process data can effectively detect potential production quality problems, provide a scientific basis and decision support for production management, extract abnormal characteristics according to the characteristic vector of the smart vest production process data, obtain abnormal data of the smart vest production process, and can monitor and identify abnormal events in the production process in real time. Abnormal production detection is performed on the abnormal data of the smart vest production process, and the time, location and cause analysis of the abnormality are provided to generate abnormal production detection data of the smart vest, thereby enhancing the reliability of the production process.

[0032] Preferably, step S5 comprises the following steps:

[0033] Step S51: performing time series analysis on the smart vest production quality analysis result data to obtain production quality time series trend data;

[0034] Step S52: classifying the abnormal situation of the smart vest production abnormality detection data to obtain production abnormality type data;

[0035] Step S53: Perform intelligent quality control on the production quality time series trend data and the production abnormality type data to generate smart vest production quality control data;

[0036] Step S54: constructing a decision model for the smart vest production quality control data to obtain a smart vest production quality adjustment control strategy model.

[0037] The present invention performs time series analysis on the smart vest production quality analysis result data, can accurately capture the changing trend of the smart vest production quality, obtain production quality time series trend data, can identify the periodic changes and long-term trends of production quality, and provide timely data support and decision-making reference for production management personnel. By performing classification analysis on the abnormal detection data in the production process of the smart vest, it can accurately identify different types of production abnormalities and obtain production abnormality type data, which can distinguish equipment failures, human operation errors and raw material abnormality types, and quickly locate the root cause of the problem for the production team. Intelligent quality control is performed according to the production quality time series trend data and production abnormality type data. Through real-time monitoring and analysis It can analyze the temporal changes and abnormal situations of production quality, automatically adjust production parameters and optimize production processes to achieve dynamic quality control of the smart vest production process, generate smart vest production quality control data, and improve the production efficiency and resource utilization of smart vests under the premise of ensuring product quality. It conducts in-depth analysis and modeling of smart vest production quality control data to construct a smart vest production quality adjustment control strategy model. The smart vest production quality adjustment control strategy model comprehensively considers the production quality temporal trend, abnormal situation classification and related production parameters and conditions, and provides accurate quality adjustment suggestions and decision support to help the production management team make the best production adjustment decisions in different situations.

[0038] Preferably, step S53 includes the following steps:

[0039] Step S531: periodically analyzing the production quality time series trend data to obtain a production quality time series trend graph;

[0040] Step S532: Calculate the peak-to-valley difference of the production quality time series trend chart to obtain production quality peak-to-valley difference data;

[0041] Step S533: Compare the production quality peak-to-valley difference data with the preset production quality standard threshold. When the production quality peak-to-valley difference data is greater than the preset production quality standard threshold, adjust the control parameters of the production quality control data to generate a production quality control parameter adjustment strategy; when the production quality peak-to-valley difference data is less than or equal to the preset production quality standard threshold, optimize the production process data to generate a production process optimization strategy; integrate the production quality control parameter adjustment strategy and the production process optimization strategy to generate a production quality control rule;

[0042] Step S534: labeling the production abnormality type data to obtain production abnormality label data;

[0043] Step S535: performing label classification extraction on the production abnormality label data to obtain label classification data;

[0044] Step S536: Analyze the production abnormality through label classification data to obtain abnormality cause analysis data; perform abnormal processing and rapid response to the production process according to the abnormality cause analysis data, and generate production abnormality control rules;

[0045] Step S537: Intelligently execute control measures for production quality control rules and production abnormality control rules to generate smart vest production quality control data.

[0046] The present invention performs periodic analysis on production quality time series trend data, and can accurately capture the periodic change law of production quality of smart vests, and obtain a production quality time series trend graph. The production quality time series trend graph helps the production management team understand the reasons for production quality fluctuations, and performs peak-to-valley difference calculation on the production quality time series trend graph to obtain production quality peak-to-valley difference data, which can accurately measure the amplitude and frequency of quality fluctuations, determine the intensity and period of quality fluctuations, and implement intelligent quality control strategies by comparing production quality peak-to-valley difference data with preset production quality standard thresholds. When quality fluctuations exceed the set threshold, production parameters are automatically adjusted to stabilize quality; when fluctuations are within the expected range, the production process is optimized to improve efficiency, and production quality control parameter adjustment strategies and production process optimization strategies are integrated into rules to generate production quality control rules. The production quality control rules integrate the dual means of parameter adjustment and process optimization, and can quickly Adapt to different production conditions and demand changes, effectively improve the product quality and production efficiency of smart vests, label production abnormality type data to obtain production abnormality label data, classify and mark different types of abnormalities, improve the readability and analysis efficiency of abnormal data, and classify and extract production abnormality label data to obtain label classification data. The label classification data enables the production management team to quickly identify common abnormal patterns and provide an important reference for abnormal handling. The production abnormalities are analyzed through the label classification data to obtain abnormal cause analysis data. The abnormal cause analysis data shows the causes of abnormal events in the production process, which helps to formulate targeted abnormal handling measures, and intelligently executes control measures for production quality control rules and production abnormality control rules. It can quickly respond to various changes and challenges in the production process, generate smart vest production quality control data, and can adjust production parameters and optimize production processes in real time.

[0047] Preferably, step S54 includes the following steps:

[0048] Step S541: Perform production quality abnormality trend analysis on the smart vest production quality control data to generate production quality problem trend distribution data;

[0049] Step S542: Performing environmental parameter impact assessment on smart vest production quality control data to generate environmental parameter impact data;

[0050] Step S543: performing weight division processing on the production quality problem trend distribution data and the environmental parameter impact data to obtain factor weight division data;

[0051] Step S544: extracting priority features from the factor weight division data to generate factor weight priority data; constructing a smart vest production quality decision model based on the factor weight priority data, and training the constructed smart vest production quality decision model using the factor weight division data to generate a smart vest production quality decision training model;

[0052] Step S545: Perform model cross-validation evaluation on the smart vest production quality decision training model to obtain smart vest production quality decision model evaluation data; adjust the model parameters of the smart vest production quality training model through the smart vest production quality decision model evaluation data to obtain the smart vest production quality adjustment control strategy model.

[0053] The present invention performs production quality abnormality trend analysis through the production quality control data of smart vests, can effectively identify and analyze the quality problem trend in the production process, generate production quality problem trend distribution data, can timely discover and solve problems affecting product quality, thereby improving the stability and reliability of the production line, performs environmental parameter impact assessment on the production quality control data of smart vests, analyzes in detail the degree of influence of various environmental factors on product quality, generates environmental parameter impact data, helps to optimize the production environment settings, and minimizes the negative impact of environmental factors on product quality, performs weight division processing on the production quality problem trend distribution data and the environmental parameter impact data, can quantify the importance and degree of influence of each factor on product quality, helps to formulate accurate quality control strategies and improvement plans, obtains factor weight division data to cope with changes and fluctuations in product quality caused by different factors, performs priority feature extraction based on the factor weight division data, realizes accurate identification and extraction of key factors with different degrees of influence on the production quality of smart vests from complex data, generates factor weight division data, and generates factor weight division data. The factor weight priority data reflects the importance of each factor in quality control and accurately reflects the key points affecting production quality. The smart vest production quality decision model is constructed according to the factor weight priority data. The previously extracted factor weight priority data is further utilized to construct a highly accurate and adaptable decision model. The constructed smart vest production quality decision model is trained using the factor weight partitioning data to ensure that the smart vest production quality decision model can correctly perform decision-making tasks and generate a smart vest production quality decision training model. The smart vest production quality decision training model is cross-validated and evaluated to ensure the robustness and reliability of the model. The smart vest production quality decision model evaluation data is obtained. According to the smart vest production quality decision model evaluation data, the parameter settings of the smart vest production quality decision model can be fine-tuned to adapt to different production environments and demand changes, and the smart vest production quality adjustment control strategy model is obtained, thereby effectively improving the flexibility and efficiency of quality control.

[0054] Preferably, step S6 comprises the following steps:

[0055] Step S61: performing real-time control and monitoring on the production process data of the smart vest according to the smart vest production quality adjustment control strategy model to obtain the smart vest production process monitoring data;

[0056] Step S62: Summarize and summarize the monitoring data of the smart vest production process to generate a production quality report and traceability record data;

[0057] Step S63: Use blockchain technology to upload the production quality report and traceability record data to the blockchain node, and distribute the production quality report and traceability record data through the blockchain node to perform smart vest production quality and safety traceability operations.

[0058] The present invention uses the smart vest production quality adjustment control strategy model to perform real-time control and monitoring of the smart vest production process data, effectively improving the response speed and accuracy of production quality management. Through real-time monitoring, abnormal conditions and potential problems in the production process can be captured instantly, so that quality control measures can be adjusted and optimized at the earliest stage, thereby obtaining the smart vest production process monitoring data, sorting and summarizing the smart vest production process monitoring data, generating a production quality report and traceability record data. The production quality report reflects the key indicators and quality data in the production process, and the traceability record data can help the production team to conduct comprehensive quality analysis and problem diagnosis, and have a clearer understanding of production efficiency and quality trends. Blockchain technology is used to The production quality report and traceability record data are uploaded to the blockchain node. Blockchain technology ensures the high security and credibility of the production quality report and traceability record data, and encrypts and distributes each data record to ensure that the data cannot be tampered with and is transparent and auditable, effectively preventing data inconsistency caused by malicious tampering or misoperation, and improving data integrity and reliability. Blockchain technology can provide an efficient traceability mechanism, making the entire process from raw materials to production process to final product traceable in real time, and blockchain technology simplifies data management and sharing processes, reduces management and transaction costs, and through distributed storage, the production team can achieve more efficient smart vest production quality and safety traceability operations.

[0059] In this specification, a smart vest production quality safety traceability system is provided, which is used to execute the above-mentioned smart vest production quality safety traceability method. The smart vest production quality safety traceability system includes:

[0060] The production link data collection module obtains the data of the smart vest production link, where the smart vest production link data includes raw material procurement stage data, monitoring and manufacturing stage data, quality inspection stage data and packaging stage data; material information is extracted from the raw material procurement stage data to obtain the smart vest material information data; production equipment operation analysis is performed on the monitoring and manufacturing stage data to generate production equipment operation data; vest quality inspection is performed on the quality inspection stage data to generate vest inspection data; finished product packaging status analysis is performed on the packaging stage data to generate finished product packaging status data;

[0061] The production data integration and storage module transmits the real-time collected material information data, production equipment operation data, vest inspection data and finished product packaging status data to the central control system through the wireless communication module for data integration, and generates an integrated data set for the production link of smart vests; unifies the data format of the integrated data set for the production link of smart vests, and generates a unified data set for the production link of smart vests; encrypts and stores the unified data set for the production link of smart vests, and generates encrypted storage data for the production link of smart vests;

[0062] The data processing module performs data preprocessing on the encrypted and stored data in the production process of the smart vest to obtain a preprocessed data set of the production process of the smart vest; extracts key features from the preprocessed data set of the production process of the smart vest to generate a data feature vector of the production process of the smart vest;

[0063] The production quality analysis module performs potential quality analysis on the characteristic vector of the smart vest production process data to generate the smart vest production quality analysis result data; performs production anomaly detection on the characteristic vector of the smart vest production process data to generate the smart vest production anomaly detection data;

[0064] Adjust the control strategy module, use the smart vest production quality analysis result data and the smart vest production anomaly detection data to perform intelligent quality control, and generate smart vest production quality control data; construct a control strategy model based on the smart vest production quality control data to obtain a smart vest production quality adjustment control strategy model;

[0065] The blockchain traceability module traces the production quality of smart vests based on the smart vest production quality adjustment control strategy model, generates production quality reports and traceability record data; uploads the production quality reports and traceability record data to the blockchain network for distributed storage to perform smart vest production quality and safety traceability operations.

[0066] The present invention constructs a smart vest production quality and safety traceability system, and the production link data acquisition module obtains the smart vest production link data, wherein the smart vest production link data includes raw material procurement stage data, monitoring and manufacturing stage data, quality inspection stage data and packaging stage data, ensuring that each production stage can obtain detailed support information, and by extracting material information from the raw material procurement stage data, it is ensured that all kinds of raw material data required for the smart vest are obtained, and the production equipment operation analysis is performed on the monitoring and manufacturing stage data to generate detailed production equipment operation data, which can monitor the equipment's operating status and efficiency in real time, and the vest quality inspection is performed on the quality inspection stage data to generate vest inspection data, and provide vest inspection quality evaluation, thereby helping Reduce the defective rate, analyze the finished product packaging status of the packaging stage data, generate finished product packaging status data, and provide the final packaging status of the smart vest. The production data integration storage module transmits the real-time collected material information data, production equipment operation data, vest inspection data and finished product packaging status data to the central control system through the wireless communication module for data integration, and generates an integrated data set for the production link of the smart vest, realizing the integration and synchronization of various data sources in the production process of the smart vest, unifying the data format of the integrated data set of the production link of the smart vest, and generating a unified data set for the production link of the smart vest, ensuring the consistency and comparability of the data within the central control system, providing an accurate basis for decision-making, and having a positive impact on the production of smart vests. The unified data set of each link is encrypted and stored, and the encrypted storage data of the smart vest production link is generated, which ensures the confidentiality and integrity of the data during storage and transmission, effectively prevents unauthorized access and tampering, and ensures the confidentiality and integrity of the data. The data processing module performs data preprocessing through the encrypted storage data of the smart vest production link to obtain the preprocessed smart vest production process data set, ensuring that the data quality and consistency reach the best level, and extracts key features of the preprocessed smart vest production process data set to generate the smart vest production process data feature vector, which effectively describes the various important aspects and key performance indicators of the production process, and converts a large amount of complex production data into a concise feature vector, production quality analysis module Through the characteristic vector of the smart vest production process data, potential quality analysis is performed to generate the smart vest production quality analysis result data, which can quickly identify and evaluate the quality problems and development trends in the production process, and help to find and correct potential quality problems early. Production anomaly detection is performed on the characteristic vector of the smart vest production process data to generate smart vest production anomaly detection data, and abnormal conditions and bad trends in the production process are discovered in time. The control strategy module is adjusted to use the smart vest production quality analysis result data and the smart vest production anomaly detection data for intelligent quality control, generate smart vest production quality control data, provide information about product quality and production status, and build a control strategy model based on the smart vest production quality control data.The smart vest production quality adjustment control strategy model is obtained, so that the production process can be monitored in real time, and the production process can be adjusted in an automated and intelligent manner. The blockchain traceability module traces the vest production quality product of the smart vest production link data according to the smart vest production quality adjustment control strategy model, and generates detailed production quality reports and traceability record data. Through accurate recording and analysis, the production details and quality status of each production batch can be traced. The production quality report provides real-time evaluation and feedback on the quality management effect in the production process, helping the production team to quickly discover problems and take measures to improve them. In order to ensure the credibility and long-term preservation of the data, the production quality report and traceability record data are uploaded to the blockchain network for distributed storage. Blockchain storage effectively prevents data tampering and loss, improves the reliability and transparency of smart vest production quality safety traceability data, and enhances data protection and verification capabilities. Therefore, the present invention improves the multi-dimensional and multi-level data comprehensive analysis problems of data analysis through Internet of Things technology, radio frequency identification technology, data analysis technology and blockchain technology. , BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 A schematic diagram of a process flow based on the quality and safety traceability method of smart vest production;

[0068] Figure 2 for Figure 1 Detailed implementation steps of step S5;

[0069] Figure 3 for Figure 2 Detailed implementation steps of step S53;

[0070] Figure 4 for Figure 2 Detailed implementation steps of step S54;

[0071] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0072] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0073] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0074] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0075] To achieve this, please refer to Figures 1 to 4 A method for tracing the production quality and safety of smart vests, the method comprising the following steps:

[0076] Step S1: Acquire the data of the smart vest production process, wherein the data of the smart vest production process includes raw material procurement stage data, monitoring and manufacturing stage data, quality inspection stage data and packaging stage data; extract material information from the raw material procurement stage data to obtain the smart vest material information data; perform production equipment operation analysis on the monitoring and manufacturing stage data to generate production equipment operation data; perform vest quality inspection on the quality inspection stage data to generate vest inspection data; perform finished product packaging status analysis on the packaging stage data to generate finished product packaging status data;

[0077] Step S2: transmitting the real-time collected material information data, production equipment operation data, vest inspection data and finished product packaging status data to the central control system through the wireless communication module for data integration to generate an integrated data set for the production link of the smart vest; unifying the data format of the integrated data set for the production link of the smart vest to generate a unified data set for the production link of the smart vest; encrypting and storing the unified data set for the production link of the smart vest to generate encrypted storage data for the production link of the smart vest;

[0078] Step S3: preprocessing the encrypted and stored data in the smart vest production process to obtain a preprocessed smart vest production process data set; extracting key features from the preprocessed smart vest production process data set to generate a smart vest production process data feature vector;

[0079] Step S4: Perform potential quality analysis on the feature vector of the smart vest production process data to generate smart vest production quality analysis result data; perform production anomaly detection on the feature vector of the smart vest production process data to generate smart vest production anomaly detection data;

[0080] Step S5: Perform intelligent quality control using the smart vest production quality analysis result data and the smart vest production anomaly detection data to generate smart vest production quality control data; construct a control strategy model based on the smart vest production quality control data to obtain a smart vest production quality adjustment control strategy model;

[0081] Step S6: According to the smart vest production quality adjustment control strategy model, the smart vest production link data is traced to the vest production quality product, and a production quality report and traceability record data are generated; the production quality report and traceability record data are uploaded to the blockchain network for distributed storage to perform the smart vest production quality and safety traceability operation.

[0082] The present invention obtains data on the production links of smart vests, wherein the data on the production links of smart vests include data from the raw material procurement stage, data from the monitoring and manufacturing stage, data from the quality inspection stage, and data from the packaging stage, so as to ensure that each key step of the production process has data support. By extracting material information from the data from the raw material procurement stage, various raw material information data required for the smart vest can be accurately obtained. The production equipment operation analysis is performed on the monitoring and manufacturing stage data to generate detailed production equipment operation data, and the operation status and efficiency of the equipment can be monitored in real time. The vest quality inspection is performed on the quality inspection stage data to generate vest inspection data, and the quality status of each smart vest is provided to help reduce the defective product rate. The finished product packaging status analysis is performed on the packaging stage data to generate finished product packaging status data, and the final packaging status of the smart vest can be provided. The material information data, production equipment operation data, vest inspection data and finished product packaging status data collected in real time are transmitted to the central control system through the wireless communication module for data integration, and the integrated data set of the smart vest production link is generated to ensure the timeliness and integrity of each data source in the production link. The data format of the integrated data set of the smart vest production link is unified to generate a unified data set of the smart vest production link. The unified data set of the smart vest production link is encrypted and stored to generate encrypted storage data of the smart vest production link, which effectively prevents unauthorized access and tampering. Data preprocessing is performed through the encrypted storage data of the smart vest production link to obtain the preprocessed smart vest production process data set, which helps to improve the quality and consistency of the data and lays the foundation for subsequent data analysis and feature extraction. The key features of the preprocessed smart vest production process data set are extracted to generate the smart vest production process data feature vector, which effectively describes various aspects and key indicators of the production process and converts a large amount of complex production data into a concise feature vector. Through the characteristic vector of the smart vest production process data, potential quality analysis is performed to generate the smart vest production quality analysis result data, which can identify and evaluate the potential quality problems and trends in the production process, help to discover and correct potential quality problems early, perform production anomaly detection on the characteristic vector of the smart vest production process data, generate smart vest production anomaly detection data, and timely discover abnormal conditions and undesirable trends in the production process. The smart vest production quality analysis result data and the smart vest production anomaly detection data are used for intelligent quality control to generate smart vest production quality control data, and obtain detailed information about product quality and production status. According to the smart vest production quality control data, a control strategy model is constructed to obtain a smart vest production quality adjustment control strategy model, which realizes real-time production monitoring of the production process and adjusts the production process in an automated and intelligent manner. According to the smart vest production quality adjustment control strategy model, the smart vest production link data is traced to the vest production quality product, and a production quality report and traceability record data are generated.Through accurate recording and analysis, the production details and quality status of each production batch can be traced. The production quality report provides real-time evaluation and feedback on the quality management effect in the production process, helping the production team to quickly find problems and take measures to improve them. The production quality report and traceability record data are uploaded to the blockchain network for distributed storage to ensure the credibility and long-term preservation of production quality data. Through blockchain storage, data tampering and loss can be effectively prevented, and the reliability and transparency of smart vest production quality safety traceability data can be improved. Therefore, the present invention improves the multi-dimensional and multi-level data comprehensive analysis problems of data analysis through Internet of Things technology, radio frequency identification technology, data analysis technology and blockchain technology.

[0083] In the embodiment of the present invention, reference Figure 1 The above is a schematic diagram of the steps of a method for tracing the quality and safety of smart vest production according to the present invention. In this example, the method for tracing the quality and safety of smart vest production includes the following steps:

[0084] Step S1: Acquire the data of the smart vest production process, wherein the data of the smart vest production process includes raw material procurement stage data, monitoring and manufacturing stage data, quality inspection stage data and packaging stage data; extract material information from the raw material procurement stage data to obtain the smart vest material information data; perform production equipment operation analysis on the monitoring and manufacturing stage data to generate production equipment operation data; perform vest quality inspection on the quality inspection stage data to generate vest inspection data; perform finished product packaging status analysis on the packaging stage data to generate finished product packaging status data;

[0085] In the embodiment of the present invention, detailed information of various materials, including material name, specification and batch number, is obtained from suppliers during the raw material procurement stage, and then these data are parsed and extracted through information extraction tools to form complete smart vest material information data. The processing of data from the monitoring manufacturing stage involves regular collection of production equipment operating status, output and process parameter data to ensure the comprehensiveness of the data. During the quality inspection stage, the smart vest is strictly inspected for quality, including appearance inspection and functional testing, and detailed vest inspection data is generated to ensure that the quality of each product meets the standards. Then, the data from the finished product packaging stage is analyzed to evaluate the integrity and appearance quality of the packaging, record the finished product packaging status data, and combine it with the quality inspection data and production data to provide complete production link data.

[0086] Step S2: transmitting the real-time collected material information data, production equipment operation data, vest inspection data and finished product packaging status data to the central control system through the wireless communication module for data integration to generate an integrated data set for the production link of the smart vest; unifying the data format of the integrated data set for the production link of the smart vest to generate a unified data set for the production link of the smart vest; encrypting and storing the unified data set for the production link of the smart vest to generate encrypted storage data for the production link of the smart vest;

[0087] In the embodiment of the present invention, by installing a wireless communication module on each smart vest production line, the data collection frequency and transmission protocol are set to ensure that material information data, production equipment operation data, vest inspection data and finished product packaging status data can be collected in real time, and can be accurately and timely transmitted to the central control system. During the transmission process, the data is encrypted using a secure encryption protocol to ensure the security and integrity of the data. After receiving the data, the central control system performs data integration processing and integrates data from different sources into an integrated data set for the production link of the smart vest. Then, the integrated data set of the production link of the smart vest is formatted in a unified manner to ensure that the structure and format of the data meet the set standards and requirements, and a unified data set for the production link of the smart vest is generated. Finally, the unified data set of the production link of the smart vest is encrypted and stored, and a secure storage device is used for storage to generate encrypted storage data for the production link of the smart vest.

[0088] Step S3: preprocessing the encrypted and stored data in the smart vest production process to obtain a preprocessed smart vest production process data set; extracting key features from the preprocessed smart vest production process data set to generate a smart vest production process data feature vector;

[0089] In the embodiment of the present invention, the encrypted and stored data of the smart vest production link are decrypted to obtain the encrypted and stored data of the smart vest production link, and the encrypted and stored data of the smart vest production link are cleaned, including processing missing values, outliers and duplicate data, to ensure the quality and integrity of the data, and obtain the smart vest production process data set. Then, the data set of the smart vest production process is converted and standardized, and the data is converted into a format suitable for subsequent analysis and processing, and standardized to eliminate the dimensional differences between different data. Then, according to the predetermined data processing process, data dimension reduction or feature selection is performed to reduce the complexity and redundant information of the data set, while retaining important data features. Finally, data mining and machine learning algorithms are used to extract key features from the smart vest production process data set to form a feature vector of the smart vest production process data for subsequent analysis and modeling tasks.

[0090] Step S4: Perform potential quality analysis on the feature vector of the smart vest production process data to generate smart vest production quality analysis result data; perform production anomaly detection on the feature vector of the smart vest production process data to generate smart vest production anomaly detection data;

[0091] In an embodiment of the present invention, by applying a statistical analysis method, a potential quality analysis is performed on the feature vector of the production process data of the smart vest, and the basic statistics of the data features, including the mean, variance and median, are calculated to understand the distribution and central trend of the data. Further, data distribution analysis is performed, including histogram and box plot visualization methods, to identify outliers and deviations of the data. Correlation analysis is used to evaluate the correlation and importance between features for subsequent quality analysis and anomaly detection. By performing production anomaly detection on the feature vector of the production process data of the smart vest, an appropriate anomaly detection method is selected, wherein the anomaly detection method includes an isolation forest algorithm, a support vector machine and a recursive neural network, and the selected anomaly detection method is applied to the feature vector of the production process data of the smart vest, samples or features that are significantly different from the normal production process are identified, and smart vest production anomaly detection data is generated, and problematic production process data points are identified for further analysis and improvement measures.

[0092] Step S5: Perform intelligent quality control using the smart vest production quality analysis result data and the smart vest production anomaly detection data to generate smart vest production quality control data; construct a control strategy model based on the smart vest production quality control data to obtain a smart vest production quality adjustment control strategy model;

[0093] In the embodiment of the present invention, by integrating the smart vest production quality analysis result data and the smart vest production abnormality detection data, the quality analysis result data is summarized and statistically analyzed, including calculating the statistical characteristics of each key dimensional quality indicator, such as the mean value, standard deviation and range. At the same time, the distribution and abnormality of the quality data are checked through the control chart and box plot visualization tools to identify potential production quality problems. Based on the integrated smart vest production quality control data, a smart vest production quality adjustment control strategy model is constructed. First, through trend analysis and correlation analysis, the change trend and key driving factors in the smart vest production quality control data are identified. Then, regression analysis and decision tree are applied to establish a smart vest production quality adjustment control strategy model, which can provide real-time quality adjustment suggestions for production quality to ensure quality stability and consistency in the production process. Through real-time monitoring and feedback mechanism, the smart vest production quality adjustment control strategy model is integrated into the production management system, and production personnel can adjust production parameters and optimize process flow according to the suggestions provided by the model to achieve intelligent quality control and continuous improvement.

[0094] Step S6: According to the smart vest production quality adjustment control strategy model, the smart vest production link data is traced to the vest production quality product, and a production quality report and traceability record data are generated; the production quality report and traceability record data are uploaded to the blockchain network for distributed storage to perform the smart vest production quality and safety traceability operation.

[0095] In an embodiment of the present invention, the vest production quality product traceability is implemented through the smart vest production quality adjustment control strategy model, and the model is used to analyze the data of the smart vest production link to identify and extract key quality control parameters and key nodes in the production process. Parameters and nodes may include production timestamps, production equipment numbers, operator information, and key information of raw material batches. Based on the identified key parameters and nodes, a production quality report and traceability record data are generated. The production quality report includes the quality assessment results of each production batch or cycle, such as the actual production results through quality indicators and quality control limits. The traceability record data records the historical changes and influencing factors of each key parameter for subsequent tracing and analysis. Using an adapted data upload interface, the encrypted production quality report and traceability record data are uploaded to the blockchain network, and multiple copies of the data are saved on each node of the network to ensure data backup and fault tolerance, and prevent data loss or tampering.

[0096] Preferably, step S1 comprises the following steps:

[0097] Step S11: using sensors to obtain data from the production process of smart vests in real time, wherein the data from the production process of smart vests includes data from the raw material procurement stage, data from the monitoring and manufacturing stage, data from the quality inspection stage, and data from the packaging stage;

[0098] Step S12: extracting material record information from the raw material procurement stage data to obtain smart vest material information data;

[0099] Step S13: locate the production equipment based on the monitoring manufacturing stage data to obtain the production equipment location data; deploy sensors on the production equipment based on the production equipment location data to obtain the production equipment sensor deployment data; construct a distributed network based on the production equipment sensor deployment data to obtain a distributed monitoring network for production equipment; collect production equipment data using the distributed monitoring network for production equipment to obtain production equipment operation data;

[0100] Step S14: Perform vest quality inspection on the quality inspection stage data to generate vest inspection data, wherein the vest quality inspection includes appearance inspection, function inspection, size inspection and durability inspection;

[0101] Step S15: Perform a finished product packaging status analysis on the packaging stage data to generate finished product packaging status data, wherein the finished product packaging status analysis includes packaging material integrity analysis and packaging process environmental condition analysis.

[0102] In the embodiment of the present invention, by installing and configuring various sensors during the production process of the smart vest, these sensors can collect and record data from the production process in real time. These data cover all stages of the entire production process, including data from the raw material procurement stage, data from the monitoring and manufacturing stage, data from the quality inspection stage, and data from the packaging stage. For the data from the raw material procurement stage, the material record information is extracted and sorted to obtain the smart vest material information data, which covers the detailed information of the various raw materials used, including supplier information, procurement time and batch number. The production equipment location data is obtained through positioning technology, and the sensors are deployed according to the production equipment location data. The sensor deployment ensures that the operating status of the production equipment and the key parameters in the production process can be fully monitored and recorded. The production equipment operation data is used to build a distributed monitoring network to realize the real-time collection and analysis of the production equipment operation data. The data from the quality inspection stage is subjected to detailed vest quality inspection, including comprehensive testing and evaluation of the appearance, function, size and durability of the vest, and the generated vest inspection data, which records the test results and quality evaluation information of each product. Analyze the data from the packaging stage, including a detailed analysis of the status of the finished product packaging, especially the integrity of the packaging materials and the environmental conditions during the packaging process, to obtain the finished product packaging status data. The finished product packaging status data contains detailed information about the packaging quality of each product, providing important data support for subsequent quality tracing and product traceability.

[0103] Preferably, step S2 comprises the following steps:

[0104] Step S21: transmitting material information data, production equipment operation data, vest inspection data and finished product packaging status data to the central control system through the wireless communication module for data integration to generate an integrated data set for the production of smart vests;

[0105] Step S22: converting the integrated data set of the smart vest production link into a standard format to obtain a converted data set of the smart vest production link; performing structured unified processing on the converted data set of the smart vest production link to generate a unified data set of the smart vest production link;

[0106] Step S23: encrypting the unified data set of the smart vest production link to obtain encrypted format data; storing the encrypted format data to generate encrypted storage data of the smart vest production link.

[0107] In an embodiment of the present invention, the data collected from each sensor is transmitted to the central control system by a pre-configured wireless communication module, wherein the data collected by the sensor includes material information data, production equipment operation data, vest inspection data and finished product packaging status data, ensuring that all key data can be transmitted to the data integration platform in a timely and accurate manner. After the data transmission is completed, the central control system integrates these data into a unified data set called the smart vest production link integrated data set. The smart vest production link integrated data set is converted to a standard format to ensure that all data conforms to a unified standard format for subsequent processing and analysis. The standard format conversion includes renaming of data fields, normalization of data types and unification of date and time formats to ensure data consistency and operability. After the conversion is completed, the smart vest production link conversion data set is obtained. The unified data set of the smart vest production link is encrypted, first, a structured unified processing is performed, including data archiving, indexing and metadata addition, so as to better manage and maintain the data. Then, the data set is encrypted, and the data is encrypted. The encrypted storage data of the smart vest production link generated finally is a strictly encrypted and structured data set for long-term storage and subsequent analysis applications.

[0108] Preferably, step S3 comprises the following steps:

[0109] Step S31: performing data preprocessing on the encrypted and stored data in the smart vest production process to generate a preprocessed smart vest production process data set, wherein the data preprocessing includes data decryption, data cleaning, data denoising and data standardization;

[0110] Step S32: performing time series extraction on the preprocessed smart vest production process data set to obtain a smart vest production process time series data set; performing spectrum conversion on the smart vest production process time series data set to generate a smart vest production process spectrum graph; performing frequency domain feature extraction on the smart vest production process spectrum graph to generate a smart vest production process data feature vector.

[0111] In an embodiment of the present invention, data decryption operation is performed by encrypting and storing data in the production link of the smart vest to restore the original format and content of the data. After decryption is completed, data cleaning is performed, including removing missing values, processing duplicate data and processing format errors. Next, data denoising is performed, and noise and outliers in the data are removed by applying filtering technology to improve the accuracy and credibility of the data. Finally, the data is standardized and the data is normalized or scaled according to the set standards so that subsequent data analysis and modeling can be carried out more effectively. Based on the pre-processed data set of the smart vest production process, time series extraction is first performed, and the data is organized in chronological order to form a time series data set of the smart vest production process. Then, the time series data set is spectrally converted, and the time domain data is converted to the frequency domain to generate a spectrum diagram of the smart vest production process. Frequency domain feature extraction is performed on the basis of the spectrum diagram to extract key frequency domain feature parameters, such as spectrum energy distribution and spectrum peak, to generate a feature vector of the smart vest production process data.

[0112] Preferably, step S4 comprises the following steps:

[0113] Step S41: extracting production process features according to the feature vector of the smart vest production process data to obtain the smart vest production process data; analyzing the potential quality problems of the smart vest production process data to generate smart vest production quality analysis result data;

[0114] Step S42: extract abnormal features according to the feature vector of the smart vest production process data to obtain abnormal data of the smart vest production process; perform abnormal production detection on the abnormal data of the smart vest production process to generate abnormal production detection data of the smart vest.

[0115] In an embodiment of the present invention, the feature vector of the production process data of the smart vest is used to extract the production process features, which involves analyzing the key parameters in the feature vector, such as production time, temperature, humidity, etc., to identify and extract the data features related to the production process of the smart vest. The data features are used to form a smart vest production process data set, which reflects the key process parameters and attributes in the production process. Subsequently, when the potential quality problem analysis of the smart vest production process data is performed, the production process data set is used for detailed analysis, including the application of statistical methods and quality control technology to identify potential quality problems and anomalies. The analysis results are converted into smart vest production quality analysis result data, which are used to improve the production process, optimize the quality control strategy and implement improvement measures. Based on the feature vector of the smart vest production process data, abnormal feature extraction is performed to identify the abnormal patterns or abnormal behaviors highlighted in the feature vector, such as abnormal production cycle and abnormal equipment operation. The extracted abnormal features are used to generate an abnormal data set of the smart vest production process. Subsequently, abnormal production detection is performed on the abnormal data set, which includes applying the recursive neural network of the anomaly detection algorithm and the generative adversarial network of the deep learning technology to identify and mark the abnormal conditions in the production process. The detection results are sorted and generate the smart vest production anomaly detection data.

[0116] As an example of the present invention, refer to Figure 2 As shown, in this example, step S5 includes:

[0117] Step S51: performing time series analysis on the smart vest production quality analysis result data to obtain production quality time series trend data;

[0118] Step S52: classifying the abnormal situation of the smart vest production abnormality detection data to obtain production abnormality type data;

[0119] Step S53: Perform intelligent quality control on the production quality time series trend data and the production abnormality type data to generate smart vest production quality control data;

[0120] Step S54: constructing a decision model for the smart vest production quality control data to obtain a smart vest production quality adjustment control strategy model.

[0121] In the embodiment of the present invention, the smart vest production quality analysis result data is used for time series analysis, and time series analysis techniques such as trend analysis, periodic analysis and seasonal analysis are applied to capture the changing trend and periodic pattern of production quality. The analysis results are sorted and archived to form production quality time series trend data, which is used to evaluate the long-term trend and direction of quality improvement. The smart vest production abnormality detection data is classified for abnormal conditions, and a classification algorithm is used, wherein the classification algorithm includes logistic regression, linear discriminant analysis and gradient boosting tree. The detected abnormalities are classified and classified according to type, and each type of abnormality will be described and recorded in detail to form a production abnormality type data set. Combined with the production quality time series trend data and the production abnormality type data, intelligent quality control is performed, including the formulation and implementation of intelligent quality control strategies to deal with the detected production quality problems and abnormal conditions. The control strategy involves automatic adjustment, real-time monitoring, alarm system and manual intervention. Finally, based on the smart vest production quality control data, a decision model is constructed, and the collected quality control data is analyzed and integrated to formulate an intelligent quality adjustment control strategy model, which is a decision support.

[0122] As an example of the present invention, refer to Figure 3 As shown, in this example, step S53 includes:

[0123] Step S531: periodically analyzing the production quality time series trend data to obtain a production quality time series trend graph;

[0124] Step S532: Calculate the peak-to-valley difference of the production quality time series trend chart to obtain production quality peak-to-valley difference data;

[0125] Step S533: Compare the production quality peak-to-valley difference data with the preset production quality standard threshold. When the production quality peak-to-valley difference data is greater than the preset production quality standard threshold, adjust the control parameters of the production quality control data to generate a production quality control parameter adjustment strategy; when the production quality peak-to-valley difference data is less than or equal to the preset production quality standard threshold, optimize the production process data to generate a production process optimization strategy; integrate the production quality control parameter adjustment strategy and the production process optimization strategy to generate a production quality control rule;

[0126] Step S534: labeling the production abnormality type data to obtain production abnormality label data;

[0127] Step S535: performing label classification extraction on the production abnormality label data to obtain label classification data;

[0128] Step S536: Analyze the production abnormality through label classification data to obtain abnormality cause analysis data; perform abnormal processing and rapid response to the production process according to the abnormality cause analysis data, and generate production abnormality control rules;

[0129] Step S537: Intelligently execute control measures for production quality control rules and production abnormality control rules to generate smart vest production quality control data.

[0130] In an embodiment of the present invention, by performing periodic analysis on production quality time series trend data, a time series analysis algorithm is used to extract periodic features to obtain a production quality time series trend graph; by performing peak-to-valley difference calculation on the production quality time series trend graph, a peak detection algorithm is used to identify peak values ​​and valley values, and the difference is calculated to obtain production quality peak-to-valley difference data; the production quality peak-to-valley difference data is compared with a preset production quality standard threshold, and when the production quality peak-to-valley difference data is greater than the preset production quality standard threshold, the control parameters in the production quality control data are adjusted to generate a production quality control parameter adjustment strategy; when the production quality peak-to-valley difference data is less than or equal to the preset production quality standard threshold, the process parameters in the production process data are optimized to generate a production process optimization strategy; the production quality control parameter adjustment is integrated Strategy and production process optimization strategy, generate production quality control rules; label the production abnormality type data, use the anomaly detection algorithm to classify and mark the data, and obtain production abnormality label data; classify and extract labels for production abnormality label data, use the clustering algorithm to classify the labels, and obtain label classification data; analyze the production abnormal situation through the label classification data, use data mining technology to extract the abnormal cause, and obtain abnormal cause analysis data; according to the abnormal cause analysis data, the production process is handled quickly to respond to abnormalities, and the automatic control system is used to generate production abnormality control rules; the control measures for production quality control rules and production abnormality control rules are intelligently executed, and the support vector machine and control system combined with the machine learning algorithm are used to generate smart vest production quality control data.

[0131] As an example of the present invention, refer to Figure 4 As shown, in this example, step S54 includes:

[0132] Step S541: Perform production quality abnormality trend analysis on the smart vest production quality control data to generate production quality problem trend distribution data;

[0133] Step S542: Performing environmental parameter impact assessment on smart vest production quality control data to generate environmental parameter impact data;

[0134] Step S543: performing weight division processing on the production quality problem trend distribution data and the environmental parameter impact data to obtain factor weight division data;

[0135] Step S544: extracting priority features from the factor weight division data to generate factor weight priority data; constructing a smart vest production quality decision model based on the factor weight priority data, and training the constructed smart vest production quality decision model using the factor weight division data to generate a smart vest production quality decision training model;

[0136] Step S545: Perform model cross-validation evaluation on the smart vest production quality decision training model to obtain smart vest production quality decision model evaluation data; adjust the model parameters of the smart vest production quality training model through the smart vest production quality decision model evaluation data to obtain the smart vest production quality adjustment control strategy model.

[0137] In the embodiment of the present invention, the production quality abnormality trend analysis is performed on the production quality control data of the smart vest, and the abnormal trend is identified by using the time series analysis method to generate the production quality problem trend distribution data; the environmental parameter impact assessment is performed on the production quality control data of the smart vest, and the impact of the environmental parameters on the quality is assessed by using the multivariate regression analysis to generate the environmental parameter impact data; the production quality problem trend distribution data and the environmental parameter impact data are weighted and processed, and the weight of each factor is calculated by using the hierarchical analysis method to obtain the factor weight division data; the factor weight division data is subjected to priority feature extraction, and the main influencing factors are extracted by using the principal component analysis to generate the factor weight priority data. According to the factor weight priority data, the key features are selected as the input of the smart vest production quality decision model, wherein the model structure of the smart vest production quality decision model includes an input layer, a hidden layer and an output layer, the factor weight priority data is input in the input layer, and multiple hidden layers are set in the hidden layer, each layer contains a number of neurons, and the activation function is selected as ReLU, and the output layer outputs the smart vest production quality decision result. The constructed smart vest production quality decision model is trained by dividing the data with factor weights to generate a smart vest production quality decision training model. The smart vest production quality decision training model is cross-validated with K value selected as 5. The performance index evaluation of the smart vest production quality decision training model includes accuracy, precision, recall and F1 value. The evaluation data of the smart vest production quality decision model is generated. The model parameters of the smart vest production quality training model are adjusted by the evaluation data of the smart vest production quality decision model. The model parameters are optimized by grid search, including the penalty parameter C and the kernel function parameter γ, to obtain the smart vest production quality adjustment control strategy model.

[0138] Preferably, step S6 comprises the following steps:

[0139] Step S61: performing real-time control and monitoring on the production process data of the smart vest according to the smart vest production quality adjustment control strategy model to obtain the smart vest production process monitoring data;

[0140] Step S62: Summarize and summarize the monitoring data of the smart vest production process to generate a production quality report and traceability record data;

[0141] Step S63: Use blockchain technology to upload the production quality report and traceability record data to the blockchain node, and distribute the production quality report and traceability record data through the blockchain node to perform smart vest production quality and safety traceability operations.

[0142] In an embodiment of the present invention, the production process data of the smart vest is controlled and monitored in real time according to the smart vest production quality adjustment control strategy model, and the real-time data is collected using a sensor network and a monitoring system to obtain the smart vest production process monitoring data; the smart vest production process monitoring data is sorted and summarized, and the production quality report and traceability record data are generated by applying data processing and summary algorithms; the production quality report and traceability record data are uploaded to the blockchain node using blockchain technology, and smart contracts and encryption algorithms are used to ensure data transmission security, and the production quality report and traceability record data are distributedly stored through the blockchain node to ensure that the data is backed up on multiple nodes to perform smart vest production quality and safety traceability operations.

[0143] In this specification, a smart vest production quality safety traceability system is provided, which is used to execute the above-mentioned smart vest production quality safety traceability method. The smart vest production quality safety traceability system includes:

[0144] The production link data collection module obtains the data of the smart vest production link, where the smart vest production link data includes raw material procurement stage data, monitoring and manufacturing stage data, quality inspection stage data and packaging stage data; material information is extracted from the raw material procurement stage data to obtain the smart vest material information data; production equipment operation analysis is performed on the monitoring and manufacturing stage data to generate production equipment operation data; vest quality inspection is performed on the quality inspection stage data to generate vest inspection data; finished product packaging status analysis is performed on the packaging stage data to generate finished product packaging status data;

[0145] The production data integration and storage module transmits the real-time collected material information data, production equipment operation data, vest inspection data and finished product packaging status data to the central control system through the wireless communication module for data integration, and generates an integrated data set for the production link of smart vests; unifies the data format of the integrated data set for the production link of smart vests, and generates a unified data set for the production link of smart vests; encrypts and stores the unified data set for the production link of smart vests, and generates encrypted storage data for the production link of smart vests;

[0146] The data processing module performs data preprocessing on the encrypted and stored data in the production process of the smart vest to obtain a preprocessed data set of the production process of the smart vest; extracts key features from the preprocessed data set of the production process of the smart vest to generate a data feature vector of the production process of the smart vest;

[0147] The production quality analysis module performs potential quality analysis on the characteristic vector of the smart vest production process data to generate the smart vest production quality analysis result data; performs production anomaly detection on the characteristic vector of the smart vest production process data to generate the smart vest production anomaly detection data;

[0148] Adjust the control strategy module, use the smart vest production quality analysis result data and the smart vest production anomaly detection data to perform intelligent quality control, and generate smart vest production quality control data; construct a control strategy model based on the smart vest production quality control data to obtain a smart vest production quality adjustment control strategy model;

[0149] The blockchain traceability module traces the production quality of smart vests based on the smart vest production quality adjustment control strategy model, generates production quality reports and traceability record data; uploads the production quality reports and traceability record data to the blockchain network for distributed storage to perform smart vest production quality and safety traceability operations.

[0150] The present invention constructs a smart vest production quality and safety traceability system, and the production link data acquisition module obtains the smart vest production link data, wherein the smart vest production link data includes raw material procurement stage data, monitoring and manufacturing stage data, quality inspection stage data and packaging stage data, ensuring that each production stage can obtain detailed support information, and by extracting material information from the raw material procurement stage data, it is ensured that all kinds of raw material data required for the smart vest are obtained, and the production equipment operation analysis is performed on the monitoring and manufacturing stage data to generate detailed production equipment operation data, which can monitor the equipment's operating status and efficiency in real time, and the vest quality inspection is performed on the quality inspection stage data to generate vest inspection data, and provide vest inspection quality evaluation, thereby helping Reduce the defective rate, analyze the finished product packaging status of the packaging stage data, generate finished product packaging status data, and provide the final packaging status of the smart vest. The production data integration storage module transmits the real-time collected material information data, production equipment operation data, vest inspection data and finished product packaging status data to the central control system through the wireless communication module for data integration, and generates an integrated data set for the production link of the smart vest, realizing the integration and synchronization of various data sources in the production process of the smart vest, unifying the data format of the integrated data set of the production link of the smart vest, and generating a unified data set for the production link of the smart vest, ensuring the consistency and comparability of the data within the central control system, providing an accurate basis for decision-making, and having a positive impact on the production of smart vests. The unified data set of each link is encrypted and stored, and the encrypted storage data of the smart vest production link is generated, which ensures the confidentiality and integrity of the data during storage and transmission, effectively prevents unauthorized access and tampering, and ensures the confidentiality and integrity of the data. The data processing module performs data preprocessing through the encrypted storage data of the smart vest production link to obtain the preprocessed smart vest production process data set, ensuring that the data quality and consistency reach the best level, and extracts key features of the preprocessed smart vest production process data set to generate the smart vest production process data feature vector, which effectively describes the important aspects and key performance indicators of the production process, and converts a large amount of complex production data into a concise feature vector, production quality analysis module Through the characteristic vector of the smart vest production process data, potential quality analysis is performed to generate the smart vest production quality analysis result data, which can quickly identify and evaluate the quality problems and development trends in the production process, and help to find and correct potential quality problems early. Production anomaly detection is performed on the characteristic vector of the smart vest production process data to generate smart vest production anomaly detection data, and abnormal conditions and bad trends in the production process are discovered in time. The control strategy module is adjusted to use the smart vest production quality analysis result data and the smart vest production anomaly detection data for intelligent quality control, generate smart vest production quality control data, provide information about product quality and production status, and build a control strategy model based on the smart vest production quality control data.The smart vest production quality adjustment control strategy model is obtained, so that the production process can be monitored in real time, and the production process can be adjusted in an automated and intelligent manner. The blockchain traceability module traces the vest production quality product of the smart vest production link data according to the smart vest production quality adjustment control strategy model, and generates detailed production quality reports and traceability record data. Through accurate recording and analysis, the production details and quality status of each production batch can be traced. The production quality report provides real-time evaluation and feedback on the quality management effect in the production process, helping the production team to quickly discover problems and take measures to improve them. In order to ensure the credibility and long-term preservation of the data, the production quality report and traceability record data are uploaded to the blockchain network for distributed storage. Blockchain storage effectively prevents data tampering and loss, improves the reliability and transparency of smart vest production quality safety traceability data, and enhances data protection and verification capabilities. Therefore, the present invention improves the multi-dimensional and multi-level data comprehensive analysis problems of data analysis through Internet of Things technology, radio frequency identification technology, data analysis technology and blockchain technology. ,

[0151] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0152] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A method for tracing the production quality and safety of smart vests, characterized in that: The following steps are involved: Step S1: Obtaining data from the production process of the smart vest, wherein the data from the production process of the smart vest includes data from the raw material procurement stage, data from the monitoring and manufacturing stage, data from the quality inspection stage, and data from the packaging stage; extracting material information from the raw material procurement stage data to obtain material information data of the smart vest; performing production equipment operation analysis on the monitoring and manufacturing stage data to generate production equipment operation data; Conduct vest quality inspection on the data in the quality inspection stage and generate vest inspection data; Analyze the finished product packaging status of the packaging stage data to generate finished product packaging status data; Step S2: The material information data, production equipment operation data, vest inspection data and finished product packaging status data collected in real time are transmitted to the central control system through the wireless communication module for data integration to generate an integrated data set of the smart vest production process; Unify the data format of the integrated data set of the smart vest production process to generate a unified data set of the smart vest production process; Perform encryption storage processing on the unified data set of the smart vest production process to generate encrypted storage data of the smart vest production process; Step S3: preprocessing the encrypted and stored data in the smart vest production process to obtain a preprocessed smart vest production process data set; extracting key features from the preprocessed smart vest production process data set to generate a smart vest production process data feature vector; Step S4: Perform potential quality analysis on the feature vector of the smart vest production process data to generate smart vest production quality analysis result data; perform production anomaly detection on the feature vector of the smart vest production process data to generate smart vest production anomaly detection data; Step S5: Perform intelligent quality control using the smart vest production quality analysis result data and the smart vest production anomaly detection data to generate smart vest production quality control data; construct a control strategy model based on the smart vest production quality control data to obtain a smart vest production quality adjustment control strategy model; Step S5 includes the following steps: Step S51: performing time series analysis on the smart vest production quality analysis result data to obtain production quality time series trend data; Step S52: classifying the abnormal situation of the smart vest production abnormality detection data to obtain production abnormality type data; Step S53: Perform intelligent quality control on the production quality time series trend data and the production abnormality type data to generate smart vest production quality control data; Step S53 includes the following steps: Step S531: periodically analyzing the production quality time series trend data to obtain a production quality time series trend graph; Step S532: Calculate the peak-to-valley difference of the production quality time series trend chart to obtain production quality peak-to-valley difference data; Step S533: Compare the production quality peak-to-valley difference data with the preset production quality standard threshold. When the production quality peak-to-valley difference data is greater than the preset production quality standard threshold, adjust the control parameters of the production quality control data to generate a production quality control parameter adjustment strategy; when the production quality peak-to-valley difference data is less than or equal to the preset production quality standard threshold, optimize the production process data to generate a production process optimization strategy; integrate the production quality control parameter adjustment strategy and the production process optimization strategy to generate a production quality control rule; Step S534: labeling the production abnormality type data to obtain production abnormality label data; Step S535: performing label classification extraction on the production abnormality label data to obtain label classification data; Step S536: Analyze the production abnormality through label classification data to obtain abnormality cause analysis data; perform abnormal processing and rapid response to the production process according to the abnormality cause analysis data, and generate production abnormality control rules; Step S537: intelligently executing control measures for production quality control rules and production abnormality control rules to generate smart vest production quality control data; Step S54: constructing a decision model for the smart vest production quality control data to obtain a smart vest production quality adjustment control strategy model; Step S6: According to the smart vest production quality adjustment control strategy model, the smart vest production link data is traced to the vest production quality product, and a production quality report and traceability record data are generated; the production quality report and traceability record data are uploaded to the blockchain network for distributed storage to perform the smart vest production quality and safety traceability operation.

2. The method for tracing the quality and safety of smart vest production according to claim 1 is characterized in that: Step S1 includes the following steps: Step S11: using sensors to obtain data from the production process of smart vests in real time, wherein the data from the production process of smart vests includes data from the raw material procurement stage, data from the monitoring and manufacturing stage, data from the quality inspection stage, and data from the packaging stage; Step S12: extracting material record information from the raw material procurement stage data to obtain smart vest material information data; Step S13: locate the production equipment based on the monitoring manufacturing stage data to obtain the production equipment location data; deploy sensors on the production equipment based on the production equipment location data to obtain the production equipment sensor deployment data; construct a distributed network based on the production equipment sensor deployment data to obtain a distributed monitoring network for production equipment; collect production equipment data using the distributed monitoring network for production equipment to obtain production equipment operation data; Step S14: Perform vest quality inspection on the quality inspection stage data to generate vest inspection data, wherein the vest quality inspection includes appearance inspection, function inspection, size inspection and durability inspection; Step S15: Perform a finished product packaging status analysis on the packaging stage data to generate finished product packaging status data, wherein the finished product packaging status analysis includes packaging material integrity analysis and packaging process environmental condition analysis.

3. The method for tracing the quality and safety of smart vest production according to claim 1 is characterized in that: Step S2 includes the following steps: Step S21: transmitting material information data, production equipment operation data, vest inspection data and finished product packaging status data to the central control system through the wireless communication module for data integration to generate an integrated data set for the production of smart vests; Step S22: converting the integrated data set of the smart vest production link into a standard format to obtain a converted data set of the smart vest production link; performing structured unified processing on the converted data set of the smart vest production link to generate a unified data set of the smart vest production link; Step S23: encrypting the unified data set of the smart vest production link to obtain encrypted format data; storing the encrypted format data to generate encrypted storage data of the smart vest production link.

4. The method for tracing the quality and safety of smart vest production according to claim 1 is characterized in that: Step S3 includes the following steps: Step S31: performing data preprocessing on the encrypted and stored data in the smart vest production process to generate a preprocessed smart vest production process data set, wherein the data preprocessing includes data decryption, data cleaning, data denoising and data standardization; Step S32: performing time series extraction on the preprocessed smart vest production process data set to obtain a smart vest production process time series data set; performing spectrum conversion on the smart vest production process time series data set to generate a smart vest production process spectrum graph; performing frequency domain feature extraction on the smart vest production process spectrum graph to generate a smart vest production process data feature vector.

5. The method for tracing the quality and safety of smart vest production according to claim 1 is characterized in that: Step S4 includes the following steps: Step S41: extracting production process features according to the feature vector of the smart vest production process data to obtain the smart vest production process data; analyzing the potential quality problems of the smart vest production process data to generate smart vest production quality analysis result data; Step S42: extract abnormal features according to the feature vector of the smart vest production process data to obtain abnormal data of the smart vest production process; perform abnormal production detection on the abnormal data of the smart vest production process to generate abnormal production detection data of the smart vest.

6. The method for tracing the quality and safety of smart vest production according to claim 1 is characterized in that: The specific steps of step S54 are: Step S541: Perform production quality abnormality trend analysis on the smart vest production quality control data to generate production quality problem trend distribution data; Step S542: Performing environmental parameter impact assessment on smart vest production quality control data to generate environmental parameter impact data; Step S543: performing weight division processing on the production quality problem trend distribution data and the environmental parameter impact data to obtain factor weight division data; Step S544: extracting priority features from the factor weight division data to generate factor weight priority data; The smart vest production quality decision model is constructed according to the factor weight priority data, and the constructed smart vest production quality decision model is trained using the factor weight division data to generate a smart vest production quality decision training model; Step S545: Perform model cross-validation evaluation on the smart vest production quality decision training model to obtain smart vest production quality decision model evaluation data; adjust the model parameters of the smart vest production quality training model through the smart vest production quality decision model evaluation data to obtain the smart vest production quality adjustment control strategy model.

7. The method for tracing the quality and safety of smart vest production according to claim 1 is characterized in that: Step S6 includes the following steps: Step S61: performing real-time control and monitoring on the production process data of the smart vest according to the smart vest production quality adjustment control strategy model to obtain the smart vest production process monitoring data; Step S62: Summarize and summarize the monitoring data of the smart vest production process to generate a production quality report and traceability record data; Step S63: Use blockchain technology to upload the production quality report and traceability record data to the blockchain node, and distribute the production quality report and traceability record data through the blockchain node to perform smart vest production quality and safety traceability operations.

8. A smart vest production quality safety traceability system, characterized in that: Used to execute the smart vest production quality safety traceability method according to claim 1, comprising: The production link data collection module obtains the data of the smart vest production link, where the smart vest production link data includes raw material procurement stage data, monitoring and manufacturing stage data, quality inspection stage data and packaging stage data; material information is extracted from the raw material procurement stage data to obtain the smart vest material information data; production equipment operation analysis is performed on the monitoring and manufacturing stage data to generate production equipment operation data; vest quality inspection is performed on the quality inspection stage data to generate vest inspection data; finished product packaging status analysis is performed on the packaging stage data to generate finished product packaging status data; The production data integration and storage module transmits the real-time collected material information data, production equipment operation data, vest inspection data and finished product packaging status data to the central control system through the wireless communication module for data integration, and generates an integrated data set for the production link of smart vests; unifies the data format of the integrated data set for the production link of smart vests, and generates a unified data set for the production link of smart vests; encrypts and stores the unified data set for the production link of smart vests, and generates encrypted storage data for the production link of smart vests; The data processing module performs data preprocessing on the encrypted and stored data in the production process of the smart vest to obtain a preprocessed data set of the production process of the smart vest; extracts key features from the preprocessed data set of the production process of the smart vest to generate a data feature vector of the production process of the smart vest; The production quality analysis module performs potential quality analysis on the characteristic vector of the smart vest production process data to generate the smart vest production quality analysis result data; performs production anomaly detection on the characteristic vector of the smart vest production process data to generate the smart vest production anomaly detection data; Adjust the control strategy module, use the smart vest production quality analysis result data and the smart vest production anomaly detection data to perform intelligent quality control, and generate smart vest production quality control data; construct a control strategy model based on the smart vest production quality control data to obtain a smart vest production quality adjustment control strategy model; The blockchain traceability module traces the production quality of smart vests based on the smart vest production quality adjustment control strategy model, generates production quality reports and traceability record data; uploads the production quality reports and traceability record data to the blockchain network for distributed storage to perform smart vest production quality and safety traceability operations.

Images