Sales chain management method based on Internet identifier analysis

Abstract

The invention relates to the technical field of Internet identifier analysis, in particular to a sales chain management method based on Internet identifier analysis. The method comprises the following steps: obtaining product full life cycle data; performing data preprocessing on the product full-life-cycle data to obtain a standard product full-life-cycle data set; extracting product types of the standard product full life cycle data set, and generating product type data; performing production process analysis on the standard product full life cycle data set according to the product type data to obtain product production process data; and performing process feature identification on the product production process data according to the product type data to obtain production process identification data. Therefore, through the Internet of Things technology, the data processing technology, the intelligent regulation and control technology and the cloud platform technology, real-time monitoring and dynamic adjustment of the production process and the quality condition of the sales chain and the supply chain are realized, so that the management efficiency of the sales chain is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of Internet identity resolution, and in particular to a sales chain management method based on Internet identity resolution. Background Art

[0002] With the rapid development of e-commerce and global trade, sales chain management has gradually become a core concern of enterprises. Traditional sales chain management methods rely on manual records and simple information system integration. Although they have played a role in the early business environment, as the complexity of the sales chain increases, these methods have gradually exposed shortcomings such as inaccurate data, information islands, and lack of real-time monitoring and traceability. In response to these challenges, Internet technology has been introduced into the field of sales chain management, especially the development of Internet identity resolution technology, which has brought new breakthroughs to sales chain management. Identity resolution technology, which was originally used for network domain name resolution and IP address allocation, has gradually been applied to the commercial field with its advantages in data identification, tracking and management. With the evolution of this technology, the sales chain management method based on Internet identity resolution has achieved accurate tracking and management of the entire life cycle of products, improved the accuracy of data processing and the transparency and traceability of the sales chain. However, existing technologies make it difficult to achieve real-time monitoring and dynamic adjustment of production processes, quality conditions and supply chains, resulting in low efficiency of sales chain management. Summary of the invention

[0003] Based on this, it is necessary to provide a sales chain management method based on Internet identity resolution to solve at least one of the above technical problems.

[0004] To achieve the above purpose, a sales chain management method based on Internet identity resolution comprises the following steps:

[0005] Step S1: Acquire product life cycle data; perform data preprocessing on the product life cycle data to obtain a standard product life cycle data set;

[0006] Step S2: extract the product type of the standard product life cycle data set to generate product type data; perform production process analysis on the standard product life cycle data set according to the product type data to obtain product production process data; perform process feature identification on the product production process data according to the product type data to obtain production process identification data;

[0007] Step S3: Perform product quality inspection on the standard product life cycle data set according to the product type data to generate product quality data; perform quality status identification on the product quality data to obtain quality status identification data; perform correlation analysis on the production process identification data according to the quality status identification data to obtain process quality correlation identification data;

[0008] Step S4: Perform supply chain monitoring on the standard product life cycle data set according to the product type data to generate product supply chain data; identify key information of the product supply chain data to obtain supply chain identification data;

[0009] Step S5: construct a product sales chain management model based on the process quality association identification data and the supply chain identification data to obtain the product sales chain management model; perform process intelligent management on the product production process data based on the product sales chain management model to generate production process management data; perform quality intelligent optimization on the product quality data based on the product sales chain management model to generate product quality optimization data; perform supply chain intelligent adjustment on the product supply chain data based on the product sales chain management model to generate product supply chain adjustment data;

[0010] Step S6: Conduct real-time sales chain monitoring and management feedback on product life cycle data through production process management data, product quality optimization data and product supply chain adjustment data, and generate a real-time sales chain monitoring and management feedback report; construct a cloud platform based on the real-time sales chain monitoring and management feedback report to obtain a sales chain management cloud platform; and execute Internet identification sales chain management operations based on the sales chain management cloud platform.

[0011] The present invention performs data preprocessing on product life cycle data to obtain a standard product life cycle data set, thereby ensuring the accuracy and availability of the data and providing a high-quality data foundation for subsequent analysis. By extracting product types and conducting production process analysis, the key links and features of the production process can be accurately identified, thus providing detailed data support for production optimization and efficiency improvement; by marking the quality status of product quality data, product quality problems can be discovered and recorded in a timely manner, which helps to improve product reliability; by marking key information of product supply chain data, the supply chain status can be monitored in real time, ensuring the stability and efficiency of the supply chain and reducing the risk of supply chain disruption; by building a product sales chain management model and applying it to product production process data, product quality data and product supply chain data, the intelligent production, quality and supply chain management is realized, management efficiency and response speed are improved, and resource allocation is optimized; by using production process management data, product quality optimization data and product supply chain adjustment data, real-time sales chain monitoring and management feedback is conducted on the product life cycle data, and real-time sales chain monitoring and management feedback reports are generated, providing real-time and accurate data support for decision-making, and improving the transparency of the entire sales chain and the ability to respond to changes; by building a sales chain management cloud platform, data sharing and collaboration efficiency are further promoted, providing platform support for continuous improvement and innovation of the sales chain. Therefore, the present invention uses Internet of Things technology, data processing technology, intelligent control technology and cloud platform technology to achieve real-time monitoring and dynamic adjustment of the production process, quality status and supply chain of the sales chain, thereby improving the management efficiency of the sales chain.

[0012] Preferably, step S2 comprises the following steps:

[0013] Step S21: performing product feature recognition on the standard product full life cycle data set to obtain product feature data; performing product type extraction on the product feature data to generate product type data;

[0014] Step S22: performing differentiated production path identification on the product type data to obtain product production path data; performing production status tracking on the standard product full life cycle data set according to the product production path data to obtain product production status data;

[0015] Step S23: analyzing the equipment usage of the standard product life cycle data set according to the product production status data to obtain product equipment usage data; performing operation status detection on the product equipment usage data to obtain product equipment operation status data;

[0016] Step S24: Integrate product production status data and product equipment operation status data to obtain product production process data; extract process dynamic variables from the product production process data to obtain process dynamic variable identification data;

[0017] Step S25: Perform process feature identification on the product production process data according to the process dynamic variable identification data to obtain production process identification data.

[0018] The present invention can accurately obtain product feature data and product type data from the standard product life cycle data set through product feature identification and product type extraction, which provides detailed product information for subsequent production management and optimization; differentiated production path identification is performed on product type data to obtain product production path data, which is helpful for monitoring the production process; production status tracking is performed on the standard product life cycle data set according to the product production path data to obtain product production status data to ensure production efficiency and product quality; equipment usage is analyzed and operation is detected to obtain product equipment usage data and product equipment operation data, which provides an accurate basis for equipment management and maintenance and helps to improve equipment utilization efficiency and reduce failure rate; production status and equipment operation data are integrated to form product production process data, and process dynamic variable identification data extracted therefrom provide data support for monitoring and control of the production process; process feature identification is performed on product production process data to obtain production process identification data, which helps to further refine production management, optimize production plans, and improve the transparency and controllability of the production process.

[0019] Preferably, step S3 comprises the following steps:

[0020] Step S31: extracting the standard product life cycle data set according to the product type data according to the product type quality to obtain product type quality data; setting quality parameters for the product type quality data to obtain product quality setting data;

[0021] Step S32: performing multi-dimensional quality parameter analysis on the product type quality data according to the product quality setting data to obtain multi-dimensional quality parameter data;

[0022] Step S33: Perform product process detection on the product type data according to the multi-dimensional quality parameter data to obtain product process data; perform product performance detection on the product type data according to the product process data to obtain product performance data; perform product quality detection on the product type data according to the product process data and the product performance data to generate product quality data;

[0023] Step S34: performing quality pattern recognition on the product quality data to obtain product quality pattern data; performing quality anomaly detection on the product quality pattern data to obtain product quality anomaly data; performing quality trend analysis on the product quality data according to the product quality anomaly data to obtain product quality trend data;

[0024] Step S35: Integrate the product quality abnormality data and the product quality trend data to obtain quality situation data; perform quality situation identification on the quality situation data to obtain quality situation identification data;

[0025] Step S36: Perform correlation analysis on the production process identification data according to the quality situation identification data to obtain process quality correlation identification data.

[0026] The present invention performs quality extraction on a standard product life cycle data set according to product type data to obtain product type quality data, and sets quality parameters for these data to generate product quality setting data to ensure consistency of product quality; uses product quality setting data to perform multi-dimensional quality parameter analysis on product type quality data to obtain multi-dimensional quality parameter data, thereby enhancing an in-depth understanding of product quality characteristics and enabling better identification and optimization of key quality attributes; performs product process and product performance testing according to the multi-dimensional quality parameter data to obtain product process data and product performance data, and generates product quality data accordingly to ensure that products meet established quality standards during the design and manufacturing process. Perform quality pattern recognition and quality anomaly detection on product quality data to obtain product quality pattern data and product quality anomaly data; perform quality trend analysis on product quality data based on product quality anomaly data to obtain product quality trend data to ensure timely detection and prevention of potential quality risks; integrate product quality anomaly data and product quality trend data to obtain quality situation data; and perform quality situation identification on quality situation data to generate quality situation identification data, which provides clear guidance for quality control and helps to quickly respond to quality issues later; perform correlation analysis on production process identification data based on quality situation identification data to obtain process quality correlation identification data. Process quality correlation identification data directly associates quality issues with the production process, providing data support for continuously improving production processes and enhancing product quality.

[0027] Preferably, step S36 includes the following steps:

[0028] Step S361: analyzing the quality influencing factors of the production process identification data according to the quality situation identification data to obtain quality influencing factor data;

[0029] Step S362: performing production process impact matching on the quality influencing factor data to obtain process quality matching data;

[0030] Step S363: performing quality feedback loop analysis on the process quality matching data to obtain process quality feedback loop data;

[0031] Step S364: performing process quality variability analysis on the process quality feedback loop data to obtain process quality variability data;

[0032] Step S365: Perform association analysis on the production process identification data according to the process quality variability data to obtain process quality association identification data.

[0033] The present invention performs quality influencing factor analysis on production process identification data through quality situation identification data to obtain quality influencing factor data, clarifies key factors affecting product quality, and provides direction for quality improvement; performs production process impact matching on quality influencing factor data to obtain process quality matching data, helps to identify specific links related to quality in the production process, and provides a basis for targeted quality control measures; performs quality feedback loop analysis on process quality matching data to obtain process quality feedback loop data, ensures the establishment of a continuous improvement mechanism, adjusts the production process through real-time feedback, and improves product quality; performs process quality variability analysis on process quality feedback loop data to obtain process quality variability data, identifies quality fluctuations in the production process, and provides data support for reducing quality deviations and improving consistency; performs association analysis on production process identification data according to process quality variability data to obtain process quality association identification data, so as to associate quality variability with specific links in the production process, and provide accurate data support for further optimizing the production process and improving product quality.

[0034] Preferably, step S4 comprises the following steps:

[0035] Step S41: extracting supply chain features from the standard product life cycle data set according to the product type data to obtain product supply chain feature data; identifying logistics elements from the product supply chain feature data to obtain supply chain logistics data;

[0036] Step S42: Tracking the logistics status of the supply chain logistics data to obtain logistics status data; monitoring the logistics status data for logistics anomalies to obtain logistics anomaly data;

[0037] Step S43: monitoring the inventory status of the product supply chain characteristic data to obtain inventory status data; recording the out-of-stock status of the inventory status data to obtain inventory out-of-stock data;

[0038] Step S44: Integrate the supply chain logistics data and inventory status data to obtain product supply chain data; identify key information of the product supply chain data to obtain supply chain identification data.

[0039] The present invention extracts supply chain characteristics from a standard product life cycle data set through product type data to obtain product supply chain characteristic data; and identifies logistics elements from the product supply chain characteristic data to generate supply chain logistics data, which provides detailed elements for an in-depth understanding of the logistics characteristics and needs of the supply chain; tracks the logistics status of the supply chain logistics data to obtain logistics status data, and performs logistics anomaly monitoring on the logistics status data to obtain logistics anomaly data, which can monitor logistics activities in real time, and promptly discover and handle abnormal situations in the logistics process; monitors the inventory status of the product supply chain characteristic data to obtain inventory status data, so as to accurately grasp the inventory level; and records the out-of-stock situation of the inventory status data to obtain inventory out-of-stock data, so as to prevent and respond to inventory shortages; integrates the supply chain logistics data and the inventory status data to obtain the product supply chain data; and identifies key information of the product supply chain data to generate supply chain identification data, which provides comprehensive data support for supply chain management, and helps to optimize supply chain operations and improve overall efficiency.

[0040] Preferably, step S5 comprises the following steps:

[0041] Step S51: constructing a product sales chain management model according to the process quality association identification data and the supply chain identification data to obtain a product sales chain management pre-model; and training the product sales chain management pre-model using the process quality association identification data and the supply chain identification data to obtain a product sales chain management training model;

[0042] Step S52: performing a model cross-validation evaluation on the product sales chain management training model to obtain product sales chain management model evaluation data; adjusting model parameters of the product sales chain management training model using the product sales chain management model evaluation data to obtain a product sales chain management model;

[0043] Step S53: Performing intelligent process management on product production process data based on the product sales chain management model to generate production process management data;

[0044] Step S54: intelligently optimizing product quality data based on the product sales chain management model to generate product quality optimization data;

[0045] Step S55: Based on the product sales chain management model, the product supply chain data is intelligently adjusted to generate product supply chain adjustment data.

[0046] The present invention constructs a product sales chain management pre-model by combining process quality association identification data and supply chain identification data, and trains it to form a product sales chain management training model, thereby ensuring that the model can accurately reflect the dynamic characteristics of the product sales chain; the product sales chain management training model is cross-validated and evaluated, and the model parameters are adjusted according to the evaluation data, and finally an optimized product sales chain management model is obtained, thereby enhancing the accuracy and reliability of the model and providing solid decision-making support for subsequent management activities; the product sales chain management model is applied to intelligently manage product production process data, generate production process management data, realize refined control of the production process, and improve production efficiency and product quality; the product sales chain management model is used to intelligently optimize product quality data, generate product quality optimization data, and can identify and implement effective quality improvement measures to ensure that product quality meets standards; based on the product sales chain management model, the product supply chain data is intelligently adjusted to generate product supply chain adjustment data, which helps to optimize supply chain operations, improve the response speed and flexibility of the supply chain, and ensure efficient operation of the supply chain.

[0047] Preferably, step S53 includes the following steps:

[0048] Step S531: Based on the product sales chain management model, the product production status data is monitored for production status anomalies to obtain product production status anomaly data; the product production status anomaly data is located for abnormal links to generate abnormal link location data;

[0049] Step S532: Perform abnormal link location and early warning processing on the product production process data through the abnormal link location data to obtain management measures for the abnormal link of the process;

[0050] Step S533: performing operation fault monitoring on the product equipment operation data based on the product sales chain management model to obtain product equipment operation fault data;

[0051] Step S534: Automated fault emergency response is performed on product production process data through product equipment operation fault data to obtain equipment operation fault management measures;

[0052] Step S535: Integrate process abnormality management measures and equipment operation failure management measures to generate production process management data.

[0053] The present invention uses a product sales chain management model to perform detailed abnormal monitoring of product production status data to obtain product production status abnormal data; and uses the product production status abnormal data to accurately locate abnormal links and generate abnormal link location data, which is helpful to quickly identify abnormal situations in the production process and effectively manage them. Using the abnormal link location data to perform early warning processing on product production process data, process abnormal link management measures are obtained, which can prevent potential production problems and ensure the stability and continuity of the production process; based on the product sales chain management model, the product equipment operation data is monitored for operation failures to obtain product equipment operation failure data, which can timely discover problems in equipment operation and reduce production interruptions; through the product equipment operation failure data, the product production process data is automatically responded to by emergency response, and equipment operation failure management measures are obtained, which enhances the response capability to equipment failures and improves the resilience of the production system; the process abnormal link management measures and equipment operation failure management measures are integrated to generate production process management data, and the integrated data can provide a comprehensive production management view, which helps to optimize production resource allocation and improve production efficiency and product quality.

[0054] Preferably, step S54 includes the following steps:

[0055] Step S541: monitoring the product quality abnormality data based on the product sales chain management model to obtain the quality process abnormality data;

[0056] Step S542: optimizing process parameters of product quality data through quality process abnormality data to obtain quality process abnormality optimization measures;

[0057] Step S543: monitoring the quality change trend of the product quality trend data based on the product sales chain management model to obtain the quality change trend data;

[0058] Step S544: Adaptively optimize product quality control data through quality change trend data to obtain quality change trend optimization measures;

[0059] Step S545: Integrate quality process anomaly optimization measures and quality change trend optimization measures to generate product quality optimization data.

[0060] The present invention monitors product quality abnormality data through a product sales chain management model to obtain quality process abnormality data, which can identify the process links that cause quality problems in the production process; optimizes process flow parameters of product quality data through quality process abnormality data to obtain quality process abnormality optimization measures, which is helpful to improve the process flow and reduce quality defects; monitors product quality trend data based on the product sales chain management model to obtain quality change trend data, which is helpful to predict and analyze long-term changes in product quality; performs adaptive quality control optimization on product quality data through quality change trend data to obtain quality change trend optimization measures, which is helpful to dynamically adjust the quality control strategy to cope with fluctuations in product quality; integrates quality process abnormality optimization measures and quality change trend optimization measures to generate product quality optimization data, providing a comprehensive improvement plan for comprehensively improving product quality.

[0061] Preferably, step S55 comprises the following steps:

[0062] Step S551: Recording the logistics abnormality time of the logistics abnormality data based on the product sales chain management model to obtain the logistics abnormality time data;

[0063] Step S552: re-plan and adjust the logistics route of the product supply chain data according to the logistics abnormality time data to obtain logistics abnormality adjustment measures;

[0064] Step S553: ​​Identify out-of-stock products based on the inventory out-of-stock data based on the product sales chain management model to obtain inventory out-of-stock product data;

[0065] Step S554: intelligently adjust the product supply chain data for replenishment according to the out-of-stock product data to obtain out-of-stock adjustment measures;

[0066] Step S555: Integrate logistics abnormality adjustment measures and inventory shortage adjustment measures to generate product supply chain adjustment data.

[0067] The present invention records the abnormal time data of logistics, tracks the abnormal events in the logistics process in detail, and provides an accurate time reference for subsequent logistics management; re-plans and adjusts the logistics route of the product supply chain data according to the abnormal time data of logistics, and obtains the abnormal logistics adjustment measures, which can optimize the logistics route and reduce future logistics delays; by identifying the data of out-of-stock products, it can accurately understand the existence of inventory shortages of products, and provide data support for replenishment decisions; intelligently replenishes the product supply chain data according to the out-of-stock product data, and obtains the out-of-stock adjustment measures, which can replenish the inventory in time and avoid the impact of out-of-stock on sales; integrates the abnormal logistics adjustment measures and the out-of-stock adjustment measures to generate the product supply chain adjustment data, and provides a comprehensive adjustment plan for supply chain management to improve the overall efficiency and responsiveness of the supply chain.

[0068] Preferably, step S6 comprises the following steps:

[0069] Step S61: Performing real-time sales chain monitoring and management feedback on product life cycle data through production process management data, product quality optimization data, and product supply chain adjustment data to obtain real-time sales chain monitoring data and real-time sales chain management feedback data;

[0070] Step S62: Integrate the real-time sales chain monitoring data and the real-time sales chain management feedback data to obtain a real-time sales chain monitoring management feedback report;

[0071] Step S63: construct a cloud platform according to the real-time sales chain monitoring management feedback report to obtain a sales chain management cloud platform to perform Internet identification sales chain management operations.

[0072] The present invention monitors and manages product life cycle data in real time through production process management data, product quality optimization data and product supply chain adjustment data, and obtains real-time sales chain monitoring data and real-time sales chain management feedback data, which helps to achieve continuous monitoring of the product sales chain, and timely discover and respond to changes in production, quality and supply chain; integrates real-time sales chain monitoring data and real-time sales chain management feedback data to obtain a real-time sales chain monitoring management feedback report, which can provide decision makers with a comprehensive overview of the sales chain status and provide data-based decision making; a cloud platform is constructed according to the real-time sales chain monitoring management feedback report to obtain a sales chain management cloud platform; the platform enables sales chain management operations to be efficiently executed in an Internet environment, thereby improving the flexibility and scalability of management operations.

[0073] The present invention performs standardized preprocessing on the data of the product's entire life cycle, generates a standard product life cycle data set, ensures the accuracy and practicality of the data, and lays a solid foundation for further data analysis. By extracting product type information from the standard product life cycle data set and deeply analyzing the production process, the key elements and characteristics in the production process can be accurately identified, providing detailed data support for improving the efficiency and quality of the production process. In terms of quality management, detailed quality status identification of product quality data helps to quickly discover and record product quality problems, thereby enhancing product stability; at the same time, marking the key information of product supply chain data can continuously track the dynamics of the supply chain, ensure its smooth operation, and reduce potential supply chain disruption risks; further, through the constructed product sales chain management model, comprehensive intelligent management of product production process data, product quality data and product supply chain data is carried out to realize intelligent processing of production, quality and supply chain management; thereby improving management efficiency, speeding up the response speed to various situations, and optimizing the use of resources. In addition, production process management data, product quality optimization data and product supply chain adjustment data are used to monitor and feedback the product life cycle data in real time, generate real-time sales chain monitoring management feedback reports, provide timely and accurate data support for decision-making, and enhance the transparency of the entire sales chain and the ability to adapt to changes; finally, by establishing a sales chain management cloud platform, data sharing and cooperation efficiency are enhanced, providing a strong platform support for the continuous optimization and innovation of the sales chain. Therefore, the present invention uses Internet of Things technology, data processing technology, intelligent control technology and cloud platform technology to achieve real-time monitoring and dynamic adjustment of the production process, quality and supply chain of the sales chain, thereby improving the management efficiency of the sales chain. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] Figure 1 A schematic diagram of the steps of a sales chain management method based on Internet identity resolution;

[0075] Figure 2 for Figure 1 Detailed implementation steps of step S5;

[0076] Figure 3 for Figure 2 Detailed implementation steps of step S55 are shown in the flowchart;

[0077] 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

[0078] 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.

[0079] 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.

[0080] 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.

[0081] To achieve this, please refer to Figures 1 to 3 , a sales chain management method based on Internet identity resolution, the method comprising the following steps:

[0082] Step S1: Acquire product life cycle data; perform data preprocessing on the product life cycle data to obtain a standard product life cycle data set;

[0083] Step S2: extract the product type of the standard product life cycle data set to generate product type data; perform production process analysis on the standard product life cycle data set according to the product type data to obtain product production process data; perform process feature identification on the product production process data according to the product type data to obtain production process identification data;

[0084] Step S3: Perform product quality inspection on the standard product life cycle data set according to the product type data to generate product quality data; perform quality status identification on the product quality data to obtain quality status identification data; perform correlation analysis on the production process identification data according to the quality status identification data to obtain process quality correlation identification data;

[0085] Step S4: Perform supply chain monitoring on the standard product life cycle data set according to the product type data to generate product supply chain data; identify key information of the product supply chain data to obtain supply chain identification data;

[0086] Step S5: construct a product sales chain management model based on the process quality association identification data and the supply chain identification data to obtain the product sales chain management model; perform process intelligent management on the product production process data based on the product sales chain management model to generate production process management data; perform quality intelligent optimization on the product quality data based on the product sales chain management model to generate product quality optimization data; perform supply chain intelligent adjustment on the product supply chain data based on the product sales chain management model to generate product supply chain adjustment data;

[0087] Step S6: Conduct real-time sales chain monitoring and management feedback on product life cycle data through production process management data, product quality optimization data and product supply chain adjustment data, and generate a real-time sales chain monitoring and management feedback report; construct a cloud platform based on the real-time sales chain monitoring and management feedback report to obtain a sales chain management cloud platform; and execute Internet identification sales chain management operations based on the sales chain management cloud platform.

[0088] The present invention performs data preprocessing on product life cycle data to obtain a standard product life cycle data set, thereby ensuring the accuracy and availability of the data and providing a high-quality data foundation for subsequent analysis. By extracting product types and conducting production process analysis, the key links and features of the production process can be accurately identified, thus providing detailed data support for production optimization and efficiency improvement; by marking the quality status of product quality data, product quality problems can be discovered and recorded in a timely manner, which helps to improve product reliability; by marking key information of product supply chain data, the supply chain status can be monitored in real time, ensuring the stability and efficiency of the supply chain and reducing the risk of supply chain disruption; by building a product sales chain management model and applying it to product production process data, product quality data and product supply chain data, the intelligent production, quality and supply chain management is realized, management efficiency and response speed are improved, and resource allocation is optimized; by using production process management data, product quality optimization data and product supply chain adjustment data, real-time sales chain monitoring and management feedback is conducted on the product life cycle data, and real-time sales chain monitoring and management feedback reports are generated, providing real-time and accurate data support for decision-making, and improving the transparency of the entire sales chain and the ability to respond to changes; by building a sales chain management cloud platform, data sharing and collaboration efficiency are further promoted, providing platform support for continuous improvement and innovation of the sales chain. Therefore, the present invention uses Internet of Things technology, data processing technology, intelligent control technology and cloud platform technology to achieve real-time monitoring and dynamic adjustment of the production process, quality status and supply chain of the sales chain, thereby improving the management efficiency of the sales chain.

[0089] In the embodiment of the present invention, reference Figure 1 The above is a schematic diagram of the steps of the sales chain management method based on Internet identifier resolution of the present invention. In this example, the sales chain management method based on Internet identifier resolution includes the following steps:

[0090] Step S1: Acquire product life cycle data; perform data preprocessing on the product life cycle data to obtain a standard product life cycle data set;

[0091] In an embodiment of the present invention, full life cycle data of a product from design, production, sales to service is collected through an integrated information system and a sensor network to obtain product full life cycle data, wherein the product full life cycle data includes product design parameters, production logs and sales records; data preprocessing is performed on the product full life cycle data, and the data preprocessing includes data cleaning to remove duplicate and incomplete records, data conversion to unify the format of data from different sources, and data normalization to eliminate dimensional and scale differences, and finally a standard product full life cycle data set is obtained.

[0092] Step S2: extract the product type of the standard product life cycle data set to generate product type data; perform production process analysis on the standard product life cycle data set according to the product type data to obtain product production process data; perform process feature identification on the product production process data according to the product type data to obtain production process identification data;

[0093] In an embodiment of the present invention, data classification technology is applied to analyze a standard product full life cycle data set to identify and extract product type information, thereby generating product type data; data analysis technology is used to deeply mine production-related data in the same data set, and the production process is refined according to the product type data to obtain product production process data; finally, feature extraction technology is implemented to analyze the product production process data, identify and annotate key production process features, and form production process identification data.

[0094] Step S3: Perform product quality inspection on the standard product life cycle data set according to the product type data to generate product quality data; perform quality status identification on the product quality data to obtain quality status identification data; perform correlation analysis on the production process identification data according to the quality status identification data to obtain process quality correlation identification data;

[0095] In an embodiment of the present invention, data screening technology is used to screen out information related to product quality from a standard product life cycle data set based on product type data to generate product quality data; data annotation technology is used to conduct a detailed review of the product quality data, annotate various quality conditions, and obtain quality condition identification data; data association analysis technology is used to match and analyze the quality condition identification data with the production process identification data, identify the correlation between the two, and form process quality association identification data.

[0096] Step S4: Perform supply chain monitoring on the standard product life cycle data set according to the product type data to generate product supply chain data; identify key information of the product supply chain data to obtain supply chain identification data;

[0097] In an embodiment of the present invention, monitoring technology is used to track and record supply chain-related activities in a standard product life cycle data set, thereby generating product supply chain data; information extraction technology is used to identify and annotate key information from the product supply chain data to form supply chain identification data.

[0098] Step S5: construct a product sales chain management model based on the process quality association identification data and the supply chain identification data to obtain the product sales chain management model; perform process intelligent management on the product production process data based on the product sales chain management model to generate production process management data; perform quality intelligent optimization on the product quality data based on the product sales chain management model to generate product quality optimization data; perform supply chain intelligent adjustment on the product supply chain data based on the product sales chain management model to generate product supply chain adjustment data;

[0099] In an embodiment of the present invention, deep learning technology is used to combine process quality association identification data with supply chain identification data to construct a product sales chain management model; the key parameters of the production process are monitored and automatically adjusted in real time through the algorithm of the product sales chain management model, thereby generating production process management data; the model is used to execute a quality control algorithm on product quality data, intelligently match and optimize product quality standards, and generate product quality optimization data; in addition, the model is also applied to product supply chain data, and the supply chain process is intelligently adjusted through the supply chain optimization algorithm to generate product supply chain adjustment data.

[0100] Step S6: Conduct real-time sales chain monitoring and management feedback on product life cycle data through production process management data, product quality optimization data and product supply chain adjustment data, and generate a real-time sales chain monitoring and management feedback report; construct a cloud platform based on the real-time sales chain monitoring and management feedback report to obtain a sales chain management cloud platform; and execute Internet identification sales chain management operations based on the sales chain management cloud platform.

[0101] In the embodiment of the present invention, integrated data processing technology is applied to combine production process management data, product quality optimization data and product supply chain adjustment data to achieve real-time sales chain monitoring and management of the entire product life cycle; specifically, multi-source data is integrated and a real-time sales chain monitoring and management feedback report is generated; then, cloud computing technology is used to build a sales chain management cloud platform based on the feedback report, which has the ability to centrally process and store data and provides real-time data access and analysis functions to support efficient sales chain management. Finally, Internet identification technology is used on the cloud platform to perform sales chain management operations, including key tasks such as product traceability, inventory monitoring and order management.

[0102] Preferably, step S2 comprises the following steps:

[0103] Step S21: performing product feature recognition on the standard product full life cycle data set to obtain product feature data; performing product type extraction on the product feature data to generate product type data;

[0104] Step S22: performing differentiated production path identification on the product type data to obtain product production path data; performing production status tracking on the standard product full life cycle data set according to the product production path data to obtain product production status data;

[0105] Step S23: analyzing the equipment usage of the standard product life cycle data set according to the product production status data to obtain product equipment usage data; performing operation status detection on the product equipment usage data to obtain product equipment operation status data;

[0106] Step S24: Integrate product production status data and product equipment operation status data to obtain product production process data; extract process dynamic variables from the product production process data to obtain process dynamic variable identification data;

[0107] Step S25: Perform process feature identification on the product production process data according to the process dynamic variable identification data to obtain production process identification data.

[0108] In the embodiment of the present invention, feature recognition technology is used to analyze the standard product life cycle data set, accurately capture the key features of the product, and form product feature data; further, type extraction technology is applied to summarize the product type from the product feature data to generate product type data; differentiated production path recognition technology is used to determine different production paths according to product type data to form product production path data; the standard product life cycle data set is tracked based on the product production path data to obtain product production status data; the product production status data is analyzed for equipment usage to obtain product equipment usage data; further operation status detection technology is used to conduct a detailed review of the equipment usage data to form product equipment operation status data; data integration technology is used to merge the product production status data with the product equipment operation status data to generate comprehensive product production process data; dynamic variable extraction technology is used to extract key dynamic variables from the production process data to form process dynamic variable identification data; based on the process dynamic variable identification data, feature identification technology is applied to carefully identify the product production process data, and finally the production process identification data is obtained, providing accurate data support for the monitoring and management of the production process.

[0109] Preferably, step S3 comprises the following steps:

[0110] Step S31: extracting the standard product life cycle data set according to the product type data according to the product type quality to obtain product type quality data; setting quality parameters for the product type quality data to obtain product quality setting data;

[0111] Step S32: performing multi-dimensional quality parameter analysis on the product type quality data according to the product quality setting data to obtain multi-dimensional quality parameter data;

[0112] Step S33: Perform product process detection on the product type data according to the multi-dimensional quality parameter data to obtain product process data; perform product performance detection on the product type data according to the product process data to obtain product performance data; perform product quality detection on the product type data according to the product process data and the product performance data to generate product quality data;

[0113] Step S34: performing quality pattern recognition on the product quality data to obtain product quality pattern data; performing quality anomaly detection on the product quality pattern data to obtain product quality anomaly data; performing quality trend analysis on the product quality data according to the product quality anomaly data to obtain product quality trend data;

[0114] Step S35: Integrate the product quality abnormality data and the product quality trend data to obtain quality situation data; perform quality situation identification on the quality situation data to obtain quality situation identification data;

[0115] Step S36: Perform correlation analysis on the production process identification data according to the quality situation identification data to obtain process quality correlation identification data.

[0116] In the embodiment of the present invention, the data classification technology is used to screen the standard product life cycle data set, and the corresponding quality-related information is extracted based on the product type data to form the product type quality data; the quality parameters of the product type quality data are set by the parameter configuration technology to generate the product quality setting data. The product quality setting data is deeply analyzed by the multi-dimensional analysis technology, specifically, the product quality is examined from different angles to obtain the multi-dimensional quality parameter data; the multi-dimensional quality parameter data is evaluated by the process detection technology to form the product process data; the product process data is further analyzed by the performance detection technology to obtain the product performance data; the product type data is tested for product quality by the quality detection technology, and the product process data and the product performance data are combined to generate the product quality data; the product quality data is analyzed by the pattern recognition technology to identify the common quality patterns to obtain the product quality pattern data; the product quality pattern data is monitored by the anomaly detection technology to find and record the quality anomaly to form the product quality anomaly data; the product quality anomaly data is analyzed by the trend analysis technology to predict the quality change trend to obtain the product quality trend data. Through data integration technology, product quality abnormality data and product quality trend data are merged to form quality status data; data annotation technology is used to classify and mark the quality status data to generate quality status identification data; association analysis technology is used to analyze the quality status identification data and production process identification data to identify the relationship between the two and obtain process quality association identification data.

[0117] Preferably, step S36 includes the following steps:

[0118] Step S361: analyzing the quality influencing factors of the production process identification data according to the quality situation identification data to obtain quality influencing factor data;

[0119] Step S362: performing production process impact matching on the quality influencing factor data to obtain process quality matching data;

[0120] Step S363: performing quality feedback loop analysis on the process quality matching data to obtain process quality feedback loop data;

[0121] Step S364: performing process quality variability analysis on the process quality feedback loop data to obtain process quality variability data;

[0122] Step S365: Perform association analysis on the production process identification data according to the process quality variability data to obtain process quality association identification data.

[0123] In an embodiment of the present invention, data analysis technology is used to conduct in-depth analysis on quality situation identification data to identify and quantify key factors affecting product quality, thereby obtaining quality influencing factor data; data matching technology is used to match quality influencing factor data with production process identification data to determine the impact of specific links in the production process on the corresponding quality, and generate process quality matching data; feedback loop analysis technology is used to evaluate process quality matching data, establish a quality feedback mechanism, and form process quality feedback loop data; variability analysis technology is used to analyze process quality feedback loop data to identify quality variability in the production process and obtain process quality variability data; and association analysis technology is used to combine process quality variability data with production process identification data for comprehensive analysis to obtain process quality association identification data, which is helpful for further optimizing the production process and improving product quality in the future.

[0124] Preferably, step S4 comprises the following steps:

[0125] Step S41: extracting supply chain features from the standard product life cycle data set according to the product type data to obtain product supply chain feature data; identifying logistics elements from the product supply chain feature data to obtain supply chain logistics data;

[0126] Step S42: Tracking the logistics status of the supply chain logistics data to obtain logistics status data; monitoring the logistics status data for logistics anomalies to obtain logistics anomaly data;

[0127] Step S43: monitoring the inventory status of the product supply chain characteristic data to obtain inventory status data; recording the out-of-stock status of the inventory status data to obtain inventory out-of-stock data;

[0128] Step S44: Integrate the supply chain logistics data and inventory status data to obtain product supply chain data; identify key information of the product supply chain data to obtain supply chain identification data.

[0129] In an embodiment of the present invention, feature extraction technology is used to analyze a standard product life cycle data set to identify supply chain features related to product types and generate product supply chain feature data; logistics element identification technology is used to extract key logistics information from product supply chain feature data to obtain supply chain logistics data; logistics status tracking technology is used to monitor supply chain logistics data, and the status of logistics activities is recorded in real time to form logistics status data; further, logistics anomaly monitoring technology is used to analyze logistics status data to identify and record any abnormal situation in the logistics process to obtain logistics anomaly data; product supply chain feature data is analyzed by inventory status monitoring technology to obtain current inventory level information to form inventory status data; out-of-stock situation recording technology is used to conduct a detailed review of inventory status data, mark any out-of-stock events, and obtain inventory out-of-stock data; data integration technology is implemented to merge supply chain logistics data with inventory status data to form a comprehensive supply chain data set, namely product supply chain data; key information identification technology is used to analyze product supply chain data to highlight key information points in the supply chain to obtain supply chain identification data.

[0130] As an example of the present invention, refer to Figure 2 As shown, in this example, step S5 includes:

[0131] Step S51: constructing a product sales chain management model according to the process quality association identification data and the supply chain identification data to obtain a product sales chain management pre-model; and training the product sales chain management pre-model using the process quality association identification data and the supply chain identification data to obtain a product sales chain management training model;

[0132] Step S52: performing a model cross-validation evaluation on the product sales chain management training model to obtain product sales chain management model evaluation data; adjusting model parameters of the product sales chain management training model using the product sales chain management model evaluation data to obtain a product sales chain management model;

[0133] Step S53: Performing intelligent process management on product production process data based on the product sales chain management model to generate production process management data;

[0134] Step S54: intelligently optimizing product quality data based on the product sales chain management model to generate product quality optimization data;

[0135] Step S55: Based on the product sales chain management model, the product supply chain data is intelligently adjusted to generate product supply chain adjustment data.

[0136] In the embodiment of the present invention, a product sales chain management model is constructed based on process quality association identification data and supply chain identification data to obtain a product sales chain management pre-model; using dimensionality reduction techniques, such as principal component analysis (PCA), the feature space is reduced to 70% of the original dimension while retaining 95% of the information to improve the operational efficiency of the model. Next, an optimization algorithm with an adaptive learning rate is used, such as Adam, whose learning rate is initially set to 0.001, and the β1 and β2 parameters are set to 0.9 and 0.999, respectively, to achieve fast and stable training of the product sales chain management pre-model. In addition, an L1 regularization term is introduced, and the regularization coefficient is set to 0.01 to enhance the generalization ability of the model and reduce the risk of overfitting, thereby obtaining a product sales chain management training model. A stratified sampling cross-validation strategy was used for the product sales chain management training model, dividing the dataset into five mutually exclusive subsets with equal data size in each subset, ensuring that each subset was used as a test set once in the model evaluation and as a training set the remaining four times; the product sales chain management model evaluation data was obtained by calculating the model performance indicators of each subset, such as precision, recall rate and F1 score; based on the product sales chain management model evaluation data, the grid search technique was applied to systematically traverse the predefined parameter grid, such as a logarithmic scale with a learning rate between 0.0001 and 0.01, and a linear scale with a regularization coefficient between 0.001 and 0.1, to find the optimal combination of model parameters, and finally the product sales chain management model was obtained. Utilizing intelligent management technology, the product sales chain management model is applied to product production process data to achieve real-time monitoring and automatic adjustment of the production process, and generate production process management data; applying quality optimization technology, the quality situation data is analyzed and optimized based on the product sales chain management model, and product quality optimization data is generated; utilizing supply chain adjustment technology, the product supply chain data is analyzed and adjusted according to the product sales chain management model, and product supply chain adjustment data is generated to optimize the response speed of the supply chain.

[0137] Preferably, step S53 includes the following steps:

[0138] Step S531: Based on the product sales chain management model, the product production status data is monitored for production status anomalies to obtain product production status anomaly data; the product production status anomaly data is located for abnormal links to generate abnormal link location data;

[0139] Step S532: Perform abnormal link location and early warning processing on the product production process data through the abnormal link location data to obtain management measures for the abnormal link of the process;

[0140] Step S533: performing operation fault monitoring on the product equipment operation data based on the product sales chain management model to obtain product equipment operation fault data;

[0141] Step S534: Automated fault emergency response is performed on product production process data through product equipment operation fault data to obtain equipment operation fault management measures;

[0142] Step S535: Integrate process abnormality management measures and equipment operation failure management measures to generate production process management data.

[0143] In the embodiment of the present invention, anomaly detection technology is used to perform real-time analysis on product production status data to identify abnormal patterns in the production process, thereby obtaining product production status abnormal data; pattern recognition technology is used to perform in-depth analysis on product production status abnormal data, accurately locate abnormal links in the production process, and generate abnormal link location data; based on the abnormal link location data, an early warning processing mechanism is implemented to monitor product production process data in real time. Once a potential abnormal link is found, the early warning system is immediately triggered to obtain process abnormal link management measures so that timely measures can be taken to prevent production interruption; equipment monitoring technology is used to perform detailed monitoring on product equipment operation data, and by analyzing the performance indicators and operation logs of the equipment, equipment failures are identified to obtain product equipment operation failure data; according to the product equipment operation failure data, the fault emergency response process is automatically started, and the product production process data is automatically processed to minimize the impact of equipment failures on production, and equipment operation failure management measures are obtained; through data fusion technology, the process abnormal link management measures and equipment operation failure management measures are integrated to generate production process management data.

[0144] Preferably, step S54 includes the following steps:

[0145] Step S541: monitoring the product quality abnormality data based on the product sales chain management model to obtain the quality process abnormality data;

[0146] Step S542: optimizing process parameters of product quality data through quality process abnormality data to obtain quality process abnormality optimization measures;

[0147] Step S543: monitoring the quality change trend of the product quality trend data based on the product sales chain management model to obtain the quality change trend data;

[0148] Step S544: Adaptively optimize product quality control data through quality change trend data to obtain quality change trend optimization measures;

[0149] Step S545: Integrate quality process anomaly optimization measures and quality change trend optimization measures to generate product quality optimization data.

[0150] In the embodiment of the present invention, monitoring technology is used to perform detailed analysis on product quality abnormality data to identify process abnormalities in the production process, thereby obtaining quality process abnormality data; based on the quality process abnormality data, parameter optimization technology is used to adjust the process flow parameters in the product quality data, specifically, the key variables in the production process are fine-tuned and optimized to reduce quality deviations, and quality process abnormality optimization measures are obtained. Trend analysis technology is used to monitor product quality trend data to identify the change pattern of product quality over time and obtain quality change trend data; adaptive control algorithms are used to perform adaptive quality control optimization on product quality data through quality change trend data, specifically, including automatically adjusting the operating conditions of production equipment, such as temperature, pressure and speed, to ensure that product quality meets normal standards. Through data integration technology, quality process abnormality optimization measures and quality change trend optimization measures are merged to generate product quality optimization data.

[0151] As an example of the present invention, refer to Figure 3 As shown, in this example, step S55 includes:

[0152] Step S551: Recording the logistics abnormality time of the logistics abnormality data based on the product sales chain management model to obtain the logistics abnormality time data;

[0153] Step S552: re-plan and adjust the logistics route of the product supply chain data according to the logistics abnormality time data to obtain logistics abnormality adjustment measures;

[0154] Step S553: ​​Identify out-of-stock products based on the inventory out-of-stock data based on the product sales chain management model to obtain inventory out-of-stock product data;

[0155] Step S554: intelligently adjust the product supply chain data for replenishment according to the out-of-stock product data to obtain out-of-stock adjustment measures;

[0156] Step S555: Integrate logistics abnormality adjustment measures and inventory shortage adjustment measures to generate product supply chain adjustment data.

[0157] In the embodiment of the present invention, time series analysis technology is used to record logistics abnormality data in detail, accurately capture the time point when logistics abnormality occurs, and thus obtain logistics abnormality time data; based on the logistics abnormality time data, supply chain optimization technology is used to re-plan and adjust the logistics routes in the product supply chain data; through algorithm analysis, the logistics path is redesigned to avoid known abnormal points, and logistics abnormality adjustment measures are obtained; classification and recognition technology is used to analyze inventory out-of-stock data, accurately identify the out-of-stock situation of specific products, and obtain inventory out-of-stock product data; based on the inventory out-of-stock product data, intelligent replenishment technology is implemented to make real-time replenishment adjustments to the product supply chain data, and inventory out-of-stock adjustment measures are generated through predictive analysis and automated replenishment logic; through data integration technology, logistics abnormality adjustment measures and inventory out-of-stock adjustment measures are merged to generate product supply chain adjustment data.

[0158] Preferably, step S6 comprises the following steps:

[0159] Step S61: Performing real-time sales chain monitoring and management feedback on product life cycle data through production process management data, product quality optimization data, and product supply chain adjustment data to obtain real-time sales chain monitoring data and real-time sales chain management feedback data;

[0160] Step S62: Integrate the real-time sales chain monitoring data and the real-time sales chain management feedback data to obtain a real-time sales chain monitoring management feedback report;

[0161] Step S63: construct a cloud platform according to the real-time sales chain monitoring management feedback report to obtain a sales chain management cloud platform to perform Internet identification sales chain management operations.

[0162] In the embodiment of the present invention, the production process management data, product quality optimization data and product supply chain adjustment data are integrated by using integration technology to realize real-time monitoring of the entire life cycle of the product; the data flow analysis technology is used to continuously track each link of the product from production to sales, collect real-time data and analyze it, and generate real-time sales chain monitoring data and real-time sales chain management feedback data; the data integration technology is applied to merge the real-time sales chain monitoring data and the real-time sales chain management feedback data to form a comprehensive real-time sales chain monitoring management feedback report, which records in detail the operating status of the sales chain, including production efficiency, product quality level and supply chain efficiency; cloud computing technology is used to build a sales chain management cloud platform based on the real-time sales chain monitoring management feedback report to perform Internet identification sales chain management operations.

[0163] The present invention performs standardized preprocessing on product life cycle data, generates a standard product life cycle data set, ensures the accuracy and practicality of the data, and lays a solid foundation for further data analysis. By extracting product type information from the standard product life cycle data set and deeply analyzing the production process, the key elements and characteristics in the production process can be accurately identified, providing detailed data support for improving the efficiency and quality of the production process. In terms of quality management, detailed quality status identification of product quality data helps to quickly discover and record product quality problems, thereby enhancing product stability; at the same time, marking the key information of product supply chain data can continuously track the dynamics of the supply chain, ensure its smooth operation, and reduce potential supply chain disruption risks; further, through the constructed product sales chain management model, the production process, product quality and supply chain data are comprehensively applied, realizing the intelligent processing of production, quality and supply chain management; thereby improving management efficiency, speeding up the response speed to various situations, and optimizing the use of resources. In addition, production process management data, product quality optimization data and product supply chain adjustment data are used to monitor and feedback the product life cycle data in real time, generate real-time sales chain monitoring management feedback reports, provide timely and accurate data support for decision-making, and enhance the transparency of the entire sales chain and the ability to adapt to changes; finally, by establishing a sales chain management cloud platform, data sharing and cooperation efficiency are enhanced, providing a strong platform support for the continuous optimization and innovation of the sales chain. Therefore, the present invention uses Internet of Things technology, data processing technology, intelligent control technology and cloud platform technology to achieve real-time monitoring and dynamic adjustment of the production process, quality and supply chain of the sales chain, thereby improving the management efficiency of the sales chain.

[0164] 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.

[0165] 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 sales chain management method based on Internet identity resolution, characterized in that: The following steps are involved: Step S1: Obtain product life cycle data; Preprocess the product life cycle data to obtain a standard product life cycle data set; Step S2: extract the product type of the standard product life cycle data set to generate product type data; perform production process analysis on the standard product life cycle data set according to the product type data to obtain product production process data; perform process feature identification on the product production process data according to the product type data to obtain production process identification data; Step S3: Perform product quality inspection on the standard product life cycle data set according to the product type data to generate product quality data; perform quality status identification on the product quality data to obtain quality status identification data; perform correlation analysis on the production process identification data according to the quality status identification data to obtain process quality correlation identification data; Step S4: Perform supply chain monitoring on the standard product life cycle data set according to the product type data to generate product supply chain data; identify key information of the product supply chain data to obtain supply chain identification data; Step S5: construct a product sales chain management model based on the process quality association identification data and the supply chain identification data to obtain the product sales chain management model; perform process intelligent management on the product production process data based on the product sales chain management model to generate production process management data; perform quality intelligent optimization on the product quality data based on the product sales chain management model to generate product quality optimization data; Based on the product sales chain management model, the product supply chain data is intelligently adjusted to generate product supply chain adjustment data; Step S6: Perform real-time sales chain monitoring and management feedback on the product life cycle data through production process management data, product quality optimization data and product supply chain adjustment data, and generate a real-time sales chain monitoring and management feedback report; construct a cloud platform based on the real-time sales chain monitoring and management feedback report to obtain a sales chain management cloud platform; Perform Internet identity sales chain management tasks based on the sales chain management cloud platform.

2. The sales chain management method based on Internet identity resolution according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: performing product feature recognition on the standard product full life cycle data set to obtain product feature data; performing product type extraction on the product feature data to generate product type data; Step S22: performing differentiated production path identification on the product type data to obtain product production path data; performing production status tracking on the standard product full life cycle data set according to the product production path data to obtain product production status data; Step S23: analyzing the equipment usage of the standard product life cycle data set according to the product production status data to obtain product equipment usage data; performing operation status detection on the product equipment usage data to obtain product equipment operation status data; Step S24: Integrate product production status data and product equipment operation status data to obtain product production process data; extract process dynamic variables from the product production process data to obtain process dynamic variable identification data; Step S25: Perform process feature identification on the product production process data according to the process dynamic variable identification data to obtain production process identification data.

3. The sales chain management method based on Internet identity resolution according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: extracting the standard product life cycle data set according to the product type data according to the product type quality to obtain product type quality data; setting quality parameters for the product type quality data to obtain product quality setting data; Step S32: performing multi-dimensional quality parameter analysis on the product type quality data according to the product quality setting data to obtain multi-dimensional quality parameter data; Step S33: Perform product process detection on the product type data according to the multi-dimensional quality parameter data to obtain product process data; perform product performance detection on the product type data according to the product process data to obtain product performance data; perform product quality detection on the product type data according to the product process data and the product performance data to generate product quality data; Step S34: performing quality pattern recognition on the product quality data to obtain product quality pattern data; performing quality anomaly detection on the product quality pattern data to obtain product quality anomaly data; performing quality trend analysis on the product quality data according to the product quality anomaly data to obtain product quality trend data; Step S35: Integrate the product quality abnormality data and the product quality trend data to obtain quality situation data; perform quality situation identification on the quality situation data to obtain quality situation identification data; Step S36: Perform correlation analysis on the production process identification data according to the quality situation identification data to obtain process quality correlation identification data.

4. The sales chain management method based on Internet identity resolution according to claim 3 is characterized in that: Step S36 includes the following steps: Step S361: analyzing the quality influencing factors of the production process identification data according to the quality situation identification data to obtain quality influencing factor data; Step S362: performing production process impact matching on the quality influencing factor data to obtain process quality matching data; Step S363: performing quality feedback loop analysis on the process quality matching data to obtain process quality feedback loop data; Step S364: performing process quality variability analysis on the process quality feedback loop data to obtain process quality variability data; Step S365: Perform association analysis on the production process identification data according to the process quality variability data to obtain process quality association identification data.

5. The sales chain management method based on Internet identity resolution according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: extracting supply chain features from the standard product life cycle data set according to the product type data to obtain product supply chain feature data; identifying logistics elements from the product supply chain feature data to obtain supply chain logistics data; Step S42: Tracking the logistics status of the supply chain logistics data to obtain logistics status data; monitoring the logistics status data for logistics anomalies to obtain logistics anomaly data; Step S43: monitoring the inventory status of the product supply chain characteristic data to obtain inventory status data; recording the out-of-stock status of the inventory status data to obtain inventory out-of-stock data; Step S44: Integrate the supply chain logistics data and inventory status data to obtain product supply chain data; identify key information of the product supply chain data to obtain supply chain identification data.

6. The sales chain management method based on Internet identity resolution according to claim 1, characterized in that: Step S5 includes the following steps: Step S51: constructing a product sales chain management model according to the process quality association identification data and the supply chain identification data to obtain a product sales chain management pre-model; and training the product sales chain management pre-model using the process quality association identification data and the supply chain identification data to obtain a product sales chain management training model; Step S52: performing a model cross-validation evaluation on the product sales chain management training model to obtain product sales chain management model evaluation data; adjusting model parameters of the product sales chain management training model using the product sales chain management model evaluation data to obtain a product sales chain management model; Step S53: Performing intelligent process management on product production process data based on the product sales chain management model to generate production process management data; Step S54: intelligently optimizing product quality data based on the product sales chain management model to generate product quality optimization data; Step S55: Based on the product sales chain management model, the product supply chain data is intelligently adjusted to generate product supply chain adjustment data.

7. The sales chain management method based on Internet identity resolution according to claim 6 is characterized in that: Step S53 includes the following steps: Step S531: Based on the product sales chain management model, the product production status data is monitored for production status anomalies to obtain product production status anomaly data; the product production status anomaly data is located for abnormal links to generate abnormal link location data; Step S532: Perform abnormal link location and early warning processing on the product production process data through the abnormal link location data to obtain management measures for the abnormal link of the process; Step S533: performing operation fault monitoring on the product equipment operation data based on the product sales chain management model to obtain product equipment operation fault data; Step S534: Automated fault emergency response is performed on product production process data through product equipment operation fault data to obtain equipment operation fault management measures; Step S535: Integrate process abnormality management measures and equipment operation failure management measures to generate production process management data.

8. The sales chain management method based on Internet identity resolution according to claim 6, characterized in that: Step S54 includes the following steps: Step S541: monitoring the product quality abnormality data based on the product sales chain management model to obtain the quality process abnormality data; Step S542: optimizing process parameters of product quality data through quality process abnormality data to obtain quality process abnormality optimization measures; Step S543: monitoring the quality change trend of the product quality trend data based on the product sales chain management model to obtain the quality change trend data; Step S544: Adaptively optimize product quality control data through quality change trend data to obtain quality change trend optimization measures; Step S545: Integrate quality process anomaly optimization measures and quality change trend optimization measures to generate product quality optimization data.

9. The sales chain management method based on Internet identity resolution according to claim 6, characterized in that: Step S55 includes the following steps: Step S551: Recording the logistics abnormality time of the logistics abnormality data based on the product sales chain management model to obtain the logistics abnormality time data; Step S552: re-plan and adjust the logistics route of the product supply chain data according to the logistics abnormality time data to obtain logistics abnormality adjustment measures; Step S553: ​​Identify out-of-stock products based on the inventory out-of-stock data based on the product sales chain management model to obtain inventory out-of-stock product data; Step S554: intelligently adjust the product supply chain data for replenishment according to the out-of-stock product data to obtain out-of-stock adjustment measures; Step S555: Integrate logistics abnormality adjustment measures and inventory shortage adjustment measures to generate product supply chain adjustment data.

10. The sales chain management method based on Internet identity resolution according to claim 1, characterized in that: Step S6 includes the following steps: Step S61: Performing real-time sales chain monitoring and management feedback on product life cycle data through production process management data, product quality optimization data, and product supply chain adjustment data to obtain real-time sales chain monitoring data and real-time sales chain management feedback data; Step S62: Integrate the real-time sales chain monitoring data and the real-time sales chain management feedback data to obtain a real-time sales chain monitoring management feedback report; Step S63: construct a cloud platform according to the real-time sales chain monitoring management feedback report to obtain a sales chain management cloud platform to perform Internet identification sales chain management operations.

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