Industrial Internet of Things intelligent supply chain management system

Abstract

The invention relates to the technical field of industrial Internet of Things and supply chain management, and discloses an industrial Internet of Things intelligent supply chain management system. The data acquisition module is used for acquiring physical data, equipment state data and environment data of each link of a supply chain in real time through industrial Internet of Things terminal equipment; edge computing nodes; the cloud server is deployed at an edge node of a factory area and is used for performing historical environment influence evaluation and real-time decision on the collected original data to obtain supply chain key data; a block chain storage module; the encryption module is used for encrypting and chaining supply chain key data to ensure that the data cannot be tampered; a cloud analysis platform; a micro-service architecture is adopted to provide supply chain key data analysis and block chain evidence storage service; a visual interaction terminal; the method is used for providing a visual interaction interface and intelligent decision support. Compared with the prior art, the method has the advantages that the data processing efficiency is improved; the supply chain management efficiency is optimized; the system credibility is enhanced; and the man-machine interaction efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of industrial Internet of Things and supply chain management, and specifically refers to an intelligent supply chain management system for industrial Internet of Things. Background Art

[0002] With the increasingly severe global environmental problems, enterprises are facing more and more pressure on green and sustainable development. As the core link of enterprise production, transportation and sales, the environmental impact of the supply chain has become the focus of social attention. The development of industrial Internet of Things (IIoT) provides technical support for improving the visualization and intelligent management of the supply chain. Enterprises can monitor the data of each link of the supply chain in real time through Internet of Things devices, discover the sources of environmental impact in time, and adjust and optimize the production process to achieve the goal of reducing environmental impact and enhancing green sustainability.

[0003] At present, when most enterprises manage the supply chain, although they gradually begin to introduce Internet of Things technology for data collection, they still lack a systematic solution in terms of environmental impact assessment and regulation. Traditional supply chain environmental management methods mostly rely on manual monitoring and post-analysis, and it is difficult to provide real-time feedback and automatic adjustment. In addition, existing supply chain visualization management systems often focus on optimizing production efficiency while ignoring the real-time regulation of environmental factors.

[0004] In short, many existing supply chain management systems have problems such as data islands, lagging response and insufficient transparency. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to overcome the above technical difficulties and provide an intelligent supply chain management system for industrial Internet of Things, which effectively solves the problems of "data islands, lagging response and insufficient transparency in existing supply chain management systems".

[0006] To solve the above technical problems, the technical solution provided by the present invention is as follows:

[0007] An intelligent supply chain management system for industrial Internet of Things, comprising:

[0008] A data perception and acquisition module; used to obtain physical data, equipment status data and environmental data of each link of the supply chain in real time through industrial Internet of Things terminal devices;

[0009] An edge computing node; deployed at the edge nodes of the factory area, used to conduct historical environmental impact assessment and real-time decision-making on the collected raw data to obtain key supply chain data;

[0010] A blockchain storage module; used to encrypt and upload key supply chain data to the chain to ensure that the data cannot be tampered with;

[0011] Cloud analysis platform; providing key data analysis of the supply chain and blockchain evidence storage services using a microservices architecture;

[0012] Visualization interaction terminal; used to provide a visualization interaction interface and intelligent decision support.

[0013] Furthermore, the data perception and acquisition module includes an RFID reader, industrial sensors, cameras, and GPS positioning devices, and supports the OPCUA protocol to achieve device interconnection.

[0014] Furthermore, the edge computing node includes: a data cleaning module for removing outliers and redundant data; a real-time analysis module for making local decisions based on a preset rule library; and a security gateway for protocol conversion and access control.

[0015] Furthermore, the blockchain storage module adopts a hierarchical architecture; the private chain stores the internal supply chain data of the enterprise, and the consortium chain is used for cross-enterprise data sharing, and automatic reconciliation and settlement are performed through smart contracts.

[0016] Furthermore, the cloud analysis platform includes: a data lake module for storing multi-source heterogeneous supply chain data; a blockchain evidence storage service module that integrates machine learning models for dynamically generating procurement plans, inventory optimization strategies, and logistics scheduling plans, and setting environmental optimization goals for key supply chain data.

[0017] Furthermore, the visualization interaction terminal supports: warehouse shelf positioning navigation based on AR technology, heat map display of production bottleneck areas, and drag-and-drop adjustment of strategy parameters.

[0018] The advantages of the present invention compared with the prior art are as follows:

[0019] 1. Improvement in data processing efficiency: The data preprocessing delay of the edge computing node is ≤8ms, and the multi-source data fusion accuracy rate is as high as 97%;

[0020] 2. Optimization of supply chain management efficiency;

[0021] 3. Enhancement of system credibility: Enhanced blockchain evidence storage performance, increased success rate of data tampering detection, and supplier credit assessment error rate ≤4%;

[0022] 4. Improvement in human-computer interaction efficiency: The device information retrieval time of the AR visualization interface is shortened from an average of 45 seconds for paper records to instant display by scanning the code, improvement in operation training efficiency: the new employee training cycle is compressed from 2 weeks to 4 days. Smart contract deployment: The business rule configuration time is reduced from 8 hours to 30 minutes. Detailed implementation manner

[0023] In the description of the present invention, unless otherwise specified, the meaning of "a plurality" is two or more. In addition, the term "comprising" and any of its variations are intended to cover non-exclusive inclusion.

[0024] The present invention will be further described in detail below in conjunction with the embodiments.

[0025] Embodiment 1

[0026] After deploying the system of the present invention in the supply chain of a certain automobile OEM:

[0027] Production side: The operation data of the stamping machine tool is collected in real time through vibration sensors, and the edge node predicts the tool wear cycle based on the LSTM algorithm, triggering a spare part procurement order 3 days in advance, reducing the unplanned downtime by 55%;

[0028] Logistics side: Combining GPS positioning and traffic big data, dynamically adjusting the JIT delivery routes of suppliers in the Yangtze River Delta region, and improving the on-time delivery rate to 97%;

[0029] Quality inspection side: Using blockchain to store the whole-process quality inspection records of key components, shortening the quality dispute handling cycle from an average of 15 days to 3 hours.

[0030] Embodiment 2:

[0031] After a certain multinational chemical group applies the system of the present invention:

[0032] Customs declaration automation: Through OCR to identify the bill of lading / packing list information, and docking with the customs system API to complete the automatic filling of declaration data, reducing the processing time of a single customs clearance document from 40 minutes to 10 minutes;

[0033] Risk control: Integrating global port strike warnings and weather disaster data, when there is congestion at the Port of Rotterdam, the intelligent decision-making automatically switches to the Port of Hamburg and recalculates the optimal inventory distribution;

[0034] Carbon footprint tracking: Based on the logistics path optimization algorithm, reducing the carbon emission per unit of cargo volume on the European to Asian route by 25%.

[0035] Embodiment 3:

[0036] After a certain chain convenience store applies the system of the present invention:

[0037] Demand forecasting: Integrating store POS data and social media sentiment analysis, the forecasting model reduces the out-of-stock rate of seasonal goods from 18% to less than 6%;

[0038] Warehouse management: Guiding the pickers to the optimal path through AR navigation, increasing the daily picking volume per capita in the warehouse by 33%;

[0039] Supplier collaboration: A supplier reliability scoring model built based on federated learning has reduced the unqualified rate of raw material batches by 40% year-on-year.

[0040] The above describes the present invention and its implementation manners, and such description is not restrictive. If those of ordinary skill in the art are inspired by it and, without departing from the gist of the present invention, design similar embodiments to the technical solution without creative efforts, they shall fall within the protection scope of the present invention.

Claims

1. An industrial Internet of Things intelligent supply chain management system, characterized in that , including: Data perception and acquisition module; It is used to obtain physical data, equipment status data and environmental data of each link in the supply chain in real time through industrial Internet of Things terminal devices; Edge computing node; Deployed at the edge nodes of the factory area, used to conduct historical environmental impact assessment and real-time decision-making on the collected raw data to obtain key supply chain data; Blockchain storage module; used to encrypt and upload key supply chain data to ensure data immutability; Cloud analysis platform; Adopting a microservices architecture to provide key supply chain data analysis and blockchain evidence storage services; Visualization interaction terminal; used to provide a visualization interaction interface and intelligent decision-making support.

2. The intelligent supply chain management system for industrial Internet of Things according to claim 1, wherein: The data perception and acquisition module includes an RFID reader, industrial sensors, cameras and GPS positioning devices, and supports the OPCUA protocol to achieve device interconnection.

3. An industrial Internet of Things intelligent supply chain management system according to claim 1, characterized in that, The edge computing node includes: a data cleaning module, used to eliminate outliers and redundant data; a real-time analysis module, which conducts local decision-making based on a preset rule library; and a security gateway, which realizes protocol conversion and access control.

4. An industrial Internet of Things intelligent supply chain management system according to claim 1, characterized in that, The blockchain storage module adopts a hierarchical architecture; The private chain stores the internal supply chain data of the enterprise, and the consortium chain is used for cross-enterprise data sharing, and automatically executes reconciliation and settlement through smart contracts.

5. An industrial Internet of Things intelligent supply chain management system according to claim 1, characterized in that: The cloud analysis platform includes: a data lake module, used to store multi-source heterogeneous supply chain data; a blockchain evidence storage service module, which integrates machine learning models, used to dynamically generate procurement plans, inventory optimization strategies and logistics scheduling plans, and set environmental optimization goals for key supply chain data.

6. An industrial Internet of Things intelligent supply chain management system according to claim 1, characterized in that: The visualization interaction terminal supports: warehouse shelf positioning navigation based on AR technology, heat map display of production bottleneck areas, and drag-and-drop adjustment of strategy parameters.