Security issues of the gold industry chain based on smart blockchain in the context of the Internet of Things
Theory of Industrial Chain security governance
Regarding the governance evaluation theory of the gold Industrial Chain’s security situation, Dale et al. explored the reasons behind the changes in the national Oil and Gas (O&G) security situation. They argued that the evolution of O&G resources lies in the changes in the financial industry and market competitiveness brought about by the supply & demand imbalance of O&G resources. A model of a sustainable development system for national O&G resource security was proposed26. Figure 1 draws a model for the national O&G resources security sustainable development system.
System model of national O&G resources security and sustainable development.
In Fig. 1, Due to the imbalance of supply and demand caused by the change of external dependence, the comprehensive effect of resources, political, economic, military, and geopolitical forces is highlighted. With the further advancement of sustainable development of the mining industry chain, the negative environmental externalities of O&G resource development and the socioeconomic development imbalance have attracted extensive attention. Consequently, technology development, energy efficiency improvement, resource structure, environmental impact, and sustainable development of the resource Industrial Chain’s security assessment have been included in the supply security assessment system. The further integration of globalization and the elements of global value chain governance profoundly impact the distribution of resources. Resource security management not only restricts resource allocation but also is closely related to technological development, environmental changes, supply and demand changes, and the legitimacy of the system. Thereupon, resource security gradually presents a binary game from resource producers and consumers to the centralized management of the country, showing the diversified development of participants, extensive management objects, and multi-level management objectives27. Figure 2 shows the evolution of resource-oriented Security Governance theory.
Evolution of resource-oriented Security Governance Theory.
Figure 2 is the evolution of resource-oriented Security Governance theory, where the governance core has shifted from a political game to economic supply and demand coordination. The governance subject has changed from national sovereignty to multi-subject participation. The governance object has expanded from mineral resources to technical optimization, and new technology, materials, and industries have been fully considered. The governance goal has evolved from ensuring production safety channels to adopting green development under sustainable development. As for the research on the value governance of Industrial Chains, Pananond et al. chose the inter-enterprise industrial organization and divided the governance of global Industrial Chains into market-oriented, modular, relational, exclusive, and vertical models based on transaction complexity, information coding, and supplier capabilities28. Figure 3 plots the governance model of the global Industrial Chain.
Governance model of the global Industrial Chain.
Figure 3 displays that the global industrial chain governance model relies on transaction cost economics for global value chain governance. Several intermediate countries enter global value chains controlled by leading firms. By undertaking industrial transfer, the three-stage development of the global transnational production chain has been completed, the domestic technological change has been realized. Additionally, the function upgrading of the global value chain has been finished, and the economic development of developed countries has been attempted to achieve convergence. Led by market forces, leading companies lock emerging economies such as China into the lower end of the value chain through rules and regulations. It is difficult for enterprises in multiple intermediate countries to enter the higher end of the global value chain controlled by developed countries. The governance model of the global industrial chain is based on the interaction between the global value chain departments (enterprises and components\material suppliers), coordination, and asymmetry of rights. The global transnational production chain is to obtain resources from exploration, development, and extraction of resources, thereby entering multiple distribution channels, multiple intermediate countries to obtain resource processing, and multiple purposes to consume resources.
Basic information on the gold Industrial Chain
China’s gold market has formed a multi-level, multi-form, and multi-functional demand system for gold processing and manufacturing, wholesale and retail, lease financing, asset allocation, and investment and trade29. Figure 4 is the flow of a gold Industrial Chain.
From the gold Industrial Chain perspective in Fig. 4, gold’s upstream, middle reaches, and downstream involve the mining industry, the smelting industry, and the gold jewelry (and gold industry and reserves), respectively. While gold sales and industrial gold are in consumer demand, gold coins, bars, and reserves are in financial demand. Besides, gold smelting & recycling is also an essential part of the industry.
China’s gold industry involves multiple participants, mainly comprehensive enterprises. Most gold enterprises cover multiple links and subdivided products in gold’s upper and middle reaches. The gold Industrial Chain’s profitability distribution is extremely uneven from link to link, as explained in Fig. 5.
Value chain distribution of the gold industry.
Apparently, exploitation, beneficiation, and smelting are the core value chain links of the gold industry and the main factors affecting the gold production cost30. Thus, optimizing these three links can directly maximize the cost of gold production. Overall, the upstream production and material selection enterprises gain the utmost profitability in the chain, and some differential enterprises in the downstream processing and sales links also have strong profitability. The profitability of the smelting in the middle reaches the most meager, and it is in the break-even balance or marginal-profit state. The upstream mining and dressing enterprises are restricted by access qualification, mineral resources, and capital investment, with the highest concentration, and the gross profit rate can generally reach 50–60%.
The mechanism of smart blockchain technology in the gold Industrial Chain security system
Smart blockchain technology provides a reliable, secure, transparent, and efficient operating environment for the gold Industrial Chain’s security system through mechanisms such as data security, traceability, smart contracts, de-trust, and enhanced security governance. Thereby, smart blockchain technology plays an important role in the gold Industrial Chain security system, and its main mechanism includes the following aspects:
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i.
Smart blockchain ensures data security and integrity in the gold Industrial Chain through the use of decentralized distributed ledgers and cryptography algorithms. Each block contains the previous block’s hash value, making tampering with the data very difficult. Any attempt to tamper with the data will be detected by other nodes, thus protecting the security of the gold Industrial Chain.
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ii.
Smart blockchain technology enables data traceability and transparency across the gold Industrial Chain. Every transaction and operation is recorded on the blockchain, tracing back to when it happened and who was involved. This transparency enables any unusual activity or violation to be tracked and detected, thereby enhancing the security of the gold Industrial Chain.
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iii.
Smart blockchain technology enables automated security mechanisms through smart contracts. Smart contracts are blockchain-based programmable code that automatically performs certain actions according to pre-set rules and conditions. In the gold Industrial Chain, smart contracts can be used to manage contracts, settle payments and monitor the supply chain, reducing the risk of intermediate links and the possibility of human intervention.
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iv.
Smart blockchain technology implements a mechanism of de-trust, enabling all participants in the gold Industrial Chain to conduct safe and reliable transactions and cooperation without completely trusting each other. Blockchain, as a shared distributed ledger, records the transactions and behaviors of all parties involved and ensures the credibility and security of transactions through algorithms and protocols.
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v.
Smart blockchain technology offers strong support for security governance and compliance of the gold Industrial Chain. Through smart contracts and data records on the blockchain, regulators and relevant parties can conduct supervision and audit more effectively. It ensures that operations in the gold Industrial Chain comply with regulatory requirements, and timely discover and deal with security risks.
The running code of smart blockchain technology in the gold Industrial Chain security system is exhibited in Table 1.
This work uses smart blockchain technology’s intelligent and trusted computing process to simulate and analyze the dynamic process of the gold Industrial Chain. And IoT is employed to collect, store, and transmit data of the gold Industrial Chain to realize early warning of its security situation.
Construction of gold Industrial Chain security model based on smart blockchain and SD
SD is a system simulation method proposed by Professor Forrester in 1956, used to analyze enterprise problems, such as production and inventory management31. Table 2 shows the basic concepts of SD.
The problem-solving process in SD is essentially a process of seeking optimization for better system function. SD emphasizes the structure of the system and analyzes the function and behavior of the system from the perspective of system structure. The system structure determines the behavior of the system. Therefore, SD is to obtain the optimal behavior of the system by finding the optimal structure of the system. SD believes that the system is a causal feedback mechanism with multiple information. Hence, after analyzing the system and getting profound and rich information, the causality feedback diagram is established for the system and then converted into a system flow chart to establish SDM. Finally, the SDM is simulated by using simulation language and software, and the real system structure is simulated. The simulation of the system structure is completed through the above process. The next is to find a better system structure. Searching for a better system structure is called strategy analysis or optimization, involving parameter optimization, structure optimization, and boundary optimization. Parameter optimization changes the system structure by changing sensitive parameters to find the optimal system behavior. Structure optimization refers to changing the system structure by increasing or decreasing the horizontal and rate variables in the model to obtain better system behavior. Boundary optimization means that the system structure changes when the boundary and boundary conditions change to obtain better system behavior. SD is to simulate the system structure through computer simulation technology, find the optimal structure of the system, and thus obtain the optimal system behavior32,33,34. Figure 6 explains SDM’s modeling process.
Figure 6 before building the SDM, a clear understanding of the research object must be formed. The system studied must be an orderly dissipative structure far from equilibrium. Then, the system-dynamics analysis is performed on the research object to establish the causal relationship diagram and flow diagram of the design object elements and the problem’s scope. Simulation experiments and calculations are conducted using the system-dynamics equation35. Equations (1)–(3) are the dynamic equation of combined external force applied to the system:
$$ \sum F = m_1 a_1 + m_2 a_2 + m_3 a_3 + \cdots + m_n a_n $$
(1)
$$ \sum F_x = m_1 a_1x + m_2 a_2x + m_3 a_3x + \cdots + m_n a_nx $$
(2)
$$ \sum F_y = m_1 a_1y + m_2 a_2y + m_3 a_3y + \cdots + m_n a_ny . $$
(3)
In Eqs. (1)–(3), \(m_1 , \ldots , m_n\) is the mass of objects in the system. \(a_1 , \ldots , a_n\) represents the acceleration of objects in the system. \(\sum F\) denotes the resultant external force on the system. \(\sum F_x\) and \(\sum F_y\) mean the resultant external force component of the system on the \(x\)-axis and \(y\)-axis, respectively. Equation (4) is the system-dynamics equation:
$$ L = L_0 + \left( R_1 – R_2 \right) \cdot \Delta t. $$
(4)
In Eq. (4), \(L\) represents the inventory of the system flow chart. \(L_0\) is the initial inventory of the system flow chart. \(R_1\) stands for system output rate. \(R_2\) indicates the system delivery rate. \(\Delta t\) refers to the system flow changes’ accumulation time.
The mechanism of SDM in the proposed system is to provide managers with a comprehensive system perspective and decision support to ensure the security and stability of the gold Industrial Chain. It is achieved through the application of dynamic modeling, risk assessment, early warning, strategy development and optimization, business decision support, and continuous improvement and optimization. SDM provides a systematic method to reveal the interdependence and complex dynamic behaviors among all links in the gold Industrial Chain, to help managers better understand the system’s operating mechanism and evolution law. The mechanism of SDM in the gold Industrial Chain security system is as follows:
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i.
SDM can dynamically model the relationship and interaction of all links in the gold Industrial Chain, covering gold mining, processing, circulation, and other links. By considering the feedback and delay effects between each link, the model can more accurately describe the behavior and evolution process of the system.
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ii.
SDM can simulate and analyze the impact of different security risk factors on the system to assess the vulnerability and security risk level of the system. Based on the analysis of the model, the potential security problems can be warned in advance, and corresponding measures can be taken to reduce the risk.
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iii.
SDM can be used to evaluate and optimize the effectiveness of different security management policies and measures. Through the model analysis, the optimal security management strategy can be found, involving resource allocation, risk management, and monitoring measures, to improve the security of the gold Industrial Chain.
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iv.
SDM can provide decision-makers with insight into the system’s overall operation and development trends. The model can help decision-makers understand the interaction between different factors, predict the development trend of the system, and make corresponding business decisions based on the information, thus optimizing the safety management of the gold Industrial Chain.
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v.
SDM is an iterative process that continuously improves and optimizes the accuracy and applicability of the model through the continuous collection and collation of actual data, and the calibration and validation of the model. This allows security management policies and measures to be constantly adjusted and improved according to the actual situation to cope with the ever-changing security risks. To sum up, a gold Industrial Chain security model based on smart blockchain and SD is implemented, as indicated in Fig. 7.
The gold Industrial Chain security model based on smart blockchain and SD.
In Fig. 7, the factors in the gold Industrial Chain’s security SDM restrict and influence each other. There are multiple positive and negative feedback relationships. Specifically, positive feedback loop: technical facility level (+) the number of mining personnel (+) mine accident rate (+) mining mortality (+) production efficiency (+) output value (+) industrial profit (+) safety investment capital (+) technical facility level. Negative feedback loop: safety investment fund (−) safety training (−) employee safety awareness (−) mining accident rate (−) mine mortality (−) industrial mining accident loss and shutdown loss (−) safety investment. The feedback loop of the gold Industrial Chain’s security SDM starts from the relationship between the various factors. Then, it evaluates the gold Industrial Chain’s safety management level and safety training through the production scale of the gold industry, economic profits, safety and production investment, technological progress, equipment level, safety officers, and mining staff, simulating and analyzing the dynamic change and evolution of gold Industrial Chain. It is based on the security submodel of smart blockchain, including blockchain data security, authentication mechanism, smart contract enforcement, and regulation. Applying blockchain technology can enhance the security, authenticity, and traceability of information in the gold Industrial Chain.
Application of IoT in the gold Industrial Chain’s security
IoT provides real-time monitoring, traceability, and security management capabilities for gold Industrial Chain security systems by connecting and transmitting physical devices, sensors, and the Internet36,37. Figure 8 shows the application of the IoT in gold Industrial Chain security management.
Application of IoT in gold Industrial Chain’s security management.
Figure 8 signifies that the perception layer can be used to monitor and collect data related to gold production, transportation, and storage. By means of temperature, humidity, and vibration sensors, gold’s environmental conditions and transportation state can be monitored in real-time to ensure the safety and quality of gold. The network layer can use various communication technologies and protocols to establish reliable and secure data transmission channels. The Wireless Sensor Network (WSN) of IoT or IoT protocol stack technology transfers the data collected by the perception layer to the subsequent application layer for processing and analysis. The application layer of IoT is the level of processing, analysis, and application of data at the perception and network layers. In the gold Industrial Chain security system, the application layer can monitor and analyze the real-time data the perception layer collects. Setting warning rules and thresholds allows potential security problems to be discovered and warned in time.
Data source and experimental environment
To study the security of the proposed IoT-based gold Industrial Chain’s security SDM, this section takes the resource reserve of China’s gold industry from 2011 to 2021 as the research data. The data source is the National Bureau of Statistics (NBS). Data collection is a key part of this work. The data is sourced from the NBS, and the agency has certified its quality. The data involves historical data on gold resource reserves, covering 2011 to 2021. These data are considered the basis of this work to analyze the safety of the gold Industrial Chain.
Then, it refers to the literature of Tan et al. to assess the security situation of China’s gold Industrial Chain38. Therefore, China’s gold Industrial Chain security is calculated by Eq. (5):
$$ GSI = \fracGSR + DES + DC3. $$
(5)
In Eq. (5), \(GSI\) (Gold Safety Industry) represents China’s gold Industrial Chain’s security index. \(GSR\) (Gold Stability Resource) is the stability of global resource supply. \(DES\) (Domestic Economic Security) denotes domestic economic security, and \(DC\) (Dominant Coexistence) stands for domestic economic security.
The construction and simulation of SDM rely on the new version of the SD professional software Vensim. Vensim runs on the Windows operating system, and its installation process includes copying the installation files and Vensim system files to the hard drive and then clicking on the installation file according to the prompts to install.
Experimental environment: The hardware configuration used in this work is Intel Core i5-7200U [email protected] GHz; Memory (Random Access Memory): 8.00 GB, Central Processing Unit (CPU): NVIDIA GeForce 94MX. The operating system is Windows 10 64-bit. This hardware environment provides sufficient computing power and resource support for the operation of the SDM to conduct SD simulations of the gold Industrial Chain security.
This work adopts the supply chain security framework as the foundation of the governance theoretical framework, which covers various aspects of the supply chain, including suppliers, logistics, information flow, and financial flow. This framework provides a theoretical basis for analyzing the security of the gold Industrial Chain and helps identify key risk factors. Smart blockchain and SD methods are chosen because they allow for establishing a multi-level, dynamic model that can capture complex interactions in the supply chain. This aligns with the theoretical framework of supply chain security governance, as it emphasizes the connections and interdependence between various nodes in the supply chain.