Steel supply chain product traceability system and method based on block chain technology

Abstract

The invention belongs to the technical field of steel transaction data, and discloses a steel supply chain product traceability system and method based on a block chain technology, and the method comprises the following steps: constructing a distributed supply chain, and setting a root node, a first target node and a first target child node of product traceability, the root node comprises a supply state index, an index key index and a historical index database; performing standardization processing on source processing in the first target sub-node to obtain sub-node data, and packaging all the sub-node data into uniform node data through the position association data; tracing target data is adaptively selected for node data through a matching strategy, the tracing target data is stored in a block chain, tracing simulation is performed on the tracing target data based on a tracing retrieval condition to update an index key index, and resource allocation and system expansibility are optimized by using a modular design and a hierarchical storage strategy.

Description

Technical Field

[0001] The present invention relates to the technical field of steel trading data. More specifically, the present invention relates to a steel supply chain product traceability system and method based on blockchain technology. Background Art

[0002] In the management of the steel supply chain, traditional methods face many challenges. For example, there are problems such as lack of trust, difficulty in traceability, and low efficiency of information sharing in traditional supply chain management. Therefore, it is necessary to introduce blockchain technology to solve these problems.

[0003] For example, Chinese Patent Application Publication No. CN112347194A discloses a steel supply chain product traceability system and method based on blockchain technology, which designs an automatically executable smart contract according to functional requirements, adds a processing link for sensitive data, and provides a traceability interface for regulatory authorities and consumers; however, there are still the following problems: In the application of blockchain technology, the data formats and interfaces of different supply chain entities are not unified, which increases the difficulty of data integration; the integrated data is stored in the corresponding storage nodes, and a complete data copy storage area needs to be configured in the storage nodes, which increases the storage burden. In addition, the consensus mechanism and encryption algorithm of the blockchain may lead to performance degradation. Sensitive data (such as trade secrets) needs to be protected, but there is a contradiction between the transparency and privacy protection of the blockchain.

[0004] In view of this, the present application proposes a steel supply chain product traceability system and method based on blockchain technology. Summary of the Invention

[0005] In order to overcome the above-mentioned defects of the prior art, the present invention proposes a steel supply chain product traceability system and method based on blockchain technology to achieve the real-time, consistency and security of data, so as to improve the availability and accessibility of data, meet the real-time requirements of steel trading, and promote supply chain collaboration and financial service optimization.

[0006] In a first aspect, the present invention provides a steel supply chain product traceability system based on blockchain technology, which is applied to a server and includes a supply chain setting module, a node setting module, a blockchain matching module and a target traceability module; each module is connected by wire and / or wirelessly; The supply chain setting module constructs a distributed supply chain. By setting the root node and hierarchical target nodes for product traceability, the root node includes supply status indicators, index key indicators and a historical indicator database; the hierarchical target nodes include a first target node and a first target sub-node, and the hierarchical target nodes are sent to the node setting module; The node setting module standardizes the source processing in the first target sub-node to obtain sub-node data, encapsulates all sub-node data into unified node data through location association data, and sends the node data to the blockchain matching module; The blockchain matching module adaptively selects traceability target data from the node data through a matching strategy, stores the traceability target data in the blockchain, and improves it using an intelligent transaction contract; sends the traceability target data to the target traceability module; The target traceability module performs traceability simulation on the traceability target data based on traceability retrieval conditions to update the index key indicators, sets the updated index key indicators at the root node, and maps to the first target node based on the root node.

[0007] As a preferred technical solution of the first aspect of the present invention, the distributed supply chain construction logic is: The steel supply chain is distributed to obtain a distributed supply chain, which includes a root node and matches the first target sub-node from the first target node. Set the root node of product traceability based on the business request target, and match the first target sub-node from the first target node based on the index key indicators; Wherein: the first target node matches and locates the index key indicators from the index key indicators of the root node; the first target sub-node provides detailed business data through the index key indicators.

[0008] As a preferred technical solution of the first aspect of the present invention, the acquisition logic of the sub-node data: The index key indicators include at least one sub-node index indicator. Based on the sub-node index indicator, business data information is obtained, and the business data information includes data type, data source, and usage scenario; The sub-node index indicator includes at least one index classification indicator and an index position indicator. Based on the index classification indicator, a slicing tool is matched, and the business data information is segmented into at least one slice of business data through the slicing tool; Each slice of business data matches at least one conversion template. The author selects any conversion template and modifies the content in the conversion template to obtain a custom conversion template; Based on the custom conversion template, selective standardization processing is performed on the slice of business data to obtain standard business data; the standard business data is associated with the current index position indicator to generate sub-node data.

[0009] As a preferred technical solution of the first aspect of the present invention, update the sub-node data based on the custom conversion template, and store the updated sub-node data and the conversion template in the historical index database; in the historical index database, store the corresponding standard business data respectively through the index position indicators corresponding to the first target sub-node, and perform time tagging on the standard business data.

[0010] As a preferred technical solution of the first aspect of the present invention, the acquisition logic of the node data is as follows: Divide the sub-node data into hot data, warm data and cold data according to the query frequency of the sub-node data, and assign different degrees of sub-node data with basic correlation degrees; Perform hierarchical analysis on all sub-node data according to the index key indicators to obtain the correlation matching degree; Obtain a positive correlation relationship by linearly fitting the correlation matching degree and the basic correlation degree, and dynamically adjust the storage strategy of the sub-node data according to the positive correlation relationship; Based on the index position indicator of the first target sub-node after the storage strategy is adjusted, extract the exactly same position information as the position index data of the first target node, and encapsulate the sub-node data according to the index position indicator within the position index data to generate node data.

[0011] As a preferred technical solution of the first aspect of the present invention, the application logic of the matching strategy: The first target node includes a data node and a data trust node, and the data node and the data trust node are associated through a matching strategy; The data node calculates and stores the node data, processes large-scale data and high-concurrency requests, and filters and extracts the traceability target data from the node data based on the pre-set matching strategy; Store the traceability target data in the data trust node, and configure the data trust node as a blockchain data trust node, and connect all the blockchain data trust nodes in series.

[0012] As a preferred technical solution of the first aspect of the present invention, the acquisition logic of the traceability target data: The node data determines multiple transaction target data and the priorities corresponding to the transaction target data; the node data determines the business request target from the multiple transaction target data according to the priority corresponding to each transaction target data; Use machine learning or a rule engine to automatically match the optimal transaction target data according to the business request target, establish a traceability chain for each transaction target data and mark it as the traceability target data, and the traceability target data includes the data source, the data flow path and the data processing history.

[0013] As a preferred technical solution of the first aspect of the present invention, the update logic of the index key indicators is as follows: Construct a digital twin model based on the traceability retrieval conditions, and simulate the traceability target data according to the digital twin model to obtain the first index key indicators; retrieve the first index key indicators to obtain the second index key indicators, and integrate the first index key indicators and the second index key indicators to obtain the reference index key indicators; If the reference index key indicators are inconsistent with the index key indicators, update the traceability retrieval conditions based on the reference index key indicators, and repeatedly execute the above operations until the reference index key indicators are consistent with the index key indicators; If the reference index key indicators are consistent with the index key indicators, perform a traceability query on the steel supply chain based on the index key indicators to obtain product traceability information, set authentication information at the storage node corresponding to the product traceability information, and desensitize the authentication information.

[0014] In a second aspect, the present invention provides a method for tracing steel supply chain products based on blockchain technology. Based on the implementation of the first aspect and applied to a server, it includes the following steps: Construct a distributed supply chain. By setting the root node and hierarchical target nodes for product traceability, the root node includes supply status indicators, index key indicators, and a historical indicator database; the hierarchical target nodes include a first target node and a first target sub-node; Standardize the source processing in the first target sub-node to obtain sub-node data, and encapsulate all sub-node data into unified node data through location-associated data; Adaptively select traceability target data from the node data through a matching strategy, store the traceability target data in the blockchain, and improve it using a smart transaction contract; Perform traceability simulation on the traceability target data based on the traceability retrieval conditions to update the index key indicators, set the updated index key indicators at the root node, and map them to the first target node based on the root node.

[0015] In a third aspect, the present invention provides an electronic device, including: a processor and a memory, wherein the memory stores a computer program that can be called by the processor; The processor executes the first aspect by calling the computer program stored in the memory.

[0016] Technical effects and advantages of the steel supply chain product traceability system and method based on blockchain technology of the present invention: Through the collaborative work of each application module, the present invention realizes the efficient management, precise traceability, and data security guarantee of the distributed supply chain, significantly improves the efficiency and accuracy of data management, ensures the immutability and transparency of data through blockchain and smart contract technologies, and optimizes resource allocation and system scalability by using modular design and hierarchical storage strategies. It is applicable to complex supply chain scenarios and has the characteristics of high efficiency, security, flexibility, and significant business value. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a schematic diagram of the distributed setup node architecture corresponding to the steel supply chain of the present invention; Figure 2 It is a schematic diagram of a steel supply chain product traceability system based on blockchain technology of the present invention; Figure 3 It is a flowchart of a method for tracing steel supply chain products based on blockchain technology of the present invention; Figure 4 It is a schematic diagram of the structure of an electronic device of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0018] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0019] In practical applications, setting the distributed system and blockchain to coexist can form a complementary relationship, where: the distributed system emphasizes high performance, scalability, and flexibility, can process large-scale data and high-concurrency requests, and disperses data storage and processing on multiple nodes to perform initialization processing on the original data; furthermore, the blockchain emphasizes decentralization, immutability, and transparency, stores the target data that requires high trust and traceability, and ensures consistency through the consensus mechanism. By combining the advantages of both, an efficient and trustworthy system can be constructed.

[0020] Embodiment 1

[0021] Please refer to Figure 2 As shown, a steel supply chain product traceability system based on blockchain technology described in this embodiment is applied to a server; it includes a supply chain setup module 100, a data access module 200, a block interaction module 300, and a security monitoring module 400; each module is connected by wire and / or wireless; Supply chain setting module 100 constructs a distributed supply chain. By setting the root node and hierarchical target nodes for product traceability, it realizes the structured management and efficient traceability of supply chain data. The hierarchical target nodes are sent to the node setting module 200.

[0022] Specifically, a distributed supply chain is obtained by performing a distributed setting on the steel supply chain. The distributed supply chain includes a root node, and first target sub-nodes are matched from the first target nodes, as Figure 1 shown. The root node is the corresponding traceability node in the current steel supply chain. The traceability node is responsible for the aggregation, coordination, and management of global data. The first target nodes are the core nodes in the steel supply link, responsible for data collection, processing, and storage in the steel supply link, such as raw material procurement, production, warehousing, logistics, sales, etc.; the first target sub-nodes are the sub-nodes under the first target nodes, responsible for the execution of specific tasks and data collection, such as a certain warehouse, production line, or transportation vehicle.

[0023] More specifically, the root node for product traceability is set based on the business request target. The root node includes supply status indicators, index key indicators, and a historical indicator database; the first target sub-nodes are matched from the first target nodes based on the index key indicators; where: the root node is used for global data management and traceability; the first target nodes match and locate the index key indicators from the index key indicators of the root node; the first target sub-nodes provide detailed business data through the index key indicators, supporting the distributed management and traceability requirements of the supply chain, and realizing the precise matching, efficient query, and full-process traceability of supply chain data.

[0024] The node setting module 200 standardizes the source processing in the first target sub-nodes to obtain sub-node data, encapsulates all sub-node data into unified node data through location-associated data, and sends the node data to the blockchain matching module 300; It should be noted that the source data in the first target sub-nodes is standardized to ensure the consistency of the data format, structure, and semantics corresponding to the sub-node data; all sub-node data is encapsulated through location-associated data (describing the geographical or logical relationships between nodes) to form unified node data, and the node data corresponds to the first target node for easy storage and management.

[0025] Specifically, the acquisition logic of the sub-node data: The index key indicators include at least one sub-node index indicator. Business data information is obtained based on the sub-node index indicators. The business data information includes data type, data source, and usage scenario; The sub-node index metrics include at least one index classification metric and an index position metric. Based on the index classification metric, a slicing tool is matched, and the business data information is segmented into at least one sliced business data by the slicing tool. Specifically, the data types include structured data (such as orders, inventory), semi-structured data (such as logs, sensor data), and unstructured data (such as pictures, documents). Each sliced business data matches at least one conversion template. The author selects any one of the conversion templates and modifies the content in the conversion template to obtain a custom conversion template. Based on the custom conversion template, selective standardization processing is performed on the sliced business data to obtain standard business data; the standard business data is associated with the current index position metric to generate sub-node data.

[0026] It should be noted that: the sub-node index metrics are index classification metrics and index position metrics; among them: the index classification metrics are business types (such as procurement, production, logistics), data types (such as orders, inventory, logs); the index position metrics: storage nodes (such as root nodes, sub-nodes), geographical locations (such as warehouse locations, distribution areas). It can be exemplified by the procurement link as: Index classification metric: purchase order.

[0027] Index position metric: supplier warehouse.

[0028] Sub-node index metric: (purchase order + supplier warehouse).

[0029] Exemplified by the production link as: Index classification metric: production batch.

[0030] Index position metric: production line number.

[0031] Sub-node index metric: (production batch + production line number).

[0032] Exemplified by the logistics link as: Index classification metric: transportation task.

[0033] Index position metric: distribution vehicle ID.

[0034] Sub-node index metric: (transportation task + distribution vehicle ID).

[0035] More specifically, based on the custom conversion template, the sub-node data is updated, and the updated sub-node data and the conversion template are stored in the historical metrics database; in the historical metrics database, the corresponding standard business data is stored respectively through the index position metric of the first target sub-node, and time stamping is performed on the standard business data.

[0036] It should be noted that the steel supply chain includes links such as raw material procurement, production, warehousing, logistics, and sales. In any link, a large amount of data storage and analysis are involved. However, in actual applications, not all data can be stored and utilized. Therefore, distributed storage nodes are deployed in each steel supply link and marked as the first target nodes to ensure that data is stored and processed nearby.

[0037] The acquisition logic of the node data is as follows: The sub-node data is divided into hot data, warm data, and cold data according to the query frequency of the sub-node data, and different degrees of sub-node data are given a basic correlation degree; based on the basic correlation degree, the importance of the sub-node data is analyzed to realize data management control.

[0038] Specifically: Hot data is data that is frequently accessed (such as real-time orders, production status), and is stored in high-speed storage media (such as in-memory databases or SSDs); Warm data: Data that is occasionally accessed (such as historical orders, inventory records), and is stored in distributed databases with moderate performance (such as MySQL, PostgreSQL); Cold data: Data that is rarely accessed (such as archived logs, historical reports), and is stored in low-cost storage media (such as object storage or tape libraries). The basic correlation degree reflects the importance and access frequency of the data, and can usually be quantified as: Hot data: High correlation degree (such as 0.8 - 1.0); Warm data: Medium correlation degree (such as 0.5 - 0.8); Cold data: Low correlation degree (such as 0.0 - 0.5); Hierarchical analysis is performed on all sub-node data according to the index key indicators to obtain the correlation matching degree. The correlation matching degree reflects the relevance of the sub-node data to the business objective, and realizes business association control. Among them, the data is classified and weighted according to the index key indicators (such as time, region, business type), and the correlation matching degree of each piece of data is calculated through weighted summation or other algorithms.

[0039] A positive correlation relationship is obtained by linearly fitting the correlation matching degree and the basic correlation degree, that is: the higher the correlation matching degree, the higher the basic correlation degree; that is: High correlation matching degree → High basic correlation degree: The data is highly relevant to the business objective, usually has a high access frequency, and should be preferentially stored in high-speed storage media; Medium correlation matching degree → Medium basic correlation degree: The data is moderately relevant to the business objective, has a moderate access frequency, and is suitable for storage in distributed databases with moderate performance; Low correlation matching degree → Low basic correlation degree: The data has a low correlation with the business objective and a low access frequency, and is suitable for storage in low-cost storage media.

[0040] Dynamically adjust the data storage strategy according to the positive correlation relationship, obtain the index position metric of the first target child node after the storage strategy is adjusted, extract exactly the same position information as the position index data of the first target node based on the index position metric, and encapsulate the child node data according to the index position metric within the position index data to generate node data.

[0041] It should be noted that in practical applications, each first target node needs to integrate data from the first target child nodes from multiple dimensions. During the integration process, the importance of the child node data is analyzed through the positive correlation relationship, so that the data storage of the current first target node has a logical storage strategy. Under the current storage strategy, the index position metrics corresponding to each first target child node are different, but the same parts are integrated as the position index data of the first target node. Then, all child node data is encapsulated according to the index position metric corresponding to the storage strategy, and the data is distributed to different storage nodes according to the index position metric. Data can be quickly located and retrieved from the relationship between the classification of the index and the position attributes.

[0042] The index key metrics can be a combination of multiple child node index metrics, that is, Preferentially allocate high-performance storage resources to data with high correlation matching degree to ensure the efficient operation of critical services. Archive data with low correlation matching degree to low-cost storage media to reduce resource occupancy, thereby achieving efficient utilization of resources and cost optimization. It is also possible to implement topological structure control of the first target child nodes and optimize the collaborative work between child nodes.

[0043] It should be noted that: Taking the steel supply chain as an example: Production link: Real-time production data (hot data) is stored in an in-memory database to ensure the real-time nature of production monitoring; historical production data (warm data) is stored in a distributed database to support production analysis; archived logs (cold data) are stored in an object storage to reduce costs.

[0044] Logistics link: Real-time transportation status (hot data) is stored in a high-speed storage medium to support logistics tracking; historical transportation records (warm data) are stored in a distributed database to support logistics analysis; archived transportation logs (cold data) are stored in a low-cost storage medium to reduce resource occupancy.

[0045] The above operations achieve comprehensive control of the first target child nodes through data classification and storage optimization, correlation matching degree analysis, and hierarchical analysis and position association. This control not only improves data management efficiency but also optimizes resource allocation and system performance, and can effectively support the efficient operation of complex supply chains.

[0046] The blockchain matching module 300 adaptively selects the traceability target data from the node data through a matching strategy, stores the traceability target data in the blockchain, and improves it using an intelligent transaction contract; and sends the traceability target data to the target traceability module 400. Specifically, the application logic of the matching strategy: The first target node includes a data node and a data trust node, and the data node and the data trust node are associated through a matching strategy. The data node calculates and stores the node data, processes large-scale data and high-concurrency requests, and filters and extracts the traceability target data from the node data based on a pre-set matching strategy. The traceability target data is stored in the data trust node, and the data trust node is configured as a blockchain data trust node. All the blockchain data trust nodes are connected in series to form a traceability system to ensure the immutability and traceability of the traceability target data.

[0047] It should be noted that: in the above setting method, based on the data trust node being configured as a blockchain data trust node, that is: the data trust node stores the hash value or digest information of the data through the blockchain to ensure the integrity and verifiability of the data; the data node stores the detailed data through a distributed system (such as IPFS, distributed database) to reduce the storage pressure on the blockchain.

[0048] The data trust node is divided into multiple trust sub-nodes, and each trust sub-node processes the traceability target information in the first target node to reduce the load of a single node. By combining the data trust node with the data node, the storage burden on the blockchain is reduced. The distributed system processes complex calculations and data analysis and stores sensitive data, and the blockchain ensures the reliable execution of the business logic and stores the hash value of the data to ensure data privacy.

[0049] More specifically, the acquisition logic of the traceability target data: The node data determines multiple transaction target data and the priorities corresponding to the transaction target data; the node data determines the business request target from the multiple transaction target data according to the priority corresponding to each transaction target data. For example, the priority is dynamically adjusted according to real-time business requirements or external conditions (such as market changes, emergency orders). In the supply chain, the priority of an emergency order can be temporarily increased to ensure rapid processing.

[0050] Using machine learning or a rule engine, automatically match the optimal transaction target data according to the business request target, establish a traceability chain for each transaction target data and mark it as the traceability target data. The traceability target data includes the data source, data flow path, and data processing history.

[0051] It should be noted that when processing transaction target data in collaboration among multiple nodes to ensure performance in high-concurrency scenarios, the business request targets are dynamically allocated according to the node load conditions to avoid single-point overload. The processing status of the transaction target data is monitored in real time to detect anomalies (such as data loss, priority conflicts). In case of anomalies, the standby node is automatically switched or the priority is adjusted to ensure business continuity; On this basis, a visualization interface is provided to display the priorities, matching results, and traceability paths of the transaction target data, and reports are automatically generated to analyze the processing efficiency and priority distribution of the business request targets.

[0052] Specifically, in raw material procurement, the priority of the procurement order is dynamically adjusted according to market price fluctuations. In production planning, the priorities of production tasks are determined by comprehensively considering delivery time, production cost, and equipment status. In logistics distribution, the optimal distribution route is intelligently matched based on real-time road conditions and vehicle status.

[0053] Through dynamic priority adjustment, multi-dimensional evaluation, intelligent matching, data traceability and verification, distributed collaboration, anomaly handling, and visualization reports, the acquisition logic of the traceability target data can be significantly expanded; the flexibility and scalability of the system are improved, and the efficiency and reliability of business processing are enhanced, which is applicable to complex supply chain scenarios.

[0054] The target traceability module 400 performs traceability simulation on the traceability target data based on the traceability retrieval conditions to update the key index indicators, sets the updated key index indicators at the root node, and maps to the first target node based on the root node.

[0055] It should be noted that the update logic of the key index indicators is as follows: A digital twin model is constructed based on the traceability retrieval conditions, and the first key index indicator is obtained by simulating the traceability target data according to the digital twin model; the first key index indicator is retrieved to obtain the second key index indicator, and the first key index indicator and the second first key index indicator are integrated to obtain the reference key index indicator; If the reference key index indicator is inconsistent with the key index indicator, the traceability retrieval conditions are updated based on the reference key index indicator, and the above operations are repeatedly executed until the reference key index indicator is consistent with the key index indicator; If the reference key index indicator is consistent with the key index indicator, a traceability query of the steel supply chain is performed based on the key index indicator to obtain product traceability information, and authentication information is set at the storage node corresponding to the product traceability information, and the authentication information is desensitized.

[0056] It should be noted that: First, the traceability retrieval conditions are rules or parameters for locating and screening traceability target data. In the digital twin model, the traceability retrieval conditions are simulated and predicted, making the simulation of the steel supply chain more in line with the actual raw material procurement, production, and logistics processes. The evaluation indicators generated during the simulation process serve as the first-index key indicators. Based on the first-index key indicators, a retrieval evaluation is conducted again to obtain the second-index key indicators for evaluating the first evaluation indicators. The first-index key indicators and the second-index key indicators are integrated to generate the reference index key indicators, which serve as the comprehensive reference indicators (reference index key indicators).

[0057] Taking the steel supply chain as an example, a digital twin model is constructed based on the traceability retrieval conditions (such as production batch numbers, time ranges), simulating the quality data of production batches to generate the first-index key indicators corresponding to the quality scores. Historical quality data is retrieved according to the quality scores to generate the second-index key indicators corresponding to the historical quality trends. The quality scores and the historical quality trends are integrated to generate the comprehensive quality indicators.

[0058] If the comprehensive quality indicators are inconsistent with the current index key indicators, the traceability retrieval conditions are updated and the operation is repeated. If they are consistent, the product traceability information is queried based on the index key indicators.

[0059] Among them: A digital twin model is constructed using simulation software (such as AnyLogic) or machine learning frameworks (such as TensorFlow); Input data: Traceability retrieval conditions (such as production batch numbers, time ranges).

[0060] Output data: First-index key indicators (such as quality scores).

[0061] A search engine (such as Elasticsearch) or database query language (such as SQL) is used for retrieval, and a data processing tool (such as Pandas) or rule engine (such as Drools) is used for integration.

[0062] Through the digital twin model and retrieval integration, the index key indicators are dynamically updated, and the consistent index key indicators are used to achieve accurate product traceability.

[0063] All traceability retrieval conditions are centrally managed in the root node, facilitating unified maintenance and update. From the global perspective of the root node, the traceability target data can be quickly located and retrieved. The traceability retrieval conditions are rules or parameters for locating and screening traceability target data; support for dynamically adding, modifying, and deleting traceability retrieval conditions to adapt to business changes.

[0064] Store the traceability retrieval conditions in the database or configuration file at the root node, and synchronize the retrieval conditions of the root node to the child nodes to ensure that the child nodes can screen data according to the global conditions; for example: use a message queue (such as Kafka) or a distributed database (such as Cassandra) to implement condition synchronization.

[0065] When the retrieval conditions of the root node change, notify the child nodes in real time to update the local conditions. By setting the traceability retrieval conditions at the root node, centralized management and efficient retrieval of the traceability target data can be achieved. Through a reasonable storage structure, interface design, and communication protocol, dynamic updates and global retrievals can be supported, which is suitable for scenarios such as complex supply chains. Although there are certain challenges, through technical optimization and architecture design, the availability and performance of the system can be significantly improved.

[0066] Embodiment 2

[0067] Please refer to Figure 3 As shown, the parts not described in detail in this embodiment can be found in the description of Embodiment 1. A method for tracing steel supply chain products based on blockchain technology is applied to a server and includes the following steps: Build a distributed supply chain by setting the root node and hierarchical target nodes for product traceability. The root node includes supply status indicators, index key indicators, and a historical indicator database; the hierarchical target nodes include a first target node and a first target child node; Standardize the source processing in the first target child node to obtain child node data, and encapsulate all child node data into unified node data through location-associated data; Adaptively select the traceability target data from the node data through a matching strategy, store the traceability target data in the blockchain, and use an intelligent transaction contract to improve it; Based on the traceability retrieval conditions, perform a traceability simulation on the traceability target data to update the index key indicators, set the updated index key indicators at the root node, and map them to the first target node based on the root node.

[0068] The construction logic of the distributed supply chain is as follows: Perform a distributed setting on the steel supply chain to obtain a distributed supply chain, which includes a root node and match the first target child node from the first target node, Set the root node for product traceability based on the business request target, and match the first target child node from the first target node based on the index key indicators; Among them: the first target node matches and locates the index key indicators from the index key indicators of the root node; the first target child node provides detailed business data through the index key indicators.

[0069] The acquisition logic of the child node data: The described index key indicators include at least one sub-node index indicator. Business data information is obtained based on the sub-node index indicator, and the business data information includes data type, data source, and usage scenario; The sub-node index indicator includes at least one index classification indicator and an index position indicator. A slicing tool is matched based on the index classification indicator, and the business data information is segmented into at least one sliced business data through the slicing tool; Each sliced business data matches at least one conversion template. The author selects any one of the conversion templates and modifies the content in the conversion template to obtain a custom conversion template; Based on the custom conversion template, selective standardization processing is performed on the sliced business data to obtain standard business data; the standard business data is associated with the current index position indicator to generate sub-node data.

[0070] Based on the custom conversion template, the sub-node data is updated, and the updated sub-node data and the conversion template are stored in the historical indicator database; in the historical indicator database, the corresponding standard business data is stored respectively through the index position indicator corresponding to the first target sub-node, and time stamping is performed on the standard business data.

[0071] The acquisition logic of the node data is as follows: The sub-node data is divided into hot data, warm data, and cold data according to the query frequency of the sub-node data, and different degrees of sub-node data are given a basic correlation degree; Hierarchical analysis is performed on all sub-node data according to the index key indicators to obtain an association matching degree; A positive correlation relationship is obtained by linearly fitting the association matching degree and the basic correlation degree, and the storage strategy of the sub-node data is dynamically adjusted according to the positive correlation relationship; According to the index position indicator of the first target sub-node after the storage strategy is adjusted, the exactly same position information is extracted based on the index position indicator as the position index data of the first target node, and the sub-node data is encapsulated in the position index data to generate node data.

[0072] The application logic of the matching strategy: The first target node includes a data node and a data trust node, and the data node and the data trust node are associated through a matching strategy; The data node calculates and stores the node data, processes large-scale data and high-concurrency requests, and filters and extracts the traceability target data from the node data based on a pre-set matching strategy; The traceability target data is stored in the data trust node, and the data trust node is configured as a blockchain data trust node, and all blockchain data trust nodes are connected in series.

[0073] The acquisition logic of the traceability target data: The node data determines multiple transaction target data and the priorities corresponding to the transaction target data; the node data determines a business request target from the multiple transaction target data according to the priority corresponding to each transaction target data; Using machine learning or a rule engine, automatically match the optimal transaction target data according to the business request target, establish a traceability chain for each transaction target data and mark it as traceability target data, where the traceability target data includes the data source, data flow path, and data processing history.

[0074] The update logic of the index key metrics is as follows: Construct a digital twin model based on the traceability retrieval conditions, simulate the traceability target data according to the digital twin model to obtain the first index key metrics; retrieve the first index key metrics to obtain the second index key metrics, and integrate the first index key metrics and the second first index key metrics to obtain the reference index key metrics; If the reference index key metrics are inconsistent with the index key metrics, update the traceability retrieval conditions based on the reference index key metrics, and repeatedly execute the above operations until the reference index key metrics are consistent with the index key metrics; If the reference index key metrics are consistent with the index key metrics, perform a traceability query on the steel supply chain based on the index key metrics to obtain product traceability information, set authentication information at the storage node corresponding to the product traceability information, and desensitize the authentication information.

[0075] Embodiment 3

[0076] An electronic device according to an exemplary embodiment includes: a processor and a memory, where a computer program that can be called by the processor is stored in the memory; The processor executes the above-mentioned steel supply chain product traceability system based on blockchain technology by calling the computer program stored in the memory.

[0077] Figure 4It is a schematic structural diagram of an electronic device provided by an embodiment of the present application. The electronic device may vary greatly due to different configurations or performances, and can include one or more processors (Central Processing Units, CPUs) and one or more memories. Among them, at least one computer program is stored in the memory, and the at least one computer program is loaded and executed by the processor to implement a steel supply chain product traceability system based on blockchain technology provided by each of the above method embodiments. The electronic device can also include other components for implementing the functions of the device. For example, the electronic device can also have components such as wired or wireless network interfaces and input / output interfaces for input and output. The embodiments of the present application will not be elaborated here.

[0078] It should be understood that in various embodiments of the present application, the magnitudes of the serial numbers of the above processes do not mean the order of execution. The order of execution of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0079] It should be understood that determining B according to A does not mean determining B only according to A, and B can also be determined according to A and / or other information.

[0080] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions described in the embodiments of the present invention are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via a wired or wireless network. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or a data center that contains a collection of one or more available media. The available medium can be a magnetic medium (such as a floppy disk, a hard disk, or a magnetic tape), an optical medium (such as a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0081] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed in the present invention can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0082] Those skilled in the art can clearly understand that for the convenience and conciseness of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0083] In several embodiments provided by the present invention, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only one way, and there can be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces, and the indirect couplings or communication connections of the devices or units can be in electrical, mechanical, or other forms.

[0084] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0085] In addition, the functional units in each embodiment of the present invention can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit.

[0086] As mentioned above, the above is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or substitutions, which should all be covered by the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

[0087] Finally: The above is only the preferred embodiment of the present invention and is not used to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should all be included in the protection scope of the present invention.

Claims

1. A steel supply chain product traceability system based on blockchain technology, which is applied to a server, and is characterized in that It includes a supply chain setting module (100), a node setting module (200), a blockchain matching module (300), and a target traceability module (400); each module is connected by wire and / or wirelessly; The supply chain setting module (100) constructs a distributed supply chain. By setting the root node and hierarchical target nodes for product traceability, the root node includes supply status indicators, index key indicators, and a historical indicator database; the hierarchical target nodes include first target nodes and first target child nodes, and the hierarchical target nodes are sent to the node setting module (200); The node setting module (200) standardizes the source processing in the first target child nodes to obtain child node data, encapsulates all child node data into unified node data through location-associated data, and sends the node data to the blockchain matching module (300); The blockchain matching module (300) adaptively selects traceability target data from the node data through a matching strategy, stores the traceability target data in the blockchain, and improves it using an intelligent transaction contract; sends the traceability target data to the target traceability module (400); The target traceability module (400) performs a traceability simulation on the traceability target data based on traceability retrieval conditions to update the index key indicators, sets the updated index key indicators at the root node, and maps them to the first target nodes based on the root node.

2. The steel supply chain product traceability system based on blockchain technology according to claim 1, wherein: The construction logic of the distributed supply chain is as follows: Perform a distributed setting on the steel supply chain to obtain a distributed supply chain, which includes a root node, and match the first target child nodes from the first target nodes. Set the root node for product traceability based on the business request target, and match the first target child nodes from the first target nodes based on the index key indicators; Among them: the first target nodes match and locate the index key indicators from the index key indicators of the root node; the first target child nodes provide detailed business data through the index key indicators.

3. The steel supply chain product traceability system based on blockchain technology according to claim 2, characterized in that: The acquisition logic of the child node data: The index key indicators include at least one child node index indicator. Based on the child node index indicator, obtain business data information, and the business data information includes data type, data source, and usage scenario; The child node index indicator includes at least one index classification indicator and an index location indicator. Based on the index classification indicator, match a slicing tool, and use the slicing tool to divide the business data information into at least one sliced business data; Each sliced business data matches at least one conversion template. The author selects any one of the conversion templates and modifies the content in the conversion template to obtain a custom conversion template; Perform selective standardization processing on the sliced business data based on the custom conversion template to obtain standard business data; associate the standard business data with the current index location indicator to generate child node data.

4. The steel supply chain product traceability system based on blockchain technology according to claim 3, characterized in that: Update the child node data based on the custom conversion template, store the updated child node data and the conversion template in the historical indicator database; store the corresponding standard business data in the historical indicator database through the index location indicator corresponding to the first target child nodes, and perform time stamping on the standard business data.

5. The steel supply chain product traceability system based on blockchain technology according to claim 4, characterized in that: The acquisition logic of the node data is as follows: Divide the child node data into hot data, warm data, and cold data according to the query frequency of the child node data, and assign different levels of child node data with a basic correlation degree; Perform hierarchical analysis on all child node data according to the index key indicators to obtain the correlation matching degree; Obtain a positive correlation relationship by linearly fitting the correlation matching degree and the basic correlation degree, and dynamically adjust the storage strategy of the child node data according to the positive correlation relationship; According to the index position indicator of the first target child node after the storage strategy is adjusted, extract the exactly same position information as the position index data of the first target node based on the index position indicator, and encapsulate the child node data according to the index position indicator within the position index data to generate node data.

6. The steel supply chain product traceability system based on blockchain technology according to claim 5, characterized in that: The application logic of the matching strategy: The first target node includes a data node and a data trust node, and the data node and the data trust node are associated through the matching strategy; The data node calculates and stores the node data, processes large-scale data and high-concurrency requests, and filters and extracts the traceability target data from the node data based on the pre-set matching strategy; Store the traceability target data in the data trust node, and configure the data trust node as a blockchain data trust node, and connect all the blockchain data trust nodes in series.

7. The product traceability system for the steel supply chain based on blockchain technology according to claim 6, characterized in that: The acquisition logic of the traceability target data: The node data determines multiple transaction target data and the priorities corresponding to the transaction target data; the node data determines the business request target from the multiple transaction target data according to the priority corresponding to each transaction target data; Use machine learning or a rule engine to automatically match the optimal transaction target data according to the business request target, establish a traceability chain for each transaction target data and mark it as the traceability target data, and the traceability target data includes the data source, data flow path, and data processing history.

8. The steel supply chain product traceability system based on blockchain technology according to claim 7, wherein: The update logic of the index key indicators is: Construct a digital twin model based on the traceability retrieval conditions, simulate the traceability target data according to the digital twin model to obtain the first index key indicator; retrieve the first index key indicator to obtain the second index key indicator, and integrate the first index key indicator and the second first index key indicator to obtain the reference index key indicator; If the reference index key indicator is inconsistent with the index key indicator, update the traceability retrieval conditions based on the reference index key indicator, and repeatedly execute the above operations until the reference index key indicator is consistent with the index key indicator; If the reference index key indicator is consistent with the index key indicator, perform a traceability query on the steel supply chain based on the index key indicator to obtain product traceability information, set authentication information at the storage node corresponding to the product traceability information, and desensitize the authentication information.

9. A method for tracing steel supply chain products based on blockchain technology, which is realized based on the steel supply chain product tracing system based on blockchain technology described in any one of claims 1-8, and is characterized in that: Including the following steps: Construct a distributed supply chain, by setting the root node and hierarchical target nodes for product traceability, the root node includes a supply status indicator, an index key indicator, and a historical indicator database; the hierarchical target nodes include a first target node and a first target child node; Standardize the source processing in the first target child node to obtain child node data, and encapsulate all child node data into unified node data through location - associated data; Adaptively select traceability target data from the node data through a matching strategy, store the traceability target data in the blockchain, and improve it using an intelligent transaction contract; Based on the traceability retrieval conditions, perform traceability simulation on the traceability target data to update the index key metrics, set the updated index key metrics at the root node, and map to the first target node based on the root node.

10. An electronic device, characterized in that, Including: A processor and a memory, wherein the memory stores a computer program that can be called by the processor; The processor executes a steel supply chain product traceability system based on blockchain technology according to any one of claims 1 - 8 by calling the computer program stored in the memory.

Images