KR20220047132A - Method and System for Enhancing Trust of Supply Chain Using Blockchain Platform with Robust Data Model and Verification Mechanisms - Google Patents

Abstract

A system and a method for strengthening trust in a supply chain using a blockchain platform with a robust data model and a verification mechanism are presented. The system for strengthening trust in a supply chain using a blockchain platform with a robust data model and a verification mechanism proposed in the present invention includes: a client that manages private and public keys, creates transactions, and sends the same to verifiers; a validator that receives transactions from the client, sends the transactions to corresponding peer nodes, and sends the transactions to execute a transaction processor while checking that all nodes agree on a generated output; and the transaction processor that causes a transaction handler to perform a predefined task on the basis of an action field representing the type of transactions received. The quality of blockchain data is improved.

Description

Method and System for Enhancing Trust of Supply Chain Using Blockchain Platform with Robust Data Model and Verification Mechanisms

The present invention relates to a method and system for strengthening trust in a supply chain using a blockchain platform with a robust data model and verification mechanism.

A supply chain is a network of objects and agents involved in producing products and moving them to clients [1]. Supply chains today are very complex. This is because in world trade, multiple stakeholders with different systems and requirements inevitably show complex relationships. As a result, managing such complex supply chains is quite difficult and expensive. According to Gartner, the supply chain management market will exceed $19 billion in total software revenue by 2021[2]. Recently, the emergence of blockchain technology has attracted tremendous attention from the supply chain and logistics industry [3]. Blockchain is a decentralized and decentralized system for sharing transaction records that are resilient to tampering. This ensures secure exchange of assets over the network and enables end-to-end visibility of data between participating nodes. Furthermore, business logic can be embedded in the blockchain through smart contracts [4], which promotes automation of business processes with little effort and increases the flexibility to expand the applicability of the blockchain.

However, due to the relatively simple data structure, the blockchain exposes some shortcomings in handling complex operations such as data retrieval and verification essential for supply chain management. In order to cope with these difficulties, an approach that integrates graph data models such as Resource Description Framework (RDF) [5] has been proposed [6]-[10]. One of the earliest studies in this direction introduces how blockchain and the Semantic Web can mutually benefit and presents potential use cases such as supply chains, data markets, and online educational credentials [6]. Kim [7] applied existing ontology such as TOVE traceability ontology to express domain knowledge of supply chain in an attempt to improve supply chain verification. Similarly, Ruta [8] proposed a flexible object discovery method in the supply chain using a logic-based semantic mediation process.

These prior technologies are intended to increase the utility of the block chain based on the expression of supply chain knowledge with a rich domain vocabulary as well as semantic reasoning technology. However, none of the prior art paid attention to thorough business logic verification before checking the block. Indeed, for the aforementioned applications to fully function as intended, it is important to ensure that semantically correct data exists on the blockchain. Since the data recorded on the blockchain is virtually immutable, it can cause serious problems for supply chain stakeholders and consumers if invalid transactions are made and propagated to the entire network without proper filtering. Therefore, without the use of systematic and fully verifiable processes, blockchain applications for supply chains can be error-prone and pose a serious threat to the trust of the distributed ledger.

The technical task of the present invention is to provide a blockchain platform for the supply chain with an anomaly detection layer in order to provide improved data quality of the blockchain. The main goal of the proposed platform is to improve the data quality of the blockchain by proactively detecting erroneous transactions against valid flows in the supply chain.

In one aspect, the supply chain trust enhancement system using a blockchain platform with a robust data model and verification mechanism proposed in the present invention manages private and public keys, creates a transaction, and transmits it to a validator, from a client to a validator. A validator that receives a transaction, sends it to its peer node, and executes that transaction processor, ensuring that all nodes agree on the output generated, and the corresponding transaction handler depending on the action field indicating the received transaction type. includes a transaction processor for performing predefined tasks.

The transaction processor includes a transaction handler, an RDF generator, an RDF store and an anomaly detector, and decodes the transaction payload encoded through the transaction handler and splits it into fields, and then, according to the action field indicating the received transaction type, the transaction handler Allows you to perform predefined tasks of transactions that add supply chain event data captured during the movement of physical objects.

The transaction processor uses the supply chain event data described in the deserialized payload to generate a triple set of graph components consisting of two vertices and an edge connecting the two vertices according to the graph creation rules through the RDF generator. After that, in relation to the set pre-stored in the RDF storage, an anomaly is detected through an anomaly detector.

The transaction processor causes the anomaly detector to interact with the validator's global state to detect anomalies, and if no anomaly is detected by the anomaly detector, the transaction handler stores the set of triples generated for the current transaction into RDF storage. to and update the block and global state.

The transaction processor causes the anomaly detector to interact with the validator's global state to detect anomalies, and if an anomaly detector detects an anomaly, invalidates the transaction, and the global state for that transaction is not updated. Instead, the transaction related to the detected anomaly is applied to the system.

A transaction payload according to an embodiment of the present invention includes only supply chain event data components captured during the movement of a physical object, and among the event data components, an event type defined in Global Traceability Standard (GTS), an identifier of a related object, Incorporates only the data needed for evidence tracking, including Key Data Element (KDE), event data simplified with timestamp, location, and business stage fields, and links to off-chain database connections.

The transaction processor is configured to manage supply chain event data captured during the movement of a physical object, via an access link stored in the transaction processor, an additional including elevation and longitude, attributes, sensor data of a particular location with respect to said event data stored in an off-chain database Access the data to detect additional anomalies.

The anomaly detector includes, for each transaction, whether the location of the related item has changed, and if so, a speed condition that reads critical data from the global state and checks whether the average movement speed is within a predetermined range, and a predetermined dwell time condition. Consistency rule-based anomaly detection using consistency rules and graph-based anomaly detection methods using graph models to ensure consistency of product lifecycles.

In another aspect, the method for strengthening trust in the supply chain using a blockchain platform with a robust data model and verification mechanism proposed by the present invention is a method in which a client manages private and public keys and creates a transaction and sends it to a validator. step, the validator receives the transaction from the client, sends the transaction to the corresponding peer node, and sends the transaction to execute the corresponding transaction processor while verifying that all nodes agree on the output generated, and an action field indicating the type of transaction received and causing the transaction handler to perform a predefined task through the transaction processor.

According to embodiments of the present invention, there are two methods for the detection of anomalous transactions, namely, presenting general consistency rules such as dwell time or rate conditions through basic consistency rule-based detection, and catching obviously erroneous transactions that violate these conditions. It can catch erroneous transactions by traversing a subset of the graph to look for anomalous patterns through time graph-based detection. By proactively detecting erroneous transactions against valid flows in the supply chain, the data quality of the blockchain can be improved.

1 is a diagram showing the configuration of a supply chain trust enhancement system using a blockchain platform equipped with a robust data model and verification mechanism according to an embodiment of the present invention.
2 is a diagram illustrating an example of a transaction according to an embodiment of the present invention.
3 is a diagram illustrating a global state type according to an embodiment of the present invention.
4 is a diagram illustrating a conversion rule for an event type according to an embodiment of the present invention.
5 is an algorithm for checking whether the life cycle order of a corresponding transaction is correct according to an embodiment of the present invention.
6 is an algorithm for checking whether all lower-level items aggregated according to a chain are disconnected from upper-level items in chronological order according to an embodiment of the present invention.
7 is a flowchart illustrating a method for strengthening trust in a supply chain using a blockchain platform equipped with a robust data model and verification mechanism according to an embodiment of the present invention.

As supply chains become extremely complex, traditional supply chain management systems reveal limitations in tracking resources for effective risk management. Recently, blockchain-based solutions for supply chains have shown the potential to overcome these limitations. However, many practical problems in this area have not been completely resolved to date. Therefore, in the present invention, one of the most important problems among them, verification of business logic in a transaction, is studied in depth.

In the present invention, we propose a new distributed ledger system for supply chain enabled by an anomaly detection framework that verifies the semantic correctness of a transaction based on business context data. The proposed blockchain data model is tailored to accurately represent events that occurred in the supply chain based on real business standards. To facilitate more efficient tracking of validation, a graph data model is utilized to represent the supply chain network. On the data model, we present a smart contract-based anomaly detection framework that verifies the anomaly of transactions. Consistency rule-based and graph-based detection methods are devised. The feasibility of the proposed model is indicated by the system implemented using Hyperledger Sawtooth. It shows how the anomaly detection layer can be connected to the system, how it interacts with other system components, and how the overall system flow works in this new feature. Evaluate the system through scenario-based simulation, and prove the accuracy and effectiveness of the proposed system for many integrated use cases. Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings.

In the present invention, we propose a blockchain platform for supply chain with anomaly detection layer to provide improved data quality of blockchain. The main goal of the proposed platform is to improve the data quality of the blockchain by proactively detecting erroneous transactions against valid flows in the supply chain. To achieve this goal, we emphasize the essential role of the underlying data model. The proposed system is built on standardized event modeling of supply chain essential for interoperability and RDF-based knowledge representation that can precisely describe and analyze supply chain components and their interactions.

Based on the proposed data model, we propose two methods for the detection of anomalous transactions, namely, the detection based on the basic consistency rule and the graph-based detection. We present general consistency rules, such as dwell time or rate conditions, and explain how we can catch obviously erroneous transactions that violate these conditions. Temporal graph-based detection is performed by traversing a subset of the graph to find anomalous patterns. A system architecture including an anomaly detection layer was designed, and the system was implemented using Hyperledger Sawtooth [11]. Prove the feasibility of the system through scenario-based simulation. The accuracy and effectiveness of the system can be verified by simulating the use case.

According to an embodiment of the present invention, a reliable data model of the blockchain for supply chain is defined based on global standards, which is an important element of interoperable data exchange as well as maintaining data quality of the blockchain.

The present invention emphasizes the need for an anomaly detection layer in a blockchain system for supply chains, and proposes smart contract-based anomaly detection for supply chain business logic verification using blockchain for the first time.

According to an embodiment of the present invention, a flexible system is constructed using Hyperledger Sawtooth, and scenario-based evaluation is performed to indicate the feasibility of the system.

In order to represent the flow of physical objects in the form of structured digital representations, automatic data capture technologies as well as unique identification schemes play a key role. Data carriers such as RFID tags and barcodes are used for automatic capture of supply chain data. An information system obtains a stream of machine-readable data associated with a physical object by scanning the data carrier. Additionally, a globally unique identification scheme can have a single, shared view of a physical object in the context of a business transaction.

A data model is needed to process a stream of context data for a uniquely identified object after automatic capture. Electronic Product Code Information Service (EPCIS) [12] is a global standard ratified by GS1 to define data models and service interfaces for creating and sharing supply chain data. In particular, the EPCIS data model enables detailed descriptions of observed events in the supply chain. The data model defines the components of supply chain event data, which represent the completion of a business phase in the four dimensions of what, when, where, and why the event.

The EPCIS data model classifies events into the following general types:

Object event: Unlike the following three event types, where the interaction between two or more entities is explicitly expressed, this event represents the completion of a single business phase of an item (for example, laptop computer A is produced). .

Aggregation Event: This event type refers to an event in which an item designated as a child item is aggregated into a parent item (temporary) or aggregated into a parent item (eg, product A is loaded/unloaded from container B).

Conversion Event: Given an input item, a conversion event transforms it into an output item (eg, transforms a raw material into a final product).

Transactional event: A specified item of this event type is related to that transaction (eg purchases products A and B for transaction T).

Hyperledger [13] is an open source project hosted by the Linux Foundation to develop systems and tools for a private blockchain that can be applied to a wide range of businesses. There are several subprojects such as Fabric [14], Sawtooth [11], and Iroha [15] developed individually and collaboratively under the Hyperledger project. Among them, using Sawtooth, it is highly modular (key elements such as a consensus algorithm and transaction processor can be accessed), and the core system is well optimized (parallel transaction execution is activated by a parallel scheduler) to build a system. . High modularity is required to build a scalable supply chain system in which actors dynamically change and adjust policies or system components. They also need to use highly optimized systems for daily processing of the large-scale, real-time data produced by the supply chain. The following are the key components of Sawtooth.

A validator is a node in charge of collecting transactions by applying a (modular) consensus algorithm and making blocks consistent across different nodes. A journal is a system unit included in a verifier that sends a transaction to a transaction processor for execution, and commits a block containing a transaction batch. The global state is also part of the validator. A key-value store that contains the results of executing a transaction in a transaction processor. Transaction processors are comparable to smart contract machines. When a transaction is received, it checks the validity of the transaction (eg data type, hash value, etc.) and updates the state according to what is defined in its handling routine.

1 is a diagram showing the configuration of a supply chain trust enhancement system using a blockchain platform equipped with a robust data model and verification mechanism according to an embodiment of the present invention.

The proposed supply chain trust enhancement system using a blockchain platform with a robust data model and verification mechanism includes a client 110 , a validator 120 , and a transaction processor 130 .

The client 110 manages the private and public keys, creates a transaction, and sends it to the verifier.

The verifier 120 receives the transaction from the client, transmits the transaction to the corresponding peer node, and transmits the transaction to execute the corresponding transaction processor while confirming that all nodes agree on the generated output.

The transaction processor 130 causes the corresponding transaction handler to perform a predefined task according to the action field indicating the received transaction type. The transaction processor 130 includes a transaction handler 131 , an RDF generator 132 , an RDF storage 133 , and an anomaly detector 134 .

The transaction processor 130 decodes the transaction payload encoded through the transaction handler 131 and divides it into fields, and then, according to the action field indicating the received transaction type, the transaction handler 131 captures during the movement of the physical object. Allows you to perform predefined tasks in transactions that add supply chain event data.

The transaction processor 130 uses the supply chain event data described in the deserialized payload, and through the RDF generator 132, a graph component composed of two vertices and an edge connecting the two vertices according to a graph generation rule. After generating a triple set consisting of

The transaction processor 130 allows the anomaly detector 134 to interact with the validator's global state to detect anomalies. If no anomaly is detected through the anomaly detector 134, the transaction handler 131 adds the triple set generated for the current transaction to the RDF storage 133 and updates the block and global state. On the other hand, when an anomaly is detected through the anomaly detector 134, the transaction is invalidated, the global state for the transaction is not updated, and the transaction related to the detected anomaly is applied to the system.

For each transaction, the anomaly detector 134 determines whether the location of the related item has changed, if so, reads critical data from the global state and checks whether the average moving speed is within a predetermined range, a speed condition and a predetermined dwell time condition Consistency rule-based anomaly detection using consistency rules including

According to an embodiment of the present invention, a transaction payload includes only supply chain event data components captured during the movement of a physical object, and among the event data components, an event type defined in Global Traceability Standard (GTS), an identifier of a related object Incorporates only the data needed for evidence tracking, including Key Data Element (KDE), event data simplified to , timestamp, location, and business phase fields, and links to off-chain database connections.

The transaction processor 130 is configured to manage the supply chain event data captured during the movement of the physical object, through the connection link stored in the transaction processor, the elevation and longitude, attributes of a specific location with respect to the event data stored in the off-chain database 140, Access to additional data including sensor data to detect additional anomalies.

A system for strengthening trust in the supply chain using a blockchain platform with a robust data model and verification mechanism according to an embodiment of the present invention will be described in more detail with reference to FIG. 1 .

The blockchain platform according to an embodiment of the present invention is built based on Hyperledger Sawtooth, and includes a client 110 , a validator 120 , and a transaction processor 130 .

After the client 110 manages its own private and public key and creates a transaction, it transmits the transaction to the verifier 120 through the REST API ( 1a in FIG. 1 ).

The validator 120 is responsible for the main task of the distributed ledger. That is, when the transaction of the client 130 is given, the verifier 120 transmits the transaction to its peer node, and executes the transaction processor 130 while confirming that all nodes agree on the generated output. . Finally, the transaction processor 130 executes the business logic of the smart contract.

There are two sources for the verifier 120 to receive transactions from the client 110 or the adjacent verifier 111 ( 1a and 1b in FIG. 1 ). The local client 110 directly connected to the verifier 120 may set all required fields of the transaction, serialize the transaction in binary format, package it in a batch, and then transmit it. Also, gossip (epidemic) used for peer-to-peer transmission, as the verifier 120 receiving the transaction from the client 110 informs the peering verifier of it. Protocol [16] is another source for validators 120 to obtain transactions.

When the verifier 120 receives the transaction, the verifier 120 forwards it to the corresponding transaction processor 130 (2 in FIG. 1), where the encoded transaction payload is decoded and divided into fields. Thereafter, the corresponding transaction handler 131 performs a predefined task according to the operation field indicating the transaction type. There are two types of transactions: those that register metadata such as predefined configuration data, and those that add supply chain event data captured during the movement of physical objects.

The present invention focuses on a procedure for processing a transaction containing supply chain event data. As a first step, considering the supply chain event data described in the deserialized payload, the RDF generator 132 generates a triple set of graph elements consisting of two vertices and an edge connecting them according to the graph generation rules. (5 in FIG. 1). Then, the generated triple set is checked by the anomaly detector 134 in relation to the existing set stored in the RDF storage 133 ( 4 in FIG. 1 ). Anomaly detector 134 also interacts with the global status and journal to retrieve both configuration data and up-to-date status of related products. If no anomaly is detected by the anomaly detector 134, the transaction handler 131 adds the triple set generated for the current transaction to the RDF storage 133 (5.2 in Fig. 1) and updates the block and global state. (5.1 and 5.3 in Fig. 1).

In contrast, if a given transaction is found to violate some consistency condition of the smart contract, it will raise an exception to invalidate the transaction. Therefore, the global state for that transaction is not updated. Instead, a transaction must be applied to the system that specifies the detection of anomalies (for example, also a summary of the specific rules being violated and the anomalous event data).

A transaction according to an embodiment of the present invention is a unit of data transmission in a block chain system. For example, given a transaction as input by a client application, the blockchain system adds new data input or return query results. For example, a machine (with an RFID or barcode tag) or a human end user can communicate with the system by sending a transaction.

In order to design a transaction according to an embodiment of the present invention, the following three requirements are considered. First, a transaction must model a digital representation of a physical resource and its interactions, taking into account the actual supply chain. Second, in order to improve the performance of distributed systems where the load of P2P communication for data synchronization becomes exponential with respect to network size, transactions need to be as lightweight as possible. Finally, several types of data registration and querying must be supported for flexible data management.

2 is a diagram illustrating an example of a transaction according to an embodiment of the present invention.

The transaction payload according to the embodiment of the present invention is designed to conform to the EPCIS data model. The EPCIS data model consists of event data and master data (ie, additional static data such as coordinates of related locations), but in the present invention, only the event data component is included in the payload. Among the incident data components, as defined in the Global Traceability Standard (GTS), the minimum data required for evidence tracing is integrated [17]. Specifically, the Key Data Element (KDE) defined in GTS is event data that is simplified to the event type, identifier of the related object, timestamp, location, and business level fields. In addition, an off-chain database connection link is included in the transaction according to an embodiment of the present invention.

The blockchain according to the embodiment of the present invention mainly deals with supply chain event data. However, managing the actual supply chain requires more detailed descriptions of events (eg, attributes such as altitude and longitude of a particular location) and numerous additional data such as sensor data (eg, temperature). This data can be managed using extra storage external to the blockchain called an off-chain database. By using the URL stored in the transaction according to the embodiment of the present invention, additional detection can be performed by accessing additional data. Moreover, the integrity of off-chain data can be maintained by monitoring the hash of the data.

A blockchain, particularly a private blockchain, according to an embodiment of the present invention is considered an extension of the Replicated State Machine (RSM) [18]. Each node in the proposed system has its own state, and the system ensures that all peering nodes reach a consensus on a unified state. The global status according to an embodiment of the present invention represents the recent status of items in the supply chain and metadata required for blockchain management. As a new transaction is registered as event data, related item information is dynamically updated, but the entire item history data is not retained. For the sake of clarity, the purpose of a global state according to an embodiment of the present invention is to immediately access and monitor the current state of an item in a supply chain or configuration setting, and to ascertain general consistency rules for anomaly detection.

Global state is maintained by persistent (key, value) storage. Addressable radix merkle trees (also called patricia trie) are used in data structures according to embodiments of the present invention. This allows (key, value) pairs to be stored in an encrypted way and greatly speeds up retrieval. State is updated in the transaction processor when the transaction processor receives a transaction from a client or other validator.

3 is a diagram illustrating a global state type according to an embodiment of the present invention.

As shown in Fig. 3, various global state types are defined according to an embodiment of the present invention. Update a specific address in the global state with different keys and values depending on the type. State types are classified into two types: event data state and metadata state. An object event is a key that stores a tuple of context data decoded in a transaction (e.g., time, location, business step, event type) and the identifier of the item (that is, title of the event). Similarly, other event types update the same tuple, except that they have extra fields representing related items. Multiple objects can be specified to represent a many-to-one or many-to-many relationship. In addition to event data, the global state can include configuration data used for rule-based anomaly detection (eg dwell time conditions and speed conditions) and metadata such as exception logs.

The anomaly detector according to an embodiment of the present invention is implemented in a transaction processor. Smart contract-based anomaly detection can be divided into two broad types: consistency rule-based and graph-based detection.

The first step in anomaly detection involves basic consistency rules that must be explicitly met for every transaction in the supply chain. In the present invention, reference is made to some of the rules defined by [19]. Here is the general rule incorporated in [19]:

Speed Conditions: The speed of movement of the product cannot exceed the maximum transport speed (eg truck, ship or airplane). Violation of this rule clearly points to counterfeit products such as duplicate tags.

Retention time conditions: The length of time a product, particularly an expiry-dated food product, such as dairy, stays in a particular location is sometimes limited. Supply chain systems need to monitor these conditions and detect any violations of predefined configurations.

A proposed solution to analyze these key rules using blockchain is:

Velocity condition: Assume that item A is associated with an event e _k with time t _k and location l _k . If the next event e _k+1 has time t _k+1 and location l _k+1 , then the distance (l _k , l _k+1 )/(t _k+1 - t _k ) is a predefined threshold range ( If it is far exceeded (between v _min and v _max ), the event e _k+1 is detected as a forgery. To implement this on the blockchain, a configuration file is used to register threshold data (v _min , v _max ) as a global state. For each transaction, the proposed system reads critical data from the global state if the location of the related item has changed and if so, checks whether the average movement speed is within the correct range.

Dwell Time Conditions: Dwell time consistency rules can be verified using configured threshold data. In this case, when the threshold data is registered in the global state, for example, a pair of an identifier of a related item and a location can be used as a key. In other words, the global state has a time limit for a specific item to stay in a specific location. Anomalies can be identified by measuring the length of time an item stays in a specific location.

In addition, the graph data model may be utilized to process more complex verification in the anomaly detector according to an embodiment of the present invention. First, we define a graph representation that considers temporal aspects for efficient data analysis. We propose an algorithm for ensuring product lifecycle consistency using a graph model. Finally, we propose a verification protocol that guarantees tamper-proof recording of graphs.

First, it is essential to analyze how items in the supply chain are related to each other over time. A disadvantage of the global state is that it cannot effectively show the relationships between supply chain entities. Therefore, the present invention uses a graph model.

According to an embodiment of the present invention, a three-part relationship between resources, often known as (s, p, o), is defined using the Resource Description Framework (RDF) [5] and W3C standards. Here, s (subject) has the relation o (object) and p (predicate). Specifically, we integrate the temporal RDF [20] by annotating the predicate with temporal information to accurately consider the temporal dynamics of the supply chain network for analysis.

4 is a diagram illustrating a conversion rule for an event type according to an embodiment of the present invention.

Similar to a global state that updates state using predefined smart contract logic in a transaction processor, graph generation logic can also be embedded in smart contracts for automatic execution. Once the transaction handler of the transaction processor receives the transaction, the contextual event data is converted into the corresponding triple set. 4 shows the conversion rules for each event type. Note that aggregation events are divided into aggregation (eg, loading) and detachment (eg, unloading). Each transaction creates two sets of triples (subject, predicate:[time], object), where predicates are annotated with time. For example, as shown in Fig. 4 (a), the object event is translated into (product EPC, atStep:[t], business step) and (product EPC, locatedAt:[t], location).

In accordance with an embodiment of the present invention, it is important to track order consistency in the business phase, assuming that transactions are registered in an asynchronous and dynamic manner. This becomes even more difficult when the system swells with large amounts of data showing complex relationships.

This work focuses on verifying the consistency of the pairwise business phase. Together with the velocity conditions and residence time conditions described above, this concept was first introduced in [19]. However, in the present invention, the problem is completely reconstructed in consideration of a more practical and complex situation, and a specific graph-based detection algorithm is proposed. The predefined terms according to the embodiment of the present invention are as follows:

Pairwise-bizstep: A business step that describes the consolidation and separation of items (eg loading-unloading, packing-unpacking, etc.)

pre-biz-list: List of business steps that precede pairwise-bizstep (eg loading, packing, etc.)

post-biz-list: business steps leading to pairwise-bizstep (eg unloading, unpacking, etc.)

When considering a series of transactions according to an embodiment of the present invention, the transaction order should ensure that: assuming that a child item C is consolidated into a parent P, the following events related to C except for separation from P are exceptional. is considered This is maintained recursively for every item that becomes part of the aggregated item chain.

Pairwise business-level consistency verification is considered a critical task if:

Accumulation of aggregation: A series of aggregation events to form a stacked stack of items (eg, a small pile of pallets contained in a larger container) can adversely affect the visibility of the aggregated items.

Integration from multiple sources of dynamic configuration: The complexity of tracking items increases for many-to-one integration mappings. It is exacerbated when these relationships emerge dynamically.

5 is an algorithm for checking whether the life cycle order of a corresponding transaction is correct according to an embodiment of the present invention.

Referring to Algorithm 1 shown in Fig. 5, G is a graph stored in the RDF storage, whereas TxG is a graph generated in a transaction (in the form of Fig. 4). Different metrics are used for consistency checking depending on whether the business phase of a given transaction represents aggregation, segregation, or otherwise. In the case of aggregation, an attempt to aggregate an already aggregated sub-item is considered an anomaly. In the case of detachment, such a transaction is anomalous if the corresponding aggregate events do not exist in the correct order. Transactions with a business stage other than aggregation or separation need to verify that the parent item to which the item belongs is separated in order. In this case, it is assumed that a series of aggregation events occur consecutively.

6 is an algorithm for checking whether all lower-level items aggregated according to a chain are disconnected from upper-level items in chronological order according to an embodiment of the present invention.

The procedure for processing the last case of Algorithm 1 of FIG. 5 is described in detail in Algorithm 2 of FIG. 6 . Algorithm 2 checks whether all aggregated child items are unlinked in chronological order with their parent items according to the chain of aggregated items (adjacent pairs each representing a child-parent relationship). This means that the last aggregated item must first be separated from the parent item. This process also verifies that the business steps are paired. Retrieve the target information using a SPARQL query to find the most recent predicate of an aggregate or dissociation (lines 2, 10).

According to an embodiment of the present invention, we propose a protocol for verifying the integrity of a graph using RDF digest calculation so that graph data stored in RDF storage is not tampered with for graph tamper-proof recording. This is a widely used mechanism for computing the fingerprint of RDF graphs in real systems: common applications include digital signatures, content-based identifier generation, and tamper-proof recording of graphs [21].

The proposed protocol is as follows. After calculating the RDF digest, the leader node adds the digest inside the transaction header and propagates the block containing the transaction to the peering node. Then, when each peering node receiving the transaction passes the anomaly detection layer, it computes a digest, and if the calculated value is the same as the digest stored in the transaction header, the node updates the RDF store and commits the transaction. In this way, nodes can reach a consensus on the validity of the graph data.

7 is a flowchart illustrating a method for strengthening trust in a supply chain using a blockchain platform equipped with a robust data model and verification mechanism according to an embodiment of the present invention.

The proposed method for strengthening trust in the supply chain using a blockchain platform with a robust data model and verification mechanism is a step 710 where the client manages private and public keys, creates a transaction, and sends it to the verifier. The verifier sends the transaction from the client. sending the transaction to the corresponding peer node, sending the transaction to execute the corresponding transaction processor while verifying that all nodes agree on the generated output ( 720 ) and the transaction processor according to the action field indicating the received transaction type and a step 730 of allowing the corresponding transaction handler to perform a predefined task.

In step 710, the client manages the private and public keys and creates a transaction and sends it to the verifier.

In step 720, the verifier receives the transaction from the client and transmits the transaction to the corresponding peer node, and transmits the transaction to execute the corresponding transaction processor while confirming that all nodes agree on the generated output.

In step 730, the corresponding transaction handler performs a predefined task through the transaction processor according to the action field indicating the received transaction type.

The transaction processor includes a transaction handler, an RDF generator, an RDF store, and an anomaly detector.

The transaction processor decodes the transaction payload encoded through the transaction handler, splits it into fields, and then adds the supply chain event data captured during the movement of the physical object by the transaction handler according to the action field indicating the type of transaction received in advance of the transaction. Let the defined task be performed.

The transaction processor uses the supply chain event data described in the deserialized payload to generate a triple set of graph components consisting of two vertices and an edge connecting the two vertices according to the graph creation rules through the RDF generator. After that, in relation to the set pre-stored in the RDF storage, the presence or absence of an anomaly is detected through an anomaly detector ( 740 ).

The transaction processor allows the anomaly detector to interact with the validator's global state to detect anomalies. If no anomaly is detected through the anomaly detector, in step 750, the transaction handler adds the triple set generated for the current transaction to the RDF storage and updates the block and global state. On the other hand, if an anomaly is detected through the anomaly detector, the transaction is invalidated in step 760, the global state for the transaction is not updated, and the transaction related to the detected anomaly is applied to the system. do.

The anomaly detector includes, for each transaction, whether the location of the related item has changed, and if so, a speed condition that reads critical data from the global state and checks whether the average movement speed is within a predetermined range, and a predetermined dwell time condition. Consistency rule-based anomaly detection using consistency rules and graph-based anomaly detection methods using graph models to ensure consistency of product lifecycles.

According to an embodiment of the present invention, a transaction payload includes only supply chain event data components captured during the movement of a physical object, and among the event data components, an event type defined in Global Traceability Standard (GTS), an identifier of a related object Incorporates only the data needed for evidence tracking, including Key Data Element (KDE), event data simplified to , timestamp, location, and business phase fields, and links to off-chain database connections.

The transaction processor is configured to manage supply chain event data captured during the movement of a physical object, via an access link stored in the transaction processor, an additional including elevation and longitude, attributes, sensor data of a particular location with respect to said event data stored in an off-chain database Access the data to detect additional anomalies.

The device described above may be implemented as a hardware component, a software component, and/or a combination of the hardware component and the software component. For example, devices and components described in the embodiments may include, for example, a processor, a controller, an arithmetic logic unit (ALU), a digital signal processor, a microcomputer, a field programmable array (FPA), It may be implemented using one or more general purpose or special purpose computers, such as a programmable logic unit (PLU), microprocessor, or any other device capable of executing and responding to instructions. The processing device may execute an operating system (OS) and one or more software applications running on the operating system. A processing device may also access, store, manipulate, process, and generate data in response to execution of the software. For convenience of understanding, although one processing device is sometimes described as being used, one of ordinary skill in the art will recognize that the processing device includes a plurality of processing elements and/or a plurality of types of processing elements. It can be seen that can include For example, the processing device may include a plurality of processors or one processor and one controller. Other processing configurations are also possible, such as parallel processors.

Software may comprise a computer program, code, instructions, or a combination of one or more thereof, which configures a processing device to operate as desired or is independently or collectively processed You can command the device. The software and/or data may be any kind of machine, component, physical device, virtual equipment, computer storage medium or apparatus, to be interpreted by or to provide instructions or data to the processing device. may be embodied in The software may be distributed over networked computer systems and stored or executed in a distributed manner. Software and data may be stored in one or more computer-readable recording media.

The method according to the embodiment may be implemented in the form of program instructions that can be executed through various computer means and recorded in a computer-readable medium. The computer-readable medium may include program instructions, data files, data structures, etc. alone or in combination. The program instructions recorded on the medium may be specially designed and configured for the embodiment, or may be known and available to those skilled in the art of computer software. Examples of the computer-readable recording medium include magnetic media such as hard disks, floppy disks and magnetic tapes, optical media such as CD-ROMs and DVDs, and magnetic such as floppy disks. - includes magneto-optical media, and hardware devices specially configured to store and execute program instructions, such as ROM, RAM, flash memory, and the like. Examples of program instructions include not only machine language codes such as those generated by a compiler, but also high-level language codes that can be executed by a computer using an interpreter or the like.

As described above, although the embodiments have been described with reference to the limited embodiments and drawings, various modifications and variations are possible from the above description by those skilled in the art. For example, the described techniques are performed in an order different from the described method, and/or the described components of the system, structure, apparatus, circuit, etc. are combined or combined in a different form than the described method, or other components Or substituted or substituted by equivalents may achieve an appropriate result.

Therefore, other implementations, other embodiments, and equivalents to the claims are also within the scope of the following claims.

<References>

[1] J. T Mentzer, W. Dewitt, J. S Keebler, S. Min, N. Nix, C. Smith, and Z. Zacharia, "Defining supply chain management," Journal of Business Logistics, vol. 22, 09 2001.

[2] "Supply chain market value to surge past 13 billion dollars by end of 2017." https://www.supplychaindigital.com/scm/supply-chain-marketvalue-surge-past-13-billion-end-2017. Accessed: 2018-12-18.

[3] "How blockchain will transform the supply chain and logistics industry." https://www.forbes.com/sites/bernardmarr/2018/03/23/howblockchain-will-transform-the-supply-chain-and-logisticsindustry/#213a04965fec. Accessed: 2018-12-18.

[4] N. Szabo, “Formalizing and securing relationships on public networks,” First Monday, vol. 2, no. 9, 1997.

[5] "RDF Primer. W3C Recommendation." https://www.w3.org/TR/rdf11-primer/. Accessed: 2019-01-04.

[6] M. D. English, S. Auer, and J. Domingue, “Block chain technologies & the semantic web: A framework for symbiotic development,” 2015.

[7] H. Kim and M. Laskowski, “Towards an ontology-driven blockchain design for supply chain provenance,” 08 2016.

[8] M. Ruta, F. Scioscia, S. Ieva, G. Capurso, and E. D. Sciascio, “Supply chain object discovery with semantic-enhanced blockchain,” in Proceedings of the 15th ACM Conference on Embedded Network Sensor Systems, SenSys 2017 , Delft, Netherlands, November 06-08, 2017, pp. 60:1-60:2, 2017.

[9] A. Third and J. Domingue, “Linked data indexing of distributed ledgers,” in Proceedings of the 26th International Conference on World Wide Web Companion, Perth, Australia, April 3-7, 2017, pp. 1431-1436, 2017.

[10] M. Sopek, P. Gradzki, W. Kosowski, D. Kuzinski, R. Trojczak, and ' R. Trypuz, "Graphchain: A distributed database with explicit semantics and chained RDF graphs," in Companion of the Web Conference 2018 on The Web Conference 2018, WWW 2018, Lyon, France, April 23-27, 2018, pp. 1171-1178, 2018.

[11] "Hyperledger Sawtooth." https://https://sawtooth.hyperledger.org/docs/core/releases/latest/. Accessed: 2018-12-18.

[12] "EPC Information Service (EPCIS)." https://www.gs1.org/sites/default/files/docs/epc/EPCIS-Standard-1.2-r-2016-09-29.pdf. Accessed: 2018-12-29.

[13] "Hyperledger." https://www.hyperledger.org/. Accessed: 2018-12-29.

[14] "Hyperledger Fabric." https://www.hyperledger.org/projects/fabric. Accessed: 2019-03-24.

[15] "Hyperledger Iroha." https://www.hyperledger.org/projects/iroha. Accessed: 2019-03-24.

[16] D. Kempe, J. M. Kleinberg, and A. J. Demers, "Spatial gossip and resource location protocols," in Proceedings on 33rd Annual ACM Symposium on Theory of Computing, July 6-8, 2001, Heraklion, Crete, Greece, pp. 163-172, 2001.

[17] "GS1 Global Traceability 2.0." https://www.gs1.org/sites/default/files/docs/traceability/GS1 Global Traceability Standard i2.pdf. Accessed: 2019-03-24.

[18] A. Aggarwal and Y. Guo, “A simple reduction from state machine replication to binary agreement in partially synchronous or asynchronous networks,” IACR Cryptology ePrint Archive, vol. 2018, p. 60, 2018.

[19] A. Ilic, T. Andersen, and F. Michaeles, “Increasing supply-chain visibility with rule-based rfid data analysis,” IEEE Internet Computing, vol. 13, pp. 31-38, Jan. 2009.

[20] A. Gomez-P 'erez and J. Euzenat, eds., 'The Semantic Web: Research and Applications, Second European Semantic Web Conference, ESWC 2005, Heraklion, Crete, Greece, May 29 - June 1, 2005, Proceedings, vol. 3532 of Lecture Notes in Computer Science, Springer, 2005.

[21] C. Sayers and A. H. Karp, “Rdf graph digest techniques and potential applications,” 05 2004.

[22] "ZeroMQ Document." http://zeromq.org/intro:read-the-manual. Accessed: 2019-04-10.

[23] "Rdflib." https://rdflib.readthedocs.io. Accessed: 2019-04-09.

Claims (16)

Clients that manage private and public keys, create transactions and send them to validators;
a verifier that receives the transaction from the client, transmits the transaction to the corresponding peer node, and transmits the transaction to execute the corresponding transaction processor while confirming that all nodes agree on the generated output; and
A transaction processor that causes the corresponding transaction handler to perform a predefined task depending on the action field indicating the type of transaction received.
A blockchain system that includes According to claim 1,
The transaction processor,
Includes transaction handlers, RDF generators, RDF stores and anomaly detectors;
A predefined of transaction that decodes the transaction payload encoded through the transaction handler and splits it into fields, and then adds the supply chain event data captured during the movement of the physical object by the transaction handler according to the action field indicating the received transaction type. to do the task
blockchain system. 3. The method of claim 2,
The transaction processor,
After generating a triple set consisting of two vertices and an edge connecting the two vertices according to the graph generation rule through the RDF generator using the supply chain event data described in the serial-to-parallel converted payload, , to detect whether anomalies are detected through an anomaly detector in relation to a set stored in advance in the RDF storage.
blockchain system. 4. The method of claim 3,
The transaction processor,
allow the anomaly detector to interact with the global state of the validator to detect anomalies;
If no anomaly is detected through the anomaly detector, the transaction handler adds the triple set generated for the current transaction to the RDF storage and updates the block and global state
blockchain system. 4. The method of claim 3,
The transaction processor,
allow the anomaly detector to interact with the global state of the validator to detect anomalies;
When an anomaly is detected through the anomaly detector, the transaction is invalidated, the global state for the transaction is not updated, and the transaction related to the detected anomaly is applied to the system.
blockchain system. 3. The method of claim 2,
The transaction payload contains only supply chain event data components captured during the movement of a physical object, and among the event data components, the event type defined in Global Traceability Standard (GTS), the identifier of the relevant object, the timestamp, location and business Integrates only the data needed for evidence tracking, including Key Data Element (KDE), event data simplified by step fields, and an off-chain database access link.
blockchain system. 7. The method of claim 6,
The transaction processor,
To manage supply chain event data captured during the movement of physical objects, access additional data, including elevation and longitude, attributes, and sensor data of a specific location, pertaining to said event data stored in an off-chain database via a connection link stored in a transaction processor to detect whether additional anomalies are
blockchain system. 3. The method of claim 2,
The anomaly detector is
For each transaction, it uses consistency rules that include a pre-determined dwell time condition and a rate condition to check whether the location of the related item has changed, if so, read critical data from the global state and check whether the average travel speed is within a pre-determined range. Consistency rules-based anomaly detection and graph-based anomaly detection methods using graph models to ensure consistency in product lifecycles.
blockchain system. The client manages the private and public keys, creates a transaction, and sends it to the verifier;
transmitting the transaction so that the verifier receives the transaction from the client, transmits the transaction to the corresponding peer node, and executes the corresponding transaction processor while confirming that all nodes agree on the generated output; and
According to the action field indicating the received transaction type, the transaction processor causes the corresponding transaction handler to perform a predefined task.
A method of operation of a blockchain system comprising a. 10. The method of claim 9,
The step of causing the transaction handler to perform a predefined task through the transaction processor according to the action field indicating the received transaction type includes:
A transaction processor including a transaction handler, an RDF generator, an RDF store and an anomaly detector decodes the transaction payload encoded through the transaction handler and splits it into fields, and then, according to the action field indicating the received transaction type, the transaction handler Enables you to perform predefined tasks in transactions that add supply chain event data captured during the movement of physical objects.
How the blockchain system works. 11. The method of claim 10,
After generating a triple set consisting of two vertices and an edge connecting the two vertices according to the graph generation rule through the RDF generator using the supply chain event data described in the serial-to-parallel converted payload, , to detect whether anomalies are detected through an anomaly detector in relation to a set stored in advance in the RDF storage.
How the blockchain system works. 12. The method of claim 11,
The anomaly detector interacts with the validator's global state to detect anomalies,
If no anomaly is detected through the anomaly detector, the transaction handler adds the triple set generated for the current transaction to the RDF storage and updates the block and global state
How the blockchain system works. 12. The method of claim 11,
allow the anomaly detector to interact with the global state of the validator to detect anomalies;
When an anomaly is detected through the anomaly detector, the transaction is invalidated, the global state for the transaction is not updated, and the transaction related to the detected anomaly is applied to the system.
How the blockchain system works. 11. The method of claim 10,
The transaction payload contains only supply chain event data components captured during the movement of a physical object, and among the event data components, the event type defined in Global Traceability Standard (GTS), the identifier of the relevant object, the timestamp, location and business Integrates only the data needed for evidence tracking, including Key Data Element (KDE), event data simplified by step fields, and an off-chain database access link.
How the blockchain system works. 15. The method of claim 14,
Additions including elevation and longitude, attributes, and sensor data of a specific location with respect to said event data stored in an off-chain database via a connection link stored in the transaction processor for the transaction processor to manage supply chain event data captured during the movement of a physical object Accessing data to detect additional anomalies
How the blockchain system works. 11. The method of claim 10,
The anomaly detector includes, for each transaction, whether the location of the related item has changed, if changed, a speed condition for reading critical data from the global state and checking whether the average moving speed is within a predetermined range, and a predetermined dwell time condition Consistency rule-based anomaly detection using consistency rules that
How the blockchain system works.

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