Multimodal transport supply chain enabling method and system based on blockchain and data exchange

Abstract

The present invention relates to a multimodal transport supply chain enabling method and system based on blockchain and data exchange, which belongs to the field of supply chain empowerment. Among them, the method includes designing a blockchain network architecture and reaching a dynamic consensus agreement, obtaining a cross-chain verification node and generating a waybill; outputting a transport resource dispatch list based on the waybill using a cross-chain data prediction model; establishing a random process decision engine for nested execution of smart contracts based on the transport resource dispatch list, for selecting the optimal contract; performing spatiotemporal integrated path optimization based on the optimal contract, generating a global path, and realizing multimodal transport supply chain empowerment. The present invention uses blockchain technology to achieve transparent sharing and secure exchange of data in all links of multimodal transport, combines smart contracts and dynamic optimization algorithms, improves supply chain efficiency, reduces costs, and ensures traceability of cargo transportation throughout the entire process.

Description

Technical Field

[0001] The present invention belongs to the field of supply chain empowerment technology, and specifically relates to a multimodal transport supply chain empowerment method and system based on blockchain and data exchange. Background Art

[0002] Current intermodal transport systems generally utilize centralized databases for data storage and electronic data exchange for information sharing. Blockchain technology has been introduced to build distributed ledgers, but a single chain architecture struggles to adapt to the diverse data formats of shipping, rail, and road transport. Existing cross-chain solutions focus on financial transaction scenarios and employ static verification node mechanisms. However, in intermodal transport scenarios, the dynamic nature of transport nodes (such as temporary port closures and sudden capacity changes) makes it difficult for static nodes to guarantee cross-chain efficiency and meet the real-time demands of transport. Traditional smart contracts are based on deterministic rules, but intermodal transport involves random events such as typhoons and traffic control, and existing solutions lack quantitative modeling of uncertainty. Existing research often employs linear programming or reinforcement learning for path planning, but these methods fail to fully consider spatiotemporal coupling, resulting in significant deviations between planned results and actual results. Summary of the Invention

[0003] In order to solve the above-mentioned problems existing in the prior art, the present invention provides a multimodal transport supply chain enabling method and system based on blockchain and data exchange.

[0004] The purpose of the present invention can be achieved through the following technical solutions:

[0005] A multimodal transport supply chain enabling method based on blockchain and data exchange, wherein the implementation of the multimodal transport supply chain enabling method comprises the following steps:

[0006] S1: Design the blockchain network architecture and reach a dynamic consensus agreement to obtain cross-chain verification nodes and generate waybills;

[0007] S2: Outputting a transportation resource dispatch list based on the waybill using a cross-chain data prediction model;

[0008] S3: Establishing a random process decision engine for nested execution of smart contracts based on the transportation resource scheduling list to select the optimal contract;

[0009] S4: Based on the optimal contract, the time-space integrated path is optimized to generate a global path and enable the multimodal transport supply chain.

[0010] Preferably, the step S1 specifically includes:

[0011] S101: Designing the blockchain network architecture. The types of blockchain network architectures include shipping chain network architecture, railway chain network architecture, and road chain network architecture. The components of the blockchain network architecture include node types and data structures.

[0012] S102: Calculate the consensus score based on the blockchain network architecture and reach the dynamic consensus protocol, select the cross-chain verification node and generate a waybill.

[0013] Preferably, the generation of the waybill in step S102 specifically includes:

[0014] S102-1: Traverse all nodes to obtain node parameters, including node data accuracy, inter-node data delay, node reputation value, node energy consumption ratio, and inter-node distance ratio;

[0015] S102-2: Obtaining a data quality item and a node efficiency item according to the node parameters;

[0016] S102-3: Obtaining the consensus score based on the data quality item and the node efficiency item;

[0017] S102-4: Select the node with the highest consensus score as the cross-chain verification node and generate a waybill.

[0018] Preferably, the mathematical description of the data quality item in step S102-2 is: ,in, is the data quality item of node i, m is the number of data types, is the k-th data accuracy of node i, λ is the attenuation coefficient, is the k-th data delay of node i, and the mathematical description of the node efficiency term is ,in, is the node effectiveness item of node i, NodeReputantion i is the node reputation value of node i, EnergyCost i is the node energy consumption ratio of node i, NodeDistance i is the ratio of the distance between node i and its adjacent nodes, and α and β are adjustment coefficients.

[0019] Preferably, the cross-chain data prediction model in step S2 specifically includes:

[0020] S201: The initiator chain initiates a request to predict the throughput of the initiator end in the next n days;

[0021] S202: Subsequent chains share encrypted capacity data;

[0022] S203: Each chain trains the model locally and uploads the parameter gradient to the coordination server;

[0023] S204: The global model is aggregated and distributed, and the prediction capability of each chain is updated to obtain the throughput prediction value.

[0024] Preferably, the step S3 specifically includes:

[0025] S301: Conduct random process modeling driven by random events to predict the impact of random events on the blockchain;

[0026] S302: Build a nested smart contract, enter the terms of the main contract, design the secondary contract and specify the trigger conditions, and compare and select the optimal contract.

[0027] Preferably, the mathematical description of the stochastic process modeling is ,in, is the posterior probability of random event j, is the prior probability of random event j, is the probability of observing data under random event j.

[0028] Preferably, the step S4 specifically includes:

[0029] S401: Divide the transportation cycle into time periods in units of x and define space-time edges;

[0030] S402: Constructing objective function and transportation constraints;

[0031] S403: Obtain the global path according to the objective function and the transportation constraints, and generate a detailed timetable.

[0032] Preferably, the mathematical description of the objective function is , where C is the node dynamic cost and is the expected cost of delay, Risk is the sum of the product of the risk probability and the loss amount of each node, μ and is the weight; the transportation constraints include flow balance, capacity constraint and time constraint.

[0033] A multimodal supply chain enabling system based on blockchain and data exchange, used to implement the multimodal supply chain enabling method described above, including a dynamic consensus module, a resource scheduling module, a random decision module, and a spatiotemporal integration module;

[0034] The dynamic consensus module is used to design the blockchain network architecture and reach a dynamic consensus protocol, obtain cross-chain verification nodes and generate waybills;

[0035] The resource scheduling module is used to output a transportation resource scheduling list based on the waybill using a cross-chain data prediction model;

[0036] The random decision module is used to establish a random process decision engine for nested execution of smart contracts based on the transportation resource scheduling list, so as to select the optimal contract;

[0037] The time-space integration module is used to optimize the time-space integration path based on the optimal contract, generate a global path, and enable the multimodal transport supply chain.

[0038] The beneficial effects of the present invention are:

[0039] (1) Dynamic election of verification nodes is achieved through a dynamic consensus protocol, which improves the cross-chain verification success rate and real-time data interoperability compared to the fixed node mechanism.

[0040] (2) Optimize resource scheduling prediction accuracy through throughput prediction.

[0041] (3) Dynamic hedging of random risks can be achieved by quantifying the chain reaction of random events such as typhoons. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] To facilitate understanding by those skilled in the art, the present invention is further described below with reference to the accompanying drawings.

[0043] Figure 1 This is a flowchart of the steps of a multimodal supply chain enabling method based on blockchain and data exchange of the present invention. DETAILED DESCRIPTION

[0044] In order to better understand the present invention, various aspects of the present invention will be described in more detail with reference to the accompanying drawings. It should be understood that these detailed descriptions are only descriptions of exemplary embodiments of the present invention and are not intended to limit the scope of the present invention in any way. Throughout the specification, the expression "and / or" includes any and all combinations of one or more of the associated listed items. As used herein, the terms "roughly", "approximately" and similar terms are used as terms to indicate approximate values, rather than as terms to indicate degree, and are intended to illustrate inherent deviations in measurements or calculated values ​​that will be recognized by those of ordinary skill in the art. In addition, in the present invention, the order in which the steps are described does not necessarily represent the order in which these processes occur in actual operation, unless otherwise specified or can be derived from the context.

[0045] It should also be understood that expressions such as "comprises," "including," "having," "includes," and / or "comprising" are open rather than closed expressions in this specification, indicating the presence of the stated features, elements, and / or components, but do not exclude the presence of one or more other features, elements, components, and / or combinations thereof. In addition, when expressions such as "at least one of..." appear after a list of listed features, they modify the entire list of features rather than just the individual elements in the list. In addition, when describing embodiments of the present invention, "may" is used to mean "one or more embodiments of the present invention." And, the term "exemplary" is intended to refer to an example or illustration.

[0046] Unless otherwise defined, all terms used herein (including engineering and scientific terms) have the same meaning as commonly understood by those skilled in the art to which this invention pertains. It should also be understood that, unless otherwise expressly stated herein, words defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant art, and should not be interpreted in an idealized or overly formal sense.

[0047] It should be noted that, in the absence of conflict, the embodiments and features of the embodiments of the present invention can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0048] Example 1:

[0049] See also Figure 1 , a multimodal supply chain empowerment method based on blockchain and data exchange, including:

[0050] S1: Design a blockchain network architecture and reach a dynamic consensus agreement to obtain cross-chain verification nodes and generate waybills. This will address the issues of high heterogeneity of multimodal transport data and low cross-chain efficiency, ensuring real-time interoperability and tamper-proof data.

[0051] S2: Outputting a transportation resource dispatch list based on the waybill using a cross-chain data prediction model;

[0052] S3: Establishing a random process decision engine for nested execution of smart contracts based on the transportation resource scheduling list to select the optimal contract;

[0053] S4: Based on the optimal contract, the time-space integrated path is optimized to generate a global path and enable the multimodal transport supply chain.

[0054] In this embodiment, a blockchain network architecture is designed and a dynamic consensus protocol is reached to obtain a cross-chain verification node and generate a waybill. This can be specifically implemented through the following steps:

[0055] S101: Design the blockchain network architecture. The types of blockchain network architecture include but are not limited to shipping chain network architecture, railway chain network architecture and road chain network architecture. The components of the blockchain network architecture include node types and data structures. For example, in a shipping chain, Port A writes the loading time and estimated arrival time of the container into the blockchain. Its node types include port (Port A, etc.), shipping company, customs, etc., and the data structure is Block 海运链 ={container ID, loading time, arrival time, temperature and humidity sequence}. A railway chain: Train B departs from station B, with a remaining capacity of 20 containers. Its node types include railway companies (the company where train B is located) and freight stations (station B, etc.). The data structure is Block 铁路链 ={train number, departure time, arrival time, remaining capacity}. A road chain: Truck C uploads its location and road condition index in real time. Its node types include logistics companies, driver terminals, and warehouses. The data structure is Block 公路链 ={vehicle ID, current location, road condition index};

[0056] S102: Calculate the consensus score based on the blockchain network architecture and reach the dynamic consensus protocol, select the cross-chain verification node and generate a waybill;

[0057] S102-1: Traverse all nodes and obtain node parameters. The node is the transit point during the transportation process (Port A, Station B, etc.). The node parameters include node data accuracy, inter-node data delay, node reputation value, node energy consumption ratio, and inter-node distance ratio;

[0058] S102-2: Obtain data quality items and node efficiency items based on the node parameters. The mathematical description of the data quality item is: ,in, is the data quality item of node i, m is the number of data types (such as port loading and unloading records, delay records, etc.), is the accuracy of the k-th type of data at node i, λ is the attenuation coefficient (based on historical data fitting, usually 0.01), is the k-th data delay of node i (dimensionless, scored, ranging from 1 to 100), and the mathematical description of the node efficiency term is ,in, is the node effectiveness item of node i, NodeReputantion i is the node reputation value of node i (based on the historical interaction success rate, ranging from 0 to 100), EnergyCost i is the node energy consumption ratio of node i (dimensionless, taking the percentage of the current energy consumption to the average energy consumption ratio of the same type of nodes), NodeDistance iis the ratio of the distance between node i and its adjacent nodes (the percentage of the distance between nodes to the total distance of the expected planned route), α and β are adjustment coefficients, with default values ​​of 0.2 and 0.1 respectively;

[0059] S102-3: Obtain the consensus score based on the data quality item and the node efficiency item, which can be mathematically described as , among which, ConsensusScore i is the consensus score of node i;

[0060] S102-4: The node with the highest consensus score is selected as the cross-chain verification node and generates a waybill, ensuring cross-chain data efficiency and reliability. Example: The shipping chain records "A123 has been loaded" and triggers a cross-chain request. The blockchain network architecture is designed based on the expected route. Input parameters: Port A unloading accuracy of 98%, data latency of 50, node reputation value of 95, node energy efficiency ratio of 90, and inter-node distance ratio of 10%. The consensus score of Port A is 3.133, the highest among all nodes, and it is selected as the cross-chain verification node. The railway chain accepts the verified data and generates a waybill: "A123 is scheduled to be shipped by B in n days."

[0061] In this embodiment, a transport resource dispatch list is output based on the waybill using a cross-chain data prediction model, which can be implemented by the following steps:

[0062] S201: The initiator chain initiates a request to predict the throughput of the initiator end in the next n days;

[0063] S202: Subsequent chains share encrypted capacity data (such as remaining capacity of trains and number of available trucks);

[0064] S203: Each chain trains the model locally and uploads the parameter gradient to the coordination server;

[0065] S204: The global model is aggregated and distributed, and the prediction capability of each chain is updated to obtain the throughput prediction value. The mathematical description of the throughput prediction value is: , where Throughput is the throughput prediction value, N is the total number of nodes, ω i is the weight of each chain model, which is dynamically allocated according to the consensus score, b is the bias term, LocalModel i is the capacity data for node i. Example: Input: Train A has a remaining capacity of 200 containers for the next 7 days, and the number of available trucks is 50. Output: The throughput of Port B (i.e., the starting point, where the port is the starting segment and the starting chain is the shipping chain) is predicted to increase by 15%, triggering the early dispatch of more cargo ships.

[0066] In this embodiment, a random process decision engine for nested execution of smart contracts is established based on the transportation resource scheduling list to select the optimal contract. This can be specifically implemented by the following steps:

[0067] S301: Perform random process modeling driven by random events to predict the impact of random events on the blockchain. The mathematical description of the random process modeling is: ,in, is the posterior probability of random event j, is the prior probability of random event j (e.g. the probability of port closure during typhoon season is 20%), is the probability of observing data under random event j (such as the typhoon path prediction accuracy). For example, the system detects a North Sea storm and needs to adjust the transportation route. At this time, the typhoon path prediction accuracy P(Date|Typhoon) = 0.85, the prior probability of port closure P(Typhoon) = 0.2, and the remaining random events P(Date|Other Events) = 0.1 and P(Other Events) = 0.8. The posterior probability is (0.85·0.2) / (0.85·0.2+0.1·0.8) = 68%. In other words, the probability that the typhoon will have a negative impact on transportation is as high as 68%, and the transportation strategy needs to be adjusted in time.

[0068] S302: Construct a nested smart contract, enter the main contract terms, design secondary contracts, and specify trigger conditions. Compare and select the optimal contract. Example: Enter the main contract terms: Goods must be shipped from location A to location B within 14 days, with a base freight of 150,000 yuan. If the delay exceeds two days, the shipper will be compensated 50,000 yuan per day. Trigger condition: The posterior probability of random event j is greater than 50%. Secondary contract: Activate "sea + air" hybrid transport, increasing costs by 200,000 yuan. If air transport capacity is insufficient, activate the "sea → rail → road" backup route. If the random process model predicts a 72% probability of typhoon-induced ocean shipping delays, the main contract terms cost approximately 222,000 yuan, increasing the time cost by two days. The secondary contract costs approximately 350,000 yuan, leaving the time cost unchanged. Based on the collaborative decision-making of multiple parties (shippers, carriers, insurance companies, etc.), the secondary contract is triggered as the optimal contract.

[0069] In this embodiment, spatiotemporal integration path optimization is performed based on the optimal contract to generate a global path and enable the multimodal supply chain. This can be specifically implemented through the following steps:

[0070] S401: Divide the transport cycle into time periods of x (e.g., x = 1h, t = 0, 1, ..., 336 represents a 14-day transport cycle). Define a spatiotemporal edge, i.e., a feasible transport mode from node i at time t to node j at time t + Δt (for example, transporting goods from port A (t = 0) to port B (t = 336) via a shipping chain, with a cost of 10,000 yuan and a risk of 5%).

[0071] S402: Construct an objective function and transportation constraints. The mathematical description of the objective function is: , where C is the node dynamic cost and (such as shipping costs fluctuate with oil prices), is the expected cost of delay (based on random process simulation), Risk is the sum of the product of the risk probability and the loss amount of each node, and μ and is the weight, and the transportation constraints include flow balance, capacity constraint and time constraint;

[0072] S403: Obtain the global path according to the objective function and the transportation constraints, and generate a detailed timetable to ensure that the process is open and transparent.

[0073] Example 2:

[0074] A multimodal supply chain enabling system based on blockchain and data exchange, including a dynamic consensus module, a resource scheduling module, a random decision module, and a time-space integration module;

[0075] The dynamic consensus module is used to design a blockchain network architecture and reach a dynamic consensus agreement, obtain a cross-chain verification node and generate a waybill, specifically: design the blockchain network architecture, the types of the blockchain network architecture include but are not limited to shipping chain network architecture, railway chain network architecture and highway chain network architecture, and the composition of the blockchain network architecture includes node type and data structure; calculate the consensus score based on the blockchain network architecture and reach the dynamic consensus agreement, select the cross-chain verification node and generate a waybill: traverse all nodes to obtain node parameters, the node is the waypoint in the transportation process, and the node parameters include node data accuracy, inter-node data delay, node reputation value, node energy consumption ratio and inter-node distance ratio; obtain data quality items and node efficiency items based on the node parameters; obtain the consensus score based on the data quality item and the node efficiency item; select the node with the highest consensus score as the cross-chain verification node and generate a waybill.

[0076] The resource scheduling module is used to output a transportation resource scheduling order based on the waybill using a cross-chain data prediction model. Specifically, the starting chain initiates a request to predict the throughput of the starting end in the next n days; subsequent chains share encrypted capacity data; each chain locally trains the model and uploads the parameter gradient to the coordination server; the global model is aggregated and issued, and the prediction capabilities of each chain are updated to obtain the throughput prediction value.

[0077] The random decision module is used to establish a random process decision engine for nested execution of smart contracts based on the transportation resource scheduling list, and is used to select the optimal contract. Specifically, it performs random process modeling driven by random events to predict the impact of random events on the blockchain; constructs nested smart contracts, enters the terms of the main contract, designs secondary contracts and specifies trigger conditions, and compares and selects the optimal contract.

[0078] The spatiotemporal integration module is used to optimize the spatiotemporal integration path based on the optimal contract, generate a global path, and enable the multimodal supply chain. Specifically, the transportation cycle is divided into time periods in units of x, and spatiotemporal edges are defined, that is, feasible transportation modes from node i at time t to node j at time t+△t; the objective function and transportation constraints are constructed, and the transportation constraints include flow balance, capacity constraints, and time constraints; the global path is obtained according to the objective function and the transportation constraints, and a detailed timetable is generated to ensure that the process is open and transparent.

[0079] The above description is merely a preferred embodiment of the present invention and does not constitute any form of limitation to the present invention. Although the present invention has been disclosed as a preferred embodiment as above, it is not intended to limit the present invention. Any person skilled in the art can make some changes or modifications to equivalent embodiments using the technical contents disclosed above without departing from the scope of the technical solution of the present invention. However, any simple modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solution of the present invention are still within the scope of the technical solution of the present invention.

Claims

1. A multimodal supply chain enabling method based on blockchain and data exchange, characterized in that: The implementation of the multimodal supply chain enabling method includes the following steps: S1: Design the blockchain network architecture and reach a dynamic consensus agreement to obtain cross-chain verification nodes and generate waybills; S2: Outputting a transportation resource dispatch list based on the waybill using a cross-chain data prediction model; S3: Establishing a random process decision engine for nested execution of smart contracts based on the transportation resource scheduling list to select the optimal contract; S4: Based on the optimal contract, perform spatiotemporal integration path optimization to generate a global path and enable the multimodal supply chain; The step S1 specifically includes: S101: Designing the blockchain network architecture. The types of blockchain network architectures include shipping chain network architecture, railway chain network architecture, and road chain network architecture. The components of the blockchain network architecture include node types and data structures. S102: Calculate the consensus score based on the blockchain network architecture and reach the dynamic consensus protocol, select the cross-chain verification node and generate a waybill; The generation of the waybill in step S102 specifically includes: S102-1: Traverse all nodes to obtain node parameters, including node data accuracy, inter-node data delay, node reputation value, node energy consumption ratio, and inter-node distance ratio; S102-2: Obtaining a data quality item and a node efficiency item according to the node parameters; S102-3: Obtaining the consensus score based on the data quality item and the node efficiency item; S102-4: Select the node with the highest consensus score as the cross-chain verification node and generate a waybill.

2. The multimodal supply chain enabling method according to claim 1, characterized in that: The mathematical description of the data quality item in step S102-2 is: ,in, is the data quality item of node i, m is the number of data types, is the k-th data accuracy of node i, λ is the attenuation coefficient, is the k-th data delay of node i, and the mathematical description of the node efficiency term is ,in, is the node effectiveness item of node i, NodeReputantion i is the node reputation value of node i, EnergyCost i is the node energy consumption ratio of node i, NodeDistance i is the ratio of the distance between node i and its adjacent nodes, and α and β are adjustment coefficients.

3. The multimodal supply chain enabling method according to claim 1, characterized in that: The cross-chain data prediction model in step S2 specifically includes: S201: The initiator chain initiates a request to predict the throughput of the initiator end in the next n days; S202: Subsequent chains share encrypted capacity data; S203: Each chain trains the model locally and uploads the parameter gradient to the coordination server; S204: The global model is aggregated and distributed, and the prediction capability of each chain is updated to obtain the throughput prediction value.

4. The multimodal supply chain enabling method according to claim 1, characterized in that: The step S3 specifically includes: S301: Conduct random process modeling driven by random events to predict the impact of random events on the blockchain; S302: Build a nested smart contract, enter the terms of the main contract, design the secondary contract and specify the trigger conditions, and compare and select the optimal contract.

5. The multimodal supply chain enabling method according to claim 4, characterized in that: The mathematical description of the stochastic process modeling is ,in, is the posterior probability of random event j, is the prior probability of random event j, is the probability of observing data under random event j.

6. The multimodal supply chain enabling method according to claim 1, characterized in that: The step S4 specifically includes: S401: Divide the transportation cycle into time periods in units of x and define space-time edges; S402: Constructing objective function and transportation constraints; S403: Obtain the global path according to the objective function and the transportation constraints, and generate a detailed timetable.

7. The multimodal supply chain enabling method according to claim 6, characterized in that: The mathematical description of the objective function is , where C is the node dynamic cost and is the expected cost of delay, Risk is the sum of the product of the risk probability and the loss amount of each node, μ and is the weight; the transportation constraints include flow balance, capacity constraint and time constraint.

8. A multimodal transport supply chain enabling system based on blockchain and data exchange, characterized by: The system is applied to the multimodal supply chain enabling method as claimed in any one of claims 1 to 7, comprising a dynamic consensus module, a resource scheduling module, a random decision module and a spatiotemporal integration module; The dynamic consensus module is used to design the blockchain network architecture and reach a dynamic consensus protocol, obtain cross-chain verification nodes and generate waybills; The resource scheduling module is used to output a transportation resource scheduling list based on the waybill using a cross-chain data prediction model; The random decision module is used to establish a random process decision engine for nested execution of smart contracts based on the transportation resource scheduling list, so as to select the optimal contract; The time-space integration module is used to optimize the time-space integration path based on the optimal contract, generate a global path, and enable the multimodal transport supply chain.

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