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

Abstract

The invention relates to a multimodal transport supply chain enabling method and system based on a block chain and data exchange, and belongs to the field of supply chain enabling. The method comprises the steps that a block chain network architecture is designed, a dynamic consensus protocol is achieved, a cross-chain verification node is obtained, and a waybill is generated; outputting a transportation resource scheduling list based on the waybill by using a cross-chain data prediction model; establishing a random process decision engine for nested execution of the smart contract based on the transportation resource scheduling list, wherein the random process decision engine is used for selecting an optimal contract; and performing space-time integration path optimization based on the optimal contract, generating a global path, and realizing multimodal transport supply chain enabling. Transparent sharing and safe exchange of data of each link of multimodal transport are realized through the block chain technology, and the intelligent contract and the dynamic optimization algorithm are combined, so that the supply chain efficiency is improved, the cost is reduced, and the traceability of the whole cargo transport process is ensured.

Description

Technical Field

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

[0002] Currently, the multimodal transport system generally uses a centralized database for data storage and realizes information sharing through electronic data interchange. Blockchain technology has been introduced to build a distributed ledger, but a single-chain architecture is difficult to adapt to the different data formats of sea, rail, and road transportation. Existing cross-chain solutions focus on financial transaction scenarios and adopt a static verification node mechanism. However, in the multimodal transport scenario, the dynamic nature of transport nodes (such as temporary port closures and sudden changes in transport capacity) makes it difficult for static nodes to ensure cross-chain efficiency and meet the real-time transport requirements. Traditional smart contracts are executed based on deterministic rules, but multimodal transport involves random events such as typhoons and traffic control, and existing solutions lack quantitative modeling of uncertainties. Existing research mostly uses linear programming or reinforcement learning for route planning, but does not fully consider spatio-temporal coupling, resulting in a large deviation between the planning results and the actual situation. Summary of the Invention

[0003] To solve the above 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 object of the present invention can be achieved by the following technical solutions: A multimodal transport supply chain enabling method based on blockchain and data exchange, the implementation of the multimodal transport supply chain enabling method includes the following steps: S1: Design a blockchain network architecture and reach a dynamic consensus protocol to obtain cross-chain verification nodes and generate a waybill; S2: Output a transport resource scheduling order based on the waybill using a cross-chain data prediction model; S3: Establish a stochastic process decision engine for nested execution of smart contracts based on the transport resource scheduling order to select the optimal contract; S4: Optimize the spatio-temporal integration path based on the optimal contract to generate a global path and realize the enabling of the multimodal transport supply chain.

[0005] Preferably, the step S1 specifically includes: S101: Design the blockchain network architecture, the types of the blockchain network architecture include a sea transport chain network architecture, a railway transport chain network architecture, and a road transport chain network architecture, and the composition of the blockchain network architecture includes node types and data structures; S102: Calculate a consensus score based on the blockchain network architecture and reach the dynamic consensus protocol, select the cross-chain verification nodes and generate a waybill.

[0006] Preferably, the generation of the waybill in step S102 specifically includes: S102-1: traverse all nodes to obtain node parameters, wherein the node parameters include 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 according to 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.

[0007] 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 type of 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.

[0008] Preferably, 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.

[0009] Preferably, the step S3 specifically includes: S301: Conduct random process modeling driven by random events to predict the impact of random events on 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.

[0010] Preferably, the mathematical description of the stochastic process modeling is , where 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.

[0011] Preferably, step S4 specifically includes: S401: Divide the transportation cycle into time periods with x as the unit, and define the spatio-temporal edges; S402: Construct the objective function and transportation constraints; S403: Obtain the global path according to the objective function and the transportation constraints, and generate a detailed timetable.

[0012] Preferably, the mathematical description of the objective function is , where C is the sum of node dynamic costs, is the expected delay cost, Risk is the sum of the products of the risk probabilities and loss amounts of each node, and μ and are weights; the transportation constraints include flow balance, transportation capacity constraints, and time constraints.

[0013] A multimodal transport supply chain enabling system based on blockchain and data exchange, used to execute the multimodal transport supply chain enabling method described above, includes a dynamic consensus module, a resource scheduling module, a stochastic decision-making module, and a spatio-temporal 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 order based on the waybill using a cross-chain data prediction model; The stochastic decision-making module is used to establish a stochastic process decision-making engine for nested execution of smart contracts based on the transportation resource scheduling order, and is used to select the optimal contract; The spatio-temporal integration module is used to optimize the spatio-temporal integration path based on the optimal contract, generate a global path, and realize the empowerment of the multimodal transport supply chain.

[0014] The beneficial effects of the present invention are: (1) Realize the dynamic election of verification nodes through the dynamic consensus protocol, improving the cross-chain verification success rate and data real-time interconnection ability compared with the fixed node mechanism.

[0015] (2) Optimize the resource scheduling prediction accuracy through throughput prediction.

[0016] (3) Realize dynamic hedging of random risks by quantifying the chain effects of random events such as typhoons. Description of the Drawings

[0017] For the convenience of those skilled in the art to understand, the present invention will be further described below in conjunction with the accompanying drawings.

[0018] Figure 1 It is a flowchart of the steps of a method for empowering a multimodal transport supply chain based on blockchain and data exchange of the present invention. Specific embodiments

[0019] To better understand the present invention, more detailed descriptions of various aspects of the present invention will be made with reference to the accompanying drawings. It should be understood that these detailed descriptions are only descriptions of the exemplary embodiments of the present invention, and do not 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, terms such as "substantially", "about" and similar terms are used as terms indicating approximation, rather than terms indicating degree, and are intended to account for the inherent deviations in measured or calculated values that would be recognized by those of ordinary skill in the art. Additionally, in the present invention, the order of description of the steps of each process does not necessarily represent the order in which these processes occur in actual operation, unless there are clear other limitations or can be deduced from the context.

[0020] It should also be understood that expressions such as "including", "including having", "having", "containing" and / or "containing having" in this specification are open-ended rather than closed-ended expressions, which mean that there are the stated features, elements and / or components, but do not exclude the existence of one or more other features, elements, components and / or their combinations. In addition, when an expression such as "at least one of..." appears after a list of listed features, it modifies the entire list of features, rather than just modifying a single element in the list. In addition, when describing the embodiments of the present invention, the use of "may" means "one or more embodiments of the present invention". And the term "exemplary" is intended to refer to an example or illustration.

[0021] Unless otherwise defined, all terms used herein (including engineering terms and scientific and technical terms) have the same meaning as commonly understood by those of ordinary skill in the art to which the present invention pertains. It should also be understood that, unless there is a clear statement in the present invention, words defined in a common dictionary 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.

[0022] It should be noted that, without conflict, the embodiments in the present invention and the features in the embodiments can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in conjunction with the embodiments.

[0023] Example 1: Please refer to Figure 1, A multimodal transport supply chain empowerment method based on blockchain and data exchange, including: S1: Design a blockchain network architecture and reach a dynamic consensus protocol to obtain cross-chain verification nodes and generate waybills, solve the problems of high heterogeneity and low cross-chain efficiency of multimodal transport data, and ensure real-time data interconnection and immutability; S2: Based on the waybill, use a cross-chain data prediction model to output a transportation resource scheduling order; S3: Based on the transportation resource scheduling order, establish a stochastic process decision-making engine for nested execution of smart contracts to select the optimal contract; S4: Based on the optimal contract, perform spatio-temporal integration path optimization to generate a global path and achieve multimodal transport supply chain empowerment.

[0024] In this embodiment, designing a blockchain network architecture and reaching a dynamic consensus protocol to obtain cross-chain verification nodes and generate waybills can be specifically implemented through the following steps: S101: Design the blockchain network architecture. The types of the blockchain network architecture include but are not limited to a maritime chain network architecture, a railway chain network architecture, and a road chain network architecture. The composition of the blockchain network architecture includes node types and data structures; Example: For a certain maritime chain: Port A writes the loading time and expected arrival time of the container into the blockchain. Its node types include ports (such as Port A), shipping companies, customs, etc., and the data structure is Block 海运链 ={Container ID, loading time, arrival time, temperature and humidity sequence}. For a certain railway chain: Train B departs from Station b with 20 remaining container capacities. Its node types include railway companies (the company where Train B is located), freight stations (such as Station b), and the data structure is Block 铁路链 ={Train number, departure time, arrival time, remaining capacity}. For a certain road chain: Truck C uploads its location and road condition index in real time. Its node types include logistics companies, driver terminals, warehouses, and the data structure is Block 公路链 ={Vehicle ID, current location, road condition index}; S102: Calculate a consensus score based on the blockchain network architecture and reach the dynamic consensus protocol, select the cross-chain verification nodes and generate waybills; S102-1: Traverse all nodes to obtain node parameters. The nodes are the waypoints (such as Port A, Station b, etc.) in the transportation process. The node parameters include node data accuracy, data delay between nodes, node reputation value, node energy consumption ratio, and distance ratio between nodes; S102-2: Obtain a data quality item and a node efficiency item according to the node parameters. The mathematical description of the data quality item is , where, 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 k-th data accuracy of 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 i is 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, and the default values ​​are 0.2 and 0.1 respectively; 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; S102-4: Select the node with the highest consensus score as the cross-chain verification node and generate a waybill to ensure data cross-chain efficiency and reliability. Example: The shipping chain records "A123 has been loaded" and triggers a cross-chain request. The blockchain network architecture is designed according to the expected route. Input parameters: Port A unloading accuracy 98%, data delay 50, node reputation value 95, node energy consumption ratio 90, node distance ratio 10%, then the consensus score of Port A is 3.133, which has the highest score among all nodes and is selected as the cross-chain verification node. The railway chain receives the verified data and generates a waybill "A123 is scheduled to be shipped by B after n days".

[0025] 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: S201: The initiator chain initiates a request to predict the throughput of the initiator end in the next n days; S202: The subsequent chain shares encrypted capacity data (such as remaining capacity of trains and available number of trucks); 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 a throughput prediction value. The mathematical description of the throughput prediction value is: , where Throughput is the predicted throughput value, N is the total number of nodes, ω i is the weight of each chain model, dynamically allocated according to the consensus score, b is the bias term, and LocalModel i is the transport capacity data of node i. Example: Input: The remaining transport capacity of Train A in the next 7 days is 200 containers, and the number of available trucks is 50. Output: It is predicted that the throughput of Port B (i.e., the starting end, the starting chain is the sea transport chain when the port is the starting section) will increase by 15%, triggering the early scheduling of more cargo ships.

[0026] In this embodiment, a stochastic process decision engine for nested execution of smart contracts is established based on the transport resource scheduling order, which is used to select the optimal contract. Specifically, it can be implemented through the following steps: S301: Conduct stochastic process modeling driven by random events to predict the impact of random events on the blockchain. The mathematical description of the stochastic process modeling is , where is the posterior probability of random event j, is the prior probability of random event j (for example, the probability of the port being closed during the typhoon season is 20%), is the probability of observing data under random event j (such as the typhoon path prediction accuracy); Example: The system detects a storm in Beihai and needs to adjust the transport route. At this time, the typhoon path prediction accuracy P(Date|typhoon)=0.85, the prior probability of the port being closed P(typhoon)=0.2, the probability of the remaining random events P(Date|other events)=0.1, and P(other events)=0.8. Then the posterior probability is (0.85·0.2) / (0.85·0.2 + 0.1·0.8)=68%, that is, the probability of the typhoon having a negative impact on transportation is as high as 68%, and the transportation strategy needs to be adjusted in time; S302: Build nested smart contracts, enter the terms of the main contract, design secondary contracts and specify the triggering conditions, and compare and select the optimal contract. Example: Enter the terms of the main contract: The goods need to be transported from Place A to Place B within 14 days, with a basic freight of 150,000 yuan. If the delay exceeds 2 days, the shipper will be compensated 50,000 yuan per day; Triggering condition: The posterior probability of random event j is higher than 50%; Secondary contract: Enable the "sea + air" combined transportation, with a cost increase of 200,000 yuan. If the air transport capacity is insufficient, start the "sea → railway → highway" alternative route; When the stochastic process model predicts that the probability of typhoon causing sea transport delay is 72%, the cost of the main contract terms is about 222,000 yuan, and the time cost increases by 2 days. The cost of the secondary contract is about 350,000 yuan, and the time cost remains unchanged. Through comprehensive collaborative decision-making among multiple parties (shippers, carriers, insurance companies, etc.), triggering the secondary contract is the optimal contract.

[0027] In this embodiment, based on the optimal contract, the spatio-temporal integration path is optimized to generate a global path, enabling the multi-modal transport supply chain, which can be specifically implemented through the following steps: S401: Divide the transportation cycle into time periods with x as the unit (for example, with 1 hour as the unit, x = 1h, t = 0, 1, …, 336 represents a transportation cycle of 14 days), and define spatio-temporal edges, that is, the feasible transportation modes from node i at time t to node j at time t + △t (example: transporting goods from Port A (t = 0) to Port B (t = 336) through the sea transportation chain, with a cost of 10,000 yuan and a risk of 5%); S402: Construct the objective function and transportation constraints. The mathematical description of the objective function is , where C is the sum of node dynamic costs (such as sea transportation costs fluctuating with oil prices), is the expected delay cost (simulated based on a stochastic process), Risk is the sum of the products of the risk probabilities and loss amounts of each node, and μ and are weights. The transportation constraints include flow balance, capacity constraint, and time constraint; S403: Obtain the global path according to the objective function and the transportation constraints, and generate a detailed timetable to ensure the transparency of the process.

[0028] Embodiment 2: A multi-modal transport supply chain enabling system based on blockchain and data exchange includes a dynamic consensus module, a resource scheduling module, a stochastic decision-making module, and a spatio-temporal 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. Specifically: design the blockchain network architecture, the types of the blockchain network architecture include but are not limited to the sea transportation chain network architecture, the railway transportation chain network architecture, and the highway transportation chain network architecture, and the composition of the blockchain network architecture includes node types and data structures; calculate the consensus score based on the blockchain network architecture and reach the dynamic consensus protocol, select the cross-chain verification nodes, and generate waybills: traverse all nodes to obtain node parameters, the nodes are the passing points in the transportation process, and the node parameters include node data accuracy, data delay between nodes, node reputation value, node energy consumption ratio, and distance ratio between nodes; obtain the data quality item and the node efficiency item according to the node parameters; obtain the consensus score according to 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 waybills.

[0029] The resource scheduling module is used to output a transportation resource scheduling list 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 capability of each chain is updated to obtain the throughput prediction value.

[0030] 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: perform random process modeling driven by random events, predict the impact of random events on the blockchain; construct 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.

[0031] The space-time integration module is used to optimize the space-time 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 space-time edges are defined, that is, the feasible transportation mode 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.

[0032] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any form. Although the present invention has been disclosed as a preferred embodiment as above, it is not used to limit the present invention. Any technical personnel in this field can make some changes or modify the technical contents disclosed above into equivalent embodiments without departing from the scope of the technical solution of the present invention. However, any simple modification, equivalent change and modification made to the above embodiments according to the technical essence of the present invention without departing from the content of the technical solution of the present invention still fall within the scope of the technical solution of the present invention.

Claims

1. A multimodal transport 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 protocol 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, the time-space integrated path is optimized to generate a global path and enable the multimodal supply chain.

2. The multimodal transport supply chain enabling method according to claim 1, wherein The step S1 specifically includes: S101: Design the blockchain network architecture, the types of the blockchain network architecture include shipping chain network architecture, railway chain network architecture and highway chain network architecture, and 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.

3. The multimodal transport supply chain enabling method according to claim 2, characterized in that, The generation of the waybill in step S102 specifically includes: S102-1: traverse all nodes to obtain node parameters, wherein the node parameters include 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 according to 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.

4. The multimodal transport supply chain enabling method according to claim 3, wherein The mathematical description of the data quality item in step S102-2 is , where is the data quality item of node i, m is the number of data types, is the accuracy rate of the k-th type of data of node i, λ is the attenuation coefficient, is the delay of the k-th type of data of node i, and the mathematical description of the node efficiency item is , where is the node efficiency 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 inter-node distance between node i and its adjacent nodes, and α and β are adjustment coefficients.

5. The multimodal transport supply chain enabling method according to claim 1, wherein 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.

6. The multimodal transport supply chain enabling method according to claim 1, wherein The step S3 specifically includes: S301: Conduct random process modeling driven by random events to predict the impact of random events on 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.

7. The multimodal transport supply chain enabling method according to claim 6, characterized in that, The mathematical description of the random process modeling is , where is the posterior probability of the random event j, is the prior probability of the random event j, is the probability of observing the data under the random event j.

8. The multimodal transport supply chain enabling method according to claim 1, wherein The step S4 specifically includes: S401: Divide the transport 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.

9. The multimodal transport supply chain enabling method according to claim 8, wherein The mathematical description of the objective function is , where C is the sum of node dynamic costs, is the expected delay cost, Risk is the sum of the products of the risk probabilities and loss amounts of each node, and μ and are weights; the transportation constraints include flow balance, capacity constraint, and time constraint.

10. A multimodal transport supply chain enabling system based on blockchain and data exchange, characterized in that, The system is applied to the multimodal supply chain enabling method as described in any one of claims 1 to 9, including a dynamic consensus module, a resource scheduling module, a random decision module and a time-space 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 transport resource scheduling list based on the waybill using a cross-chain data prediction model; The random decision-making module is used to establish a random process decision-making engine for nested execution of smart contracts based on the transportation resource scheduling order, and is used to select the optimal contract; The spatio-temporal integration module is used to optimize the spatio-temporal integration path based on the optimal contract, generate a global path, and realize the empowerment of the multimodal transport supply chain.

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