Product lifecycle data reduction modeling and on-chain and off-chain mapping method and system

Abstract

The application provides a product full life cycle data reduction modeling and on-chain and off-chain mapping method and system, which comprises the following steps: establishing a maker chain network, and including all participants in the product full life cycle into the maker chain network; obtaining production transaction data of each participant in the product production process, and respectively utilizing an intelligent agent to perform reduction modeling on the production transaction data of each participant to obtain a corresponding transaction model; mapping each transaction model into a data entry, and obtaining digital twin data of the corresponding participant in combination with physical label information of the product; establishing a corresponding block for each participant; and finally, connecting each block by using a consensus mechanism in a time stamp order to form a block chain, and constructing a product full life cycle digital twin model; the application can reduce the product data model of the product data entry mapped to the physical world product manufacturing event, improve the security calculation efficiency of multi-modal data, and reduce the operation and maintenance cost.

Description

Technical Field

[0001] The present invention relates to the field of blockchain technology, and more specifically, to a method and system for data simplification modeling and on-chain and off-chain mapping for the entire product life cycle. Background Art

[0002] To meet consumers' growing demand for personalization, companies need more flexible supply and production chains to provide more personalized products and services. This requires social resources to self-organize in a timely manner through swarm intelligence and jointly create open-architecture products. However, while open-architecture products can guarantee industry standards and interfaces within the social manufacturing paradigm, they lack a decision-making support mechanism to form consensus, making it difficult to achieve effective connections between manufacturers.

[0003] The growing demand for personalization requires social resources to leverage the self-organization of crowd intelligence to collaboratively create open-architecture products. This social manufacturing paradigm expands manufacturers' need for product authenticity and quality tracking. Product lifecycle quality assurance and anti-counterfeiting are becoming increasingly important. A new decentralized blockchain-driven model, called Makerchain, has been proposed to manage the social manufacturing network credit between various manufacturers. A new anti-counterfeiting method based on chemical signatures has also been proposed to represent the unique features of personalized products. Binding unique signature data to blockchain and other functional databases has been implemented and is expected to make manufacturing service transactions between manufacturers more trustworthy. Based on the automatic execution mechanism of smart contracts between manufacturers, decentralized manufacturing networks can automate transactions between manufacturers and provide third-party verification of product lifecycles through a series of historical events.

[0004] Enabling rapid commissioning and decommissioning of participants can effectively save workload and costs while ensuring trust in the decentralized manufacturing paradigm. Therefore, it is very necessary to explore a self-organizing method that adapts to the characteristics of collaborative decentralization to make manufacturing service transactions between manufacturers more trustworthy.

[0005] Existing patent documents disclose a digital twin data management method for the entire life cycle of a product. The specific process is: building a peer-to-peer network and incorporating all participants in the entire life cycle of the product into the network; creating blocks, including block headers and block bodies; the block header is the identity proof of the block and is unique, and the block body is used to store the data of the digital twin in the form of a standard "Transaction"; in timestamp order, different blocks are connected using the proof-of-work consensus mechanism to form a full life cycle data blockchain of the digital twin; however, in this prior art method, due to the large number of participants and complex data types, the security calculation efficiency of multimodal data is low and the operation and maintenance cost is high. Therefore, there is an urgent need to study a data simplification method. Summary of the Invention

[0006] In order to overcome the defects of low computational efficiency and high operation and maintenance costs of multimodal data in the above-mentioned prior art, the present invention provides a method and system for data simplification modeling and on-chain and off-chain mapping of the entire product life cycle, which can simplify the product data model that maps product data entries to physical world product manufacturing events, improve the security computing efficiency of multimodal data, and reduce operation and maintenance costs.

[0007] In order to solve the above technical problems, the technical solutions of the present invention are as follows:

[0008] A method for simplifying product lifecycle data modeling and on-chain and off-chain mapping includes the following steps:

[0009] S1: Establish a maker chain network and include all participants in the entire product life cycle into the maker chain network;

[0010] S2: Obtain the production transaction data of each participant in the Maker Chain network during the product production process. Each participant constructs an intelligent agent locally and uses the intelligent agent to simplify and model the production transaction data to obtain a transaction model for each participant.

[0011] S3: Mapping the transaction model of each participant into a data entry, and synchronizing the physical tag information with the data entry through the link provided by the physical tag of the product to obtain the digital twin data of each participant;

[0012] S4: Create a corresponding block for each participant, where the block includes a block header and a block body; the block header stores the unique identity certificate of the block; and the block body stores the digital twin data of the participant;

[0013] S5: Connect each of the blocks in timestamp order using a consensus mechanism to form a blockchain, and build a digital twin model of the product's entire life cycle.

[0014] Preferably, in step S1, the participants in the Maker Chain network include: designers, demanders, manufacturers, prosumers, verifiers and regulators.

[0015] Preferably, the production transaction data in step S2 includes: product attributes, metadata and service data.

[0016] Preferably, the product attributes include: physical characteristics, chemical characteristics, energy consumption properties, material properties, manufacturing characteristics and quality characteristics of the product;

[0017] The metadata includes: product structure data, function data and numerical control data;

[0018] The service data includes: physical events in the product production process, machine tool data and position data.

[0019] Preferably, in step S2, using an agent to perform simplified modeling on production transaction data includes:

[0020] Establishing a product design collaborative data dictionary in the intelligent agent to uniformly represent the production transaction data in the same data format;

[0021] The product design collaborative data dictionary stores several mapping rules; each mapping rule is used to convert the format of production transaction data into another format.

[0022] Preferably, in step S3, a hash algorithm is used to map the transaction model of each participant into a data entry.

[0023] Preferably, in step S3, the physical label information of the product is specifically a QR code or an RFID tag.

[0024] Preferably, in the block header of step S4, the unique identity proof of the block includes: the hash value of the previous block, the Merkle root, the technical data, the timestamp and the Nonce random number, and the Nonce random number is used to verify the valid hash value of the next block.

[0025] The present invention also provides a product lifecycle data simplification modeling and on-chain and off-chain mapping system, which applies the above-mentioned product lifecycle data simplification modeling and on-chain and off-chain mapping method, including:

[0026] Maker Chain Network Construction Unit: used to establish the Maker Chain network and include all participants in the entire product life cycle into the Maker Chain network;

[0027] Simplified modeling unit: used to obtain the production transaction data of each participant in the Maker Chain network during the product production process. Each participant constructs an intelligent agent locally and uses the intelligent agent to perform simplified modeling on the production transaction data to obtain the transaction model of each participant.

[0028] Data mapping unit: used to map the transaction model of each participant into a data entry, and synchronize the physical tag information with the data entry through the link provided by the physical tag of the product to obtain the digital twin data of each participant;

[0029] Block construction unit: used to create a corresponding block for each participant, the block including a block header and a block body; the block header stores the unique identity certificate of the block; the block body stores the digital twin data of the participant;

[0030] Digital twin unit: used to connect each of the blocks in timestamp order using a consensus mechanism to form a blockchain, and build a digital twin model of the product's entire life cycle.

[0031] The present invention also provides a computer-readable storage medium, characterized in that a computer program is stored on the computer-readable storage medium, and the computer program implements the steps in the above method when executed by a processor.

[0032] Compared with the prior art, the beneficial effects of the technical solution of the present invention are:

[0033] The present invention provides a method and system for data simplification modeling and on-chain and off-chain mapping of a product's entire life cycle. First, a maker chain network is established, and all participants in the product's entire life cycle are included in the maker chain network. Then, the production transaction data of each participant in the maker chain network during the product production process is obtained. Each participant constructs an intelligent agent locally and uses the intelligent agent to simplify and model the production transaction data to obtain a transaction model for each participant. Then, each participant's transaction model is mapped to a data entry, and the digital twin data of each participant is obtained in combination with the physical tag information of the product. A corresponding block is established for each participant, and the block includes a block header and a block body. The block header stores the block's unique identity certificate; the block body stores the participant's digital twin data. Finally, each block is connected in timestamp order using a consensus mechanism to form a blockchain, thereby constructing a digital twin model of the product's entire life cycle.

[0034] The present invention relies on blockchain technology and digital twin technology, performs data simplification modeling through a unified mapping structure to represent data, and then performs on-chain and off-chain mapping of data. The present invention can build a digital twin model of the entire life cycle of personalized artifacts to handle the social manufacturing network credit between various manufacturers and reflect the uniqueness of personalized products. In this model, the simplified data on the chain is mapped to the original data off the chain through a hash algorithm, which can improve the security computing efficiency of multimodal data and reduce operation and maintenance costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 This is a flow chart of a method for data simplification modeling and on-chain and off-chain mapping of a product throughout its life cycle, as provided in Example 1.

[0036] Figure 2 This is a schematic diagram of the Maker Chain network provided in Example 2.

[0037] Figure 3 This is the architecture diagram of the product life cycle data simplification modeling and on-chain and off-chain mapping method provided in Example 2.

[0038] Figure 4 This is a schematic diagram of the block structure provided in Example 2.

[0039] Figure 5 This is a schematic diagram of the data processing flow provided in Example 2.

[0040] Figure 6 This is a structural diagram of a product lifecycle data simplification modeling and on-chain and off-chain mapping system provided in Example 3. DETAILED DESCRIPTION

[0041] The accompanying drawings are for illustrative purposes only and are not to be construed as limiting this patent;

[0042] In order to better illustrate this embodiment, some parts in the drawings may be omitted, enlarged, or reduced, and do not represent the actual product size;

[0043] It is understandable to those skilled in the art that some well-known structures and descriptions thereof may be omitted in the drawings.

[0044] The technical solution of the present invention is further described below with reference to the accompanying drawings and embodiments.

[0045] Example 1

[0046] like Figure 1 As shown, this embodiment provides a method for data simplification modeling and on-chain and off-chain mapping of a product throughout its life cycle, including the following steps:

[0047] S1: Establish a maker chain network and include all participants in the entire product life cycle into the maker chain network;

[0048] S2: Obtain the production transaction data of each participant in the Maker Chain network during the product production process. Each participant constructs an intelligent agent locally and uses the intelligent agent to simplify and model the production transaction data to obtain a transaction model for each participant.

[0049] S3: Mapping the transaction model of each participant into a data entry, and synchronizing the physical tag information with the data entry through the link provided by the physical tag of the product to obtain the digital twin data of each participant;

[0050] S4: Create a corresponding block for each participant, where the block includes a block header and a block body; the block header stores the unique identity certificate of the block; and the block body stores the digital twin data of the participant;

[0051] S5: Connect each of the blocks in timestamp order using a consensus mechanism to form a blockchain, and build a digital twin model of the product's entire life cycle.

[0052] In the specific implementation process, the Maker Chain network is first established to include all participants in the entire product life cycle into the Maker Chain network;

[0053] Then, the production transaction data of each participant in the Maker Chain network during the product production process is obtained. Each participant builds an intelligent agent locally and uses the intelligent agent to simplify and model the production transaction data to obtain the transaction model of each participant.

[0054] The transaction model of each participant is then mapped to a data entry, and the physical tag information and data entry are synchronized and updated through the link provided by the physical tag of the product, thereby obtaining the digital twin data of each participant. The physical tag information is the real-world twin of the transaction model data. The so-called "integration" refers to this twin relationship. The data identifying the physical tag can be obtained through scanners, sensors, cameras and other industrial Internet of Things technologies, which connect cyberspace with physical manufacturing systems. The so-called physical tag is a data tag attached to the workpiece (including QR code, RFID tag), which is the link between cyberspace and physical space and is used for quality control and anti-counterfeiting. The physical tag is a physical entity in the real world (and therefore not stored in the blockchain). The physical tag is a mapping of the product information in the blockchain, and the digital twin of the product information is realized through mapping.

[0055] A corresponding block is created for each participant. The block includes a block header and a block body. The block header stores the unique identity of the block; the block body stores the digital twin data of the participant.

[0056] Finally, each block is connected in timestamp order using a consensus mechanism to form a blockchain, building a digital twin model of the product's entire life cycle.

[0057] This method relies on blockchain technology and digital twin technology, and performs data simplification modeling through a unified mapping structure to represent data, and then performs on-chain and off-chain mapping of data. This method can build a digital twin model of the entire life cycle of personalized artifacts to handle the social manufacturing network credit between various manufacturers and reflect the uniqueness of personalized products. In this model, the simplified data on the chain is mapped to the original data off the chain through a hash algorithm, which can improve the security computing efficiency of multimodal data and reduce operation and maintenance costs.

[0058] Example 2

[0059] This embodiment provides a method for data simplification modeling and on-chain and off-chain mapping throughout the product lifecycle, including the following steps:

[0060] S1: Establish a maker chain network and include all participants in the entire product life cycle into the maker chain network;

[0061] S2: Obtain the production transaction data of each participant in the Maker Chain network during the product production process. Each participant constructs an intelligent agent locally and uses the intelligent agent to simplify and model the production transaction data to obtain a transaction model for each participant.

[0062] S3: Mapping the transaction model of each participant into a data entry, and synchronizing the physical tag information with the data entry through the link provided by the physical tag of the product to obtain the digital twin data of each participant;

[0063] S4: Create a corresponding block for each participant, where the block includes a block header and a block body; the block header stores the unique identity certificate of the block; and the block body stores the digital twin data of the participant;

[0064] S5: Connect each of the blocks in timestamp order using a consensus mechanism to form a blockchain, and build a digital twin model of the product's entire life cycle.

[0065] In step S1, the participants in the Maker Chain network include: designers, demanders, manufacturers, prosumers, verifiers and regulators;

[0066] The production transaction data in step S2 includes: product attributes, metadata and service data;

[0067] The product attributes include: physical characteristics, chemical characteristics, energy consumption properties, material properties, manufacturing characteristics and quality characteristics of the product;

[0068] The metadata includes: product structure data, function data and numerical control data;

[0069] The service data includes: physical events, machine tool data and position data during the product production process;

[0070] In step S2, using an agent to perform simplified modeling on production transaction data includes:

[0071] A product design collaborative data dictionary is established in the agent to uniformly represent the production transaction data in the same data format. Data reduction is a dictionary logical structure that meets the integration requirements of various heterogeneous data models. The dictionary is established to better establish the transaction model.

[0072] The product design collaborative data dictionary contains several mapping rules; each of the mapping rules is used to convert the format of production transaction data into another format;

[0073] In step S3, the transaction model of each participant is mapped to a data entry using a hash algorithm. The complete original data (the transaction model data of the product's full life cycle information) is stored in an off-chain database or storage system. This data is not directly stored on the blockchain to conserve blockchain storage resources. The transaction model of each participant is mapped to a data entry using a hash algorithm. The data stored on the chain includes a hash value of the original data (this hash value can be used for search and verification when verification or access to the original data is required).

[0074] In step S3, the physical label information of the product is specifically a QR code or an RFID tag;

[0075] In the block header of step S4, the unique identity proof of the block includes: the hash value of the previous block, the Merkle root, the technical data, the timestamp and the Nonce random number, and the Nonce random number is used to verify the valid hash value of the next block.

[0076] In the specific implementation process, the Maker Chain network is first established to include all participants in the entire product life cycle into the Maker Chain network;

[0077] like Figure 2 The figure below is a schematic diagram of a large-scale personalized Maker Chain model. The Maker Chain is a vibrant community where designers, demanders, manufacturers, prosumers, verifiers, regulators, and various makers are interconnected in a decentralized network, with the common goal of creating personalized products in an open architecture. Blockchain provides an online self-organizing environment for makers in the Maker Chain.

[0078] A group of decentralized makers self-organize in the Maker Chain and collaboratively handle tasks / demands through regular confirmation and adjustment by verifiers and regulators. Personalized manufacturing tasks / demands can be matched to a suitable manufacturer based on smart contracts and decentralized applications. Each manufacturer can act as a blockchain node, with computing, storage, and network services to manage many machines, equipment, and artifacts. Each maker has a unique address to identify itself and synchronizes copies of all blocks as a member of the Maker Chain. Makers who have passed the manufacturing service capability verification can obtain a large number of personalized requirements. Designers requiring manufacturing services can verify the manufacturer's capabilities or put forward personalized service requirements on the Maker Chain. Regulators can verify the requirements put forward by designers.

[0079] Every manufacturing service match between a demander and a provider is recorded as a transaction in the blockchain. A block typically contains many transactions (e.g., manufacturing service matches) over a period of time. The blockchain is a log of historical matching events and a prerequisite for maintaining trust relationships in the Maker Chain. It can serve as a reference for trust evaluation between makers and a starting point for improving the efficiency of decentralized cooperation. By integrating contextual data mining algorithms into smart contracts and decentralized applications, the Maker Chain brings makers together in different time communities based on common interests and consensus. By integrating social self-organizing algorithms into smart contracts and decentralized applications, the Maker Chain can enhance the flexibility of manufacturing process management to meet a large number of personalized needs.

[0080] Manufacturers with unique identity addresses in the Maker Chain will be given the ability to write manufacturing service events into the permissioned blockchain. The manufacturing service events recorded by the smart gateway will be batch processed and collected as transactions uploaded to the blockchain. During the production process, the generated production transaction data will be used to first implement product model simplification in the local intelligent agent. As a transaction model in the blockchain network, the transaction data model includes the following three types of information:

[0081] a. Product attributes: including physical characteristics, chemical characteristics, energy consumption properties, material properties, manufacturing characteristics, and quality characteristics;

[0082] b. Metadata: including structural data, functional data and numerical control data;

[0083] c. Service data: including physical events, machine tools, locations, smart workpieces, and manufacturing services;

[0084] like Figure 3 The following is an architectural diagram of data reduction and on-chain / off-chain data mapping in this method;

[0085] Simplify the above three types of data, establish a product design collaborative data dictionary, and unify the mapping structure of data representation;

[0086] Each participant’s transaction model is then mapped into a data entry and saved together with the product’s physical tag information as each participant’s digital twin data;

[0087] One drawback of Makerchain's decentralized self-organization is its limited storage capacity, encompassing both the blockchain network that runs the system and the data tags attached to each blockchain node. To address this limited capacity issue, most raw data is not stored directly on the blockchain, to avoid taking up excessive space and degrading the performance of the consensus algorithm. Instead, a digital twin system is constructed to synchronize actual manufacturing data in a fully replicated manner, recording the hash of the primary manufacturing data on the blockchain. This reduces storage costs and privatizes the manufacturing data uploaded to Makerchain.

[0088] Data tags attached to artifacts (such as QR codes and RFID tags) serve as a link between cyberspace and physical space, used for quality control and anti-counterfeiting. This data is encoded into Makerchain along with the physical artifact information for easy analysis. The identification of each artifact's data tag is linked to a specific blockchain transaction on Makerchain, serving as the starting anchor for mapping the artifact's digital twin to the physical space throughout its lifecycle. Information abstraction eliminates the synchronization process between data tags and blockchain.

[0089] Incorporating manufacturing process data into digital twins will create significant value for the continuous improvement of Makerchain. One of the challenges is how to achieve interoperability between the cyberspace and physical space of Makerchain. The figure below illustrates the construction principle of a lifecycle digital twin model for a personalized artifact. A set of data entries digitally references parent blockchain transactions and is also synchronously paired in data tags on the artifact (such as RFID tags and QR codes). Data tags provide direct links to their associated digital twin data. These data entries that make up the digital twin include not only unique chemical signature data, but also data such as personalized requirements and design parameters. By uploading a series of transactions, including data on all events related to the artifact, the digital twin of the artifact is securitized for lifecycle quality tracking and overall anti-counterfeiting.

[0090] While blockchain is suitable for the custody chain of abstract artifacts and ensuring trust, data synchronization with other network systems in the Maker Chain still needs to be carried out for system management and analysis. On the one hand, blockchain can serve as a mandatory proof of untampered data obtained from the manufacturing system, which is crucial for product life cycle quality assurance and anti-counterfeiting. On the other hand, other entity-relational databases are also effective for the retrieval of historical transactions.

[0091] Then, a corresponding block is created for each participant. The block includes a block header and a block body. The block header stores the unique identity of the block; the block body stores the digital twin data of the participant.

[0092] A block consists of a series of transactions, which are aggregated and verified by decentralized maker nodes. Each block contains an immutable hash of the previous block to which it is directly connected, ultimately forming a blockchain that captures manufacturing data related to certain machines and workpieces. The collection of all transactions can be used to represent the manufacturing event chain of a personalized product through a graphical formal state block deduction model. Blocks describe the time-sensitive state and location changes of the work-in-progress flow. In essence, a blockchain is composed of multiple discrete manufacturing events.

[0093] Each blockchain (Tx) of a personalized product is prefixed with the original parent Tx hash; e.g. Figure 4 Figure 1 illustrates the structure of these transactions and the event data attached to each transaction; the block header contains the previous block hash, Merkle root, technical data, timestamp, and a Nonce random number used to verify the valid hash of the subsequent block; the number of transactions included in a single block affects the block size, which can be optimized on the blockchain infrastructure;

[0094] Finally, each block is connected in timestamp order using a consensus mechanism to form a blockchain, building a digital twin model of the product's entire life cycle.

[0095] like Figure 5 Shown is a schematic diagram of the data processing process in this method;

[0096] This method relies on blockchain technology and digital twin technology, and performs data simplification modeling through a unified mapping structure to represent data, and then performs on-chain and off-chain mapping of data. This method can build a digital twin model of the entire life cycle of personalized artifacts to handle the social manufacturing network credit between various manufacturers and reflect the uniqueness of personalized products. In this model, the simplified data on the chain is mapped to the original data off the chain through a hash algorithm, which can improve the security computing efficiency of multimodal data and reduce operation and maintenance costs.

[0097] Example 3

[0098] like Figure 6 As shown, this embodiment provides a product lifecycle data simplification modeling and on-chain and off-chain mapping system, applying the product lifecycle data simplification modeling and on-chain and off-chain mapping method described in Example 1 or 2, including:

[0099] Makerchain network construction unit 301: used to establish a makerchain network and include all participants in the entire product life cycle into the makerchain network;

[0100] Simplified modeling unit 302: used to obtain the production transaction data of each participant in the Maker Chain network during the product production process. Each participant constructs an intelligent agent locally and uses the intelligent agent to perform simplified modeling on the production transaction data to obtain a transaction model for each participant.

[0101] Data mapping unit 303: used to map the transaction model of each participant into a data entry, and synchronize the physical tag information with the data entry through the link provided by the physical tag of the product to obtain the digital twin data of each participant;

[0102] Block construction unit 304: used to create a corresponding block for each participant, the block including a block header and a block body; the block header stores the unique identity of the block; the block body stores the digital twin data of the participant;

[0103] Digital twin unit 305: used to connect each of the blocks in timestamp order using a consensus mechanism to form a blockchain, and build a digital twin model of the product's entire life cycle.

[0104] In the specific implementation process, first, the maker chain network construction unit 301 establishes the maker chain network and incorporates all participants in the entire life cycle of the product into the maker chain network;

[0105] Then, the simplified modeling unit 302 obtains the production transaction data of each participant in the Maker Chain network during the product production process. Each participant constructs an intelligent agent locally and uses the intelligent agent to perform simplified modeling on the production transaction data to obtain the transaction model of each participant.

[0106] Afterwards, the data mapping unit 303 maps the transaction model of each participant into a data entry, and synchronizes the physical tag information with the data entry through the link provided by the physical tag of the product, thereby obtaining the digital twin data of each participant;

[0107] The block construction unit 304 creates a corresponding block for each participant. The block includes a block header and a block body. The block header stores the unique identity of the block; the block body stores the digital twin data of the participant.

[0108] Finally, the digital twin unit 305 connects each block in the order of timestamps using the consensus mechanism to form a blockchain, building a digital twin model of the product's entire life cycle;

[0109] This system relies on blockchain technology and digital twin technology to perform data simplification modeling through a unified mapping structure to represent data, and then performs on-chain and off-chain mapping of data. This system can build a digital twin model of the entire life cycle of personalized artifacts to handle the social manufacturing network credit between various manufacturers and reflect the uniqueness of personalized products. In this model, the simplified data on the chain is mapped to the original data off the chain through a hash algorithm, which can improve the security computing efficiency of multimodal data and reduce operation and maintenance costs.

[0110] The same or similar reference numerals correspond to the same or similar components;

[0111] The terms used in the drawings to describe positional relationships are for illustrative purposes only and should not be construed as limiting this patent;

[0112] Obviously, the above embodiments of the present invention are merely examples for the purpose of clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. Those skilled in the art will appreciate that other variations or modifications can be made based on the above description. It is not necessary and impossible to enumerate all embodiments here. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the claims of the present invention.

Claims

1. A method for data simplification modeling and on-chain and off-chain mapping of product lifecycle, characterized by: The following steps are involved: S1: Establish a maker chain network and include all participants in the entire product life cycle into the maker chain network; The Maker Chain network is a decentralized network, in which participants include: designers, demanders, manufacturers, prosumers, verifiers and regulators; S2: Obtain the production transaction data of each participant in the Maker Chain network during the product production process. Each participant constructs an intelligent agent locally and uses the intelligent agent to simplify and model the production transaction data to obtain a transaction model for each participant. Among them, using intelligent agents to simplify and model production transaction data includes: Establishing a product design collaborative data dictionary in the intelligent agent to uniformly represent the production transaction data in the same data format; The product design collaborative data dictionary contains several mapping rules; each of the mapping rules is used to convert the format of production transaction data into another format; S3: Mapping the transaction model of each participant into a data entry, and synchronizing the physical tag information with the data entry through the link provided by the physical tag of the product to obtain the digital twin data of each participant; The data entry digitally references the parent blockchain transaction and is also paired with a physical tag on the product; S4: Create a corresponding block for each participant, where the block includes a block header and a block body; the block header stores the unique identity certificate of the block; and the block body stores the digital twin data of the participant; S5: Connect each of the blocks in timestamp order using a consensus mechanism to form a blockchain, and build a digital twin model of the product's entire life cycle.

2. A product lifecycle data simplification modeling and on-chain and off-chain mapping method according to claim 1, characterized in that: The production transaction data in step S2 includes: product attributes, metadata and service data.

3. A product life cycle data simplification modeling and on-chain and off-chain mapping method according to claim 2, characterized in that: The product attributes include: physical characteristics, chemical characteristics, energy consumption properties, material properties, manufacturing characteristics and quality characteristics of the product; The metadata includes: product structure data, function data and numerical control data; The service data includes: physical events in the product production process, machine tool data and position data.

4. A product lifecycle data simplification modeling and on-chain and off-chain mapping method according to claim 1, characterized in that: In step S3, the transaction model of each participant is mapped into a data entry using a hash algorithm.

5. A product lifecycle data simplification modeling and on-chain and off-chain mapping method according to claim 1, characterized in that: In step S3, the physical label information of the product is specifically a QR code or an RFID tag.

6. A product life cycle data simplification modeling and on-chain and off-chain mapping method according to claim 1, characterized in that: In the block header of step S4, the unique identity proof of the block includes: the hash value of the previous block, the Merkle root, the technical data, the timestamp and the Nonce random number, and the Nonce random number is used to verify the valid hash value of the next block.

7. A product lifecycle data simplification modeling and on-chain and off-chain mapping system, applying the product lifecycle data simplification modeling and on-chain and off-chain mapping method described in any one of claims 1 to 6, comprising: Maker Chain Network Construction Unit: used to establish the Maker Chain network and include all participants in the entire product life cycle into the Maker Chain network; Simplified modeling unit: used to obtain the production transaction data of each participant in the Maker Chain network during the product production process. Each participant constructs an intelligent agent locally and uses the intelligent agent to perform simplified modeling on the production transaction data to obtain the transaction model of each participant. Data mapping unit: used to map the transaction model of each participant into a data entry, and synchronize the physical tag information with the data entry through the link provided by the physical tag of the product to obtain the digital twin data of each participant; Block construction unit: used to create a corresponding block for each participant, wherein the block includes a block header and a block body; the block header stores the unique identity certificate of the block; The block stores the digital twin data of the participant; Digital twin unit: used to connect each of the blocks in timestamp order using a consensus mechanism to form a blockchain, and build a digital twin model of the product's entire life cycle.

8. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in the method according to any one of claims 1 to 6 are implemented.

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