Blockchain-based blow molding product full life cycle quality traceability and process sharing system

Abstract

The application provides a kind of blow molding product full life cycle quality tracing and process sharing system based on blockchain, belongs to intelligent manufacturing and industrial data security technical field.The system includes data acquisition and storage module, for obtaining production information and storing key characteristic value or hash value in blockchain;Multi-party collaborative management module, for configuring dynamic data access rights for participants through smart contract;And process knowledge asset management module, for extracting process parameter set from blockchain data that is strongly associated with high-quality output results, encapsulating as verifiable process knowledge package, and realizing its pricing, transaction and feedback loop through smart contract.The method realizes cross-enterprise trusted data tracing, safe sharing and value realization of process knowledge through the collaborative work of the above modules, improves product quality consistency and industry knowledge reuse efficiency.

Description

Technical Field

[0001] The embodiments of the present invention relate to the fields of intelligent manufacturing and industrial data security technology, and in particular to a blockchain-based technology for quality traceability and process sharing of blow-molded products, specifically including corresponding systems, methods, equipment and storage media. Background Technology

[0002] Blow molding is a key process in the production of hollow plastic products, widely used in packaging, medical, and automotive industries. In the contract manufacturing of high-end blow-molded products such as medical consumables, brand owners (clients) have extremely high requirements for product quality consistency, production process traceability, and process reliability. Current technologies typically employ Manufacturing Execution Systems (MES) or Internet of Things (IoT) platforms for production data collection and monitoring, but the following problems still exist: First, the data lacks credibility and tamper resistance. Existing systems store data on the processor's local server, creating "data silos" that make it difficult for clients, raw material suppliers, and others to access and verify the data in real time. In the event of a quality dispute, data cannot be mutually recognized by all parties, making it difficult to determine liability.

[0003] Second, process knowledge is difficult to retain and reuse. Blow molding is highly dependent on operator experience, and the correlation between key process parameters (such as preform temperature and blowing pressure curves) and final product quality (such as wall thickness uniformity) is tacit knowledge. Existing technologies only record data and lack mechanisms for automatically identifying, reliably encapsulating, and effectively sharing high-quality processes. This results in high-quality processes not being able to be safely reused across different production lines and factories, leading to a waste of knowledge.

[0004] Third, there is a lack of data governance mechanisms adapted to complex collaborative scenarios. In the context of "multiple clients, small batches, and high customization" in processing with supplied materials, different clients have varying requirements for data visibility and process confidentiality. Existing systems have rigid access control, making it impossible to achieve dynamic, fine-grained data authorization based on individual production work orders, and it is difficult to balance the needs of data sharing and privacy protection.

[0005] Therefore, issues such as how to build a cross-enterprise, reliable data traceability system for blow molding production; how to transform implicit blow molding process experience into verifiable and tradable digital assets; and how to achieve flexible and secure data sharing under complex supply chain collaboration urgently need to be addressed.

[0006] While existing blockchain-based supply chain traceability systems exist, most only ensure data immutability and lack structured encapsulation and asset-based circulation mechanisms for process knowledge. Furthermore, current access control is largely based on fixed roles, making it difficult to adapt to flexible production scenarios requiring individualized solutions. Therefore, existing systems exhibit significant shortcomings in areas such as process knowledge reuse, cross-enterprise collaboration efficiency, and the balance between data privacy and sharing.

[0007] The embodiments of the present invention are improvements made to solve the above problems. Summary of the Invention

[0008] The purpose of this invention is to provide a blockchain-based system and method for enabling trusted quality traceability and the sharing of process knowledge assets throughout the entire lifecycle of blow-molded products. Implementing this invention can solve problems such as low data reliability, difficulty in accumulating and reusing process knowledge, and low efficiency in multi-party collaboration under traditional blow molding processing models.

[0009] To achieve the aforementioned objective, in a first aspect, embodiments of the present invention provide a blockchain-based system for quality traceability and process sharing of blow-molded products, the technical solution of which is: The system includes a data acquisition and storage module, a multi-party collaborative management module, and a process knowledge asset management module.

[0010] The data acquisition and storage module is used to acquire material information, equipment status information and process parameter sets during the blow molding production process, and store the key feature values ​​or hash values ​​of the information on the blockchain.

[0011] The multi-party collaborative management module is used to configure dynamic data access permissions for supply chain participants based on smart contracts deployed on the blockchain. Specifically, it includes configuring a first permission set for the client for the same production work order, allowing access to the complete quality inspection report and process parameter summary; and configuring a second permission set for the raw material supplier, allowing access only to usage statistics information related to the batches of raw materials supplied by the supplier. The content of the first and second permission sets is automatically generated and executed by the smart contract based on the work order information, and the permissions are associated with the production work order.

[0012] The process knowledge asset management module is used to extract a set of process parameters that are strongly correlated with high-quality output results from the data stored on the blockchain, package them into verifiable process knowledge packages, and realize the pricing, transaction authorization, and process application feedback loop based on transaction results of the process knowledge packages through smart contracts.

[0013] In this embodiment of the invention, an immutable, full-lifecycle data chain is established through edge-side trusted data collection and blockchain notarization, resolving cross-enterprise data trust issues. Dynamic permission management based on work orders is implemented through smart contracts, meeting the data security and sharing requirements of complex collaborative production scenarios. By reliably binding high-quality process parameter sets with on-chain verified quality results and designing their pricing, trading, and feedback mechanisms, blow molding process knowledge is transformed for the first time into digital assets that can circulate and appreciate in value while protecting the rights of all parties, forming a continuously optimized industrial knowledge ecosystem. Through the collaborative work of these three modules, the complex technical problems of low reliability in quality traceability, difficulty in accumulating process knowledge, and low efficiency in multi-party collaboration are systematically solved.

[0014] As a preferred option, the data acquisition and evidence storage module includes: An edge trusted computing unit is configured to be deployed on the blow molding production equipment side to receive raw process parameters collected by sensors and to perform feature extraction and integrity verification on the raw process parameters. The equipment identification unit is used to assign a unique identification code to the blow-molded product or carrier corresponding to each production work order; The edge trusted computing unit is also used to bind the verified process parameter feature value, the unique identifier and the timestamp to generate evidence-based transaction data and send it to the blockchain.

[0015] As a preferred option, the process knowledge asset management module includes: The knowledge extraction unit is configured to identify continuous production batches that meet preset yield conditions from the blockchain and extract the corresponding set of process parameters for that batch. The trusted binding unit is configured to associate the process parameter set with the batch quality inspection results recorded on the blockchain and confirmed by multiple parties, and generate process knowledge package metadata containing the association proof.

[0016] A smart contract unit, wherein the smart contract defines transaction terms associated with the process knowledge package, the transaction terms including usage price, scope of authorization and validity verification conditions; The smart contract is configured to grant the requesting party access to the process knowledge package in response to a received transaction request and payment credential.

[0017] Secondly, embodiments of the present invention provide a blockchain-based method for quality traceability and process sharing of blow-molded products.

[0018] This method is applied to the system described in the first aspect above, including: acquiring material information, equipment status information, and process parameter sets during the blow molding production process, and storing the key feature values ​​or hash values ​​of the information on the blockchain; configuring dynamic data access permissions for supply chain participants based on production work orders using smart contracts deployed on the blockchain; extracting process parameter sets strongly correlated with high-quality output results from the data stored on the blockchain, and encapsulating them into verifiable process knowledge packages; and implementing pricing, transaction authorization, and process application feedback loops based on transaction results for the process knowledge packages through smart contracts. The specific feedback loop mechanism can be as follows: after applying the knowledge package, the system automatically collects the yield rate and key indicators of the new batch and compares them with the original knowledge package records. If the yield rate increases by ≥1%, the score of the knowledge package is increased; otherwise, it is recorded as conditional feedback for subsequent matching and optimization.

[0019] In this embodiment of the invention, the method realizes a closed loop of trusted storage of production data, flexible authorization, and process knowledge through technical processes, transforming management rules into automatically executable code, thereby improving the transparency and collaborative efficiency of the entire supply chain.

[0020] Thirdly, embodiments of the present invention provide an electronic device.

[0021] The electronic device includes one or more processors and a memory, the memory storing one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement the method described in the second aspect above.

[0022] Fourthly, embodiments of the present invention provide a computer-readable storage medium.

[0023] The computer-readable storage medium stores a computer program that, when executed by a processor, implements the method described in the second aspect above.

[0024] Furthermore, the above summary does not enumerate all the features required for embodiments of the present invention, and other combinations of these feature groups may also constitute embodiments of the present invention. Attached Figure Description

[0025] To more clearly illustrate the technical solutions in the embodiments of the present invention or the background art, the accompanying drawings used in the embodiments of the present invention or the background art will be described below.

[0026] Figure 1 This is a schematic diagram of the architecture of a blockchain-based blow molding product quality traceability and process sharing system provided in one embodiment of the present invention; Figure 2 This is a schematic diagram illustrating a scenario where the data acquisition and evidence storage module and a blow molding machine work together, according to an embodiment of the present invention. Figure 3This is a schematic diagram of the workflow of the process knowledge asset management module provided in one embodiment of the present invention; Figure 4 This is a flowchart of a blockchain-based method for quality traceability and process sharing of blow-molded products, provided in one embodiment of the present invention. Figure 5 This is a structural block diagram of an electronic device provided in one embodiment of the present invention. Detailed Implementation

[0027] To make the technical means, creative features, objectives and effects of the embodiments of the present invention easier to understand, the embodiments of the present invention are further described below in conjunction with the figures and specific embodiments. It should be understood that the specific embodiments described herein are merely for explaining the embodiments of the present invention and are not intended to limit the embodiments of the present invention.

[0028] It should be noted that the terms "strong correlation" and "high-quality output" in this text refer to: a statistically significant relationship (e.g., correlation coefficient ≥ 0.7) established through correlation analysis or rule matching between the process parameter set and output quality indicators. "High-quality output results" refers to outputs that meet preset quality standards, including a yield rate ≥ 99% and key physical indicators conforming to national standards. "Process knowledge package" refers to a structured data package containing the process parameter set, hashes of associated quality results, digital signatures, terms of use, and scoring information. "Edge trusted computing unit" refers to hardware or a combination of hardware and software modules deployed on the production equipment side, possessing data verification, feature extraction, and on-chain signature capabilities.

[0029] To better understand the embodiments of the present invention, please refer to Figure 1 As shown, in a feasible implementation, this embodiment of the invention provides a blockchain-based blow molding product quality traceability and process sharing system. The system architecture includes a data acquisition and storage layer 101 deployed in the blow molding production workshop, a blockchain core layer 102 deployed via cloud services or privately, and an application service layer 103 for different users.

[0030] Data acquisition and evidence storage layer 101 may specifically include: Edge Trusted Gateway 1011: For example, an industrial gateway or industrial control computer with a TEE (Trusted Execution Environment) is used. It has a built-in SHA-256 hash algorithm and RSA digital signature mechanism to perform real-time verification and feature extraction on the collected process parameters, generate data packets with timestamps, sign them and upload them to the blockchain. It connects to devices such as blow molding machine controllers, mold temperature controllers, and electronic scales through industrial buses (such as Modbus, Profinet) or analog signal interfaces to obtain process parameters in real time, such as barrel temperature, screw speed, mold temperature, blowing pressure curve (which can collect parameters such as blowing synchronization time difference), and ambient temperature and humidity.

[0031] Identification and coding equipment 1012: such as a QR code marking machine or RFID reader, generates and assigns a unique traceability code (which can be associated with the UDI code of medical devices) to each pallet or each blow-molded product carrier (such as a turnover box) based on the production order (work order) information.

[0032] The edge trusted gateway 1011 has a built-in data processing program that filters and denoises the received raw process parameters and calculates key feature values ​​(such as the average, peak, and variance of the blowing pressure within a production cycle). Simultaneously, the edge trusted gateway 1011 generates a data packet containing the feature value, the corresponding traceability code, a timestamp, and the gateway device's digital signature, and calculates the hash value of this data packet. Subsequently, the edge trusted gateway 1011 uploads the hash value and the plaintext (optionally encrypted) key feature value to the blockchain core layer 102 for notarization via HTTPS.

[0033] The core blockchain layer 102, for example, can be built using the Hyperledger Fabric consortium blockchain framework, with participating nodes including blow molding plant M, principal B, raw material supplier S, etc. This layer is deployed with: Evidence storage smart contract 1021: Used to receive and verify data from the edge side, and write hash values ​​and other data into the blockchain immutable ledger.

[0034] Access control smart contract 1022 and process knowledge transaction smart contract 1023.

[0035] Example of smart contract permission rules (code snippet): function grantAccess(string memory orderId, address user, Role role)public { require(orders[orderId].exists, "Order not found"); if (role == Role.Client) { allowAccess(orderId, user, AccessType.Full); } else if (role == Role.Supplier) { allowAccess(orderId, user, AccessType.MaterialOnly); } } The code symbols represent the following: symbol Reference meaning Data type / enumeration definition orderId Production work order unique identifier string memory (string type) orders[orderId].exists Order Existence Check bool (Boolean value), orders is an order mapping table address user Blockchain user address (participant identification) address (Ethereum address type) Role User Role Enumeration enum Role { Client, Supplier, Manufacturer, Auditor} Role.Client Client Role The enumerated values ​​represent the brand owner or the contract manufacturer. Role.Supplier role of raw material supplier Enumerated values, representing raw material suppliers AccessType.Full Full access type enum AccessType { Full, MaterialOnly, ReadOnly} The multi-party collaborative management module's functionality is primarily implemented by the access control smart contract 1022. When a new work order is created, the system instantiates an access control policy for that work order within the contract. For example, the policy can be defined as follows: the client (node ​​B) can query all quality inspection reports and process parameter summaries under this work order; the raw material supplier (node ​​S) can only query the total usage and average yield rate of the raw material with batch number "Batch-2023-001" across all relevant work orders. All data query requests must pass the smart contract's verification, and the contract automatically executes the predefined policy.

[0036] Application service layer 103 provides web or mobile clients for all participants to submit work orders, view traceability reports, and initiate process knowledge transactions.

[0037] In this embodiment, the authenticity of source data is ensured by an edge trusted gateway, tamper-proofing is achieved by combining blockchain notarization, and dynamic, fine-grained data access control is achieved by using smart contracts. This effectively solves the contradiction between trust and security in cross-organizational data sharing and lays the foundation for high-quality traceability.

[0038] In one feasible implementation, see Figure 3 As shown, this embodiment describes in detail the aforementioned process knowledge asset management module (corresponding to...). Figure 1 The workflow of the 103rd layer logic module and the 1023rd contract in the system includes the following: Step S301: Generate and trustworthy process knowledge package.

[0039] The knowledge extraction unit continuously monitors the blockchain ledger. When the system identifies that the final on-chain quality inspection record for a production batch (e.g., 1000 infusion bags continuously produced under work order "PO-2023-005") shows a yield rate consistently higher than a preset threshold (e.g., 99.5%), and all key performance indicators (e.g., burst pressure) meet the standards, knowledge extraction is automatically triggered. Specifically, the knowledge extraction unit triggers extraction based on preset rules (e.g., yield rate ≥ 99.5% for 3 consecutive batches), extracts a parameter set, and binds it to the hash of the on-chain quality inspection record. For example, it associates the quality inspection result of "blowing pressure curve variance ≤ 0.05MPa" with "burst pressure ≥ 0.8MPa" to generate knowledge package metadata. This unit extracts the complete process parameter set corresponding to this batch (denoted as "parameter set A") from the ledger. Parameter set A includes, but is not limited to, the aforementioned characteristic values ​​of the blowing pressure curve, the set values ​​of heating temperatures for each segment, cooling time, etc.

[0040] Subsequently, the trusted binding unit generates a process knowledge package. The data structure of this knowledge package includes: parameter set A, a pointer to the transaction hash of the high-quality inspection record of this batch on the chain, a generation timestamp, and the digital signature of the generator (e.g., "Production Line A - Team A"). This process strongly associates the abstract "good process" with the objective, multi-party confirmed "good result" on the chain, forming a trusted process knowledge package.

[0041] Step S302: Knowledge package pricing and on-chain registration.

[0042] The pricing of knowledge packages can adopt a dynamic model: a base price + a usage frequency coefficient + an effectiveness score coefficient. For example, the base price is 100 tokens, increasing by 10 tokens for each successful application, and increasing by 5 tokens for every 0.1 increase in the effectiveness score. The generator can set a usage price (e.g., 100 tokens / use) and usage terms (e.g., limited to producing similar specifications of products) for "Craft Knowledge Package A" through the application interface. This information, along with the knowledge package's metadata hash, is registered by calling the Craft Knowledge Transaction Smart Contract 1023, generating a unique, addressable knowledge asset identifier on the blockchain.

[0043] Step S303: Knowledge Package Trading and Application.

[0044] When another production unit (such as "Production Line B - Team B") receives an order for a similar product, its operator can initiate a query in the system. The recommendation and verification unit matches "Process Knowledge Package A" from the on-chain knowledge base and recommends it based on the product specifications of the new order (such as "500ml LDPE infusion bag") and the raw material grade. After Team B confirms and pays the fee, smart contract 1023 executes the transaction, authorizing Team B's edge trusted gateway to download "Parameter Set A".

[0045] Team B's equipment adjusted production parameters according to "Parameter Set A" for trial production. Production data and results were automatically uploaded to the blockchain.

[0046] Step S304: Application feedback and knowledge iteration.

[0047] The recommendation and verification unit compares and analyzes the production quality results of Team B after applying "Process Knowledge Package A" with the historical results recorded within the knowledge package. If the application effect meets expectations (e.g., the yield rate is also higher than 99%), the successful application record will serve as positive feedback, associated with "Process Knowledge Package A," and improve its "applicability score." Conversely, if the application fails, the record will serve as conditional feedback, providing a basis for more accurate recommendations in the future.

[0048] In this embodiment, technical means are used to automatically identify high-quality processes from production data, encapsulate them into digital assets with on-chain credit backing, and establish a closed loop of secure transactions and effect verification based on smart contracts, thereby improving the efficiency of the accumulation and dissemination of industry process level.

[0049] In one feasible implementation, see Figure 4 As shown, this embodiment describes a blockchain-based method for quality traceability and process sharing of blow-molded products. This method can be executed by a program deployed on a server or edge computing device and includes the following steps: S401: Trusted data collection and on-chain evidence storage.

[0050] In response to the blow molding machine start signal, the edge trusted gateway 1011 begins collecting production data. The data is preprocessed and a notarization request containing a unique traceability code is generated. The notarization smart contract 1021 on the blockchain is invoked to hash the data and upload it to the chain. For example, the "blowing pressure stability index: 0.05MPa" is collected and uploaded to the chain.

[0051] S402: Dynamic permission policy execution.

[0052] When client B requests to view certain work order data, the request triggers the access control smart contract 1022. The contract automatically verifies B's identity and permissions and returns the data view that B is authorized to access from on-chain or off-chain storage.

[0053] S403: Operation of converting process knowledge into assets.

[0054] The system automatically or manually performs the generation (S301), registration (S302), transaction and recommendation (S303), and application feedback processing of process knowledge packages (S304).

[0055] S404: Full lifecycle traceability query.

[0056] When a user scans the traceability code on a product, the application service layer 103 queries the blockchain for the hashes of all on-chain events associated with that code (raw material warehousing, production parameters, quality inspection, factory exit, etc.) and retrieves detailed data from off-chain storage. After comparing the hashes, a complete and tamper-proof traceability report is presented.

[0057] In this embodiment, the method organically integrates three major processes: quality traceability, collaborative management, and knowledge sharing. By using blockchain and smart contracts, each step is automated and made trustworthy, realizing the value flow of process knowledge and providing a brand-new digital collaborative production management model for the blow molding industry.

[0058] In one feasible implementation, see Figure 5As shown, this embodiment provides an electronic device 500 that implements the above method. The electronic device 500 can be a server, an industrial control computer, or a high-performance edge computing gateway.

[0059] The electronic device 500 includes at least one processor 501 (e.g., CPU), a memory 502, a communication interface 503, and a bus 504. The memory 502 stores a computer program that, when executed by the processor 501, implements the steps described in Embodiment 3. The communication interface 503 is used for data interaction with the blow molding machine PLC, sensors, blockchain nodes, and other systems.

[0060] This invention also provides a computer-readable storage medium, such as an optical disc, a USB flash drive, or a hard disk. The storage medium stores a computer program, which, when read and executed by a computer or processor 501, causes the computer or device to perform the methods described in the foregoing embodiments.

[0061] It should be noted that the foregoing embodiments can be implemented individually or in combination without contradiction. The embodiments of the present invention do not impose restrictions on the specific model of the blow molding machine, the selection of the blockchain (e.g., an Ethereum private chain can also be used), or the programming language of the smart contract (e.g., Solidity, Go).

[0062] To better understand the technical solution and beneficial effects of the present invention, several specific embodiments will be used for further explanation below.

[0063] In a specific application scenario, let's take the contract manufacturing of a medical infusion bag (specification: 1000ml, LDPE material) as an example. Company A, the client, issues work order "PO-MD2023-001" to company B, the processing plant. Before processing begins, company B's operator submits the work order information through the application service layer. The system automatically instantiates a strategy for this work order in the access control smart contract: Company A can view all quality inspection data and process summaries; the raw material supplier, "Chemical International," can only query the total consumption of its supplied "LDPE-medical grade-batch number A2023." During production, the edge trusted gateway collects data showing that when production line 1 continuously produces 10,000 products, its key process parameter, "blowing pressure stability index" (calculating the variance of the self-blowing device pressure curve), remains stable at 0.03MPa, and the yield rate of the final quality inspection record on the blockchain for this batch is 99.8%. The system automatically triggers the binding of the "Process Knowledge Package-P001" containing this parameter set with the on-chain quality inspection record hash. Production Line 1 team sets a trial price of 50 yuan per use and registers it on the chain. A week later, Production Line 2 receives a similar order, and the system recommends and authorizes its paid use of the "Process Knowledge Package-P001". After application, the yield rate of the first batch of 1000 products produced by Production Line 2 increases from the historical average of 98.5% to 99.6%. This example demonstrates the value loop from data collection and knowledge generation to transaction application.

[0064] In another specific application scenario, a control experiment was designed to objectively verify the effect of the system. The control group used a traditional MES system to manage two independent production lines (production line A and production line B) to produce the same plastic bottle. The experimental group used the system of this invention to manage the same two production lines (production line C and production line D) and enabled the process knowledge sharing function. During the one-month production period, the following key data were recorded: (1) Cross-line quality consistency: The standard deviation of the key dimension (outer diameter of the bottle mouth) of the products produced by the experimental group (production lines C and D) was 0.05 mm, which was significantly lower than the 0.12 mm of the control group (production lines A and B). (2) Process optimization efficiency: In the experimental group, production line C was the first to explore a set of optimization parameters that reduced the defect rate by 2%. After production line D purchased and used this knowledge package, production line D reached a similar optimization level within one day; while in the control group, production line B took a week to initially reproduce the optimization effect of production line A through traditional experience exchange, and the effect was unstable. (3) Abnormal traceability time: When a batch sealing failure occurred, the experimental group used blockchain traceability to accurately locate the abnormal mold temperature sensor reading of a certain device on production line D within a specific time period within 10 minutes; the control group, on the other hand, had to manually check multiple isolated logs, which took more than 4 hours. This embodiment verifies the technical advantages of the present invention in improving quality consistency, accelerating knowledge dissemination, and improving traceability efficiency.

[0065] In yet another specific application scenario, combined with Figures 1 to 3 See also Figure 1 When the identification and coding device (1012) affixes the traceability code "ID-001" to the turnover box, the event hash is recorded by the evidence storage contract (1021). Simultaneously, the associated production data (feature values) collected by the edge trusted gateway (1011) are also uploaded to the blockchain. See also... Figure 3 Assuming the batch is of excellent quality, the process knowledge transaction contract (1023) will generate a knowledge package. When another factory queries and purchases this knowledge package through its application service layer (103), the contract (1023) authorizes its edge gateway to download the parameters. See also Figure 2 The blow molding machine (201)'s sensor (202) operates according to the new parameters, and the production data is uploaded back to the chain, forming a closed loop. This example clearly illustrates... Figure 1 , 2 The components described in section 3 work together in the actual data flow and business flow, thus concretizing the abstract architecture.

[0066] In one possible application scenario, a processing plant uses a batch of raw materials from a new supplier, whose melt flow index differs slightly from commonly used raw materials. During the initial production phase using the existing process, a small number of products exhibited uneven wall thickness. Traditionally, this would require engineers to spend considerable time troubleshooting. However, in this system, abnormal process data (such as abnormal melt pressure fluctuation curves) collected in real-time by the edge gateway, along with the quality inspection results for "uneven wall thickness," are simultaneously uploaded to the blockchain. A process knowledge package for "high melt flow index raw material adaptation" is matched and recommended from the system's knowledge base. This knowledge package originates from another production line that has handled similar situations in the past, and its core adjustment strategy is to "increase the temperature of one section of the barrel by 5°C and reduce the screw speed by 10%." After the processing plant paid for and applied this knowledge package, it adjusted its process, and subsequent production quickly returned to normal. This embodiment demonstrates that the system of this invention not only shares "successful experiences" but also accumulates "abnormal handling solutions" into knowledge assets, thereby possessing the ability to handle unknown production fluctuations and quickly restore production stability.

[0067] In other specific application scenarios, four parties are involved: the client, the processing plant, the third-party testing agency, and the logistics provider. The work order stipulates that the testing report issued by the testing agency serves as the basis for settlement. After the system is deployed, the testing agency, acting as a blockchain node, directly uploads the hash value of its encrypted testing report to the chain, while the full text of the report is encrypted and stored on its own server. During settlement, the smart contract automatically verifies: ① whether the report hash exists on the chain (authenticity); ② whether the report signing time is after production is completed (logic); ③ whether the client and the processing party's digital signatures confirm receipt (consensus). After all verifications pass, the contract automatically triggers the payment instruction. This process eliminates disputes regarding report forgery, tampering, or delayed transmission, and the testing agency's original data does not need to be exposed to other parties, achieving minimal data disclosure while maintaining necessary transparency. This verifies the system's technical effectiveness in balancing trust and privacy in complex collaborations.

[0068] In another application scenario, a large blow molding processing enterprise owns blow molding machines from multiple brands and different production years, with varying data interface protocols (such as Siemens S7, Mitsubishi MC, Omron Host Link, etc.). The edge trusted gateway (1011) of this invention is designed as a containerized software module that supports multi-protocol conversion and can load corresponding driver plugins for different devices. The gateway uniformly converts heterogeneous data into a standardized JSON format before performing feature extraction and on-chain processing. Meanwhile, in addition to supporting major consortium blockchain frameworks, the blockchain core layer (102) also has an interface for its notarization contract (1021) that is compatible with notarization calls on public blockchains such as Ethereum, so as to connect with a wider range of supply chain systems in the future.

[0069] It should be understood that the terms "one embodiment," "an embodiment," "a feasible implementation," or "some implementations" used throughout the specification mean that a specific feature, structure, or characteristic related to the embodiment is included in at least one embodiment of the present invention. Therefore, "one embodiment," "an embodiment," "a feasible implementation," or "some implementations" appearing throughout the specification do not necessarily refer to the same embodiment. Furthermore, these specific features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. Those skilled in the art should also recognize that the embodiments described in the specification are optional embodiments, and the actions and modules involved are not necessarily essential to the embodiments of the present invention.

[0070] The above description is merely a specific embodiment of the present invention, but the protection scope of the embodiments of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the embodiments of the present invention should be included within the protection scope of the embodiments of the present invention. Therefore, the protection scope of the embodiments of the present invention should be determined by the protection scope of the claims.

Claims

1. A blockchain-based system for quality traceability and process sharing of blow-molded products, characterized in that, include: The data acquisition and storage module is used to acquire material information, equipment status information and process parameter sets during the blow molding production process, and store the key feature values ​​or hash values ​​of the information in the blockchain; The multi-party collaborative management module is used to configure dynamic data access permissions for supply chain participants based on smart contracts deployed on the blockchain, and the permissions are associated with production work orders. The process knowledge asset management module is used to extract a set of process parameters that are strongly correlated with high-quality output results from the data stored in the blockchain, encapsulate them into a verifiable process knowledge package, and realize the pricing, transaction authorization, and effect score update of the process knowledge package based on application feedback through the smart contract.

2. The system according to claim 1, characterized in that, The data acquisition and evidence storage module includes: An edge trusted computing unit is configured to be deployed on the blow molding production equipment side to receive raw process parameters collected by sensors and to perform feature extraction and integrity verification on the raw process parameters. The equipment identification unit is used to assign a unique identification code to the blow-molded product or carrier corresponding to each production work order; The edge trusted computing unit is also used to bind the verified process parameter feature value, the unique identifier and the timestamp to generate evidence-based transaction data and send it to the blockchain.

3. The system according to claim 1, characterized in that, The process knowledge asset management module includes: The knowledge extraction unit is configured to identify continuous production batches that meet preset yield conditions from the blockchain and extract the corresponding set of process parameters for that batch. The trusted binding unit is configured to associate the set of process parameters with the batch quality inspection results recorded on the blockchain and confirmed by multiple parties, and generate process knowledge package metadata containing the association proof.

4. The system according to claim 3, characterized in that, The process knowledge asset management module also includes: A smart contract unit, wherein the smart contract defines transaction terms associated with the process knowledge package, the transaction terms including usage price, scope of authorization and validity verification conditions; The smart contract is configured to grant the requesting party access to the process knowledge package in response to a received transaction request and payment credential.

5. The system according to claim 4, characterized in that, The process knowledge asset management module also includes: The recommendation and verification unit is configured to match the traded process knowledge package from the blockchain based on the product specifications and raw material information of the new work order, and generate process parameter recommendations. The recommendation and verification unit is further configured to collect new production data and quality results after the recommended process knowledge package is applied, and feed the new results back to the blockchain to update the application effectiveness score of the process knowledge package.

6. The system according to claim 1, characterized in that, The dynamic data access permissions configured in the multi-party collaborative management module include: For the same production work order, configure the first set of permissions for the client, allowing access to the complete quality inspection report and process parameter summary; Configure a second permission set for raw material suppliers, allowing them to access only usage statistics related to batches of raw materials supplied by them; The contents of the first permission set and the second permission set are automatically generated and executed by the smart contract based on the work order information.

7. The system according to claim 1, characterized in that, The system also includes: The adjustment and positioning module is used to horizontally adjust the installation position of the sensor in the data acquisition and storage module according to different blow molding molds or product specifications, and to fix the adjusted position by positioning components.

8. A blockchain-based method for quality traceability and process sharing of blow-molded products, characterized in that, Applied to the system as described in any one of claims 1-7, the method comprises: Obtain material information, equipment status information, and process parameter sets during the blow molding production process, and store the key feature values ​​or hash values ​​of the information in the blockchain; Based on smart contracts deployed on the blockchain, dynamic data access permissions are configured for supply chain participants according to production work orders. Extract the set of process parameters that are strongly correlated with high-quality output results from the data stored in the blockchain, and encapsulate them into a verifiable process knowledge package; The smart contract enables a closed loop for pricing the process knowledge package, authorizing transactions, and updating performance scores based on application feedback.

9. An electronic device, characterized in that, include: One or more processors; Memory, used to store one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors perform the method as described in claim 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method as described in claim 8.

Images