Agricultural product full-process traceability system and method based on block chain and Internet of Things technology

Abstract

The invention relates to an agricultural product full-process traceability system and method based on a block chain and an internet of things technology, and belongs to the technical field of agricultural product information. The method comprises the following steps: carrying out real-time data acquisition on a production process by deploying a sensor, and carrying out encryption verification on a data uplink process by utilizing AI anomaly analysis and zero-knowledge proof; all data are stored in a distributed account book in a block form, so that the data are ensured not to be tampered. Meanwhile, a third-party decentralized verification platform is introduced, so that the authority and transparency of data auditing are improved; and at the consumer end, through visual display and trust index evaluation, the consumer can efficiently judge the quality safety and brand trust of the agricultural products, so that a complete'data acquisition-data verification-data storage-data display-trust evaluation 'closed-loop system is constructed, and a powerful guarantee is provided for safe circulation and brand construction of the agricultural products.

Description

TECHNICAL FIELD

[0001] The present application relates to a blockchain and Internet of Things technology-based agricultural product whole-process traceability system and method, belonging to the field of agricultural product information technology. BACKGROUND

[0002] With the increasing concern of consumers for food safety and quality, agricultural product traceability technology is widely used in agriculture, food supply chain and retail industry. Although there have been attempts to use blockchain for agricultural product traceability in existing technologies, most of them have the following defects: Fraudulent original data: The original data in the production process of agricultural products may be tampered with by humans or the sensor data may be falsified.

[0003] Fraudulent uploading verification process: When uploading data to the blockchain, the fraudulent verification process cannot be effectively prevented, and if malicious tampering occurs, the existing traceability system has no complete defense capability.

[0004] Insufficient data storage security: Although blockchain has tamper-proofing, the existing storage method has problems of redundant data management and data privacy protection.

[0005] Inadequate data display verification mechanism: The external verification mechanism lacks authority and credibility, which may lead to false information being misidentified.

[0006] Subjective trust is difficult to quantify: Consumers' trust in goods is based on brand, reputation and other factors, which is difficult to quantify and manage through the system.

[0007] In existing technologies, a single blockchain application cannot integrate the whole process of Internet of Things data collection, data uploading verification, data storage, data display and brand trust evaluation, resulting in system vulnerabilities and incomplete trust chain. SUMMARY

[0008] To solve the above-mentioned shortcomings of existing technologies, the present application provides a blockchain and Internet of Things technology-based agricultural product whole-process traceability system and method, which collects, verifies, uploads, stores and displays data at each link from production to consumption of agricultural products, and combines a decentralized authentication mechanism with brand trust management, to realize a whole-process transparent and tamper-proof traceability mechanism, thereby effectively preventing data falsification.

[0009] The technical solution of the present application is: a blockchain and Internet of Things technology-based agricultural product whole-process traceability system, which comprises: A trusted perception and original data collection anti-fraud system for collecting environmental parameters, farming operation data and growth state data in the production link of agricultural products, and performing trusted perception and anti-fraud verification; A data chaining verification module for multiple verifications of the original data, including AI analysis anomaly detection, zero-knowledge proof, and BFP consensus algorithm verification; A blockchain data storage module for storing the verified data in a chain structure in a distributed ledger; A decentralized verification platform module for introducing a third-party institution to independently audit the label data; A consumer-end data display and trust evaluation module for displaying the traceability information to consumers and building a dynamic trust index.

[0010] Further, the trusted perception and original data acquisition anti-counterfeiting system includes the following processes: Tamper-proof hardware terminal data acquisition: The tamper-proof hardware terminal includes environmental parameter monitoring sensors, agricultural operation record sensors, and crop health monitoring sensors deployed in the production base for real-time data acquisition. The tamper-proof hardware terminal has a trusted execution environment (TEE) and a hardware security module (HSM) built-in, running a hardened firmware; Multiple authentication binding: The sensor devices of the tamper-proof hardware terminal are bound to a unique physical identifier, and the device identity uniqueness and legality are ensured through zero-knowledge proof or physically unclonable function technology; Real-time data fingerprint generation: After data collection, the original data hash fingerprint (RDH) is calculated in real time in TEE / HSM, and the device ID, timestamp, and geographic location coordinates are attached; Abnormal local verification: The trusted perception and original data acquisition anti-counterfeiting system has a lightweight model built-in for reasonable verification of real-time data range, and abnormal data is marked and alarmed in real time.

[0011] Further, the data chaining verification module uses an AI model to analyze whether the data is abnormal and combines zero-knowledge proof technology for data authenticity verification; specifically including: BFP verification node network: A decentralized verification node network composed of industry association certified independent detection agencies (third parties), large-scale planting and breeding base representatives, and government regulatory departments. Verification nodes need to stake tokens and establish a reputation mechanism; Original data hash fingerprint (RDH) and original data packet formatting: The collection device submits RDH, device certificate, original data packet, and metadata to the local front-end server or edge node; Multi-source cross-validation challenge: BFP verification nodes randomly receive verification tasks, including: requiring the device to remotely perform a specified operation in response to the challenge; calling multiple sensor data in the same period and nearby location for cross comparison; calling the biological feature binding record of the operator; the verification node uses secure multi-party computation (MPC) or homomorphic encryption technology to perform data comparison under the premise of protecting privacy; Zero-knowledge proof generation: After the verification node successfully passes the challenge, a zero-knowledge proof ZKP is generated using zk-SNARKs / STARKs, proving that the original data is from a legitimate device and has not been tampered with before being transmitted to the blockchain, i.e., the data packet integrity corresponding to RDH is verified; Malicious behavior punishment mechanism: If a node forges verification results or colludes, it will be confiscated and lose its node qualification and be recorded publicly.

[0012] Further, the blockchain data storage module adopts BFP consensus algorithm, IPFS storage technology and distributed storage structure to ensure data tamper-proof and resistance to single-point attacks; specifically including: Multi-consortium chain / hybrid chain deployment: Core traceability data is written into multiple independent consortium chains with different consensus mechanisms; Data sharding and dynamic isolated storage: After homomorphic encryption or verifiable encryption of original data, processed intermediate data, and non-core traceability data, they are divided into multiple encrypted fragments, which are stored in decentralized storage networks, cloud storage, or even physical offline media; Data availability and integrity proof generation: Regularly or on-demand trigger availability and integrity challenge for off-chain stored data; distributed storage network generates PoDA through verifiable delay function VDF or zero-knowledge proof, proving that all data fragments are complete and available and have not been tampered with; challenge tasks are executed by BFP network nodes or randomly selected verifiers.

[0013] Further, the decentralized verification platform module is registered as a verification node on the blockchain by a third-party institution with legal qualifications for independent auditing and verification; specifically including: Tokenized authorized access mechanism: Users obtain high-privilege or standard-view access credentials by holding specific NFTs or paying a small amount of tokens; this credential itself is a chain-based NFT; Tamper-proof data dashboard: Terminal APP or Web-based dashboard generated based on real-time blockchain data; the display interface is forced to include the following elements: Full data hash root: Root hash value of all associated data is displayed; Verification node certificate: List of nodes participating in BFP verification with digital signatures; Zero-knowledge proof verification entry: Provides a button for users to directly input ZKP for public verification; Data source map: Intuitively displays data collection points, BFP verification points, and storage locations; Tamper-proof operation log: Timestamp and operator digital signature for all key operations including data generation, verification, on-chain storage, and storage call; Open API integration with external verification agencies: Provide standard API interfaces for decentralized third-party verification agencies to access; these agencies can: Random sampling: Sample and test the original physical products pointed by RDH, upload the results, i.e. hash value, to the chain for comparison; Live audit broadcast: Access live monitoring video stream by authorization to conduct online notarization; Publish independent verification report: Cast the verification report into NFT and anchor it on the blockchain, so that users can clearly see how many independent verification agencies, at what time, verified which indicators of which batch.

[0014] Further, the trust evaluation module generates a product dynamic trust index based on the purchase behavior, evaluation data, social media data and third-party audit results of consumers, and binds it with the blockchain data chain.

[0015] The application also provides a whole-process traceability method for agricultural products based on blockchain and Internet of Things technology, which comprises the following steps: S1, deploying sensors in the production link of agricultural products to collect environmental, operation and growth state data; S2, sending the data to the edge computing node through the Internet of Things middleware for preliminary data preprocessing and AI anomaly detection; S3, using zero-knowledge proof technology to complete the encryption verification of data on-chain; S4, after verification by the BFP consensus algorithm, the data is written into the blockchain in the form of blocks; S5, independent auditing of on-chain data by a decentralized verification platform; S6, displaying all data and trust indexes on the consumer side, allowing third parties to verify them and evaluate the trust indexes.

[0016] Further, the display of all data and trust indexes supports real-time access of terminal devices, query of blockchain browsers and online verification of third-party platforms.

[0017] Further, the evaluation of the trust index is dynamically calculated by a machine learning model and combined with consumer social media feedback data for evaluation.

[0018] The application has the following advantages: 1. The application collects real-time data during the production process by deploying sensors, and uses AI anomaly analysis and zero-knowledge proof to encrypt and verify the data on-chain process; 2. All data is stored in the form of blocks in a distributed ledger, ensuring that the data cannot be tampered with; 3. The application introduces a third-party decentralized verification platform to improve the authority and transparency of data auditing; 4. On the consumer side, the application realizes efficient judgment of consumers on the quality and safety of agricultural products and brand trust through visual display and trust index evaluation, thereby assisting in building a complete "data collection-data verification-data storage-data display-trust evaluation" closed-loop system, providing strong protection for the safe circulation and brand construction of agricultural products, and the application can improve the authenticity and transparency of agricultural product traceability data, prevent data falsification, and assist in enhancing consumer trust. BRIEF DESCRIPTION OF DRAWINGS

[0019] Figure 1 is the system overall architecture diagram in the application; Figure 2 is the complete traceability process timing diagram in the application. DETAILED DESCRIPTION

[0020] In order to more clearly understand the above-mentioned purposes, features and advantages of the application, the application will be further described in detail below in combination with the drawings and specific embodiments.

[0021] It should be noted that in the following description, many specific details are set forth in order to provide a thorough understanding of the application, however, the application can also have other embodiments and variations, therefore, the protection scope of the application is not limited by the specific embodiments disclosed below. The application provides an agricultural product whole-process traceability system based on blockchain and Internet of Things technology, and the specific embodiments of the application are as follows: The agricultural product whole-process traceability system based on blockchain and Internet of Things technology comprises: A trusted perception and original data acquisition anti-fake system is used to collect environmental parameters, farming operation data and growth state data in the production link of agricultural products, and to perform trusted perception and anti-fake verification; A data on-chain verification module is used to perform multiple verifications on the original data, including AI analysis anomaly detection, zero-knowledge proof and BFP consensus algorithm verification; A blockchain data storage module is used to store the verified data in a chain structure in a distributed ledger; A decentralized verification platform module is used to introduce a third party to independently audit the label data; A consumer-side data display and trust evaluation module is used to display traceability information to consumers and build a dynamic trust index.

[0022] Further, the trusted perception and original data acquisition anti-fake system comprises the following processes: Tamper-proof hardware terminal collects data: The tamper-proof hardware terminal includes environmental parameter monitoring sensors (temperature and humidity, light, soil fertility, water quality, CO2, video monitoring, etc.) deployed in production bases (soil, greenhouse, breeding farm, etc.), agricultural operation record sensors (fertilizer / pesticide recording instrument, harvesting equipment sensor), and crop health monitoring sensors for real-time data collection. The tamper-proof hardware terminal has a trusted execution environment (TEE) and a hardware security module (HSM) built-in, running a hardened firmware; Multiple authentication binding: The sensor devices of the tamper-proof hardware terminal are bound with unique physical identifiers (such as non-removable digital identity chips, laser-etched physical two-dimensional codes), and the device identity uniqueness and legitimacy are ensured through zero-knowledge proof or physical unclonable function technology; Real-time data fingerprint generation: After data collection, the Raw Data Hash (RDH) is calculated in real time in the TEE / HSM, and the device ID, timestamp, and geographic location coordinates are attached; Abnormal local verification: The trusted perception and original data collection anti-fraud system has a lightweight model built-in, which performs rationality verification on real-time data range (such as whether the temperature is within the sensor range), and marks and alarms abnormal data in real time.

[0023] The trusted perception and original data collection anti-fraud system is mainly used to solve the problem of fake original data; Main role: Ensure the authenticity, integrity of data collection process and the credibility of device identity from the physical device level, prevent fake sensor data or simulated data upload. RDH is the anchor point of subsequent data chaining.

[0024] Further, the data chaining verification module uses an AI model to analyze whether the data is abnormal, and uses zero-knowledge proof technology to verify the authenticity of the data; specifically including: BFP verification node network: A decentralized verification node network composed of industry association certified independent testing agencies (third parties), large-scale planting and breeding base representatives, and government regulatory departments; verification nodes need to stake Tokens and establish a reputation mechanism; Raw data hash fingerprint RDH and raw data packet formatting: The collection device submits RDH, device certificate, raw data packet (encrypted or desensitized), metadata (timestamp, location, operator ID) to the local front-end server or edge node; Multi-source cross-validation challenge: BFP verification nodes randomly receive verification tasks, including: requiring devices to remotely perform a specified operation (such as taking a photo, uploading specific parameters) to respond to the challenge; calling multiple sensor data in the same period and nearby location for cross comparison; calling the biological features (such as fingerprints) of the operators for binding records; the verification node uses secure multi-party computation MPC or homomorphic encryption technology to compare data while protecting privacy; Zero-knowledge evidence generation: After the verification node successfully passes the challenge, it uses zk-SNARKs / STARKs to generate a small-volume, high-verification-efficiency zero-knowledge proof ZKP, proving that the original data is from the authenticated legal device and has not been tampered with before being transmitted to the blockchain, i.e., the data packet integrity corresponding to RDH is verified; Malicious behavior punishment mechanism: If a node forges verification results or colludes, it will be confiscated Token, lose node qualification and be recorded publicly.

[0025] The data on-chain verification module is mainly used to solve the problem of tampering with on-chain data; Main role: Introduce a verifiable, multi-party, and cryptographically strong pre-on-chain link to completely solve the core trust black box of "data preparation on-chain". The generated ZKP is the core trust carrier for data on-chain.

[0026] Further, the blockchain data storage module adopts BFP consensus algorithm, IPFS storage technology and distributed storage structure to ensure that data cannot be tampered with and can resist single-point attacks; specifically including: Multi-consortium chain / hybrid chain deployment: core traceability data (such as RDH, ZKP, and key operation records) are written into multiple independent, different consensus mechanism (such as BFP, PBFT, PoA, PoS) consortium chain networks (which can be maintained by different industry organizations, government agencies, and head enterprises); Data sharding and dynamic isolated storage: After homomorphic encryption or verifiable encryption of raw data, processed intermediate data, and non-core traceability data, the data is divided into multiple encrypted fragments, which are stored in decentralized storage networks (such as IPFS / Filecoin + Arweave's permanent storage layer), cloud storage (distributed by multiple manufacturers), and even physical offline media (such as time-locked capsules that can be unlocked after a specified period of time); Data availability and integrity proof generation: periodically (such as every 24 hours) or on-demand trigger availability and integrity challenges for off-chain stored data; distributed storage networks generate PoDA using verifiable delay functions VDF or zero-knowledge proofs to prove that all data fragments are complete and available and have not been tampered with; challenge tasks are performed by BFP network nodes or randomly selected verifiers.

[0027] The blockchain data storage module is mainly used to solve the problem of data storage fraud. Main functions: Anti-single blockchain failure / controlled: multiple chains write to any chain simultaneously, other chains still retain original data, attack cost increases dramatically.

[0028] Protecting the safety of non-chain full data: sharded storage + dynamic PoDA verification, even if attackers break into storage nodes or cloud service providers, it is difficult to easily obtain complete or tamper with data. Privacy data is protected.

[0029] Improve data long-term availability: Arweave and other permanent storage + dynamic verification to ensure that critical data is never lost.

[0030] Further, the decentralized verification platform module is registered as a verification node on the blockchain by a third-party institution with legal qualifications, for independent auditing and verification; Specifically includes: Tokenized access authorization mechanism: users (consumers, regulators) obtain high-privilege or standard-view access credentials by holding specific NFTs or paying a small amount of tokens (to the verification node network); This credential itself is a chain-based NFT; Tamper-proof data dashboard: terminal APP or Web-based dashboard generated based on real-time blockchain data; The display interface is forced to include the following elements: Full data hash root (Merkle Root): display the root hash value of all related data; Verification node certificate: list of nodes participating in BFP verification with digital signatures; Zero-knowledge proof verification entry: provides a button for users to directly input ZKP for public verification (without knowing the original data); Data source map: visually displays data collection points, BFP verification points, and storage locations; Non-tamperable operation log: timestamps and operator digital signatures for all key operations, including data generation, verification, on-chain storage, and storage calls; Open API for integration with external verification agencies: provides standard API interfaces for decentralized third-party verification agencies (such as SGS, local detection centers, university laboratories, and community oversight groups) to access; These agencies can: Random sampling: sample and test (pesticides, ingredients, etc.) original physical products (such as randomly purchased agricultural products) pointed to by RDH, and upload the results, i.e., hash values, to the chain for comparison; Live audit streaming: authorized access to live monitoring video streams for online notarization; Publish independent verification report (NFT form): cast the verification report into NFT and anchor it on the blockchain, so that users can clearly see how many independent verification agencies, at what time, verified which indicators of which batch.

[0031] The decentralized verification platform module is mainly used to solve the problem of data display fraud; Main role: terminal display is no longer monopolized by the platform, users get verifiable, full-view trust window. The results of decentralized external verification agencies are directly linked to the chain, forming a strong, auditable trust endorsement, preventing platform "selective display" or tampering with data.

[0032] Further, the trust evaluation module generates a dynamic trust index of the product based on the consumer's purchase behavior, evaluation data, social media data, and third-party audit results, and binds it with the blockchain data chain. Specifically, it includes: Multi-dimensional reputation index model: based on on-chain data, automatically calculate a dynamic updated trust reputation index of agricultural product production units (plots / batches), which includes: Objective behavior indicators (70%): BFP verification pass rate / verification response speed.

[0033] Data PoDA verification historical success rate.

[0034] External verification agency sampling compliance rate / number of verification reports.

[0035] Abnormal data event frequency and handling timeliness.

[0036] Data integrity and availability score.

[0037] Market feedback indicators (30%): consumer code scanning evaluation (real-name binding to prevent fake orders), return rate / complaint rate (associated with source batch chain code), brand historical credit record (if involving big brands).

[0038] Decentralized oracle access: access reputable decentralized oracle networks to obtain external market data (such as e-commerce evaluation platform data API) and chain it.

[0039] Trust graph display: in the terminal display area, visualize the reputation index sub-item scores, verification agency graph, and data flow path. Abstract trust is converted into quantifiable, traceable data.

[0040] Brand value anchoring and linkage: provide interfaces to allow well-known brands to use their existing brand reputation profiles (audited by authoritative agencies) as one of the trusted input sources, or to link their reputation scores with on-chain reputation indexes for display (such as "brand credibility + on-chain verifiable data" dual authentication).

[0041] The trust evaluation module is mainly used for assisting in solving the problem of subjective trust; Main role: convert subjective trust (brand) into trust index based on on-chain objective data and dynamic calculation. Through the superposition of quantitative indicators and external verification reports, even when the brand information is unknown, consumers can establish rational trust for unknown but verifiable high-quality agricultural products. Improve the transparency and overall credibility of the entire supply chain.

[0042] The application also provides an agricultural product whole-process traceability method based on a blockchain and an Internet of Things technology, and the method comprises the following steps: S1, deploying sensors in the production link of agricultural products to collect environment, operation and growth state data; S2, sending the data to an edge computing node through an Internet of Things middleware to perform preliminary data preprocessing and AI anomaly detection; S3, using zero-knowledge proof technology to complete encryption verification of data uplink; S4, after verification by a BFP consensus algorithm, writing the data into a blockchain in the form of a block; S5, independently auditing the on-chain data by a decentralized verification platform; S6, displaying all the data and the trust index on the consumer side, allowing a third party to verify the same, and evaluating the trust index.

[0043] Further, the display of all the data and the trust index supports real-time access of terminal equipment, query of a blockchain browser and online verification of a third-party platform.

[0044] Further, the evaluation of the trust index is dynamically calculated by using a machine learning model and combined with consumer social media feedback data.

[0045] In embodiment 2, the application also provides an agricultural product whole-process traceability method based on a blockchain and an Internet of Things technology, and the method comprises the following steps: S1, collecting data at the production end; Deploying multifunctional intelligent sensors (environmental sensors, crop health sensors and operation record sensors) in planting bases, breeding farms and processing plants; The sensor data is sent to an edge computing node through a wireless communication interface (such as LoRa and NB-IoT); The edge node performs data preprocessing and anomaly value detection to ensure the accuracy of the uploaded data; An AI model is used to analyze and identify possible data abnormal behaviors (such as artificial forgery and equipment failure) by using a machine learning algorithm; A hash value is generated for the original data as a unique fingerprint of the data.

[0046] S2, data chaining and verification; After data processing, initiate a data chaining request to the blockchain node, the request includes: Hash value; Timestamp; Geographical coordinates; Source node signature; Use zero-knowledge proof (ZKP) to encrypt and verify the data chaining process; The verification result is automatically audited and updated by the smart contract; After verification, the data is packaged as a blockchain transaction and time-stamped, hashed and signed as a block, forming a complete data chain.

[0047] S3, blockchain data storage; Data uses a hybrid storage structure, including: Upload data in a chain structure on the blockchain to ensure it cannot be tampered with; The original data is saved in the cloud server as a backup data source; Data is stored and backed up through distributed nodes to prevent single point failure; Establish multiple structure data index, allow external node efficient retrieval of specific product traceability information; Redundancy mechanism and encryption method are used in data storage structure to protect data security.

[0048] S4, decentralized third-party verification; External agencies obtain verification identity through blockchain registration platform; Verification agencies can conduct independent audits based on blockchain public data; The verification process uses DApp to operate, and the audit results are uploaded to the blockchain platform after completion; The audit results include: Whether there is data anomaly; Whether it meets the planting specifications; Whether it has passed the quality inspection process; Audit results support on-chain traceability, enhancing consumer trust in products.

[0049] S5, consumer-side data display and trust evaluation; Consumers access the complete data chain of agricultural products through mobile applications, web portals, or blockchain browsers; The display content includes: Growth environment data; Operation records; Audit results; Consumer ratings; Trust index; The system assesses the brand and product through an AI scoring model to generate a dynamic trust index; Consumers can make product selection decisions based on the trust index, thereby improving market trust and purchasing behavior.

[0050] Embodiment 3: The present application also provides a full-process traceability system for agricultural products based on blockchain and Internet of Things technology. The system of the present embodiment comprises: A trusted perception terminal (module 1) is used to complete device identity authentication and binding, a multi-chain network (module 3), a BFP verification node network (module 2), a third-party verification agency (module 4), and a reputation model (module 5); The specific workflow of the applied system comprises: 1. Deployment and initialization: deploy the trusted perception terminal (module 1), the multi-chain network (module 3), the BFP verification node network (module 2), the third-party verification agency (module 4), and the reputation model (module 5) at the agricultural production base; 2. Data collection and generation: module 1 collects environmental / operational data and generates RDH in TEE / HSM, and sends it to the front edge node; 3. BFP strong verification: the front edge node submits the data packet and RDH to the BFP verification node network (module 2); the verification node initiates a challenge and generates a zero-knowledge proof ZKP. The verification result (ZKP + node signature list) is returned; 4. On-chain storage: RDH, ZKP, key metadata (timestamp, location, operation ID, device ID), and verification node list hash are written into multiple consortium chains (module 3) as an indivisible transaction batch through a consensus mechanism; 5. Data sharding and secure storage: non-core traceability data is encrypted and sharded and stored in a distributed storage network and cloud platform / offline medium (module 3); the system triggers PoDA verification (module 3) regularly; 6. Authentication access and display: users scan the code through the APP and use NFT / Token authorization (module 4); the system generates a dashboard (module 4&5) containing all verifiable elements (Merkle Root, ZKP verification entry, verification agency certificate, tamper-proof log, and reputation index display); 7. External verification and collaboration: the third-party verification agency (module 4) accesses through the API for sampling inspection / onsite audit / report release, and the results (hash or NFT) are uploaded to the chain; the oracle (module 5) obtains market feedback and uploads it to the chain; 8. Dynamic reputation calculation: chain data, external verification results, and market feedback are continuously input (module 5), and the reputation index model is automatically updated and displayed; 9. Interaction and audit: Users can verify ZKPs (module 4), view reputation reports (module 5), trace the entire process (modules 1-5). Regulatory or audit bodies can access the full data view.

[0051] The above merely illustrates the specific embodiments of the present application, but the protection scope of the present application is not limited thereto, any person skilled in the art can easily think of the changes or replacements within the technical range disclosed by the present application, which should be covered within the protection scope of the present application; the embodiments of the present application and the features in the embodiments can be combined with each other without conflict. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A full-process traceability system for agricultural products based on blockchain and IoT technologies, characterized by: The system includes: The Trusted Sensing and Raw Data Acquisition Anti-counterfeiting System is used to collect environmental parameters, agricultural operation data, and growth status data in the production process of agricultural products, and to perform trusted sensing and anti-counterfeiting verification. The data on-chain verification module is used to perform multiple verifications on the original data, including AI analysis anomaly detection, zero-knowledge proof, and BFP consensus algorithm verification. The blockchain data storage module is used to store verified data in a chain structure in a distributed ledger; The decentralized verification platform module is used to introduce third-party organizations to independently review the tag data; The consumer-side data display and trust assessment module is used to show consumers traceability information and build a dynamic trust index.

2. The agricultural product full-process traceability system based on blockchain and Internet of Things technology according to claim 1, characterized in that, The trusted sensing and raw data acquisition anti-counterfeiting system includes the following process: Data collection via tamper-proof hardware terminal: The tamper-proof hardware terminal includes environmental parameter monitoring sensors, agricultural operation record sensors, and crop health monitoring sensors deployed in the production base for real-time data collection; the tamper-proof hardware terminal has a built-in Trusted Execution Environment (TEE) and Hardware Security Module (HSM) and runs fixed firmware; Multi-factor authentication binding: The sensor devices of the tamper-proof hardware terminal are bound to a unique physical identifier, and the uniqueness and legitimacy of the device identity are ensured through zero-knowledge proof or physical unclonable function technology; Real-time data fingerprint generation: After data acquisition, the original data hash fingerprint RDH is calculated in real time within the TEE / HSM, and the device ID, timestamp, and geographic location coordinates are appended. Local anomaly verification: The trusted perception and original data acquisition anti-counterfeiting system has a built-in lightweight model that verifies the reasonableness of the real-time data range, and marks and alarms abnormal data in real time.

3. The agricultural product full-process traceability system based on blockchain and Internet of Things technology according to claim 1, characterized in that, The data on-chain verification module uses an AI model to analyze whether the data is abnormal and combines zero-knowledge proof technology to verify the authenticity of the data; specifically, it includes: BFP Validator Network: A decentralized validator network jointly composed of independent testing institutions certified by industry associations (i.e., third parties), representatives of large-scale planting and breeding bases, and government regulatory departments; validator nodes need to stake tokens and establish a reputation mechanism. Raw data hash fingerprint RDH and raw data packet formatting: The acquisition device submits the RDH, device certificate, raw data packet, and metadata to the local front-end server or edge node; Multi-source cross-validation challenge: BFP verification nodes randomly receive verification tasks, which include: requiring the device to remotely perform a specified operation in response to the challenge; retrieving and cross-comparing data from multiple sensors at the same time and nearby locations; retrieving the operator's biometric binding records; and the verification node using secure multi-party computation (MPC) or homomorphic encryption technology to perform data comparison while protecting privacy. Zero-knowledge proof generation: After the verification node successfully passes the challenge, it uses zk-SNARKs / STARKs to generate a zero-knowledge proof ZKP to prove that the original data originated from a certified legitimate device and was not tampered with before being transmitted to the blockchain. In other words, the integrity of the data packet corresponding to RDH is verified. Malicious behavior punishment mechanism: If a node forges verification results or colludes to cheat, its staked tokens will be confiscated, its node qualification will be lost, and its record will be made public.

4. The agricultural product full-process traceability system based on blockchain and Internet of Things technology according to claim 1, characterized in that, The blockchain data storage module employs the BFP consensus algorithm, IPFS storage technology, and a distributed storage structure to ensure data immutability and resistance to single-point attacks; specifically, it includes: Multi-consortium chain / hybrid chain deployment: Core traceability data is simultaneously written to multiple independent consortium chain networks with different consensus mechanisms; Data sharding and dynamic isolated storage: After homomorphic or verifiable encryption, the raw data, processed intermediate data, and non-core traceability data of the verification log are divided into multiple encrypted fragments, which are distributed and stored in: decentralized storage networks, cloud storage, or even physical offline media. Data availability and integrity proof generation: Availability and integrity challenges for off-chain stored data are triggered periodically or on demand; the distributed storage network generates PoDA through verifiable delay functions (VDFs) or zero-knowledge proofs to prove that all data fragments are complete, available, and have not been tampered with; the challenge tasks are performed by BFP network nodes or randomly selected verifiers.

5. The agricultural product full-process traceability system based on blockchain and Internet of Things technology according to claim 1, characterized in that, The decentralized verification platform module is registered as a verification node on the blockchain by a legally qualified third-party organization, used for independent auditing and verification; specifically, it includes: Tokenized Authorization Access Mechanism: Users obtain access credentials for high-level privileges or standard views by holding specific NFTs or paying micro-tokens; these credentials are themselves on-chain NFTs. Anti-tamper data dashboard: The terminal APP or web platform displays a dashboard generated in real time based on blockchain data; the display interface is required to include the following elements: Full data hash root: Displays the root hash value of all related data; Verify node certificates: A list of digital signatures of nodes participating in BFP verification; Zero-knowledge proof verification entry: A button is provided so that users can directly enter ZKP for public verification; Data source map: intuitively displays data collection points, BFP verification points, and storage locations; Immutable operation logs: all critical operations include timestamps and operator digital signatures for data generation, verification, on-chain storage, and storage calls; Open API Integration with External Verification Agencies: Provides standard API interfaces for decentralized third-party verification agencies to access; these agencies can: Random sampling inspection: Sampling inspection is carried out on the original physical products pointed to by RDH, and the results, i.e. hash values, are uploaded to the on-chain comparison point; Live on-site audit: Online notarization is conducted by authorizing access to the on-site monitoring video stream; Publish independent verification reports: Cast verification reports into NFTs and anchor them on the blockchain. Users can clearly see how many independent verification agencies, when, and which batches and metrics were verified.

6. The agricultural product full-process traceability system based on blockchain and Internet of Things technology according to claim 1, characterized in that, The trust assessment module generates a dynamic trust index for products based on consumer purchasing behavior, evaluation data, social media data, and third-party audit results, and binds it to the blockchain data chain.

7. A method for full-process traceability of agricultural products based on blockchain and IoT technologies, characterized in that: The method includes: S1. Deploy sensors in the agricultural production process to collect environmental, operational, and growth status data; S2. Data is sent to edge computing nodes via IoT middleware for preliminary data preprocessing and AI anomaly detection. S3. Use zero-knowledge proof technology to complete the encrypted verification of data on the blockchain; S4. After verification through the BFP consensus algorithm, the data is written into the blockchain in the form of blocks; S5. On-chain data is independently audited by a decentralized verification platform; S6. Display all data and trust index to consumers, and allow third parties to verify and evaluate the trust index.

8. The method for full-process traceability of agricultural products based on blockchain and Internet of Things technology according to claim 7, characterized in that, The system displays all data and trust indices, supporting real-time access from terminal devices, queries via blockchain explorers, and online verification through third-party platforms.

9. The method for full-process traceability of agricultural products based on blockchain and Internet of Things technology according to claim 7, characterized in that, The trust index is evaluated using a machine learning model for dynamic calculation, combined with consumer social media feedback data.

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