Block chain-based food processing additive safety detection system and method

Abstract

The invention discloses a food processing additive safety detection system and method based on a block chain, and the system comprises a data collection unit which is connected with a block chain unit, and the block chain unit is connected with an application unit. The data acquisition unit is used for acquiring content data of food additives and acquisition scene data of the food additives, and the block chain unit performs verification, early warning, encryption and evidence storage on the data acquired by the data acquisition unit through an intelligent contract module, a consensus algorithm verification module, an early warning module, an encryption module and an evidence storage module. And the application unit is linked, and the traceability, supervision and enterprise management of the food additives by the user are realized through the application unit. The method has the advantages of random sampling inspection of the food processing additives based on the block chain, data tampering prevention and multiple detection.

Description

Technical Field

[0001] The present invention relates to the fields of blockchain technology and food processing detection technology, and in particular to a blockchain-based food processing additive safety detection system and method. Background Art

[0002] Currently, food safety is receiving increasing attention. Regarding the safety of food additives, the rational use of additives during food processing can improve food quality, color, flavor, and extend shelf life. However, additive safety testing is crucial. There are 23 categories and over 2,000 types of food additives, each with strict dosage requirements. Exceeding the limit or using beyond the scope may have adverse effects on human health. To ensure food safety, safety testing of the types and content of food additives is necessary.

[0003] There are currently many problems with additive safety testing: on the one hand, test data is easily tampered with. From sampling and testing to result recording, human factors may cause data distortion, making it difficult for regulatory authorities to obtain true and accurate information; test samples are easily deliberately prepared, and test data cannot reflect authenticity and reliability. It is difficult to ensure that test samples are processed samples. In addition, the authority of test results is difficult to guarantee, and it is difficult to retain random test evidence and authoritative test data.

[0004] In summary, there is at least one of the following technical problems: Test data is easily tampered with. From sampling and testing to recording results, human factors may cause data distortion, making it difficult for regulatory authorities to obtain true and accurate information; Test samples can easily be deliberately prepared, test data cannot reflect authenticity and reliability, and it is difficult to ensure that test samples are processed samples. In addition, the authority of the test results is difficult to guarantee, and it is difficult to retain random test evidence and authoritative test data. Summary of the Invention

[0005] The main purpose of the present invention is to provide a blockchain-based food processing additive safety detection system and method to solve the technical problems in the existing technology that detection data is easily tampered with, human factors may cause data distortion from sampling, detection to result recording, making it difficult for regulatory authorities to obtain true and accurate information; detection samples are easily prepared deliberately, detection data cannot reflect authenticity and reliability, and it is difficult to ensure that the detection samples are processed samples. In addition, the authority of the detection results is difficult to guarantee, and it is difficult to retain random detection evidence and authoritative detection data.

[0006] In order to achieve the above-mentioned purpose, according to one aspect of the present invention, a blockchain-based food processing additive safety detection system is provided, including: a data acquisition unit, wherein the data acquisition unit is connected to a blockchain unit, and the blockchain unit is connected to an application unit. The data acquisition unit is used to collect content data of food additives and food additive collection scenario data. The blockchain unit verifies, warns, encrypts and stores evidence on the data collected by the data acquisition unit through a smart contract module, a consensus algorithm verification module, an early warning module, an encryption module and an evidence storage module, and links to the application unit, so as to realize user traceability, supervision and enterprise management of food additives through the application unit.

[0007] Preferably, the data acquisition unit includes a random sensing detection module, a third-party testing agency data import module and a production enterprise data docking module, the random sensing detection module includes a first random acquisition terminal, a second random acquisition terminal and an N-th random acquisition terminal, totaling N; the third-party testing agency data import module includes a first third-party acquisition terminal, a second third-party acquisition terminal and an N-th third-party acquisition terminal, totaling N; the production enterprise data docking module includes a first production enterprise acquisition terminal, a second production enterprise acquisition terminal and an N-th production enterprise acquisition terminal, totaling N.

[0008] Preferably, the blockchain unit includes a smart contract verification module, a consensus algorithm verification module, a smart contract early warning module, a block evidence module and a data encryption module. The smart contract verification module presets the additive safety standard rules and the enterprise's real scenario data, and performs security and matching verification on the food additive content data and food additive collection scenario data collected by the data collection unit. The consensus algorithm module adopts PBFT or PoS algorithm to ensure the consistency of data of each node. When the smart contract verification module fails to verify the data, the smart pre-contract early warning module issues an early warning. When the smart contract verification module passes the verification, the block existence module stores the evidence and encrypts it through the data encryption module.

[0009] Preferably, the application unit includes an enterprise management background, a traceability query module and a supervision and audit module. The traceability query module is used to provide consumers with a scanning and traceability function, displaying the full-link detection report of additives and transparent information on production and addition data; the supervision and audit module is used to provide regulatory authorities with real-time data monitoring, violation tracking, historical records, and audit functions of food additive collection scenario data.

[0010] Preferably, the data acquisition unit is provided with an acquisition system, a camera system and a locking system. The acquisition system includes a solid sampling end, a liquid sampling end or both a solid sampling end and a liquid sampling end. The acquisition system is connected to the camera system. The camera system and the acquisition system are remotely linked to the blockchain unit and the supervision and monitoring center. The supervision and monitoring center controls the acquisition time node of the data acquisition unit. The general control center controls the random sensing detection module set in the food processing enterprise to perform random sampling and collection. The random sensing detection module controls the acquisition system and the camera system to start at the same time. The camera system first collects the factory environment, and then uses the acquisition system to randomly collect food on the production line under the condition of camera synchronization, and detects it through the food additive detection module of the acquisition system. After collection, the data collected by the camera system and the acquisition system are locked and the collected data is transmitted to the blockchain.

[0011] Preferably, the blockchain unit verifies the food additive content data and food additive collection scene data collected by the random sensing detection module through the smart contract verification module, verifies them with the preset food safety standard rules, verifies them with the historical data of previous third-party testing agencies, verifies them with the historical data uploaded by the production enterprises themselves, and matches them with the real scenes of the enterprises stored in the blockchain. If the food additive content exceeds the threshold or the actual collection scene does not match the preset real collection scene, an early warning will be issued through the smart contract early warning module. If it meets the requirements, it will be recorded through the block existence module, and the required data will be encrypted through the data encryption module.

[0012] Preferably, the random sensing detection modules are distributed in the production plants of various food processing enterprises and are interconnected with the control center of the regulatory department. Through the control center of the regulatory department, random control is carried out to conduct random inspections of the safety of food additives. The camera system is consistent with the acquisition system to ensure randomness in time while ensuring the reliability and authenticity of the inspected food additives.

[0013] Preferably, the third-party testing agency data import module is set in each third-party testing agency. By connecting the third-party testing agency with the blockchain unit, the reliability of the characteristic test results is guaranteed. If an authoritative identification certificate is required, the food additive test samples collected by the collection system are collected and locked in the collection system through the locking system. Then the regulatory department authorizes and unlocks them. After unlocking, they are transferred to the third-party testing agency for authoritative testing to ensure that when random inspections determine that there are problems, they are then authoritatively tested by the third-party testing agency, and a food additive safety test certificate is issued, and the test results are transmitted to the blockchain unit.

[0014] Preferably, the enterprise management background of the application unit is set in the food processing enterprise for retrieving food additive safety test data, the traceability query module of the application unit is set in the ordinary user APP, for ordinary users to retrieve and view food additive safety test data at any time, and the supervision and audit module of the application unit is set in the supervision department, for providing supervision of food additive safety to the supervision department.

[0015] According to another aspect of the present invention, a method for using a blockchain-based food processing additive safety detection system is provided, comprising: Multi-dimensional data collection is combined with random sampling, and data is collected through multiple channels using random sensing detection modules, third-party testing agency data import modules, and production enterprise data docking modules. Random sampling is also implemented through random sensing detection modules to ensure the comprehensiveness, randomness, and authenticity of the data. Apply blockchain technology to food additive safety testing, using smart contracts, consensus algorithms, data encryption, and evidence storage to achieve trusted verification, secure storage, and tamper-proof data. Build a collaborative system involving manufacturers, regulatory authorities, and consumers, and achieve data sharing and transparent management through application units; Carry out scenario-based data collection and verification. The camera system and the acquisition system collect data synchronously, and the detection scene data is combined with the food additive content data for verification to further enhance the credibility and reliability of the data.

[0016] The application of the technical solution of the present invention has the following technical effects: By distributing random sensor detection modules throughout the production facilities of food processing companies and connecting them to the regulatory authorities' control center, random sampling and data collection can be conducted according to regulatory authorities' instructions. Simultaneously controlling the acquisition system and camera system, this system enables simultaneous data collection of the factory environment and food on the production line. This ensures the randomness and authenticity of the sampling, prevents human interference in the test results, improves the credibility of the test data, and provides reliable data support for regulatory authorities. A third-party testing agency data import module, installed at each third-party testing agency, imports their test results into the blockchain unit, providing an authoritative reference for data verification and comparison. This enriches the data sources and increases the credibility and authority of the data. When further confirmation of sampling results is required, the professional testing capabilities of third-party testing agencies can be leveraged to ensure the accuracy of the test results. The production enterprise data connection module connects with the data systems of food processing companies, transferring relevant data collected by the production companies to the blockchain unit for data verification and analysis. This enables production companies to actively participate in food additive safety supervision and provides a more comprehensive data perspective for the entire system, facilitating more accurate assessments of food additive safety. The sampling system includes solid and liquid sampling terminals, respectively, for sampling solid and liquid food additives, meeting the testing requirements of different types of food additives. In conjunction with the camera system, it enables random sampling of food on the production line. This ensures the representativeness and authenticity of the collected food additive samples, providing an accurate sample basis for subsequent testing and analysis. After acquisition, the locking system locks the data collected by the camera system and the acquisition system to prevent data tampering or loss, and then transmits the locked data to the blockchain unit. This ensures the integrity and security of the data from acquisition to transmission, ensuring the credibility of the uploaded data and providing reliable support for subsequent data applications. The camera system's detection scene data acquisition module first captures the factory environment during random inspections by the random sensing detection module, recording the actual food production scene to provide a basis for verifying the authenticity of the captured scene. This helps verify that the collection process complies with regulatory requirements, prevents companies from artificially creating collection scenes, and improves the credibility of the test data. The detection sample image data acquisition module simultaneously captures food image data on the production line while the acquisition system is sampling food on the production line. This includes information such as the food's appearance and condition, and verifies it with the sampled data. It further enhances the integrity and credibility of the data and provides richer information for subsequent test result analysis and traceability queries.

[0017] Through the random sensing detection module, the regulatory authorities remotely trigger random inspections, and the forced linkage camera system realizes dual randomness in time and space, with random inspection time and random geographical location, to prevent premeditated cheating. Through the locking system, the sampling container is physically locked, and only the supervisory authority can unlock it to solve the risk of sample transfer and substitution. Cooperating with blockchain evidence storage, a "physical-digital" double insurance is formed. The detection scene encryption module encrypts the collection environment video with the national secret algorithm to prevent the video evidence from being tampered with, meeting the judicial-level evidence storage requirements; the triple verification mechanism, cross-verification of corporate historical data, third-party reports, and random inspection data breaks the data silos, and any unilateral data anomaly immediately triggers an early warning.

[0018] The detection scene encryption module encrypts the detection scene data and test sample image data collected by the camera system to protect data privacy and security. This prevents detection scene data from being leaked or tampered with, ensuring data authenticity and integrity, and safeguarding the interests of all parties involved, including businesses and regulators. The smart contract verification module strictly verifies food additive content data and related collection scene data according to pre-set safety standards and rules. Immediate warnings are issued upon detection of anomalies, effectively preventing substandard food additives from entering the market and ensuring food safety. From data collection to on-chain storage and final traceability, all data is clearly accessible, achieving transparency across the entire food additive industry chain. This ensures consumer confidence, effective oversight by regulators, and eases self-management and optimization for businesses. The regulatory audit module within the application unit allows regulators to quickly and easily access food additive-related data, conduct real-time monitoring and auditing, and promptly identify and address violations, significantly improving the timeliness and effectiveness of oversight. The enterprise management backend facilitates unified management and analysis of food additive safety testing data, helping businesses optimize production processes and improve product quality. Consumers can clearly understand the detailed information of food additives through the traceability query module, thereby enhancing their trust in the company and promoting the healthy development of the company. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The accompanying drawings, which constitute part of this application, are intended to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are intended to explain the present invention and do not constitute an undue limitation of the present invention. In the accompanying drawings: Figure 1 A schematic diagram of the structure of a food processing additive safety detection system based on blockchain according to the present invention is shown; Figure 2 Shown Figure 1 A modular structure view of the blockchain-based food processing additive safety testing system; Figure 3 Shown Figure 1A structural view of the data acquisition unit of the blockchain-based food processing additive safety testing system; Figure 4 Shown Figure 1 Blockchain unit structure view of the blockchain-based food processing additive safety detection system; Figure 5 Shown Figure 1 The application unit structure view of the blockchain-based food processing additive safety detection system; Figure 6 Shown Figure 1 The random collection end structure view of the blockchain-based food processing additive safety detection system; Figure 7 Shown Figure 1 A structural view of the camera system in the blockchain-based food processing additive safety detection system; Figure 8 Shown Figure 1 A structural view of the collection system for the blockchain-based food processing additive safety testing system.

[0020] The above drawings include the following reference numerals: Data acquisition unit 1; Blockchain unit 2; smart contract verification module 2-1; consensus algorithm verification module 2-2; smart contract early warning module 2-3; block evidence module 2-4; data encryption module 2-6; Application unit 3; Enterprise management background 3-1; Traceability query module 3-2; Supervision and audit module 3-3; Random sensing detection module 4; first random acquisition terminal 4-1; second random acquisition terminal 4-2; Nth random acquisition terminal 4-3; Third-party testing agency data import module 5; first third-party collection terminal 5-1; second third-party collection terminal 5-2; Nth third-party collection terminal 5-N; Production enterprise data docking module 6; first production enterprise collection terminal 6-1; second production enterprise collection terminal 6-3; Nth production enterprise collection terminal 6-N; Sampling system 7; solid sampling end 7-1; liquid sampling end 7-2; Locking system 8; Camera system 9; detection scene data acquisition module 9-1; detection sample image data acquisition module 9-2; detection scene encryption module 9-3. DETAILED DESCRIPTION

[0021] It should be noted that, in the absence of conflict, the embodiments and features of the embodiments in this application can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0022] like Figures 1 to 8 As shown, an embodiment of the present invention provides a food processing additive safety detection system based on blockchain, including: a data acquisition unit 1, the data acquisition unit 1 is connected to a blockchain unit 2, the blockchain unit 2 is connected to an application unit 3, the data acquisition unit 1 is used to collect food additive content data and food additive collection scenario data, the blockchain unit 2 verifies, warns, encrypts and stores the data collected by the data acquisition unit 1 through a smart contract verification module 2-1, a consensus algorithm verification module 2-2, a smart contract early warning module 2-3, a data encryption module 2-6 and a block evidence module 2-4, and links to the application unit 3, so that users can trace, supervise and manage food additives through the application unit 3. The data acquisition unit 1 is also connected to the control center of the regulatory department of the application unit 3.

[0023] In this embodiment, the data acquisition unit 1 includes a random sensing detection module 4, a third-party testing agency data import module 5 and a production enterprise data docking module 6, the random sensing detection module 4 includes a first random acquisition terminal 4-1, a second random acquisition terminal 4-2 and an N-th random acquisition terminal 4-3, totaling N; the third-party testing agency data import module 5 includes a first third-party acquisition terminal 5-1, a second third-party acquisition terminal 5-2 and an N-th third-party acquisition terminal 5-N, totaling N; the production enterprise data docking module 6 includes a first production enterprise acquisition terminal 6-1, a second production enterprise acquisition terminal 6-3 and an N-th production enterprise acquisition terminal 6-N, totaling N. The data acquisition unit 1 is provided with a sampling system 7, a camera system 9 and a locking system 8. The sampling system 7 includes a solid sampling terminal 7-1, a liquid sampling terminal 7-2, or both a solid sampling terminal 7-1 and a liquid sampling terminal 7-2. The sampling system 7 is connected to the camera system 9. The camera system 9 and the sampling system 7 are remotely linked to the blockchain unit 2 and the supervision and monitoring center. The supervision and monitoring center controls the collection time node of the data acquisition unit 1. The general control center controls the random sensing detection module 4 set in the food processing enterprise to perform random sampling and collection. The random sensing detection module 4 controls the sampling system 7 and the camera system 9 to start at the same time. The camera system 9 first collects the factory environment, and then uses the sampling system 7 to randomly collect the food on the production line under the condition of synchronous camera shooting, and detects it through the food additive detection module of the sampling system 7. After collection, the data collected by the camera system 9 and the sampling system 7 are locked and the collected data is transmitted to the blockchain. Random sensing and detection modules 4 are distributed throughout the production facilities of various food processing enterprises and interconnected with the regulatory authorities' control center. Through the regulatory authorities' control center, random sampling of food additive safety is controlled and coordinated with sampling system 7, ensuring both random timing and the reliability and authenticity of the sampling. Third-party testing agency data import modules 5 are located at each third-party testing agency. By connecting these third-party testing agencies to blockchain unit 2, the reliability of characteristic testing results is ensured. If an authoritative authentication certificate is required, food additive test samples collected by sampling system 7 are locked in sampling system 7 via locking system 8. The regulatory authorities then authorize unlocking the unlocked samples, which are then transferred to a third-party testing agency for authoritative testing. If a problem is identified during random sampling, the third-party testing agency authorizes testing and issues a food additive safety test certificate, with the test results transmitted to blockchain unit 2. Data collection unit 1, serving as the fundamental data source for the entire system, is responsible for collecting food additive content data and food additive collection scenario data. By collecting data from different channels (random sensing and detection module 4, third-party testing agency data import module 5, and manufacturer data connection module 6), the comprehensiveness and diversity of the data are ensured.It provides a rich and accurate data foundation for subsequent testing, verification, traceability and other work, enabling the entire system to operate and make decisions based on comprehensive data.

[0024] In this embodiment, random sensing detection modules 4 are distributed throughout the production plants of various food processing enterprises and interconnected with the control center of the regulatory authorities. They conduct random sampling and collection according to the regulatory authorities' instructions, and simultaneously control the activation of the sampling system 7 and the camera system 9 to achieve simultaneous collection of data from the factory environment and food on the production line. This ensures the randomness and authenticity of the sampling, avoids human interference with the test results, improves the credibility of the test data, and provides reliable data support for the regulatory authorities. A third-party testing agency data import module 5 is installed at each third-party testing agency and imports the test results data from third-party testing agencies into the blockchain unit 2, providing an authoritative reference for data verification and comparison. This enriches the data sources and increases the credibility and authority of the data. When further confirmation of the sampling results is required, the professional testing capabilities of the third-party testing agencies can be leveraged to ensure the accuracy of the test results. The production enterprise data docking module 6 docks with the data system of the food processing enterprise and transmits relevant data collected by the production enterprise (such as data on additive usage during the production process) to the blockchain unit 2, participating in data verification and analysis. This system enables manufacturers to proactively participate in food additive safety oversight and provides a more comprehensive data perspective for the entire system, facilitating a more accurate assessment of food additive safety. The sampling system 7 includes a solid sampling terminal 7-1 and a liquid sampling terminal 7-2, which sample solid and liquid food additives, respectively, to meet the testing requirements of different types of food additives. In conjunction with the camera system 9, random sampling of food on the production line is achieved. This ensures the representativeness and authenticity of the collected food additive samples, providing an accurate sample basis for subsequent testing and analysis. After collection is complete, the locking system 8 locks the data collected by the camera system 9 and the sampling system 7 to prevent data tampering or loss, and then transmits the locked data to the blockchain unit 2. This ensures the integrity and security of the data from collection to transmission, ensuring the credibility of the uploaded data and providing reliable support for subsequent data applications. During random inspections by the random sensing detection module 4, the detection scene data acquisition module 9-1 of the camera system 9 first collects data from the factory environment, recording the actual scene conditions of food production, providing a basis for verifying the authenticity of the collected scene. This helps verify compliance with regulatory requirements during the collection process, prevents companies from fabricating false collection scenarios, and enhances the credibility of test data. The test sample image data acquisition module 9-2 simultaneously collects food image data, including information such as the food's appearance and condition, while the sampling system 7 samples food on the production line. This data is then used to verify the sampled data. This further enhances data integrity and credibility, providing richer information for subsequent test result analysis and traceability queries.

[0025] The detection scene encryption module 9-3 encrypts the detection scene data and detection sample image data collected by the camera system 9 to protect the privacy and security of the data. This prevents the detection scene data from being leaked or tampered with, ensuring the authenticity and integrity of the data and safeguarding the interests of all parties involved, including enterprises and regulatory authorities.

[0026] In this embodiment, the blockchain unit 2 includes a smart contract verification module 2-1, a consensus algorithm verification module 2-2, a smart contract early warning module 2-3, a block evidence module 2-4 and a data encryption module 2-6. The smart contract verification module 2-1 presets the additive safety standard rules and the enterprise's real scenario data, and performs security and matching verification on the food additive content data and food additive collection scenario data collected by the data collection unit 1. The consensus algorithm module adopts PBFT or PoS algorithm to ensure the consistency of data of each node. When the smart contract verification module 2-1 fails to verify the data, the smart contract early warning module 2-3 issues an early warning. When the smart contract verification module 2-1 passes the verification, the evidence is stored through the block evidence module 2-4 and encrypted through the data encryption module 2-6. Blockchain unit 2 verifies the food additive content data and food additive collection scenario data collected by random sensor detection module 4 through smart contract verification module 2-1. This data is verified against preset food safety standards, historical data from previous third-party testing agencies, historical data uploaded by the manufacturer, and matched with the company's actual scenarios stored on the blockchain. If the food additive content exceeds the threshold or the actual collection scenario does not match the preset real-world collection scenario, a warning is issued through smart contract warning module 2-3. If it meets the requirements, the blockchain evidence module 2-4 stores the evidence, and the required data is encrypted through data encryption module 2-6. Smart contract verification module 2-1 presets additive safety standards and real-world enterprise scenario data, and verifies the security and compatibility of the data collected by data collection unit 1, including comparison with its own preset rules, previous third-party testing data, and historical data from the manufacturer. It can accurately determine whether the food additive content exceeds the standard and whether the collection scenario is authentic, effectively screening out data that does not meet safety requirements and ensuring the safety of food additives. Consensus Algorithm Verification Module 2-2 uses PBFT or PoS algorithms to ensure data consistency across blockchain nodes, enabling all participating nodes to reach consensus and jointly maintain data on the blockchain. This ensures reliable data storage and transmission on the blockchain, avoids data conflicts and inconsistencies, and enhances the stability and credibility of the entire system. Smart Contract Early Warning Module 2-3 issues a timely warning when data verified by Smart Contract Verification Module 2-1 fails, alerting relevant parties (such as companies and regulatory authorities) of potential food additive safety issues. This enables all parties to take swift action, preventing further circulation of substandard food additives and mitigating safety risks. Blockchain Evidence Module 2-4 stores data verified by Smart Contract Verification Module 2-1 in a blockchain, ensuring immutability and traceability. This provides a solid data foundation for subsequent traceability inquiries, regulatory audits, and other tasks, enhancing the credibility and authority of the data.Data encryption modules 2-6 encrypt relevant data on the blockchain to protect data privacy and security, preventing data leakage and malicious tampering. This ensures the security of corporate trade secrets, consumer personal information, and test data, and strengthens user trust in the system.

[0027] In this embodiment, the application unit 3 includes an enterprise management background 3-1, a traceability query module 3-2 and a supervision and audit module 3-3. The traceability query module 3-2 is used to provide consumers with a scanning and tracing function, displaying the full-link detection report of additives and transparent information on production and addition data; the supervision and audit module 3-3 is used to provide the supervision department with real-time data monitoring, violation tracking, historical records, and audit functions for food additive collection scene data. The enterprise management background 3-1 of the application unit 3 is set in the food processing enterprise for retrieving food additive safety test data. The traceability query module 3-2 of the application unit 3 is set in the ordinary user APP for ordinary users to retrieve and view food additive safety test data at any time. The supervision and audit module 3-3 of the application unit 3 is set in the supervision department for providing supervision of food additive safety to the supervision department. The enterprise management background 3-1 is set in the food processing enterprise, which is convenient for the enterprise to retrieve and view food additive safety test data, and is convenient for the enterprise to manage and monitor product quality. It helps enterprises to promptly discover problems in the production process, optimize production processes, improve product quality, and enhance the market competitiveness of enterprises.

[0028] The traceability query module 3-2 provides consumers with a scanning and traceability function, displaying full-link food additive testing reports, transparent information on production and additive data, etc., allowing consumers to clearly understand the relevant information about additives in the food they purchase. This enhances consumers' trust in food quality and safety, protects consumers' right to know, and promotes the healthy development of the food industry. The supervision and audit module 3-3 provides regulatory authorities with real-time data monitoring, violation tracking, historical record query, and audit functions for food additive collection scenarios, helping them to fully understand the safety status of food additives. This improves the work efficiency and supervision effectiveness of regulatory authorities, enabling them to promptly detect and handle violations, and ensure the safety and stability of the food market.

[0029] Working principle: The random sensing detection module 4, third-party testing agency data import module 5, and production enterprise data docking module 6 in the data collection unit 1 each perform their respective functions. The random sensing detection module 4, through its multiple random sampling terminals, conducts random sampling and sampling of the production plants of various food processing enterprises under the instructions of the regulatory control center. During the sampling process, the sampling system 7 (including the solid sampling terminal 7-1 and the liquid sampling terminal 7-2) works in conjunction with the camera system 9. The camera system 9 first collects images of the factory environment and then, while simultaneously capturing images, randomly collects food on the production line. This data is then tested using the food additive detection module. The collected data is locked by the locking system 8 and transmitted to the blockchain unit 2. The third-party testing agency data import module 5 imports the test results of each third-party testing agency into the blockchain unit 2. The production enterprise data docking module 6 is responsible for transmitting the relevant data collected by the production enterprise to the blockchain unit 2.

[0030] After blockchain unit 2 receives the data, smart contract verification module 2-1 first verifies the collected data for security and compatibility based on pre-set additive safety standards and real-world enterprise data. It also compares and verifies the data with historical data collected by third-party testing agencies and historical data uploaded by the manufacturer. If the data fails verification, smart contract early warning module 2-3 immediately issues an alert, prompting relevant parties to address the issue promptly. If the data passes verification, block evidence module 2-4 stores the data on the blockchain, and data encryption module 2-6 encrypts the data to ensure its security and privacy.

[0031] The Enterprise Management Backend 3-1, Traceability Query Module 3-2, and Supervision and Audit Module 3-3 in Application Unit 3 provide corresponding services for enterprises, consumers, and regulatory authorities, respectively. The Enterprise Management Backend 3-1 allows food processing enterprises to retrieve and view food additive safety test data, facilitating internal management and quality control. The Traceability Query Module 3-2 is set up in the general user app, and consumers can retrieve and view food additive safety test data at any time by scanning or other means, including full-link test reports and production additive data transparency information. The Supervision and Audit Module 3-3 is set up in the regulatory authorities, providing them with real-time data monitoring, violation tracking, historical record query, and audit of food additive collection scenario data, helping them to effectively perform their supervisory duties.

[0032] Another embodiment of the present invention provides a detection method for a food processing additive safety detection system based on blockchain, comprising: Multi-dimensional data collection is combined with random sampling, and data is collected through multiple channels using the random sensing detection module 4, the third-party testing agency data import module 5, and the production enterprise data docking module 6. Random sampling is also implemented through the random sensing detection module 4 to ensure the comprehensiveness, randomness, and authenticity of the data. Apply blockchain technology to food additive safety testing, using the smart contract verification module 2-1, consensus algorithm verification module 2-2, data encryption module 2-6, and block evidence module 2-4 to achieve trusted verification, secure storage, and tamper-proof data. Build a collaborative system involving manufacturers, regulatory authorities, and consumers, and achieve data sharing and transparent management through application unit 3; Carry out scenario-based data collection and verification. The camera system 9 and the sampling system 7 collect data synchronously, and the detection scene data is combined with the food additive content data for verification to further enhance the credibility and reliability of the data.

[0033] From the above description, it can be seen that the above embodiments of the present invention achieve the following technical effects: By distributing random sensing detection modules 4 across the production plants of various food processing enterprises and interconnecting them with the control center of the regulatory authorities, random sampling and collection are carried out according to the instructions of the regulatory authorities. Simultaneously, the sampling system 7 and the camera system 9 are controlled to be activated simultaneously, achieving the simultaneous collection of data from the factory environment and the food on the production line. This ensures the randomness and authenticity of the sampling, avoids the interference of human factors on the test results, improves the credibility of the test data, and provides reliable data support for the regulatory authorities. A third-party testing agency data import module 5 is set up at each third-party testing agency and imports the test result data of the third-party testing agency into the blockchain unit 2, providing an authoritative reference for data verification and comparison. This enriches the data sources and increases the credibility and authority of the data. When further confirmation of the sampling results is needed, the professional testing capabilities of the third-party testing agency can be used to ensure the accuracy of the test results. The production enterprise data docking module 6 docks with the data system of the food processing enterprise and transmits the relevant data collected by the production enterprise to the blockchain unit 2, participating in the data verification and analysis. This enables production enterprises to actively participate in the safety supervision of food additives, while also providing a more comprehensive data perspective for the entire system, helping to more accurately assess the safety status of food additives. The sampling system 7 includes a solid sampling terminal 7-1 and a liquid sampling terminal 7-2, which respectively sample solid and liquid food additives to meet the testing requirements of different types of food additives. In conjunction with the camera system 9, it enables random sampling of food on the production line. This ensures the representativeness and authenticity of the collected food additive samples, providing an accurate sample basis for subsequent testing and analysis. After collection is complete, the locking system 8 locks the data collected by the camera system 9 and the sampling system 7 to prevent data tampering or loss, and then transmits the locked data to the blockchain unit 2. This ensures the integrity and security of the data from collection to transmission, ensuring the credibility of the uploaded data and providing reliable protection for subsequent data applications. During random inspections by the random sensing detection module 4, the detection scene data acquisition module 9-1 of the camera system 9 first collects data from the factory environment, recording the actual scene of food production, providing a basis for verifying the authenticity of the collected scene. This helps verify whether the collection process complies with regulatory requirements, prevents companies from artificially creating false collection scenes, and improves the credibility of the test data. The test sample image data acquisition module 9-2 simultaneously collects food image data, including information such as the food's appearance and condition, while the sampling system 7 samples the food on the production line. This data is then used to verify the sampled data. This further enhances the integrity and credibility of the data and provides richer information for subsequent test result analysis and traceability queries.

[0034] Through the random sensing detection module 4, the regulatory authorities remotely trigger random inspections, and the forced linkage camera system 9 realizes dual randomness in time and space, with random inspection time and random geographical location, to prevent premeditated cheating. Through the locking system 8, the sampling container is physically locked, and only the supervisory authority can unlock it to solve the risk of sample transfer and substitution, and cooperate with the blockchain to form a "physical-digital" double insurance. The detection scene encryption module 9-3 encrypts the collection environment video with the national secret algorithm to prevent the video evidence from being tampered with, meeting the judicial-level evidence requirements; the triple verification mechanism, cross-verification of corporate historical data, third-party reports, and random inspection data breaks the data silos, and any unilateral data anomaly immediately triggers an early warning.

[0035] The detection scene encryption module 9-3 encrypts the detection scene data and test sample image data collected by the camera system 9 to protect data privacy and security. This prevents the detection scene data from being leaked or tampered with, ensuring its authenticity and integrity, and safeguarding the interests of all parties involved, including enterprises and regulatory authorities. The smart contract verification module 2-1 strictly verifies food additive content data and related collection scene data according to pre-set safety standards and rules. Once an anomaly is detected, a timely warning is issued, effectively preventing substandard food additives from entering the market and ensuring food safety. From data collection to on-chain storage and final traceability, all data is clearly accessible, achieving transparency across the entire food additive industry chain. This ensures consumer confidence, effective regulatory oversight, and eases self-management and optimization for enterprises. Using the regulatory audit module 3-3 in the application unit 3, regulatory authorities can quickly and easily access food additive-related data, conduct real-time monitoring and auditing, and promptly identify and address violations, significantly improving the timeliness and effectiveness of supervision. The enterprise management backend 3-1 facilitates unified management and analysis of food additive safety testing data for enterprises, helping them optimize production processes and improve product quality. Consumers can clearly understand the detailed information of food additives through the traceability query module 3-2, thereby enhancing their trust in the company and promoting the healthy development of the company.

[0036] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be within the scope of protection of the present invention.

Claims

1. A food processing additive safety detection system based on blockchain, characterized in that: include: A data collection unit, wherein the data collection unit is connected to a blockchain unit, and the blockchain unit is connected to an application unit. The data collection unit is used to collect content data of food additives and food additive collection scenario data. The blockchain unit verifies, warns, encrypts and stores evidence of the data collected by the data collection unit through a smart contract module, a consensus algorithm verification module, an early warning module, an encryption module and an evidence storage module, and links to the application unit, thereby enabling users to trace, supervise and manage food additives through the application unit.

2. The food processing additive safety detection system based on blockchain as claimed in claim 1, characterized in that: The data acquisition unit includes a random sensing detection module, a third-party testing agency data import module and a production enterprise data docking module. The random sensing detection module includes a first random acquisition terminal, a second random acquisition terminal and an N-th random acquisition terminal, totaling N; the third-party testing agency data import module includes a first third-party acquisition terminal, a second third-party acquisition terminal and an N-th third-party acquisition terminal, totaling N; the production enterprise data docking module includes a first production enterprise acquisition terminal, a second production enterprise acquisition terminal and an N-th production enterprise acquisition terminal, totaling N.

3. The food processing additive safety detection system based on blockchain as claimed in claim 1, characterized in that: The blockchain unit includes a smart contract verification module, a consensus algorithm verification module, a smart contract early warning module, a block evidence module and a data encryption module. The smart contract verification module presets additive safety standard rules and real-world scenario data of the enterprise, and performs security and matching verification on the content data of food additives and the food additive collection scenario data collected by the data collection unit. The consensus algorithm module adopts PBFT or PoS algorithm to ensure the consistency of data of each node. When the smart contract verification module fails to verify the data, the smart pre-contract early warning module issues an early warning. When the smart contract verification module passes the verification, the block existence module stores the evidence and encrypts it through the data encryption module.

4. The food processing additive safety detection system based on blockchain as claimed in claim 1, characterized in that: The application unit includes an enterprise management background, a traceability query module and a supervision and audit module. The traceability query module is used to provide consumers with a scanning and traceability function, displaying the full-link additive detection report and transparent information on production and additive data; the supervision and audit module is used to provide regulatory authorities with real-time data monitoring, violation tracking, historical records, and audit functions for food additive collection scenario data.

5. The food processing additive safety detection system based on blockchain as claimed in claim 1, characterized in that: The data acquisition unit is provided with an acquisition system, a camera system and a locking system. The acquisition system includes a solid sampling end, a liquid sampling end or both a solid sampling end and a liquid sampling end. The acquisition system is connected to the camera system. The camera system and the acquisition system are remotely linked to the blockchain unit and the supervision and monitoring center. The supervision and monitoring center controls the acquisition time node of the data acquisition unit. The general control center controls the random sensing detection module set in the food processing enterprise to perform random sampling and collection. The random sensing detection module controls the acquisition system and the camera system to start at the same time. The camera system first collects the factory environment, and then uses the acquisition system to randomly collect food on the production line under the condition of camera synchronization, and detects it through the food additive detection module of the acquisition system. After collection, the data collected by the camera system and the acquisition system are locked and the collected data is transmitted to the blockchain.

6. The food processing additive safety detection system based on blockchain as claimed in claim 1, characterized in that: The blockchain unit verifies the food additive content data and food additive collection scene data collected by the random sensing detection module through the smart contract verification module, verifies them against the preset food safety standard rules, verifies them against the historical data of previous third-party testing agencies, verifies them against the historical data uploaded by the production enterprises, and matches them with the real scenes of the enterprises stored in the blockchain. If the food additive content exceeds the threshold or the actual collection scene does not match the preset real collection scene, an early warning will be issued through the smart contract early warning module. If it meets the requirements, it will be recorded through the block existence module, and the required data will be encrypted through the data encryption module.

7. The food processing additive safety detection system based on blockchain as claimed in claim 1, characterized in that: The random sensing detection modules are distributed in the production plants of various food processing enterprises and are interconnected with the control center of the regulatory department. Through the control center of the regulatory department, random control is carried out to conduct random inspections of the safety of food additives. The camera system is consistent with the acquisition system to ensure randomness in time while ensuring the reliability and authenticity of the inspected food additives.

8. The food processing additive safety detection system based on blockchain as claimed in claim 1, characterized in that: The third-party testing agency data import module is set up in each third-party testing agency. By connecting the third-party testing agency with the blockchain unit, the reliability of the characteristic test results is guaranteed. If an authoritative identification certificate is required, the food additive test samples collected by the collection system are collected and locked in the collection system through the locking system. The regulatory department then authorizes and unlocks them. After unlocking, they are transferred to the third-party testing agency for authoritative testing to ensure that when random inspections determine that there are problems, they are then authoritatively tested by the third-party testing agency, and a food additive safety test certificate is issued, and the test results are transmitted to the blockchain unit.

9. The food processing additive safety detection system based on blockchain as claimed in claim 1, characterized in that: The enterprise management background of the application unit is set in the food processing enterprise for retrieving food additive safety test data. The traceability query module of the application unit is set in the ordinary user APP for ordinary users to retrieve and view food additive safety test data at any time. The supervision and audit module of the application unit is set in the supervision department for providing supervision of food additive safety to the supervision department.

10. A detection method for a blockchain-based food processing additive safety detection system, based on the blockchain-based food processing additive safety detection system according to any one of claims 1 to 9, characterized in that: include: Combining multi-dimensional data collection with random sampling, we use a random sensing detection module, a third-party testing agency data import module, and a manufacturer data docking module to collect data through multiple channels. We also use the random sensing detection module to implement random sampling to ensure the comprehensiveness, randomness, and authenticity of the data. Apply blockchain technology to food additive safety testing, using smart contracts, consensus algorithms, data encryption, and evidence storage to achieve trusted verification, secure storage, and tamper-proof data. Build a collaborative system involving manufacturers, regulatory authorities, and consumers, and achieve data sharing and transparent management through application units; Carry out scenario-based data collection and verification. The camera system and the acquisition system collect data synchronously, and the detection scene data is combined with the food additive content data for verification to further enhance the credibility and reliability of the data.

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