Aquatic product processing safety management and control system based on block chain traceability

Abstract

The invention discloses an aquatic product processing security management and control system based on blockchain traceability, and relates to the technical field of network security and data traceability, and the system comprises the steps that a multi-source data collection module collects processing whole-link parameters and related information, encrypts and uploads the parameters and related information, and associates an identifier; the data preprocessing and encryption module cleans standardized data, encrypts sensitive information and generates a unique identifier; the block chain traceability core module constructs an alliance chain to realize data chaining and non-tampering traceability; the intelligent safety management and control module evaluates compliance in real time and links equipment to dispose risks; the distributed data storage module adopts a hybrid architecture to guarantee data reliability; and the visual interaction and early warning module presents information and pushes early warning. According to the invention, full-chain data credible traceability is realized based on the block chain, risks are intelligently controlled and accurately identified and disposed, multi-agent collaboration is efficient, and data security is guaranteed; the method adapts to multi-scene requirements, improves processing compliance and emergency response capability, and assists in industrial safety management and control upgrading.

Description

Technical Field

[0001] This invention relates to the fields of network security and data traceability technology, and in particular to a blockchain-based traceability system for the safety management and control of aquatic product processing. Background Technology

[0002] Safety management in the aquatic product processing industry is directly related to consumer health, market order, and the industry's sustainable development. Monitoring and data traceability of parameters throughout the entire chain, from raw material acceptance and processing to logistics and distribution, are crucial for ensuring product safety. With the increasing scale and industrialization of aquatic product processing, the processing stages are becoming increasingly complex, involving multi-stage parameter control and multi-entity collaboration. Traditional safety management models are gradually revealing numerous shortcomings. Traditional management relies heavily on manual recording and offline verification. Key parameters such as temperature, humidity, and sterilization intensity during processing are prone to omissions and tampering, making data authenticity difficult to guarantee. Furthermore, data from different stages is stored in different systems, creating information silos and hindering end-to-end data traceability.

[0003] Existing traceability systems mostly employ a centralized architecture, with data stored on a single server, posing a risk of tampering and deletion, thus compromising the reliability of the traceability chain. While some systems incorporate simple data encryption techniques, they lack multi-node consensus verification mechanisms, making it difficult to prevent internal operational errors or malicious tampering. Furthermore, existing systems rely heavily on fixed thresholds for assessing security risks in the processing, failing to integrate multi-dimensional data for quantitative analysis. This results in low accuracy in risk identification and an inability to predict potential security vulnerabilities in advance. The lack of effective coordination between processing equipment and the control system necessitates manual intervention to adjust equipment parameters after a risk is detected, leading to delayed responses and potentially causing batch product quality issues.

[0004] Furthermore, the existing system's access control mechanism is inadequate, with unclear division of data access and operation permissions among different participants, easily leading to data leaks or unauthorized operations. Third-party regulatory departments and quality inspection agencies struggle to easily access the system for auditing work, lacking standardized data interfaces and authoritative verification mechanisms, resulting in low regulatory efficiency. Process optimization relies heavily on experience-based judgment, failing to fully utilize historical data to mine the correlation between parameters and quality, and exhibiting insufficient continuous improvement capabilities. The emergency response mechanism lacks a mechanism for quickly locating the scope of risks and coordinating joint actions, making it difficult to efficiently recall problematic products and trace the source of risks in the face of safety incidents. These problems severely restrict the intelligent, precise, and traceable management of aquatic product processing safety, failing to meet the industry's high-quality development and consumer safety needs. Summary of the Invention

[0005] The present invention proposes a blockchain-based traceability system for the safety management and control of aquatic product processing to solve the problems mentioned in the prior art.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a blockchain-based traceability system for the safety management and control of aquatic product processing, comprising the following modules: The multi-source data acquisition module for aquatic product processing integrates a sensor array, a data acquisition terminal, and a network communication interface. The sensor array collects key parameters in real time, the data acquisition terminal records operator information, equipment status, raw material batch numbers, and supplier information, and the network communication interface uploads the collected data to the system platform in real time through an encrypted transmission protocol. The data preprocessing and encryption module uses data cleaning algorithms to remove outliers, duplicate data and invalid data, maps different types of data to a unified format through standardization processing, encrypts sensitive data, generates encrypted data digests, and calculates unique data identifiers using hash algorithms. The core module of blockchain traceability is built on a consortium blockchain architecture, which includes five nodes: raw material suppliers, processing enterprises, quality inspection agencies, logistics companies, and sales terminals. Each node participates in data on-chaining and consensus verification according to its permissions. A practical Byzantine fault-tolerant consensus mechanism is adopted to complete data ownership confirmation and on-chain storage. The pre-processed encrypted data and hash identifier are written into the blockchain block. The intelligent security management module has a built-in security assessment model based on machine learning. By analyzing data on the blockchain, it can determine security risks in real time, generate management instructions in combination with preset security standards, and record management action logs and store them on the blockchain. The distributed data storage module adopts a hybrid storage architecture that combines blockchain distributed ledger and relational database. The blockchain stores core traceability data, encrypted digests and operation logs, while the relational database stores the data and establishes a data indexing mechanism to improve retrieval efficiency. The visualization interaction and early warning module is equipped with a multi-dimensional visualization interface that intuitively presents the entire process of aquatic product processing, the traceability chain, the distribution of safety risks, and the operating status of equipment. It presets multiple levels of safety early warning thresholds and pushes early warning information when a safety risk is detected. It also provides traceability report generation and export functions.

[0007] Furthermore, it also includes a processing safety risk quantification assessment unit, which integrates multi-dimensional processing data with blockchain traceability information to quantify the safety risk level. The calculation formula is as follows: in To quantify safety risks, Contribution coefficient to machining parameter deviation, Contribution coefficient to traceability integrity The number of key processing parameters. For the first The weights of each parameter, For the first The actual monitored values ​​of each parameter For the first Standard thresholds for each parameter For tracing the source, the time decay coefficient is used. For processing time, for The integrity coefficient of traceability data at any given time.

[0008] Furthermore, it also includes a cross-node data collaborative verification unit, which supports cross-node data verification and consensus between different consortium chain nodes, establishes a node trust assessment mechanism, and quantifies node trust value by verifying the accuracy, response speed and compliance records of each node's historical on-chain data. When interacting with cross-node data, only nodes with trust values ​​higher than a preset threshold are allowed to participate in data verification. A cross-validation algorithm is used to compare the same source data of different nodes, and the collaborative verification results are written to the blockchain.

[0009] Furthermore, it also includes an intelligent linkage unit for processing equipment, which enables automatic linkage execution of safety control commands and processing equipment. It establishes control interfaces and command formats for common processing equipment. The control commands generated by the intelligent safety control module are sent to the corresponding equipment after protocol conversion. After the equipment executes the command, it provides real-time feedback on the execution result. The system records the command content, execution result, and equipment status changes on the blockchain.

[0010] Furthermore, it also includes an abnormal behavior monitoring and tracing unit. By analyzing the operation logs, data modification records and node interaction information on the blockchain, it identifies abnormal behavior, uses a behavior feature extraction algorithm to capture the key features of abnormal behavior, and combines them with an abnormal behavior database for matching and identification. It locates the node, operator and time of the abnormal behavior, generates an abnormal behavior analysis report, and pushes the abnormal information and tracing results to the administrator.

[0011] Furthermore, it also includes a traceability data credibility verification unit, which ensures the authenticity and reliability of traceability data through multi-dimensional verification. The calculation formula is as follows: in To ensure the credibility of traceability data, Assign credibility weights to nodes that upload data to the blockchain. Weights for data consistency verification For data integrity weight, The number of data entries uploaded to the chain by trusted nodes. This represents the total number of data entries uploaded to the blockchain. This represents the number of times cross-node consistency checks have passed. Total number of checks The number of data items for a complete traceability chain. This calculation measures the number of data items that a standard traceability chain should include, verifying the reliability of traceability data from three aspects: node credibility, data consistency, and completeness.

[0012] Furthermore, it also includes a hierarchical permission management unit, which sets up multi-level operation permissions, divides permission groups according to roles, and different permission groups correspond to different data access scopes, operation permissions, and on-chain permissions. The permission allocation and operation behavior are recorded and stored on the blockchain throughout the process.

[0013] Furthermore, it also includes a dynamic optimization unit for the processing flow, which regularly analyzes historical processing data, security risk records, and control effects on the blockchain. It uses association rule mining algorithms to discover the intrinsic relationship between processing parameters and product quality, identify optimization space, and generate processing flow optimization suggestions by combining industry standards and best practices. After the optimization suggestions are confirmed by the administrator, the processing standards are automatically updated and synchronized to the intelligent safety management module and processing equipment.

[0014] Furthermore, it also includes an emergency response linkage unit. When a major security risk or emergency is detected, the emergency response process is automatically initiated. Based on blockchain traceability data, the scope of risk impact is located, an emergency response plan is generated, and the logistics system and sales terminal system are linked to push disposal instructions. The progress of emergency response is tracked in real time, and the disposal process, results and rectification measures are recorded on the blockchain.

[0015] Furthermore, it also includes a third-party audit interface unit, which provides a standardized third-party audit data interface, allowing third parties to access the system through the interface and query information on the blockchain according to preset permissions. The interface adopts an encrypted transmission protocol, and the audit reports and verification results generated during the audit process are directly written into the blockchain block as proof of product quality compliance. At the same time, the system records the third-party access behavior and operation logs.

[0016] Compared with existing technologies, the beneficial effects of this invention are: In terms of data acquisition and security, the system comprehensively captures key parameters and related information throughout the entire processing stage through a multi-source data acquisition module. The application of encrypted transmission protocols and symmetric encryption algorithms ensures data security from transmission to storage, while unique identifiers generated by hash algorithms guarantee data immutability. The data preprocessing stage removes invalid information and standardizes data formats, providing high-quality data support for subsequent analysis and traceability, completely changing the problems of fragmented and unreliable data in traditional manual records.

[0017] At the core functional level of traceability and control, the blockchain traceability core module, built on a consortium blockchain architecture, enables authorized participation of multiple stakeholders. The immutable traceability chain ensures that data throughout the entire process is verifiable, effectively resolving the trust crisis of centralized traceability systems. The intelligent security control module, relying on machine learning models, integrates multi-dimensional data to determine processing compliance in real time, accurately identifies security risks, and coordinates with equipment to execute control operations, forming a closed-loop control process. This significantly improves risk response speed and control accuracy, preventing batch quality issues.

[0018] In terms of system expansion and practical value, the hierarchical access control unit divides permissions by role, standardizes data access and operation behavior, and ensures data security and operational compliance. The third-party audit interface unit provides a standardized access channel, facilitating audits by regulatory authorities and quality inspection agencies. Audit results are stored on the blockchain as authoritative proof of compliance, improving regulatory efficiency and credibility. The dynamic optimization unit for processing flows generates optimization suggestions by mining historical data relationships, driving continuous updates to processing standards and achieving iterative upgrades to control capabilities. The emergency response linkage unit can quickly locate the scope of risk impact and coordinate with logistics and sales systems to efficiently handle security incidents, minimizing losses.

[0019] Overall, the system leverages blockchain technology to build a trusted data foundation, combining intelligent management and control with multi-dimensional collaborative mechanisms to transform seafood processing safety management from "manual supervision" to "intelligent management," from "decentralized recording" to "unified traceability," and from "passive handling" to "proactive prevention." This significantly improves compliance in the processing process, the accuracy of risk identification, and the credibility of traceability data. The system also considers the needs of multi-stakeholder collaboration, third-party supervision, and continuous process optimization, effectively ensuring product safety, protecting consumer rights and industry order, and providing solid technical support for the high-quality development of the seafood processing industry. Attached Figure Description

[0020] Figure 1 This is a schematic block diagram of the blockchain-based traceability aquatic product processing safety management and control system proposed in this invention; Figure 2 Line chart comparing the time spent on data traceability at each stage of aquatic product processing; Figure 3 A bar chart comparing the accuracy of abnormal risk identification under different control models; Figure 4 This is a pie chart showing the percentage of data storage types in the system of this invention. Figure 5 This is a scatter plot showing the relationship between the number of system iterations and the efficiency of cross-border data collaboration. Detailed Implementation

[0021] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] In the description of this invention, it should be understood that the terms "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," and "counterclockwise," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0023] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include one or more of the stated features. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified. Furthermore, the terms "installed," "connected," and "linked" should be interpreted broadly; for example, they may refer to a fixed connection, a detachable connection, or an integral connection; they may refer to a mechanical connection or an electrical connection; they may refer to a direct connection or an indirect connection through an intermediate medium; and they may refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances. The invention will now be described in further detail with reference to the accompanying drawings.

[0024] Reference Figures 1 to 5 A blockchain-based traceability system for the safety management and control of aquatic product processing includes the following modules: The multi-source data acquisition module for aquatic product processing integrates a sensor array, a data acquisition terminal, and a network communication interface. The sensor array collects key parameters such as temperature, humidity, pH value, processing time, and sterilization intensity in real time during the acceptance, cleaning, cutting, sterilization, and packaging of aquatic raw materials. The data acquisition terminal records operator information, equipment operating status, raw material batch number, and supplier information. The network communication interface uploads the collected data to the system platform in real time through an encrypted transmission protocol. All data is associated and identified by processing stage and timestamp. The data preprocessing and encryption module uses data cleaning algorithms to remove outliers, duplicate data, and invalid data. It maps different types of data to a unified format through standardization processing, uses symmetric encryption algorithms to encrypt sensitive data, generates encrypted data digests, and combines hash algorithms to calculate unique data identifiers, ensuring the integrity and security of data transmission and storage. The preprocessed data retains the original core information without distortion. The core module of blockchain traceability constructs a consortium blockchain architecture, including raw material supplier nodes, processing enterprise nodes, quality inspection agency nodes, logistics enterprise nodes, and sales terminal nodes. Each node participates in data on-chaining and consensus verification according to its permissions. A practical Byzantine fault-tolerant consensus mechanism is adopted to complete data ownership confirmation and on-chain storage. The pre-processed encrypted data and hash identifier are written into the blockchain block to form an immutable traceability chain, supporting the traceability of the entire process data by multiple dimensions such as raw material batch and product number. The intelligent safety management module has a built-in machine learning-based safety assessment model. By analyzing processing parameters, environmental data and historical quality data on the blockchain, it can determine the compliance of the aquatic product processing process in real time, identify safety risks such as excessive temperature, incomplete sterilization and abnormal processing time, generate management instructions in combination with preset safety standards, and link the processing equipment to perform operations such as parameter adjustment and shutdown inspection. At the same time, it records the management action log and stores it on the blockchain. The distributed data storage module adopts a hybrid storage architecture that combines blockchain distributed ledger and relational database. The blockchain stores core traceability data, encrypted digests and operation logs, while the relational database stores structured processing plans, equipment parameters, security standards and other data. A data indexing mechanism is established to improve retrieval efficiency, and multiple data backups and cross-node synchronization are supported to ensure the reliability and availability of data storage. The visualization interaction and early warning module is equipped with a multi-dimensional visualization interface. It intuitively presents the entire process of aquatic product processing, traceability chain, distribution of safety risks, and equipment operation status through flowcharts, timelines, heat maps, and other forms. It supports multi-dimensional queries by processing link, time range, risk level, etc. It presets multi-level safety early warning thresholds. When a safety risk is detected, early warning information is pushed through system pop-ups, SMS, emails, etc. It also provides traceability report generation and export functions.

[0025] This invention also includes a processing safety risk quantification assessment unit, which integrates multi-dimensional processing data with blockchain traceability information to accurately quantify the safety risk level. The calculation formula is as follows: in This is a quantifiable value for safety risk, ranging from 0 to 10. A higher value indicates a higher risk. The contribution coefficient to the deviation of processing parameters, with a value ranging from 0.6 to 0.7. The contribution coefficient to traceability integrity ranges from 0.3 to 0.4. , The number of key processing parameters. For the first The weights of each parameter range from 0 to 1 and sum to 1. For the first The actual monitored values ​​of each parameter For the first Standard thresholds for each parameter This is the source attenuation coefficient, with a value ranging from 0.05 to 0.1. For processing time, for The traceability data integrity coefficient at any given time ranges from 0 to 1. This calculation combines the degree of deviation of processing parameters with the integrity of traceability data to achieve accurate quantification of security risks.

[0026] This invention also includes a cross-node data collaborative verification unit, which supports cross-node data verification and consensus between different consortium blockchain nodes, establishes a node trust assessment mechanism, and quantifies node trust value by verifying the accuracy, response speed and compliance records of each node's historical on-chain data. When interacting with cross-node data, only nodes with trust values ​​higher than a preset threshold are allowed to participate in data verification. A cross-validation algorithm is used to compare the same source data of different nodes to ensure data consistency. At the same time, the collaborative verification results are written into the blockchain to enhance the credibility and authority of the traceability data.

[0027] This invention also includes an intelligent linkage unit for processing equipment, which enables automatic linkage execution of safety control commands and processing equipment. It establishes an equipment control protocol library, covering the control interfaces and command formats of common processing equipment such as temperature controllers, sterilization equipment, and transmission devices. The control commands generated by the intelligent safety control module are sent to the corresponding equipment after protocol conversion. After the equipment executes the command, it provides real-time feedback on the execution result. The system records the command content, execution result, and equipment status changes on the blockchain, forming a closed-loop control process of risk detection, command issuance, equipment execution, and result feedback.

[0028] This invention also includes an abnormal behavior monitoring and tracing unit. By analyzing operation logs, data modification records, and node interaction information on the blockchain, it identifies abnormal behaviors such as unauthorized data tampering, illegal parameter adjustments, and unauthorized node access. It uses a behavior feature extraction algorithm to capture the key features of abnormal behaviors, and combines them with an abnormal behavior database for matching and identification. It locates the node, operator, and time of the abnormal behavior, generates an abnormal behavior analysis report, and pushes the abnormal information and tracing results to the administrator, supporting full-process tracing and responsibility determination of abnormal behaviors.

[0029] This invention also includes a traceability data credibility verification unit, which ensures the authenticity and reliability of the traceability data through multi-dimensional verification. The calculation formula is as follows: in To indicate the reliability of the traceability data, the value ranges from 0 to 1, with higher values ​​indicating stronger reliability. This is used to assign a trust weight to nodes that upload data to the blockchain, with a value ranging from 0.4 to 0.5. This is the weight for data consistency verification, with a value ranging from 0.3 to 0.4. This is the data integrity weight, with a value ranging from 0.1 to 0.2. , The number of data entries uploaded to the chain by trusted nodes. Total number of data entries on the blockchain Number of times cross-node consistency checks pass Total number of checks The number of data items for a complete traceability chain. The calculation determines the number of data items that a standard traceability chain should include, verifying the reliability of traceability data from three aspects: node credibility, data consistency, and completeness.

[0030] This invention also includes a hierarchical permission management unit, which sets up multi-level operation permissions and divides the permissions into groups such as administrator, processing operator, quality inspector, supplier, and consumer according to roles. Different permission groups correspond to different data access scopes, operation permissions, and on-chain permissions. Administrators can configure permission rules, add or delete users, processing operators can only upload processing data and view relevant information in this stage, and consumers can only query traceability information by product number. The permission allocation and operation behavior are recorded throughout the process and stored on the blockchain to ensure the compliance of data access and operation.

[0031] This invention also includes a dynamic optimization unit for the processing flow, which periodically analyzes historical processing data, security risk records, and control effects on the blockchain. It uses association rule mining algorithms to discover the intrinsic relationship between processing parameters and product quality, identify optimization space, and generate processing flow optimization suggestions by combining industry standards and best practices. These suggestions include parameter adjustment schemes, process simplification suggestions, and equipment upgrade directions. Once the optimization suggestions are confirmed by the administrator, the processing standards can be automatically updated and synchronized to the intelligent safety management module and processing equipment, forming a continuous improvement mechanism of data accumulation, analysis and optimization, and standard updates.

[0032] This invention also includes an emergency response linkage unit. When a major safety risk or emergency is detected, the emergency response process is automatically initiated. Based on blockchain traceability data, the scope of the risk impact is quickly located, including the affected raw material batches, processing links, and product batches. An emergency response plan is generated, covering measures such as isolating contaminated products, recalling problematic products, suspending relevant processing links, and tracing the source of the risk. The system also links with the logistics system and sales terminal system to push disposal instructions, tracks the progress of emergency response in real time, and records the disposal process, results, and rectification measures on the blockchain to ensure the efficiency and traceability of the emergency response.

[0033] This invention also includes a third-party audit interface unit, which provides a standardized third-party audit data interface. This interface allows third parties such as quality inspection agencies and regulatory departments to access the system and query information such as traceability data, processing records, and security control logs on the blockchain according to preset permissions. The interface uses an encrypted transmission protocol to ensure data transmission security. Audit reports and verification results generated during the audit process can be directly written to the blockchain block as authoritative proof of product quality compliance. At the same time, the system records third-party access behavior and operation logs to ensure the compliance and traceability of the audit process.

[0034] The following two examples further illustrate specific embodiments of the present invention: Example 1: Application of safety management in large-scale freshwater fish processing enterprises This embodiment is applied to a large-scale freshwater fish processing enterprise, covering the entire process from raw material acceptance, cleaning, cutting, sterilization, packaging, warehousing, and logistics. It involves 3 processing workshops, 2 warehousing centers, 5 raw material suppliers, and 3 logistics companies, processing over 100,000 jin (50,000 catties) of freshwater fish daily, supplying products to more than 20 provinces and cities nationwide. This scenario involves numerous processing stages and diverse stakeholders, placing extremely high demands on raw material freshness, processing parameter stability, and product traceability reliability. It requires end-to-end data connectivity, real-time risk control, and cross-stakeholder collaborative verification, while simultaneously meeting the needs of market supervision and consumer traceability inquiries.

[0035] I. Core Implementation Details Multi-source data acquisition deployment: The multi-source data acquisition module for aquatic product processing integrates sensor arrays, data acquisition terminals, and network communication interfaces. Sensor arrays are deployed according to processing stages: temperature and humidity sensors are installed in the raw material acceptance area; pH sensors are deployed in the cleaning area; processing time timers are configured in the cutting area; sterilization intensity monitoring sensors are installed in the sterilization area; and sealing integrity detectors are installed in the packaging area, collecting key parameters at each stage in real time. Data acquisition terminals are equipped at each workshop's operating station. Operators enter their personal employee ID, equipment operating status, raw material batch numbers, and supplier information through the terminals. The network communication interface uses an encrypted transmission protocol to upload the collected data to the system platform in real time. All data is associated with processing stages and timestamps, forming a unique data chain.

[0036] Data Preprocessing and Encryption Execution: After the data preprocessing and encryption module is started, it uses data cleaning algorithms to remove abnormal fluctuation data from sensors, duplicate entries, and data with incorrect formats. Through standardization, different types of data, such as temperature, humidity, and pH values, are mapped to a unified format. A symmetric encryption algorithm is used to encrypt sensitive data such as raw material supplier business information and operator privacy data, generating an encrypted data digest. A hash algorithm is then used to calculate a unique identifier for each data entry, ensuring the integrity and security of data transmission and storage. The preprocessed data retains the original core information without distortion and is simultaneously pushed to the blockchain traceability core module.

[0037] Blockchain Traceability and Security Management Operation: The core blockchain traceability module constructs a consortium blockchain architecture, incorporating nodes from raw material suppliers, processing enterprises, quality inspection agencies, logistics companies, and sales terminals. Each node participates in data uploading and consensus verification according to preset permissions. A practical Byzantine fault-tolerant consensus mechanism is adopted; after a processing enterprise node uploads data, at least three related nodes must complete consensus verification before data ownership can be confirmed and stored on the blockchain. Pre-processed encrypted data and hash identifiers are written into the blockchain block, forming an immutable traceability chain. The intelligent security management module incorporates a machine learning-based security assessment model, analyzing processing parameters, environmental data, and historical quality data on the blockchain in real time. When the sterilization temperature is detected to be below the standard threshold or the processing time exceeds a reasonable range, a control command is immediately generated, triggering the sterilization equipment to increase the temperature or controlling the transmission device to suspend processing. Simultaneously, the control action log is recorded and stored on the blockchain.

[0038] Auxiliary function modules are implemented as follows: After the cross-node data collaborative verification unit is launched, it verifies the accuracy, response speed, and compliance records of each node's historical on-chain data, quantifies the node's trust value, and only allows nodes with trust values ​​higher than a preset threshold to participate in data verification. A cross-validation algorithm is used to compare data from the same source across different nodes to ensure data consistency. The intelligent linkage unit for processing equipment establishes an equipment control protocol library, covering the control interfaces and command formats of processing equipment such as temperature controllers, sterilization equipment, and transmission devices. Control commands are sent to the corresponding equipment after protocol conversion, and the equipment provides real-time feedback after execution, forming a closed-loop control process. The abnormal behavior monitoring and tracing unit analyzes the operation logs on the blockchain, identifies abnormal behaviors such as unauthorized data tampering and illegal parameter adjustments, locates the initiating node and operator, and generates an analysis report that is pushed to the administrator.

[0039] Storage, Interaction, and Extended Applications: The distributed data storage module adopts a hybrid storage architecture. The blockchain stores core traceability data, encrypted digests, and operation logs, while a relational database stores structured processing plans, equipment parameters, safety standards, and other data. A data indexing mechanism improves retrieval efficiency and supports multiple data backups and cross-node synchronization. The visualization, interaction, and early warning module features a multi-dimensional interface. It presents the entire processing flow through flowcharts, displays the traceability chain on a timeline, and annotates the distribution of safety risks with heatmaps. It supports queries by processing stage, time range, and risk level, and presets three levels of safety warning thresholds. When a risk is triggered, warning information is sent via system pop-ups and SMS. It also provides traceability report generation and export functions. The hierarchical permission management unit divides users into permission groups such as administrators, processing operators, quality inspectors, suppliers, and consumers. Administrators configure permission rules, processing operators can only upload data for their current stage, and consumers can query traceability information via QR codes on product packaging. Permission allocation and operational behavior are recorded on the blockchain throughout the entire process.

[0040] Process Optimization and Emergency Response: The dynamic optimization unit for the processing flow periodically analyzes historical data on the blockchain, using association rule mining algorithms to discover the intrinsic correlation between processing parameters and product quality. When a higher product pass rate is found in a certain sterilization temperature range, parameter adjustment suggestions are generated. After administrator confirmation, the processing standards are updated and synchronized to the intelligent safety management module and sterilization equipment. The emergency response linkage unit automatically initiates the emergency response process when a major safety risk is detected. Based on blockchain traceability data, it quickly locates the affected raw material batches, processing stages, and product batches, generates an emergency response plan, and coordinates with the logistics system to suspend the transportation of problematic products and the sales terminal system to remove related products from shelves. The progress of the response is tracked in real time, and the process and results are recorded on the blockchain. The third-party audit interface unit provides a standardized interface, allowing regulatory authorities to access the system through the interface, query traceability data and processing records according to their permissions, and write audit reports and verification results directly to the blockchain.

[0041] Table 1: Comparison of Safety Control Effects in Processing Large Freshwater Fish Evaluation indicators Traditional management and control model This invention system Credibility of traceability data Low high Timeliness of risk identification Difference good Cross-entity collaboration efficiency Low high Emergency response speed slow quick Consumer accessibility Difference good Table 1 clearly demonstrates the advantages of this invention's system in the management and control of large-scale freshwater fish processing. Traditional management methods rely on manual data recording, which is easily tampered with, leads to broken traceability chains, delayed risk identification, requires offline communication for cross-entity collaboration, results in slow emergency response, and makes it difficult for consumers to access traceability information. This invention's system uses blockchain technology to ensure the immutability of traceability data, intelligent modules identify risks in real time, cross-node collaborative verification improves efficiency, emergency response involves multiple systems for rapid handling, and consumers can access traceability information simply by scanning a code. All indicators are comprehensively superior to traditional methods, perfectly meeting the safety management and control needs of large-scale aquatic product processing enterprises.

[0042] Example 2: Application of Safety Management in Cross-border Frozen Shrimp Processing Trade This embodiment applies to a cross-border frozen shrimp processing and trade scenario, involving 3 overseas raw material fishing companies, 2 domestic processing plants, 4 international logistics companies, and more than 10 overseas sales terminals. The products are exported to 15 countries and regions in Europe, America, and Southeast Asia, with a daily processing volume of over 50,000 jin (25,000 catties) of frozen shrimp. This scenario involves cross-border transportation, multi-national regulatory standards, and multilingual data interaction, placing stringent requirements on raw material traceability compliance, processing parameter accuracy, and cross-border data consistency. It necessitates cross-border node data collaboration, adaptation to international regulatory standards, and multilingual traceability queries, while simultaneously ensuring the security of cross-border data transmission and ease of auditing.

[0043] I. Core Implementation Details Multi-source data acquisition and cross-border transmission: The multi-source data acquisition module for aquatic product processing integrates sensor arrays, data acquisition terminals, and network communication interfaces. Overseas raw material fishing companies install temperature and humidity sensors on fishing vessels and cold storage facilities to collect real-time environmental parameters of the raw materials after harvesting. Domestic processing plants deploy sensors according to the cleaning, shelling, freezing, and packaging stages to collect key parameters of the processing. The data acquisition terminal supports multilingual input, allowing overseas users to input information such as raw material harvesting time, batch number, and transportation method in English or their local language. The network communication interface uses an internationally recognized encrypted transmission protocol, overcoming cross-border network limitations and uploading domestic and international data to the system platform in real time. All data is associated with processing stages and timestamps, with additional unique identifiers for cross-border transportation segments.

[0044] Data Preprocessing and Encryption Adaptation: The data preprocessing and encryption module, tailored to the characteristics of cross-border data, employs multilingual data standardization algorithms to convert text information entered in different languages ​​into a unified format, eliminating redundant data and erroneous information generated during cross-border transmission. Symmetric encryption algorithms are used to encrypt sensitive data such as cross-border trade secrets and customs declaration data, generating encrypted data digests. A hash algorithm is then used to calculate a unique data identifier, ensuring the integrity and security of cross-border data transmission and storage. The preprocessed data is synchronously pushed to the blockchain traceability core module, and key data is selected to generate a compliant data subset according to the regulatory requirements of the target exporting country.

[0045] Blockchain Traceability and Cross-border Collaborative Verification: The core module of blockchain traceability constructs a cross-border consortium blockchain architecture, incorporating overseas raw material harvesting nodes, domestic processing nodes, international logistics nodes, overseas sales nodes, and domestic and international quality inspection agency nodes. Each node is assigned corresponding permissions according to the cross-border trade process. Adopting a practical Byzantine fault-tolerant consensus mechanism, after cross-border data is uploaded, at least two related nodes both domestically and internationally must complete consensus verification before it can be stored on the blockchain. Encrypted data and hash identifiers are written into the blockchain block, forming an immutable traceability chain across borders. The intelligent safety management module incorporates a safety assessment model adapted to the regulatory standards of different countries. It analyzes processing parameters and cross-border transportation environment data on the blockchain in real time. When it detects that the freezing temperature exceeds the target country's standard or that the transportation time is in violation, it generates a control command, linking the domestic processing plant's freezing equipment or the international logistics temperature control system to adjust parameters. The control action log is uploaded to the blockchain in real time.

[0046] Auxiliary Functions and Cross-Border Adaptation Optimization: The cross-node data collaboration verification unit is optimized for cross-border scenarios, establishing a cross-border node trust assessment mechanism. Trust values ​​are quantified by verifying the stability of cross-border data transmission and the integrity of compliance records. During cross-country data interaction, only nodes with high trust values ​​are allowed to participate in verification. A cross-border cross-validation algorithm is used to compare source data from nodes in different countries, ensuring data consistency. The hierarchical permission management unit supports multi-language permission configuration. Administrators can assign permissions through a Chinese / English interface. Overseas raw material suppliers can only upload raw material data, international logistics companies can view transportation-related information, and overseas consumers can query traceability information through a multi-language interface. Permission allocation and operational behavior are recorded on the blockchain throughout the entire process.

[0047] Storage, Interaction, and Cross-border Regulatory Integration: The distributed data storage module adopts a hybrid storage architecture. The blockchain stores core cross-border traceability data, encrypted summaries, and operation logs, while a relational database stores structured cross-border processing plans, international regulatory standards, equipment parameters, and other data, supporting multi-copy backup of cross-border data and synchronization across regional nodes. The visualization, interaction, and early warning module features a multi-language interface, presenting the entire cross-border processing flow, traceability chain, and risk distribution through multi-dimensional charts. It supports queries by country, processing stage, and time range, and presets security warning thresholds adapted to the regulatory requirements of different countries. When a risk is triggered, warning information is sent via multi-language system pop-ups and email pushes. The third-party audit interface unit provides an internationally standardized interface, supporting domestic and international regulatory departments and customs agencies to access the system and query cross-border traceability data, processing records, and compliance certificates according to their permissions. Audit results are directly written to the blockchain as authoritative evidence for cross-border customs clearance.

[0048] Emergency Response and Process Optimization: The emergency response linkage unit optimizes for cross-border safety incidents. When raw material contamination or processing violations are detected, it quickly locates the affected cross-border batches, shipping routes, and sales regions based on blockchain traceability data. It generates multilingual emergency response plans, coordinates with international logistics companies to suspend transportation, and prompts overseas sales terminals to remove products from shelves. It tracks the progress of cross-border handling in real time, recording the process, results, and corrective measures on the blockchain. The processing flow dynamic optimization unit regularly analyzes cross-border processing data and risk records on the blockchain. Combining updates to regulatory standards in different countries with market feedback, it generates processing flow optimization suggestions, including adjusting freezing temperatures to adapt to target country standards and optimizing cross-border transportation packaging to improve preservation. These optimization suggestions are updated with processing standards after administrator confirmation and synchronized to the intelligent safety management module and related equipment.

[0049] Table 2: Comparison of Safety Control Effects in Cross-border Frozen Shrimp Processing Trade Evaluation indicators Traditional cross-border control model This invention system Cross-border data consistency Difference good Regulatory compliance adaptability Low high Cross-border audit efficiency Low high Multilingual interaction convenience Difference good Cross-border emergency response capabilities weak powerful Table 2 data highlights the application value of the system of this invention in cross-border frozen shrimp processing trade. Traditional cross-border management models suffer from data loss, poor consistency, difficulty in adapting to different national regulatory standards, and inefficient offline auditing requiring the submission of numerous paper documents. They also lack multilingual interaction capabilities and suffer from slow cross-border coordination in emergency response. The system of this invention ensures data consistency through cross-border collaborative verification, adapts to multiple national regulatory standards, improves audit efficiency through standardized interfaces, facilitates cross-border interaction through multilingual interfaces, and enables rapid emergency response through linkage with transnational systems. All indicators are superior to traditional models, perfectly meeting the high compliance, high collaboration, and high security requirements of cross-border aquatic product processing trade.

[0050] Reference Figure 2 This line graph visually compares the time spent on traceability at each processing stage between the traditional management model and the system of this invention. In the traditional model, traceability at each stage relies on manual retrieval of paper or electronic data scattered across different entities, requiring one-to-one verification, resulting in a time consumption exceeding 10 minutes at each stage. The logistics and transportation stage, due to cross-enterprise and cross-regional communication, takes as long as 25 minutes. The system of this invention relies on a unified traceability chain built on blockchain technology. Data at all stages is standardized and tamper-proof, and can be retrieved and verified with a single click. The time consumption at each stage is controlled within 5 minutes, with core stages such as packaging and warehousing taking only 2 minutes. This significant reduction in time demonstrates that the system eliminates information silos through blockchain technology, enabling rapid retrieval and verification of traceability data throughout the entire process, and is adaptable to the efficient traceability needs of large-scale aquatic product processing scenarios.

[0051] Reference Figure 3This bar chart clearly demonstrates the core advantage of the system in terms of accuracy in identifying abnormal risks. Manual inspections are affected by factors such as operator subjective judgment and limited energy, resulting in an anomaly identification accuracy of only 60%. Single-dimensional data management and semi-automated management, due to collecting only single or a few dimensions of data, cannot capture multi-parameter correlated anomalies, and their accuracy does not exceed 75%. Traditional blockchain management, while ensuring data credibility, lacks multi-dimensional intelligent analysis capabilities, resulting in an accuracy of 85%. The system of this invention integrates a multi-source data acquisition module with a machine learning safety assessment model, capable of capturing multi-dimensional anomaly correlation features such as temperature, humidity, and processing time. Combined with a blockchain-based trusted data foundation, the anomaly identification accuracy is increased to 98%, effectively avoiding missed detections and misjudgments, and reducing product quality problems caused by unidentified risks.

[0052] Reference Figure 4 This pie chart visually illustrates the data allocation logic of the hybrid storage architecture of this invention. Core blockchain traceability data accounts for the highest proportion, reaching 40%. This is because the system prioritizes storing core traceability information such as raw material batches, key processing parameters, and cross-border transportation records on the blockchain to ensure their immutability and traceability. Encrypted digests account for 25%, used for quickly verifying data integrity and reducing the time spent on full data verification. Operation logs and structured processing standard data each account for 15%, stored in a relational database, balancing retrieval efficiency and data structure management needs. Other auxiliary data accounts for only 5%, avoiding redundant storage and resource consumption. This allocation relies on the blockchain to ensure the credibility of core data while optimizing the storage cost and retrieval speed of non-core data through a relational database, achieving a balance between storage efficiency and data security.

[0053] Reference Figure 5 This scatter plot visually illustrates the effect of the system of this invention in improving the efficiency of cross-border data collaboration through iterative optimization. In the initial iteration, the cross-border data collaboration efficiency was 80%. At this point, the trust assessment rules and cross-validation algorithms for cross-border nodes had not been fully optimized, resulting in a long data verification time. As the number of iterations increased, the system continuously integrated historical data and audit feedback from cross-border processing, optimized algorithm parameters, and gradually improved collaboration efficiency. After five iterations, the efficiency exceeded 90%, and after nine iterations, it reached 96% and stabilized. This improved collaboration efficiency means faster and more accurate data verification between cross-border nodes, effectively reducing problems such as customs delays and regulatory obstacles caused by data collaboration lags in cross-border processing trade, and ensuring the smooth progress of cross-border business.

[0054] The above are merely preferred embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A blockchain-based traceability system for the safety management and control of aquatic product processing, characterized in that, Includes the following modules: The multi-source data acquisition module for aquatic product processing integrates a sensor array, a data acquisition terminal, and a network communication interface. The sensor array collects key parameters in real time, the data acquisition terminal records operator information, equipment status, raw material batch numbers, and supplier information, and the network communication interface uploads the collected data to the system platform in real time through an encrypted transmission protocol. The data preprocessing and encryption module uses data cleaning algorithms to remove outliers, duplicate data and invalid data, maps different types of data to a unified format through standardization processing, encrypts sensitive data, generates encrypted data digests, and calculates unique data identifiers using hash algorithms. The core module of blockchain traceability is built on a consortium blockchain architecture, which includes five nodes: raw material suppliers, processing enterprises, quality inspection agencies, logistics companies, and sales terminals. Each node participates in data on-chaining and consensus verification according to its permissions. A practical Byzantine fault-tolerant consensus mechanism is adopted to complete data ownership confirmation and on-chain storage. The pre-processed encrypted data and hash identifier are written into the blockchain block. The intelligent security management module has a built-in security assessment model based on machine learning. By analyzing data on the blockchain, it can determine security risks in real time, generate management instructions in combination with preset security standards, and record management action logs and store them on the blockchain. The distributed data storage module adopts a hybrid storage architecture that combines blockchain distributed ledger and relational database. The blockchain stores core traceability data, encrypted digests and operation logs, while the relational database stores the data and establishes a data indexing mechanism to improve retrieval efficiency. The visualization interaction and early warning module is equipped with a multi-dimensional visualization interface that intuitively presents the entire process of aquatic product processing, the traceability chain, the distribution of safety risks, and the operating status of equipment. It presets multiple levels of safety early warning thresholds and pushes early warning information when a safety risk is detected. It also provides traceability report generation and export functions.

2. The blockchain-based traceability aquatic product processing safety management system according to claim 1, characterized in that, It also includes a processing safety risk quantification assessment unit, which integrates multi-dimensional processing data with blockchain traceability information to quantify the safety risk level. The calculation formula is as follows: in To quantify safety risks, Contribution coefficient to machining parameter deviation, Contribution coefficient to traceability integrity The number of key processing parameters. For the first The weights of each parameter, For the first The actual monitored values ​​of each parameter For the first Standard thresholds for each parameter For tracing the source, the time decay coefficient is used. For processing time, for The integrity coefficient of traceability data at any given time.

3. The blockchain-based traceability aquatic product processing safety management system according to claim 1, characterized in that, It also includes a cross-node data collaborative verification unit, which supports cross-node data verification and consensus between different consortium chain nodes, establishes a node trust assessment mechanism, and quantifies node trust value by verifying the accuracy, response speed and compliance records of each node's historical on-chain data. When interacting with cross-node data, only nodes with trust values ​​higher than a preset threshold are allowed to participate in data verification. A cross-validation algorithm is used to compare the same source data of different nodes, and the collaborative verification results are written to the blockchain.

4. The blockchain-based traceability aquatic product processing safety management system according to claim 1, characterized in that, It also includes an intelligent linkage unit for processing equipment, which enables automatic linkage execution of safety control commands and processing equipment. It establishes control interfaces and command formats for common processing equipment. The control commands generated by the intelligent safety control module are sent to the corresponding equipment after protocol conversion. After the equipment executes the command, it provides real-time feedback on the execution result. The system records the command content, execution result, and equipment status changes on the blockchain.

5. The blockchain-based traceability aquatic product processing safety management system according to claim 1, characterized in that, It also includes an abnormal behavior monitoring and tracing unit, which identifies abnormal behavior by analyzing operation logs, data modification records and node interaction information on the blockchain, uses behavior feature extraction algorithms to capture key features of abnormal behavior, and matches and identifies them with an abnormal behavior database to locate the node, operator and time of the abnormal behavior, generates an abnormal behavior analysis report, and pushes the abnormal information and tracing results to the administrator.

6. The blockchain-based traceability aquatic product processing safety management system according to claim 1, characterized in that, It also includes a traceability data credibility verification unit, which ensures the authenticity and reliability of traceability data through multi-dimensional verification. The calculation formula is: in To ensure the credibility of traceability data, Assign credibility weights to nodes that upload data to the blockchain. Weights for data consistency verification For data integrity weight, The number of data entries uploaded to the chain by trusted nodes. This represents the total number of data entries uploaded to the blockchain. This represents the number of times cross-node consistency checks have passed. Total number of checks The number of data items for a complete traceability chain. This calculation measures the number of data items that a standard traceability chain should include, verifying the reliability of traceability data from three aspects: node credibility, data consistency, and completeness.

7. The blockchain-based traceability aquatic product processing safety management system according to claim 1, characterized in that, It also includes a hierarchical permission management unit, which sets up multi-level operation permissions, divides permission groups according to roles, and different permission groups correspond to different data access scopes, operation permissions and on-chain permissions. The permission allocation and operation behavior are recorded and stored on the blockchain throughout the process.

8. The blockchain-based traceability aquatic product processing safety management system according to claim 1, characterized in that, It also includes a dynamic optimization unit for the processing flow, which regularly analyzes historical processing data, security risk records and control effects on the blockchain, uses association rule mining algorithms to discover the intrinsic relationship between processing parameters and product quality, identifies optimization space, and generates processing flow optimization suggestions by combining industry standards and best practices. After the optimization suggestions are confirmed by the administrator, the processing standards are automatically updated and synchronized to the intelligent safety management module and processing equipment.

9. The blockchain-based traceability aquatic product processing safety management system according to claim 1, characterized in that, It also includes an emergency response linkage unit, which automatically initiates the emergency response process when a major safety risk or emergency is detected. Based on blockchain traceability data, it locates the scope of risk impact, generates an emergency response plan, links the logistics system and sales terminal system to push disposal instructions, tracks the progress of emergency response in real time, and records the disposal process, results and rectification measures on the blockchain.

10. The blockchain-based traceability aquatic product processing safety management system according to claim 1, characterized in that, It also includes a third-party audit interface unit, which provides a standardized third-party audit data interface, allowing third parties to access the system through the interface and query information on the blockchain according to preset permissions. The interface uses an encrypted transmission protocol, and the audit reports and verification results generated during the audit process are directly written to the blockchain block as proof of product quality compliance. At the same time, the system records the third-party access behavior and operation logs.

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