A green agricultural product whole life cycle traceability management system and method

Abstract

This invention provides a green agricultural product full life cycle traceability management system and method, belonging to the field of agricultural product traceability management technology. This invention assigns a unique identifier code containing type, origin, and batch number to each product, and combines IoT technology to achieve real-time data collection throughout the entire process from planting and processing to logistics and warehousing. Edge computing nodes are used to filter and repair erroneous data through a rule base of integrity and rationality. Subsequently, a staged safety scoring and dynamic weight allocation mechanism is introduced to quantify the safety status of each stage into an intuitive comprehensive traceability credibility index, facilitating a clear understanding of the traceability results. Finally, relying on blockchain technology, the full life cycle data and credibility index are stored immutably in a distributed manner, and a convenient retrieval mechanism is established. This not only achieves precise positioning of responsibility throughout the entire process and rapid emergency response, but also highlights the green and safe attributes of agricultural products through quantitative indicators. Furthermore, the application of edge computing and distributed architecture effectively reduces operating costs.

Description

Technical Field

[0001] This invention relates to the field of agricultural product traceability management technology, and in particular to a green agricultural product full life cycle traceability management system and method. Background Technology

[0002] With the increasing demand from consumers for green agricultural products, establishing a full life-cycle traceability system has become a key means to ensure food safety and enhance product trust. However, existing agricultural product traceability technologies still have the following significant shortcomings: Traditional traceability relies heavily on manual reporting or single-point IoT data collection. Data is easily tampered with or omitted during transmission, and there is a lack of automatic screening and repair mechanisms for abnormal data such as excessive environmental parameters or missing operation records, resulting in unreliable source data.

[0003] Secondly, most systems use centralized server storage. Once the central node is attacked or malfunctions, it is easy to lose data throughout the entire chain. Moreover, the data is controlled by a single organization, making it difficult for consumers to fully trust it. Furthermore, existing technologies mostly provide simple information listings, such as only displaying the place of origin and logistics tracking number. It is difficult to quantify and score the safety of each stage, such as planting, processing, and logistics. Consumers cannot intuitively judge the authenticity and credibility of the product, and it is difficult to effectively highlight the safety attributes of agricultural products.

[0004] Therefore, it is necessary to provide a green agricultural product full life cycle traceability management system and method to solve the above-mentioned technical problems. Summary of the Invention

[0005] To address the aforementioned technical problems, this invention provides a green agricultural product full life cycle traceability management system and method to solve the problems in the existing technology, such as the lack of real-time and authenticity assurance in data collection, security risks in storage, and fragmented display of traceability results without quantitative evaluation.

[0006] This invention provides a method for full life-cycle traceability management of green agricultural products, comprising the following steps: S1. Assign a unique identifier to each type of agricultural product and combine it with the Internet of Things to build a data collection mechanism for the life cycle of agricultural products to collect life cycle data of each type of agricultural product; S2. Use edge computing nodes to filter out missing or erroneous information in the lifecycle data and obtain the verified dataset. S3. Based on the regional or related agricultural products and their by-products, divide information traceability nodes to form a distributed traceability network; S4. Assess the safety scores of each stage in the green life cycle of agricultural products, and allocate weight coefficients for each information traceability node in combination with logistics and warehousing. S5. Based on each information traceability node and its assigned corresponding weight coefficient, the verified dataset is weighted and calculated to generate a comprehensive traceability credibility index for the entire life cycle of agricultural products. S6. Based on the type of agricultural product, data including unique identifiers, life cycle data of each type of agricultural product, and a comprehensive traceability credibility index will be synchronized to the blockchain network for distributed storage through a consensus mechanism, and a retrieval mechanism will be established.

[0007] Preferably, the specific steps of S1 include: S101. Assign a unique identification code to each specific product in each type of agricultural product. The identification code contains the agricultural product type, place of origin, and batch information. S102. At each stage of planting, processing, logistics and warehousing, environmental data, operation records and life cycle data of logistics trajectory are collected in real time through Internet of Things sensing technology. S103. Bind the collected lifecycle data with a unique identifier to form a structured data stream and upload it to the edge computing node.

[0008] Preferably, the specific steps of S2 include: S201. The edge computing node receives the collected lifecycle data and filters out missing or erroneous information through a preset rule base, which includes data integrity rules and data rationality rules. S202. Mark the erroneous data or repair it through interpolation algorithm, remove invalid data, and generate a verified dataset.

[0009] Preferably, the specific steps of S3 include: S301. Based on the geographical region or the processing chain relationship between agricultural products and their by-products, the traceability process is broken down into multiple information traceability nodes; S302. By connecting the various pre-defined information traceability nodes through network technology, a distributed traceability network is formed. The information traceability nodes are interconnected through encrypted channels, enabling data interaction and sharing.

[0010] Preferably, the specific steps of S4 include: S401. Pre-set corresponding safety assessment indicators for each stage of the green life cycle of agricultural products; S402. Extract data corresponding to each stage and safety assessment indicators from the verified dataset, and calculate the safety score of each stage in the green life cycle of agricultural products using the grading method based on the collected data and the preset assessment standards. The grading method includes dividing safety levels and setting corresponding scores for each safety level. For each safety level, specific assessment standards are formulated. Based on the collected data corresponding to the safety assessment indicators of agricultural products at each stage, the safety level of the stage is determined by comparing with the assessment standards, so as to assess its specific safety score. S403. Based on the degree of impact of logistics and warehousing on each information traceability node, formulate weight allocation principles, and in accordance with the weight allocation principles and combined with the security scores at each stage, assign corresponding weight coefficients to each information traceability node.

[0011] Preferably, the specific steps of S5 include: S501. Extract the data values ​​related to agricultural product traceability for each information traceability node from the verified dataset; S502. Using the weighted average method, the data value of each information traceability node is multiplied by its corresponding weight coefficient, and the calculation results of all nodes are added together to obtain the comprehensive traceability credibility index of agricultural products throughout their entire life cycle.

[0012] Preferably, the specific steps of S6 include: S601. Using the unique identifier of the specific product allocation in agricultural products as an index, package the life cycle data and comprehensive traceability credibility index of agricultural products into data blocks; S602. Verify the validity of the block through the consensus mechanism, synchronize it to each node of the blockchain network for distributed storage, and build a corresponding index structure in the blockchain network for traceability.

[0013] A green agricultural product full life cycle traceability management system includes: The data acquisition module is used to assign a unique identifier to each type of agricultural product and to build a data acquisition mechanism for the life cycle of agricultural products in conjunction with the Internet of Things to collect life cycle data of each type of agricultural product. The data verification module is used to filter out missing or erroneous information in lifecycle data using edge computing nodes and obtain a verified dataset. The node segmentation module is used to segment information traceability nodes based on regions or related agricultural products and their by-products, forming a distributed traceability network. The weighting module is used to evaluate the safety scores of each stage in the green life cycle of agricultural products, and to allocate the weight coefficients of each information traceability node in combination with logistics and warehousing. The trusted computing module is used to perform weighted calculations on the verified dataset based on each information traceability node and its assigned corresponding weight coefficients, and generate a comprehensive traceability trust index for the entire life cycle of agricultural products. The distributed storage module is used to synchronize data including unique identifiers, lifecycle data of various types of agricultural products, and comprehensive traceability credibility index to the blockchain network for distributed storage based on the type of agricultural product, and to establish a retrieval mechanism.

[0014] Compared with related technologies, the green agricultural product full life cycle traceability management system and method provided by the present invention has the following beneficial effects: This invention assigns each product a unique identifier containing its type, origin, and batch number. Combined with IoT technology, it enables real-time data collection throughout the entire process from planting and processing to logistics and warehousing. Edge computing nodes are used to filter and correct erroneous data using a rule base of integrity and rationality, ensuring the accuracy and completeness of the basic data. Secondly, by constructing a distributed encrypted traceability network based on geographical regions or processing chains, single points of failure are avoided. Subsequently, a staged security scoring and dynamic weight allocation mechanism is introduced to quantify the security status of each link into an intuitive comprehensive traceability credibility index, facilitating a clear understanding of the traceability results. Finally, relying on blockchain technology, the entire lifecycle data and credibility index are stored immutably in a distributed manner, and a convenient retrieval mechanism is established. This not only achieves precise identification of responsibility throughout the entire process and rapid emergency response but also highlights the green and safe attributes of agricultural products through quantitative indicators. Furthermore, the application of edge computing and distributed architecture effectively reduces operating costs. Attached Figure Description

[0015] Figure 1 This is a flowchart illustrating a green agricultural product lifecycle traceability management method according to the present invention; Figure 2 This is a system block diagram of a green agricultural product full life cycle traceability management system according to the present invention. Detailed Implementation

[0016] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0017] Example 1

[0018] In the specific implementation process, such as Figure 1 As shown, a method for full life-cycle traceability management of green agricultural products includes the following steps: S1. Assign a unique identifier to each type of agricultural product and combine it with the Internet of Things to build a data collection mechanism for the life cycle of agricultural products to collect life cycle data of each type of agricultural product; S2. Use edge computing nodes to filter out missing or erroneous information in the lifecycle data and obtain the verified dataset. S3. Based on the regional or related agricultural products and their by-products, divide information traceability nodes to form a distributed traceability network; S4. Assess the safety scores of each stage in the green life cycle of agricultural products, and allocate weight coefficients for each information traceability node in combination with logistics and warehousing. S5. Based on each information traceability node and its assigned corresponding weight coefficient, the verified dataset is weighted and calculated to generate a comprehensive traceability credibility index for the entire life cycle of agricultural products. S6. Based on the type of agricultural product, data including unique identifiers, life cycle data of each type of agricultural product, and a comprehensive traceability credibility index will be synchronized to the blockchain network for distributed storage through a consensus mechanism, and a retrieval mechanism will be established.

[0019] In the specific implementation process, the specific steps of S1 include: S101. Assign a unique identification code to each specific product in each type of agricultural product. The identification code contains the agricultural product type, place of origin, and batch information.

[0020] Specifically, each product needs to be assigned a unique identification code containing two key pieces of information: agricultural product type and origin / batch information. This unique identification code can be generated using methods such as numerical coding or a combination of letters and numbers. For example, for an agricultural product like apples, a coding rule could be set: the first few digits represent the agricultural product type, such as "AP" for apples; the middle few digits represent the origin, such as "BJ" for Beijing; and the last few digits represent the batch, such as "001" for the first batch. Therefore, the unique identification code for an apple might be "AP-BJ-001".

[0021] S102. At each stage of planting, processing, logistics and warehousing, environmental data, operation records and life cycle data of logistics trajectory are collected in real time through Internet of Things sensing technology.

[0022] Specifically, data is collected at each stage using corresponding IoT sensor devices. For example, soil moisture sensors, temperature sensors, and light sensors are installed in planting fields; process parameter monitoring equipment and operation recorders are installed in processing workshops; GPS positioning devices and speed sensors are installed on transport vehicles; and temperature and humidity sensors and ventilation monitoring equipment are installed in warehouses.

[0023] S103. Bind the collected lifecycle data with a unique identifier to form a structured data stream and upload it to the edge computing node.

[0024] Specifically, after collecting lifecycle data of agricultural products at each stage, this data is associated with the unique identifier previously assigned to the agricultural products, so that each set of data can be clearly associated with a specific agricultural product. After binding, the data is organized in a certain format to form a structured data stream for subsequent processing and analysis. Finally, the organized structured data stream is uploaded to the edge computing node via the network.

[0025] In the specific implementation process, the specific steps of S2 include: S201. The edge computing node receives the collected lifecycle data and filters out missing or erroneous information through a preset rule base, which includes data integrity rules and data rationality rules.

[0026] Specifically, edge computing nodes receive agricultural product lifecycle data from various data collection terminals, such as devices that collect data through IoT sensing technology at each stage of planting, processing, logistics, and warehousing. The edge computing nodes then filter the received data according to a preset rule base. For example, when recording environmental data during the planting stage of agricultural products, it should include data from multiple dimensions such as soil moisture, temperature, and light. If data for any one dimension is missing, it indicates that the data is incomplete. The data rationality rule determines whether the data is within a reasonable range. For example, the temperature of agricultural products during the warehousing stage has a reasonable range based on the characteristics of different agricultural products. If the collected temperature data exceeds this range, it is judged as erroneous data.

[0027] In this embodiment, a batch of apple lifecycle data was collected, including temperature and humidity data of the transport vehicles during the logistics stage. The preset rule base specifies that the temperature of vehicles transporting apples should be between 0-10℃, and the humidity should be between 70%-90%. After receiving the data, the edge computing node found that the temperature data collected at a certain moment was 15℃, exceeding the preset reasonable range, and also found that humidity data records were missing for a certain time period. Based on data integrity and data reasonableness rules, the edge computing node marked this temperature data and the time period with missing humidity data as potentially erroneous or incomplete.

[0028] S202. Mark the erroneous data or repair it through interpolation algorithm, remove invalid data, and generate a verified dataset.

[0029] Specifically, for incorrectly labeled data, a specific label field can be set. When erroneous data is detected, the corresponding label value is filled into this field. For interpolation algorithm repair, common existing techniques include linear interpolation and polynomial interpolation. For example, using linear interpolation, if the values ​​of two adjacent correct data points are known, a reasonable value can be calculated and filled for missing or incorrect data points in between based on the values ​​of these two points and the distance between them. For removing invalid data, the program can directly delete the data row or segment containing the invalid data from the dataset.

[0030] In this embodiment, a batch of apple life cycle data was collected. When the temperature of the vehicle transporting the apples was found to be 15℃ at a certain moment, which exceeded the preset reasonable range, the erroneous data of 15℃ was repaired by a linear interpolation algorithm. The preset temperatures of the two correct data points before and after the erroneous data point were 8℃ and 6℃, respectively, and the time interval between them and the erroneous data point was the same. Therefore, the repaired temperature can be calculated by linear interpolation as (8+6) / 2=7℃.

[0031] In the specific implementation process, the specific steps of S3 include: S301. Based on the geographical region or the processing chain relationship between agricultural products and their by-products, the traceability process is broken down into multiple information traceability nodes.

[0032] Specifically, we collect geographical information, including the distribution of planting, processing, and sales areas; at the same time, we sort out the processing chain from agricultural products to agricultural by-products, clarify the sequence and specific content of each processing link, and then break down the process into multiple information traceability nodes according to geographical regions and processing chains, and number and name each node for subsequent management and identification.

[0033] In this embodiment, the apples used in this green agricultural product are mainly grown in multiple orchards in region A. These orchards can serve as an information traceability node, designated as "Region A Planting Node". After harvesting, the apples are transported to an apple processing plant in region B for initial processing such as cleaning, sorting, and packaging. This processing plant in region B is an information traceability node, designated as "Region B Initial Processing Node". Subsequently, some apples are transported to region C for further processing into juice. The juice processing plant in region C is another information traceability node, designated as "Region C Juice Processing Node". Finally, the processed apples and their agricultural byproducts are distributed to various sales areas, such as supermarkets and fruit shops. These sales areas can also serve as information traceability nodes, such as "Region D Supermarket Sales Node". S302. By connecting the various pre-defined information traceability nodes through network technology, a distributed traceability network is formed. The information traceability nodes are interconnected through encrypted channels, enabling data interaction and sharing.

[0034] Specifically, select appropriate network technologies, such as the Internet, local area networks, or dedicated networks, to connect the various predefined information tracing nodes. For the connection between nodes, wired networks (such as fiber optics and Ethernet) or wireless networks (such as Wi-Fi and 4G / 5G) can be used. Secondly, in terms of establishing encrypted channels, existing encryption protocols such as SSL / TLS can be used to encrypt and protect data transmission.

[0035] In its implementation, S4 includes the following specific steps: S401. Pre-set corresponding safety assessment indicators for each stage of the green life cycle of agricultural products.

[0036] Specifically, historical safety data and industry standards are acquired to pre-set corresponding safety assessment indicators for each stage of the green life cycle of agricultural products. For example, during the planting stage, the safety assessment indicators may include the heavy metal content in the soil, the pesticide residue in irrigation water, and the proportion of organic fertilizer used; during the processing stage, the indicators may include the air cleanliness of the processing workshop, the pesticide residue removal rate after washing vegetables, and whether illegal additives are used; during the logistics stage, the indicators may include whether the transportation time exceeds the shelf life of the vegetables; and during the storage stage, the indicators may include the temperature and humidity control of the warehouse and the storage time of the vegetables.

[0037] S402. Extract data corresponding to each stage and safety assessment indicators from the verified dataset, and calculate the safety score of each stage in the green life cycle of agricultural products using the grading method based on the collected data and the preset assessment standards. The grading method includes dividing safety levels and setting corresponding scores for each safety level. For each safety level, specific assessment standards are formulated. Based on the collected data corresponding to the safety assessment indicators of agricultural products at each stage, the safety level of the stage is determined by comparing with the assessment standards, so as to assess its specific safety score.

[0038] In this embodiment, the preset evaluation criteria for soil heavy metal content in a type of organic vegetable during the planting stage are as follows: lead content less than 0.2 mg / kg is excellent (score 100 points), 0.2-0.5 mg / kg is good (score 80 points), 0.5-1 mg / kg is acceptable (score 60 points), and greater than 1 mg / kg is unacceptable (score 40 points). From the validated dataset, the soil lead content of a certain planting area was extracted to be 0.3 mg / kg. According to the evaluation criteria, this data belongs to the good level, corresponding to a safety score of 80 points. Similarly, other safety evaluation indicators during the planting stage, as well as indicators for processing, logistics, and warehousing, are evaluated and calculated to obtain a safety score for each stage.

[0039] S403. Based on the degree of impact of logistics and warehousing on each information traceability node, formulate weight allocation principles, and in accordance with the weight allocation principles and combined with the security scores at each stage, assign corresponding weight coefficients to each information traceability node.

[0040] Specifically, based on historical life cycle data of agricultural products, statistical analysis methods are used to analyze the impact of logistics and warehousing on various information traceability nodes throughout the entire green life cycle of agricultural products. For example, long-term storage may lead to the deterioration of agricultural products and affect their safety; while efficient logistics and transportation can reduce the loss and deterioration risk of agricultural products during transportation. Among these, the information traceability nodes with greater impact are assigned higher weight coefficients.

[0041] In this embodiment, for vegetable agricultural products, the weight allocation principle for the four information traceability nodes of planting, processing, logistics and warehousing is as follows: based on the statistical analysis of their historical data, it is determined that the logistics and warehousing links have a greater impact on the safety of vegetables, so they are assigned a weight coefficient of 0.3 respectively; while the planting link is the foundation and is assigned a weight coefficient of 0.25; the processing link is assigned a weight coefficient of 0.15.

[0042] In its implementation, S5 includes the following specific steps: S501. Extract the data values ​​related to agricultural product traceability for each information traceability node from the verified dataset.

[0043] Specifically, by using database query statements and based on the pre-defined correspondence between information traceability nodes and data fields, data is extracted from the verified dataset to obtain the data values ​​related to agricultural product traceability for each information traceability node.

[0044] S502. Using the weighted average method, the data value of each information traceability node is multiplied by its corresponding weight coefficient, and the calculation results of all nodes are added together to obtain the comprehensive traceability credibility index of agricultural products throughout their entire life cycle.

[0045] In this embodiment, for a certain vegetable agricultural product, the weighted score for the planting stage is 80×0.25=20 points, the weighted score for the processing stage is 80×0.15=12 points, the weighted score for the logistics stage is 60×0.3=18 points, and the weighted score for the warehousing stage is 80×0.3=24 points. The sum of the calculation results of all nodes is 20+12+18+24=74, which is the comprehensive traceability credibility index of the entire life cycle of the vegetable agricultural product.

[0046] In its implementation, S6 includes the following specific steps: S601. Using the unique identifier of the specific product allocation in agricultural products as an index, package the life cycle data and comprehensive traceability credibility index of agricultural products into data blocks.

[0047] Specifically, appropriate identifiers are selected from the unique identifiers assigned to specific agricultural products as indexes. The collected lifecycle data and comprehensive traceability credibility index are organized according to a certain data structure and format, and packaged into a data block using the unique identifier as an index.

[0048] S602. Verify the validity of the block through the consensus mechanism, synchronize it to each node of the blockchain network for distributed storage, and build a corresponding index structure in the blockchain network for traceability.

[0049] Specifically, the packaged data blocks are submitted to the blockchain network. Each node in the network verifies the validity of the block according to a preset consensus mechanism, such as proof-of-work or proof-of-stake. The verification includes the integrity, accuracy, and compliance with blockchain rules. After verification, the block is synchronized to various nodes in the blockchain network for distributed storage.

[0050] Example 2

[0051] like Figure 2 As shown, a green agricultural product full life cycle traceability management system includes: The data acquisition module is used to assign a unique identifier to each type of agricultural product and to build a data acquisition mechanism for the life cycle of agricultural products in conjunction with the Internet of Things to collect life cycle data of each type of agricultural product. The data verification module is used to filter out missing or erroneous information in lifecycle data using edge computing nodes and obtain a verified dataset. The node segmentation module is used to segment information traceability nodes based on regions or related agricultural products and their by-products, forming a distributed traceability network. The weighting module is used to evaluate the safety scores of each stage in the green life cycle of agricultural products, and to allocate the weight coefficients of each information traceability node in combination with logistics and warehousing. The trusted computing module is used to perform weighted calculations on the verified dataset based on each information traceability node and its assigned corresponding weight coefficients, and generate a comprehensive traceability trust index for the entire life cycle of agricultural products. The distributed storage module is used to synchronize data including unique identifiers, lifecycle data of various types of agricultural products, and comprehensive traceability credibility index to the blockchain network for distributed storage based on the type of agricultural product, and to establish a retrieval mechanism.

[0052] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0053] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, including read-only memory (ROM), random access memory (RAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), one-time programmable read-only memory (OTPROM), electrically-Erasable Programmable Read-Only Memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, disk storage, magnetic tape storage, or any other computer-readable medium capable of carrying or storing data.

[0054] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

Claims

1. A method for full life-cycle traceability management of green agricultural products, characterized in that, Includes the following steps: S1. Assign a unique identifier to each type of agricultural product and combine it with the Internet of Things to build a data collection mechanism for the life cycle of agricultural products to collect life cycle data of each type of agricultural product; S2. Use edge computing nodes to filter out missing or erroneous information in the lifecycle data and obtain the verified dataset. S3. Based on the regional or related agricultural products and their by-products, divide information traceability nodes to form a distributed traceability network; S4. Assess the safety scores of each stage in the green life cycle of agricultural products, and allocate weight coefficients for each information traceability node in combination with logistics and warehousing. S5. Based on each information traceability node and its assigned corresponding weight coefficient, the verified dataset is weighted and calculated to generate a comprehensive traceability credibility index for the entire life cycle of agricultural products. S6. Based on the type of agricultural product, data including unique identifiers, life cycle data of each type of agricultural product, and a comprehensive traceability credibility index will be synchronized to the blockchain network for distributed storage through a consensus mechanism, and a retrieval mechanism will be established.

2. The method for full life-cycle traceability management of green agricultural products according to claim 1, characterized in that, The specific steps of S1 include: S101. Assign a unique identification code to each specific product in each type of agricultural product. The identification code contains the agricultural product type, place of origin, and batch information. S102. At each stage of planting, processing, logistics and warehousing, environmental data, operation records and life cycle data of logistics trajectory are collected in real time through Internet of Things sensing technology. S103. Bind the collected lifecycle data with a unique identifier to form a structured data stream and upload it to the edge computing node.

3. The method for full life-cycle traceability management of green agricultural products according to claim 1, characterized in that, The specific steps of S2 include: S201. The edge computing node receives the collected lifecycle data and filters out missing or erroneous information through a preset rule base, which includes data integrity rules and data rationality rules. S202. Mark the erroneous data or repair it through interpolation algorithm, remove invalid data, and generate a verified dataset.

4. The method for full life-cycle traceability management of green agricultural products according to claim 1, characterized in that, The specific steps of S3 include: S301. Based on the geographical region or the processing chain relationship between agricultural products and their by-products, the traceability process is broken down into multiple information traceability nodes; S302. By connecting the various pre-defined information traceability nodes through network technology, a distributed traceability network is formed. The information traceability nodes are interconnected through encrypted channels, enabling data interaction and sharing.

5. The method for full life-cycle traceability management of green agricultural products according to claim 1, characterized in that, The specific steps of S4 include: S401. Pre-set corresponding safety assessment indicators for each stage of the green life cycle of agricultural products; S402. Extract data corresponding to each stage and safety assessment indicators from the verified dataset, and calculate the safety score of each stage in the green life cycle of agricultural products using the grading method based on the collected data and the preset assessment standards. The grading method includes dividing safety levels and setting corresponding scores for each safety level. For each safety level, specific assessment standards are formulated. Based on the collected data corresponding to the safety assessment indicators of agricultural products at each stage, the safety level of the stage is determined by comparing with the assessment standards, so as to assess its specific safety score. S403. Based on the degree of impact of logistics and warehousing on each information traceability node, formulate weight allocation principles, and in accordance with the weight allocation principles and combined with the security scores at each stage, assign corresponding weight coefficients to each information traceability node.

6. The method for full life-cycle traceability management of green agricultural products according to claim 1, characterized in that, The specific steps of S5 include: S501. Extract the data values ​​related to agricultural product traceability for each information traceability node from the verified dataset; S502. Using the weighted average method, the data value of each information traceability node is multiplied by its corresponding weight coefficient, and the calculation results of all nodes are added together to obtain the comprehensive traceability credibility index of agricultural products throughout their entire life cycle.

7. The method for full life-cycle traceability management of green agricultural products according to claim 1, characterized in that, The specific steps of S6 include: S601. Using the unique identifier of the specific product allocation in agricultural products as an index, package the life cycle data and comprehensive traceability credibility index of agricultural products into data blocks; S602. Verify the validity of the block through the consensus mechanism, synchronize it to each node of the blockchain network for distributed storage, and build a corresponding index structure in the blockchain network for traceability.

8. A green agricultural product lifecycle traceability management system, employing a green agricultural product lifecycle traceability management method as described in any one of claims 1-7, characterized in that, The management system includes: The data acquisition module is used to assign a unique identifier to each type of agricultural product and to build a data acquisition mechanism for the life cycle of agricultural products in conjunction with the Internet of Things to collect life cycle data of each type of agricultural product. The data verification module is used to filter out missing or erroneous information in lifecycle data using edge computing nodes and obtain a verified dataset. The node segmentation module is used to segment information traceability nodes based on regions or related agricultural products and their by-products, forming a distributed traceability network. The weighting module is used to evaluate the safety scores of each stage in the green life cycle of agricultural products, and to allocate the weight coefficients of each information traceability node in combination with logistics and warehousing. The trusted computing module is used to perform weighted calculations on the verified dataset based on each information traceability node and its assigned corresponding weight coefficients, and generate a comprehensive traceability trust index for the entire life cycle of agricultural products. The distributed storage module is used to synchronize data including unique identifiers, lifecycle data of various types of agricultural products, and comprehensive traceability credibility index to the blockchain network for distributed storage based on the type of agricultural product, and to establish a retrieval mechanism.

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