Blockchain single fruit level traceability method and system for orchard
By generating a unique identification code for each apple and using blockchain technology for evidence storage, the problems of traceability accuracy and data security in the orchard traceability system have been solved, enabling precise traceability and reliable pricing at the single-fruit level, thereby enhancing the market competitiveness of fruit products and farmers' income.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- TONGCHUAN ZHAOJIN HAITANG ECOLOGICAL IND CO LTD
- Filing Date
- 2026-03-31
- Publication Date
- 2026-06-19
AI Technical Summary
The existing orchard quality and safety traceability system suffers from insufficient traceability accuracy, limited information dimensions, and low data security and credibility. It cannot achieve unique identification and refined traceability at the single fruit level, and the information is easily tampered with, resulting in damage to the authenticity and credibility of the traceability data.
Each apple is given a unique identification code using blockchain technology. A QR code is marked on the base of the fruit stem using laser micro-engraving technology. Key life cycle data is collected and encrypted and stored on the FISCO-BCOS consortium blockchain. Consumers can scan the code to verify the authenticity of the data in real time and make accurate pricing based on the trusted data.
It achieves precise traceability at the individual fruit level, ensuring data security and immutability, enhancing consumer trust and brand value, and enabling differentiated pricing through reliable data, thereby improving the competitiveness of fruit products in the market and farmers' income.
Smart Images
Figure CN122243528A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of agricultural product quality and safety traceability technology, and in particular to a blockchain-based single-fruit traceability method and system for orchards. Background Technology
[0002] Orchard quality and safety traceability refers to recording information from all stages of fruit tree planting, fertilization, pesticide application, harvesting, packaging, storage, and transportation, and using technologies such as QR codes and RFID to assign unique identifiers to products. Consumers can scan the code to check data such as origin, production records, and test reports, achieving transparent supervision from orchard to table. This system can help fruit farmers standardize production and strengthen quality control, enhance consumer trust, and accurately trace the source and quickly recall products in the event of safety issues, ensuring fruit safety and brand value.
[0003] However, the current orchard quality and safety traceability system still has obvious shortcomings. First, the traceability accuracy is insufficient, only able to locate at the "batch-barcode" level, unable to achieve unique identification of individual fruits and refined traceability. Second, the information dimensions are limited, lacking key quality data such as sugar-acid ratio and firmness, as well as environmental information such as carbon footprint, making it difficult to meet consumers' deeper needs for quality and transparency. Finally, data security and credibility are questionable, and the existing system is unable to prevent information tampering, resulting in damage to the authenticity and credibility of traceability data, which restricts the trust value and application effect of the traceability system. Summary of the Invention
[0004] The purpose of this invention is to provide a blockchain-based single-fruit traceability method and system for orchards, in order to solve the problems mentioned in the background art.
[0005] To achieve the above objectives, the present invention provides the following technical solution: a blockchain-based single-fruit traceability method for orchards, comprising: S1: Single fruit identification coding and physical marking. A corresponding UID is generated for each apple, and the UID is converted into a QR code and marked on the base of the fruit stem using laser micro-engraving technology. S2: Key life cycle data collection, collecting relevant operational and quality data at seven key nodes of apple cultivation, pruning, hormones, fertilizer and water, pesticide residues, harvesting, and cold chain. S3: Data encryption and blockchain notarization, which generates corresponding hash values for the collected data from each node in real time, and writes the hash values into the FISCO-BCOS consortium blockchain for notarization within a specified time. S4: Consumer scanning and information retrieval. Consumers scan the QR code on the fruit stem with their mobile devices, and the system retrieves and verifies the entire process data of the single fruit stored on the blockchain. S5: Core information display and verification, clearly displaying the core information of the single fruit to consumers, mainly including sugar-acid ratio, firmness, color a* value, pesticide residue test results and carbon footprint; S6: Value realization and brand premium, based on the complete and credible traceability information presented, to accurately price the single fruit.
[0006] Preferably, in step S1, the UID is encoded using a five-level hierarchical structure, accurate to the physical location of a single fruit's growth. The physical location includes, in sequence, the planting area, the tree row, the tree body, the fruit branch, and the single fruit.
[0007] Preferably, step S1 includes the following steps: S11: In the system's database, a batch of available UIDs are created in real time for each physical location. When bagging the fruit, an unused UID is assigned to the fruit from the system based on the fruit's actual physical location, and this UID is bound to the fruit's initial information in the system. S12: Using the UID string as input data, the QR code generation algorithm is used to convert the UID data into a two-dimensional matrix graphic; S13: Using laser marking equipment, precisely locate the base of the apple stem, adjust the laser power, speed and frequency, and clearly engrave the QR code on the base of the apple stem.
[0008] Preferably, step S2 includes the following steps: S21: Planting data collection, including basic data such as fruit tree seedling variety, rootstock type, planting date, planting density, and trunk height; S22: Pruning data collection, including the specific time of winter and summer pruning, the pruning techniques used, the degree of pruning, and information on the personnel performing the pruning; S23: Data collection on hormone use, including the name, concentration, timing of administration, method of administration, and safety interval of the hormones used; S24: Fertilizer and water data collection, including the type of fertilizer, fertilizer name and composition, precise dosage, irrigation method and water volume, and execution date for each application; S25: Data collection for pesticide residue testing. Safety testing is performed before harvesting. The enzyme inhibition method is used to test the pesticide residues in the samples, and the test items, the acceptable range and the result data are recorded. S26: Harvesting data collection, including harvesting date and time, batch, maturity index at harvest time, and information on personnel responsible for harvesting operations; S27: Cold chain logistics data collection, including post-harvest storage and transportation environment, collecting continuous temperature and humidity records from warehousing, pre-cooling, storage to transportation throughout the entire process, as well as the start and end times and location information of each link.
[0009] Preferably, step S3 includes the following steps: S31: Convert the data fields collected in step S2 into a unified string format, and sort all data key-value pairs in a specific order to generate a standard string sequence; S32: The unique string obtained after standardization is fed into the cryptographic hash function for calculation to obtain the corresponding hash value; S33: After generating the hash value, the system calls the smart contract deployed on the FISCO-BCOS consortium blockchain. By sending a transaction to the contract, the hash value, timestamp, and related individual UID index information representing a specific batch of data are packaged and submitted to the blockchain network within a specified time. The nodes in the network verify the consensus and finally record it in a new block.
[0010] Preferably, step S4 includes the following steps: S41: Consumers use a scanning application on their smartphones to scan the tiny QR code at the base of the apple stem. Once the scanning software successfully recognizes the code, it will parse out the corresponding five-level UID. S42: Consumers use mobile devices to send a data request to the system's backend server using the parsed UID as the query keyword. After receiving the request, the system will recalculate a hash value for the corresponding original data. At the same time, the system will query the corresponding hash value stored in the S3 stage from the FISCO-BCOS blockchain based on the UID and compare this newly calculated hash value with the original hash value retrieved from the blockchain. S43, if the two hash values match, the system will then prepare to send the complete and reliable original data to the consumer's mobile application; if the two hash values do not match, the system will return a warning message to the consumer indicating that the data verification failed.
[0011] Preferably, step S5 includes the following steps: S51: Visual presentation of trusted status. The system clearly informs consumers through clear visual elements on the information display interface that all traceability information currently displayed has been verified by blockchain hash value and has not been tampered with since it was recorded, thereby establishing a trustworthy interaction foundation in the first place. If the verification fails, a warning sign will be displayed simultaneously. S52: Interpretation of core quality and safety indicators, which displays the sugar-acid ratio, hardness, color a* value, pesticide residue test results, and carbon footprint in the form of reports.
[0012] Preferably, step S6 includes the following steps: S61: Traceability data value assessment. The system performs multi-dimensional value analysis on verified traceability data, including: food quality rating represented by sugar-acid ratio, safety level assessment represented by pesticide residue test results, environmental value assessment represented by carbon footprint data, and trust premium calculation brought about by transparency in the entire process from planting to cold chain. S62: Construction of dynamic market pricing model. Based on the value assessment results, a dynamic pricing algorithm is established that includes basic production costs, quality added coefficient, safety premium coefficient, environmental value coefficient and market supply and demand parameters. S63: Implementation of differentiated pricing strategy. Based on the output of the pricing model, implement tiered pricing for apples that pass traceability verification: standard pricing is implemented for those that meet the benchmark value, quality and safety premium is given for those with excellent sugar-acid ratio and no detected pesticide residues, and additional environmental premium is given for those with excellent carbon footprint performance. S64: Brand value delivery and market education. Clearly demonstrate the basis for premium pricing in the sales process. Through packaging labels, sales scripts, and QR code page explanations, clearly explain to consumers the specific value points corresponding to higher prices. S65: Premium effect tracking and strategy optimization, continuously monitor the market acceptance of different premium strategies, collect data on consumers' attention to traceability information and willingness to pay, and dynamically adjust the parameter settings of the pricing model based on sales data and feedback.
[0013] Preferably, the pricing model uses the following formula for assessing the value of traceable data: ; Among them, V represents the comprehensive value assessment, Q represents the food quality score brought by the sugar-acid ratio, S represents the safety level of the pesticide residue test results, E represents the environmental value assessment of the carbon footprint, and T represents the trust premium brought by the transparency of the entire process from planting to cold chain. When the comprehensive value assessment is greater than the premium standard, pricing can be made.
[0014] This invention also provides a blockchain-based single-fruit traceability system for orchards, comprising: The single-fruit identification coding and physical marking module generates a corresponding UID for each apple, converts the UID into a QR code, and marks it on the base of the fruit stem using laser micro-engraving technology; The key lifecycle data acquisition module collects relevant operational and quality data at seven key nodes in apple production: planting, pruning, hormones, fertilizer and water, pesticide residues, harvesting, and cold chain. The data encryption and blockchain notarization module generates corresponding hash values for the collected data from each node in real time, and writes the hash values into the FISCO-BCOS consortium blockchain for notarization within a specified time. The consumer scanning and information retrieval module allows consumers to scan the QR code on the fruit stem using their mobile devices, and the system then retrieves and verifies the entire process data of the single fruit stored on the blockchain. The core information display and verification module clearly displays the core information of the single fruit to consumers, including sugar-acid ratio, firmness, color a* value, pesticide residue test results, and carbon footprint. The Value Realization and Brand Premium module accurately prices the single fruit based on the complete and credible traceability information displayed.
[0015] The technical effects and advantages of this invention are as follows: This invention assigns a unique identity to each apple and laser-engraves it, giving each apple a unique, physically identifiable identifier. This refines the traceability granularity from the vague batch level to the individual fruit. Simultaneously, it collects structured data at seven key lifecycle nodes, including planting, pruning, fertilization and irrigation, pesticide residues, harvesting, and cold chain. Using a hash algorithm and the FISCO-BCOS consortium blockchain, it generates a unique digital fingerprint for each stage of the data and permanently stores it. This process ensures that all recorded information cannot be tampered with after generation. When a consumer scans the code, the system compares the hash value on the blockchain with the recalculated data hash value in real time, thus providing a verifiable technical guarantee for the authenticity of each piece of information. This invention directly transforms reliable data into pricing criteria and market competitiveness. The system presents consumers with specific core indicators that have been verified by blockchain, including sugar-acid ratio, hardness, pesticide residue test results, and carbon footprint. Based on this quantifiable and reliable data, orchards can establish a dynamic pricing algorithm. This algorithm integrates basic costs, quality parameters, safety levels, and environmental performance to output differentiated prices, enabling the monetization of superior attributes that were previously unrecognizable by the market, making it easy to use. Attached Figure Description
[0016] Figure 1 This is a flowchart of the blockchain-based single-fruit traceability method for orchards according to the present invention. Detailed Implementation
[0017] 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.
[0018] This invention provides, for example Figure 1The method shown is a blockchain-based single-fruit traceability method for orchards, including S1: single-fruit identity encoding and physical marking, generating a corresponding UID for each apple, converting the UID into a QR code, and marking it on the base of the fruit stem using laser micro-engraving technology to ensure that each fruit has a unique identity, which facilitates subsequent traceability and management and improves the traceability and security of the fruit. S2: Key lifecycle data collection. At the seven key nodes of apple planting, pruning, hormones, fertilizer and water, pesticide residues, harvesting and cold chain, corresponding operation and quality data are collected respectively. By comprehensively recording the production information of each link, the integrity and accuracy of the data are ensured, providing a basis for subsequent decision-making and quality control. S3: Data encryption and blockchain notarization generate corresponding hash values for the collected data from each node in real time, and write the hash values into the FISCO-BCOS consortium blockchain for notarization within a specified time, ensuring the security and immutability of the data and improving consumers' trust in the traceability system. S4: Consumer scanning and information retrieval. Consumers scan the QR code on the fruit stem with their mobile devices. The system then retrieves and verifies the entire process data of the single fruit stored on the blockchain, enabling consumers to easily obtain product information and enhancing the transparency and confidence in their purchasing decisions. S5: Core information display and verification. Clearly display the core information of the single fruit to consumers, including sugar-acid ratio, firmness, color a* value, pesticide residue test results and carbon footprint. Through the visualization of information, consumers can quickly understand the product quality and improve the shopping experience and product value perception. S6: Value Realization and Brand Premium. Based on the complete and credible traceability information presented, the price of each apple is accurately determined. Through scientific pricing strategies, the true value of the apple can be better reflected, while creating more economic benefits for farmers and promoting the sustainable development of the industry.
[0019] In step S1, the UID is encoded using a five-level hierarchical structure, accurate to the physical location of a single fruit. The physical location includes, in order, the planting area, the tree row, the tree body, the fruit branch, and the single fruit.
[0020] Step S1 includes the following steps: S11: In the system's database, a batch of available UIDs are created in real time for each physical location. When bagging the fruit, an unused UID is assigned to the fruit from the system based on its actual physical location. This UID is bound to the fruit's initial information in the system to ensure that each fruit has a unique identifier for easy traceability. S12: Using the UID string as input data, a QR code generation algorithm is used to convert the UID data into a two-dimensional matrix graphic. The generation of QR codes makes information storage more efficient, and consumers can quickly obtain relevant information by scanning the code, thus improving the user experience. S13: Using laser marking equipment, the laser is precisely positioned at the base of the apple stem. The power, speed, and frequency of the laser are adjusted to clearly engrave the QR code on the base of the apple stem. Laser marking technology ensures the clarity and durability of the QR code, making it less prone to damage during transportation and storage.
[0021] The steps described above, by generating a unique UID for each apple and marking it at the base of the stem using laser micro-engraving technology, ensure that each fruit has a unique identifier. This approach not only enhances the traceability and security of the fruit but also lays a solid foundation for subsequent quality management and traceability systems. Consumers can obtain detailed information by scanning a QR code, increasing their trust in the product. Simultaneously, it provides producers with precise traceability capabilities, enhancing their brand image.
[0022] Step S2 includes the following steps: S21: Planting data collection, including basic data such as the variety of fruit tree seedlings, rootstock type, planting date, planting density, and trunk height. Detailed recording of planting process data helps to analyze the growth characteristics of different varieties and optimize cultivation strategies. S22: Pruning data collection, including the specific time of winter and summer pruning, the pruning techniques used, the degree of pruning, and the information of the personnel performing the pruning. By recording pruning information, the impact of pruning on fruit quality can be assessed, thereby improving fruit yield and quality. S23: Hormone use data collection, including the name, concentration, application period, application method, and safety interval of the hormones used. This data ensures the transparency and compliance of hormone use and helps consumers trust the safety of the fruit. S24: Fertilizer and water data collection, including the type of fertilizer, fertilizer name and composition, precise dosage, irrigation method and water volume, and execution date for each application. Detailed fertilizer and water management records support scientific fertilization and irrigation, and promote healthy fruit growth. S25: Data collection for pesticide residue testing. Safety testing is carried out before harvesting. The enzyme inhibition method is used to test samples for pesticide residues. The test items, the acceptable range and the result data are recorded to ensure that the fruit meets safety standards before it is put on the market and to protect the health of consumers. S26: Harvesting data collection, including harvest date and time, batch, maturity index at harvest, and information on personnel responsible for harvesting, helps track changes in fruit quality and optimize harvesting timing; S27: Cold chain logistics data collection, including post-harvest storage and transportation environment, collecting continuous temperature and humidity records throughout the entire process from warehousing, pre-cooling, storage to transportation, as well as the start and end times and location information of each link. By recording cold chain information, the freshness and quality of the fruit during transportation are ensured.
[0023] The above steps involve comprehensive data collection at seven key stages of apple production: planting, pruning, hormones, fertilization and irrigation, pesticide residues, harvesting, and cold chain logistics. This ensures the integrity and accuracy of information throughout the production process. By recording data at each stage, producers can better analyze and optimize planting and management strategies, thereby improving fruit yield and quality. This also provides an important basis for subsequent quality control and improvement, promoting the scientific and standardized development of agricultural production.
[0024] Step S3 includes the following steps: S31: Convert the data fields collected in step S2 into a unified string format, and sort all data key-value pairs in a specific order to generate a standard string sequence. Standardization ensures data consistency and helps with subsequent hash calculations. S32: The unique string obtained after standardization is fed into the cryptographic hash function for calculation to obtain the corresponding hash value. The generation of the hash value provides technical protection for the secure storage of data and prevents data from being tampered with. S33: After generating the hash value, the system calls the smart contract deployed on the FISCO-BCOS consortium blockchain. By sending a transaction to the contract, the hash value, timestamp, and related individual UID index information representing a specific batch of data are packaged and submitted to the blockchain network within a specified time. The nodes in the network perform consensus verification and finally record it in a new block, ensuring that all data is securely stored on the blockchain and improving the transparency and credibility of the data.
[0025] The above steps generate hash values for each node's data in real time and store them in the blockchain, ensuring data security and immutability, increasing consumer trust in the traceability system, and ensuring the authenticity and transparency of information. Through blockchain technology, both producers and consumers can obtain reliable data sources, further enhancing brand credibility and market competitiveness. This secure evidence storage mechanism provides strong protection for the entire supply chain and reduces the risk of information asymmetry.
[0026] Step S4 includes the following steps: S41: Consumers use a scanning application on their smartphones to scan the tiny QR code at the base of the apple stem. Once the scanning software successfully recognizes the code, it will parse out the corresponding five-level UID, allowing consumers to quickly obtain detailed information about the fruit. S42: Consumers use their mobile devices to send a data request to the system's backend server using the parsed UID as the query keyword. After receiving the request, the system will recalculate a hash value for the corresponding original data. At the same time, the system will query the corresponding hash value stored in the S3 stage from the FISCO-BCOS blockchain based on the UID, and compare this newly calculated hash value with the original hash value retrieved from the blockchain to ensure the accuracy and security of the information. S43, if the two hash values match, the system will then prepare to send the complete and reliable original data to the consumer's mobile application. If the two hash values do not match, the system will return a warning message to the consumer indicating that the data verification failed. This step establishes a chain of trust between the consumer and the product, ensuring the authenticity of the information.
[0027] These steps allow consumers to easily access Apple's complete data process by scanning a QR code with their mobile devices, enhancing transparency and confidence in their purchasing decisions. This interactive approach enables consumers to understand the product's origin and quality in real time, improving the shopping experience. Simultaneously, transparent information dissemination encourages producers to pay more attention to quality control during planting and management, creating a virtuous cycle and raising industry standards.
[0028] Step S5 includes the following steps: S51: Visualized presentation of trusted status. The system clearly informs consumers through clear visual elements on the information display interface that all traceability information currently displayed has been verified by blockchain hash value and has not been tampered with since it was recorded, thereby establishing a trustworthy interactive foundation in the first place. If the verification fails, a warning sign will be displayed simultaneously. This step enhances consumers' trust and improves the brand image. S52: Interpretation of core quality and safety indicators. The sugar-acid ratio, hardness, color a* value, pesticide residue test results, and carbon footprint are displayed in the form of reports. Through detailed data interpretation, consumers can have a more comprehensive understanding of the product's quality and safety, promoting rational consumption.
[0029] The above steps clearly display core information about apples, such as sugar-acid ratio, firmness, color a* value, and pesticide residue test results. Consumers can quickly understand the product quality. The visual information display not only enhances the consumer shopping experience but also promotes rational consumption and brand loyalty. Consumers' clear understanding of product quality helps drive market demand for high-quality fruit, thereby encouraging producers to continuously improve product quality and safety standards.
[0030] Step S6 includes the following steps: S61: Traceability data value assessment. The system performs multi-dimensional value analysis on verified traceability data, including: food quality rating represented by sugar-acid ratio, safety level assessment represented by pesticide residue test results, environmental value assessment represented by carbon footprint data, and trust premium calculation brought about by transparency in the entire process from planting to cold chain. This step helps producers and consumers better understand the market value of products. S62: Dynamic market pricing model construction. Based on the value assessment results, a dynamic pricing algorithm is established that includes basic production costs, quality added coefficient, safety premium coefficient, environmental value coefficient, and market supply and demand parameters. This pricing model can flexibly respond to market changes and ensure the competitiveness of products. S63: Implementation of differentiated pricing strategy. Based on the output of the pricing model, implement tiered pricing for apples that pass traceability verification: standard pricing is implemented for those that meet the benchmark value, quality and safety premium is given for those with excellent sugar-acid ratio and no detected pesticide residues, and additional environmental premium is given for those with excellent carbon footprint performance. S64: Brand value delivery and market education. Clearly demonstrate the basis for premium pricing in the sales process. Through packaging labels, sales scripts, and QR code pages, clearly explain to consumers the specific value points corresponding to higher prices. This strategy aims to incentivize high-quality production, raise overall market standards, and enhance consumers' identification with the brand and products by educating them, thereby promoting brand loyalty. S65: Premium effect tracking and strategy optimization, continuously monitor the market acceptance of different premium strategies, collect data on consumers' attention to traceability information and willingness to pay, and dynamically adjust the parameter settings of the pricing model based on sales data and feedback to ensure the effectiveness and market adaptability of the pricing strategy and to respond to changes in consumer demand in a timely manner.
[0031] The pricing model uses the following formula for assessing the value of traceable data: ; Among them, V represents the comprehensive value assessment, Q represents the edible quality score based on the sugar-acid ratio, which is obtained by laboratory testing of the sugar and acid ratio in the fruit, S represents the safety level of pesticide residue test results, which is obtained by analyzing pesticide residues in fruit samples and setting limits according to national food safety standards. Qualified fruits get high scores. The calculation method is as follows: safe fruits get 10 points, slightly exceeding the limit gets 5 points, and seriously exceeding the limit gets 0 points. E represents the environmental value assessment of carbon footprint, which is calculated by assessing the greenhouse gas emissions generated by the fruit during production, transportation and cold chain. Fruits with low carbon footprint will get higher scores. T represents the trust premium brought by the transparency of the entire process from planting to cold chain, which is based on the degree of consumer recognition of information transparency. The higher the transparency, the higher the score. When the comprehensive value assessment is greater than the premium standard, a price can be set.
[0032] When the overall value assessment (V) exceeds the preset premium standard, pricing can be implemented, ensuring that the product price matches its actual quality and safety. This enhances consumer trust and loyalty to the brand. Furthermore, through a transparent traceability system, consumers can clearly understand the value behind the high price and are willing to pay more for high-quality, safe, and environmentally friendly apples. This helps increase overall sales and profit margins. In addition, this data-driven pricing model encourages fruit growers and producers to continuously optimize production processes and improve fruit quality, further promoting sustainable agricultural development and enhancing brand image. Ultimately, this approach not only maximizes economic benefits but also promotes consumer awareness of healthy and safe food, raising the overall quality standards of the market.
[0033] This invention also provides a blockchain-based single-fruit traceability system for orchards, comprising: The single-fruit identification coding and physical marking module generates a corresponding UID for each apple, converts the UID into a QR code, and marks it on the base of the fruit stem using laser micro-engraving technology; The key lifecycle data acquisition module collects relevant operational and quality data at seven key nodes in apple production: planting, pruning, hormones, fertilizer and water, pesticide residues, harvesting, and cold chain. The data encryption and blockchain notarization module generates corresponding hash values for the collected data from each node in real time, and writes the hash values into the FISCO-BCOS consortium blockchain for notarization within a specified time. The consumer scanning and information retrieval module allows consumers to scan the QR code on the fruit stem using their mobile devices, and the system then retrieves and verifies the entire process data of the single fruit stored on the blockchain. The core information display and verification module clearly displays the core information of the single fruit to consumers, including sugar-acid ratio, firmness, color a* value, pesticide residue test results, and carbon footprint. The Value Realization and Brand Premium module accurately prices the single fruit based on the complete and credible traceability information displayed.
[0034] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.
Claims
1. A blockchain-based single-fruit traceability method for orchards, characterized in that, Includes the following steps: S1: Single fruit identification coding and physical marking. A corresponding UID is generated for each apple, and the UID is converted into a QR code and marked on the base of the fruit stem using laser micro-engraving technology. S2: Key life cycle data collection, collecting relevant operational and quality data at seven key nodes of apple cultivation, pruning, hormones, fertilizer and water, pesticide residues, harvesting, and cold chain. S3: Data encryption and blockchain notarization, which generates corresponding hash values for the collected data from each node in real time, and writes the hash values into the FISCO-BCOS consortium blockchain for notarization within a specified time. S4: Consumer scanning and information retrieval. Consumers scan the QR code on the fruit stem with their mobile devices, and the system retrieves and verifies the entire process data of the single fruit stored on the blockchain. S5: Core information display and verification, clearly displaying the core information of the single fruit to consumers, mainly including sugar-acid ratio, firmness, color a* value, pesticide residue test results and carbon footprint; S6: Value realization and brand premium, based on the complete and credible traceability information presented, to accurately price the single fruit.
2. The blockchain-based single-fruit traceability method for orchards according to claim 1, characterized in that, In step S1, the UID is encoded using a five-level hierarchical structure, accurate to the physical location of a single fruit's growth. The physical location includes, in order, the planting area, tree row, tree body, fruit branch, and single fruit.
3. The blockchain-based single-fruit traceability method for orchards according to claim 1, characterized in that, Step S1 includes the following steps: S11: In the system's database, a batch of available UIDs are created in real time for each physical location. When bagging the fruit, an unused UID is assigned to the fruit from the system based on the fruit's actual physical location, and this UID is bound to the fruit's initial information in the system. S12: Using the UID string as input data, a QR code generation algorithm is used to convert the UID data into a two-dimensional matrix graphic; S13: Using laser marking equipment, precisely locate the base of the apple stem, adjust the laser power, speed and frequency, and clearly engrave the QR code on the base of the apple stem.
4. The blockchain-based single-fruit traceability method for orchards according to claim 1, characterized in that, Step S2 includes the following steps: S21: Planting data collection, including basic data such as fruit tree seedling variety, rootstock type, planting date, planting density, and trunk height; S22: Pruning data collection, including the specific time of winter and summer pruning, the pruning techniques used, the degree of pruning, and information on the personnel performing the pruning; S23: Data collection on hormone use, including the name, concentration, timing of administration, method of administration, and safety interval of the hormones used; S24: Fertilizer and water data collection, including the type of fertilizer, fertilizer name and composition, precise dosage, irrigation method and water volume, and execution date for each application; S25: Data collection for pesticide residue testing. Safety testing is performed before harvesting. The enzyme inhibition method is used to test the pesticide residues in the samples, and the test items, the acceptable range and the result data are recorded. S26: Harvesting data collection, including harvesting date and time, batch, maturity index at harvest time, and information on personnel responsible for harvesting operations; S27: Cold chain logistics data collection, including post-harvest storage and transportation environment, collecting continuous temperature and humidity records from warehousing, pre-cooling, storage to transportation throughout the entire process, as well as the start and end times and location information of each link.
5. The blockchain-based single-fruit traceability method for orchards according to claim 1, characterized in that, Step S3 includes the following steps: S31: Convert the data fields collected in step S2 into a unified string format, and sort all data key-value pairs in a specific order to generate a standard string sequence; S32: The unique string obtained after standardization is fed into the cryptographic hash function for calculation to obtain the corresponding hash value; S33: After generating the hash value, the system calls the smart contract deployed on the FISCO-BCOS consortium blockchain. By sending a transaction to the contract, the hash value, timestamp, and related individual UID index information representing a specific batch of data are packaged and submitted to the blockchain network within a specified time. The nodes in the network verify the consensus and finally record it in a new block.
6. The blockchain-based single-fruit traceability method for orchards according to claim 1, characterized in that, Step S4 includes the following steps: S41: Consumers use a scanning application on their smartphones to scan the tiny QR code at the base of the apple stem. Once the scanning software successfully recognizes the code, it will parse out the corresponding five-level UID. S42: Consumers use mobile devices to send a data request to the system's backend server using the parsed UID as the query keyword. After receiving the request, the system will recalculate a hash value for the corresponding original data. At the same time, the system will query the corresponding hash value stored in the S3 stage from the FISCO-BCOS blockchain based on the UID and compare this newly calculated hash value with the original hash value retrieved from the blockchain. S43, if the two hash values match, the system will then prepare to send the complete and reliable original data to the consumer's mobile application; if the two hash values do not match, the system will return a warning message to the consumer indicating that the data verification failed.
7. The blockchain-based single-fruit traceability method for orchards according to claim 1, characterized in that, Step S5 includes the following steps: S51: Visual presentation of trusted status. The system clearly informs consumers through clear visual elements on the information display interface that all traceability information currently displayed has been verified by blockchain hash value and has not been tampered with since it was recorded, thereby establishing a trustworthy interaction foundation in the first place. If the verification fails, a warning sign will be displayed simultaneously. S52: Interpretation of core quality and safety indicators, which displays the sugar-acid ratio, hardness, color a* value, pesticide residue test results, and carbon footprint in the form of reports.
8. The blockchain-based single-fruit traceability method for orchards according to claim 1, characterized in that, Step S6 includes the following steps: S61: Traceability data value assessment. The system performs multi-dimensional value analysis on verified traceability data, including: food quality rating represented by sugar-acid ratio, safety level assessment represented by pesticide residue test results, environmental value assessment represented by carbon footprint data, and trust premium calculation brought about by transparency in the entire process from planting to cold chain. S62: Construction of dynamic market pricing model. Based on the value assessment results, a dynamic pricing algorithm is established that includes basic production costs, quality added coefficient, safety premium coefficient, environmental value coefficient and market supply and demand parameters. S63: Implementation of differentiated pricing strategy. Based on the output of the pricing model, implement tiered pricing for apples that pass traceability verification: standard pricing is implemented for those that meet the benchmark value, quality and safety premium is given for those with excellent sugar-acid ratio and no detected pesticide residues, and additional environmental premium is given for those with excellent carbon footprint performance. S64: Brand value delivery and market education. Clearly demonstrate the basis for premium pricing in the sales process. Through packaging labels, sales scripts, and QR code page explanations, clearly explain to consumers the specific value points corresponding to higher prices. S65: Premium effect tracking and strategy optimization, continuously monitor the market acceptance of different premium strategies, collect data on consumers' attention to traceability information and willingness to pay, and dynamically adjust the parameter settings of the pricing model based on sales data and feedback.
9. The blockchain-based single-fruit traceability method for orchards according to claim 8, characterized in that, The pricing model uses the following formula for assessing the value of traceable data: ； Among them, V represents the comprehensive value assessment, Q represents the food quality score brought by the sugar-acid ratio, S represents the safety level of the pesticide residue test results, E represents the environmental value assessment of the carbon footprint, and T represents the trust premium brought by the transparency of the entire process from planting to cold chain. When the comprehensive value assessment is greater than the premium standard, pricing can be made.
10. A blockchain-based single-fruit traceability system for orchards, applied to the blockchain-based single-fruit traceability method for orchards as described in any one of claims 1-9, characterized in that, include: The single-fruit identification coding and physical marking module generates a corresponding UID for each apple, converts the UID into a QR code, and marks it on the base of the fruit stem using laser micro-engraving technology; The key lifecycle data acquisition module collects relevant operational and quality data at seven key nodes in apple production: planting, pruning, hormones, fertilizer and water, pesticide residues, harvesting, and cold chain. The data encryption and blockchain notarization module generates corresponding hash values for the collected data from each node in real time, and writes the hash values into the FISCO-BCOS consortium blockchain for notarization within a specified time. The consumer scanning and information retrieval module allows consumers to scan the QR code on the fruit stem using their mobile devices. The system then retrieves and verifies the entire process data of the single fruit stored on the blockchain. The core information display and verification module clearly displays the core information of the single fruit to consumers, mainly including sugar-acid ratio, firmness, color a* value, pesticide residue test results, and carbon footprint; The Value Realization and Brand Premium module accurately prices the single fruit based on the complete and credible traceability information displayed.