Ginseng product digital intelligence development design method and system based on block chain

Abstract

The invention provides a block chain-based ginseng product digital intelligence development design method and system. The method relates to the technical field of block chains, and comprises the following steps: converting block chain record data into automatic monitoring data through an intelligent contract mechanism; and performing quality authentication on the automatic monitoring data to form quality management authentication data, wherein the quality authentication comprises quality standard matching and authentication log generation. And performing identity authentication on the quality management authentication data to form digital identity authentication data, wherein the identity authentication comprises a digital signature verification method and a multi-party verification mechanism. According to the ginseng product digital intelligence development design method and system based on the block chain, through a block chain recording processing technology, it is ensured that key data in a ginseng product production chain are safely and reliably recorded, all the data are processed through distributed network nodes, the integrity and non-tampering performance of the data are ensured, and the data processing efficiency is improved. And a foundation is laid for subsequent automatic monitoring and quality management.

Description

Technical Field

[0001] This invention relates to the field of blockchain technology, specifically to a blockchain-based digital development and design method and system for ginseng products. Background Technology

[0002] Currently, the ginseng industry has developed into a large-scale industrial chain, involving multiple stages such as planting, processing, transportation, and sales. Enterprises utilize advanced information management systems to improve production efficiency and ensure product quality. Existing technologies employ information technology, leveraging database systems and the Internet of Things (IoT) to monitor ginseng planting, processing, and logistics in real time, ensuring timely recording and effective management of product information. These systems collect and analyze data generated during the production process in an automated manner, reducing manual intervention and improving the transparency and controllability of the production process.

[0003] However, existing technologies still have significant shortcomings. The production and management of current ginseng products rely on centralized platforms and human intervention, leading to vulnerabilities in data security, accuracy, and traceability. Traditional database systems are controlled by a single administrator, limiting information sharing and cross-platform data flow, and increasing the difficulty of management and certification processes. Existing quality traceability mechanisms only record data at certain specific stages, failing to achieve full traceability. The lack of a complete chain of data integration and sharing mechanisms results in an inadequate product quality assurance system, affecting the transparency and controllability of the entire production process. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a blockchain-based digital development and design method and system for ginseng products. The technical problem this invention aims to solve is: how to address the data security, accuracy, and traceability issues caused by centralized platforms and human intervention in existing technologies through blockchain records, smart contract mechanisms, and automated monitoring processes.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a blockchain-based digital development and design method for ginseng products, comprising: S1. Key data in the ginseng product production chain are processed using blockchain to form the blockchain record data; S2. The blockchain-recorded data is converted into automated monitoring data through a smart contract mechanism; S3. Perform quality certification on the automated monitoring data to form quality management certification data, wherein the quality certification includes quality standard matching and certification log generation; S4. Perform identity authentication on the quality management certification data to form digital identity authentication data, wherein the identity authentication includes digital signature verification and multi-party verification mechanism; S5. The digital identity authentication data is encrypted and stored to form the encrypted storage data.

[0006] Preferably, the key data includes planting data, processing data, transportation data, and quality inspection data, and the blockchain record processing adopts distributed network nodes, which include data verification nodes and consensus nodes.

[0007] Preferably, the smart contract mechanism includes preset condition triggering, data verification, and real-time feedback. The preset condition triggering automatically starts the automated monitoring data, the data verification checks the legality of the automated monitoring data, and the real-time feedback identifies anomalies during the automatic start-up process and the legality check. The synergistic effect of the automatic start-up process, the legality check, and the anomaly identification forms the automated monitoring data.

[0008] Preferably, the automatic startup process adopts an automated triggering rule, which includes condition threshold judgment and time period check; the legality check includes data consistency verification and standard compliance detection; and the anomaly identification includes data deviation detection and abnormal fluctuation warning.

[0009] Preferably, the quality standard matching adopts a ginseng quality-environment correlation model. Historical production data is collected through sensors, and the ginseng quality-environment correlation model is constructed based on this data. This model correlates the historical production data with saponin content to form a ginseng-environment correlation index. Environmental factors are extracted from the historical production data through this correlation to form an environmental feature vector. The environmental feature vector is then subjected to multiple regression analysis with the saponin content to form the ginseng-environment correlation index. This index includes temperature influence coefficient, soil moisture influence coefficient, soil nutrient influence coefficient, and a comprehensive quality evaluation value. The ginseng-environment correlation index is then matched with the automated monitoring data through environmental detection and analysis. The detection and analysis matching process uses a numerical comparison method to match the ginseng-environment correlation index with the automated monitoring data, forming an environmental matching degree vector. This environmental matching degree vector is then weighted to generate quality control data, with the ginseng-environment correlation index serving as the weight in the weighted calculation. The weighted calculation normalizes and sums the ginseng-environment correlation index and the environmental matching degree vector to generate a quality control score. This quality control score is then associated with the automated monitoring data, and the association annotation binds the quality control score and the automated monitoring data to data fields. This data field binding forms the quality control data. The saponin content is obtained through online monitoring equipment. The certification log generation includes the following steps: S31. Collect the actual growth years of ginseng through an agricultural management platform, which includes a planting archive and a growth cycle database; S32. Compare and verify the actual growth year of the ginseng with the blockchain recorded data, and generate a year verification report through the comparison and verification; S33. Compare and confirm the consistency of the quality control data and the year verification report. The data comparison and consistency confirmation means that when the quality control data meets the quality standard and the year of the year verification report is true, the quality control data and the year verification report are determined to be consistent. The quality management certification data is formed based on the consistency of the two.

[0010] Preferably, the digital signature verification method performs digital signature processing on the quality management certification data to form a signature value, and the multi-party verification mechanism performs node verification on the signature value to form the digital identity authentication data.

[0011] Preferably, the digital signature processing employs an elliptic curve cryptography algorithm, which uses a private key as a signature parameter and performs elliptic curve operations on the quality management authentication data based on the signature parameter to generate the signature value.

[0012] Preferably, the node verification includes the following steps: S41. Perform node-independent verification on the signature value to form a preliminary verification result. The node-independent verification includes public key verification, hash comparison, and timestamp verification. S42. The preliminary verification results are confirmed by a consensus mechanism to form a consistent verification result. The consensus mechanism confirmation includes consensus algorithm confirmation and consistency verification. S43. The verification consistency results are processed to form the digital identity authentication data.

[0013] Preferably, the encrypted storage process includes an encryption key and a blockchain network, wherein the encryption key includes a symmetric encryption key and an asymmetric encryption key, and the blockchain network includes a decentralized distributed ledger and encrypted protected storage nodes.

[0014] A blockchain-based intelligent development and design system for ginseng products includes: Blockchain Recording Module: The blockchain recording module performs blockchain recording processing on key data in the ginseng product production chain. The blockchain recording module forms the blockchain recording data through the blockchain recording processing, and the recording processing adopts distributed network nodes. Automated monitoring module: The automated monitoring module converts the blockchain recorded data into automated monitoring data. The conversion adopts a smart contract mechanism, which includes preset condition triggering, data verification, and real-time feedback. Quality certification module: The quality certification module performs quality certification on the automated monitoring data, and the quality certification module generates quality management certification data through the quality certification. The management certification process includes quality standard matching and certification log generation. Decentralized authentication module: The decentralized authentication module performs identity authentication on the quality management authentication data. The decentralized authentication module forms digital identity authentication data through the identity authentication. The identity authentication includes digital signature verification method and multi-party verification mechanism. Encrypted storage module: The encrypted storage module performs encrypted storage processing on the digital identity authentication data. The encrypted storage module forms the encrypted storage data through the encrypted storage processing, which includes an encryption key and a blockchain network.

[0015] This invention provides a blockchain-based digital development and design method and system for ginseng products. It offers the following advantages:

[0016] This blockchain-based digital development and design method and system for ginseng products ensures that key data in the ginseng product production chain is recorded securely and reliably through blockchain recording and processing technology. All data is processed through distributed network nodes, guaranteeing data integrity and immutability, laying the foundation for subsequent automated monitoring and quality management.

[0017] The adoption of smart contract mechanisms and automated monitoring of data transformation enables real-time monitoring and data verification. Through quality standard matching and authentication log generation by the quality certification module, detailed data authentication is performed, and combined with a decentralized authentication module to achieve digital identity authentication, ensuring data legitimacy and security. The encrypted storage module's encrypted storage processing ensures data security during storage, preventing data leakage and tampering. Attached Figure Description

[0018] Figure 1 This is a schematic diagram of the system structure of the present invention; Figure 2 This is a flowchart of the data processing of the present invention; Figure 3 This is a flowchart of the smart contract monitoring process of the present invention; Figure 4 This is a flowchart of the identity authentication process of the present invention; Figure 5 This is a flowchart of the encrypted storage process of the present invention; Figure 6 This is a flowchart of the quality certification process for this invention. Detailed Implementation

[0019] 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.

[0020] Example 1 like Figure 1-6 As shown, this embodiment of the invention provides a blockchain-based digital development and design method for ginseng products, including: S1. Performing blockchain recording processing on key data of the ginseng product production chain to form blockchain record data. Key data includes planting data, processing data, transportation data, and quality inspection data. The blockchain recording processing employs distributed network nodes, including data verification nodes and consensus nodes.

[0021] S2. Blockchain-recorded data is transformed into automated monitoring data through smart contract mechanisms. These mechanisms include preset condition triggering, data verification, and real-time feedback. Preset condition triggering automatically initiates the automated monitoring data processing; data verification checks the legality of the data; and real-time feedback identifies anomalies during the automatic initiation and legality checks. The synergistic effect of automatic initiation, legality checks, and anomaly identification forms the automated monitoring data. Automatic initiation employs automated triggering rules, including condition threshold judgments and time period checks. Legality checks include data consistency verification and standard compliance testing. Anomaly identification includes data deviation detection and abnormal fluctuation warnings.

[0022] S3. Quality certification is performed on automated monitoring data to form quality management certification data. Quality certification includes quality standard matching and certification log generation. Quality standard matching uses a ginseng quality-environment correlation model. Historical production data is collected through sensors, and a ginseng quality-environment correlation model is constructed based on this data. This model correlates historical production data with saponin content to form a ginseng-environment correlation index. Environmental factors are extracted from the historical production data through this correlation to form an environmental feature vector. This environmental feature vector is then used in a multiple regression analysis with saponin content to form the ginseng-environment correlation index. The ginseng-environment correlation index includes the influence coefficients of temperature, soil moisture, and soil nutrients, as well as a comprehensive quality evaluation value. This ginseng-environment correlation index is then matched with the automated monitoring data through environmental detection and analysis. Environmental monitoring and analysis matching uses a numerical comparison method to match and calculate the ginseng-environment correlation index with automated monitoring data. This matching calculation forms an environmental matching degree vector. The environmental matching degree vector is then weighted to generate quality control data, with the ginseng-environment correlation index serving as the weight in the weighting calculation. The weighted calculation then normalizes and sums the ginseng-environment correlation index and the environmental matching degree vector to generate a quality control score. This quality control score is then associated and labeled with the automated monitoring data. This association and labeling binds the quality control score and the automated monitoring data into data fields, thus forming the quality control data. Saponin content is obtained through online monitoring equipment. The certification log generation includes the following steps:

[0023] S31. Collect the actual growth years of ginseng through an agricultural management platform, which includes a planting archive and a growth cycle database.

[0024] S32. Compare and verify the actual growth year of the ginseng with the data recorded on the blockchain, and generate a year verification report through comparison and verification.

[0025] S33. Compare and confirm the consistency of the quality control data and the annual verification report. If the quality control data meets the quality standards and the annual verification report is accurate, the quality control data and the annual verification report are considered to be consistent. Based on the consistency of the two, quality management certification data is formed.

[0026] S4. The quality management certification data undergoes identity authentication to form digital identity authentication data. Identity authentication includes digital signature verification and multi-party verification mechanisms. The digital signature verification method processes the quality management certification data into a signature value, and the multi-party verification mechanism performs node verification on the signature value to form the digital identity authentication data. The digital signature processing uses an elliptic curve cryptography algorithm. The elliptic curve cryptography algorithm uses the private key as a signature parameter, and performs elliptic curve operations on the quality management certification data based on the signature parameter to generate a signature value. Node verification includes the following steps:

[0027] S41. Perform node-independent verification on the signature value to form a preliminary verification result. Node-independent verification includes public key verification, hash comparison and timestamp verification.

[0028] S42. The preliminary verification results are confirmed by a consensus mechanism to form a consistent verification result. The consensus mechanism confirmation includes consensus algorithm confirmation and consistency verification.

[0029] S43. The verification consistency results are integrated and processed to form digital identity authentication data.

[0030] S5. Encrypt the digital identity authentication data to form encrypted storage data. The encrypted storage process includes encryption keys and a blockchain network. The encryption keys include symmetric encryption keys and asymmetric encryption keys. The blockchain network includes a decentralized distributed ledger and encrypted storage nodes.

[0031] A blockchain-based intelligent development and design system for ginseng products includes: Blockchain Recording Module: The blockchain recording module performs blockchain recording processing on key data in the ginseng product production chain. The blockchain recording module forms blockchain record data through blockchain recording processing, and the recording processing adopts distributed network nodes.

[0032] Automated monitoring module: The automated monitoring module transforms blockchain-recorded data into automated monitoring data. The transformation adopts a smart contract mechanism, which includes preset condition triggering, data verification, and real-time feedback.

[0033] Quality Certification Module: The quality certification module performs quality certification on automated monitoring data. The quality certification module generates quality management certification data through quality certification. The management certification process includes quality standard matching and certification log generation.

[0034] Decentralized Authentication Module: The decentralized authentication module performs identity authentication on quality management authentication data. The decentralized authentication module generates digital identity authentication data through identity authentication, which includes digital signature verification and multi-party verification mechanisms.

[0035] Encrypted storage module: The encrypted storage module performs encrypted storage processing on digital identity authentication data. The encrypted storage module forms encrypted storage data through encrypted storage processing, which includes encryption keys and blockchain network.

[0036] By applying blockchain records and smart contract mechanisms, the data at every stage of the production process is ensured to be transparent and tamper-proof, enabling full traceability of the product. Based on the distributed node processing method of blockchain, the main responsibility of data verification nodes is to verify the validity and legality of data in the blockchain network. They are responsible for performing legality checks such as data consistency verification and standard compliance testing to ensure that the data entering the blockchain meets the preset standards and requirements. Consensus nodes participate in the consensus mechanism of the blockchain network, ensuring that all nodes reach a consensus on the data processing results. Through consensus algorithms and consistency checks, it is ensured that the data recorded in the blockchain is recognized and confirmed as valid by all nodes.

[0037] The smart contract mechanism automatically triggers the monitoring process based on preset conditions. This automation reduces human intervention, improves production efficiency, and lowers the risk of operational errors. Automated monitoring data includes planting data, processing data, transportation data, and quality inspection data. Planting data covers environmental monitoring information such as temperature, humidity, and soil nutrients; processing data records process and time parameters; transportation data focuses on temperature, humidity, and delivery time during logistics; and quality inspection data includes indicators such as saponin content. These data are further refined into environmental monitoring data, production data, and quality control data.

[0038] By comparing with preset quality standards, the quality certification module ensures that each batch of products meets quality requirements. Based on a weighted calculation method using a ginseng quality-environment correlation model, real-time feedback and anomaly detection mechanisms can quickly identify quality deviations. The automated monitoring system should monitor more environmental factors such as temperature, humidity, soil nutrients, and light, helping to reflect the impact of changes in the ginseng's growing environment on quality. Simultaneously, increasing the collection of production process data on moisture content and equipment status allows for more comprehensive quality control.

[0039] The ginseng quality-environment correlation model, based on the correlation analysis between historical production data and saponin content, extracts the influence coefficients of environmental factors such as temperature, soil moisture, and soil nutrients on ginseng quality, and normalizes these coefficients to serve as weighting parameters. After standardizing each environmental factor, the quality control score is calculated using the following weighted formula:

[0040] in, It is a quality control score that reflects the combined impact of environmental factors. The weight of the i-th environmental factor is provided by the ginseng quality-environment correlation model. It is the standardized value of the i-th environmental factor, representing the degree of matching between the current production environment and historical data.

[0041] By monitoring the quality control scores in real time, the system will automatically issue an alarm if the score is found to be below the standard, and make timely adjustments to ensure that the quality of ginseng always meets the requirements.

[0042] The year verification report is generated by comparing the following specific data: If the actual planting time matches the planting time recorded on the blockchain, and the actual harvest time matches the harvest date recorded on the blockchain, then the data is considered a match. Similarly, if actual environmental parameters such as temperature, soil moisture, and nutrient content match the environmental data recorded on the blockchain, then a match is also considered established. If the actual planting time does not match the planting time recorded on the blockchain, or the actual harvest date does not match the blockchain record, the report will clearly indicate the specific data discrepancies. If there are differences in environmental parameters such as temperature, humidity, or nutrient content, the report will list the inconsistencies in detail and analyze possible reasons such as data entry errors or blockchain record lag. Through this comparison, the verification report ensures the accuracy and authenticity of the ginseng growth year data.

[0043] Digital signature verification and multi-party authentication mechanisms are used to authenticate quality management data, improving data security and trustworthiness. Encrypted storage technology ensures data security and privacy protection during storage and transmission.

[0044] By leveraging the decentralized nature of blockchain, the data verification process does not rely on a single third party. The credibility and tamper resistance of the system are enhanced through independent verification by multiple nodes, ensuring the independence and impartiality of the quality management process.

[0045] By combining historical production data with real-time monitoring data, the ginseng quality-environment correlation model dynamically adjusts the quality standard matching method according to different production conditions, ensuring more accurate quality control that meets market demands.

[0046] Example 2 This embodiment presents a blockchain-based digital development and design method and system for ginseng products. By applying blockchain and smart contract technologies, it achieves automated monitoring and management of the planting, processing, and transportation processes of ginseng products, ensuring data transparency, real-time performance, and traceability. The specific implementation method is as follows:

[0047] 1. Data collection and blockchain recording A company produces and sells high-quality ginseng, with a production chain including three stages: planting, transportation, and quality testing. The company uses blockchain technology to record all key data and uses smart contracts to automate the processing and verification of the data.

[0048] Planting stage: At 08:00 on November 20, 2025, the temperature and humidity sensors installed at the planting base recorded a soil temperature of 27°C and a humidity of 60%.

[0049] Data from the planting stage is uploaded to the blockchain via the blockchain recording module, generating the following data records: Sensor ID: 001, Timestamp: 2025-11-20-08:00, Temperature: 27℃, Humidity: 60%.

[0050] Transportation phase: At 09:00 on November 21, 2025, the transportation company began transporting ginseng products from the planting base to the processing plant. Temperature and humidity sensors installed on the transport vehicles recorded a temperature of 24°C and a humidity of 55% during the transportation process.

[0051] During the transportation phase, data is uploaded to the blockchain via the blockchain recording module, generating the following data records: Sensor ID: 002, Timestamp: 2025-11-21-09:00, Temperature: 24℃, Humidity: 55%.

[0052] Quality inspection stage: At 09:00 on November 23, 2025, quality inspection was completed at the processing plant. The quality inspection equipment recorded the quality inspection data, including the following indicators:

[0053] The ginseng weighs 1.2kg, has a sugar content of 18%, and its appearance score is out of 100. The appearance score is 92. The timestamp is 2025-11-23-9:00.

[0054] During the quality inspection phase, data is uploaded to the blockchain via the blockchain recording module, generating the following data records: Sensor ID: 003, Timestamp: 2025-11-23-09:00, Ginseng weight: 1.2kg, Sugar content: 18%, Appearance score: 92 points.

[0055] 2. The smart contract mechanism converts the initial data into automated monitoring data. Preset conditions for triggering: Planting stage: The smart contract sets a soil temperature threshold of 25℃. If the temperature exceeds the threshold, the monitoring program is automatically triggered. At 08:00 on November 20, 2025, the recorded soil temperature was 27℃, exceeding the threshold, and the system automatically started the monitoring and processing program.

[0056] Transportation Phase: The smart contract sets the transportation time to no more than 48 hours. If the transportation time exceeds this limit, monitoring data processing will be triggered. If the transportation time is 50 hours, the system will automatically initiate the processing procedure if the preset time is exceeded.

[0057] Data verification: Data consistency verification: The smart contract compares the temperature data during the planting stage with the temperature data during the transportation stage. It detects that the temperature difference between the two is 3℃, which exceeds the preset fluctuation range of ±2℃, and marks it as abnormal data.

[0058] Standard compliance testing: During transportation, the temperature should be maintained below 25℃. The temperature recorded during transportation was 24℃, which complies with industry standards, and the data has been verified.

[0059] Quality inspection data verification: Sugar content compliance test: According to industry standards, the sugar content should be between 18% and 22%. The test result for this test was 18%, which meets the standard.

[0060] Appearance rating compliance test: The industry standard requires an appearance rating of no less than 85 points. The current appearance rating is 92 points, which meets the standard.

[0061] Real-time feedback and anomaly detection: Data Deviation Detection: During transportation, at 12:00 on November 21, 2025, the recorded temperature was 35℃, far exceeding the set standard of 25℃. The smart contract detected a temperature deviation of 11℃, triggering the anomaly detection mechanism and issuing an alert.

[0062] Abnormal fluctuation warning: If the temperature fluctuates abnormally during transportation, such as rising from 24℃ to 35℃ and the fluctuation range exceeds the set threshold of ±3℃, the system will automatically issue a warning and notify the logistics department to take measures.

[0063] 3. Automatic startup and collaborative processing Planting process: When the smart contract detects a soil temperature of 27℃, exceeding the set threshold of 25℃, the system automatically initiates data verification and anomaly detection. The system checks planting standards to ensure that the temperature data meets the requirements. If the temperature fluctuates too much, the system will automatically adjust the planting environment.

[0064] In this case, the temperature of 27°C meets the planting standard, and the data continues to be saved to the blockchain.

[0065] Transportation process: The transit time is 50 hours. If it exceeds 48 hours, the smart contract will trigger a processing procedure to notify the transit team to check the transit plan and optimize the transit arrangements.

[0066] During transportation, the temperature suddenly rose from 24°C to 35°C, exceeding the set range. The smart contract initiated the exception handling process, issuing a warning and notifying logistics management personnel, requiring immediate inspection and adjustment of the transportation equipment or temperature control facilities.

[0067] Quality inspection process: Quality inspection data upload and verification: After the quality inspection data is uploaded to the blockchain, the smart contract automatically verifies the sugar content and appearance score to ensure that it meets industry standards.

[0068] Quality Certification: The system generates a quality certification mark to ensure that the batch of ginseng meets quality standards.

[0069] All data is stored via blockchain, ensuring transparency and immutability. Data at each stage is verified and processed by smart contracts, and anomalies are alerted and addressed through a real-time feedback mechanism.

[0070] 4. Results and Advantages Through the above implementation, data at every stage of the production process is accurately and in real-time recorded and monitored. Smart contracts enable automatic data verification, anomaly detection, and feedback, improving the level of automation in production management. Blockchain storage ensures the traceability and transparency of all key data, guaranteeing product quality meets standards and enabling the timely detection of potential problems during transportation.

[0071] The advantages of this embodiment include: Data transparency: All key data is recorded through blockchain to ensure that the data is not tampered with.

[0072] Real-time monitoring and automatic feedback: Smart contracts monitor production data in real time and automatically report abnormal situations, improving production efficiency and quality control.

[0073] Traceability: Through blockchain, the entire process of product data, from planting to transportation, can be traced, ensuring quality and safety.

[0074] In summary, this embodiment, by combining blockchain and smart contract technologies, enables real-time recording, automatic verification, and monitoring of key data in the ginseng product production chain. The system automatically identifies and reports anomalies such as temperature fluctuations and transportation delays during production and transportation, improving the automation level of production management. Blockchain technology ensures data transparency, immutability, and high traceability, guaranteeing product quality and the reliability of the transportation process.

[0075] Example 3 This embodiment presents a blockchain-based digital development and design method and system for ginseng products. It utilizes blockchain technology to achieve quality traceability and authentication throughout the entire ginseng product production process, ensuring the authenticity, integrity, and immutability of relevant data. The specific implementation method is as follows:

[0076] At a ginseng production company, for the batch of ginseng products produced in the first quarter of 2025, with batch number 2025001, blockchain-based digital identity authentication technology was used. The production data includes planting data, processing data, transportation data, and quality inspection data. All data is recorded through the blockchain and ultimately forms digital identity authentication data to ensure the traceability of product quality.

[0077] 1. Digital signature verification method On March 1, 2025, production data for batch 2025001 was digitally signed using private key 1. The specific data recorded is as follows:

[0078] Planting data: Planting time: January 15, 2025, temperature: 18℃, humidity: 75%, soil pH: 6.5, planting location: Changchun City, Jilin Province, China.

[0079] Processing data: Processing start time: February 20, 2025; processing temperature: 65℃; processing humidity: 60%; processing time: 24 hours; processing technology: high-temperature steaming.

[0080] Transportation data: Transportation start time: February 22, 2025; transportation temperature: 20℃; transportation duration: 48 hours; transportation method: cold chain transportation; transportation company: Changchun Logistics Co., Ltd.

[0081] Quality inspection data: Testing date: February 25, 2025; Testing standard: GB / T17128-2019; Heavy metal content: 0.02 mg / kg, which meets the national standard; Pesticide residue: Not detected.

[0082] The signature value generated by the above data using the elliptic curve cryptography algorithm is: 12345.

[0083] 2. Independent node verification In a blockchain network, multiple verification nodes independently verify the signature value 12345. The verification process includes:

[0084] Public key verification: Node A uses public key 1 to verify whether the signature value 12345 was generated by private key 1.

[0085] Hash comparison: Node B calculates the hash value of the above data, 67890, and compares it with the hash value in the signature value to ensure that the data has not been tampered with.

[0086] Timestamp verification: Node C verifies the timestamp of the data to confirm that the signature date of February 25, 2025 is within the range of the data record.

[0087] Nodes A, B, and C return verification results v1, v2, and v3, respectively.

[0088] 3. Consensus Mechanism Confirmation After a node passes independent verification, the system enters the consensus mechanism confirmation phase: Consensus Algorithm Confirmation: The Proof-of-Work (PoW) consensus algorithm is used. Nodes D, E, and F confirm the verification results v1, v2, and v3. All nodes reach a consensus within 30 seconds, confirming the validity of the data.

[0089] Consistency check: Nodes D, E, and F further perform consistency checks on the verification results to ensure that the verification results of all nodes are consistent.

[0090] All nodes eventually confirm the validity of the data and reach a consensus through the consensus mechanism.

[0091] 4. Data integration and processing After the consensus mechanism confirms the agreement, node G integrates the data to verify the consistency results. The final integrated data is as follows:

[0092] The digital identity authentication data integrates the verification results v1, v2, and v3 of nodes A, B, and C, the signature value 12345, the hash value 67890, and the timestamp February 25, 2025, ultimately forming digital identity authentication data 001.

[0093] Digital identity authentication data 001 represents the unique identifier for batch 2025001 of ginseng products and contains the following information: The product batch number is 2025001. Planting data is from January 15, 2025: 18℃, 75% concentration, pH: 6.5. Processing data is from February 20, 2025: 65℃, 60% concentration, 24 hours. Transportation data is from February 22, 2025: 20℃, 48 hours. Quality testing data is from February 25, 2025: heavy metal content: 0.02 mg / kg, pesticide residue: not detected.

[0094] 5. Digital identity authentication data storage Digital identity authentication data 001 is encrypted and stored in a blockchain network to ensure the immutability and long-term traceability of the data.

[0095] Any consumer or regulatory body can use the blockchain system to check the production process, quality testing results, and compliance with relevant standards for ginseng products, ensuring that the quality is traceable and meets national and industry standards.

[0096] This embodiment uses blockchain technology to record and authenticate data throughout the entire process of ginseng product cultivation, processing, transportation, and quality testing, ensuring the high authenticity and integrity of data at each stage. Through digital signature processing, independent node verification, and consensus mechanisms, the data is ensured to be tamper-proof and traceable, providing consumers and regulatory agencies with reliable and transparent quality assurance, enhancing market trust in the product, and helping to meet the compliance requirements of relevant industries.

[0097] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A blockchain-based digital development and design method for ginseng products, characterized in that, include: S1. Key data in the ginseng product production chain are processed using blockchain to form the blockchain record data; S2. The blockchain-recorded data is converted into automated monitoring data through a smart contract mechanism; S3. Perform quality certification on the automated monitoring data to form quality management certification data, wherein the quality certification includes quality standard matching and certification log generation; S4. Perform identity authentication on the quality management certification data to form digital identity authentication data, wherein the identity authentication includes digital signature verification and multi-party verification mechanism; S5. The digital identity authentication data is encrypted and stored to form the encrypted storage data.

2. The blockchain-based digital development and design method for ginseng products, as described in claim 1, is characterized in that: The key data includes planting data, processing data, transportation data, and quality inspection data. The blockchain record processing adopts distributed network nodes, which include data verification nodes and consensus nodes.

3. The blockchain-based digital development and design method for ginseng products, as described in claim 1, is characterized in that: The smart contract mechanism includes preset condition triggering, data verification, and real-time feedback. The preset condition triggering automatically starts the automated monitoring data, the data verification checks the legality of the automated monitoring data, and the real-time feedback identifies anomalies during the automatic start-up process and the legality check. The synergistic effect of the automatic start-up process, the legality check, and the anomaly identification forms the automated monitoring data.

4. The blockchain-based digital development and design method for ginseng products, as described in claim 3, is characterized in that: The automatic startup process employs automated triggering rules, which include condition threshold judgment and time period checks. The legality checks include data consistency verification and standard compliance detection. The anomaly identification includes data deviation detection and abnormal fluctuation warning.

5. The blockchain-based digital development and design method for ginseng products, as described in claim 1, is characterized in that: The quality standard matching adopts a ginseng quality-environment correlation model. Historical production data is collected through sensors, and the ginseng quality-environment correlation model is constructed based on this data. This model correlates the historical production data with saponin content to form a ginseng-environment correlation index. Environmental factors are extracted from the historical production data through this correlation to form an environmental feature vector. This environmental feature vector is then subjected to multiple regression analysis with the saponin content to form the ginseng-environment correlation index. The ginseng-environment correlation index includes temperature influence coefficient, soil moisture influence coefficient, soil nutrient influence coefficient, and comprehensive quality evaluation value. The ginseng-environment correlation index is then matched with the automated monitoring data through environmental detection analysis. The analysis and matching process uses a numerical comparison method to match the ginseng-environment correlation index with the automated monitoring data, forming an environmental matching degree vector. This vector is then weighted to generate quality control data, with the ginseng-environment correlation index serving as the weight in the weighted calculation. The weighted calculation normalizes and sums the ginseng-environment correlation index and the environmental matching degree vector to generate a quality control score. This score is then associated with the automated monitoring data, and the association annotation binds the quality control score to the automated monitoring data's data fields. This data field binding forms the quality control data. The saponin content is obtained through online monitoring equipment. The certification log generation includes the following steps: S31. Collect the actual growth years of ginseng through an agricultural management platform, which includes a planting archive and a growth cycle database; S32. Compare and verify the actual growth year of the ginseng with the blockchain recorded data, and generate a year verification report through the comparison and verification; S33. Compare and confirm the consistency of the quality control data and the year verification report. The data comparison and consistency confirmation means that when the quality control data meets the quality standard and the year of the year verification report is true, the quality control data and the year verification report are determined to be consistent. The quality management certification data is formed based on the consistency of the two.

6. The blockchain-based digital development and design method for ginseng products, as described in claim 1, is characterized in that: The digital signature verification method performs digital signature processing on the quality management certification data to form a signature value, and the multi-party verification mechanism performs node verification on the signature value to form the digital identity authentication data.

7. The blockchain-based digital development and design method for ginseng products, as described in claim 6, is characterized in that: The digital signature processing employs an elliptic curve cryptography algorithm, which uses a private key as a signature parameter and performs elliptic curve operations on the quality management authentication data based on the signature parameter to generate the signature value.

8. The blockchain-based digital development and design method for ginseng products, as described in claim 6, is characterized in that: The node verification includes the following steps: S41. Perform node-independent verification on the signature value to form a preliminary verification result. The node-independent verification includes public key verification, hash comparison, and timestamp verification. S42. The preliminary verification results are confirmed by a consensus mechanism to form a consistent verification result. The consensus mechanism confirmation includes consensus algorithm confirmation and consistency verification. S43. The verification consistency results are processed to form the digital identity authentication data.

9. The blockchain-based digital development and design method for ginseng products, as described in claim 1, is characterized in that: The encrypted storage process includes encryption keys and a blockchain network. The encryption keys include symmetric encryption keys and asymmetric encryption keys. The blockchain network includes a decentralized distributed ledger and encrypted storage nodes.

10. A blockchain-based digital development and design system for ginseng products, as described in any one of claims 1-9, characterized in that... The work includes the following steps: Blockchain Recording Module: The blockchain recording module performs blockchain recording processing on key data in the ginseng product production chain. The blockchain recording module forms the blockchain recording data through the blockchain recording processing, and the recording processing adopts distributed network nodes. Automated monitoring module: The automated monitoring module converts the blockchain recorded data into automated monitoring data. The conversion adopts a smart contract mechanism, which includes preset condition triggering, data verification, and real-time feedback. Quality certification module: The quality certification module performs quality certification on the automated monitoring data, and the quality certification module generates quality management certification data through the quality certification. The management certification process includes quality standard matching and certification log generation. Decentralized authentication module: The decentralized authentication module performs identity authentication on the quality management authentication data. The decentralized authentication module forms digital identity authentication data through the identity authentication. The identity authentication includes digital signature verification method and multi-party verification mechanism. Encrypted storage module: The encrypted storage module performs encrypted storage processing on the digital identity authentication data. The encrypted storage module forms the encrypted storage data through the encrypted storage processing, which includes an encryption key and a blockchain network.

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