Green product authentication and tracing system based on block chain

Abstract

The invention relates to the technical field of data processing, and discloses a block chain-based green product authentication traceability system. The system comprises a data acquisition module for acquiring product full-life-cycle green attribute data by using a multi-source sensing device; the block chain evidence storage module is used for hierarchically encrypting the data based on a hybrid hash algorithm to generate a non-tampering account book record; the intelligent contract auditing module automatically generates a green authentication result through a multi-rule verification model; the traceability query module is used for constructing a privacy protection query channel to output a verifiable traceability chain by applying a zero-knowledge proof technology; and the dynamic optimization module is used for adjusting the authentication standard according to the environmental policy change. According to the system, comprehensive acquisition, safe storage, accurate authentication, reliable tracing and standard dynamic optimization of green product data are realized, the authentication credibility and the supervision efficiency are improved, and the development of the green industry is promoted.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and particularly to a blockchain-based green product certification and traceability system. Background Art

[0002] With the continuous improvement of global environmental awareness, the green product market has developed rapidly. Consumers' demand for green products is increasing day by day. They not only focus on the functions and quality of products, but also pay more attention to the environmental protection attributes of products throughout the entire life cycle, such as production, transportation, and use.

[0003] There are many problems with traditional product certification and traceability methods. On the one hand, data management is decentralized and lacks credibility. Throughout the life cycle of a product, it involves multiple links such as raw material suppliers, manufacturers, transporters, and sellers. The data of each link is often independently managed by different entities, with inconsistent data formats and various storage methods. This makes the data easily tampered with or lost, and it is difficult to ensure the authenticity and integrity of the data, resulting in a lack of credibility in the certification results.

[0004] On the other hand, the traceability efficiency is low and it is difficult to achieve accurate traceability. Most existing traceability systems are based on centralized databases. When the data volume is large, the query speed is slow, and it is difficult to quickly locate the specific information of a product from a large amount of data. Once a quality problem or environmental protection violation occurs, it is impossible to quickly and accurately trace back to the source of the problem, bringing great difficulties to supervision and consumer rights protection. Taking the clothing industry as an example, when it is found that a batch of clothing has exceeded the environmental protection indicators, due to the complex supply chain information, it is very difficult to quickly determine whether the problem occurred in the raw material procurement link, the production and processing link, or the transportation and storage link.

[0005] In addition, the traditional certification and traceability system is difficult to adapt to the dynamic changes of environmental policies. Environmental policies are constantly updated, and the certification standards for green products are also continuously adjusted. However, the traditional system lacks the ability to automatically adjust and optimize, and requires manual updates of certification standards and processes. This is not only inefficient, but also prone to errors and omissions, and cannot accurately certify and supervise green products in a timely and effective manner.

[0006] With the rise of blockchain technology, its characteristics such as decentralization, immutability, and traceability provide new ideas for solving the problems of green product certification and traceability. However, at present, the research and practice of applying blockchain technology to green product certification and traceability are still in the exploratory stage. Existing solutions still have deficiencies in aspects such as the comprehensiveness of data collection, the security of encrypted storage, the accuracy of certification and review, and the adaptability to policy changes, and cannot fully meet the needs of the market and supervision. Therefore, it is of great practical significance to develop an efficient, reliable, intelligent blockchain-based green product certification and traceability system that can adapt to policy changes. Summary of the Invention

[0007] The object of the present invention is to provide a blockchain-based green product certification and traceability system to solve the problems raised in the above-mentioned background technology.

[0008] To achieve the above object, the present invention provides the following technical solution: A blockchain-based green product certification and traceability system, the system includes: Data acquisition module: used to collect green attribute data of the entire product life cycle through multi-source perception devices; Blockchain evidence storage module: based on a hybrid hashing algorithm, hierarchically encrypt the green attribute data to generate an immutable distributed ledger record; Smart contract audit module: input the distributed ledger record into a preset multi-rule verification model to automatically generate a green certification result; Traceability query module: use zero-knowledge proof technology to construct a privacy protection query channel, and output a verifiable traceability chain of product green attributes according to user requests; Dynamic optimization module: construct a multi-objective certification parameter optimization model according to changes in environmental policies, and use gradient descent and constraint relaxation algorithms to adaptively adjust the certification standards.

[0009] Preferably, the collection of green attribute data of the entire product life cycle through multi-source perception devices includes: The multi-source perception devices include a carbon emission monitor, an energy consumption sensor, a logistics tracking tag, and an environmental humidity sensor; Align the carbon emission monitor data and the energy consumption sensor data in space and time to construct a product carbon footprint distribution map; perform multi-scale feature decomposition on the logistics tracking tag data and the environmental humidity sensor data to generate a supply chain state feature sequence; Construct a two-stream feature fusion network. The first stream uses a three-dimensional convolutional network to extract the spatial correlation features of the carbon footprint distribution map, and the second stream uses a temporal attention network to extract the dynamic evolution features of the supply chain state feature sequence; Fuse the spatial correlation features and the dynamic evolution features through a cross-modal graph neural network to generate a comprehensive green attribute dataset including raw material traceability, production process energy consumption, and transportation path optimization.

[0010] Preferably, the hierarchical encryption of the green attribute data based on the hybrid hashing algorithm includes: Divide the green attribute data into a basic layer, a process layer, and a result layer according to business types; use a one-way hashing function to generate a digest value for the basic layer data; use a chained hashing structure to ensure data continuity for the process layer data; introduce a Merkle tree structure for the result layer data to achieve batch verification; Construct a dynamic key management mechanism, generate differentiated encryption keys according to data levels, and achieve distributed storage through key sharding technology.

[0011] Preferably, the multi - rule verification model adopts a multi - channel parallel decision - making architecture, including: Construct a rule graph, where the nodes include national environmental protection standard nodes, industry certification specification nodes, and enterprise - defined rule nodes, and the node attributes include threshold ranges, effective times, and weight coefficients; Adopt a two - level attention mechanism. The first level filters associated rules through rule matching degree calculation, and the second level filters expired rules through timeliness verification; Integrate the verification results of each channel based on a weighted voting mechanism, and output a green certification report including compliance scores and anomaly marks.

[0012] Preferably, the zero - knowledge proof technology is achieved by combining ring signature and homomorphic encryption, including: Encode the product green attribute data into a set of verifiable claims; use the ring signature algorithm to hide user identity information; perform homomorphic encryption processing on the claim set to support logical operation verification in the ciphertext state.

[0013] Preferably, the multi - objective authentication parameter optimization model adopts a decomposition and coordination strategy, including: Model the authentication standard parameters as a multi - constraint non - linear optimization problem, where the decision variables include carbon emission caps, recycled material ratios, and energy consumption efficiency coefficients; Initialize the relaxed sub - problems and calculate the approximate Pareto - front solution, and use the dynamic sensitivity analysis algorithm to adjust the objective function weights according to policy changes; In the decomposition stage, divide the global problem into multiple single - objective sub - problems based on the objective decomposition technology; in the coordination stage, introduce the shadow price mechanism to balance the resource conflicts between sub - problems.

[0014] Preferably, the three - dimensional convolutional network adopts a multi - granularity pooling structure, including: Divide the carbon footprint distribution map into multi - level grids according to geographical regions, and each level of grid stores the carbon emission intensity and time - accumulated amount; In the feature extraction stage, use dilated convolutional kernels to capture cross - region associations, and in the feature compression stage, retain multi - scale information through spatial pyramid pooling; Introduce a channel attention mechanism to adaptively weight the feature maps.

[0015] Preferably, the chained hash structure is achieved through timestamp binding, including: Construct a hash verification index table to record the dual - verification information of the location hash and timestamp hash of data blocks.

[0016] Preferably, the timeliness verification is achieved through a sliding time window, including: Set the effective time interval and priority label for each rule; construct a time window sliding model to dynamically intercept the current valid rule subset; Adopt an expired rule recycling mechanism to move the rules beyond the time window into the historical rule library and freeze their verification weights.

[0017] Preferably, the dynamic sensitivity analysis algorithm is implemented based on an online learning framework, including: Collect the objective function gradient and constraint relaxation degree during the historical optimization process as training samples; Construct a Gaussian process regression model to fit the non-linear relationship between the weight coefficient and the external policy variable; Online update the model parameters through an incremental Kalman filter algorithm and adjust the sensitivity threshold of the authentication standard in real time.

[0018] Compared with the prior art, the beneficial effects of the present invention are: The blockchain-based green product certification and traceability system of the present invention has many significant beneficial effects. In terms of data management, the data collection module realizes the comprehensive collection of green attribute data of the product throughout the life cycle with the help of multi-source perception devices such as carbon emission monitors, energy consumption sensors, logistics tracking tags, and environmental humidity sensors. This method can obtain product information from multiple dimensions to ensure the integrity and accuracy of the data. For example, during the production process of electronic products, not only can the energy consumption and carbon emissions during the production process be monitored, but also the environmental information during transportation can be grasped through logistics tracking tags, providing rich data support for comprehensively evaluating the green attributes of the product.

[0019] The blockchain evidence storage module uses a hybrid hashing algorithm to encrypt the data layer by layer to generate an immutable distributed ledger record. This encrypted storage method greatly improves the security and credibility of the data. On the one hand, encrypt by dividing levels according to business types, and use different technologies such as one-way hashing functions, chained hashing structures, and Merkle tree structures for different levels of data characteristics to ensure the integrity and continuity of the data. For example, the chained hashing structure is bound by a timestamp to verify the continuity of the data in terms of time and location, preventing the data from being tampered with. On the other hand, the dynamic key management mechanism generates differentiated encryption keys according to the data levels and stores them distributedly through key sharding technology, further enhancing the confidentiality of the data and effectively preventing data leakage.

[0020] The multi - rule verification model of the intelligent contract audit module improves the accuracy and fairness of authentication. This model constructs a rule graph, integrating national environmental protection standards, industry certification norms, and enterprise - defined rules. It uses a two - level attention mechanism to screen associated rules and filter expired rules, and integrates verification results based on a weighted voting mechanism, outputting a green certification report containing compliance scores and anomaly marks. This makes the certification process more scientific and rigorous, and can more accurately evaluate whether a product meets green standards. For example, in the food industry, by comprehensively considering rules at different levels, it is possible to comprehensively evaluate various links of food from raw material procurement, production and processing to packaging and transportation, avoiding the limitations of single - rule evaluation.

[0021] The traceability query module uses zero - knowledge proof technology to construct a privacy - protected query channel. While protecting user privacy, it ensures that users can obtain a verifiable traceability chain of the green attributes of products. The combination of ring signature and homomorphic encryption can not only hide user identity information but also support logical operation verification in the ciphertext state. When consumers query product information, they don't need to worry about personal privacy leakage, and at the same time can effectively verify the green attributes of products, enhancing consumers' trust in green products.

[0022] The dynamic optimization module constructs a multi - objective certification parameter optimization model according to changes in environmental policies, and uses gradient descent and constraint relaxation algorithms to adaptively adjust certification standards. Modeling the certification standard parameters as a multi - constraint non - linear optimization problem, it adjusts the weights of the objective function according to policy changes through a dynamic sensitivity analysis algorithm, and uses a decomposition and coordination strategy to balance resource conflicts between sub - problems. This enables the system to keep up with policy changes in a timely manner, ensuring the timeliness and reasonableness of certification standards. For example, when the country raises the carbon emission requirements for a certain type of product, the system can automatically adjust the certification parameters and re - evaluate the green attributes of products to ensure that the certification results comply with the latest policies.

[0023] In addition, the various modules of this system work together to form a complete and efficient green product certification and traceability system. It not only improves the quality and efficiency of green product certification, but also provides a powerful supervision tool for government regulatory departments, promoting the healthy development of the green industry. At the same time, it enhances consumers' awareness and trust in green products, and promotes the prosperity of the green consumption market. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 is the working principle diagram of the blockchain - based green product certification and traceability system described in the present invention; Figure 2 is the working diagram of the multi - rule verification model; Figure 3 is the schematic diagram of zero - knowledge proof privacy protection; Figure 4It is a schematic diagram of feature extraction for a 3D convolutional network. Specific implementation manners

[0025] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0026] Please refer to Figures 1 - 4 , the present invention provides a blockchain-based green product certification and traceability system, aiming to achieve effective management and monitoring of the entire life cycle of green products, and ensure the authenticity, reliability and traceability of the green attributes of products. This system mainly consists of the following core modules working together to achieve the goal: Data acquisition module: Use multi-source sensing devices to comprehensively collect green attribute data of the entire life cycle of products. These multi-source sensing devices cover carbon emission monitors, energy consumption sensors, logistics tracking tags, environmental humidity sensors, etc. The carbon emission monitor is used to monitor the carbon emissions of products in each link, the energy consumption sensor is responsible for recording the energy consumption during the production and transportation of products, the logistics tracking tag can track the location information of products in the supply chain in real time, and the environmental humidity sensor monitors the humidity of the environment where the products are located. Through these devices, data related to the green attributes of products are obtained from multiple dimensions, providing a basis for subsequent analysis.

[0027] Blockchain evidence storage module: Use a hybrid hashing algorithm to perform hierarchical encryption processing on the collected green attribute data, thereby generating an immutable distributed ledger record. The green attribute data is divided into a basic layer, a process layer and a result layer according to the business type. For the data characteristics of different levels, different hashing technologies are used for processing. At the same time, a dynamic key management mechanism is constructed, differential encryption keys are generated according to the data levels, and the key sharding technology is used to achieve distributed storage, enhancing the security and reliability of data storage.

[0028] Intelligent contract audit module: Input the distributed ledger record stored in the blockchain into a preset multi-rule verification model, and the model will automatically generate a green certification result. The multi-rule verification model adopts a multi-channel parallel decision-making architecture. By constructing a rule graph, integrating multi-faceted rules such as national environmental protection standards, industry certification specifications, and enterprise-defined rules, using a two-level attention mechanism to screen effective rules, and integrating verification results based on a weighted voting mechanism, a green certification report including compliance scores and anomaly marks is finally output to objectively and fairly evaluate the green attributes of products.

[0029] Traceability Query Module: Construct a privacy - protected query channel using zero - knowledge proof technology. This technology combines ring signature and homomorphic encryption to encode the green attribute data of products into a set of verifiable claims. The ring signature algorithm is used to hide the user's identity information, and the set of claims is processed by homomorphic encryption to support logical operation verification in the ciphertext state. When a user issues a query request, the system can output a verifiable traceability chain of the product's green attributes, meeting the user's query requirements for product green attribute information while protecting the user's privacy.

[0030] Dynamic Optimization Module: Construct a multi - objective authentication parameter optimization model according to environmental policy changes, and adaptively adjust the authentication criteria using gradient descent and constraint relaxation algorithms. Model the authentication standard parameters as a multi - constraint non - linear optimization problem, where the decision variables include the carbon emission cap, the proportion of recycled materials, and the energy consumption efficiency coefficient, etc. By initializing the relaxation sub - problem, calculating the approximate solution of the Pareto front, and using the dynamic sensitivity analysis algorithm to adjust the objective function weights according to policy changes, divide the global problem into multiple single - objective sub - problems in the decomposition stage, and introduce the shadow price mechanism in the coordination stage to balance the resource conflicts between sub - problems, enabling the authentication criteria to be optimized in a timely manner with the change of environmental policies, and ensuring the adaptability and effectiveness of the system.

[0031] The following further illustrates the implementation of the present invention in combination with Embodiments 1 to 6.

[0032] Embodiment 1: In this embodiment, the specific process of green attribute data collection and processing in the data collection module is further elaborated, especially the content related to the three - dimensional convolutional network.

[0033] First, the carbon emission monitor, energy consumption sensor, logistics tracking tag, and environmental humidity sensor in the multi - source perception device work together. The carbon emission monitor and energy consumption sensor collect data at different times and locations. To construct an accurate product carbon footprint distribution map, it is necessary to perform spatio - temporal alignment on these two types of data. For example, during the product production process, the carbon emission monitor and energy consumption sensor may collect data at different time intervals. Through time synchronization algorithms and spatial mapping methods, the carbon emission data and energy consumption data are accurately matched with the corresponding production links and geographical locations, thereby constructing a carbon footprint distribution map that reflects the carbon emissions and energy consumption of the product at different stages.

[0034] Multi-scale feature decomposition is performed on logistics tracking tag data and environmental humidity sensor data. Logistics tracking tags record the transportation trajectory information of products in the supply chain, and environmental humidity sensors record the humidity changes in the environment in which products are transported. Through multi-scale analysis methods such as wavelet transform, these data are decomposed from different time scales and spatial scales, and features reflecting changes in supply chain status are extracted to generate supply chain status feature sequences. For example, in the process of transporting products from the production site to the sales site, logistics tracking tag data can reflect information such as transportation speed and residence time, and environmental humidity sensor data can reflect the environmental humidity conditions at different transportation stages. Multi-scale feature decomposition can analyze this information at different scales and mine more valuable features.

[0035] Then, a two-stream feature fusion network is constructed. The first stream uses a three-dimensional convolutional network to extract the spatial correlation features of the carbon footprint distribution map. The three-dimensional convolutional network uses a multi-granularity pooling structure to divide the carbon footprint distribution map into multi-level grids according to geographical regions. Each grid level stores the carbon emission intensity and time accumulation. Assume that the carbon footprint distribution map is divided into Level grid, in In the grid, carbon emission intensity is expressed as Indicates that the time accumulation is expressed as In the feature extraction stage, the dilated convolution kernel is used to capture cross-regional associations. The dilated convolution kernel can expand the receptive field of the convolution kernel without increasing the number of parameters, thereby better capturing the carbon emission correlation information between different regions. In the feature compression stage, multi-scale information is retained through spatial pyramid pooling. Spatial pyramid pooling fuses features of different scales, allowing the network to learn local and global feature information at the same time. At the same time, the channel attention mechanism is introduced to adaptively weight the feature map. The channel attention mechanism calculates the importance of each channel and assigns different weights to different channels, so that the network pays more attention to the channel information that is important for green attribute analysis.

[0036] The second stream uses a temporal attention network to extract the dynamic evolution characteristics of the supply chain status feature sequence. The temporal attention network can automatically adjust the degree of attention to the features at different times according to the importance of the features at different times, so as to better capture the dynamic changes of the supply chain status. Finally, the spatial correlation features and dynamic evolution features are fused through a cross-modal graph neural network to generate a comprehensive green attribute data set including raw material traceability, production process energy consumption and transportation route optimization. The cross-modal graph neural network can effectively fuse data features from different modalities, organically combine the spatial characteristics of the carbon footprint distribution map with the temporal characteristics of the supply chain status feature sequence, and provide comprehensive and accurate data support for subsequent green product certification and traceability.

[0037] Embodiment 2: When performing hierarchical encryption on green attribute data, the data is divided into a basic layer, a process layer, and a result layer according to the business type. For the basic layer data, a one-way hash function is used to generate a digest value. The one-way hash function is irreversible. The basic layer data is input into the one-way hash function , and the generated digest value can uniquely identify the basic layer data and cannot restore the original data through the digest value, ensuring the security of the data.

[0038] For the process layer data, a chained hash structure is used to ensure data continuity. The chained hash structure is implemented through timestamp binding, constructing a hash verification index table to record the dual verification information of the position hash and the timestamp hash of the data block. Assume that the data blocks are in chronological order as , for the data block , its position hash is , and the timestamp hash is , where is the generation time of the data block . In the hash verification index table, record the information of each data block. When it is necessary to verify the continuity of the data, by comparing the hash values and the timestamp hash values of adjacent data blocks, if is calculated based on the content of and , and , then it can be proved that the data is continuous in time and position, effectively preventing the data from being tampered with or deleted.

[0039] For the result layer data, a Merkle tree structure is introduced to achieve batch verification. The Merkle tree organizes the result layer data into a tree structure, and each leaf node stores the hash value of the data block, and the non-leaf nodes are obtained by calculating the hash values of their child nodes. Through this structure, the integrity of a large amount of data can be quickly verified, improving the verification efficiency.

[0040] At the same time, a dynamic key management mechanism is constructed. Different encryption keys are generated according to the data levels. Assume that the basic layer key is , the process layer key is , and the result layer key is . These keys are divided into multiple fragments through key sharding technology and distributed and stored on different nodes. For example, the key is divided into fragments , and are stored in different nodes respectively. Only when these nodes cooperate together can the complete key be restored, further enhancing the security of the data.

[0041] Example 3: The multi - rule verification model adopts a multi - channel parallel decision - making architecture. First, a rule graph is constructed. The nodes in the rule graph include national environmental protection standard nodes, industry certification specification nodes, and enterprise - defined rule nodes. Each node has attributes. Among them, the threshold range is used to judge whether the product complies with the corresponding rule. For example, in the national environmental protection standard node, the carbon emission threshold for the product is specified as , if the actual carbon emission of the product exceeds , then it does not meet the standard; the effective time stipulates the valid period of the rule, represented by and indicating the start effective time and end effective time of the rule; the weight coefficient reflects the importance of the rule in the comprehensive evaluation, represented by .

[0042] A two - level attention mechanism is adopted to screen the rules. The first level screens the associated rules by calculating the rule matching degree. Suppose the green attribute data of the product to be verified is , for each rule node , calculate its matching degree with the data , which can be calculated by methods such as cosine similarity. The rules with a matching degree higher than a certain threshold are screened out and enter the next - level verification. The second level filters out the expired rules through timeliness verification. The timeliness verification is realized through a sliding time window. Set the effective time interval and priority label for each rule. Construct a time - window sliding model to dynamically intercept the current valid rule subset. For example, the current time is , the time - window size is , then the current valid rule subset is the rule set that satisfies . At the same time, an expired rule recycling mechanism is adopted to move the rules beyond the time window into the historical rule library and freeze their verification weights to avoid the interference of expired rules on the certification results.

[0043] Integrate the verification results of each channel based on the weighted voting mechanism. For the selected valid rules, conduct weighted voting according to their weight coefficients . Suppose there are valid rules in total, and the verification result of each rule is ( takes values of passed or not passed), then the calculation method of the comprehensive verification result is: . Finally, output a green certification report containing compliance scores and anomaly marks. The compliance scores are calculated based on the weighted voting results and other relevant indicators, and the anomaly marks are used to indicate in which aspects the product does not meet the rule requirements.

[0044] Example 4: This example focuses on elaborating the specific implementation process of the zero - knowledge proof technology in the traceability query module.

[0045] The zero - knowledge proof technology is implemented by combining ring signature and homomorphic encryption. First, the green attribute data of the product is encoded into a set of verifiable claims.

[0046] Then, the ring signature algorithm is used to hide the user identity information. The ring signature algorithm allows the signer to hide their true identity among a set of users. Suppose the set of users is , and the signer is , the ring signature algorithm will generate a signature such that the verifier cannot determine which user in the set the signer is from the signature, but can verify the legality of the signature.

[0047] Next, the set of claims is processed by homomorphic encryption to support logical operation verification in the ciphertext state. The homomorphic encryption algorithm can perform specific operations on ciphertext without decrypting the data. For example, for two green attribute data and , their ciphertexts are and respectively, and an addition operation can be performed in the ciphertext state, where represents the addition operation defined by the homomorphic encryption algorithm. When the user issues a query request, the system uses these encryption and signature technologies to obtain relevant data from the blockchain while protecting the user's privacy, and generates a verifiable traceability chain of the product's green attributes through ciphertext verification and other means. The user can verify and trace the green attributes of the product based on the information in the traceability chain.

[0048] Example 5: This example details the specific implementation process of the multi - objective authentication parameter optimization model and the dynamic sensitivity analysis algorithm in the dynamic optimization module.

[0049] The multi - objective authentication parameter optimization model adopts a decomposition and coordination strategy. First, the authentication standard parameters are modeled as a multi - constraint non - linear optimization problem. The decision variables include the carbon emission cap , the proportion of recycled materials , and the energy consumption efficiency coefficient etc. Taking the carbon emission cap as an example, it is restricted by various factors such as national environmental protection policies and industry development trends. At the same time, there are also some constraint conditions, such as the restriction of the production process on the energy consumption efficiency coefficient, and the restriction of raw material supply on the proportion of recycled materials, etc.

[0050] Initialize the relaxation sub-problem and calculate the approximate Pareto front solution. In the initialization stage, set some initial relaxation variables. For example, for the constraint on the carbon emission cap, set the relaxation variable such that the constraint condition becomes . Through some optimization algorithms, such as genetic algorithms, calculate the approximate Pareto front solutions under different objective function weights. The approximate Pareto front solutions represent the set of optimal solutions that can be achieved when making trade-offs among multiple objectives.

[0051] Adopt the dynamic sensitivity analysis algorithm to adjust the objective function weights according to policy changes. The dynamic sensitivity analysis algorithm is implemented based on an online learning framework. First, collect the objective function gradients and constraint relaxation degrees in the historical optimization process as training samples. Update the model parameters online through the incremental Kalman filter algorithm, and adjust the sensitivity threshold of the certification standard in real time. The incremental Kalman filter algorithm can continuously update the model parameters according to new sample data, enabling the model to better adapt to policy changes, thereby dynamically adjusting the sensitivity threshold of the certification standard to ensure the rationality and effectiveness of the certification standard.

[0052] In the decomposition stage, divide the global problem into multiple single-objective sub-problems based on the objective decomposition technique. For example, decompose the optimization problem of the green product certification standard into sub-problems with the goal of minimizing carbon emissions, maximizing the proportion of recycled materials, and improving energy consumption efficiency, etc. In the coordination stage, introduce the shadow price mechanism to balance the resource conflicts among sub-problems. The shadow price mechanism assigns a shadow price to each sub-problem, reflecting the scarcity of resources in different sub-problems, so as to allocate resources reasonably and achieve the overall optimization goal.

[0053] Example 6: This example supplements and explains other aspects related to the system that were not elaborated in detail in the above examples.

[0054] In terms of the overall architecture of the system, data is exchanged between various modules through efficient interfaces. For example, after the data acquisition module collects green attribute data, it transmits the data to the blockchain evidence storage module through a secure and stable interface for encrypted storage. The data stored in the blockchain evidence storage module can be called by the smart contract audit module and the traceability query module. The interface design between these modules ensures the accuracy and timeliness of data transmission. At the same time, security measures such as encrypted transmission are adopted to prevent data from being stolen or tampered with during transmission.

[0055] In terms of the deployment of the system, a distributed deployment method can be adopted, where each module is deployed on different server nodes to improve the performance and reliability of the system. For example, the blockchain evidence storage module can be deployed on multiple blockchain nodes to achieve redundant storage and high availability of data by leveraging the distributed characteristics of the blockchain. The intelligent contract review module can be deployed on multiple servers with strong computing capabilities according to the volume of business to improve the review efficiency. The traceability query module can be deployed on edge servers close to users to reduce the response time of user queries.

[0056] In terms of the maintenance and update of the system, a perfect version management mechanism has been established. When there are new technological or policy changes, the system can be upgraded in a timely manner. For example, when new environmental protection standards are introduced by the state, the rule graph in the intelligent contract review module can be updated in a timely manner, and the multi-objective authentication parameter optimization model can also adjust decision variables and constraint conditions according to the new policy requirements. At the same time, the running state of the system is monitored in real time, and data during the system operation, such as the processing time and resource consumption of each module, is collected to detect potential problems in a timely manner and optimize them.

[0057] In addition, the system also considers the compatibility with other relevant systems. For example, during the data collection phase, the data collected by multi-source sensing devices may need to be docked with the enterprise's existing production management system. Therefore, a unified data format and interface specification are designed to ensure the smooth interaction of data. In terms of traceability query, the verifiable traceability chain generated by the system can be docked with the query platform of government regulatory departments to facilitate the supervision and management of green products by regulatory departments. Through these measures, the practicality and scalability of the system in actual applications are ensured, and the needs of green product certification and traceability can be better met.

[0058] It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or device.

[0059] Although the embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention, and the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A blockchain-based green product certification and traceability system, characterized in that, Including: Data acquisition module: used to collect green attribute data of the product throughout its life cycle through multi-source sensing devices; Blockchain evidence storage module: based on a hybrid hashing algorithm, hierarchically encrypt the green attribute data to generate an immutable distributed ledger record; Smart contract audit module: input the distributed ledger record into a preset multi-rule verification model to automatically generate a green certification result; Traceability query module: use zero-knowledge proof technology to construct a privacy protection query channel and output a verifiable traceability chain of the product's green attributes according to user requests; Dynamic optimization module: construct a multi-objective certification parameter optimization model according to environmental policy changes, and use gradient descent and constraint relaxation algorithms to adaptively adjust the certification standards.

2. The green product certification and traceability system according to claim 1, characterized in that, The collection of green attribute data of the product throughout its life cycle through multi-source sensing devices includes: The multi-source sensing devices include a carbon emission monitor, an energy consumption sensor, a logistics tracking tag, and an environmental humidity sensor; Perform spatio-temporal alignment on the carbon emission monitor data and the energy consumption sensor data to construct a product carbon footprint distribution map; perform multi-scale feature decomposition on the logistics tracking tag data and the environmental humidity sensor data to generate a supply chain state feature sequence; Construct a two-stream feature fusion network. The first stream uses a three-dimensional convolutional network to extract the spatial correlation features of the carbon footprint distribution map, and the second stream uses a temporal attention network to extract the dynamic evolution features of the supply chain state feature sequence; Fuse the spatial correlation features and the dynamic evolution features through a cross-modal graph neural network to generate a comprehensive green attribute dataset including raw material traceability, production process energy consumption, and transportation path optimization.

3. The green product certification and traceability system according to claim 1, characterized in that, The hierarchical encryption of the green attribute data based on the hybrid hashing algorithm includes: Divide the green attribute data into a basic layer, a process layer, and a result layer according to business types; use a one-way hashing function to generate a digest value for the basic layer data; use a chained hashing structure to ensure data continuity for the process layer data; introduce a Merkle tree structure for the result layer data to achieve batch verification; Construct a dynamic key management mechanism, generate different encryption keys according to data levels, and achieve distributed storage through key sharding technology.

4. The green product certification and traceability system according to claim 1, characterized in that, The multi-rule verification model adopts a multi-channel parallel decision-making architecture, including: Construct a rule graph, the nodes include national environmental protection standard nodes, industry certification specification nodes, and enterprise-defined rule nodes, and the node attributes include threshold ranges, effective times, and weight coefficients; Adopt a two-level attention mechanism. The first level filters associated rules through rule matching degree calculation, and the second level filters expired rules through timeliness verification; Integrate the verification results of each channel based on a weighted voting mechanism and output a green certification report including compliance scores and anomaly marks.

5. The green product certification and traceability system according to claim 1, characterized in that, The zero-knowledge proof technology is achieved by combining ring signature and homomorphic encryption, including: Encode the product green attribute data into a set of verifiable statements; use the ring signature algorithm to hide user identity information; perform homomorphic encryption processing on the statement set to support logical operation verification in the ciphertext state.

6. The green product certification and traceability system according to claim 1, characterized in that, The multi-objective certification parameter optimization model adopts a decomposition and coordination strategy, including: Model the certification standard parameters as a multi-constraint non-linear optimization problem, where the decision variables include the carbon emission cap, the proportion of recycled materials, and the energy consumption efficiency coefficient; Initialize the relaxation sub-problem and calculate the approximate solution of the Pareto front. Use the dynamic sensitivity analysis algorithm to adjust the objective function weights according to policy changes; In the decomposition stage, divide the global problem into multiple single-objective sub-problems based on the objective decomposition technique; in the coordination stage, introduce the shadow price mechanism to balance the resource conflicts between sub-problems.

7. The green product certification and traceability system according to claim 2, characterized in that, The three-dimensional convolutional network adopts a multi-granularity pooling structure, including: Divide the carbon footprint distribution map into multi-level grids according to geographical regions, and each level of grid stores the carbon emission intensity and the time cumulative amount; In the feature extraction stage, use dilated convolutional kernels to capture cross-region correlations, and in the feature compression stage, retain multi-scale information through spatial pyramid pooling; Introduce a channel attention mechanism to adaptively weight the feature maps.

8. The green product certification and traceability system according to claim 3, characterized in that, The chained hash structure is implemented through timestamp binding, including: Construct a hash verification index table to record the dual verification information of the location hash and the timestamp hash of the data block.

9. The green product certification and traceability system according to claim 4, characterized in that, The timeliness verification is implemented through a sliding time window, including: Set the effective time interval and priority label for each rule; construct a time window sliding model to dynamically intercept the currently valid rule subset; Adopt an expired rule recycling mechanism to move the rules beyond the time window into the historical rule library and freeze their verification weights.

10. The green product certification and traceability system according to claim 6, characterized in that, The dynamic sensitivity analysis algorithm is implemented based on an online learning framework, including: Collect the objective function gradients and constraint relaxation degrees in the historical optimization process as training samples; Construct a Gaussian process regression model to fit the non-linear relationship between the weight coefficients and external policy variables; Online update the model parameters through the incremental Kalman filter algorithm to adjust the sensitivity threshold of the certification standard in real time.

Images