A method and system for managing basin pollution discharge rights based on a consortium chain and a knowledge graph

Abstract

The application discloses a kind of based on alliance chain and knowledge graph's basin pollution discharge right management method and system, it is related to environmental protection technical field and information technology field.The management method and system aim to provide a transparent, efficient and intelligent solution for pollution discharge management in the basin by combining the tamper-proof and distributed ledger characteristics of alliance chain and the data correlation and intelligent analysis capabilities of knowledge graph.The present application builds a Hyperledger fabric platform;A set of reward and punishment mechanism algorithm is written using smart contract, and automatic reward and punishment operations are performed according to preset rules;At the same time, the pollution discharge right transaction information is recorded on the chain, and the basin knowledge graph is constructed according to the transaction results and river topology structure;Finally, the environmental data of the basin is analyzed using knowledge graph reasoning technology to assist users in making more scientific and reasonable transaction decisions.

Description

Technical Field

[0001] This application relates to the field of environmental protection technology, and in particular to a watershed pollution discharge rights management method and system based on consortium blockchain and knowledge graph. Background Technology

[0002] With the development of society and economy and the acceleration of urbanization, watershed aquatic ecosystems, with water as the main component, are the primary places for human survival and social development, and the most fundamental arteries for the development of industries such as industry, agriculture, and water conservancy. Smart water management is committed to realizing the vision of "intelligent decision-making and efficient service," striving to create a more scientific management model and a better user experience by automating business processes and providing convenient services. According to statistics, the market size of smart water management in my country was approximately RMB 18.8 billion in 2023, and is expected to reach RMB 28 billion by 2027, with a compound annual growth rate of 10.5%. According to data from the China Water Rights Exchange, the total volume of water rights transactions in 2023 was 539 million cubic meters, an increase of 289 million cubic meters compared to 2022, representing a year-on-year increase of 115.4%; the total transaction amount was RMB 142 million, an increase of RMB 58.387 million compared to 2022, representing a year-on-year increase of 69.8%; and the total number of transactions was 5,760, an increase of 2,253 transactions compared to 2022, representing a year-on-year increase of 64.2%. However, against this backdrop of rapid development, my country has accumulated a vast amount of knowledge in this field. Due to the lack of unified knowledge management, it cannot be effectively reused, nor can a complete and systematic knowledge framework be formed, resulting in high costs for knowledge acquisition and utilization. Simultaneously, in the current river basin pollution rights trading system, the secondary market for pollution rights trading remains sluggish, and the trading process suffers from problems such as opacity, lack of mutual trust, insecurity, and difficulty in traceability. The authenticity of pollution data reported by river basin pollution-discharging enterprises cannot be guaranteed, and there is a lack of corresponding incentive and penalty mechanisms. To ensure fair and transparent trading, reduce the cost of knowledge utilization, and improve the overall efficiency of pollution rights trading, how to construct an effective knowledge management and trading support system has become an urgent problem to be solved.

[0003] Chinese patent document CN111143472A discloses a pollution management method, device, and medium based on a consortium blockchain. Chinese patent document CN114092239A discloses a pollution monitoring tokenized trading system based on the Internet of Things and a consortium blockchain. Both patents focus on pollution monitoring, but fail to improve the efficiency of pollution rights trading or provide decision support. This invention aims to effectively integrate the advantages of multiple technologies, using smart contracts for rewarding and penalizing enterprises, and knowledge graph reasoning capabilities to improve overall transaction efficiency and provide users with visualized decision guidance. Summary of the Invention

[0004] The purpose of this invention is to provide a watershed pollution discharge rights management method and system based on consortium blockchain and knowledge graph, which improves the transparency and efficiency of overall transactions through the following improvements. Specifically, this application introduces a reward and penalty algorithm based on the water environment capacity (WEC) of the existing Hyperledger Fabric system, without affecting the health of the ecosystem. By monitoring the water quality parameters of each river segment, the WEC value of each segment can be calculated. Then, different reward and penalty standards are set according to these WEC values. Enterprises that improve the WEC value of their river segment to above the preset standard through pollution control measures will be given certain economic rewards. Conversely, if an enterprise fails to effectively control pollution discharge, resulting in the WEC value of its river segment falling below the prescribed standard, it will be subject to corresponding fines or other forms of penalties. This not only helps to incentivize enterprises to actively improve water quality but also promotes the rational allocation of resources. Considering that various factors may affect water quality, this invention sets a dynamically adjustable WEC threshold. Thus, the reward and penalty standards can be flexibly adjusted according to the actual situation, ensuring the adaptability of the management strategy while avoiding unreasonable restrictions caused by a fixed threshold. Simultaneously, it incorporates the automatic execution capabilities of smart contracts, setting up automatic reward and penalty execution at specified times to reduce human resource consumption. Furthermore, this invention combines blockchain with the CompGCN model, leveraging blockchain's advantage in transaction transparency to ensure that every pollution discharge rights transaction record is immutable and transparent. It also integrates knowledge graph technology for intelligent decision support. By semantically processing a large number of on-chain historical transaction records, a knowledge graph covering multiple dimensions of information is constructed. This knowledge graph not only intuitively displays the relationships between various river sections and their changing trends over time, but also assists managers in formulating more precise policy measures, guiding enterprises to maximize economic benefits within the framework of legality and compliance. The CompGCN model is used to train and infer complex relationships in the knowledge graph, capturing potential connections between entities through deep learning, providing decision-makers with scientific evidence and technical support.

[0005] The technical solution adopted in this invention is as follows:

[0006] This application provides a method and system for managing watershed pollution discharge rights based on consortium blockchain and knowledge graph. The specific steps of the modeling method are as follows:

[0007] S1: Construct a hyperledger transaction network that includes multiple organizational nodes and authoritative institutions;

[0008] S2: After a transaction is completed between users, the relevant transaction data will be recorded in the blockchain to ensure the transparency, security and immutability of the transaction.

[0009] S3: Calculate the water quality of each basin based on relevant formulas and river topology, and record the obtained WEC values ​​in the structure defined by the smart contract;

[0010] S4: Use smart contracts to write a water environment capacity reward and punishment algorithm, and preset reward and punishment conditions based on dynamic thresholds;

[0011] S5: Construct a knowledge graph based on real water quality data and river topology in each watershed;

[0012] S6: Using knowledge reasoning techniques, calculate the loss function score of the transaction data triples using the CompGCN model.

[0013] As a further step in this scheme, the specific steps of S1 are as follows:

[0014] S1.1 Build the Hyperledger network and define the sorting nodes and peer nodes for each organization.

[0015] S1.2. Write contract parameters, setting WEC values ​​(wecValues), reward amount (rewardAmount), penalty amount (penaltyAmount), deadline (deadline), threshold (threshold), etc.

[0016] S1.3 The definition of events such as OrganizationRegistered, TradeCompleted, RewardOrPenaltyClaimed, and TradeFailed enables more granular and transparent operation recording and tracking in consortium blockchains.

[0017] As a further step in this scheme, the specific steps of S2 are as follows:

[0018] S2.1 When an organization node initiates a transaction, each organization member (node) first performs a preliminary verification upon receiving the transaction request broadcast by the user. This includes checking the validity of the digital signature, ensuring the correct transaction format, and confirming that the transaction initiator has sufficient pollution rights balance, etc.

[0019] S2.2 Verify the consistency between the data of the new transaction and other data already recorded on the consortium blockchain. This step helps prevent double-spending or duplicate transactions and ensures that all transactions follow the same logic and rules.

[0020] As a further step in this plan, the specific steps of S3 are as follows:

[0021] S3.1 Clean and preprocess the collected data to ensure data quality and consistency, and remove outliers or fill in missing values.

[0022] S3.2. Based on relevant formulas, water quality standards, and the specific conditions of the watershed, determine the key parameters used to calculate the WEC (Water Environment Capacity) value.

[0023] The formula for water environment capacity W is as follows:

[0024]

[0025] Where Q0 represents the upstream design flow rate (m³ / s). 3 / s), Cs is the water quality standard at the control section (mg / L), C0 is the design water quality upstream (mg / L), and q is the inflow rate of the wastewater boundary tributary (m³). 3 / s), Cq is the influent water quality of the boundary tributary of the sewage (mg / L), K is the degradation coefficient of the pollutant (1 / d), and V is the water volume of the river section (m³). 3 ).

[0026] The formula for calculating the degradation coefficient is as follows:

[0027]

[0028] Where Cu and Cd are the pollutant concentrations (mg / L) at the upper and lower cross sections, respectively, u is the average flow velocity (m / s), and x is the river length (km).

[0029] S3.3. Using the remaining water environment capacity or the amount of pollutant reduction as evaluation indicators, and the upper and lower limits of the water environment capacity as water quality protection benchmarks, the relationship between pollutants and water environment capacity is clarified by judging the water quality status of the watershed section at various times, and this is used as the emission reduction indicator for the river section to determine the amount of pollutant reduction in each river section under different water periods, thereby achieving the protection of the watershed's water ecological environment.

[0030] The relevant calculation formulas are as follows:

[0031] X i =W i -P i (3)

[0032] Where Xi: the remaining water environment capacity or pollution reduction amount of river segment i, t / a; Wi: the water environment capacity of river segment i, t / a; Pi: the amount of pollutants entering the river in river segment i, t / a.

[0033] The final determined WEC value is recorded in the structure defined by the smart contract.

[0034] As a further step in this plan, the specific steps of S4 are as follows:

[0035] S4.1. Use the Solidity programming language to refine the smart contract code and create a .sol file containing the water environment capacity reward and penalty logic. This file defines the structure, variables, functions, and events of the smart contract, ensuring that all rules and conditions can be automatically executed on the consortium blockchain.

[0036] S4.2 Set dynamic thresholds and transaction deadlines according to specific circumstances, and write preset conditions and triggering events into the contract to ensure that the behavior of all participants is constrained by the smart contract rules.

[0037] S4.3 Upon reaching the deadline, automatically assign preset rewards and penalties to companies that meet or fail to meet the requirements.

[0038] As a further step in this solution, the specific steps of S5 are as follows:

[0039] S5.1 Process the on-chain transaction data and convert it into triples.

[0040] S5.2 Construct a knowledge graph based on the transformed data and display it using neo4j.

[0041] As a further step in this solution, the specific steps of S6 are as follows:

[0042] S6.1 Perform necessary preprocessing on the constructed knowledge graph, such as noise removal, missing value filling, and data format standardization, to improve the quality of subsequent reasoning.

[0043] S6.2. Set the form of the loss function, initialize the CompGCN model, and adjust parameters such as the model learning rate, number of hidden layer units, and activation function according to the specific application scenario.

[0044] S6.3. The trained dataset is input with the current user's WEC value. The corresponding true score is obtained through the loss function. The highest score is retained in the form of triples, and a new knowledge graph is generated to provide users with intelligent decision-making guidance.

[0045] The beneficial effects of this application are as follows:

[0046] 1. In traditional watershed water environment management, some enterprises may tamper with monitoring data to evade supervision, resulting in ineffective control of environmental pollution problems. Introducing Hyperledger Fabric technology, because the consortium blockchain is a distributed, immutable distributed ledger, all transaction information is publicly recorded on the chain. Each block contains the hash value of the previous block, forming an immutable chain structure. All participants can view transaction records but cannot tamper with them. This transparency ensures the authenticity and reliability of the data, allowing the public and regulatory authorities to view water quality conditions and transaction details at any time, enhancing the effectiveness of social supervision.

[0047] 2. Compared to traditional reward and penalty methods, smart contracts offer an efficient, transparent, and tamper-proof solution for implementing automated reward and penalty mechanisms, significantly reducing reliance on manual intervention and improving efficiency. This is particularly suitable for areas requiring multi-party participation and strict oversight, such as pollution rights trading management. Specifically, smart contracts automatically execute reward and penalty measures based on preset rules and conditions when specific triggering conditions are met. We use chaincode to formulate and execute a reward and penalty mechanism based on Environmental Capacity (WEC). Each river segment trades its rights according to its WEC value. River segments with better water quality sell their additional pollution rights to river segments with poorer water quality, and the latter pay a fee to purchase these rights from the former. Furthermore, if a river segment with better water quality still has a WEC value higher than the government-designated standard after selling its pollution rights, it receives a financial reward from the government. Conversely, if a river segment with poorer water quality still has a WEC value lower than the government-designated standard after the trading deadline, the government imposes a financial penalty. This mechanism can not only improve transaction efficiency, but also incentivize each section of the river to actively improve water quality and achieve sustainable development of the basin's ecological environment system.

[0048] 3. Knowledge graph technology is introduced, utilizing tools such as RDF Mapping Tools to convert structured data into a standard format. This constructs a structure covering key entities such as transaction subjects, transaction types, transaction amounts, and their relationships, and presents this information in a structured and hierarchical manner through a semantic network. This facilitates users' viewing of information such as transaction details and water quality conditions in various river basins.

[0049] 4. As a knowledge reasoning tool, CompGCN leverages its strengths in handling complex graph structures to perform in-depth mining and correlation analysis of information within knowledge graphs, capturing relationships between entities and inferring potential, unobserved connections. Finally, the results derived through CompGCN reasoning are presented to users in the form of a graph, allowing them to easily access this information and perform queries, analyses, and transaction decisions as needed. Attached Figure Description

[0050] The accompanying drawings are provided to further understand this application and form part of the specification. They are used together with the embodiments of this application to explain this application and do not constitute a limitation thereof.

[0051] Figure 1 This is a flowchart of the watershed pollution discharge rights management method based on consortium blockchain and knowledge graph in this application;

[0052] Figure 2 This is a sample diagram of the Fabric system architecture for this application;

[0053] Figure 3 This is an example diagram of a smart contract model in the embodiments of this application;

[0054] Figure 4 This is a diagram of the overall model architecture in the embodiments of this application; Detailed Implementation

[0055] The technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0056] Example 1

[0057] Reference Figures 1-4 This application provides a method and system for managing watershed pollution discharge rights based on consortium blockchain and knowledge graph. The specific steps of the method are as follows:

[0058] S1: Construct a Hyperledger transaction network, which includes multiple organizational nodes and authoritative institutions. The specific steps are as follows:

[0059] S1.1 Build the Hyperledger network and define the sorting nodes and peer nodes for each organization.

[0060] S1.2. Write contract parameters, setting WEC values ​​(wecValues), reward amount (rewardAmount), penalty amount (penaltyAmount), deadline (deadline), threshold (threshold), etc.

[0061] S1.3 The definition of events such as OrganizationRegistered, TradeCompleted, RewardOrPenaltyClaimed, and TradeFailed enables more granular and transparent operation recording and tracking in consortium blockchains.

[0062] S2: Users will conduct transactions and record the transaction data on the blockchain. The specific steps are as follows:

[0063] S2.1 When an organization node initiates a transaction, each organization member (node) first performs a preliminary verification upon receiving the transaction request broadcast by the user. This includes checking the validity of the digital signature, ensuring the correct transaction format, and confirming that the transaction initiator has sufficient pollution rights balance, etc.

[0064] S2.2 Verify the consistency between the data of the new transaction and other data already recorded on the consortium blockchain. This step helps prevent double-spending or duplicate transactions and ensures that all transactions follow the same logic and rules.

[0065] S3: Calculate the water quality of each basin based on relevant formulas and river topology to obtain the WEC value. The specific steps are as follows:

[0066] S3.1 Clean and preprocess the collected data to ensure data quality and consistency, removing outliers or filling in missing values. The cleaned data will be in the following format:

[0067] River section number Water flow pollutant concentration volume area 1 2.11cms 49080mg / L 38104m3 103km2 ... ... ... ... ...

[0068] S3.2. Based on relevant formulas, water quality standards, and the specific conditions of the watershed, determine the key parameters used to calculate the WEC (Water Environment Capacity) value.

[0069] S3.3 Define a structure for each user containing their WEC value and other relevant information. Create a map using the address or other unique identifier as the key to quickly look up the WEC value for a specific user. Record the finalized WEC value in the structure.

[0070] S4: Develop a water environment capacity reward and penalty algorithm using smart contracts, and preset reward and penalty conditions based on dynamic thresholds. The specific steps are as follows:

[0071] S4.1. Use the Solidity programming language to complete the smart contract code and create a .sol file containing the water environment capacity reward and penalty logic, including parameters such as threshold, reward and penalty amount, and deadline. The file type is as follows:

[0072] Data types Access modifiers Parameter name Int256 Public Threshold Uint256 Public Rewardamount Mapping Public Wecvalues Address Public Organizations

[0073] This .sol file defines the structure, variables, functions, and events of the smart contract, ensuring that all rules and conditions can be executed automatically on the consortium blockchain.

[0074] S4.2 Set dynamic thresholds and transaction deadlines according to specific circumstances, and write preset conditions and triggering events into the contract to ensure that the behavior of all participants is constrained by the smart contract rules.

[0075] S4.3 Upon reaching the deadline, automatically assign preset rewards and penalties to companies that meet or fail to meet the requirements.

[0076] S5: Construct a knowledge graph based on real water quality data and river topology for each watershed. The specific steps are as follows:

[0077] S5.1 Process the on-chain transaction data and transform it into entity-relationship-entity triples. Construct a knowledge graph based on the transformed data and display it using Neo4j.

[0078] S6: Using knowledge reasoning techniques, calculate the loss function score of the transaction data triples using the CompGCN model. The specific steps are as follows:

[0079] S6.1 Perform necessary preprocessing on the constructed knowledge graph, such as noise removal, missing value imputation, and data format standardization, to improve the quality of subsequent inference. Define the form of the loss function, initialize the CompGCN model, and update the formula as follows:

[0080]

[0081] Where Nv represents the neighboring nodes of v, Xu and Zr represent the initial features of node u and relation r, hv represents the update of node v, and Wr represents the specific parameters of the relation type.

[0082] Then, adjust parameters such as the model learning rate, the number of hidden layer units, and the activation function according to the specific application scenario.

[0083] S6.2. The trained dataset is input with the current user's WEC value. The corresponding true score is obtained through the loss function. The highest score is retained in the form of triples, and a new knowledge graph is generated to provide users with intelligent decision-making guidance.

[0084] In summary, this example demonstrates a watershed pollution rights management method using consortium blockchain and knowledge graph. By leveraging consortium blockchain technology, a fair and transparent trading environment is constructed, smart contracts are used for automatic rewards and penalties, and finally, a knowledge graph is built to provide users with decision-making guidance.

[0085] Note that the above description only illustrates the preferred embodiments of this application and the technical principles of their application. The embodiments of this application are not limited to the specific examples detailed herein. Those skilled in the art can make various obvious modifications, reconfigurations, and substitutions without departing from the core ideas of the embodiments of this application. Although the embodiments of this application have been described in detail through the above specific embodiments, the embodiments of this application are far more extensive than those described herein. Many other equivalent implementation methods can be covered without departing from the basic concept of the embodiments of this application. The scope of protection of the embodiments of this application is defined by the appended claims. The above description of one embodiment of this application is only a preferred embodiment and should not be considered as limiting the scope of implementation of this application. All equivalent changes and improvements made within the scope of this application should still fall within the patent coverage of this application.

Claims

1. A watershed pollution discharge rights management method based on consortium blockchain and knowledge graph, characterized in that, The specific steps of this method are as follows: S1: Construct a hyperledger transaction network that includes multiple organizational nodes and authoritative institutions; S2: After a transaction is completed between users, the relevant transaction data will be recorded in the blockchain; S3: Calculate the water quality of each basin based on the river topology, and record the obtained water environmental capacity (WEC) value into the structure defined by the smart contract; S4: Use smart contracts to write a water environment capacity reward and punishment algorithm, and preset reward and punishment conditions based on dynamic thresholds; S5: Construct a knowledge graph based on real water quality data and river topology in each basin; S6: Using knowledge reasoning techniques, calculate the loss function score of the transaction data triples using the CompGCN model, wherein S6 includes: S6.1 Preprocessing of the constructed knowledge graph by removing noise, filling in missing values, and standardizing data format; S6.

2. Set the form of the loss function, initialize the CompGCN model, and adjust the model learning rate, number of hidden layer units, and activation function according to the scenario; S6.

3. The trained dataset is input with the current user's WEC value. The corresponding scores are obtained through the loss function. The highest score is saved in the form of triples. A new knowledge graph is generated to provide users with intelligent decision-making guidance.

2. The watershed pollution discharge rights management method based on consortium blockchain and knowledge graph as described in claim 1, characterized in that, S1 includes: S1.1 Build the Hyperledger network and define the sorting nodes and peer nodes for each organization; S1.2, Write contract parameters, set WEC value, reward amount, penalty amount, deadline, and threshold; S1.3 Definitions of the OrganizationRegistered, TradeCompleted, RewardOrPenaltyClaimed, and TradeFailed events.

3. The watershed pollution discharge rights management method based on consortium blockchain and knowledge graph as described in claim 1, characterized in that, S2 includes: S2.1 When an organization node initiates a transaction, each organization member, upon receiving the transaction request broadcast by the user, first performs a preliminary verification, which includes checking the validity of the digital signature, ensuring the correctness of the transaction format, confirming that the transaction initiator has sufficient pollution rights balance and sufficient qualifications to participate in the transaction. S2.2 Verify the consistency between the data of the new transaction and other data already recorded on the consortium blockchain.

4. The watershed pollution discharge rights management method based on consortium blockchain and knowledge graph as described in claim 1, characterized in that, S3 includes: S3.1 Clean and preprocess the collected data to remove outliers or fill in missing values; S3.

2. Based on water quality standards and the specific conditions of the watershed, determine the key parameters used to calculate the WEC value; S3.3 Define a structure for each user that contains WEC values ​​and other relevant information, and create a mapping using the address or other unique identifier as the key.

5. The watershed pollution discharge rights management method based on consortium blockchain and knowledge graph as described in claim 1, characterized in that, S4 includes: S4.1 Use the Solidity programming language to improve the smart contract code and create a .sol file containing the water environment capacity reward and punishment logic. This file defines the structure, variables, functions and events of the smart contract. S4.2 Set dynamic thresholds and transaction deadlines, and write preset conditions and triggering events into the contract; S4.3 Upon reaching the deadline, automatically assign preset rewards and penalties to companies that meet or fail to meet the requirements.

6. The watershed pollution discharge rights management method based on consortium blockchain and knowledge graph as described in claim 1, characterized in that, S5 includes: S5.1 Process the on-chain transaction data and convert it into the form of triples; S5.2 Construct a knowledge graph based on the transformed data and display it using neo4j.

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