Drainage basin emission right management method and system based on alliance chain and knowledge graph

Abstract

The invention discloses a drainage basin pollution discharge right management method and system based on an alliance chain and a knowledge graph, and relates to the technical field of environmental protection and the technical field of information. According to the management method and system, a transparent, efficient and intelligent solution is provided for pollution emission management in the drainage basin by combining the non-tampering and distributed account book characteristics of the alliance chain and the data association and intelligent analysis capability of the knowledge graph. The method comprises the following steps of: establishing a Hyperledger fabric platform; compiling a set of reward and punishment mechanism algorithm by using an intelligent contract, and performing automatic reward and punishment operation according to a preset rule; meanwhile, pollution discharge right transaction information is recorded on a chain, and a basin knowledge graph is constructed according to a transaction result and a river topological structure; and finally, basin environment data is analyzed by using a knowledge graph reasoning technology, and a user is assisted to make a transaction decision more scientifically and reasonably.

Description

Technical Field

[0001] The present application relates to the field of environmental protection technology, and in particular to a method and system for managing watershed pollution discharge rights based on alliance chain and knowledge graph. Background Art

[0002] With the development of social economy and the acceleration of urbanization, the water basin water environment ecosystem with water as the main body is the main place for human survival and social development, and it is also the most basic artery for the development of industries such as industry, agriculture, and water conservancy. Smart water affairs are committed to realizing the vision of "intelligent decision-making and efficient service". By automating business processes and providing convenient services, they strive to create a more scientific management model and a better user experience. According to statistics, the scale of my country's smart water affairs market in 2023 is about 18.8 billion yuan, and it is expected to reach 28 billion yuan by 2027, with a compound annual growth rate of 10.5%. According to data from the China Water Rights Exchange, the total transaction volume of water rights transactions in 2023 is 539 million cubic meters, an increase of 289 million cubic meters from 2022, a year-on-year increase of 115.4%; the total transaction amount is 142 million yuan, an increase of 58.387 million yuan from 2022, a year-on-year increase of 69.8%; the total number of transactions is 5,760, an increase of 2,253 transactions from 2022, a year-on-year increase of 64.2%. However, in this context of rapid development, my country has accumulated a lot of knowledge in this field. Due to the lack of unified knowledge management, it is impossible to reuse it better and effectively form a complete and systematic knowledge system, which leads to high costs for knowledge acquisition and its utilization. At the same time, in the current basin emission rights trading system. The secondary market for emission rights trading continues to be sluggish, and there are problems such as opacity, lack of mutual trust, insecurity, and difficulty in tracing the source in the trading process. The authenticity of the pollution data reported by the basin pollution-discharging enterprises cannot be guaranteed, and there is a lack of corresponding incentive and punishment mechanisms. In order to ensure the fairness and transparency of transactions, reduce the cost of knowledge utilization, and improve the overall efficiency of pollution trading, how to build an effective knowledge management and transaction support system has become an urgent problem to be solved.

[0003] A pollution management method, device and medium based on alliance chain disclosed in the Chinese patent document with publication number CN111143472A. A pollution monitoring tokenized trading system based on Internet of Things and alliance chain disclosed in the Chinese patent document with publication number CN114092239A. The above patents all focus on the supervision of pollution, but cannot improve the efficiency of pollution rights trading and provide decision support. The present invention aims to effectively integrate the advantages of multiple technologies, and through the comprehensive application of smart contracts, reward and punish enterprises, as well as the reasoning function of knowledge graphs, so as to improve the efficiency of the overall transaction and provide users with visual decision-making guidance. Summary of the invention

[0004] The invention aims to provide a method and system for managing river basin pollution discharge rights based on alliance chain and knowledge graph, and improve the transparency and efficiency of the overall transaction through the following improvements. Specifically, the present application introduces a reward and punishment algorithm based on the water environment capacity on the prior art Hyperledger fabric system, without affecting the health of the ecosystem. By monitoring the water quality parameters of each river section, the WEC value of each river section can be calculated. Then, different reward and punishment standards are set according to these WEC values. For those enterprises that make the WEC value of their river section higher than the preset standard through governance measures, a certain economic reward will be given. On the contrary, if an enterprise fails to effectively control pollution discharge, resulting in the WEC value of the river section it is responsible for being lower than the prescribed standard, it will be subject to corresponding fines or other forms of punishment. This not only helps to motivate enterprises to actively improve water quality, but also promotes the rational allocation of resources. Considering that various factors may affect the water quality, the present invention sets a dynamically adjustable WEC threshold. As a result, the reward and punishment standards can be flexibly adjusted according to the actual situation, which not only ensures the adaptability of the management strategy, but also avoids the unreasonable restrictions caused by fixed thresholds. At the same time, it combines the automatic execution capability of smart contracts, sets the specified time to automatically execute rewards and punishments, and reduces human resource consumption. In addition, the present invention combines blockchain with the CompGCN model, and uses the advantages of blockchain in transaction transparency to ensure that each emission rights transaction record cannot be tampered with and is open and transparent. It combines knowledge graph technology for intelligent decision support. By semantically processing a large number of historical transaction records on the chain, a knowledge graph covering multiple dimensions of information is constructed. This knowledge graph can not only intuitively display the correlation between various river sections and their changing trends over time, but also assist managers in formulating more accurate policies and measures, and guide enterprises to maximize economic benefits under the premise of legality and compliance. The CompGCN model is used to train and infer complex relationships in the knowledge graph, capture the potential connections between entities through deep learning, and provide decision makers with a scientific basis and technical support.

[0005] The technical solution adopted by the present invention is as follows:

[0006] The embodiment of the present application provides a method and system for managing river basin pollution discharge rights based on alliance chain and knowledge graph. The specific steps of the modeling method are as follows:

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

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

[0009] S3: Calculate the water quality of each river basin based on relevant formulas and river topology, and record the obtained WEC value into 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 the real water quality data of each river basin and the river topology structure;

[0012] S6: Using knowledge reasoning technology, the CompGCN model is used to calculate the loss function score of the transaction data triples.

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

[0014] S1.1. Build a hyperledger network and define the sorting nodes and peer nodes of each organization.

[0015] S1.2. Write contract parameters, set WEC value (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 can achieve more detailed and transparent operation recording and tracking in the alliance chain.

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

[0018] S2.1. The organization node initiates the transaction. After receiving the transaction request broadcast by the user, each organization member (node) first conducts preliminary verification. This includes checking the validity of the digital signature, ensuring the correctness of the transaction format, and confirming that the transaction initiator has sufficient emission rights balance.

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

[0020] As a further solution of this scheme, the specific steps of S3 are as follows:

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

[0022] S3.2. Determine the key parameters for calculating the WEC (Water Environment Capacity) value based on relevant formulas, water quality standards and the specific conditions of the watershed.

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

[0024]

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

[0026] The degradation coefficient calculation formula is as follows:

[0027]

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

[0029] S3.3. Use the remaining water environment capacity or the amount of pollutant reduction as the evaluation index, and the upper and lower limits of the water environment capacity as the water quality assurance benchmark. By judging the water quality conditions of the basin section at different times, clarify the relationship between pollutants and water environment capacity, and use it as the emission reduction index of the river section, determine the amount of pollutant reduction in each river section under different water periods, and thus achieve the protection of the basin water ecological environment.

[0030] The relevant calculation formula is as follows:

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

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

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

[0034] As a further solution of this scheme, the specific steps of S4 are as follows:

[0035] 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 to ensure that all rules and conditions can be automatically executed on the alliance chain.

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

[0037] S4.3. When the deadline is reached, the preset rewards and penalties will be automatically given to the enterprises that meet the requirements and those that do not.

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

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

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

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

[0042] S6.1. Perform necessary preprocessing on the constructed knowledge graph, such as removing noise, filling missing values, and standardizing data formats, to improve the quality of subsequent reasoning.

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

[0044] S6.3. Train the processed data set, input the current user's WEC value, obtain their corresponding real scores through the loss function, retain the highest score in the form of triples and generate a new knowledge graph to provide users with intelligent decision-making guidance.

[0045] The beneficial effects of this application are:

[0046] 1. In traditional river basin water environment management, some companies may tamper with monitoring data to evade supervision, resulting in environmental pollution problems that cannot be effectively controlled. With the introduction of Hyperledger Fabric technology, since the consortium chain is a distributed, tamper-proof distributed ledger, all transaction information is publicly recorded on the chain. Each block contains the hash value of the previous block, forming an unchangeable chain structure. All participants can view transaction records, but they cannot tamper with them. This transparency ensures the authenticity and reliability of the data. The public and regulatory authorities can view water quality conditions and transaction details at any time, enhancing the effectiveness of social supervision;

[0047] 2. Compared with the traditional reward and punishment method, smart contracts provide an efficient, transparent and tamper-proof solution for realizing automatic reward and punishment mechanisms, greatly reducing reliance on manual labor and improving efficiency. It is especially suitable for areas that require multi-party participation and strict supervision, such as emission rights trading management. Specifically, smart contracts automatically execute reward and punishment measures when specific trigger conditions are met through preset rules and conditions. We use chain codes to formulate and implement a reward and punishment mechanism based on water environment capacity (WEC). Each river section trades according to its own WEC value. The river section with better water quality sells its additional emission rights to the river section with poorer water quality. The river section with poorer water quality needs to pay to purchase the emission rights of the river section with better water quality. At the same time, if the WEC value of the river section with better water quality is still higher than the government-specified standard after the sale of emission rights, it will also receive financial rewards from the government. On the contrary, if the WEC value of the river section with poorer water quality is still lower than the government-specified standard after the transaction is closed, the government will impose financial penalties on it. This mechanism can not only improve transaction efficiency, but also encourage each river section to actively improve water quality and achieve sustainable development of the river basin ecological environment system;

[0048] 3. Introduce knowledge graph technology, use RDF Mapping Tools and other tools to convert structured data into a standard format, build key entities and their relationships including transaction subjects, transaction types, transaction type amounts, etc., and present this information in a structured and hierarchical manner through a semantic network. This makes it convenient for users to view transaction status, water quality in each river basin, and other information.

[0049] 4. As a knowledge reasoning tool, CompGCN uses its advantages in processing complex graph structures to conduct in-depth mining and association analysis of information in the knowledge graph, capture the relationship between entities, and infer potential, not directly observed connections. Finally, the results of CompGCN reasoning are intuitively displayed to users in the form of graphs, allowing users to easily access this information, query, analyze, and make trading decisions as needed. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] The accompanying drawings are used to provide further understanding of the present application and constitute a part of the specification. They are used together with the embodiments of the present application to explain the present application and do not constitute a limitation of the present application.

[0051] Figure 1 This is a flow chart of the watershed pollution rights management method based on the alliance chain and knowledge graph for 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 the smart contract model in the embodiment of this application;

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

[0055] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments.

[0056] Example 1

[0057] Reference Figure 1-Figure 4 The embodiment of the present application provides a method and system for managing river basin pollution discharge rights based on alliance chain and knowledge graph. The specific steps of the method are as follows:

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

[0059] S1.1. Build a hyperledger network and define the sorting nodes and peer nodes of each organization.

[0060] S1.2. Write contract parameters, set WEC value (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 can achieve more detailed and transparent operation recording and tracking in the alliance chain.

[0062] S2: Users will conduct transactions between each other and upload transaction data to the chain. The specific steps are as follows:

[0063] S2.1. The organization node initiates the transaction. After receiving the transaction request broadcast by the user, each organization member (node) first conducts preliminary verification. This includes checking the validity of the digital signature, ensuring the correctness of the transaction format, and confirming that the transaction initiator has sufficient emission rights balance.

[0064] S2.2. Verify the consistency between the data of the new transaction and other data recorded on the alliance chain. 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 river basin according to the relevant formula 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, remove outliers or fill in missing values. The cleaned data is in the following format:

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

[0068] S3.2. Determine the key parameters for calculating the WEC (Water Environment Capacity) value based on relevant formulas, water quality standards and the specific conditions of the watershed.

[0069] S3.3. Define a structure for each user that contains the WEC value and other relevant information. Use the address or other unique identifier as the key to create a map to quickly find the WEC value for a specific user. Record the finalized WEC value in the structure.

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

[0071] 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, including parameters such as thresholds, reward and punishment amounts, and deadlines. The types are 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 automatically executed on the consortium chain.

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

[0075] S4.3. When the deadline is reached, the preset rewards and penalties will be automatically given to the enterprises that meet the requirements and those that do not.

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

[0077] S5.1. Process the transaction data on the chain and convert it into entity-relationship-entity triples. Build a knowledge graph based on the converted data and display it using neo4j.

[0078] S6: Use knowledge reasoning technology to 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 removing noise, filling missing values, and standardizing data formats, to improve the quality of subsequent reasoning. Set 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 relationship r, hv represents the update of node v, and Wr represents the specific parameters of the relationship type.

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

[0083] S6.2. Train the processed data set, input the current user's WEC value, obtain their corresponding real scores through the loss function, retain the highest score in the form of triples and generate a new knowledge graph to provide users with intelligent decision-making guidance.

[0084] To sum up, this example is a watershed emission rights management method based on alliance chain and knowledge graph. Through alliance chain technology, a fair and transparent trading environment is built, and smart contracts are used for automatic rewards and punishments. Finally, a knowledge graph is constructed to provide users with decision-making guidance.

[0085] Note that the above content only describes the preferred implementation of the embodiment of the present application and the technical principles of its application. The embodiment of the present application is not limited to the specific examples described in detail here. For those skilled in the art, a variety of obvious modifications, reconfigurations and replacements can be made without departing from the core idea of ​​the embodiment of the present application. Although the embodiment of the present application has been described in more detail through the above specific embodiments, the embodiment of the present application is far more than this. Without departing from the basic concept of the embodiment of the present application, more other equivalent implementations can also be covered, and the protection scope of the embodiment of the present application is defined by the attached claims. An embodiment of the present application is described in detail above, but the content is only a preferred embodiment of the present application and cannot be considered to be used to limit the scope of implementation of the present application. All equal changes and improvements made according to the scope of application of this application should still belong to the scope of patent coverage of this application.

Claims

1. A method and system for river basin pollution discharge rights management based on alliance chain and knowledge graph, characterized in that: The specific steps of this method are as follows: S1: Build a hyperledger transaction network that includes multiple organizational nodes and authoritative institutions; S2: After transactions are conducted between users, the relevant transaction data will be recorded in the blockchain to ensure the transparency, security and immutability of the transactions. S3: Calculate the water quality of each river basin based on relevant formulas and river topology, and record the obtained 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 the real water quality data of each river basin and the river topology structure; S6: Using knowledge reasoning technology, the CompGCN model is used to calculate the loss function score of the transaction data triples.

2. According to claim 1, a method and system for managing river basin pollution discharge rights based on alliance chain and knowledge graph, characterized in that: The S1 includes: S1.

1. Build a hyperledger network and define the sorting nodes and peer nodes of each organization. S1.

2. Write contract parameters, set WEC value (wecValues), reward amount (rewardAmount), penalty amount (penaltyAmount), deadline (deadline), threshold (threshold), etc. S1.

3. The definition of events such as OrganizationRegistered, TradeCompleted, RewardOrPenaltyClaimed and TradeFailed can achieve more detailed and transparent operation recording and tracking in the alliance chain.

3. According to claim 1, a method and system for managing river basin pollution discharge rights based on alliance chain and knowledge graph, characterized in that: The S2 includes: S2.

1. The organization node initiates the transaction. After receiving the transaction request broadcast by the user, each organization member (node) first conducts preliminary verification. This includes checking the validity of the digital signature, ensuring the correctness of the transaction format, confirming that the transaction initiator has sufficient emission rights balance and sufficient qualifications to participate in the transaction, etc. S2.

2. Verify the consistency between the data of the new transaction and other data recorded on the alliance chain. This step helps prevent double spending or duplicate transactions and ensures that all transactions follow the same logic and rules.

4. According to claim 1, a method and system for managing river basin pollution discharge rights based on alliance chain and knowledge graph, characterized in that: The S3 includes: S3.

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

2. Determine the key parameters for calculating the WEC (Water Environment Capacity) value based on relevant formulas, water quality standards and the specific conditions of the watershed. S3.

3. Define a structure for each user that contains the WEC value and other relevant information. Use the address or other unique identifier as the key to create a map to quickly find the WEC value for a specific user. Record the finalized WEC value in the structure.

5. According to claim 1, a method and system for managing river basin pollution discharge rights based on alliance chain and knowledge graph, characterized in that: The 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 to ensure that all rules and conditions can be automatically executed on the alliance chain. S4.

2. Set dynamic thresholds and transaction deadlines based on specific circumstances, write preset conditions and trigger events into the contract, and ensure that the behavior of all participants is subject to the rules of the smart contract. S4.

3. When the deadline is reached, the preset rewards and penalties will be automatically given to the enterprises that meet the requirements and those that do not.

6. A method and system for managing river basin pollution discharge rights based on alliance chain and knowledge graph according to claim 1, characterized in that: The S5 includes: S5.

1. Process the transaction data on the chain and convert it into triples. S5.

2. Build a knowledge graph based on the converted data and display it using neo4j.

7. According to claim 1, a method and system for managing river basin pollution discharge rights based on alliance chain and knowledge graph, characterized in that: The S6 includes: S6.

1. Perform necessary preprocessing on the constructed knowledge graph, such as removing noise, filling missing values, and standardizing data formats, to improve the quality of subsequent reasoning. S6.

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

3. Train the processed data set, input the current user's WEC value, obtain their corresponding real scores through the loss function, retain the highest score in the form of triples and generate a new knowledge graph to provide users with intelligent decision-making guidance.

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