Energy scheduling and coal supply management method based on block chain

Abstract

The invention discloses an energy scheduling and coal supply management method based on a block chain, and the method achieves the remarkable improvement of energy scheduling and coal supply management through the application of a block chain technology. The distributed account book enhances the data transparency, the zero-knowledge proof and the multi-level authority management improve the data security, the intelligent contract improves the scheduling response speed, and the carbon emission monitoring and incentive mechanism enhances the environmental protection property. Through combined action of the technical means, energy supply chain management is more efficient, safer and more flexible, sustainable development is supported, and strict requirements of a modern energy management system for real-time performance and environmental protection are met.

Description

Technical Field

[0001] The present application relates to the technical field of energy scheduling and coal supply management based on blockchain, and in particular to a method for energy scheduling and coal supply management based on blockchain. Background Art

[0002] In the current supply chain management and scheduling process of the energy industry, how to effectively improve resource utilization and reduce management costs has always been a key challenge facing the industry. Especially in the coal mining, transportation and storage links, the traditional energy supply chain management model usually relies on a centralized database system for inventory management, energy scheduling and transaction settlement. However, this centralized model has obvious limitations in the following aspects:

[0003] Insufficient data transparency: Since the energy supply chain involves multiple participants, such as coal mining companies, transportation companies, power companies, etc., the parties are often restricted by the centralized system when sharing data and lack unified data standards. This model makes it difficult for all parties to achieve effective information exchange, resulting in a lack of transparency in resource scheduling, which is prone to information lags and scheduling errors.

[0004] Security and privacy protection issues: In traditional supply chain management, data from all parties is often concentrated in one or more central databases, which can easily become targets of attacks and cause data leaks. In addition, different participants have different data privacy protection needs, and centralized databases are difficult to meet the different needs of all parties for data access rights, resulting in an increased risk of data leaks.

[0005] Slow dispatch response: In the existing energy dispatch system, dispatch decisions are often based on fixed logic of historical data, which makes it difficult to adapt to the changing energy demand in real time. This model results in a failure to respond quickly during peak demand periods and difficulty in adjusting resource allocation in time during low demand periods, resulting in energy waste or insufficient supply.

[0006] Lack of environmental monitoring and environmental incentive mechanisms: Traditional energy supply chain management systems usually focus on resource supply efficiency, but lack monitoring methods for environmental factors such as carbon emissions. In addition, due to the lack of an effective incentive mechanism, it is difficult for all parties in the supply chain to take the initiative to take environmental protection measures, which is not conducive to achieving sustainable development goals.

[0007] In order to solve the above problems, the industry has begun to explore the application of blockchain technology in supply chain management in recent years. Blockchain technology has the characteristics of decentralization, data immutability and multi-party consensus, and can establish a transparent and trusted distributed ledger among multiple parties. This technology has been applied to logistics, food traceability and other fields to a certain extent, and has improved the transparency and data security of the supply chain through distributed storage and smart contracts. However, the current blockchain technology still has shortcomings in the flexibility of energy scheduling and resource allocation, and it is difficult to meet the energy industry's higher requirements for real-time scheduling, efficient settlement and environmental monitoring. Summary of the invention

[0008] This application provides a blockchain-based energy scheduling and coal supply management method, which aims to solve the problem that the current blockchain technology still has deficiencies in the flexibility of energy scheduling and resource allocation, and is difficult to meet the energy industry's higher requirements for real-time scheduling, efficient settlement and environmental monitoring.

[0009] A method for energy dispatching and coal supply management based on blockchain, the method comprising:

[0010] Data collection and verification: Through multiple data collection nodes such as coal mining, transportation, and storage, real-time collection of coal mining volume, transportation status, inventory data, and energy demand data is collected. The dynamic data verification mechanism of weighted verification is used to ensure the authenticity and integrity of the data, and data privacy is protected through zero-knowledge proof technology;

[0011] Data upload and permission management: upload the verified data to the blockchain alliance chain network to form a distributed ledger, and set different permission levels according to the node role to achieve secure data sharing and access control;

[0012] Smart contract condition triggering: Based on multi-layer conditional logic, smart contracts automatically trigger replenishment instructions and scheduling path optimization operations when energy demand reaches the preset threshold, thereby improving supply chain management efficiency.

[0013] In the above scheme, optionally, the data verification step further includes a privacy protection scheme that combines zero-knowledge proof with a weighted verification mechanism, which ensures the authenticity and integrity of the data before uploading it to the blockchain through dynamic verification to prevent unauthorized data leakage.

[0014] In the above scheme, optionally, the blockchain storage step adopts a consortium chain model, configures different permission levels for each data collection node, energy production unit and demander, and ensures the security and transparency of data when shared across nodes through hierarchical access control.

[0015] In the above scheme, optionally, the smart contract condition triggering step includes setting adaptive thresholds that are automatically adjusted based on historical supply and demand data, and dynamically setting scheduling thresholds using statistical analysis or machine learning models to optimize energy scheduling strategies under different demand fluctuation conditions.

[0016] The above scheme may optionally also include an edge computing analysis step, which completes data preprocessing and analysis in real time at the data source through distributed edge computing nodes, triggers an early warning signal when data anomalies occur, and assists scheduling decisions through the blockchain network.

[0017] In the above scheme, the optional prediction and dynamic optimization step is based on the LSTM model and multi-objective optimization algorithm in deep learning to predict the trend of future energy demand, automatically generate resource allocation optimization plans, and dynamically adjust transportation routes and replenishment plans.

[0018] The above scheme optionally also includes a carbon emission monitoring step, which records and chains carbon emission data through smart contracts during each energy dispatch and coal supply operation, forms a traceable environmental protection ledger, provides real-time monitoring reference for each node, and supports the audit of environmental protection behavior.

[0019] In the above scheme, data privacy protection is optionally implemented through zero-knowledge proof technology and a ring encryption structure to ensure the security of data during cross-node transmission, while preventing the risk of leakage caused by reverse data transmission by setting a one-way data transmission mode.

[0020] The above scheme may optionally include a multi-signature payment step driven by a smart contract, in which the payment smart contract automatically triggers the payment process after confirming that the resource delivery conditions have been met, and ensures the security and transparency of the transaction through a multi-signature payment mechanism. The payment conditions include the trigger conditions of transportation completion or delivery acceptance confirmation.

[0021] In the above scheme, the smart contract optionally includes green priority logic, giving priority to energy supply solutions with lower carbon emissions, setting transaction priorities for low-carbon nodes through incentives such as transaction sorting and fee reductions, and incentivizing environmentally friendly behavior and reducing carbon emissions.

[0022] Compared with the prior art, this application has at least the following beneficial effects:

[0023] This application is based on further analysis and research of existing technical problems, and recognizes that the current blockchain technology still has deficiencies in the flexibility of energy scheduling and resource allocation, and it is difficult to meet the energy industry's higher requirements for real-time scheduling, efficient settlement and environmental monitoring. Through the application of blockchain technology, this application has achieved significant improvements in energy scheduling and coal supply management. Distributed ledgers enhance data transparency, zero-knowledge proofs and multi-level authority management improve data security, smart contracts improve scheduling response speed, and carbon emission monitoring and incentive mechanisms enhance environmental protection. These technical means work together to make energy supply chain management more efficient, secure, and flexible, and support sustainable development, meeting the strict requirements of modern energy management systems for real-time and environmental protection. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 A flowchart of a blockchain-based energy scheduling and coal supply management method provided for one embodiment of the present application. DETAILED DESCRIPTION

[0025] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0026] In one embodiment, Figure 1 As shown, a method for energy dispatching and coal supply management based on blockchain is provided, comprising the following steps:

[0027] Data collection and verification: Through multiple data collection nodes such as coal mining, transportation, and storage, real-time collection of coal mining volume, transportation status, inventory data, and energy demand data is collected. The dynamic data verification mechanism of weighted verification is used to ensure the authenticity and integrity of the data, and data privacy is protected through zero-knowledge proof technology;

[0028] Data upload and permission management: upload the verified data to the blockchain alliance chain network to form a distributed ledger, and set different permission levels according to the node role to achieve secure data sharing and access control;

[0029] Smart contract condition triggering: Based on multi-layer conditional logic, smart contracts automatically trigger replenishment instructions and scheduling path optimization operations when energy demand reaches the preset threshold, thereby improving supply chain management efficiency.

[0030] This embodiment uses the blockchain storage method of the distributed ledger. The application scheme shares energy supply chain data on multiple nodes, allowing multiple nodes such as coal mining, transportation, and warehousing to upload and verify data in real time, thereby achieving data transparency among all parties involved and reducing information lags and data island problems. The distributed ledger provides a reliable and tamper-proof data foundation for all parties, which helps to achieve fast and efficient decision-making in resource scheduling. Using zero-knowledge proof and weighted verification mechanisms, the data is dynamically verified before being uploaded to the blockchain to ensure data authenticity, and a multi-level permission management mechanism is used to limit data access rights of different nodes, avoiding centralized data storage and data leakage risks. Through the above-mentioned security and privacy protection measures, the scheme effectively improves the information security of all parties in the data sharing process and ensures the reliability of supply chain data.

[0031] This embodiment scheme is based on a smart contract with multi-layer conditional logic. When the energy demand reaches the preset threshold, it can automatically trigger scheduling operations such as replenishment instructions and path optimization. The dynamic adaptive threshold set in the smart contract is adjusted according to historical data, so that the system can quickly adapt to the real-time changing needs, meet the different scheduling needs during peak and trough periods, and avoid energy waste and resource shortages. Through carbon emission monitoring and green priority logic in smart contracts, this application scheme records carbon emission data during each energy scheduling and coal supply process, and forms a traceable environmental protection account book to provide environmental monitoring basis for all participants. In addition, the smart contract gives priority to low-carbon emission resource sources, sets transaction priorities for low-carbon nodes, encourages environmental protection behaviors through incentives such as transaction sorting and fee reductions, and helps promote sustainable development in the supply chain. It significantly improves the transparency, security, response speed and environmental protection of energy scheduling and coal supply management, making it more in line with the high requirements of modern energy management systems for real-time and environmental protection.

[0032] In this embodiment, the data verification step further includes a privacy protection scheme that combines zero-knowledge proof with a weighted verification mechanism, which ensures the authenticity and integrity of the data before uploading it to the blockchain through dynamic verification to prevent unauthorized data leakage.

[0033] This embodiment implements data privacy protection through zero-knowledge proof technology, so that the authenticity of the data can be verified without disclosing the specific content before uploading it to the blockchain. Zero-knowledge proof provides great privacy protection in the supply chain, especially when it comes to commercially sensitive energy data, it can ensure that the privacy of the data owner is not affected. Compared with traditional permission control or simple encryption, zero-knowledge proof allows all parties to complete data verification without disclosing specific information, thereby ensuring transparency while avoiding the risk of data leakage. The weighted verification mechanism further enhances the verification efficiency and accuracy of the data. In traditional data verification methods, data verification usually relies on fixed verification standards, which may not be able to adapt to the diverse data characteristics in the supply chain. The weighted verification mechanism allows different weights to be assigned according to the importance of the data source and node, such as assigning higher weights to the data of key nodes (such as production nodes or transportation nodes), ensuring the flexibility and accuracy of data verification. In this way, the system can dynamically adjust the weight coefficient of data verification, adapt to the energy data verification needs in different scenarios, and improve the efficiency of data verification.

[0034] The dynamic data verification mechanism that combines zero-knowledge proof and weighted verification can effectively prevent unauthorized data tampering or fraud. This dual mechanism not only logically guarantees the accuracy of the data, but also improves the ability to respond to data anomalies through weighted verification. For example, when data anomalies occur in the transportation link, the system can quickly identify the source of the anomaly and combine zero-knowledge proof to ensure that the problematic data will not affect the transparency and credibility of the entire supply chain.

[0035] Before uploading data to the blockchain network, screening and verifying the data through zero-knowledge proof and weighted verification can prevent unverified or unauthorized data from flowing into the blockchain system, avoid the blockchain system from being burdened with invalid data, and save storage resources. At the same time, through this dual protection mechanism, all parties in the supply chain can share data more confidently, reducing resistance caused by privacy concerns.

[0036] This embodiment significantly improves the reliability and security of data sharing and privacy protection in the energy supply chain by combining zero-knowledge proof and weighted verification mechanism, thereby achieving more reliable and efficient data management.

[0037] In this embodiment, the blockchain storage step adopts a consortium chain model, configures different permission levels for each data collection node, energy production unit and demander, and ensures the security and transparency of data when shared across nodes through hierarchical access control.

[0038] This embodiment adopts the alliance chain model. The system sets up multi-level permission control in the blockchain network, and different nodes are assigned different data access rights according to their roles. For example, the data collection node may only be able to upload and view its own data, while the production unit can access real-time inventory and demand data, and the demand side can view the summary information after scheduling. This permission control mechanism not only ensures data security, but also makes data access more flexible, thereby improving the information flow efficiency of the entire supply chain.

[0039] The alliance chain model provides stronger data privacy protection for each node. Since different nodes have different access rights to data, the system can ensure that the data finds a balance between public and confidential, avoiding the exposure of sensitive data to irrelevant parties. Unlike traditional centralized management methods, the alliance chain enhances data security through distributed storage technology. Even if individual nodes are attacked, it will not affect the data integrity and security of the overall system.

[0040] In the traditional supply chain, the centralized data management mode is prone to single point failure, which makes the system less resistant to risks. However, through the distributed structure of the alliance chain, the system can save a complete copy of the data at each node, thereby enhancing the risk resistance. Even if a node fails, the system can still operate normally. For supply chain systems involving energy scheduling and resource allocation, this distributed storage risk resistance is particularly important. Traditional centralized supply chain systems often slow down the scheduling response due to data transmission delays. Through the alliance chain, the data transmission between nodes does not need to go through the central node, but is directly connected point-to-point. In this way, the overall response speed of the system is improved. Especially in energy scheduling scenarios with high real-time requirements, this distributed transmission mode can ensure the rapid execution and adjustment of scheduling instructions, thereby optimizing resource utilization. The multi-level permission mechanism of the alliance chain ensures that each node can only access data that matches its permission level, which not only improves the transparency of data sharing, but also enhances the credibility of the system. Each participant in the supply chain can clearly understand the information they can obtain according to their permissions, and cannot access other people's data without authorization. This transparent permission allocation mechanism improves the credibility of the system and enables all parties to trust and rely on the data flow of the system more.

[0041] This embodiment optimizes the data security, system risk resistance, response speed and data sharing transparency of the energy supply chain through the alliance chain model and multi-level permission mechanism, and significantly improves the stability and credibility of the system.

[0042] In this embodiment, the smart contract condition triggering step includes adaptive threshold setting that is automatically adjusted based on historical supply and demand data, and dynamically setting scheduling thresholds using statistical analysis or machine learning models to optimize energy scheduling strategies under different demand fluctuation conditions.

[0043] Adaptive threshold setting enables the system to automatically adjust the dispatch trigger conditions according to historical data or real-time demand changes. In traditional systems, the threshold of energy dispatch is usually fixed, which makes it difficult to respond quickly to demand fluctuations. The introduction of adaptive thresholds enables the system to dispatch resources in advance during peak periods to avoid supply shortages; and reduce the dispatch frequency during trough periods to avoid resource waste, significantly improving the flexibility of energy dispatch. Through statistical analysis or machine learning models to automatically analyze historical data, the system can update the adaptive threshold in real time and respond quickly to drastic changes in demand. Compared with the traditional mode, adaptive threshold setting avoids the delay caused by lagging manual adjustment or fixed thresholds, thereby improving the system's response speed and real-time dispatch capability, which is particularly suitable for energy management scenarios with frequent demand changes. Adaptive thresholds not only improve the flexibility of dispatch, but also effectively optimize resource allocation. By dynamically adjusting the dispatch frequency according to supply and demand conditions, the system can better match the relationship between supply and demand, reduce unnecessary resource mobilization and inventory backlogs, and reduce dispatch costs. In the coal supply chain, this dynamic optimization mechanism helps to strike a balance between cost control and full utilization of resources, thereby achieving higher economic benefits. Under the control of adaptive thresholds, the system can identify and warn of possible imbalances in supply and demand in advance, and maintain supply stability by dynamically adjusting the dispatch volume. For example, when a significant increase in demand is predicted, the system can automatically increase the dispatch frequency to avoid supply shortages caused by a sudden increase in demand, thereby ensuring the continuous stability of energy supply and preventing unexpected supply interruptions.

[0044] Adaptive threshold settings enable the system to better adapt to supply chain environments of different sizes and demand types. Whether it is a small regional supply chain or a large cross-regional energy network, adaptive thresholds can automatically optimize scheduling strategies according to different scenarios, ensuring that the system always maintains efficiency and stability in different application scenarios, improving the scalability of the system.

[0045] The adaptive threshold technology solution of this embodiment significantly improves the system's response speed, scheduling flexibility, resource utilization, supply stability and scalability during the energy scheduling process, enabling the energy scheduling system to meet changing needs and complex application environments with higher efficiency.

[0046] In this embodiment, an edge computing analysis step is also included, in which data preprocessing and analysis are completed in real time at the data source through distributed edge computing nodes, and an early warning signal is triggered when abnormal data occurs, and scheduling decisions are assisted by the blockchain network.

[0047] This embodiment deploys distributed edge computing nodes at data collection points to complete real-time preprocessing and analysis of data, and immediately triggers an early warning when an abnormality occurs. This technical solution provides higher real-time, reliability and intelligence for the energy dispatching and coal supply management system. The main technical effects are reflected in the following aspects:

[0048] After deploying edge computing nodes at data collection points, the system can analyze data in real time at the source of data generation (such as coal mining points or transportation nodes) without transmitting all data to the central server for processing. Through local processing, edge nodes can quickly identify key information in the data and immediately issue warnings when data is abnormal or deviates from the preset standards, greatly shortening the delay time of data analysis and warnings, and providing support for rapid response of supply chain management. Traditional centralized energy dispatching systems are prone to processing bottlenecks when facing a surge in data traffic, thereby affecting the response efficiency of the system. Edge computing nodes are scattered at various data collection points, have independent computing and analysis capabilities, and can complete data processing without relying on central servers. This distributed computing method reduces the load on the central server, allowing the system to maintain efficient operation when facing high-frequency data updates, and improves the overall response efficiency of the system and the real-time nature of dispatching decisions. In the process of energy dispatching and coal supply management, abnormal events such as transportation delays and abnormal inventory data may affect the smooth operation of the supply chain. Edge computing nodes can detect anomalies at the moment data is generated, trigger warnings immediately, and feed back anomaly information to the blockchain network to ensure that the system can take measures as soon as possible after an abnormal event occurs to reduce the negative impact of the anomaly. For example, when an edge node detects a delay at a transportation node, the system can immediately reallocate transportation resources or schedule replenishment, improving the robustness and resilience of the supply chain.

[0049] In traditional centralized systems, all data needs to be transmitted to the central server for processing, which requires very high bandwidth and storage resources. However, with real-time preprocessing through edge computing nodes, only key information and abnormal data need to be uploaded to the blockchain network, reducing the bandwidth requirements and costs of data transmission. The application of edge computing not only saves storage and bandwidth resources, but also reduces unnecessary data processing steps, making data transmission more efficient and economical.

[0050] Since edge computing nodes have independent data processing capabilities, the system can flexibly increase or decrease the number of edge nodes according to specific application requirements. For example, more edge nodes can be deployed in new mining areas or key nodes, while fewer can be deployed in areas with low demand, thereby achieving on-demand expansion. This flexible edge computing layout method gives the system higher scalability and can flexibly adapt to changes in demand in a changing supply chain environment, further enhancing the adaptability and efficiency of the energy dispatch system.

[0051] Through the real-time preprocessing and early warning mechanism of distributed edge computing nodes, the system's data processing real-time, abnormal response speed, bandwidth utilization and system scalability have been greatly improved, providing intelligent and flexible technical support for energy scheduling and supply management.

[0052] In this embodiment, the prediction and dynamic optimization steps are based on the LSTM model and multi-objective optimization algorithm in deep learning to predict the trend of future energy demand, automatically generate resource allocation optimization plans, and dynamically adjust transportation routes and replenishment plans.

[0053] Through deep learning prediction and multi-objective optimization, this embodiment enables the system to identify and respond to demand changes in advance, achieve accurate scheduling, resource optimization and efficient management of the supply chain, and provide more intelligent and automated technical support for the energy supply chain.

[0054] In this embodiment, a carbon emission monitoring step is also included. Through smart contracts, carbon emission data is recorded and uploaded to the chain during each energy dispatch and coal supply operation, forming a traceable environmental protection ledger, providing real-time monitoring reference for each node, and supporting the audit of environmental protection behavior.

[0055] This embodiment realizes real-time monitoring, data traceability and environmental protection incentives of carbon emissions in the energy supply chain through carbon emission monitoring, environmental protection ledger and green priority logic, promotes environmental protection management and sustainable development of the supply chain, and has significant environmental protection technical effects.

[0056] In this embodiment, data privacy protection is achieved through zero-knowledge proof technology and a ring encryption structure to ensure the security of data during cross-node transmission. At the same time, by setting a one-way data transmission mode, the risk of leakage caused by reverse transmission of data is prevented.

[0057] This embodiment significantly improves the privacy protection and transmission security of energy supply chain data through the combination of zero-knowledge proof and ring encryption structure, and provides a more secure and efficient transmission guarantee for multi-node data sharing.

[0058] In this embodiment, a multi-signature payment step driven by a smart contract is also included, in which the payment smart contract automatically triggers the payment process after confirming that the resource delivery conditions are met, and ensures the security and transparency of the transaction through a multi-signature payment mechanism. The payment conditions include the trigger conditions of transportation completion or delivery acceptance confirmation.

[0059] This embodiment provides a secure, transparent and automated guarantee for transaction payments in the energy supply chain through a multi-signature payment mechanism driven by smart contracts, significantly reduces transaction risks and the possibility of capital loss, and improves the payment efficiency and resilience of the system.

[0060] In this embodiment, the smart contract includes green priority logic, giving priority to energy supply options with lower carbon emissions, setting transaction priorities for low-carbon nodes through incentives such as transaction sorting and fee reductions, incentivizing environmentally friendly behavior and reducing carbon emissions.

[0061] This embodiment provides technical support for the low-carbon transformation of the supply chain through incentives based on green priority logic and a carbon emission priority selection mechanism, achieves environmental incentives, transparent management, compliance support, and brand value enhancement, and significantly promotes the sustainable development of the energy supply chain.

[0062] The technical features of the above embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

Claims

1. A method for energy dispatching and coal supply management based on blockchain, characterized in that: The following steps are involved: Data collection and verification: Through multiple data collection nodes such as coal mining, transportation, and storage, real-time collection of coal mining volume, transportation status, inventory data, and energy demand data is collected. The dynamic data verification mechanism of weighted verification is used to ensure the authenticity and integrity of the data, and data privacy is protected through zero-knowledge proof technology; Data upload and permission management: upload the verified data to the blockchain alliance chain network to form a distributed ledger, and set different permission levels according to the node role to achieve secure data sharing and access control; Smart contract condition triggering: Based on multi-layer conditional logic, smart contracts automatically trigger replenishment instructions and scheduling path optimization operations when energy demand reaches the preset threshold, thereby improving supply chain management efficiency.

2. The method according to claim 1, characterized in that The data verification step further includes a privacy protection scheme that combines zero-knowledge proof with a weighted verification mechanism, which ensures the authenticity and integrity of the data before uploading it to the blockchain through dynamic verification to prevent unauthorized data leakage.

3. The method according to claim 1, characterized in that The blockchain storage step adopts a consortium chain model, configuring different permission levels for each data collection node, energy production unit and demander, and ensuring the security and transparency of data when shared across nodes through hierarchical access control.

4. The method according to claim 1, characterized in that: The smart contract condition triggering steps include adaptive threshold setting that is automatically adjusted based on historical supply and demand data, and dynamically setting scheduling thresholds using statistical analysis or machine learning models to optimize energy scheduling strategies under different demand fluctuations.

5. The method according to claim 1, characterized in that It also includes edge computing analysis steps, which completes data preprocessing and analysis in real time at the data source through distributed edge computing nodes, triggers early warning signals when data anomalies occur, and assists scheduling decisions through the blockchain network.

6. The method according to claim 1, characterized in that The prediction and dynamic optimization step uses the LSTM model and multi-objective optimization algorithm in deep learning to predict future energy demand trends, automatically generate resource allocation optimization plans, and dynamically adjust transportation routes and replenishment plans.

7. The method according to claim 1, characterized in that It also includes a carbon emission monitoring step, which records and chains carbon emission data through smart contracts during each energy dispatch and coal supply operation, forming a traceable environmental ledger, providing real-time monitoring reference for each node, and supporting the audit of environmental protection behavior.

8. The method according to claim 1, characterized in that Data privacy protection is achieved through zero-knowledge proof technology and ring encryption structure to ensure the security of data during cross-node transmission. At the same time, by setting a one-way data transmission mode, the risk of leakage caused by reverse data transmission is prevented.

9. The method according to claim 1, characterized in that: It also includes a smart contract-driven multi-signature payment step, in which the payment smart contract automatically triggers the payment process after confirming that the resource delivery conditions have been met, and ensures the security and transparency of the transaction through a multi-signature payment mechanism. The payment conditions include the trigger conditions of transportation completion or delivery acceptance confirmation.

10. The method according to claim 1, characterized in that Smart contracts include green priority logic, giving priority to energy supply options with lower carbon emissions, setting transaction priorities for low-carbon nodes through incentives such as transaction sorting and fee reductions, incentivizing environmentally friendly behavior and reducing carbon emissions.

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